Zhu, Xiang; Zhang, Dianwen
2013-01-01
We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy.
Xiang Zhu; Dianwen Zhang
2013-01-01
We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetim...
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.
Evaluation of Model Fit in Cognitive Diagnosis Models
Hu, Jinxiang; Miller, M. David; Huggins-Manley, Anne Corinne; Chen, Yi-Hsin
2016-01-01
Cognitive diagnosis models (CDMs) estimate student ability profiles using latent attributes. Model fit to the data needs to be ascertained in order to determine whether inferences from CDMs are valid. This study investigated the usefulness of some popular model fit statistics to detect CDM fit including relative fit indices (AIC, BIC, and CAIC),…
Evaluation of model fit in nonlinear multilevel structural equation modeling
Directory of Open Access Journals (Sweden)
Karin eSchermelleh-Engel
2014-03-01
Full Text Available Evaluating model fit in nonlinear multilevel structural equation models (MSEM presents a challenge as no adequate test statistic is available. Nevertheless, using a product indicator approach a likelihood ratio test for linear models is provided which may also be useful for nonlinear MSEM. The main problem with nonlinear models is that product variables are nonnormally distributed. Although robust test statistics have been developed for linear SEM to ensure valid results under the condition of nonnormality, they were not yet investigated for nonlinear MSEM. In a Monte Carlo study, the performance of the robust likelihood ratio test was investigated for models with single-level latent interaction effects using the unconstrained product indicator approach. As overall model fit evaluation has a potential limitation in detecting the lack of fit at a single level even for linear models, level-specific model fit evaluation was also investigated using partially saturated models. Four population models were considered: a model with interaction effects at both levels, an interaction effect at the within-group level, an interaction effect at the between-group level, and a model with no interaction effects at both levels. For these models the number of groups, predictor correlation, and model misspecification was varied. The results indicate that the robust test statistic performed sufficiently well. Advantages of level-specific model fit evaluation for the detection of model misfit are demonstrated.
Evaluation of model fit in nonlinear multilevel structural equation modeling.
Schermelleh-Engel, Karin; Kerwer, Martin; Klein, Andreas G
2014-01-01
Evaluating model fit in nonlinear multilevel structural equation models (MSEM) presents a challenge as no adequate test statistic is available. Nevertheless, using a product indicator approach a likelihood ratio test for linear models is provided which may also be useful for nonlinear MSEM. The main problem with nonlinear models is that product variables are non-normally distributed. Although robust test statistics have been developed for linear SEM to ensure valid results under the condition of non-normality, they have not yet been investigated for nonlinear MSEM. In a Monte Carlo study, the performance of the robust likelihood ratio test was investigated for models with single-level latent interaction effects using the unconstrained product indicator approach. As overall model fit evaluation has a potential limitation in detecting the lack of fit at a single level even for linear models, level-specific model fit evaluation was also investigated using partially saturated models. Four population models were considered: a model with interaction effects at both levels, an interaction effect at the within-group level, an interaction effect at the between-group level, and a model with no interaction effects at both levels. For these models the number of groups, predictor correlation, and model misspecification was varied. The results indicate that the robust test statistic performed sufficiently well. Advantages of level-specific model fit evaluation for the detection of model misfit are demonstrated.
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.
Biomedical model fitting and error analysis.
Costa, Kevin D; Kleinstein, Steven H; Hershberg, Uri
2011-09-20
This Teaching Resource introduces students to curve fitting and error analysis; it is the second of two lectures on developing mathematical models of biomedical systems. The first focused on identifying, extracting, and converting required constants--such as kinetic rate constants--from experimental literature. To understand how such constants are determined from experimental data, this lecture introduces the principles and practice of fitting a mathematical model to a series of measurements. We emphasize using nonlinear models for fitting nonlinear data, avoiding problems associated with linearization schemes that can distort and misrepresent the data. To help ensure proper interpretation of model parameters estimated by inverse modeling, we describe a rigorous six-step process: (i) selecting an appropriate mathematical model; (ii) defining a "figure-of-merit" function that quantifies the error between the model and data; (iii) adjusting model parameters to get a "best fit" to the data; (iv) examining the "goodness of fit" to the data; (v) determining whether a much better fit is possible; and (vi) evaluating the accuracy of the best-fit parameter values. Implementation of the computational methods is based on MATLAB, with example programs provided that can be modified for particular applications. The problem set allows students to use these programs to develop practical experience with the inverse-modeling process in the context of determining the rates of cell proliferation and death for B lymphocytes using data from BrdU-labeling experiments.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...
Direct model fitting to combine dithered ACS images
Mahmoudian, Haniyeh
2013-01-01
The information lost in images of undersampled CCD cameras can be recovered with the technique of `dithering'. A number of subexposures is taken with sub-pixel shifts in order to record structures on scales smaller than a pixel. The standard method to combine such exposures, `Drizzle', averages after reversing the displacements, including rotations and distortions. More sophisticated methods are available to produce, e.g., Nyquist sampled representations of band-limited inputs. While the combined images produced by these methods can be of high quality, their use as input for forward-modelling techniques in gravitational lensing is still not optimal, because the residual artefacts still affect the modelling results in unpredictable ways. In this paper we argue for an overall modelling approach that takes into account the dithering and the lensing without the intermediate product of a combined image. As one building block we introduce an alternative approach to combine dithered images by direct model fitting wi...
On assessing model fit for distribution-free longitudinal models under missing data.
Wu, P; Tu, X M; Kowalski, J
2014-01-15
The generalized estimating equation (GEE), a distribution-free, or semi-parametric, approach for modeling longitudinal data, is used in a wide range of behavioral, psychotherapy, pharmaceutical drug safety, and healthcare-related research studies. Most popular methods for assessing model fit are based on the likelihood function for parametric models, rendering them inappropriate for distribution-free GEE. One rare exception is a score statistic initially proposed by Tsiatis for logistic regression (1980) and later extended by Barnhart and Willamson to GEE (1998). Because GEE only provides valid inference under the missing completely at random assumption and missing values arising in most longitudinal studies do not follow such a restricted mechanism, this GEE-based score test has very limited applications in practice. We propose extensions of this goodness-of-fit test to address missing data under the missing at random assumption, a more realistic model that applies to most studies in practice. We examine the performance of the proposed tests using simulated data and demonstrate the utilities of such tests with data from a real study on geriatric depression and associated medical comorbidities.
Direct model fitting to combine dithered ACS images
Mahmoudian, H.; Wucknitz, O.
2013-08-01
The information lost in images of undersampled CCD cameras can be recovered with the technique of "dithering". A number of subexposures is taken with sub-pixel shifts in order to record structures on scales smaller than a pixel. The standard method to combine such exposures, "Drizzle", averages after reversing the displacements, including rotations and distortions. More sophisticated methods are available to produce, e.g., Nyquist sampled representations of band-limited inputs. While the combined images produced by these methods can be of high quality, their use as input for forward-modelling techniques in gravitational lensing is still not optimal, because the residual artefacts still affect the modelling results in unpredictable ways. In this paper we argue for an overall modelling approach that takes into account the dithering and the lensing without the intermediate product of a combined image. As one building block we introduce an alternative approach to combine dithered images by direct model fitting with a least-squares approach including a regularization constraint. We present tests with simulated and real data that show the quality of the results. The additional effects of gravitational lensing and the convolution with an instrumental point spread function can be included in a natural way, avoiding the possible systematic errors of previous procedures.
Bayesian Data-Model Fit Assessment for Structural Equation Modeling
Levy, Roy
2011-01-01
Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes…
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.
Kompaneets Model Fitting of the Orion-Eridanus Superbubble
Pon, Andy; Bally, John; Heiles, Carl
2014-01-01
Winds and supernovae from OB associations create large cavities in the interstellar medium referred to as superbubbles. The Orion molecular clouds are the nearest high mass star-forming region and have created a highly elongated, 20 degree x 45 degree, superbubble. We fit Kompaneets models to the Orion-Eridanus superbubble and find that a model where the Eridanus side of the superbubble is oriented away from the Sun provides a marginal fit. Because this model requires an unusually small scale height of 40 pc and has the superbubble inclined 35 degrees from the normal to the Galactic plane, we propose that this model should be treated as a general framework for modeling the Orion-Eridanus superbubble, with a secondary physical mechanism not included in the Kompaneets model required to fully account for the orientation and elongation of the superbubble.
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 consider
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
A neutrino model fit to the CMB power spectrum
Shanks, T; Schewtschenko, J A; Whitbourn, J R
2014-01-01
The current standard cosmological model, LCDM, provides an excellent fit to the WMAP and Planck CMB data. However, the model has well known problems. For example, the cosmological constant is fine tuned to 1 part in 10^100 and the cold dark matter (CDM) particle is not yet detected in the laboratory. Here we seek an alternative model to LCDM which makes minimal assumptions about new physics. This is based on previous work by Shanks who investigated a model which assumed neither exotic particles nor a cosmological constant but instead postulated a low Hubble constant (H_0) to help allow a baryon density which was compatible with an inflationary model with zero spatial curvature. However, the recent Planck results make it more difficult to reconcile such a model with the cosmic microwave background (CMB) temperature fluctuations. Here we relax the previous assumptions to assess the effects of assuming standard model neutrinos of moderate mass (~5eV) but with no CDM and no cosmological constant. If we assume a l...
Parametric Models of Periodogram
Indian Academy of Sciences (India)
P. Mohan; A. Mangalam; S. Chattopadhyay
2014-09-01
The maximum likelihood estimator is used to determine fit parameters for various parametric models of the Fourier periodogram followed by the selection of the best-fit model amongst competing models using the Akaike information criteria. This analysis, when applied to light curves of active galactic nuclei can be used to infer the presence of quasi-periodicity and break or knee frequencies. The extracted information can be used to place constraints on the mass, spin and other properties of the putative central black hole and the region surrounding it through theoretical models involving disk and jet physics.
A neutrino model fit to the CMB power spectrum
Shanks, T.; Johnson, R. W. F.; Schewtschenko, J. A.; Whitbourn, J. R.
2014-12-01
The standard cosmological model, Λ cold dark matter (ΛCDM), provides an excellent fit to cosmic microwave background (CMB) data. However, the model has well-known problems. For example, the cosmological constant, Λ, is fine-tuned to 1 part in 10100 and the CDM particle is not yet detected in the laboratory. Shanks previously investigated a model which assumed neither exotic particles nor a cosmological constant but instead postulated a low Hubble constant (H0) to allow a baryon density compatible with inflation and zero spatial curvature. However, recent Planck results make it more difficult to reconcile such a model with CMB power spectra. Here, we relax the previous assumptions to assess the effects of assuming three active neutrinos of mass ≈5 eV. If we assume a low H0 ≈ 45 km s-1 Mpc-1 then, compared to the previous purely baryonic model, we find a significantly improved fit to the first three peaks of the Planck power spectrum. Nevertheless, the goodness of fit is still significantly worse than for ΛCDM and would require appeal to unknown systematic effects for the fit ever to be considered acceptable. A further serious problem is that the amplitude of fluctuations is low (σ8 ≈ 0.2), making it difficult to form galaxies by the present day. This might then require seeds, perhaps from a primordial magnetic field, to be invoked for galaxy formation. These and other problems demonstrate the difficulties faced by models other than ΛCDM in fitting ever more precise cosmological data.
Rapid world modeling: Fitting range data to geometric primitives
Energy Technology Data Exchange (ETDEWEB)
Feddema, J.; Little, C.
1996-12-31
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.
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)
2015-01-07
Jan 7, 2015 ... 2Hydrology and Water Quality, Agricultural and Biological Engineering ... This general methodology is applied to a reservoir model of the Okavango ... Global sensitivity and uncertainty analysis (GSA/UA) system- ... and weighing risks between decisions (Saltelli et al., 2008). ...... resources and support.
3D Building Model Fitting Using A New Kinetic Framework
Brédif, Mathieu; Pierrot-Deseilligny, Marc; Maître, Henri
2008-01-01
We describe a new approach to fit the polyhedron describing a 3D building model to the point cloud of a Digital Elevation Model (DEM). We introduce a new kinetic framework that hides to its user the combinatorial complexity of determining or maintaining the polyhedron topology, allowing the design of a simple variational optimization. This new kinetic framework allows the manipulation of a bounded polyhedron with simple faces by specifying the target plane equations of each of its faces. It proceeds by evolving continuously from the polyhedron defined by its initial topology and its initial plane equations to a polyhedron that is as topologically close as possible to the initial polyhedron but with the new plane equations. This kinetic framework handles internally the necessary topological changes that may be required to keep the faces simple and the polyhedron bounded. For each intermediate configurations where the polyhedron looses the simplicity of its faces or its boundedness, the simplest topological mod...
Geometrical model fitting for interferometric data: GEM-FIND
Klotz, D; Paladini, C; Hron, J; Wachter, G
2012-01-01
We developed the tool GEM-FIND that allows to constrain the morphology and brightness distribution of objects. The software fits geometrical models to spectrally dispersed interferometric visibility measurements in the N-band using the Levenberg-Marquardt minimization method. Each geometrical model describes the brightness distribution of the object in the Fourier space using a set of wavelength-independent and/or wavelength-dependent parameters. In this contribution we numerically analyze the stability of our nonlinear fitting approach by applying it to sets of synthetic visibilities with statistically applied errors, answering the following questions: How stable is the parameter determination with respect to (i) the number of uv-points, (ii) the distribution of points in the uv-plane, (iii) the noise level of the observations?
Mechanical Response of Polycarbonate with Strength Model Fits
2012-02-01
is used as free -parameter to improve the quality of the fit. ̇ is the strain rate and ?̇? is the reference strain rate for which 1/s was used...experimental data. Table 3. ZA model parameters. Bo= 0.006715948 1/K B1= 0.00009503 1/K Bpa = 550 MPa Bopa= 48 MPa ωa= -8 ▬ ωb= -0.01 ▬ β= 0.5...Hybrid Hard/Ductile All-Plastic-and Glass-Plastic-Based Composites ; ARL-TR-3155; U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, February
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.
Impact of Missing Data on Person-Model Fit and Person Trait Estimation
Zhang, Bo; Walker, Cindy M.
2008-01-01
The purpose of this research was to examine the effects of missing data on person-model fit and person trait estimation in tests with dichotomous items. Under the missing-completely-at-random framework, four missing data treatment techniques were investigated including pairwise deletion, coding missing responses as incorrect, hotdeck imputation,…
Does the Foreign Income Shock in a Small Open Economy DSGE Model Fit Croatian Data?
Arčabić, Vladimir; Globan, Tomislav; Nadoveza, Ozana; Rogić Dumančić, Lucija; Tica, Josip
2016-01-01
The paper compares theoretical impulse response functions from a DSGE model for a small open economy with an empirical VAR model estimated for the Croatian economy. The theoretical model fits the data well as long as monetary policy is modelled as a fixed exchange rate regime. The paper considers only a foreign output gap shock. A positive foreign shock increases domestic GDP and prices and decreases terms of trade, which is in compliance with theoretical assumptions. Interest rates behave di...
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.
Is Model Fitting Necessary for Model-Based fMRI?
Directory of Open Access Journals (Sweden)
Robert C Wilson
2015-06-01
Full Text Available Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derived approximation of the true generative regressor. Then, as a specific test case, we examined the sensitivity of model-based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previously-published datasets. We found that even gross errors in the learning rate lead to only minute changes in the neural results. Our findings thus suggest that precise model fitting is not always necessary for model-based fMRI. They also highlight the difficulty in using fMRI data for arbitrating between different models or model parameters. While these specific results pertain only to the effect of learning rate in simple reinforcement learning models, we provide a template for testing for effects of different parameters in other models.
Is Model Fitting Necessary for Model-Based fMRI?
Wilson, Robert C; Niv, Yael
2015-06-01
Model-based analysis of fMRI data is an important tool for investigating the computational role of different brain regions. With this method, theoretical models of behavior can be leveraged to find the brain structures underlying variables from specific algorithms, such as prediction errors in reinforcement learning. One potential weakness with this approach is that models often have free parameters and thus the results of the analysis may depend on how these free parameters are set. In this work we asked whether this hypothetical weakness is a problem in practice. We first developed general closed-form expressions for the relationship between results of fMRI analyses using different regressors, e.g., one corresponding to the true process underlying the measured data and one a model-derived approximation of the true generative regressor. Then, as a specific test case, we examined the sensitivity of model-based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previously-published datasets. We found that even gross errors in the learning rate lead to only minute changes in the neural results. Our findings thus suggest that precise model fitting is not always necessary for model-based fMRI. They also highlight the difficulty in using fMRI data for arbitrating between different models or model parameters. While these specific results pertain only to the effect of learning rate in simple reinforcement learning models, we provide a template for testing for effects of different parameters in other models.
Kompaneets Model Fitting of the Orion-Eridanus Superbubble II: Thinking Outside of Barnard's Loop
Pon, Andy; Alves, Joao; Bally, John; Basu, Shantanu; Tielens, Alexander G G M
2016-01-01
The Orion star-forming region is the nearest active high-mass star-forming region and has created a large superbubble, the Orion-Eridanus superbubble. Recent work by Ochsendorf et al. (2015) has extended the accepted boundary of the superbubble. We fit Kompaneets models of superbubbles expanding in exponential atmospheres to the new, larger shape of the Orion-Eridanus superbubble. We find that this larger morphology of the superbubble is consistent with the evolution of the superbubble being primarily controlled by expansion into the exponential Galactic disk ISM if the superbubble is oriented with the Eridanus side farther from the Sun than the Orion side. Unlike previous Kompaneets model fits that required abnormally small scale heights for the Galactic disk (<40 pc), we find morphologically consistent models with scale heights of 80 pc, similar to that expected for the Galactic disk.
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.
A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting
Soltani-Mohammadi, Saeed; Safa, Mohammad
2016-09-01
Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
Cantó, J.; Curiel, S.; Martínez-Gómez, E.
2009-07-01
Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
other aspects, the properties of a method for parameter estimation in stochastic differential equations is considered within the field of heat dynamics of buildings. In the second paper a lack-of-fit test for stochastic differential equations is presented. The test can be applied to both linear and non-linear...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...... stochastic differential equations. Some applications are presented in the papers. In the summary report references are made to a number of other applications. Resumé på dansk: Nærværende afhandling består af ti artikler publiceret i perioden 1996-1999 samt et sammendrag og en perspektivering heraf. I...
Model fitting of kink waves in the solar atmosphere: Gaussian damping and time-dependence
Morton, R. J.; Mooroogen, K.
2016-09-01
Aims: Observations of the solar atmosphere have shown that magnetohydrodynamic waves are ubiquitous throughout. Improvements in instrumentation and the techniques used for measurement of the waves now enables subtleties of competing theoretical models to be compared with the observed waves behaviour. Some studies have already begun to undertake this process. However, the techniques employed for model comparison have generally been unsuitable and can lead to erroneous conclusions about the best model. The aim here is to introduce some robust statistical techniques for model comparison to the solar waves community, drawing on the experiences from other areas of astrophysics. In the process, we also aim to investigate the physics of coronal loop oscillations. Methods: The methodology exploits least-squares fitting to compare models to observational data. We demonstrate that the residuals between the model and observations contain significant information about the ability for the model to describe the observations, and show how they can be assessed using various statistical tests. In particular we discuss the Kolmogorov-Smirnoff one and two sample tests, as well as the runs test. We also highlight the importance of including any observational trend line in the model-fitting process. Results: To demonstrate the methodology, an observation of an oscillating coronal loop undergoing standing kink motion is used. The model comparison techniques provide evidence that a Gaussian damping profile provides a better description of the observed wave attenuation than the often used exponential profile. This supports previous analysis from Pascoe et al. (2016, A&A, 585, L6). Further, we use the model comparison to provide evidence of time-dependent wave properties of a kink oscillation, attributing the behaviour to the thermodynamic evolution of the local plasma.
Lévy Flights and Self-Similar Exploratory Behaviour of Termite Workers: Beyond Model Fitting
Miramontes, Octavio; DeSouza, Og; Paiva, Leticia Ribeiro; Marins, Alessandra; Orozco, Sirio
2014-01-01
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. PMID:25353958
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.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
Canto, J; Martinez-Gomez, E; 10.1051/0004-6361/200911740
2009-01-01
Context. Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims. We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (Asexual Genetic Algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two e...
Interactive Dimensioning of Parametric Models
Kelly, T.
2015-05-01
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. © 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
Kinetic modelling of RDF pyrolysis: Model-fitting and model-free approaches.
Çepelioğullar, Özge; Haykırı-Açma, Hanzade; Yaman, Serdar
2016-02-01
In this study, refuse derived fuel (RDF) was selected as solid fuel and it was pyrolyzed in a thermal analyzer from room temperature to 900°C at heating rates of 5, 10, 20, and 50°C/min in N2 atmosphere. The obtained thermal data was used to calculate the kinetic parameters using Coats-Redfern, Friedman, Flylnn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods. As a result of Coats-Redfern model, decomposition process was assumed to be four independent reactions with different reaction orders. On the other hand, model free methods demonstrated that activation energy trend had similarities for the reaction progresses of 0.1, 0.2-0.7 and 0.8-0.9. The average activation energies were found between 73-161kJ/mol and it is possible to say that FWO and KAS models produced closer results to the average activation energies compared to Friedman model. Experimental studies showed that RDF may be a sustainable and promising feedstock for alternative processes in terms of waste management strategies.
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Nested by design: model fitting and interpretation in a mixed model era
National Research Council Canada - National Science Library
Schielzeth, Holger; Nakagawa, Shinichi; Freckleton, Robert
2013-01-01
...‐effects models offer a powerful framework to do so. Nested effects can usually be fitted using the syntax for crossed effects in mixed models, provided that the coding reflects implicit nesting...
Adams, Matthew P.; Collier, Catherine J.; Uthicke, Sven; Ow, Yan X.; Langlois, Lucas; O’Brien, Katherine R.
2017-01-01
When several models can describe a biological process, the equation that best fits the data is typically considered the best. However, models are most useful when they also possess biologically-meaningful parameters. In particular, model parameters should be stable, physically interpretable, and transferable to other contexts, e.g. for direct indication of system state, or usage in other model types. As an example of implementing these recommended requirements for model parameters, we evaluated twelve published empirical models for temperature-dependent tropical seagrass photosynthesis, based on two criteria: (1) goodness of fit, and (2) how easily biologically-meaningful parameters can be obtained. All models were formulated in terms of parameters characterising the thermal optimum (Topt) for maximum photosynthetic rate (Pmax). These parameters indicate the upper thermal limits of seagrass photosynthetic capacity, and hence can be used to assess the vulnerability of seagrass to temperature change. Our study exemplifies an approach to model selection which optimises the usefulness of empirical models for both modellers and ecologists alike.
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
Semi-Parametric Modelling of Correlation Dynamics
C.M. Hafner (Christian); D.J.C. van Dijk (Dick); Ph.H.B.F. Franses (Philip Hans)
2005-01-01
textabstractIn this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate GARCH-type specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only
Hierarchical Shrinkage Priors and Model Fitting for High-dimensional Generalized Linear Models
Yi, Nengjun; Ma, Shuangge
2013-01-01
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/). PMID:23192052
Linear Parametric Model Checking of Timed Automata
DEFF Research Database (Denmark)
Hune, Tohmas Seidelin; Romijn, Judi; Stoelinga, Mariëlle
2001-01-01
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......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...
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 model estimates (5.6 ± 5.6 km) was nearly half that of LS estimates (11.6 ± 8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw 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.
Energy Technology Data Exchange (ETDEWEB)
McFee, J.E., E-mail: jemcfee@telus.net; Mosquera, C.M.; Faust, A.A.
2016-08-21
An analysis of digitized pulse waveforms from experiments with LaBr{sub 3}(Ce) and LaCl{sub 3}(Ce) detectors is presented. Pulse waveforms from both scintillator types were captured in the presence of {sup 22}Na and {sup 60}Co sources and also background alone. Two methods to extract pulse shape discrimination (PSD) parameters and estimate energy spectra were compared. The first involved least squares fitting of the pulse waveforms to a physics-based model of one or two exponentially modified Gaussian functions. The second was the conventional gated integration method. The model fitting method produced better PSD than gated integration for LaCl{sub 3}(Ce) and higher resolution energy spectra for both scintillator types. A disadvantage to the model fitting approach is that it is more computationally complex and about 5 times slower. LaBr{sub 3}(Ce) waveforms had a single decay component and showed no ability for alpha/electron PSD. LaCl{sub 3}(Ce) was observed to have short and long decay components and alpha/electron discrimination was observed.
Modeling personnel turnover in the parametric organization
Dean, Edwin B.
1991-01-01
A model is developed for simulating the dynamics of a newly formed organization, credible during all phases of organizational development. The model development process is broken down into the activities of determining the tasks required for parametric cost analysis (PCA), determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the model, implementing the model, and testing it. The model, parameterized by the likelihood of job function transition, has demonstrated by the capability to represent the transition of personnel across functional boundaries within a parametric organization using a linear dynamical system, and the ability to predict required staffing profiles to meet functional needs at the desired time. The model can be extended by revisions of the state and transition structure to provide refinements in functional definition for the parametric and extended organization.
Roxburgh, Ian W.
2015-01-01
Aims: Our aim is to describe the theory of surface layer independent model fitting by phase matching and to apply this to the stars HD 49933 observed by CoRoT, and HD 177153 (aka Perky) observed by Kepler. Methods: We use theoretical analysis, phase shifts, and model fitting. Results: We define the inner and outer phase shifts of a frequency set of a model star and show that the outer phase shifts are (almost) independent of degree ℓ, and that a function of the inner phase shifts (the phase function) collapses to an ℓ independent function of frequency in the outer layers. We then show how to use this result in a model fitting technique to find a best fit model to an observed frequency set by calculating the inner phase shifts of a model using the observed frequencies and determining the extent to which the phase function collapses to a single function of frequency in the outer layers. This technique does not depend on the radial order n assigned to the observed frequencies. We give two examples applying this technique to the frequency sets of HD 49933 observed by CoRoT and HD 177153 (aka Perky) observed by Kepler, for which measurements of angular diameters and bolometric fluxes are available. For HD 49933 we find a very wide range of models to be consistent with the data (all with convective core overshooting) - and conclude that the data is not precise enough to make any useful restrictions on the structure of this star. For HD 177153 our best fit models have no convective cores, masses in the range 1.15-1.17 M⊙, ages of 4.45-4.70 × 109 yr, Z in the range 0.021-0.024, XH = 0.71-0.72, Y = 0.256 - 0.266 and mixing length parameter α = 1.8. We compare our results to those of previous studies. We contrast the phase matching technique to that using the ratios of small to large separations, showing that it avoids the problem of correlated errors in separation ratio fitting and of assigning radial order n to the modes.
Bridge Engineering-Oriented Parametric Model
Institute of Scientific and Technical Information of China (English)
周凌远; 李乔
2004-01-01
A new model is proposed to improve the efficiency of structural modeling. In this model, the bridge structural components are expressed with component description, parametric description and geometric description in a software system. This model provides both convenience and flexibility for users in structural modeling process. The object-oriented method is applied in the model implementation. A bridge analysis preprocessor is developed on the basis of this model. It provides an effective way for bridge modeling.
Parametric modelling of a knee joint prosthesis.
Khoo, L P; Goh, J C; Chow, S L
1993-01-01
This paper presents an approach for the establishment of a parametric model of knee joint prosthesis. Four different sizes of a commercial prosthesis are used as an example in the study. A reverse engineering technique was employed to reconstruct the prosthesis on CATIA, a CAD (computer aided design) system. Parametric models were established as a result of the analysis. Using the parametric model established and the knee data obtained from a clinical study on 21 pairs of cadaveric Asian knees, the development of a prototype prosthesis that suits a patient with a very small knee joint is presented. However, it was found that modification to certain parameters may be inevitable due to the uniqueness of the Asian knee. An avenue for rapid modelling and eventually economical production of a customized knee joint prosthesis for patients is proposed and discussed.
Model fitting of kink waves in the solar atmosphere: Gaussian damping and time-dependence
Morton, R J
2016-01-01
{Observations of the solar atmosphere have shown that magnetohydrodynamic waves are ubiquitous throughout. Improvements in instrumentation and the techniques used for measurement of the waves now enables subtleties of competing theoretical models to be compared with the observed waves behaviour. Some studies have already begun to undertake this process. However, the techniques employed for model comparison have generally been unsuitable and can lead to erroneous conclusions about the best model. The aim here is to introduce some robust statistical techniques for model comparison to the solar waves community, drawing on the experiences from other areas of astrophysics. In the process, we also aim to investigate the physics of coronal loop oscillations. } {The methodology exploits least-squares fitting to compare models to observational data. We demonstrate that the residuals between the model and observations contain significant information about the ability for the model to describe the observations, and show...
A fungal growth model fitted to carbon-limited dynamics of Rhizoctonia solani
Jeger, M.J.; Lamour, A.; Gilligan, C.A.; Otten, W.
2008-01-01
Here, a quasi-steady-state approximation was used to simplify a mathematical model for fungal growth in carbon-limiting systems, and this was fitted to growth dynamics of the soil-borne plant pathogen and saprotroph Rhizoctonia solani. The model identified a criterion for invasion into
The Shape of Dark Matter Haloes II. The Galactus HI Modelling & Fitting Tool
Peters, S P C; Allen, R J; Freeman, K C
2016-01-01
We present a new HI modelling tool called \\textsc{Galactus}. The program has been designed to perform automated fits of disc-galaxy models to observations. It includes a treatment for the self-absorption of the gas. The software has been released into the public domain. We describe the design philosophy and inner workings of the program. After this, we model the face-on galaxy NGC2403, using both self-absorption and optically thin models, showing that self-absorption occurs even in face-on galaxies. It is shown that the maximum surface brightness plateaus seen in Paper I of this series are indeed signs of self-absorption. The apparent HI mass of an edge-on galaxy can be drastically lower compared to that same galaxy seen face-on. The Tully-Fisher relation is found to be relatively free from self-absorption issues.
Uchiyama, Takanori; Minamitani, Haruyuki; Sakata, Makoto
1990-01-01
The complex maximum entropy method and complex autoregressive model fitting with the singular value decomposition method (SVD) were applied to the free induction decay signal data obtained with a Fourier transform nuclear magnetic resonance spectrometer to estimate superresolved NMR spectra. The practical estimation of superresolved NMR spectra are shown on the data of phosphorus-31 nuclear magnetic resonance spectra. These methods provide sharp peaks and high signal-to-noise ratio compared with conventional fast Fourier transform. The SVD method was more suitable for estimating superresolved NMR spectra than the MEM because the SVD method allowed high-order estimation without spurious peaks, and it was easy to determine the order and the rank.
Improved cosmological model fitting of Planck data with a dark energy spike
Park, Chan-Gyung
2015-06-01
The Λ cold dark matter (Λ CDM ) model is currently known as the simplest cosmology model that best describes observations with a minimal number of parameters. Here we introduce a cosmology model that is preferred over the conventional Λ CDM one by constructing dark energy as the sum of the cosmological constant Λ and an additional fluid that is designed to have an extremely short transient spike in energy density during the radiation-matter equality era and an early scaling behavior with radiation and matter densities. The density parameter of the additional fluid is defined as a Gaussian function plus a constant in logarithmic scale-factor space. Searching for the best-fit cosmological parameters in the presence of such a dark energy spike gives a far smaller chi-square value by about 5 times the number of additional parameters introduced and narrower constraints on the matter density and Hubble constant compared with the best-fit Λ CDM model. The significant improvement in reducing the chi square mainly comes from the better fitting of the Planck temperature power spectrum around the third (ℓ≈800 ) and sixth (ℓ≈1800 ) acoustic peaks. The likelihood ratio test and the Akaike information criterion suggest that the model of a dark energy spike is strongly favored by the current cosmological observations over the conventional Λ CDM model. However, based on the Bayesian information criterion which penalizes models with more parameters, the strong evidence supporting the presence of a dark energy spike disappears. Our result emphasizes that the alternative cosmological parameter estimation with even better fitting of the same observational data is allowed in Einstein's gravity.
The shape of dark matter haloes - II. The GALACTUS H I modelling & fitting tool
Peters, S. P. C.; van der Kruit, P. C.; Allen, R. J.; Freeman, K. C.
2017-01-01
We present a new H I modelling tool called GALACTUS. The program has been designed to perform automated fits of disc-galaxy models to observations. It includes a treatment for the self-absorption of gas. The software has been released into the public domain. We describe the design philosophy and inner workings of the program. After this, we model the face-on galaxy NGC 2403 using both self-absorption and optically thin models, showing that self-absorption occurs even in face-on galaxies. These results are then used to model an edge-on galaxy. It is shown that the maximum surface brightness plateaus seen in Paper I of this series are indeed signs of self-absorption. The apparent H I mass of an edge-on galaxy can be drastically lower compared with that same galaxy seen face-on. The Tully-Fisher relation is found to be relatively free from self-absorption issues.
Non-covalent interactions at electrochemical interfaces: one model fits all?
Cabello, Gema; Leiva, Ezequiel P M; Gutiérrez, Claudio; Cuesta, Angel
2014-07-21
The shift with increasing concentration of alkali-metal cations of the potentials of both the spike and the hump observed in the cyclic voltammograms of Pt(111) electrodes in sulfuric acid solutions is shown to obey the simple model recently developed by us to explain the effect of non-covalent interactions at the electrical double layer. The results suggest that the model, originally developed to describe the effect of alkali-metal cations on the cyclic voltammogram of cyanide-modified Pt(111) electrodes, is of general applicability and can explain quantitatively the effect of cations on the properties of the electrical double layer.
Heliospheric Propagation of Coronal Mass Ejections: Drag-Based Model Fitting
Žic, T; Temmer, M
2015-01-01
The so-called drag-based model (DBM) simulates analytically the propagation of coronal mass ejections (CMEs) in interplanetary space and allows the prediction of their arrival times and impact speeds at any point in the heliosphere ("target"). The DBM is based on the assumption that beyond a distance of about 20 solar radii from the Sun, the dominant force acting on CMEs is the "aerodynamic" drag force. In the standard form of DBM, the user provisionally chooses values for the model input parameters, by which the kinematics of the CME over the entire Sun--"target" distance range is defined. The choice of model input parameters is usually based on several previously undertaken statistical studies. In other words, the model is used by ad hoc implementation of statistics-based values of the input parameters, which are not necessarily appropriate for the CME under study. Furthermore, such a procedure lacks quantitative information on how well the simulation reproduces the coronagraphically observed kinematics of ...
