A Tool for Parameter-space Explorations

Science.gov (United States)

Murase, Yohsuke; Uchitane, Takeshi; Ito, Nobuyasu

A software for managing simulation jobs and results, named "OACIS", is presented. It controls a large number of simulation jobs executed in various remote servers, keeps these results in an organized way, and manages the analyses on these results. The software has a web browser front end, and users can submit various jobs to appropriate remote hosts from a web browser easily. After these jobs are finished, all the result files are automatically downloaded from the computational hosts and stored in a traceable way together with the logs of the date, host, and elapsed time of the jobs. Some visualization functions are also provided so that users can easily grasp the overview of the results distributed in a high-dimensional parameter space. Thus, OACIS is especially beneficial for the complex simulation models having many parameters for which a lot of parameter searches are required. By using API of OACIS, it is easy to write a code that automates parameter selection depending on the previous simulation results. A few examples of the automated parameter selection are also demonstrated.

Online State Space Model Parameter Estimation in Synchronous Machines

Directory of Open Access Journals (Sweden)

Z. Gallehdari

2014-06-01

The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.

On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

Science.gov (United States)

Yamazaki, Keisuke

2012-07-01

Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

Charge distributions in transverse coordinate space and in impact parameter space

Energy Technology Data Exchange (ETDEWEB)

Hwang, Dae Sung [Department of Physics, Sejong University, Seoul 143-747 (Korea, Republic of)], E-mail: dshwang@slac.stanford.edu; Kim, Dong Soo [Department of Physics, Kangnung National University, Kangnung 210-702 (Korea, Republic of); Kim, Jonghyun [Department of Physics, Sejong University, Seoul 143-747 (Korea, Republic of)

2008-11-27

We study the charge distributions of the valence quarks inside nucleon in the transverse coordinate space, which is conjugate to the transverse momentum space. We compare the results with the charge distributions in the impact parameter space.

Charge distributions in transverse coordinate space and in impact parameter space

OpenAIRE

Hwang, Dae Sung; Kim, Dong Soo; Kim, Jonghyun

2008-01-01

We study the charge distributions of the valence quarks inside nucleon in the transverse coordinate space, which is conjugate to the transverse momentum space. We compare the results with the charge distributions in the impact parameter space.

Systematic parameter inference in stochastic mesoscopic modeling

Energy Technology Data Exchange (ETDEWEB)

Lei, Huan; Yang, Xiu [Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Li, Zhen [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States); Karniadakis, George Em, E-mail: george_karniadakis@brown.edu [Division of Applied Mathematics, Brown University, Providence, RI 02912 (United States)

2017-02-01

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are “sparse”. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

B→τν: Opening up the charged Higgs parameter space with R-parity violation

International Nuclear Information System (INIS)

Bose, Roshni; Kundu, Anirban

2012-01-01

The theoretically clean channel B + →τ + ν shows a close to 3σ discrepancy between the Standard Model prediction and the data. This in turn puts a strong constraint on the parameter space of a two-Higgs doublet model, including R-parity conserving supersymmetry. The constraint is so strong that it almost smells of fine-tuning. We show how the parameter space opens up with the introduction of suitable R-parity violating interactions, and release the tension between data and theory.

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

Science.gov (United States)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Exploration of DGVM Parameter Solution Space Using Simulated Annealing: Implications for Forecast Uncertainties

Science.gov (United States)

Wells, J. R.; Kim, J. B.

2011-12-01

Parameters in dynamic global vegetation models (DGVMs) are thought to be weakly constrained and can be a significant source of errors and uncertainties. DGVMs use between 5 and 26 plant functional types (PFTs) to represent the average plant life form in each simulated plot, and each PFT typically has a dozen or more parameters that define the way it uses resource and responds to the simulated growing environment. Sensitivity analysis explores how varying parameters affects the output, but does not do a full exploration of the parameter solution space. The solution space for DGVM parameter values are thought to be complex and non-linear; and multiple sets of acceptable parameters may exist. In published studies, PFT parameters are estimated from published literature, and often a parameter value is estimated from a single published value. Further, the parameters are "tuned" using somewhat arbitrary, "trial-and-error" methods. BIOMAP is a new DGVM created by fusing MAPSS biogeography model with Biome-BGC. It represents the vegetation of North America using 26 PFTs. We are using simulated annealing, a global search method, to systematically and objectively explore the solution space for the BIOMAP PFTs and system parameters important for plant water use. We defined the boundaries of the solution space by obtaining maximum and minimum values from published literature, and where those were not available, using +/-20% of current values. We used stratified random sampling to select a set of grid cells representing the vegetation of the conterminous USA. Simulated annealing algorithm is applied to the parameters for spin-up and a transient run during the historical period 1961-1990. A set of parameter values is considered acceptable if the associated simulation run produces a modern potential vegetation distribution map that is as accurate as one produced by trial-and-error calibration. We expect to confirm that the solution space is non-linear and complex, and that

Parameter estimation in stochastic rainfall-runoff models

DEFF Research Database (Denmark)

Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur

2006-01-01

A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...

Parameter space of experimental chaotic circuits with high-precision control parameters

Energy Technology Data Exchange (ETDEWEB)

Sousa, Francisco F. G. de; Rubinger, Rero M. [Instituto de Física e Química, Universidade Federal de Itajubá, Itajubá, MG (Brazil); Sartorelli, José C., E-mail: sartorelli@if.usp.br [Universidade de São Paulo, São Paulo, SP (Brazil); Albuquerque, Holokx A. [Departamento de Física, Universidade do Estado de Santa Catarina, Joinville, SC (Brazil); Baptista, Murilo S. [Institute of Complex Systems and Mathematical Biology, SUPA, University of Aberdeen, Aberdeen (United Kingdom)

2016-08-15

We report high-resolution measurements that experimentally confirm a spiral cascade structure and a scaling relationship of shrimps in the Chua's circuit. Circuits constructed using this component allow for a comprehensive characterization of the circuit behaviors through high resolution parameter spaces. To illustrate the power of our technological development for the creation and the study of chaotic circuits, we constructed a Chua circuit and study its high resolution parameter space. The reliability and stability of the designed component allowed us to obtain data for long periods of time (∼21 weeks), a data set from which an accurate estimation of Lyapunov exponents for the circuit characterization was possible. Moreover, this data, rigorously characterized by the Lyapunov exponents, allows us to reassure experimentally that the shrimps, stable islands embedded in a domain of chaos in the parameter spaces, can be observed in the laboratory. Finally, we confirm that their sizes decay exponentially with the period of the attractor, a result expected to be found in maps of the quadratic family.

Research on Geometric Positioning Algorithm of License Plate in Multidimensional Parameter Space

Directory of Open Access Journals (Sweden)

Yinhua Huan

2014-05-01

Full Text Available Considering features of vehicle license plate location method which commonly used, in order to search a consistent location for reference images with license plates feature in multidimensional parameter space, a new algorithm of geometric location is proposed. Geometric location algorithm main include model training and real time search. Which not only adapt the gray-scale linearity and the gray non-linear changes, but also support changes of scale and angle. Compared with the mainstream locating software, numerical results shows under the same test conditions that the position deviation of geometric positioning algorithm is less than 0.5 pixel. Without taking into account the multidimensional parameter space, Geometric positioning algorithm position deviation is less than 1.0 pixel and angle deviation is less than 1.0 degree taking into account the multidimensional parameter space. This algorithm is robust, simple, practical and is better than the traditional method.

Recovering a Probabilistic Knowledge Structure by Constraining Its Parameter Space

Science.gov (United States)

Stefanutti, Luca; Robusto, Egidio

2009-01-01

In the Basic Local Independence Model (BLIM) of Doignon and Falmagne ("Knowledge Spaces," Springer, Berlin, 1999), the probabilistic relationship between the latent knowledge states and the observable response patterns is established by the introduction of a pair of parameters for each of the problems: a lucky guess probability and a careless…

Rapid Computation of Thermodynamic Properties over Multidimensional Nonbonded Parameter Spaces Using Adaptive Multistate Reweighting.

Science.gov (United States)

Naden, Levi N; Shirts, Michael R

2016-04-12

We show how thermodynamic properties of molecular models can be computed over a large, multidimensional parameter space by combining multistate reweighting analysis with a linear basis function approach. This approach reduces the computational cost to estimate thermodynamic properties from molecular simulations for over 130,000 tested parameter combinations from over 1000 CPU years to tens of CPU days. This speed increase is achieved primarily by computing the potential energy as a linear combination of basis functions, computed from either modified simulation code or as the difference of energy between two reference states, which can be done without any simulation code modification. The thermodynamic properties are then estimated with the Multistate Bennett Acceptance Ratio (MBAR) as a function of multiple model parameters without the need to define a priori how the states are connected by a pathway. Instead, we adaptively sample a set of points in parameter space to create mutual configuration space overlap. The existence of regions of poor configuration space overlap are detected by analyzing the eigenvalues of the sampled states' overlap matrix. The configuration space overlap to sampled states is monitored alongside the mean and maximum uncertainty to determine convergence, as neither the uncertainty or the configuration space overlap alone is a sufficient metric of convergence. This adaptive sampling scheme is demonstrated by estimating with high precision the solvation free energies of charged particles of Lennard-Jones plus Coulomb functional form with charges between -2 and +2 and generally physical values of σij and ϵij in TIP3P water. We also compute entropy, enthalpy, and radial distribution functions of arbitrary unsampled parameter combinations using only the data from these sampled states and use the estimates of free energies over the entire space to examine the deviation of atomistic simulations from the Born approximation to the solvation free

Robust estimation of hydrological model parameters

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A. Bárdossy

2008-11-01

Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

"A space-time ensemble Kalman filter for state and parameter estimation of groundwater transport models"

Science.gov (United States)

Briseño, Jessica; Herrera, Graciela S.

2010-05-01

Herrera (1998) proposed a method for the optimal design of groundwater quality monitoring networks that involves space and time in a combined form. The method was applied later by Herrera et al (2001) and by Herrera and Pinder (2005). To get the estimates of the contaminant concentration being analyzed, this method uses a space-time ensemble Kalman filter, based on a stochastic flow and transport model. When the method is applied, it is important that the characteristics of the stochastic model be congruent with field data, but, in general, it is laborious to manually achieve a good match between them. For this reason, the main objective of this work is to extend the space-time ensemble Kalman filter proposed by Herrera, to estimate the hydraulic conductivity, together with hydraulic head and contaminant concentration, and its application in a synthetic example. The method has three steps: 1) Given the mean and the semivariogram of the natural logarithm of hydraulic conductivity (ln K), random realizations of this parameter are obtained through two alternatives: Gaussian simulation (SGSim) and Latin Hypercube Sampling method (LHC). 2) The stochastic model is used to produce hydraulic head (h) and contaminant (C) realizations, for each one of the conductivity realizations. With these realization the mean of ln K, h and C are obtained, for h and C, the mean is calculated in space and time, and also the cross covariance matrix h-ln K-C in space and time. The covariance matrix is obtained averaging products of the ln K, h and C realizations on the estimation points and times, and the positions and times with data of the analyzed variables. The estimation points are the positions at which estimates of ln K, h or C are gathered. In an analogous way, the estimation times are those at which estimates of any of the three variables are gathered. 3) Finally the ln K, h and C estimate are obtained using the space-time ensemble Kalman filter. The realization mean for each one

Entropy considerations in constraining the mSUGRA parameter space

International Nuclear Information System (INIS)

Nunez, Dario; Sussman, Roberto A.; Zavala, Jesus; Nellen, Lukas; Cabral-Rosetti, Luis G.; Mondragon, Myriam

2006-01-01

We explore the use of two criteria to constraint the allowed parameter space in mSUGRA models. Both criteria are based in the calculation of the present density of neutralinos as dark matter in the Universe. The first one is the usual ''abundance'' criterion which is used to calculate the relic density after the ''freeze-out'' era. To compute the relic density we used the numerical public code micrOMEGAs. The second criterion applies the microcanonical definition of entropy to a weakly interacting and self-gravitating gas evaluating then the change in the entropy per particle of this gas between the ''freeze-out'' era and present day virialized structures (i.e systems in virial equilibrium). An ''entropy-consistency'' criterion emerges by comparing theoretical and empirical estimates of this entropy. The main objective of our work is to determine for which regions of the parameter space in the mSUGRA model are both criteria consistent with the 2σ bounds according to WMAP for the relic density: 0.0945 < ΩCDMh2 < 0.1287. As a first result, we found that for A0 = 0, sgnμ +, small values of tanβ are not favored; only for tanβ ≅ 50 are both criteria significantly consistent

Scattering-parameter extraction and calibration techniques for RF free-space material characterization

DEFF Research Database (Denmark)

Kaniecki, M.; Saenz, E.; Rolo, L.

2014-01-01

This paper demonstrates a method for material characterization (permittivity, permeability, loss tangent) based on the scattering parameters. The performance of the extraction algorithm will be shown for modelled and measured data. The measurements were carried out at the European Space Agency...

SP_Ace: a new code to derive stellar parameters and elemental abundances

Science.gov (United States)

Boeche, C.; Grebel, E. K.

2016-03-01

Context. Ongoing and future massive spectroscopic surveys will collect large numbers (106-107) of stellar spectra that need to be analyzed. Highly automated software is needed to derive stellar parameters and chemical abundances from these spectra. Aims: We developed a new method of estimating the stellar parameters Teff, log g, [M/H], and elemental abundances. This method was implemented in a new code, SP_Ace (Stellar Parameters And Chemical abundances Estimator). This is a highly automated code suitable for analyzing the spectra of large spectroscopic surveys with low or medium spectral resolution (R = 2000-20 000). Methods: After the astrophysical calibration of the oscillator strengths of 4643 absorption lines covering the wavelength ranges 5212-6860 Å and 8400-8924 Å, we constructed a library that contains the equivalent widths (EW) of these lines for a grid of stellar parameters. The EWs of each line are fit by a polynomial function that describes the EW of the line as a function of the stellar parameters. The coefficients of these polynomial functions are stored in a library called the "GCOG library". SP_Ace, a code written in FORTRAN95, uses the GCOG library to compute the EWs of the lines, constructs models of spectra as a function of the stellar parameters and abundances, and searches for the model that minimizes the χ2 deviation when compared to the observed spectrum. The code has been tested on synthetic and real spectra for a wide range of signal-to-noise and spectral resolutions. Results: SP_Ace derives stellar parameters such as Teff, log g, [M/H], and chemical abundances of up to ten elements for low to medium resolution spectra of FGK-type stars with precision comparable to the one usually obtained with spectra of higher resolution. Systematic errors in stellar parameters and chemical abundances are presented and identified with tests on synthetic and real spectra. Stochastic errors are automatically estimated by the code for all the parameters

Review of the different methods to derive average spacing from resolved resonance parameters sets

International Nuclear Information System (INIS)

Fort, E.; Derrien, H.; Lafond, D.

1979-12-01

The average spacing of resonances is an important parameter for statistical model calculations, especially concerning non fissile nuclei. The different methods to derive this average value from resonance parameters sets have been reviewed and analyzed in order to tentatively detect their respective weaknesses and propose recommendations. Possible improvements are suggested

A fractional factorial probabilistic collocation method for uncertainty propagation of hydrologic model parameters in a reduced dimensional space

Science.gov (United States)

Wang, S.; Huang, G. H.; Huang, W.; Fan, Y. R.; Li, Z.

2015-10-01

In this study, a fractional factorial probabilistic collocation method is proposed to reveal statistical significance of hydrologic model parameters and their multi-level interactions affecting model outputs, facilitating uncertainty propagation in a reduced dimensional space. The proposed methodology is applied to the Xiangxi River watershed in China to demonstrate its validity and applicability, as well as its capability of revealing complex and dynamic parameter interactions. A set of reduced polynomial chaos expansions (PCEs) only with statistically significant terms can be obtained based on the results of factorial analysis of variance (ANOVA), achieving a reduction of uncertainty in hydrologic predictions. The predictive performance of reduced PCEs is verified by comparing against standard PCEs and the Monte Carlo with Latin hypercube sampling (MC-LHS) method in terms of reliability, sharpness, and Nash-Sutcliffe efficiency (NSE). Results reveal that the reduced PCEs are able to capture hydrologic behaviors of the Xiangxi River watershed, and they are efficient functional representations for propagating uncertainties in hydrologic predictions.

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis.

Science.gov (United States)

Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas

2013-01-01

Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline's modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

Approaches to automatic parameter fitting in a microscopy image segmentation pipeline: An exploratory parameter space analysis

Directory of Open Access Journals (Sweden)

Christian Held

2013-01-01

Full Text Available Introduction: Research and diagnosis in medicine and biology often require the assessment of a large amount of microscopy image data. Although on the one hand, digital pathology and new bioimaging technologies find their way into clinical practice and pharmaceutical research, some general methodological issues in automated image analysis are still open. Methods: In this study, we address the problem of fitting the parameters in a microscopy image segmentation pipeline. We propose to fit the parameters of the pipeline′s modules with optimization algorithms, such as, genetic algorithms or coordinate descents, and show how visual exploration of the parameter space can help to identify sub-optimal parameter settings that need to be avoided. Results: This is of significant help in the design of our automatic parameter fitting framework, which enables us to tune the pipeline for large sets of micrographs. Conclusion: The underlying parameter spaces pose a challenge for manual as well as automated parameter optimization, as the parameter spaces can show several local performance maxima. Hence, optimization strategies that are not able to jump out of local performance maxima, like the hill climbing algorithm, often result in a local maximum.

Parameter-space metric of semicoherent searches for continuous gravitational waves

International Nuclear Information System (INIS)

Pletsch, Holger J.

2010-01-01

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

A growing social network model in geographical space

Science.gov (United States)

Antonioni, Alberto; Tomassini, Marco

2017-09-01

In this work we propose a new model for the generation of social networks that includes their often ignored spatial aspects. The model is a growing one and links are created either taking space into account, or disregarding space and only considering the degree of target nodes. These two effects can be mixed linearly in arbitrary proportions through a parameter. We numerically show that for a given range of the combination parameter, and for given mean degree, the generated network class shares many important statistical features with those observed in actual social networks, including the spatial dependence of connections. Moreover, we show that the model provides a good qualitative fit to some measured social networks.

Shape, size, and robustness: feasible regions in the parameter space of biochemical networks.

Directory of Open Access Journals (Sweden)

Adel Dayarian

2009-01-01

Full Text Available The concept of robustness of regulatory networks has received much attention in the last decade. One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional. In this paper, we show that, in addition to volume, the geometry of this region has important consequences for the robustness and the fragility of a network. We develop an approximation within which we could algebraically specify the feasible region. We analyze the segment polarity gene network to illustrate our approach. The study of random walks in the parameter space and how they exit the feasible region provide us with a rich perspective on the different modes of failure of this network model. In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other. Our method provides a more complete measure of robustness to parameter variation. As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks.

Ground level enhancement (GLE) energy spectrum parameters model

Science.gov (United States)

Qin, G.; Wu, S.

2017-12-01

We study the ground level enhancement (GLE) events in solar cycle 23 with the four energy spectra parameters, the normalization parameter C, low-energy power-law slope γ 1, high-energy power-law slope γ 2, and break energy E0, obtained by Mewaldt et al. 2012 who fit the observations to the double power-law equation. we divide the GLEs into two groups, one with strong acceleration by interplanetary (IP) shocks and another one without strong acceleration according to the condition of solar eruptions. We next fit the four parameters with solar event conditions to get models of the parameters for the two groups of GLEs separately. So that we would establish a model of energy spectrum for GLEs for the future space weather prediction.

Exploring short-GRB afterglow parameter space for observations in coincidence with gravitational waves

Science.gov (United States)

Saleem, M.; Resmi, L.; Misra, Kuntal; Pai, Archana; Arun, K. G.

2018-03-01

Short duration Gamma Ray Bursts (SGRB) and their afterglows are among the most promising electromagnetic (EM) counterparts of Neutron Star (NS) mergers. The afterglow emission is broad-band, visible across the entire electromagnetic window from γ-ray to radio frequencies. The flux evolution in these frequencies is sensitive to the multidimensional afterglow physical parameter space. Observations of gravitational wave (GW) from BNS mergers in spatial and temporal coincidence with SGRB and associated afterglows can provide valuable constraints on afterglow physics. We run simulations of GW-detected BNS events and assuming that all of them are associated with a GRB jet which also produces an afterglow, investigate how detections or non-detections in X-ray, optical and radio frequencies can be influenced by the parameter space. We narrow down the regions of afterglow parameter space for a uniform top-hat jet model, which would result in different detection scenarios. We list inferences which can be drawn on the physics of GRB afterglows from multimessenger astronomy with coincident GW-EM observations.

Model parameter learning using Kullback-Leibler divergence

Science.gov (United States)

Lin, Chungwei; Marks, Tim K.; Pajovic, Milutin; Watanabe, Shinji; Tung, Chih-kuan

2018-02-01

In this paper, we address the following problem: For a given set of spin configurations whose probability distribution is of the Boltzmann type, how do we determine the model coupling parameters? We demonstrate that directly minimizing the Kullback-Leibler divergence is an efficient method. We test this method against the Ising and XY models on the one-dimensional (1D) and two-dimensional (2D) lattices, and provide two estimators to quantify the model quality. We apply this method to two types of problems. First, we apply it to the real-space renormalization group (RG). We find that the obtained RG flow is sufficiently good for determining the phase boundary (within 1% of the exact result) and the critical point, but not accurate enough for critical exponents. The proposed method provides a simple way to numerically estimate amplitudes of the interactions typically truncated in the real-space RG procedure. Second, we apply this method to the dynamical system composed of self-propelled particles, where we extract the parameter of a statistical model (a generalized XY model) from a dynamical system described by the Viscek model. We are able to obtain reasonable coupling values corresponding to different noise strengths of the Viscek model. Our method is thus able to provide quantitative analysis of dynamical systems composed of self-propelled particles.

Exploiting intrinsic fluctuations to identify model parameters.

Science.gov (United States)

Zimmer, Christoph; Sahle, Sven; Pahle, Jürgen

2015-04-01

Parameterisation of kinetic models plays a central role in computational systems biology. Besides the lack of experimental data of high enough quality, some of the biggest challenges here are identification issues. Model parameters can be structurally non-identifiable because of functional relationships. Noise in measured data is usually considered to be a nuisance for parameter estimation. However, it turns out that intrinsic fluctuations in particle numbers can make parameters identifiable that were previously non-identifiable. The authors present a method to identify model parameters that are structurally non-identifiable in a deterministic framework. The method takes time course recordings of biochemical systems in steady state or transient state as input. Often a functional relationship between parameters presents itself by a one-dimensional manifold in parameter space containing parameter sets of optimal goodness. Although the system's behaviour cannot be distinguished on this manifold in a deterministic framework it might be distinguishable in a stochastic modelling framework. Their method exploits this by using an objective function that includes a measure for fluctuations in particle numbers. They show on three example models, immigration-death, gene expression and Epo-EpoReceptor interaction, that this resolves the non-identifiability even in the case of measurement noise with known amplitude. The method is applied to partially observed recordings of biochemical systems with measurement noise. It is simple to implement and it is usually very fast to compute. This optimisation can be realised in a classical or Bayesian fashion.

Emulation of a complex global aerosol model to quantify sensitivity to uncertain parameters

Directory of Open Access Journals (Sweden)

L. A. Lee

2011-12-01

Full Text Available Sensitivity analysis of atmospheric models is necessary to identify the processes that lead to uncertainty in model predictions, to help understand model diversity through comparison of driving processes, and to prioritise research. Assessing the effect of parameter uncertainty in complex models is challenging and often limited by CPU constraints. Here we present a cost-effective application of variance-based sensitivity analysis to quantify the sensitivity of a 3-D global aerosol model to uncertain parameters. A Gaussian process emulator is used to estimate the model output across multi-dimensional parameter space, using information from a small number of model runs at points chosen using a Latin hypercube space-filling design. Gaussian process emulation is a Bayesian approach that uses information from the model runs along with some prior assumptions about the model behaviour to predict model output everywhere in the uncertainty space. We use the Gaussian process emulator to calculate the percentage of expected output variance explained by uncertainty in global aerosol model parameters and their interactions. To demonstrate the technique, we show examples of cloud condensation nuclei (CCN sensitivity to 8 model parameters in polluted and remote marine environments as a function of altitude. In the polluted environment 95 % of the variance of CCN concentration is described by uncertainty in the 8 parameters (excluding their interaction effects and is dominated by the uncertainty in the sulphur emissions, which explains 80 % of the variance. However, in the remote region parameter interaction effects become important, accounting for up to 40 % of the total variance. Some parameters are shown to have a negligible individual effect but a substantial interaction effect. Such sensitivities would not be detected in the commonly used single parameter perturbation experiments, which would therefore underpredict total uncertainty. Gaussian process

Constraining the mSUGRA (minimal supergravity) parameter space using the entropy of dark matter halos

Energy Technology Data Exchange (ETDEWEB)

Nunez, Dario; Zavala, Jesus; Nellen, Lukas; Sussman, Roberto A [Instituto de Ciencias Nucleares, Universidad Nacional Autonoma de Mexico (ICN-UNAM), AP 70-543, Mexico 04510 DF (Mexico); Cabral-Rosetti, Luis G [Departamento de Posgrado, Centro Interdisciplinario de Investigacion y Docencia en Educacion Tecnica (CIIDET), Avenida Universidad 282 Pte., Col. Centro, Apartado Postal 752, C. P. 76000, Santiago de Queretaro, Qro. (Mexico); Mondragon, Myriam, E-mail: nunez@nucleares.unam.mx, E-mail: jzavala@nucleares.unam.mx, E-mail: jzavala@shao.ac.cn, E-mail: lukas@nucleares.unam.mx, E-mail: sussman@nucleares.unam.mx, E-mail: lgcabral@ciidet.edu.mx, E-mail: myriam@fisica.unam.mx [Instituto de Fisica, Universidad Nacional Autonoma de Mexico (IF-UNAM), Apartado Postal 20-364, 01000 Mexico DF (Mexico); Collaboration: For the Instituto Avanzado de Cosmologia, IAC

2008-05-15

We derive an expression for the entropy of a dark matter halo described using a Navarro-Frenk-White model with a core. The comparison of this entropy with that of dark matter in the freeze-out era allows us to constrain the parameter space in mSUGRA models. Moreover, combining these constraints with the ones obtained from the usual abundance criterion and demanding that these criteria be consistent with the 2{sigma} bounds for the abundance of dark matter: 0.112{<=}{Omega}{sub DM}h{sup 2}{<=}0.122, we are able to clearly identify validity regions among the values of tan{beta}, which is one of the parameters of the mSUGRA model. We found that for the regions of the parameter space explored, small values of tan{beta} are not favored; only for tan {beta} Asymptotically-Equal-To 50 are the two criteria significantly consistent. In the region where the two criteria are consistent we also found a lower bound for the neutralino mass, m{sub {chi}}{>=}141 GeV.

Constraining the mSUGRA (minimal supergravity) parameter space using the entropy of dark matter halos

International Nuclear Information System (INIS)

Núñez, Darío; Zavala, Jesús; Nellen, Lukas; Sussman, Roberto A; Cabral-Rosetti, Luis G; Mondragón, Myriam

2008-01-01

We derive an expression for the entropy of a dark matter halo described using a Navarro–Frenk–White model with a core. The comparison of this entropy with that of dark matter in the freeze-out era allows us to constrain the parameter space in mSUGRA models. Moreover, combining these constraints with the ones obtained from the usual abundance criterion and demanding that these criteria be consistent with the 2σ bounds for the abundance of dark matter: 0.112≤Ω DM h 2 ≤0.122, we are able to clearly identify validity regions among the values of tanβ, which is one of the parameters of the mSUGRA model. We found that for the regions of the parameter space explored, small values of tanβ are not favored; only for tan β ≃ 50 are the two criteria significantly consistent. In the region where the two criteria are consistent we also found a lower bound for the neutralino mass, m χ ≥141 GeV

Determining frequentist confidence limits using a directed parameter space search

International Nuclear Information System (INIS)

Daniel, Scott F.; Connolly, Andrew J.; Schneider, Jeff

2014-01-01

We consider the problem of inferring constraints on a high-dimensional parameter space with a computationally expensive likelihood function. We propose a machine learning algorithm that maps out the Frequentist confidence limit on parameter space by intelligently targeting likelihood evaluations so as to quickly and accurately characterize the likelihood surface in both low- and high-likelihood regions. We compare our algorithm to Bayesian credible limits derived by the well-tested Markov Chain Monte Carlo (MCMC) algorithm using both multi-modal toy likelihood functions and the seven yr Wilkinson Microwave Anisotropy Probe cosmic microwave background likelihood function. We find that our algorithm correctly identifies the location, general size, and general shape of high-likelihood regions in parameter space while being more robust against multi-modality than MCMC.

