Baillet, S.; Mosher, J. C.; Jerbi, K.; Leahy, R. M.
2001-01-01
Reliable estimation of the local spatial extent of neural activity is a key to the quantitative analysis of MEG sources across subjects and conditions. In association with an understanding of the temporal dynamics among multiple areas, this would represent a major advance in electrophysiological source imaging. Parametric current dipole approaches to MEG (and EEG) source localization can rapidly generate a physical model of neural current generators using a limited number of parameters. However, physiological interpretation of these models is often difficult, especially in terms of the spatial extent of the true cortical activity. In new approaches using multipolar source models [3, 5], similar problems remain in the analysis of the higher-order source moments as parameters of cortical extent. Image-based approaches to the inverse problem provide a direct estimate of cortical current generators, but computationally expensive nonlinear methods are required to produce focal sources [1,4]. Recent efforts describe how a cortical patch can be grown until a best fit to the data is reached in the least-squares sense [6], but computational considerations necessitate that the growth be seeded in predefined regions of interest. In a previous study [2], a source obtained using a parametric model was remapped onto the cortex by growing a patch of cortical dipoles in the vicinity of the parametric source until the forward MEG or EEG fields of the parametric and cortical sources matched. The source models were dipoles and first-order multipoles. We propose to combine the parametric and imaging methods for MEG source characterization to take advantage of (i) the parsimonious and computationally efficient nature of parametric source localization methods and (ii) the anatomical and physiological consistency of imaging techniques that use relevant a priori information. By performing the cortical remapping imaging step by matching the multipole expansions of the original parametric
Parametric Modeling of Electron Beam Loss in Synchrotron Light Sources
Sayyar-Rodsari, B.; Schweiger, C.; Hartman, E.; Corbett, J.; Lee, M.; Lui, P.; Paterson, E.; /SLAC
2007-11-28
Synchrotron light is used for a wide variety of scientific disciplines ranging from physical chemistry to molecular biology and industrial applications. As the electron beam circulates, random single-particle collisional processes lead to decay of the beam current in time. We report a simulation study in which a combined neural network (NN) and first-principles (FP) model is used to capture the decay in beam current due to Touschek, Bremsstrahlung, and Coulomb effects. The FP block in the combined model is a parametric description of the beam current decay where model parameters vary as a function of beam operating conditions (e.g. vertical scraper position, RF voltage, number of the bunches, and total beam current). The NN block provides the parameters of the FP model and is trained (through constrained nonlinear optimization) to capture the variation in model parameters as operating condition of the beam changes. Simulation results will be presented to demonstrate that the proposed combined framework accurately models beam decay as well as variation to model parameters without direct access to parameter values in the model.
Parametric Explosion Spectral Model
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.
Modeling and optimization of photon pair sources based on spontaneous parametric down-conversion
Kolenderski, Piotr; Banaszek, Konrad; Wasilewski, Wojciech
2009-07-15
We address the problem of efficient modeling of photon pairs generated in spontaneous parametric down-conversion and coupled into single-mode fibers. It is shown that when the range of relevant transverse wave vectors is restricted by the pump and fiber modes, the computational complexity can be reduced substantially with the help of the paraxial approximation, while retaining the full spectral characteristics of the source. This approach can serve as a basis for efficient numerical calculations or can be combined with analytically tractable approximations of the phase-matching function. We introduce here a cosine-Gaussian approximation of the phase-matching function that works for a broader range of parameters than the Gaussian model used previously. The developed modeling tools are used to evaluate characteristics of the photon pair sources such as the pair production rate and the spectral purity quantifying frequency correlations. Strategies to generate spectrally uncorrelated photons, necessary in multiphoton interference experiments, are analyzed with respect to trade-offs between parameters of the source.
Model and parametric uncertainty in source-based kinematic models of earthquake ground motion
Hartzell, Stephen; Frankel, Arthur; Liu, Pengcheng; Zeng, Yuehua; Rahman, Shariftur
2011-01-01
Four independent ground-motion simulation codes are used to model the strong ground motion for three earthquakes: 1994 Mw 6.7 Northridge, 1989 Mw 6.9 Loma Prieta, and 1999 Mw 7.5 Izmit. These 12 sets of synthetics are used to make estimates of the variability in ground-motion predictions. In addition, ground-motion predictions over a grid of sites are used to estimate parametric uncertainty for changes in rupture velocity. We find that the combined model uncertainty and random variability of the simulations is in the same range as the variability of regional empirical ground-motion data sets. The majority of the standard deviations lie between 0.5 and 0.7 natural-log units for response spectra and 0.5 and 0.8 for Fourier spectra. The estimate of model epistemic uncertainty, based on the different model predictions, lies between 0.2 and 0.4, which is about one-half of the estimates for the standard deviation of the combined model uncertainty and random variability. Parametric uncertainty, based on variation of just the average rupture velocity, is shown to be consistent in amplitude with previous estimates, showing percentage changes in ground motion from 50% to 300% when rupture velocity changes from 2.5 to 2.9 km/s. In addition, there is some evidence that mean biases can be reduced by averaging ground-motion estimates from different methods.
NASA Astrophysics Data System (ADS)
Lambrakos, S. G.
2016-04-01
A general methodology for inverse thermal analysis of steady-state energy deposition in plate structures, typically welds, is extended with respect to its formulation. This methodology is in terms of numerical-analytical basis functions, which provide parametric representations of weld-temperature histories that can be adopted as input data to various types of computational procedures, such as those for prediction of solid-state phase transformations and mechanical response. The extension of the methodology presented here concerns construction of numerical-analytical basis functions and their associated parameterizations, which permit optimal and convenient parameter optimization with respect to different types of weld-workpiece boundary conditions, energy source characteristics, and experimental measurements adoptable as weld-temperature history constraints. Prototype inverse thermal analyses of a steel weld are presented that provide proof of concept for inverse thermal analysis using these basis functions.
Wey, Andrew; Connett, John; Rudser, Kyle
2015-07-01
For estimating conditional survival functions, non-parametric estimators can be preferred to parametric and semi-parametric estimators due to relaxed assumptions that enable robust estimation. Yet, even when misspecified, parametric and semi-parametric estimators can possess better operating characteristics in small sample sizes due to smaller variance than non-parametric estimators. Fundamentally, this is a bias-variance trade-off situation in that the sample size is not large enough to take advantage of the low bias of non-parametric estimation. Stacked survival models estimate an optimally weighted combination of models that can span parametric, semi-parametric, and non-parametric models by minimizing prediction error. An extensive simulation study demonstrates that stacked survival models consistently perform well across a wide range of scenarios by adaptively balancing the strengths and weaknesses of individual candidate survival models. In addition, stacked survival models perform as well as or better than the model selected through cross-validation. Finally, stacked survival models are applied to a well-known German breast cancer study.
Parametric Modeling for Fluid Systems
NASA Technical Reports Server (NTRS)
Pizarro, Yaritzmar Rosario; Martinez, Jonathan
2013-01-01
Fluid Systems involves different projects that require parametric modeling, which is a model that maintains consistent relationships between elements as is manipulated. One of these projects is the Neo Liquid Propellant Testbed, which is part of Rocket U. As part of Rocket U (Rocket University), engineers at NASA's Kennedy Space Center in Florida have the opportunity to develop critical flight skills as they design, build and launch high-powered rockets. To build the Neo testbed; hardware from the Space Shuttle Program was repurposed. Modeling for Neo, included: fittings, valves, frames and tubing, between others. These models help in the review process, to make sure regulations are being followed. Another fluid systems project that required modeling is Plant Habitat's TCUI test project. Plant Habitat is a plan to develop a large growth chamber to learn the effects of long-duration microgravity exposure to plants in space. Work for this project included the design and modeling of a duct vent for flow test. Parametric Modeling for these projects was done using Creo Parametric 2.0.
Optimal parametrization of electrodynamical battery model using model selection criteria
NASA Astrophysics Data System (ADS)
Suárez-García, Andrés; Alfonsín, Víctor; Urréjola, Santiago; Sánchez, Ángel
2015-07-01
This paper describes the mathematical parametrization of an electrodynamical battery model using different model selection criteria. A good modeling technique is needed by the battery management units in order to increase battery lifetime. The elements of battery models can be mathematically parametrized to enhance their implementation in simulation environments. In this work, the best mathematical parametrizations are selected using three model selection criteria: the coefficient of determination (R2), the Akaike Information Criterion (AIC) and the Bayes Information Criterion (BIC). The R2 criterion only takes into account the error of the mathematical parametrizations, whereas AIC and BIC consider complexity. A commercial 40 Ah lithium iron phosphate (LiFePO4) battery is modeled and then simulated for contrasting. The OpenModelica open-source modeling and simulation environment is used for doing the battery simulations. The mean percent error of the simulations is 0.0985% for the models parametrized with R2 , 0.2300% for the AIC ones, and 0.3756% for the BIC ones. As expected, the R2 selected the most precise, complex and slowest mathematical parametrizations. The AIC criterion chose parametrizations with similar accuracy, but simpler and faster than the R2 ones.
Parametric Model Checking with VerICS
NASA Astrophysics Data System (ADS)
Knapik, Michał; Niewiadomski, Artur; Penczek, Wojciech; Półrola, Agata; Szreter, Maciej; Zbrzezny, Andrzej
The paper presents the verification system verICS, extended with the three new modules aimed at parametric verification of Elementary Net Systems, Distributed Time Petri Nets, and a subset of UML. All the modules exploit Bounded Model Checking for verifying parametric reachability and the properties specified in the logic PRTECTL - the parametric extension of the existential fragment of CTL.
Lensed: Forward parametric modelling of strong lenses
NASA Astrophysics Data System (ADS)
Tessore, Nicolas; Bellagamba, Fabio; Metcalf, R. Benton
2015-05-01
Lensed performs forward parametric modelling of strong lenses. Using a provided model, Lensed renders the expected image of the lensing event for a large number of parameter settings, thereby exploring the space of possible realizations of the observation. It compares the expectation to the observed image by calculating the likelihood that the observation was indeed produced by the assumed model, thus reconstructing the probability distribution over the parameter space of the model. Written in C, the code uses a massively parallel ray-tracing kernel to perform the necessary calculations on a graphics processing unit (GPU), making the precise rendering of the background lensed sources fast and allowing the simultaneous optimization of tens of parameters for the selected model.
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.
Parametric Cost Models for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip
2010-01-01
A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.
Towards an Empirically Based Parametric Explosion Spectral Model
Ford, S R; Walter, W R; Ruppert, S; Matzel, E; Hauk, T; Gok, R
2009-08-31
Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never before been tested. The focus of our work is on the local and regional distances (< 2000 km) and phases (Pn, Pg, Sn, Lg) necessary to see small explosions. We are developing a parametric model of the nuclear explosion seismic source spectrum that is compatible with the earthquake-based geometrical spreading and attenuation models developed using the Magnitude Distance Amplitude Correction (MDAC) techniques (Walter and Taylor, 2002). The explosion parametric model will be particularly important in regions without any prior explosion data for calibration. The model is being developed using the available body of seismic data at local and regional distances for past nuclear explosions at foreign and domestic test sites. Parametric modeling is a simple and practical approach for widespread monitoring applications, prior to the capability to carry out fully deterministic modeling. The achievable goal of our parametric model development is to be able to predict observed local and regional distance seismic amplitudes for event identification and yield determination in regions with incomplete or no prior history of underground nuclear testing. The relationship between the parametric equations and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source.
Modeling personnel turnover in the parametric organization
NASA Technical Reports Server (NTRS)
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.
Modeling personnel turnover in the parametric organization
NASA Technical Reports Server (NTRS)
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.
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.
Marmarelis, Vasilis Z.; Berger, Theodore W.
2009-01-01
Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP. PMID:18506609
Quantifying parametric uncertainty in the Rothermel model
S. Goodrick
2008-01-01
The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spreadmodel (implemented in software such as fire spread models in the United States. This model consists of a non-linear system of equations that relates environmentalvariables (input parameter groups...
THz-wave parametric sources and imaging applications
NASA Astrophysics Data System (ADS)
Kawase, Kodo
2004-12-01
We have studied the generation of terahertz (THz) waves by optical parametric processes based on laser light scattering from the polariton mode of nonlinear crystals. Using parametric oscillation of MgO-doped LiNbO3 crystal pumped by a nano-second Q-switched Nd:YAG laser, we have realized a widely tunable coherent THz-wave sources with a simple configuration. We have also developed a novel basic technology for THz imaging, which allows detection and identification of chemicals by introducing the component spatial pattern analysis. The spatial distributions of the chemicals were obtained from terahertz multispectral trasillumination images, using absorption spectra previously measured with a widely tunable THz-wave parametric oscillator. Further we have applied this technique to the detection and identification of illicit drugs concealed in envelopes. The samples we used were methamphetamine and MDMA, two of the most widely consumed illegal drugs in Japan, and aspirin as a reference.
Parametric structural modeling of insect wings.
Mengesha, T E; Vallance, R R; Barraja, M; Mittal, R
2009-09-01
Insects produce thrust and lift forces via coupled fluid-structure interactions that bend and twist their compliant wings during flapping cycles. Insight into this fluid-structure interaction is achieved with numerical modeling techniques such as coupled finite element analysis and computational fluid dynamics, but these methods require accurate and validated structural models of insect wings. Structural models of insect wings depend principally on the shape, dimensions and material properties of the veins and membrane cells. This paper describes a method for parametric modeling of wing geometry using digital images and demonstrates the use of the geometric models in constructing three-dimensional finite element (FE) models and simple reduced-order models. The FE models are more complete and accurate than previously reported models since they accurately represent the topology of the vein network, as well as the shape and dimensions of the veins and membrane cells. The methods are demonstrated by developing a parametric structural model of a cicada forewing.
Incorporating parametric uncertainty into population viability analysis models
McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.
2011-01-01
Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.
Parametric identification of human operator models
NASA Technical Reports Server (NTRS)
Ninz, N. R.
1982-01-01
The accurate and efficient identification of the human operator is still a need in human factors engineering especially concerning multivariable control. Control theoretic identification methods need to be tested with human operator models under realistic boundary conditons. The requirements and criteria for the use of parametric methods, selected models as well as the Maximum Likelihood Method and the Extended Kalman Filter are displayed. The experiments and results are comparatively discussed from the point of practical engineering.
Parametric Cost Models for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Henrichs, Todd; Dollinger, Courtney
2010-01-01
Multivariable parametric cost models for space telescopes provide several benefits to designers and space system project managers. They identify major architectural cost drivers and allow high-level design trades. They enable cost-benefit analysis for technology development investment. And, they provide a basis for estimating total project cost. A survey of historical models found that there is no definitive space telescope cost model. In fact, published models vary greatly [1]. Thus, there is a need for parametric space telescopes cost models. An effort is underway to develop single variable [2] and multi-variable [3] parametric space telescope cost models based on the latest available data and applying rigorous analytical techniques. Specific cost estimating relationships (CERs) have been developed which show that aperture diameter is the primary cost driver for large space telescopes; technology development as a function of time reduces cost at the rate of 50% per 17 years; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and increasing mass reduces cost.
Semi-Parametric Generalized Linear Models.
1985-08-01
is nonsingular, upper triangular, and of full rank r. It is known (Dongarra et al., 1979) that G-1 FT is the Moore - Penrose inverse of L . Therefore... GENERALIZED LINEAR pq Mathematics Research Center University of Wisconsin-Madison 610 Walnut Street Madison, Wisconsin 53705 TI C August 1985 E T NOV 7 8...North Carolina 27709 -. -.. . - -.-. g / 6 O5’o UNIVERSITY OF WISCONSIN-MADISON MATHD4ATICS RESEARCH CENTER SD4I-PARAMETRIC GENERALIZED LINEAR MODELS
Degeneracies in parametrized modified gravity models
Hojjati, Alireza
2013-01-01
We study degeneracies between parameters in some of the widely used parametrized modified gravity models. We investigate how different observables from a future photometric weak lensing survey such as LSST, correlate the effects of these parameters and to what extent the degeneracies are broken. We also study the impact of other degenerate effects, namely massive neutrinos and some of the weak lensing systematics, on the correlations.
Hyperbolic and semi-parametric models in finance
NASA Astrophysics Data System (ADS)
Bingham, N. H.; Kiesel, Rüdiger
2001-02-01
The benchmark Black-Scholes-Merton model of mathematical finance is parametric, based on the normal/Gaussian distribution. Its principal parametric competitor, the hyperbolic model of Barndorff-Nielsen, Eberlein and others, is briefly discussed. Our main theme is the use of semi-parametric models, incorporating the mean vector and covariance matrix as in the Markowitz approach, plus a non-parametric part, a scalar function incorporating features such as tail-decay. Implementation is also briefly discussed.
Ground-Based Telescope Parametric Cost Model
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Rowell, Ginger Holmes
2004-01-01
A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.
Parametric Model of an Aerospike Rocket Engine
NASA Technical Reports Server (NTRS)
Korte, J. J.
2000-01-01
A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHTI multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.
Parametric Model of an Aerospike Rocket Engine
NASA Technical Reports Server (NTRS)
Korte, J. J.
2000-01-01
A suite of computer codes was assembled to simulate the performance of an aerospike engine and to generate the engine input for the Program to Optimize Simulated Trajectories. First an engine simulator module was developed that predicts the aerospike engine performance for a given mixture ratio, power level, thrust vectoring level, and altitude. This module was then used to rapidly generate the aerospike engine performance tables for axial thrust, normal thrust, pitching moment, and specific thrust. Parametric engine geometry was defined for use with the engine simulator module. The parametric model was also integrated into the iSIGHT multidisciplinary framework so that alternate designs could be determined. The computer codes were used to support in-house conceptual studies of reusable launch vehicle designs.
Acoustic intensity in the interaction region of a parametric source
NASA Astrophysics Data System (ADS)
Lauchle, G. C.; Gabrielson, T. B.; van Tol, D. J.; Kottke, N. F.; McConnell, J. A.
2003-10-01
The goal of this project was to measure acoustic intensity in the strong interaction region of a parametric source in order to obtain a clear definition of the source-generation region and to separate the local generation (the reactive field) from propagation (the real or active field). The acoustic intensity vector was mapped in the interaction region of a parametric projector at Lake Seneca. The source was driven with primary signals at 22 kHz and 27 kHz. Receiving sensors were located 8.5 meters from the projector. At that range, the secondary at 5 kHz was between 40 and 45 dB below either primary. For the primary levels used, the plane-wave shock inception distance would have been at least 14 meters. Furthermore, the Rayleigh distance for the projector was about 4 meters so the measurements at 8.5 meters were in the strong interaction region but not in saturation. Absorption was negligible over these ranges. The intensity measurements were made at fixed range but varying azimuth angle and varying depth thus developing a two-dimensional cross-section of the secondary beam. Measurements of both the active and reactive intensity vectors will be presented along with a discussion of measurement error. [Work supported by ONR Code 321SS.
Modeling Personnel Turnover in the Parametric Organization
NASA Technical Reports Server (NTRS)
Dean, Edwin B.
1991-01-01
A primary issue in organizing a new parametric cost analysis function is to determine the skill mix and number of personnel required. The skill mix can be obtained by a functional decomposition of the tasks required within the organization and a matrixed correlation with educational or experience backgrounds. The number of personnel is a function of the skills required to cover all tasks, personnel skill background and cross training, the intensity of the workload for each task, migration through various tasks by personnel along a career path, personnel hiring limitations imposed by management and the applicant marketplace, personnel training limitations imposed by management and personnel capability, and the rate at which personnel leave the organization for whatever reason. Faced with the task of relating all of these organizational facets in order to grow a parametric cost analysis (PCA) organization from scratch, it was decided that a dynamic model was required in order to account for the obvious dynamics of the forming organization. The challenge was to create such a simple model which would be credible during all phases of organizational development. The model development process was broken down into the activities of determining the tasks required for PCA, determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the dynamic model, implementing the dynamic model, and testing the dynamic model.
Smirr, Jean-Loup; Guilbaud, Sylvain; Ghalbouni, Joe; Frey, Robert; Diamanti, Eleni; Alléaume, Romain; Zaquine, Isabelle
2011-01-17
Fast characterization of pulsed spontaneous parametric down conversion (SPDC) sources is important for applications in quantum information processing and communications. We propose a simple method to perform this task, which only requires measuring the counts on the two output channels and the coincidences between them, as well as modeling the filter used to reduce the source bandwidth. The proposed method is experimentally tested and used for a complete evaluation of SPDC sources (pair emission probability, total losses, and fidelity) of various bandwidths. This method can find applications in the setting up of SPDC sources and in the continuous verification of the quality of quantum communication links.
Mathematically trivial control of sound using a parametric beam focusing source.
Tanaka, Nobuo; Tanaka, Motoki
2011-01-01
By exploiting a case regarded as trivial, this paper presents global active noise control using a parametric beam focusing source (PBFS). As with a dipole model, one is used for a primary sound source and the other for a control sound source, the control effect for minimizing a total acoustic power depends on the distance between the two. When the distance becomes zero, the total acoustic power becomes null, hence nothing less than a trivial case. Because of the constraints in practice, there exist difficulties in placing a control source close enough to a primary source. However, by projecting a sound beam of a parametric array loudspeaker onto the target sound source (primary source), a virtual sound source may be created on the target sound source, thereby enabling the collocation of the sources. In order to further ensure feasibility of the trivial case, a PBFS is then introduced in an effort to meet the size of the two sources. Reflected sound wave of the PBFS, which is tantamount to the virtual sound source output, aims to suppress the primary sound. Finally, a numerical analysis as well as an experiment is conducted, verifying the validity of the proposed methodology.
Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models
ERIC Educational Resources Information Center
Song, Xin-Yuan; Xia, Ye-Mao; Pan, Jun-Hao; Lee, Sik-Yum
2011-01-01
Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the "L[subscript nu]"-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider…
Model Comparison of Bayesian Semiparametric and Parametric Structural Equation Models
ERIC Educational Resources Information Center
Song, Xin-Yuan; Xia, Ye-Mao; Pan, Jun-Hao; Lee, Sik-Yum
2011-01-01
Structural equation models have wide applications. One of the most important issues in analyzing structural equation models is model comparison. This article proposes a Bayesian model comparison statistic, namely the "L[subscript nu]"-measure for both semiparametric and parametric structural equation models. For illustration purposes, we consider…
Approximately Integrable Linear Statistical Models in Non-Parametric Estimation
1990-08-01
OTIC I EL COPY Lfl 0n Cf) NAPPROXIMATELY INTEGRABLE LINEAR STATISTICAL MODELS IN NON- PARAMETRIC ESTIMATION by B. Ya. Levit University of Maryland...Integrable Linear Statistical Models in Non- Parametric Estimation B. Ya. Levit Sumnmary / The notion of approximately integrable linear statistical models...models related to the study of the "next" order optimality in non- parametric estimation . It appears consistent to keep the exposition at present at the
Parametric modeling of quantile regression coefficient functions.
Frumento, Paolo; Bottai, Matteo
2016-03-01
Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile regression model depend on the order of the quantile being estimated. For example, the coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the regression coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package.
Unifying framework for decomposition models of parametric and non-parametric image registration
NASA Astrophysics Data System (ADS)
Ibrahim, Mazlinda; Chen, Ke
2017-08-01
Image registration aims to find spatial transformations such that the so-called given template image becomes similar in some sense to the reference image. Methods in image registration can be divided into two classes (parametric or non-parametric) based on the degree of freedom of the given method. In parametric image registration, the transformation is governed by a finite set of image features or by expanding the transformation in terms of basis functions. Meanwhile, in non-parametric image registration, the problem is modelled as a functional minimisation problem via the calculus of variations. In this paper, we provide a unifying framework for decomposition models for image registration which combine parametric and non-parametric models. Several variants of the models are presented with focus on the affine, diffusion and linear curvature models. An effective numerical solver is provided for the models as well as experimental results to show the effectiveness, robustness and accuracy of the models. The decomposition model of affine and linear curvature outperforms the competing models based on tested images.
uvmcmcfit: Parametric models to interferometric data fitter
NASA Astrophysics Data System (ADS)
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).
Energy scaling of terahertz-wave parametric sources.
Tang, Guanqi; Cong, Zhenhua; Qin, Zengguang; Zhang, Xingyu; Wang, Weitao; Wu, Dong; Li, Ning; Fu, Qiang; Lu, Qingming; Zhang, Shaojun
2015-02-23
Terahertz-wave parametric oscillators (TPOs) have advantages of room temperature operation, wide tunable range, narrow line-width, good coherence. They have also disadvantage of small pulse energy. In this paper, several factors preventing TPOs from generating high-energy THz pulses and the corresponding solutions are analyzed. A scheme to generate high-energy THz pulses by using the combination of a TPO and a Stokes-pulse-injected terahertz-wave parametric generator (spi-TPG) is proposed and demonstrated. A TPO is used as a source to generate a seed pulse for the surface-emitted spi-TPG. The time delay between the pump and Stokes pulses is adjusted to guarantee they have good temporal overlap. The pump pulses have a large pulse energy and a large beam size. The Stokes beam is enlarged to make its size be larger than the pump beam size to have a large effective interaction volume. The experimental results show that the generated THz pulse energy from the spi-TPG is 1.8 times as large as that obtained from the TPO for the same pumping pulse energy density of 0.90 J/cm(2) and the same pumping beam size of 3.0 mm. When the pumping beam sizes are 5.0 and 7.0 mm, the enhancement times are 3.7 and 7.5, respectively. The spi-TPG here is similar to a difference frequency generator; it can also be used as a Stokes pulse amplifier.
Parametric System Model for a Stirling Radioisotope Generator
NASA Technical Reports Server (NTRS)
Schmitz, Paul C.
2014-01-01
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 Stirling Radioisotope Generator (SRG). Using the General Purpose Heat Source (GPHS approximately 250 watt thermal) modules as the thermal building block around 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.
Parametric System Model for a Stirling Radioisotope Generator
NASA Technical Reports Server (NTRS)
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.
Toward an Empirically-based Parametric Explosion Spectral Model
NASA Astrophysics Data System (ADS)
Ford, S. R.; Walter, W. R.; Ruppert, S.; Matzel, E.; Hauk, T. F.; Gok, R.
2010-12-01
Small underground nuclear explosions need to be confidently detected, identified, and characterized in regions of the world where they have never occurred. We develop a parametric model of the nuclear explosion seismic source spectrum derived from regional phases (Pn, Pg, and Lg) that is compatible with earthquake-based geometrical spreading and attenuation. Earthquake spectra are fit with a generalized version of the Brune spectrum, which is a three-parameter model that describes the long-period level, corner-frequency, and spectral slope at high-frequencies. These parameters are then correlated with near-source geology and containment conditions. There is a correlation of high gas-porosity (low strength) with increased spectral slope. However, there are trade-offs between the slope and corner-frequency, which we try to independently constrain using Mueller-Murphy relations and coda-ratio techniques. The relationship between the parametric equation and the geologic and containment conditions will assist in our physical understanding of the nuclear explosion source, and aid in the prediction of observed local and regional distance seismic amplitudes for event identification and yield determination in regions with incomplete or no prior history of underground nuclear testing.
Using a Parametric Solid Modeler as an Instructional Tool
ERIC Educational Resources Information Center
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…
Using a Parametric Solid Modeler as an Instructional Tool
ERIC Educational Resources Information Center
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 Wave Transformation Models on Natural Beaches
NASA Astrophysics Data System (ADS)
Apotsos, A. A.; Raubenheimer, B.; Elgar, S.; Guza, R. T.
2006-12-01
Seven parametric models for wave height transformation across the surf zone [e.g., Thornton and Guza, 1983] are tested with observations collected between the shoreline and about 5-m water depth during 2 experiments on a barred beach near Duck, NC, and between the shoreline and about 3.5-m water depth during 2 experiments on unbarred beaches near La Jolla, CA. Offshore wave heights ranged from about 0.1 to 3.0 m. Beach profiles were surveyed approximately every other day. The models predict the observations well. Root-mean-square errors between observed and simulated wave heights are small in water depths h > 2 m (average rms errors < 10%), and increase with decreasing depth for h < 2 m (average rms errors > 20%). The lowest rms errors (i.e., the most accurate predictions) are achieved by tuning a free parameter, γ, in each model. To tune the models accurately to the data considered here, observations are required at 3 to 5 locations, and must span the surf zone. No tuned or untuned model provides the best predictions for all data records in any one experiment. The best fit γ's for each model-experiment pair are represented well with an empirical hyperbolic tangent curve based on the inverse Iribarren number. In 3 of the 4 data sets, estimating γ for each model using an average curve based on the predictions and observations from all 4 experiments typically improves model-data agreement relative to using a constant or previously determined empirical γ. The best fit γ's at the 4th experiment (conducted off La Jolla, CA) are roughly 20% smaller than the γ's for the other 3 experiments, and thus using the experiment-averaged curve increases prediction errors. Possible causes for the smaller γ's at the 4th experiment will be discussed. Funded by ONR and NSF.
Parametric Testing of Launch Vehicle FDDR Models
NASA Technical Reports Server (NTRS)
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.
Forecasting Marine Corps Enlisted Attrition Through Parametric Modeling
2009-03-01
OF PAGES 85 14. SUBJECT TERMS Forecasting, Attrition, Marine Corps NEAS losses, Gompertz Model, Survival Analysis 16. PRICE CODE 17. SECURITY...18 1. Parametric Proportional Hazards Models ......................................18 2. Gompertz Models...19 a. Gompertz Hazard Function....................................................19 b. Gompertz Cumulative
Mixing parametrizations for ocean climate modelling
NASA Astrophysics Data System (ADS)
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
Penalized Likelihood for General Semi-Parametric Regression Models.
1985-05-01
should be stressed that q, while it may be somewhat less than n, will still be ’large’, and parametric estimation of £ will not be appropriate...Partial spline models for the semi- parametric estimation of functions of several variables, in Statistical Analysis of Time Series, Tokyo: Institute of
A new parametrization and minimal model for glacier calving
NASA Astrophysics Data System (ADS)
Lüthi, Martin; Vieli, Andreas; Mercenier, Rémy
2017-04-01
The iceberg calving process influences the geometry of a tidewater glacier, and is in turn controlled by the terminus geometry through the stress field which controls damage and fracture of the ice. A simple parametrization of the stress field at the glacier terminus is obtained from the results of a Finite Element model with varying water depths. Using this stress field in an isotropic damage evolution equation yields calving rates in dependence of calving front thickness and water depth. These parametrized calving rates compare favorably with observations, and extend well established parametrizations. The proposed calving parametrization is easy to implement in numerical ice sheet models. Using these parametrized calving rates in a minimal calving model allows us to analyze the intricate feedbacks of the calving process, to reproduce observed tidewater glacier dynamics, and to analyze the stability of glacier termini.
Variable selection in semi-parametric models
Zhang, Hongmei; Maity, Arnab; Arshad, Hasan; Holloway, John; Karmaus, Wilfried
2014-01-01
We propose Bayesian variable selection methods in semi-parametric models in the framework of partially linear Gaussian and problit regressions. Reproducing kernels are utilized to evaluate possibly non-linear joint effect of a set of variables. Indicator variables are introduced into the reproducing kernels for the inclusion or exclusion of a variable. Different scenarios based on posterior probabilities of including a variable are proposed to select important variables. Simulations are used to demonstrate and evaluate the methods. It was found that the proposed methods can efficiently select the correct variables regardless of the feature of the effects, linear or non-linear in an unknown form. The proposed methods are applied to two real data sets to identify cytosine phosphate guanine methylation sites associated with maternal smoking and cytosine phosphate guanine sites associated with cotinine levels with creatinine levels adjusted. The selected methylation sites have the potential to advance our understanding of the underlying mechanism for the impact of smoking exposure on health outcomes, and consequently benefit medical research in disease intervention. PMID:23990355
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.
Global Nonlinear Parametric Modeling with Application to F-16 Aerodynamics
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
1998-01-01
A global nonlinear parametric modeling technique is described and demonstrated. The technique uses multivariate orthogonal modeling functions generated from the data to determine nonlinear model structure, then expands each retained modeling function into an ordinary multivariate polynomial. The final model form is a finite multivariate power series expansion for the dependent variable in terms of the independent variables. Partial derivatives of the identified models can be used to assemble globally valid linear parameter varying models. The technique is demonstrated by identifying global nonlinear parametric models for nondimensional aerodynamic force and moment coefficients from a subsonic wind tunnel database for the F-16 fighter aircraft. Results show less than 10% difference between wind tunnel aerodynamic data and the nonlinear parameterized model for a simulated doublet maneuver at moderate angle of attack. Analysis indicated that the global nonlinear parametric models adequately captured the multivariate nonlinear aerodynamic functional dependence.
Validation of a Parametric Approach for 3d Fortification Modelling: Application to Scale Models
NASA Astrophysics Data System (ADS)
Jacquot, K.; Chevrier, C.; Halin, G.
2013-02-01
Parametric modelling approach applied to cultural heritage virtual representation is a field of research explored for years since it can address many limitations of digitising tools. For example, essential historical sources for fortification virtual reconstructions like plans-reliefs have several shortcomings when they are scanned. To overcome those problems, knowledge based-modelling can be used: knowledge models based on the analysis of theoretical literature of a specific domain such as bastioned fortification treatises can be the cornerstone of the creation of a parametric library of fortification components. Implemented in Grasshopper, these components are manually adjusted on the data available (i.e. 3D surveys of plans-reliefs or scanned maps). Most of the fortification area is now modelled and the question of accuracy assessment is raised. A specific method is used to evaluate the accuracy of the parametric components. The results of the assessment process will allow us to validate the parametric approach. The automation of the adjustment process can finally be planned. The virtual model of fortification is part of a larger project aimed at valorising and diffusing a very unique cultural heritage item: the collection of plans-reliefs. As such, knowledge models are precious assets when automation and semantic enhancements will be considered.
THz-wave parametric source and its imaging applications
NASA Astrophysics Data System (ADS)
Kawase, Kodo
2004-08-01
Widely tunable coherent terahertz (THz) wave generation has been demonstrated based on the parametric oscillation using MgO doped LiNbO3 crystal pumped by a Q-switched Nd:YAG laser. This method exhibits multiple advantages like wide tunability, coherency and compactness of its system. We have developed a novel basic technology for terahertz (THz) imaging, which allows detection and identification of chemicals by introducing the component spatial pattern analysis. The spatial distributions of the chemicals were obtained from terahertz multispectral transillumination images, using absorption spectra previously measured with a widely tunable THz-wave parametric oscillator. Further we have applied this technique to the detection and identification of illicit drugs concealed in envelopes. The samples we used were methamphetamine and MDMA, two of the most widely consumed illegal drugs in Japan, and aspirin as a reference.
Broadband picosecond radiation source based on noncollinear optical parametric amplifier
Arakcheev, V G; Morozov, V B; Vereshchagin, A K; Vereshchagin, K A; Tunkin, V G; Yakovlev, D V
2014-04-28
Amplification of broadband radiation of modeless dye laser by a noncollinear optical parametric amplifier based on a KTP crystal has been implemented upon pumping by 63-ps second-harmonic pulses of a Nd : YAG laser. Pulses with a bandwidth of 21 nm, a duration of 26 ps and an energy of 1.2 mJ have been obtained at the centre wavelength of 685 nm. (nonlinear optical phenomena)
Semi-parametric estimation in failure time mixture models.
Taylor, J M
1995-09-01
A mixture model is an attractive approach for analyzing failure time data in which there are thought to be two groups of subjects, those who could eventually develop the endpoint and those who could not develop the endpoint. The proposed model is a semi-parametric generalization of the mixture model of Farewell (1982). A logistic regression model is proposed for the incidence part of the model, and a Kaplan-Meier type approach is used to estimate the latency part of the model. The estimator arises naturally out of the EM algorithm approach for fitting failure time mixture models as described by Larson and Dinse (1985). The procedure is applied to some experimental data from radiation biology and is evaluated in a Monte Carlo simulation study. The simulation study suggests the semi-parametric procedure is almost as efficient as the correct fully parametric procedure for estimating the regression coefficient in the incidence, but less efficient for estimating the latency distribution.
Stochastic parametrizations and model uncertainty in the Lorenz '96 system.
Arnold, H M; Moroz, I M; Palmer, T N
2013-05-28
Simple chaotic systems are useful tools for testing methods for use in numerical weather simulations owing to their transparency and computational cheapness. The Lorenz system was used here; the full system was defined as 'truth', whereas a truncated version was used as a testbed for parametrization schemes. Several stochastic parametrization schemes were investigated, including additive and multiplicative noise. The forecasts were started from perfect initial conditions, eliminating initial condition uncertainty. The stochastically generated ensembles were compared with perturbed parameter ensembles and deterministic schemes. The stochastic parametrizations showed an improvement in weather and climate forecasting skill over deterministic parametrizations. Including a temporal autocorrelation resulted in a significant improvement over white noise, challenging the standard idea that a parametrization should only represent sub-gridscale variability. The skill of the ensemble at representing model uncertainty was tested; the stochastic ensembles gave better estimates of model uncertainty than the perturbed parameter ensembles. The forecasting skill of the parametrizations was found to be linked to their ability to reproduce the climatology of the full model. This is important in a seamless prediction system, allowing the reliability of short-term forecasts to provide a quantitative constraint on the accuracy of climate predictions from the same system.
The parametrization of radio source coordinates in VLBI and its impact on the CRF
NASA Astrophysics Data System (ADS)
Karbon, Maria; Heinkelmann, Robert; Mora-Diaz, Julian; Xu, Minghui; Nilsson, Tobias; Schuh, Harald
2016-04-01
dramatically in time. Hence, each source would have to be modeled individually. Considering this, the shear amount of sources, in our study more than 600 are included, sets practical limitations. We decided to use the multivariate adaptive regression splines (MARS) procedure to parametrize the source coordinates, as they allow a great deal of automation as it combines recursive partitioning and spline fitting in an optimal way. The algorithm finds the ideal knot positions for the splines and thus the best number of polynomial pieces to fit the data. We investigate linear and cubic splines determined by MARS to "human" determined linear splines and their impact on the CRF. Within this work we try to answer the following questions: How can we find optimal criteria for the definition of the defining and unstable sources? What are the best polynomials for the individual categories? How much can we improve the CRF by extending the parametrization of the sources?
Parametric Modeling of Transverse Phase Space of an RF Photoinjector
Hartman, E.; Sayyar-Rodsari, B.; Schweiger, C.A.; Lee, M.J.; Lui, P.; Paterson, Ewan; Schmerge, J.F.; /SLAC
2008-01-24
High brightness electron beam sources such as rf photo-injectors as proposed for SASE FELs must consistently produce the desired beam quality. We report the results of a study in which a combined neural network (NN) and first-principles (FP) model is used to model the transverse phase space of the beam as a function of quadrupole strength, while beam charge, solenoid field, accelerator gradient, and linac voltage and phase are kept constant. The parametric transport matrix between the exit of the linac section and the spectrometer screen constitutes the FP component of the combined model. The NN block provides the parameters of the transport matrix as functions of quad current. Using real data from SLAC Gun Test Facility, we will highlight the significance of the constrained training of the NN block and show that the phase space of the beam is accurately modeled by the combined NN and FP model, while variations of beam matrix parameters with the quad current are correctly captured. We plan to extend the combined model in the future to capture the effects of variations in beam charge, solenoid field, and accelerator voltage and phase.
Incident Duration Modeling Using Flexible Parametric Hazard-Based Models
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. PMID:25530753
NASA Astrophysics Data System (ADS)
Blom, Philip S.; Marcillo, Omar E.
2017-03-01
A method is developed to apply acoustic tomography methods to a localized network of infrasound arrays with intention of monitoring the atmosphere state in the region around the network using non-local sources without requiring knowledge of the precise source location or non-local atmosphere state. Closely spaced arrays provide a means to estimate phase velocities of signals that can provide limiting bounds on certain characteristics of the atmosphere. Larger spacing between such clusters provide a means to estimate celerity from propagation times along multiple unique stratospherically or thermospherically ducted propagation paths and compute more precise estimates of the atmosphere state. In order to avoid the commonly encountered complex, multimodal distributions for parametric atmosphere descriptions and to maximize the computational efficiency of the method, an optimal parametrization framework is constructed. This framework identifies the ideal combination of parameters for tomography studies in specific regions of the atmosphere and statistical model selection analysis shows that high quality corrections to the middle atmosphere winds can be obtained using as few as three parameters. Comparison of the resulting estimates for synthetic data sets shows qualitative agreement between the middle atmosphere winds and those estimated from infrasonic traveltime observations.
NASA Astrophysics Data System (ADS)
Metzger, W. J.; Novák, T.; Csörgő, T.; Kittel, W.
A short review of Bose-Einstein correlations in hadronic e+e- annihilation is presented. Bose-Einstein correlations of pairs of identical charged pions in hadronic Z-boson decays are analyzed in terms of various parametrizations. A good description is achieved using a Lévy stable distribution in conjunction with a hadronization model having highly correlated configuration and momentum space, the τ-model. Using these results, the source function is reconstructed.
Housing price prediction: parametric versus semi-parametric spatial hedonic models
NASA Astrophysics Data System (ADS)
Montero, José-María; Mínguez, Román; Fernández-Avilés, Gema
2017-08-01
House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.
Parametric Model for Astrophysical Proton-Proton Interactions and Applications
Karlsson, Niklas
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
Modeling shell morphology of an epitoniid species with parametric equations
NASA Astrophysics Data System (ADS)
Bernido, Christopher C.; Carpio-Bernido, M. Victoria; Sadudaquil, Jerome A.; Salas, Rochelle I.; Mangyao, Justin Ericson A.; Halasan, Lorenzo C.; Baja, Paz Kenneth S.; Jumawan, Ethel Jade V.
2017-08-01
An epitoniid specimen under the genus Cycloscala is mathematically modeled using parametric equations which allow comparison of growth functions and parameter values with other specimens of the same genus. This mathematical modeling approach may supplement the currently used genetic and microscopy methods in the taxonomic classification of species.
Kang, Jiqiang; Wei, Xiaoming; Li, Bowen; Wang, Xie; Yu, Luoqin; Tan, Sisi; Jinata, Chandra; Wong, Kenneth K. Y.