An improved cosmological model fitting of Planck data with a dark energy spike
Park, Chan-Gyung
2015-01-01
The $\\Lambda$ cold dark matter ($\\Lambda\\textrm{CDM}$) model is currently known as the simplest cosmology model that best describes observations with minimal number of parameters. Here we introduce a cosmology model that is preferred over the conventional $\\Lambda\\textrm{CDM}$ one by constructing dark energy as the sum of the cosmological constant $\\Lambda$ and the additional fluid that is designed to have an extremely short transient spike in energy density during the radiation-matter equality era and the early scaling behavior with radiation and matter densities. The density parameter of the additional fluid is defined as a Gaussian function plus a constant in logarithmic scale-factor space. Searching for the best-fit cosmological parameters in the presence of such a dark energy spike gives a far smaller chi-square value by about five times the number of additional parameters introduced and narrower constraints on matter density and Hubble constant compared with the best-fit $\\Lambda\\textrm{CDM}$ model. The...
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…
Visualization-Directed Interactive Model-Fitting to Spectral Data Cubes
Fluke, Christopher J; Barnes, David G
2010-01-01
Spectral datasets obtained at radio frequencies and optical/IR wavelengths are increasing in complexity as new facilities and instruments come online, resulting in an increased need to visualize and quantitatively analyze the velocity structures. As the visible structure in spectral data cubes is not purely spatial, additional insight is required to relate structures in 2D space plus line-of-sight velocity to their true three-dimensional (3D) structures. This can be achieved through the use of models that are converted to velocity-space representations. We have used the S2PLOT programming library to enable intuitive, interactive comparison between 3D models and spectral data, with potential for improved understanding of the spatial configurations. We also report on the use of 3D Cartesian shapelets to support quantitative analysis.
Can a first-order exponential decay model fit heart rate recovery after resistance exercise?
Bartels-Ferreira, Rhenan; de Sousa, Élder D; Trevizani, Gabriela A; Silva, Lilian P; Nakamura, Fábio Y; Forjaz, Cláudia L M; Lima, Jorge Roberto P; Peçanha, Tiago
2015-03-01
The time-constant of postexercise heart rate recovery (HRRτ ) obtained by fitting heart rate decay curve by a first-order exponential fitting has being used to assess cardiac autonomic recovery after endurance exercise. The feasibility of this model was not tested after resistance exercise (RE). The aim of this study was to test the goodness of fit of the first-order exponential decay model to fit heart rate recovery (HRR) after RE. Ten healthy subjects participated in the study. The experimental sessions occurred in two separated days and consisted of performance of 1 set of 10 repetitions at 50% or 80% of the load achieved on the one-repetition maximum test [low-intensity (LI) and high-intensity (HI) sessions, respectively]. Heart rate (HR) was continuously registered before and during exercise and also for 10 min of recovery. A monoexponential equation was used to fit the HRR curve during the postexercise period using different time windows (i.e. 30, 60, 90, … 600 s). For each time window, (i) HRRτ was calculated and (ii) variation of HR explained by the model (R(2) goodness of fit index) was assessed. The HRRτ showed stabilization from 360 and 420 s on LI and HI, respectively. Acceptable R(2) values were observed from the 360 s on LI (R(2) > 0.65) and at all tested time windows on HI (R(2) > 0.75). In conclusion, this study showed that using a minimum length of monitoring (~420 s) HRR after RE can be adequately modelled by a first-order exponential fitting. © 2014 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.
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.
Cheng, Yuan-Chieh; Chen, Jia-Hong; Chang, Rong-Jie; Wang, Chung-Yen; Hsu, Wei-Yao; Wang, Pei-Jen
2015-09-01
Contact lenses are typically measured by the wet-box method because of the high optical power resulting from the anterior central curvature of cornea, even though the back vertex power of the lenses are small. In this study, an optical measurement system based on the Shack-Hartmann wavefront principle was established to investigate the aberrations of soft contact lenses. Fitting conditions were micmicked to study the optical design of an eye model with various topographical shapes in the anterior cornea. Initially, the contact lenses were measured by the wet-box method, and then by fitting the various topographical shapes of cornea to the eye model. In addition, an optics simulation program was employed to determine the sources of errors and assess the accuracy of the system. Finally, samples of soft contact lenses with various Diopters were measured; and, both simulations and experimental results were compared for resolving the controversies of fitting contact lenses to an eye model for optical measurements. More importantly, the results show that the proposed system can be employed for study of primary aberrations in contact lenses.
Efficient parallel implementation of active appearance model fitting algorithm on GPU.
Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou
2014-01-01
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.
Understanding Systematics in ZZ Ceti Model Fitting to Enable Differential Seismology
Fuchs, J. T.; Dunlap, B. H.; Clemens, J. C.; Meza, J. A.; Dennihy, E.; Koester, D.
2017-03-01
We are conducting a large spectroscopic survey of over 130 Southern ZZ Cetis with the Goodman Spectrograph on the SOAR Telescope. Because it employs a single instrument with high UV throughput, this survey will both improve the signal-to-noise of the sample of SDSS ZZ Cetis and provide a uniform dataset for model comparison. We are paying special attention to systematics in the spectral fitting and quantify three of those systematics here. We show that relative positions in the log g -Teff plane are consistent for these three systematics.
Understanding Systematics in ZZ Ceti Model Fitting to Enable Differential Seismology
Fuchs, J T; Clemens, J C; Meza, J A; Dennihy, E; Koester, D
2016-01-01
We are conducting a large spectroscopic survey of over 130 Southern ZZ Cetis with the Goodman Spectrograph on the SOAR Telescope. Because it employs a single instrument with high UV throughput, this survey will both improve the signal-to-noise of the sample of SDSS ZZ Cetis and provide a uniform dataset for model comparison. We are paying special attention to systematics in the spectral fitting and quantify three of those systematics here. We show that relative positions in the $\\log{g}$-$T_{\\rm eff}$ plane are consistent for these three systematics.
Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.
Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L
2017-02-01
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.
The conceptual basis of mathematics in cardiology IV: statistics and model fitting.
Bates, Jason H T; Sobel, Burton E
2003-06-01
This is the fourth in a series of four articles developed for the readers of Coronary Artery Disease. Without language ideas cannot be articulated. What may not be so immediately obvious is that they cannot be formulated either. One of the essential languages of cardiology is mathematics. Unfortunately, medical education does not emphasize, and in fact, often neglects empowering physicians to think mathematically. Reference to statistics, conditional probability, multicompartmental modeling, algebra, calculus and transforms is common but often without provision of genuine conceptual understanding. At the University of Vermont College of Medicine, Professor Bates developed a course designed to address these deficiencies. The course covered mathematical principles pertinent to clinical cardiovascular and pulmonary medicine and research. It focused on fundamental concepts to facilitate formulation and grasp of ideas. This series of four articles was developed to make the material available for a wider audience. The articles will be published sequentially in Coronary Artery Disease. Beginning with fundamental axioms and basic algebraic manipulations they address algebra, function and graph theory, real and complex numbers, calculus and differential equations, mathematical modeling, linear system theory and integral transforms and statistical theory. The principles and concepts they address provide the foundation needed for in-depth study of any of these topics. Perhaps of even more importance, they should empower cardiologists and cardiovascular researchers to utilize the language of mathematics in assessing the phenomena of immediate pertinence to diagnosis, pathophysiology and therapeutics. The presentations are interposed with queries (by Coronary Artery Disease abbreviated as CAD) simulating the nature of interactions that occurred during the course itself. Each article concludes with one or more examples illustrating application of the concepts covered to
Comparing PyMorph and SDSS photometry. I. Background sky and model fitting effects
Fischer, J.-L.; Bernardi, M.; Meert, A.
2017-01-01
A number of recent estimates of the total luminosities of galaxies in the SDSS are significantly larger than those reported by the SDSS pipeline. This is because of a combination of three effects: one is simply a matter of defining the scale out to which one integrates the fit when defining the total luminosity, and amounts on average to ≤0.1 mags even for the most luminous galaxies. The other two are less trivial and tend to be larger; they are due to differences in how the background sky is estimated and what model is fit to the surface brightness profile. We show that PyMorph sky estimates are fainter than those of the SDSS DR7 or DR9 pipelines, but are in excellent agreement with the estimates of Blanton et al. (2011). Using the SDSS sky biases luminosities by more than a few tenths of a magnitude for objects with half-light radii ≥7 arcseconds. In the SDSS main galaxy sample these are typically luminous galaxies, so they are not necessarily nearby. This bias becomes worse when allowing the model more freedom to fit the surface brightness profile. When PyMorph sky values are used, then two component Sersic-Exponential fits to E+S0s return more light than single component deVaucouleurs fits (up to ˜0.2 mag), but less light than single Sersic fits (0.1 mag). Finally, we show that PyMorph fits of Meert et al. (2015) to DR7 data remain valid for DR9 images. Our findings show that, especially at large luminosities, these PyMorph estimates should be preferred to the SDSS pipeline values.
Comparing pymorph and SDSS photometry - I. Background sky and model fitting effects
Fischer, J.-L.; Bernardi, M.; Meert, A.
2017-05-01
A number of recent estimates of the total luminosities of galaxies in the SDSS are significantly larger than those reported by the Sloan Digital Sky Survey (SDSS) pipeline. This is because of a combination of three effects: one is simply a matter of defining the scale out to which one integrates the fit when defining the total luminosity, and amounts on average to ≤0.1 mag even for the most luminous galaxies. The other two are less trivial and tend to be larger; they are due to differences in how the background sky is estimated and what model is fit to the surface brightness profile. We show that pymorph sky estimates are fainter than those of the Sloan Digital Sky Servey Data Release 7 or Data Release 9 pipelines, but are in excellent agreement with the estimates of Blanton et al. Using the SDSS sky biases luminosities by more than a few tenths of a magnitude for objects with half-light radii ≥7 arcsec. In the SDSS main galaxy sample, these are typically luminous galaxies, so they are not necessarily nearby. This bias becomes worse when allowing the model more freedom to fit the surface brightness profile. When pymorph sky values are used, then two-component Sérsic-exponential fits to E+S0s return more light than single component deVaucouleurs fits (up to ˜0.2 mag), but less light than single Sérsic fits (0.1 mag). Finally, we show that pymorph fits of Meert et al. to DR7 data remain valid for DR9 images. Our findings show that, especially at large luminosities, these pymorph estimates should be preferred to the SDSS pipeline values.
AN ANIMAL MODEL FITS FOR STUDYING DIVERGENCES AMONG DIABETIC MICROVASCULAR COMPLICATIONS
Directory of Open Access Journals (Sweden)
Stella Maris Martínez
2005-08-01
Full Text Available SUMMARYA comparison is made between data reported by Kanauchi et al (1998 in patients with a rare occurring divergence (advanced nephropathy without retinopathy and others, obtained in a similar line of rats (eSS, accepted as a general model for type 2 diabetes. This comparison reveals attracting analogies from different standpoints (methods employed, age, gender, lack of obesity, duration and control of diabetes, biochemical - total urinary protein excretion, serum creatinine and clearance of creatinine - and microscopic analysis. Such analogies allow proposing to eSS as an animal model for the particular study of the referred nephro- retinian divergences as well as others, opportunely reported in diabetic patients.RESUMENSe comunica una comparación hecha entre datos reportados por Kanauchi et al (1998 en pacientes con una rara divergencia (neuropatía avanzada sin retinopatía y otros obtenidos en una línea similar de ratas (eSS, aceptada como modelo general para el estudio de la diabetes tipo 2. Dicha comparación arroja analogía atractivas desde distintos puntos de vista (métodos empleados, edad, género, ausencia de obesidad, duración y control de la diabetes, análisis bioquímicos - excreción proteica urinaria total, creatininemia y clearance de creatinina y microscópicos. Tales analogías permiten proponer a las ratas eSS como modelo para el estudio particular de las referidas divergencias reno-retinianas así como de otras, oportunamente comunicadas en pacientes diabéticos.
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.
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
Systematic effects on the size-luminosity relation: dependence on model fitting and morphology
Bernardi, M; Vikram, V; Huertas-Company, M; Mei, S; Shankar, F; Sheth, R K
2012-01-01
We quantify the systematics in the size-luminosity relation of galaxies in the SDSS main sample which arise from fitting different 1- and 2-component model profiles to the images. In objects brighter than L*, fitting a single Sersic profile to what is really a two-component SerExp system leads to biases: the half-light radius is increasingly overestimated as n of the fitted single component increases; it is also overestimated at B/T ~ 0.6. However, the net effect on the R-L relation is small, except for the most luminous tail, where it curves upwards towards larger sizes. We also study how this relation depends on morphological type. Our analysis is one of the first to use Bayesian-classifier derived weights, rather than hard cuts, to define morphology. Crudely, there appear to be only two relations: one for early-types (Es, S0s and Sa's) and another for late-types (Sbs and Scds). However, closer inspection shows that within the early-type sample S0s tend to be 15% smaller than Es of the same luminosity, and,...
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...
parfm : Parametric Frailty Models in R
Directory of Open Access Journals (Sweden)
Marco Munda
2012-11-01
Full Text Available Frailty models are getting more and more popular to account for overdispersion and/or clustering in survival data. When the form of the baseline hazard is somehow known in advance, the parametric estimation approach can be used advantageously. Nonetheless, there is no unified widely available software that deals with the parametric frailty model. The new parfm package remedies that lack by providing a wide range of parametric frailty models in R. The gamma, inverse Gaussian, and positive stable frailty distributions can be specified, together with five different baseline hazards. Parameter estimation is done by maximising the marginal log-likelihood, with right-censored and possibly left-truncated data. In the multivariate setting, the inverse Gaussian may encounter numerical difficulties with a huge number of events in at least one cluster. The positive stable model shows analogous difficulties but an ad-hoc solution is implemented, whereas the gamma model is very resistant due to the simplicity of its Laplace transform.
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.
Probabilistic Reachability for Parametric Markov Models
DEFF Research Database (Denmark)
Hahn, Ernst Moritz; Hermanns, Holger; Zhang, Lijun
2011-01-01
Given a parametric Markov model, we consider the problem of computing the rational function expressing the probability of reaching a given set of states. To attack this principal problem, Daws has suggested to first convert the Markov chain into a finite automaton, from which a regular expression...... is computed. Afterwards, this expression is evaluated to a closed form function representing the reachability probability. This paper investigates how this idea can be turned into an effective procedure. It turns out that the bottleneck lies in the growth of the regular expression relative to the number...... 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...
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
Product directivity models for parametric loudspeakers.
Shi, Chuang; Gan, Woon-Seng
2012-03-01
In a recent work, the beamsteering characteristics of parametric loudspeakers were validated in an experiment. It was shown that based on the product directivity model, the locations and amplitudes of the mainlobe and grating lobes could be predicted within acceptable errors. However, the measured amplitudes of sidelobes have not been able to match the theoretical results accurately. In this paper, the original theories behind the product directivity model are revisited, and three modified product directivity models are proposed: (i) the advanced product directivity model, (ii) the exponential product directivity model, and (iii) the combined product directivity model. The proposed product directivity models take the radii of equivalent Gaussian sources into account and obtain better predictions of sidelobes for the difference frequency waves. From the comparison between measurement results and numerical solutions, all the proposed models outperform the original product directivity model in terms of selected sidelobe predictions by about 10 dB.
Parametric Regression Models Using Reversed Hazard Rates
Directory of Open Access Journals (Sweden)
Asokan Mulayath Variyath
2014-01-01
Full Text Available Proportional hazard regression models are widely used in survival analysis to understand and exploit the relationship between survival time and covariates. For left censored survival times, reversed hazard rate functions are more appropriate. In this paper, we develop a parametric proportional hazard rates model using an inverted Weibull distribution. The estimation and construction of confidence intervals for the parameters are discussed. We assess the performance of the proposed procedure based on a large number of Monte Carlo simulations. We illustrate the proposed method using a real case example.
Falsifying Oscillation Properties of Parametric Biological Models
Directory of Open Access Journals (Sweden)
Thao Dang
2013-08-01
Full Text Available We propose an approach to falsification of oscillation properties of parametric biological models, based on the recently developed techniques for testing continuous and hybrid systems. In this approach, an oscillation property can be specified using a hybrid automaton, which is then used to guide the exploration in the state and input spaces to search for the behaviors that do not satisfy the property. We illustrate the approach with the Laub-Loomis model for spontaneous oscillations during the aggregation stage of Dictyostelium.
Modeling Interconnect Variability Using Efficient Parametric Model Order Reduction
Li, Peng; Li, Xin; Pileggi, Lawrence T; Nassif, Sani R
2011-01-01
Assessing IC manufacturing process fluctuations and their impacts on IC interconnect performance has become unavoidable for modern DSM designs. However, the construction of parametric interconnect models is often hampered by the rapid increase in computational cost and model complexity. In this paper we present an efficient yet accurate parametric model order reduction algorithm for addressing the variability of IC interconnect performance. The efficiency of the approach lies in a novel combination of low-rank matrix approximation and multi-parameter moment matching. The complexity of the proposed parametric model order reduction is as low as that of a standard Krylov subspace method when applied to a nominal system. Under the projection-based framework, our algorithm also preserves the passivity of the resulting parametric models.
Raykov, Tenko; Lee, Chun-Lung; Marcoulides, George A.; Chang, Chi
2013-01-01
The relationship between saturated path-analysis models and their fit to data is revisited. It is demonstrated that a saturated model need not fit perfectly or even well a given data set when fit to the raw data is examined, a criterion currently frequently overlooked by researchers utilizing path analysis modeling techniques. The potential of…
Parametric uncertainty modeling for robust control
DEFF Research Database (Denmark)
Rasmussen, K.H.; Jørgensen, Sten Bay
1999-01-01
The dynamic behaviour of a non-linear process can often be approximated with a time-varying linear model. In the presented methodology the dynamics is modeled non-conservatively as parametric uncertainty in linear lime invariant models. The obtained uncertainty description makes it possible...... method can be utilized in identification of a nominal model with uncertainty description. The method is demonstrated on a binary distillation column operating in the LV configuration. The dynamics of the column is approximated by a second order linear model, wherein the parameters vary as the operating...... to perform robustness analysis on a control system using the structured singular value. The idea behind the proposed method is to fit a rational function to the parameter variation. The parameter variation can then be expressed as a linear fractional transformation (LFT), It is discussed how the proposed...
Hierarchical Geometric Constraint Model for Parametric Feature Based Modeling
Institute of Scientific and Technical Information of China (English)
高曙明; 彭群生
1997-01-01
A new geometric constraint model is described,which is hierarchical and suitable for parametric feature based modeling.In this model,different levels of geometric information are repesented to support various stages of a design process.An efficient approach to parametric feature based modeling is also presented,adopting the high level geometric constraint model.The low level geometric model such as B-reps can be derived automatically from the hig level geometric constraint model,enabling designers to perform their task of detailed design.
Neitzel, Anne-Christin; Stamer, Eckhard; Junge, Wolfgang; Thaller, Georg
2015-05-01
Laboratory somatic cell count (LSCC) records are usually recorded monthly and provide an important information source for breeding and herd management. Daily milk viscosity detection in composite milking (expressed as drain time) with an automated on-line California Mastitis Test (CMT) could serve immediately as an early predictor of udder diseases and might be used as a selection criterion to improve udder health. The aim of the present study was to clarify the relationship between the well-established LSCS and the new trait,'drain time', and to estimate their correlations to important production traits. Data were recorded on the dairy research farm Karkendamm in Germany. Viscosity sensors were installed on every fourth milking stall in the rotary parlour to measure daily drain time records. Weekly LSCC and milk composition data were available. Two data sets were created containing records of 187,692 milkings from 320 cows (D1) and 25,887 drain time records from 311 cows (D2). Different fixed effect models, describing the log-transformed drain time (logDT), were fitted to achieve applicable models for further analysis. Lactation curves were modelled with standard parametric functions (Ali and Schaeffer, Legendre polynomials of second and third degree) of days in milk (DIM). Random regression models were further applied to estimate the correlations between cow effects between logDT and LSCS with further important production traits. LogDT and LSCS were strongest correlated in mid-lactation (r = 0.78). Correlations between logDT and production traits were low to medium. Highest correlations were reached in late lactation between logDT and milk yield (r = -0.31), between logDT and protein content (r = 0.30) and in early as well as in late lactation between logDT and lactose content (r = -0.28). The results of the present study show that the drain time could be used as a new trait for daily mastitis control.
uvmcmcfit: Parametric models to interferometric data fitter
Bussmann, Shane; Leung, Tsz Kuk (Daisy); Conley, Alexander
2016-06-01
Uvmcmcfit fits parametric models to interferometric data. It is ideally suited to extract the maximum amount of information from marginally resolved observations with interferometers like the Atacama Large Millimeter Array (ALMA), Submillimeter Array (SMA), and Plateau de Bure Interferometer (PdBI). uvmcmcfit uses emcee (ascl:1303.002) to do Markov Chain Monte Carlo (MCMC) and can measure the goodness of fit from visibilities rather than deconvolved images, an advantage when there is strong gravitational lensing and in other situations. uvmcmcfit includes a pure-Python adaptation of Miriad’s (ascl:1106.007) uvmodel task to generate simulated visibilities given observed visibilities and a model image and a simple ray-tracing routine that allows it to account for both strongly lensed systems (where multiple images of the lensed galaxy are detected) and weakly lensed systems (where only a single image of the lensed galaxy is detected).
Lipschitz Parametrization of Probabilistic Graphical Models
Honorio, Jean
2012-01-01
We show that the log-likelihood of several probabilistic graphical models is Lipschitz continuous with respect to the lp-norm of the parameters. We discuss several implications of Lipschitz parametrization. We present an upper bound of the Kullback-Leibler divergence that allows understanding methods that penalize the lp-norm of differences of parameters as the minimization of that upper bound. The expected log-likelihood is lower bounded by the negative lp-norm, which allows understanding the generalization ability of probabilistic models. The exponential of the negative lp-norm is involved in the lower bound of the Bayes error rate, which shows that it is reasonable to use parameters as features in algorithms that rely on metric spaces (e.g. classification, dimensionality reduction, clustering). Our results do not rely on specific algorithms for learning the structure or parameters. We show preliminary results for activity recognition and temporal segmentation.
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. m(
Parametric hazard rate models for long-term sickness absence
Koopmans, Petra C.; Roelen, Corne A. M.; Groothoff, Johan W.
2009-01-01
In research on the time to onset of sickness absence and the duration of sickness absence episodes, Cox proportional hazard models are in common use. However, parametric models are to be preferred when time in itself is considered as independent variable. This study compares parametric hazard rate m
Using a Parametric Solid Modeler as an Instructional Tool
Devine, Kevin L.
2008-01-01
This paper presents the results of a quasi-experimental study that brought 3D constraint-based parametric solid modeling technology into the high school mathematics classroom. This study used two intact groups; a control group and an experimental group, to measure the extent to which using a parametric solid modeler during instruction affects…
Parametric hazard rate models for long-term sickness absence
Koopmans, Petra C.; Roelen, Corne A. M.; Groothoff, Johan W.
2009-01-01
In research on the time to onset of sickness absence and the duration of sickness absence episodes, Cox proportional hazard models are in common use. However, parametric models are to be preferred when time in itself is considered as independent variable. This study compares parametric hazard rate m
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.
Mixing parametrizations for ocean climate modelling
Gusev, Anatoly; Moshonkin, Sergey; Diansky, Nikolay; Zalesny, Vladimir
2016-04-01
The algorithm is presented of splitting the total evolutionary equations for the turbulence kinetic energy (TKE) and turbulence dissipation frequency (TDF), which is used to parameterize the viscosity and diffusion coefficients in ocean circulation models. The turbulence model equations are split into the stages of transport-diffusion and generation-dissipation. For the generation-dissipation stage, the following schemes are implemented: the explicit-implicit numerical scheme, analytical solution and the asymptotic behavior of the analytical solutions. The experiments were performed with different mixing parameterizations for the modelling of Arctic and the Atlantic climate decadal variability with the eddy-permitting circulation model INMOM (Institute of Numerical Mathematics Ocean Model) using vertical grid refinement in the zone of fully developed turbulence. The proposed model with the split equations for turbulence characteristics is similar to the contemporary differential turbulence models, concerning the physical formulations. At the same time, its algorithm has high enough computational efficiency. Parameterizations with using the split turbulence model make it possible to obtain more adequate structure of temperature and salinity at decadal timescales, compared to the simpler Pacanowski-Philander (PP) turbulence parameterization. Parameterizations with using analytical solution or numerical scheme at the generation-dissipation step of the turbulence model leads to better representation of ocean climate than the faster parameterization using the asymptotic behavior of the analytical solution. At the same time, the computational efficiency left almost unchanged relative to the simple PP parameterization. Usage of PP parametrization in the circulation model leads to realistic simulation of density and circulation with violation of T,S-relationships. This error is majorly avoided with using the proposed parameterizations containing the split turbulence model
A finite element parametric modeling technique of aircraft wing structures
Institute of Scientific and Technical Information of China (English)
Tang Jiapeng; Xi Ping; Zhang Baoyuan; Hu Bifu
2013-01-01
A finite element parametric modeling method of aircraft wing structures is proposed in this paper because of time-consuming characteristics of finite element analysis pre-processing. The main research is positioned during the preliminary design phase of aircraft structures. A knowledge-driven system of fast finite element modeling is built. Based on this method, employing a template parametric technique, knowledge including design methods, rules, and expert experience in the process of modeling is encapsulated and a finite element model is established automatically, which greatly improves the speed, accuracy, and standardization degree of modeling. Skeleton model, geometric mesh model, and finite element model including finite element mesh and property data are established on parametric description and automatic update. The outcomes of research show that the method settles a series of problems of parameter association and model update in the pro-cess of finite element modeling which establishes a key technical basis for finite element parametric analysis and optimization design.
Cai, Li
2015-06-01
Lord and Wingersky's (Appl Psychol Meas 8:453-461, 1984) recursive algorithm for creating summed score based likelihoods and posteriors has a proven track record in unidimensional item response theory (IRT) applications. Extending the recursive algorithm to handle multidimensionality is relatively simple, especially with fixed quadrature because the recursions can be defined on a grid formed by direct products of quadrature points. However, the increase in computational burden remains exponential in the number of dimensions, making the implementation of the recursive algorithm cumbersome for truly high-dimensional models. In this paper, a dimension reduction method that is specific to the Lord-Wingersky recursions is developed. This method can take advantage of the restrictions implied by hierarchical item factor models, e.g., the bifactor model, the testlet model, or the two-tier model, such that a version of the Lord-Wingersky recursive algorithm can operate on a dramatically reduced set of quadrature points. For instance, in a bifactor model, the dimension of integration is always equal to 2, regardless of the number of factors. The new algorithm not only provides an effective mechanism to produce summed score to IRT scaled score translation tables properly adjusted for residual dependence, but leads to new applications in test scoring, linking, and model fit checking as well. Simulated and empirical examples are used to illustrate the new applications.
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.
Variable Relation Parametric Model on Graphics Modelon for Collaboration Design
Institute of Scientific and Technical Information of China (English)
DONG Yu-de; ZHAO Han; LI Yan-feng
2005-01-01
A new approach to variable relation parametric model for collaboration design based on the graphic modelon has been put forward. The paper gives a parametric description model of graphic modelon, and relating method for different graphic modelon based on variable constraint. At the same time, with the aim of engineering application in the collaboration design, the autonmous constraint in modelon and relative constraint between two modelons are given. Finally, with the tool of variable and relation dbase, the solving method of variable relating and variable-driven among different graphic modelon in a part, and doubleacting variable relating parametric method among different parts for collaboration are given.
Two parametric tropical cyclone models for storm surge modeling
Institute of Scientific and Technical Information of China (English)
WANG Zhi-li
2010-01-01
In this paper,the two parametric tropical cyclone models for storm surge modeling are further developed.The analytical expressions of tangential and radial velocity distribution are derived from the governing momentum equations,based on the general symmetric pressure distribution proposed by Holland and Fujita.On the basis of the data of several tropical cyclones that occurred in East China Ocean,the shape parameter in pressure model is estimated.Finally,the Fred cyclone(typhoon 199417)is calculated,and comparisons of measured and calculated air pressures and wind speed are presented.
Directory of Open Access Journals (Sweden)
Yu eBai
2014-08-01
Full Text Available Humans are capable of correcting their actions based on actions performed in the past, and this ability enables them to adapt to a changing environment. The computational field of reinforcement learning (RL has provided a powerful explanation for understanding such processes. Recently, the dual learning system, modeled as a hybrid model that incorporates value update based on reward-prediction error and learning rate modulation based on the surprise signal, has gained attention as a model for explaining various neural signals. However, the functional significance of the hybrid model has not been established. In the present study, we used computer simulation in a reversal learning task to address functional significance. The hybrid model was found to perform better than the standard RL model in a large parameter setting. These results suggest that the hybrid model is more robust against mistuning of parameters compared to the standard RL model when decision makers continue to learn stimulus-reward contingencies, which make an abrupt changes. The parameter fitting results also indicated that the hybrid model fit better than the standard RL model for more than 50% of the participants, which suggests that the hybrid model has more explanatory power for the behavioral data than the standard RL model.
Building information modeling based on intelligent parametric technology
Institute of Scientific and Technical Information of China (English)
ZENG Xudong; TAN Jie
2007-01-01
In order to push the information organization process of the building industry,promote sustainable architectural design and enhance the competitiveness of China's building industry,the author studies building information modeling (BIM) based on intelligent parametric modeling technology.Building information modeling is a new technology in the field of computer aided architectural design,which contains not only geometric data,but also the great amount of engineering data throughout the lifecycle of a building.The author also compares BIM technology with two-dimensional CAD technology,and demonstrates the advantages and characteristics of intelligent parametric modeling technology.Building information modeling,which is based on intelligent parametric modeling technology,will certainly replace traditional computer aided architectural design and become the new driving force to push forward China's building industry in this information age.
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.
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.
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.
Incident duration modeling using flexible parametric hazard-based models.
Li, Ruimin; Shang, Pan
2014-01-01
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.
Tillema, Sandra; ter Bogt, Henk
2016-01-01
The degree of auditor independence is an important issue in the performance auditing literature. However, little attention has been paid to the influence of the context in which an audit body operates. This paper investigates how an audit model with a high degree of auditor independence, which is co
Tillema, Sandra; ter Bogt, Henk
2016-01-01
The degree of auditor independence is an important issue in the performance auditing literature. However, little attention has been paid to the influence of the context in which an audit body operates. This paper investigates how an audit model with a high degree of auditor independence, which is co
Energy Technology Data Exchange (ETDEWEB)
Onaka, T.; Jong, T. de; Willems, F.J. (Amsterdam Univ. (NL))
1989-12-01
We have fitted dust shell models to the IRAS LRS spectra of 109 M Mira variables. The main assumptions in the model calculations are: (i) the dust shell is spherical and optically thin, (ii) the dust grains consist of aluminum oxide and amorphous magnesium silicate, (iii) the mass loss rate is constant, (iv) the stellar photosphere is characterized by R = 3 x 10{sup 13} cm and T = 2500 K. Best fit models are calculated for each star. A model is completely determined by five parameters: the dust temperatures at the inner boundaries of the aluminum oxide and silicate dust shells, the column densities of each dust grain component, and the distance to the star. It turns out that the 1 - 200 {mu}m infrared energy distributions calculated for the best fit parameters also provide quite satisfactory fits to the observed near- and far-infrared broad-band data for most sources. The material presented here forms the basis for a study of dust condensation in the circumstellar shells around Mira variables.
Institute of Scientific and Technical Information of China (English)
Jesus Mellado; Edgar Sepulveda; Jose E Garcia; Alvaro Rodriguez; Maria A De Santiago; Francisco G Veliz; Miguel Mellado
2014-01-01
Nineteen multiparous barren Holstein cows were subjected to an induction of lactation protocol for 21 d administering estradiol cypionate (2 mg kg-1 of body weight (BW) d-1, on day 1 to 14), progesterone (0.10 mg kg-1 of BW, on day 1 to 7), lfumethasone (0.03 mg kg-1 of BW, on day 18 to 20) and recombinant bovine somatotropin (rbST;500 mg per cow, on day 1, 6, 16 and 21). At the end of lactation and with a minimum of a 2-mon dry period, the same cows were again hormonally induced into lactation. Cows in both lactations were not artiifcially inseminated, they were milked 3 times daily and received rbST throughout lactation. Mean accumulated milk yield at 305 d in milk (DIM) did not differ between the ifrst and second induced lactations ((9 710 ±1 728) vs. (9 309±2 150) kg;mean±SD). Total milk yield ((12 707±3 406) vs. (12 306±4 218) kg;mean±SD) and lactation length ((405±100) vs. (410±91) d;mean±SD) were not different between the ifrst and second induced lactations. In a second study, 15 empirical models including exponential, power law, yield-density, sigmoidal and miscellaneous models were compared for their suitability by modeling 12-mon (n=334), 18-mon (n=164) and 29-mon (n=22) lactation cycles of Holsteins cows induced into lactation and treated with rbST throughout the lactation. Hoerl (Y=ab1/xxc), Wood (Y=axb exp(cx)) and Dhanoa (Y=ax(bc)exp(cx)) models were equally suitable to describe 12-mon lactations. An exponential model with ifve parameters (Y=exp(a+bx+cd2+e/x)) showed the best ift for milk yield for 18-mon lactations. The rational model (Y=a+bx/1+cx+dx2) was found to produce the closest ift for 29-mon lactations. It was concluded that, with the protocol used in the present study, multiparous cows respond favorably to a second cycle of induced lactation, with milk yield similar to that experienced during the ifrst cycle. Thus, dairy producers might be able to lengthen the productive life of infertile high producing cows with a renewal of
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?
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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
Parametric models of reflectance spectra for dyed fabrics
Aiken, Daniel C.; Ramsey, Scott; Mayo, Troy; Lambrakos, Samuel G.; Peak, Joseph
2016-05-01
This study examines parametric modeling of NIR reflectivity spectra for dyed fabrics, which provides for both their inverse and direct modeling. The dye considered for prototype analysis is triarylamine dye. The fabrics considered are camouflage textiles characterized by color variations. The results of this study provide validation of the constructed parametric models, within reasonable error tolerances for practical applications, including NIR spectral characteristics in camouflage textiles, for purposes of simulating NIR spectra corresponding to various dye concentrations in host fabrics, and potentially to mixtures of dyes.