Lumped-parameter models

Energy Technology Data Exchange (ETDEWEB)

Ibsen, Lars Bo; Liingaard, M.

2006-12-15

A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. In this technical report the steps of establishing a lumped-parameter model are presented. Following sections are included in this report: Static and dynamic formulation, Simple lumped-parameter models and Advanced lumped-parameter models. (au)

Replicate periodic windows in the parameter space of driven oscillators

Energy Technology Data Exchange (ETDEWEB)

Medeiros, E.S., E-mail: esm@if.usp.br [Instituto de Fisica, Universidade de Sao Paulo, Sao Paulo (Brazil); Souza, S.L.T. de [Universidade Federal de Sao Joao del-Rei, Campus Alto Paraopeba, Minas Gerais (Brazil); Medrano-T, R.O. [Departamento de Ciencias Exatas e da Terra, Universidade Federal de Sao Paulo, Diadema, Sao Paulo (Brazil); Caldas, I.L. [Instituto de Fisica, Universidade de Sao Paulo, Sao Paulo (Brazil)

2011-11-15

Highlights: > We apply a weak harmonic perturbation to control chaos in two driven oscillators. > We find replicate periodic windows in the driven oscillator parameter space. > We find that the periodic window replication is associated with the chaos control. - Abstract: In the bi-dimensional parameter space of driven oscillators, shrimp-shaped periodic windows are immersed in chaotic regions. For two of these oscillators, namely, Duffing and Josephson junction, we show that a weak harmonic perturbation replicates these periodic windows giving rise to parameter regions correspondent to periodic orbits. The new windows are composed of parameters whose periodic orbits have the same periodicity and pattern of stable and unstable periodic orbits already existent for the unperturbed oscillator. Moreover, these unstable periodic orbits are embedded in chaotic attractors in phase space regions where the new stable orbits are identified. Thus, the observed periodic window replication is an effective oscillator control process, once chaotic orbits are replaced by regular ones.

Multimedia Mapping using Continuous State Space Models

DEFF Research Database (Denmark)

Lehn-Schiøler, Tue

2004-01-01

In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in 'face space'. Simulations...... are performed on recordings of 3-5 sec. video sequences with sentences from the Timit database. The model is able to construct an image sequence from an unknown noisy speech sequence fairly well even though the number of training examples are limited....

Forward modeling of space-borne gravitational wave detectors

International Nuclear Information System (INIS)

Rubbo, Louis J.; Cornish, Neil J.; Poujade, Olivier

2004-01-01

Planning is underway for several space-borne gravitational wave observatories to be built in the next 10 to 20 years. Realistic and efficient forward modeling will play a key role in the design and operation of these observatories. Space-borne interferometric gravitational wave detectors operate very differently from their ground-based counterparts. Complex orbital motion, virtual interferometry, and finite size effects complicate the description of space-based systems, while nonlinear control systems complicate the description of ground-based systems. Here we explore the forward modeling of space-based gravitational wave detectors and introduce an adiabatic approximation to the detector response that significantly extends the range of the standard low frequency approximation. The adiabatic approximation will aid in the development of data analysis techniques, and improve the modeling of astrophysical parameter extraction

Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

Directory of Open Access Journals (Sweden)

Gergely Takács

2014-01-01

Full Text Available This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

Investigations of the sensitivity of a coronal mass ejection model (ENLIL) to solar input parameters

DEFF Research Database (Denmark)

Falkenberg, Thea Vilstrup; Vršnak, B.; Taktakishvili, A.

2010-01-01

Understanding space weather is not only important for satellite operations and human exploration of the solar system but also to phenomena here on Earth that may potentially disturb and disrupt electrical signals. Some of the most violent space weather effects are caused by coronal mass ejections...... (CMEs), but in order to predict the caused effects, we need to be able to model their propagation from their origin in the solar corona to the point of interest, e.g., Earth. Many such models exist, but to understand the models in detail we must understand the primary input parameters. Here we...... investigate the parameter space of the ENLILv2.5b model using the CME event of 25 July 2004. ENLIL is a time‐dependent 3‐D MHD model that can simulate the propagation of cone‐shaped interplanetary coronal mass ejections (ICMEs) through the solar system. Excepting the cone parameters (radius, position...

Monitoring Murder Crime in Namibia Using Bayesian Space-Time Models

Directory of Open Access Journals (Sweden)

Isak Neema

2012-01-01

Full Text Available This paper focuses on the analysis of murder in Namibia using Bayesian spatial smoothing approach with temporal trends. The analysis was based on the reported cases from 13 regions of Namibia for the period 2002–2006 complemented with regional population sizes. The evaluated random effects include space-time structured heterogeneity measuring the effect of regional clustering, unstructured heterogeneity, time, space and time interaction and population density. The model consists of carefully chosen prior and hyper-prior distributions for parameters and hyper-parameters, with inference conducted using Gibbs sampling algorithm and sensitivity test for model validation. The posterior mean estimate of the parameters from the model using DIC as model selection criteria show that most of the variation in the relative risk of murder is due to regional clustering, while the effect of population density and time was insignificant. The sensitivity analysis indicates that both intrinsic and Laplace CAR prior can be adopted as prior distribution for the space-time heterogeneity. In addition, the relative risk map show risk structure of increasing north-south gradient, pointing to low risk in northern regions of Namibia, while Karas and Khomas region experience long-term increase in murder risk.

Effects of two-temperature parameter and thermal nonlocal parameter on transient responses of a half-space subjected to ramp-type heating

Science.gov (United States)

Xue, Zhang-Na; Yu, Ya-Jun; Tian, Xiao-Geng

2017-07-01

Based upon the coupled thermoelasticity and Green and Lindsay theory, the new governing equations of two-temperature thermoelastic theory with thermal nonlocal parameter is formulated. To more realistically model thermal loading of a half-space surface, a linear temperature ramping function is adopted. Laplace transform techniques are used to get the general analytical solutions in Laplace domain, and the inverse Laplace transforms based on Fourier expansion techniques are numerically implemented to obtain the numerical solutions in time domain. Specific attention is paid to study the effect of thermal nonlocal parameter, ramping time, and two-temperature parameter on the distributions of temperature, displacement and stress distribution.

Space, time, and the third dimension (model error)

Science.gov (United States)

Moss, Marshall E.

1979-01-01

The space-time tradeoff of hydrologic data collection (the ability to substitute spatial coverage for temporal extension of records or vice versa) is controlled jointly by the statistical properties of the phenomena that are being measured and by the model that is used to meld the information sources. The control exerted on the space-time tradeoff by the model and its accompanying errors has seldom been studied explicitly. The technique, known as Network Analyses for Regional Information (NARI), permits such a study of the regional regression model that is used to relate streamflow parameters to the physical and climatic characteristics of the drainage basin.The NARI technique shows that model improvement is a viable and sometimes necessary means of improving regional data collection systems. Model improvement provides an immediate increase in the accuracy of regional parameter estimation and also increases the information potential of future data collection. Model improvement, which can only be measured in a statistical sense, cannot be quantitatively estimated prior to its achievement; thus an attempt to upgrade a particular model entails a certain degree of risk on the part of the hydrologist.

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study.

Science.gov (United States)

Fan, Ming; Kuwahara, Hiroyuki; Wang, Xiaolei; Wang, Suojin; Gao, Xin

2015-11-01

Parameter estimation is a challenging computational problem in the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter estimation of gene circuit models from such time-series mRNA data has become an important method for quantitatively dissecting the regulation of gene expression. By focusing on the modeling of gene circuits, we examine here the performance of three types of state-of-the-art parameter estimation methods: population-based methods, online methods and model-decomposition-based methods. Our results show that certain population-based methods are able to generate high-quality parameter solutions. The performance of these methods, however, is heavily dependent on the size of the parameter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, online methods and model decomposition-based methods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fast methods with local search as a subsequent refinement procedure can substantially increase the quality of their parameter estimates to the level on par with the best solution obtained from the population-based methods while maintaining high computational speed. These suggest that such hybrid methods can be a promising alternative to the more commonly used population-based methods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatory mechanisms makes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.

Science.gov (United States)

Lehnert, Teresa; Timme, Sandra; Pollmächer, Johannes; Hünniger, Kerstin; Kurzai, Oliver; Figge, Marc Thilo

2015-01-01

Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM), because this level of model complexity allows estimating a priori unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM) is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e., least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment. In the future, spatio-temporal simulations of whole-blood samples may enable timely

Space dependence of reactivity parameters on reactor dynamic perturbation measurements

International Nuclear Information System (INIS)

Maletti, R.; Ziegenbein, D.

1985-01-01

Practical application of reactor-dynamic perturbation measurements for on-power determination of differential reactivity weight of control rods and power coefficients of reactivity has shown a significant dependence of parameters on the position of outcore detectors. The space dependence of neutron flux signal in the core of a VVER-440-type reactor was measured by means of 60 self-powered neutron detectors. The greatest neutron flux alterations are located close to moved control rods and in height of the perturbation position. By means of computations, detector positions can be found in the core in which the one-point model is almost valid. (author)

Determination of Geometric Parameters of Space Steel Constructions

Directory of Open Access Journals (Sweden)

Jitka Suchá

2005-06-01

Full Text Available The paper contains conclusions of the PhD thesis „Accuracy of determination of geometric parameters of space steel construction using geodetic methods“. Generally it is a difficult task with high requirements for the accuracy and reliability of results, i.e. space coordinates of assessed points on a steel construction. A solution of this task is complicated by the effects of atmospheric influences to begin with the temperature, which strongly affects steel constructions. It is desirable to eliminate the influence of the temperature for the evaluation of the geometric parameters. A choice of an efficient geodetic method, which fulfils demanding requirements, is often affected with a constrained place in an immediate neighbourhood of the measured construction. These conditions disable the choice of efficient points configuration of a geodetic micro network, e.g. the for forward intersection. In addition, points of a construction are often hardly accessible and therefore marking is difficult. The space polar method appears efficient owing to the mentioned reasons and its advantages were increased with the implementation of self-adhesive reflex targets for the distance measurement which enable the ermanent marking of measured points already in the course of placing the construction.

Parameter space of general gauge mediation

International Nuclear Information System (INIS)

Rajaraman, Arvind; Shirman, Yuri; Smidt, Joseph; Yu, Felix

2009-01-01

We study a subspace of General Gauge Mediation (GGM) models which generalize models of gauge mediation. We find superpartner spectra that are markedly different from those of typical gauge and gaugino mediation scenarios. While typical gauge mediation predictions of either a neutralino or stau next-to-lightest supersymmetric particle (NLSP) are easily reproducible with the GGM parameters, chargino and sneutrino NLSPs are generic for many reasonable choices of GGM parameters.

Effect of solar wind plasma parameters on space weather

International Nuclear Information System (INIS)

Rathore, Balveer S.; Gupta, Dinesh C.; Kaushik, Subhash C.

2015-01-01

Today's challenge for space weather research is to quantitatively predict the dynamics of the magnetosphere from measured solar wind and interplanetary magnetic field (IMF) conditions. Correlative studies between geomagnetic storms (GMSs) and the various interplanetary (IP) field/plasma parameters have been performed to search for the causes of geomagnetic activity and develop models for predicting the occurrence of GMSs, which are important for space weather predictions. We find a possible relation between GMSs and solar wind and IMF parameters in three different situations and also derived the linear relation for all parameters in three situations. On the basis of the present statistical study, we develop an empirical model. With the help of this model, we can predict all categories of GMSs. This model is based on the following fact: the total IMF B total can be used to trigger an alarm for GMSs, when sudden changes in total magnetic field B total occur. This is the first alarm condition for a storm's arrival. It is observed in the present study that the southward B z component of the IMF is an important factor for describing GMSs. A result of the paper is that the magnitude of B z is maximum neither during the initial phase (at the instant of the IP shock) nor during the main phase (at the instant of Disturbance storm time (Dst) minimum). It is seen in this study that there is a time delay between the maximum value of southward B z and the Dst minimum, and this time delay can be used in the prediction of the intensity of a magnetic storm two-three hours before the main phase of a GMS. A linear relation has been derived between the maximum value of the southward component of B z and the Dst, which is Dst = (−0.06) + (7.65) B z +t. Some auxiliary conditions should be fulfilled with this, for example the speed of the solar wind should, on average, be 350 km s −1 to 750 km s −1 , plasma β should be low and, most importantly, plasma temperature

The impact of structural error on parameter constraint in a climate model

Science.gov (United States)

McNeall, Doug; Williams, Jonny; Booth, Ben; Betts, Richard; Challenor, Peter; Wiltshire, Andy; Sexton, David

2016-11-01

Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections of future climate change. We use observations of forest fraction to constrain carbon cycle and land surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error. Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the climate model, we use history matching to rule out input parameter settings where the corresponding climate model output is judged sufficiently different from observations, even allowing for uncertainty. Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to tune the simulator leads to a larger underestimate of the Amazon forest fraction. We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the simulated Amazon, including missing processes in the land surface component and a bias in the climatology of the Amazon.

Exactly solvable string models of curved space-time backgrounds

International Nuclear Information System (INIS)

Russo, J.G.

1995-01-01

We consider a new 3-parameter class of exact 4-dimensional solutions in closed string theory and solve the corresponding string model, determining the physical spectrum and the partition function. The background fields (4-metric, antisymmetric tensor, two Kaluza-Klein vector fields, dilaton and modulus) generically describe axially symmetric stationary rotating (electro)magnetic flux-tube type universes. Backgrounds of this class include both the ''dilatonic'' (a=1) and ''Kaluza-Klein'' (a=√(3)) Melvin solutions and the uniform magnetic field solution, as well as some singular space-times. Solvability of the string σ-model is related to its connection via duality to a simpler model which is a ''twisted'' product of a flat 2-space and a space dual to 2-plane. We discuss some physical properties of this model (tachyonic instabilities in the spectrum, gyromagnetic ratio, issue of singularities, etc.). It provides one of the first examples of a consistent solvable conformal string model with explicit D=4 curved space-time interpretation. (orig.)

Parameter estimation in nonlinear models for pesticide degradation

International Nuclear Information System (INIS)

Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.

1991-01-01

A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)

Constraining the mSUGRA parameter space through entropy and abundance criteria

International Nuclear Information System (INIS)

Cabral-Rosetti, Luis G.; Mondragon, Myriam; Nunez, Dario; Sussman, Roberto A.; Zavala, Jesus; Nellen, Lukas

2007-01-01

We explore the use of two criteria to constrain the allowed parameter space in mSUGRA models; both criteria are based in the calculation of the present density of neutralinos χ0 as Dark Matter in the Universe. The first one is the usual ''abundance'' criterion that requieres that present neutralino relic density complies with 0.0945 < ΩCDMh2 < 0.1287, which are the 2σ bounds according to WMAP. To calculate the relic density we use the public numerical code micrOMEGAS. The second criterion is the original idea presented in [3] that basically applies the microcanonical definition of entropy to a weakly interacting and self-gravitating gas, and then evaluate the change in entropy per particle of this gas between the freeze-out era and present day virialized structures. An 'entropy consistency' criterion emerges by comparing theoretical and empirical estimates of this entropy. One of the objetives of the work is to analyze the joint application of both criteria, already done in [3], to see if their results, using approximations for the calculations of the relic density, agree with the results coming from the exact numerical results of micrOMEGAS. The main objetive of the work is to use this method to constrain the parameter space in mSUGRA models that are inputs for the calculations of micrOMEGAS, and thus to get some bounds on the predictions for the SUSY spectra

Laser dimpling process parameters selection and optimization using surrogate-driven process capability space

Science.gov (United States)

Ozkat, Erkan Caner; Franciosa, Pasquale; Ceglarek, Dariusz

2017-08-01

Remote laser welding technology offers opportunities for high production throughput at a competitive cost. However, the remote laser welding process of zinc-coated sheet metal parts in lap joint configuration poses a challenge due to the difference between the melting temperature of the steel (∼1500 °C) and the vapourizing temperature of the zinc (∼907 °C). In fact, the zinc layer at the faying surface is vapourized and the vapour might be trapped within the melting pool leading to weld defects. Various solutions have been proposed to overcome this problem over the years. Among them, laser dimpling has been adopted by manufacturers because of its flexibility and effectiveness along with its cost advantages. In essence, the dimple works as a spacer between the two sheets in lap joint and allows the zinc vapour escape during welding process, thereby preventing weld defects. However, there is a lack of comprehensive characterization of dimpling process for effective implementation in real manufacturing system taking into consideration inherent changes in variability of process parameters. This paper introduces a methodology to develop (i) surrogate model for dimpling process characterization considering multiple-inputs (i.e. key control characteristics) and multiple-outputs (i.e. key performance indicators) system by conducting physical experimentation and using multivariate adaptive regression splines; (ii) process capability space (Cp-Space) based on the developed surrogate model that allows the estimation of a desired process fallout rate in the case of violation of process requirements in the presence of stochastic variation; and, (iii) selection and optimization of the process parameters based on the process capability space. The proposed methodology provides a unique capability to: (i) simulate the effect of process variation as generated by manufacturing process; (ii) model quality requirements with multiple and coupled quality requirements; and (iii

Frequentist analysis of the parameter space of minimal supergravity

Energy Technology Data Exchange (ETDEWEB)

Buchmueller, O.; Colling, D. [Imperial College, London (United Kingdom). High Energy Physics Group; Cavanaugh, R. [Fermi National Accelerator Laboratory, Batavia, IL (United States); Illinois Univ., Chicago, IL (US). Physics Dept.] (and others)

2010-12-15

We make a frequentist analysis of the parameter space of minimal supergravity (mSUGRA), in which, as well as the gaugino and scalar soft supersymmetry-breaking parameters being universal, there is a specific relation between the trilinear, bilinear and scalar supersymmetry-breaking parameters, A{sub 0}=B{sub 0}+m{sub 0}, and the gravitino mass is fixed by m{sub 3/2}=m{sub 0}. We also consider a more general model, in which the gravitino mass constraint is relaxed (the VCMSSM). We combine in the global likelihood function the experimental constraints from low-energy electroweak precision data, the anomalous magnetic moment of the muon, the lightest Higgs boson mass M{sub h}, B physics and the astrophysical cold dark matter density, assuming that the lightest supersymmetric particle (LSP) is a neutralino. In the VCMSSM, we find a preference for values of m{sub 1/2} and m{sub 0} similar to those found previously in frequentist analyses of the constrained MSSM (CMSSM) and a model with common non-universal Higgs masses (NUHM1). On the other hand, in mSUGRA we find two preferred regions: one with larger values of both m{sub 1/2} and m{sub 0} than in the VCMSSM, and one with large m{sub 0} but small m{sub 1/2}. We compare the probabilities of the frequentist fits in mSUGRA, the VCMSSM, the CMSSM and the NUHM1: the probability that mSUGRA is consistent with the present data is significantly less than in the other models. We also discuss the mSUGRA and VCMSSM predictions for sparticle masses and other observables, identifying potential signatures at the LHC and elsewhere. (orig.)

Constraining statistical-model parameters using fusion and spallation reactions

Directory of Open Access Journals (Sweden)

Charity Robert J.

2011-10-01

Full Text Available The de-excitation of compound nuclei has been successfully described for several decades by means of statistical models. However, such models involve a large number of free parameters and ingredients that are often underconstrained by experimental data. We show how the degeneracy of the model ingredients can be partially lifted by studying different entrance channels for de-excitation, which populate different regions of the parameter space of the compound nucleus. Fusion reactions, in particular, play an important role in this strategy because they fix three out of four of the compound-nucleus parameters (mass, charge and total excitation energy. The present work focuses on fission and intermediate-mass-fragment emission cross sections. We prove how equivalent parameter sets for fusion-fission reactions can be resolved using another entrance channel, namely spallation reactions. Intermediate-mass-fragment emission can be constrained in a similar way. An interpretation of the best-fit IMF barriers in terms of the Wigner energies of the nascent fragments is discussed.

Quantum metric spaces as a model for pregeometry

International Nuclear Information System (INIS)

Alvarez, E.; Cespedes, J.; Verdaguer, E.

1992-01-01

A new arena for the dynamics of spacetime is proposed, in which the basic quantum variable is the two-point distance on a metric space. The scaling dimension (that is, the Kolmogorov capacity) in the neighborhood of each point then defines in a natural way a local concept of dimension. We study our model in the region of parameter space in which the resulting spacetime is not too different from a smooth manifold

Hadronic total cross-sections through soft gluon summation in impact parameter space

International Nuclear Information System (INIS)

Grau, A.

1999-01-01

IThe Bloch-Nordsieck model for the parton distribution of hadrons in impact parameter space, constructed using soft gluon summation, is investigated in detail. Its dependence upon the infrared structure of the strong coupling constant α s is discussed, both for finite as well as singular, but integrable, α s . The formalism is applied to the prediction of total proton-proton and proton-antiproton cross-sections, where screening, due to soft gluon emission from the initial valence quarks, becomes evident

Parameter estimation in space systems using recurrent neural networks

Science.gov (United States)

Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.

1991-01-01

The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.

Space-time modeling of electricity spot prices

DEFF Research Database (Denmark)

Abate, Girum Dagnachew; Haldrup, Niels

In this paper we derive a space-time model for electricity spot prices. A general spatial Durbin model that incorporates the temporal as well as spatial lags of spot prices is presented. Joint modeling of space-time effects is necessarily important when prices and loads are determined in a network...... in the spot price dynamics. Estimation of the spatial Durbin model show that the spatial lag variable is as important as the temporal lag variable in describing the spot price dynamics. We use the partial derivatives impact approach to decompose the price impacts into direct and indirect effects and we show...... that price effects transmit to neighboring markets and decline with distance. In order to examine the evolution of the spatial correlation over time, a time varying parameters spot price spatial Durbin model is estimated using recursive estimation. It is found that the spatial correlation within the Nord...

Parameter Estimation of Spacecraft Fuel Slosh Model

Science.gov (United States)

Gangadharan, Sathya; Sudermann, James; Marlowe, Andrea; Njengam Charles

2004-01-01

Fuel slosh in the upper stages of a spinning spacecraft during launch has been a long standing concern for the success of a space mission. Energy loss through the movement of the liquid fuel in the fuel tank affects the gyroscopic stability of the spacecraft and leads to nutation (wobble) which can cause devastating control issues. The rate at which nutation develops (defined by Nutation Time Constant (NTC can be tedious to calculate and largely inaccurate if done during the early stages of spacecraft design. Pure analytical means of predicting the influence of onboard liquids have generally failed. A strong need exists to identify and model the conditions of resonance between nutation motion and liquid modes and to understand the general characteristics of the liquid motion that causes the problem in spinning spacecraft. A 3-D computerized model of the fuel slosh that accounts for any resonant modes found in the experimental testing will allow for increased accuracy in the overall modeling process. Development of a more accurate model of the fuel slosh currently lies in a more generalized 3-D computerized model incorporating masses, springs and dampers. Parameters describing the model include the inertia tensor of the fuel, spring constants, and damper coefficients. Refinement and understanding the effects of these parameters allow for a more accurate simulation of fuel slosh. The current research will focus on developing models of different complexity and estimating the model parameters that will ultimately provide a more realistic prediction of Nutation Time Constant obtained through simulation.

Superfield Lax formalism of supersymmetric sigma model on symmetric spaces

International Nuclear Information System (INIS)

Saleem, U.; Hassan, M.

2006-01-01

We present a superfield Lax formalism of the superspace sigma model based on the target space G/H and show that a one-parameter family of flat superfield connections exists if the target space G/H is a symmetric space. The formalism has been related to the existence of an infinite family of local and non-local superfield conserved quantities. A few examples have been given to illustrate the results. (orig.)

Evasive Maneuvers in Space Debris Environment and Technological Parameters

Directory of Open Access Journals (Sweden)

Antônio D. C. Jesus

2012-01-01

Full Text Available We present a study of collisional dynamics between space debris and an operational vehicle in LEO. We adopted an approach based on the relative dynamics between the objects on a collisional course and with a short warning time and established a semianalytical solution for the final trajectories of these objects. Our results show that there are angular ranges in 3D, in addition to the initial conditions, that favor the collisions. These results allowed the investigation of a range of technological parameters for the spacecraft (e.g., fuel reserve that allow a safe evasive maneuver (e.g., time available for the maneuver. The numerical model was tested for different values of the impact velocity and relative distance between the approaching objects.

The level density parameters for fermi gas model

International Nuclear Information System (INIS)

Zuang Youxiang; Wang Cuilan; Zhou Chunmei; Su Zongdi

1986-01-01

Nuclear level densities are crucial ingredient in the statistical models, for instance, in the calculations of the widths, cross sections, emitted particle spectra, etc. for various reaction channels. In this work 667 sets of more reliable and new experimental data are adopted, which include average level spacing D D , radiative capture width Γ γ 0 at neutron binding energy and cumulative level number N 0 at the low excitation energy. They are published during 1973 to 1983. Based on the parameters given by Gilbert-Cameon and Cook the physical quantities mentioned above are calculated. The calculated results have the deviation obviously from experimental values. In order to improve the fitting, the parameters in the G-C formula are adjusted and new set of level density parameters is obsained. The parameters is this work are more suitable to fit new measurements

Joint Model and Parameter Dimension Reduction for Bayesian Inversion Applied to an Ice Sheet Flow Problem

Science.gov (United States)

Ghattas, O.; Petra, N.; Cui, T.; Marzouk, Y.; Benjamin, P.; Willcox, K.

2016-12-01

Model-based projections of the dynamics of the polar ice sheets play a central role in anticipating future sea level rise. However, a number of mathematical and computational challenges place significant barriers on improving predictability of these models. One such challenge is caused by the unknown model parameters (e.g., in the basal boundary conditions) that must be inferred from heterogeneous observational data, leading to an ill-posed inverse problem and the need to quantify uncertainties in its solution. In this talk we discuss the problem of estimating the uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem--i.e., the posterior probability density--is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal sliding coefficient field). To overcome these twin computational challenges, it is essential to exploit problem structure (e.g., sensitivity of the data to parameters, the smoothing property of the forward model, and correlations in the prior). To this end, we present a data-informed approach that identifies low-dimensional structure in both parameter space and the forward model state space. This approach exploits the fact that the observations inform only a low-dimensional parameter space and allows us to construct a parameter-reduced posterior. Sampling this parameter-reduced posterior still requires multiple evaluations of the forward problem, therefore we also aim to identify a low dimensional state space to reduce the computational cost. To this end, we apply a proper orthogonal decomposition (POD) approach to approximate the state using a low-dimensional manifold constructed using ``snapshots'' from the parameter reduced posterior, and the discrete

Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development.

Science.gov (United States)

Tøndel, Kristin; Niederer, Steven A; Land, Sander; Smith, Nicolas P

2014-05-20

Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms. The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input-output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on

Exactly solvable string models of curved space-time backgrounds

CERN Document Server

Russo, J.G.; Russo, J G; Tseytlin, A A

1995-01-01

We consider a new 3-parameter class of exact 4-dimensional solutions in closed string theory and solve the corresponding string model, determining the physical spectrum and the partition function. The background fields (4-metric, antisymmetric tensor, two Kaluza-Klein vector fields, dilaton and modulus) generically describe axially symmetric stationary rotating (electro)magnetic flux-tube type universes. Backgrounds of this class include both the dilatonic Melvin solution and the uniform magnetic field solution discussed earlier as well as some singular space-times. Solvability of the string sigma model is related to its connection via duality to a much simpler looking model which is a "twisted" product of a flat 2-space and a space dual to 2-plane. We discuss some physical properties of this model as well as a number of generalizations leading to larger classes of exact 4-dimensional string solutions.

HL-LHC parameter space and scenarios

International Nuclear Information System (INIS)

Bruning, O.S.