2016-01-01
We proposed a sensitivity enhancement method of the interference-based signal detection approach and applied it on a swept-source optical coherence tomography (SS-OCT) system through all-fiber optical parametric amplifier (FOPA) and parametric balanced detector (BD). The parametric BD was realized by combining the signal and phase conjugated idler band that was newly-generated through FOPA, and specifically by superimposing these two bands at a photodetector. The sensitivity enhancement by FOPA and parametric BD in SS-OCT were demonstrated experimentally. The results show that SS-OCT with FOPA and SS-OCT with parametric BD can provide more than 9 dB and 12 dB sensitivity improvement, respectively, when compared with the conventional SS-OCT in a spectral bandwidth spanning over 76 nm. To further verify and elaborate their sensitivity enhancement, a bio-sample imaging experiment was conducted on loach eyes by conventional SS-OCT setup, SS-OCT with FOPA and parametric BD at different illumination power levels. All these results proved that using FOPA and parametric BD could improve the sensitivity significantly in SS-OCT systems. PMID:27446655
Kang, Jiqiang; Wei, Xiaoming; Li, Bowen; Wang, Xie; Yu, Luoqin; Tan, Sisi; Jinata, Chandra; Wong, Kenneth K Y
2016-04-01
We proposed a sensitivity enhancement method of the interference-based signal detection approach and applied it on a swept-source optical coherence tomography (SS-OCT) system through all-fiber optical parametric amplifier (FOPA) and parametric balanced detector (BD). The parametric BD was realized by combining the signal and phase conjugated idler band that was newly-generated through FOPA, and specifically by superimposing these two bands at a photodetector. The sensitivity enhancement by FOPA and parametric BD in SS-OCT were demonstrated experimentally. The results show that SS-OCT with FOPA and SS-OCT with parametric BD can provide more than 9 dB and 12 dB sensitivity improvement, respectively, when compared with the conventional SS-OCT in a spectral bandwidth spanning over 76 nm. To further verify and elaborate their sensitivity enhancement, a bio-sample imaging experiment was conducted on loach eyes by conventional SS-OCT setup, SS-OCT with FOPA and parametric BD at different illumination power levels. All these results proved that using FOPA and parametric BD could improve the sensitivity significantly in SS-OCT systems.
ANSYS PARAMETRIC MODEL FOR TANK DST-AY
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This report documents the parametric ANSYS models developed for dome load analyses of double-shell tanks. The default model parameters are specific to the AY tanks but can be easily modified for tank-specific analysis of AN, AW, AP, AZ or SY tanks. Both axisymmetric slice and full 360 degree models are provided. The purpose of this calculation is to develop a parametric finite element analysis model of the Hanford Site underground waste storage tanks. This is not an analysis. Instead, the present calculation develops a parametric model of the double shell tank DST-AY, which is based on Buyer-supplied as-built drawings and information for the analyses of record (AOR) for Double-Shell Tanks (DSTs), encompassing the existing tank load conditions. The computer model has various parameters that can be either changed directly or easily added by a knowledgeable ANSYS user. These parameters are modified to consider field conditions, such as in-situ wall thickness of primary steel tank, dead and live loads, moving loads, berm loads, soil overburden depth plus surrounding soil, internal waste level and waste specific gravity, internal vapor pressure, and thermal loads within the tank. This document contains sample calculations that demonstrate how various aspects of the parametric model function. These sample calculations in this document are not to be used for assessing the structural integrity of the DST-AY tanks at the Hanford Site.
NASA Astrophysics Data System (ADS)
Wei, Jie; Zhang, Chun-Hui; Wang, Qin
2017-02-01
We present a new scheme on implementing the passive quantum key distribution with thermal distributed parametric down-conversion source. In this scheme, only one-intensity decoy state is employed, but we can achieve very precise estimation on the single-photon-pulse contribution by utilizing those built-in decoy states. Moreover, we compare the new scheme with other practical methods, i.e., the standard three-intensity decoy-state BB84 protocol using either weak coherent states or parametric down-conversion source. Through numerical simulations, we demonstrate that our new scheme can drastically improve both the secure transmission distance and the key generation rate.
Bayesian non-parametrics and the probabilistic approach to modelling
Ghahramani, Zoubin
2013-01-01
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman’s coalescent, Dirichlet diffusion trees and Wishart processes. PMID:23277609
Update on Parametric Cost Models for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl. H. Philip; Henrichs, Todd; Luedtke, Alexander; West, Miranda
2011-01-01
Since the June 2010 Astronomy Conference, an independent review of our cost data base discovered some inaccuracies and inconsistencies which can modify our previously reported results. This paper will review changes to the data base, our confidence in those changes and their effect on various parametric cost models
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
NASA Astrophysics Data System (ADS)
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.
Source finding, parametrization, and classification for the extragalactic Effelsberg-Bonn H i Survey
NASA Astrophysics Data System (ADS)
Flöer, L.; Winkel, B.; Kerp, J.
2014-09-01
Context. Source extraction for large-scale H i surveys currently involves large amounts of manual labor. For data volumes expected from future H i surveys with upcoming facilities, this approach is not feasible any longer. Aims: We describe the implementation of a fully automated source finding, parametrization, and classification pipeline for the Effelsberg-Bonn H i Survey (EBHIS). With future radio astronomical facilities in mind, we want to explore the feasibility of a completely automated approach to source extraction for large-scale H i surveys. Methods: Source finding is implemented using wavelet denoising methods, which previous studies show to be a powerful tool, especially in the presence of data defects. For parametrization, we automate baseline fitting, mask optimization, and other tasks based on well-established algorithms, currently used interactively. For the classification of candidates, we implement an artificial neural network, which is trained on a candidate set comprised of false positives from real data and simulated sources. Using simulated data, we perform a thorough analysis of the algorithms implemented. Results: We compare the results from our simulations to the parametrization accuracy of the H i Parkes All-Sky Survey (HIPASS) survey. Even though HIPASS is more sensitive than EBHIS in its current state, the parametrization accuracy and classification reliability match or surpass the manual approach used for HIPASS data.
Automated parametrical antenna modelling for ambient assisted living applications
NASA Astrophysics Data System (ADS)
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.
Extended Range Hydrological Predictions: Uncertainty Associated with Model Parametrization
NASA Astrophysics Data System (ADS)
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.
Bayesian non parametric modelling of Higgs pair production
NASA Astrophysics Data System (ADS)
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.
Scene Parsing With Integration of Parametric and Non-Parametric Models
NASA Astrophysics Data System (ADS)
Shuai, Bing; Zuo, Zhen; Wang, Gang; Wang, Bing
2016-05-01
We adopt Convolutional Neural Networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.
Scene Parsing With Integration of Parametric and Non-Parametric Models.
Shuai, Bing; Zuo, Zhen; Wang, Gang; Wang, Bing
2016-05-01
We adopt convolutional neural networks (CNNs) to be our parametric model to learn discriminative features and classifiers for local patch classification. Based on the occurrence frequency distribution of classes, an ensemble of CNNs (CNN-Ensemble) are learned, in which each CNN component focuses on learning different and complementary visual patterns. The local beliefs of pixels are output by CNN-Ensemble. Considering that visually similar pixels are indistinguishable under local context, we leverage the global scene semantics to alleviate the local ambiguity. The global scene constraint is mathematically achieved by adding a global energy term to the labeling energy function, and it is practically estimated in a non-parametric framework. A large margin-based CNN metric learning method is also proposed for better global belief estimation. In the end, the integration of local and global beliefs gives rise to the class likelihood of pixels, based on which maximum marginal inference is performed to generate the label prediction maps. Even without any post-processing, we achieve the state-of-the-art results on the challenging SiftFlow and Barcelona benchmarks.
Parametric Modeling and Fault Tolerant Control
NASA Technical Reports Server (NTRS)
Wu, N. Eva; Ju, Jianhong
2000-01-01
Fault tolerant control is considered for a nonlinear aircraft model expressed as a linear parameter-varying system. By proper parameterization of foreseeable faults, the linear parameter-varying system can include fault effects as additional varying parameters. A recently developed technique in fault effect parameter estimation allows us to assume that estimates of the fault effect parameters are available on-line. Reconfigurability is calculated for this model with respect to the loss of control effectiveness to assess the potentiality of the model to tolerate such losses prior to control design. The control design is carried out by applying a polytopic method to the aircraft model. An error bound on fault effect parameter estimation is provided, within which the Lyapunov stability of the closed-loop system is robust. Our simulation results show that as long as the fault parameter estimates are sufficiently accurate, the polytopic controller can provide satisfactory fault-tolerance.
Non-Parametric Model Drift Detection
2016-07-01
Drift Detection for the Machine Translation Task .................................................................. 15 CONCLUSIONS...framework on two tasks in NLP domain, topic modeling, and machine translation. Our main findings are summarized as follows: • We can measure important...Introduction Most machine learning methods operate under the assumption that the training and the test data are sampled from the same distribution
Cosmic star formation probed via parametric stack-fitting of known sources to radio imaging
NASA Astrophysics Data System (ADS)
Roseboom, I. G.; Best, P. N.
2014-04-01
The promise of multiwavelength astronomy has been tempered by the large disparity in sensitivity and resolution between different wavelength regimes. Here, we present a statistical approach which attempts to overcome this by fitting parametric models directly to image data. Specifically, we fit a model for the radio luminosity function (LF) of star-forming galaxies to pixel intensity distributions at 1.4 GHz coincident with near-IR selected sources in COSMOS. Taking a mass-limited sample in redshift bins across the range 0 < z < 4, we are able to fit the radio LF with ˜0.2 dex precision in the key parameters (e.g. Φ*,L*). Good agreement is seen between our results and those using standard methods at radio and other wavelengths. Integrating our LFs to get the star formation rate density, we find that galaxies with M* > 109.5 M⊙ contribute ≳50 per cent of cosmic star formation at 0 < z < 4. The scalability of our approach is empirically estimated, with the precision in LF parameter estimates found to scale with the number of sources in the stack, Ns, as ∝ √{N_s}. This type of approach will be invaluable in the multiwavelength analysis of upcoming surveys with the Square Kilometre Array pathfinder facilities: LOFAR, ASKAP and MeerKAT.
Parametric modeling of zoom lens barrels
NASA Astrophysics Data System (ADS)
Pierce, Charles W.
2001-12-01
Today's customer requires zoom lens designs that are compact, inexpensive, and at six-sigma quality levels. While incorporating these customer requirements, a design team must often work within compressed design cycles and minimal product development budgets. These customer and project constraints, coupled with the inherent complexity of a zoom lens module, force the design team to try new and innovative techniques to deliver their products. This paper presents the methods used to develop lens barrels for several zoom lens module projects at Eastman Kodak Company. The lens barrel, a critical interface between the mechanical and optical systems, presented a technical barrier from both an engineering analysis and manufacturing perspective. The method used to overcome these barriers consisted of identifying several key functional parameters, creating a parameter-driven 3-D solid model in a commercially available CAD system, and then using the model to make iterative, data-driven design decisions while leveraging the model to create engineering drawings and the necessary prototypes and production tooling. As a result, the designs were able to meet their size, cost, and design cycle time requirements while realizing a better than anticipated first pass yield and quality level.
Interfacing a quantum dot with a spontaneous parametric down-conversion source
NASA Astrophysics Data System (ADS)
Huber, Tobias; Prilmüller, Maximilian; Sehner, Michael; Solomon, Glenn S.; Predojević, Ana; Weihs, Gregor
2017-09-01
Quantum networks require interfacing stationary and flying qubits. These flying qubits are usually nonclassical states of light. Here we consider two of the leading source technologies for nonclassical light, spontaneous parametric down-conversion and single semiconductor quantum dots. Down-conversion delivers high-grade entangled photon pairs, whereas quantum dots excel at producing single photons. We report on an experiment that joins these two technologies and investigates the conditions under which optimal interference between these dissimilar light sources may be achieved.
Parametric design calculations using Green's function to determine unique source
NASA Astrophysics Data System (ADS)
Carter, L. L.; Lan, J. S.
1991-01-01
The energy dependence of the current incident on a macrocell can in principle be determined if the Green's function and the interior flux due to the total current are known. The Green's function is the flux within some portion of the macrocell due to a unit source in each energy group. It was demonstrated that the appropriate solution for the current is obtained for the higher energy groups in a fast reactor example. However, the lower energy groups are very sensitive to the downscattering and the solution even led to negative values for the incident partial current in some energy groups, which is not physical. In this particular example, the preliminary design study was insensitive to the incident current of the lower energy groups. For problems where the lower groups are important, it would seem prudent to abandon the exact solution and use a weighted least squares solution. Such a weighted least squares solution could assign importance for obtaining nearly the exact solution for as many energy groups as possible, while simultaneously making slight adjustments in the higher energy currents to obtain downscatter contributions that will approximately perserve the flux in the lower energy groups.
Single-variable parametric cost models for space telescopes
NASA Astrophysics Data System (ADS)
Stahl, H. Philip; Henrichs, Todd; Smart, Christian; Prince, Frank A.
2010-07-01
Parametric cost models are routinely used to plan missions, compare concepts, and justify technology investments. Unfortunately, there is no definitive space telescope cost model. For example, historical cost estimating relationships (CERs) based on primary mirror diameter vary by an order of magnitude. We present new single-variable cost models for space telescope optical telescope assembly (OTA). They are based on data collected from 30 different space telescope missions. Standard statistical methods are used to derive CERs for OTA cost versus aperture diameter and mass. The results are compared with previously published models
ERIC Educational Resources Information Center
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…
ERIC Educational Resources Information Center
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
NASA Astrophysics Data System (ADS)
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.
Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia
2004-01-01
A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.
Multivariable Parametric Cost Model for Ground Optical Telescope Assembly
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia
2005-01-01
A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.
On the influence of model parametrization in elastic full waveform tomography
NASA Astrophysics Data System (ADS)
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
NASA Astrophysics Data System (ADS)
Alexander, Preston; McDonld, Jackson; Harrington, Jason; Smith, R. Seth
2014-03-01
During the past year, a quantum optics laboratory was constructed and tested at Francis Marion University. A spontaneous parametric downconversion source was used to create pairs of correlated photons for use in single photon tests of quantum mechanics. Photons from a spontaneous parametric downconversion source were detected with single photon counting modules that were purchased through the Advanced Laboratory Physics Association (ALPHA). The effect of pump polarization on the output intensity was studied. Coincidences between pairs of correlated photons were counted and plotted as a function of the angle between the single photon detectors, in order to perform a test of Conservation of Momentum. The laboratory will be used to perform single photon tests of quantum mechanics, including the Grangier experiment, single photon interference, quantum state measurement, and tests of local realism.
Dynamics of the Kitaev chain model under parametric pumping
NASA Astrophysics Data System (ADS)
Zvyagin, A. A.
2014-07-01
Dynamics of the Kitaev chain model under the effect of parametric pumping is studied. Two contributions to dynamical characteristics are considered: from the extended eigenstates and from the edge bound state (zero Majorana modes). It is shown that in the dynamical regime the frequencies of Rabi oscillations for zero Majorana modes are much larger than those related to gapped extended states. In the steady-state regime, the Rabi oscillations are blurred due to relaxation processes, and only oscillations of the characteristics of the model with the pumping frequency exist, producing absorption of the pumping power by extended states. Experimental realizations of the considered effect are discussed.
Parametric Structural Model for a Mars Entry Concept
NASA Technical Reports Server (NTRS)
Lane, Brittney M.; Ahmed, Samee W.
2017-01-01
This paper outlines the process of developing a parametric model for a vehicle that can withstand Earth launch and Mars entry conditions. This model allows the user to change a variety of parameters ranging from dimensions and meshing to materials and atmospheric entry angles to perform finite element analysis on the model for the specified load cases. While this work focuses on an aeroshell for Earth launch aboard the Space Launch System (SLS) and Mars entry, the model can be applied to different vehicles and destinations. This specific project derived from the need to deliver large payloads to Mars efficiently, safely, and cheaply. Doing so requires minimizing the structural mass of the body as much as possible. The code developed for this project allows for dozens of cases to be run with the single click of a button. The end result of the parametric model gives the user a sense of how the body reacts under different loading cases so that it can be optimized for its purpose. The data are reported in this paper and can provide engineers with a good understanding of the model and valuable information for improving the design of the vehicle. In addition, conclusions show that the frequency analysis drives the design and suggestions are made to reduce the significance of normal modes in the design.
Parametric uncertainty modeling for application to robust control
NASA Technical Reports Server (NTRS)
Belcastro, Christine M.; Chang, B.-C.; Fischl, Robert
1993-01-01
Viewgraphs and a paper on parametric uncertainty modeling for application to robust control are included. Advanced robust control system analysis and design is based on the availability of an uncertainty description which separates the uncertain system elements from the nominal system. Although this modeling structure is relatively straightforward to obtain for multiple unstructured uncertainties modeled throughout the system, it is difficult to formulate for many problems involving real parameter variations. Furthermore, it is difficult to ensure that the uncertainty model is formulated such that the dimension of the resulting model is minimal. A procedure for obtaining an uncertainty model for real uncertain parameter problems in which the uncertain parameters can be represented in a multilinear form is presented. Furthermore, the procedure is formulated such that the resulting uncertainty model is minimal (or near minimal) relative to a given state space realization of the system. The approach is demonstrated for a multivariable third-order example problem having four uncertain parameters.
Model reduction for parametric instability analysis in shells conveying fluid
NASA Astrophysics Data System (ADS)
Kochupillai, Jayaraj; Ganesan, N.; Padmanabhan, Chandramouli
2003-05-01
Flexible pipes conveying fluid are often subjected to parametric excitation due to time-periodic flow fluctuations. Such systems are known to exhibit complex instability phenomena such as divergence and coupled-mode flutter. Investigators have typically used weighted residual techniques, to reduce the continuous system model into a discrete model, based on approximation functions with global support, for carrying out stability analysis. While this approach is useful for straight pipes, modelling based on FEM is needed for the study of complicated piping systems, where the approximation functions used are local in support. However, the size of the problem is now significantly larger and for computationally efficient stability analysis, model reduction is necessary. In this paper, model reduction techniques are developed for the analysis of parametric instability in flexible pipes conveying fluids under a mean pressure. It is shown that only those linear transformations which leave the original eigenvalues of the linear time invariant system unchanged are admissible. The numerical technique developed by Friedmann and Hammond (Int. J. Numer. Methods Eng. Efficient 11 (1997) 1117) is used for the stability analysis. One of the key research issues is to establish criteria for deciding the basis vectors essential for an accurate stability analysis. This paper examines this issue in detail and proposes new guidelines for their selection.
Two-parametric model of electron beam in computational dosimetry for radiation processing
NASA Astrophysics Data System (ADS)
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.
A simple pond parametrization for malaria transmission models
NASA Astrophysics Data System (ADS)
Tompkins, A. M.; Asare, E.; Amekudzi, L. K.
2012-04-01
In order to model malaria effectively using a dynamical modelling approach, a realistic representation of the surface hydrology is required. Achieving this goal is hindered by the fact that key vector breeding sites are small in spatial scale, ranging from small permanent ponds to temporary puddles. This small spatial scale confounds modelling efforts as the topography on such small scales is unknown, and also renders detection by remote sensing techniques difficult implying a requirement of in-situ measurements. Results from ongoing measurements of breeding sites in Kumasi (Ghana) are shown, along with attempts to reproduce these using a simple pond 'parametrization'. The significant impact of the pond model implementation and settings on malaria simulations using the new VECTRI dynamical disease model is demonstrated.
Single-frequency mid-infrared optical parametric oscillator source for coherent laser radar.
Hanson, F; Poirier, P; Arbore, M A
2001-11-15
We report on the design and characterization of a highly coherent mid-IR source at 3.57mum based on a single-frequency optical parametric oscillator. Detailed frequency and amplitude noise spectra have been measured. The rms intensity noise from 1.2 to 1000 Hz was 0.03%, and a rms frequency drift of 8 kHz in 1 ms was observed. We have also demonstrated the utility of this source for coherent laser radar applications by measuring micro-Doppler spectra from vibrating targets.
Humphrey, Victor F; Robinson, Stephen P; Smith, John D; Martin, Michael J; Beamiss, Graham A; Hayman, Gary; Carroll, Nicholas L
2008-08-01
A technique for evaluating the underwater acoustic performance of panels under simulated ocean conditions in a laboratory test facility is described. The method uses a parametric array as a source of sound within a test vessel capable of simulating ocean depths down to 700 m and water temperatures from 2 to 35 degrees C. The reflection loss and transmission loss of the test panel may be determined at frequencies from a few kilohertz to 50 kHz. The use of the parametric array enables wideband measurements to be undertaken with short-duration pulses and reduces the effects of diffraction from the panel edges. An acoustic filter is used to truncate the array in order to provide a source-free measurement region and to simplify the measurement process. The difficulties of establishing a parametric array in the confined space of the vessel are outlined, and the experimental procedures adopted are described. The techniques were validated by undertaking measurements on two test objects that have predictable behavior. The potential of the technique is also illustrated with experimental results for test panels for hydrostatic pressures up to 2.8 MPa. An extensive discussion of the measurement limitations is included.
Fine, D R; Lurie, R E; Candy, G P
1994-11-01
Renal function is often characterized by the activity/time curves obtained by imaging the aorta and kidney. Non-parametric deconvolution of the activity/time curves is clinically useful as a diagnostic tool in determining renal transit times. Typically non-parametric deconvolution is performed using a technique that does not require a priori information, e.g. matrix-based and Fourier-transform methods. Using data filtering and conservation of mass constraints, non-parametric deconvolution continues to exhibit noise in the deconvolved curves. This noise hampers the identification of renal transit times. Given the shortcomings of non-parametric deconvolution, a parametric model of the renal response has been developed. Our model is shown to be anatomically and physiologically plausible. In this paper, the parametric model structure is used, in conjunction with experimental data, to estimate renal physiological parameters. These parameters include the filtration fraction, renal blood transit time and urine transit times. The model parameters are then related to the minimum transit time (MinTT), mean transit time (MTT), glomerular filtration rate (GFR) and parenchymal transit time index (PTTI). As deconvolution techniques often produce negative artifacts, Fine et al developed a technique to determine an aorta background to minimize this effect. In this paper this work is extended to determine a reasonable renal background from aorta activity/time curves. Non-parametric deconvolution is used to provide initial estimates of model parameters. The model is then fitted to twelve healthy background-corrected kidneys by an iterative parameter-estimation technique. The normal values correspond to those reported in the literature. These normal values are then used to identify renal arterial stenosis in two renal hypertensive patients. The results suggest that parametric identification, based on a renal-retention-function model, may provide additional anatomical and
Automated, Parametric Geometry Modeling and Grid Generation for Turbomachinery Applications
NASA Technical Reports Server (NTRS)
Harrand, Vincent J.; Uchitel, Vadim G.; Whitmire, John B.
2000-01-01
The objective of this Phase I project is to develop a highly automated software system for rapid geometry modeling and grid generation for turbomachinery applications. The proposed system features a graphical user interface for interactive control, a direct interface to commercial CAD/PDM systems, support for IGES geometry output, and a scripting capability for obtaining a high level of automation and end-user customization of the tool. The developed system is fully parametric and highly automated, and, therefore, significantly reduces the turnaround time for 3D geometry modeling, grid generation and model setup. This facilitates design environments in which a large number of cases need to be generated, such as for parametric analysis and design optimization of turbomachinery equipment. In Phase I we have successfully demonstrated the feasibility of the approach. The system has been tested on a wide variety of turbomachinery geometries, including several impellers and a multi stage rotor-stator combination. In Phase II, we plan to integrate the developed system with turbomachinery design software and with commercial CAD/PDM software.
Model for coherence transfer in a backward optical parametric oscillator
NASA Astrophysics Data System (ADS)
Montes, Carlos; Aschieri, Pierre; Picozzi, Antonio
2011-09-01
The mirrorless backward optical parametric oscillator (BOPO), where the signal and idler waves are propagating in opposite directions, will establish a distributed feedback mechanism and thus optical parametric oscillation without the need to apply mirrors or external feedback to the cavity. It has been recently demonstrated experimentally by exploiting the periodic poling technique in second-order nonlinear crystals, that the sub-micrometer structured medium achieves an efficient quasi-phase-matching of the three wave interaction in the backward configuration. A remarkable property of such BOPO is the high degree of coherence of the backward wave component, whose spectrum may be several order of magnitudes narrower than that of the pump, due to the convectioninduced phase-locking mechanism. Experimentally and numerically proved the transfer of coherent phase modulations from the pump wave to the parametrically down-converted waves, we show here that this is also possible for a broad bandwidth spectrally incoherent pump. In order to accurately describe the nonlinear counter-propagation dynamics of the three dispersive waves, we have developed for the first time to our knowledge a new numerical scheme which combines the method of the trajectories usually employed to solve the three-wave interaction and the intraband group velocity dispersion effect is performed in the spectral domain with the help of the Fast Fourier Transform (FFT) technique. The model accurately conserves the number of photons and the Manley-Rowe invariants. This allowed us to predict various configurations of MOPOs in which, thanks to the convection-induced phase-locking mechanism, a highly coherent backward wave is spontaneously generated from a highly incoherent pump wave.
Uncertainties in volcanic plume modeling: A parametric study using FPLUME
NASA Astrophysics Data System (ADS)
Macedonio, G.; Costa, A.; Folch, A.
2016-10-01
We carry out a parametric study in order to identify and quantify the effects of uncertainties on pivotal parameters controlling the dynamics of volcanic plumes. The study builds upon numerical simulations 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 study (Costa et al., 2016). The parametric study quantifies the effect of typical uncertainties on total mass eruption rate, column height, mixture exit velocity, temperature and water content, and particle size. Moreover, a sensitivity study 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 that control plume height are the mass eruption rate and the air entrainment coefficient, especially for weak plumes.
Parametric Thermal Soak Model for Earth Entry Vehicles
NASA Technical Reports Server (NTRS)
Agrawal, Parul; Samareh, Jamshid; Doan, Quy D.
2013-01-01
The analysis and design of an Earth Entry Vehicle (EEV) is multidisciplinary in nature, requiring the application many disciplines. An integrated tool called Multi Mission System Analysis for Planetary Entry Descent and Landing or M-SAPE is being developed as part of Entry Vehicle Technology project under In-Space Technology program. Integration of a multidisciplinary problem is a challenging task. Automation of the execution process and data transfer among disciplines can be accomplished to provide significant benefits. Thermal soak analysis and temperature predictions of various interior components of entry vehicle, including the impact foam and payload container are part of the solution that M-SAPE will offer to spacecraft designers. The present paper focuses on the thermal soak analysis of an entry vehicle design based on the Mars Sample Return entry vehicle geometry and discusses a technical approach to develop parametric models for thermal soak analysis that will be integrated into M-SAPE. One of the main objectives is to be able to identify the important parameters and to develop correlation coefficients so that, for a given trajectory, can estimate the peak payload temperature based on relevant trajectory parameters and vehicle geometry. The models are being developed for two primary thermal protection (TPS) materials: 1) carbon phenolic that was used for Galileo and Pioneer Venus probes and, 2) Phenolic Impregnated Carbon Ablator (PICA), TPS material for Mars Science Lab mission. Several representative trajectories were selected from a very large trade space to include in the thermal analysis in order to develop an effective parametric thermal soak model. The selected trajectories covered a wide range of heatload and heatflux combinations. Non-linear, fully transient, thermal finite element simulations were performed for the selected trajectories to generate the temperature histories at the interior of the vehicle. Figure 1 shows the finite element model
Two-parametric fractional statistics models for anyons
NASA Astrophysics Data System (ADS)
Rovenchak, Andrij
2014-08-01
In the paper, two-parametric models of fractional statistics are proposed in order to determine the functional form of the distribution function of free anyons. From the expressions of the second and third virial coefficients, an approximate correspondence is shown to hold for three models, namely, the nonadditive Polychronakos statistics and both the incomplete and the nonadditive modifications of the Haldane-Wu statistics. The difference occurs only in the fourth virial coefficient leading to a small correction in the equation of state. For the two generalizations of the Haldane-Wu statistics, the solutions for the statistics parameters g, q exist in the whole domain of the anyonic parameter α ∈ [0; 1], unlike the nonadditive Polychronakos statistics. It is suggested that the search for the expression of the anyonic distribution function should be made within some modifications of the Haldane-Wu statistics.
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.
Multi-copy entanglement purification with practical spontaneous parametric down conversion sources
NASA Astrophysics Data System (ADS)
Zhang, Shuai-Shuai; Shu, Qi; Zhou, Lan; Sheng, Yu-Bo
2017-06-01
Entanglement purification is to distill the high quality entanglement from the low quality entanglement with local operations and classical communications. It is one of the key technologies in long-distance quantum communication. We discuss an entanglement purification protocol (EPP) with spontaneous parametric down conversion (SPDC) sources, in contrast to previous EPP with multi-copy mixed states, which requires ideal entanglement sources. We show that the SPDC source is not an obstacle for purification, but can benefit the fidelity of the purified mixed state. This EPP works for linear optics and is feasible in current experiment technology. Project supported by the National Natural Science Foundation of China (Grant Nos. 11474168 and 61401222), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20151502), the Qing Lan Project in Jiangsu Province, China, and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China.
Parametric overdispersed frailty models for current status data.
Abrams, Steven; Aerts, Marc; Molenberghs, Geert; Hens, Niel
2017-03-27
Frailty models have a prominent place in survival analysis to model univariate and multivariate time-to-event data, often complicated by the presence of different types of censoring. In recent years, frailty modeling gained popularity in infectious disease epidemiology to quantify unobserved heterogeneity using Type I interval-censored serological data or current status data. In a multivariate setting, frailty models prove useful to assess the association between infection times related to multiple distinct infections acquired by the same individual. In addition to dependence among individual infection times, overdispersion can arise when the observed variability in the data exceeds the one implied by the model. In this article, we discuss parametric overdispersed frailty models for time-to-event data under Type I interval-censoring, building upon the work by Molenberghs et al. () and Hens et al. (). The proposed methodology is illustrated using bivariate serological data on hepatitis A and B from Flanders, Belgium anno 1993-1994. Furthermore, the relationship between individual heterogeneity and overdispersion at a stratum-specific level is studied through simulations. Although it is important to account for overdispersion, one should be cautious when modeling both individual heterogeneity and overdispersion based on current status data as model selection is hampered by the loss of information due to censoring.
Parametric Thermal Models of the Transient Reactor Test Facility (TREAT)
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.
Stochastic parametrization of model errors using nested model in the context of data assimilation
NASA Astrophysics Data System (ADS)
Barth, Alexander; Vandenbulcke, Luc; Alvera Azcarate, Aida; Beckers, Jean-Marie
2017-04-01
A major difficulty in data assimilation is to adequately specify the model error covariances. For ensemble assimilation schemes all uncertain aspects of the model would need to be perturbed within their range of uncertainty. While progress have been made to address the error source external to an ocean model, such as atmospheric fields, bathymetry and boundary conditions, the intrinsic model error is rarely addressed. The objective of the study is to improve our knowledge on the intrinsic model error due to the finite resolution and to propose statistical parameterization usable in ensemble simulations. To study the impact of resolution on the model simulation, a two-way nested ROMS model is implemented. The modelling system is composed by a Ligurian Sea model at 1/60 degree nested in the CMEMS Mediterranean Model (one-way). A high-resolution NW Corsican model at 1/180 degree (about 530 m) is nested in the Ligurian Sea model (two-way). The fact that the model equations are solved twice provides an interesting opportunity to gain some insight about the model error due to resolution and to derive an empirical stochastic parametrization of subgrid-scale processes. During the two-way nesting feedback, the difference between the coarse model grid and the averaged fine model result is computed. The statistical properties of the feedback increment is studied and related to the parameters resolved on the coarse model grid. In favorable cases, a significant part of the variance of the feedback increment can be related to fronts also resolved in the coarse model grid which allows to derive empirical statistical parameterizations of the subgrid scale processes.
First-Order Parametric Model of Reflectance Spectra for Dyed Fabrics
2016-02-19
absorbing dyes, fabrics, and parametric reflectance models must first be examined. The theory underlying the parametric model, as well as the dyes...insights in this effort. References 1. A. Tarantola: Inverse Problem Theory and Methods for Model Parameter Estimation, SIAM, Philadelphia, PA
Parametric study of the Incompletely Stirred Reactor modeling
Mobini, K.; Bilger, R.W.
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)
Testing wave-function-collapse models using parametric heating of a trapped nanosphere
NASA Astrophysics Data System (ADS)
Goldwater, Daniel; Paternostro, Mauro; Barker, P. F.
2016-07-01
We propose a mechanism for testing the theory of collapse models such as continuous spontaneous localization (CSL) by examining the parametric heating rate of a trapped nanosphere. The random localizations of the center of mass for a given particle predicted by the CSL model can be understood as a stochastic force embodying a source of heating for the nanosphere. We show that by utilizing a Paul trap to levitate the particle and optical cooling, it is possible to reduce environmental decoherence to such a level that CSL dominates the dynamics and contributes the main source of heating. We show that this approach allows measurements to be made on the time scale of seconds and that the free parameter λcsl which characterizes the model ought to be testable to values as low as 10-12 Hz.
Assessment of parametric uncertainty for groundwater reactive transport modeling
NASA Astrophysics Data System (ADS)
Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun
2014-05-01
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
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
User's manual for heat-pump seasonal-performance model (SPM) with selected parametric examples
Not Available
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)
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.
A Bayesian Non-Parametric Potts Model with Application to Pre-Surgical FMRI Data
Johnson, Timothy D.; Liu, Zhuqing; Bartsch, Andreas J.; Nichols, Thomas E.
2013-01-01
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 manuscript we present a non-parametric Potts model and apply it to an FMRI 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 belong 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. PMID:22627277
Validating Timed Models of Deployment Components with Parametric Concurrency
NASA Astrophysics Data System (ADS)
Broch Johnsen, Einar; Owe, Olaf; Schlatte, Rudolf; Tapia Tarifa, Silvia Lizeth
Many software systems today are designed without assuming a fixed underlying architecture, and may be adapted for sequential, multicore, or distributed deployment. Examples of such systems are found in, e.g., software product lines, service-oriented computing, information systems, embedded systems, operating systems, and telephony. Models of such systems need to capture and range over relevant deployment scenarios, so it is interesting to lift aspects of low-level deployment concerns to the abstraction level of the modeling language. This paper proposes an abstract model of deployment components for concurrent objects, extending the Creol modeling language. The deployment components are parametric in the amount of concurrency they provide; i.e., they vary in processing resources. We give a formal semantics of deployment components and characterize equivalence between deployment components which differ in concurrent resources in terms of test suites. Our semantics is executable on Maude, which allows simulations and test suites to be applied to a deployment component with different concurrent resources.
Numerical model of solar dynamic radiator for parametric analysis
NASA Technical Reports Server (NTRS)
Rhatigan, Jennifer L.
1989-01-01
Growth power requirements for Space Station Freedom will be met through addition of 25 kW solar dynamic (SD) power modules. The SD module rejects waste heat from the power conversion cycle to space through a pumped-loop, multi-panel, deployable radiator. The baseline radiator configuration was defined during the Space Station conceptual design phase and is a function of the state point and heat rejection requirements of the power conversion unit. Requirements determined by the overall station design such as mass, system redundancy, micrometeoroid and space debris impact survivability, launch packaging, costs, and thermal and structural interaction with other station components have also been design drivers for the radiator configuration. Extensive thermal and power cycle modeling capabilities have been developed which are powerful tools in Station design and analysis, but which prove cumbersome and costly for simple component preliminary design studies. In order to aid in refining the SD radiator to the mature design stage, a simple and flexible numerical model was developed. The model simulates heat transfer and fluid flow performance of the radiator and calculates area mass and impact survivability for many combinations of flow tube and panel configurations, fluid and material properties, and environmental and cycle variations. A brief description and discussion of the numerical model, it's capabilities and limitations, and results of the parametric studies performed is presented.
Multivariable parametric cost model for space and ground telescopes
NASA Astrophysics Data System (ADS)
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).
Sowa, Michael G; Smith, Michael S D; Kendall, Catherine; Bock, Erika R; Ko, Alex C-T; Choo-Smith, Lin-P'ing; Stone, Nicholas
2006-08-01
Identification and quantification of molecular species are central applications of molecular spectroscopy. In complex multicomponent systems like tissue samples, linear parametric models are often used to estimate the relative concentrations of the biochemical components of the sample. In situations where not all of the components of the sample are known or modeled, such parametric models can suffer from omitted variable bias and result in skewed estimates of component concentrations. We propose a semi-parametric approach that tries to avoid this omitted variable bias by effectively including unknown covariates as a non-parametric term in the regression equation. Constituent concentrations estimated with such partial linear models should outperform strict parametric linear models when the user has limited information on the composition of a multi-constituent system.
An Improved Parametrized Representation of the Secondary He Neutral Flow in its Source Region
NASA Astrophysics Data System (ADS)
Wood, Brian E.
2017-09-01
Analysis of data from the Interstellar Boundary EXplorer (IBEX) has revealed the presence of a flow of neutral helium through the inner solar system that most likely emanates from the outer heliosheath, where a distinct population of neutral He is produced by charge exchange processes. This secondary He flow has been modeled using codes designed to study interstellar flows through the heliosphere, but a laminar flow is not a good approximation for the outer heliosheath. I present a simple parametrization for a more appropriate divergent flow, and demonstrate how the secondary He particles might provide a means to remotely measure the divergence of the ISM flow around the heliopause.
Open Source Molecular Modeling
Pirhadi, Somayeh; Sunseri, Jocelyn; Koes, David Ryan
2016-01-01
The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. PMID:27631126
Je, Yub; Lee, Haksue; Park, Jongkyu; Moon, Wonkyu
2010-06-01
An ultrasonic radiator is developed to generate a difference frequency sound from two frequencies of ultrasound in air with a parametric array. A design method is proposed for an ultrasonic radiator capable of generating highly directive, high-amplitude ultrasonic sound beams at two different frequencies in air based on a modification of the stepped-plate ultrasonic radiator. The stepped-plate ultrasonic radiator was introduced by Gallego-Juarez et al. [Ultrasonics 16, 267-271 (1978)] in their previous study and can effectively generate highly directive, large-amplitude ultrasonic sounds in air, but only at a single frequency. Because parametric array sources must be able to generate sounds at more than one frequency, a design modification is crucial to the application of a stepped-plate ultrasonic radiator as a parametric array source in air. The aforementioned method was employed to design a parametric radiator for use in air. A prototype of this design was constructed and tested to determine whether it could successfully generate a difference frequency sound with a parametric array. The results confirmed that the proposed single small-area transducer was suitable as a parametric radiator in air.
Design and evaluation of a parametric model for cardiac sounds.
Ibarra-Hernández, Roilhi F; Alonso-Arévalo, Miguel A; Cruz-Gutiérrez, Alejandro; Licona-Chávez, Ana L; Villarreal-Reyes, Salvador
2017-08-09
Heart sound analysis plays an important role in the auscultative diagnosis process to detect the presence of cardiovascular diseases. In this paper we propose a novel parametric heart sound model that accurately represents normal and pathological cardiac audio signals, also known as phonocardiograms (PCG). The proposed model considers that the PCG signal is formed by the sum of two parts: one of them is deterministic and the other one is stochastic. The first part contains most of the acoustic energy. This part is modeled by the Matching Pursuit (MP) algorithm, which performs an analysis-synthesis procedure to represent the PCG signal as a linear combination of elementary waveforms. The second part, also called residual, is obtained after subtracting the deterministic signal from the original heart sound recording and can be accurately represented as an autoregressive process using the Linear Predictive Coding (LPC) technique. We evaluate the proposed heart sound model by performing subjective and objective tests using signals corresponding to different pathological cardiac sounds. The results of the objective evaluation show an average Percentage of Root-Mean-Square Difference of approximately 5% between the original heart sound and the reconstructed signal. For the subjective test we conducted a formal methodology for perceptual evaluation of audio quality with the assistance of medical experts. Statistical results of the subjective evaluation show that our model provides a highly accurate approximation of real heart sound signals. We are not aware of any previous heart sound model rigorously evaluated as our proposal. Copyright © 2017 Elsevier Ltd. All rights reserved.
Parametric plate-bridge dynamic filter model of violin radiativity.
Bissinger, George
2012-07-01
A hybrid, deterministic-statistical, parametric "dynamic filter" model of the violin's radiativity profile [characterized by an averaged-over-sphere, mean-square radiativity (R(ω)(2))] is developed based on the premise that acoustic radiation depends on (1) how strongly it vibrates [characterized by the averaged-over-corpus, mean-square mobility (Y(ω)(2))] and (2) how effectively these vibrations are turned into sound, characterized by the radiation efficiency, which is proportional to (R(ω)(2))/(Y(ω)(2)). Two plate mode frequencies were used to compute 1st corpus bending mode frequencies using empirical trend lines; these corpus bending modes in turn drive cavity volume flows to excite the two lowest cavity modes A0 and A1. All widely-separated, strongly-radiating corpus and cavity modes in the low frequency deterministic region are then parameterized in a dual-Helmholtz resonator model. Mid-high frequency statistical regions are parameterized with the aid of a distributed-excitation statistical mobility function (no bridge) to help extract bridge filter effects associated with (a) bridge rocking mode frequency changes and (b) bridge-corpus interactions from 14-violin-average, excited-via-bridge (Y(ω)(2)) and (R(ω)(2)). Deterministic-statistical regions are rejoined at ~630 Hz in a mobility-radiativity "trough" where all violin quality classes had a common radiativity. Simulations indicate that typical plate tuning has a significantly weaker effect on radiativity profile trends than bridge tuning.
NASA Astrophysics Data System (ADS)
Ferrero, Enrico; Mortarini, Luca; Purghè, Federico
2017-04-01
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.