Directory of Open Access Journals (Sweden)
J. G. Hemann
2009-01-01
Full Text Available A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM_{2.5} concentrations, comprised of 39 chemical species from nine pollutant sources. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A circular block bootstrap is used to create replicate datasets, with the same receptor model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series, and this classification is used to align factor orderings across the model results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input factors used to create the synthetic data assesses bias. The results indicate that variability in factor contribution estimates does not necessarily encompass model error: contribution estimates can have small associated variability across results yet also be very biased. These findings are likely dependent on characteristics of the data.
Matsushita, Satoki; Matsuo, Hiroshi; Pardo, Juan R.; Radford, Simon J. E.
1999-10-01
A second observing run aimed to measure the millimeter and submillimeter-wave (150-1500 GHz or 2 mm-200 mu m) atmospheric opacity was carried out with a Fourier Transform Spectrometer (FTS) at Pampa la Bola, 4800 m above sea level in northern Chile. We obtained high transmission spectra, showing up to ~ 67% transmission at submillimeter-wave windows. The observed spectra can be well modeled by newly developed radiative-transfer calculations. Correlations between 220 GHz and submillimeter-wave opacities were reanalized, including the new data set. The results show almost identical trends as the ones resulting from the first measurements. We also identified supra-terahertz windows (located around 1035 GHz, 1350 GHz, and 1500 GHz), which could not be seen in our first measurements. Opacity correlations between the 220 GHz and these new windows are derived for the first time. Combined with a statistical study of the 225 GHz opacity data of the Chajnantor site (7 km apart from Pampa la Bola), it is estimated that submillimeter-wave observations can be done with zenith opacity less than 1.0 (at the most transparent frequency in those windows) for about 50% of the winter season.
DEVELOPING PARAMETRIC BUILDING MODELS – THE GANDIS USE CASE
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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.
SEMIPARAMETRIC VERSUS PARAMETRIC CLASSIFICATION MODELS - AN APPLICATION TO DIRECT MARKETING
BULT, [No Value
1993-01-01
In this paper we are concerned with estimation of a classification model using semiparametric and parametric methods. Benefits and limitations of semiparametric models in general, and of Manski's maximum score method in particular, are discussed. The maximum score method yields consistent estimates
A New Method for Characterizing Single Parametric Model Potential
Institute of Scientific and Technical Information of China (English)
P.S. Vyas; P.N. Gajjar; B.Y. Thakore; A.R. Jani
2008-01-01
A novel approach of characterizing single parametric model potential is proposed by equating total pair wise force to zero.Our well-established single parametric model potential is characterized using the proposed idea and compared the obtained parameter with parameters computed by previously used approaches.Thus characterized pseudopotential is then tested to compute total energy of alkali metals.The results establish the reliability of proposed idea of making total pair wise force to zero in determining the parameter of the pseudopotential.
Diagnostics and future evolution analysis of the two parametric models
Yang, Guang; Meng, Xinhe
2016-01-01
In this paper, we apply three diagnostics including $Om$, Statefinder hierarchy and the growth rate of perturbations into discriminating the two parametric models for the effective pressure with the $\\Lambda$CDM model. By using the $Om$ diagnostic, we find that both the model 1 and the model 2 can be hardly distinguished from each other as well as the $\\Lambda$CDM model in terms of 68\\% confidence level. As a supplement, by using the Statefinder hierarchy diagnostics and the growth rate of perturbations, we discover that not only can our two parametric models be well distinguished from $\\Lambda$CDM model, but also, by comparing with $Om$ diagnostic, the model 1 and the model 2 can be distinguished better from each other. In addition, we also explore the fate of universe evolution of our two models by means of the rip analysis.
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......In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state...... to handle multi-contrast (vector-valued intensities) MR data. We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility...
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.
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Maydeu-Olivares, Albert
2005-04-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in their data. To verify this conjecture, we compare the fit of these models to the Social Problem Solving Inventory-Revised, whose scales were designed to be unidimensional. A calibration and a cross-validation sample of new observations were used. We also included the following parametric models in the comparison: Bock's nominal model, Masters' partial credit model, and Thissen and Steinberg's extension of the latter. All models were estimated using full information maximum likelihood. We also included in the comparison a normal ogive model version of Samejima's model estimated using limited information estimation. We found that for all scales Samejima's model outperformed all other parametric IRT models in both samples, regardless of the estimation method employed. The non-parametric model outperformed all parametric models in the calibration sample. However, the graded model outperformed MFS in the cross-validation sample in some of the scales. We advocate employing the graded model estimated using limited information methods in modeling Likert-type data, as these methods are more versatile than full information methods to capture the multidimensionality that is generally present in personality data.
Parametric time delay modeling for floating point units
Fahmy, Hossam A. H.; Liddicoat, Albert A.; Flynn, Michael J.
2002-12-01
A parametric time delay model to compare floating point unit implementations is proposed. This model is used to compare a previously proposed floating point adder using a redundant number representation with other high-performance implementations. The operand width, the fan-in of the logic gates and the radix of the redundant format are used as parameters to the model. The comparison is done over a range of operand widths, fan-in and radices to show the merits of each implementation.
Automated parametrical antenna modelling for ambient assisted living applications
Kazemzadeh, R.; John, W.; Mathis, W.
2012-09-01
In this paper a parametric modeling technique for a fast polynomial extraction of the physically relevant parameters of inductively coupled RFID/NFC (radio frequency identification/near field communication) antennas is presented. The polynomial model equations are obtained by means of a three-step procedure: first, full Partial Element Equivalent Circuit (PEEC) antenna models are determined by means of a number of parametric simulations within the input parameter range of a certain antenna class. Based on these models, the RLC antenna parameters are extracted in a subsequent model reduction step. Employing these parameters, polynomial equations describing the antenna parameter with respect to (w.r.t.) the overall antenna input parameter range are extracted by means of polynomial interpolation and approximation of the change of the polynomials' coefficients. The described approach is compared to the results of a reference PEEC solver with regard to accuracy and computation effort.
Correlated Non-Parametric Latent Feature Models
Doshi-Velez, Finale
2012-01-01
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.
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’ 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
Scarpa, Bruno; Dorigo, Tommaso
2017-03-01
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.
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.
Parametric modelling of nonstationary platform deck motions
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.
-sense-stationary processes. Then the time series are modelled by the maximum entropy method which is formulated here for spectral estimation of platform deck displacements. The lower order maximum entropy spectra of nonstationary platform deck displacements are compared...
A Parametric Modelling Method for Dexterous Finger Reachable Workspaces
2016-01-01
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-finge...
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.
Non-Parametric Model Drift Detection
2016-07-01
Analysis Division Information Directorate This report is published in the interest of scientific and technical...took place on datasets made up of text documents. The difference between datasets used to estimate potential error (drop in accuracy) that the model...Assistant, Extraction of executable rules from regulatory text 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 19a
PARAMETRIC MODELING, CREATIVITY, AND DESIGN: TWO EXPERIENCES WITH ARCHITECTURE’ STUDENTS
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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.
Foreground Bias From Parametric Models of Far-IR Dust Emission
Kogut, A
2016-01-01
We use simple toy models of far-IR dust emission to estimate the accuracy to which the polarization of the cosmic microwave background can be recovered using multi-frequency fits, if the parametric form chosen for the fitted dust model differs from the actual dust emission. Commonly used approximations to the far-IR dust spectrum yield CMB residuals comparable to or larger than the sensitivities expected for the next generation of CMB missions, despite fitting the combined CMB + foreground emission to precision 0.1% or better. The Rayleigh-Jeans approximation to the dust spectrum biases the fitted dust spectral index by Delta beta_d = 0.2 and the inflationary B-mode amplitude by Delta r = 0.03. Fitting the dust to a modified blackbody at a single temperature biases the best-fit CMB by Delta r > 0.003 if the true dust spectrum contains multiple temperature components. A 13-parameter model fitting two temperature components reduces this bias by an order of magnitude if the true dust spectrum is in fact a simple...
A new pressure-parametrization unified dark fluid model
Energy Technology Data Exchange (ETDEWEB)
Wang, Deng [Nankai University, Theoretical Physics Division, Chern Institute of Mathematics, Tianjin (China); Yan, Yang-Jie; Meng, Xin-He [Nankai University, Department of Physics, Tianjin (China)
2017-04-15
We propose a new pressure-parametrization model to explain the accelerated expansion of the late-time Universe by considering the baryon matter and dark contents (dark matter and dark energy) as a unified dark fluid. To realize this model more physically, we reconstruct it with the quintessence and phantom scalar fields, respectively. We use the recent cosmological data to constrain this model, distinguish it from the standard cosmological model and find that the value of the Hubble constant H{sub 0} = 68.34{sup +0.53}{sub -0.92} supports the global measurement by the Planck satellite at the 1σ confidence level. (orig.)
A Novel Parametric Model For The Human Respiratory System
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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…
Modeling of autoresonant control of a parametrically excited screen machine
Abolfazl Zahedi, S.; Babitsky, Vladimir
2016-10-01
Modelling of nonlinear dynamic response of a screen machine described by the nonlinear coupled differential equations and excited by the system of autoresonant control is presented. The displacement signal of the screen is fed to the screen excitation directly by means of positive feedback. Negative feedback is used to fix the level of screen amplitude response within the expected range. The screen is anticipated to vibrate with a parametric resonance and the excitation, stabilization and control response of the system are studied in the stable mode. Autoresonant control is thoroughly investigated and output tracking is reported. The control developed provides the possibility of self-tuning and self-adaptation mechanisms that allow the screen machine to maintain a parametric resonant mode of oscillation under a wide range of uncertainty of mass and viscosity.
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.
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
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.
Major, Jason T.; Johnson, Wendy; Deary, Ian J.
2012-01-01
Three prominent theories of intelligence, the Cattell-Horn-Carroll (CHC), extended fluid-crystallized (Gf-Gc) and verbal-perceptual-image rotation (VPR) theories, provide differing descriptions of the structure of intelligence (McGrew, 2009; Horn & Blankson, 2005; Johnson & Bouchard, 2005b). To compare these theories, models representing them were…
Directory of Open Access Journals (Sweden)
Ali Zare
2011-10-01
Full Text Available Survival analysis is a set of methods used for analysis of the data which exist until the occurrence of an event. This study aimed to compare the results of the use of the semi-parametric Cox model with parametric models to determine the factors influencing the length of stay of patients in the inpatient units of Women Hospital in Tehran, Iran. In this historical cohort study all 3421 charts of the patients admitted to Obstetrics, Surgery and Oncology units in 2008 were reviewed and the required patient data such as medical insurance coverage types, admission months, days and times, inpatient units, final diagnoses, the number of diagnostic tests, admission types were collected. The patient length of stay in hospitals leading to recoverys was considered as a survival variable. To compare the semi-parametric Cox model and parametric (including exponential, Weibull, Gompertz, log-normal, log-logistic and gamma models and find the best model fitted to studied data, Akaike's Information Criterion (AIC and Cox-Snell residual were used. P<0.05 was considered as statistically significant. AIC and Cox-Snell residual graph showed that the gamma model had the lowest AIC (4288.598 and the closest graph to the bisector. The results of the gamma model showed that factors affecting the patient length of stay were admission day, inpatient unit, related physician specialty, emergent admission, final diagnosis and the number of laboratory tests, radiographies and sonographies (P<0.05. The results showed that the gamma model provided a better fit to the studied data than the Cox proportional hazards model. Therefore, it is better for researchers of healthcare field to consider this model in their researches about the patient length of stay (LOS if the assumption of proportional hazards is not fulfilled.
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.
A Parametric Model for Barred Equilibrium Beach Profiles
2014-05-10
A parametric model for barred equilibrium beach profiles Robert A. Holman a,⁎, David M. Lalejini a, Kacey Edwards b, Jay Veeramony b a Marine...a limited cross-shore span. Coastal Engineering 90 (2014) 85–94 ⁎ Corresponding author. Tel.: +1 541 737 2914. E-mail addresses: holman ...coas.oregonstate.edu (R.A. Holman ), David.Lalejini@nrlssc.navy.mil (D.M. Lalejini), kacey.edwards@nrlssc.navy.mil (K. Edwards), jay.veeramony@nrlssc.navy.mil (J
Modeling of Self-Pumped Singly Resonant Optical Parametric Oscillator
Deng, Chengxian
2016-01-01
A model of the steady-state operating, self-pumped singly resonant optical parametric oscillator (SPSRO) has been developed. The characteristics of quasi three-level laser gain medium pumped longitudinally have been taken into account. The characteristics of standing wave cavity, reabsorption losses, focusing Gaussian beams of the pump laser, fundamental laser and signal wave have been considered in the analyses. Furthermore, The power characteristics of threshold and efficiency have been analyzed, employing a Yb3+-doped periodically poled lithium niobate co-doped with MgO (Yb3+:MgO:PPLN) as the medium of laser gain and second-order nonlinear crystal.
O'Neil, Sean F; Mac, Amy; Rhodes, Gillian; Webster, Michael A
2015-12-01
Recently, we proposed that the aftereffects of adapting to facial age are consistent with a renormalization of the perceived age (e.g., so that after adapting to a younger or older age, all ages appear slightly older or younger, respectively). This conclusion has been challenged by arguing that the aftereffects can also be accounted for by an alternative model based on repulsion (in which facial ages above or below the adapting age are biased away from the adaptor). However, we show here that this challenge was based on allowing the fitted functions to take on values which are implausible and incompatible across the different adapting conditions. When the fits are constrained or interpreted in terms of standard assumptions about normalization and repulsion, then the two analyses both agree in pointing to a pattern of renormalization in age aftereffects.
Parametric Models of NIR Transmission and Reflectivity Spectra for Dyed Fabrics
2015-07-29
parametric model Eq.(1a), and assuming background subtraction with a scale factor Co, the modeled transmission component of the dyes as a function of...and inversely proportional to the level of scattering. The experimental quantity defined by Eq.(7) is modeled parametrically by the scaled absorption...Naval Research Laboratory Washington, DC 20375-5320 NRL/MR/5708--15-9629 Parametric Models of NIR Transmission and Reflectivity Spectra for Dyed
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.
Coupled mode parametric resonance in a vibrating screen model
Slepyan, Leonid I
2013-01-01
We consider a simple dynamic model of the vibrating screen operating in the parametric resonance (PR) mode. This model was used in the course of designing and setting of such a screen in LPMC. The PR-based screen compares favorably with conventional types of such machines, where the transverse oscillations are excited directly. It is characterized by larger values of the amplitude and by insensitivity to damping in a rather wide range. The model represents an initially strained system of two equal masses connected by a linearly elastic string. Self-equilibrated, longitudinal, harmonic forces act on the masses. Under certain conditions this results in transverse, finite-amplitude oscillations of the string. The problem is reduced to a system of two ordinary differential equations coupled by the geometric nonlinearity. Damping in both the transverse and longitudinal oscillations is taken into account. Free and forced oscillations of this mass-string system are examined analytically and numerically. The energy e...
Semi-parametric regression: Efficiency gains from modeling the nonparametric part
Yu, Kyusang; Park, Byeong U; 10.3150/10-BEJ296
2011-01-01
It is widely admitted that structured nonparametric modeling that circumvents the curse of dimensionality is important in nonparametric estimation. In this paper we show that the same holds for semi-parametric estimation. We argue that estimation of the parametric component of a semi-parametric model can be improved essentially when more structure is put into the nonparametric part of the model. We illustrate this for the partially linear model, and investigate efficiency gains when the nonparametric part of the model has an additive structure. We present the semi-parametric Fisher information bound for estimating the parametric part of the partially linear additive model and provide semi-parametric efficient estimators for which we use a smooth backfitting technique to deal with the additive nonparametric part. We also present the finite sample performances of the proposed estimators and analyze Boston housing data as an illustration.
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.
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.
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.
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. PMID:25814963
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.
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).
The Ponzano-Regge model and parametric representation
Li, Dan
2011-01-01
We give a parametric representation of the effective noncommutative field theory derived from a $\\kappa$-deformation of the Ponzano-Regge model and define a generalized Kirchhoff polynomial with $\\kappa$-correction terms, obtained in a $\\kappa$-linear approximation. We then consider the corresponding graph hypersurfaces and the question of how the presence of the correction term affects their motivic nature. We look in particular at the tetrahedron graph, which is the basic case of relevance to quantum gravity. With the help of computer calculations, we verify that the number of points over finite fields of the corresponding hypersurface does not fit polynomials with integer coefficients, hence the hypersurface of the tetrahedron is not polynomially countable. This shows that the correction term can change significantly the motivic properties of the hypersurfaces, with respect to the classical case.
Parametric study of the Incompletely Stirred Reactor modeling
Energy Technology Data Exchange (ETDEWEB)
Mobini, K. [Department of Mechanical Engineering, Shahid Rajaee University, Lavizan, Tehran (Iran); Bilger, R.W. [School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Sydney (Australia)
2009-09-15
The Incompletely Stirred Reactor (ISR) is a generalization of the widely-used Perfectly Stirred Reactor (PSR) model and allows for incomplete mixing within the reactor. Its formulation is based on the Conditional Moment Closure (CMC) method. This model is applicable to nonpremixed combustion with strong recirculation such as in a gas turbine combustor primary zone. The model uses the simplifying assumptions that the conditionally-averaged reactive-scalar concentrations are independent of position in the reactor: this results in ordinary differential equations in mixture fraction space. The simplicity of the model permits the use of very complex chemical mechanisms. The effects of the detailed chemistry can be found while still including the effects of micromixing. A parametric study is performed here on an ISR for combustion of methane at overall stoichiometric conditions to investigate the sensitivity of the model to different parameters. The focus here is on emissions of nitric oxide and carbon monoxide. It is shown that the most important parameters in the ISR model are reactor residence time, the chemical mechanism and the core-averaged Probability Density Function (PDF). Using several different shapes for the core-averaged PDF, it is shown that use of a bimodal PDF with a low minimum at stoichiometric mixture fraction and a large variance leads to lower nitric oxide formation. The 'rich-plus-lean' mixing or staged combustion strategy for combustion is thus supported. (author)
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
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.
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
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.
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).
Extended Range Hydrological Predictions: Uncertainty Associated with Model Parametrization
Joseph, J.; Ghosh, S.; Sahai, A. K.
2016-12-01
The better understanding of various atmospheric processes has led to improved predictions of meteorological conditions at various temporal scale, ranging from short term which cover a period up to 2 days to long term covering a period of more than 10 days. Accurate prediction of hydrological variables can be done using these predicted meteorological conditions, which would be helpful in proper management of water resources. Extended range hydrological simulation includes the prediction of hydrological variables for a period more than 10 days. The main sources of uncertainty in hydrological predictions include the uncertainty in the initial conditions, meteorological forcing and model parametrization. In the present study, the Extended Range Prediction developed for India for monsoon by Indian Institute of Tropical Meteorology (IITM), Pune is used as meteorological forcing for the Variable Infiltration Capacity (VIC) model. Sensitive hydrological parameters, as derived from literature, along with a few vegetation parameters are assumed to be uncertain and 1000 random values are generated given their prescribed ranges. Uncertainty bands are generated by performing Monte-Carlo Simulations (MCS) for the generated sets of parameters and observed meteorological forcings. The basins with minimum human intervention, within the Indian Peninsular region, are identified and validation of results are carried out using the observed gauge discharge. Further, the uncertainty bands are generated for the extended range hydrological predictions by performing MCS for the same set of parameters and extended range meteorological predictions. The results demonstrate the uncertainty associated with the model parametrisation for the extended range hydrological simulations. Keywords: Extended Range Prediction, Variable Infiltration Capacity model, Monte Carlo Simulation.
Free-form geometric modeling by integrating parametric and implicit PDEs.
Du, Haixia; Qin, Hong
2007-01-01
Parametric PDE techniques, which use partial differential equations (PDEs) defined over a 2D or 3D parametric domain to model graphical objects and processes, can unify geometric attributes and functional constraints of the models. PDEs can also model implicit shapes defined by level sets of scalar intensity fields. In this paper, we present an approach that integrates parametric and implicit trivariate PDEs to define geometric solid models containing both geometric information and intensity distribution subject to flexible boundary conditions. The integrated formulation of second-order or fourth-order elliptic PDEs permits designers to manipulate PDE objects of complex geometry and/or arbitrary topology through direct sculpting and free-form modeling. We developed a PDE-based geometric modeling system for shape design and manipulation of PDE objects. The integration of implicit PDEs with parametric geometry offers more general and arbitrary shape blending and free-form modeling for objects with intensity attributes than pure geometric models.
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
Institute of Scientific and Technical Information of China (English)
ZHAO Xiaoliang; ZHU Zhemin; DU Gonghuan; TANG Haiqing; LI Shui; MIAO Rongxing
2001-01-01
A theoretical model is presented to describe the parametric acoustic field generated by a piston radiator. In the model, the high-frequency primary wave interaction region that is truncated by a low-pass acoustic filter can be viewed as a cylindrical source within the Rayleigh distance of the piston. When the radius of the piston is much smaller than the length of the parametric region, this model is reduced to the Berketey's End-Fire Line Array model. Comparison between numerical calculations and experimental measurement show that the generated parametric sound field (especially near the axis) agrees well with the experiment results.
Hollow cathode modeling: II. Physical analysis and parametric study
Sary, Gaétan; Garrigues, Laurent; Boeuf, Jean-Pierre
2017-05-01
A numerical emissive hollow cathode model which couples plasma and thermal aspects of the NASA NSTAR cathode has been presented in a companion paper and simulation results obtained using the plasma model were compared to experimental data. We now compare simulation results with measurements using the full coupled model. Inside the cathode, the simulated plasma density profile agrees with the experimental data up to the ±50% experimental uncertainty while the simulated emitter temperature differs from measurements by at most 5 K. We then proceed to an analysis of the cathode discharge both inside the cathode where electron emission is dominant and outside in the near plume where electron transport instabilities are important. As observed previously in the literature, the total emitted electron current is much larger (34 {{A}}) than the set discharge current collected at the anode (13 {{A}}) while ionization plays a negligible role. Extracted electrons are emitted from a region much shorter than the full emitter (0.9 {{cm}} versus 2.5 {{cm}}). The influence of an applied axial magnetic field in the plume is also assessed and we observe that it leads to a 10-fold increase of the plasma density 1 cm downstream of the orifice entrance while the simulated discharge potential at the anode is increased from 10 {{V}} up to 35.5 {{V}}. Lastly, we perform a parametric study on both the operating point (discharge current, mass flow rate) and design (inner radius) of the cathode. The simulated useful operating envelope is shown to be limited at low discharge current mostly because of the probable ion sputtering of the emitter and at high discharge current because of emitter evaporation, plasma oscillations and sputtering of the keeper electrode. The behavior of the cathode is also analyzed w.r.t. its internal radius and simulation results show that the useful emitter length scales linearly with the cathode radius.
Parametric Hidden Markov Models for Recognition and Synthesis of Movements
DEFF Research Database (Denmark)
Herzog, Dennis; Krüger, Volker; Grest, Daniel
2008-01-01
the applicability for online recognition based on very noisy 3D tracking data. The use of a parametric representation of movements is shown in a robot demo, where a robot removes objects from a table as demonstrated by an advisor. The synthesis for motor control is performed for arbitrary table-top positions.......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 understanding the whole meaning of a movement of a human, the recognition of its type, likewise its parameterization are important. Only both together convey the whole meaning. Vice versa, for mimicry, the synthesis of movements for the motor control of a robot needs to be parameterized, e.g., by the relative...
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....... However, it is well accepted that one contribution to the noise performance originates from vacuum fluctuations. In this work we show a novel approach to predict the spontaneous radiation from a parametric amplifier. In the approach the propagating fields are treated as a sum of a classical mean field...
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...
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.
About Nested Circuits Markov in one Parametric Queueing Model
Directory of Open Access Journals (Sweden)
Rafik A. Simonyan
2013-01-01
Full Text Available In operation the single-channel queuing system with several Poisson entering flows and with Kleynrok's parametric discipline is considered. The Markov circuit which is received on a basis a vector of processes of the maximum priorities of flows of calls is completely studied
APT cost scaling: Preliminary indications from a Parametric Costing Model (PCM)
Energy Technology Data Exchange (ETDEWEB)
Krakowski, R.A.
1995-02-03
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 level{close_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 curve{close_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.
A proposal for a consistent parametrization of earth models
Forbriger, Thomas; Friederich, Wolfgang
2005-08-01
The current way to parametrize earth models in terms of real-valued seismic velocities and quality factors is incomplete as it does not specify how complex-valued viscoelastic moduli or complex velocities should be computed from them. Various ways to do this can be found in the literature. Depending on the context they may specify (1) the real part of the viscoelastic modulus, (2) the absolute value of the viscoelastic modulus, (3) the real part of complex velocity or (4) the phase velocity of a propagating plane wave. We propose here to exclusively use the first alternative because it is the only one which allows both a flexible choice of elastic parameters and a mathematically rigorous evaluation of the complex-valued viscoelastic moduli. The other definitions only permit an evaluation of viscoelastic moduli if the tabulated quality factors are directly associated with the listed velocities. Ignoring the subtle differences between the three definitions leads to variations in viscoelastic moduli which are second order in 1/Q where Q is a quality factor. This may be the reason why the topic has never been discussed in the literature. In case of shallow seismic media, however, where quality factors may assume values of less than 10, the subtle differences become noticeable in synthetic seismograms. It is then essential to use the same definition in all algorithms to make results comparable. Matters become worse for anisotropic media, which are commonly specified in terms of real elastic moduli and quality factors for effective isotropic moduli. In that case, the complex-valued viscoelastic moduli cannot be determined uniquely. However, interpreting the tabulated constants as the real parts of the complex-valued viscoelastic moduli at least allows a consistent definition, which respects the relative magnitude of the anelastic and anisotropic parts compared to the elastic parts. It should be noted that all these considerations apply to complex-valued viscoelastic
Hawke, Jesse L; Stallings, Michael C; Wadsworth, Sally J; DeFries, John C
2008-03-01
Although a comparison of concordance rates for deviant scores in identical and fraternal twin pairs can provide prima facie evidence for a genetic etiology, information is not fully utilized when continuous measures are analyzed in a dichotomous manner. Thus, DeFries and Fulker (Behav Genet 15:467-473, 1985; Acta Genet Med Gemellol, 37:205-216, 1988) developed a regression-based methodology (DF analysis) to assess genetic etiology in both selected and unselected twin samples. While the DF analysis is a very versatile and relatively powerful statistical approach, it is not easily extended to the multivariate case. In contrast, structural equation models may be readily extended to analyze multivariate data sets (Neale and Cardon, Methodology for genetic studies of twins and families, 1992). However, such methodologies may yield biased estimates of additive genetic, shared environmental, and non-shared environmental influences when multivariate models are fitted to selected twin data. Therefore, the Pearson-Aitken (PA) selection formula (Aitken, Proc Edinburgh Math Soc B, 4:106-110, 1934) was used to analyze reading performance data from twins with reading difficulties (selected sample) and a population of normally-achieving twin pairs (control sample). As a comparison, DF models were also fitted to these same data sets. In general, resulting estimates of additive genetic, shared environmental, and non-shared environmental influences were similar when the DF and PA models were fitted to the data. However, the PA selection formula may be more readily generalized to the multivariate case.
Artamonov, A. A.; Mishev, A. L.; Usoskin, I. G.
2016-11-01
Results of a comparison of a new model CRAC:EPII (Cosmic Ray Atmospheric Cascade: Electron Precipitation Induced Ionization) with a commonly used parametric model of atmospheric ionization is presented. The CRAC:EPII is based on a Monte Carlo simulation of precipitating electrons propagation and interaction with matter in the Earth's atmosphere. It explicitly considers energy deposit: ionization, pair production, Compton scattering, generation of Bremsstrahlung high energy photons, photo-ionization and annihilation of positrons, multiple scattering as physical processes accordingly. Propagation of precipitating electrons and their interactions with air is simulated with the GEANT4 simulation tool PLANETOCOSMICS code using NRLMSISE-00 atmospheric model. Ionization yields are computed and compared with a parametrization model for different energies of incident precipitating energetic electrons, using simulated fluxes of mono-energetic particles. A good agreement between the two models is achieved in the mesosphere but the contribution of Bremsstrahlung in the stratosphere, which is not accounted for in the parametric models, is found significant. As an example, we calculated profiles of the ion production rates in the middle and upper atmosphere (below 100 km) on the basis of balloon-born measured spectra of precipitating electrons for 30-October-2002 and 07-January-2004.
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) ar...... ensure that the synthesized movements can be applied to different configurations of the external world and are thus suitable for actions that involve the manipulation of objects....
Parametrized Post-Newtonian Limit of Teleparallel Dark Energy Model
Li, Jung-Tsung; Geng, Chao-Qiang
2013-01-01
We study the post-Newtonian limit in the teleparallel equivalent of General Relativity with a scalar field which non-minimally couples to gravity. The metric perturbation is obtained from the vierbein field expansion with respect to the Minkowski background. Due to the structure of the teleparallel gravity Lagrangian, the potential of the scalar field shows no effect to the parametrized post-Newtonian parameters, and compatible results with Solar System observations are found.
Lara, Jesús R; Saremi, Naseem T; Castillo, Martin J; Hoddle, Mark S
2016-04-01
Oligonychus perseae (Acari: Tetranychidae) is an important foliar spider mite pest of 'Hass' avocados in several commercial production areas of the world. In California (USA), O. perseae densities in orchards can exceed more than 100 mites per leaf and this makes enumerative counting prohibitive for field sampling. In this study, partial enumerative mite counts along half a vein on an avocado leaf, an industry recommended practice known as the "half-vein method", was evaluated for accuracy using four data sets with a combined total of more than 485,913 motile O. perseae counted on 3849 leaves. Sampling simulations indicated that the half-vein method underestimated mite densities in a range of 15-60 %. This problem may adversely affect management of this pest in orchards and potentially compromise the results of field research requiring accurate mite density estimation. To address this limitation, four negative binomial regression models were fit to count data in an attempt to rescue the half-vein method for estimating mite densities. These models were incorporated into sampling plans and evaluated for their ability to estimate mite densities on whole leaves within 30-tree blocks of avocados. Model 3, a revised version of the original half-vein model, showed improvement in providing reliable estimates of O. perseae densities for making assessments of general leaf infestation densities across orchards in southern California. The implications of these results for customizing the revised half-vein method as a potential field sampling tool and for experimental research in avocado production in California are discussed.
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.
Rollett, Tanja; Isavnin, Alexey; Davies, Jackie A; Kubicka, Manuel; Amerstorfer, Ute V; Harrison, Richard A
2016-01-01
In this study, we present a new method for forecasting arrival times and speeds of coronal mass ejections (CMEs) at any location in the inner heliosphere. This new approach enables the adoption of a highly flexible geometrical shape for the CME front with an adjustable CME angular width and an adjustable radius of curvature of its leading edge, i.e. the assumed geometry is elliptical. Using, as input, STEREO heliospheric imager (HI) observations, a new elliptic conversion (ElCon) method is introduced and combined with the use of drag-based model (DBM) fitting to quantify the deceleration or acceleration experienced by CMEs during propagation. The result is then used as input for the Ellipse Evolution Model (ElEvo). Together, ElCon, DBM fitting, and ElEvo form the novel ElEvoHI forecasting utility. To demonstrate the applicability of ElEvoHI, we forecast the arrival times and speeds of 21 CMEs remotely observed from STEREO/HI and compare them to in situ arrival times and speeds at 1 AU. Compared to the commonl...
Santander-Garcia, M; Koning, N; Steffen, W
2014-01-01
Modern instrumentation in radioastronomy constitutes a valuable tool for studying the Universe: ALMA has reached unprecedented sensitivities and spatial resolution, while Herschel/HIFI has opened a new window for probing molecular warm gas (~50-1000 K). On the other hand, the SHAPE software has emerged in the last few years as a standard tool for determining the morphology and velocity field of different kinds of gaseous emission nebulae via spatio-kinematical modelling. SHAPE implements radiative transfer solving, but it is only available for atomic species and not for molecules. Being aware of the growing importance of the development of tools for simplifying the analyses of molecular data, we introduce shapemol, a complement to SHAPE with which we intend to fill the so far under-developed molecular niche. shapemol enables user-friendly, spatio-kinematic modeling with accurate non-LTE calculations of excitation and radiative transfer in CO lines. It allows radiative transfer solving in the 12CO and 13CO J=1...
Santander-García, M.; Bujarrabal, V.; Koning, N.; Steffen, W.
2015-01-01
Context. Modern instrumentation in radioastronomy constitutes a valuable tool for studying the Universe: ALMA has reached unprecedented sensitivities and spatial resolution, while Herschel/HIFI has opened a new window (most of the sub-mm and far-infrared ranges are only accessible from space) for probing molecular warm gas (~50-1000 K). On the other hand, the software SHAPE has emerged in the past few years as a standard tool for determining the morphology and velocity field of different kinds of gaseous emission nebulae via spatio-kinematical modelling. Standard SHAPE implements radiative transfer solving, but it is only available for atomic species and not for molecules. Aims: Being aware of the growing importance of the development of tools for simplifying the analyses of molecular data from new-era observatories, we introduce the computer code shapemol, a complement to SHAPE, with which we intend to fill the so-far under-developed molecular niche. Methods: shapemol enables user-friendly, spatio-kinematic modelling with accurate non-LTE calculations of excitation and radiative transfer in CO lines. Currently, it allows radiative transfer solving in the 12CO and 13CO J = 1-0 to J = 17-16 lines, but its implementation permits easily extending the code to different transitions and other molecular species, either by the code developers or by the user. Used along SHAPE, shapemol allows easily generating synthetic maps to test against interferometric observations, as well as synthetic line profiles to match single-dish observations. Results: We give a full description of how shapemol works, and we discuss its limitations and the sources of uncertainty to be expected in the final synthetic profiles or maps. As an example of the power and versatility of shapemol, we build a model of the molecular envelope of the planetary nebula NGC 6302 and compare it with 12CO and 13CO J = 2-1 interferometric maps from SMA and high-J transitions from Herschel/HIFI. We find the
Djuris, Jelena; Nikolakakis, Ioannis; Ibric, Svetlana; Djuric, Zorica; Kachrimanis, Kyriakos
2013-05-01
Hot-melt extrusion (HME) is a dust- and solvent-free continuous process enabling the preparation of a variety of solid dosage forms containing solid dispersions of poorly soluble drugs into thermoplastic polymers. Miscibility of drug and polymer is a prerequisite for stable solid dispersion formation. The present study investigates the feasibility of forming solid dispersions of carbamazepine (CBZ) into polyethyleneglycol-polyvinyl caprolactam-polyvinyl acetate grafted copolymer (Soluplus) by hot-melt extrusion. Physicochemical properties of the raw materials, extrudates, co-melted products, and corresponding physical mixtures were characterized by thermo-gravimetric analysis (TGA), differential scanning calorimetry (DSC), attenuated total reflectance infrared (ATR-FTIR) spectroscopy and hot stage microscopy (HSM), while miscibility of CBZ and Soluplus was estimated on the basis of the Flory-Huggins theory, Hansen solubility parameters, and solid-liquid equilibrium equation. It was found that hot-melt extrusion of carbamazepine and Soluplus is feasible on a single-screw hot-melt extruder without the addition of plasticizers. DSC analysis and FTIR spectroscopy revealed that a molecular dispersion is formed when the content of CBZ does not exceed ∼5% w/w while higher CBZ content results in a microcrystalline dispersion of CBZ form III crystals, with the molecularly dispersed percentage increasing with extrusion temperature, at the risk of inducing transformation to the undesirable form I of CBZ. Thermodynamic modeling elucidated potential limitations and temperature dependence of solubility/dispersibility of carbamazepine in Soluplus hot-melt extrudates. The results obtained by thermodynamic models are in agreement with the findings of the HME processing, encouraging therefore their further application in the HME process development.