2012-01-01

The HL-LHC project aims at a total integrated luminosity of approximately 3000 fb -1 over the lifetime of the HL-LHC. Assuming an exploitation period of ca. 10 years this goal implies an annual integrated luminosity of approximately 200 fb -1 to 300 fb -1 per year. This paper looks at potential beam parameters that are compatible with the HL-LHC performance goals and discusses briefly potential variation in the parameter space. It is shown that the design goal of the HL-LHC project can only be achieved with a full upgrade of the injector complex and the operation with β* values close to 0.15 m. Significant margins for leveling can be achieved for β* values close to 0.15 m. However, these margins can only be harvested during the HL-LHC operation if the required leveling techniques have been demonstrated in operation

Approximate Bayesian Computation by Subset Simulation using hierarchical state-space models

Science.gov (United States)

Vakilzadeh, Majid K.; Huang, Yong; Beck, James L.; Abrahamsson, Thomas

2017-02-01

A new multi-level Markov Chain Monte Carlo algorithm for Approximate Bayesian Computation, ABC-SubSim, has recently appeared that exploits the Subset Simulation method for efficient rare-event simulation. ABC-SubSim adaptively creates a nested decreasing sequence of data-approximating regions in the output space that correspond to increasingly closer approximations of the observed output vector in this output space. At each level, multiple samples of the model parameter vector are generated by a component-wise Metropolis algorithm so that the predicted output corresponding to each parameter value falls in the current data-approximating region. Theoretically, if continued to the limit, the sequence of data-approximating regions would converge on to the observed output vector and the approximate posterior distributions, which are conditional on the data-approximation region, would become exact, but this is not practically feasible. In this paper we study the performance of the ABC-SubSim algorithm for Bayesian updating of the parameters of dynamical systems using a general hierarchical state-space model. We note that the ABC methodology gives an approximate posterior distribution that actually corresponds to an exact posterior where a uniformly distributed combined measurement and modeling error is added. We also note that ABC algorithms have a problem with learning the uncertain error variances in a stochastic state-space model and so we treat them as nuisance parameters and analytically integrate them out of the posterior distribution. In addition, the statistical efficiency of the original ABC-SubSim algorithm is improved by developing a novel strategy to regulate the proposal variance for the component-wise Metropolis algorithm at each level. We demonstrate that Self-regulated ABC-SubSim is well suited for Bayesian system identification by first applying it successfully to model updating of a two degree-of-freedom linear structure for three cases: globally

Moving to continuous facial expression space using the MPEG-4 facial definition parameter (FDP) set

Science.gov (United States)

Karpouzis, Kostas; Tsapatsoulis, Nicolas; Kollias, Stefanos D.

2000-06-01

Research in facial expression has concluded that at least six emotions, conveyed by human faces, are universally associated with distinct expressions. Sadness, anger, joy, fear, disgust and surprise are categories of expressions that are recognizable across cultures. In this work we form a relation between the description of the universal expressions and the MPEG-4 Facial Definition Parameter Set (FDP). We also investigate the relation between the movement of basic FDPs and the parameters that describe emotion-related words according to some classical psychological studies. In particular Whissel suggested that emotions are points in a space, which seem to occupy two dimensions: activation and evaluation. We show that some of the MPEG-4 Facial Animation Parameters (FAPs), approximated by the motion of the corresponding FDPs, can be combined by means of a fuzzy rule system to estimate the activation parameter. In this way variations of the six archetypal emotions can be achieved. Moreover, Plutchik concluded that emotion terms are unevenly distributed through the space defined by dimensions like Whissel's; instead they tend to form an approximately circular pattern, called 'emotion wheel,' modeled using an angular measure. The 'emotion wheel' can be defined as a reference for creating intermediate expressions from the universal ones, by interpolating the movement of dominant FDP points between neighboring basic expressions. By exploiting the relation between the movement of the basic FDP point and the activation and angular parameters we can model more emotions than the primary ones and achieve efficient recognition in video sequences.

Validation of nuclear models used in space radiation shielding applications

International Nuclear Information System (INIS)

Norman, Ryan B.; Blattnig, Steve R.

2013-01-01

A program of verification and validation has been undertaken to assess the applicability of models to space radiation shielding applications and to track progress as these models are developed over time. In this work, simple validation metrics applicable to testing both model accuracy and consistency with experimental data are developed. The developed metrics treat experimental measurement uncertainty as an interval and are therefore applicable to cases in which epistemic uncertainty dominates the experimental data. To demonstrate the applicability of the metrics, nuclear physics models used by NASA for space radiation shielding applications are compared to an experimental database consisting of over 3600 experimental cross sections. A cumulative uncertainty metric is applied to the question of overall model accuracy, while a metric based on the median uncertainty is used to analyze the models from the perspective of model development by examining subsets of the model parameter space.

An open-source job management framework for parameter-space exploration: OACIS

Science.gov (United States)

Murase, Y.; Uchitane, T.; Ito, N.

2017-11-01

We present an open-source software framework for parameter-space exporation, named OACIS, which is useful to manage vast amount of simulation jobs and results in a systematic way. Recent development of high-performance computers enabled us to explore parameter spaces comprehensively, however, in such cases, manual management of the workflow is practically impossible. OACIS is developed aiming at reducing the cost of these repetitive tasks when conducting simulations by automating job submissions and data management. In this article, an overview of OACIS as well as a getting started guide are presented.

Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?

Energy Technology Data Exchange (ETDEWEB)

Yi, Boram; Kang, Doo Kyoung; Kim, Tae Hee [Ajou University School of Medicine, Department of Radiology, Suwon, Gyeonggi-do (Korea, Republic of); Yoon, Dukyong [Ajou University School of Medicine, Department of Biomedical Informatics, Suwon (Korea, Republic of); Jung, Yong Sik; Kim, Ku Sang [Ajou University School of Medicine, Department of Surgery, Suwon (Korea, Republic of); Yim, Hyunee [Ajou University School of Medicine, Department of Pathology, Suwon (Korea, Republic of)

2014-05-15

To find out any correlation between dynamic contrast-enhanced (DCE) model-based parameters and model-free parameters, and evaluate correlations between perfusion parameters with histologic prognostic factors. Model-based parameters (Ktrans, Kep and Ve) of 102 invasive ductal carcinomas were obtained using DCE-MRI and post-processing software. Correlations between model-based and model-free parameters and between perfusion parameters and histologic prognostic factors were analysed. Mean Kep was significantly higher in cancers showing initial rapid enhancement (P = 0.002) and a delayed washout pattern (P = 0.001). Ve was significantly lower in cancers showing a delayed washout pattern (P = 0.015). Kep significantly correlated with time to peak enhancement (TTP) (ρ = -0.33, P < 0.001) and washout slope (ρ = 0.39, P = 0.002). Ve was significantly correlated with TTP (ρ = 0.33, P = 0.002). Mean Kep was higher in tumours with high nuclear grade (P = 0.017). Mean Ve was lower in tumours with high histologic grade (P = 0.005) and in tumours with negative oestrogen receptor status (P = 0.047). TTP was shorter in tumours with negative oestrogen receptor status (P = 0.037). We could acquire general information about the tumour vascular physiology, interstitial space volume and pathologic prognostic factors by analyzing time-signal intensity curve without a complicated acquisition process for the model-based parameters. (orig.)

Space-time modeling of soil moisture

Science.gov (United States)

Chen, Zijuan; Mohanty, Binayak P.; Rodriguez-Iturbe, Ignacio

2017-11-01

A physically derived space-time mathematical representation of the soil moisture field is carried out via the soil moisture balance equation driven by stochastic rainfall forcing. The model incorporates spatial diffusion and in its original version, it is shown to be unable to reproduce the relative fast decay in the spatial correlation functions observed in empirical data. This decay resulting from variations in local topography as well as in local soil and vegetation conditions is well reproduced via a jitter process acting multiplicatively over the space-time soil moisture field. The jitter is a multiplicative noise acting on the soil moisture dynamics with the objective to deflate its correlation structure at small spatial scales which are not embedded in the probabilistic structure of the rainfall process that drives the dynamics. These scales of order of several meters to several hundred meters are of great importance in ecohydrologic dynamics. Properties of space-time correlation functions and spectral densities of the model with jitter are explored analytically, and the influence of the jitter parameters, reflecting variabilities of soil moisture at different spatial and temporal scales, is investigated. A case study fitting the derived model to a soil moisture dataset is presented in detail.

Model Adaptation in Parametric Space for POD-Galerkin Models

Science.gov (United States)

Gao, Haotian; Wei, Mingjun

2017-11-01

The development of low-order POD-Galerkin models is largely motivated by the expectation to use the model developed with a set of parameters at their native values to predict the dynamic behaviors of the same system under different parametric values, in other words, a successful model adaptation in parametric space. However, most of time, even small deviation of parameters from their original value may lead to large deviation or unstable results. It has been shown that adding more information (e.g. a steady state, mean value of a different unsteady state, or an entire different set of POD modes) may improve the prediction of flow with other parametric states. For a simple case of the flow passing a fixed cylinder, an orthogonal mean mode at a different Reynolds number may stabilize the POD-Galerkin model when Reynolds number is changed. For a more complicated case of the flow passing an oscillatory cylinder, a global POD-Galerkin model is first applied to handle the moving boundaries, then more information (e.g. more POD modes) is required to predicate the flow under different oscillatory frequencies. Supported by ARL.

Reduced-order modelling of parameter-dependent, linear and nonlinear dynamic partial differential equation models.

Science.gov (United States)

Shah, A A; Xing, W W; Triantafyllidis, V

2017-04-01

In this paper, we develop reduced-order models for dynamic, parameter-dependent, linear and nonlinear partial differential equations using proper orthogonal decomposition (POD). The main challenges are to accurately and efficiently approximate the POD bases for new parameter values and, in the case of nonlinear problems, to efficiently handle the nonlinear terms. We use a Bayesian nonlinear regression approach to learn the snapshots of the solutions and the nonlinearities for new parameter values. Computational efficiency is ensured by using manifold learning to perform the emulation in a low-dimensional space. The accuracy of the method is demonstrated on a linear and a nonlinear example, with comparisons with a global basis approach.

On the Consistency of Bootstrap Testing for a Parameter on the Boundary of the Parameter Space

DEFF Research Database (Denmark)

Cavaliere, Giuseppe; Nielsen, Heino Bohn; Rahbek, Anders

2017-01-01

It is well known that with a parameter on the boundary of the parameter space, such as in the classic cases of testing for a zero location parameter or no autoregressive conditional heteroskedasticity (ARCH) effects, the classic nonparametric bootstrap – based on unrestricted parameter estimates...... – leads to inconsistent testing. In contrast, we show here that for the two aforementioned cases, a nonparametric bootstrap test based on parameter estimates obtained under the null – referred to as ‘restricted bootstrap’ – is indeed consistent. While the restricted bootstrap is simple to implement...... in practice, novel theoretical arguments are required in order to establish consistency. In particular, since the bootstrap is analysed both under the null hypothesis and under the alternative, non-standard asymptotic expansions are required to deal with parameters on the boundary. Detailed proofs...

Overall feature of EAST operation space by using simple Core-SOL-Divertor model

International Nuclear Information System (INIS)

Hiwatari, R.; Hatayama, A.; Zhu, S.; Takizuka, T.; Tomita, Y.

2005-01-01

We have developed a simple Core-SOL-Divertor (C-S-D) model to investigate qualitatively the overall features of the operational space for the integrated core and edge plasma. To construct the simple C-S-D model, a simple core plasma model of ITER physics guidelines and a two-point SOL-divertor model are used. The simple C-S-D model is applied to the study of the EAST operational space with lower hybrid current drive experiments under various kinds of trade-off for the basic plasma parameters. Effective methods for extending the operation space are also presented. As shown by this study for the EAST operation space, it is evident that the C-S-D model is a useful tool to understand qualitatively the overall features of the plasma operation space. (author)

Geometry on the parameter space of the belief propagation algorithm on Bayesian networks

Energy Technology Data Exchange (ETDEWEB)

Watanabe, Yodai [National Institute of Informatics, Research Organization of Information and Systems, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430 (Japan); Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198 (Japan)

2006-01-30

This Letter considers a geometrical structure on the parameter space of the belief propagation algorithm on Bayesian networks. The statistical manifold of posterior distributions is introduced, and the expression for the information metric on the manifold is derived. The expression is used to construct a cost function which can be regarded as a measure of the distance in the parameter space.

A joint-space numerical model of metabolic energy expenditure for human multibody dynamic system.

Science.gov (United States)

Kim, Joo H; Roberts, Dustyn

2015-09-01

Metabolic energy expenditure (MEE) is a critical performance measure of human motion. In this study, a general joint-space numerical model of MEE is derived by integrating the laws of thermodynamics and principles of multibody system dynamics, which can evaluate MEE without the limitations inherent in experimental measurements (phase delays, steady state and task restrictions, and limited range of motion) or muscle-space models (complexities and indeterminacies from excessive DOFs, contacts and wrapping interactions, and reliance on in vitro parameters). Muscle energetic components are mapped to the joint space, in which the MEE model is formulated. A constrained multi-objective optimization algorithm is established to estimate the model parameters from experimental walking data also used for initial validation. The joint-space parameters estimated directly from active subjects provide reliable MEE estimates with a mean absolute error of 3.6 ± 3.6% relative to validation values, which can be used to evaluate MEE for complex non-periodic tasks that may not be experimentally verifiable. This model also enables real-time calculations of instantaneous MEE rate as a function of time for transient evaluations. Although experimental measurements may not be completely replaced by model evaluations, predicted quantities can be used as strong complements to increase reliability of the results and yield unique insights for various applications. Copyright © 2015 John Wiley & Sons, Ltd.

The standard model on non-commutative space-time

International Nuclear Information System (INIS)

Calmet, X.; Jurco, B.; Schupp, P.; Wohlgenannt, M.; Wess, J.

2002-01-01

We consider the standard model on a non-commutative space and expand the action in the non-commutativity parameter θ μν . No new particles are introduced; the structure group is SU(3) x SU(2) x U(1). We derive the leading order action. At zeroth order the action coincides with the ordinary standard model. At leading order in θ μν we find new vertices which are absent in the standard model on commutative space-time. The most striking features are couplings between quarks, gluons and electroweak bosons and many new vertices in the charged and neutral currents. We find that parity is violated in non-commutative QCD. The Higgs mechanism can be applied. QED is not deformed in the minimal version of the NCSM to the order considered. (orig.)

Multi-parameter variational calculations for the (2+1)-dimensional U(1) lattice gauge theory and the XY model

International Nuclear Information System (INIS)

Heys, D.W.; Stump, D.R.

1987-01-01

Variational calculations are described that use multi-parameter trial wave functions for the U(1) lattice gauge theory in two space dimensions, and for the XY model. The trial functions are constructed as the exponential of a linear combination of states from the strong-coupling basis of the model, with the coefficients treated as variational parameters. The expectation of the hamiltonian is computed by the Monte Carlo method, using a reweighting technique to evaluate expectation values in finite patches of the parameter space. The trial function for the U(1) gauge theory involves six variational parameters, and its weak-coupling behaviour is in reasonable agreement with theoretical expectations. (orig.)

Secondary structure classification of amino-acid sequences using state-space modeling

OpenAIRE

Brunnert, Marcus; Krahnke, Tillmann; Urfer, Wolfgang

2001-01-01

The secondary structure classification of amino acid sequences can be carried out by a statistical analysis of sequence and structure data using state-space models. Aiming at this classification, a modified filter algorithm programmed in S is applied to data of three proteins. The application leads to correct classifications of two proteins even when using relatively simple estimation methods for the parameters of the state-space models. Furthermore, it has been shown that the assumed initial...

Estimation methods for nonlinear state-space models in ecology

DEFF Research Database (Denmark)

Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro

2011-01-01

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...

Parameter choice in Banach space regularization under variational inequalities

International Nuclear Information System (INIS)

Hofmann, Bernd; Mathé, Peter

2012-01-01

The authors study parameter choice strategies for the Tikhonov regularization of nonlinear ill-posed problems in Banach spaces. The effectiveness of any parameter choice for obtaining convergence rates depends on the interplay of the solution smoothness and the nonlinearity structure, and it can be expressed concisely in terms of variational inequalities. Such inequalities are link conditions between the penalty term, the norm misfit and the corresponding error measure. The parameter choices under consideration include an a priori choice, the discrepancy principle as well as the Lepskii principle. For the convenience of the reader, the authors review in an appendix a few instances where the validity of a variational inequality can be established. (paper)

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

Science.gov (United States)

El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher

2018-01-01

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

Energy Technology Data Exchange (ETDEWEB)

Khawli, Toufik Al; Eppelt, Urs; Hermanns, Torsten [RWTH Aachen University, Chair for Nonlinear Dynamics, Steinbachstr. 15, 52047 Aachen (Germany); Gebhardt, Sascha [RWTH Aachen University, Virtual Reality Group, IT Center, Seffenter Weg 23, 52074 Aachen (Germany); Kuhlen, Torsten [Forschungszentrum Jülich GmbH, Institute for Advanced Simulation (IAS), Jülich Supercomputing Centre (JSC), Wilhelm-Johnen-Straße, 52425 Jülich (Germany); Schulz, Wolfgang [Fraunhofer, ILT Laser Technology, Steinbachstr. 15, 52047 Aachen (Germany)

2016-06-08

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.

Cosmological space-times with resolved Big Bang in Yang-Mills matrix models

Science.gov (United States)

Steinacker, Harold C.

2018-02-01

We present simple solutions of IKKT-type matrix models that can be viewed as quantized homogeneous and isotropic cosmological space-times, with finite density of microstates and a regular Big Bang (BB). The BB arises from a signature change of the effective metric on a fuzzy brane embedded in Lorentzian target space, in the presence of a quantized 4-volume form. The Hubble parameter is singular at the BB, and becomes small at late times. There is no singularity from the target space point of view, and the brane is Euclidean "before" the BB. Both recollapsing and expanding universe solutions are obtained, depending on the mass parameters.

The constitutive distributed parameter model of multicomponent chemical processes in gas, fluid and solid phase

International Nuclear Information System (INIS)

Niemiec, W.

1985-01-01

In the literature of distributed parameter modelling of real processes is not considered the class of multicomponent chemical processes in gas, fluid and solid phase. The aim of paper is constitutive distributed parameter physicochemical model, constructed on kinetics and phenomenal analysis of multicomponent chemical processes in gas, fluid and solid phase. The mass, energy and momentum aspects of these multicomponent chemical reactions and adequate phenomena are utilized in balance operations, by conditions of: constitutive invariance for continuous media with space and time memories, reciprocity principle for isotropic and anisotropic nonhomogeneous media with space and time memories, application of definitions of following derivative and equation of continuity, to the construction of systems of partial differential constitutive state equations, in the following derivative forms for gas, fluid and solid phase. Couched in this way all physicochemical conditions of multicomponent chemical processes in gas, fluid and solid phase are new form of constitutive distributed parameter model for automatics and its systems of equations are new form of systems of partial differential constitutive state equations in sense of phenomenal distributed parameter control

Statistical learning modeling method for space debris photometric measurement

Science.gov (United States)

Sun, Wenjing; Sun, Jinqiu; Zhang, Yanning; Li, Haisen

2016-03-01

Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

The standard model on non-commutative space-time

Energy Technology Data Exchange (ETDEWEB)

Calmet, X.; Jurco, B.; Schupp, P.; Wohlgenannt, M. [Sektion Physik, Universitaet Muenchen (Germany); Wess, J. [Sektion Physik, Universitaet Muenchen (Germany); Max-Planck-Institut fuer Physik, Muenchen (Germany)

2002-03-01

We consider the standard model on a non-commutative space and expand the action in the non-commutativity parameter {theta}{sup {mu}}{sup {nu}}. No new particles are introduced; the structure group is SU(3) x SU(2) x U(1). We derive the leading order action. At zeroth order the action coincides with the ordinary standard model. At leading order in {theta}{sup {mu}}{sup {nu}} we find new vertices which are absent in the standard model on commutative space-time. The most striking features are couplings between quarks, gluons and electroweak bosons and many new vertices in the charged and neutral currents. We find that parity is violated in non-commutative QCD. The Higgs mechanism can be applied. QED is not deformed in the minimal version of the NCSM to the order considered. (orig.)

Parameter estimation methods for gene circuit modeling from time-series mRNA data: a comparative study

KAUST Repository

Fan, M.

2015-03-29

Parameter estimation is a challenging computational problemin the reverse engineering of biological systems. Because advances in biotechnology have facilitated wide availability of time-series gene expression data, systematic parameter esti- mation of gene circuitmodels fromsuch time-series mRNA data has become an importantmethod for quantitatively dissecting the regulation of gene expression. By focusing on themodeling of gene circuits, we examine here the perform- ance of three types of state-of-the-art parameter estimation methods: population-basedmethods, onlinemethods and model-decomposition-basedmethods. Our results show that certain population-basedmethods are able to generate high- quality parameter solutions. The performance of thesemethods, however, is heavily dependent on the size of the param- eter search space, and their computational requirements substantially increase as the size of the search space increases. In comparison, onlinemethods andmodel decomposition-basedmethods are computationally faster alternatives and are less dependent on the size of the search space. Among other things, our results show that a hybrid approach that augments computationally fastmethods with local search as a subsequent refinement procedure can substantially increase the qual- ity of their parameter estimates to the level on par with the best solution obtained fromthe population-basedmethods whilemaintaining high computational speed. These suggest that such hybridmethods can be a promising alternative to themore commonly used population-basedmethods for parameter estimation of gene circuit models when limited prior knowledge about the underlying regulatorymechanismsmakes the size of the parameter search space vastly large. © The Author 2015. Published by Oxford University Press.

A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data.

Science.gov (United States)

Luo, Jianhua; Mou, Zhiying; Qin, Binjie; Li, Wanqing; Ogunbona, Philip; Robini, Marc C; Zhu, Yuemin

2017-12-09

Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of an image in the k-space, the singular k-space model (SKM) is proposed in terms of the k-space functions of a singularizing operator. The singularizing operator is constructed by combining basic difference operators. An algorithm is developed to reliably estimate the model parameters from undersampled k-space data. The estimated parameters are then used to recover the missing k-space data through the model, subsequently achieving high-quality reconstruction of the image using inverse Fourier transform. Experiments on physical phantom and real brain MR images have shown that the proposed SKM method constantly outperforms the popular total variation (TV) and the classical zero-filling (ZF) methods regardless of the undersampling rates, the noise levels, and the image structures. For the same objective quality of the reconstructed images, the proposed method requires much less k-space data than the TV method. The SKM method is an effective method for fast MRI reconstruction from the undersampled k-space data. Graphical abstract Two Real Images and their sparsified images by singularizing operator.

Model parameters estimation and sensitivity by genetic algorithms

International Nuclear Information System (INIS)

Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca

2003-01-01

In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The

A stochastic fractional dynamics model of space-time variability of rain

Science.gov (United States)

Kundu, Prasun K.; Travis, James E.

2013-09-01

varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, which allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and time scales. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and on the Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to fit the second moment statistics of radar data at the smaller spatiotemporal scales. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well at these scales without any further adjustment.

The MSSM Parameter Space with Non-Universal Higgs Masses

CERN Document Server

Ellis, Jonathan Richard; Santoso, Y; Ellis, John; Olive, Keith A.; Santoso, Yudi

2002-01-01

Without assuming that Higgs masses have the same values as other scalar masses at the input GUT scale, we combine constraints on the minimal supersymmetric extension of the Standard Model (MSSM) coming from the cold dark matter density with the limits from direct searches at accelerators such as LEP, indirect measurements such as b to s gamma decay and the anomalous magnetic moment of the muon. The requirement that Higgs masses-squared be positive at the GUT scale imposes important restrictions on the MSSM parameter space, as does the requirement that the LSP be neutral. We analyze the interplay of these constraints in the (mu, m_A), (mu, m_{1/2}), (m_{1/2}, m_0) and (m_A, tan beta) planes. These exhibit new features not seen in the corresponding planes in the constrained MSSM in which universality is extended to Higgs masses.

FORECASTING COSMOLOGICAL PARAMETER CONSTRAINTS FROM NEAR-FUTURE SPACE-BASED GALAXY SURVEYS

International Nuclear Information System (INIS)

Pavlov, Anatoly; Ratra, Bharat; Samushia, Lado

2012-01-01

The next generation of space-based galaxy surveys is expected to measure the growth rate of structure to a level of about one percent over a range of redshifts. The rate of growth of structure as a function of redshift depends on the behavior of dark energy and so can be used to constrain parameters of dark energy models. In this work, we investigate how well these future data will be able to constrain the time dependence of the dark energy density. We consider parameterizations of the dark energy equation of state, such as XCDM and ωCDM, as well as a consistent physical model of time-evolving scalar field dark energy, φCDM. We show that if the standard, specially flat cosmological model is taken as a fiducial model of the universe, these near-future measurements of structure growth will be able to constrain the time dependence of scalar field dark energy density to a precision of about 10%, which is almost an order of magnitude better than what can be achieved from a compilation of currently available data sets.

Tuning a space-time scalable PI controller using thermal parameters

Energy Technology Data Exchange (ETDEWEB)

Riverol, C. [University of West Indies, Chemical Engineering Department, St. Augustine, Trinidad (Trinidad and Tobago); Pilipovik, M.V. [Armach Engineers, Urb. Los Palos Grandes, Project Engineering Department, Caracas (Venezuela)

2005-03-01

The paper outlines the successful empirical design and validation of a space-time PI controller based on study of the controlled variable output as function of time and space. The developed control was implemented on two heat exchanger systems (falling film evaporator and milk pasteurizer). The strategy required adding a new term over the classical PI controller, such that a new parameter should be tuned. Measurements made on commercial installations have confirmed the validity of the new controller. (orig.)

Fast estimation of space-robots inertia parameters: A modular mathematical formulation

Science.gov (United States)

Nabavi Chashmi, Seyed Yaser; Malaek, Seyed Mohammad-Bagher

2016-10-01

This work aims to propose a new technique that considerably helps enhance time and precision needed to identify ;Inertia Parameters (IPs); of a typical Autonomous Space-Robot (ASR). Operations might include, capturing an unknown Target Space-Object (TSO), ;active space-debris removal; or ;automated in-orbit assemblies;. In these operations generating precise successive commands are essential to the success of the mission. We show how a generalized, repeatable estimation-process could play an effective role to manage the operation. With the help of the well-known Force-Based approach, a new ;modular formulation; has been developed to simultaneously identify IPs of an ASR while it captures a TSO. The idea is to reorganize the equations with associated IPs with a ;Modular Set; of matrices instead of a single matrix representing the overall system dynamics. The devised Modular Matrix Set will then facilitate the estimation process. It provides a conjugate linear model in mass and inertia terms. The new formulation is, therefore, well-suited for ;simultaneous estimation processes; using recursive algorithms like RLS. Further enhancements would be needed for cases the effect of center of mass location becomes important. Extensive case studies reveal that estimation time is drastically reduced which in-turn paves the way to acquire better results.

Constraints on pre-big-bang parameter space from CMBR anisotropies

International Nuclear Information System (INIS)

Bozza, V.; Gasperini, M.; Giovannini, M.; Veneziano, G.

2003-01-01

The so-called curvaton mechanism--a way to convert isocurvature perturbations into adiabatic ones--is investigated both analytically and numerically in a pre-big-bang scenario where the role of the curvaton is played by a sufficiently massive Kalb-Ramond axion of superstring theory. When combined with observations of CMBR anisotropies at large and moderate angular scales, the present analysis allows us to constrain quite considerably the parameter space of the model: in particular, the initial displacement of the axion from the minimum of its potential and the rate of evolution of the compactification volume during pre-big-bang inflation. The combination of theoretical and experimental constraints favors a slightly blue spectrum of scalar perturbations, and/or a value of the string scale in the vicinity of the SUSY GUT scale

Constraints on pre-big bang parameter space from CMBR anisotropies

CERN Document Server

Bozza, Valerio; Giovannini, Massimo; Veneziano, Gabriele

2003-01-01

The so-called curvaton mechanism --a way to convert isocurvature perturbations into adiabatic ones-- is investigated both analytically and numerically in a pre-big bang scenario where the role of the curvaton is played by a sufficiently massive Kalb--Ramond axion of superstring theory. When combined with observations of CMBR anisotropies at large and moderate angular scales, the present analysis allows us to constrain quite considerably the parameter space of the model: in particular, the initial displacement of the axion from the minimum of its potential and the rate of evolution of the compactification volume during pre-big bang inflation. The combination of theoretical and experimental constraints favours a slightly blue spectrum of scalar perturbations, and/or a value of the string scale in the vicinity of the SUSY-GUT scale.