A versatile design for resonant guided-wave parametric down-conversion sources for quantum repeaters
NASA Astrophysics Data System (ADS)
Brecht, Benjamin; Luo, Kai-Hong; Herrmann, Harald; Silberhorn, Christine
2016-05-01
Quantum repeaters—fundamental building blocks for long-distance quantum communication—are based on the interaction between photons and quantum memories. The photons must fulfil stringent requirements on central frequency, spectral bandwidth and purity in order for this interaction to be efficient. We present a design scheme for monolithically integrated resonant photon-pair sources based on parametric down-conversion in nonlinear waveguides, which facilitate the generation of such photons. We investigate the impact of different design parameters on the performance of our source. The generated photon spectral bandwidths can be varied between several tens of MHz up to around 1 GHz, facilitating an efficient coupling to different memories. The central frequency of the generated photons can be coarsely tuned by adjusting the pump frequency, poling period and sample temperature, and we identify stability requirements on the pump laser and sample temperature that can be readily fulfilled with off-the-shelf components. We find that our source is capable of generating high-purity photons over a wide range of photon bandwidths. Finally, the PDC emission can be frequency fine-tuned over several GHz by simultaneously adjusting the sample temperature and pump frequency. We conclude our study with demonstrating the adaptability of our source to different quantum memories.
Modeling neuron-glia interactions: from parametric model to neuromorphic hardware.
Ghaderi, Viviane S; Allam, Sushmita L; Ambert, N; Bouteiller, J-M C; Choma, J; Berger, T W
2011-01-01
Recent experimental evidence suggests that glial cells are more than just supporting cells to neurons - they play an active role in signal transmission in the brain. We herein propose to investigate the importance of these mechanisms and model neuron-glia interactions at synapses using three approaches: A parametric model that takes into account the underlying mechanisms of the physiological system, a non-parametric model that extracts its input-output properties, and an ultra-low power, fast processing, neuromorphic hardware model. We use the EONS (Elementary Objects of the Nervous System) platform, a highly elaborate synaptic modeling platform to investigate the influence of astrocytic glutamate transporters on postsynaptic responses in the detailed micro-environment of a tri-partite synapse. The simulation results obtained using EONS are then used to build a non-parametric model that captures the essential features of glutamate dynamics. The structure of the non-parametric model we use is specifically designed for efficient hardware implementation using ultra-low power subthreshold CMOS building blocks. The utilization of the approach described allows us to build large-scale models of neuron/glial interaction and consequently provide useful insights on glial modulation during normal and pathological neural function.
Parametric Dielectric Model of Comet Churyumov-Gerasimenko
NASA Astrophysics Data System (ADS)
Heggy, E.; Palmer, E. M.; Kofman, W. W.; Clifford, S. M.; Righter, K.; Herique, A.
2012-12-01
In 2014, the European Space Agency's Rosetta mission is scheduled to rendezvous with Comet 67P/Churyumov-Gerasimenko (Comet 67P). Rosetta's CONSERT experiment aims to explore the cometary nucleus' geophysical properties using radar tomography. The expected scientific return and inversion algorithms are mainly dependent on our understanding of the dielectric properties of the comet nucleus and how they vary with the spatial distribution of geophysical parameters. Using observations of comets 9P/Tempel 1 and 81P/Wild 2 in combination with dielectric laboratory measurements of temperature, porosity, and dust-to-ice mass ratio dependencies for cometary analog material, we have constructed two hypothetical three-dimensional parametric dielectric models of Comet 67P's nucleus to assess different dielectric scenarios of the inner structure. Our models suggest that dust-to-ice mass ratios and porosity variations generate the most significant measurable dielectric contrast inside the comet nucleus, making it possible to explore the structural and compositional hypotheses of cometary nuclei. Surface dielectric variations, resulting from temperature changes induced by solar illumination of the comet's faces, have also been modeled and suggest that the real part of the dielectric constant varies from 1.9 to 3.0, hence changing the surface radar reflectivity. For CONSERT, this variation could be significant at low incidence angles, when the signal propagates through a length of dust mantle comparable to the wavelength. The overall modeled dielectric permittivity spatial and temporal variations are therefore consistent with the expected deep penetration of CONSERT's transmitted wave through the nucleus. It is also clear that changes in the physical properties of the nucleus induce sufficient variation in the dielectric properties of cometary material to allow their inversion from radar tomography.
Robust parametric models of runoff characteristics at the mesoscale
NASA Astrophysics Data System (ADS)
Samaniego, Luis; Bárdossy, András
2005-03-01
Many hydrologic studies report that runoff characteristics such as means or extremes of a given basin may be modified due to climatic and/or land use/cover changes and that the magnitude of these changes largely depends on the geographic location and the scale at which the study is carried out. Identifying the main causes of variability at the mesoscale, however, is a challenging task because of the lack of data regarding the spatial distribution of relevant explanatory variables and, if they exist, because of their high uncertainty. This study proposes a general method to find a robust non-linear model by solving a constrained multiobjective optimization problem whose solution space is composed of all feasible combinations of given explanatory variables. As a result, a model that simultaneously fulfills several criteria such as parsimony, robustness, significance, and overall performance is expected. Furthermore, it does not require assumptions regarding the sampling distributions neither of the parameters nor of the estimators because their p-values are estimated by a non-parametric technique. Finally, there is no limitation with respect to the functional form adopted for a given model and its estimator because a generalized reduced gradient algorithm is used for the calibration of its parameters. The proposed method was tested in the upper catchment of the Neckar River (Germany) covering an area of approximately 4000 km 2. The objective of this study was to detect trends and responses of runoff characteristics in mesoscale catchments due to changes of climatic or land use/cover conditions. In this case, the explained variables are the specific total discharge in summer and winter whereas the explanatory variables comprise several physiographic, land cover and climatic characteristics evaluated for 46 subcatchments during the period 1961-1993. The results of the study indicate a significant gain in performance and robustness of the selected models compared to
A non-parametric model for the cosmic velocity field
NASA Astrophysics Data System (ADS)
Branchini, E.; Teodoro, L.; Frenk, C. S.; Schmoldt, I.; Efstathiou, G.; White, S. D. M.; Saunders, W.; Sutherland, W.; Rowan-Robinson, M.; Keeble, O.; Tadros, H.; Maddox, S.; Oliver, S.
1999-09-01
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. The two methods, which give very similar results, have been tested and calibrated on mock PSCz catalogues constructed from cosmological N-body simulations. The denser sampling provided by the PSCz survey compared with previous IRAS galaxy surveys allows an improved reconstruction of the density and velocity fields out to large distances. The most striking feature of the model velocity field is a coherent large-scale streaming motion along the baseline connecting Perseus-Pisces, the Local Supercluster, the Great Attractor and the Shapley Concentration. We find no evidence for back-infall on to the Great Attractor. Instead, material behind and around the Great Attractor is inferred to be streaming towards the Shapley Concentration, aided by the compressional push of two large nearby underdensities. The PSCz model velocities compare well with those predicted from the 1.2-Jy redshift survey of IRAS galaxies and, perhaps surprisingly, with those predicted from the distribution of Abell/ACO clusters, out to 140h^-1Mpc. Comparison of the real-space density fields (or, alternatively, the peculiar velocity fields) inferred from the PSCz and cluster catalogues gives a relative (linear) bias parameter between clusters and IRAS galaxies of b_c=4.4+/-0.6. Finally, we implement a likelihood analysis that uses all the available information on peculiar velocities in our local Universe to estimate beta_Omega 0 0.6 b_0.6 -0.15 +0.22 (1sigma), where b is the bias parameter for IRAS galaxies.
Hollow cathode modeling: II. Physical analysis and parametric study
NASA Astrophysics Data System (ADS)
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.
Coupled mode parametric resonance in a vibrating screen model
NASA Astrophysics Data System (ADS)
Slepyan, Leonid I.; Slepyan, Victor I.
2014-02-01
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 exchange between the longitudinal and transverse modes of free oscillations is demonstrated. An exact analytical solution is found for the forced oscillations, where the coupling plays the role of a stabilizer. In a more general case, the harmonic analysis is used with neglect of the higher harmonics. Explicit expressions for all parameters of the steady nonlinear oscillations are determined. The domains are found where the analytically obtained steady oscillation regimes are stable. Over the frequency ranges, where the steady oscillations exist, a perfect correspondence is found between the amplitudes obtained analytically and numerically. Illustrations based on the analytical and numerical simulations are presented.
Comparison of parametric methods for modeling corneal surfaces
NASA Astrophysics Data System (ADS)
Bouazizi, Hala; Brunette, Isabelle; Meunier, Jean
2017-02-01
Corneal topography is a medical imaging technique to get the 3D shape of the cornea as a set of 3D points of its anterior and posterior surfaces. From these data, topographic maps can be derived to assist the ophthalmologist in the diagnosis of disorders. In this paper, we compare three different mathematical parametric representations of the corneal surfaces leastsquares fitted to the data provided by corneal topography. The parameters obtained from these models reduce the dimensionality of the data from several thousand 3D points to only a few parameters and could eventually be useful for diagnosis, biometry, implant design etc. The first representation is based on Zernike polynomials that are commonly used in optics. A variant of these polynomials, named Bhatia-Wolf will also be investigated. These two sets of polynomials are defined over a circular domain which is convenient to model the elevation (height) of the corneal surface. The third representation uses Spherical Harmonics that are particularly well suited for nearly-spherical object modeling, which is the case for cornea. We compared the three methods using the following three criteria: the root-mean-square error (RMSE), the number of parameters and the visual accuracy of the reconstructed topographic maps. A large dataset of more than 2000 corneal topographies was used. Our results showed that Spherical Harmonics were superior with a RMSE mean lower than 2.5 microns with 36 coefficients (order 5) for normal corneas and lower than 5 microns for two diseases affecting the corneal shapes: keratoconus and Fuchs' dystrophy.
A high-repetition-rate PPLN mid-infrared optical parametric oscillator source
NASA Astrophysics Data System (ADS)
Mason, Paul D.; Wood, Nicholas J.
2004-12-01
High average power sources operating in the 3 to 5 μm mid-infrared waveband are of interest for a wide variety of applications. We present design and performance results for a high-power engineered breadboard mid-IR source based on near-infrared pumped periodically-poled lithium niobate (PPLN) optical parametric oscillator (OPO) technology. The source design utilises a pair of singly-resonant PPLN OPOs pumped by a commercial 40 Watt, Q-switched, diode-pumped Nd:YLF laser. The mid-IR outputs from each OPO are polarisation recombined into a single output beam. A twin OPO design was chosen to minimise the effect of optical absorption, reduce thermal loading within each PPLN crystal and provide additional flexibility by offering the option for dual-wavelength mid-IR operation. An average output power approaching 4 Watts has been obtained with a corresponding slope efficiency of 15%. The mid-infrared beam is 6 times diffraction limited. Laser operation is controlled by a remote PC link and power, spectral and temporal pulse diagnostics are included within the system.
Continuous-wave, singly resonant parametric oscillator-based mid-infrared optical vortex source.
Aadhi, A; Sharma, Varun; Singh, R P; Samanta, G K
2017-09-15
We report on a high-power, continuous-wave source of optical vortices tunable in the mid-infrared (mid-IR) wavelength range. Using the orbital angular momentum (OAM) conservation of the parametric processes and the threshold conditions of the cavity modes of the singly resonant optical parametric oscillator (SRO), we have transferred the OAM of the pump beam at the near-infrared wavelength to the idler beam tunable in the mid-IR. Pumped with a vortex beam of order lp=1 at 1064 nm, the SRO, configured in a four curved mirror-based ring cavity with a 50 mm long MgO-doped periodically poled LiNbO3 crystal, produces an idler beam with an output power in excess of 2 W in a vortex spatial profile with the order li=1, tunable across 2217-3574 nm and corresponding signal beam in Gaussian intensity distribution across 1515-2046 nm. For pump vortices of the order lp=1 and 2, and a power of 22 W, the SRO produces idler vortices of the same order as that of the pump beam with a maximum power of 5.23 and 2.3 W, corresponding to near-IR to mid-IR vortex conversion efficiency of 23.8% and 10.4%, respectively. The idler vortex beam has a spectral width, and a passive rms power stability of 101 MHz and 4.9% over 2 h, respectively.
Parametric model of ventilators simulated in OpenFOAM and Elmer
NASA Astrophysics Data System (ADS)
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.
Application of Parametric Models to a Survival Analysis of Hemodialysis Patients
Montaseri, Maryam; Charati, Jamshid Yazdani; Espahbodi, Fateme
2016-01-01
Background Hemodialysis is the most common renal replacement therapy in patients with end stage renal disease (ESRD). Objectives The present study compared the performance of various parametric models in a survival analysis of hemodialysis patients. Methods This study consisted of 270 hemodialysis patients who were referred to Imam Khomeini and Fatima Zahra hospitals between November 2007 and November 2012. The Akaike information criterion (AIC) and residuals review were used to compare the performance of the parametric models. The computations were done using STATA Software, with significance accepted at a level of 0.05. Results The results of a multivariate analysis of the variables in the parametric models showed that the mean serum albumin and the clinic attended were the most important predictors in the survival of the hemodialysis patients (P < 0.05). Among the parametric models tested, the results indicated that the performance of the Weibull model was the highest. Conclusions Parametric models may provide complementary data for clinicians and researchers about how risks vary over time. The Weibull model seemed to show the best fit among the parametric models of the survival of hemodialysis patients. PMID:27896235
Identification of the 1PL Model with Guessing Parameter: Parametric and Semi-Parametric Results
ERIC Educational Resources Information Center
San Martin, Ernesto; Rolin, Jean-Marie; Castro, Luis M.
2013-01-01
In this paper, we study the identification of a particular case of the 3PL model, namely when the discrimination parameters are all constant and equal to 1. We term this model, 1PL-G model. The identification analysis is performed under three different specifications. The first specification considers the abilities as unknown parameters. It is…
Parametric Modelling of As-Built Beam Framed Structure in Bim Environment
NASA Astrophysics Data System (ADS)
Yang, X.; Koehl, M.; Grussenmeyer, P.
2017-02-01
A complete documentation and conservation of a historic timber roof requires the integration of geometry modelling, attributional and dynamic information management and results of structural analysis. Recently developed as-built Building Information Modelling (BIM) technique has the potential to provide a uniform platform, which provides possibility to integrate the traditional geometry modelling, parametric elements management and structural analysis together. The main objective of the project presented in this paper is to develop a parametric modelling tool for a timber roof structure whose elements are leaning and crossing beam frame. Since Autodesk Revit, as the typical BIM software, provides the platform for parametric modelling and information management, an API plugin, able to automatically create the parametric beam elements and link them together with strict relationship, was developed. The plugin under development is introduced in the paper, which can obtain the parametric beam model via Autodesk Revit API from total station points and terrestrial laser scanning data. The results show the potential of automatizing the parametric modelling by interactive API development in BIM environment. It also integrates the separate data processing and different platforms into the uniform Revit software.
Parametric Models of NIR Transmission and Reflectivity Spectra for Dyed Fabrics
2015-07-29
control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 5d. PROJECT NUMBER 5e...Unlimited Unclassified Unlimited 36 Daniel Aiken (202) 279-5293 Parametric Modeling Inverse /direct analysis Dielectric function This study examines...parametric modeling of near-infrared (NIR) transmission and reflectivity spectra for dyed fabrics, which provides for both their inverse and direct
A proposal for a consistent parametrization of earth models
NASA Astrophysics Data System (ADS)
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
Open source molecular modeling.
Pirhadi, Somayeh; Sunseri, Jocelyn; Koes, David Ryan
2016-09-01
The success of molecular modeling and computational chemistry efforts are, by definition, dependent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. An updated online version of this catalog can be found at https://opensourcemolecularmodeling.github.io. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.
The Impact of Sources of Variability on Parametric Response Mapping of Lung CT Scans.
Boes, Jennifer L; Bule, Maria; Hoff, Benjamin A; Chamberlain, Ryan; Lynch, David A; Stojanovska, Jadranka; Martinez, Fernando J; Han, Meilan K; Kazerooni, Ella A; Ross, Brian D; Galbán, Craig J
2015-09-01
Parametric response mapping (PRM) of inspiration and expiration computed tomography (CT) images improves the radiological phenotyping of chronic obstructive pulmonary disease (COPD). PRM classifies individual voxels of lung parenchyma as normal, emphysematous, or nonemphysematous air trapping. In this study, bias and noise characteristics of the PRM methodology to CT and clinical procedures were evaluated to determine best practices for this quantitative technique. Twenty patients of varying COPD status with paired volumetric inspiration and expiration CT scans of the lungs were identified from the baseline COPD-Gene cohort. The impact of CT scanner manufacturer and reconstruction kernels were evaluated as potential sources of variability in PRM measurements along with simulations to quantify the impact of inspiration/expiration lung volume levels, misregistration, and image spacing on PRM measurements. Negligible variation in PRM metrics was observed when CT scanner type and reconstruction were consistent and inspiration/expiration lung volume levels were near target volumes. CT scanner Hounsfield unit drift occurred but remained difficult to ameliorate. Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements. PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels. As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented.
The Impact of Sources of Variability on Parametric Response Mapping of Lung CT Scans
Boes, Jennifer L.; Bule, Maria; Hoff, Benjamin A.; Chamberlain, Ryan; Lynch, David A.; Stojanovska, Jadranka; Martinez, Fernando J.; Han, Meilan K.; Kazerooni, Ella A.; Ross, Brian D.; Galbán, Craig J.
2015-01-01
Parametric response mapping (PRM) of inspiration and expiration computed tomography (CT) images improves the radiological phenotyping of chronic obstructive pulmonary disease (COPD). PRM classifies individual voxels of lung parenchyma as normal, emphysematous, or nonemphysematous air trapping. In this study, bias and noise characteristics of the PRM methodology to CT and clinical procedures were evaluated to determine best practices for this quantitative technique. Twenty patients of varying COPD status with paired volumetric inspiration and expiration CT scans of the lungs were identified from the baseline COPD-Gene cohort. The impact of CT scanner manufacturer and reconstruction kernels were evaluated as potential sources of variability in PRM measurements along with simulations to quantify the impact of inspiration/expiration lung volume levels, misregistration, and image spacing on PRM measurements. Negligible variation in PRM metrics was observed when CT scanner type and reconstruction were consistent and inspiration/expiration lung volume levels were near target volumes. CT scanner Hounsfield unit drift occurred but remained difficult to ameliorate. Increasing levels of image misregistration and CT slice spacing were found to have a minor effect on PRM measurements. PRM-derived values were found to be most sensitive to lung volume levels and mismatched reconstruction kernels. As with other quantitative imaging techniques, reliable PRM measurements are attainable when consistent clinical and CT protocols are implemented. PMID:26568983
NASA Astrophysics Data System (ADS)
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.
Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory
NASA Technical Reports Server (NTRS)
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.
Parametric regression model for survival data: Weibull regression model as an example
2016-01-01
Weibull regression model is one of the most popular forms of parametric regression model that it provides estimate of baseline hazard function, as well as coefficients for covariates. Because of technical difficulties, Weibull regression model is seldom used in medical literature as compared to the semi-parametric proportional hazard model. To make clinical investigators familiar with Weibull regression model, this article introduces some basic knowledge on Weibull regression model and then illustrates how to fit the model with R software. The SurvRegCensCov package is useful in converting estimated coefficients to clinical relevant statistics such as hazard ratio (HR) and event time ratio (ETR). Model adequacy can be assessed by inspecting Kaplan-Meier curves stratified by categorical variable. The eha package provides an alternative method to model Weibull regression model. The check.dist() function helps to assess goodness-of-fit of the model. Variable selection is based on the importance of a covariate, which can be tested using anova() function. Alternatively, backward elimination starting from a full model is an efficient way for model development. Visualization of Weibull regression model after model development is interesting that it provides another way to report your findings. PMID:28149846
Parametric uncertainties in global model simulations of black carbon column mass concentration
NASA Astrophysics Data System (ADS)
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
Modeling of finite-amplitude sound beams: second order fields generated by a parametric loudspeaker.
Yang, Jun; Sha, Kan; Gan, Woon-Seng; Tian, Jing
2005-04-01
The nonlinear interaction of sound waves in air has been applied to sound reproduction for audio applications. A directional audible sound can be generated by amplitude-modulating the ultrasound carrier with an audio signal, then transmitting it from a parametric loudspeaker. This brings the need of a computationally efficient model to describe the propagation of finite-amplitude sound beams for the system design and optimization. A quasilinear analytical solution capable of fast numerical evaluation is presented for the second-order fields of the sum-, difference-frequency and second harmonic components. It is based on a virtual-complex-source approach, wherein the source field is treated as an aggregation of a set of complex virtual sources located in complex distance, then the corresponding fundamental sound field is reduced to the computation of sums of simple functions by exploiting the integrability of Gaussian functions. By this result, the five-dimensional integral expressions for the second-order sound fields are simplified to one-dimensional integrals. Furthermore, a substantial analytical reduction to sums of single integrals also is derived for an arbitrary source distribution when the basis functions are expressible as a sum of products of trigonometric functions. The validity of the proposed method is confirmed by a comparison of numerical results with experimental data previously published for the rectangular ultrasonic transducer.
NASA Astrophysics Data System (ADS)
Daneshkhah, Alireza; Remesan, Renji; Chatrabgoun, Omid; Holman, Ian P.
2016-09-01
This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment.
Pradhan, Biswabrata; Dewanji, Anup
2009-07-10
In this work, we consider the parametric estimation of quality adjusted lifetime (QAL) distribution in progressive illness-death models. The main idea of this paper is to derive the theoretical distribution of QAL for the progressive illness-death models, under parametric models for the sojourn time distributions in different states, and then replace the model parameters by their estimates obtained by standard techniques of survival analysis. The method of estimation of the model parameters is also described. A data set of IBCSG Trial V has been analyzed for illustration. Extension to more general illness-death models is also discussed.
A convolution model for computing the far-field directivity of a parametric loudspeaker array.
Shi, Chuang; Kajikawa, Yoshinobu
2015-02-01
This paper describes a method to compute the far-field directivity of a parametric loudspeaker array (PLA), whereby the steerable parametric loudspeaker can be implemented when phased array techniques are applied. The convolution of the product directivity and the Westervelt's directivity is suggested, substituting for the past practice of using the product directivity only. Computed directivity of a PLA using the proposed convolution model achieves significant improvement in agreement to measured directivity at a negligible computational cost.
2001-03-14
parametric estimation algorithms of the interference components simplify and improve existing STAP methods. The resulting modeling and processing methods provide new parametric tools to estimate and mitigate the Doppler ambiguous clutter. The estimation algorithms the authors propose enable the estimation of the interference signals using the observations in only a single range gate. The proposed method is particularly suitable for non-stationary clutter and jamming environments. The approach provides a new analytical insight into the STAP
Kernel based model parametrization and adaptation with applications to battery management systems
NASA Astrophysics Data System (ADS)
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
Efficient parametric analysis of the chemical master equation through model order reduction.
Waldherr, Steffen; Haasdonk, Bernard
2012-07-02
Stochastic biochemical reaction networks are commonly modelled by the chemical master equation, and can be simulated as first order linear differential equations through a finite state projection. Due to the very high state space dimension of these equations, numerical simulations are computationally expensive. This is a particular problem for analysis tasks requiring repeated simulations for different parameter values. Such tasks are computationally expensive to the point of infeasibility with the chemical master equation. In this article, we apply parametric model order reduction techniques in order to construct accurate low-dimensional parametric models of the chemical master equation. These surrogate models can be used in various parametric analysis task such as identifiability analysis, parameter estimation, or sensitivity analysis. As biological examples, we consider two models for gene regulation networks, a bistable switch and a network displaying stochastic oscillations. The results show that the parametric model reduction yields efficient models of stochastic biochemical reaction networks, and that these models can be useful for systems biology applications involving parametric analysis problems such as parameter exploration, optimization, estimation or sensitivity analysis.
Parametric Modeling in the CAE Process: Creating a Family of Models
NASA Technical Reports Server (NTRS)
Brown, Christopher J.
2011-01-01
This Presentation meant as an example - Give ideas of approaches to use - The significant benefit of PARAMETRIC geometry based modeling The importance of planning before you build Showcase some NX capabilities - Mesh Controls - Associativity - Divide Face - Offset Surface Reminder - This only had to be done once! - Can be used for any cabinet in that "family" Saves a lot of time if pre-planned Allows re-use in the future
A parametric model of the spectral periodicity of stimulus frequency otoacoustic emissions
NASA Astrophysics Data System (ADS)
Lineton, Ben; Lutman, Mark E.
2003-08-01
A model for estimating the spectral period of stimulus frequency otoacoustic emissions (SFOAEs) is presented. The model characterizes the frequency spectrum of an SFOAE in terms of four parameters which can be directly related to cochlear mechanical quantities featuring in the theory of SFOAE generation proposed by Zweig and Shera [J. Acoust. Soc. Am. 98, 2018-2047 (1995)]. The results of applying the parametric model to SFOAEs generated by cochlear models suggest that it gives a sensitive measure of spectral period. It is concluded that the parametric model may be a useful tool for detecting small changes in cochlear function using SFOAE measurements.
Model reduction techniques for fast blood flow simulation in parametrized geometries.
Manzoni, Andrea; Quarteroni, Alfio; Rozza, Gianluigi
2012-01-01
In this paper, we propose a new model reduction technique aimed at real-time blood flow simulations on a given family of geometrical shapes of arterial vessels. Our approach is based on the combination of a low-dimensional shape parametrization of the computational domain and the reduced basis method to solve the associated parametrized flow equations. We propose a preliminary analysis carried on a set of arterial vessel geometries, described by means of a radial basis functions parametrization. In order to account for patient-specific arterial configurations, we reconstruct the latter by solving a suitable parameter identification problem. Real-time simulation of blood flows are thus performed on each reconstructed parametrized geometry, by means of the reduced basis method. We focus on a family of parametrized carotid artery bifurcations, by modelling blood flows using Navier-Stokes equations and measuring distributed outputs such as viscous energy dissipation or vorticity. The latter are indexes that might be correlated with the assessment of pathological risks. The approach advocated here can be applied to a broad variety of (different) flow problems related with geometry/shape variation, for instance related with shape sensitivity analysis, parametric exploration and shape design. Copyright © 2011 John Wiley & Sons, Ltd.
Simulation of parametric model towards the fixed covariate of right censored lung cancer data
NASA Astrophysics Data System (ADS)
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.
Single-arm phase II trial design under parametric cure models.
Wu, Jianrong
2015-01-01
The current practice of designing single-arm phase II survival trials is limited under the exponential model. Trial design under the exponential model may not be appropriate when a portion of patients are cured. There is no literature available for designing single-arm phase II trials under the parametric cure model. In this paper, a test statistic is proposed, and a sample size formula is derived for designing single-arm phase II trials under a class of parametric cure models. Extensive simulations showed that the proposed test and sample size formula perform very well under different scenarios. Copyright © 2015 John Wiley & Sons, Ltd.
Single-Arm Phase II Trial Design Under Parametric Cure Models
Wu, Jianrong
2015-01-01
The current practice of designing single-arm phase II survival trials is limited under the exponential model. Trial design under the exponential model may not be appropriate when a portion of patients are cured. There is no literature available for designing single-arm phase II trials under the parametric cure model. In this article, a test statistic is proposed, and a sample size formula is derived for designing single-arm phase II trials under a class of parametric cure models. Extensive simulations showed that the proposed test and sample size formula perform very well under different scenarios. PMID:25846141
NASA Astrophysics Data System (ADS)
Hemmings, J. C. P.; Challenor, P. G.; Yool, A.
2014-09-01
Biogeochemical ocean circulation models used to investigate the role of plankton ecosystems in global change rely on adjustable parameters to compensate for missing biological complexity. In principle, optimal parameter values can be estimated by fitting models to observational data, including satellite ocean colour products such as chlorophyll that achieve good spatial and temporal coverage of the surface ocean. However, comprehensive parametric analyses require large ensemble experiments that are computationally infeasible with global 3-D simulations. Site-based simulations provide an efficient alternative but can only be used to make reliable inferences about global model performance if robust quantitative descriptions of their relationships with the corresponding 3-D simulations can be established. The feasibility of establishing such a relationship is investigated for an intermediate complexity biogeochemistry model (MEDUSA) coupled with a widely-used global ocean model (NEMO). A site-based mechanistic emulator is constructed for surface chlorophyll output from this target model as a function of model parameters. The emulator comprises an array of 1-D simulators and a statistical quantification of the uncertainty in their predictions. The unknown parameter-dependent biogeochemical environment, in terms of initial tracer concentrations and lateral flux information required by the simulators, is a significant source of uncertainty. It is approximated by a mean environment derived from a small ensemble of 3-D simulations representing variability of the target model behaviour over the parameter space of interest. The performance of two alternative uncertainty quantification schemes is examined: a direct method based on comparisons between simulator output and a sample of known target model "truths" and an indirect method that is only partially reliant on knowledge of target model output. In general, chlorophyll records at a representative array of oceanic sites
Parametrically Guided Generalized Additive Models with Application to Mergers and Acquisitions Data
Fan, Jianqing; Maity, Arnab; Wang, Yihui; Wu, Yichao
2012-01-01
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. PMID:23645976
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.
SAMPLE AOR CALCULATION USING ANSYS SLICE PARAMETRIC MODEL FOR TANK SST-SX
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS slice 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 the 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 FULL PARAMETRIC MODEL FOR TANK SST-SX
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS parametric 360-degree 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 full model for the single shell tank (SST) SX to deal with asymmetry loading conditions 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 SLICE PARAMETRIC MODEL FOR TANK SST-BX
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS slice parametric model for single-shell tank BX and provides sample calculation for analysis-of-record mechanical load conditions. The purpose of this calculation is to develop a parametric model for the single shell tank (SST) BX, and provide a sample analysis of the SST-BX tank based on analysis of record (AOR) loads. The SST-BX 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
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.
SAMPLE AOR CALCULATION USING ANSYS AXISYMMETRIC PARAMETRIC MODEL FOR TANK SST-SX
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 SLICE PARAMETRIC MODEL FOR TANK SST-S
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS slice 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 the single shell tank (SST) S, and provide a sample analysis of the 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.
SAMPLE AOR CALCULATION USING ANSYS SLICE PARAMETRIC MODEL FOR TANK SST-AX
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS slice parametric model for single-shell tank AX and provides sample calculation for analysis-of-record mechanical load conditions. The purpose of this calculation is to develop a parametric model for the single shell tank (SST) AX, and provide a sample analysis of the SST-AX tank based on analysis of record (AOR) loads. The SST-AX 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-AX
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS axisymmetric parametric model for single-shell tank AX 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) AX, and provide a sample analysis of SST-AX tank based on analysis of record (AOR) loads. The SST-AX 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-A
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 SLICE PARAMETRIC MODEL FOR TANK SST-A
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS slice 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 the single shell tank (S) A, and provide a sample analysis of the SST-S 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.
Parametric Mass Modeling for Mars Entry, Descent and Landing System Analysis Study
NASA Technical Reports Server (NTRS)
Samareh, Jamshid A.; Komar, D. R.
2011-01-01
This paper provides an overview of the parametric mass models used for the Entry, Descent, and Landing Systems Analysis study conducted by NASA in FY2009-2010. The study examined eight unique exploration class architectures that included elements such as a rigid mid-L/D aeroshell, a lifting hypersonic inflatable decelerator, a drag supersonic inflatable decelerator, a lifting supersonic inflatable decelerator implemented with a skirt, and subsonic/supersonic retro-propulsion. Parametric models used in this study relate the component mass to vehicle dimensions and mission key environmental parameters such as maximum deceleration and total heat load. The use of a parametric mass model allows the simultaneous optimization of trajectory and mass sizing parameters.
Tatarinova, Tatiana; Neely, Michael; Bartroff, Jay; van Guilder, Michael; Yamada, Walter; Bayard, David; Jelliffe, Roger; Leary, Robert; Chubatiuk, Alyona; Schumitzky, Alan
2013-04-01
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approaches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org.
Neely, Michael; Bartroff, Jay; van Guilder, Michael; Yamada, Walter; Bayard, David; Jelliffe, Roger; Leary, Robert; Chubatiuk, Alyona; Schumitzky, Alan
2013-01-01
Population pharmacokinetic (PK) modeling methods can be statistically classified as either parametric or nonparametric (NP). Each classification can be divided into maximum likelihood (ML) or Bayesian (B) approazches. In this paper we discuss the nonparametric case using both maximum likelihood and Bayesian approaches. We present two nonparametric methods for estimating the unknown joint population distribution of model parameter values in a pharmacokinetic/pharmacodynamic (PK/PD) dataset. The first method is the NP Adaptive Grid (NPAG). The second is the NP Bayesian (NPB) algorithm with a stick-breaking process to construct a Dirichlet prior. Our objective is to compare the performance of these two methods using a simulated PK/PD dataset. Our results showed excellent performance of NPAG and NPB in a realistically simulated PK study. This simulation allowed us to have benchmarks in the form of the true population parameters to compare with the estimates produced by the two methods, while incorporating challenges like unbalanced sample times and sample numbers as well as the ability to include the covariate of patient weight. We conclude that both NPML and NPB can be used in realistic PK/PD population analysis problems. The advantages of one versus the other are discussed in the paper. NPAG and NPB are implemented in R and freely available for download within the Pmetrics package from www.lapk.org. PMID:23404393
Maji, Partha Sona; Roy Chaudhuri, Partha
2016-03-21
In this article, we have presented a new design methodology of obtaining wide band parametric sources based on highly nonlinear chalcogenide material of As{sub 2}S{sub 3}. The dispersion profile of the photonic crystal fiber (PCF) has been engineered wisely by reducing the diameter of the second air-hole ring to have a favorable higher order dispersion parameter. The parametric gain dependence upon fiber length, pump power, and different pumping wavelengths has been investigated in detail. Based upon the nonlinear four wave mixing phenomenon, we are able to achieve a wideband parametric amplifier with peak gain of 29 dB with FWHM of ≈2000 nm around the IR wavelength by proper tailoring of the dispersion profile of the PCF with a continuous wave Erbium (Er{sup 3+})-doped ZBLAN fiber laser emitting at 2.8 μm as the pump source with an average power of 5 W. The new design methodology will unleash a new dimension to the chalcogenide material based investigation for wavelength translation around IR wavelength band.
NASA Astrophysics Data System (ADS)
Maji, Partha Sona; Roy Chaudhuri, Partha
2016-03-01
In this article, we have presented a new design methodology of obtaining wide band parametric sources based on highly nonlinear chalcogenide material of As2S3. The dispersion profile of the photonic crystal fiber (PCF) has been engineered wisely by reducing the diameter of the second air-hole ring to have a favorable higher order dispersion parameter. The parametric gain dependence upon fiber length, pump power, and different pumping wavelengths has been investigated in detail. Based upon the nonlinear four wave mixing phenomenon, we are able to achieve a wideband parametric amplifier with peak gain of 29 dB with FWHM of ≈2000 nm around the IR wavelength by proper tailoring of the dispersion profile of the PCF with a continuous wave Erbium (Er3+)-doped ZBLAN fiber laser emitting at 2.8 μm as the pump source with an average power of 5 W. The new design methodology will unleash a new dimension to the chalcogenide material based investigation for wavelength translation around IR wavelength band.
Evaluation of Parametric Wind Models for Wave and Storm Surge Modelling of Hurricane Sandy
NASA Astrophysics Data System (ADS)
Bennett, V. C. C.; Mulligan, R. P.
2016-02-01
Hurricane Sandy made landfall in October, 2012, on the New Jersey coast south of Fire Island, New York. Fire Island is a barrier island on the south side of Long Island, and was severely impacted during Hurricane Sandy with extensive overwash and erosion leading to the breaching of the island in three locations. The purpose of this study is to investigate the offshore waves and storm surge generated by Hurricane Sandy, such that the storm impacts on nearshore hydrodynamics, sediment transport, and morphological changes of the barrier island and back-barrier bay can be evaluated. The Delft3D circulation model coupled to the SWAN wave model are used to simulate the storm event over a regional grid of the New York Bight and western continental shelf in the Atlantic Ocean. Three different spatially-varying wind fields are evaluated and compared to wind observations, including: the parametric Holland model, a parametric Generalized Asymmetric Holland Model, and results from the Regional Atmospheric Modelling System (Weatherflow). The winds are used to drive the coupled hydrodynamic and wave models; and the wave statistics, directional wave spectra, and storm surge elevations are compared to observations at several offshore buoys and coastal monitoring sites to investigate the impact of the complex wind field on sea surface evolution.
NASA Astrophysics Data System (ADS)
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.
Zhang, Yu; Manjavacas, Alejandro; Hogan, Nathaniel J; Zhou, Linan; Ayala-Orozco, Ciceron; Dong, Liangliang; Day, Jared K; Nordlander, Peter; Halas, Naomi J
2016-05-11
Active optical processes such as amplification and stimulated emission promise to play just as important a role in nanoscale optics as they have in mainstream modern optics. The ability of metallic nanostructures to enhance optical nonlinearities at the nanoscale has been shown for a number of nonlinear and active processes; however, one important process yet to be seen is optical parametric amplification. Here, we report the demonstration of surface plasmon-enhanced difference frequency generation by integration of a nonlinear optical medium, BaTiO3, in nanocrystalline form within a plasmonic nanocavity. These nanoengineered composite structures support resonances at pump, signal, and idler frequencies, providing large enhancements of the confined fields and efficient coupling of the wavelength-converted idler radiation to the far-field. This nanocomplex works as a nanoscale tunable infrared light source and paves the way for the design and fabrication of a surface plasmon-enhanced optical parametric amplifier.
Discrete K-valued Logic for Multi-parametrical Modeling of a Robotic Agent
NASA Astrophysics Data System (ADS)
Bykovsky, A. Yu.
K-valued Allen-Givone algebra is potentially a good tool for multi-parametric modeling of robotic and multi-agent systems, because a multiple-valued truth table can be directly applied for the accumulation of expert knowledge and the reconstruction of switching functions. The computational cost for their minimization will limit the real information capacity of such a model.
Maximum likelihood parametric blur identification based on a continuous spatial domain model.
Pavlovic, G; Tekalp, A M
1992-01-01
A formulation for maximum-likelihood (ML) blur identification based on parametric modeling of the blur in the continuous spatial coordinates is proposed. Unlike previous ML blur identification methods based on discrete spatial domain blur models, this formulation makes it possible to find the ML estimate of the extent, as well as other parameters, of arbitrary point spread functions that admit a closed-form parametric description in the continuous coordinates. Experimental results are presented for the cases of 1-D uniform motion blur, 2-D out-of-focus blur, and 2-D truncated Gaussian blur at different signal-to-noise ratios.
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes
Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D.
2016-01-01
This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. PMID:26993062
ERIC Educational Resources Information Center
Dyehouse, Melissa A.
2009-01-01
This study compared the model-data fit of a parametric item response theory (PIRT) model to a nonparametric item response theory (NIRT) model to determine the best-fitting model for use with ordinal-level alternate assessment ratings. The PIRT Generalized Graded Unfolding Model (GGUM) was compared to the NIRT Mokken model. Chi-square statistics…
Parametrizing coarse grained models for molecular systems at equilibrium
NASA Astrophysics Data System (ADS)
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.
Formation of parametric images using mixed-effects models: a feasibility study.
Huang, Husan-Ming; Shih, Yi-Yu; Lin, Chieh
2016-03-01
Mixed-effects models have been widely used in the analysis of longitudinal data. By presenting the parameters as a combination of fixed effects and random effects, mixed-effects models incorporating both within- and between-subject variations are capable of improving parameter estimation. In this work, we demonstrate the feasibility of using a non-linear mixed-effects (NLME) approach for generating parametric images from medical imaging data of a single study. By assuming that all voxels in the image are independent, we used simulation and animal data to evaluate whether NLME can improve the voxel-wise parameter estimation. For testing purposes, intravoxel incoherent motion (IVIM) diffusion parameters including perfusion fraction, pseudo-diffusion coefficient and true diffusion coefficient were estimated using diffusion-weighted MR images and NLME through fitting the IVIM model. The conventional method of non-linear least squares (NLLS) was used as the standard approach for comparison of the resulted parametric images. In the simulated data, NLME provides more accurate and precise estimates of diffusion parameters compared with NLLS. Similarly, we found that NLME has the ability to improve the signal-to-noise ratio of parametric images obtained from rat brain data. These data have shown that it is feasible to apply NLME in parametric image generation, and the parametric image quality can be accordingly improved with the use of NLME. With the flexibility to be adapted to other models or modalities, NLME may become a useful tool to improve the parametric image quality in the future. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
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
Data-based stochastic subgrid-scale parametrization: an approach using cluster-weighted modelling.
Kwasniok, Frank
2012-03-13
A new approach for data-based stochastic parametrization of unresolved scales and processes in numerical weather and climate prediction models is introduced. The subgrid-scale model is conditional on the state of the resolved scales, consisting of a collection of local models. A clustering algorithm in the space of the resolved variables is combined with statistical modelling of the impact of the unresolved variables. The clusters and the parameters of the associated subgrid models are estimated simultaneously from data. The method is implemented and explored in the framework of the Lorenz '96 model using discrete Markov processes as local statistical models. Performance of the cluster-weighted Markov chain scheme is investigated for long-term simulations as well as ensemble prediction. It clearly outperforms simple parametrization schemes and compares favourably with another recently proposed subgrid modelling scheme also based on conditional Markov chains.
Efficient model reduction of parametrized systems by matrix discrete empirical interpolation
NASA Astrophysics Data System (ADS)
Negri, Federico; Manzoni, Andrea; Amsallem, David
2015-12-01
In this work, we apply a Matrix version of the so-called Discrete Empirical Interpolation (MDEIM) for the efficient reduction of nonaffine parametrized systems arising from the discretization of linear partial differential equations. Dealing with affinely parametrized operators is crucial in order to enhance the online solution of reduced-order models (ROMs). However, in many cases such an affine decomposition is not readily available, and must be recovered through (often) intrusive procedures, such as the empirical interpolation method (EIM) and its discrete variant DEIM. In this paper we show that MDEIM represents a very efficient approach to deal with complex physical and geometrical parametrizations in a non-intrusive, efficient and purely algebraic way. We propose different strategies to combine MDEIM with a state approximation resulting either from a reduced basis greedy approach or Proper Orthogonal Decomposition. A posteriori error estimates accounting for the MDEIM error are also developed in the case of parametrized elliptic and parabolic equations. Finally, the capability of MDEIM to generate accurate and efficient ROMs is demonstrated on the solution of two computationally-intensive classes of problems occurring in engineering contexts, namely PDE-constrained shape optimization and parametrized coupled problems.