Moraes, L.E.; Kebreab, E.; Strathe, A.B.; France, J.; Dijkstra, J.; Casper, D.; Fadel, J.G.
2014-01-01
Linear and non-linear models have been extensively utilised for the estimation of net and metabolisable energy requirements and for the estimation of the efficiencies of utilising dietary energy for maintenance and tissue gain. In growing animals, biological principles imply that energy retention ra
Directory of Open Access Journals (Sweden)
Stender Birgit
2016-09-01
Full Text Available Eikonal models are useful to compute approximate solutions of cardiac excitation propagation in a computationally efficient way. In this work the underlying conduction velocities for different cell types were computed solving the classical bidomain model equations for planar wavefront propagation. It was further investigated how changes in the conductivity tensors within the bidomain model analytically correspond to changes in the conduction velocity. The error in the presence of local front curvature for the derived eikonal model parametrization were analyzed. The conduction velocity simulated based on the bidomain model was overestimated by a maximum of 10%.
Above-Threshold Poles in Model-Independent Form Factor Parametrizations
Grinstein, Benjamin
2015-01-01
The model-independent parametrization for exclusive hadronic form factors commonly used for semileptonic decays is generalized to allow for the inclusion of above-threshold resonant poles of known mass and width. We discuss the interpretation of such poles, particularly with respect to the analytic structure of the relevant two-point Green's function in which they reside. Their presence has a remarkably small effect on the parametrization, as we show explicitly for the case of $D \\to \\pi e^+ \
Kernel based model parametrization and adaptation with applications to battery management systems
Weng, Caihao
With the wide spread use of energy storage systems, battery state of health (SOH) monitoring has become one of the most crucial challenges in power and energy research, as SOH significantly affects the performance and life cycle of batteries as well as the systems they are interacting with. Identifying the SOH and adapting of the battery energy/power management system accordingly are thus two important challenges for applications such as electric vehicles, smart buildings and hybrid power systems. This dissertation focuses on the identification of lithium ion battery capacity fading, and proposes an on-board implementable model parametrization and adaptation framework for SOH monitoring. Both parametric and non-parametric approaches that are based on kernel functions are explored for the modeling of battery charging data and aging signature extraction. A unified parametric open circuit voltage model is first developed to improve the accuracy of battery state estimation. Several analytical and numerical methods are then investigated for the non-parametric modeling of battery data, among which the support vector regression (SVR) algorithm is shown to be the most robust and consistent approach with respect to data sizes and ranges. For data collected on LiFePO 4 cells, it is shown that the model developed with the SVR approach is able to predict the battery capacity fading with less than 2% error. Moreover, motivated by the initial success of applying kernel based modeling methods for battery SOH monitoring, this dissertation further exploits the parametric SVR representation for real-time battery characterization supported by test data. Through the study of the invariant properties of the support vectors, a kernel based model parametrization and adaptation framework is developed. The high dimensional optimization problem in the learning algorithm could be reformulated as a parameter estimation problem, that can be solved by standard estimation algorithms such as the
Wang, Bao; Wei, Guowei
2016-01-01
In this work, a systematic protocol is proposed to automatically parametrize implicit solvent models with polar and nonpolar components. The proposed protocol utilizes the classical Poisson model or the Kohn-Sham density functional theory (KSDFT) based polarizable Poisson model for modeling polar solvation free energies. For the nonpolar component, either the standard model of surface area, molecular volume, and van der Waals interactions, or a model with atomic surface areas and molecular volume is employed. Based on the assumption that similar molecules have similar parametrizations, we develop scoring and ranking algorithms to classify solute molecules. Four sets of radius parameters are combined with four sets of charge force fields to arrive at a total of 16 different parametrizations for the Poisson model. A large database with 668 experimental data is utilized to validate the proposed protocol. The lowest leave-one-out root mean square (RMS) error for the database is 1.33k cal/mol. Additionally, five s...
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
Adaptivity Assessment of Regional Semi-Parametric VTEC Modeling to Different Data Distributions
Durmaz, Murat; Onur Karslıoǧlu, Mahmut
2014-05-01
Semi-parametric modelling of Vertical Total Electron Content (VTEC) combines parametric and non-parametric models into a single regression model for estimating the parameters and functions from Global Positioning System (GPS) observations. The parametric part is related to the Differential Code Biases (DCBs), which are fixed unknown parameters of the geometry-free linear combination (or the so called ionospheric observable). On the other hand, the non-parametric component is referred to the spatio-temporal distribution of VTEC which is estimated by applying the method of Multivariate Adaptive Regression B-Splines (BMARS). BMARS algorithm builds an adaptive model by using tensor product of univariate B-splines that are derived from the data. The algorithm searches for best fitting B-spline basis functions in a scale by scale strategy, where it starts adding large scale B-splines to the model and adaptively decreases the scale for including smaller scale features through a modified Gram-Schmidt ortho-normalization process. Then, the algorithm is extended to include the receiver DCBs where the estimates of the receiver DCBs and the spatio-temporal VTEC distribution can be obtained together in an adaptive semi-parametric model. In this work, the adaptivity of regional semi-parametric modelling of VTEC based on BMARS is assessed in different ground-station and data distribution scenarios. To evaluate the level of adaptivity the resulting DCBs and VTEC maps from different scenarios are compared not only with each other but also with CODE distributed GIMs and DCB estimates .
Research on Mixer Parametric Modeling System Based on Redevelopment of ANSYS
Directory of Open Access Journals (Sweden)
Bin Zheng
2015-01-01
Full Text Available In this paper, the mixer parametric modeling system software was developed by using VB which was taken as the foreground development program, and the paper combined with ANSYS software to create the finite element model of mixer blade and cylinder for the following numerical simulation of the flow field and parameter optimization of mixer. The software user interface was developed by VB and the pre-process model was created by invoking APDL of ANSYS in background. Therefore, the operation of modeling, meshing, component-building of mixer blade and cylinder were completed by using APDL and the graphic and text were outputted and displayed on the mixer parametric modeling system user interface which was developed by VB. Practice proved that it is convenient to modify the mixer solid model created by the parametric design language of ANSYS due to the similar structure.
A Rapid Model Fitting Tool Suite Project
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,...
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...
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.
Applying Statistical Models and Parametric Distance Measures for Music Similarity Search
Lukashevich, Hanna; Dittmar, Christian; Bastuck, Christoph
Automatic deriving of similarity relations between music pieces is an inherent field of music information retrieval research. Due to the nearly unrestricted amount of musical data, the real-world similarity search algorithms have to be highly efficient and scalable. The possible solution is to represent each music excerpt with a statistical model (ex. Gaussian mixture model) and thus to reduce the computational costs by applying the parametric distance measures between the models. In this paper we discuss the combinations of applying different parametric modelling techniques and distance measures and weigh the benefits of each one against the others.
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......, acoustic performance can inform the geometry and material logic of the design. In this way, the architectural design and the acoustic analysis model become linked....
Whitening of Background Brain Activity via Parametric Modeling
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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.
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.
Directory of Open Access Journals (Sweden)
Jingjing Wu
2015-01-01
Full Text Available A robust particle filter (PF and its application to fault/defect detection of nonlinear system are investigated in this paper. First, an adaptive parametric model is exploited as the observation model for a nonlinear system. Second, by incorporating the parametric model, particle filter is employed to estimate more accurate hidden states for the nonlinear stochastic system. Third, by formulating the problem of defect detection within the hypothesis testing framework, the statistical properties of the proposed testing are established. Finally, experimental results demonstrate the effectiveness and robustness of the proposed detector on real defect detection and localization in images.
SAMPLE AOR CALCULATION USING ANSYS AXISYMMETRIC PARAMETRIC MODEL FOR TANK SST-A
Energy Technology Data Exchange (ETDEWEB)
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS axisymmetric parametric model for single-shell tank A and provides sample calculation for analysis-of-record mechanical load conditions. The purpose of this calculation is to develop a parametric model for single shell tank (SST) A, and provide a sample analysis of SST-A tank based on analysis of record (AOR) loads. The SST-A model is based on buyer-supplied as-built drawings and information for the AOR for SSTs, encompassing the existing tank load conditions, and evaluates stresses and deformations throughout the tank and surrounding soil mass.
SAMPLE AOR CALCULATION USING ANSYS AXISYMMETRIC PARAMETRIC MODEL FOR TANK SST-SX
Energy Technology Data Exchange (ETDEWEB)
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS axisymmetric parametric model for single-shell tank SX and provides sample calculation for analysis-of-record mechanical load conditions. The purpose of this calculation is to develop a parametric model for single shell tank (SST) SX, and provide a sample analysis of the SST-SX tank based on analysis of record (AOR) loads. The SST-SX model is based on buyer-supplied as-built drawings and information for the AOR for SSTs, encompassing the existing tank load conditions, and evaluates stresses and deformations throughout the tank and surrounding soil mass.
SAMPLE AOR CALCULATION USING ANSYS AXISYMMETRIC PARAMETRIC MODEL FOR TANK SST-S
Energy Technology Data Exchange (ETDEWEB)
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS axisymmetric parametric model for single-shell tank S and provides sample calculation for analysis-of-record mechanical load conditions. The purpose of this calculation is to develop a parametric model for single shell tank (SST) S, and provide a sample analysis of SST-S tank based on analysis of record (AOR) loads. The SST-S model is based on buyer-supplied as-built drawings and information for the AOR for SSTs, encompassing the existing tank load conditions, and evaluates stresses and deformations throughout the tank and surrounding soil mass.
Energy Technology Data Exchange (ETDEWEB)
Kim, Su Jin; Lee, Jae Sung; Kim, Yu Kyeong; Lee, Dong Soo [Seoul National University College of Medicine, Seoul (Korea, Republic of)
2007-07-01
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{sub 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 [{sup 11}C]MeNTI PET for opioid receptor.
Parametrizing coarse grained models for molecular systems at equilibrium
Kalligiannaki, E.
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.
Parametrizing coarse grained models for molecular systems at equilibrium
Kalligiannaki, E.; Chazirakis, A.; Tsourtis, A.; Katsoulakis, M. A.; Plecháč, P.; Harmandaris, V.
2016-10-01
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.
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.
Baldasaro, Ruth E; Bauer, Daniel J
2011-11-30
Many approaches have been proposed to estimate interactions among latent variables. These methods often assume a specific functional form for the interaction, such as a bilinear interaction. Theory is seldom specific enough to provide a functional form for an interaction, however, so a more exploratory, diagnostic approach may often be required. Bauer (2005) proposed a semiparametric approach that allows for the estimation of interaction effects of unknown functional form among latent variables. A structural equation mixture model (SEMM) is first fit to the data. Then an approximation of the interaction is obtained by aggregating over the mixing components. A simulation study is used to examine the performance of this semiparametric approach to two parametric approaches: the latent moderated structures approach (Klein & Moosbrugger, 2000) and the unconstrained product-indicator approach (Marsh, Wen, & Hau, 2004). Data were generated from four functional forms: main effects only, quadratic trend, bilinear interaction, and exponential interaction. Estimates of bias and root mean squared error of approximation were calculated by comparing the surface used to generate the data and the model-implied surface constructed from each approach. As expected, the parametric approaches were more efficient than the SEMM. For the main effects model, bias was similar for both the SEMM and parametric approaches. For the bilinear interaction, the parametric approaches provided nearly identical results, although the SEMM approach was slightly more biased. When the parametric approaches assumed a bilinear interaction and the data were generated from a quadratic trend or an exponential interaction, the parametric approaches generated biased estimates of the true surface. The SEMM approach approximated the true data generation surface with a similarly low level of bias for all the nonlinear surfaces. For example, Figure 1 shows the true surface for the bilinear interaction along with the
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...... statistic. Under rather weak assumptions on the drift and volatility we prove weak convergence of the test statistic to a centered mixed Gaussian distribution. As a consequence we obtain a test, which is consistent for any fixed alternative. Moreover, we present a parametric bootstrap procedure which...
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...... present a parametric bootstrap procedure which provides a better approximation of the distribution of the test statistic. Finally, it is demonstrated by means of Monte Carlo study that the range-based test is more powerful than the return-based test when comparing at the same sampling frequency....
Testing the trajectory difference in a semi-parametric longitudinal model.
Niu, Feiyang; Zhou, Jianhui; Le, Thu H; Ma, Jennie Z
2017-06-01
Motivated by a genetic investigation on the progressive decline in renal function in a clinical trial study of kidney disease, we develop a practical test for evaluating the group difference in trajectories under a semi-parametric modeling framework. For the temporal patterns or trajectories of longitudinal data, B-splines are used to approximate the function non-parametrically. Such approximation asymptotically converts the problem of testing trajectory difference into the significance test of regression coefficients that can be simply estimated by generalized estimating equations. To select the optimal number of inner knots for B-splines, a cross-validation procedure is performed using the criterion of the generalized residual sum of squares. The new proposed test successfully detects a significant difference of underlying genetic impact on the progression of renal disease, which is not captured by the parametric approach.
Directory of Open Access Journals (Sweden)
Eloranta Sandra
2011-06-01
Full Text Available Abstract Background When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Methods Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. Results We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Conclusions Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models.
Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach.
Yu, Rongjie; Wang, Xuesong; Yang, Kui; Abdel-Aty, Mohamed
2016-10-01
Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.
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.
The Parametric Model for PLC Reference Chanells and its Verification in Real PLC Environment
2008-01-01
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.
Rojano, Teresa; García-Campos, Montserrat
2017-01-01
This article reports the outcomes of a study that seeks to investigate the role of feedback, by way of an intelligent support system in natural language, in parametrized modelling activities carried out by a group of tertiary education students. With such a system, it is possible to simultaneously display on a computer screen a dialogue window and…
Beer-Lambert-Law Parametric Model of Reflectance Spectra for Dyed Fabrics
2016-06-06
A3) Next, it is reasonable to assume that the function IT(z), which is monotonically ...used as the test substrate for NIR dye application. The results of this study provide validation of the constructed parametric model within reasonable ...other spectral features depend on dye-solvent interactions [22,23,24]. These permittivity functions, however, should be considered as “ reasonable
Woudt, Edwin; de Boer, Pieter-Tjerk; van Ommeren, Jan C.W.
2007-01-01
Previous work on state-dependent adaptive importance sampling techniques for the simulation of rare events in Markovian queueing models used either no smoothing or a parametric smoothing technique, which was known to be non-optimal. In this paper, we introduce the use of kernel smoothing in this con
Directory of Open Access Journals (Sweden)
M. O. Kostin
2010-09-01
Full Text Available The probabilistic model of parametric reliability of power electromagnetic valve contactors of rolling stock which helps to evaluate the probability of failures in condition of switching a contactor (the tractive force during the whole process of operation should be greater than the resulting counteracting force is proposed in the paper.
Directory of Open Access Journals (Sweden)
Uri UDIN
2014-06-01
Full Text Available This article proposes usage of Pontryagin maximum principle for parametrical identification of mathematical vessel’s model. Proposed method has a special perspective for identification in real time mode, when the parameters identified can be used for forecasting of coming maneuvers.
Dustmann, C.; van Soest, A.H.O.
1999-01-01
We consider both a parametric and a semiparametric method to account for classification errors on the dependent variable in an ordered response model. The methods are applied to the analysis of self-reported speaking fluency of male immigrants in Germany. We find some substantial differences in para
Dustmann, C.; van Soest, A.H.O.
1999-01-01
We consider both a parametric and a semiparametric method to account for classification errors on the dependent variable in an ordered response model. The methods are applied to the analysis of self-reported speaking fluency of male immigrants in Germany. We find some substantial differences in
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.
DEFF Research Database (Denmark)
Grislain-Letrémy, Céline; Katossky, Arthur
2014-01-01
on the housing values strongly differs among these three areas, even if the areas all surround chemical and petrochemical industries. We compare the results from both standard parametric and more flexible, semiparametric models of hedonic property. We show that the parametric model might structurally lead...
Degeneracies of parametric lens model families near folds and cusps
Wagner, Jenny
2015-01-01
We develop an approach to select families of lens models that can describe doubly and triply gravitationally lensed images near folds and cusps using the model-independent ratios of lensing-potential derivatives derived in Wagner & Bartelmann (2015). Models are selected by comparing these model-independent ratios of potential derivatives to (numerically determined) ratios of potential derivatives along critical curves for entire lens model families in a given range of parameter values. This comparison returns parameter ranges which lens model families can reproduce observation within, as well as sections of the critical curve where image sets of the observed type can appear. If the model-independent potential-derivative ratios inferred from the observation fall outside the range of these ratios derived for the lens model family, the entire family can be excluded as a feasible model in the given volume in parameter space. We employ this approach for the family of singular isothermal spheres with external s...
Artamonov, A A; Usoskin, I G
2016-01-01
A new model CRAC:EPII (Cosmic Ray Atmospheric Cascade: Electron Precipitation Induced Ionization) is presented. The CRAC:EPII is based on Monte Carlo simulation of precipitating electrons propagation and interaction with matter in the Earth atmosphere. It explicitly considers energy deposit: ionization, pair production, Compton scattering, generation of Bremsstrahlung high energy photons, photo-ionization and annihilation of positrons, multiple scattering as physical processes accordingly. The propagation of precipitating electrons and their interactions with atmospheric molecules is carried out with the GEANT4 simulation tool PLANETOCOSMICS code using NRLMSISE 00 atmospheric model. The ionization yields is compared with an analytical parametrization for various energies of incident precipitating electron, using a flux of mono-energetic particles. A good agreement between the two models is achieved. Subsequently, on the basis of balloon-born measured spectra of precipitating electrons at 30.10.2002 and 07.01....
Vorontsov, Sergei V
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 pseudo modes (interference peaks at frequencies above the atmospheric acoustic cutoff).
Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu
2017-01-01
Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.
On the influence of model parametrization in elastic full waveform tomography
Köhn, D.; De Nil, D.; Kurzmann, A.; Przebindowska, A.; Bohlen, T.
2012-10-01
Elastic Full Waveform Tomography (FWT) aims to reduce the misfit between recorded and modelled data, to deduce a very detailed model of elastic material parameters in the underground. The choice of the elastic model parameters to be inverted affects the convergence and quality of the reconstructed subsurface model. Using the Cross-Triangle-Squares (CTS) model three elastic parametrizations, Lamé parameters m1 = [λ, μ, ρ], seismic velocities m2 = [Vp, Vs, ρ] and seismic impedances m3 = [Ip, Is, ρ] for far-offset reflection seismic acquisition geometries with explosive point sources and free-surface condition are studied. In each CTS model the three elastic parameters are assigned to three different geometrical objects that are spatially separated. The results of the CTS model study reveal a strong requirement of a sequential frequency inversion from low to high frequencies to reconstruct the density model. Using only high-frequency data, cross-talk artefacts have an influence on the quantitative reconstruction of the material parameters, while for a sequential frequency inversion only structural artefacts, representing the boundaries of different model parameters, are present. During the inversion, the Lamé parameters, seismic velocities and impedances could be reconstructed well. However, using the Lamé parametrization ?-artefacts are present in the λ model, while similar artefacts are suppressed when using seismic velocities or impedances. The density inversion shows the largest ambiguity for all parametrizations. However, the artefacts are again more dominant, when using the Lamé parameters and suppressed for seismic velocity and impedance parametrization. The afore mentioned results are confirmed for a geologically more realistic modified Marmousi-II model. Using a conventional streamer acquisition geometry the P-velocity, S-velocity and density models of the subsurface were reconstructed successfully and are compared with the results of the Lam
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.
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.
Flexible parametric modelling of the cause-specific cumulative incidence function.
Lambert, Paul C; Wilkes, Sally R; Crowther, Michael J
2016-12-22
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.
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.
Logistic distributed activation energy model--Part 1: Derivation and numerical parametric study.
Cai, Junmeng; Jin, Chuan; Yang, Songyuan; Chen, Yong
2011-01-01
A new distributed activation energy model is presented using the logistic distribution to mathematically represent the pyrolysis kinetics of complex solid fuels. A numerical parametric study of the logistic distributed activation energy model is conducted to evaluate the influences of the model parameters on the numerical results of the model. The parameters studied include the heating rate, reaction order, frequency factor, mean of the logistic activation energy distribution, standard deviation of the logistic activation energy distribution. The parametric study addresses the dependence on the forms of the calculated α-T and dα/dT-T curves (α: reaction conversion, T: temperature). The study results would be very helpful to the application of the logistic distributed activation energy model, which is the main subject of the next part of this series.
Robust Near-Hovering Flight Controller for Model-Scale Helicopters Via Parametric Approach
Institute of Scientific and Technical Information of China (English)
Zhigang Zhou; Yongan Zhang∗
2015-01-01
This paper aims to provide a parametric design for robust flight controller of the model⁃scale helicopter. The main contributions lie in two aspects. Firstly, under near⁃hovering condition, a procedure is presented for simplification of the highly nonlinear and under⁃actuated model of the model⁃scale helicopter. This nonlinear system is linearized around the trim values of the chosen flight mode, followed by decomposing this high⁃order linear model into three lower⁃order subsystems according to the coupling properties among channels. After decomposition, the three subsystems are obtained which include the coupling subsystem between the roll ( pitch) motion and the lateral ( longitudinal) motion, the subsystem of the yaw motion and the subsystem of the vertical motion. Secondly, by using eigenstructure assignment, the problem of flight controller design can be converted into solving two optimization problems and the linear robust controllers of these subsystems are designed through solving these optimization problems. Besides, this paper contrasts and analyzed the performances of the LQR controller and the parametric controller. The results demonstrate the effectiveness and the robustness against the parametric perturbations of the parametric controller.
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.
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.
Two-parametric model of electron beam in computational dosimetry for radiation processing
Lazurik, V. M.; Lazurik, V. T.; Popov, G.; Zimek, Z.
2016-07-01
Computer simulation of irradiation process of various materials with electron beam (EB) can be applied to correct and control the performances of radiation processing installations. Electron beam energy measurements methods are described in the international standards. The obtained results of measurements can be extended by implementation computational dosimetry. Authors have developed the computational method for determination of EB energy on the base of two-parametric fitting of semi-empirical model for the depth dose distribution initiated by mono-energetic electron beam. The analysis of number experiments show that described method can effectively consider random displacements arising from the use of aluminum wedge with a continuous strip of dosimetric film and minimize the magnitude uncertainty value of the electron energy evaluation, calculated from the experimental data. Two-parametric fitting method is proposed for determination of the electron beam model parameters. These model parameters are as follow: E0 - energy mono-energetic and mono-directional electron source, X0 - the thickness of the aluminum layer, located in front of irradiated object. That allows obtain baseline data related to the characteristic of the electron beam, which can be later on applied for computer modeling of the irradiation process. Model parameters which are defined in the international standards (like Ep- the most probably energy and Rp - practical range) can be linked with characteristics of two-parametric model (E0, X0), which allows to simulate the electron irradiation process. The obtained data from semi-empirical model were checked together with the set of experimental results. The proposed two-parametric model for electron beam energy evaluation and estimation of accuracy for computational dosimetry methods on the base of developed model are discussed.
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.
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...
A parametric ribcage geometry model accounting for variations among the adult population.
Wang, Yulong; Cao, Libo; Bai, Zhonghao; Reed, Matthew P; Rupp, Jonathan D; Hoff, Carrie N; Hu, Jingwen
2016-09-06
The objective of this study is to develop a parametric ribcage model that can account for morphological variations among the adult population. Ribcage geometries, including 12 pair of ribs, sternum, and thoracic spine, were collected from CT scans of 101 adult subjects through image segmentation, landmark identification (1016 for each subject), symmetry adjustment, and template mesh mapping (26,180 elements for each subject). Generalized procrustes analysis (GPA), principal component analysis (PCA), and regression analysis were used to develop a parametric ribcage model, which can predict nodal locations of the template mesh according to age, sex, height, and body mass index (BMI). Two regression models, a quadratic model for estimating the ribcage size and a linear model for estimating the ribcage shape, were developed. The results showed that the ribcage size was dominated by the height (p=0.000) and age-sex-interaction (p=0.007) and the ribcage shape was significantly affected by the age (p=0.0005), sex (p=0.0002), height (p=0.0064) and BMI (p=0.0000). Along with proper assignment of cortical bone thickness, material properties and failure properties, this parametric ribcage model can directly serve as the mesh of finite element ribcage models for quantifying effects of human characteristics on thoracic injury risks.
Brayton power conversion system parametric design modelling for nuclear electric propulsion
Ashe, Thomas L.; Otting, William 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.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-12-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.
Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions
2013-01-01
Background Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. Methods There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. Results An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. Conclusion A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies. PMID:23384310
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
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 ...
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.
Multivariate parametric random effect regression models for fecundability studies.
Ecochard, R; Clayton, D G
2000-12-01
Delay until conception is generally described by a mixture of geometric distributions. Weinberg and Gladen (1986, Biometrics 42, 547-560) proposed a regression generalization of the beta-geometric mixture model where covariates effects were expressed in terms of contrasts of marginal hazards. Scheike and Jensen (1997, Biometrics 53, 318-329) developed a frailty model for discrete event times data based on discrete-time analogues of Hougaard's results (1984, Biometrika 71, 75-83). This paper is on a generalization to a three-parameter family distribution and an extension to multivariate cases. The model allows the introduction of explanatory variables, including time-dependent variables at the subject-specific level, together with a choice from a flexible family of random effect distributions. This makes it possible, in the context of medically assisted conception, to include data sources with multiple pregnancies (or attempts at pregnancy) per couple.
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.
Freiman, M; Kronman, A; Esses, S J; Joskowicz, L; Sosna, J
2010-01-01
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.
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.
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 ...
Wind Farm parametrization in the mesoscale model WRF
DEFF Research Database (Denmark)
Volker, Patrick; Badger, Jake; Hahmann, Andrea N.
2012-01-01
The project’s objective is to investigate and develop methods for prediction of mesoscale climate, wake effects and atmospheric feedbacks, for scenarios where large portions of the sea are covered with wind farms. The atmospheric flow is simulated with the WRF mesoscale model, since it has signif...
Statistical inference on parametric part for partially linear single-index model
Institute of Scientific and Technical Information of China (English)
ZHANG RiQuan; HUANG ZhenSheng
2009-01-01
Statistical inference on parametric part for the partially linear single-index model (PLSIM) is considered in this paper.A profile least-squares technique for estimating the parametric part is proposed and the asymptotic normality of the profile least-squares estimator is given.Based on the estimator,a generalized likelihood ratio (GLR) test is proposed to test whether parameters on linear part for the model is under a contain linear restricted condition.Under the null model,the proposed GLR statistic follows asymptotically the X2-distribution with the scale constant and degree of freedom independent of the nuisance parameters,known as Wilks phenomenon.Both simulated and real data examples are used to illustrate our proposed methods.
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
Lithium-Sulfur (Li-S) batteries are an emerging energy storage technology, which draw interest due to its high theoretical specific capacity (approx. 1675 Ah/kg) and theoretical energy density of almost 2600 Wh/kg. In order to analyse their dynamic behaviour and to determine their suitability...... 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...
Statistical inference on parametric part for partially linear single-index model
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Statistical inference on parametric part for the partially linear single-index model (PLSIM) is considered in this paper. A profile least-squares technique for estimating the parametric part is proposed and the asymptotic normality of the profile least-squares estimator is given. Based on the estimator, a generalized likelihood ratio (GLR) test is proposed to test whether parameters on linear part for the model is under a contain linear restricted condition. Under the null model, the proposed GLR statistic follows asymptotically the χ2-distribution with the scale constant and degree of freedom independent of the nuisance parameters, known as Wilks phenomenon. Both simulated and real data examples are used to illustrate our proposed methods.
parametric modeling and computing of multi-tower suspension bridge based on ANSYS
Institute of Scientific and Technical Information of China (English)
Feng Zhaoxiang; Zhao An; Song Jianyong; Yang Yun
2012-01-01
Based on FEM （finite element method） program ANSYS and the OpenGL graphics, this paper develops the parametric modeling module and the computing module of the multi-tower suspension bridge, the modules being embedded into the ANSYS system, and the parametric modeling module parameters can be entered by way of interface, which can fast establish a multi-tower suspension bridge model. Calculation module can establish load conditions for the features of road bridge and specifications, in which multiple conditions can be defined and solved automatically. Postprocessing part of the solution also serves the results of the subtotals and selects the output, so that the results of the output and finishing work have become more convenient and easier, and also the results can be saved in word, excel and other different file types.
Quintessence a natural model to parametrize the cosmological constant
Macorra, A D L
2003-01-01
We show how a scalar field with gravitational interaction only, i e. quintessence, can account for present day acceleration of the universe and it gives the correct acoustic scale and peaks of the CMP,R anisotropy. We show that the quintessence field can be naturally be described by the fermion condensates of a non-abelian gauge group. This gauge group is unified with the standard model gauge groups. The model has no free parameters. Even the initial energy density at the unification scale and at the condensation scale are fixed by the number of degrees of freedom of the gauge group. We study the evolution of all fields from the unification scale and we calculate the relevant cosmological quantities. (Author)
Modelling and Simulation for Energy Production Parametric Dependence in Greenhouses
Directory of Open Access Journals (Sweden)
Maurizio Carlini
2010-01-01
Full Text Available Greenhouses crops in Italy are made by using prefabricated structures, leaving out the preliminary study of optical and thermal exchanges between the external environment and the greenhouse, dealing with heating and cooling and the effects of air conditioning needed for plant growth. This involves rather significant costs that directs the interest of designers, builders, and farmers in order to seek constructive solutions to optimize the system of such emissions. This work was done by building a model of gases using TRNSYS software, and these gases then have been checked for compliance. The model was constructed considering an example of a prefabricated greenhouse, located in central of Italy. Aspects of the structural components, and thermal and optical properties are analyzed in order to achieve a representation of reality.
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.
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 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...... of the presented modelling approach....
Parametric nonlinear lumped element model for circular CMUTs in collapsed mode.
Aydoğdu, Elif; Ozgurluk, Alper; Atalar, Abdullah; Köymen, Hayrettin
2014-01-01
We present a parametric equivalent circuit model for a circular CMUT in collapsed mode. First, we calculate the collapsed membrane deflection, utilizing the exact electrical force distribution in the analytical formulation of membrane deflection. Then we develop a lumped element model of collapsed membrane operation. The radiation impedance for collapsed mode is also included in the model. The model is merged with the uncollapsed mode model to obtain a simulation tool that handles all CMUT behavior, in transmit or receive. Large- and small-signal operation of a single CMUT can be fully simulated for any excitation regime. The results are in good agreement with FEM simulations.
An immersed boundary model of the cochlea with parametric forcing
Ko, William
2015-01-01
The cochlea or inner ear has a remarkable ability to amplify sound signals. This is understood to derive at least in part from some active process that magnifies vibrations of the basilar membrane (BM) and the cochlear partition in which it is embedded, to the extent that it overcomes the effect of viscous damping from the surrounding cochlear fluid. Many authors have associated this amplification ability to some type of mechanical resonance within the cochlea, however there is still no consensus regarding the precise cause of amplification. Our work is inspired by experiments showing that the outer hair cells within the cochlear partition change their lengths when stimulated, which can in turn cause periodic distortions of the BM and other structures in the cochlea. This paper investigates a novel fluid-mechanical resonance mechanism that derives from hydrodynamic interactions between an oscillating BM and the surrounding cochlear fluid. We present a model of the cochlea based on the immersed boundary method...
Modeling Sodium Iodide Detector Response Using Parametric Equations
2013-03-22
and the source are kept in a constant geometry using a thin wooden plank . Both are moved back as a unit in 10 cm increments...using a thin wooden plank . Both are moved back as a unit in 10 cm increments. Similar to the MCNP model, the source and detector remained in a...simulated particles Error % Max Backscatter 0 9.29E‐04 1% 100% 10 3.86E‐04 2% 42% 20 1.98E‐04 2% 21% 30 1.16E‐04 3% 13% 40 8.01E
Microprocessor-controlled colonic peristalsis: dynamic parametric modeling in dogs.
Rashev, Peter Z; Amaris, Manuel; Bowes, Kenneth L; Mintchev, Martin P
2002-05-01
The study aimed at completing a model of functional colonic electric stimulation and testing it for artificial recreation of peristalsis in dogs. Dynamic measurements of invoked single contractions obtained from two unconscious dogs as well as previously reported static contraction properties were utilized to suggest the optimal stimulation parameters of: (1) length of the stimulating electrodes, (2) separation between the successive electrode sets, (3) duration, and (4) phase lag between the stimuli sequentially applied to the electrode sets. The derived electrode configuration and stimulation pattern were adjusted for different anatomical dimensions and tested in distended colon full of viscous content. Forward and backward propagating peristaltic waves were invoked in two other unconscious dogs, indicating that the recreation of colonic peristalsis under microprocessor control is feasible.