Modal Analysis and Model Correlation of the Mir Space Station

Science.gov (United States)

Kim, Hyoung M.; Kaouk, Mohamed

2000-01-01

This paper will discuss on-orbit dynamic tests, modal analysis, and model refinement studies performed as part of the Mir Structural Dynamics Experiment (MiSDE). Mir is the Russian permanently manned Space Station whose construction first started in 1986. The MiSDE was sponsored by the NASA International Space Station (ISS) Phase 1 Office and was part of the Shuttle-Mir Risk Mitigation Experiment (RME). One of the main objectives for MiSDE is to demonstrate the feasibility of performing on-orbit modal testing on large space structures to extract modal parameters that will be used to correlate mathematical models. The experiment was performed over a one-year span on the Mir-alone and Mir with a Shuttle docked. A total of 45 test sessions were performed including: Shuttle and Mir thruster firings, Shuttle-Mir and Progress-Mir dockings, crew exercise and pushoffs, and ambient noise during night-to-day and day-to-night orbital transitions. Test data were recorded with a variety of existing and new instrumentation systems that included: the MiSDE Mir Auxiliary Sensor Unit (MASU), the Space Acceleration Measurement System (SAMS), the Russian Mir Structural Dynamic Measurement System (SDMS), the Mir and Shuttle Inertial Measurement Units (IMUs), and the Shuttle payload bay video cameras. Modal analysis was performed on the collected test data to extract modal parameters, i.e. frequencies, damping factors, and mode shapes. A special time-domain modal identification procedure was used on free-decay structural responses. The results from this study show that modal testing and analysis of large space structures is feasible within operational constraints. Model refinements were performed on both the Mir alone and the Shuttle-Mir mated configurations. The design sensitivity approach was used for refinement, which adjusts structural properties in order to match analytical and test modal parameters. To verify the refinement results, the analytical responses calculated using

Multiplicity distributions in impact parameter space

International Nuclear Information System (INIS)

Wakano, Masami

1976-01-01

A definition for the average multiplicity of pions as a function of momentum transfer and total energy in the high energy proton-proton collisions is proposed by using the n-pion production differential cross section with the given momentum transfer from a proton to other final products and the given energy of the latter. Contributions from nondiffractive and diffractive processes are formulated in a multi-Regge model. We define a relationship between impact parameter and momentum transfer in the sense of classical theory for inelastic processes and we obtain the average multiplicity of pions as a function of impact parameter and total energy from the corresponding quantity afore-mentioned. By comparing this quantity with the square root of the opaqueness at given impact parameter, we conclude that the overlap of localized constituents is important in determining the opaqueness at given impact parameter in a collision of two hadrons. (auth.)

Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data.

Science.gov (United States)

Drummond, Alexei J; Nicholls, Geoff K; Rodrigo, Allen G; Solomon, Wiremu

2002-07-01

Molecular sequences obtained at different sampling times from populations of rapidly evolving pathogens and from ancient subfossil and fossil sources are increasingly available with modern sequencing technology. Here, we present a Bayesian statistical inference approach to the joint estimation of mutation rate and population size that incorporates the uncertainty in the genealogy of such temporally spaced sequences by using Markov chain Monte Carlo (MCMC) integration. The Kingman coalescent model is used to describe the time structure of the ancestral tree. We recover information about the unknown true ancestral coalescent tree, population size, and the overall mutation rate from temporally spaced data, that is, from nucleotide sequences gathered at different times, from different individuals, in an evolving haploid population. We briefly discuss the methodological implications and show what can be inferred, in various practically relevant states of prior knowledge. We develop extensions for exponentially growing population size and joint estimation of substitution model parameters. We illustrate some of the important features of this approach on a genealogy of HIV-1 envelope (env) partial sequences.

A new Bayesian recursive technique for parameter estimation

Science.gov (United States)

Kaheil, Yasir H.; Gill, M. Kashif; McKee, Mac; Bastidas, Luis

2006-08-01

The performance of any model depends on how well its associated parameters are estimated. In the current application, a localized Bayesian recursive estimation (LOBARE) approach is devised for parameter estimation. The LOBARE methodology is an extension of the Bayesian recursive estimation (BARE) method. It is applied in this paper on two different types of models: an artificial intelligence (AI) model in the form of a support vector machine (SVM) application for forecasting soil moisture and a conceptual rainfall-runoff (CRR) model represented by the Sacramento soil moisture accounting (SAC-SMA) model. Support vector machines, based on statistical learning theory (SLT), represent the modeling task as a quadratic optimization problem and have already been used in various applications in hydrology. They require estimation of three parameters. SAC-SMA is a very well known model that estimates runoff. It has a 13-dimensional parameter space. In the LOBARE approach presented here, Bayesian inference is used in an iterative fashion to estimate the parameter space that will most likely enclose a best parameter set. This is done by narrowing the sampling space through updating the "parent" bounds based on their fitness. These bounds are actually the parameter sets that were selected by BARE runs on subspaces of the initial parameter space. The new approach results in faster convergence toward the optimal parameter set using minimum training/calibration data and fewer sets of parameter values. The efficacy of the localized methodology is also compared with the previously used BARE algorithm.

An evaluation of behavior inferences from Bayesian state-space models: A case study with the Pacific walrus

Science.gov (United States)

Beatty, William; Jay, Chadwick V.; Fischbach, Anthony S.

2016-01-01

State-space models offer researchers an objective approach to modeling complex animal location data sets, and state-space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two-state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state-space models, and reconcile these parameters with the study species and its expected behaviors.

Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

Science.gov (United States)

Wentworth, Mami Tonoe

verification strategies to assess the accuracy of those techniques, which we illustrate in the context of the HIV model. Finally, we examine active subspace methods as an alternative to parameter subset selection techniques. The objective of active subspace methods is to determine the subspace of inputs that most strongly affect the model response, and to reduce the dimension of the input space. The major difference between active subspace methods and parameter selection techniques is that parameter selection identifies influential parameters whereas subspace selection identifies a linear combination of parameters that impacts the model responses significantly. We employ active subspace methods discussed in [22] for the HIV model and present a verification that the active subspace successfully reduces the input dimensions.

A parameter identification problem arising from a two-dimensional airfoil section model

International Nuclear Information System (INIS)

Cerezo, G.M.

1994-01-01

The development of state space models for aeroelastic systems, including unsteady aerodynamics, is particularly important for the design of highly maneuverable aircraft. In this work we present a state space formulation for a special class of singular neutral functional differential equations (SNFDE) with initial data in C(-1, 0). This work is motivated by the two-dimensional airfoil model presented by Burns, Cliff and Herdman in. In the same authors discuss the validity of the assumptions under which the model was formulated. They pay special attention to the derivation of the evolution equation for the circulation on the airfoil. This equation was coupled to the rigid-body dynamics of the airfoil in order to obtain a complete set of functional differential equations that describes the composite system. The resulting mathematical model for the aeroelastic system has a weakly singular component. In this work we consider a finite delay approximation to the model presented in. We work with a scalar model in which we consider the weak singularity appearing in the original problem. The main goal of this work is to develop numerical techniques for the identification of the parameters appearing in the kernel of the associated scalar integral equation. Clearly this is the first step in the study of parameter identification for the original model and the corresponding validation of this model for the aeroelastic system

Model Optimization Identification Method Based on Closed-loop Operation Data and Process Characteristics Parameters

Directory of Open Access Journals (Sweden)

Zhiqiang GENG

2014-01-01

Full Text Available Output noise is strongly related to input in closed-loop control system, which makes model identification of closed-loop difficult, even unidentified in practice. The forward channel model is chosen to isolate disturbance from the output noise to input, and identified by optimization the dynamic characteristics of the process based on closed-loop operation data. The characteristics parameters of the process, such as dead time and time constant, are calculated and estimated based on the PI/PID controller parameters and closed-loop process input/output data. And those characteristics parameters are adopted to define the search space of the optimization identification algorithm. PSO-SQP optimization algorithm is applied to integrate the global search ability of PSO with the local search ability of SQP to identify the model parameters of forward channel. The validity of proposed method has been verified by the simulation. The practicability is checked with the PI/PID controller parameter turning based on identified forward channel model.

Modeling and Parameter Identification Involving 3-Hydroxypropionaldehyde Inhibitory Effects in Glycerol Continuous Fermentation

Directory of Open Access Journals (Sweden)

Zhaohua Gong

2012-01-01

Full Text Available Mathematical modeling and parameter estimation are critical steps in the optimization of biotechnological processes. In the 1,3-propanediol (1,3-PD production by glycerol fermentation process under anaerobic conditions, 3-hydroxypropionaldehyde (3-HPA accumulation would arouse an irreversible cessation of the fermentation process. Considering 3-HPA inhibitions to cells growth and to activities of enzymes, we propose a novel mathematical model to describe glycerol continuous cultures. Some properties of the above model are discussed. On the basis of the concentrations of extracellular substances, a parameter identification model is established to determine the kinetic parameters in the presented system. Through the penalty function technique combined with an extension of the state space method, an improved genetic algorithm is then constructed to solve the parameter identification model. An illustrative numerical example shows the appropriateness of the proposed model and the validity of optimization algorithm. Since it is difficult to measure the concentrations of intracellular substances, a quantitative robustness analysis method is given to infer whether the model is plausible for the intracellular substances. Numerical results show that the proposed model is of good robustness.

Numerical Modeling of Ophthalmic Response to Space

Science.gov (United States)

Nelson, E. S.; Myers, J. G.; Mulugeta, L.; Vera, J.; Raykin, J.; Feola, A.; Gleason, R.; Samuels, B.; Ethier, C. R.

2015-01-01

To investigate ophthalmic changes in spaceflight, we would like to predict the impact of blood dysregulation and elevated intracranial pressure (ICP) on Intraocular Pressure (IOP). Unlike other physiological systems, there are very few lumped parameter models of the eye. The eye model described here is novel in its inclusion of the human choroid and retrobulbar subarachnoid space (rSAS), which are key elements in investigating the impact of increased ICP and ocular blood volume. Some ingenuity was required in modeling the blood and rSAS compartments due to the lack of quantitative data on essential hydrodynamic quantities, such as net choroidal volume and blood flowrate, inlet and exit pressures, and material properties, such as compliances between compartments.

A state-space-based prognostics model for lithium-ion battery degradation

International Nuclear Information System (INIS)

Xu, Xin; Chen, Nan

2017-01-01

This paper proposes to analyze the degradation of lithium-ion batteries with the sequentially observed discharging profiles. A general state-space model is developed in which the observation model is used to approximate the discharging profile of each cycle, the corresponding parameter vector is treated as the hidden state, and the state-transition model is used to track the evolution of the parameter vector as the battery ages. The EM and EKF algorithms are adopted to estimate and update the model parameters and states jointly. Based on this model, we construct prediction on the end of discharge times for unobserved cycles and the remaining useful cycles before the battery failure. The effectiveness of the proposed model is demonstrated using a real lithium-ion battery degradation data set. - Highlights: • Unifying model for Li-Ion battery SOC and SOH estimation. • Extended Kalman filter based efficient inference algorithm. • Using voltage curves in discharging to have wide validity.

Determination of charged particle beam parameters with taking into account of space charge

International Nuclear Information System (INIS)

Ishkhanov, B.S.; Poseryaev, A.V.; Shvedunov, V.I.

2005-01-01

One describes a procedure to determine the basic parameters of a paraxial axially-symmetric beam of charged particles taking account of space charge contribution. The described procedure is based on application of the general equation for beam envelope. Paper presents data on its convergence and resistance to measurement errors. The position determination error of crossover (stretching) and radius of beam in crossover is maximum 15% , while the emittance determination error depends on emittance and space charge correlation. The introduced procedure was used to determine parameters of the available electron gun 20 keV energy beam with 0.64 A current. The derived results turned to agree closely with the design parameters [ru

On discourse space modeling

OpenAIRE

Казыдуб, Надежда

2013-01-01

Discourse space is a complex structure that incorporates different levels and dimensions. The paper focuses on developing a multidisciplinary approach that is congruent to the complex character of the modern discourse. Two models of discourse space are proposed here. The Integrated Model reveals the interaction of different categorical mechanisms in the construction of the discourse space. The Evolutionary Model describes the historical roots of the modern discourse. It also reveals historica...

State space model extraction of thermohydraulic systems – Part I: A linear graph approach

International Nuclear Information System (INIS)

Uren, K.R.; Schoor, G. van

2013-01-01

Thermohydraulic simulation codes are increasingly making use of graphical design interfaces. The user can quickly and easily design a thermohydraulic system by placing symbols on the screen resembling system components. These components can then be connected to form a system representation. Such system models may then be used to obtain detailed simulations of the physical system. Usually this kind of simulation models are too complex and not ideal for control system design. Therefore, a need exists for automated techniques to extract lumped parameter models useful for control system design. The goal of this first paper, in a two part series, is to propose a method that utilises a graphical representation of a thermohydraulic system, and a lumped parameter modelling approach, to extract state space models. In this methodology each physical domain of the thermohydraulic system is represented by a linear graph. These linear graphs capture the interaction between all components within and across energy domains – hydraulic, thermal and mechanical. These linear graphs are analysed using a graph-theoretic approach to derive reduced order state space models. These models capture the dominant dynamics of the thermohydraulic system and are ideal for control system design purposes. The proposed state space model extraction method is demonstrated by considering a U-tube system. A non-linear state space model is extracted representing both the hydraulic and thermal domain dynamics of the system. The simulated state space model is compared with a Flownex ® model of the U-tube. Flownex ® is a validated systems thermal-fluid simulation software package. - Highlights: • A state space model extraction methodology based on graph-theoretic concepts. • An energy-based approach to consider multi-domain systems in a common framework. • Allow extraction of transparent (white-box) state space models automatically. • Reduced order models containing only independent state

Large model-space calculation of the nuclear level density parameter

International Nuclear Information System (INIS)

Agrawal, B.K.; Samaddar, S.K.; De, J.N.; Shlomo, S.

1998-01-01

Recently, several attempts have been made to obtain nuclear level density (ρ) and level density parameter (α) within the microscopic approaches based on path integral representation of the partition function. The results for the inverse level density parameter K es and the level density as a function of excitation energy are presented

Anomalous solute transport in saturated porous media: Relating transport model parameters to electrical and nuclear magnetic resonance properties

Science.gov (United States)

Swanson, Ryan D; Binley, Andrew; Keating, Kristina; France, Samantha; Osterman, Gordon; Day-Lewis, Frederick D.; Singha, Kamini

2015-01-01

The advection-dispersion equation (ADE) fails to describe commonly observed non-Fickian solute transport in saturated porous media, necessitating the use of other models such as the dual-domain mass-transfer (DDMT) model. DDMT model parameters are commonly calibrated via curve fitting, providing little insight into the relation between effective parameters and physical properties of the medium. There is a clear need for material characterization techniques that can provide insight into the geometry and connectedness of pore spaces related to transport model parameters. Here, we consider proton nuclear magnetic resonance (NMR), direct-current (DC) resistivity, and complex conductivity (CC) measurements for this purpose, and assess these methods using glass beads as a control and two different samples of the zeolite clinoptilolite, a material that demonstrates non-Fickian transport due to intragranular porosity. We estimate DDMT parameters via calibration of a transport model to column-scale solute tracer tests, and compare NMR, DC resistivity, CC results, which reveal that grain size alone does not control transport properties and measured geophysical parameters; rather, volume and arrangement of the pore space play important roles. NMR cannot provide estimates of more-mobile and less-mobile pore volumes in the absence of tracer tests because these estimates depend critically on the selection of a material-dependent and flow-dependent cutoff time. Increased electrical connectedness from DC resistivity measurements are associated with greater mobile pore space determined from transport model calibration. CC was hypothesized to be related to length scales of mass transfer, but the CC response is unrelated to DDMT.

An Integrated Approach to Parameter Learning in Infinite-Dimensional Space

Energy Technology Data Exchange (ETDEWEB)

Boyd, Zachary M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Wendelberger, Joanne Roth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-09-14

The availability of sophisticated modern physics codes has greatly extended the ability of domain scientists to understand the processes underlying their observations of complicated processes, but it has also introduced the curse of dimensionality via the many user-set parameters available to tune. Many of these parameters are naturally expressed as functional data, such as initial temperature distributions, equations of state, and controls. Thus, when attempting to find parameters that match observed data, being able to navigate parameter-space becomes highly non-trivial, especially considering that accurate simulations can be expensive both in terms of time and money. Existing solutions include batch-parallel simulations, high-dimensional, derivative-free optimization, and expert guessing, all of which make some contribution to solving the problem but do not completely resolve the issue. In this work, we explore the possibility of coupling together all three of the techniques just described by designing user-guided, batch-parallel optimization schemes. Our motivating example is a neutron diffusion partial differential equation where the time-varying multiplication factor serves as the unknown control parameter to be learned. We find that a simple, batch-parallelizable, random-walk scheme is able to make some progress on the problem but does not by itself produce satisfactory results. After reducing the dimensionality of the problem using functional principal component analysis (fPCA), we are able to track the progress of the solver in a visually simple way as well as viewing the associated principle components. This allows a human to make reasonable guesses about which points in the state space the random walker should try next. Thus, by combining the random walker's ability to find descent directions with the human's understanding of the underlying physics, it is possible to use expensive simulations more efficiently and more quickly arrive at the

The magnetically driven imploding liner parameter space of the ATLAS capacitor bank

CERN Document Server

Lindemuth, I R; Faehl, R J; Reinovsky, R E

2001-01-01

Summary form only given, as follows. The Atlas capacitor bank (23 MJ, 30 MA) is now operational at Los Alamos. Atlas was designed primarily to magnetically drive imploding liners for use as impactors in shock and hydrodynamic experiments. We have conducted a computational "mapping" of the high-performance imploding liner parameter space accessible to Atlas. The effect of charge voltage, transmission inductance, liner thickness, liner initial radius, and liner length has been investigated. One conclusion is that Atlas is ideally suited to be a liner driver for liner-on-plasma experiments in a magnetized target fusion (MTF) context . The parameter space of possible Atlas reconfigurations has also been investigated.

DUAL STATE-PARAMETER UPDATING SCHEME ON A CONCEPTUAL HYDROLOGIC MODEL USING SEQUENTIAL MONTE CARLO FILTERS

Science.gov (United States)

Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin

Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.

Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)

Science.gov (United States)

Santos, Léonard; Thirel, Guillaume; Perrin, Charles

2018-04-01

In many conceptual rainfall-runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called operator splitting. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall-runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall-runoff models and make the resolution of the representation difficult, are first replaced by a so-called Nash cascade and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions.

Science.gov (United States)

Vernon, Ian; Liu, Junli; Goldstein, Michael; Rowe, James; Topping, Jen; Lindsey, Keith

2018-01-02

Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data of various forms. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology. Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embeded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to a model of hormonal crosstalk in Arabidopsis root development, which has 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches between model output and observed trend data. The multiple insights into the model's structure that this analysis provides are discussed. The methodology is applied to a second related model, and the biological consequences of the resulting comparison, including the evaluation of gene functions, are described. Bayesian uncertainty analysis for complex models using both emulators and history matching is shown to be a powerful technique that can greatly aid the study of a large class of systems biology models. It both provides insight into model behaviour

Estimating Parameters in Physical Models through Bayesian Inversion: A Complete Example

KAUST Repository

Allmaras, Moritz

2013-02-07

All mathematical models of real-world phenomena contain parameters that need to be estimated from measurements, either for realistic predictions or simply to understand the characteristics of the model. Bayesian statistics provides a framework for parameter estimation in which uncertainties about models and measurements are translated into uncertainties in estimates of parameters. This paper provides a simple, step-by-step example-starting from a physical experiment and going through all of the mathematics-to explain the use of Bayesian techniques for estimating the coefficients of gravity and air friction in the equations describing a falling body. In the experiment we dropped an object from a known height and recorded the free fall using a video camera. The video recording was analyzed frame by frame to obtain the distance the body had fallen as a function of time, including measures of uncertainty in our data that we describe as probability densities. We explain the decisions behind the various choices of probability distributions and relate them to observed phenomena. Our measured data are then combined with a mathematical model of a falling body to obtain probability densities on the space of parameters we seek to estimate. We interpret these results and discuss sources of errors in our estimation procedure. © 2013 Society for Industrial and Applied Mathematics.

Mapping from Speech to Images Using Continuous State Space Models

DEFF Research Database (Denmark)

Lehn-Schiøler, Tue; Hansen, Lars Kai; Larsen, Jan

2005-01-01

In this paper a system that transforms speech waveforms to animated faces are proposed. The system relies on continuous state space models to perform the mapping, this makes it possible to ensure video with no sudden jumps and allows continuous control of the parameters in 'face space...... a subjective point of view the model is able to construct an image sequence from an unknown noisy speech sequence even though the number of training examples are limited.......'. The performance of the system is critically dependent on the number of hidden variables, with too few variables the model cannot represent data, and with too many overfitting is noticed. Simulations are performed on recordings of 3-5 sec.\\$\\backslash\\$ video sequences with sentences from the Timit database. From...

Efficiently enclosing the compact binary parameter space by singular-value decomposition

International Nuclear Information System (INIS)

Cannon, Kipp; Hanna, Chad; Keppel, Drew

2011-01-01

Gravitational-wave searches for the merger of compact binaries use matched filtering as the method of detecting signals and estimating parameters. Such searches construct a fine mesh of filters covering a signal parameter space at high density. Previously it has been shown that singular-value decomposition can reduce the effective number of filters required to search the data. Here we study how the basis provided by the singular-value decomposition changes dimension as a function of template-bank density. We will demonstrate that it is sufficient to use the basis provided by the singular-value decomposition of a low-density bank to accurately reconstruct arbitrary points within the boundaries of the template bank. Since this technique is purely numerical, it may have applications to interpolating the space of numerical relativity waveforms.

State Space Modeling Using SAS

Directory of Open Access Journals (Sweden)

Rajesh Selukar

2011-05-01

Full Text Available This article provides a brief introduction to the state space modeling capabilities in SAS, a well-known statistical software system. SAS provides state space modeling in a few different settings. SAS/ETS, the econometric and time series analysis module of the SAS system, contains many procedures that use state space models to analyze univariate and multivariate time series data. In addition, SAS/IML, an interactive matrix language in the SAS system, provides Kalman filtering and smoothing routines for stationary and nonstationary state space models. SAS/IML also provides support for linear algebra and nonlinear function optimization, which makes it a convenient environment for general-purpose state space modeling.

Subchannel and bundle friction factors and flow split parameters for laminar transition and turbulent longitudinal flows in wire wrap spaced hexagonal arrays

International Nuclear Information System (INIS)

Hawley, J.T.; Chiu, C.; Todreas, N.E.; Rohsenow, W.M.

1980-01-01

Correlations are presented for subchannel and bundle friction factors and flowsplit parameters for laminar, transition and turbulent longitudinal flows in wire wrap spaced hexagonal arrays. These results are obtained from pressure drop models of flow in individual subchannels. For turbulent flow, an existing pressure drop model for flow in edge subchannels is extended, and the resulting edge subchannel friction factor is identified. Using the expressions for flowsplit parameters and the equal pressure drops assumption, the interior subchannel and bundle friction factors are obtained. For laminar flow, models are developed for pressure drops of individual subchannels. From these models, expressions for the subchannel friction factors are identified and expressions for the flowsplit parameters are derived

Subchannel and bundle friction factors and flowsplit parameters for laminar, transition, and turbulent longitudinal flows in wire-wrap spaced hexagonal arrays. [LMFBR

Energy Technology Data Exchange (ETDEWEB)

Hawley, J.T.; Chiu, C.; Rohsenow, W.M.; Todreas, N.E.

1980-08-01

Correlations are presented for subchannel and bundle friction factors and flowsplit parameters for laminar, transition and turbulent longitudinal flows in wire wrap spaced hexagonal arrays. These results are obtained from pressure drop models of flow in individual subchannels. For turbulent flow, an existing pressure drop model for flow in edge subchannels is extended, and the resulting edge subchannel friction factor is identified. Using the expressions for flowsplit parameters and the equal pressured drop assumption, the interior subchannel and bundle friction factors are obtained. For laminar flow, models are developed for pressure drops of individual subchannels. From these models, expressions for the subchannel friction factors are identified and expressions for the flowsplit parameters are derived.

Model parameter updating using Bayesian networks

International Nuclear Information System (INIS)

Treml, C.A.; Ross, Timothy J.

2004-01-01

This paper outlines a model parameter updating technique for a new method of model validation using a modified model reference adaptive control (MRAC) framework with Bayesian Networks (BNs). The model parameter updating within this method is generic in the sense that the model/simulation to be validated is treated as a black box. It must have updateable parameters to which its outputs are sensitive, and those outputs must have metrics that can be compared to that of the model reference, i.e., experimental data. Furthermore, no assumptions are made about the statistics of the model parameter uncertainty, only upper and lower bounds need to be specified. This method is designed for situations where a model is not intended to predict a complete point-by-point time domain description of the item/system behavior; rather, there are specific points, features, or events of interest that need to be predicted. These specific points are compared to the model reference derived from actual experimental data. The logic for updating the model parameters to match the model reference is formed via a BN. The nodes of this BN consist of updateable model input parameters and the specific output values or features of interest. Each time the model is executed, the input/output pairs are used to adapt the conditional probabilities of the BN. Each iteration further refines the inferred model parameters to produce the desired model output. After parameter updating is complete and model inputs are inferred, reliabilities for the model output are supplied. Finally, this method is applied to a simulation of a resonance control cooling system for a prototype coupled cavity linac. The results are compared to experimental data.

Space Environment Modeling

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Collection includes presentation materials and outputs from operational space environment models produced by the NOAA Space Weather Prediction Center (SWPC) and...

Comparison of three-dimensional parameters of Halo CMEs using three cone models

Science.gov (United States)

Na, H.; Moon, Y.; Jang, S.; Lee, K.

2012-12-01

Halo coronal mass ejections (HCMEs) are a major cause of geomagnetic storms and their three dimensional structures are important for space weather. In this study, we compare three cone models: an elliptical cone model, an ice-cream cone model, and an asymmetric cone model. These models allow us to determine the three dimensional parameters of HCMEs such as radial speed, angular width, and the angle (γ) between sky plane and cone axis. We compare these parameters obtained from three models using 62 well-observed HCMEs observed by SOHO/LASCO from 2001 to 2002. Then we obtain the root mean square error (RMS error) between maximum measured projection speeds and their calculated projection speeds from the cone models. As a result, we find that the radial speeds obtained from the models are well correlated with one another (R > 0.84). The correlation coefficients between angular widths are ranges from 0.04 to 0.53 and those between γ values are from -0.15 to 0.47, which are much smaller than expected. The reason may be due to different assumptions and methods. The RMS errors between the maximum measured projection speeds and the maximum estimated projection speeds of the elliptical cone model, the ice-cream cone model, and the asymmetric cone model are 213 km/s, 254 km/s, and 267 km/s, respectively. And we obtain the correlation coefficients between the location from the models and the flare location (R > 0.75). Finally, we discuss strengths and weaknesses of these models in terms of space weather application.

PARAMETER ESTIMATION IN BREAD BAKING MODEL

Directory of Open Access Journals (Sweden)

Hadiyanto Hadiyanto

2012-05-01

Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels.  Abstrak  PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan

Nonlinear Parameter-Varying AeroServoElastic Reduced Order Model for Aerostructural Sensing and Control, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The overall goal of the project is to develop reliable reduced order modeling technologies to automatically generate nonlinear, parameter-varying (PV),...

Cost prediction model for various payloads and instruments for the Space Shuttle Orbiter

Science.gov (United States)

Hoffman, F. E.

1984-01-01

The following cost parameters of the space shuttle were undertaken: (1) to develop a cost prediction model for various payload classes of instruments and experiments for the Space Shuttle Orbiter; and (2) to show the implications of various payload classes on the cost of: reliability analysis, quality assurance, environmental design requirements, documentation, parts selection, and other reliability enhancing activities.

Spiral computed tomography phase-space source model in the BEAMnrc/EGSnrc Monte Carlo system: implementation and validation

International Nuclear Information System (INIS)

Kim, Sangroh; Yoshizumi, Terry T; Yin Fangfang; Chetty, Indrin J

2013-01-01

Currently, the BEAMnrc/EGSnrc Monte Carlo (MC) system does not provide a spiral CT source model for the simulation of spiral CT scanning. We developed and validated a spiral CT phase-space source model in the BEAMnrc/EGSnrc system. The spiral phase-space source model was implemented in the DOSXYZnrc user code of the BEAMnrc/EGSnrc system by analyzing the geometry of spiral CT scan—scan range, initial angle, rotational direction, pitch, slice thickness, etc. Table movement was simulated by changing the coordinates of the isocenter as a function of beam angles. Some parameters such as pitch, slice thickness and translation per rotation were also incorporated into the model to make the new phase-space source model, designed specifically for spiral CT scan simulations. The source model was hard-coded by modifying the ‘ISource = 8: Phase-Space Source Incident from Multiple Directions’ in the srcxyznrc.mortran and dosxyznrc.mortran files in the DOSXYZnrc user code. In order to verify the implementation, spiral CT scans were simulated in a CT dose index phantom using the validated x-ray tube model of a commercial CT simulator for both the original multi-direction source (ISOURCE = 8) and the new phase-space source model in the DOSXYZnrc system. Then the acquired 2D and 3D dose distributions were analyzed with respect to the input parameters for various pitch values. In addition, surface-dose profiles were also measured for a patient CT scan protocol using radiochromic film and were compared with the MC simulations. The new phase-space source model was found to simulate the spiral CT scanning in a single simulation run accurately. It also produced the equivalent dose distribution of the ISOURCE = 8 model for the same CT scan parameters. The MC-simulated surface profiles were well matched to the film measurement overall within 10%. The new spiral CT phase-space source model was implemented in the BEAMnrc/EGSnrc system. This work will be beneficial in estimating the

Spiral computed tomography phase-space source model in the BEAMnrc/EGSnrc Monte Carlo system: implementation and validation.