Investigating Microvibration Sources Modelling
NASA Astrophysics Data System (ADS)
Addari, Daniele; Aglietti, Guglielmo S.; Remedia, Marcello
2014-06-01
One of the challenges related to microvibration is investigating the characterisation of the microvibration sources (here emphasis is given to reaction wheel assemblies) on board satellites. This usually involves series of experiments to characterise the hardware and produce representative models. Here we present a methodology that gives good estimates covering a wide frequency range and reduce the complexity of the test campaign.In addition, a practical example of coupling between a reaction wheel assembly and a structural panel where the coupled loads have been estimated using the mathematical model and compared with experimental test results (retrieved using an interface load transducer) will be presented, giving indications of the level of accuracy that can be expected from this type of analyses.
Wavelength tunable parametric mid-IR source pumped by a high power picosecond thin-disk laser
NASA Astrophysics Data System (ADS)
Vyvlečka, Michal; Novák, Ondřej; Smrž, Martin; Endo, Akira; Mocek, Tomáš
2017-05-01
High average power wavelength tunable picosecond mid-IR source based on parametric down-conversion is being developed. The conversion system is pumped by a Yb:YAG thin-disk laser delivering 100 W of average power at 100 kHz repetition rate, 1030 nm wavelength, and 3 ps pulse width. First, part of the beam pumps an optical parametric generator (OPG) consisting of a PPLN crystal. The generated wavelength is determined by PPLN's poling period and temperature. Signal beam covered wavelength range between 1.46 mμ and 1.95 mμ. The corresponding idler wavelengths are 3.5 mμ and 2.18 mμ, respectively. Signal beam of about 20 mW was generated at 2 W pumping and double pass arrangement of the OPG stage. The signal pulse energy is further boosted in an optical parametric amplifier (OPA) consisting of two KTP crystals. The signal beam was amplified to 2 W at pumping of 38 W. The idler beam is taken out of the OPA stage as well. Wavelength tuning by KTP crystals' phase-matching angle change was achieved in ranges and 1.7 - 1.95 μm and 2.18 - 2.62 mμ for signal and idler beam, respectively.
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.
2011-01-01
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. PMID:21696598
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.
NASA Astrophysics Data System (ADS)
Breinl, Korbinian
2017-04-01
It was examined whether a lumped hydrological model driven with lumped daily precipitation time series from a single-site rainfall generator can produce equally good simulation results compared to using a multi-site rainfall generator, where synthetic precipitation is first generated at multiple sites and subsequently lumped. Driving a lumped hydrological model with synthetic rainfall time series from stochastic rainfall generation is a fast methodology in hydrological impact assessment, for example for the assessment of low frequent extreme flows when long synthetic discharge time series are required. The use of a lumped hydrological model appears to justify the application of a straightforward single-site "Richardson type" rainfall generator, where rainfall observations from several sites in the catchment are first lumped and then used for parametric distribution fitting. An alternative approach is the application of a multi-site rainfall generator, where rainfall is first generated at all available rainfall sites and subsequently lumped to feed the hydrological model. The higher complexity of multi-site rainfall generators makes the application of a single-site approach attractive as the latter can be set up fairly easily. This study revealed, however, that well-established parametric rainfall distributions for single-site rainfall observations are not suitable for lumped rainfall time series in the Alpine catchments examined, and can lead to bias in the simulation of extreme flows when using a single-site rainfall generator. The issue can be avoided by either using a multi-site rainfall generator, which is considerably less sensitive to the choice of the parametric rainfall distribution or by a careful choice of the parametric rainfall distribution fitted to lumped rainfall time series when using a single-site rainfall generator. In this study three different rainfall generators were tested: two different single-site "Richardson type" models (one with and one
ERIC Educational Resources Information Center
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…
Visual Literacy and the Integration of Parametric Modeling in the Problem-Based Curriculum
ERIC Educational Resources Information Center
Assenmacher, Matthew Benedict
2013-01-01
This quasi-experimental study investigated the application of visual literacy skills in the form of parametric modeling software in relation to traditional forms of sketching. The study included two groups of high school technical design students. The control and experimental groups involved in the study consisted of two randomly selected groups…
A Parametric Cost Model for Estimating Acquisition Costs of Conventional U.S. Navy Surface Ships.
1999-09-01
techniques return cost estimating relationships able to predict average procurement cost from ship light displacement, ship overall length, ship ... propulsion shaft horsepower or number of propulsion engines. The formulated parametric cost model is approximate and appropriate only for rough order of
NASA Astrophysics Data System (ADS)
Wan, Chenchen
Optical frequency combs are coherent light sources consist of thousands of equally spaced frequency lines. Frequency combs have achieved success in applications of metrology, spectroscopy and precise pulse manipulation and control. The most common way to generate frequency combs is based on mode-locked lasers which has the output spectrum of comb structures. To generate stable frequency combs, the output from mode-locked lasers need to be phase stabilized. The whole comb lines will be stabilized if the pulse train repetition rate corresponding to comb spacing and the pulse carrier envelope offset (CEO) frequency are both stabilized. The output from a laser always has fluctuations in parameters known as noise. In laser applications, noise is an important factor to limit the performance and often need to be well controlled. For example in precision measurement such as frequency metrology and precise spectroscopy, low laser intensity and phase noise is required. In mode-locked lasers there are different types of noise like intensity noise, pulse temporal position noise also known as timing jitter, optical phase noise. In term for frequency combs, these noise dynamics is more complex and often related. Understanding the noise behavior is not only of great interest in practical applications but also help understand fundamental laser physics. In this dissertation, the noise of frequency combs and mode-locked lasers will be studied in two projects. First, the CEO frequency phase noise of a synchronously pumped doubly resonant optical parametric oscillators (OPO) will be explored. This is very important for applications of the OPO as a coherent frequency comb source. Another project will focus on the intensity noise coupling in a soliton fiber oscillator, the finding of different noise coupling in soliton pulses and the dispersive waves generated from soliton perturbation can provide very practical guidance for low noise soliton laser design. OPOs are used to generate
Preliminary Multi-Variable Parametric Cost Model for Space Telescopes
NASA Technical Reports Server (NTRS)
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.
Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana
2015-03-01
The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology. Copyright © 2014 John Wiley & Sons, Ltd.
Modeling Sodium Iodide Detector Response Using Parametric Equations
2013-03-22
Detection Methodologies In 2001 a group of woodcutters in Lja, Georgia found two ‘objects’ in the forest (unshielded strontium -90 sources, each approx...especially between 10 and 20 cm. Comparing the backscatter at 100 cm shows that 89 % of the maximum backscatter registers in the detector versus the 82
NASA Astrophysics Data System (ADS)
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.
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
Ojeda-Guillén, D.; Mota, R. D.; Granados, V. D.
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.
Moore, Julia L
2014-01-01
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 nonlinear 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 vs. 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. PMID:24443079
Flexible parametric modelling of the cause-specific cumulative incidence function.
Lambert, Paul C; Wilkes, Sally R; Crowther, Michael J
2017-04-30
Competing risks arise with time-to-event data when individuals are at risk of more than one type of event and the occurrence of one event precludes the occurrence of all other events. A useful measure with competing risks is the cause-specific cumulative incidence function (CIF), which gives the probability of experiencing a particular event as a function of follow-up time, accounting for the fact that some individuals may have a competing event. When modelling the cause-specific CIF, the most common model is a semi-parametric proportional subhazards model. In this paper, we propose the use of flexible parametric survival models to directly model the cause-specific CIF where the effect of follow-up time is modelled using restricted cubic splines. The models provide smooth estimates of the cause-specific CIF with the important advantage that the approach is easily extended to model time-dependent effects. The models can be fitted using standard survival analysis tools by a combination of data expansion and introducing time-dependent weights. Various link functions are available that allow modelling on different scales and have proportional subhazards, proportional odds and relative absolute risks as particular cases. We conduct a simulation study to evaluate how well the spline functions approximate subhazard functions with complex shapes. The methods are illustrated using data from the European Blood and Marrow Transplantation Registry showing excellent agreement between parametric estimates of the cause-specific CIF and those obtained from a semi-parametric model. We also fit models relaxing the proportional subhazards assumption using alternative link functions and/or including time-dependent effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Analysis of survival in breast cancer patients by using different parametric models
NASA Astrophysics Data System (ADS)
Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti
2017-09-01
In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.
Change point models for cognitive tests using semi-parametric maximum likelihood
van den Hout, Ardo; Muniz-Terrera, Graciela; Matthews, Fiona E.
2013-01-01
Random-effects change point models are formulated for longitudinal data obtained from cognitive tests. The conditional distribution of the response variable in a change point model is often assumed to be normal even if the response variable is discrete and shows ceiling effects. For the sum score of a cognitive test, the binomial and the beta-binomial distributions are presented as alternatives to the normal distribution. Smooth shapes for the change point models are imposed. Estimation is by marginal maximum likelihood where a parametric population distribution for the random change point is combined with a non-parametric mixing distribution for other random effects. An extension to latent class modelling is possible in case some individuals do not experience a change in cognitive ability. The approach is illustrated using data from a longitudinal study of Swedish octogenarians and nonagenarians that began in 1991. Change point models are applied to investigate cognitive change in the years before death. PMID:23471297
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. Crown Copyright Â© 2010. Published by Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Brustlein, Sophie; Ferrand, Patrick; Walther, Nico; Brasselet, Sophie; Billaudeau, Cyrille; Marguet, Didier; Rigneault, Hervé
2011-02-01
We present the assets and constraints of using optical parametric oscillators (OPOs) to perform point scanning nonlinear microscopy and spectroscopy with special emphasis on coherent Raman spectroscopy. The difterent possible configurations starting with one OPO and two OPOs are described in detail and with comments that are intended to be practically useful for the user. Explicit examples on test samples such as nonlinear organic crystal, polystyrene beads, and fresh mouse tissues are given. Special emphasis is given to background-free coherent Raman anti-Stokes scattering (CARS) imaging, including CARS hyperspectral imaging in a fully automated mode with commercial OPOs.
Numerical Models of Broad Bandwidth Nanosecond Optical Parametric Oscillators
Bowers, M.S.; Gehr, R.J.; Smith, A.V.
1998-10-14
We describe results from three new methods of numerically modeling broad-bandwidth, nanosecond OPO's in the plane-wave approximate ion. They account for differences in group velocities among the three mixing waves, and also include a qutt~ttun noise model.
Flow Control Predictions using URANS Modeling: A Parametric Study
NASA Technical Reports Server (NTRS)
Rumsey, Christopher L.; Greenblatt, David
2007-01-01
A computational study was performed for steady and oscillatory flow control over a hump model with flow separation to assess how well the steady and unsteady Reynolds-averaged Navier-Stokes equations predict trends due to Reynolds number, control magnitude, and control frequency. As demonstrated previously, the hump model case is useful because it clearly demonstrates a failing in all known turbulence models: they under-predict the turbulent shear stress in the separated region and consequently reattachment occurs too far downstream. In spite of this known failing, three different turbulence models were employed to determine if trends can be captured even though absolute levels are not. The three turbulence models behaved similarly. Overall they showed very similar trends as experiment for steady suction, but only agreed qualitatively with some of the trends for oscillatory control.
Automatic Parametrization of Somatosensory Evoked Potentials With Chirp Modeling.
Vayrynen, Eero; Noponen, Kai; Vipin, Ashwati; Thow, X Y; Al-Nashash, Hasan; Kortelainen, Jukka; All, Angelo
2016-09-01
In this paper, an approach using polynomial phase chirp signals to model somatosensory evoked potentials (SEPs) is proposed. SEP waveforms are assumed as impulses undergoing group velocity dispersion while propagating along a multipath neural connection. Mathematical analysis of pulse dispersion resulting in chirp signals is performed. An automatic parameterization of SEPs is proposed using chirp models. A Particle Swarm Optimization algorithm is used to optimize the model parameters. Features describing the latencies and amplitudes of SEPs are automatically derived. A rat model is then used to evaluate the automatic parameterization of SEPs in two experimental cases, i.e., anesthesia level and spinal cord injury (SCI). Experimental results show that chirp-based model parameters and the derived SEP features are significant in describing both anesthesia level and SCI changes. The proposed automatic optimization based approach for extracting chirp parameters offers potential for detailed SEP analysis in future studies. The method implementation in Matlab technical computing language is provided online.
Development of lake parametrization in the INMCM climate model
NASA Astrophysics Data System (ADS)
Bogomolov, V.; Stepanenko, V.; Volodin, E.
2016-11-01
Land surface schemes (LSS or terrestrial models) are a crucial component of both Numerical Weather Prediction (NWP) systems and climate models. An important land- surface type is lakes. This paper presents the mechanism of incorporation of a model LAKE into a coupled general circulation model of the atmosphere and ocean INMCM4, with a space resolution 2° to 1.5° and 21 levels in height, with two-way interaction. A new map for 14 land types distribution was created using a digital map of inland waters for the entire globe. The digital map of water bodies includes the fraction of lake area on the land surface, and the average depth of water bodies, both on the grid of the climate model. This digital map is based on a dataset consisting of 14 000 freshwater lakes. In order to increase the time step in the LAKE model, the k-ε parameterization has been replaced by the parameterization of Henderson-Sellers. With the amended INMCM4 model, numerical experiments were carried out to simulate global climate during the second half of the XX century. The effects of the new lake parameterization on the surface temperature and heat fluxes are analyzed.
Evaluation of wave runup predictions from numerical and parametric models
Stockdon, Hilary F.; Thompson, David M.; Plant, Nathaniel G.; Long, Joseph W.
2014-01-01
Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.
Numerical model of solar dynamic radiator for parametric analysis
NASA Technical Reports Server (NTRS)
Rhatigan, Jennifer L.
1989-01-01
Growth power requirements for Space Station Freedom will be met through addition of 25 kW solar dynamic (SD) power modules. Extensive thermal and power cycle modeling capabilities have been developed which are powerful tools in Station design and analysis, but which prove cumbersome and costly for simple component preliminary design studies. In order to aid in refining the SD radiator to the mature design stage, a simple and flexible numerical model was developed. The model simulates heat transfer and fluid flow performance of the radiator and calculates area mass and impact survivability for many combinations of flow tube and panel configurations, fluid and material properties, and environmental and cycle variations.
Numerical model of solar dynamic radiator for parametric analysis
NASA Technical Reports Server (NTRS)
Rhatigan, Jennifer L.
1989-01-01
Growth power requirements for Space Station Freedom will be met through addition of 25 kW solar dynamic (SD) power modules. Extensive thermal and power cycle modeling capabilities have been developed which are powerful tools in Station design and analysis, but which prove cumbersome and costly for simple component preliminary design studies. In order to aid in refining the SD radiator to the mature design stage, a simple and flexible numerical model was developed. The model simulates heat transfer and fluid flow performance of the radiator and calculates area mass and impact survivability for many combinations of flow tube and panel configurations, fluid and material properties, and environmental and cycle variations.
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.
Parametric reduced models for the nonlinear Schrödinger equation.
Harlim, John; Li, Xiantao
2015-05-01
Reduced models for the (defocusing) nonlinear Schrödinger equation are developed. In particular, we develop reduced models that only involve the low-frequency modes given noisy observations of these modes. The ansatz of the reduced parametric models are obtained by employing a rational approximation and a colored-noise approximation, respectively, on the memory terms and the random noise of a generalized Langevin equation that is derived from the standard Mori-Zwanzig formalism. The parameters in the resulting reduced models are inferred from noisy observations with a recently developed ensemble Kalman filter-based parametrization method. The forecasting skill across different temperature regimes are verified by comparing the moments up to order four, a two-time correlation function statistics, and marginal densities of the coarse-grained variables.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-01-01
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. PMID:23275882
Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions
2013-01-01
Background Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. Methods There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. Results An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. Conclusion A key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies. PMID:23384310
Brayton Power Conversion System Parametric Design Modelling for Nuclear Electric Propulsion
NASA Technical Reports Server (NTRS)
Ashe, Thomas L.; Otting, William D.
1993-01-01
The parametrically based closed Brayton cycle (CBC) computer design model was developed for inclusion into the NASA LeRC overall Nuclear Electric Propulsion (NEP) end-to-end systems model. The code is intended to provide greater depth to the NEP system modeling which is required to more accurately predict the impact of specific technology on system performance. The CBC model is parametrically based to allow for conducting detailed optimization studies and to provide for easy integration into an overall optimizer driver routine. The power conversion model includes the modeling of the turbines, alternators, compressors, ducting, and heat exchangers (hot-side heat exchanger and recuperator). The code predicts performance to significant detail. The system characteristics determined include estimates of mass, efficiency, and the characteristic dimensions of the major power conversion system components. These characteristics are parametrically modeled as a function of input parameters such as the aerodynamic configuration (axial or radial), turbine inlet temperature, cycle temperature ratio, power level, lifetime, materials, and redundancy.
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.
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.
Geometric Model for a Parametric Study of the Blended-Wing-Body Airplane
NASA Technical Reports Server (NTRS)
Mastin, C. Wayne; Smith, Robert E.; Sadrehaghighi, Ideen; Wiese, Micharl R.
1996-01-01
A parametric model is presented for the blended-wing-body airplane, one concept being proposed for the next generation of large subsonic transports. The model is defined in terms of a small set of parameters which facilitates analysis and optimization during the conceptual design process. The model is generated from a preliminary CAD geometry. From this geometry, airfoil cross sections are cut at selected locations and fitted with analytic curves. The airfoils are then used as boundaries for surfaces defined as the solution of partial differential equations. Both the airfoil curves and the surfaces are generated with free parameters selected to give a good representation of the original geometry. The original surface is compared with the parametric model, and solutions of the Euler equations for compressible flow are computed for both geometries. The parametric model is a good approximation of the CAD model and the computed solutions are qualitatively similar. An optimal NURBS approximation is constructed and can be used by a CAD model for further refinement or modification of the original geometry.
Bifurcation analysis of parametrically excited bipolar disorder model
NASA Astrophysics Data System (ADS)
Nana, Laurent
2009-02-01
Bipolar II disorder is characterized by alternating hypomanic and major depressive episode. We model the periodic mood variations of a bipolar II patient with a negatively damped harmonic oscillator. The medications administrated to the patient are modeled via a forcing function that is capable of stabilizing the mood variations and of varying their amplitude. We analyze analytically, using perturbation method, the amplitude and stability of limit cycles and check this analysis with numerical simulations.
NASA Astrophysics Data System (ADS)
Hamerly, Ryan; Marandi, Alireza; Jankowski, Marc; Fejer, M. M.; Yamamoto, Yoshihisa; Mabuchi, Hideo
2016-12-01
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 simultonlike 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.
Novel parametric reduced order model for aeroengine blade dynamics
NASA Astrophysics Data System (ADS)
Yuan, Jie; Allegri, Giuliano; Scarpa, Fabrizio; Rajasekaran, Ramesh; Patsias, Sophoclis
2015-10-01
The work introduces a novel reduced order model (ROM) technique to describe the dynamic behavior of turbofan aeroengine blades. We introduce an equivalent 3D frame model to describe the coupled flexural/torsional mode shapes, with their relevant natural frequencies and associated modal masses. The frame configurations are identified through a structural identification approach based on a simulated annealing algorithm with stochastic tunneling. The cost functions are constituted by linear combinations of relative errors associated to the resonance frequencies, the individual modal assurance criteria (MAC), and on either overall static or modal masses. When static masses are considered the optimized 3D frame can represent the blade dynamic behavior with an 8% error on the MAC, a 1% error on the associated modal frequencies and a 1% error on the overall static mass. When using modal masses in the cost function the performance of the ROM is similar, but the overall error increases to 7%. The approach proposed in this paper is considerably more accurate than state-of-the-art blade ROMs based on traditional Timoshenko beams, and provides excellent accuracy at reduced computational time when compared against high fidelity FE models. A sensitivity analysis shows that the proposed model can adequately predict the global trends of the variations of the natural frequencies when lumped masses are used for mistuning analysis. The proposed ROM also follows extremely closely the sensitivity of the high fidelity finite element models when the material parameters are used in the sensitivity.
Principles of parametric estimation in modeling language competition
Zhang, Menghan; Gong, Tao
2013-01-01
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. PMID:23716678
Lagoudas model for optomechanical mountings: parametric study and characterization campaign
NASA Astrophysics Data System (ADS)
Rigamonti, D.; Zanetti, F.; Riva, M.; Villa, E.; Passaretti, F.; Zerbi, F. M.
2013-04-01
This paper is a study on the numerical modeling and the accordance between model and experiment of the behavior of Shape Memory Alloys (SMA) used as functional devices for application in Instrumentations for Astronomy. Some NiTi alloy samples was characterized using different experimental techniques, with the purpose of obtaining the material parameters, necessary to evaluate the correspondence between the simulation and the experimental behavior of the materials. The sensibility of the computational model to the variation of this parameters for the materials was investigated as well. Opto-mechanical mounting with pseudoelastic kinematic behavior and damping of launch loads onto optical elements are feasible applications that are investigated in this paper. The practical realization of a scaled down prototype is described. The device was thought for ground-based applications and made up of four small flexures that support an optical component and was designed and modeled in order to be able to evaluate the mechanical effects of different materials. The results of numerical modeling was compared to the data obtained from the prototype. We obtained a first evaluation of the development, selection and processing of NiTi-based supports for optomechanical applications and verified the performances of a complete system as a respect to an analogous system made up using traditional materials like steels.
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.
Song, Guo-Zhu; Wu, Fang-Zhou; Zhang, Mei; Yang, Guo-Jian
2016-01-01
Quantum repeater is the key element in quantum communication and quantum information processing. Here, we investigate the possibility of achieving a heralded quantum repeater based on the scattering of photons off single emitters in one-dimensional waveguides. We design the compact quantum circuits for nonlocal entanglement generation, entanglement swapping, and entanglement purification, and discuss the feasibility of our protocols with current experimental technology. In our scheme, we use a parametric down-conversion source instead of ideal single-photon sources to realize the heralded quantum repeater. Moreover, our protocols can turn faulty events into the detection of photon polarization, and the fidelity can reach 100% in principle. Our scheme is attractive and scalable, since it can be realized with artificial solid-state quantum systems. With developed experimental technique on controlling emitter-waveguide systems, the repeater may be very useful in long-distance quantum communication. PMID:27350159
Biomass steam gasification--an extensive parametric modeling study.
Schuster, G; Löffler, G; Weigl, K; Hofbauer, H
2001-03-01
A model for steam gasification of biomass was developed by applying thermodynamic equilibrium calculations. With this model, the simulation of a decentralized combined heat and power station based on a dual fluidized-bed steam gasifier was carried out. Fuel composition (ultimate analysis and moisture content) and the operating parameters, temperature and amount of gasification agent, were varied over a wide range. Their influences on amount, composition, and heating value of product gas and process efficiencies were evaluated. It was shown that the accuracy of an equilibrium model for the gas composition is sufficient for thermodynamic considerations. Net electric efficiency of about 20% can be expected with a rather simple process. Sensitivity analysis showed that gasification temperature and fuel oxygen content were the most significant parameters determining the chemical efficiency of the gasification.
Modelling and validation of magnetorheological brake responses using parametric approach
NASA Astrophysics Data System (ADS)
Z, Zainordin A.; A, Abdullah M.; K, Hudha
2013-12-01
Magnetorheological brake (MR Brake) is one x-by-wire systems which performs better than conventional brake systems. MR brake consists of a rotating disc that is immersed with Magnetorheological Fluid (MR Fluid) in an enclosure of an electromagnetic coil. The applied magnetic field will increase the yield strength of the MR fluid where this fluid was used to decrease the speed of the rotating shaft. The purpose of this paper is to develop a mathematical model to represent MR brake with a test rig. The MR brake model is developed based on actual torque characteristic which is coupled with motion of a test rig. Next, the experimental are performed using MR brake test rig and obtained three output responses known as angular velocity response, torque response and load displacement response. Furthermore, the MR brake was subjected to various current. Finally, the simulation results of MR brake model are then verified with experimental results.
PARAMETRIC STUDY OF GROUND SOURCE HEAT PUMP SYSTEM FOR HOT AND HUMID CLMATE
Zhu, Jiang; Tao, Yong X.
2011-12-15
The U-tube sizes and varied thermal conductivity with different grout materials are studied based on the benchmark residential building in Hot-humid Pensacola, Florida. In this study, the benchmark building is metered and the data is used to validate the simulation model. And a list of comparative simulation cases with varied parameter value are simulated to study the importance of pipe size and grout to the ground source heat pump energy consumption. The simulation software TRNSYS [1] is employed to fulfill this task. The results show the preliminary energy saving based on varied parameters. Future work needs to be conducted for the cost analysis, include the installation cost from contractor and materials cost.
NASA Astrophysics Data System (ADS)
Vu, H. X.; Bezzerides, B.; DuBois, D. F.
1999-11-01
A fully kinetic, reduced-description particle-in-cell (RPIC) model is presented in which deviations from quasineutrality, electron and ion kinetic effects, and nonlinear interactions between low-frequency and high-frequency parametric instabilities are modeled correctly. The model is based on a reduced description where the electromagnetic field is represented by three separate temporal envelopes in order to model parametric instabilities with low-frequency and high-frequency daughter waves. Because temporal envelope approximations are invoked, the simulation can be performed on the electron time scale instead of the time scale of the light waves. The electrons and ions are represented by discrete finite-size particles, permitting electron and ion kinetic effects to be modeled properly. The Poisson equation is utilized to ensure that space-charge effects are included. The RPIC model is fully three dimensional and has been implemented in two dimensions on the Accelerated Strategic Computing Initiative (ASCI) parallel computer at Los Alamos National Laboratory, and the resulting simulation code has been named ASPEN. We believe this code is the first particle-in-cell code capable of simulating the interaction between low-frequency and high-frequency parametric instabilites in multiple dimensions. Test simulations of stimulated Raman scattering, stimulated Brillouin scattering, and Langmuir decay instability are presented.
CuBe: parametric modeling of 3D foveal shape using cubic Bézier
Yadav, Sunil Kumar; Motamedi, Seyedamirhosein; Oberwahrenbrock, Timm; Oertel, Frederike Cosima; Polthier, Konrad; Paul, Friedemann; Kadas, Ella Maria; Brandt, Alexander U.
2017-01-01
Optical coherence tomography (OCT) allows three-dimensional (3D) imaging of the retina, and is commonly used for assessing pathological changes of fovea and macula in many diseases. Many neuroinflammatory conditions are known to cause modifications to the fovea shape. In this paper, we propose a method for parametric modeling of the foveal shape. Our method exploits invariant features of the macula from OCT data and applies a cubic Bézier polynomial along with a least square optimization to produce a best fit parametric model of the fovea. Additionally, we provide several parameters of the foveal shape based on the proposed 3D parametric modeling. Our quantitative and visual results show that the proposed model is not only able to reconstruct important features from the foveal shape, but also produces less error compared to the state-of-the-art methods. Finally, we apply the model in a comparison of healthy control eyes and eyes from patients with neuroinflammatory central nervous system disorders and optic neuritis, and show that several derived model parameters show significant differences between the two groups. PMID:28966857
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.
Parametric linear modeling of circular cMUT membranes in vacuum.
Köymen, Hayrettin; Senlik, Muhammed N; Atalar, Abdullah; Olcum, Selim
2007-06-01
We present a lumped element parametric model for the clamped circular membrane of a capacitive micromachined ultrasonic transducer (cMUT). The model incorporates an electrical port and two sets of acoustic ports, through which the cMUT couples to the medium. The modeling approach is based on matching a lumped element model and the mechanical impedance of the cMUT membrane at the resonance frequencies in vacuum. Very good agreement between finite element simulation results and model impedance is obtained. Equivalent circuit model parameters can be found from material properties and membrane dimensions without a need for finite element simulation.
Parametric Modeling as a Technology of Rapid Prototyping in Light Industry
NASA Astrophysics Data System (ADS)
Tomilov, I. N.; Grudinin, S. N.; Frolovsky, V. D.; Alexandrov, A. A.
2016-04-01
The paper deals with the parametric modeling method of virtual mannequins for the purposes of design automation in clothing industry. The described approach includes the steps of generation of the basic model on the ground of the initial one (obtained in 3D-scanning process), its parameterization and deformation. The complex surfaces are presented by the wireframe model. The modeling results are evaluated with the set of similarity factors. Deformed models are compared with their virtual prototypes. The results of modeling are estimated by the standard deviation factor.
Li, Bernadette; Cairns, John A; Robb, Matthew L; Johnson, Rachel J; Watson, Christopher J E; Forsythe, John L; Oniscu, Gabriel C; Ravanan, Rommel; Dudley, Christopher; Roderick, Paul; Metcalfe, Wendy; Tomson, Charles R; Bradley, J Andrew
2016-05-25
The influence of donor and recipient factors on outcomes following kidney transplantation is commonly analysed using Cox regression models, but this approach is not useful for predicting long-term survival beyond observed data. We demonstrate the application of a flexible parametric approach to fit a model that can be extrapolated for the purpose of predicting mean patient survival. The primary motivation for this analysis is to develop a predictive model to estimate post-transplant survival based on individual patient characteristics to inform the design of alternative approaches to allocating deceased donor kidneys to those on the transplant waiting list in the United Kingdom. We analysed data from over 12,000 recipients of deceased donor kidney or combined kidney and pancreas transplants between 2003 and 2012. We fitted a flexible parametric model incorporating restricted cubic splines to characterise the baseline hazard function and explored a range of covariates including recipient, donor and transplant-related factors. Multivariable analysis showed the risk of death increased with recipient and donor age, diabetic nephropathy as the recipient's primary renal diagnosis and donor hypertension. The risk of death was lower in female recipients, patients with polycystic kidney disease and recipients of pre-emptive transplants. The final model was used to extrapolate survival curves in order to calculate mean survival times for patients with specific characteristics. The use of flexible parametric modelling techniques allowed us to address some of the limitations of both the Cox regression approach and of standard parametric models when the goal is to predict long-term survival.
Framework for the Parametric System Modeling of Space Exploration Architectures
NASA Technical Reports Server (NTRS)
Komar, David R.; Hoffman, Jim; Olds, Aaron D.; Seal, Mike D., II
2008-01-01
This paper presents a methodology for performing architecture definition and assessment prior to, or during, program formulation that utilizes a centralized, integrated architecture modeling framework operated by a small, core team of general space architects. This framework, known as the Exploration Architecture Model for IN-space and Earth-to-orbit (EXAMINE), enables: 1) a significantly larger fraction of an architecture trade space to be assessed in a given study timeframe; and 2) the complex element-to-element and element-to-system relationships to be quantitatively explored earlier in the design process. Discussion of the methodology advantages and disadvantages with respect to the distributed study team approach typically used within NASA to perform architecture studies is presented along with an overview of EXAMINE s functional components and tools. An example Mars transportation system architecture model is used to demonstrate EXAMINE s capabilities in this paper. However, the framework is generally applicable for exploration architecture modeling with destinations to any celestial body in the solar system.
Parametric Estimation in a Recurrent Competing Risks Model.
Taylor, Laura L; Peña, Edsel A
2013-01-01
A resource-efficient approach to making inferences about the distributional properties of the failure times in a competing risks setting is presented. Efficiency is gained by observing recurrences of the competing risks over a random monitoring period. The resulting model is called the recurrent competing risks model (RCRM) and is coupled with two repair strategies whenever the system fails. Maximum likelihood estimators of the parameters of the marginal distribution functions associated with each of the competing risks and also of the system lifetime distribution function are presented. Estimators are derived under perfect and partial repair strategies. Consistency and asymptotic properties of the estimators are obtained. The estimation methods are applied to a data set of failures for cars under warranty. Simulation studies are used to ascertain the small sample properties and the efficiency gains of the resulting estimators.
Parametrized Aeorelastic Reduced-Order Modeling of Fighters
2009-08-21
turbine in-flow environment, AIAA Paper 2004-1004. (19] M. Hong, K. Bhatia, G . SenGupta, K. T., G . Kuruvila, W. Silva, R. Bartels, R. Biedron ...Aeorelastic Reduced-Order Modeling of Fighters Sb. GRANT NUMBER F A9 5 50-06-024 7-P0002 Sc. PROGRAM ELEMENT NUMBER 6. AUTHOR( S ) Sd. PROJECT NUMBER...Farhat, Charbel Se. TASK NUMBER Sf. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION Stanford
Bayesian parametrization of coarse-grain dissipative dynamics models
NASA Astrophysics Data System (ADS)
Dequidt, Alain; Solano Canchaya, Jose G.
2015-08-01
We introduce a new bottom-up method for the optimization of dissipative coarse-grain models. The method is based on Bayesian optimization of the likelihood to reproduce a coarse-grained reference trajectory obtained from analysis of a higher resolution molecular dynamics trajectory. This new method is related to force matching techniques, but using the total force on each grain averaged on a coarse time step instead of instantaneous forces. It has the advantage of not being limited to pairwise short-range interactions in the coarse-grain model and also yields an estimation of the friction parameter controlling the dynamics. The theory supporting the method is exposed in a practical perspective, with an analytical solution for the optimal set of parameters. The method was first validated by using it on a system with a known optimum. The new method was then tested on a simple system: n-pentane. The local molecular structure of the optimized model is in excellent agreement with the reference system. An extension of the method allows to get also an excellent agreement for the equilibrium density. As for the dynamic properties, they are also very satisfactory, but more sensitive to the choice of the coarse-grain representation. The quality of the final force field depends on the definition of the coarse grain degrees of freedom and interactions. We consider this method as a serious alternative to other methods like iterative Boltzmann inversion, force matching, and Green-Kubo formulae.
Magnetoviscoelasticity parametric model of an MR elastomer vibration mitigation device
NASA Astrophysics Data System (ADS)
Zhu, Jun-Tao; Xu, Zhao-Dong; Guo, Ying-Qing
2012-07-01
Both experimental and modeling studies of magnetic field induced viscoelastic properties of magnetorheological (MR) elastomers under different loading cases are discussed. Anisotropic MR elastomer (MRE) samples with different concentrations of carbonyl iron powder, natural rubber and additives are fabricated and four MRE vibration mitigation devices are manufactured to investigate the dynamic viscoelastic properties of MREs under varying magnetic fields, displacement amplitudes and frequencies in the shear mode. The characteristics of the dynamic properties of the MRE devices are obtained in terms of the experimentally determined shear storage modulus and loss factor. These results demonstrate that the MREs exhibit variable stiffness and damping properties. Based on the studies of properties of viscoelastic materials and the experimental results of MREs, a parameter model is proposed to describe MRE performances. The four parameters under various working conditions, such as magnetic field, displacement amplitude and frequency, are identified by using the Matlab optimization algorithm. Comparisons between experimental and numerical results are discussed, and the results show that the proposed parameter model can describe the performances of MRE devices very well.
Derraz, Foued; Forzy, Gérard; Delebarre, Arnaud; Taleb-Ahmed, Abdelmalik; Oussalah, Mourad; Peyrodie, Laurent; Verclytte, Sebastien
2015-11-01
Prostate contours delineation on Magnetic Resonance (MR) images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Traditional active contours-based delineation algorithms are typically quite successful for piecewise constant images. Nevertheless, when MR images have diffuse edges or multiple similar objects (e.g. bladder close to prostate) within close proximity, such approaches have proven to be unsuccessful. In order to mitigate these problems, we proposed a new framework for bi-stage contours delineation algorithm based on directional active contours (DAC) incorporating prior knowledge of the prostate shape. We first explicitly addressed the prostate contour delineation problem based on fast globally DAC that incorporates both statistical and parametric shape prior model. In doing so, we were able to exploit the global aspects of contour delineation problem by incorporating a user feedback in contours delineation process where it is shown that only a small amount of user input can sometimes resolve ambiguous scenarios raised by DAC. In addition, once the prostate contours have been delineated, a cost functional is designed to incorporate both user feedback interaction and the parametric shape prior model. Using data from publicly available prostate MR datasets, which includes several challenging clinical datasets, we highlighted the effectiveness and the capability of the proposed algorithm. Besides, the algorithm has been compared with several state-of-the-art methods. Copyright © 2015 John Wiley & Sons, Ltd.
Klein, Katelyn F; Hu, Jingwen; Reed, Matthew P; Hoff, Carrie N; Rupp, Jonathan D
2015-10-01
Statistical models were developed that predict male and female femur geometry as functions of age, body mass index (BMI), and femur length as part of an effort to develop lower-extremity finite element models with geometries that are parametric with subject characteristics. The process for developing these models involved extracting femur geometry from clinical CT scans of 62 men and 36 women, fitting a template finite element femur mesh to the surface geometry of each patient, and then programmatically determining thickness at each nodal location. Principal component analysis was then performed on the thickness and geometry nodal coordinates, and linear regression models were developed to predict principal component scores as functions of age, BMI, and femur length. The average absolute errors in male and female external surface geometry model predictions were 4.57 and 4.23 mm, and the average absolute errors in male and female thickness model predictions were 1.67 and 1.74 mm. The average error in midshaft cortical bone areas between the predicted geometries and the patient geometries was 4.4%. The average error in cortical bone area between the predicted geometries and a validation set of cadaver femur geometries across 5 shaft locations was 2.9%.
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%.
Nonlinear parametric model for Granger causality of time series
NASA Astrophysics Data System (ADS)
Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano
2006-06-01
The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.
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.
Towards the generation of a parametric foot model using principal component analysis: A pilot study.
Scarton, Alessandra; Sawacha, Zimi; Cobelli, Claudio; Li, Xinshan
2016-06-01
There have been many recent developments in patient-specific models with their potential to provide more information on the human pathophysiology and the increase in computational power. However they are not yet successfully applied in a clinical setting. One of the main challenges is the time required for mesh creation, which is difficult to automate. The development of parametric models by means of the Principle Component Analysis (PCA) represents an appealing solution. In this study PCA has been applied to the feet of a small cohort of diabetic and healthy subjects, in order to evaluate the possibility of developing parametric foot models, and to use them to identify variations and similarities between the two populations. Both the skin and the first metatarsal bones have been examined. Besides the reduced sample of subjects considered in the analysis, results demonstrated that the method adopted herein constitutes a first step towards the realization of a parametric foot models for biomechanical analysis. Furthermore the study showed that the methodology can successfully describe features in the foot, and evaluate differences in the shape of healthy and diabetic subjects.
Otero, Marta; Zabkova, Miriam; Rodrigues, Alírio E
2005-09-01
Thermal parametric pumping is a cyclic adsorptive process based on periodic changes in the bed temperature simultaneously with flow reversal. This is an innovative technology which may allow removing phenolic compounds from waste solutions to be recovered and recycled. The recovery and/or purification of liquid streams containing phenol and 4-nitrophenol by adsorptive parametric pumping was studied in this work. An automated parametric pumping pilot unit was operated in semi-continuous recuperative mode. The adsorbent used was the polymeric resin Sephabeads SP206 (Mitsubishi Kasei Corporation, Japan) and temperatures of the hot and the cold half-cycles were 333 and 293 K, respectively. Basic data were obtained from batch equilibrium experiments and fixed-bed adsorption. Different experimental conditions were run and two simplified models were used to simulate the results: an equilibrium model and a linear driving force (LDF) model. Experimental and simulated results using the LDF model were in quite good agreement. Purification levels below three orders of magnitude lower than the concentration of the feed solution were obtained for phenol and 4-nitrophenol.
Small parametric model for nonlinear dynamics of large scale cyclogenesis with wind speed variations
NASA Astrophysics Data System (ADS)
Erokhin, Nikolay; Shkevov, Rumen; Zolnikova, Nadezhda; Mikhailovskaya, Ludmila
2016-07-01
It is performed a numerical investigation of a self consistent small parametric model (SPM) for large scale cyclogenesis (RLSC) by usage of connected nonlinear equations for mean wind speed and ocean surface temperature in the tropical cyclone (TC). These equations may describe the different scenario of temporal dynamics of a powerful atmospheric vortex during its full life cycle. The numerical calculations have shown that relevant choice of SPMTs incoming parameters allows to describe the seasonal behavior of regional large scale cyclogenesis dynamics for a given number of TC during the active season. It is shown that SPM allows describe also the variable wind speed variations inside the TC. Thus by usage of the nonlinear small parametric model it is possible to study the features of RLSCTs temporal dynamics during the active season in the region given and to analyze the relationship between regional cyclogenesis parameters and different external factors like the space weather including the solar activity level and cosmic rays variations.
Volterra model of the parametric array loudspeaker operating at ultrasonic frequencies.
Shi, Chuang; Kajikawa, Yoshinobu
2016-11-01
The parametric array loudspeaker (PAL) is an application of the parametric acoustic array in air, which can be applied to transmit a narrow audio beam from an ultrasonic emitter. However, nonlinear distortion is very perceptible in the audio beam. Modulation methods to reduce the nonlinear distortion are available for on-axis far-field applications. For other applications, preprocessing techniques are wanting. In order to develop a preprocessing technique with general applicability to a wide range of operating conditions, the Volterra filter is investigated as a nonlinear model of the PAL in this paper. Limitations of the standard audio-to-audio Volterra filter are elaborated. An improved ultrasound-to-ultrasound Volterra filter is proposed and empirically demonstrated to be a more generic Volterra model of the PAL.
Parametric study of extended end-plate connection using finite element modeling
NASA Astrophysics Data System (ADS)
Mureşan, Ioana Cristina; Bâlc, Roxana
2017-07-01
End-plate connections with preloaded high strength bolts represent a convenient, fast and accurate solution for beam-to-column joints. The behavior of framework joints build up with this type of connection are sensitive dependent on geometrical and material characteristics of the elements connected. This paper presents results of parametric analyses on the behavior of a bolted extended end-plate connection using finite element modeling program Abaqus. This connection was experimentally tested in the Laboratory of Faculty of Civil Engineering from Cluj-Napoca and the results are briefly reviewed in this paper. The numerical model of the studied connection was described in detail in [1] and provides data for this parametric study.
SAMPLE AOR CALCULATION USING ANSYS PARAMETRIC MODEL FOR TANK SST-AY
JULYK, L.J.; MACKEY, T.C.