Parametric CAD and Fea Model of a Saddle Tapping Tee
DEFF Research Database (Denmark)
A. Kristensen, Anders Schmidt; Lund Jepsen, Kristian
2007-01-01
pressure 30[bar]. A parametric verification model is established based on considerations regarding the compaction of the gasket and the stress level in the brackets of the clamp. The required minimum compaction pressure in the gasket is 1.4[N/mm] according to the European Norm for pressure vessels DS EN...... 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...
Parametric City Scale Energy Modeling Perspectives on using Termite in city scaled models
DEFF Research Database (Denmark)
Negendahl, Kristoffer; Nielsen, Toke Rammer
for fully parametric district- and city-size simulations of yearly building energy consumption with the same precisions of energy use as the tool simulates on each and every building. The poster demonstrates some of the parametric flexibilities in using Termite e.g. planning for optimal synergetic envelope...
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.
Gosselin, Jeremy M.; Dosso, Stan E.; Cassidy, John F.; Quijano, Jorge E.; Molnar, Sheri; Dettmer, Jan
2017-10-01
This paper develops and applies a Bernstein-polynomial parametrization to efficiently represent general, gradient-based profiles in nonlinear geophysical inversion, with application to ambient-noise Rayleigh-wave dispersion data. Bernstein polynomials provide a stable parametrization in that small perturbations to the model parameters (basis-function coefficients) result in only small perturbations to the geophysical parameter profile. A fully nonlinear Bayesian inversion methodology is applied to estimate shear wave velocity (VS) profiles and uncertainties from surface wave dispersion data extracted from ambient seismic noise. The Bayesian information criterion is used to determine the appropriate polynomial order consistent with the resolving power of the data. Data error correlations are accounted for in the inversion using a parametric autoregressive model. The inversion solution is defined in terms of marginal posterior probability profiles for VS as a function of depth, estimated using Metropolis-Hastings sampling with parallel tempering. This methodology is applied to synthetic dispersion data as well as data processed from passive array recordings collected on the Fraser River Delta in British Columbia, Canada. Results from this work are in good agreement with previous studies, as well as with co-located invasive measurements. The approach considered here is better suited than `layered' modelling approaches in applications where smooth gradients in geophysical parameters are expected, such as soil/sediment profiles. Further, the Bernstein polynomial representation is more general than smooth models based on a fixed choice of gradient type (e.g. power-law gradient) because the form of the gradient is determined objectively by the data, rather than by a subjective parametrization choice.
Survival Analysis of Patients with Breast Cancer using Weibull Parametric Model.
Baghestani, Ahmad Reza; Moghaddam, Sahar Saeedi; Majd, Hamid Alavi; Akbari, Mohammad Esmaeil; Nafissi, Nahid; Gohari, Kimiya
2015-01-01
The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
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.
Parametric links among Monte Carlo, phase-field, and sharp-interface models of interfacial motion.
Liu, Pu; Lusk, Mark T
2002-12-01
Parametric links are made among three mesoscale simulation paradigms: phase-field, sharp-interface, and Monte Carlo. A two-dimensional, square lattice, 1/2 Ising model is considered for the Monte Carlo method, where an exact solution for the interfacial free energy is known. The Monte Carlo mobility is calibrated as a function of temperature using Glauber kinetics. A standard asymptotic analysis relates the phase-field and sharp-interface parameters, and this allows the phase-field and Monte Carlo parameters to be linked. The result is derived without bulk effects but is then applied to a set of simulations with the bulk driving force included. An error analysis identifies the domain over which the parametric relationships are accurate.
Hashemi-Kia, M.; Toossi, M.
1990-01-01
As a result of this work, a reduction procedure has been developed which can be applied to large finite element model of airframe type structures. This procedure, which is tailored to be used with MSC/NASTRAN finite element code, is applied to the full airframe dynamic finite element model of AH-64A Attack Helicopter. The applicability of the resulting reduced model to parametric and optimization studies is examined. Through application of the design sensitivity analysis, the viability and efficiency of this reduction technique has been demonstrated in a vibration reduction study.
An estimating equation for parametric shared frailty models with marginal additive hazards
DEFF Research Database (Denmark)
Pipper, Christian Bressen; Martinussen, Torben
2004-01-01
Multivariate failure time data arise when data consist of clusters in which the failure times may be dependent. A popular approach to such data is the marginal proportional hazards model with estimation under the working independence assumption. In some contexts, however, it may be more reasonable...... to use the marginal additive hazards model. We derive asymptotic properties of the Lin and Ying estimators for the marginal additive hazards model for multivariate failure time data. Furthermore we suggest estimating equations for the regression parameters and association parameters in parametric shared...
Directory of Open Access Journals (Sweden)
Tristan Perez
2009-01-01
Full Text Available This article describes a Matlab toolbox for parametric identification of fluid-memory models associated with the radiation forces ships and offshore structures. Radiation forces are a key component of force-to- motion models used in simulators, motion control designs, and also for initial performance evaluation of wave-energy converters. The software described provides tools for preparing non-parmatric data and for identification with automatic model-order detection. The identification problem is considered in the frequency domain.
Vitković, Nikola; Mitić, Jelena; Manić, Miodrag; Trajanović, Miroslav; Husain, Karim; Petrović, Slađana; Arsić, Stojanka
2015-01-01
Geometrically accurate and anatomically correct 3D models of the human bones are of great importance for medical research and practice in orthopedics and surgery. These geometrical models can be created by the use of techniques which can be based on input geometrical data acquired from volumetric methods of scanning (e.g., Computed Tomography (CT)) or on the 2D images (e.g., X-ray). Geometrical models of human bones created in such way can be applied for education of medical practitioners, preoperative planning, etc. In cases when geometrical data about the human bone is incomplete (e.g., fractures), it may be necessary to create its complete geometrical model. The possible solution for this problem is the application of parametric models. The geometry of these models can be changed and adapted to the specific patient based on the values of parameters acquired from medical images (e.g., X-ray). In this paper, Method of Anatomical Features (MAF) which enables creation of geometrically precise and anatomically accurate geometrical models of the human bones is implemented for the creation of the parametric model of the Human Mandible Coronoid Process (HMCP). The obtained results about geometrical accuracy of the model are quite satisfactory, as it is stated by the medical practitioners and confirmed in the literature.
National Research Council Canada - National Science Library
L. MuhamadSafiih; A. A. Kamil; M. T. Abu Osman
2014-01-01
... this problem is through the use of semi-parametric method. However, the uncertainties and ambiguities exist in the models, particularly the relationship between the endogenous and exogenous variables...
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.
Parametrization of orographic thermal effect on the deep convection triggering in Global Model
Jingmei, Y.; Jean-Yves, G.; Alain, L.
2013-05-01
The work is based on the hypothesis that anabatic winds (or valley breeze) is an important mechanism of deep convection triggering. Induced by the temperature difference between the mountain surface and the environmental air, anabatic winds own a kinetic energy which may eventually overcome the Planet Boundary Layer inhibition (CIN, Convective Inhibition) and allows the associated convection to develop into the free troposphere. This sub-grid scale phenomenon needs a special parametrization in general circulation models (GCMs). Its lack of representation in present GCM versions is thought of being the cause of the deficit of deep convection systems genesis observed in certain orographical zones, as Mount Cameroun in West Africa for example. A valley breeze parametrization has been designed and built in a GCM (LMDZ). The model computes kinetic energy of the valley breeze in relation to the sub-grid scale orographical characteristics (elevation, slope, orientation). It consists of a grid slim layer along the mountain surface. It is coupled with a multi-layers conductive-capacitive soil model. The coupling is accomplished by using the energy budget at the surface of the mountain. The model was tested in the dynamical mode by systematic sensitivity analysis to the principal parameters and to the environmental conditions. It has then been implemented in the 1D version of the GCM (SCM, Single Column Model), coupled with the Emanuel deep convection scheme, and tested against a radiative-convective equilibrium case and the Hapex campaign case. The stationnary solution of the aeraulic part of the model has been adopted for the GCM. The parametrization finally has been introduced in the 3D version of the GCM, in the diagnostic mode (without coupling to the convection process). It gives a spatial distribution of the triggering frequency of deep convection in coherence with that of the satellite image observation in the West Africa region, during the West African Monsoon
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.
Constrained parametric model for simultaneous inference of two cumulative incidence functions.
Shi, Haiwen; Cheng, Yu; Jeong, Jong-Hyeon
2013-01-01
We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds-rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds-rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite-sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.
Spectroscopic ellipsometric study of Ge nanocrystals embedded in SiO{sub 2} using parametric models
Energy Technology Data Exchange (ETDEWEB)
Petrik, P.; Fried, M. [Research Institute for Technical Physics and Materials Science, Budapest (Hungary); Dana, A.; Aydinli, A. [Institute of Materials Science and Nanotechnology, Bilkent University, Ankara (Turkey); Foss, S.; Finstad, T.G. [University of Oslo, Department of Physics, Blindern, Oslo (Norway); Basa, P.
2008-05-15
Ge-rich SiO{sub 2} layers on top of Si substrates were deposited using plasma enhanced chemical vapour deposition. Ge nanocrystals embedded in the SiO{sub 2} layers were formed by high temperature annealing. The samples were measured and evaluated by spectroscopic ellipsometry. Effective medium theory (EMT) and parametric semiconductor models have been used to model the dielectric function of the layers. Systematic dependences of the layer thickness and the oscillator parameters have been found on the annealing temperature (nanocrystal size). (copyright 2008 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Parametric-based brain Magnetic Resonance Elastography using a Rayleigh damping material model.
Petrov, Andrii Y; Sellier, Mathieu; Docherty, Paul D; Chase, J Geoffrey
2014-10-01
The three-parameter Rayleigh damping (RD) model applied to time-harmonic Magnetic Resonance Elastography (MRE) has potential to better characterise fluid-saturated tissue systems. However, it is not uniquely identifiable at a single frequency. One solution to this problem involves simultaneous inverse problem solution of multiple input frequencies over a broad range. As data is often limited, an alternative elegant solution is a parametric RD reconstruction, where one of the RD parameters (μI or ρI) is globally constrained allowing accurate identification of the remaining two RD parameters. This research examines this parametric inversion approach as applied to in vivo brain imaging. Overall, success was achieved in reconstruction of the real shear modulus (μR) that showed good correlation with brain anatomical structures. The mean and standard deviation shear stiffness values of the white and gray matter were found to be 3±0.11kPa and 2.2±0.11kPa, respectively, which are in good agreement with values established in the literature or measured by mechanical testing. Parametric results with globally constrained μI indicate that selecting a reasonable value for the μI distribution has a major effect on the reconstructed ρI image and concomitant damping ratio (ξd). More specifically, the reconstructed ρI image using a realistic μI=333Pa value representative of a greater portion of the brain tissue showed more accurate differentiation of the ventricles within the intracranial matter compared to μI=1000Pa, and ξd reconstruction with μI=333Pa accurately captured the higher damping levels expected within the vicinity of the ventricles. Parametric RD reconstruction shows potential for accurate recovery of the stiffness characteristics and overall damping profile of the in vivo living brain despite its underlying limitations. Hence, a parametric approach could be valuable with RD models for diagnostic MRE imaging with single frequency data. Copyright © 2014
Institute of Scientific and Technical Information of China (English)
LIM; C.W.
2010-01-01
Nonlinear combination parametric resonance is investigated for an axially accelerating viscoelastic string.The governing equation of in-planar motion of the string is established by introducing a coordinate transform in the Eulerian equation of a string with moving boundaries.The string under investigation is constituted by the standard linear solid model in which the material,not partial,time derivative was used.The governing equation leads to the Mote model for transverse vibration by omitting the longitudinal component and higher order terms.The Kirchhoff model is derived from the Mote model by replacing the tension with the averaged tension over the string.The two models are respectively analyzed via the method of multiple scales for principal parametric resonance.The amplitudes and the existence conditions of steady-state response and its stability can be numerically determined.Numerical calculations demonstrate the effects of the string material parameters,the initial tension,and the axial speed fluctuation amplitude.The outcomes of the two models are qualitatively and quantitatively compared.
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...
Multi-scale hierarchical approach for parametric mapping: assessment on multi-compartmental models.
Rizzo, G; Turkheimer, F E; Bertoldo, A
2013-02-15
This paper investigates a new hierarchical method to apply basis function to mono- and multi-compartmental models (Hierarchical-Basis Function Method, H-BFM) at a voxel level. This method identifies the parameters of the compartmental model in its nonlinearized version, integrating information derived at the region of interest (ROI) level by segmenting the cerebral volume based on anatomical definition or functional clustering. We present the results obtained by using a two tissue-four rate constant model with two different tracers ([(11)C]FLB457 and [carbonyl-(11)C]WAY100635), one of the most complex models used in receptor studies, especially at the voxel level. H-BFM is robust and its application on both [(11)C]FLB457 and [carbonyl-(11)C]WAY100635 allows accurate and precise parameter estimates, good quality parametric maps and a low percentage of voxels out of physiological bound (approach for PET quantification by using compartmental modeling at the voxel level. In particular, different from other proposed approaches, this method can also be used when the linearization of the model is not appropriate. We expect that applying it to clinical data will generate reliable parametric maps. Copyright © 2012 Elsevier Inc. All rights reserved.
A photometric approach to parametric modelling for optimising multisegmented photodetector rings
Yoon, P. S.; Siddons, D. P.
2013-06-01
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.
Advanced parametrical modelling of 24 GHz radar sensor IC packaging components
Kazemzadeh, R.; John, W.; Wellmann, J.; Bala, U. B.; Thiede, A.
2011-08-01
This paper deals with the development of an advanced parametrical modelling concept for packaging components of a 24 GHz radar sensor IC used in automotive driver assistance systems. For fast and efficient design of packages for system-in-package modules (SiP), a simplified model for the description of parasitic electromagnetic effects within the package is desirable, as 3-D field computation becomes inefficient due to the high density of conductive elements of the various signal paths in the package. By using lumped element models for the characterization of the conductive components, a fast indication of the design's signal-quality can be gained, but so far does not offer enough flexibility to cover the whole range of geometric arrangements of signal paths in a contemporary package. This work pursues to meet the challenge of developing a flexible and fast package modelling concept by defining parametric lumped-element models for all basic signal path components, e.g. bond wires, vias, strip lines, bumps and balls.
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.
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)
Energy Technology Data Exchange (ETDEWEB)
Logan, R W; Nitta, C K; Chidester, S K
2006-02-28
One of the final steps in building a numerical model of a physical, mechanical, thermal, or chemical process, is to assess its accuracy as well as its sensitivity to input parameters and modeling technique. In this work, we demonstrate one simple process to take a top-down or integral view of the model, one which can implicitly reflect any couplings between parameters, to assess the importance of each aspect of modeling technique. We illustrate with an example of a comparison of a finite element model with data for violent reaction of explosives in accident scenarios. We show the relative importance of each of the main parametric inputs, and the contributions of model form and grid convergence. These can be directly related to the importance factors for the system being analyzed as a whole, and help determine which factors need more attention in future analyses and tests.
Energy Technology Data Exchange (ETDEWEB)
Logan, R W; Nitta, C K; Chidester, S K
2006-02-28
One of the final steps in building a numerical model of a physical, mechanical, thermal, or chemical process, is to assess its accuracy as well as its sensitivity to input parameters and modeling technique. In this work, we demonstrate one simple process to take a top-down or integral view of the model, one which can implicitly reflect any couplings between parameters, to assess the importance of each aspect of modeling technique. We illustrate with an example of a comparison of a finite element model with data for violent reaction of explosives in accident scenarios. We show the relative importance of each of the main parametric inputs, and the contributions of model form and grid convergence. These can be directly related to the importance factors for the system being analyzed as a whole, and help determine which factors need more attention in future analyses and tests.
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.
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.
Parametric Model for the Response of a Photo-multiplier Tube
Energy Technology Data Exchange (ETDEWEB)
Aguilar, M.; Alcaraz, J.; Berdugo, J.; Casaus, J.; Delgado, C.; Diaz, C.; Lanciotti, E.; Mana, C.; Marin, J.; Martinez, G.; Molla, M.; Palomares, C.; Rodriguez, J.; Sanchez, E.; Sevilla, A.; Torrento, A.
2005-07-01
When a photon impinges upon a photon-multiplier tube, an electron is emitted with certain probability and, after several amplification stages, an electron shower is collected at the anode. However, when the first electron is emitted from one of the amplification dynodes or the photon-multiplier is operated under untoward conditions (external magnetic fields...) smaller showers are collected. In this paper, we present a bi-parametric model which describers the response of a photo-multiplier tube over a wide range of circumstances. (Author)
Research on Parametric Process Planning Technology Based on Three-dimensional Part Model
Institute of Scientific and Technical Information of China (English)
DING Yufeng; WEI Zhongling
2006-01-01
CAPP(Computer Aided Process Planning) has already become the bottleneck of CAD/CAM system. Present two-dimensional CAPP system which is used in the enterprise needs to be input information and data again, because it can not draw data from CAD(Computer Aided Design)model automatically after building CAD model and drawing. This has influenced extensive use of CAPP system because of its low efficiency. In this paper, three-dimensional model is built by using the parametric method, the process file can be produced directly through drawing corresponding characteristic and parameter from the model with the aid of process database. This improves the efficiency of product development. Visual C++ 6.0 and SQL Server 2000 are used to develop WTJDCAPP prototype system based on component model and SolidWorks three-dimensional CAD platform. Engine valve-seat is taken as concrete object to validate of the technology.
Institute of Scientific and Technical Information of China (English)
Juan J. Cuadrado Gallego; Daniel Rodríguez; Miguel (A)ngel Sicilia; Miguel Garre Rubio; Angel García Crespo
2007-01-01
Parametric software effort estimation models usually consists of only a single mathematical relationship. Withthe advent of software repositories containing data from heterogeneous projects, these types of models suffer from pooradjustment and predictive accuracy. One possible way to alleviate this problem is the use of a set of mathematical equationsobtained through dividing of the historical project datasets according to different parameters into subdatasets called parti-tions. In turn, partitions are divided into clusters that serve as a tool for more accurate models. In this paper, we describethe process, tool and results of such approach through a case study using a publicly available repository, ISBSG. Resultssuggest the adequacy of the technique as an extension of existing single-expression models without making the estimationprocess much more complex that uses a single estimation model. A tool to support the process is also presented.
Development of Parametric Mass and Volume Models for an Aerospace SOFC/Gas Turbine Hybrid System
Tornabene, Robert; Wang, Xiao-yen; Steffen, Christopher J., Jr.; Freeh, Joshua E.
2005-01-01
In aerospace power systems, mass and volume are key considerations to produce a viable design. The utilization of fuel cells is being studied for a commercial aircraft electrical power unit. Based on preliminary analyses, a SOFC/gas turbine system may be a potential solution. This paper describes the parametric mass and volume models that are used to assess an aerospace hybrid system design. The design tool utilizes input from the thermodynamic system model and produces component sizing, performance, and mass estimates. The software is designed such that the thermodynamic model is linked to the mass and volume model to provide immediate feedback during the design process. It allows for automating an optimization process that accounts for mass and volume in its figure of merit. Each component in the system is modeled with a combination of theoretical and empirical approaches. A description of the assumptions and design analyses is presented.
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)
Battaglia, Nick; Cen, Renyue; Loeb, Abraham
2012-01-01
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 box, we show that the density and reionization-redshift fields are highly correlated on large scales (>~ 1 Mpc/h). 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 which can be reduced to one free parameter when we fit the two bias parameters to simulations 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 reionizati...
Parametric models to relate spike train and LFP dynamics with neural information processing.
Banerjee, Arpan; Dean, Heather L; Pesaran, Bijan
2012-01-01
Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial
Ferrero, Enrico; Mortarini, Luca; Purghè, Federico
2016-11-01
A model for the evaluation of the concentration fluctuation variance is coupled with a one-particle Lagrangian stochastic model and results compared to a wind-tunnel simulation experiment. In this model the concentration variance evolves along the particle trajectories according to the same Langevin equation used for the simulation of the velocity field, and its dissipation is taken into account through a decay term with a finite time scale. Indeed, while the mean concentration is conserved, the concentration variance is not and our model takes into account its dissipation. A simple parametrization for the dissipation time scale is proposed and it is found that it depends linearly on time and on the ratio between the size and the height of the source through a scaling factor of 1 / 3.
Developing two non-parametric performance models for higher learning institutions
Kasim, Maznah Mat; Kashim, Rosmaini; Rahim, Rahela Abdul; Khan, Sahubar Ali Muhamed Nadhar
2016-08-01
Measuring the performance of higher learning Institutions (HLIs) is a must for these institutions to improve their excellence. This paper focuses on formation of two performance models: efficiency and effectiveness models by utilizing a non-parametric method, Data Envelopment Analysis (DEA). The proposed models are validated by measuring the performance of 16 public universities in Malaysia for year 2008. However, since data for one of the variables is unavailable, an estimate was used as a proxy to represent the real data. The results show that average efficiency and effectiveness scores were 0.817 and 0.900 respectively, while six universities were fully efficient and eight universities were fully effective. A total of six universities were both efficient and effective. It is suggested that the two proposed performance models would work as complementary methods to the existing performance appraisal method or as alternative methods in monitoring the performance of HLIs especially in Malaysia.
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.
Energy Technology Data Exchange (ETDEWEB)
Gaussens, J.; Paillot, H. [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1965-07-01
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) [French] Les auteurs definissent les notions de valeurs d'usage et de prix du plutonium. Ils donnent un 'modele parametre simplifie' simulant l'equilibre de l'office et de la demande dans le temps concernant le plutonium et le prix qui decoule de la rarete relative de ce metal, compte tenu des parametres techniques et economiques de fonctionnement des divers reacteurs en presence. Ce modele est suffisamment simple pour permettre des calculs manuels et etablir des liaisons claires entre les divers parametres. L'utilisation de la technique des programmes lineaires permet par ailleurs une extension considerable du modele. Cette note comprend trois parties: I - Expose general de l'etude (sans expose du detail des calculs) II - Developpement mathematique du modele parametre simplifie et application (on precise les donnees de base et le resultat des calculs) III - Annexes (donnant le detail des calculs de la partie II). (auteurs)
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.
Estimating the loss in expectation of life due to cancer using flexible parametric survival models.
Andersson, Therese M-L; Dickman, Paul W; Eloranta, Sandra; Lambe, Mats; Lambert, Paul C
2013-12-30
A useful summary measure for survival data is the expectation of life, which is calculated by obtaining the area under a survival curve. The loss in expectation of life due to a certain type of cancer is the difference between the expectation of life in the general population and the expectation of life among the cancer patients. This measure is used little in practice as its estimation generally requires extrapolation of both the expected and observed survival. A parametric distribution can be used for extrapolation of the observed survival, but it is difficult to find a distribution that captures the underlying shape of the survival function after the end of follow-up. In this paper, we base our extrapolation on relative survival, because it is more stable and reliable. Relative survival is defined as the observed survival divided by the expected survival, and the mortality analogue is excess mortality. Approaches have been suggested for extrapolation of relative survival within life-table data, by assuming that the excess mortality has reached zero (statistical cure) or has stabilized to a constant. We propose the use of flexible parametric survival models for relative survival, which enables estimating the loss in expectation of life on individual level data by making these assumptions or by extrapolating the estimated linear trend at the end of follow-up. We have evaluated the extrapolation from this model using data on four types of cancer, and the results agree well with observed data.
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.
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.
Grain-scale modeling and splash parametrization for aeolian sand transport
Lämmel, Marc; Dzikowski, Kamil; Kroy, Klaus; Oger, Luc; Valance, Alexandre
2017-02-01
The collision of a spherical grain with a granular bed is commonly parametrized by the splash function, which provides the velocity of the rebounding grain and the velocity distribution and number of ejected grains. Starting from elementary geometric considerations and physical principles, like momentum conservation and energy dissipation in inelastic pair collisions, we derive a rebound parametrization for the collision of a spherical grain with a granular bed. Combined with a recently proposed energy-splitting model [Ho et al., Phys. Rev. E 85, 052301 (2012), 10.1103/PhysRevE.85.052301] that predicts how the impact energy is distributed among the bed grains, this yields a coarse-grained but complete characterization of the splash as a function of the impact velocity and the impactor-bed grain-size ratio. The predicted mean values of the rebound angle, total and vertical restitution, ejection speed, and number of ejected grains are in excellent agreement with experimental literature data and with our own discrete-element computer simulations. We extract a set of analytical asymptotic relations for shallow impact geometries, which can readily be used in coarse-grained analytical modeling or computer simulations of geophysical particle-laden flows.
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.
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.
A periodic charge-dipole electrostatic model: parametrization for silver slabs.
Bodrenko, I V; Sierka, M; Fabiano, E; Della Sala, F
2012-10-07
We present an extension of the charge-dipole model for the description of periodic systems. This periodic charge-dipole electrostatic model (PCDEM) allows one to describe the linear response of periodic structures in terms of charge- and dipole-type gaussian basis functions. The long-range electrostatic interaction is efficiently described by means of the continuous fast multipole method. As a first application, the PCDEM method is applied to describe the polarizability of silver slabs. We find that for a correct description of the polarizability of the slabs both charges and dipoles are required. However a continuum set of parametrizations, i.e., different values of the width of charge- and dipole-type gaussians, leads to an equivalent and accurate description of the slabs polarizability but a completely unphysical description of induced charge-density inside the slab. We introduced the integral squared density measure which allows one to obtain a unique parametrization which accurately describes both the polarizability and the induced density profile inside the slab. Finally the limits of the electrostatic approximations are also pointed out.
Parametric models to relate spike train and LFP dynamics with neural information processing
Directory of Open Access Journals (Sweden)
Arpan eBanerjee
2012-07-01
Full Text Available Spike trains and local field potentials resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Trial-by-trial spike-field correlation in visual response onset times are higher when the unified model is used, matching with corresponding values obtained using earlier trial-averaged measures on a previously published data set. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior.
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.
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.).
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.
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.
2016-11-01
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. We aim to establish that a single model can generally reproduce the observed properties of these jet-like events. Methods: 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 β on the generation and properties of solar-like jets. Results: The parametric study validates our model of jets for plasma β ranging from 10-3 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 β ≤ 1. We introduces the new result that the plasma β 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 enable us to understand the energisation, triggering, and driving processes of jet-like events. Our model enables 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.
Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.
Han, Chao; House, Leanna; Leman, Scotland C
2016-01-01
Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.
Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.
Directory of Open Access Journals (Sweden)
Chao Han
Full Text Available Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.
Directory of Open Access Journals (Sweden)
Sandra Teodorescu
2013-11-01
Full Text Available The present paper describes a series of parametric distributions used for modeling non-life insurance payments data.Of those listed, special attention is paid to the transformed Beta distribution family.This distribution as well as those which are obtained from it(special cases of four-parameter transformed Beta distribution are used in the modeling of high costs, or even extreme ones.In the literature it follows the tail behaviour of distributions depending on the parameters, because the insurance payments data are tipically highly positively skewed and distributed with large upper tails.In the paper is described the tail behavior of the distribution in the left and right side respectively, and deduced from it, a general case.There are also some graphs of probability density function for one of the transformed Beta family members, which comes to reinforce the comments made.
Parametric Modeling and Moving Simulation of Vibrating Screen and Tubers on Potato Harvester
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Huali Yu
2015-02-01
Full Text Available In order to overcome the behindhand and inefficient design of potato diggers, feature-based parametric modeling software Autodesk Inventor was used for modeling of potato diggers. The swing sieve, movement simulation with ADAMS was carried out. The complex velocity acceleration and displacement curves were analysed. Collision pressure curves were analysed too. Its velocity is less than or equal to 500 mm/s and its acceleration is more than or equal to 2.5 m/s2 and less than or equal to 20 m/s2. Test results indicated that Collision pressures of small and medium tubers are 120 Newton and 250 Newton, respectively, which are all smaller than damaging pressure. Potato can be transferred freely and damage rate is less than or equal to 4% when the frequency is 5.5 Hz and the swing is 30 mm.
Martinez, L C; Calzado, A
2016-01-01
A parametric model is used for the calculation of the CT number of some selected human tissues of known compositions (Hi) in two hybrid systems, one SPECT-CT and one PET-CT. Only one well characterized substance, not necessarily tissue-like, needs to be scanned with the protocol of interest. The linear attenuation coefficients of these tissues for some energies of interest (μ(i)) have been calculated from their tabulated compositions and the NIST databases. These coefficients have been compared with those calculated with the bilinear model from the CT number (μ(B)i). No relevant differences have been found for bones and lung. In the soft tissue region, the differences can be up to 5%. These discrepancies are attributed to the different chemical composition for the tissues assumed by both methods.
Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R
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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.
Heat Transfer Parametric System Identification
1993-06-01
Transfer Parametric System Identification 6. AUTHOR(S Parker, Gregory K. 7. PERFORMING ORGANIZATION NAME(S) AND AOORESS(ES) 8. PERFORMING ORGANIZATION...distribution is unlimited. Heat Transfer Parametric System Identification by Gregory K. Parker Lieutenant, United States Navy BS., DeVry Institute of...Modeling Concept ........ ........... 3 2. Lumped Parameter Approach ...... ......... 4 3. Parametric System Identification ....... 4 B. BASIC MODELING
Semiparametric modeling: Correcting low-dimensional model error in parametric models
Berry, Tyrus; Harlim, John
2016-03-01
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.
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
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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.
Mandal, Subhamoy; Nagaraj, Yeshaswini; Ben, Xose Luis Dean; Razansky, Daniel
2015-01-01
In this article, we present a novel scheme for segmenting the image boundary (with the background) in optoacoustic small animal in vivo imaging systems. The method utilizes a multiscale edge detection algorithm to generate a binary edge map. A scale dependent morphological operation is employed to clean spurious edges. Thereafter, an ellipse is fitted to the edge map through constrained parametric transformations and iterative goodness of fit calculations. The method delimits the tissue edges through the curve fitting model, which has shown high levels of accuracy. Thus, this method enables segmentation of optoacoutic images with minimal human intervention, by eliminating need of scale selection for multiscale processing and seed point determination for contour mapping.
Mandal, S; Viswanath, P S; Yeshaswini, N; Dean-Ben, X L; Razansky, D
2015-08-01
In this article, we present a novel scheme for segmenting the image boundary (with the background) in optoacoustic small animal in vivo imaging systems. The method utilizes a multiscale edge detection algorithm to generate a binary edge map. A scale dependent morphological operation is employed to clean spurious edges. Thereafter, an ellipse is fitted to the edge map through constrained parametric transformations and iterative goodness of fit calculations. The method delimits the tissue edges through the curve fitting model, which has shown high levels of accuracy. Thus, this method enables segmentation of optoacoutic images with minimal human intervention, by eliminating need of scale selection for multiscale processing and seed point determination for contour mapping.
Non-parametric Reconstruction of Cluster Mass Distribution from Strong Lensing Modelling Abell 370
Abdel-Salam, H M; Williams, L L R
1997-01-01
We describe a new non-parametric technique for reconstructing the mass distribution in galaxy clusters with strong lensing, i.e., from multiple images of background galaxies. The observed positions and redshifts of the images are considered as rigid constraints and through the lens (ray-trace) equation they provide us with linear constraint equations. These constraints confine the mass distribution to some allowed region, which is then found by linear programming. Within this allowed region we study in detail the mass distribution with minimum mass-to-light variation; also some others, such as the smoothest mass distribution. The method is applied to the extensively studied cluster Abell 370, which hosts a giant luminous arc and several other multiply imaged background galaxies. Our mass maps are constrained by the observed positions and redshifts (spectroscopic or model-inferred by previous authors) of the giant arc and multiple image systems. The reconstructed maps obtained for A370 reveal a detailed mass d...
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.
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.
Kozlovská, Mária; Čabala, Jozef; Struková, Zuzana
2014-11-01
Information technology is becoming a strong tool in different industries, including construction. The recent trend of buildings designing is leading up to creation of the most comprehensive virtual building model (Building Information Model) in order to solve all the problems relating to the project as early as in the designing phase. Building information modelling is a new way of approaching to the design of building projects documentation. Currently, the building site layout as a part of the building design documents has a very little support in the BIM environment. Recently, the research of designing the construction process conditions has centred on improvement of general practice in planning and on new approaches to construction site layout planning. The state of art in field of designing the construction process conditions indicated an unexplored problem related to connection of knowledge system with construction site facilities (CSF) layout through interactive modelling. The goal of the paper is to present the methodology for execution of 3D construction site facility allocation model (3D CSF-IAM), based on principles of parametric and interactive modelling.
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
The use of algorithmic behavioural transfer functions in parametric EO system performance models
Hickman, Duncan L.; Smith, Moira I.