Science.gov (United States)

Kim, Sangroh; Yoshizumi, Terry T; Yin, Fang-Fang; Chetty, Indrin J

2013-04-21

Currently, the BEAMnrc/EGSnrc Monte Carlo (MC) system does not provide a spiral CT source model for the simulation of spiral CT scanning. We developed and validated a spiral CT phase-space source model in the BEAMnrc/EGSnrc system. The spiral phase-space source model was implemented in the DOSXYZnrc user code of the BEAMnrc/EGSnrc system by analyzing the geometry of spiral CT scan-scan range, initial angle, rotational direction, pitch, slice thickness, etc. Table movement was simulated by changing the coordinates of the isocenter as a function of beam angles. Some parameters such as pitch, slice thickness and translation per rotation were also incorporated into the model to make the new phase-space source model, designed specifically for spiral CT scan simulations. The source model was hard-coded by modifying the 'ISource = 8: Phase-Space Source Incident from Multiple Directions' in the srcxyznrc.mortran and dosxyznrc.mortran files in the DOSXYZnrc user code. In order to verify the implementation, spiral CT scans were simulated in a CT dose index phantom using the validated x-ray tube model of a commercial CT simulator for both the original multi-direction source (ISOURCE = 8) and the new phase-space source model in the DOSXYZnrc system. Then the acquired 2D and 3D dose distributions were analyzed with respect to the input parameters for various pitch values. In addition, surface-dose profiles were also measured for a patient CT scan protocol using radiochromic film and were compared with the MC simulations. The new phase-space source model was found to simulate the spiral CT scanning in a single simulation run accurately. It also produced the equivalent dose distribution of the ISOURCE = 8 model for the same CT scan parameters. The MC-simulated surface profiles were well matched to the film measurement overall within 10%. The new spiral CT phase-space source model was implemented in the BEAMnrc/EGSnrc system. This work will be beneficial in estimating the spiral

Model based Computerized Ionospheric Tomography in space and time

Science.gov (United States)

Tuna, Hakan; Arikan, Orhan; Arikan, Feza

2018-04-01

Reconstruction of the ionospheric electron density distribution in space and time not only provide basis for better understanding the physical nature of the ionosphere, but also provide improvements in various applications including HF communication. Recently developed IONOLAB-CIT technique provides physically admissible 3D model of the ionosphere by using both Slant Total Electron Content (STEC) measurements obtained from a GPS satellite - receiver network and IRI-Plas model. IONOLAB-CIT technique optimizes IRI-Plas model parameters in the region of interest such that the synthetic STEC computations obtained from the IRI-Plas model are in accordance with the actual STEC measurements. In this work, the IONOLAB-CIT technique is extended to provide reconstructions both in space and time. This extension exploits the temporal continuity of the ionosphere to provide more reliable reconstructions with a reduced computational load. The proposed 4D-IONOLAB-CIT technique is validated on real measurement data obtained from TNPGN-Active GPS receiver network in Turkey.

Effects of space-dependent cross sections on core physics parameters for compact fast spectrum space power reactors

International Nuclear Information System (INIS)

Lell, R.M.; Hanan, N.A.

1987-01-01

Effects of multigroup neutron cross section generation procedures on core physics parameters for compact fast spectrum reactors have been examined. Homogeneous and space-dependent multigroup cross section sets were generated in 11 and 27 groups for a representative fast reactor core. These cross sections were used to compute various reactor physics parameters for the reference core. Coarse group structure and neglect of space-dependence in the generation procedure resulted in inaccurate computations of reactor flux and power distributions and in significant errors regarding estimates of core reactivity and control system worth. Delayed neutron fraction was insensitive to cross section treatment, and computed reactivity coefficients were only slightly sensitive. However, neutron lifetime was found to be very sensitive to cross section treatment. Deficiencies in multigroup cross sections are reflected in core nuclear design and, consequently, in system mechanical design

Model-Based Trade Space Exploration for Near-Earth Space Missions

Science.gov (United States)

Cohen, Ronald H.; Boncyk, Wayne; Brutocao, James; Beveridge, Iain

2005-01-01

We developed a capability for model-based trade space exploration to be used in the conceptual design of Earth-orbiting space missions. We have created a set of reusable software components to model various subsystems and aspects of space missions. Several example mission models were created to test the tools and process. This technique and toolset has demonstrated itself to be valuable for space mission architectural design.

Nonlinear State Space Modeling and System Identification for Electrohydraulic Control

Directory of Open Access Journals (Sweden)

Jun Yan

2013-01-01

Full Text Available The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and then use a modified recursive least square method with iterative estimation of internal variables to identify all the unknown parameters simultaneously. It is found that the proposed H-W model approximates the actual system better than the independent Hammerstein, Wiener, and ARX models. The prediction error of the H-W model is about 13%, 54%, and 58% less than the Hammerstein, Wiener, and ARX models, respectively.

Nonlinear Parameter-Varying AeroServoElastic Reduced Order Model for Aerostructural Sensing and Control, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — The overall goal of the project is to develop reliable reduced order modeling technologies to automatically generate parameter-varying (PV), aeroservoelastic (ASE)...

LAMOST DR1: Stellar Parameters and Chemical Abundances with SP_Ace

Science.gov (United States)

Boeche, C.; Smith, M. C.; Grebel, E. K.; Zhong, J.; Hou, J. L.; Chen, L.; Stello, D.

2018-04-01

We present a new analysis of the LAMOST DR1 survey spectral database performed with the code SP_Ace, which provides the derived stellar parameters {T}{{eff}}, {log}g, [Fe/H], and [α/H] for 1,097,231 stellar objects. We tested the reliability of our results by comparing them to reference results from high spectral resolution surveys. The expected errors can be summarized as ∼120 K in {T}{{eff}}, ∼0.2 in {log}g, ∼0.15 dex in [Fe/H], and ∼0.1 dex in [α/Fe] for spectra with S/N > 40, with some differences between dwarf and giant stars. SP_Ace provides error estimations consistent with the discrepancies observed between derived and reference parameters. Some systematic errors are identified and discussed. The resulting catalog is publicly available at the LAMOST and CDS websites.

Photovoltaic module parameters acquisition model

Energy Technology Data Exchange (ETDEWEB)

Cibira, Gabriel, E-mail: cibira@lm.uniza.sk; Koščová, Marcela, E-mail: mkoscova@lm.uniza.sk

2014-09-01

Highlights: • Photovoltaic five-parameter model is proposed using Matlab{sup ®} and Simulink. • The model acquisits input sparse data matrix from stigmatic measurement. • Computer simulations lead to continuous I–V and P–V characteristics. • Extrapolated I–V and P–V characteristics are in hand. • The model allows us to predict photovoltaics exploitation in different conditions. - Abstract: This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I–V and P–V characteristics for PV module based on equivalent electrical circuit. Then, limited I–V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model.

Photovoltaic module parameters acquisition model

International Nuclear Information System (INIS)

Cibira, Gabriel; Koščová, Marcela

2014-01-01

Highlights: • Photovoltaic five-parameter model is proposed using Matlab ® and Simulink. • The model acquisits input sparse data matrix from stigmatic measurement. • Computer simulations lead to continuous I–V and P–V characteristics. • Extrapolated I–V and P–V characteristics are in hand. • The model allows us to predict photovoltaics exploitation in different conditions. - Abstract: This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I–V and P–V characteristics for PV module based on equivalent electrical circuit. Then, limited I–V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model

Finite frequency shear wave splitting tomography: a model space search approach

Science.gov (United States)

Mondal, P.; Long, M. D.

2017-12-01

Observations of seismic anisotropy provide key constraints on past and present mantle deformation. A common method for upper mantle anisotropy is to measure shear wave splitting parameters (delay time and fast direction). However, the interpretation is not straightforward, because splitting measurements represent an integration of structure along the ray path. A tomographic approach that allows for localization of anisotropy is desirable; however, tomographic inversion for anisotropic structure is a daunting task, since 21 parameters are needed to describe general anisotropy. Such a large parameter space does not allow a straightforward application of tomographic inversion. Building on previous work on finite frequency shear wave splitting tomography, this study aims to develop a framework for SKS splitting tomography with a new parameterization of anisotropy and a model space search approach. We reparameterize the full elastic tensor, reducing the number of parameters to three (a measure of strength based on symmetry considerations for olivine, plus the dip and azimuth of the fast symmetry axis). We compute Born-approximation finite frequency sensitivity kernels relating model perturbations to splitting intensity observations. The strong dependence of the sensitivity kernels on the starting anisotropic model, and thus the strong non-linearity of the inverse problem, makes a linearized inversion infeasible. Therefore, we implement a Markov Chain Monte Carlo technique in the inversion procedure. We have performed tests with synthetic data sets to evaluate computational costs and infer the resolving power of our algorithm for synthetic models with multiple anisotropic layers. Our technique can resolve anisotropic parameters on length scales of ˜50 km for realistic station and event configurations for dense broadband experiments. We are proceeding towards applications to real data sets, with an initial focus on the High Lava Plains of Oregon.

State and parameter estimation of state-space model with entry-wise correlated uniform noise

Czech Academy of Sciences Publication Activity Database

Pavelková, Lenka; Kárný, Miroslav

2014-01-01

Roč. 28, č. 11 (2014), s. 1189-1205 ISSN 0890-6327 R&D Projects: GA TA ČR TA01030123; GA ČR GA13-13502S Institutional research plan: CEZ:AV0Z1075907 Keywords : state-space models * bounded noise * filtering problems * estimation algorithms * uncertain dynamic systems Subject RIV: BC - Control Systems Theory Impact factor: 1.346, year: 2014 http://library.utia.cas.cz/separaty/2014/AS/pavelkova-0422958.pdf

Analytical model for calculation of the thermo hydraulic parameters in a fuel rod assembly

Energy Technology Data Exchange (ETDEWEB)

Cesna, B., E-mail: benas@mail.lei.l [Lithuanian Energy Institute, Laboratory of Nuclear Installation Safety, Breslaujos g. 3, LT-44403 Kaunas (Lithuania)

2010-11-15

Research highlights: {yields} Proposed calculation model can be used for rapid calculation of the bundles with rods spaced by wire wrapping or honey type spacer grids. {yields} Model estimate three flow cross mixture mechanisms. {yields} Program DARS is enable to analyses experimental results. - Abstract: The paper presents the procedure of the cellular calculation of thermo hydraulic parameters of a single-phase gas flow in a fuel rod assembly. The procedure is implemented in the DARS program. The program is intended for calculation of the distribution of the gaseous coolant parameters and wall temperatures in case of arbitrary, geometrically specified, arrangement of the rods in fuel assembly and in case of arbitrary, functionally specified in space, heat release in the rods. In mathematical model the flow cross-section of the channel of intricate shape is conventionally divided to elementary cells formed by straight lines, which connect the centers of rods. Within the limits of a single cell the coolant parameters and the temperature of the corresponding part of the rod surface are assumed constant. The entire fuel assembly is viewed as a system of parallel interconnected channels. Program DARS is illustrated by calculation of a temperature mode of 85-rod assembly with spacers of wire wrapping on the rods.

Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data

Directory of Open Access Journals (Sweden)

Silvia de Haan-Rietdijk

2017-10-01

Full Text Available The Experience Sampling Method is a common approach in psychological research for collecting intensive longitudinal data with high ecological validity. One characteristic of ESM data is that it is often unequally spaced, because the measurement intervals within a day are deliberately varied, and measurement continues over several days. This poses a problem for discrete-time (DT modeling approaches, which are based on the assumption that all measurements are equally spaced. Nevertheless, DT approaches such as (vector autoregressive modeling are often used to analyze ESM data, for instance in the context of affective dynamics research. There are equivalent continuous-time (CT models, but they are more difficult to implement. In this paper we take a pragmatic approach and evaluate the practical relevance of the violated model assumption in DT AR(1 and VAR(1 models, for the N = 1 case. We use simulated data under an ESM measurement design to investigate the bias in the parameters of interest under four different model implementations, ranging from the true CT model that accounts for all the exact measurement times, to the crudest possible DT model implementation, where even the nighttime is treated as a regular interval. An analysis of empirical affect data illustrates how the differences between DT and CT modeling can play out in practice. We find that the size and the direction of the bias in DT (VAR models for unequally spaced ESM data depend quite strongly on the true parameter in addition to data characteristics. Our recommendation is to use CT modeling whenever possible, especially now that new software implementations have become available.

Legal Parameters of Space Tourism

OpenAIRE

Smith, Lesley Jane; Hörl, Kay-Uwe

2004-01-01

The commercial concept of space tourism raises important legal issues not specifically addressed by first generation rules of international spacelaw. The principles established in the nineteen sixties and seventies were inspired by the philosophy that exploration of space was undertaken by and for the benefit of mankind. Technical developments since then haveincreased the potential for new space applications, with a corresponding increase in commercial interest in space. If space tourism is t...

A relation between the Barbero-Immirzi parameter and the standard model

Energy Technology Data Exchange (ETDEWEB)

Broda, Boguslaw, E-mail: bobroda@uni.lodz.p [Department of Theoretical Physics, University of Lodz, Pomorska 149/153, PL-90-236 Lodz (Poland); Szanecki, Michal, E-mail: michalszanecki@wp.p [Department of Theoretical Physics, University of Lodz, Pomorska 149/153, PL-90-236 Lodz (Poland)

2010-06-07

It has been shown that Sakharov's induced, from the fields entering the standard model, Barbero-Immirzi parameter {gamma} assumes, in the framework of Euclidean formalism, the UV cutoff-independent value, 1/9. The calculus uses the Schwinger's proper-time formalism, the Seeley-DeWitt heat-kernel expansion, and it is akin to the derivation of the ABJ chiral anomaly in space-time with torsion.

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

International Nuclear Information System (INIS)

Lovejoy, S.; Lima, M. I. P. de

2015-01-01

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time

Sensitivity Analysis and Parameter Estimation for a Reactive Transport Model of Uranium Bioremediation

Science.gov (United States)

Meyer, P. D.; Yabusaki, S.; Curtis, G. P.; Ye, M.; Fang, Y.

2011-12-01

A three-dimensional, variably-saturated flow and multicomponent biogeochemical reactive transport model of uranium bioremediation was used to generate synthetic data . The 3-D model was based on a field experiment at the U.S. Dept. of Energy Rifle Integrated Field Research Challenge site that used acetate biostimulation of indigenous metal reducing bacteria to catalyze the conversion of aqueous uranium in the +6 oxidation state to immobile solid-associated uranium in the +4 oxidation state. A key assumption in past modeling studies at this site was that a comprehensive reaction network could be developed largely through one-dimensional modeling. Sensitivity analyses and parameter estimation were completed for a 1-D reactive transport model abstracted from the 3-D model to test this assumption, to identify parameters with the greatest potential to contribute to model predictive uncertainty, and to evaluate model structure and data limitations. Results showed that sensitivities of key biogeochemical concentrations varied in space and time, that model nonlinearities and/or parameter interactions have a significant impact on calculated sensitivities, and that the complexity of the model's representation of processes affecting Fe(II) in the system may make it difficult to correctly attribute observed Fe(II) behavior to modeled processes. Non-uniformity of the 3-D simulated groundwater flux and averaging of the 3-D synthetic data for use as calibration targets in the 1-D modeling resulted in systematic errors in the 1-D model parameter estimates and outputs. This occurred despite using the same reaction network for 1-D modeling as used in the data-generating 3-D model. Predictive uncertainty of the 1-D model appeared to be significantly underestimated by linear parameter uncertainty estimates.

Elastoplastic constitutive models parameters for unsaturated compacted bentonite sand buffer (BSB)

International Nuclear Information System (INIS)

Priyanto, D.; Man, A.; Dixon, D.; Blatz, J.

2010-01-01

(ps) of the BSB is defined from laboratory tests to be 0.31 MPa for s = 10 MPa results in k ∼ 0.031, where k is the slope of ps-line describing an increase of tensile strength with suction. The BBM assumes that ps continues to increase with suction. Considering that for s > s 0 , p o and λ(s) become constant for BSB, ps should also be constant for s > 30 MPa and results in k ∼ 0 for s > 30 MPa. Finally, the yield surface in p-s space under isotropic loading condition (q = 0) has the LC-, ps-, and SI-lines as the boundaries. The results of triaxial tests monitored during shearing with various p-q stress paths and constant suction within a range of 0-125 MPa are used to determine the yield and critical state functions. Two types of failures (e.g., ductile and brittle) depending on the p-s-q stress states were observed. Critical state slopes M interpreted from these test results show that M increases with s for s < s 0 and becomes constant for s > s 0 . The original BBM assumed that M is a constant. Based on this observation, a bi-linear function is created. This function has M increasing with s from ∼ 0.3 to 1.1 for s ∼ 0 to 30 MPa and remaining constant M for s > 30 MPa. This paper introduces two modifications of the original BBM to simulate BSB by creating two bi-linear functions for M and p s (i.e., M = M(s) and k = k(s)). Stress volume relationship in p-s-v space and critical state surface in p-s-q space created using this model match the results of laboratory test results. In some computer codes, implementation of an elastoplastic model requires substitution of p with the Bishop's effective stress (p' = p + S r .s), where S r is the degree of saturation. The magnitude of the BBM parameters in p-q-s space will change when p'-s-v space is used. This paper will discuss the transformation of these parameters. Finally, this set of BBM parameters is used to simulate the H-M behaviour of the BSB in triaxial tests using a computer code (e.g., FLAC

Progress on reference input parameter library for nuclear model calculations of nuclear data (III)

International Nuclear Information System (INIS)

Su Zongdi; Liu Jianfeng; Huang Zhongfu

1997-01-01

A new set of the average neutron resonance spacings D 0 and neutron strength functions S 0 for 309 nuclei were reestimated on the basis of the resolved resonance parameters reevaluated from BNL-325, ENDF/B-6, JEF-2, and JENDL-3, and the cumulative number N 0 of low low lying levels for 344 nuclei were also reevaluated by means of histograms. Three sets of level density parameters for the Gilbert-Cameron (GC) formula, back-shifted Fermi gas model(BS) and generated superfluid model (GSM) have been reesitmated by fitting the D 0 and N 0 values of CENPL.LRD-2

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

Science.gov (United States)

Campbell, D A; Chkrebtii, O

2013-12-01

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

Determination of HCME 3-D parameters using a full ice-cream cone model

Science.gov (United States)

Na, Hyeonock; Moon, Yong-Jae; Lee, Harim

2016-05-01

It is very essential to determine three dimensional parameters (e.g., radial speed, angular width, source location) of Coronal Mass Ejections (CMEs) for space weather forecast. Several cone models (e.g., an elliptical cone model, an ice-cream cone model, an asymmetric cone model) have been examined to estimate these parameters. In this study, we investigate which cone type is close to a halo CME morphology using 26 CMEs: halo CMEs by one spacecraft (SOHO or STEREO-A or B) and as limb CMEs by the other ones. From cone shape parameters of these CMEs such as their front curvature, we find that near full ice-cream cone type CMEs are much closer to observations than shallow ice-cream cone type CMEs. Thus we develop a new cone model in which a full ice-cream cone consists of many flat cones with different heights and angular widths. This model is carried out by the following steps: (1) construct a cone for given height and angular width, (2) project the cone onto the sky plane, (3) select points comprising the outer boundary, and (4) minimize the difference between the estimated projection speeds with the observed ones. By applying this model to 12 SOHO/LASCO halo CMEs, we find that 3-D parameters from our method are similar to those from other stereoscopic methods (a geometrical triangulation method and a Graduated Cylindrical Shell model) based on multi-spacecraft data. We are developing a general ice-cream cone model whose front shape is a free parameter determined by observations.

Parameter Estimation of Partial Differential Equation Models.

Science.gov (United States)

Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

2013-01-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

Habitability Concept Models for Living in Space

Science.gov (United States)

Ferrino, M.

2002-01-01

As growing trends show, living in "space" has acquired new meanings, especially considering the utilization of the International Space Station (ISS) with regard to group interaction as well as individual needs in terms of time, space and crew accommodations. In fact, for the crew, the Spaced Station is a combined Laboratory-Office/Home and embodies ethical, social, and cultural aspects as additional parameters to be assessed to achieve a user centered architectural design of crew workspace. Habitability Concept Models can improve the methods and techniques used to support the interior design and layout of space architectures and at the same time guarantee a human focused approach. This paper discusses and illustrates some of the results obtained for the interior design of a Habitation Module for the ISS. In this work, two different but complementary approaches are followed. The first is "object oriented" and based on Video Data (American and Russian) supported by Proxemic methods (Edward T. Hall, 1963 and Francesca Pregnolato, 1998). This approach offers flexible and adaptive design solutions. The second is "subject oriented" and based on a Virtual Reality environment. With this approach human perception and cognitive aspects related to a specific crew task are considered. Data obtained from these two approaches are used to verify requirements and advance the design of the Habitation Module for aspects related to man machine interfaces (MMI), ergonomics, work and free-time. It is expected that the results achieved can be applied to future space related projects.

Space Environment Modelling with the Use of Artificial Intelligence Methods

Science.gov (United States)

Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

1996-12-01

Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore

Model-based verification method for solving the parameter uncertainty in the train control system

International Nuclear Information System (INIS)

Cheng, Ruijun; Zhou, Jin; Chen, Dewang; Song, Yongduan

2016-01-01

This paper presents a parameter analysis method to solve the parameter uncertainty problem for hybrid system and explore the correlation of key parameters for distributed control system. For improving the reusability of control model, the proposed approach provides the support for obtaining the constraint sets of all uncertain parameters in the abstract linear hybrid automata (LHA) model when satisfying the safety requirements of the train control system. Then, in order to solve the state space explosion problem, the online verification method is proposed to monitor the operating status of high-speed trains online because of the real-time property of the train control system. Furthermore, we construct the LHA formal models of train tracking model and movement authority (MA) generation process as cases to illustrate the effectiveness and efficiency of the proposed method. In the first case, we obtain the constraint sets of uncertain parameters to avoid collision between trains. In the second case, the correlation of position report cycle and MA generation cycle is analyzed under both the normal and the abnormal condition influenced by packet-loss factor. Finally, considering stochastic characterization of time distributions and real-time feature of moving block control system, the transient probabilities of wireless communication process are obtained by stochastic time petri nets. - Highlights: • We solve the parameters uncertainty problem by using model-based method. • We acquire the parameter constraint sets by verifying linear hybrid automata models. • Online verification algorithms are designed to monitor the high-speed trains. • We analyze the correlation of key parameters and uncritical parameters. • The transient probabilities are obtained by using reliability analysis.

Method of solving conformal models in D-dimensional space I

International Nuclear Information System (INIS)

Fradkin, E.S.; Palchik, M.Y.

1996-01-01

We study the Hilbert space of conformal field theory in D-dimensional space. The latter is shown to have model-independent structure. The states of matter fields and gauge fields form orthogonal subspaces. The dynamical principle fixing the choice of model may be formulated either in each of these subspaces or in their direct sum. In the latter case, gauge interactions are necessarily present in the model. We formulate the conditions specifying the class of models where gauge interactions are being neglected. The anomalous Ward identities are derived. Different values of anomalous parameters (D-dimensional analogs of a central charge, including operator ones) correspond to different models. The structure of these models is analogous to that of 2-dimensional conformal theories. Each model is specified by D-dimensional analog of null vector. The exact solutions of the simplest models of this type are examined. It is shown that these models are equivalent to Lagrangian models of scalar fields with a triple interaction. The values of dimensions of such fields are calculated, and the closed sets of differential equations for higher Green functions are derived. Copyright copyright 1996 Academic Press, Inc

Quality assessment for radiological model parameters

International Nuclear Information System (INIS)

Funtowicz, S.O.

1989-01-01

A prototype framework for representing uncertainties in radiological model parameters is introduced. This follows earlier development in this journal of a corresponding framework for representing uncertainties in radiological data. Refinements and extensions to the earlier framework are needed in order to take account of the additional contextual factors consequent on using data entries to quantify model parameters. The parameter coding can in turn feed in to methods for evaluating uncertainties in calculated model outputs. (author)

Modeling nonstationarity in space and time.

Science.gov (United States)

Shand, Lyndsay; Li, Bo

2017-09-01

We propose to model a spatio-temporal random field that has nonstationary covariance structure in both space and time domains by applying the concept of the dimension expansion method in Bornn et al. (2012). Simulations are conducted for both separable and nonseparable space-time covariance models, and the model is also illustrated with a streamflow dataset. Both simulation and data analyses show that modeling nonstationarity in both space and time can improve the predictive performance over stationary covariance models or models that are nonstationary in space but stationary in time. © 2017, The International Biometric Society.

Space debris: modeling and detectability

Science.gov (United States)

Wiedemann, C.; Lorenz, J.; Radtke, J.; Kebschull, C.; Horstmann, A.; Stoll, E.

2017-01-01

High precision orbit determination is required for the detection and removal of space debris. Knowledge of the distribution of debris objects in orbit is necessary for orbit determination by active or passive sensors. The results can be used to investigate the orbits on which objects of a certain size at a certain frequency can be found. The knowledge of the orbital distribution of the objects as well as their properties in accordance with sensor performance models provide the basis for estimating the expected detection rates. Comprehensive modeling of the space debris environment is required for this. This paper provides an overview of the current state of knowledge about the space debris environment. In particular non-cataloged small objects are evaluated. Furthermore, improvements concerning the update of the current space debris model are addressed. The model of the space debris environment is based on the simulation of historical events, such as fragmentations due to explosions and collisions that actually occurred in Earth orbits. The orbital distribution of debris is simulated by propagating the orbits considering all perturbing forces up to a reference epoch. The modeled object population is compared with measured data and validated. The model provides a statistical distribution of space objects, according to their size and number. This distribution is based on the correct consideration of orbital mechanics. This allows for a realistic description of the space debris environment. Subsequently, a realistic prediction can be provided concerning the question, how many pieces of debris can be expected on certain orbits. To validate the model, a software tool has been developed which allows the simulation of the observation behavior of ground-based or space-based sensors. Thus, it is possible to compare the results of published measurement data with simulated detections. This tool can also be used for the simulation of sensor measurement campaigns. It is

The Legion Support for Advanced Parameter-Space Studies on a Grid

National Research Council Canada - National Science Library

Natrajan, Anand; Humphrey, Marty A; Grimshaw, Andrew S

2006-01-01

.... Legion provides tools and services that support advanced parameter-space studies, i.e., studies that make complex demands such as transparent access to distributed files, fault-tolerance and security. We demonstrate these benefits with a protein-folding experiment in which a molecular simulation package was run over a grid managed by Legion.

Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies

Science.gov (United States)

Suh, Peter M.; Conyers, Howard Jason; Mavris, Dimitri N.

2015-01-01

This report introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing-edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio, and number of control surfaces. Using this information, the generalized forces are computed using the doublet-lattice method. Using Roger's approximation, a rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. All parameters can be easily modified if desired. The focus of this report is on tool presentation, verification, and validation. These processes are carried out in stages throughout the report. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool, therefore, the flutter speed and frequency for a clamped plate are computed using damping-versus-velocity and frequency-versus-velocity analysis. The computational results are compared to a previously published computational analysis and wind-tunnel results for the same structure. A case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to damping-versus-velocity and frequency-versus-velocity analysis, including the analysis of the model in response to a 1-cos gust.

Parameter Estimation of Partial Differential Equation Models

KAUST Repository

Xun, Xiaolei

2013-09-01

Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.

Parameter Estimation for Thurstone Choice Models

Energy Technology Data Exchange (ETDEWEB)

Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-04-24

We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.