2003-06-19
This document documents the ANSYS parametric model for double-shell tank AY and provides sample calculation for analysis-of-record mechanical load conditions. The purpose of this calculation is to provide a sample analysis of the DST-AY tanks based on AOR loads, plus loads identified in the Statement of Work (SOW) for CHG contract 92879. This is not an analysis. Instead, the present calculation utilizes the parametric model generated for the double shell tank DST-AY, which is based on Buyer-supplied as-built drawings and information for the analyses of record (AOR) for Double-Shell Tanks (DSTs), encompassing the existing tank load conditions, and evaluates stresses and deformations throughout the tank and surrounding soil mass.
Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.
Xu, Xiaoguang; Kypraios, Theodore; O'Neill, Philip D
2016-10-01
This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings. © The Author 2016. Published by Oxford University Press.
Oracle estimation of parametric models under boundary constraints.
Wong, Kin Yau; Goldberg, Yair; Fine, Jason P
2016-12-01
In many classical estimation problems, the parameter space has a boundary. In most cases, the standard asymptotic properties of the estimator do not hold when some of the underlying true parameters lie on the boundary. However, without knowledge of the true parameter values, confidence intervals constructed assuming that the parameters lie in the interior are generally over-conservative. A penalized estimation method is proposed in this article to address this issue. An adaptive lasso procedure is employed to shrink the parameters to the boundary, yielding oracle inference which adapt to whether or not the true parameters are on the boundary. When the true parameters are on the boundary, the inference is equivalent to that which would be achieved with a priori knowledge of the boundary, while if the converse is true, the inference is equivalent to that which is obtained in the interior of the parameter space. The method is demonstrated under two practical scenarios, namely the frailty survival model and linear regression with order-restricted parameters. Simulation studies and real data analyses show that the method performs well with realistic sample sizes and exhibits certain advantages over standard methods. © 2016, The International Biometric Society.
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.
TVC (Thrust Vector Control) Jet Vane Thermal Modeling Using Parametric System Identification
1988-03-18
Parametric system identification procedures, using the software package MATRIXx, are applied to the problem of simulating the thermal response of a...Boundary layer convection and stagnation point heating are considered as thermal inputs, and the associated resistances are estimated. System ... identification is used to determine the appropriate values for the convective resistances and the vane mount thermal sink. The identified model, which is linear
NASA Astrophysics Data System (ADS)
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.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood
Davenport, Clemontina A.; Maity, Arnab; Wu, Yichao
2015-01-01
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. PMID:26146469
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.
Tsoumpas, Charalampos; Turkheimer, Federico E; Thielemans, Kris
2008-04-01
In dynamic positron emission tomography (PET) studies, the time changing activity of the radiotracer is measured through multiple consecutive frames. Subsequent pixel-by-pixel application of the appropriate kinetic model provides quantitative information in terms of images of the distribution of the physiological parameter of interest. In this context, iterative reconstruction methods may be used to reconstruct for each time frame a static image of appreciable higher quality than the analytical algorithms, especially in low-count cases. Furthermore, if the reconstruction algorithm also models the kinetics of the measured counts, the parametric image is expected to be of even higher quality. In this work, we investigate the methodology to directly reconstruct parametric images in three-dimensional PET when the kinetic model is linear in its parameters (Patlak plot) and compare with indirectly estimated parametric maps, where the radioactivity distribution was estimated by the filtered back projection and ordered subsets expectation maximization algorithms. Both real and simulated data for tracers with irreversible kinetics in brain studies are included. The results demonstrate appreciable smaller standard deviation and mean squared error characteristics for the direct reconstruction. However, some regions may converge slowly. The FBP and ordered subsets expectation maximization (OSEM) indirect estimations have a similar level of bias after matching their resolutions, but OSEM has smaller standard deviation.
A bulk parametrization of melting snowflakes with explicit liquid water fraction for the COSMO model
NASA Astrophysics Data System (ADS)
Frick, C.; Seifert, A.; Wernli, H.
2013-11-01
A new snow melting parametrization is presented for the non-hydrostatic limited-area COSMO ("consortium for small-scale modelling") model. In contrast to the standard cloud microphysics of the COSMO model, which instantaneously transfers the meltwater from the snow to the rain category, the new scheme explicitly considers the liquid water fraction of the melting snowflakes. These semi-melted hydrometeors have characteristics (e.g., shape and fall speed) that differ from those of dry snow and rain droplets. Where possible, theoretical considerations and results from vertical wind tunnel laboratory experiments of melting snowflakes are used as the basis for characterising the melting snow as a function of its liquid water fraction. These characteristics include the capacitance, the ventilation coefficient, and the terminal fall speed. For the bulk parametrization, a critical diameter is introduced. It is assumed that particles smaller than this diameter, which increases during the melting process, have completely melted, i.e., they are converted to the rain category. The values of the bulk integrals are calculated with a finite difference method and approximately represented by polynomial functions, which allows an efficient implementation of the parametrization. Two case studies of (wet) snowfall in Germany are presented to illustrate the potential of the new snow melting parametrization. It is shown that the new scheme (i) produces wet snow instead of rain in some regions with surface temperatures slightly above the freezing point, (ii) simulates realistic atmospheric melting layers with a gradual transition from dry snow to melting snow to rain, and (iii) leads to a slower snow melting process. In the future, it will be important to thoroughly validate the scheme, also with radar data and to further explore its potential for improved surface precipitation forecasts for various meteorological conditions.
NASA Astrophysics Data System (ADS)
Saffin, Leo; Methven, John; Gray, Sue
2016-04-01
Numerical models of the atmosphere combine a dynamical core, which approximates solutions to the adiabatic and frictionless governing equations, with the tendencies arising from the parametrization of physical processes. Tracers of potential vorticity (PV) can be used to accumulate the tendencies of parametrized physical processes and diagnose their impacts on the large-scale dynamics. This is due to two key properties of PV, conservation following an air mass and invertibility which relates the PV distribution to the balanced dynamics of the atmosphere. Applying the PV tracers to many short forecasts allows for a systematic investigation of the behaviour of parametrized physical processes. The forecasts are 2.5 day lead time forecasts run using the Met Office Unified Model (MetUM) initialised at 0Z for each day in November/December/January 2013/14. The analysis of the PV tracers has been focussed on regions where diabatic processes can be important (tropopause ridges and troughs, frontal regions and the boundary layer top). The tropopause can be described as a surface of constant PV with a sharp PV gradient. Previous work using the PV tracers in individual case studies has shown that parametrized physical processes act to enhance the tropopause PV contrast which can affect the Rossby wave phase speed. The short forecasts show results consistent with a systematic enhancement of tropopause PV contrast by diabatic processes and show systematically different behaviour between ridges and troughs. The implication of this work is that a failure to correctly represent the effects of diabatic processes on the tropopause in models can lead to poor Rossby wave evolution and potentially downstream forecast busts.
Shang, Yaping; Xu, Jiangming; Wang, Peng; Li, Xiao; Zhou, Pu; Xu, Xiaojun
2016-09-19
The longterm stability of the laser system is very important in many applications. In this letter, an ultra-stable, broadband, mid-infrared (MIR) optical parametric oscillator (OPO) pumped by a super-fluorescent fiber source is demonstrated. An idler MIR output power of 11.3 W with excellent beam quality was obtained and the corresponding pump-to-idler conversion slope efficiency was 15.9%. Furthermore, during 1h measurement at full power operation, the peak-to-peak fluctuation of idler output power was less than 1.9% and the corresponding standard deviation was less than 0.4% RMS, which was much better than that of a traditional single mode fiber laser pumped OPO system (10.9% for peak-to-peak fluctuation and 1.8% RMS for the standard deviation) in another experiment for comparison. To our knowledge, this is the first demonstration on a high-power, ultra-stable OPO system by using the modefree pump source, which offered an effective approach to achieve an ultra-stable MIR source and broadened the range of the super-fluorescent fiber source applications.
Tunable Monochromatic X-ray Source Based on Parametric X-ray Radiation at LEBRA, Nihon University
Hayakawa, Y.; Sato, I.; Hayakawa, K.; Tanaka, T.; Kuwada, T.; Sakai, T.; Nogami, K.; Nakao, K.; Inagaki, M.; Mori, A.
2007-01-19
The monochromatic X-ray source based on parametric X-ray radiation (PXR) was developed by using the electron beam from the 125-MeV linac at Nihon University. The X-ray generating system consists of two silicon perfect-crystal plates to offer a wide tunability. The system has actually been providing the energy dispersive monochromatic X-ray beam in the region of 6 to 20 keV, using Si(111)-plane for the target and the second crystals. Since the X-ray beam from the PXR generator has rather high energy resolution and coherency, X-ray absorption fine structure (XAFS) measurement and phase-contrast imaging are possible applications of PXR. Actually, preliminary experiments on energy dispersive XAFS measurement and refraction-contrast imaging have been successfully carried out using the PXR beam.
Third-order spontaneous parametric down-conversion in thin optical fibers as a photon-triplet source
Corona, Maria; Garay-Palmett, Karina; U'Ren, Alfred B.
2011-09-15
We study the third-order spontaneous parametric down-conversion (TOSPDC) process, as a means to generate entangled photon triplets. Specifically, we consider thin optical fibers as the nonlinear medium to be used as the basis for TOSPDC in configurations where phase matching is attained through the use of more than one fiber transverse modes. Our analysis in this paper, which follows from our earlier paper [Opt. Lett. 36, 190-192 (2011)], aims to supply experimentalists with the details required in order to design a TOSPDC photon-triplet source. Specifically, our analysis focuses on the photon triplet state, on the rate of emission, and on the TOSPDC phase-matching characteristics for the cases of frequency-degenerate and frequency nondegenerate TOSPDC.
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. PMID:25309338
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.
NASA Technical Reports Server (NTRS)
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.
Improvement of the Analytical Model of a Laminated Core Parametric Motor
NASA Astrophysics Data System (ADS)
Tajima, Katsubumi; Sato, Tadashi; Sakamoto, Yoshinori
A laminated core parametric induction motor has desirable features and the planer structure to make it possible to reduce the production cost of the motor by mass production. In the past work, we showed the validity to apply the two-dimensional reluctance network analytical model to the dynamic analysis of the motor while the rotor is driving. In this paper, we investigate the improvement the accuracy of the analytical method of the motor by using new reluctance network analytical model of the motor. In this model, the magnetic circuits of the stator and the rotor are connected by the variable reluctances that are expressed as the function of the rotating angle.
Modeling a MEMS deformable mirror using non-parametric estimation techniques.
Guzmán, Dani; Juez, Francisco Javier de Cos; Myers, Richard; Guesalaga, Andrés; Lasheras, Fernando Sánchez
2010-09-27
Using non-parametric estimation techniques, we have modeled an area of 126 actuators of a micro-electro-mechanical deformable mirror with 1024 actuators. These techniques produce models applicable to open-loop adaptive optics, where the turbulent wavefront is measured before it hits the deformable mirror. The model's input is the wavefront correction to apply to the mirror and its output is the set of voltages to shape the mirror. Our experiments have achieved positioning errors of 3.1% rms of the peak-to-peak wavefront excursion.
Kimstrand, Peter; Traneus, Erik; Ahnesjö, Anders; Tilly, Nina
2008-07-07
Collimators are routinely used in proton radiotherapy to laterally confine the field and improve the penumbra. Collimator scatter contributes up to 15% of the local dose and is therefore important to include in treatment planning dose calculation. We present a method for reconstruction of the collimator scatter phase space based on the parametrization of pre-calculated scatter kernels. Collimator scatter distributions, generated by the Monte Carlo (MC) package GEANT4.8.2, were scored differential in direction and energy. The distributions were then parametrized so as to enable a fast reconstruction by sampling. MC calculated dose distributions in water based on the parametrized phase space were compared to full MC simulations that included the collimator in the simulation geometry, as well as to experimental data. The experiments were performed at the scanned proton beam line at the The Svedberg Laboratory (TSL) in Uppsala, Sweden. Dose calculations using the parametrization of this work and the full MC for isolated typical cases of collimator scatter were compared by means of the gamma index. The result showed that in total 96.7% (99.3%) of the voxels fulfilled the gamma 2.0%/2.0 mm (3.0%/3.0 mm) criterion. The dose distribution for a collimated field was calculated based on the phase space created by the collimator scatter model incorporated into the generation of the phase space of a scanned proton beam. Comparing these dose distributions to full MC simulations, including particle transport in the MLC, yielded that in total for 18 different collimated fields, 99.1% of the voxels satisfied the gamma 1.0%/1.0 mm criterion and no voxel exceeded the gamma 2.6%/2.6 mm criterion. The dose contribution of collimator scatter along the central axis as predicted by the model showed good agreement with experimental data.
NASA Astrophysics Data System (ADS)
Kimstrand, Peter; Traneus, Erik; Ahnesjö, Anders; Tilly, Nina
2008-07-01
Collimators are routinely used in proton radiotherapy to laterally confine the field and improve the penumbra. Collimator scatter contributes up to 15% of the local dose and is therefore important to include in treatment planning dose calculation. We present a method for reconstruction of the collimator scatter phase space based on the parametrization of pre-calculated scatter kernels. Collimator scatter distributions, generated by the Monte Carlo (MC) package GEANT4.8.2, were scored differential in direction and energy. The distributions were then parametrized so as to enable a fast reconstruction by sampling. MC calculated dose distributions in water based on the parametrized phase space were compared to full MC simulations that included the collimator in the simulation geometry, as well as to experimental data. The experiments were performed at the scanned proton beam line at the The Svedberg Laboratory (TSL) in Uppsala, Sweden. Dose calculations using the parametrization of this work and the full MC for isolated typical cases of collimator scatter were compared by means of the gamma index. The result showed that in total 96.7% (99.3%) of the voxels fulfilled the gamma 2.0%/2.0 mm (3.0%/3.0 mm) criterion. The dose distribution for a collimated field was calculated based on the phase space created by the collimator scatter model incorporated into the generation of the phase space of a scanned proton beam. Comparing these dose distributions to full MC simulations, including particle transport in the MLC, yielded that in total for 18 different collimated fields, 99.1% of the voxels satisfied the gamma 1.0%/1.0 mm criterion and no voxel exceeded the gamma 2.6%/2.6 mm criterion. The dose contribution of collimator scatter along the central axis as predicted by the model showed good agreement with experimental data.
Parametric bootstrap methods for testing multiplicative terms in GGE and AMMI models.
Forkman, Johannes; Piepho, Hans-Peter
2014-09-01
The genotype main effects and genotype-by-environment interaction effects (GGE) model and the additive main effects and multiplicative interaction (AMMI) model are two common models for analysis of genotype-by-environment data. These models are frequently used by agronomists, plant breeders, geneticists and statisticians for analysis of multi-environment trials. In such trials, a set of genotypes, for example, crop cultivars, are compared across a range of environments, for example, locations. The GGE and AMMI models use singular value decomposition to partition genotype-by-environment interaction into an ordered sum of multiplicative terms. This article deals with the problem of testing the significance of these multiplicative terms in order to decide how many terms to retain in the final model. We propose parametric bootstrap methods for this problem. Models with fixed main effects, fixed multiplicative terms and random normally distributed errors are considered. Two methods are derived: a full and a simple parametric bootstrap method. These are compared with the alternatives of using approximate F-tests and cross-validation. In a simulation study based on four multi-environment trials, both bootstrap methods performed well with regard to Type I error rate and power. The simple parametric bootstrap method is particularly easy to use, since it only involves repeated sampling of standard normally distributed values. This method is recommended for selecting the number of multiplicative terms in GGE and AMMI models. The proposed methods can also be used for testing components in principal component analysis. © 2014, The International Biometric Society.
Parametric analysis of three dimensional flow models applied to tidal energy sites in Scotland
NASA Astrophysics Data System (ADS)
Rahman, Anas; Venugopal, Vengatesan
2017-04-01
This paper presents a detailed parametric analysis on various input parameters of two different numerical models, namely Telemac3D and Delft3D, used for the simulation of tidal current flow at potential tidal energy sites in the Pentland Firth in Scotland. The motivation behind this work is to investigate the influence of the input parameters on the above 3D models, as the majority of past research has mainly focused on using the 2D depth-averaged flow models for this region. An extended description of the models setup, along with the utilised parameters is provided. The International Hydrographic Organisation (IHO) tidal gauges and Acoustic Doppler and Current Profiler (ADCP) measurements are used in calibrating model output to ensure the robustness of the models. Extensive parametric study on the impact of varying drag coefficients, roughness formulae and turbulence models has been investigated and reported. The results indicate that both Telemac3D and Delft3D models are able to produce excellent comparison against measured data; however, with Delft3D, the model parameters which provided higher correlation with the measured data, are found to be different from those reported in the previous literature, which could be attributed to the choice of boundary conditions and the mesh size.
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.
Parametric analysis of the statistical model of the stick-slip process
NASA Astrophysics Data System (ADS)
Lima, Roberta; Sampaio, Rubens
2017-06-01
In this paper it is performed a parametric analysis of the statistical model of the response of a dry-friction oscillator. The oscillator is a spring-mass system which moves over a base with a rough surface. Due to this roughness, the mass is subject to a dry-frictional force modeled as a Coulomb friction. The system is stochastically excited by an imposed bang-bang base motion. The base velocity is modeled by a Poisson process for which a probabilistic model is fully specified. The excitation induces in the system stochastic stick-slip oscillations. The system response is composed by a random sequence alternating stick and slip-modes. With realizations of the system, a statistical model is constructed for this sequence. In this statistical model, the variables of interest of the sequence are modeled as random variables, as for example, the number of time intervals in which stick or slip occur, the instants at which they begin, and their duration. Samples of the system response are computed by integration of the dynamic equation of the system using independent samples of the base motion. Statistics and histograms of the random variables which characterize the stick-slip process are estimated for the generated samples. The objective of the paper is to analyze how these estimated statistics and histograms vary with the system parameters, i.e., to make a parametric analysis of the statistical model of the stick-slip process.
NASA Astrophysics Data System (ADS)
Andreadis, G. M.; Podias, A. K. M.; Tsiakaras, P. E.
In the present work, a model-based parametric analysis of the performance of a direct ethanol polymer electrolyte membrane fuel cell (DE-PEMFC) is conducted with the purpose to investigate the effect of several parameters on the cell's operation. The analysis is based on a previously validated one-dimensional mathematical model that describes the operation of a DE-PEMFC in steady state. More precisely, the effect of several operational and structural parameters on (i) the ethanol crossover rate from the anode to the cathode side of the cell, (ii) the parasitic current generation (mixed potential formation) and (iii) the total cell performance is investigated. According to the model predictions it was found that the increase of the ethanol feed concentration leads to higher ethanol crossover rates, higher parasitic currents and higher mixed potential values resulting in the decrease of the cell's power density. However there is an optimum ethanol feed concentration (approximately 1.0 mol L -1) for which the cell power density reaches its highest value. The platinum (Pt) loading of the anode and the cathode catalytic layers affects strongly the cell performance. Higher values of Pt loading of the catalytic layers increase the specific reaction surface area resulting in higher cell power densities. An increase of the anode catalyst loading compared to an equal one of the cathode catalyst loading has greater impact on the cell's power density. Another interesting finding is that increasing the diffusion layers' porosity up to a certain extent, improves the cell power density despite the fact that the parasitic current increases. This is explained by the fact that the reactants' concentrations over the catalysts are increased, leading to lower activation overpotential values, which are the main source of the total cell overpotentials. Moreover, the use of a thicker membrane leads to lower ethanol crossover rate, lower parasitic current and lower mixed potential values
Global analysis of parametric sensitivity of precipitation in the Community Atmosphere Model (CAM5)
NASA Astrophysics Data System (ADS)
Qian, Y.; Yan, H.; Zhao, C.; Hou, Z.; Wang, H.; Rasch, P. J.; Klein, S. A.; Lucas, D.; Tannahill, J.
2013-12-01
In this study, we investigate the sensitivity of precipitation characteristics, including mean, extreme and diurnal cycle, to dozens of uncertain parameters mainly related to cloud and aerosol processes in the Community Atmosphere Model (CAM5). We adopt both the Latin hypercube sampling and quasi-Monte Carlo sampling approaches to effectively explore the high-dimensional parameter space and then conduct two large sets of simulations (1356 in total). The CAM5 ensemble simulates the mean precipitation reasonably well, but fails to capture the diurnal cycle of precipitation over land. The phase of diurnal precipitation associated with the convection propagation over Central US seems to be more related to model structural errors rather than the parametric uncertainties. Parametric calibration could possibly improve CAM5 precipitation over regions, such as Tropical Western Pacific, having relatively weak diurnal cycle and high model parameter identifiability. The precipitation variance is large and the diurnal cycle is strong over South America and Central Africa, where parametric calibration can possibly improve the model prediction of mean precipitation but not the diurnal cycle. Variance-based sensitivity analysis using a generalized linear model (GLM) is conducted to examine the relative contributions of individual parameter perturbations and their interactions to the global and regional precipitation. We characterize the global spatial distribution as well as scale (global vs. local) and seasonal dependence of parametric sensitivity of precipitation, and identify a few parameters that dominate the behavior of the mean, extremes or diurnal cycle of precipitation, respectively. Results suggest that the model-simulated precipitation is remarkably sensitive to a few cloud-related parameters, while aerosols have minor impact on the diurnal cycle of precipitation in the current CAM5. The interactions among the selected parameters contribute a relatively small portion to
Bos, E J; Scholten, T; Song, Y; Verlinden, J C; Wolff, J; Forouzanfar, T; Helder, M N; van Zuijlen, P
2015-04-01
Ear reconstruction is a tedious and demanding surgical procedure and the implant framework used is essential for the esthetic result. The outcome of a reconstructed ear, however, is not necessarily limited to the implant shape but rather to the available options of transplantable tissue for coverage. Apart from the visual aesthetics, ear reconstruction subsequently also requires implant dimensions to be adapted to the surgical possibilities. In this article, we have brought different disciplines together to develop a customizable ear model for 3D printing of ear implants. Computed tomography (CT) scans were made of 4 human cadaver ears before and after soft tissue dissection using a Discovery 750 High Definition Freedom Edition scanner (GE, Milwaukee, WI, USA) and subsequently converted into an STL data set using Mimics Software (Materialise, Leuven, Belgium). These scans were then used to develop a fully adjustable parametric model based on the essential ear anatomy using Rhinoceros and Grasshopper software. To determine the quality of the developed models, directed Hausdorff distance (DHD) was applied as the basis for measuring the similarity between the parametric model and the ear cartilage scanning data. Two methods were used. The mean directed Haussdorff distance (MDHD) was calculated based on the distribution of point sets showing an average similarity of 0.8 mm (±0.05 mm). The mean similarity coefficient (SC) of the model and scan surfaces was 94% with a 2-mm threshold. This study shows that a parametric standard model could be used as a feasible method to generate custom implants based on existing ear images. Copyright © 2015 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.
Parametrization of orographic thermal effect on the deep convection triggering in Global Model
NASA Astrophysics Data System (ADS)
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
Modeling Magnetospheric Sources
NASA Technical Reports Server (NTRS)
Walker, Raymond J.; Ashour-Abdalla, Maha; Ogino, Tatsuki; Peroomian, Vahe; Richard, Robert L.
2001-01-01
We have used global magnetohydrodynamic, simulations of the interaction between the solar wind and magnetosphere together with single particle trajectory calculations to investigate the sources of plasma entering the magnetosphere. In all of our calculations solar wind plasma primarily enters the magnetosphere when the field line on which it is convecting reconnects. When the interplanetary magnetic field has a northward component the reconnection is in the polar cusp region. In the simulations plasma in the low latitude boundary layer (LLBL) can be on either open or closed field lines. Open field lines occur when the high latitude reconnection occurs in only one cusp. In the MHD calculations the ionosphere does not contribute significantly to the LLBL for northward IMF. The particle trajectory calculations show that ions preferentially enter in the cusp region where they can be accelerated by non-adiabatic motion across the high latitude electric field. For southward IMF in the MHD simulations the plasma in the middle and inner magnetosphere comes from the inner (ionospheric) boundary of the simulation. Solar wind plasma on open field lines is confined to high latitudes and exits the tailward boundary of the simulation without reaching the plasma sheet. The LLBL is populated by both ionospheric and solar wind plasma. When the particle trajectories are included solar wind ions can enter the middle magnetosphere. We have used both the MHD simulations and the particle calculations to estimate source rates for the magnetosphere which are consistent with those inferred from observations.
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.
Parametric sensitivity analysis of an agro-economic model of management of irrigation water
NASA Astrophysics Data System (ADS)
El Ouadi, Ihssan; Ouazar, Driss; El Menyari, Younesse
2015-04-01
The current work aims to build an analysis and decision support tool for policy options concerning the optimal allocation of water resources, while allowing a better reflection on the issue of valuation of water by the agricultural sector in particular. Thus, a model disaggregated by farm type was developed for the rural town of Ait Ben Yacoub located in the east Morocco. This model integrates economic, agronomic and hydraulic data and simulates agricultural gross margin across in this area taking into consideration changes in public policy and climatic conditions, taking into account the competition for collective resources. To identify the model input parameters that influence over the results of the model, a parametric sensitivity analysis is performed by the "One-Factor-At-A-Time" approach within the "Screening Designs" method. Preliminary results of this analysis show that among the 10 parameters analyzed, 6 parameters affect significantly the objective function of the model, it is in order of influence: i) Coefficient of crop yield response to water, ii) Average daily gain in weight of livestock, iii) Exchange of livestock reproduction, iv) maximum yield of crops, v) Supply of irrigation water and vi) precipitation. These 6 parameters register sensitivity indexes ranging between 0.22 and 1.28. Those results show high uncertainties on these parameters that can dramatically skew the results of the model or the need to pay particular attention to their estimates. Keywords: water, agriculture, modeling, optimal allocation, parametric sensitivity analysis, Screening Designs, One-Factor-At-A-Time, agricultural policy, climate change.
Animal models of source memory.
Crystal, Jonathon D
2016-01-01
Source memory is the aspect of episodic memory that encodes the origin (i.e., source) of information acquired in the past. Episodic memory (i.e., our memories for unique personal past events) typically involves source memory because those memories focus on the origin of previous events. Source memory is at work when, for example, someone tells a favorite joke to a person while avoiding retelling the joke to the friend who originally shared the joke. Importantly, source memory permits differentiation of one episodic memory from another because source memory includes features that were present when the different memories were formed. This article reviews recent efforts to develop an animal model of source memory using rats. Experiments are reviewed which suggest that source memory is dissociated from other forms of memory. The review highlights strengths and weaknesses of a number of animal models of episodic memory. Animal models of source memory may be used to probe the biological bases of memory. Moreover, these models can be combined with genetic models of Alzheimer's disease to evaluate pharmacotherapies that ultimately have the potential to improve memory.
Parametric modeling of quantile regression coefficient functions with censored and truncated data.
Frumento, Paolo; Bottai, Matteo
2017-02-09
Quantile regression coefficient functions describe how the coefficients of a quantile regression model depend on the order of the quantile. A method for parametric modeling of quantile regression coefficient functions was discussed in a recent article. The aim of the present work is to extend the existing framework to censored and truncated data. We propose an estimator and derive its asymptotic properties. We discuss goodness-of-fit measures, present simulation results, and analyze the data that motivated this article. The described estimator has been implemented in the R package qrcm.
X-1 to X-Wings: Developing a Parametric Cost Model
NASA Technical Reports Server (NTRS)
Sterk, Steve; McAtee, Aaron
2015-01-01
In todays cost-constrained environment, NASA needs an X-Plane database and parametric cost model that can quickly provide rough order of magnitude predictions of cost from initial concept to first fight of potential X-Plane aircraft. This paper takes a look at the steps taken in developing such a model and reports the results. The challenges encountered in the collection of historical data and recommendations for future database management are discussed. A step-by-step discussion of the development of Cost Estimating Relationships (CERs) is then covered.
Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study
2010-01-01
Background Traditional methods for analyzing clinical and epidemiological cohort study data have been focused on the first occurrence of a health outcome. However, in many situations, recurrent event data are frequently observed. It is inefficient to use methods for the analysis of first events to analyse recurrent event data. Methods We applied several semi-parametric proportional hazards models to analyze the risk of recurrent myocardial infarction (MI) events based on data from a very large randomized placebo-controlled trial of cholesterol-lowering drug. The backward selection procedure was used to select the significant risk factors in a model. The best fitting model was selected using the log-likelihood ratio test, Akaike Information and Bayesian Information Criteria. Results A total of 8557 persons were included in the LIPID study. Risk factors such as age, smoking status, total cholesterol and high density lipoprotein cholesterol levels, qualifying event for the acute coronary syndrome, revascularization, history of stroke or diabetes, angina grade and treatment with pravastatin were significant for development of both first and subsequent MI events. No significant difference was found for the effects of these risk factors between the first and subsequent MI events. The significant risk factors selected in this study were the same as those selected by the parametric conditional frailty model. Estimates of the relative risks and 95% confidence intervals were also similar between these two methods. Conclusions Our study shows the usefulness and convenience of the semi-parametric proportional hazards models for the analysis of recurrent event data, especially in estimation of regression coefficients and cumulative risks. PMID:20356409
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
A Parametric Study of Erupting Flux Rope Rotation: Modeling the 'Cartwheel CME' on 9 April 2008
NASA Technical Reports Server (NTRS)
Kliem, B.; Toeroek, T.; Thompson, W. T.
2012-01-01
The rotation of erupting filaments in the solar corona is addressed through a parametric simulation study of unstable, rotating flux ropes in bipolar force-free initial equilibrium. The Lorentz force due to the external shear-field component and the relaxation of tension in the twisted field are the major contributors to the rotation in this model, while reconnection with the ambient field is of minor importance, due to the field's simple structure. In the low-beta corona, the rotation is not guided by the changing orientation of the vertical field component's polarity inversion line with height. The model yields strong initial rotations which saturate in the corona and differ qualitatively from the profile of rotation vs. height obtained in a recent simulation of an eruption without preexisting flux rope. Both major mechanisms writhe the flux rope axis, converting part of the initial twist helicity, and produce rotation profiles which, to a large part, are very similar within a range of shear-twist combinations. A difference lies in the tendency of twist-driven rotation to saturate at lower heights than shear-driven rotation. For parameters characteristic of the source regions of erupting filaments and coronal mass ejections, the shear field is found to be the dominant origin of rotations in the corona and to be required if the rotation reaches angles of order 90 degrees and higher; it dominates even if the twist exceeds the threshold of the helical kink instability. The contributions by shear and twist to the total rotation can be disentangled in the analysis of observations if the rotation and rise profiles are simultaneously compared with model calculations. The resulting twist estimate allows one to judge whether the helical kink instability occurred. This is demonstrated for the erupting prominence in the "Cartwheel CME" on 9 April 2008, which has shown a rotation of approximately 115 deg. up to a height of 1.5 Solar R above the photosphere. Out of a range of
Multipli-Entangled Photons from a Spontaneous Parametric Down-Conversion Source
2011-01-01
Alsing, Corey J. Peters (AFRL/RITA); Enrique J. Galvez ( Colgate University, Hamilton, NY) 5d. PROJECT NUMBER QIS0 5e. TASK NUMBER PR 5f...and Enrique J. Galvez Colgate University, Hamilton, NY (USA) 1. ABSTRACT In this work, we discuss a novel compact source that generates six
Application of a hazard-based visual predictive check to evaluate parametric hazard models.
Huh, Yeamin; Hutmacher, Matthew M
2016-02-01
Parametric models used in time to event analyses are evaluated typically by survival-based visual predictive checks (VPC). Kaplan-Meier survival curves for the observed data are compared with those estimated using model-simulated data. Because the derivative of the log of the survival curve is related to the hazard--the typical quantity modeled in parametric analysis--isolation, interpretation and correction of deficiencies in the hazard model determined by inspection of survival-based VPC's is indirect and thus more difficult. The purpose of this study is to assess the performance of nonparametric hazard estimators of hazard functions to evaluate their viability as VPC diagnostics. Histogram-based and kernel-smoothing estimators were evaluated in terms of bias of estimating the hazard for Weibull and bathtub-shape hazard scenarios. After the evaluation of bias, these nonparametric estimators were assessed as a method for VPC evaluation of the hazard model. The results showed that nonparametric hazard estimators performed reasonably at the sample sizes studied with greater bias near the boundaries (time equal to 0 and last observation) as expected. Flexible bandwidth and boundary correction methods reduced these biases. All the nonparametric estimators indicated a misfit of the Weibull model when the true hazard was a bathtub shape. Overall, hazard-based VPC plots enabled more direct interpretation of the VPC results compared to survival-based VPC plots.
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 (<8%). The computational time depends on the number of basis functions selected and can be compatible with clinical use (~6h for a single subject analysis). The novel method is a robust 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.
NASA Astrophysics Data System (ADS)
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 photometric approach to parametric modelling for optimising multisegmented photodetector rings
NASA Astrophysics Data System (ADS)
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.
Battaglia, N.; Trac, H.; Cen, R.; Loeb, A.
2013-10-20
We present a new method for modeling inhomogeneous cosmic reionization on large scales. Utilizing high-resolution radiation-hydrodynamic simulations with 2048{sup 3} dark matter particles, 2048{sup 3} gas cells, and 17 billion adaptive rays in a L = 100 Mpc h {sup –1} box, we show that the density and reionization redshift fields are highly correlated on large scales (∼> 1 Mpc h {sup –1}). This correlation can be statistically represented by a scale-dependent linear bias. We construct a parametric function for the bias, which is then used to filter any large-scale density field to derive the corresponding spatially varying reionization redshift field. The parametric model has three free parameters that can be reduced to one free parameter when we fit the two bias parameters to simulation results. We can differentiate degenerate combinations of the bias parameters by combining results for the global ionization histories and correlation length between ionized regions. Unlike previous semi-analytic models, the evolution of the reionization redshift field in our model is directly compared cell by cell against simulations and performs well in all tests. Our model maps the high-resolution, intermediate-volume radiation-hydrodynamic simulations onto lower-resolution, larger-volume N-body simulations (∼> 2 Gpc h {sup –1}) in order to make mock observations and theoretical predictions.
Advanced parametrical modelling of 24 GHz radar sensor IC packaging components
NASA Astrophysics Data System (ADS)
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.
Analyzing resilience properties in oscillatory biological systems using parametric model checking.
Andreychenko, Alexander; Magnin, Morgan; Inoue, Katsumi
2016-11-01
Automated verification of living organism models allows us to gain previously unknown knowledge about underlying biological processes. In this paper we show how parametric time model checking can be applied to define the time behavior of biological oscillatory systems more precisely. In particular, we focus on the resilience properties of such systems. This notion was introduced to understand the behavior of biological systems (e.g. the mammalian circadian rhythm) that are reactive and adaptive enough to endorse major changes in their environment (e.g. jet-lags, day-night alternating work-time). We formalize these properties through parametric TCTL and investigate the influence of environmental conditions changes on the resilience of living organisms under the uncertainty in parameters. In particular, we discuss the influence of various perturbations, e.g. artificial jet-lag or components knock-out on the parameters controlling the oscillatory behavior. This analysis is crucial when it comes to model elicitation for dynamic biological systems. We demonstrate the applicability of this technique using a simplified model of circadian clock and discuss its results with regard to other previous studies based on hybrid modeling. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Fischer, Cornelia; Bartlome, Richard; Sigrist, Markus W.
2005-04-01
In this paper, we present first results of a spectral characterisation of doping substances using a resonant optoacoustic cell and a Nd:YAG laser pumped optical parametric generation (OPG) laser source in the mid-infrared wavelength range between 3.0 and 4.0 μm with periodically poled LiNbO3 as nonlinear medium for the frequency conversion. Single spectra covering a wavelength range of about 220 nm can be conducted within less than 2 hours (3s averaging time, 7s between consecutive data points, about 0.3nm step-width). Despite the large linewidth of the OPG source of 240 GHz (8 cm-1), the laser spectrometer is well suited for the spectral analysis of these large organic molecules as they exhibit structured continuum absorption over a wide spectral range rather than isolated absorption peaks. We present measured spectra of ephedrine, alprenolol, ethacrynic acid, etc. and discuss the potential of laser-based detection of doping substances both as a supplement to existing methods and in view of a fast in situ screening technique at sporting events.
NASA Astrophysics Data System (ADS)
Weaver, R.; Plesko, C. S.; Gisler, G. R.
2013-12-01
We are performing detailed hydrodynamic simulations of the interaction from a strong explosion with sample Asteroid objects. The purpose of these simulations is to apply modern hydrodynamic codes that have been well verified and validated (V&V) to the problem of mitigating the hazard from a potentially hazardous object (PHO), an asteroid or comet that is on an Earth crossing orbit. The code we use for these simulations is the RAGE code from Los Alamos National Laboratory [1-6]. Initial runs were performed using a spherical object. Next we ran simulations using the shape form from a known asteroid: 25143 Itokawa. This particular asteroid is not a PHO but we use its shape to consider the influence of non-spherical objects. The initial work was performed using 2D cylindrically symmetric simulations and simple geometries. We then performed a major fully 3D simulation. For an Itokawa size object (~500 m) and an explosion energies ranging from 0.5 - 1 megatons, the velocities imparted to all of the PHO "rocks" in all cases were many m/s. The velocities calculated were much larger than escape velocity and would preclude re-assembly of the fragments. The dispersion of the asteroid remnants is very directional from a surface burst, with all fragments moving away from the point of the explosion. This detail can be used to time the intercept for maximum movement off the original orbit. Results from these previous studies will be summarized for background. In the new work presented here we show a variety of parametric studies around these initial simulations. We modified the explosion energy by +/- 20% and varied the internal composition from a few large "rocks" to several hundred smaller rocks. The results of these parametric studies will be presented. We have also extended our work [6],[7] to stand-off nuclear bursts and will present the initial results for the energy deposition by a generic source into the non-uniform composition asteroid. The goal of this new work is to
Economic policy optimization based on both one stochastic model and the parametric control theory
NASA Astrophysics Data System (ADS)
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)
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.
Toward a curse of dimensionality appropriate (CODA) asymptotic theory for semi-parametric models.
Robins, J M; Ritov, Y
We argue, that due to the curse of dimensionality, there are major difficulties with any pure or smoothed likelihood-based method of inference in designed studies with randomly missing data when missingness depends on a high-dimensional vector of variables. We study in detail a semi-parametric superpopulation version of continuously stratified random sampling. We show that all estimators of the population mean that are uniformly consistent or that achieve an algebraic rate of convergence, no matter how slow, require the use of the selection (randomization) probabilities. We argue that, in contrast to likelihood methods which ignore these probabilities, inverse selection probability weighted estimators continue to perform well achieving uniform n 1/2-rates of convergence. We propose a curse of dimensionality appropriate (CODA) asymptotic theory for inference in non- and semi-parametric models in an attempt to formalize our arguments. We discuss whether our results constitute a fatal blow to the likelihood principle and study the attitude toward these that a committed subjective Bayesian would adopt. Finally, we apply our CODA theory to analyse the effect of the 'curse of dimensionality' in several interesting semi-parametric models, including a model for a two-armed randomized trial with randomization probabilities depending on a vector of continuous pretreatment covariates X. We provide substantive settings under which a subjective Bayesian would ignore the randomization probabilities in analysing the trial data. We then show that any statistician who ignores the randomization probabilities is unable to construct nominal 95 per cent confidence intervals for the true treatment effect that have both: (i) an expected length which goes to zero with increasing sample size; and (ii) a guaranteed expected actual coverage rate of at least 95 per cent over the ensemble of trials analysed by the statistician during his or her lifetime. However, we derive a new interval
Hybrid Model of Inhomogeneous Solar Wind Plasma Heating by Alfven Wave Spectrum: Parametric Studies
NASA Technical Reports Server (NTRS)
Ofman, L.
2010-01-01
Observations of the solar wind plasma at 0.3 AU and beyond show that a turbulent spectrum of magnetic fluctuations is present. Remote sensing observations of the corona indicate that heavy ions are hotter than protons and their temperature is anisotropic (T(sub perpindicular / T(sub parallel) >> 1). We study the heating and the acceleration of multi-ion plasma in the solar wind by a turbulent spectrum of Alfvenic fluctuations using a 2-D hybrid numerical model. In the hybrid model the protons and heavy ions are treated kinetically as particles, while the electrons are included as neutralizing background fluid. This is the first two-dimensional hybrid parametric study of the solar wind plasma that includes an input turbulent wave spectrum guided by observation with inhomogeneous background density. We also investigate the effects of He++ ion beams in the inhomogeneous background plasma density on the heating of the solar wind plasma. The 2-D hybrid model treats parallel and oblique waves, together with cross-field inhomogeneity, self-consistently. We investigate the parametric dependence of the perpendicular heating, and the temperature anisotropy in the H+-He++ solar wind plasma. It was found that the scaling of the magnetic fluctuations power spectrum steepens in the higher-density regions, and the heating is channeled to these regions from the surrounding lower-density plasma due to wave refraction. The model parameters are applicable to the expected solar wind conditions at about 10 solar radii.
Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models.
Zeng, Ping; Zhou, Xiang
2017-09-06
Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.Genetic prediction of complex traits with polygenic architecture has wide application from animal breeding to disease prevention. Here, Zeng and Zhou develop a non-parametric genetic prediction method based on latent Dirichlet Process regression models.
Hybrid Model of Inhomogeneous Solar Wind Plasma Heating by Alfven Wave Spectrum: Parametric Studies
NASA Technical Reports Server (NTRS)
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.
Model rotor high-speed impulsive noise - Parametric variations and full-scale comparisons
NASA Astrophysics Data System (ADS)
Splettstoesser, W. R.; Schultz, K. J.; Schmitz, F. H.; Boxwell, D. A.
1983-05-01
The results of a 1/7-scale model of the AH-1 series helicopter main rotor test in the German-Dutch anechoic wind tunnel are discussed, with emphasis given on exploring the important scaling parameters of helicopter-rotor high-speed impulsive noise. Nondimensional parameters are derived from the governing equations and employed to compare the model rotor measurements with full-scale investigations, using an equivalent in-flight technique. The peak acoustic pressure, impulsive noise directivity, and acoustic waveform of the model are found to scale well in shape and in amplitude with full-scale results. Parametric variations of the model-rotor acoustic measurements, such as the change of the high-speed impulsive noise level over a range of advancing-tip Mach numbers at constant advance ratio or constant velocity, are presented. It is concluded that model-scale rotors can be used to explore potential acoustic design innovations on full-scale helicopters.