2015-10-01
The use of mathematical models to predict the overall performance of an electro-optic (EO) system is well-established as a methodology and is used widely to support requirements definition, system design, and produce performance predictions. Traditionally these models have been based upon cascades of transfer functions based on established physical theory, such as the calculation of signal levels from radiometry equations, as well as the use of statistical models. However, the performance of an EO system is increasing being dominated by the on-board processing of the image data and this automated interpretation of image content is complex in nature and presents significant modelling challenges. Models and simulations of EO systems tend to either involve processing of image data as part of a performance simulation (image-flow) or else a series of mathematical functions that attempt to define the overall system characteristics (parametric). The former approach is generally more accurate but statistically and theoretically weak in terms of specific operational scenarios, and is also time consuming. The latter approach is generally faster but is unable to provide accurate predictions of a system's performance under operational conditions. An alternative and novel architecture is presented in this paper which combines the processing speed attributes of parametric models with the accuracy of image-flow representations in a statistically valid framework. An additional dimension needed to create an effective simulation is a robust software design whose architecture reflects the structure of the EO System and its interfaces. As such, the design of the simulator can be viewed as a software prototype of a new EO System or an abstraction of an existing design. This new approach has been used successfully to model a number of complex military systems and has been shown to combine improved performance estimation with speed of computation. Within the paper details of the approach
Reduced-Order Modeling of Parametrically Excited Micro-Electro-Mechanical Systems (MEMS
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Sangram Redkar
2010-01-01
Full Text Available Reduced-order modeling is a systematic way of constructing models with smaller number of states that can capture the “essential dynamics” of the large-scale systems, accurately. In this paper, reduced-order modeling and control techniques for parametrically excited MEMS are presented. The techniques proposed here use the Lyapunov-Floquet (L-F transformation that makes the linear part of transformed equations time invariant. In this work, three model reduction techniques for MEMS are suggested. First method is simply an application of the well-known Guyan-like reduction method to nonlinear systems. The second technique is based on singular perturbation, where the transformed system dynamics is partitioned as fast and slow dynamics and the system of differential equations is converted into a differential algebraic (DAE system. In the third technique, the concept of invariant manifold for time-periodic systems is used. The “time periodic invariant manifold” based technique yields “reducibility conditions”. This is an important result because it helps us to understand the various types of resonances present in the system. These resonances indicate a tight coupling between the system states, and in order to retain the dynamic characteristics, one has to preserve all these “resonant” states in the reduced-order model. Thus, if the “reducibility conditions” are satisfied, only then a nonlinear order reduction based on invariant manifold approach is possible. It is found that the invariant manifold approach yields the most accurate results followed by the nonlinear projection and linear technique. These methodologies are general, free from small parameter assumptions, and can be applied to a variety of MEM systems like resonators, sensors and filters. The reduced-order models can be used for parametric study, sensitivity analysis and/or controller design. The controller design is based on the reduced-order system. Thus, first the
Wang, Bao; Zhao, Zhixiong; Wei, Guo-Wei
2016-09-01
In this work, a systematic protocol is proposed to automatically parametrize the non-polar part of implicit solvent models with polar and non-polar components. The proposed protocol utilizes either the classical Poisson model or the Kohn-Sham density functional theory based polarizable Poisson model for modeling polar solvation free energies. Four sets of radius parameters are combined with four sets of charge force fields to arrive at a total of 16 different parametrizations for the polar component. For the non-polar component, either the standard model of surface area, molecular volume, and van der Waals interactions or a model with atomic surface areas and molecular volume is employed. To automatically parametrize a non-polar model, we develop scoring and ranking algorithms to classify solute molecules. The their non-polar parametrization is obtained based on the assumption that similar molecules have similar parametrizations. A large database with 668 experimental data is collected and employed to validate the proposed protocol. The lowest leave-one-out root mean square (RMS) error for the database is 1.33 kcal/mol. Additionally, five subsets of the database, i.e., SAMPL0-SAMPL4, are employed to further demonstrate that the proposed protocol. The optimal RMS errors are 0.93, 2.82, 1.90, 0.78, and 1.03 kcal/mol, respectively, for SAMPL0, SAMPL1, SAMPL2, SAMPL3, and SAMPL4 test sets. The corresponding RMS errors for the polarizable Poisson model with the Amber Bondi radii are 0.93, 2.89, 1.90, 1.16, and 1.07 kcal/mol, respectively.
Institute of Scientific and Technical Information of China (English)
Ning WANG; Kui-hua WANG; Wen-bing WU
2013-01-01
In this paper,a model named fictitious soil pile was introduced to solve the boundary coupled problem at the pile tip.In the model,the soil column between pile tip and bedrock was treated as a fictitious pile,which has the same properties as the local soil.The tip of the fictitious soil pile was assumed to rest on a rigid rock and no tip movement was allowed.In combination with the plane strain theory,the analytical solutions of vertical vibration response of piles in a frequency domain and the corresponding semi-analytical solutions in a time domain were obtained using the Laplace transforms and inverse Fourier transforms.A parametric study of pile response at the pile tip and head showed that the thickness and layering of the stratum between pile tip and bedrock have a significant influence on the complex impedances.Finally,two applications of the analytical model were presented.One is to identify the defects of the pile shaft,in which the proposed model was proved to be accurate to identify the location as well as the length of pile defects.Another application of the model is to identify the sediment thickness under the pile tip.The results showed that the sediment can lead to the decrease of the pile stiffness and increase of the damping,especially when the pile is under a low frequency load.
A parametric approach to construct femur models and their fixation plates
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Xiaozhong Chen
2016-05-01
Full Text Available Although anatomic plates reflect an important breakthrough in the treatment of distal femur fractures, there are still some patients experiencing healing complications. For individual differences in bone morphology and fractures, the development of patient specific plates is very complex and needs a long cycle. In this study, a parametric approach was proposed to conveniently construct femur models and design their fixation plates. First, the typical femur anatomy was described with the average femur model. Second, five surface features were defined to represent the femur surface model by setting up parameterization and parameter constraints. Third, according to the fracture information of a specific patient, customized plate surface with a suitable contour was created from the reconstructed femur model. Finally, the femur plate was represented by feature parameterization, and the hierarchical constraints between femur parameters and plate parameters were built to construct a plate model. The experimental results showed that the proposed method could effectively represent femur surface shape features and intuitively construct and edit individualized plates with high-level parameters. The method is competitive in time saving and design convenience and may provide a basic tool for digital restoration of incomplete femurs and the design of patient specific femur plates.
A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines
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Baoshou Zhang
2017-03-01
Full Text Available Under the inspiration of polar coordinates, a novel parametric modeling and optimization method for Savonius wind turbines was proposed to obtain the highest power output, in which a quadratic polynomial curve was bent to describe a blade. Only two design parameters are needed for the shape-complicated blade. Therefore, this novel method reduces sampling scale. A series of transient simulations was run to get the optimal performance coefficient (power coefficient C p for different modified turbines based on computational fluid dynamics (CFD method. Then, a global response surface model and a more precise local response surface model were created according to Kriging Method. These models defined the relationship between optimization objective Cp and design parameters. Particle swarm optimization (PSO algorithm was applied to find the optimal design based on these response surface models. Finally, the optimal Savonius blade shaped like a “hook” was obtained. Cm (torque coefficient, Cp and flow structure were compared for the optimal design and the classical design. The results demonstrate that the optimal Savonius turbine has excellent comprehensive performance. The power coefficient Cp is significantly increased from 0.247 to 0.262 (6% higher. The weight of the optimal blade is reduced by 17.9%.
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 unstaggered. The stability of these states is investigated analytically and numerically. The nonlinear dynamics of the Bloch states are described by a complex Ginzburg-Landau equation with linear and nonlinear parametric driving. The switching between the staggered and unstaggered Bloch states under...
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.
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…
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…
DEFF Research Database (Denmark)
Niero, M.; Di Felice, F.; Ren, Jingzheng;
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...
Scholl, Joep H G; van de Ven, Peter M; van Puijenbroek, Eugène P
2015-01-01
OBJECTIVES: The aim of this study was to investigate whether the time to onset (TTO) of common adverse drug reactions (ADRs) of antidiabetic drugs could be modeled using parametric distributions and whether these TTO distributions were dependent on patient characteristics. Furthermore, information r
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.
Reis, Yara; Bernardo-Faura, Marti; Richter, Daniela; Wolf, Thomas; Brors, Benedikt; Hamacher-Brady, Anne; Eils, Roland; Brady, Nathan R
2012-01-01
Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis.
Directory of Open Access Journals (Sweden)
Yara Reis
Full Text Available Mitochondria exist as a network of interconnected organelles undergoing constant fission and fusion. Current approaches to study mitochondrial morphology are limited by low data sampling coupled with manual identification and classification of complex morphological phenotypes. Here we propose an integrated mechanistic and data-driven modeling approach to analyze heterogeneous, quantified datasets and infer relations between mitochondrial morphology and apoptotic events. We initially performed high-content, multi-parametric measurements of mitochondrial morphological, apoptotic, and energetic states by high-resolution imaging of human breast carcinoma MCF-7 cells. Subsequently, decision tree-based analysis was used to automatically classify networked, fragmented, and swollen mitochondrial subpopulations, at the single-cell level and within cell populations. Our results revealed subtle but significant differences in morphology class distributions in response to various apoptotic stimuli. Furthermore, key mitochondrial functional parameters including mitochondrial membrane potential and Bax activation, were measured under matched conditions. Data-driven fuzzy logic modeling was used to explore the non-linear relationships between mitochondrial morphology and apoptotic signaling, combining morphological and functional data as a single model. Modeling results are in accordance with previous studies, where Bax regulates mitochondrial fragmentation, and mitochondrial morphology influences mitochondrial membrane potential. In summary, we established and validated a platform for mitochondrial morphological and functional analysis that can be readily extended with additional datasets. We further discuss the benefits of a flexible systematic approach for elucidating specific and general relationships between mitochondrial morphology and apoptosis.
Bilgili, D; Ryu, D; Ergönül, Ö; Ebrahimi, N
2016-03-01
Infectious diseases that can be spread directly or indirectly from one person to another are caused by pathogenic microorganisms such as bacteria, viruses, parasites, or fungi. Infectious diseases remain one of the greatest threats to human health and the analysis of infectious disease data is among the most important application of statistics. In this article, we develop Bayesian methodology using parametric bivariate accelerated lifetime model to study dependency between the colonization and infection times for Acinetobacter baumannii bacteria which is leading cause of infection among the hospital infection agents. We also study their associations with covariates such as age, gender, apache score, antibiotics use 3 months before admission and invasive mechanical ventilation use. To account for singularity, we use Singular Bivariate Extreme Value distribution to model residuals in Bivariate Accelerated lifetime model under the fully Bayesian framework. We analyze a censored data related to the colonization and infection collected in five major hospitals in Turkey using our methodology. The data analysis done in this article is for illustration of our proposed method and can be applied to any situation that our model can be used.
Development of a parametric containment event tree model of a severe PWR accident
Energy Technology Data Exchange (ETDEWEB)
Okkonen, T. [OTO-Consulting Ay, Helsinki (Finland)
1996-06-01
The study supports the development project of STUK on `Living` PSA Level 2. The main work objective is to develop review tools for the Level 2 PSA studies underway at the utilities. The SPSA (STUK PSA) code is specifically designed for the purpose. In this work, SPSA is utilized as the Level 2 programming and calculation tool. A containment event tree (CET) model is built for analysis of severe accidents at the Loviisa pressurized water reactor (PWR) 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 include new research results, and so it facilitates the Living PSA concept on Level 2 as well. The analyses of the study are limited to severe accidents starting from full-power operation and leading to core melting at a low primary system pressure. Severe accident progression from five plant damage states (PDSs) is examined, however the integration with Level 1 is deferred to more definitive, integrated, safety assessments. (34 refs., 5 figs., 9 tabs.).
Model for straight and helical solar jets. I. Parametric studies of the magnetic field geometry
Pariat, E.; Dalmasse, K.; DeVore, C. R.; Antiochos, S. K.; Karpen, J. T.
2015-01-01
Context. Jets are dynamic, impulsive, well-collimated plasma events developing 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. Studying their dynamics can help us to better understand the processes acting in larger eruptive events (e.g., flares and coronal mass ejections) as well as mass, magnetic helicity, and energy transfer at all scales in the solar atmosphere. The relative simplicity of their magnetic geometry and topology, compared with larger solar active events, makes jets ideal candidates for studying the fundamental role of reconnection in energetic events. Methods: In this study, using our recently developed numerical solver ARMS, we present several parametric studies of a 3D numerical magneto-hydrodynamic model of solar-jet-like events. We studied the impact of the magnetic field inclination and photospheric field distribution on the generation and properties of two morphologically different types of solar jets, straight and helical, which can account for the observed so-called standard and blowout jets. Results: Our parametric studies validate our model of jets for different geometric properties of the magnetic configuration. We find that a helical jet is always triggered for the range of parameters we tested. This demonstrates that the 3D magnetic null-point configuration is a very robust structure for the energy storage and impulsive release characteristic of helical jets. In certain regimes determined by magnetic geometry, a straight jet precedes the onset of a helical jet. We show that the reconnection occurring during the straight-jet phase influences the triggering of the helical jet. Conclusions: Our results allow us to better understand the energization, triggering, and driving processes of straight and helical jets. Our model predicts the impulsiveness and energetics of jets in terms of the surrounding
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%.
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.
Cabras, Stefano; Castellanos, Maria Eugenia; Perra, Silvia
2014-11-20
This paper considers the problem of selecting a set of regressors when the response variable is distributed according to a specified parametric model and observations are censored. Under a Bayesian perspective, the most widely used tools are Bayes factors (BFs), which are undefined when improper priors are used. In order to overcome this issue, fractional (FBF) and intrinsic (IBF) BFs have become common tools for model selection. Both depend on the size, Nt , of a minimal training sample (MTS), while the IBF also depends on the specific MTS used. In the case of regression with censored data, the definition of an MTS is problematic because only uncensored data allow to turn the improper prior into a proper posterior and also because full exploration of the space of the MTSs, which includes also censored observations, is needed to avoid bias in model selection. To address this concern, a sequential MTS was proposed, but it has the drawback of an increase of the number of possible MTSs as Nt becomes random. For this reason, we explore the behaviour of the FBF, contextualizing its definition to censored data. We show that these are consistent, providing also the corresponding fractional prior. Finally, a large simulation study and an application to real data are used to compare IBF, FBF and the well-known Bayesian information criterion.
A Recurrent Network Model of Somatosensory Parametric Working Memory in the Prefrontal Cortex
Miller, Paul; Brody, Carlos D; Romo, Ranulfo; Wang, Xiao-Jing
2015-01-01
A parametric working memory network stores the information of an analog stimulus in the form of persistent neural activity that is monotonically tuned to the stimulus. The family of persistent firing patterns with a continuous range of firing rates must all be realizable under exactly the same external conditions (during the delay when the transient stimulus is withdrawn). How this can be accomplished by neural mechanisms remains an unresolved question. Here we present a recurrent cortical network model of irregularly spiking neurons that was designed to simulate a somatosensory working memory experiment with behaving monkeys. Our model reproduces the observed positively and negatively monotonic persistent activity, and heterogeneous tuning curves of memory activity. We show that fine-tuning mathematically corresponds to a precise alignment of cusps in the bifurcation diagram of the network. Moreover, we show that the fine-tuned network can integrate stimulus inputs over several seconds. Assuming that such time integration occurs in neural populations downstream from a tonically persistent neural population, our model is able to account for the slow ramping-up and ramping-down behaviors of neurons observed in prefrontal cortex. PMID:14576212
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).
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.
Basic parametric analysis for a multi-state model in hospital epidemiology.
von Cube, Maja; Schumacher, Martin; Wolkewitz, Martin
2017-07-20
The extended illness-death model is a useful tool to study the risks and consequences of hospital-acquired infections (HAIs). The statistical quantities of interest are the transition-specific hazard rates and the transition probabilities as well as attributable mortality (AM) and the population-attributable fraction (PAF). In the most general case calculation of these expressions is mathematically complex. When assuming time-constant hazards calculation of the quantities of interest is facilitated. In this situation the transition probabilities can be expressed in closed mathematical forms. The estimators for AM and PAF can be easily derived from these forms. In this paper, we show how to explicitly calculate all the transition probabilities of an extended-illness model with constant hazards. Using a parametric model to estimate the time-constant transition specific hazard rates of a data example, the transition probabilities, AM and PAF can be directly calculated. With a publicly available data example, we show how the approach provides first insights into principle time-dynamics and data structure. Assuming constant hazards facilitates the understanding of multi-state processes. Even in a non-constant hazards setting, the approach is a helpful first step for a comprehensive investigation of complex data.
Logistic regression model for diagnosis of transition zone prostate cancer on multi-parametric MRI
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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.)
Letort, Veronique; Mathieu, Amélie; De Reffye, Philippe; Constant, Thiéry
2010-01-01
Functional-structural models provide detailed representations of tree growth and their application to forestry seems full of prospects. However, owing to the complexity of tree architecture, parametric identification of such models remains a critical issue. We present the GreenLab approach for modelling tree growth. It simulates tree growth plasticity in response to changes of their internal level of trophic competition, especially topological development and cambial growth. The model includes a simplified representation of tree architecture, based on a species-specific description of branching patterns. We study whether those simplifications allow enough flexibility to reproduce with the same set of parameters the growth of two observed understorey beech trees (Fagus sylvatica L.) of different ages in different environmental conditions. The parametric identification of the model is global, i.e. all parameters are estimated simultaneously, potentially providing a better description of interactions between sub...
Integrated System-Level Optimization for Concurrent Engineering With Parametric Subsystem Modeling
Schuman, Todd; DeWeck, Oliver L.; Sobieski, Jaroslaw
2005-01-01
The introduction of concurrent design practices to the aerospace industry has greatly increased the productivity of engineers and teams during design sessions as demonstrated by JPL's Team X. Simultaneously, advances in computing power have given rise to a host of potent numerical optimization methods capable of solving complex multidisciplinary optimization problems containing hundreds of variables, constraints, and governing equations. Unfortunately, such methods are tedious to set up and require significant amounts of time and processor power to execute, thus making them unsuitable for rapid concurrent engineering use. This paper proposes a framework for Integration of System-Level Optimization with Concurrent Engineering (ISLOCE). It uses parametric neural-network approximations of the subsystem models. These approximations are then linked to a system-level optimizer that is capable of reaching a solution quickly due to the reduced complexity of the approximations. The integration structure is described in detail and applied to the multiobjective design of a simplified Space Shuttle external fuel tank model. Further, a comparison is made between the new framework and traditional concurrent engineering (without system optimization) through an experimental trial with two groups of engineers. Each method is evaluated in terms of optimizer accuracy, time to solution, and ease of use. The results suggest that system-level optimization, running as a background process during integrated concurrent engineering sessions, is potentially advantageous as long as it is judiciously implemented.
A model for straight and helical solar jets: II. Parametric study of the plasma beta
Pariat, E; DeVore, C R; Antiochos, S K; Karpen, J T
2016-01-01
Jets are dynamic, impulsive, well-collimated plasma events that develop at many different scales and in different layers of the solar atmosphere. 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. 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 prop...
Simulation of Moving Loads in Elastic Multibody Systems With Parametric Model Reduction Techniques
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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.
Wang, Can; Xi, Jin-Ying; Hu, Hong-Ying; Kang, In-Sun
2011-03-01
A new type of a combined ultraviolet (UV)-biofilter system for air pollution control is developed. In this paper, two conceptual mathematical submodels of the UV reactor and standalone biofilter are developed. All model parameters have been determined by independent experiments or have been taken from literature. Results from UV and the standalone biofilter submodels are in a good agreement with experimental data. However, the performance of the combined system has significantly deviated from those of the UV or standalone submodels because of the stimulating effects of UV irradiation products on the subsequent biofilter performance. A modified model that considers the stimulating effects has agreed well with experimental data over a wide range of operating conditions. Further analysis of the primary parametric sensitivity of the model has shown that inlet chlorobenzene concentrations, gas empty-bed residence time in the UV reactor, and light intensity are important operating conditions.
Kim, Kue Bum; Kwon, Hyun-Han; Han, Dawei
2015-11-01
In this paper, we present a comparative study of bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations/models. In traditional bias correction schemes, the statistics of the simulated model outputs are adjusted to those of the observation data. However, the model output and the observation data are only one case (i.e., realization) out of many possibilities, rather than being sampled from the entire population of a certain distribution due to internal climate variability. This issue has not been considered in the bias correction schemes of the existing climate change studies. Here, three approaches are employed to explore this issue, with the intention of providing a practical tool for bias correction of daily rainfall for use in hydrologic models ((1) conventional method, (2) non-informative Bayesian method, and (3) informative Bayesian method using a Weather Generator (WG) data). The results show some plausible uncertainty ranges of precipitation after correcting for the bias of RCM precipitation. The informative Bayesian approach shows a narrower uncertainty range by approximately 25-45% than the non-informative Bayesian method after bias correction for the baseline period. This indicates that the prior distribution derived from WG may assist in reducing the uncertainty associated with parameters. The implications of our results are of great importance in hydrological impact assessments of climate change because they are related to actions for mitigation and adaptation to climate change. Since this is a proof of concept study that mainly illustrates the logic of the analysis for uncertainty-based bias correction, future research exploring the impacts of uncertainty on climate impact assessments and how to utilize uncertainty while planning mitigation and adaptation strategies is still needed.
Dynamic modelling and stability parametric analysis of a flexible spacecraft with fuel slosh
Gasbarri, Paolo; Sabatini, Marco; Pisculli, Andrea
2016-10-01
Modern spacecraft often contain large quantities of liquid fuel to execute station keeping and attitude manoeuvres for space missions. In general the combined liquid-structure system is very difficult to model, and the analyses are based on some assumed simplifications. A realistic representation of the liquid dynamics inside closed containers can be approximated by an equivalent mechanical system. This technique can be considered a very useful mathematical tool for solving the complete dynamics problem of a space-system containing liquid. Thus they are particularly useful when designing a control system or to study the stability margins of the coupled dynamics. The commonly used equivalent mechanical models are the mass-spring models and the pendulum models. As far as the spacecraft modelling is concerned they are usually considered rigid; i.e. no flexible appendages such as solar arrays or antennas are considered when dealing with the interaction of the attitude dynamics with the fuel slosh. In the present work the interactions among the fuel slosh, the attitude dynamics and the flexible appendages of a spacecraft are first studied via a classical multi-body approach. In particular the equations of attitude and orbit motion are first derived for the partially liquid-filled flexible spacecraft undergoing fuel slosh; then several parametric analyses will be performed to study the stability conditions of the system during some assigned manoeuvers. The present study is propaedeutic for the synthesis of advanced attitude and/or station keeping control techniques able to minimize and/or reduce an undesired excitation of the satellite flexible appendages and of the fuel sloshing mass.
Analytic parametrizations of the non-perturbative Pomeron and QCD-inspired models
Nicolescu, Basarab; Ezhela, Vladimir V; Gauron, P; Kang, K; Kuyanov, Yu V; Lugovsky, S B; Tkachenko, N P; Kuyanov, Yu. V.
2002-01-01
We consider several classes of analytic parametrizations of hadronic scattering amplitudes, and compare their predictions to all available forward data (proton- proton, antiproton-proton, pion-proton, kaon-proton, photon-proton, photon- photon, sigma-proton). Although these parametrizations are very close for energy larger than 9 GeV, it turns out that they differ markedly at low energy, where a universal Pomeron term ~(ln s)**2 enables one to extend the fit down to 4 GeV.
Noh, Seong Jin; Rakovec, Oldrich; Kumar, Rohini; Samaniego, Luis
2016-04-01
There have been tremendous improvements in distributed hydrologic modeling (DHM) which made a process-based simulation with a high spatiotemporal resolution applicable on a large spatial scale. Despite of increasing information on heterogeneous property of a catchment, DHM is still subject to uncertainties inherently coming from model structure, parameters and input forcing. Sequential data assimilation (DA) may facilitate improved streamflow prediction via DHM using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is, however, often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. If parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by DHM may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we present a global multi-parametric ensemble approach to incorporate parametric uncertainty of DHM in DA to improve streamflow predictions. To effectively represent and control uncertainty of high-dimensional parameters with limited number of ensemble, MPR method is incorporated with DA. Lagged particle filtering is utilized to consider the response times and non-Gaussian characteristics of internal hydrologic processes. The hindcasting experiments are implemented to evaluate impacts of the proposed DA method on streamflow predictions in multiple European river basins
A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit
Kim, K. Han; Young, Karen S.; Bernal, Yaritza; Boppana, Abhishektha; Vu, Linh Q.; Benson, Elizabeth A.; Jarvis, Sarah; Rajulu, Sudhakar L.
2016-01-01
Shoulder injury is one of the most severe risks that have the potential to impair crewmembers' performance and health in long duration space flight. Overall, 64% of crewmembers experience shoulder pain after extra-vehicular training in a space suit, and 14% of symptomatic crewmembers require surgical repair (Williams & Johnson, 2003). Suboptimal suit fit, in particular at the shoulder region, has been identified as one of the predominant risk factors. However, traditional suit fit assessments and laser scans represent only a single person's data, and thus may not be generalized across wide variations of body shapes and poses. The aim of this work is to develop a software tool based on a statistical analysis of a large dataset of crewmember body shapes. This tool can accurately predict the skin deformation and shape variations for any body size and shoulder pose for a target population, from which the geometry can be exported and evaluated against suit models in commercial CAD software. A preliminary software tool was developed by statistically analyzing 150 body shapes matched with body dimension ranges specified in the Human-Systems Integration Requirements of NASA ("baseline model"). Further, the baseline model was incorporated with shoulder joint articulation ("articulation model"), using additional subjects scanned in a variety of shoulder poses across a pre-specified range of motion. Scan data was cleaned and aligned using body landmarks. The skin deformation patterns were dimensionally reduced and the co-variation with shoulder angles was analyzed. A software tool is currently in development and will be presented in the final proceeding. This tool would allow suit engineers to parametrically generate body shapes in strategically targeted anthropometry dimensions and shoulder poses. This would also enable virtual fit assessments, with which the contact volume and clearance between the suit and body surface can be predictively quantified at reduced time and
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Ayman A. El-Badawy
2000-01-01
Full Text Available We investigated the design of a neural-network-based adaptive control system for a smart structural dynamic model of the twin tails of an F-15 tail section. A neural network controller was developed and tested in computer simulation for active vibration suppression of the model subjected to parametric excitation. First, an emulator neural network was trained to represent the structure to be controlled and thus used in predicting the future responses of the model. Second, a neurocontroller to determine the necessary control action on the structure was developed. The control was implemented through the application of a smart material actuator. A strain gauge sensor was assumed to be on each tail. Results from computer-simulation studies have shown great promise for control of the vibration of the twin tails under parametric excitation using artificial neural networks.
DEFF Research Database (Denmark)
Niero, Monia; Di Felice, Francesco; Ren, Jingzheng
2014-01-01
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......-linear regression allowed to define a correlation between the life cycle impact assessment (LCIA) category indicators considered and the most influencing parameters.The definition of LCI parametric model in the wooden pallet sector can effectively be used for calculating the environmental impacts of products......; 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...
DEFF Research Database (Denmark)
Niero, M.; Di Felice, F.; Ren, Jingzheng
study of a SME in the wooden pallet sector, investigating to what extent the use of parametric LCI models can be beneficial both in evaluating the environmental impacts of similar products and in providing a preliminary assessment of the potential environmental impacts of new products. We developed...... 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...... in the design process. This modelling approach can be replicated in other manufacturing sectors, provided the products being examined present similar characteristics....
DEFF Research Database (Denmark)
Niero, Monia; Di Felice, F.; Ren, J.
2014-01-01
study of a SME in the wooden pallet sector, investigating to what extent the use of parametric LCI models can be beneficial both in evaluating the environmental impacts of similar products and in providing a preliminary assessment of the potential environmental impacts of new products. We developed...... 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...... in the design process. This modelling approach can be replicated in other manufacturing sectors, provided the products being examined present similar characteristics....
DEFF Research Database (Denmark)
Niero, Monia; Felice, Francesco, Di; Ren, Jingzheng
2014-01-01
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......-based regression and one multiple non-linear regression allowed to define a correlation between the life cycle impact assessment (LCIA) category indicators considered and the most influencing parameters. Conclusions The definition of LCI parametric model in the wooden pallet sector can effectively be used......; 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...
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
Directory of Open Access Journals (Sweden)
H.-W. Wong
2010-04-01
Full Text Available Condensation trails (contrails formed from water vapor emissions behind aircraft engines are the most uncertain components of the aviation impacts on climate change. To gain improved knowledge of contrail and contrail-induced cirrus cloud formation, understanding of contrail ice particle formation immediately after aircraft engines is needed. Despite many efforts spent in modeling the microphysics of ice crystal formation in jet regime (with a plume age <5 s, systematic understanding of parametric effects of variables affecting contrail ice particle formation is still limited. In this work, we apply a microphysical parcel modeling approach to study contrail ice particle formation in near-field aircraft plumes up to 1000 m downstream of an aircraft engine in the soot-rich regime (soot number emission index >1×10^{15} (kg-fuel^{−1} at cruise. The effects of dilution history, ion-mediated nucleation, ambient relative humidity, fuel sulfur contents, and initial soot emissions were investigated. Our simulation results suggest that ice particles are mainly formed by water condensation on emitted soot particles. The growth of ice coated soot particles is driven by water vapor emissions in the first 1000 m and by ambient relative humidity afterwards. The presence of chemi-ions does not significantly contribute to the formation of ice particles in the soot-rich regime, and the effect of fuel sulfur contents is small over the range typical of standard jet fuels. The initial properties of soot emissions play the most critical role, and our calculations suggest that higher number concentration and smaller size of contrail particle nuclei may be able to effectively suppress the formation of contrail ice particles. Further modeling and experimental studies are needed to verify if our findings can provide a possible approach for contrail mitigation.
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Dario Sacco
2007-01-01
Full Text Available Groundwater nitrate contamination is a source of rising concern that has been faced through the introduction of several regulations in different countries. However the methodologies used in the definition of Nitrate Vulnerable Zones are not included in the regulations. The aim of this work was to compare different methodologies, used to asses groundwater nitrate contamination risks, based on parametric systems or simulation modelling. The work was carried out in Piedmont, Italy, in an area characterised by intensive animal husbandry, high N load, a shallow water table and a coarse type of sub-soil sediments. Only N loads from agricultural non-point sources were considered. Different methodologies with different level of information have been compared to determine the groundwater nitrate contamination risk assessment: N load, IPNOA index, the intrinsic contamination risk from nitrates, leached N and N concentration of the soil solution estimated by the simulation model. The good correlation between the IPNOA index and the intrinsic nitrate contamination risk revealed that the parameters that describe the soil in this area did not lead to a different classification of the parcels. The intrinsic nitrate contamination risk was greatly influenced by N fertilisation, however the effect of the soils increased the variability in comparison to the IPNOA index. The leached N and N concentration in the leaching were closely correlated. The dilution effect of percolated water was almost negligible. Both methodologies were slightly correlated to the N fertilisation and the two indexes. The correlations related to the intrinsic nitrate contamination risk was higher than those related to IPNOA, and this means that the effect of taking into account soil parameters increases the correlation to the prediction of the simulation model.
Energy Technology Data Exchange (ETDEWEB)
Carman, R.J. [Centre for Lasers and Applications, Macquarie University, North Ryde, Sydney, New South Wales 2109 (Australia)
1997-07-01
A self-consistent computer model was used to simulate the plasma kinetics (radially resolved) and parametric behaviour of an 18 mm bore (6 W) copper vapour laser for a wide range of optimum and non-optimum operating conditions. Good quantitative agreement was obtained between modelled results and experimental data including the temporal evolution of the 4p{sup 2}P{sub 3/2}, 4s{sup 2} {sup 2}D{sub 5/2} and 4s{sup 2}{sup 2}D{sub 3/2} Cu laser level populations derived from hook method measurements. The modelled results show that the two most important parameters that affect laser behaviour are the ground state copper density and the peak electron temperature T{sub e}. For a given pulse repetition frequency (prf), maximum laser power is achieved by matching the copper atom density to the input pulse energy thereby maintaining the peak T{sub e} at around 3 eV. However, there is a threshold wall temperature (and copper density) above which the plasma tube becomes thermally unstable. At low prf ({lt}8 kHz), this thermal instability limits the attainable copper density (and consequently the laser output power) to values below the optimum for matching to the input pulse energy. For higher prf values ({gt}8 kHz), the copper density can be matched to the input pulse energy to give maximum laser power because the corresponding wall temperature then falls below the threshold temperature for thermal instability. For prf {gt}14 kHz, the laser output becomes highly annular across the tube diameter due to a severe depletion of the copper atom density on axis caused by radial ion pumping. {copyright} {ital 1997 American Institute of Physics.}
Institute of Scientific and Technical Information of China (English)
廖小锋; 刘济明; 张东凯; 靳勇; 张勇; 闫国华; 王敏
2012-01-01
Several typical models of light-response curve of leaf net photosynthesis, such as non-rectangular hyperbolic model, rectangular hyperbolic model, prompted rectangular hyperbolic model and index function model, were tested on wild Drepanostachyum luodianense, and the applicability of these models also were investigated. The results were summarized as follows: ① the plant' t analytical solution of maximum net photosynmetic rate (Pnmax) and saturation point (Isat) couldn' t be solved by rectangular hyperbola model, non-rectangular hyperbola model and exponential equation model, while the values simulated by combining other methods existed a big difference compared with the measured value, and the data under photoinhibition couldn' t be processed by these three models; ② the quadratic polynomial model could process the data under photoinhibition to a certain extent, but the photosynthetic parameters simulated by it also existed larger difference compared with the measured value, even generated logic error, ③ the photosynthetic parameters simulated by modified rectangular hyperbola model all were close to the measured values ,and this model also could well process the data under photoinhibition; ④ the Pnmax, Isat compensation point(Ic), respiration rate(Rd) and initial quantum efticiency(a) of wild D. luodianense simulated by modified rectangular hyperbola model were 8.53 μmol·m-2s-1, 1 750.75 μmol·m-2s-1, 21.40 μmol·m-2s-1, 1.06 μmol·m-2s-1 and 0.054 respectively.%应用5种典型的光响应模型对野生小蓬竹叶片光响应曲线进行了拟合,并探讨了几种模型在光响应研究中的适用性.结果表明:①直角双曲线、非直角双曲线及指数函数模型无法求取植物最大净光合速率(Pnmax)和光饱和点(Isat)的解析解,而结合其它方法拟合的相应值却与实测值相差很大,同时也不能处理光抑制部分的光响应数据；②二次多项式模型能够一定程度地处理光抑制部分的光
A Nonparametric Approach for Assessing Goodness-of-Fit of IRT Models in a Mixed Format Test
Liang, Tie; Wells, Craig S.
2015-01-01
Investigating the fit of a parametric model plays a vital role in validating an item response theory (IRT) model. An area that has received little attention is the assessment of multiple IRT models used in a mixed-format test. The present study extends the nonparametric approach, proposed by Douglas and Cohen (2001), to assess model fit of three…
Directory of Open Access Journals (Sweden)
H.-W. Wong
2009-10-01
Full Text Available Condensation trails (contrails formed from water vapor emissions behind aircraft engines are the most uncertain components of the aviation impacts on climate change. To gain improved knowledge of contrail and contrail-induced cirrus cloud formation, understanding of contrail ice particle formation immediately after aircraft engines is needed. Despite many efforts spent in modeling the microphysics of ice crystal formation in jet regime (with a plume age <5 s, systematic understanding of parametric effects of variables affecting contrail ice particle formation is still limited. In this work, we apply a one-dimensional modeling approach to study contrail ice particle formation in near-field aircraft plumes up to 1000 m downstream of an aircraft engine in the soot-rich regime (soot number emission index >1×10^{15} (kg-fuel^{−1} at cruise. The effects of ion-mediated nucleation, ambient relative humidity, fuel sulfur content, and initial soot emissions were investigated. Our simulation results suggest that ice particles are mainly formed by water condensation on emitted soot particles. The growth of ice coated soot particles is driven by water vapor emissions in the first 1000 m and by ambient relative humidity afterwards. The presence of chemi-ions does not significantly contribute to the formation of ice particles, and the effect of fuel sulfur content is small over the range typical of standard jet fuels. The initial properties of soot emissions play the most critical role, and our calculations suggest that higher number concentration and smaller size of contrail particle nuclei may be able to effectively suppress the formation of contrail ice particles, providing a possible approach for contrail mitigation.