Planning ahead for asteroid and comet hazard mitigation, phase 1: parameter space exploration and scenario modeling

Energy Technology Data Exchange (ETDEWEB)

Plesko, Catherine S [Los Alamos National Laboratory; Clement, R Ryan [Los Alamos National Laboratory; Weaver, Robert P [Los Alamos National Laboratory; Bradley, Paul A [Los Alamos National Laboratory; Huebner, Walter F [Los Alamos National Laboratory

2009-01-01

The mitigation of impact hazards resulting from Earth-approaching asteroids and comets has received much attention in the popular press. However, many questions remain about the near-term and long-term, feasibility and appropriate application of all proposed methods. Recent and ongoing ground- and space-based observations of small solar-system body composition and dynamics have revolutionized our understanding of these bodies (e.g., Ryan (2000), Fujiwara et al. (2006), and Jedicke et al. (2006)). Ongoing increases in computing power and algorithm sophistication make it possible to calculate the response of these inhomogeneous objects to proposed mitigation techniques. Here we present the first phase of a comprehensive hazard mitigation planning effort undertaken by Southwest Research Institute and Los Alamos National Laboratory. We begin by reviewing the parameter space of the object's physical and chemical composition and trajectory. We then use the radiation hydrocode RAGE (Gittings et al. 2008), Monte Carlo N-Particle (MCNP) radiation transport (see Clement et al., this conference), and N-body dynamics codes to explore the effects these variations in object properties have on the coupling of energy into the object from a variety of mitigation techniques, including deflection and disruption by nuclear and conventional munitions, and a kinetic impactor.

Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0

Directory of Open Access Journals (Sweden)

L. Santos

2018-04-01

Full Text Available In many conceptual rainfall–runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called operator splitting. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall–runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs, which are frequent in rainfall–runoff models and make the resolution of the representation difficult, are first replaced by a so-called Nash cascade and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

Identification of Affine Linear Parameter Varying Models for Adaptive Interventions in Fibromyalgia Treatment.

Science.gov (United States)

Dos Santos, P Lopes; Deshpande, Sunil; Rivera, Daniel E; Azevedo-Perdicoúlis, T-P; Ramos, J A; Younger, Jarred

2013-12-31

There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.

Han's model parameters for microalgae grown under intermittent illumination: Determined using particle swarm optimization.

Science.gov (United States)

Pozzobon, Victor; Perre, Patrick

2018-01-21

This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.

Parameters of electromagnetic weather in near-terrestrial space determining the effects on biosystems

International Nuclear Information System (INIS)

Oraevskij, V.N.; Golyshev, S.A.; Levitin, A.E.; Breus, T.K.; Ivanova, S.V.; Komarov, F.I.; Rapoport, S.I.

1995-01-01

Space and time distribution of the electric and magnetic fields and current systems in the near terrestrial space (electromagnetic weather) were studied in connection with ambulance calls in Moscow, Russia, related to the cardia-vascular diseases. The some examples of the correlations between the solar activity parameters and geomagnetic variations and the events of the extreme number of ambulance calls were presented. 4 refs., 5 figs., 2 tabs

IASM: Individualized activity space modeler

Science.gov (United States)

Hasanzadeh, Kamyar

2018-01-01

Researchers from various disciplines have long been interested in analyzing and describing human mobility patterns. Activity space (AS), defined as an area encapsulating daily human mobility and activities, has been at the center of this interest. However, given the applied nature of research in this field and the complexity that advanced geographical modeling can pose to its users, the proposed models remain simplistic and inaccurate in many cases. Individualized Activity Space Modeler (IASM) is a geographic information system (GIS) toolbox, written in Python programming language using ESRI's Arcpy module, comprising four tools aiming to facilitate the use of advanced activity space models in empirical research. IASM provides individual-based and context-sensitive tools to estimate home range distances, delineate activity spaces, and model place exposures using individualized geographical data. In this paper, we describe the design and functionality of IASM, and provide an example of how it performs on a spatial dataset collected through an online map-based survey.

Are Model Transferability And Complexity Antithetical? Insights From Validation of a Variable-Complexity Empirical Snow Model in Space and Time

Science.gov (United States)

Lute, A. C.; Luce, Charles H.

2017-11-01

The related challenges of predictions in ungauged basins and predictions in ungauged climates point to the need to develop environmental models that are transferable across both space and time. Hydrologic modeling has historically focused on modelling one or only a few basins using highly parameterized conceptual or physically based models. However, model parameters and structures have been shown to change significantly when calibrated to new basins or time periods, suggesting that model complexity and model transferability may be antithetical. Empirical space-for-time models provide a framework within which to assess model transferability and any tradeoff with model complexity. Using 497 SNOTEL sites in the western U.S., we develop space-for-time models of April 1 SWE and Snow Residence Time based on mean winter temperature and cumulative winter precipitation. The transferability of the models to new conditions (in both space and time) is assessed using non-random cross-validation tests with consideration of the influence of model complexity on transferability. As others have noted, the algorithmic empirical models transfer best when minimal extrapolation in input variables is required. Temporal split-sample validations use pseudoreplicated samples, resulting in the selection of overly complex models, which has implications for the design of hydrologic model validation tests. Finally, we show that low to moderate complexity models transfer most successfully to new conditions in space and time, providing empirical confirmation of the parsimony principal.

Parametric cost models for space telescopes

Science.gov (United States)

Stahl, H. Philip; Henrichs, Todd; Dollinger, Courtnay

2017-11-01

Multivariable parametric cost models for space telescopes provide several benefits to designers and space system project managers. They identify major architectural cost drivers and allow high-level design trades. They enable cost-benefit analysis for technology development investment. And, they provide a basis for estimating total project cost. A survey of historical models found that there is no definitive space telescope cost model. In fact, published models vary greatly [1]. Thus, there is a need for parametric space telescopes cost models. An effort is underway to develop single variable [2] and multi-variable [3] parametric space telescope cost models based on the latest available data and applying rigorous analytical techniques. Specific cost estimating relationships (CERs) have been developed which show that aperture diameter is the primary cost driver for large space telescopes; technology development as a function of time reduces cost at the rate of 50% per 17 years; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and increasing mass reduces cost.

Parametric Cost Models for Space Telescopes

Science.gov (United States)

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.

Estimation of k-ε parameters using surrogate models and jet-in-crossflow data

Energy Technology Data Exchange (ETDEWEB)

Lefantzi, Sophia [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ray, Jaideep [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Arunajatesan, Srinivasan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Dechant, Lawrence [Sandia National Lab. (SNL-CA), Livermore, CA (United States)

2014-11-01

We demonstrate a Bayesian method that can be used to calibrate computationally expensive 3D RANS (Reynolds Av- eraged Navier Stokes) models with complex response surfaces. Such calibrations, conditioned on experimental data, can yield turbulence model parameters as probability density functions (PDF), concisely capturing the uncertainty in the parameter estimates. Methods such as Markov chain Monte Carlo (MCMC) estimate the PDF by sampling, with each sample requiring a run of the RANS model. Consequently a quick-running surrogate is used instead to the RANS simulator. The surrogate can be very difficult to design if the model's response i.e., the dependence of the calibration variable (the observable) on the parameter being estimated is complex. We show how the training data used to construct the surrogate can be employed to isolate a promising and physically realistic part of the parameter space, within which the response is well-behaved and easily modeled. We design a classifier, based on treed linear models, to model the "well-behaved region". This classifier serves as a prior in a Bayesian calibration study aimed at estimating 3 k - ε parameters ( C _μ, C _ε2 , C _ε1 ) from experimental data of a transonic jet-in-crossflow interaction. The robustness of the calibration is investigated by checking its predictions of variables not included in the cal- ibration data. We also check the limit of applicability of the calibration by testing at off-calibration flow regimes. We find that calibration yield turbulence model parameters which predict the flowfield far better than when the nomi- nal values of the parameters are used. Substantial improvements are still obtained when we use the calibrated RANS model to predict jet-in-crossflow at Mach numbers and jet strengths quite different from those used to generate the ex- perimental (calibration) data. Thus the primary reason for poor predictive skill of RANS, when using nominal

Automated parameter estimation for biological models using Bayesian statistical model checking.

Science.gov (United States)

Hussain, Faraz; Langmead, Christopher J; Mi, Qi; Dutta-Moscato, Joyeeta; Vodovotz, Yoram; Jha, Sumit K

2015-01-01

Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. We have developed a new algorithmic technique for discovering parameters in complex stochastic models of biological systems given behavioral specifications written in a formal mathematical logic. Our algorithm uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to automatically synthesize parameters of probabilistic biological models.

MODIS/Terra+Aqua BRDF/Albedo Model Parameters 16-Day L3 Global 1km SIN Grid V005

Data.gov (United States)

National Aeronautics and Space Administration — The MODerate-resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters product (MCD43B1) contains three-dimensional (3D) data sets providing users...

My Life with State Space Models

DEFF Research Database (Denmark)

Lundbye-Christensen, Søren

2007-01-01

. The conceptual idea behind the state space model is that the evolution over time in the object we are observing and the measurement process itself are modelled separately. My very first serious analysis of a data set was done using a state space model, and since then I seem to have been "haunted" by state space...

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models.

Science.gov (United States)

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

Science.gov (United States)

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation

Triple Junction InGaP/GaAs/Ge Solar Cell Optimization: The Design Parameters for a 36.2% Efficient Space Cell Using Silvaco ATLAS Modeling & Simulation

OpenAIRE

Tsutagawa, Michael H.; Michael, Sherif

2009-01-01

This paper presents the design parameters for a triple junction InGaP/GaAs/Ge space solar cell with a simulated maximum efficiency of 36.28% using Silvaco ATLAS Virtual Wafer Fabrication tool. Design parameters include the layer material, doping concentration, and thicknesses.

The dynamics of blood biochemical parameters in cosmonauts during long-term space flights

Science.gov (United States)

Markin, Andrei; Strogonova, Lubov; Balashov, Oleg; Polyakov, Valery; Tigner, Timoty

Most of the previously obtained data on cosmonauts' metabolic state concerned certain stages of the postflight period. In this connection, all conclusions, as to metabolism peculiarities during the space flight, were to a large extent probabilistic. The purpose of this work was study of metabolism characteristics in cosmonauts directly during long-term space flights. In the capillary blood samples taken from a finger, by "Reflotron IV" biochemical analyzer, "Boehringer Mannheim" GmbH, Germany, adapted to weightlessness environments, the activity of GOT, GPT, CK, gamma-GT, total and pancreatic amylase, as well as concentration of hemoglobin, glucose, total bilirubin, uric acid, urea, creatinine, total, HDL- and LDL cholesterol, triglycerides had been determined. HDL/LDL-cholesterol ratio also was computed. The crewmembers of 6 main missions to the "Mir" orbital station, a total of 17 cosmonauts, were examined. Biochemical tests were carryed out 30-60 days before lounch, and in the flights different stages between the 25-th and the 423-rd days of flights. In cosmonauts during space flight had been found tendency to increase, in compare with basal level, GOT, GPT, total amylase activity, glucose and total cholesterol concentration, and tendency to decrease of CK activity, hemoglobin, HDL-cholesterol concentration, and HDL/LDL — cholesterol ratio. Some definite trends in variations of other determined biochemical parameters had not been found. The same trends of mentioned biochemical parameters alterations observed in majority of tested cosmonauts, allows to suppose existence of connection between noted metabolic alterations with influence of space flight conditions upon cosmonaut's body. Variations of other studied blood biochemical parameters depends on, probably, pure individual causes.

The modified turning bands (MTB) model for space-time rainfall. I. Model definition and properties

Science.gov (United States)

Mellor, Dale

1996-02-01

A new stochastic model of space-time rainfall, the Modified Turning Bands (MTB) model, is proposed which reproduces, in particular, the movements and developments of rainbands, cluster potential regions and raincells, as well as their respective interactions. The ensemble correlation structure is unsuitable for practical estimation of the model parameters because the model is not ergodic in this statistic, and hence it cannot easily be measured from a single real storm. Thus, some general theory on the internal covariance structure of a class of stochastic models is presented, of which the MTB model is an example. It is noted that, for the MTB model, the internal covariance structure may be measured from a single storm, and can thus be used for model identification.

EXTRACTION OF SPATIAL PARAMETERS FROM CLASSIFIED LIDAR DATA AND AERIAL PHOTOGRAPH FOR SOUND MODELING

Directory of Open Access Journals (Sweden)

S. Biswas

2012-07-01

Full Text Available Prediction of outdoor sound levels in 3D space is important for noise management, soundscaping etc. Sound levels at outdoor can be predicted using sound propagation models which need terrain parameters. The existing practices of incorporating terrain parameters into models are often limited due to inadequate data or inability to determine accurate sound transmission paths through a terrain. This leads to poor accuracy in modelling. LIDAR data and Aerial Photograph (or Satellite Images provide opportunity to incorporate high resolution data into sound models. To realize this, identification of building and other objects and their use for extraction of terrain parameters are fundamental. However, development of a suitable technique, to incorporate terrain parameters from classified LIDAR data and Aerial Photograph, for sound modelling is a challenge. Determination of terrain parameters along various transmission paths of sound from sound source to a receiver becomes very complex in an urban environment due to the presence of varied and complex urban features. This paper presents a technique to identify the principal paths through which sound transmits from source to receiver. Further, the identified principal paths are incorporated inside the sound model for sound prediction. Techniques based on plane cutting and line tracing are developed for determining principal paths and terrain parameters, which use various information, e.g., building corner and edges, triangulated ground, tree points and locations of source and receiver. The techniques developed are validated through a field experiment. Finally efficacy of the proposed technique is demonstrated by developing a noise map for a test site.

Sensitivity analysis of specific activity model parameters for environmental transport of 3H and dose assessment

International Nuclear Information System (INIS)

Rout, S.; Mishra, D.G.; Ravi, P.M.; Tripathi, R.M.

2016-01-01

Tritium is one of the radionuclides likely to get released to the environment from Pressurized Heavy Water Reactors. Environmental models are extensively used to quantify the complex environmental transport processes of radionuclides and also to assess the impact to the environment. Model parameters exerting the significant influence on model results are identified through a sensitivity analysis (SA). SA is the study of how the variation (uncertainty) in the output of a mathematical model can be apportioned, qualitatively or quantitatively, to different sources of variation in the input parameters. This study was designed to identify the sensitive model parameters of specific activity model (TRS 1616, IAEA) for environmental transfer of 3 H following release to air and then to vegetation and animal products. Model includes parameters such as air to soil transfer factor (CRs), Tissue Free Water 3 H to Organically Bound 3 H ratio (Rp), Relative humidity (RH), WCP (fractional water content) and WEQp (water equivalent factor) any change in these parameters leads to change in 3 H level in vegetation and animal products consequently change in dose due to ingestion. All these parameters are function of climate and/or plant which change with time, space and species. Estimation of these parameters at every time is a time consuming and also required sophisticated instrumentation. Therefore it is necessary to identify the sensitive parameters and freeze the values of least sensitive parameters at constant values for more accurate estimation of 3 H dose in short time for routine assessment

The influence of model parameters on catchment-response

International Nuclear Information System (INIS)

Shah, S.M.S.; Gabriel, H.F.; Khan, A.A.

2002-01-01

This paper deals with the study of influence of influence of conceptual rainfall-runoff model parameters on catchment response (runoff). A conceptual modified watershed yield model is employed to study the effects of model-parameters on catchment-response, i.e. runoff. The model is calibrated, using manual parameter-fitting approach, also known as trial and error parameter-fitting. In all, there are twenty one (21) parameters that control the functioning of the model. A lumped parametric approach is used. The detailed analysis was performed on Ling River near Kahuta, having catchment area of 56 sq. miles. The model includes physical parameters like GWSM, PETS, PGWRO, etc. fitting coefficients like CINF, CGWS, etc. and initial estimates of the surface-water and groundwater storages i.e. srosp and gwsp. Sensitivity analysis offers a good way, without repetititious computations, the proper weight and consideration that must be taken when each of the influencing factor is evaluated. Sensitivity-analysis was performed to evaluate the influence of model-parameters on runoff. The sensitivity and relative contributions of model parameters influencing catchment-response are studied. (author)

Parameter Sensitivity of High–Order Equivalent Circuit Models Of Turbine Generator

Directory of Open Access Journals (Sweden)

T. Niewierowicz–Swiecicka

2010-01-01

Full Text Available This work shows the results of a parametric sensitivity analysis applied to a state–space representation of high–order two–axis equivalent circuits (ECs of a turbo generator (150 MVA, 120 MW, 13.8 kV y 50 Hz. The main purpose of this study is to evaluate each parameter impact on the transient response of the analyzed two–axis models –d–axis ECs with one to five damper branches and q–axis ECs from one to four damper branches–. The parametric sensitivity concept is formulated in a general context and the sensibility function is established from the generator response to a short circuit condition. Results ponder the importance played by each parameter in the model behavior. The algorithms were design within MATLAB® environment. The study gives way to conclusions on electromagnetic aspects of solid rotor synchronous generators that have not been previously studied. The methodology presented here can be applied to any other physical system.

Parameter optimization of thermal-model-oriented control law for PEM fuel cell stack via novel genetic algorithm

International Nuclear Information System (INIS)

Li Xi; Deng Zhonghua; Wei Dong; Xu Chunshan; Cao Guangyi

2011-01-01

Highlights: →We build up the thermal expressions of PEMFC stack. → The expressions are converted into the affine state space control-oriented model for the VSC strategy. → The NGA is developed to optimize the parameter of thermal-model-oriented control law. → Numerical results demonstrate the effectiveness and rationality of the method proposed. - Abstract: It is critical to understand and manage the thermal effects in optimizing the performance and durability of proton exchange membrane fuel cell (PEMFC) stack. And building up the control-oriented thermal model of PEMFC stack is necessary. The thermal model, a set of differential equations, is established according to the conservation equations of mass and energy, which can be used to reflect truly the actual temperature response of PEMFC stack, however, the expressions of the model are too complicated to be used in the design of control. For this reason, the expressions are converted into the affine state space control-oriented model in detail for the variable structure control (VSC) strategy. Meanwhile, the accurate model must be established for the VSC and the parameters of VSC laws should be optimized. Consequently, a novel genetic algorithm (NGA) is developed to optimize the parameter of thermal-model-oriented control law for PEMFC stack. Finally, numerical test results demonstrate the effectiveness and rationality of the method proposed in this paper. It lays the foundation for the realization of online thermal management of PEMFC stack based on VSC.

On parameter estimation in deformable models

DEFF Research Database (Denmark)

Fisker, Rune; Carstensen, Jens Michael

1998-01-01

Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian form...

Cost Modeling for Space Telescope

Science.gov (United States)

Stahl, H. Philip

2011-01-01

Parametric cost models are an important tool for planning missions, compare concepts and justify technology investments. This paper presents on-going efforts to develop single variable and multi-variable cost models for space telescope optical telescope assembly (OTA). These models are based on data collected from historical 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.

Complexity, parameter sensitivity and parameter transferability in the modelling of floodplain inundation

Science.gov (United States)

Bates, P. D.; Neal, J. C.; Fewtrell, T. J.

2012-12-01

In this we paper we consider two related questions. First, we address the issue of how much physical complexity is necessary in a model in order to simulate floodplain inundation to within validation data error. This is achieved through development of a single code/multiple physics hydraulic model (LISFLOOD-FP) where different degrees of complexity can be switched on or off. Different configurations of this code are applied to four benchmark test cases, and compared to the results of a number of industry standard models. Second we address the issue of how parameter sensitivity and transferability change with increasing complexity using numerical experiments with models of different physical and geometric intricacy. Hydraulic models are a good example system with which to address such generic modelling questions as: (1) they have a strong physical basis; (2) there is only one set of equations to solve; (3) they require only topography and boundary conditions as input data; and (4) they typically require only a single free parameter, namely boundary friction. In terms of complexity required we show that for the problem of sub-critical floodplain inundation a number of codes of different dimensionality and resolution can be found to fit uncertain model validation data equally well, and that in this situation Occam's razor emerges as a useful logic to guide model selection. We find also find that model skill usually improves more rapidly with increases in model spatial resolution than increases in physical complexity, and that standard approaches to testing hydraulic models against laboratory data or analytical solutions may fail to identify this important fact. Lastly, we find that in benchmark testing studies significant differences can exist between codes with identical numerical solution techniques as a result of auxiliary choices regarding the specifics of model implementation that are frequently unreported by code developers. As a consequence, making sound

Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver

Science.gov (United States)

Kang, Ling; Zhou, Liwei

2018-02-01

Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.

Nonlinear Prediction As A Tool For Determining Parameters For Phase Space Reconstruction In Meteorology

Science.gov (United States)

Miksovsky, J.; Raidl, A.

Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.

Preliminary Cost Model for Space Telescopes

Science.gov (United States)

Stahl, H. Philip; Prince, F. Andrew; Smart, Christian; Stephens, Kyle; Henrichs, Todd

2009-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. However, great care is required. Some space telescope cost models, such as those based only on mass, lack sufficient detail to support such analysis and may lead to inaccurate conclusions. Similarly, using ground based telescope models which include the dome cost will also lead to inaccurate conclusions. This paper reviews current and historical models. Then, based on data from 22 different NASA space telescopes, this paper tests those models and presents preliminary analysis of single and multi-variable space telescope cost models.

Dynamical quantum Hall effect in the parameter space.

Science.gov (United States)

Gritsev, V; Polkovnikov, A

2012-04-24

Geometric phases in quantum mechanics play an extraordinary role in broadening our understanding of fundamental significance of geometry in nature. One of the best known examples is the Berry phase [M.V. Berry (1984), Proc. Royal. Soc. London A, 392:45], which naturally emerges in quantum adiabatic evolution. So far the applicability and measurements of the Berry phase were mostly limited to systems of weakly interacting quasi-particles, where interference experiments are feasible. Here we show how one can go beyond this limitation and observe the Berry curvature, and hence the Berry phase, in generic systems as a nonadiabatic response of physical observables to the rate of change of an external parameter. These results can be interpreted as a dynamical quantum Hall effect in a parameter space. The conventional quantum Hall effect is a particular example of the general relation if one views the electric field as a rate of change of the vector potential. We illustrate our findings by analyzing the response of interacting spin chains to a rotating magnetic field. We observe the quantization of this response, which we term the rotational quantum Hall effect.

An accelerated life test model for harmonic drives under a segmental stress history and its parameter optimization

Directory of Open Access Journals (Sweden)

Zhang Chao

2015-12-01

Full Text Available Harmonic drives have various distinctive advantages and are widely used in space drive mechanisms. Accelerated life test (ALT is commonly conducted to shorten test time and reduce associated costs. An appropriate ALT model is needed to predict the lifetime of harmonic drives with ALT data. However, harmonic drives which are used in space usually work under a segmental stress history, and traditional ALT models can hardly be used in this situation. This paper proposes a dedicated ALT model for harmonic drives applied in space systems. A comprehensive ALT model is established and genetic algorithm (GA is adopted to obtain optimal parameters in the model using the Manson fatigue damage rule to describe the fatigue failure process and a cumulative damage method to calculate and accumulate the damage caused by each segment in the stress history. An ALT of harmonic drives was carried out and experimental results show that this model is acceptable and effective.

Space Station Freedom (SSF) Data Management System (DMS) performance model data base

Science.gov (United States)

Stovall, John R.

1993-01-01

The purpose of this document was originally to be a working document summarizing Space Station Freedom (SSF) Data Management System (DMS) hardware and software design, configuration, performance and estimated loading data from a myriad of source documents such that the parameters provided could be used to build a dynamic performance model of the DMS. The document is published at this time as a close-out of the DMS performance modeling effort resulting from the Clinton Administration mandated Space Station Redesign. The DMS as documented in this report is no longer a part of the redesigned Space Station. The performance modeling effort was a joint undertaking between the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) Flight Data Systems Division (FDSD) and the NASA Ames Research Center (ARC) Spacecraft Data Systems Research Branch. The scope of this document is limited to the DMS core network through the Man Tended Configuration (MTC) as it existed prior to the 1993 Clinton Administration mandated Space Station Redesign. Data is provided for the Standard Data Processors (SDP's), Multiplexer/Demultiplexers (MDM's) and Mass Storage Units (MSU's). Planned future releases would have added the additional hardware and software descriptions needed to describe the complete DMS. Performance and loading data through the Permanent Manned Configuration (PMC) was to have been included as it became available. No future releases of this document are presently planned pending completion of the present Space Station Redesign activities and task reassessment.

Effects of OCR Errors on Ranking and Feedback Using the Vector Space Model.

Science.gov (United States)

Taghva, Kazem; And Others

1996-01-01

Reports on the performance of the vector space model in the presence of OCR (optical character recognition) errors in information retrieval. Highlights include precision and recall, a full-text test collection, smart vector representation, impact of weighting parameters, ranking variability, and the effect of relevance feedback. (Author/LRW)

Simultaneous fitting of statistical-model parameters to symmetric and asymmetric fission cross sections

International Nuclear Information System (INIS)

Mancusi, D; Charity, R J; Cugnon, J

2013-01-01

The de-excitation of compound nuclei has been successfully described for several decades by means of statistical models. However, accurate predictions require some fine-tuning of the model parameters. This task can be simplified by studying several entrance channels, which populate different regions of the parameter space of the compound nucleus. Fusion reactions play an important role in this strategy because they minimise the uncertainty on the entrance channel by fixing mass, charge and excitation energy of the compound nucleus. If incomplete fusion is negligible, the only uncertainty on the compound nucleus comes from the spin distribution. However, some de-excitation channels, such as fission, are quite sensitive to spin. Other entrance channels can then be used to discriminate between equivalent parameter sets. The focus of this work is on fission and intermediate-mass-fragment emission cross sections of compound nuclei with 70 70 ≲ A ≲ 240. 240. The statistical de-excitation model is GEMINI++. The choice of the observables is natural in the framework of GEMINI++, which describes fragment emission using a fissionlike formalism. Equivalent parameter sets for fusion reactions can be resolved using the spallation entrance channel. This promising strategy can lead to the identification of a minimal set of physical ingredients necessary for a unified quantitative description of nuclear de-excitation.

Multiobjective constraints for climate model parameter choices: Pragmatic Pareto fronts in CESM1

Science.gov (United States)

Langenbrunner, B.; Neelin, J. D.

2017-09-01

Global climate models (GCMs) are examples of high-dimensional input-output systems, where model output is a function of many variables, and an update in model physics commonly improves performance in one objective function (i.e., measure of model performance) at the expense of degrading another. Here concepts from multiobjective optimization in the engineering literature are used to investigate parameter sensitivity and optimization in the face of such trade-offs. A metamodeling technique called cut high-dimensional model representation (cut-HDMR) is leveraged in the context of multiobjective optimization to improve GCM simulation of the tropical Pacific climate, focusing on seasonal precipitation, column water vapor, and skin temperature. An evolutionary algorithm is used to solve for Pareto fronts, which are surfaces in objective function space along which trade-offs in GCM performance occur. This approach allows the modeler to visualize trade-offs quickly and identify the physics at play. In some cases, Pareto fronts are small, implying that trade-offs are minimal, optimal parameter value choices are more straightforward, and the GCM is well-functioning. In all cases considered here, the control run was found not to be Pareto-optimal (i.e., not on the front), highlighting an opportunity for model improvement through objectively informed parameter selection. Taylor diagrams illustrate that these improvements occur primarily in field magnitude, not spatial correlation, and they show that specific parameter updates can improve fields fundamental to tropical moist processes—namely precipitation and skin temperature—without significantly impacting others. These results provide an example of how basic elements of multiobjective optimization can facilitate pragmatic GCM tuning processes.

Parameter identification in ODE models with oscillatory dynamics: a Fourier regularization approach

Science.gov (United States)

Chiara D'Autilia, Maria; Sgura, Ivonne; Bozzini, Benedetto

2017-12-01

In this paper we consider a parameter identification problem (PIP) for data oscillating in time, that can be described in terms of the dynamics of some ordinary differential equation (ODE) model, resulting in an optimization problem constrained by the ODEs. In problems with this type of data structure, simple application of the direct method of control theory (discretize-then-optimize) yields a least-squares cost function exhibiting multiple ‘low’ minima. Since in this situation any optimization algorithm is liable to fail in the approximation of a good solution, here we propose a Fourier regularization approach that is able to identify an iso-frequency manifold {{ S}} of codimension-one in the parameter space \

New space--time model for hadron--nucleus collisions at high energies

International Nuclear Information System (INIS)

Bialkowski, G.; Chiu, C.B.; Tow, D.M.