Development of Parametric Mass and Volume Models for an Aerospace SOFC/Gas Turbine Hybrid System
NASA Technical Reports Server (NTRS)
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.
NASA Astrophysics Data System (ADS)
Ma, L. W. Lorraine; Ebrahimi, Mehran
2017-03-01
A mathematical formulation for intensity-based slice-to-volume registration is proposed. The approach is flexible and accommodates various regularization schemes, similarity measures, and optimizers. The framework is evaluated by registering 2D and 3D cardiac magnetic resonance (MR) images obtained in vivo, aimed at real- time MR-guided applications. Rigid-body and affine transformations are used to validate the parametric model. Target registration error (TRE), Jaccard, and Dice indices are used to evaluate the algorithm and demonstrate the accuracy of the registration scheme on both simulated and clinical data. Registration with the affine model appeared to be more robust than with the rigid model in controlled cases. By simply extending the rigid model to an affine model, alignment of the cardiac region generally improved, without the need for complex dissimilarity measures or regularizers.
A non-parametric consistency test of the ΛCDM model with Planck CMB data
NASA Astrophysics Data System (ADS)
Aghamousa, Amir; Hamann, Jan; Shafieloo, Arman
2017-09-01
Non-parametric reconstruction methods, such as Gaussian process (GP) regression, provide a model-independent way of estimating an underlying function and its uncertainty from noisy data. We demonstrate how GP-reconstruction can be used as a consistency test between a given data set and a specific model by looking for structures in the residuals of the data with respect to the model's best-fit. Applying this formalism to the Planck temperature and polarisation power spectrum measurements, we test their global consistency with the predictions of the base ΛCDM model. Our results do not show any serious inconsistencies, lending further support to the interpretation of the base ΛCDM model as cosmology's gold standard.
Raybaut, Myriam; Schmid, Thomas; Godard, Antoine; Mohamed, Ajmal K; Lefebvre, Michel; Marnas, Fabien; Flamant, Pierre; Bohman, Axel; Geiser, Peter; Kaspersen, Peter
2009-07-01
We report on a 2.05 microm nanosecond master oscillator power amplifier optical parametric source for CO2 differential-absorption lidar. The master oscillator consists of an entangled-cavity nanosecond optical parametric oscillator based on a type II periodically poled lithium niobate crystal that provides highly stable single-longitudinal-mode radiation. The signal emission is amplified by a multistage parametric amplifier to generate up to 11 mJ in a nearly diffraction-limited beam with an M2 quality factor of approximately 1.5 while maintaining single-longitudinal-mode emission with a frequency stability better than 3 MHz rms. This approach can be readily applied to the detection of various greenhouse gases.
Nakhaee, F; Law, M
2011-03-01
Parametric survival models have not previously been applied to survival following a diagnosis of HIV/AIDS in Australia. Four different parametric models--exponential, Weibull, log-normal and log-logistic--were applied to data both on HIV-positive cases and on cases diagnosed with AIDS collected through the national HIV/AIDS surveillance system. Using likelihood based goodness-of-fit criteria the Weibull model was found to be the best-fitted model for predicting survival following a diagnosis of HIV infection without and with a diagnosis of AIDS. Several covariates-age, sex, combined HIV exposure category, CD4 cell counts, antiretroviral treatment and AIDS-defining illnesses--were included in the parametric model to predict factors associated with future mortality. Predicted deaths were in agreement with the observed deaths following HIV infection and AIDS. The Weibull model will be applied for future projections of deaths from HIV/AIDS.
Parametric modeling of energy filtering by energy barriers in thermoelectric nanocomposites
Zianni, Xanthippi E-mail: xzianni@gmail.com; Narducci, Dario
2015-01-21
We present a parametric modeling of the thermoelectric transport coefficients based on a model previously used to interpret experimental measurements on the conductivity, σ, and Seebeck coefficient, S, in highly Boron-doped polycrystalline Si, where a very significant thermoelectric power factor (TPF) enhancement was observed. We have derived analytical formalism for the transport coefficients in the presence of an energy barrier assuming thermionic emission over the barrier for (i) non-degenerate and (ii) degenerate one-band semiconductor. Simple generic parametric equations are found that are in agreement with the exact Boltzmann transport formalism in a wide range of parameters. Moreover, we explore the effect of energy barriers in 1-d composite semiconductors in the presence of two phases: (a) the bulk-like phase and (b) the barrier phase. It is pointed out that significant TPF enhancement can be achieved in the composite structure of two phases with different thermal conductivities. The TPF enhancement is estimated as a function of temperature, the Fermi energy position, the type of scattering, and the barrier height. The derived modeling provides guidance for experiments and device design.
Shamshery, Pulkit; Wang, Ruo-Qian; Tran, Davis V; Winter V, Amos G
2017-01-01
Drip irrigation is a means of distributing the exact amount of water a plant needs by dripping water directly onto the root zone. It can produce up to 90% more crops than rain-fed irrigation, and reduce water consumption by 70% compared to conventional flood irrigation. Drip irrigation may enable millions of poor farmers to rise out of poverty by growing more and higher value crops, while not contributing to overconsumption of water. Achieving this impact will require broadening the engineering knowledge required to design new, low-cost, low-power drip irrigation technology, particularly for poor, off-grid communities in developing countries. For more than 50 years, pressure compensating (PC) drip emitters-which can maintain a constant flow rate under variations in pressure, to ensure uniform water distribution on a field-have been designed and optimized empirically. This study presents a parametric model that describes the fluid and solid mechanics that govern the behavior of a common PC emitter architecture, which uses a flexible diaphragm to limit flow. The model was validated by testing nine prototypes with geometric variations, all of which matched predicted performance to within R2 = 0.85. This parametric model will enable irrigation engineers to design new drip emitters with attributes that improve performance and lower cost, which will promote the use of drip irrigation throughout the world.
A parametric sizing model for Molten Regolith Electrolysis reactors to produce oxygen on the Moon
NASA Astrophysics Data System (ADS)
Schreiner, Samuel S.; Sibille, Laurent; Dominguez, Jesus A.; Hoffman, Jeffrey A.
2016-04-01
We present a parametric sizing model for a Molten Regolith Electrolysis (MRE) reactor that produces oxygen and molten metals from lunar regolith. The model has a foundation of regolith material property models validated using data from Apollo samples and simulants. A multiphysics simulation of an MRE reactor is developed and leveraged to generate a database linking reactor design and performance trends. A novel design methodology is created which utilizes this database to parametrically design an MRE reactor that can (1) sustain the required current, operating temperature, and mass of molten regolith to meet a desired oxygen production level, (2) operate for long periods of time by protecting the reactor walls from the corrosive molten regolith with a layer of solid "frozen" regolith, and (3) 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. Sensitivity analyses are presented for several design variables, including operating temperature, regolith feedstock composition, and the degree of operational flexibility.
Wang, Ruo-Qian; Tran, Davis V.; Winter V, Amos G.
2017-01-01
Drip irrigation is a means of distributing the exact amount of water a plant needs by dripping water directly onto the root zone. It can produce up to 90% more crops than rain-fed irrigation, and reduce water consumption by 70% compared to conventional flood irrigation. Drip irrigation may enable millions of poor farmers to rise out of poverty by growing more and higher value crops, while not contributing to overconsumption of water. Achieving this impact will require broadening the engineering knowledge required to design new, low-cost, low-power drip irrigation technology, particularly for poor, off-grid communities in developing countries. For more than 50 years, pressure compensating (PC) drip emitters—which can maintain a constant flow rate under variations in pressure, to ensure uniform water distribution on a field—have been designed and optimized empirically. This study presents a parametric model that describes the fluid and solid mechanics that govern the behavior of a common PC emitter architecture, which uses a flexible diaphragm to limit flow. The model was validated by testing nine prototypes with geometric variations, all of which matched predicted performance to within R2 = 0.85. This parametric model will enable irrigation engineers to design new drip emitters with attributes that improve performance and lower cost, which will promote the use of drip irrigation throughout the world. PMID:28384355
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
Baneshi, Mohammad-Reza; Bahmanbijari, Bahareh; Mahdian, Reza; Haji-Maghsoodi, Saeide; Nikbakht, Roya
2014-04-01
The Cox model is the dominant tool in clinical trials to compare treatment options. This model does not specify any specific form to the hazard function. On the other hand, parametric models allow the researcher to consider an appropriate shape of hazard function for the event of interest. The aim of this article is to compare performance of Cox and parametric models. We used data collected in a prospective clinical trial that aimed to compare performance of nasal intermittent positive pressure ventilation (NIPPV) and nasal continuous positive airway pressure (NCPAP) treatments in terms of survival of newborn infants who had respiratory distress syndrome (RDS). Performance of Cox, exponential, Weibull, and log-logistic models were compared in terms of goodness of fit. Fitting the Cox model, we have seen that infants who received NCPAP were 4.23 (Hazard Ratio= 4.23, 95% Confidence Interval: 1.87-9.59) times more likely to fail than those received NIPPV (P=0.001). Adequacy of the exponential model was rejected. We have seen a decreasing hazard rate over time, in both treatment groups. This decrease was sharper in NCPAP group. Akiake information criterion corresponded to the log-logistic model and was lower than all other models followed by Weibull model. Our results demonstrate the benefit of parametric survival models over traditional Cox regression model in terms of modeling of shape of hazard function. We saw a decreasing hazard that confirms the flexibility of parametric models in terms of the modeling of hazard rate.
Developing two non-parametric performance models for higher learning institutions
NASA Astrophysics Data System (ADS)
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.
Evaluation of parametric models by the prediction error in colorectal cancer survival analysis
Baghestani, Ahmad Reza; Gohari, Mahmood Reza; Orooji, Arezoo; Pourhoseingholi, Mohamad Amin; Zali, Mohammad Reza
2015-01-01
Aim: The aim of this study is to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) using parametric models and select the best model by predicting error’s technique. Background: Survival models are statistical techniques to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. Patients and methods: A total of 600 colorectal cancer patients who admitted to the Cancer Registry Center of Gastroenterology and Liver Disease Research Center, Taleghani Hospital, Tehran, were followed at least for 5 years and have completed selected information for this study. Body Mass Index (BMI), Sex, family history of CRC, tumor site, stage of disease and histology of tumor included in the analysis. The survival time was compared by the Log-rank test and multivariate analysis was carried out using parametric models including Log normal, Weibull and Log logistic regression. For selecting the best model, the prediction error by apparent loss was used. Results: Log rank test showed a better survival for females, BMI more than 25, patients with early stage at diagnosis and patients with colon tumor site. Prediction error by apparent loss was estimated and indicated that Weibull model was the best one for multivariate analysis. BMI and Stage were independent prognostic factors, according to Weibull model. Conclusion: In this study, according to prediction error Weibull regression showed a better fit. Prediction error would be a criterion to select the best model with the ability to make predictions of prognostic factors in survival analysis. PMID:26328040
NASA Astrophysics Data System (ADS)
Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino
2017-03-01
Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.
NASA Astrophysics Data System (ADS)
Wen-Long, Tian; Zhao-Hua, Wang; Jiang-Feng, Zhu; Zhi-Yi, Wei
2016-01-01
We demonstrate a widely tunable near-infrared source from 767 nm to 874 nm generated by the intracavity second harmonic generation (SHG) in an optical parametric oscillator pumped by a Yb:LYSO solid-state laser. The home-made Yb:LYSO oscillator centered at 1035 nm delivers an average power of 2 W and a pulse duration as short as 351 fs. Two MgO doped periodically poled lithium niobates (MgO:PPLN) with grating periods of 28.5-31.5 μm in steps of 0.5 μm and 19.5-21.3 μm in steps of 0.2 μm are used for the OPO and intracavity SHG, respectively. The maximum average output power of 180 mW at 798 nm was obtained and the output pulses have pulse duration of 313 fs at 792 nm if a sech2-pulse shape was assumed. In addition, tunable signal femtosecond pulses from 1428 nm to 1763 nm are also realized with the maximum average power of 355 mW at 1628 nm. Project supported by the National Key Basic Research Program of China (Grant No. 2013CB922402), the National Key Scientific Instruments Development Program of China (Grant No. 2012YQ120047), the National Natural Science Foundation of China (Grant Nos. 61205130 and 11174361), and the Key Deployment Project of Chinese Academy of Sciences (Grant No. KJZD-EW-L11-03).
Gu, Chenglin; Hu, Minglie; Fan, Jintao; Song, Youjian; Liu, Bowen; Chai, Lu; Wang, Chingyue; Reid, Derryck T
2015-03-09
We report a high average power tunable 51 MHz femtosecond ultraviolet (UV) laser source based on an intra-cavity sum frequency mixing optical parametric oscillator (OPO) pumped by a fiber laser. The UV laser is generated by sum frequency generation (SFG) between the second harmonic of a mode-locked Yb-fiber laser and the signal of the OPO. A non-collinear configuration is used in the SFG to compensate the group velocity mismatch, and to increase the SFG conversion efficiency dramatically. Tunable ultraviolet pulses within the wavelength range from 385 to 400 nm have been produced with a maximum average power of 402 mW and a pulse width of 286 fs at 2 W Yb-fiber laser pump, corresponding to 20.1% near-infrared to UV conversion efficiency at 387 nm. To our knowledge, this is the first demonstration of tunable femtosecond UV pulse generation from a fiber laser pumped OPO, and is also the highest average power tunable UV femtosecond pulses from an OPO.
High-power, Yb-fiber-laser-pumped, picosecond parametric source tunable across 752-860 nm.
Kumar, S Chaitanya; Kimmelma, O; Ebrahim-Zadeh, M
2012-05-01
We report a stable, high-power source of picosecond pulses in the near-infrared based on intracavity second harmonic generation (SHG) of a MgO:PPLN optical parametric oscillator synchronously pumped at 81 MHz by a mode-locked Yb-fiber laser. By exploiting the large spectral acceptance bandwidth for Type I (oo→e) SHG in β-BaB2O4 and a 5 mm crystal, we have generated picosecond pulses over 752-860 nm spectral range under minimal angle tuning, with as much as 3.5 W of output power at 778 nm and >2 W over 73% of the tuning range, in good beam quality with TEM00 spatial profile and M2<1.4. The SHG output pulses have durations of 15.2 ps, with a spectral bandwidth of ∼3.4 nm at 784 nm. In addition, the oscillator simultaneously provides a signal power of >1 W over 1505-1721 nm (25 THz) and idler power >1.8 W over 2787-3630 nm (25 THz), corresponding to a total (signal plus idler) tuning range of 1059 nm. The SHG, signal, and idler output exhibit passive long-term power stability better than 1.6%, 1.3%, and 1.6% rms, respectively, over 14 h.
Development of a parametric simulation model for forecasting goal-oriented treatment outcomes.
Yuan, Yong; Chen, Roland S; L'Italien, Gilbert; Karaniewsky, Robert
2004-01-01
Treatment-to-goal (TTG) analyses are frequently used to predict guideline-directed population control rates for drug therapies based on mean efficacy data. Nevertheless, estimates are commonly inaccurate because variability in efficacy is not considered. A new methodology was developed to improve TTG forecasting. Patient-level blood pressure (BP) lowering data sets, designed to simulate clinical trial results, were generated for testing from three underlying distributions: normal, lognormal, and beta. To emulate real-world conditions where patient-level data are unavailable, two approaches were considered: parametric--simulated BP lowering data were generated using the mean and standard deviation of the test data sets; and point-estimate--BP lowering was uniformly assigned as the mean lowering. BP control (systolic BP < 140 and diastolic BP < 90 mmHg) was forecasted by subtracting values generated by these two methods from baseline BP values in untreated hypertensive patients (n = 2483) from the Third National Health and Nutrition Examination Survey. Estimated control rates were compared to analyses where the patient-level data sets were bootstrapped. We assumed mean (+/- SD) BP lowering of 20 (12) mmHg systolic and 14 (7) mmHg diastolic. Parametric method predicted a BP control rate of 66.9% [95% confidence interval (CI) 65.7-67.9], similar to the bootstrapping approach (67.3%, 95% CI 65.9-68.8). The control rate projected based on the point-estimate method was 75.5%. The point-estimate method frequently led to substantially different results under a wide range of model assumptions. A new parametric-based forecasting method, which addresses underlying variability, improves on estimates based on mean efficacy only. In the absence of patient-level data, this method is generalizable to different therapeutic areas.
Grain-scale modeling and splash parametrization for aeolian sand transport
NASA Astrophysics Data System (ADS)
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.
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.
Developing integrated parametric planning models for budgeting and managing complex projects
NASA Technical Reports Server (NTRS)
Etnyre, Vance A.; Black, Ken U.
1988-01-01
The applicability of integrated parametric models for the budgeting and management of complex projects is investigated. Methods for building a very flexible, interactive prototype for a project planning system, and software resources available for this purpose, are discussed and evaluated. The prototype is required to be sensitive to changing objectives, changing target dates, changing costs relationships, and changing budget constraints. To achieve the integration of costs and project and task durations, parametric cost functions are defined by a process of trapezoidal segmentation, where the total cost for the project is the sum of the various project cost segments, and each project cost segment is the integral of a linearly segmented cost loading function over a specific interval. The cost can thus be expressed algebraically. The prototype was designed using Lotus-123 as the primary software tool. This prototype implements a methodology for interactive project scheduling that provides a model of a system that meets most of the goals for the first phase of the study and some of the goals for the second phase.
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.
NASA Technical Reports Server (NTRS)
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.
Kvist, Kajsa; Gerster, Mette; Andersen, Per Kragh; Kessing, Lars Vedel
2007-12-30
For recurrent events there is evidence that misspecification of the frailty distribution can cause severe bias in estimated regression coefficients (Am. J. Epidemiol 1998; 149:404-411; Statist. Med. 2006; 25:1672-1684). In this paper we adapt a procedure originally suggested in (Biometrika 1999; 86:381-393) for parallel data for checking the gamma frailty to recurrent events. To apply the model checking procedure, a consistent non-parametric estimator for the marginal gap time distributions is needed. This is in general not possible due to induced dependent censoring in the recurrent events setting, however, in (Biometrika 1999; 86:59-70) a non-parametric estimator for the joint gap time distributions based on the principle of inverse probability of censoring weights is suggested. Here, we attempt to apply this estimator in the model checking procedure and the performance of the method is investigated with simulations and applied to Danish registry data. The method is further investigated using the usual Kaplan-Meier estimator and a marginalized estimator for the marginal gap time distributions. We conclude that the procedure only works when the recurrent event is common and when the intra-individual association between gap times is weak.
NASA Astrophysics Data System (ADS)
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.
A shape constrained parametric active contour model for breast contour detection.
Lee, Juhun; Muralidhar, Gautam S; Reece, Gregory P; Markey, Mia K
2012-01-01
Quantitative measures of breast morphology can help a breast cancer survivor to understand outcomes of reconstructive surgeries. One bottleneck of quantifying breast morphology is that there are only a few reliable automation algorithms for detecting the breast contour. This study proposes a novel approach for detecting the breast contour, which is based on a parametric active contour model. In addition to employing the traditional parametric active contour model, the proposed approach enforces a mathematical shape constraint based on the catenary curve, which has been previously shown to capture the overall shape of the breast contour reliably. The mathematical shape constraint regulates the evolution of the active contour and helps the contour evolve towards the breast, while minimizing the undesired effects of other structures such as, the nipple/areola and scars. The efficacy of the proposed approach was evaluated on anterior posterior photographs of women who underwent or were scheduled for breast reconstruction surgery including autologous tissue reconstruction. The proposed algorithm shows promising results for detecting the breast contour.
Dai, Heng; Ye, Ming; Walker, Anthony P.; ...
2017-03-28
A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods withmore » variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. Here, for demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models of hydraulic conductivity. Each process model has its own random parameters. Finally, the new process sensitivity index is mathematically general, and can be applied to a wide range of problems in hydrology and beyond.« less
NASA Astrophysics Data System (ADS)
Dai, Heng; Ye, Ming; Walker, Anthony P.; Chen, Xingyuan
2017-04-01
A hydrological model consists of multiple process level submodels, and each submodel represents a process key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this study develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis. A process sensitivity index is derived as a single summary measure of relative process importance, and the index includes variance in model outputs caused by uncertainty in both process models and their parameters. For demonstration, the new index is used to assign importance to the processes of recharge and geology in a synthetic study of groundwater reactive transport modeling. The recharge process is simulated by two models that convert precipitation to recharge, and the geology process is simulated by two models 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.
NASA Astrophysics Data System (ADS)
Karakatsanis, Nicolas A.; Rahmim, Arman
2014-03-01
Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis. In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear generalized Patlak 4D nested reconstruction algorithm. A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed acquisitions. Quantitative analysis on the Ki images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for tumor diagnosis and treatment response monitoring.
Parametric Study of Synthetic-Jet-Based Flow Control on a Vertical Tail Model
NASA Astrophysics Data System (ADS)
Monastero, Marianne; Lindstrom, Annika; Beyar, Michael; Amitay, Michael
2015-11-01
Separation control over the rudder of the vertical tail of a commercial airplane using synthetic-jet-based flow control can lead to a reduction in tail size, with an associated decrease in drag and increase in fuel savings. A parametric, experimental study was undertaken using an array of finite span synthetic jets to investigate the sensitivity of the enhanced vertical tail side force to jet parameters, such as jet spanwise spacing and jet momentum coefficient. A generic wind tunnel model was designed and fabricated to fundamentally study the effects of the jet parameters at varying rudder deflection and model sideslip angles. Wind tunnel results obtained from pressure measurements and tuft flow visualization in the Rensselaer Polytechnic Subsonic Wind Tunnel show a decrease in separation severity and increase in model performance in comparison to the baseline, non-actuated case. The sensitivity to various parameters will be presented.
Bayesian Spatial Semi-Parametric Modeling of HIV Variation in Kenya
Ngesa, Oscar; Mwambi, Henry; Achia, Thomas
2014-01-01
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. PMID:25061669
A model for straight and helical solar jets. II. Parametric study of the plasma beta
NASA Astrophysics Data System (ADS)
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.
Modeling and Simulation of a Parametrically Resonant Micromirror With Duty-Cycled Excitation.
Shahid, Wajiha; Qiu, Zhen; Duan, Xiyu; Li, Haijun; Wang, Thomas D; Oldham, Kenn R
2014-12-01
High frequency large scanning angle electrostatically actuated microelectromechanical systems (MEMS) mirrors are used in a variety of applications involving fast optical scanning. A 1-D parametrically resonant torsional micromirror for use in biomedical imaging is analyzed here with respect to operation by duty-cycled square waves. Duty-cycled square wave excitation can have significant advantages for practical mirror regulation and/or control. The mirror's nonlinear dynamics under such excitation is analyzed in a Hill's equation form. This form is used to predict stability regions (the voltage-frequency relationship) of parametric resonance behavior over large scanning angles using iterative approximations for nonlinear capacitance behavior of the mirror. Numerical simulations are also performed to obtain the mirror's frequency response over several voltages for various duty cycles. Frequency sweeps, stability results, and duty cycle trends from both analytical and simulation methods are compared with experimental results. Both analytical models and simulations show good agreement with experimental results over the range of duty cycled excitations tested. This paper discusses the implications of changing amplitude and phase with duty cycle for robust open-loop operation and future closed-loop operating strategies.
Modeling and Simulation of a Parametrically Resonant Micromirror With Duty-Cycled Excitation
Shahid, Wajiha; Qiu, Zhen; Duan, Xiyu; Li, Haijun; Wang, Thomas D.; Oldham, Kenn R.
2014-01-01
High frequency large scanning angle electrostatically actuated microelectromechanical systems (MEMS) mirrors are used in a variety of applications involving fast optical scanning. A 1-D parametrically resonant torsional micromirror for use in biomedical imaging is analyzed here with respect to operation by duty-cycled square waves. Duty-cycled square wave excitation can have significant advantages for practical mirror regulation and/or control. The mirror’s nonlinear dynamics under such excitation is analyzed in a Hill’s equation form. This form is used to predict stability regions (the voltage-frequency relationship) of parametric resonance behavior over large scanning angles using iterative approximations for nonlinear capacitance behavior of the mirror. Numerical simulations are also performed to obtain the mirror’s frequency response over several voltages for various duty cycles. Frequency sweeps, stability results, and duty cycle trends from both analytical and simulation methods are compared with experimental results. Both analytical models and simulations show good agreement with experimental results over the range of duty cycled excitations tested. This paper discusses the implications of changing amplitude and phase with duty cycle for robust open-loop operation and future closed-loop operating strategies. PMID:25506188
NASA Astrophysics Data System (ADS)
Alsing, Paul M.
2015-04-01
In this paper we extend the investigation of Adami and Ver Steeg (2014 Class. Quantum Grav. 31 075015) to treat the process of black hole (BH) particle emission effectively as the analogous quantum optical process of parametric down conversion with a dynamical (depleted versus non-depleted) ‘pump’ source mode which models the evaporating BH energy degree of freedom. We investigate both the short time (non-depleted pump) and long time (depleted pump) regimes of the quantum state and its impact on the Holevo channel capacity for commu.nicating information from the far past to the far future in the presence of Hawking radiation. The new feature introduced in this work is the coupling of the emitted Hawking radiation modes through the common BH ‘source pump’ mode which phenomenologically represents a quantized energy degree of freedom of the gravitational field. This (zero-dimensional) model serves as a simplified arena to explore BH particle production/evaporation and back-action effects under an explicitly unitary evolution that enforces quantized energy/particle conservation. Within our analogous quantum optical model we examine the entanglement between two emitted particle/anti-particle and anti-particle/particle pairs coupled via the BH evaporating ‘pump’ source. We also analytically and dynamically verify the ‘Page information time’ for our model, which refers to the conventionally held belief that the information in the BH radiation becomes significant after the BH has evaporated half its initial energy into the outgoing radiation. Lastly, we investigate the effect of BH particle production/evaporation on two modes in the exterior region of the BH event horizon that are initially maximally entangled, when one mode falls inward and interacts with the BH, and the other remains forever outside and non-interacting.
Time-varying linear and nonlinear parametric model for Granger causality analysis.
Li, Yang; Wei, Hua-Liang; Billings, Steve A; Liao, Xiao-Feng
2012-04-01
Statistical measures such as coherence, mutual information, or correlation are usually applied to evaluate the interactions between two or more signals. However, these methods cannot distinguish directions of flow between two signals. The capability to detect causalities is highly desirable for understanding the cooperative nature of complex systems. The main objective of this work is to present a linear and nonlinear time-varying parametric modeling and identification approach that can be used to detect Granger causality, which may change with time and may not be detected by traditional methods. A numerical example, in which the exact causal influences relationships, is presented to illustrate the performance of the method for time-varying Granger causality detection. The approach is applied to EEG signals to track and detect hidden potential causalities. One advantage of the proposed model, compared with traditional Granger causality, is that the results are easier to interpret and yield additional insights into the transient directed dynamical Granger causality interactions.
Ramos, German; Cobos, Maximo
2013-09-01
Parametric methods for modeling the perceptually relevant features of head-related transfer functions (HRTFs) are very important for the development of low-cost immersive sound applications. This letter describes an efficient method based on a low-order infinite impulse response filter implemented by a chain of second order sections of conventional shelving and peak audio filters. The parameters (central frequency, gain, and quality factor) are numerically adjusted by iteratively fitting the frequency response of the filter to the desired HRTF. Besides allowing for low-order binaural models, the proposed approach provides an efficient way to synthesize HRTFs for non-measured angles by applying a simple interpolation between the parameters from neighboring responses. Additionally, the HRTF database size is significantly reduced.
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
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. PMID:26905728
NASA Astrophysics Data System (ADS)
Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino
2017-04-01
Among different approaches to bias correct climate model (CM) results, distribution mapping has been identified as the most efficient one in reproducing the statistics of rainfall at regional scales, and at temporal resolutions suitable to run hydrologic models (e.g. daily). Yet, its implementation remains at a basic level, based on empirical distributions derived from control samples (referred to as non-parametric, or empirical distribution mapping), which makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of CM results, and may lead to significant biases, especially when focus is on extreme rainfall estimation. In an effort to address these shortcomings, we use a two component theoretical distribution model (i.e. a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates) to propose a parametric bias correction procedure suited for regional frequency analysis. The latter is implemented by proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data (KUD). To assess the performance of the suggested parametric approach relative to non-parametric distribution mapping, we use daily raingauge measurements from a dense network in the island of Sardinia (Italy), and climate model rainfall data from 4 CMs of the ENSEMBLES project, to apply both methods to different combinations of control and validation periods. The obtained results shed light on the competitive advantages of the parametric approach relative to the non-parametric one, with the former being more accurate and considerably less sensitive to the characteristics of the control period, independent of the climate model used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available
Savitsky, Terrance D; Paddock, Susan M
2014-03-01
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 subject's 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 allow 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++.
Parametric Resonance Revisited
NASA Astrophysics Data System (ADS)
van den Broeck, C.; Bena, I.
The phenomenon of parametric resonance is revisited. Several physical examples are reviewed and an exactly solvable model is discussed. A mean field theory is presented for globally coupled parametric oscillators with randomly distributed phases. A new type of collective instability appears, which is similar in nature to that of noise induced phase transitions.
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
Fuzzy modeling for chaotic systems via interval type-2 T-S fuzzy model with parametric uncertainty
NASA Astrophysics Data System (ADS)
Hasanifard, Goran; Gharaveisi, Ali Akbar; Vali, Mohammad Ali
2014-02-01
A motivation for using fuzzy systems stems in part from the fact that they are particularly suitable for processes when the physical systems or qualitative criteria are too complex to model and they have provided an efficient and effective way in the control of complex uncertain nonlinear systems. To realize a fuzzy model-based design for chaotic systems, it is mostly preferred to represent them by T-S fuzzy models. In this paper, a new fuzzy modeling method has been introduced for chaotic systems via the interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model. An IT2 fuzzy model is proposed to represent a chaotic system subjected to parametric uncertainty, covered by the lower and upper membership functions of the interval type-2 fuzzy sets. Investigating many well-known chaotic systems, it is obvious that nonlinear terms have a single common variable or they depend only on one variable. If it is taken as the premise variable of fuzzy rules and another premise variable is defined subject to parametric uncertainties, a simple IT2 T-S fuzzy dynamical model can be obtained and will represent many well-known chaotic systems. This IT2 T-S fuzzy model can be used for physical application, chaotic synchronization, etc. The proposed approach is numerically applied to the well-known Lorenz system and Rossler system in MATLAB environment.
NASA Astrophysics Data System (ADS)
Karbon, Maria; Heinkelmann, Robert; Mora-Diaz, Julian; Xu, Minghui; Nilsson, Tobias; Schuh, Harald
2017-07-01
The radio sources within the most recent celestial reference frame (CRF) catalog ICRF2 are represented by a single, time-invariant coordinate pair. The datum sources were chosen mainly according to certain statistical properties of their position time series. Yet, such statistics are not applicable unconditionally, and also ambiguous. However, ignoring systematics in the source positions of the datum sources inevitably leads to a degradation of the quality of the frame and, therefore, also of the derived quantities such as the Earth orientation parameters. One possible approach to overcome these deficiencies is to extend the parametrization of the source positions, similarly to what is done for the station positions. We decided to use the multivariate adaptive regression splines algorithm to parametrize the source coordinates. It allows a great deal of automation, by combining recursive partitioning and spline fitting in an optimal way. The algorithm finds the ideal knot positions for the splines and, thus, the best number of polynomial pieces to fit the data autonomously. With that we can correct the ICRF2 a priori coordinates for our analysis and eliminate the systematics in the position estimates. This allows us to introduce also special handling sources into the datum definition, leading to on average 30 % more sources in the datum. We find that not only the CPO can be improved by more than 10 % due to the improved geometry, but also the station positions, especially in the early years of VLBI, can benefit greatly.
NASA Astrophysics Data System (ADS)
Karbon, Maria; Heinkelmann, Robert; Mora-Diaz, Julian; Xu, Minghui; Nilsson, Tobias; Schuh, Harald
2016-09-01
The radio sources within the most recent celestial reference frame (CRF) catalog ICRF2 are represented by a single, time-invariant coordinate pair. The datum sources were chosen mainly according to certain statistical properties of their position time series. Yet, such statistics are not applicable unconditionally, and also ambiguous. However, ignoring systematics in the source positions of the datum sources inevitably leads to a degradation of the quality of the frame and, therefore, also of the derived quantities such as the Earth orientation parameters. One possible approach to overcome these deficiencies is to extend the parametrization of the source positions, similarly to what is done for the station positions. We decided to use the multivariate adaptive regression splines algorithm to parametrize the source coordinates. It allows a great deal of automation, by combining recursive partitioning and spline fitting in an optimal way. The algorithm finds the ideal knot positions for the splines and, thus, the best number of polynomial pieces to fit the data autonomously. With that we can correct the ICRF2 a priori coordinates for our analysis and eliminate the systematics in the position estimates. This allows us to introduce also special handling sources into the datum definition, leading to on average 30 % more sources in the datum. We find that not only the CPO can be improved by more than 10 % due to the improved geometry, but also the station positions, especially in the early years of VLBI, can benefit greatly.
NASA Technical Reports Server (NTRS)
Borisenkov, Y. P.
1974-01-01
A small parametric, nonadiabatic model for precomputation of meteorological fields on the basis of complete equations, along with its energetic analogs is described. The model incorporates integral characteristics of the components of the wind speed and the analogous functions of the total fluxes of the ocean, and uses a Cartesian isobaric system of coordinates.
Karev, Georgy P; Novozhilov, Artem S; Koonin, Eugene V
2006-01-01
Background: One of the mechanisms that ensure cancer robustness is tumor heterogeneity, and its effects on tumor cells dynamics have to be taken into account when studying cancer progression. There is no unifying theoretical framework in mathematical modeling of carcinogenesis that would account for parametric heterogeneity. Results: Here we formulate a modeling approach that naturally takes stock of inherent cancer cell heterogeneity and illustrate it with a model of interaction between a tumor and an oncolytic virus. We show that several phenomena that are absent in homogeneous models, such as cancer recurrence, tumor dormancy, and others, appear in heterogeneous setting. We also demonstrate that, within the applied modeling framework, to overcome the adverse effect of tumor cell heterogeneity on the outcome of cancer treatment, a heterogeneous population of an oncolytic virus must be used. Heterogeneity in parameters of the model, such as tumor cell susceptibility to virus infection and the ability of an oncolytic virus to infect tumor cells, can lead to complex, irregular evolution of the tumor. Thus, quasi-chaotic behavior of the tumor-virus system can be caused not only by random perturbations but also by the heterogeneity of the tumor and the virus. Conclusion: The modeling approach described here reveals the importance of tumor cell and virus heterogeneity for the outcome of cancer therapy. It should be straightforward to apply these techniques to mathematical modeling of other types of anticancer therapy. Reviewers: Leonid Hanin (nominated by Arcady Mushegian), Natalia Komarova (nominated by Orly Alter), and David Krakauer. PMID:17018145
Tropical cyclone wind field asymmetry—Development and evaluation of a new parametric model
NASA Astrophysics Data System (ADS)
Olfateh, Mohammad; Callaghan, David P.; Nielsen, Peter; Baldock, Tom E.
2017-01-01
A new parametric model is developed to describe the wind field asymmetry commonly observed in tropical cyclones or hurricanes in a reference frame fixed at its center. Observations from 21 hurricanes from the North Atlantic basin and TC Roger (1993) in the Coral Sea are analyzed to determine the azimuthal and radial asymmetry typical in these mesoscale systems after removing the forward speed. On the basis of the observations, a new asymmetric directional wind model is proposed which adjusts the widely used Holland (1980) axisymmetric wind model to account for the action of blocking high-pressure systems, boundary layer friction, and forward speed. The model is tested against the observations and demonstrated to capture the physical features of asymmetric cyclones and provides a better fit to observed winds than the Holland model. Optimum values and distributions of the model parameters are derived for use in statistical modeling. Finally, the model is used to investigate the asymmetric character of TC systems, including the azimuth of the maximum wind speed, the degree of asymmetry, and the relationship between asymmetry and forward speed.
Quantitative vertebral morphometry based on parametric modeling of vertebral bodies in 3D.
Stern, D; Njagulj, V; Likar, B; Pernuš, F; Vrtovec, T
2013-04-01
Quantitative vertebral morphometry (QVM) was performed by parametric modeling of vertebral bodies in three dimensions (3D). Identification of vertebral fractures in two dimensions is a challenging task due to the projective nature of radiographic images and variability in the vertebral shape. By generating detailed 3D anatomical images, computed tomography (CT) enables accurate measurement of vertebral deformations and fractures. A detailed 3D representation of the vertebral body shape is obtained by automatically aligning a parametric 3D model to vertebral bodies in CT images. The parameters of the 3D model describe clinically meaningful morphometric vertebral body features, and QVM in 3D is performed by comparing the parameters to their statistical values. Thresholds and parameters that best discriminate between normal and fractured vertebral bodies are determined by applying statistical classification analysis. The proposed QVM in 3D was applied to 454 normal and 228 fractured vertebral bodies, yielding classification sensitivity of 92.5% at 7.5% specificity, with corresponding accuracy of 92.5% and precision of 86.1%. The 3D shape parameters that provided the best separation between normal and fractured vertebral bodies were the vertebral body height and the inclination and concavity of both vertebral endplates. The described QVM in 3D is able to efficiently and objectively discriminate between normal and fractured vertebral bodies and identify morphological cases (wedge, (bi)concavity, or crush) and grades (1, 2, or 3) of vertebral body fractures. It may be therefore valuable for diagnosing and predicting vertebral fractures in patients who are at risk of osteoporosis.
Adams, Matthew S.; Scott, Serena J.; Salgaonkar, Vasant A.; Sommer, Graham; Diederich, Chris J.
2016-01-01
Purpose To investigate endoluminal ultrasound applicator configurations for volumetric thermal ablation and hyperthermia of pancreatic tumors using 3D acoustic and biothermal finite element models. Materials and Methods Parametric studies compared endoluminal heating performance for varying applicator transducer configurations (planar, curvilinear-focused, or radial-diverging), frequencies (1–5 MHz), and anatomical conditions. Patient-specific pancreatic head and body tumor models were used to evaluate feasibility of generating hyperthermia and thermal ablation using an applicator positioned in the duodenal or stomach lumen. Temperature and thermal dose were calculated to define ablation (>240 EM43°C) and moderate hyperthermia (40–45 °C) boundaries, and to assess sparing of sensitive tissues. Proportional-integral control was incorporated to regulate maximum temperature to 70–80 °C for ablation and 45 °C for hyperthermia in target regions. Results Parametric studies indicated that 1–3 MHz planar transducers are most suitable for volumetric ablation, producing 5–8 cm3 lesion volumes for a stationary 5 minute sonication. Curvilinear-focused geometries produce more localized ablation to 20–45 mm depth from the GI tract and enhance thermal sparing (Tmax<42 °C) of the luminal wall. Patient anatomy simulations show feasibility in ablating 60.1–92.9% of head/body tumor volumes (4.3–37.2 cm3) with dose <15 EM43°C in the luminal wall for 18–48 min treatment durations, using 1–3 applicator placements in GI lumen. For hyperthermia, planar and radial-diverging transducers could maintain up to 8 cm3 and 15 cm3 of tissue, respectively, between 40–45 °C for a single applicator placement. Conclusions Modeling studies indicate the feasibility of endoluminal ultrasound for volumetric thermal ablation or hyperthermia treatment of pancreatic tumor tissue. PMID:27097663
Mace, Andy; Rudolph, David L.; Kachanoski , R. Gary
1998-01-01
The performance of parametric models used to describe soil water retention (SWR) properties and predict unsaturated hydraulic conductivity (K) as a function of volumetric water content (θ) is examined using SWR and K(θ) data for coarse sand and gravel sediments. Six 70 cm long, 10 cm diameter cores of glacial outwash were instrumented at eight depths with porous cup ten-siometers and time domain reflectometry probes to measure soil water pressure head (h) and θ, respectively, for seven unsaturated and one saturated steady-state flow conditions. Forty-two θ(h) and K(θ) relationships were measured from the infiltration tests on the cores. Of the four SWR models compared in the analysis, the van Genuchten (1980) equation with parameters m and n restricted according to the Mualem (m = 1 - 1/n) criterion is best suited to describe the θ(h) relationships. The accuracy of two models that predict K(θ) using parameter values derived from the SWR models was also evaluated. The model developed by van Genuchten (1980) based on the theoretical expression of Mualem (1976) predicted K(θ) more accurately than the van Genuchten (1980) model based on the theory of Burdine (1953). A sensitivity analysis shows that more accurate predictions of K(θ) are achieved using SWR model parameters derived with residual water content (θr) specified according to independent measurements of θ at values of h where θ/h ∼ 0 rather than model-fit θr values. The accuracy of the model K(θ) function improves markedly when at least one value of unsaturated K is used to scale the K(θ) function predicted using the saturated K. The results of this investigation indicate that the hydraulic properties of coarse-grained sediments can be accurately described using the parametric models. In addition, data collection efforts should focus on measuring at least one value of unsaturated hydraulic conductivity and as complete a set of SWR data as possible, particularly in the dry range.
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.
Semiparametric modeling: Correcting low-dimensional model error in parametric models
NASA Astrophysics Data System (ADS)
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.
Ma, Weiping; Feng, Yang; Chen, Kani; Ying, Zhiliang
2015-11-01
Motivated by modeling and analysis of mass-spectrometry data, a semi- and nonparametric model is proposed that consists of linear parametric components for individual location and scale and a nonparametric regression function for the common shape. A multi-step approach is developed that simultaneously estimates the parametric components and the nonparametric function. Under certain regularity conditions, it is shown that the resulting estimators is consistent and asymptotic normal for the parametric part and achieve the optimal rate of convergence for the nonparametric part when the bandwidth is suitably chosen. Simulation results are presented to demonstrate the effectiveness and finite-sample performance of the method. The method is also applied to a SELDI-TOF mass spectrometry data set from a study of liver cancer patients.