Directory of Open Access Journals (Sweden)
Alireza Ganjovi
2014-09-01
Full Text Available A kinetic model is used based on Particle in Cell - Monte Carlo Collision (PIC-MCC model, for parametric study of the damage due to partial discharges (PD activity into the surroundings dielectrics of a narrow channel encapsulated within the volume of a dielectric material. The parameters studied are applied electric field, channel dimensions and gas pressure. After employing an electric field across a dielectric material which contains a narrow channel, repeated ionization process starts in the gaseous medium of narrow channel. Charged particles, especially electrons, gain energy from the electric field across narrow channel and cause damage into dielectric surfaces of narrow channel on impact. The dependence of the electron energy distribution function (EEDF on the applied electric field is considered. These estimations are performed based on the number of C-H bond-scissions produced by the impacting electrons of a single PD pulse. Regarding this technique, the consequent damage into the solid dielectric and the time required to increase surface conductivity, is computed. The formation of acid molecules due to interaction of PD pulse with polymer surface in presence of air and humidity causes changes in the surface conductivity of the surrounding dielectrics of the narrow channels. It is observed that the extent of damage caused by a PD is primarily determined by the total number of impacting electrons which are capable of producing bond-scission at the dielectric. Parameters that effectively cause an increase in the number of energetic electrons will increase effective damage as well as surface conductivity of surrounding dielectrics.
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
Institute of Scientific and Technical Information of China (English)
吕纯濂
2000-01-01
Algorithms for iteratively refining the parameter estimates andresiduals from the fitting of a regression model using QR decompositionmethods are described. It is shown that if square root free algorithmsfor performing the QR decomposition are used the related iterativerefinement algorithms can also be square root free. Testing of thealgorithms is carried out and comments made about accuracies ofparameter estimates
Sharba, A B; Zepf, M; Borghesi, M; Sarri, G
2016-01-01
We present a comprehensive model for predicting the full performance of a second harmonic generationoptical parametric amplification system that aims at enhancing the temporal contrast of laser pulses. The model simultaneously takes into account all the main parameters at play in the system such as the group velocity mismatch, the beam divergence, the spectral content, the pump depletion, and the length of the nonlinear crystals. We monitor the influence of the initial parameters of the input pulse and the interdependence of the two related non-linear processes on the performance of the system and show its optimum configuration. The influence of the initial beam divergence on the spectral and the temporal characteristics of the generated pulse is discussed. In addition, we show that using a crystal slightly longer than the optimum length and introducing small delay between the seed and the pump ensures maximum efficiency and compensates for the spectral shift in the optical parametric amplification stage in c...
Gonzales-Martínez, Rolando
2009-01-01
Time series of obligations with the public are important to liquidity risk management in emerging economies, but a traditional parametric VaR model could give imprecise measures of liquidity risk if the series do not approach a normal (Gaussian) distribution. To overcome this flaw of parametric gaussian VaR models, this study suggest a parametric VaR model with indirect calibration (VaR-i) with a beta-parameter calibrated to be successful in backtesting tests, according to the empirical distr...
Directory of Open Access Journals (Sweden)
HARPREET KAUR SAINI
2014-10-01
Full Text Available Skin detection is active research area in the field of computer vision which can be applied in the application of face detection, eye detection, etc. These detection helps in various applications such as driver fatigue monitoring system, surveillance system etc. In Computer vision applications, the color model and representations of the human image in color model is one of major module to detect the skin pixels. The mainstream technology is based on the individual pixels and selection of the pixels to detect the skin part in the whole image. In this thesis implementation, we presents a novel technique for skin color detection incorporating with explicit region based and parametric based approach which gives the better efficiency and performances in terms of skin detection in human images. Color models and image quantization technique is used to extract the regions of the images and to represent the image in a particular color model such as RGB and HSV, and then the parametric based approach is applied by selecting the low level skin features are applied to extract the skin and non-skin pixels of the images. In the first step, our technique uses the state-of-the-art non-parametric approach which we call the template based technique or explicitly defined skin regions technique. Then the low level features of the human skin are being extracted such as edge, corner detection which is also known as parametric method. The experimental results depict the improvement in detection rate of the skin pixels by this novel approach. And in the end we discuss the experimental results to prove the algorithmic improvements.
Marmarelis, Vasilis Z; Shin, Dae C; Zhang, Yaping; Kautzky-Willer, Alexandra; Pacini, Giovanni; D'Argenio, David Z
2013-07-01
Modeling studies of the insulin-glucose relationship have mainly utilized parametric models, most notably the minimal model (MM) of glucose disappearance. This article presents results from the comparative analysis of the parametric MM and a nonparametric Laguerre based Volterra Model (LVM) applied to the analysis of insulin modified (IM) intravenous glucose tolerance test (IVGTT) data from a clinical study of gestational diabetes mellitus (GDM). An IM IVGTT study was performed 8 to 10 weeks postpartum in 125 women who were diagnosed with GDM during their pregnancy [population at risk of developing diabetes (PRD)] and in 39 control women with normal pregnancies (control subjects). The measured plasma glucose and insulin from the IM IVGTT in each group were analyzed via a population analysis approach to estimate the insulin sensitivity parameter of the parametric MM. In the nonparametric LVM analysis, the glucose and insulin data were used to calculate the first-order kernel, from which a diagnostic scalar index representing the integrated effect of insulin on glucose was derived. Both the parametric MM and nonparametric LVM describe the glucose concentration data in each group with good fidelity, with an improved measured versus predicted r² value for the LVM of 0.99 versus 0.97 for the MM analysis in the PRD. However, application of the respective diagnostic indices of the two methods does result in a different classification of 20% of the individuals in the PRD. It was found that the data based nonparametric LVM revealed additional insights about the manner in which infused insulin affects blood glucose concentration. © 2013 Diabetes Technology Society.
Batsidis, Apostolos; Pardo, Leandro; Zografos, Konstantinos
2011-01-01
This paper studies the change point problem for a general parametric, univariate or multivariate family of distributions. An information theoretic procedure is developed which is based on general divergence measures for testing the hypothesis of the existence of a change. For comparing the accuracy of the new test-statistic a simulation study is performed for the special case of a univariate discrete model. Finally, the procedure proposed in this paper is illustrated through a classical change-point example.
Institute of Scientific and Technical Information of China (English)
冉雍
2016-01-01
重大工程建设一般会有定期的沉降和变形监测，本研究利用具有规律变化的Logistic和Gompertz曲线模型进行拟合，并以某大型发电厂为研究对象，利用近15年的监测数据，建立预测模型并进行精度评估。研究结果表明，若监测数据具有一定程度的稳定性，并对计算时监测数据进行合理取舍，对采取的全区、分区平均值或单一点高度值的检测数据，运用Logistic和Gompertz曲线模型来预测大型建筑物的沉降情况是可行的。%The major engineering construction regularly has the settlement and deformation monitoring, a regular variation of Logistic and Gompertz curve fitting model was used in this paper.Choosing a large power plant as the research object, the prediction model was established and the accuracy evaluated was conducted by using the monitoring data of recently 15 years.The research results showed that if the test data was stability in a certain degree, and the monitoring data was trade-off in reasonable, the Logistic and Gompertz curve model was feasible to predict the settlement of the large buildings condition by taking the entire district, partition or single point height value of the average detection data.
Directory of Open Access Journals (Sweden)
Ana Lúcia Souza da Silva
2004-10-01
Full Text Available Vários tipos de resíduos têm sido propostos para modelos de sobrevivência, sendo os mais adequados resultado dos resíduos generalizados de Cox e Snell (1968. O objetivo com este trabalho é avaliar a adequacidade de modelos por meio de gráficos de diagnósticos gerados a partir dos resíduos generalizados de Cox-Snell. Para ilustrar a teoria, foram feitas três aplicações. A primeira aplicação visou a ilustrar a lógica existente entre a plotagem dos resíduos ordenados de três distribuições, normal (0,1, logística (0,1 e valor extremo (0,1 versus as estatísticas de ordem esperadas desses resíduos de acordo com as distribuições assumidas. Para a segunda aplicação, foram utilizados dados de tempo de vida de isolantes, obtidos em Nelson (1990. A partir da verificação por meio dos gráficos de diagnósticos utilizando-se os resíduos generalizados de Cox-Snell, encontrou-se que o modelo apropriado para o tempo de vida dos isolantes era o log-normal. Para a terceira aplicação, foram analisados dados censurados referentes ao tempo de vida de pacientes, obtidos em Collett (1994. Avaliou-se a adequacidade de vários modelos por meio dos resíduos de Cox-Snell adaptados para dados de sobrevivência. Pelos resultados constatou-se que o modelo Weibull foi o mais adequado.Several kinds of residuals have been proposed for survival models, the most suitable for this purpose are Cox and Snell (1968 generalized residuals. The objective of this work was to evaluate the adequacy of models by graphical diagnostics using Cox-Snell generalized residuals. To illustrate the theory three applications were considered. The first application sought to illustrate the heuristics by plotting ordered residuals from three distributions: normal (0,1, logistics (0,1 and extreme value (0,1, versus the expected order statistics of these residuals in consonance with the assumed distributions. The second application consisted of lifetime data of electric
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
A parametric FE modeling of brake for non-linear analysis
Directory of Open Access Journals (Sweden)
Ibrahim Ahmed, Yasser Fatouh, Wael Aly
2014-01-01
Full Text Available A parametric modeling of a drum brake based on 3-D Finite Element Methods (FEM for non-contact analysis is presented. Many parameters are examined during this study such as the effect of drum-lining interface stiffness, coefficient of friction, and line pressure on the interface contact. Firstly, the modal analysis of the drum brake is also studied to get the natural frequency and instability of the drum to facilitate transforming the modal elements to non-contact elements. It is shown that the Unsymmetric solver of the modal analysis is efficient enough to solve this linear problem after transforming the non-linear behavior of the contact between the drum and the lining to a linear behavior. A SOLID45 which is a linear element is used in the modal analysis and then transferred to non-linear elements which are Targe170 and Conta173 that represent the drum and lining for contact analysis study. The contact analysis problems are highly non-linear and require significant computer resources to solve it, however, the contact problem give two significant difficulties. Firstly, the region of contact is not known based on the boundary conditions such as line pressure, and drum and friction material specs. Secondly, these contact problems need to take the friction into consideration. Finally, it showed a good distribution of the nodal reaction forces on the slotted lining contact surface and existing of the slot in the middle of the lining can help in wear removal due to the friction between the lining and the drum. Accurate contact stiffness can give a good representation for the pressure distribution between the lining and the drum. However, a full contact of the front part of the slotted lining could occur in case of 20, 40, 60 and 80 bar of piston pressure and a partially contact between the drum and lining can occur in the rear part of the slotted lining.
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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.
DEFF Research Database (Denmark)
Montazeri, Najmeh; Nielsen, Ulrik Dam; Jensen, Jørgen Juncher
2016-01-01
Shipboard wave estimation has been of interest in recent years for the purpose of decision support. In this paper, estimation of sea state is studied using ship responses and a parametric description of directional wave spectra. A set of parameters, characterising a given wave spectrum is estimated...
Amsallem, David; Tezaur, Radek; Farhat, Charbel
2016-12-01
A comprehensive approach for real-time computations using a database of parametric, linear, projection-based reduced-order models (ROMs) based on arbitrary underlying meshes is proposed. In the offline phase of this approach, the parameter space is sampled and linear ROMs defined by linear reduced operators are pre-computed at the sampled parameter points and stored. Then, these operators and associated ROMs are transformed into counterparts that satisfy a certain notion of consistency. In the online phase of this approach, a linear ROM is constructed in real-time at a queried but unsampled parameter point by interpolating the pre-computed linear reduced operators on matrix manifolds and therefore computing an interpolated linear ROM. The proposed overall model reduction framework is illustrated with two applications: a parametric inverse acoustic scattering problem associated with a mockup submarine, and a parametric flutter prediction problem associated with a wing-tank system. The second application is implemented on a mobile device, illustrating the capability of the proposed computational framework to operate in real-time.
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
Lua, Yuan J.; Liu, Wing K.; Belytschko, Ted
1992-01-01
A stochastic damage model for predicting the rupture of a brittle multiphase material is developed, based on the microcrack-macrocrack interaction. The model, which incorporates uncertainties in locations, orientations, and numbers of microcracks, characterizes damage by microcracking and fracture by macrocracking. A parametric study is carried out to investigate the change of the stress intensity at the macrocrack tip by the configuration of microcracks. The inherent statistical distribution of the fracture toughness arising from the intrinsic random nature of microcracks is explored using a statistical approach. For this purpose, a computer simulation model is introduced, which incorporates a statistical characterization of geometrical parameters of a random microcrack array.
Li, Bin; Chen, Kan; Tian, Lianfang; Yeboah, Yao; Ou, Shanxing
2013-01-01
The segmentation and detection of various types of nodules in a Computer-aided detection (CAD) system present various challenges, especially when (1) the nodule is connected to a vessel and they have very similar intensities; (2) the nodule with ground-glass opacity (GGO) characteristic possesses typical weak edges and intensity inhomogeneity, and hence it is difficult to define the boundaries. Traditional segmentation methods may cause problems of boundary leakage and "weak" local minima. This paper deals with the above mentioned problems. An improved detection method which combines a fuzzy integrated active contour model (FIACM)-based segmentation method, a segmentation refinement method based on Parametric Mixture Model (PMM) of juxta-vascular nodules, and a knowledge-based C-SVM (Cost-sensitive Support Vector Machines) classifier, is proposed for detecting various types of pulmonary nodules in computerized tomography (CT) images. Our approach has several novel aspects: (1) In the proposed FIACM model, edge and local region information is incorporated. The fuzzy energy is used as the motivation power for the evolution of the active contour. (2) A hybrid PMM Model of juxta-vascular nodules combining appearance and geometric information is constructed for segmentation refinement of juxta-vascular nodules. Experimental results of detection for pulmonary nodules show desirable performances of the proposed method.
Geloun, Joseph Ben
2014-01-01
We consider the parametric representation of the amplitudes of Abelian models in the so-called framework of rank $d$ Tensorial Group Field Theory. These models are called Abelian because their fields live on $U(1)^D$. We concentrate on the case when these models are endowed with particular kinetic terms involving a linear power in momenta. New dimensional regularization and renormalization schemes are introduced for particular models in this class: a rank 3 tensor model, an infinite tower of matrix models $\\phi^{2n}$ over $U(1)$, and a matrix model over $U(1)^2$. For all divergent amplitudes, we identify a domain of meromorphicity in a strip determined by the real part of the group dimension $D$. From this point, the ordinary subtraction program is applied and leads to convergent and analytic renormalized integrals. Furthermore, we identify and study in depth the Symanzik polynomials provided by the parametric amplitudes of generic rank $d$ Abelian models. We find that these polynomials do not satisfy the ord...
Ben Geloun, Joseph; Toriumi, Reiko
2015-09-01
We consider the parametric representation of the amplitudes of Abelian models in the so-called framework of rank d tensorial group field theory. These models are called Abelian because their fields live on copies of U(1)D. We concentrate on the case when these models are endowed with particular kinetic terms involving a linear power in momenta. A new dimensional regularization is introduced for particular models in this class: a rank 3 tensor model, an infinite tower of matrix models ϕ2n over U(1), and a matrix model over U(1)2. We prove that all divergent amplitudes are meromorphic functions in the complexified group dimension D ∈ ℂ. From this point, a standard subtraction program yielding analytic renormalized integrals could be applied. Furthermore, we identify and study in depth the Symanzik polynomials provided by the parametric amplitudes of generic rank d Abelian models. We find that these polynomials do not satisfy the ordinary Tutte's rules (contraction/deletion). By scrutinizing the "face"-structure of these polynomials, we find a generalized polynomial which turns out to be stable only under contraction.
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.
Refractive Index of Humid Air in the Infrared: Model Fits
Mathar, R J
2006-01-01
The theory of summation of electromagnetic line transitions is used to tabulate the Taylor expansion of the refractive index of humid air over the basic independent parameters (temperature, pressure, humidity, wavelength) in five separate infrared regions from the H to the Q band at a fixed percentage of Carbon Dioxide. These are least-squares fits to raw, highly resolved spectra for a set of temperatures from 10 to 25 C, a set of pressures from 500 to 1023 hPa, and a set of relative humidities from 5 to 60%. These choices reflect the prospective application to characterize ambient air at mountain altitudes of astronomical telescopes.
Issues in Evaluating Model Fit With Missing Data
Davey, Adam
2005-01-01
Effects of incomplete data on fit indexes remain relatively unexplored. We evaluate a wide set of fit indexes (?[squared], root mean squared error of appproximation, Normed Fit Index [NFI], Tucker-Lewis Index, comparative fit index, gamma-hat, and McDonald's Centrality Index) varying conditions of sample size (100-1,000 in increments of 50),…
A Model Fitting Approach to Depict the Global Textile Trade
Institute of Scientific and Technical Information of China (English)
区健勋; 陈若瀚
2004-01-01
Since the mercantilism era, economists have built up trade theories to explain the rationale and patterns of world trade.In this paper, the explanatory power of the international product life cycle (IPLC) theory for describing the trends and patterns of the global textile trade, one of the most geographically dispersed export items in both developed and developing countries/regions, is discussed. Data at SITC two-digit level (SITC 65 ) were collected and time series regressions were performed to analyze the value trends and world shares of textile exports from 1990 to 2000 for selected developed economies. It was found that some developed economies have increased their world shares in textile exports, which indicated that global trade shift in the textile industry may not follow what the IPLC has suggested.
van de Walle, Axel; Rouleau, Lucie; Deckers, Elke; Desmet, Wim
2015-01-01
In many engineering applications, viscoelastic treatments are used to suppress vibrations of lightly damped structures. Computational methods provide powerful tools for the design and analysis of these structures. The most commonly used method to model the dynamics of complex structures is the finite element method. Its use, however, often results in very large and computationally demanding models, especially when viscoelastic material behaviour has to be taken into account. To alleviate this...
Hamerly, Ryan; Jankowski, Marc; Fejer, Martin M; Yamamoto, Yoshihisa; Mabuchi, Hideo
2016-01-01
We develop reduced models that describe half-harmonic generation in a synchronously-pumped optical parametric oscillator above threshold, where nonlinearity, dispersion, and group-velocity mismatch are all relevant. These models are based on (1) an eigenmode expansion for low pump powers, (2) a simulton-like sech-pulse ansatz for intermediate powers, and (3) dispersionless box-shaped pulses for high powers. Analytic formulas for pulse compression, degenerate vs. nondegenerate operation, and stability are derived and compared to numerical and experimental results.
Uncertainties in volcanic plume modeling: a parametric study using FPLUME model
Macedonio, Giovanni; Costa, Antonio; Folch, Arnau
2016-04-01
Tephra transport and dispersal models are commonly used for volcanic hazard assessment and tephra dispersal (ash cloud) forecasts. The proper quantification of the parameters defining the source term in the dispersal models, and in particular the estimation of the mass eruption rate, plume height, and particle vertical mass distribution, is of paramount importance for obtaining reliable results in terms of particle mass concentration in the atmosphere and loading on the ground. The study builds upon numerical simulations of using FPLUME, an integral steady-state model based on the Buoyant Plume Theory, generalized in order to account for volcanic processes (particle fallout and re-entrainment, water phase changes, effects of wind, etc). As reference cases for strong and weak plumes, we consider the cases defined during the IAVCEI Commission on tephra hazard modeling inter-comparison exercise. The goal was to explore the leading order role of each parameter in order to assess which should be better constrained to better quantify the eruption source parameters for use by the dispersal models. Moreover, a sensitivity analysis investigates the role of wind entrainment and intensity, atmospheric humidity, water phase changes, and particle fallout and re-entrainment. Results show that the leading-order parameters are the mass eruption rate and the air entrainment coefficient, specially for weak plumes.
DEFF Research Database (Denmark)
Rosthøj, Susanne; Keiding, Niels
2004-01-01
When studying a regression model measures of explained variation are used to assess the degree to which the covariates determine the outcome of interest. Measures of predictive accuracy are used to assess the accuracy of the predictions based on the covariates and the regression model. We give...... a detailed and general introduction to the two measures and the estimation procedures. The framework we set up allows for a study of the effect of misspecification on the quantities estimated. We also introduce a generalization to survival analysis....
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.
Parametric modelling and optimization of building structures with Rhinoceros-Grasshopper
Soklič, Rok
2015-01-01
This graduation thesis comprises the use of modern geometry design software in the process of structural design. By implementing parametrically designed geometry and interactively linking 3D computer graphics application software with structural analysis software, tasks involved in structural design (e.g. automatic design data transfer) can be significantly improved. In the first part of the thesis, the basics of NURBS (Non-uniform Rational Basis Spline) mathematical elements are briefly pres...
Energy Technology Data Exchange (ETDEWEB)
Santos, Roberto Hugo Melo dos; Figueiro, Wilson Mouzer [Bahia Univ., Salvador, BA (Brazil). Inst. de Geociencias]. E-mail: rms@cpgg.ufba.br; fgueiro@cpgg.ufba.br
2003-07-01
The developed algorithm in this work was based on the finite difference method applied to the wave equation, assuming that the Earth has an acoustic behavior. The seismic modeling was implemented numerically by means of the finite differences method (MDF), employing regular nets, and applied to the derivatives of time and fourth order to derivatives of the space. Two-dimensional geological models was represented by two distinct kind of parametrizations: in blocs (P B) and using trigonometric polynomials (PPT). With the objective of jumping the advantages of using the PPT front P B, mainly in what it tells respect the economy of space of memory in program of finite difference and simplification of the equation in the inversion strategies, simulations of the propagation of waves ware presented in several models acted by different parametrizations (P B and PPT) using applied MDF the equation of the wave and generating synthetic seismograms that they are compared amongst themselves. As a result of this work we can detach the great economy of space of memory in the use of PPT, in whole PPT the model is defined for the coefficients of the polynomial that start to be the parameters of the model, and PPT simplifies the representation of more complicated models. (author)
Energy Technology Data Exchange (ETDEWEB)
Al Mamon, Abdulla; Das, Sudipta [Visva-Bharati, Department of Physics, Santiniketan (India)
2015-06-15
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.)
User's manual for heat-pump seasonal-performance model (SPM) with selected parametric examples
Energy Technology Data Exchange (ETDEWEB)
1982-06-30
The Seasonal Performance Model (SPM) was developed to provide an accurate source of seasonal energy consumption and cost predictions for the evaluation of heat pump design options. The program uses steady state heat pump performance data obtained from manufacturers' or Computer Simulation Model runs. The SPM was originally developed in two forms - a cooling model for central air conditioners and heat pumps and a heating model for heat pumps. The original models have undergone many modifications, which are described, to improve the accuracy of predictions and to increase flexibility for use in parametric evaluations. Insights are provided into the theory and construction of the major options, and into the use of the available options and output variables. Specific investigations provide examples of the possible applications of the model. (LEW)
Lee, Young-Hee; Ahn, Kwang-Deuk; Lee, Yong Hee
2016-12-01
We have developed a parametrization of tidal effects for use in the Noah land-surface model and have validated the land-surface model using observations taken over a tidal flat of the western coast of South Korea. The parametrization is based on the energy budget of a water layer with varying thickness above the soil. During flood tide, heat transfer by the moving water is considered in addition to the surface energy budget. In addition, partial penetration of solar radiation through the water layer is considered in the surface energy budget, and the water thickness varying with time is used as an additional input. Seven days with clear-sky conditions and westerly winds during the daytime are selected for validation of the model. Two simulations are performed in an offline mode: a control simulation without the tidal effect (CONTROL) and a simulation with the tidal effect (TIDE). Comparisons of results have been made with eddy-covariance measurements and soil temperature data for the tidal flats. Observations show that inundation significantly reduces both sensible and latent heat fluxes during daytime, which is well simulated in the TIDE simulation. The diurnal variation and magnitude of soil temperature are better simulated in the TIDE than in the CONTROL simulation. Some underestimation of the latent heat flux over the water surface is partly attributed to the use of one layer of water and the underestimated roughness length at this site. In addition, other model deficiencies are discussed.
Directory of Open Access Journals (Sweden)
P.A. López Pérez
2016-06-01
Full Text Available The main objective of this work was to design a software sensor device based on state observer for a class of continuous bioreactor with application to heavy metal removal and locally analyze the observability properties of the considered system, considering parametric uncertainties. First, an alternative phenomenological model of the main state variables of the process was formulated, considering an unstructured kinetic approach based on Levenspiel product inhibition model; this kinetic model was experimentally validated. The kinetic model was used as a benchmark plant and extended for continuous operation in order to analyze the local observability properties, considering several sets of measured outputs that produce observable subspaces of different dimensions. In addition, we present a nonlinear observer, which is robust against parametric uncertainties, to estimate the observable states of the bioreactor. The convergence of the proposed methodology was analyzed using Lyapunov stability theory. Numerical experiments were done in order to show the performance of the proposed observer and the observability properties of the system.
Lee, Young-Hee; Ahn, Kwang-Deuk; Lee, Yong Hee
2016-06-01
We have developed a parametrization of tidal effects for use in the Noah land-surface model and have validated the land-surface model using observations taken over a tidal flat of the western coast of South Korea. The parametrization is based on the energy budget of a water layer with varying thickness above the soil. During flood tide, heat transfer by the moving water is considered in addition to the surface energy budget. In addition, partial penetration of solar radiation through the water layer is considered in the surface energy budget, and the water thickness varying with time is used as an additional input. Seven days with clear-sky conditions and westerly winds during the daytime are selected for validation of the model. Two simulations are performed in an offline mode: a control simulation without the tidal effect (CONTROL) and a simulation with the tidal effect (TIDE). Comparisons of results have been made with eddy-covariance measurements and soil temperature data for the tidal flats. Observations show that inundation significantly reduces both sensible and latent heat fluxes during daytime, which is well simulated in the TIDE simulation. The diurnal variation and magnitude of soil temperature are better simulated in the TIDE than in the CONTROL simulation. Some underestimation of the latent heat flux over the water surface is partly attributed to the use of one layer of water and the underestimated roughness length at this site. In addition, other model deficiencies are discussed.
污染线性模型的非参数估计%NON-PARAMETRIC ESTIMATION IN CONTAMINATED LINEAR MODEL
Institute of Scientific and Technical Information of China (English)
柴根象; 孙燕; 杨筱菡
2001-01-01
In this paper, the following contaminated linear model is considered: yi=(1-ε)xτiβ+zi, 1≤i≤n, where r.v.'s ｛yi｝ are contaminated with errors ｛zi｝. To assume that the errors have the finite moment of order 2 only. The non-parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.
Vale, A
2007-01-01
We revisit the longstanding question of whether first brightest cluster galaxies are statistically drawn from the same distribution as other cluster galaxies or are "special", using the new non-parametric, empirically based model presented in Vale&Ostriker (2006) for associating galaxy luminosity with halo/subhalo masses. We introduce scatter in galaxy luminosity at fixed halo mass into this model, building a conditional luminosity function (CLF) by considering two possible models: a simple lognormal and a model based on the distribution of concentration in haloes of a given mass. We show that this model naturally allows an identification of halo/subhalo systems with groups and clusters of galaxies, giving rise to a clear central/satellite galaxy distinction. We then use these results to build up the dependence of brightest cluster galaxy (BCG) magnitudes on cluster luminosity, focusing on two statistical indicators, the dispersion in BCG magnitude and the magnitude difference between first and second bri...
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.
Rahmim, Arman; Zhou, Yun; Tang, Jing; Lu, Lijun; Sossi, Vesna; Wong, Dean F.
2012-02-01
Due to high noise levels in the voxel kinetics, development of reliable parametric imaging algorithms remains 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 Neuroimage 44 661-70), 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, Inverse Problems 14 1455-67) 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 versus bias quantitative measurements were performed in various regions of the brain. Direct 4D EM reconstruction resulted in notable qualitative and quantitative accuracy
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
A non-parametric framework for estimating threshold limit values
Directory of Open Access Journals (Sweden)
Ulm Kurt
2005-11-01
Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.
Indian Academy of Sciences (India)
N Deo
2002-02-01
This paper summarizes some work that I have been doing on eigenvalue correlators of random matrix models which show some interesting behavior. First we consider matrix models with gaps in their spectrum or density of eigenvalues. The density–density correlators of these models depend on whether , where is the size of the matrix, takes even or odd values. The fact that this dependence persists in the large thermodynamic limit is an unusual property and may have consequences in the study of one electron effects in mesoscopic systems. Secondly, we study the parametric and cross correlators of the Harish Chandra–Itzykson–Zuber matrix model. The analytic expressions determine how the correlators change as a parameter (e.g. the strength of a perturbation in the Hamiltonian of the chaotic system or external magnetic ﬁeld on a sample of material) is varied. The results are relevant for the conductance ﬂuctuations in disordered mesoscopic systems.
Energy Technology Data Exchange (ETDEWEB)
Pfafferott, J.; Herkel, S.; Jaeschke, M. [Fraunhofer-Institute for Solar Energy Systems, Freiburg (Germany)
2003-12-01
At the new institute building of Fraunhofer ISE, both mechanical and free night ventilation is used for passive cooling of the offices. The results from a long-term monitoring show, that room temperatures are comfortable even at high ambient air temperatures. In two offices, experiments were carried out in order to determine the efficiency of night ventilation dependent on air change rate, solar and internal heat gains. The aim is to identify characteristic building parameters and to determine the night ventilation effect with these parameters. The experiments (one room with and one without night ventilation) are evaluated by using both a parametric model and the ESP-r building simulation programme. Both models are merged in order to develop a method for data evaluation in office buildings with night ventilation and to provide a simple model for integration in a building management system. (Author)
Zhang, Xiaojing; Musson-Genon, Luc; Dupont, Eric; Milliez, Maya; Carissimo, Bertrand
2014-05-01
A detailed numerical simulation of a radiation fog event with a single column model is presented, which takes into account recent developments in microphysical parametrizations. One-dimensional simulations are performed using the computational fluid dynamics model Code_Saturne and the results are compared to a very detailed in situ dataset collected during the ParisFog campaign, which took place near Paris, France, during the winter 2006-2007. Special attention is given to the detailed and complete diurnal simulations and to the role of microphysics in the fog life cycle. The comparison between the simulated and the observed visibility, in the single-column model case study, shows that the evolution of radiation fog is correctly simulated. Sensitivity simulations show that fog development and dissipation are sensitive to the droplet-size distribution through sedimentation/deposition processes but the aerosol number concentration in the coarse mode has a low impact on the time of fog formation.
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
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...
On The Robustness of z=0-1 Galaxy Size Measurements Through Model and Non-Parametric Fits
Mosleh, Moein; Franx, Marijn
2013-01-01
We present the size-stellar mass relations of nearby (z=0.01-0.02) SDSS galaxies, for samples selected by color, morphology, Sersic index n, and specific star formation rate. Several commonly-employed size measurement techniques are used, including single Sersic fits, two-component Sersic models and a non-parametric method. Through simple simulations we show that the non-parametric and two-component Sersic methods provide the most robust effective radius measurements, while those based on single Sersic profiles are often overestimates, especially for massive red/early-type galaxies. Using our robust sizes, we show that for all sub-samples, the mass-size relations are shallow at low stellar masses and steepen above ~3-4 x 10^{10}\\Msun. The mass-size relations for galaxies classified as late-type, low-n, and star-forming are consistent with each other, while blue galaxies follow a somewhat steeper relation. The mass-size relations of early-type, high-n, red, and quiescent galaxies all agree with each other but ...
Cheng, Huihui; Luo, Zhengqian; Ye, Chenchun; Huang, Yizhong; Liu, Chun; Cai, Zhiping
2013-01-20
Mid-infrared fiber optical parametric oscillators (MIR FOPOs) based on the degenerate four-wave mixing (DFWM) of tellurite photonic crystal fibers (PCFs) are proposed and modeled for the first time. Using the DFWM coupled-wave equations, numerical simulations are performed to analyze the effects of tellurite PCFs, single-resonant cavity, and pump source on the MIR FOPO performances. The numerical results show that: (1) although a longer tellurite PCF can decrease the pump threshold of MIR FOPOs to a few watts only, the high conversion-efficiency of MIR idler usually requires a short-length optimum PCF with low loss; (2) compared with the single-pass DFWM configurations of the MIR fiber sources published previously, the stable oscillation of signal light in single-resonant cavity can significantly promote the MIR idler output efficiency. With a suggested tellurite PCF as parametric gain medium, the theoretical prediction indicates that such a MIR FOPO could obtain a wide MIR-tunable range and a high conversion efficiency of more than 10%.
Parametric modelling of the 3.6um to 8um colour distributions of galaxies in the SWIRE Survey
Davoodi, P; Evans, T; Fang, F; Farrah, D; Gonzalez-Solares, E; Jarrett, T; Lonsdale, C; Oliver, S; Polletta, M C; Rowan-Robinson, M; Savage, R S; Shupe, D L; Siana, B; Smith, H E; Surace, J; Waddington, I; Xu, C K; Babbedge, Tom; Davoodi, Payam; Evans, Tracey; Fang, Fan; Farrah, Duncan; Gonzalez-Solares, Eduardo; Jarrett, Tom; Lonsdale, Carol; Oliver, Seb; Polletta, Maria del Carmen; Rowan-Robinson, Michael; Savage, Richard S.; Shupe, David L.; Siana, Brian; Smith, Harding E.; Surace, Jason; Waddington, Ian
2006-01-01
We fit a parametric model comprising a mixture of multi-dimensional Gaussian functions to the 3.6 to 8um colour and optical photo-z distribution of galaxy populations in the ELAIS-N1 and Lockman Fields of SWIRE. For 16,698 sources in ELAIS-N1 we find our data are best modelled (in the sense of the Bayesian Information Criterion) by the sum of four Gaussian distributions or modes (C_a, C_b, C_c and C_d). We compare the fit of our empirical model with predictions from existing semi-analytic and phenomological models. We infer that our empirical model provides a better description of the mid-infrared colour distribution of the SWIRE survey than these existing models. This colour distribution test is thus a powerful model discriminator and complementary to comparisons of number counts. We use our model to provide a galaxy classification scheme and explore the nature of the galaxies in the different modes of the model. C_a consists of dusty star-forming systems such as ULIRG's. Low redshift late-type spirals are f...