1976-12-01

A new space-time model for hadron-nucleus collisions is proposed, where particles at the instant of creation are immature and their maturity rate is enhanced in the presence of other hadronic matter, as in a nucleus. With only one free parameter, the model can explain dn/sub A//sup p//d eta, dn/sub A//sup pi//d eta, R/sub A//sup p/(E/sub L/), and the A-dependences of sigma/sub in/sup pA/ and sigma/sub in/sup pi A/

A hybrid method of estimating pulsating flow parameters in the space-time domain

Science.gov (United States)

Pałczyński, Tomasz

2017-05-01

This paper presents a method for estimating pulsating flow parameters in partially open pipes, such as pipelines, internal combustion engine inlets, exhaust pipes and piston compressors. The procedure is based on the method of characteristics, and employs a combination of measurements and simulations. An experimental test rig is described, which enables pressure, temperature and mass flow rate to be measured within a defined cross section. The second part of the paper discusses the main assumptions of a simulation algorithm elaborated in the Matlab/Simulink environment. The simulation results are shown as 3D plots in the space-time domain, and compared with proposed models of phenomena relating to wave propagation, boundary conditions, acoustics and fluid mechanics. The simulation results are finally compared with acoustic phenomena, with an emphasis on the identification of resonant frequencies.

Classical algorithms for automated parameter-search methods in compartmental neural models - A critical survey based on simulations using neuron

International Nuclear Information System (INIS)

Mutihac, R.; Mutihac, R.C.; Cicuttin, A.

2001-09-01

gradient-descent techniques are adequate if the parameter space is low-dimensional, relatively smooth, and has a few local minima (e.g., parameterizing single-neuron compartmental models). Only the fast algorithms and/or a decent (low) number of model parameters are candidates for automated parameter search because of practical reasons. Eventually, the size of the parameter space may be reduced and/or parallel supercomputers may be used. Data overfitting may negatively affect the generalization ability of the model. Bayesian methods include Occam's factor, which set the preference for simpler models. Proliferation of (neural) models raises the question of rigorous criteria for comparing the overall performance of various models designed to match the same type of data. Bayesian methods provide the best framework to assess the neural models quantitatively. Paradoxically, parameter-search methods may sometimes be more useful when they fail by discarding unrealistic mechanisms used in the model design, rather than fitting experimental data to an alleged model

An optical flow-based state-space model of the vocal folds

DEFF Research Database (Denmark)

Granados, Alba; Brunskog, Jonas

2017-01-01

High-speed movies of the vocal fold vibration are valuable data to reveal vocal fold features for voice pathology diagnosis. This work presents a suitable Bayesian model and a purely theoretical discussion for further development of a framework for continuum biomechanical features estimation. A l...... to capture different deformation patterns between the computed optical flow and the finite element deformation, controlled by the choice of the model tissue parameters........ A linear and Gaussian nonstationary state-space model is proposed and thoroughly discussed. The evolution model is based on a self-sustained three-dimensional finite element model of the vocal folds, and the observation model involves a dense optical flow algorithm. The results show that the method is able...

An optical flow-based state-space model of the vocal folds.

Science.gov (United States)

Granados, Alba; Brunskog, Jonas

2017-06-01

High-speed movies of the vocal fold vibration are valuable data to reveal vocal fold features for voice pathology diagnosis. This work presents a suitable Bayesian model and a purely theoretical discussion for further development of a framework for continuum biomechanical features estimation. A linear and Gaussian nonstationary state-space model is proposed and thoroughly discussed. The evolution model is based on a self-sustained three-dimensional finite element model of the vocal folds, and the observation model involves a dense optical flow algorithm. The results show that the method is able to capture different deformation patterns between the computed optical flow and the finite element deformation, controlled by the choice of the model tissue parameters.

Logic-based models in systems biology: a predictive and parameter-free network analysis method.

Science.gov (United States)

Wynn, Michelle L; Consul, Nikita; Merajver, Sofia D; Schnell, Santiago

2012-11-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network's dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples.

Parametric modeling of the intervertebral disc space in 3D: application to CT images of the lumbar spine.

Science.gov (United States)

Korez, Robert; Likar, Boštjan; Pernuš, Franjo; Vrtovec, Tomaž

2014-10-01

Gradual degeneration of intervertebral discs of the lumbar spine is one of the most common causes of low back pain. Although conservative treatment for low back pain may provide relief to most individuals, surgical intervention may be required for individuals with significant continuing symptoms, which is usually performed by replacing the degenerated intervertebral disc with an artificial implant. For designing implants with good bone contact and continuous force distribution, the morphology of the intervertebral disc space and vertebral body endplates is of considerable importance. In this study, we propose a method for parametric modeling of the intervertebral disc space in three dimensions (3D) and show its application to computed tomography (CT) images of the lumbar spine. The initial 3D model of the intervertebral disc space is generated according to the superquadric approach and therefore represented by a truncated elliptical cone, which is initialized by parameters obtained from 3D models of adjacent vertebral bodies. In an optimization procedure, the 3D model of the intervertebral disc space is incrementally deformed by adding parameters that provide a more detailed morphometric description of the observed shape, and aligned to the observed intervertebral disc space in the 3D image. By applying the proposed method to CT images of 20 lumbar spines, the shape and pose of each of the 100 intervertebral disc spaces were represented by a 3D parametric model. The resulting mean (±standard deviation) accuracy of modeling was 1.06±0.98mm in terms of radial Euclidean distance against manually defined ground truth points, with the corresponding success rate of 93% (i.e. 93 out of 100 intervertebral disc spaces were modeled successfully). As the resulting 3D models provide a description of the shape of intervertebral disc spaces in a complete parametric form, morphometric analysis was straightforwardly enabled and allowed the computation of the corresponding

International Space Station Model Correlation Analysis

Science.gov (United States)

Laible, Michael R.; Fitzpatrick, Kristin; Hodge, Jennifer; Grygier, Michael

2018-01-01

This paper summarizes the on-orbit structural dynamic data and the related modal analysis, model validation and correlation performed for the International Space Station (ISS) configuration ISS Stage ULF7, 2015 Dedicated Thruster Firing (DTF). The objective of this analysis is to validate and correlate the analytical models used to calculate the ISS internal dynamic loads and compare the 2015 DTF with previous tests. During the ISS configurations under consideration, on-orbit dynamic measurements were collected using the three main ISS instrumentation systems; Internal Wireless Instrumentation System (IWIS), External Wireless Instrumentation System (EWIS) and the Structural Dynamic Measurement System (SDMS). The measurements were recorded during several nominal on-orbit DTF tests on August 18, 2015. Experimental modal analyses were performed on the measured data to extract modal parameters including frequency, damping, and mode shape information. Correlation and comparisons between test and analytical frequencies and mode shapes were performed to assess the accuracy of the analytical models for the configurations under consideration. These mode shapes were also compared to earlier tests. Based on the frequency comparisons, the accuracy of the mathematical models is assessed and model refinement recommendations are given. In particular, results of the first fundamental mode will be discussed, nonlinear results will be shown, and accelerometer placement will be assessed.

Reconciling Planck with the local value of H0 in extended parameter space

Directory of Open Access Journals (Sweden)

Eleonora Di Valentino

2016-10-01

Full Text Available The recent determination of the local value of the Hubble constant by Riess et al., 2016 (hereafter R16 is now 3.3 sigma higher than the value derived from the most recent CMB anisotropy data provided by the Planck satellite in a ΛCDM model. Here we perform a combined analysis of the Planck and R16 results in an extended parameter space, varying simultaneously 12 cosmological parameters instead of the usual 6. We find that a phantom-like dark energy component, with effective equation of state w=−1.29−0.12+0.15 at 68% c.l. can solve the current tension between the Planck dataset and the R16 prior in an extended ΛCDM scenario. On the other hand, the neutrino effective number is fully compatible with standard expectations. This result is confirmed when including cosmic shear data from the CFHTLenS survey and CMB lensing constraints from Planck. However, when BAO measurements are included we find that some of the tension with R16 remains, as also is the case when we include the supernova type Ia luminosity distances from the JLA catalog.

Universally sloppy parameter sensitivities in systems biology models.

Directory of Open Access Journals (Sweden)

Ryan N Gutenkunst

2007-10-01

Full Text Available Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Universally sloppy parameter sensitivities in systems biology models.

Science.gov (United States)

Gutenkunst, Ryan N; Waterfall, Joshua J; Casey, Fergal P; Brown, Kevin S; Myers, Christopher R; Sethna, James P

2007-10-01

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

Harmonic Instability Assessment Using State-Space Modeling and Participation Analysis in Inverter-Fed Power Systems

DEFF Research Database (Denmark)

Wang, Yanbo; Wang, Xiongfei; Blaabjerg, Frede

2017-01-01

parameters on the harmonic instability of the power system. Moreover, the harmonic-frequency oscillation modes are identified, where participation analysis is presented to evaluate the contributions of different states to these modes and to further reveal how the system gives rise to harmonic instability......This paper presents a harmonic instability analysis method using state-space modeling and participation analysis in the inverter-fed ac power systems. A full-order state-space model for the droop-controlled Distributed Generation (DG) inverter is built first, including the time delay of the digital...... control system, inner current and voltage control loops, and outer droop-based power control loop. Based on the DG inverter model, an overall state-space model of a two-inverter-fed system is established. The eigenvalue-based stability analysis is then presented to assess the influence of controller...

Model-based thermal system design optimization for the James Webb Space Telescope

Science.gov (United States)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-10-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

Directory of Open Access Journals (Sweden)

Jonathan R Karr

2015-05-01

Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

Science.gov (United States)

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Quantum sensing of the phase-space-displacement parameters using a single trapped ion

Science.gov (United States)

Ivanov, Peter A.; Vitanov, Nikolay V.

2018-03-01

We introduce a quantum sensing protocol for detecting the parameters characterizing the phase-space displacement by using a single trapped ion as a quantum probe. We show that, thanks to the laser-induced coupling between the ion's internal states and the motion mode, the estimation of the two conjugated parameters describing the displacement can be efficiently performed by a set of measurements of the atomic state populations. Furthermore, we introduce a three-parameter protocol capable of detecting the magnitude, the transverse direction, and the phase of the displacement. We characterize the uncertainty of the two- and three-parameter problems in terms of the Fisher information and show that state projective measurement saturates the fundamental quantum Cramér-Rao bound.

Exploring parameter constraints on quintessential dark energy: The exponential model

International Nuclear Information System (INIS)

Bozek, Brandon; Abrahamse, Augusta; Albrecht, Andreas; Barnard, Michael

2008-01-01

We present an analysis of a scalar field model of dark energy with an exponential potential using the Dark Energy Task Force (DETF) simulated data models. Using Markov Chain Monte Carlo sampling techniques we examine the ability of each simulated data set to constrain the parameter space of the exponential potential for data sets based on a cosmological constant and a specific exponential scalar field model. We compare our results with the constraining power calculated by the DETF using their 'w 0 -w a ' parametrization of the dark energy. We find that respective increases in constraining power from one stage to the next produced by our analysis give results consistent with DETF results. To further investigate the potential impact of future experiments, we also generate simulated data for an exponential model background cosmology which cannot be distinguished from a cosmological constant at DETF 'Stage 2', and show that for this cosmology good DETF Stage 4 data would exclude a cosmological constant by better than 3σ

Test models for improving filtering with model errors through stochastic parameter estimation

International Nuclear Information System (INIS)

Gershgorin, B.; Harlim, J.; Majda, A.J.

2010-01-01

The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.

On the identifiability of inertia parameters of planar Multi-Body Space Systems

Science.gov (United States)

Nabavi-Chashmi, Seyed Yaser; Malaek, Seyed Mohammad-Bagher

2018-04-01

This work describes a new formulation to study the identifiability characteristics of Serially Linked Multi-body Space Systems (SLMBSS). The process exploits the so called "Lagrange Formulation" to develop a linear form of Equations of Motion w.r.t the system Inertia Parameters (IPs). Having developed a specific form of regressor matrix, we aim to expedite the identification process. The new approach allows analytical as well as numerical identification and identifiability analysis for different SLMBSSs' configurations. Moreover, the explicit forms of SLMBSSs identifiable parameters are derived by analyzing the identifiability characteristics of the robot. We further show that any SLMBSS designed with Variable Configurations Joint allows all IPs to be identifiable through comparing two successive identification outcomes. This feature paves the way to design new class of SLMBSS for which accurate identification of all IPs is at hand. Different case studies reveal that proposed formulation provides fast and accurate results, as required by the space applications. Further studies might be necessary for cases where planar-body assumption becomes inaccurate.

A Two-Phase Space Resection Model for Accurate Topographic Reconstruction from Lunar Imagery with PushbroomScanners.

Science.gov (United States)

Xu, Xuemiao; Zhang, Huaidong; Han, Guoqiang; Kwan, Kin Chung; Pang, Wai-Man; Fang, Jiaming; Zhao, Gansen

2016-04-11

Exterior orientation parameters' (EOP) estimation using space resection plays an important role in topographic reconstruction for push broom scanners. However, existing models of space resection are highly sensitive to errors in data. Unfortunately, for lunar imagery, the altitude data at the ground control points (GCPs) for space resection are error-prone. Thus, existing models fail to produce reliable EOPs. Motivated by a finding that for push broom scanners, angular rotations of EOPs can be estimated independent of the altitude data and only involving the geographic data at the GCPs, which are already provided, hence, we divide the modeling of space resection into two phases. Firstly, we estimate the angular rotations based on the reliable geographic data using our proposed mathematical model. Then, with the accurate angular rotations, the collinear equations for space resection are simplified into a linear problem, and the global optimal solution for the spatial position of EOPs can always be achieved. Moreover, a certainty term is integrated to penalize the unreliable altitude data for increasing the error tolerance. Experimental results evidence that our model can obtain more accurate EOPs and topographic maps not only for the simulated data, but also for the real data from Chang'E-1, compared to the existing space resection model.

Higgs boson resonance parameters and the finite temperature phase transition in a chirally invariant Higgs-Yukawa model

Energy Technology Data Exchange (ETDEWEB)

Bulava, John; Jansen, Karl [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Gerhold, Philip; Kallarackal, Jim; Nagy, Attila [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany). John von Neumann-Inst. fuer Computing NIC; Humbolt-Univ. Berlin (Germany)

2011-12-15

We study a chirally invariant Higgs-Yukawa model regulated on a space-time lattice. We calculate Higgs boson resonance parameters and mass bounds for various values of the mass of the degenerate fermion doublet. Also, first results on the phase transition temperature are presented. In general, this model may be relevant for BSM scenarios with a heavy fourth generation of quarks. (orig.)

Qualitative models for space system engineering

Science.gov (United States)

Forbus, Kenneth D.

1990-01-01

The objectives of this project were: (1) to investigate the implications of qualitative modeling techniques for problems arising in the monitoring, diagnosis, and design of Space Station subsystems and procedures; (2) to identify the issues involved in using qualitative models to enhance and automate engineering functions. These issues include representing operational criteria, fault models, alternate ontologies, and modeling continuous signals at a functional level of description; and (3) to develop a prototype collection of qualitative models for fluid and thermal systems commonly found in Space Station subsystems. Potential applications of qualitative modeling to space-systems engineering, including the notion of intelligent computer-aided engineering are summarized. Emphasis is given to determining which systems of the proposed Space Station provide the most leverage for study, given the current state of the art. Progress on using qualitative models, including development of the molecular collection ontology for reasoning about fluids, the interaction of qualitative and quantitative knowledge in analyzing thermodynamic cycles, and an experiment on building a natural language interface to qualitative reasoning is reported. Finally, some recommendations are made for future research.

A Gaussian mixture model based cost function for parameter estimation of chaotic biological systems

Science.gov (United States)

Shekofteh, Yasser; Jafari, Sajad; Sprott, Julien Clinton; Hashemi Golpayegani, S. Mohammad Reza; Almasganj, Farshad

2015-02-01

As we know, many biological systems such as neurons or the heart can exhibit chaotic behavior. Conventional methods for parameter estimation in models of these systems have some limitations caused by sensitivity to initial conditions. In this paper, a novel cost function is proposed to overcome those limitations by building a statistical model on the distribution of the real system attractor in state space. This cost function is defined by the use of a likelihood score in a Gaussian mixture model (GMM) which is fitted to the observed attractor generated by the real system. Using that learned GMM, a similarity score can be defined by the computed likelihood score of the model time series. We have applied the proposed method to the parameter estimation of two important biological systems, a neuron and a cardiac pacemaker, which show chaotic behavior. Some simulated experiments are given to verify the usefulness of the proposed approach in clean and noisy conditions. The results show the adequacy of the proposed cost function.

An IBM PC-based math model for space station solar array simulation

Science.gov (United States)

Emanuel, E. M.

1986-01-01

This report discusses and documents the design, development, and verification of a microcomputer-based solar cell math model for simulating the Space Station's solar array Initial Operational Capability (IOC) reference configuration. The array model is developed utilizing a linear solar cell dc math model requiring only five input parameters: short circuit current, open circuit voltage, maximum power voltage, maximum power current, and orbit inclination. The accuracy of this model is investigated using actual solar array on orbit electrical data derived from the Solar Array Flight Experiment/Dynamic Augmentation Experiment (SAFE/DAE), conducted during the STS-41D mission. This simulator provides real-time simulated performance data during the steady state portion of the Space Station orbit (i.e., array fully exposed to sunlight). Eclipse to sunlight transients and shadowing effects are not included in the analysis, but are discussed briefly. Integrating the Solar Array Simulator (SAS) into the Power Management and Distribution (PMAD) subsystem is also discussed.

Full parameter scan of the Zee model: exploring Higgs lepton flavor violation

Energy Technology Data Exchange (ETDEWEB)

Herrero-García, Juan [ARC Center of Excellence for Particle Physics at the Terascale, University of Adelaide,Adelaide, SA 5005 (Australia); Department of Physics, School of Engineering Sciences, KTH Royal Institute of Technology,AlbaNova University Center, Roslagstullsbacken 21, 106 91 Stockholm (Sweden); Ohlsson, Tommy; Riad, Stella; Wirén, Jens [Department of Physics, School of Engineering Sciences, KTH Royal Institute of Technology,AlbaNova University Center, Roslagstullsbacken 21, 106 91 Stockholm (Sweden)

2017-04-21

We study the general Zee model, which includes an extra Higgs scalar doublet and a new singly-charged scalar singlet. Neutrino masses are generated at one-loop level, and in order to describe leptonic mixing, both the Standard Model and the extra Higgs scalar doublets need to couple to leptons (in a type-III two-Higgs doublet model), which necessarily generates large lepton flavor violating signals, also in Higgs decays. Imposing all relevant phenomenological constraints and performing a full numerical scan of the parameter space, we find that both normal and inverted neutrino mass orderings can be fitted, although the latter is disfavored with respect to the former. In fact, inverted ordering can only be accommodated if θ{sub 23} turns out to be in the first octant. A branching ratio for h→τμ of up to 10{sup −2} is allowed, but it could be as low as 10{sup −6}. In addition, if future expected sensitivities of τ→μγ are achieved, normal ordering can be almost completely tested. Also, μe conversion is expected to probe large parts of the parameter space, excluding completely inverted ordering if no signal is observed. Furthermore, non-standard neutrino interactions are found to be smaller than 10{sup −6}, which is well below future experimental sensitivity. Finally, the results of our scan indicate that the masses of the additional scalars have to be below 2.5 TeV, and typically they are lower than that and therefore within the reach of the LHC and future colliders.

Lumped Parameter Models of the Central Nervous System for VIIP Research

Science.gov (United States)

Vera, J.; Mulugeta, L.; Nelson, E. S.; Raykin, J.; Feola, A.; Gleason, R.; Samuels, B.; Myers, J. G.

2015-01-01

INTRODUCTION: Current long-duration missions to the International Space Station and future exploration-class missions beyond low-Earth orbit, such as to Mars and asteroids, expose astronauts to increased risk of Visual Impairment and Intracranial Pressure (VIIP) syndrome [1]. It has been hypothesized that the headward shift of cerebral spinal fluid (CSF) and blood in microgravity may cause significant elevation of intracranial pressure (ICP), which in turn induces VIIP syndrome through biomechanical pathways [1, 2]. However, there is insufficient evidence to confirm this hypothesis. In this light, we are developing lumped-parameter models of fluid transport in the central nervous system (CNS) as a means to simulate the influence of microgravity on ICP. The CNS models will also be used in concert with the lumped parameter and finite element models of the eye described in the realted IWS abstracts submitted by Nelson et al., Feola et al. and Ethier et al. METHODS: We have developed a nine compartment CNS model (Figure 1) capable of both time-dependent and steady state fluid transport simulations, based on the works of Stevens et al. [3]. The breakdown of compartments within the model includes: vascular (3), CSF (2), brain (1) and extracranial (3). The boundary pressure in the Central Arteries [A] node is prescribed using an oscillating pressure function PA(t) simulating the carotid pulsatile pressure wave as developed by Linninger et al. [4]. For each time step, pressures are integrated through time using an adaptive-timestep 4th and 5th order Runga-Kutta solver. Once pressures are found, constitutive equations are used to solve for flowrates (Q) between each compartment. In addition to fluid flow between the different compartments, compliance (C) interactions between neighboring compartments are represented. We are also developing a second CNS model based on the works of Linninger et al. [4] which takes a more granular approach to represent the interactions of the

The manifold model for space-time

International Nuclear Information System (INIS)

Heller, M.

1981-01-01

Physical processes happen on a space-time arena. It turns out that all contemporary macroscopic physical theories presuppose a common mathematical model for this arena, the so-called manifold model of space-time. The first part of study is an heuristic introduction to the concept of a smooth manifold, starting with the intuitively more clear concepts of a curve and a surface in the Euclidean space. In the second part the definitions of the Csub(infinity) manifold and of certain structures, which arise in a natural way from the manifold concept, are given. The role of the enveloping Euclidean space (i.e. of the Euclidean space appearing in the manifold definition) in these definitions is stressed. The Euclidean character of the enveloping space induces to the manifold local Euclidean (topological and differential) properties. A suggestion is made that replacing the enveloping Euclidean space by a discrete non-Euclidean space would be a correct way towards the quantization of space-time. (author)

A Hybrid 3D Indoor Space Model

Directory of Open Access Journals (Sweden)

A. Jamali

2016-10-01

Full Text Available GIS integrates spatial information and spatial analysis. An important example of such integration is for emergency response which requires route planning inside and outside of a building. Route planning requires detailed information related to indoor and outdoor environment. Indoor navigation network models including Geometric Network Model (GNM, Navigable Space Model, sub-division model and regular-grid model lack indoor data sources and abstraction methods. In this paper, a hybrid indoor space model is proposed. In the proposed method, 3D modeling of indoor navigation network is based on surveying control points and it is less dependent on the 3D geometrical building model. This research proposes a method of indoor space modeling for the buildings which do not have proper 2D/3D geometrical models or they lack semantic or topological information. The proposed hybrid model consists of topological, geometrical and semantical space.

Computational Modeling of Space Physiology

Science.gov (United States)

Lewandowski, Beth E.; Griffin, Devon W.

2016-01-01

The Digital Astronaut Project (DAP), within NASAs Human Research Program, develops and implements computational modeling for use in the mitigation of human health and performance risks associated with long duration spaceflight. Over the past decade, DAP developed models to provide insights into space flight related changes to the central nervous system, cardiovascular system and the musculoskeletal system. Examples of the models and their applications include biomechanical models applied to advanced exercise device development, bone fracture risk quantification for mission planning, accident investigation, bone health standards development, and occupant protection. The International Space Station (ISS), in its role as a testing ground for long duration spaceflight, has been an important platform for obtaining human spaceflight data. DAP has used preflight, in-flight and post-flight data from short and long duration astronauts for computational model development and validation. Examples include preflight and post-flight bone mineral density data, muscle cross-sectional area, and muscle strength measurements. Results from computational modeling supplement space physiology research by informing experimental design. Using these computational models, DAP personnel can easily identify both important factors associated with a phenomenon and areas where data are lacking. This presentation will provide examples of DAP computational models, the data used in model development and validation, and applications of the model.

Scoping the parameter space for demo and the engineering test

International Nuclear Information System (INIS)

Meier, W R.

1999-01-01

In our IFE development plan, we have set a goal of building an Engineering Test Facility (ETF) for a total cost of $2B and a Demo for $3B. In Mike Campbell s presentation at Madison, we included a viewgraph with an example Demo that had 80 to 250 MWe of net power and showed a plausible argument that it could cost less than $3B. In this memo, I examine the design space for the Demo and then briefly for the ETF. Instead of attempting to estimate the costs of the drivers, I pose the question in a way to define R ampersand D goals: As a function of key design and performance parameters, how much can the driver cost if the total facility cost is limited to the specified goal? The design parameters examined for the Demo included target gain, driver energy, driver efficiency, and net power output. For the ETF; the design parameters are target gain, driver energy, and target yield. The resulting graphs of allowable driver cost determine the goals that the driver R ampersand D programs must seek to meet

Pump Component Model in SPACE Code

International Nuclear Information System (INIS)

Kim, Byoung Jae; Kim, Kyoung Doo

2010-08-01

This technical report describes the pump component model in SPACE code. A literature survey was made on pump models in existing system codes. The models embedded in SPACE code were examined to check the confliction with intellectual proprietary rights. Design specifications, computer coding implementation, and test results are included in this report

Stabilization and discontinuity-capturing parameters for space-time flow computations with finite element and isogeometric discretizations

Science.gov (United States)

Takizawa, Kenji; Tezduyar, Tayfun E.; Otoguro, Yuto

2018-04-01

Stabilized methods, which have been very common in flow computations for many years, typically involve stabilization parameters, and discontinuity-capturing (DC) parameters if the method is supplemented with a DC term. Various well-performing stabilization and DC parameters have been introduced for stabilized space-time (ST) computational methods in the context of the advection-diffusion equation and the Navier-Stokes equations of incompressible and compressible flows. These parameters were all originally intended for finite element discretization but quite often used also for isogeometric discretization. The stabilization and DC parameters we present here for ST computations are in the context of the advection-diffusion equation and the Navier-Stokes equations of incompressible flows, target isogeometric discretization, and are also applicable to finite element discretization. The parameters are based on a direction-dependent element length expression. The expression is outcome of an easy to understand derivation. The key components of the derivation are mapping the direction vector from the physical ST element to the parent ST element, accounting for the discretization spacing along each of the parametric coordinates, and mapping what we have in the parent element back to the physical element. The test computations we present for pure-advection cases show that the parameters proposed result in good solution profiles.

Incorporating model parameter uncertainty into inverse treatment planning

International Nuclear Information System (INIS)

Lian Jun; Xing Lei

2004-01-01

Radiobiological treatment planning depends not only on the accuracy of the models describing the dose-response relation of different tumors and normal tissues but also on the accuracy of tissue specific radiobiological parameters in these models. Whereas the general formalism remains the same, different sets of model parameters lead to different solutions and thus critically determine the final plan. Here we describe an inverse planning formalism with inclusion of model parameter uncertainties. This is made possible by using a statistical analysis-based frameset developed by our group. In this formalism, the uncertainties of model parameters, such as the parameter a that describes tissue-specific effect in the equivalent uniform dose (EUD) model, are expressed by probability density function and are included in the dose optimization process. We found that the final solution strongly depends on distribution functions of the model parameters. Considering that currently available models for computing biological effects of radiation are simplistic, and the clinical data used to derive the models are sparse and of questionable quality, the proposed technique provides us with an effective tool to minimize the effect caused by the uncertainties in a statistical sense. With the incorporation of the uncertainties, the technique has potential for us to maximally utilize the available radiobiology knowledge for better IMRT treatment

Emulating a flexible space structure: Modeling

Science.gov (United States)

Waites, H. B.; Rice, S. C.; Jones, V. L.

1988-01-01

Control Dynamics, in conjunction with Marshall Space Flight Center, has participated in the modeling and testing of Flexible Space Structures. Through the series of configurations tested and the many techniques used for collecting, analyzing, and modeling the data, many valuable insights have been gained and important lessons learned. This paper discusses the background of the Large Space Structure program, Control Dynamics' involvement in testing and modeling of the configurations (especially the Active Control Technique Evaluation for Spacecraft (ACES) configuration), the results from these two processes, and insights gained from this work.