Parametric study of a Hill-type hyperelastic skeletal muscle model.
Lu, Y T; Beldie, L; Walker, B; Richmond, S; Middleton, J
2011-05-01
Hill's one-dimensional three-element model has often been used for formulating a three-dimensional skeletal muscle constitutive model, which generally involves several material parameters. However, only few of these parameters have physical meanings and can be experimentally determined. In this paper, a parametric study of a Hill-type hyperelastic skeletal muscle model is performed. First, the Hill-type hyperelastic skeletal muscle model is formulated, containing 13 material parameters. The values or value ranges of these parameters are discussed. The muscle model is then used to predict the behaviour of New Zealand white rabbit hind leg muscle tibialis anterior and a sensitivity study of several parameters is performed. Results show that some parameters in the muscle model can be experimentally determined, some parameters have their own value ranges and the muscle model can predict the experimental data by tuning the parameters within their value ranges. The results from the sensitivity study can help understand how some parameters influence the total muscle stress.
The Analytical Parametrization of Fusion Barrier by Using the Skyrme Energy-Density Function Model
NASA Astrophysics Data System (ADS)
Zanganeh, V.; Mirzaei, M.; N., Wang
2015-08-01
Using the skyrme energy density formalism, a pocket formula is introduced for barrier heights and positions of 95 fusion reactions (48 ≤ ZP ZT ≤ 1520) with respect to the charge and mass numbers of the interacting nuclei. It is shown that the parameterized values of RB and VB are able to reproduce the corresponding experimental data with good accuracy. Moreover, the absolute errors of our formulas are less than those obtained using the analytical parametrization forms of the fusion barrier based on the proximity versions. The ability of the parameterized forms of the barrier heights and its positions to reproduce the experimental data of the fusion cross section have been analyzed using the Wong model.
NASA Technical Reports Server (NTRS)
Dittmar, J. H.
1977-01-01
A high tip speed turboprop is being considered as a future energy conservative airplane. The high tip speed of the propeller combined with the cruise speed of the airplane may result in supersonic relative flow on the propeller tips. These supersonic blade sections could generate noise that is a cabin environment problem. An existing supersonic propeller noise model was parametrically investigated to identify and evaluate the noise reduction variables. Both independent and interdependent parameter variations (constant propeller thrust) were performed. The noise reductions indicated by the independent investigation varied from sizable in the case of reducing Mach number to minimal for adjusting the thickness and loading distributions. The noise reduction possibilities of decreasing relative Mach number were further investigated during the interdependent variations. The interdependent investigation indicated that significant noise reductions could be achieved by increasing the propeller diameter and/or increasing the number of propeller blades while maintaining a constant propeller thrust.
Skew-Quad Parametric-Resonance Ionization Cooling: Theory and Modeling
Afanaciev, Andre; Derbenev, Yaroslav S.; Morozov, Vasiliy; Sy, Amy; Johnson, Rolland P.
2015-09-01
Muon beam ionization cooling is a key component for the next generation of high-luminosity muon colliders. To reach adequately high luminosity without excessively large muon intensities, it was proposed previously to combine ionization cooling with techniques using a parametric resonance (PIC). Practical implementation of PIC proposal is a subject of this report. We show that an addition of skew quadrupoles to a planar PIC channel gives enough flexibility in the design to avoid unwanted resonances, while meeting the requirements of radially-periodic beam focusing at ionization-cooling plates, large dynamic aperture and an oscillating dispersion needed for aberration corrections. Theoretical arguments are corroborated with models and a detailed numerical analysis, providing step-by-step guidance for the design of Skew-quad PIC (SPIC) beamline.
Moment stability for a predator-prey model with parametric dichotomous noises
NASA Astrophysics Data System (ADS)
Jin, Yan-Fei
2015-06-01
In this paper, we investigate the solution moment stability for a Harrison-type predator-prey model with parametric dichotomous noises. Using the Shapiro-Loginov formula, the equations for the first-order and second-order moments are obtained and the corresponding stable conditions are given. It is found that the solution moment stability depends on the noise intensity and correlation time of noise. The first-order and second-order moments become unstable with the decrease of correlation time. That is, the dichotomous noise can improve the solution moment stability with respect to Gaussian white noise. Finally, some numerical results are presented to verify the theoretical analyses. Project supported by the National Natural Science Foundation of China (Grant No. 11272051).
Parametric geometric model and shape optimization of an underwater glider with blended-wing-body
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Ollinaho, Pirkka; Lock, Sarah-Jane; Leutbecher, Martin; Bechtold, Peter; Beljaars, Anton; Bozzo, Alessio; Forbes, Richard M.; Haiden, Thomas; Hogan, Robin J.; Sandu, Irina
2017-04-01
Ensemble prediction systems rely on representations of the uncertainties in the model itself, in addition to the initial conditions, to produce reliable forecasts. We present a novel approach for representing the model uncertainties through perturbations in the model closure parameters. Spatially and temporally changing perturbations are drawn from prescribed distributions. Unique perturbation patterns are applied to 20 parameters and variables in the ECMWF IFS parametrizations of (a) turbulent diffusion and subgrid orography, (b) convection, (c) clouds and large-scale precipitation, and (d) radiation. Sensitivity of the SPP scheme is studied through altering the spatial and temporal dimensions of the perturbations as well as through changes in the prescribed distributions. The scheme is benchmarked against the ECMWF operational stochastic physics scheme, SPPT. Differences between the schemes are discussed in short-, medium-, and climatological-ranges. In short-range forecasts (less than 24 h), the two schemes display similar skill. However, in the medium-range (up to forecast day 15), the SPPT scheme produces more skilful ensembles for a given set of fixed initial condition perturbations. When comparing long model integrations the SPP scheme displays a better fit to a range of variables. A closer study of the model tendencies in the short ranges indicates that the two schemes represent different aspects of model uncertainty.
Parametric Packet-Layer Model for Evaluation Audio Quality in Multimedia Streaming Services
NASA Astrophysics Data System (ADS)
Egi, Noritsugu; Hayashi, Takanori; Takahashi, Akira
We propose a parametric packet-layer model for monitoring audio quality in multimedia streaming services such as Internet protocol television (IPTV). This model estimates audio quality of experience (QoE) on the basis of quality degradation due to coding and packet loss of an audio sequence. The input parameters of this model are audio bit rate, sampling rate, frame length, packet-loss frequency, and average burst length. Audio bit rate, packet-loss frequency, and average burst length are calculated from header information in received IP packets. For sampling rate, frame length, and audio codec type, the values or the names used in monitored services are input into this model directly. We performed a subjective listening test to examine the relationships between these input parameters and perceived audio quality. The codec used in this test was the Advanced Audio Codec-Low Complexity (AAC-LC), which is one of the international standards for audio coding. On the basis of the test results, we developed an audio quality evaluation model. The verification results indicate that audio quality estimated by the proposed model has a high correlation with perceived audio quality.
Fernandez Lastra, C; Gonzalez Lopez, F; Dominguez-Gil, A; Mariño, E L
1988-07-01
The aim of the present study was to attempt to discriminate between single- and two-compartment kinetic models used for calculating the pharmacokinetic parameters of bentazepam when the plasma concentrations of different administrations are used as initial data during multiple dosage regimes. Determination of the best estimated pharmacokinetic parameters was performed using non-linear regression analysis, weighting the data as a function of the error of the analytical technique. Bentazepam was administered at a dose of 25 mg orally at intervals of 8, 12 or 24 h to a total of 9 patients. The mean values of the parameters established for the single-compartment model were Ka = 2.024 h-1; Vd = 2.198 l/kg and Ke = 0.130 h-1. For the two-compartment model these values were: Ka = 2.134 h-1, Vc = 2.049 l/kg, K10 = 0.154 h-1, K12 = 0.042 h-1 and K21 = 0.103 h-1. By application of the MAICE test mean AIC values of 41.62 and 42.52 were obtained for the single- and two-compartment models, respectively. The most suitable kinetic model was determined for each patient according to the predictive nature of the individual parameters of the two kinetic models and by analysis of the residuals of the non-linear regressions of the parametric estimation.
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Mayo, T.; Lin, N.
2016-02-01
The fidelity of hurricane storm surge forecasts is largely based on accurate specification of the storm wind forcing. In both real-time forecasting and long-term risk analysis, parametric wind models are often used to describe the surface wind field. The surface wind field can be estimated as the sum of an axisymmetric wind profile of the storm and a background wind field of the environment. The Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model is the operational storm surge model of the National Hurricane Center, and uses a radial wind speed profile that is dependent on the maximum windspeed Vmax and its radius. The same profile is used to nonlinearly scale the translation speed of the storm to model the background wind field. However, recent advances have shown that the surface wind field may be more accurately characterized by including Coriolis effects in the storm wind profile, and scaling the translation speed by a constant factor. In this work, we apply these findings to the SLOSH model. We also remove the iterative procedure the SLOSH model uses to solve for Vmax from pressure gradients, and input the true value directly. We hindcast historical hurricanes to show that storm surges can be more accurately estimated with these changes.
Peng, Nie; Bang-Fa, Ni; Wei-Zhi, Tian
2013-02-01
Application of effective interaction depth (EID) principle for parametric normalization of full energy peak efficiencies at different counting positions, originally for quasi-point sources, has been extended to bulky sources (within ∅30 mm×40 mm) with arbitrary matrices. It is also proved that the EID function for quasi-point source can be directly used for cylindrical bulky sources (within ∅30 mm×40 mm) with the geometric center as effective point source for low atomic number (Z) and low density (D) media and high energy γ-rays. It is also found that in general EID for bulky sources is dependent upon Z and D of the medium and the energy of the γ-rays in question. In addition, the EID principle was theoretically verified by MCNP calculations.
Wallis, Thomas S A; Funke, Christina M; Ecker, Alexander S; Gatys, Leon A; Wichmann, Felix A; Bethge, Matthias
2017-10-01
Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.
The use of algorithmic behavioural transfer functions in parametric EO system performance models
NASA Astrophysics Data System (ADS)
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
NASA Astrophysics Data System (ADS)
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.
NASA Technical Reports Server (NTRS)
Rosenberg, Leigh; Hihn, Jairus; Roust, Kevin; Warfield, Keith
2000-01-01
This paper presents an overview of a parametric cost model that has been built at JPL to estimate costs of future, deep space, robotic science missions. Due to the recent dramatic changes in JPL business practices brought about by an internal reengineering effort known as develop new products (DNP), high-level historic cost data is no longer considered analogous to future missions. Therefore, the historic data is of little value in forecasting costs for projects developed using the DNP process. This has lead to the development of an approach for obtaining expert opinion and also for combining actual data with expert opinion to provide a cost database for future missions. In addition, the DNP cost model has a maximum of objective cost drivers which reduces the likelihood of model input error. Version 2 is now under development which expands the model capabilities, links it more tightly with key design technical parameters, and is grounded in more rigorous statistical techniques. The challenges faced in building this model will be discussed, as well as it's background, development approach, status, validation, and future plans.
Parametric retrieval model for estimating aerosol size distribution via the AERONET, LAGOS station.
Emetere, Moses Eterigho; Akinyemi, Marvel Lola; Akin-Ojo, Omololu
2015-12-01
The size characteristics of atmospheric aerosol over the tropical region of Lagos, Southern Nigeria were investigated using two years of continuous spectral aerosol optical depth measurements via the AERONET station for four major bands i.e. blue, green, red and infrared. Lagos lies within the latitude of 6.465°N and longitude of 3.406°E. Few systems of dispersion model was derived upon specified conditions to solve challenges on aerosols size distribution within the Stokes regime. The dispersion model was adopted to derive an aerosol size distribution (ASD) model which is in perfect agreement with existing model. The parametric nature of the formulated ASD model shows the independence of each band to determine the ASD over an area. The turbulence flow of particulates over the area was analyzed using the unified number (Un). A comparative study via the aid of the Davis automatic weather station was carried out on the Reynolds number, Knudsen number and the Unified number. The Reynolds and Unified number were more accurate to describe the atmospheric fields of the location. The aerosols loading trend in January to March (JFM) and August to October (ASO) shows a yearly 15% retention of aerosols in the atmosphere. The effect of the yearly aerosol retention can be seen to partly influence the aerosol loadings between October and February. Copyright © 2015 Elsevier Ltd. All rights reserved.
Ji, Songbai; Ghadyani, Hamidreza; Bolander, Richard P.; Beckwith, Jonathan G.; Ford, James C.; Mcallister, Thomas W.; Flashman, Laura A.; Paulsen, Keith D.; Ernstrom, Karin; Jain, Sonia; Raman, Rema; Zhang, Liying; Greenwald, Richard M.
2015-01-01
A number of human head finite element (FE) models have been developed from different research groups over the years to study the mechanisms of traumatic brain injury. These models can vary substantially in model features and parameters, making it important to evaluate whether simulation results from one model are readily comparable with another, and whether response-based injury thresholds established from a specific model can be generalized when a different model is employed. The purpose of this study is to parametrically compare regional brain mechanical responses from three validated head FE models to test the hypothesis that regional brain responses are dependent on the specific head model employed as well as the region of interest (ROI). The Dartmouth Scaled and Normalized Model (DSNM), the Simulated Injury Monitor (SIMon), and the Wayne State University Head Injury Model (WSUHIM) were selected for comparisons. For model input, 144 unique kinematic conditions were created to represent the range of head impacts sustained by male collegiate hockey players during play. These impacts encompass the 50th, 95th, and 99th percentile peak linear and rotational accelerations at 16 impact locations around the head. Five mechanical variables (strain, strain rate, strain × strain rate, stress, and pressure) in seven ROIs reported from the FE models were compared using Generalized Estimating Equation statistical models. Highly significant differences existed among FE models for nearly all output variables and ROIs. The WSUHIM produced substantially higher peak values for almost all output variables regardless of the ROI compared to the DSNM and SIMon models (p < 0.05). DSNM also produced significantly different stress and pressure compared with SIMon for all ROIs (p < 0.05), but such differences were not consistent across ROIs for other variables. Regardless of FE model, most output variables were highly correlated with linear and rotational peak accelerations. The
Parametric modeling for quantitative analysis of pulmonary structure to function relationships
NASA Astrophysics Data System (ADS)
Haider, Clifton R.; Bartholmai, Brian J.; Holmes, David R., III; Camp, Jon J.; Robb, Richard A.
2005-04-01
While lung anatomy is well understood, pulmonary structure-to-function relationships such as the complex elastic deformation of the lung during respiration are less well documented. Current methods for studying lung anatomy include conventional chest radiography, high-resolution computed tomography (CT scan) and magnetic resonance imaging with polarized gases (MRI scan). Pulmonary physiology can be studied using spirometry or V/Q nuclear medicine tests (V/Q scan). V/Q scanning and MRI scans may demonstrate global and regional function. However, each of these individual imaging methods lacks the ability to provide high-resolution anatomic detail, associated pulmonary mechanics and functional variability of the entire respiratory cycle. Specifically, spirometry provides only a one-dimensional gross estimate of pulmonary function, and V/Q scans have poor spatial resolution, reducing its potential for regional assessment of structure-to-function relationships. We have developed a method which utilizes standard clinical CT scanning to provide data for computation of dynamic anatomic parametric models of the lung during respiration which correlates high-resolution anatomy to underlying physiology. The lungs are segmented from both inspiration and expiration three-dimensional (3D) data sets and transformed into a geometric description of the surface of the lung. Parametric mapping of lung surface deformation then provides a visual and quantitative description of the mechanical properties of the lung. Any alteration in lung mechanics is manifest by alterations in normal deformation of the lung wall. The method produces a high-resolution anatomic and functional composite picture from sparse temporal-spatial methods which quantitatively illustrates detailed anatomic structure to pulmonary function relationships impossible for translational methods to provide.
NASA Astrophysics Data System (ADS)
Nagorkin, M. N.; Fyodorov, V. P.; Nagorkina, V. V.
2017-02-01
The article discusses the formation of tribotechnical characteristics of cylindrical sliding friction joints by means of simulation. Experiments were conducted on physical models of joints. The parametric reliability of tribotechnological parameters of burnish operation were determined for the studied tribotechnological system.
ERIC Educational Resources Information Center
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…
ERIC Educational Resources Information Center
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…
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.
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.
NASA Astrophysics Data System (ADS)
Giron Palomares, Jose Benjamin; Hsieh, Sheng-Jen
2014-05-01
A methodology based on active infrared thermography to study and characterize hidden solder joint shapes on a multi cover PCB assembly was investigated. A numerical model was developed to simulate the active thermography methodology and was proven to determine the grand average cooling rates with maximum errors of 8.85% (one cover) and 13.36% (two covers). A parametric analysis was performed by varying the number of covers, heat flux provided, and the amount of heating time. Grand average cooling rate distances among contiguous solder joint shapes, as well as solder joints discriminability, were determined to be directly proportional to heat flux, and inversely proportional to the number of covers and heating time. Finally, a mathematical model was developed to determine the appropriate total amount of energy needed to discriminate among hidden solder joints with a "good" discriminability for one and two covers, and a "regular" discriminability for up to five covers. The mathematical model was proven to predict the total amount of energy to achieve a "good" discriminability for one cover within a 10% of error with respect to the experimental active thermography model.
Jet-induced ground effects on a parametric flat-plate model in hover
NASA Technical Reports Server (NTRS)
Wardwell, Douglas A.; Hange, Craig E.; Kuhn, Richard E.; Stewart, Vearl R.
1993-01-01
The jet-induced forces generated on short takeoff and vertical landing (STOVL) aircraft when in close proximity to the ground can have a significant effect on aircraft performance. Therefore, accurate predictions of these aerodynamic characteristics are highly desirable. Empirical procedures for estimating jet-induced forces during the vertical/short takeoff and landing (V/STOL) portions of the flight envelope are currently limited in accuracy. The jet-induced force data presented significantly add to the current STOVL configurations data base. Further development of empirical prediction methods for jet-induced forces, to provide more configuration diversity and improved overall accuracy, depends on the viability of this STOVL data base. The data base may also be used to validate computational fluid dynamics (CFD) analysis codes. The hover data obtained at the NASA Ames Jet Calibration and Hover Test (JCAHT) facility for a parametric flat-plate model is presented. The model tested was designed to allow variations in the planform aspect ratio, number of jets, nozzle shape, and jet location. There were 31 different planform/nozzle configurations tested. Each configuration had numerous pressure taps installed to measure the pressures on the undersurface of the model. All pressure data along with the balance jet-induced lift and pitching-moment increments are tabulated. For selected runs, pressure data are presented in the form of contour plots that show lines of constant pressure coefficient on the model undersurface. Nozzle-thrust calibrations and jet flow-pressure survey information are also provided.
Bayesian kinematic earthquake source models
NASA Astrophysics Data System (ADS)
Minson, S. E.; Simons, M.; Beck, J. L.; Genrich, J. F.; Galetzka, J. E.; Chowdhury, F.; Owen, S. E.; Webb, F.; Comte, D.; Glass, B.; Leiva, C.; Ortega, F. H.
2009-12-01
Most coseismic, postseismic, and interseismic slip models are based on highly regularized optimizations which yield one solution which satisfies the data given a particular set of regularizing constraints. This regularization hampers our ability to answer basic questions such as whether seismic and aseismic slip overlap or instead rupture separate portions of the fault zone. We present a Bayesian methodology for generating kinematic earthquake source models with a focus on large subduction zone earthquakes. Unlike classical optimization approaches, Bayesian techniques sample the ensemble of all acceptable models presented as an a posteriori probability density function (PDF), and thus we can explore the entire solution space to determine, for example, which model parameters are well determined and which are not, or what is the likelihood that two slip distributions overlap in space. Bayesian sampling also has the advantage that all a priori knowledge of the source process can be used to mold the a posteriori ensemble of models. Although very powerful, Bayesian methods have up to now been of limited use in geophysical modeling because they are only computationally feasible for problems with a small number of free parameters due to what is called the "curse of dimensionality." However, our methodology can successfully sample solution spaces of many hundreds of parameters, which is sufficient to produce finite fault kinematic earthquake models. Our algorithm is a modification of the tempered Markov chain Monte Carlo (tempered MCMC or TMCMC) method. In our algorithm, we sample a "tempered" a posteriori PDF using many MCMC simulations running in parallel and evolutionary computation in which models which fit the data poorly are preferentially eliminated in favor of models which better predict the data. We present results for both synthetic test problems as well as for the 2007 Mw 7.8 Tocopilla, Chile earthquake, the latter of which is constrained by InSAR, local high
NASA Astrophysics Data System (ADS)
Franzmann, E. L.; Fiege, J. D.
2016-12-01
We introduce a software package called PolCat for modelling magnetized molecular cloud cores using submillimetre linear polarization and continuum intensity maps from thermal dust emission. Our PolCat modelling software builds a three-dimensional triaxial core model via the use of consecutive parametrized coordinate transformations, and produces simulated polarization maps to fit to observational datasets. We utilize a multi-objective evolutionary optimizer to search the parameter space to simultaneously minimize χ2 for the intensity and polarization position angle maps. The aim of this paper is to test PolCat by applying it to several artificial data sets, characterizing the capabilities and performance of the code using approximately 400 test runs. We find that PolCat is able to distinguish between polarization maps of twisted and non-twisted field geometries and identify the symmetry of the twist when one exists in the data. PolCat generally obtains the correct shapes of cores when fit to models with the correct field geometry. We characterized the degeneracy of our models due to orientation, finding that there are at least eight degenerate core orientations that produce identical polarization maps for the case of triaxial cores. The degeneracy increases with core symmetry. We expect PolCat to be a useful tool for modelling observational polarization data sets. Our tests demonstrate that the code can often eliminate incorrect field configurations, while finding a range or potential models that can explain the data. Physical considerations can often further reduce the set of allowed models, resulting in reasonable constraints on field geometry.
A new parametrized model of the global horizontal and vertical ionospheric current system
NASA Astrophysics Data System (ADS)
Laundal, Karl M.; Finlay, Christopher C.; Olsen, Nils; Reistad, Jone P.; Tenfjord, Paul; Snekvik, Kristian; Østgaard, Nikolai
2017-04-01
We present a newly developed global empirical model of the ionospheric magnetic disturbance field, and the associated horizontal and vertical currents. The field is represented in terms of spherical harmonics, parametrized in terms of solar wind drivers, dipole tilt angle, and the F10.7 index. The model parameters are estimated by using magnetic field measurements from ESA's CHAMP and Swarm missions. The model represents an improvement compared to other empirical models of ionospheric currents by the following three characteristics: 1) Distortions due to Earth's main magnetic field are taken into account and essentially corrected for by use of magnetic apex coordinates. This allows us to interpret resulting currents independently of longitudinal, hemispheric, and temporal variations in the Earth's magnetic field. 2) We do not impose any symmetry between hemispheres, so that inter-hemispheric differences can be investigated. 3) We estimate both the Birkeland currents (and its closure) and the horizontal divergence-free currents (the equivalent current) simultaneously. They can be combined to calculate the true height-integrated horizontal current. This is only possible, without additional data or assumptions, because we use magnetic field measurements from low Earth orbit. In this presentation we compare modeled magnetic field perturbations at ground and in space with independent observations. We find that the total field aligned currents in the model are very well correlated with the total currents measured by AMPERE. We also show that, on time scales of > 1h, the model is well correlated with measured ground magnetic field perturbations. Neither AMPERE nor any ground magnetometers were used to estimate model coefficients.
2011-01-01
Background Competing risks, which are particularly encountered in medical studies, are an important topic of concern, and appropriate analyses must be used for these data. One feature of competing risks is the cumulative incidence function, which is modeled in most studies using non- or semi-parametric methods. However, parametric models are required in some cases to ensure maximum efficiency, and to fit various shapes of hazard function. Methods We have used the stable distributions family of Hougaard to propose a new four-parameter distribution by extending a two-parameter log-logistic distribution, and carried out a simulation study to compare the cumulative incidence estimated with this distribution with the estimates obtained using a non-parametric method. To test our approach in a practical application, the model was applied to a set of real data on fertility history. Conclusions The results of simulation studies showed that the estimated cumulative incidence function was more accurate than non-parametric estimates in some settings. Analyses of real data indicated that the proposed distribution showed a much better fit to the data than the other distributions tested. Therefore, the new distribution is recommended for practical applications to parameterize the cumulative incidence function in competing risk settings. PMID:22074546
NASA Astrophysics Data System (ADS)
Wang, W. L.; Yu, D. S.; Zhou, Z.
2015-10-01
Due to the high-speed operation of modern rail vehicles and severe in-service environment of their hydraulic dampers, it has become important to establish more practical and accurate damper models and apply those models in high-speed transit problem studies. An improved full parametric model with actual in-service parameters, such as variable viscous damping, comprehensive stiffness and small mounting clearance was established for a rail vehicle's axle-box hydraulic damper. A subtle variable oil property model was built and coupled to the modelling process, which included modelling of the dynamic flow losses and the relief-valve system dynamics. The experiments validated the accuracy and robustness of the established full in-service parametric model and simulation which captured the damping characteristics over an extremely wide range of excitation speeds. Further simulations were performed using the model to uncover the effects of key in-service parameter variations on the nominal damping characteristics of the damper. The obtained in-service parametric model coupled all of the main factors that had significant impacts on the damping characteristics, so that the model could be useful in more extensive parameter effects analysis, optimal specification and product design optimisation of hydraulic dampers for track-friendliness, ride comfort and other high-speed transit problems.
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
Solar tower power plant using a particle-heated steam generator: Modeling and parametric study
NASA Astrophysics Data System (ADS)
Krüger, Michael; Bartsch, Philipp; Pointner, Harald; Zunft, Stefan
2016-05-01
Within the framework of the project HiTExStor II, a system model for the entire power plant consisting of volumetric air receiver, air-sand heat exchanger, sand storage system, steam generator and water-steam cycle was implemented in software "Ebsilon Professional". As a steam generator, the two technologies fluidized bed cooler and moving bed heat exchangers were considered. Physical models for the non-conventional power plant components as air- sand heat exchanger, fluidized bed coolers and moving bed heat exchanger had to be created and implemented in the simulation environment. Using the simulation model for the power plant, the individual components and subassemblies have been designed and the operating parameters were optimized in extensive parametric studies in terms of the essential degrees of freedom. The annual net electricity output for different systems was determined in annual performance calculations at a selected location (Huelva, Spain) using the optimized values for the studied parameters. The solution with moderate regenerative feed water heating has been found the most advantageous. Furthermore, the system with moving bed heat exchanger prevails over the system with fluidized bed cooler due to a 6 % higher net electricity yield.
Towards a Multi-Variable Parametric Cost Model for Ground and Space Telescopes
NASA Technical Reports Server (NTRS)
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).
Update on Multi-Variable Parametric Cost Models for Ground and Space Telescopes
NASA Technical Reports Server (NTRS)
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.
NASA Technical Reports Server (NTRS)
Gersh-Range, Jessica A.; Arnold, William R.; Peck, Mason A.; Stahl, H. Philip
2011-01-01
Since future astrophysics missions require space telescopes with apertures of at least 10 meters, there is a need for on-orbit assembly methods that decouple the size of the primary mirror from the choice of launch vehicle. One option is to connect the segments edgewise using mechanisms analogous to damped springs. To evaluate the feasibility of this approach, a parametric ANSYS model that calculates the mode shapes, natural frequencies, and disturbance response of such a mirror, as well as of the equivalent monolithic mirror, has been developed. This model constructs a mirror using rings of hexagonal segments that are either connected continuously along the edges (to form a monolith) or at discrete locations corresponding to the mechanism locations (to form a segmented mirror). As an example, this paper presents the case of a mirror whose segments are connected edgewise by mechanisms analogous to a set of four collocated single-degree-of-freedom damped springs. The results of a set of parameter studies suggest that such mechanisms can be used to create a 15-m segmented mirror that behaves similarly to a monolith, although fully predicting the segmented mirror performance would require incorporating measured mechanism properties into the model. Keywords: segmented mirror, edgewise connectivity, space telescope
NASA Technical Reports Server (NTRS)
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.
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.
Parametric modeling and stagger angle optimization of an axial flow fan
NASA Astrophysics Data System (ADS)
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%.
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.
NASA Astrophysics Data System (ADS)
Hong, Sung-Kwon; Epureanu, Bogdan I.; Castanier, Matthew P.
2014-09-01
The goal of this work is to develop a numerical model for the vibration of hybrid electric vehicle (HEV) battery packs to enable probabilistic forced response simulations for the effects of variations. There are two important types of variations that affect their structural response significantly: the prestress that is applied when joining the cells within a pack; and the small, random structural property discrepancies among the cells of a battery pack. The main contributions of this work are summarized as follows. In order to account for these two important variations, a new parametric reduced order model (PROM) formulation is derived by employing three key observations: (1) the stiffness matrix can be parameterized for different levels of prestress, (2) the mode shapes of a battery pack with cell-to-cell variation can be represented as a linear combination of the mode shapes of the nominal system, and (3) the frame holding each cell has vibratory motion. A numerical example of an academic battery pack with pouch cells is presented to demonstrate that the PROM captures the effects of both prestress and structural variation on battery packs. The PROM is validated numerically by comparing full-order finite element models (FEMs) of the same systems.
Parametric study of a two-shaft gas turbine cycle model of power plant
NASA Astrophysics Data System (ADS)
Ibrahim, Thamir K.; Rahman, M. M.
2012-09-01
In this paper, the parametric study of a two shafts gas turbine cycle model of the power plant was proposed. The power output, compression work, specific fuel consumption and thermal efficiency are evaluated with respect to the cycle temperature and compression ratio for a typical set of operating conditions. Two shafts gas turbine cycle with realistic parameters is modeled. The computational model was developed utilizing the MATLAB codes. Turbine work found to be decreases as ambient temperature increases as well as the thermal efficiency decreases. It can be seen that the thermal efficiency and power output increases linearly with increases of compression ratio while decreases of ambient temperature. The power of the simulated two shafts gas turbine reach to 135MW, which is higher than the simple gas-turbine cycle (Baiji gas turbine power plant, power < 131MW). The specific fuel consumption increases with increases of ambient temperature as well as the lower turbine inlet temperature. Even though at the lower turbine inlet temperature is decrement the thermal efficiency dramatically and the power output increases linearly with increases of compression ratio and decreases the ambient temperature.
Parametric uncertainty analysis of pulse wave propagation in a model of a human arterial network
Xiu Dongbin Sherwin, Spencer J.
2007-10-01
Reduced models of human arterial networks are an efficient approach to analyze quantitative macroscopic features of human arterial flows. The justification for such models typically arise due to the significantly long wavelength associated with the system in comparison to the lengths of arteries in the networks. Although these types of models have been employed extensively and many issues associated with their implementations have been widely researched, the issue of data uncertainty has received comparatively little attention. Similar to many biological systems, a large amount of uncertainty exists in the value of the parameters associated with the models. Clearly reliable assessment of the system behaviour cannot be made unless the effect of such data uncertainty is quantified. In this paper we present a study of parametric data uncertainty in reduced modelling of human arterial networks which is governed by a hyperbolic system. The uncertain parameters are modelled as random variables and the governing equations for the arterial network therefore become stochastic. This type stochastic hyperbolic systems have not been previously systematically studied due to the difficulties introduced by the uncertainty such as a potential change in the mathematical character of the system and imposing boundary conditions. We demonstrate how the application of a high-order stochastic collocation method based on the generalized polynomial chaos expansion, combined with a discontinuous Galerkin spectral/hp element discretization in physical space, can successfully simulate this type of hyperbolic system subject to uncertain inputs with bounds. Building upon a numerical study of propagation of uncertainty and sensitivity in a simplified model with a single bifurcation, a systematical parameter sensitivity analysis is conducted on the wave dynamics in a multiple bifurcating human arterial network. Using the physical understanding of the dynamics of pulse waves in these types of
Hess, Jeremy J.; Ebi, Kristie L.; Markandya, Anil; Balbus, John M.; Wilkinson, Paul; Haines, Andy; Chalabi, Zaid
2014-01-01
simultaneously improving health. Citation: Remais JV, Hess JJ, Ebi KL, Markandya A, Balbus JM, Wilkinson P, Haines A, Chalabi Z. 2014. Estimating the health effects of greenhouse gas mitigation strategies: addressing parametric, model, and valuation challenges. Environ Health Perspect 122:447–455; http://dx.doi.org/10.1289/ehp.1306744 PMID:24583270
Integrated System-Level Optimization for Concurrent Engineering With Parametric Subsystem Modeling
NASA Technical Reports Server (NTRS)
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.
Kimel-Naor, Shani; Abboud, Shimon; Arad, Marina
2016-08-01
Osteoporosis is defined as bone microstructure deterioration resulting a decrease of bone's strength. Measured bone mineral density (BMD) constitutes the main tool for Osteoporosis diagnosis, management, and defines patient's fracture risk. In the present study, parametric electrical impedance tomography (pEIT) method was examined for monitoring BMD, using a computerized simulation model and preliminary real measurements. A numerical solver was developed to simulate surface potentials measured over a 3D computerized pelvis model. Varying cortical and cancellous BMD were simulated by changing bone conductivity and permittivity. Up to 35% and 16% change was found in the real and imaginary modules of the calculated potential, respectively, while BMD changes from 100% (normal) to 60% (Osteoporosis). Negligible BMD relative error was obtained with SNR>60 [dB]. Position changes errors indicate that for long term monitoring, measurement should be taken at the same geometrical configuration with great accuracy. The numerical simulations were compared to actual measurements that were acquired from a healthy male subject using a five electrodes belt bioimpedance device. The results suggest that pEIT may provide an inexpensive easy to use tool for frequent monitoring BMD in small clinics during pharmacological treatment, as a complementary method to DEXA test.
NASA Astrophysics Data System (ADS)
Ding, Baocang; Pan, Hongguang
2016-08-01
The output feedback robust model predictive control (MPC), for the linear parameter varying (LPV) system with norm-bounded disturbance, is addressed, where the model parametric matrices are only known to be bounded within a polytope. The previous techniques of norm-bounding technique, quadratic boundedness (QB), dynamic output feedback, and ellipsoid (true-state bound; TSB) refreshment formula for guaranteeing recursive feasibility, are fused into the newly proposed approaches. In the notion of QB, the full Lyapunov matrix is applied for the first time in this context. The single-step dynamic output feedback robust MPC, where the infinite-horizon control moves are parameterised as a dynamic output feedback law, is the main topic of this paper, while the multi-step method is also suggested. In order to strictly guarantee the physical constraints, the outer bound of the true state replaces the true state itself, so tightness of this bound has a major effect on the control performance. In order to tighten the TSB, a procedure for refreshing the real-time ellipsoid based on that of the last sampling instant is given. This paper is conclusive for the past results and far-reaching for the future researches. Two benchmark examples are given to show the effectiveness of the novel results.
Deformable mirror models for open-loop adaptive optics using non-parametric estimation techniques
NASA Astrophysics Data System (ADS)
Guzmán, Dani; De Cos Juez, Francisco Javier; Myers, Richard; Sánchez Lasheras, Fernando; Young, Laura K.; Guesalaga, Andrés
2010-07-01
Open-loop adaptive optics is a technique in which the turbulent wavefront is measured before it hits the deformable mirror for correction; therefore the correct control of the mirror in open-loop is key in achieving the expected level of correction. In this paper, we present non-parametric estimation techniques to model deformable mirrors working in open-loop. We have results with mirrors characterized by non-linear behavior: a Xinetics electrostrictive mirror and a Boston Micromachines MEMS mirror. The inputs for these models are the wavefront corrections to apply to the mirror and the outputs are the set of voltages to shape the mirror. We have performed experiments on both mirrors, achieving Go-To errors relative to peak-to-peak wavefront excursion in the order of 1 % RMS for the Xinetics mirror and 3 % RMS for the Boston mirror . These techniques are trained with interferometric data from the mirror under control; therefore they do not depend on the physical parameters of the device.
Application of a Momentum Source Model to the RAH-66 Comanche FANTAIL
NASA Technical Reports Server (NTRS)
Nygaard, Tor A.; Dimanlig, Arsenio C.; Meadowcroft, Edward T.
2004-01-01
A Momentum Source Model has been revised and implemented in the flow solver OVERFLOW-D. In this approach, the fan forces are evaluated from two-dimensional airfoil tables as a function of local Mach number and angle-of-attack and applied as source terms in the discretized Navier-Stokes equations. The model revisions include a new model for forces in the tip region and axial distribution of the source terms. The model revisions improve the results significantly. The Momentum Source Model agrees well with a discrete blade model for all computed collective pitch angles. The two models agree well with experimental data for thrust vs. torque. The Momentum Source Model is a good complement to Discrete Blade Models for ducted fan computations. The lower computational and labor costs make parametric studies, optimization studies and interactional aerodynamics studies feasible for cases beyond what is practical with a Discrete Blade Model today.
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.
Vorontsov, Sergei V.; Jefferies, Stuart M. E-mail: stuartj@ifa.hawaii.edu
2013-11-20
We describe a global parametric model for the observed power spectra of solar oscillations of intermediate and low degree. A physically motivated parameterization is used as a substitute for a direct description of mode excitation and damping as these mechanisms remain poorly understood. The model is targeted at the accurate fitting of power spectra coming from Doppler-velocity measurements and uses an adaptive response function that accounts for both the vertical and horizontal components of the velocity field on the solar surface and for possible instrumental and observational distortions. The model is continuous in frequency, can easily be adapted to intensity measurements, and extends naturally to the analysis of high-frequency pseudomodes (interference peaks at frequencies above the atmospheric acoustic cutoff).
NASA Astrophysics Data System (ADS)
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.
Haque, Md Mazharul; Washington, Simon
2014-01-01
The use of mobile phones while driving is more prevalent among young drivers-a less experienced cohort with elevated crash risk. The objective of this study was to examine and better understand the reaction times of young drivers to a traffic event originating in their peripheral vision whilst engaged in a mobile phone conversation. The CARRS-Q advanced driving simulator was used to test a sample of young drivers on various simulated driving tasks, including an event that originated within the driver's peripheral vision, whereby a pedestrian enters a zebra crossing from a sidewalk. Thirty-two licensed drivers drove the simulator in three phone conditions: baseline (no phone conversation), hands-free and handheld. In addition to driving the simulator each participant completed questionnaires related to driver demographics, driving history, usage of mobile phones while driving, and general mobile phone usage history. The participants were 21-26 years old and split evenly by gender. Drivers' reaction times to a pedestrian in the zebra crossing were modelled using a parametric accelerated failure time (AFT) duration model with a Weibull distribution. Also tested where two different model specifications to account for the structured heterogeneity arising from the repeated measures experimental design. The Weibull AFT model with gamma heterogeneity was found to be the best fitting model and identified four significant variables influencing the reaction times, including phone condition, driver's age, license type (provisional license holder or not), and self-reported frequency of usage of handheld phones while driving. The reaction times of drivers were more than 40% longer in the distracted condition compared to baseline (not distracted). Moreover, the impairment of reaction times due to mobile phone conversations was almost double for provisional compared to open license holders. A reduction in the ability to detect traffic events in the periphery whilst distracted
Dynamic modelling and stability parametric analysis of a flexible spacecraft with fuel slosh
NASA Astrophysics Data System (ADS)
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.
Progress in optical parametric oscillators
NASA Technical Reports Server (NTRS)
Fan, Y. X.; Byer, R. L.
1984-01-01
It is pointed out that tunable coherent sources are very useful for many applications, including spectroscopy, chemistry, combustion diagnostics, and remote sensing. Compared with other tunable sources, optical parametric oscillators (OPO) offer the potential advantage of a wide wavelength operating range, which extends from 0.2 micron to 25 microns. The current status of OPO is examined, taking into account mainly advances made during the last decade. Attention is given to early LiNbO3 parametric oscillators, problems which have prevented wide use of parametric oscillators, the demonstration of OPO's using urea and AgGaS2, progress related to picosecond OPO's, a breakthrough in nanosecond parametric oscillators, the first demonstration of a waveguide and fiber parametric amplification and generation, the importance of chalcopyrite crystals, and theoretical work performed with the aim to understand the factors affecting the parametric oscillator performance.
Modelling Mt. Etna mantle sources
NASA Astrophysics Data System (ADS)
Casetta, Federico; Giacomoni, Pier Paolo; Coltorti, Massimo; Ferlito, Carmelo; Bonadiman, Costanza
2017-04-01
The mantle source beneath Mt. Etna is matter of a longstanding and controversial debate, due to the absence of mantle xenoliths, the evolved nature of the erupted magmas and their geochemical variations. This study is focused on the modelling of the petrogenetic processes responsible for the production of Mt. Etna magmas and their variation through time, by means of a comparison with the Hyblean lavas (Southern Sicily), their evolution and mantle source(s). Samples from all Mt. Etna eruptive events, from the tholeiites to the nowadays K-rich eruptions, were used to a backward reconstruction of the primitive magma compositions, taking into account the fO2 of the magmatic system and its effect on mineral-melt Fe partitioning. The eutectic melting proportions and the modal composition of the Mt. Etna mantle source, obtained by a mass balance melting model, allowed to: i) compare the etnean inferred primary magmas with the Ol-hosted melt inclusions (MI) composition and with the Hyblean real primary magmas; ii) define some petrologic and geodynamic constraints on the Hyblean-Mt. Etna area taking also in account the compositions of the Hyblean xenoliths. A 2% to 17% addition of dunitic to wehrlitic assemblages (Ol + Cpx in progressive equilibrium) to Mt. Etna less evolved lavas allowed to equilibrate the Mt. Etna primitive magmas (mg# = 68) compositions for Timpe, AAV, Ellittico, Mongibello and Post-1971 stages to mantle conditions; Ol with Fo=88). The calculated Lh source is constituted by Ol + Opx + Cpx + Cr-Sp, with addition of small amounts (4.3%) of Amph and Phlog. Decreasing partial melting degrees (from 19% to 13-10%) and a change in Amph and Phlog eutectic melting proportions can explain the entire Mt. Etna compositional range, from the tholeiitic event to the Post-1971 LILE-enriched episodes, leading to the production of primary magmas characterized by a 0.6 to 1.2 wt% H2O content. Some speculation between geodynamic and magmatic evolution of the articulated and
NASA Astrophysics Data System (ADS)
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
NASA Technical Reports Server (NTRS)
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
An Evaluation of Parametric and Nonparametric Models of Fish Population Response.