Shi, Yuhan; Duan, Qingyun
2017-04-01
Earth System Models (ESMs) are an important tool for understanding past climate evolution and for predicting future climate change. However, the ESM model outputs contain significant uncertainties. A major source of uncertainties is from the specification of model parameters. Specification of ESM model parameters is complicated as most ESMs contain a large number of model parameters. Further, ESMs simulate many different climatic variables and are computationally expensive to run. In this study, we intend to use a design of experiment approach to evaluate the parametric sensitivities of different climatic variables simulated by LOVECLIM, an Earth System Model of Intermediate Complexity (EMIC). Three sensitivity analysis methods are used to explore the sensitivities of different outputs of LOVECLIM, such as global mean temperature, global land/ocean precipitation and evaporation to different model parameters. A newly developed software package, Uncertainty Quantification Python Laboratory (UQ-PyL), is employed to execute the sensitivity analysis. A total of 23 adjustable parameters of the model were considered. This presentation will present the preliminary results of parameter sensitivity analysis, which, in turn, should form the basis for further optimization of the model parameters to better simulate the climate system.
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.
Jaspers, Stijn; Verbeke, Geert; Böhning, Dankmar; Aerts, Marc
2016-01-01
In the last decades, considerable attention has been paid to the collection of antimicrobial resistance data, with the aim of monitoring non-wild-type isolates. This monitoring is performed based on minimum inhibition concentration (MIC) values, which are collected through dilution experiments. We present a semi-parametric mixture model to estimate the entire MIC density on the continuous scale. The parametric first component is extended with a non-parametric second component and a new back-fitting algorithm, based on the Vertex Exchange Method, is proposed. Our data example shows how to estimate the MIC density for Escherichia coli tested for ampicillin and how to use this estimate for model-based classification. A simulation study was performed, showing the promising behavior of the new method, both in terms of density estimation as well as classification.
Parametric studies of magnetic-optic imaging using finite-element models
Chao, C.; Udpa, L.; Xuan, L.; Fitzpatrick, G.; Thorne, D.; Shih, W.
2000-05-01
Magneto-optic imaging is a relatively new sensor application of bubble memory technology to NDI. The Magneto-Optic Imager (MOI) uses a magneto-optic (MO) sensor to produce analog images of magnetic flux leakage from surface and subsurface defects. The flux leakage is produced by eddy current induction techniques in nonferrous metals and magnetic yokes are used in ferromagnetic materials. The technique has gained acceptance in the aircraft maintenance industry for use to detect surface-breaking cracks and corrosion. Until recently, much of the MOI development has been empirical in nature since the electromagnetic processes that produce images are rather complex. The availability of finite element techniques to numerically solve Maxwell's equations, in conjunction with MOI observations, allows greater understanding of the capabilities of the instrument. In this paper, we present a systematic set of finite element calculations along with MOI measurements on specific defects to quantify the current capability of the MOI as well as its desired performance. Parametric studies including effects of liftoff and proximity of edges are also studied.—This material is based upon work supported by the Federal Aviation Administration under Contract #DTFA03-98-D-00008, Delivery Order #IA013 and performed at Iowa State University's Center for NDE as part of the Center for Aviation Systems Reliability program.
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.
Energy Technology Data Exchange (ETDEWEB)
Lozano, Sebastian; Gutierrez, Ester [University of Seville, E.S.I., Department of Industrial Management, Camino de los Descubrimientos, s/n, 41092 Sevilla (Spain)
2008-07-15
In this paper, a non-parametric approach based in Data Envelopment Analysis (DEA) is proposed as an alternative to the Kaya identity (a.k.a ImPACT). This Frontier Method identifies and extends existing best practices. Population and GDP are considered as input and output, respectively. Both primary energy consumption and Greenhouse Gas (GHG) emissions are considered as undesirable outputs. Several Linear Programming models are formulated with different aims, namely: (a) determine efficiency levels, (b) estimate maximum GDP compatible with given levels of population, energy intensity and carbonization intensity, and (c) estimate the minimum level of GHG emissions compatible with given levels of population, GDP, energy intensity or carbonization index. The United States of America case is used as illustration of the proposed approach. (author)
Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad
2015-11-01
One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.
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.
Brauer, Claudia; Torfs, Paul; Teuling, Ryan; Uijlenhoet, Remko
2014-05-01
We present the Wageningen Lowland Runoff Simulator (WALRUS), a novel rainfall-runoff model to fill the gap between complex, spatially distributed models for lowland catchments and simple, parametric models for mountainous catchments. From observations and experience from two Dutch field sites (the Hupsel Brook catchment and the Cabauw polder), we identified key processes for runoff generation in lowland catchments and important feedbacks between components in the hydrological system. We used this knowledge to design a parametric model which can be used all over the world in both freely draining lowland catchments and polders with controlled water levels. While using only four parameters which require calibration, WALRUS explicitly accounts for processes that are important in lowland areas: (1) Groundwater-unsaturated zone coupling: WALRUS contains one soil reservoir, which is divided effectively by the (dynamic) groundwater table into a groundwater zone and a vadose zone. The condition of this soil reservoir is described by two strongly dependent variables: the groundwater depth and the storage deficit (the effective thickness of empty pores). This implementation enables capillary rise when the top soil has dried through evapotranspiration. (2) Wetness-dependent flowroutes: The storage deficit determines the division of rain water between the soil reservoir (slow routes: infiltration, percolation and groundwater flow) and a quickflow reservoir (quick routes: drainpipe, macropore and overland flow). (3) Groundwater-surface water feedbacks: Surface water forms an explicit part of the model structure. Drainage depends on the difference between surface water level and groundwater level (rather than groundwater level alone), allowing for feedbacks and infiltration of surface water into the soil. (4) Seepage and surface water supply: Groundwater seepage and surface water supply or extraction (pumping) are added to or subtracted from the soil or surface water reservoir
Luo, Zhiwen; Li, Yuguo
2011-10-01
This paper reports the results of a parametric CFD study on idealized city models to investigate the potential of slope flow in ventilating a city located in a mountainous region when the background synoptic wind is absent. Examples of such a city include Tokyo in Japan, Los Angeles and Phoenix in the US, and Hong Kong. Two types of buoyancy-driven flow are considered, i.e., slope flow from the mountain slope (katabatic wind at night and anabatic wind in the daytime), and wall flow due to heated/cooled urban surfaces. The combined buoyancy-driven flow system can serve the purpose of dispersing the accumulated urban air pollutants when the background wind is weak or absent. The microscopic picture of ventilation performance within the urban structures was evaluated in terms of air change rate (ACH) and age of air. The simulation results reveal that the slope flow plays an important role in ventilating the urban area, especially in calm conditions. Katabatic flow at night is conducive to mitigating the nocturnal urban heat island. In the present parametric study, the mountain slope angle and mountain height are assumed to be constant, and the changing variables are heating/cooling intensity and building height. For a typical mountain of 500 m inclined at an angle of 20° to the horizontal level, the interactive structure is very much dependent on the ratio of heating/cooling intensity as well as building height. When the building is lower than 60 m, the slope wind dominates. When the building is as high as 100 m, the contribution from the urban wall flow cannot be ignored. It is found that katabatic wind can be very beneficial to the thermal environment as well as air quality at the pedestrian level. The air change rate for the pedestrian volume can be as high as 300 ACH.
Energy Technology Data Exchange (ETDEWEB)
Kostenko, I.F.
1983-01-01
A method is described for building a parametric model based on automatic scanning of structural images. Parameters such as the porosity index, the hydraulic radius, the specific surface and the sinuosity of the pores are measured. The function of distribution of the pores by size is built.
Directory of Open Access Journals (Sweden)
J Yazdani Charati
2015-06-01
Full Text Available Background & aim: One of the most common clinical problems among individuals is thyroid nodule diseases which are characterized by one or more nodules in the thyroid and are usually benign. It can be said that thyroid cancer is the most common endocrine cancer worldwide. This study aimed to determine the risk factors for cancer in patients with thyroid nodule in Mazandaran province,Iran, using parametric survival analysis. Methods: In the present historical cohort study, 26,730 patients with thyroid nodules who were referred to health care centers from July 2002 to March 2008 were identified. Parametric log-normal and log-logistic models were compared with and without taking frailty into account. The criterion for comparing models was Akaike's criterion. All calculations were performed with the SPSS software and the significance level was considered 0.05. Results: The mean time of the conversion of thyroid nodules to cancer in patients was found to be 29.32 months. Using Kaplan-Meier method, survival rates of one year, five years and ten years of nodule conversion to cancer was calculated 94.6, 88.6 and respectively. According to the log rank test age (p=0.03, hypothyroidism (p=0.01, bilateral nodules (p <0.001, a multi-nodular goiter (p <0.001, TSH hormone (p <0.001, T4 hormones (p = 0.005, cholesterol (p = 0.03, creatinin levels (p = 0.001 a significant relationship was seen. Based on the Akaike's criterion, the lognormal model which takes frailty into account best fits to the data. Conclusion: Based on the log-normal model with frailty, It can be concluded that the thyroid nodule patients with abnormal TSH hormone are 6.55 times more likely to develop risk of thyroid cancer than patients who had normal TSH hormone overall. This model also indicated that patients who had heart palpitations are 5.52 times more likely to develop risk of cancer than patients who did not have heart palpitations.
Ivanova, Violeta M.; Sousa, Rita; Murrihy, Brian; Einstein, Herbert H.
2014-06-01
This paper presents results from research conducted at MIT during 2010-2012 on modeling of natural rock fracture systems with the GEOFRAC three-dimensional stochastic model. Following a background summary of discrete fracture network models and a brief introduction of GEOFRAC, the paper provides a thorough description of the newly developed mathematical and computer algorithms for fracture intensity, aperture, and intersection representation, which have been implemented in MATLAB. The new methods optimize, in particular, the representation of fracture intensity in terms of cumulative fracture area per unit volume, P32, via the Poisson-Voronoi Tessellation of planes into polygonal fracture shapes. In addition, fracture apertures now can be represented probabilistically or deterministically whereas the newly implemented intersection algorithms allow for computing discrete pathways of interconnected fractures. In conclusion, results from a statistical parametric study, which was conducted with the enhanced GEOFRAC model and the new MATLAB-based Monte Carlo simulation program FRACSIM, demonstrate how fracture intensity, size, and orientations influence fracture connectivity.
Zhu, Xiaowei; Iungo, G. Valerio; Leonardi, Stefano; Anderson, William
2017-02-01
For a horizontally homogeneous, neutrally stratified atmospheric boundary layer (ABL), aerodynamic roughness length, z_0, is the effective elevation at which the streamwise component of mean velocity is zero. A priori prediction of z_0 based on topographic attributes remains an open line of inquiry in planetary boundary-layer research. Urban topographies - the topic of this study - exhibit spatial heterogeneities associated with variability of building height, width, and proximity with adjacent buildings; such variability renders a priori, prognostic z_0 models appealing. Here, large-eddy simulation (LES) has been used in an extensive parametric study to characterize the ABL response (and z_0) to a range of synthetic, urban-like topographies wherein statistical moments of the topography have been systematically varied. Using LES results, we determined the hierarchical influence of topographic moments relevant to setting z_0. We demonstrate that standard deviation and skewness are important, while kurtosis is negligible. This finding is reconciled with a model recently proposed by Flack and Schultz (J Fluids Eng 132:041203-1-041203-10, 2010), who demonstrate that z_0 can be modelled with standard deviation and skewness, and two empirical coefficients (one for each moment). We find that the empirical coefficient related to skewness is not constant, but exhibits a dependence on standard deviation over certain ranges. For idealized, quasi-uniform cubic topographies and for complex, fully random urban-like topographies, we demonstrate strong performance of the generalized Flack and Schultz model against contemporary roughness correlations.
Institute of Scientific and Technical Information of China (English)
林作铨; 李未
1995-01-01
Parametric logic is introduced. The language, semantics and axiom system of parametric logic are defined. Completeness theorem of parametric logic is provided. Parametric logic has formal ability powerful enough to capture a wide class of logic as its special cases, and therefore can be viewed as a uniform basis for modern logics.
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
Rigatos, G; Rigatou, E; Djida, J D
2015-01-01
The derivative-free nonlinear Kalman filter is proposed for state estimation and fault diagnosis in distributed parameter systems of the wave-type and particularly in the Peyrard-Bishop-Dauxois model of DNA dynamics. At a first stage, a nonlinear filtering approach is introduced for estimating the dynamics of the Peyrard-Bishop-Dauxois 1D nonlinear wave equation, through the processing of a small number of measurements. It is shown that the numerical solution of the associated partial differential equation results in a set of nonlinear ordinary differential equations. With the application of a diffeomorphism that is based on differential flatness theory it is shown that an equivalent description of the system is obtained in the linear canonical (Brunovsky) form. This transformation enables to obtain local estimates about the state vector of the DNA model through the application us of the standard Kalman filter recursion. At a second stage, the local statistical approach to fault diagnosis is used to perform fault diagnosis for this distributed parameter system by processing with statistical tools the differences (residuals) between the output of the Kalman filter and the measurements obtained from the distributed parameter system. Optimal selection of the fault threshold is succeeded by using the local statistical approach to fault diagnosis. The efficiency of the proposed filtering approach in the problem of fault diagnosis for parametric change detection, in nonlinear wave-type models of DNA dynamics, is confirmed through simulation experiments.
Institute of Scientific and Technical Information of China (English)
Ma Yuezhou; Ma Wenbin; Qu Min; Chen Jianhong
2006-01-01
For on-line monitoring of welding quality, the characteristics of the arc sound signals in short circuit CO2 GMAW were analyzed in the time and frequency domains. The arc sound presents a series of ringing-like oscillations that occur at the end of short circuit i. e. the moment of arc re-ignition, and distributes mainly in the frequency band below 10 kHz. A concept of the arc tone channel and its equivalent electrical model were suggested, which is considered a time-dependent distributed parametric system of which the transmission properties depend upon the geometric and physical characteristics of the arc and surroundings, and is excited by the sound source results from the change of arc energy so that results in arc sound. The linear prediction coding ( LPC) model is an estimation of the tone channel. The radial basis function ( RBF) neural networks were built for on-line pattern recognition of the gas-lack in welding, in which the input vectors were formed with the LPC coefficients. The test results proved that the LPC model of arc sound and the RBF networks are feasible in on-line quality monitoring.
Müller, E; Wongwathanarat, A
2011-01-01
Time-dependent and direction-dependent neutrino and gravitational-wave (GW) signatures are presented for a set of 3D hydrodynamic models of parametrized, neutrino-driven supernova explosions of non-rotating 15 and 20 solar mass stars. We employ an approximate treatment of neutrino transport. Due to the excision of the high-density core of the proto-neutron star and the use of an axis-free overset grid, the models can be followed from the post-bounce accretion phase for more than one second without imposing any symmetry restrictions. GW and neutrino emission exhibit the generic time-dependent features known from 2D models. Non-radial hydrodynamic mass motions in the accretion layer and their interaction with the outer layers of the proto-neutron star together with anisotropic neutrino emission give rise to a GW signal with an amplitude of ~5-20 cm and frequencies 100--500 Hz. The GW emission from mass motions reaches a maximum before the explosion sets in. Afterwards the GW signal exhibits a low-frequency modu...
Shah, Anoop D; Bartlett, Jonathan W; Carpenter, James; Nicholas, Owen; Hemingway, Harry
2014-03-15
Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research. The "true" imputation model may contain nonlinearities which are not included in default imputation models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions and does not require a particular regression model to be specified. We compared parametric MICE with a random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000 persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research using Linked Bespoke Studies and Electronic Records; 2001-2010) with complete data on all covariates. Variables were artificially made "missing at random," and the bias and efficiency of parameter estimates obtained using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard ratios, but random forest was more efficient and produced narrower confidence intervals. The second study used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better. This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in which some patients have missing data.
Casarini, L.; Bonometto, S. A.; Tessarotto, E.; Corasaniti, P.-S.
2016-08-01
We discuss an extension of the Coyote emulator to predict non-linear matter power spectra of dark energy (DE) models with a scale factor dependent equation of state of the form w = w0+(1-a)wa. The extension is based on the mapping rule between non-linear spectra of DE models with constant equation of state and those with time varying one originally introduced in ref. [40]. Using a series of N-body simulations we show that the spectral equivalence is accurate to sub-percent level across the same range of modes and redshift covered by the Coyote suite. Thus, the extended emulator provides a very efficient and accurate tool to predict non-linear power spectra for DE models with w0-wa parametrization. According to the same criteria we have developed a numerical code that we have implemented in a dedicated module for the CAMB code, that can be used in combination with the Coyote Emulator in likelihood analyses of non-linear matter power spectrum measurements. All codes can be found at https://github.com/luciano-casarini/pkequal.
A Semi-Parametric Bayesian Mixture Modeling Approach for the Analysis of Judge Mediated Data
Muckle, Timothy Joseph
2010-01-01
Existing methods for the analysis of ordinal-level data arising from judge ratings, such as the Multi-Facet Rasch model (MFRM, or the so-called Facets model) have been widely used in assessment in order to render fair examinee ability estimates in situations where the judges vary in their behavior or severity. However, this model makes certain…
Stationary solution and parametric estimation for Bilinear model driven by ARCH noises
Institute of Scientific and Technical Information of China (English)
潘家柱; 李国栋; 谢衷洁
2002-01-01
Bilinear model driven by ARCH (1) noises is proposed. Existence, uniqueness and form of sta-tionary solution to this new model are presented. Maximum likelihood estimation of the model is discussedand some simulation results are given to evaluate our algorithm.
Parametric Adjustments to the Rankine Vortex Wind Model for Gulf of Mexico Hurricanes
2012-11-01
Rankine Vortex (RV) model [25], the SLOSH model [28], the Holland model [29], the vortex simulation model [30], and the Willoughby and Rahn model [31...www.asme.org/terms/Terms_Use.cfm where Pn ¼ Pc 20:69 þ 1:33Vm þ 0:11u (3) Willoughby et al. [34] provide an alternative formula to estimate Rm as a function of...MacAfee and Pearson [26], and Willoughby et al. [34] also made adjustments which were tailored for mid- latitude applications. 3 Adjustments to the RV
Snorradóttir, Bergthóra S; Jónsdóttir, Fjóla; Sigurdsson, Sven Th; Másson, Már
2014-08-01
A model is presented for transdermal drug delivery from single-layered silicone matrix systems. The work is based on our previous results that, in particular, extend the well-known Higuchi model. Recently, we have introduced a numerical transient model describing matrix systems where the drug dissolution can be non-instantaneous. Furthermore, our model can describe complex interactions within a multi-layered matrix and the matrix to skin boundary. The power of the modelling approach presented here is further illustrated by allowing the possibility of a donor solution. The model is validated by a comparison with experimental data, as well as validating the parameter values against each other, using various configurations with donor solution, silicone matrix and skin. Our results show that the model is a good approximation to real multi-layered delivery systems. The model offers the ability of comparing drug release for ibuprofen and diclofenac, which cannot be analysed by the Higuchi model because the dissolution in the latter case turns out to be limited. The experiments and numerical model outlined in this study could also be adjusted to more general formulations, which enhances the utility of the numerical model as a design tool for the development of drug-loaded matrices for trans-membrane and transdermal delivery.
Wouters, Hendrik; Demuzere, Matthias; Blahak, Ulrich; Fortuniak, Krzysztof; Maiheu, Bino; Camps, Johan; Tielemans, Daniël; van Lipzig, Nicole P. M.
2016-09-01
This paper presents the Semi-empirical URban canopY parametrization (SURY) v1.0, which bridges the gap between bulk urban land-surface schemes and explicit-canyon schemes. Based on detailed observational studies, modelling experiments and available parameter inventories, it offers a robust translation of urban canopy parameters - containing the three-dimensional information - into bulk parameters. As a result, it brings canopy-dependent urban physics to existing bulk urban land-surface schemes of atmospheric models. At the same time, SURY preserves a low computational cost of bulk schemes for efficient numerical weather prediction and climate modelling at the convection-permitting scales. It offers versatility and consistency for employing both urban canopy parameters from bottom-up inventories and bulk parameters from top-down estimates. SURY is tested for Belgium at 2.8 km resolution with the COSMO-CLM model (v5.0_clm6) that is extended with the bulk urban land-surface scheme TERRA_URB (v2.0). The model reproduces very well the urban heat islands observed from in situ urban-climate observations, satellite imagery and tower observations, which is in contrast to the original COSMO-CLM model without an urban land-surface scheme. As an application of SURY, the sensitivity of atmospheric modelling with the COSMO-CLM model is addressed for the urban canopy parameter ranges from the local climate zones of http://WUDAPT.org. City-scale effects are found in modelling the land-surface temperatures, air temperatures and associated urban heat islands. Recommendations are formulated for more precise urban atmospheric modelling at the convection-permitting scales. It is concluded that urban canopy parametrizations including SURY, combined with the deployment of the WUDAPT urban database platform and advancements in atmospheric modelling systems, are essential.
Energy Technology Data Exchange (ETDEWEB)
Yan, Huiping; Qian, Yun; Lin, Guang; Leung, Lai-Yung R.; Yang, Ben; Fu, Q.
2014-03-25
Convective parameterizations used in weather and climate models all display sensitivity to model resolution and variable skill in different climatic regimes. Although parameters in convective schemes can be calibrated using observations to reduce model errors, it is not clear if the optimal parameters calibrated based on regional data can robustly improve model skill across different model resolutions and climatic regimes. In this study, this issue is investigated using a regional modeling framework based on the Weather Research and Forecasting (WRF) model. To quantify the response and sensitivity of model performance to model parameters, we identified five key input parameters and specified their ranges in the Kain-Fritsch (KF) convection scheme in WRF and calibrated them across different spatial resolutions, climatic regimes, and radiation schemes using observed precipitation data. Results show that the optimal values for the five input parameters in the KF scheme are close and model sensitivity and error exhibit similar dependence on the input parameters for all experiments conducted in this study despite differences in the precipitation climatology. We found that the model overall performances in simulating precipitation are more sensitive to the coefficients of downdraft (Pd) and entrainment (Pe) mass flux and starting height of downdraft (Ph). However, we found that rainfall biases, which are probably more related to structural errors, still exist over some regions in the simulation even with the optimal parameters, suggesting further studies are needed to identify the sources of uncertainties and reduce the model biases or structural errors associated with missed or misrepresented physical processes and/or potential problems with the modeling framework.
Parametric sensitivity analysis of a test cell thermal model using spectral analysis
Mara, Thierry Alex; Garde, François
2012-01-01
The paper deals with an empirical validation of a building thermal model. We put the emphasis on sensitivity analysis and on research of inputs/residual correlation to improve our model. In this article, we apply a sensitivity analysis technique in the frequency domain to point out the more important parameters of the model. Then, we compare measured and predicted data of indoor dry-air temperature. When the model is not accurate enough, recourse to time-frequency analysis is of great help to identify the inputs responsible for the major part of error. In our approach, two samples of experimental data are required. The first one is used to calibrate our model the second one to really validate the optimized model.
Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling
Peterson, J. R.; Marshall, P. J.; Andersson, K.
2005-01-01
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 t...
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.
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.
Kulmala, A; Tenhunen, M
2012-11-07
The signal of the dosimetric detector is generally dependent on the shape and size of the sensitive volume of the detector. In order to optimize the performance of the detector and reliability of the output signal the effect of the detector size should be corrected or, at least, taken into account. The response of the detector can be modelled using the convolution theorem that connects the system input (actual dose), output (measured result) and the effect of the detector (response function) by a linear convolution operator. We have developed the super-resolution and non-parametric deconvolution method for determination of the cylinder symmetric ionization chamber radial response function. We have demonstrated that the presented deconvolution method is able to determine the radial response for the Roos parallel plate ionization chamber with a better than 0.5 mm correspondence with the physical measures of the chamber. In addition, the performance of the method was proved by the excellent agreement between the output factors of the stereotactic conical collimators (4-20 mm diameter) measured by the Roos chamber, where the detector size is larger than the measured field, and the reference detector (diode). The presented deconvolution method has a potential in providing reference data for more accurate physical models of the ionization chamber as well as for improving and enhancing the performance of the detectors in specific dosimetric problems.
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.
A Parametric Energy Model for Energy Management of Long Belt Conveyors
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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.
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
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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…
Modeling the Soil Moisture Parametrization in a Snow Dominated Mountainous Region
Kikine, Daniel; Sensoy, Aynur; Sorman, Arda
2016-04-01
The study quantifies the effects of both the soil moisture accounting and the temperature index in the event based as well as the continuous simulation of a model in a snow dominated basin. Physically based watershed model parameters are required to reproduce the historical flows and forecast the stream flows. This study demonstrates that parameterization of hydrological model is a favorable approach to perform forecasting because it employs the relationship of the calibrated model parameters and those of the watershed's physical properties. With this consideration, the temperature index (degree-day) snowmelt and the soil moisture accounting models within the Hydrologic Engineering Center's hydrologic modeling system (HEC-HMS) are applied to the Upper Euphrates watershed. The versatile 14-parameter soil moisture accounting (SMA) algorithm is utilized for a better simulation and parameterization of the watershed. The methodology was exemplified by performing various independent simulations using the meteorological data and the observed stream discharges. The soil moisture parameters were calibrated and modified according to their statistical relationships with the land use for the 2002 - 2008 period, the obtained parameter set are then validated for the 2009 - 2012 period. Model outputs are evaluated in comparison to satellite derived soil moisture and snow water equivalent data. Deterministic Numerical Weather Prediction data are used together with the conceptual model to forecast runoff for the melting period of the year 2015.
Sparse Linear Parametric Modeling of Room Acoustics with Orthonormal Basis Functions
DEFF Research Database (Denmark)
Vairetti, G.; von Waterschoot, T.; Moonen, M.;
2014-01-01
Orthonormal Basis Function (OBF) models provide a stable and well-conditioned representation of a linear system. When used for the modeling of room acoustics, useful information about the true dynamics of the system can be introduced by a proper selection of a set of poles, which however appear non...
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.
A parametrized non-equilibrium wall-model for large-eddy simulations
Hickel, Stefan; Bodart, Julien; Larsson, Johan
2015-01-01
Wall-models are essential for enabling large-eddy simulations (LESs) of realistic problems at high Reynolds numbers. The present study is focused on approaches that directly model the wall shear stress, specifically on filling the gap between models based on wall-normal ordinary differential equations (ODEs) that assume equilibrium and models based on full partial differential equations (PDEs) that do not. We develop ideas for how to incorporate non-equilibrium effects (most importantly, strong pressure-gradient effects) in the wall-model while still solving only wall-normal ODEs. We test these ideas using two reference databases: an adverse pressure-gradient turbulent boundary-layer and a shock/boundary-layer interaction problem, both of which lead to separation and re-attachment of the turbulent boundary layer.
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.)
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.
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M. Ghil
2008-05-01
Full Text Available We consider a delay differential equation (DDE model for El-Niño Southern Oscillation (ENSO variability. The model combines two key mechanisms that participate in ENSO dynamics: delayed negative feedback and seasonal forcing. We perform stability analyses of the model in the three-dimensional space of its physically relevant parameters. Our results illustrate the role of these three parameters: strength of seasonal forcing b, atmosphere-ocean coupling κ, and propagation period τ of oceanic waves across the Tropical Pacific. Two regimes of variability, stable and unstable, are separated by a sharp neutral curve in the (b, τ plane at constant κ. The detailed structure of the neutral curve becomes very irregular and possibly fractal, while individual trajectories within the unstable region become highly complex and possibly chaotic, as the atmosphere-ocean coupling κ increases. In the unstable regime, spontaneous transitions occur in the mean "temperature" (i.e., thermocline depth, period, and extreme annual values, for purely periodic, seasonal forcing. The model reproduces the Devil's bleachers characterizing other ENSO models, such as nonlinear, coupled systems of partial differential equations; some of the features of this behavior have been documented in general circulation models, as well as in observations. We expect, therefore, similar behavior in much more detailed and realistic models, where it is harder to describe its causes as completely.
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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.
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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
Frick, Maximilian; Sippel, Judith; Cacace, Mauro; Scheck-Wenderoth, Magdalena
2016-04-01
The goal of this study was to quantify the influence of the geological structure and geophysical parametrization of model units on the geothermal field as calculated by 3D numerical simulations of coupled fluid and heat transport for the subsurface of Berlin, Germany. The study area is located in the Northeast German Basin which is filled with several kilometers of sediments. This sedimentary infill includes the clastic sedimentary units Middle Buntsandstein and Sedimentary Rotliegend which are of particular interest for geothermal exploration. Previous studies conducted in the Northeast German Basin have already shown the geometries and properties of the geological units majorly control the distribution of subsurface temperatures. In this study we followed a two-step approach, where we first improved an existing structural model by integrating newly available 57 geological cross-sections, well data and deep seismics (down to ~4 km). Secondly, we performed a sensitivity analysis investigating the effects of varying physical fluid and rock properties on the subsurface temperature field. The results of this study show, that the structural configuration of model units exerts the highest influence on the geothermal field (up to ± 23 K at 1000 m below sea level). Here, the Rupelian clay aquitard, displaying a heterogeneous thickness distribution, locally characterized by hydrogeological windows (i.e. domains of no thickness) enabling intra-aquifer groundwater circulation has been identified as major controlling factor. The new structural configuration of this unit (more continuous, less numerous hydrogeological windows) also leads to a reduction of the influence of different boundary conditions and heat transport mechanisms considered. Additionally, the models results show that calculated temperatures highly depend on geophysical properties of model units whereas the hydraulic conductivity of the Cenozoic succession was identified as most dominant, leading to changes
APDL语言在ANSYS参数化建模中的应用%Application of APDL Language in ANSYS Parametric Modeling
Institute of Scientific and Technical Information of China (English)
杨胜; 刘淑芬; 白恒
2015-01-01
There are many modeling method based on ANSYS software ,the paper mainly introduces the application of the APDL language in modeling. Perforated inclined plate is taken as an example to discuss an approach to parametric modeling with APDL Language. Single and multiple parameters input interface customization and the macros are used to realize parametric modeling.%ANSYS的建模方法有多种，文中主要介绍APDL语言在建模中的应用。以一带孔斜板零件为例，论述了APDL语言的参数化建模方法。通过单参数输入界面和多参数输入界面的订制，宏命令的使用等方法分别实现了斜板零件的参数化建模。
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.
Fitting parametric models of diffusion MRI in regions of partial volume
Eaton-Rosen, Zach; Cardoso, M. J.; Melbourne, Andrew; Orasanu, Eliza; Bainbridge, Alan; Kendall, Giles S.; Robertson, Nicola J.; Marlow, Neil; Ourselin, Sebastien
2016-03-01
Regional analysis is normally done by fitting models per voxel and then averaging over a region, accounting for partial volume (PV) only to some degree. In thin, folded regions such as the cerebral cortex, such methods do not work well, as the partial volume confounds parameter estimation. Instead, we propose to fit the models per region directly with explicit PV modeling. In this work we robustly estimate region-wise parameters whilst explicitly accounting for partial volume effects. We use a high-resolution segmentation from a T1 scan to assign each voxel in the diffusion image a probabilistic membership to each of k tissue classes. We rotate the DW signal at each voxel so that it aligns with the z-axis, then model the signal at each voxel as a linear superposition of a representative signal from each of the k tissue types. Fitting involves optimising these representative signals to best match the data, given the known probabilities of belonging to each tissue type that we obtained from the segmentation. We demonstrate this method improves parameter estimation in digital phantoms for the diffusion tensor (DT) and `Neurite Orientation Dispersion and Density Imaging' (NODDI) models. The method provides accurate parameter estimates even in regions where the normal approach fails completely, for example where partial volume is present in every voxel. Finally, we apply this model to brain data from preterm infants, where the thin, convoluted, maturing cortex necessitates such an approach.
Strubbe, David A.; Grossman, Jeffrey C.
Classical inter-atomic potentials can be successful at predicting the vibrations of materials at system sizes intractable by quantum methods. However, to predict Raman spectra, electrons must be re-introduced, for example via a bond-polarizability model which attributes the polarizability to cylindrically symmetrical inter-atomic bonds. Parameters in assumed functional forms are fit to experimental spectra, and then a Raman intensity can be computed for each mode. In the case of amorphous silicon, the existing models do not show satisfactory agreement with experimental spectra. To generate a more accurate and transferable bond-polarizability model, we have instead begun with ab initio calculated Raman tensors for a set of a-Si:H structures [DA Strubbe et al., arXiv:1511.01139]. This atomistic data set al.lows us to obtain parameters and functional forms for a general model, without confounding errors from the potentials. This Raman model can be used to study large structural models with relevance for photovoltaics, such as medium- and long-range order in a-Si:H, nanocrystalline Si, amorphous/crystalline interfaces, or a-Si:H nanowires, at sizes that would be inaccessible for ab initio calculations. We analyze the applicability of this approach to other materials systems.
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Demétrius David da Silva
2010-08-01
Full Text Available Little is known about the mineralization dynamic of organic nitrogen contained in swine manure, so models need to be adjusted for its prediction. The objective of the present study was to parameterize and assess models of organic nitrogen mineralization in soil treated with swine raising wastewater (SRW at different temperatures and water contents. Samples of 57.3 cm3 of dystrophic Red-Yellow Latosol were mixed with SRW at the application dose of 400 kg ha-1 nitrogen, placed in plastic cups and incubated at four different temperatures (15, 20, 25 and 35°C and water contents corresponding to tensions of 10, 30, 200 and 1500 kPa. Samples were removed from the incubated soil after 3, 6, 12, 24, 48 and 96 days to quantify the ammonium and nitrate concentrations. The parameters of the soil organic nitrogen mineralization models were determined from the organic nitrogen mineralization values obtained over the different incubation periods. The value of the potentially mineralizable nitrogen (N0 in soil with application of SRW was superior that of the soil without application of SRW. The mineralization constant (k in soil with application of SRW was always superior that of the soil without application of SRW. There was a tendency for the simple exponential model to underestimate the values of the mineralized nitrogen concentration. In most of the situations the potential model was more efficient than the simple exponential model to predict the mineralization of the organic nitrogen.