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.
Orun, A B; Goodyer, E; Seker, H; Smith, G; Uslan, V; Chauhan, D
2014-11-01
Optical and parametric skin imaging methods which can efficiently identify invisible sub-skin features or subtle changes in skin layers are very important for accurate optical skin modelling. In this study, a hybrid method is introduced that helps develop a parametric optical skin model by utilizing interdisciplinary techniques including light back-scatter analysis, laser speckle imaging, image-texture analysis and Bayesian inference methods. The model aims to detect subtle skin changes and hence very early signs of skin abnormalities/diseases. Light back-scatter and laser speckle image textural analysis are applied onto the normal and abnormal skin regions (lesions) to generate set of attributes/parameters. These are then optimized by Bayesian inference method in order to build an optimized parametric model. The attributes selected by Bayesian inference method in the optimization stage were used to build an optimized model and then successfully verified. It was clearly proven that Bayesian inference based optimization process yields good results to build an optimized skin model. The outcome of this study clearly shows the applicability of this hybrid method in the analysis of skin features and is therefore expected to lead development of non-invasive and low-cost instrument for early detection of skin changes. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Testing of the Trim Tab Parametric Model in NASA Langley's Unitary Plan Wind Tunnel
NASA Technical Reports Server (NTRS)
Murphy, Kelly J.; Watkins, Anthony N.; Korzun, Ashley M.; Edquist, Karl T.
2013-01-01
In support of NASA's Entry, Descent, and Landing technology development efforts, testing of Langley's Trim Tab Parametric Models was conducted in Test Section 2 of NASA Langley's Unitary Plan Wind Tunnel. The objectives of these tests were to generate quantitative aerodynamic data and qualitative surface pressure data for experimental and computational validation and aerodynamic database development. Six component force-and-moment data were measured on 38 unique, blunt body trim tab configurations at Mach numbers of 2.5, 3.5, and 4.5, angles of attack from -4deg to +20deg, and angles of sideslip from 0deg to +8deg. Configuration parameters investigated in this study were forebody shape, tab area, tab cant angle, and tab aspect ratio. Pressure Sensitive Paint was used to provide qualitative surface pressure mapping for a subset of these flow and configuration variables. Over the range of parameters tested, the effects of varying tab area and tab cant angle were found to be much more significant than varying tab aspect ratio relative to key aerodynamic performance requirements. Qualitative surface pressure data supported the integrated aerodynamic data and provided information to aid in future analyses of localized phenomena for trim tab configurations.
Edge Sharpness Assessment by Parametric Modeling: Application to Magnetic Resonance Imaging.
Ahmad, R; Ding, Y; Simonetti, O P
2015-05-01
In biomedical imaging, edge sharpness is an important yet often overlooked image quality metric. In this work, a semi-automatic method to quantify edge sharpness in the presence of significant noise is presented with application to magnetic resonance imaging (MRI). The method is based on parametric modeling of image edges. First, an edge map is automatically generated and one or more edges-of-interest (EOI) are manually selected using graphical user interface. Multiple exclusion criteria are then enforced to eliminate edge pixels that are potentially not suitable for sharpness assessment. Second, at each pixel of the EOI, an image intensity profile is read along a small line segment that runs locally normal to the EOI. Third, the profiles corresponding to all EOI pixels are individually fitted with a sigmoid function characterized by four parameters, including one that represents edge sharpness. Last, the distribution of the sharpness parameter is used to quantify edge sharpness. For validation, the method is applied to simulated data as well as MRI data from both phantom imaging and cine imaging experiments. This method allows for fast, quantitative evaluation of edge sharpness even in images with poor signal-to-noise ratio. Although the utility of this method is demonstrated for MRI, it can be adapted for other medical imaging applications.
NASA Astrophysics Data System (ADS)
Speyer, Gavriel; Kaczkowski, Peter; Brayman, Andrew; Crum, Lawrence
2010-03-01
Accurate monitoring of high intensity focused ultrasound (HIFU) surgery is critical to ensuring proper treatment. Pulse-echo diagnostic ultrasound (DU) is a recognized modality for identifying temperature differentials using speckle tracking between two DU radio frequency (RF) frames [2], [4]. This observation has motivated non-parametric temperature estimation, which associates temperature changes directly with the displacement estimates. We present an estimation paradigm termed displacement mode analysis (DMA), which uses physical modeling to associate particular patterns of observed displacement, called displacement modes, with corresponding modes of variation in the administered therapy. This correspondence allows DMA to estimate therapy directly using a linear combination of displacement modes, imbuing these displacement estimates into the reference using interpolation, and by aligning with the treatment frame, providing a therapy estimate with the heating modes. Since DMA is maximum likelihood estimation (MLE), the accuracy of its estimates can be assessed a priori, providing error bounds for estimates of applied heating, temperature, and thermal dose. Predicted performance is verified using both simulation and experiment for a point exposure of 4.2 Watts of electrical power in alginate, a tissue mimicking phantom.
Martinez-Murcia, Francisco J; Górriz, Juan M; Ramírez, Javier; Ortiz, Andres
2016-11-01
The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer's disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's disease neuroimaging initiative (ADNI).
Stephenson, J; Chadwick, B L; Playle, R A; Treasure, E T
2010-01-01
Caries in primary teeth is an ongoing issue in children's dental health. Its quantification is affected by clustering of data within children and the concurrent risk of exfoliation of primary teeth. This analysis of caries data of 103,776 primary molar tooth surfaces from a cohort study of 2,654 British children aged 4-5 years at baseline applied multilevel competing risks survival analysis methodology to identify factors significantly associated with caries occurrence in primary tooth surfaces in the presence of the concurrent risk of exfoliation, and assessed the effect of exfoliation on caries development. Multivariate multilevel parametric survival models were applied at surface level to the analysis of the sound-carious and sound-exfoliation transitions to which primary tooth surfaces are subject. Socio-economic class, fluoridation status and surface type were found to be the strongest predictors of primary caries, with the highest rates of occurrence and lowest median survival times associated with occlusal surfaces of children from poor socio-economic class living in non-fluoridated areas. The concurrent risk of exfoliation was shown to reduce the distinction in survival experience between different types of surfaces, and between surfaces of teeth from children of different socio-economic class or fluoridation status. Clustering of data had little effect on inferences of parameter significance.
Readout IC requirement trends based on a simplified parametric seeker model.
Osborn, Thor D.
2010-03-01
Modern space based optical sensors place substantial demands on the focal plane array readout integrated circuit. Active pixel readout designs offer direct access to individual pixel data but require analog to digital conversion at or near each pixel. Thus, circuit designers must create precise, fundamentally analog circuitry within tightly constrained areas on the integrated circuit. Rapidly changing phenomena necessitate tradeoffs between sampling and conversion speed, data precision, and heat generation adjacent the detector array, especially of concern for thermally sensitive space grade infrared detectors. A simplified parametric model is presented that illustrates seeker system performance and analog to digital conversion requirements trends in the visible through mid-wave infrared, for varying sample rate. Notional limiting-case Earth optical backgrounds were generated using MODTRAN4 with a range of cloud extremes and approximate practical albedo limits for typical surface features from a composite of the Mosart and Aster spectral albedo databases. The dynamic range requirements imposed by these background spectra are discussed in the context of optical band selection and readout design impacts.
NASA Astrophysics Data System (ADS)
Fripp, Jurgen; Crozier, Stuart; Warfield, Simon K.; Ourselin, Sébastien
2006-03-01
Subdivision surfaces and parameterization are desirable for many algorithms that are commonly used in Medical Image Analysis. However, extracting an accurate surface and parameterization can be difficult for many anatomical objects of interest, due to noisy segmentations and the inherent variability of the object. The thin cartilages of the knee are an example of this, especially after damage is incurred from injuries or conditions like osteoarthritis. As a result, the cartilages can have different topologies or exist in multiple pieces. In this paper we present a topology preserving (genus 0) subdivision-based parametric deformable model that is used to extract the surfaces of the patella and tibial cartilages in the knee. These surfaces have minimal thickness in areas without cartilage. The algorithm inherently incorporates several desirable properties, including: shape based interpolation, sub-division remeshing and parameterization. To illustrate the usefulness of this approach, the surfaces and parameterizations of the patella cartilage are used to generate a 3D statistical shape model.
NASA Astrophysics Data System (ADS)
Wouters, Hendrik; Blahak, Ulrich; Helmert, Jürgen; Raschendorfer, Matthias; Demuzere, Matthias; Fay, Barbara; Trusilova, Kristina; Mironov, Dmitrii; Reinert, Daniel; Lüthi, Daniel; Machulskaya, Ekaterina
2015-04-01
In order to address urban climate at the regional scales, a new efficient urban land-surface parametrization TERRA_URB has been developed and coupled to the atmospheric numerical model COSMO-CLM. Hereby, several new advancements for urban land-surface models are introduced which are crucial for capturing the urban surface-energy balance and its seasonal dependency in the mid-latitudes. This includes a new PDF-based water-storage parametrization for impervious land, the representation of radiative absorption and emission by greenhouse gases in the infra-red spectrum in the urban canopy layer, and the inclusion of heat emission from human activity. TERRA_URB has been applied in offline urban-climate studies during European observation campaigns at Basel (BUBBLE), Toulouse (CAPITOUL), and Singapore, and currently applied in online studies for urban areas in Belgium, Germany, Switzerland, Helsinki, Singapore, and Melbourne. Because of its computational efficiency, high accuracy and its to-the-point conceptual easiness, TERRA_URB has been selected to become the standard urban parametrization of the atmospheric numerical model COSMO(-CLM). This allows for better weather forecasts for temperature and precipitation in cities with COSMO, and an improved assessment of urban outdoor hazards in the context of global climate change and urban expansion with COSMO-CLM. We propose additional extensions to TERRA_URB towards a more robust representation of cities over the world including their structural design. In a first step, COSMO's standard EXTernal PARarameter (EXTPAR) tool is updated for representing the cities into the land cover over the entire globe. Hereby, global datasets in the standard EXTPAR tool are used to retrieve the 'Paved' or 'sealed' surface Fraction (PF) referring to the presence of buildings and streets. Furthermore, new global data sets are incorporated in EXTPAR for describing the Anthropogenic Heat Flux (AHF) due to human activity, and optionally the
Refinement of numerical models and parametric study of SOFC stack performance
NASA Astrophysics Data System (ADS)
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
Visible BaB2O4 optical parametric oscillator pumped at 355 nm by a single-axial-mode pulsed source
NASA Technical Reports Server (NTRS)
Fan, Y. X.; Eckardt, R. C.; Byer, R. L.; Nolting, J.; Wallenstein, R.
1988-01-01
A visible BaB2O4 optical parametric oscillator (OPO) pumped by a single-axial-mode 355-nm source has been demonstrated. An average output power of 140 mW with a signal wave conversion efficiency of 13 percent and an idler conversion efficiency of 11 percent for a total conversion efficiency of 24 percent has been achieved. The oscillator has continuously tuned from 412 nm to 2.55 microns limited by the infrared transmission range of the crystal. Through injection seeding, single-axial-mode OPO operation with a corresponding OPO linewidth of less than 3 GHz was obtained.
Visible BaB2O4 optical parametric oscillator pumped at 355 nm by a single-axial-mode pulsed source
NASA Technical Reports Server (NTRS)
Fan, Y. X.; Eckardt, R. C.; Byer, R. L.; Nolting, J.; Wallenstein, R.
1988-01-01
A visible BaB2O4 optical parametric oscillator (OPO) pumped by a single-axial-mode 355-nm source has been demonstrated. An average output power of 140 mW with a signal wave conversion efficiency of 13 percent and an idler conversion efficiency of 11 percent for a total conversion efficiency of 24 percent has been achieved. The oscillator has continuously tuned from 412 nm to 2.55 microns limited by the infrared transmission range of the crystal. Through injection seeding, single-axial-mode OPO operation with a corresponding OPO linewidth of less than 3 GHz was obtained.
NASA Astrophysics Data System (ADS)
Wong, H.-W.; Miake-Lye, R. C.
2010-04-01
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 >1×1015 (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.
Foreground Bias from Parametric Models of Far-IR Dust Emission
NASA Technical Reports Server (NTRS)
Kogut, A.; Fixsen, D. J.
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 plus foreground emission to precision 0.1 percent or better. The Rayleigh-Jeans approximation to the dust spectrum biases the fitted dust spectral index by (Delta)(Beta)(sub 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) greater than 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 superposition of emission at different temperatures, but fails at the level (Delta)(r) = 0.006 for dust whose spectral index varies with frequency. Restricting the observing frequencies to a narrow region near the foreground minimum reduces these biases for some dust spectra but can increase the bias for others. Data at THz frequencies surrounding the peak of the dust emission can mitigate these biases while providing a direct determination of the dust temperature profile.
Foreground Bias from Parametric Models of Far-IR Dust Emission
NASA Technical Reports Server (NTRS)
Kogut, A.; Fixsen, D. J.
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 plus foreground emission to precision 0.1 percent or better. The Rayleigh-Jeans approximation to the dust spectrum biases the fitted dust spectral index by (Delta)(Beta)(sub 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) greater than 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 superposition of emission at different temperatures, but fails at the level (Delta)(r) = 0.006 for dust whose spectral index varies with frequency. Restricting the observing frequencies to a narrow region near the foreground minimum reduces these biases for some dust spectra but can increase the bias for others. Data at THz frequencies surrounding the peak of the dust emission can mitigate these biases while providing a direct determination of the dust temperature profile.
Moore, Julia L; Liang, Song; Akullian, Adam; Remais, Justin V
2012-12-01
Developmental models, such as degree-day models, are commonly used to predict the impact of future climate change on the intensity, distribution, and timing of the transmission of infectious diseases, particularly those caused by pathogens carried by vectors or intermediate hosts. Resulting projections can be useful in policy discussions concerning regional or national responses to future distributions of important infectious diseases. Although the simplicity of degree-day models is appealing, little work has been done to analyze their ability to make reliable projections of the distribution of important pathogens, vectors, or intermediate hosts in the presence of the often considerable parametric uncertainty common to such models. Here, a population model of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, was used to investigate the sensitivity of host range predictions in Sichuan Province, China, to uncertainty in two key degree-day model parameters: delta(min) (minimum temperature threshold for development) and K (total degree-days required for completion of snail development). The intent was to examine the consequences of parametric uncertainty in a plausible biological model, rather than to generate the definitive model. Results indicate that model output, the seasonality of population dynamics, and range predictions, particularly along the edge of the range, are highly sensitive to changes in model parameters, even at levels of parametric uncertainty common to such applications. Caution should be used when interpreting the results of degree-day models used to generate predictions of disease distribution and risk under scenarios of future climate change, and predictions should be considered most reliable when the temperature ranges used in projections resemble those used to estimate model parameters. Given the potential for substantial changes in degree-day model output with modest changes in parameter values, caution is warranted when
NASA Astrophysics Data System (ADS)
Hong, Sung-Kwon; Castanier, Matthew P.; Epureanu, Bogdan I.
2009-03-01
Modeling and re-analysis techniques are proposed for predicting the dynamic response of complex structures that have suffered damage in one or more of their components. When such damages are present, the model of the healthy structure may no longer capture the system-level response or the loading from the rest of the structure on the damaged components. Hence, novel models that allow for an accurate re-analysis of the response of damaged structures are needed in important applications, including damage detection. Herein, such models are obtained by using a reduced order modeling approach based on component mode synthesis. Because the resonant response of a complex structure is often sensitive to component uncertainties (in geometric parameters such as thickness, material properties such as Young's modulus, etc.), novel parametric reduced order models (PROMs) are developed. In previous work, PROMs have been applied for handling uncertainties in a single substructure. Herein, PROMs are extended to the general case of multiple substructures with uncertain parameters or damage. Two damage cases are considered: severe structural deformation (dents), and cracks. For the first damage case, an approximate method based on static mode compensation (SMC) is used to perform fast re-analysis of the vibration response of the damaged structure. The re-analysis is performed through a range of locations and severity levels of the damage. For selected damage locations and levels, the SMC approximation is compared to full finite element analysis to demonstrate the accuracy and computational time savings for the new method. For the second damage case (cracks), the vibration problem becomes nonlinear due to the intermittent contact of the crack faces. Therefore, to estimate the resonant frequencies for a cracked structure, the bi-linear frequency approximation (BFA) is used for cracks of various lengths. Since BFA is based on linear analyses, it is fast and particularly well suited for
Moore, Julia L; Liang, Song; Akullian, Adam; Remais, Justin V
2013-01-01
Developmental models, such as degree-day models, are commonly used to predict the impact of future climate change on the intensity, distribution, and timing of the transmission of infectious diseases, particularly those caused by pathogens carried by vectors or intermediate hosts. Resulting projections can be useful in policy discussions concerning regional or national responses to future distributions of important infectious diseases. Though the simplicity of degree-day models is appealing, little work has been done to analyze their ability to make reliable projections of the distribution of important pathogens, vectors, or intermediate hosts in the presence of the often considerable parametric uncertainty common to such models. Here, a population model of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, was used to investigate the sensitivity of host range predictions in Sichuan Province, China, to uncertainty in two key degree-day model parameters (δmin and K). The intent was to examine the consequences of parametric uncertainty in a plausible biological model, rather than to generate the definitive model. Results indicate that model output, the seasonality of population dynamics, and range predictions, particularly along the edge of the range, are highly sensitive to changes in model parameters, even at levels of parametric uncertainty common to such applications. Caution should be used when interpreting the results of degree-day models used to generate predictions of disease distribution and risk under scenarios of future climate change, and predictions should be considered most reliable when the temperature ranges used in projections resemble those used to estimate model parameters. Given the potential for substantial changes in output with modest changes in parameter values, particular concern is warranted when results will be used to inform policy and management decisions. PMID:23387122
Marmarelis, Vasilis Z.; Shin, Dae C.; Zhang, Yaping; Kautzky-Willer, Alexandra; Pacini, Giovanni; D’Argenio, David Z.
2013-01-01
Background: 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). Methods: 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. Results: 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. Conclusions: It was found that the data based nonparametric LVM revealed additional insights about the manner in which infused insulin affects blood glucose concentration. PMID:23911176
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.
Nollo, G; Porta, A; Faes, L; Del Greco, M; Disertori, M; Ravelli, F
2001-04-01
Spectral and cross-spectral analysis of R-R interval and systolic arterial pressure (SAP) spontaneous fluctuations have been proposed for noninvasive evaluation of baroreflex sensitivity (BRS). However, results are not in good agreement with clinical measurements. In this study, a bivariate parametric autoregressive model with exogenous input (ARXAR model), able to divide the R-R variability into SAP-related and -unrelated parts, was used to quantify the gain (alpha(ARXAR)) of the baroreflex regulatory mechanism. For performance assessing, two traditional noninvasive methods based on frequency domain analysis [spectral, baroreflex gain by autogressive model (alpha(AR)); cross-spectral, baroreflex gain by bivariate autoregressive model (alpha(2AR))] and one based on the time domain [baroreflex gain by sequence analysis (alpha(SEQ))] were considered and compared with the baroreflex gain by phenylephrine test (alpha(PHE)). The BRS evaluation was performed on 30 patients (61 +/- 10 yr) with recent (10 +/- 3 days) myocardial infarction. The ARXAR model allowed dividing the R-R variability (950 +/- 1,099 ms(2)) into SAP-related (256 +/- 418 ms(2)) and SAP-unrelated (694 +/- 728 ms(2)) parts. alpha(AR) (12.2 +/- 6.1 ms/mmHg) and alpha(2AR) (8.9 +/- 5.6 ms/mmHg) as well as alpha(SEQ) (12.6 +/- 7.1 ms/mmHg) overestimated BRS assessed by alpha(PHE) (6.4 +/- 4.7 ms/mmHg), whereas the ARXAR index gave a comparable value (alpha(ARXAR) = 5.4 +/- 3.3 ms/mmHg). All noninvasive methods were significantly correlated to alpha(PHE) (alpha(ARXAR) and alpha(SEQ) were more correlated than the other indexes). Thus the baroreflex gain obtained describing the causal dependence of R-R interval on SAP showed a good agreement with alpha(PHE) and may provide additional information regarding the gain estimation in the frequency domain.
NASA Astrophysics Data System (ADS)
Gu, Yongxian
The demand of portable power generation systems for both domestic and military applications has driven the advances of mesoscale internal combustion engine systems. This dissertation was devoted to the gasdynamic modeling and parametric study of the mesoscale internal combustion swing engine/generator systems. First, the system-level thermodynamic modeling for the swing engine/generator systems has been developed. The system performance as well as the potentials of both two- and four-stroke swing engine systems has been investigated based on this model. Then through parameterc studies, the parameters that have significant impacts on the system performance have been identified, among which, the burn time and spark advance time are the critical factors related to combustion process. It is found that the shorter burn time leads to higher system efficiency and power output and the optimal spark advance time is about half of the burn time. Secondly, the turbulent combustion modeling based on levelset method (G-equation) has been implemented into the commercial software FLUENT. Thereafter, the turbulent flame propagation in a generic mesoscale combustion chamber and realistic swing engine chambers has been studied. It is found that, in mesoscale combustion engines, the burn time is dominated by the mean turbulent kinetic energy in the chamber. It is also shown that in a generic mesoscale combustion chamber, the burn time depends on the longest distance between the initial ignition kernel to its walls and by changing the ignition and injection locations, the burn time can be reduced by a factor of two. Furthermore, the studies of turbulent flame propagation in real swing engine chambers show that the combustion can be enhanced through in-chamber turbulence augmentation and with higher engine frequency, the burn time is shorter, which indicates that the in-chamber turbulence can be induced by the motion of moving components as well as the intake gas jet flow. The burn time
Assessing Model Characterization of Single Source ...
Aircraft measurements made downwind from specific coal fired power plants during the 2013 Southeast Nexus field campaign provide a unique opportunity to evaluate single source photochemical model predictions of both O3 and secondary PM2.5 species. The model did well at predicting downwind plume placement. The model shows similar patterns of an increasing fraction of PM2.5 sulfate ion to the sum of SO2 and PM2.5 sulfate ion by distance from the source compared with ambient based estimates. The model was less consistent in capturing downwind ambient based trends in conversion of NOX to NOY from these sources. Source sensitivity approaches capture near-source O3 titration by fresh NO emissions, in particular subgrid plume treatment. However, capturing this near-source chemical feature did not translate into better downwind peak estimates of single source O3 impacts. The model estimated O3 production from these sources but often was lower than ambient based source production. The downwind transect ambient measurements, in particular secondary PM2.5 and O3, have some level of contribution from other sources which makes direct comparison with model source contribution challenging. Model source attribution results suggest contribution to secondary pollutants from multiple sources even where primary pollutants indicate the presence of a single source. The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, deci
Numerical Model Of Parametric Oscillator In Proton Exchanged Ti:LiNbO3 Waveguides
NASA Astrophysics Data System (ADS)
Bava, G. P.; Montrosset, I.
1986-11-01
Integrated Optic Parametric oscillators in TIPE resonating structures are analyzed as regards the pump threshold power minimization. The amount of proton exchange has been chosen to satisfy phase-matching condition and the Ti:LiNbO3 waveguide parameters used for optimization. A comparison between TIPE and Ti structures will be discussed.
NASA Astrophysics Data System (ADS)
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.
Alper, Kenneth; Raghavan, Manoj; Isenhart, Robert; Howard, Bryant; Doyle, Werner; John, Roy; Prichep, Leslie
2008-02-01
This preliminary study sought to localize epileptogenic regions in patients with partial epilepsy by analysis of interictal EEG activity utilizing variable resolution electromagnetic tomography (VARETA), a three-dimensional quantitative electroencephalographic (QEEG) frequency-domain distributed source modeling technique. The very narrow band (VNB) spectra spanned the frequency range 0.39 Hz to 19.1 Hz, in 0.39 Hz steps. These VNB spectra were compared to normative data and transformed to provide Z-scores for every scalp derivation, and the spatial distributions of the probable EEG generators of the most abnormal values were displayed on slices from a probabilistic MRI atlas. Each voxel was color-coded to represent the significance of the deviation relative to age appropriate normative values. We compared the resulting three-dimensional images to the localization of epileptogenic regions based on invasive intracranial EEG recordings of seizure onsets. The VARETA image indicated abnormal interictal spectral power values in regions of seizure onset identified by invasive monitoring, mainly in delta and theta range (1.5 to 8.0 Hz). The VARETA localization of the most abnormal voxel was congruent with the epileptogenic regions identified by intracranial recordings with regard to hemisphere in all 6 cases, and with regard to lobe in 5 cases. In contrast, abnormal findings with routine EEG agreed with invasive monitoring with regard to hemisphere in 3 cases and with regard to lobe in 2 cases. These results suggest that analysis of background interictal EEG utilizing distributed source models should be investigated further in clinical epilepsy.
NASA Astrophysics Data System (ADS)
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
NASA Technical Reports Server (NTRS)
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. PMID:23690876
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.
Continuous-wave optical parametric source for terahertz waves tunable from 1 to 4.5 THz frequency
NASA Astrophysics Data System (ADS)
Kießling, Jens; Buse, Karsten; Vodopyanov, Konstantin L.; Breunig, Ingo
2014-02-01
We demonstrate the continuous-wave operation of a cascade that has been successfully applied so far only for picosecond systems: A doubly-resonant optical-parametric oscillator (OPO) based on lithium niobate generates signal and idler waves close to degeneracy. Subsequently, these two light fields are converted to a terahertz wave via difference frequency mixing in an orientation-patterned gallium arsenide crystal placed inside the OPO cavity. Using this scheme, we achieved tunability from 1 to 4:5 THz frequency, a linewidth smaller than 10 MHz, and a Gaussian beam profile. The output power is of the order of tens of μW, with a scalability into the milliwatt regime.
NASA Astrophysics Data System (ADS)
Boswell, R. W.; Sutherland, O.; Charles, C.; Squire, J. P.; Chang Díaz, F. R.; Glover, T. W.; Jacobson, V. T.; Chavers, D. G.; Bengtson, R. D.; Bering, E. A.; Goulding, R. H.; Light, M.
2004-11-01
Decay waves have been observed in the megahertz range in the helium plasma generated by the variable specific impulse magnetoplasma rocket magnetoplasma thruster. They are measured using one of the tips of a triple probe connected to a 50 Ω input of a spectrum analyzer via a dc block (a small capacitor). The maximum amplitude of all waves is in the center of the plasma and does not appear correlated to the radial electron density or temperature profiles. The waves seem to be generated close to the helicon antenna that was 91 cm "upstream" from the measuring Langmuir probe. A possible explanation is parametric decay of the large amplitude helicon wave that also generates the plasma.
Parametric Identification of Systems Via Linear Operators.
1978-09-01
A general parametric identification /approximation model is developed for the black box identification of linear time invariant systems in terms of... parametric identification techniques derive from the general model as special cases associated with a particular linear operator. Some possible
NASA Astrophysics Data System (ADS)
McCorkel, Joel
2016-09-01
Many inter-consistency efforts force empirical agreement between satellite and airborne sensors viewing a source nearly coincident in time and geometry that ensures consistency between sensors rather than relying on a physical understanding of the source. Several research groups organized a campaign at Algodones Dunes in March 2015 in an effort to measure and characterize parameters that can be used for a source model that will enable this physical understanding. This work will provide an overview of the parameters retrieved from airborne and ground-based measurements made during the campaign. Examples of model-based predictions of at-sensor radiance will be shown for Landsat and MODIS. This approach will provide insight into uncertainties of sensor inter-consistency studies and allow for documented SI-traceability and associate error budget. The Algodones model and subsequent test site models can be used for the assessing inter-calibration accuracies of the upcoming Climate Absolute Reflectance and Refractivity Observatory (CLARREO) Pathfinder mission.
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.
NASA Astrophysics Data System (ADS)
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.
[Review of urban nonpoint source pollution models].
Wang, Long; Huang, Yue-Fei; Wang, Guang-Qian
2010-10-01
The development history of urban nonpoint source pollution models is reviewed. Features, applicability and limitations of seven popular urban nonpoint source pollution models (SWMM, STORM, SLAMM, HSPF, DR3M-QUAL, MOUSE, and HydroWorks) are discussed. The methodology and research findings of uncertainty in urban nonpoint source pollution modeling are presented. Analytical probabilistic models for estimation of urban nonpoint sources are also presented. The research achievements of urban nonpoint source pollution models in China are summarized. The shortcomings and gaps of approaches on urban nonpoint source pollution models are pointed out. Improvements in modeling of pollutants buildup and washoff, sediments and pollutants transport, and pollutants biochemical reactions are desired for those seven popular models. Most of the models developed by researchers in China are empirical models, so that they can only applied for specific small areas and have inadequate accuracy. Future approaches include improving capability in fate and transport simulation of sediments and pollutants, exploring methodologies of modeling urban nonpoint source pollution in regions with little data or incomplete information, developing stochastic models for urban nonpoint source pollution simulation, and applying GIS to facilitate urban nonpoint source pollution simulation.
Cadarso-Suárez, Carmen; Roca-Pardiñas, Javier; Figueiras, Adolfo; González-Manteiga, Wenceslao
2005-04-30
The generalized additive, model (GAM) is a powerful and widely used tool that allows researchers to fit, non-parametrically, the effect of continuous predictors on a transformation of the mean response variable. Such a transformation is given by a so-called link function, and in GAMs this link function is assumed to be known. Nevertheless, if an incorrect choice is made for the link, the resulting GAM is misspecified and the results obtained may be misleading. In this paper, we propose a modified version of the local scoring algorithm that allows for the non-parametric estimation of the link function, by using local linear kernel smoothers. To better understand the effect that each covariate produces on the outcome, results are expressed in terms of the non-parametric odds ratio (OR) curves. Bootstrap techniques were used to correct the bias in the OR estimation and to construct point-wise confidence intervals. A simulation study was carried out to assess the behaviour of the resulting estimates. The proposed methodology was illustrated using data from the AIDS Register of Galicia (NW Spain), with a view to assessing the effect of the CD4 lymphocyte count on the probability of being AIDS-diagnosed via Tuberculosis (TB). This application shows how the link's flexibility makes it possible to obtain OR curve estimates that are less sensitive to the presence of outliers and unusual values that are often present in the extremes of the covariate distributions.
Grating lobe elimination in steerable parametric loudspeaker.
Shi, Chuang; Gan, Woon-Seng
2011-02-01
In the past two decades, the majority of research on the parametric loudspeaker has concentrated on the nonlinear modeling of acoustic propagation and pre-processing techniques to reduce nonlinear distortion in sound reproduction. There are, however, very few studies on directivity control of the parametric loudspeaker. In this paper, we propose an equivalent circular Gaussian source array that approximates the directivity characteristics of the linear ultrasonic transducer array. By using this approximation, the directivity of the sound beam from the parametric loudspeaker can be predicted by the product directivity principle. New theoretical results, which are verified through measurements, are presented to show the effectiveness of the delay-and-sum beamsteering structure for the parametric loudspeaker. Unlike the conventional loudspeaker array, where the spacing between array elements must be less than half the wavelength to avoid spatial aliasing, the parametric loudspeaker can take advantage of grating lobe elimination to extend the spacing of ultrasonic transducer array to more than 1.5 wavelengths in a typical application.
NASA Astrophysics Data System (ADS)
Wu, Zhiliang; Wang, Shuxin; Zhang, Lianhong; Hu, S. Jack
This paper presents an analytical model of the electrical contact resistance between the carbon paper gas diffusion layers (GDLs) and the graphite bipolar plates (BPPs) in a proton exchange membrane (PEM) fuel cell. The model is developed based on the classical statistical contact theory for a PEM fuel cell, using the same probability distributions of the GDL structure and BPP surface profile as previously described in Wu et al. [Z. Wu, Y. Zhou, G. Lin, S. Wang, S.J. Hu, J. Power Sources 182 (2008) 265-269] and Zhou et al. [Y. Zhou, G. Lin, A.J. Shih, S.J. Hu, J. Power Sources 163 (2007) 777-783]. Results show that estimates of the contact resistance compare favorably with experimental data by Zhou et al. [Y. Zhou, G. Lin, A.J. Shih, S.J. Hu, J. Power Sources 163 (2007) 777-783]. Factors affecting the contact behavior are systematically studied using the analytical model, including the material properties of the two contact bodies and factors arising from the manufacturing processes. The transverse Young's modulus of chopped carbon fibers in the GDL and the surface profile of the BPP are found to be significant to the contact resistance. The factor study also sheds light on the manufacturing requirements of carbon fiber GDLs for a better contact performance in PEM fuel cells.
NASA Astrophysics Data System (ADS)
Barrientos Barria, Jessica; Dobroc, Alexandre; Coudert-Alteirac, Hélène; Raybaut, Myriam; Cézard, Nicolas; Dherbecourt, Jean-Baptiste; Schmid, Thomas; Faure, Basile; Souhaité, Grégoire; Pelon, Jacques; Melkonian, Jean-Michel; Godard, Antoine; Lefebvre, Michel
2014-10-01
We report on the remote sensing capability of an integrated path differential absorption lidar (IPDIAL) instrument, for multi-species gas detection and monitoring in the 3.3-3.7 µm range. This instrument is based on an optical parametric source composed of a master oscillator-power amplifier scheme—whose core building block is a nested cavity optical parametric oscillator—emitting up to 10 µJ at 3.3 µm. Optical pumping is realized with an innovative single-frequency, 2-kHz repetition rate, nanosecond microchip laser, amplified up to 200 µJ per pulse in a single-crystal fiber amplifier. Simultaneous monitoring of mean atmospheric water vapor and methane concentrations was performed over several days by use of a topographic target, and water vapor concentration measurements show good agreement compared with an in situ hygrometer measurement. Performances of the IPDIAL instrument are assessed in terms of concentration measurement uncertainties and maximum remote achievable range.
NASA Astrophysics Data System (ADS)
Foyo-Moreno, I.; Vida, J.; Olmo, F. J.; Alados-Arboledas, L.
2000-11-01
Since the discovery of the ozone depletion in Antarctic and the globally declining trend of stratospheric ozone concentration, public and scientific concern has been raised in the last decades. A very important consequence of this fact is the increased broadband and spectral UV radiation in the environment and the biological effects and heath risks that may take place in the near future. The absence of widespread measurements of this radiometric flux has lead to the development and use of alternative estimation procedures such as the parametric approaches. Parametric models compute the radiant energy using available atmospheric parameters. Some parametric models compute the global solar irradiance at surface level by addition of its direct beam and diffuse components. In the present work, we have developed a comparison between two cloudless sky parametrization schemes. Both methods provide an estimation of the solar spectral irradiance that can be integrated spectrally within the limits of interest. For this test we have used data recorded in a radiometric station located at Granada (37.180°N, 3.580°W, 660 m a.m.s.l.), an inland location. The database includes hourly values of the relevant variables covering the years 1994-95. The performance of the models has been tested in relation to their predictive capability of global solar irradiance in the UV range (290-385 nm). After our study, it appears that information concerning the aerosol radiative effects is fundamental in order to obtain a good estimation. The original version of SPCTRAL2 provides estimates of the experimental values with negligible mean bias deviation. This suggests not only the appropriateness of the model but also the convenience of the aerosol features fixed in it to Granada conditions. SMARTS2 model offers increased flexibility concerning the selection of different aerosol models included in the code and provides the best results when the selected models are those considered as urban
NASA Astrophysics Data System (ADS)
Restrepo, Louis Fernando
The close location of most DOE non-reactor nuclear facilities to site boundaries and the potential for having receptors in the proximity of such facilities makes it extremely important to accurately address the impact of plume rise and building wake effects on the consequences to such individuals. Unfortunately, there is no current single computer code or model that adequately address the consequences to receptors postulated to be located within the building wake of such facilities. Existing state-of-the-art models have relied on over- simplistic plume rise and parametric wake models that were developed based on very limited amount of data or assumptions, thus potentially leading to large errors in calculations. Building wake and plume rise models implemented in existing consequence computer codes have been identified and evaluated. These models come from an extensive literature review of dispersion, transport, and consequence modeling of airborne radioactive material releases that extends over 25 years. This dissertation focuses on the evaluation of existing state-of-the-art parametric building wake dispersion models by the use of computational fluid dynamic (CFD) codes, developing potential improvements to such models, and comparing the results of such improvements to those generated by CFD models and models implemented in state- of-the-art computer codes. This dissertation also presents new dispersion models and a new analytical parametric model to deal with transient releases that decay or transform during transport.
Parametric modeling of wideband piezoelectric polymer sensors: Design for optoacoustic applications.
Fernández Vidal, A; Ciocci Brazzano, L; Matteo, C L; Sorichetti, P A; González, M G
2017-09-01
In this work, we present a three-dimensional model for the design of wideband piezoelectric polymer sensors which includes the geometry and the properties of the transducer materials. The model uses FFT and numerical integration techniques in an explicit, semi-analytical approach. To validate the model, we made electrical and mechanical measurements on homemade sensors for optoacoustic applications. Each device was implemented using a polyvinylidene fluoride thin film piezoelectric polymer with a thickness of 25 μm. The sensors had detection areas in the range between 0.5 mm(2) and 35 mm(2) and were excited by acoustic pressure pulses of 5 ns (FWHM) from a source with a diameter around 10 μm. The experimental data obtained from the measurements agree well with the model results. We discuss the relative importance of the sensor design parameters for optoacoustic applications and we provide guidelines for the optimization of devices.
Parametric modeling of wideband piezoelectric polymer sensors: Design for optoacoustic applications
NASA Astrophysics Data System (ADS)
Fernández Vidal, A.; Ciocci Brazzano, L.; Matteo, C. L.; Sorichetti, P. A.; González, M. G.
2017-09-01
In this work, we present a three-dimensional model for the design of wideband piezoelectric polymer sensors which includes the geometry and the properties of the transducer materials. The model uses FFT and numerical integration techniques in an explicit, semi-analytical approach. To validate the model, we made electrical and mechanical measurements on homemade sensors for optoacoustic applications. Each device was implemented using a polyvinylidene fluoride thin film piezoelectric polymer with a thickness of 25 μm. The sensors had detection areas in the range between 0.5 mm2 and 35 mm2 and were excited by acoustic pressure pulses of 5 ns (FWHM) from a source with a diameter around 10 μm. The experimental data obtained from the measurements agree well with the model results. We discuss the relative importance of the sensor design parameters for optoacoustic applications and we provide guidelines for the optimization of devices.
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.
Zhang, Kai; Cao, Libo; Wang, Yulong; Hwang, Eunjoo; Reed, Matthew P; Forman, Jason; Hu, Jingwen
2017-10-01
Field data analyses have shown that obesity significantly increases the occupant injury risks in motor vehicle crashes, but the injury assessment tools for people with obesity are largely lacking. The objectives of this study were to use a mesh morphing method to rapidly generate parametric finite element models with a wide range of obesity levels and to evaluate their biofidelity against impact tests using postmortem human subjects (PMHS). Frontal crash tests using three PMHS seated in a vehicle rear seat compartment with body mass index (BMI) from 24 to 40 kg/m(2) were selected. To develop the human models matching the PMHS geometry, statistical models of external body shape, rib cage, pelvis, and femur were applied to predict the target geometry using age, sex, stature, and BMI. A mesh morphing method based on radial basis functions was used to rapidly morph a baseline human model into the target geometry. The model-predicted body excursions and injury measures were compared to the PMHS tests. Comparisons of occupant kinematics and injury measures between the tests and simulations showed reasonable correlations across the wide range of BMI levels. The parametric human models have the capability to account for the obesity effects on the occupant impact responses and injury risks. © 2017 The Obesity Society.
Parametric Rietveld refinement
Stinton, Graham W.; Evans, John S. O.
2007-01-01
In this paper the method of parametric Rietveld refinement is described, in which an ensemble of diffraction data collected as a function of time, temperature, pressure or any other variable are fitted to a single evolving structural model. Parametric refinement offers a number of potential benefits over independent or sequential analysis. It can lead to higher precision of refined parameters, offers the possibility of applying physically realistic models during data analysis, allows the refinement of ‘non-crystallographic’ quantities such as temperature or rate constants directly from diffraction data, and can help avoid false minima. PMID:19461841
NASA Astrophysics Data System (ADS)
Semerok, A.; Fomichev, S. V.; Jabbour, C.; Lacour, J.-L.; Tabarant, M.; Chartier, F.
2017-10-01
Multi-parametric theoretical studies to analyze the effect of both the matter properties (absorption coefficient, thermal conductivity and diffusivity) and the heating field parameters (spatial distribution and pulse duration) on the resulted temperature distribution are presented. For heating in sub-micrometric range (< 1 μm), a low dependence of heating temperature distribution on the sample thermal properties and heating source duration was observed. Nano-ablation thresholds are found to be increasing inversely with heating source dimensions. The simulation results demonstrated a good agreement with the nanometer-size craters (100 nm diameters, 10 nm depth) obtained experimentally with a tip-enhanced near-field ablation (4 ns laser pulse duration, 266 nm wavelength) of Si- and Au-samples.
NASA Technical Reports Server (NTRS)
Smialek, James L.
2002-01-01
An equation has been developed to model the iterative scale growth and spalling process that occurs during cyclic oxidation of high temperature materials. Parabolic scale growth and spalling of a constant surface area fraction have been assumed. Interfacial spallation of the only the thickest segments was also postulated. This simplicity allowed for representation by a simple deterministic summation series. Inputs are the parabolic growth rate constant, the spall area fraction, oxide stoichiometry, and cycle duration. Outputs include the net weight change behavior, as well as the total amount of oxygen and metal consumed, the total amount of oxide spalled, and the mass fraction of oxide spalled. The outputs all follow typical well-behaved trends with the inputs and are in good agreement with previous interfacial models.
Research on Finite Element Model Generating Method of General Gear Based on Parametric Modelling
NASA Astrophysics Data System (ADS)
Lei, Yulong; Yan, Bo; Fu, Yao; Chen, Wei; Hou, Liguo
2017-06-01
Aiming at the problems o