Fast Estimation Method of Space-Time Two-Dimensional Positioning Parameters Based on Hadamard Product

Directory of Open Access Journals (Sweden)

Haiwen Li

2018-01-01

Full Text Available The estimation speed of positioning parameters determines the effectiveness of the positioning system. The time of arrival (TOA and direction of arrival (DOA parameters can be estimated by the space-time two-dimensional multiple signal classification (2D-MUSIC algorithm for array antenna. However, this algorithm needs much time to complete the two-dimensional pseudo spectral peak search, which makes it difficult to apply in practice. Aiming at solving this problem, a fast estimation method of space-time two-dimensional positioning parameters based on Hadamard product is proposed in orthogonal frequency division multiplexing (OFDM system, and the Cramer-Rao bound (CRB is also presented. Firstly, according to the channel frequency domain response vector of each array, the channel frequency domain estimation vector is constructed using the Hadamard product form containing location information. Then, the autocorrelation matrix of the channel response vector for the extended array element in frequency domain and the noise subspace are calculated successively. Finally, by combining the closed-form solution and parameter pairing, the fast joint estimation for time delay and arrival direction is accomplished. The theoretical analysis and simulation results show that the proposed algorithm can significantly reduce the computational complexity and guarantee that the estimation accuracy is not only better than estimating signal parameters via rotational invariance techniques (ESPRIT algorithm and 2D matrix pencil (MP algorithm but also close to 2D-MUSIC algorithm. Moreover, the proposed algorithm also has certain adaptability to multipath environment and effectively improves the ability of fast acquisition of location parameters.

Linking Time and Space Scales in Distributed Hydrological Modelling - a case study for the VIC model

Science.gov (United States)

Melsen, Lieke; Teuling, Adriaan; Torfs, Paul; Zappa, Massimiliano; Mizukami, Naoki; Clark, Martyn; Uijlenhoet, Remko

2015-04-01

/24 degree, if in the end you only look at monthly runoff? In this study an attempt is made to link time and space scales in the VIC model, to study the added value of a higher spatial resolution-model for different time steps. In order to do this, four different VIC models were constructed for the Thur basin in North-Eastern Switzerland (1700 km²), a tributary of the Rhine: one lumped model, and three spatially distributed models with a resolution of respectively 1x1 km, 5x5 km, and 10x10 km. All models are run at an hourly time step and aggregated and calibrated for different time steps (hourly, daily, monthly, yearly) using a novel Hierarchical Latin Hypercube Sampling Technique (Vořechovský, 2014). For each time and space scale, several diagnostics like Nash-Sutcliffe efficiency, Kling-Gupta efficiency, all the quantiles of the discharge etc., are calculated in order to compare model performance over different time and space scales for extreme events like floods and droughts. Next to that, the effect of time and space scale on the parameter distribution can be studied. In the end we hope to find a link for optimal time and space scale combinations.

Joint Data Assimilation and Parameter Calibration in on-line groundwater modelling using Sequential Monte Carlo techniques

Science.gov (United States)

Ramgraber, M.; Schirmer, M.

2017-12-01

As computational power grows and wireless sensor networks find their way into common practice, it becomes increasingly feasible to pursue on-line numerical groundwater modelling. The reconciliation of model predictions with sensor measurements often necessitates the application of Sequential Monte Carlo (SMC) techniques, most prominently represented by the Ensemble Kalman Filter. In the pursuit of on-line predictions it seems advantageous to transcend the scope of pure data assimilation and incorporate on-line parameter calibration as well. Unfortunately, the interplay between shifting model parameters and transient states is non-trivial. Several recent publications (e.g. Chopin et al., 2013, Kantas et al., 2015) in the field of statistics discuss potential algorithms addressing this issue. However, most of these are computationally intractable for on-line application. In this study, we investigate to what extent compromises between mathematical rigour and computational restrictions can be made within the framework of on-line numerical modelling of groundwater. Preliminary studies are conducted in a synthetic setting, with the goal of transferring the conclusions drawn into application in a real-world setting. To this end, a wireless sensor network has been established in the valley aquifer around Fehraltorf, characterized by a highly dynamic groundwater system and located about 20 km to the East of Zürich, Switzerland. By providing continuous probabilistic estimates of the state and parameter distribution, a steady base for branched-off predictive scenario modelling could be established, providing water authorities with advanced tools for assessing the impact of groundwater management practices. Chopin, N., Jacob, P.E. and Papaspiliopoulos, O. (2013): SMC2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75 (3), p. 397-426. Kantas, N., Doucet, A., Singh, S

Impact parameter analysis and soft QCD dynamics

International Nuclear Information System (INIS)

Carvalho, P.A.S.; Martini, A.F.; Menon, M.J.

2002-01-01

In a recent paper, based on the hypothesis of light-cone dipole representation for gluon Bremsstrahlung, Kopeliovich et al. developed a dynamical model for the elastic hadronic amplitude. The model has been applied to pp and p (bar) p scattering and the effects of unitarity and peripheral interactions have been investigated in the impact parameter representation. In this communication, making use of a model independent extraction of the scattering amplitude in the impact parameter space (early developed), we represent a comparative study between the predictions from the dynamical model and the impact parameter analysis. (author)

Space Shuttle and Space Station Radio Frequency (RF) Exposure Analysis

Science.gov (United States)

Hwu, Shian U.; Loh, Yin-Chung; Sham, Catherine C.; Kroll, Quin D.

2005-01-01

This paper outlines the modeling techniques and important parameters to define a rigorous but practical procedure that can verify the compliance of RF exposure to the NASA standards for astronauts and electronic equipment. The electromagnetic modeling techniques are applied to analyze RF exposure in Space Shuttle and Space Station environments with reasonable computing time and resources. The modeling techniques are capable of taking into account the field interactions with Space Shuttle and Space Station structures. The obtained results illustrate the multipath effects due to the presence of the space vehicle structures. It's necessary to include the field interactions with the space vehicle in the analysis for an accurate assessment of the RF exposure. Based on the obtained results, the RF keep out zones are identified for appropriate operational scenarios, flight rules and necessary RF transmitter constraints to ensure a safe operating environment and mission success.

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

Science.gov (United States)

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

2014-01-01

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

Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods

Directory of Open Access Journals (Sweden)

W. Castaings

2009-04-01

Full Text Available Variational methods are widely used for the analysis and control of computationally intensive spatially distributed systems. In particular, the adjoint state method enables a very efficient calculation of the derivatives of an objective function (response function to be analysed or cost function to be optimised with respect to model inputs.

In this contribution, it is shown that the potential of variational methods for distributed catchment scale hydrology should be considered. A distributed flash flood model, coupling kinematic wave overland flow and Green Ampt infiltration, is applied to a small catchment of the Thoré basin and used as a relatively simple (synthetic observations but didactic application case.

It is shown that forward and adjoint sensitivity analysis provide a local but extensive insight on the relation between the assigned model parameters and the simulated hydrological response. Spatially distributed parameter sensitivities can be obtained for a very modest calculation effort (~6 times the computing time of a single model run and the singular value decomposition (SVD of the Jacobian matrix provides an interesting perspective for the analysis of the rainfall-runoff relation.

For the estimation of model parameters, adjoint-based derivatives were found exceedingly efficient in driving a bound-constrained quasi-Newton algorithm. The reference parameter set is retrieved independently from the optimization initial condition when the very common dimension reduction strategy (i.e. scalar multipliers is adopted.

Furthermore, the sensitivity analysis results suggest that most of the variability in this high-dimensional parameter space can be captured with a few orthogonal directions. A parametrization based on the SVD leading singular vectors was found very promising but should be combined with another regularization strategy in order to prevent overfitting.

Establishing statistical models of manufacturing parameters

International Nuclear Information System (INIS)

Senevat, J.; Pape, J.L.; Deshayes, J.F.

1991-01-01

This paper reports on the effect of pilgering and cold-work parameters on contractile strain ratio and mechanical properties that were investigated using a large population of Zircaloy tubes. Statistical models were established between: contractile strain ratio and tooling parameters, mechanical properties (tensile test, creep test) and cold-work parameters, and mechanical properties and stress-relieving temperature

Logic-based models in systems biology: a predictive and parameter-free network analysis method†

Science.gov (United States)

Wynn, Michelle L.; Consul, Nikita; Merajver, Sofia D.

2012-01-01

Highly complex molecular networks, which play fundamental roles in almost all cellular processes, are known to be dysregulated in a number of diseases, most notably in cancer. As a consequence, there is a critical need to develop practical methodologies for constructing and analysing molecular networks at a systems level. Mathematical models built with continuous differential equations are an ideal methodology because they can provide a detailed picture of a network’s dynamics. To be predictive, however, differential equation models require that numerous parameters be known a priori and this information is almost never available. An alternative dynamical approach is the use of discrete logic-based models that can provide a good approximation of the qualitative behaviour of a biochemical system without the burden of a large parameter space. Despite their advantages, there remains significant resistance to the use of logic-based models in biology. Here, we address some common concerns and provide a brief tutorial on the use of logic-based models, which we motivate with biological examples. PMID:23072820

Some tests for parameter constancy in cointegrated VAR-models

DEFF Research Database (Denmark)

Hansen, Henrik; Johansen, Søren

1999-01-01

Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations, and anot...... be applied to test the constancy of the long-run parameters in the cointegrated VAR-model. All results are illustrated using a model for the term structure of interest rates on US Treasury securities. ......Some methods for the evaluation of parameter constancy in vector autoregressive (VAR) models are discussed. Two different ways of re-estimating the VAR model are proposed; one in which all parameters are estimated recursively based upon the likelihood function for the first observations......, and another in which the cointegrating relations are estimated recursively from a likelihood function, where the short-run parameters have been concentrated out. We suggest graphical procedures based on recursively estimated eigenvalues to evaluate the constancy of the long-run parameters in the model...

Edge Modeling by Two Blur Parameters in Varying Contrasts.

Science.gov (United States)

Seo, Suyoung

2018-06-01

This paper presents a method of modeling edge profiles with two blur parameters, and estimating and predicting those edge parameters with varying brightness combinations and camera-to-object distances (COD). First, the validity of the edge model is proven mathematically. Then, it is proven experimentally with edges from a set of images captured for specifically designed target sheets and with edges from natural images. Estimation of the two blur parameters for each observed edge profile is performed with a brute-force method to find parameters that produce global minimum errors. Then, using the estimated blur parameters, actual blur parameters of edges with arbitrary brightness combinations are predicted using a surface interpolation method (i.e., kriging). The predicted surfaces show that the two blur parameters of the proposed edge model depend on both dark-side edge brightness and light-side edge brightness following a certain global trend. This is similar across varying CODs. The proposed edge model is compared with a one-blur parameter edge model using experiments of the root mean squared error for fitting the edge models to each observed edge profile. The comparison results suggest that the proposed edge model has superiority over the one-blur parameter edge model in most cases where edges have varying brightness combinations.

DRAGON solutions to the 3D transport benchmark over a range in parameter space

International Nuclear Information System (INIS)

Martin, Nicolas; Hebert, Alain; Marleau, Guy

2010-01-01

DRAGON solutions to the 'NEA suite of benchmarks for 3D transport methods and codes over a range in parameter space' are discussed in this paper. A description of the benchmark is first provided, followed by a detailed review of the different computational models used in the lattice code DRAGON. Two numerical methods were selected for generating the required quantities for the 729 configurations of this benchmark. First, S N calculations were performed using fully symmetric angular quadratures and high-order diamond differencing for spatial discretization. To compare S N results with those of another deterministic method, the method of characteristics (MoC) was also considered for this benchmark. Comparisons between reference solutions, S N and MoC results illustrate the advantages and drawbacks of each methods for this 3-D transport problem.

A general-model-space diagrammatic perturbation theory

International Nuclear Information System (INIS)

Hose, G.; Kaldor, U.

1980-01-01

A diagrammatic many-body perturbation theory applicable to arbitrary model spaces is presented. The necessity of having a complete model space (all possible occupancies of the partially-filled shells) is avoided. This requirement may be troublesome for systems with several well-spaced open shells, such as most atomic and molecular excited states, as a complete model space spans a very broad energy range and leaves out states within that range, leading to poor or no convergence of the perturbation series. The method presented here would be particularly useful for such states. The solution of a model problem (He 2 excited Σ + sub(g) states) is demonstrated. (Auth.)

TU-AB-BRC-05: Creation of a Monte Carlo TrueBeam Model by Reproducing Varian Phase Space Data

International Nuclear Information System (INIS)

O’Grady, K; Davis, S; Seuntjens, J

2016-01-01

Purpose: To create a Varian TrueBeam 6 MV FFF Monte Carlo model using BEAMnrc/EGSnrc that accurately reproduces the Varian representative dataset, followed by tuning the model’s source parameters to accurately reproduce in-house measurements. Methods: A BEAMnrc TrueBeam model for 6 MV FFF has been created by modifying a validated 6 MV Varian CL21EX model. Geometric dimensions and materials were adjusted in a trial and error approach to match the fluence and spectra of TrueBeam phase spaces output by the Varian VirtuaLinac. Once the model’s phase space matched Varian’s counterpart using the default source parameters, it was validated to match 10 × 10 cm"2 Varian representative data obtained with the IBA CC13. The source parameters were then tuned to match in-house 5 × 5 cm"2 PTW microDiamond measurements. All dose to water simulations included detector models to include the effects of volume averaging and the non-water equivalence of the chamber materials, allowing for more accurate source parameter selection. Results: The Varian phase space spectra and fluence were matched with excellent agreement. The in-house model’s PDD agreement with CC13 TrueBeam representative data was within 0.9% local percent difference beyond the first 3 mm. Profile agreement at 10 cm depth was within 0.9% local percent difference and 1.3 mm distance-to-agreement in the central axis and penumbra regions, respectively. Once the source parameters were tuned, PDD agreement with microDiamond measurements was within 0.9% local percent difference beyond 2 mm. The microDiamond profile agreement at 10 cm depth was within 0.6% local percent difference and 0.4 mm distance-to-agreement in the central axis and penumbra regions, respectively. Conclusion: An accurate in-house Monte Carlo model of the Varian TrueBeam was achieved independently of the Varian phase space solution and was tuned to in-house measurements. KO acknowledges partial support by the CREATE Medical Physics Research

Application of a free parameter model to plastic scintillation samples

Energy Technology Data Exchange (ETDEWEB)

Tarancon Sanz, Alex, E-mail: alex.tarancon@ub.edu [Departament de Quimica Analitica, Universitat de Barcelona, Diagonal 647, E-08028 Barcelona (Spain); Kossert, Karsten, E-mail: Karsten.Kossert@ptb.de [Physikalisch-Technische Bundesanstalt (PTB), Bundesallee 100, 38116 Braunschweig (Germany)

2011-08-21

In liquid scintillation (LS) counting, the CIEMAT/NIST efficiency tracing method and the triple-to-double coincidence ratio (TDCR) method have proved their worth for reliable activity measurements of a number of radionuclides. In this paper, an extended approach to apply a free-parameter model to samples containing a mixture of solid plastic scintillation microspheres and radioactive aqueous solutions is presented. Several beta-emitting radionuclides were measured in a TDCR system at PTB. For the application of the free parameter model, the energy loss in the aqueous phase must be taken into account, since this portion of the particle energy does not contribute to the creation of scintillation light. The energy deposit in the aqueous phase is determined by means of Monte Carlo calculations applying the PENELOPE software package. To this end, great efforts were made to model the geometry of the samples. Finally, a new geometry parameter was defined, which was determined by means of a tracer radionuclide with known activity. This makes the analysis of experimental TDCR data of other radionuclides possible. The deviations between the determined activity concentrations and reference values were found to be lower than 3%. The outcome of this research work is also important for a better understanding of liquid scintillation counting. In particular the influence of (inverse) micelles, i.e. the aqueous spaces embedded in the organic scintillation cocktail, can be investigated. The new approach makes clear that it is important to take the energy loss in the aqueous phase into account. In particular for radionuclides emitting low-energy electrons (e.g. M-Auger electrons from {sup 125}I), this effect can be very important.

Identifyability measures to select the parameters to be estimated in a solid-state fermentation distributed parameter model.

Science.gov (United States)

da Silveira, Christian L; Mazutti, Marcio A; Salau, Nina P G

2016-07-08

Process modeling can lead to of advantages such as helping in process control, reducing process costs and product quality improvement. This work proposes a solid-state fermentation distributed parameter model composed by seven differential equations with seventeen parameters to represent the process. Also, parameters estimation with a parameters identifyability analysis (PIA) is performed to build an accurate model with optimum parameters. Statistical tests were made to verify the model accuracy with the estimated parameters considering different assumptions. The results have shown that the model assuming substrate inhibition better represents the process. It was also shown that eight from the seventeen original model parameters were nonidentifiable and better results were obtained with the removal of these parameters from the estimation procedure. Therefore, PIA can be useful to estimation procedure, since it may reduce the number of parameters that can be evaluated. Further, PIA improved the model results, showing to be an important procedure to be taken. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:905-917, 2016. © 2016 American Institute of Chemical Engineers.

Properties of Griffin-Hill-Wheeler spaces - 2. one-parameters and two-conjugate parameter families of generator states

International Nuclear Information System (INIS)

Passos, E.J.V. de; Toledo Piza, A.F.R. de.

The properties of the subspaces of the many-body Hilbert space which are associated with the use of the Generator Coordinate Method (GCM) in connection with one parameter, and with two-conjugate parameter families of generator states are examined in detail. It is shown that natural orthonormal base vectors in each case are immediately related to Peierls-Voccoz and Peierls-Thouless projections respectively. Through the formal consideration of a canonical transformation to collective, P and Q, and intrinsic degrees of freedom, the properties of the GCM subspaces with respect to the kinematical separation of these degrees of freedom are discussed in detail. An application is made, using the ideas developed in this paper, a) to translation; b) to illustrate the qualitative understanting of the content of existing GCM calculations of giant ressonances in light nuclei and c) to the definition of appropriate asymptotic states in current GCM descriptions of scattering [pt

PHOTOGRAMMETRIC MODEL BASED METHOD OF AUTOMATIC ORIENTATION OF SPACE CARGO SHIP RELATIVE TO THE INTERNATIONAL SPACE STATION

Directory of Open Access Journals (Sweden)

Y. B. Blokhinov

2012-07-01

Full Text Available The technical problem of creating the new Russian version of an automatic Space Cargo Ship (SCS for the International Space Station (ISS is inseparably connected to the development of a digital video system for automatically measuring the SCS position relative to ISS in the process of spacecraft docking. This paper presents a method for estimating the orientation elements based on the use of a highly detailed digital model of the ISS. The input data are digital frames from a calibrated video system and the initial values of orientation elements, these can be estimated from navigation devices or by fast-and-rough viewpoint-dependent algorithm. Then orientation elements should be defined precisely by means of algorithmic processing. The main idea is to solve the exterior orientation problem mainly on the basis of contour information of the frame image of ISS instead of ground control points. A detailed digital model is used for generating raster templates of ISS nodes; the templates are used to detect and locate the nodes on the target image with the required accuracy. The process is performed for every frame, the resulting parameters are considered to be the orientation elements. The Kalman filter is used for statistical support of the estimation process and real time pose tracking. Finally, the modeling results presented show that the proposed method can be regarded as one means to ensure the algorithmic support of automatic space ships docking.

Probability Distribution for Flowing Interval Spacing

International Nuclear Information System (INIS)

S. Kuzio

2004-01-01

Fracture spacing is a key hydrologic parameter in analyses of matrix diffusion. Although the individual fractures that transmit flow in the saturated zone (SZ) cannot be identified directly, it is possible to determine the fractured zones that transmit flow from flow meter survey observations. The fractured zones that transmit flow as identified through borehole flow meter surveys have been defined in this report as flowing intervals. The flowing interval spacing is measured between the midpoints of each flowing interval. The determination of flowing interval spacing is important because the flowing interval spacing parameter is a key hydrologic parameter in SZ transport modeling, which impacts the extent of matrix diffusion in the SZ volcanic matrix. The output of this report is input to the ''Saturated Zone Flow and Transport Model Abstraction'' (BSC 2004 [DIRS 170042]). Specifically, the analysis of data and development of a data distribution reported herein is used to develop the uncertainty distribution for the flowing interval spacing parameter for the SZ transport abstraction model. Figure 1-1 shows the relationship of this report to other model reports that also pertain to flow and transport in the SZ. Figure 1-1 also shows the flow of key information among the SZ reports. It should be noted that Figure 1-1 does not contain a complete representation of the data and parameter inputs and outputs of all SZ reports, nor does it show inputs external to this suite of SZ reports. Use of the developed flowing interval spacing probability distribution is subject to the limitations of the assumptions discussed in Sections 5 and 6 of this analysis report. The number of fractures in a flowing interval is not known. Therefore, the flowing intervals are assumed to be composed of one flowing zone in the transport simulations. This analysis may overestimate the flowing interval spacing because the number of fractures that contribute to a flowing interval cannot be

Environmental Transport Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

M. A. Wasiolek

2003-06-27

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699

Environmental Transport Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Wasiolek, M. A.

2003-01-01

This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699], Section 6.2). Parameter values

Using SpaceClaimTD Direct for Modeling Components with Complex Geometries for the Thermal Desktop-Based Advanced Stirling Radioisotope Generator Model

Science.gov (United States)

Fabanich, William A., Jr.

2014-01-01

SpaceClaim/TD Direct has been used extensively in the development of the Advanced Stirling Radioisotope Generator (ASRG) thermal model. This paper outlines the workflow for that aspect of the task and includes proposed best practices and lessons learned. The ASRG thermal model was developed to predict component temperatures and power output and to provide insight into the prime contractor's thermal modeling efforts. The insulation blocks, heat collectors, and cold side adapter flanges (CSAFs) were modeled with this approach. The model was constructed using mostly TD finite difference (FD) surfaces/solids. However, some complex geometry could not be reproduced with TD primitives while maintaining the desired degree of geometric fidelity. Using SpaceClaim permitted the import of original CAD files and enabled the defeaturing/repair of those geometries. TD Direct (a SpaceClaim add-on from CRTech) adds features that allowed the "mark-up" of that geometry. These so-called "mark-ups" control how finite element (FE) meshes are to be generated through the "tagging" of features (e.g. edges, solids, surfaces). These tags represent parameters that include: submodels, material properties, material orienters, optical properties, and radiation analysis groups. TD aliases were used for most tags to allow analysis to be performed with a variety of parameter values. "Domain-tags" were also attached to individual and groups of surfaces and solids to allow them to be used later within TD to populate objects like, for example, heaters and contactors. These tools allow the user to make changes to the geometry in SpaceClaim and then easily synchronize the mesh in TD without having to redefine the objects each time as one would if using TDMesher. The use of SpaceClaim/TD Direct helps simplify the process for importing existing geometries and in the creation of high fidelity FE meshes to represent complex parts. It also saves time and effort in the subsequent analysis.

Using SpaceClaim/TD Direct for Modeling Components with Complex Geometries for the Thermal Desktop-Based Advanced Stirling Radioisotope Generator Model

Science.gov (United States)

Fabanich, William

2014-01-01

SpaceClaim/TD Direct has been used extensively in the development of the Advanced Stirling Radioisotope Generator (ASRG) thermal model. This paper outlines the workflow for that aspect of the task and includes proposed best practices and lessons learned. The ASRG thermal model was developed to predict component temperatures and power output and to provide insight into the prime contractors thermal modeling efforts. The insulation blocks, heat collectors, and cold side adapter flanges (CSAFs) were modeled with this approach. The model was constructed using mostly TD finite difference (FD) surfaces solids. However, some complex geometry could not be reproduced with TD primitives while maintaining the desired degree of geometric fidelity. Using SpaceClaim permitted the import of original CAD files and enabled the defeaturing repair of those geometries. TD Direct (a SpaceClaim add-on from CRTech) adds features that allowed the mark-up of that geometry. These so-called mark-ups control how finite element (FE) meshes were generated and allowed the tagging of features (e.g. edges, solids, surfaces). These tags represent parameters that include: submodels, material properties, material orienters, optical properties, and radiation analysis groups. TD aliases were used for most tags to allow analysis to be performed with a variety of parameter values. Domain-tags were also attached to individual and groups of surfaces and solids to allow them to be used later within TD to populate objects like, for example, heaters and contactors. These tools allow the user to make changes to the geometry in SpaceClaim and then easily synchronize the mesh in TD without having to redefine these objects each time as one would if using TD Mesher.The use of SpaceClaim/TD Direct has helped simplify the process for importing existing geometries and in the creation of high fidelity FE meshes to represent complex parts. It has also saved time and effort in the subsequent analysis.

Uncertainty, Sensitivity Analysis, and Causal Identification in the Arctic using a Perturbed Parameter Ensemble of the HiLAT Climate Model

Energy Technology Data Exchange (ETDEWEB)

Hunke, Elizabeth Clare [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urrego Blanco, Jorge Rolando [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urban, Nathan Mark [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-02-12

Coupled climate models have a large number of input parameters that can affect output uncertainty. We conducted a sensitivity analysis of sea ice proper:es and Arc:c related climate variables to 5 parameters in the HiLAT climate model: air-ocean turbulent exchange parameter (C), conversion of water vapor to clouds (cldfrc_rhminl) and of ice crystals to snow (micro_mg_dcs), snow thermal conduc:vity (ksno), and maximum snow grain size (rsnw_mlt). We used an elementary effect (EE) approach to rank their importance for output uncertainty. EE is an extension of one-at-a-time sensitivity analyses, but it is more efficient in sampling multi-dimensional parameter spaces. We looked for emerging relationships among climate variables across the model ensemble, and used causal discovery algorithms to establish potential pathways for those relationships.

Parameter retrieval of chiral metamaterials based on the state-space approach.

Science.gov (United States)

Zarifi, Davoud; Soleimani, Mohammad; Abdolali, Ali

2013-08-01

This paper deals with the introduction of an approach for the electromagnetic characterization of homogeneous chiral layers. The proposed method is based on the state-space approach and properties of a 4×4 state transition matrix. Based on this, first, the forward problem analysis through the state-space method is reviewed and properties of the state transition matrix of a chiral layer are presented and proved as two theorems. The formulation of a proposed electromagnetic characterization method is then presented. In this method, scattering data for a linearly polarized plane wave incident normally on a homogeneous chiral slab are combined with properties of a state transition matrix and provide a powerful characterization method. The main difference with respect to other well-established retrieval procedures based on the use of the scattering parameters relies on the direct computation of the transfer matrix of the slab as opposed to the conventional calculation of the propagation constant and impedance of the modes supported by the medium. The proposed approach allows avoiding nonlinearity of the problem but requires getting enough equations to fulfill the task which was provided by considering some properties of the state transition matrix. To demonstrate the applicability and validity of the method, the constitutive parameters of two well-known dispersive chiral metamaterial structures at microwave frequencies are retrieved. The results show that the proposed method is robust and reliable.

A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model

Energy Technology Data Exchange (ETDEWEB)

Covey, C; Brandon, S; Bremer, P T; Domyancis, D; Garaizar, X; Johannesson, G; Klein, R; Klein, S A; Lucas, D D; Tannahill, J; Zhang, Y

2011-10-27

Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide.

A method for model identification and parameter estimation

International Nuclear Information System (INIS)

Bambach, M; Heinkenschloss, M; Herty, M

2013-01-01

We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)

Reduced order modeling and parameter identification of a building energy system model through an optimization routine

International Nuclear Information System (INIS)

Harish, V.S.K.V.; Kumar, Arun

2016-01-01

Highlights: • A BES model based on 1st principles is developed and solved numerically. • Parameters of lumped capacitance model are fitted using the proposed optimization routine. • Validations are showed for different types of building construction elements. • Step response excitations for outdoor air temperature and relative humidity are analyzed. - Abstract: Different control techniques together with intelligent building technology (Building Automation Systems) are used to improve energy efficiency of buildings. In almost all control projects, it is crucial to have building energy models with high computational efficiency in order to design and tune the controllers and simulate their performance. In this paper, a set of partial differential equations are formulated accounting for energy flow within the building space. These equations are then solved as conventional finite difference equations using Crank–Nicholson scheme. Such a model of a higher order is regarded as a benchmark model. An optimization algorithm has been developed, depicted through a flowchart, which minimizes the sum squared error between the step responses of the numerical and the optimal model. Optimal model of the construction element is nothing but a RC-network model with the values of Rs and Cs estimated using the non-linear time invariant constrained optimization routine. The model is validated with comparing the step responses with other two RC-network models whose parameter values are selected based on a certain criteria. Validations are showed for different types of building construction elements viz., low, medium and heavy thermal capacity elements. Simulation results show that the optimal model closely follow the step responses of the numerical model as compared to the responses of other two models.

On discrete models of space-time

International Nuclear Information System (INIS)

Horzela, A.; Kempczynski, J.; Kapuscik, E.; Georgia Univ., Athens, GA; Uzes, Ch.

1992-02-01

Analyzing the Einstein radiolocation method we come to the conclusion that results of any measurement of space-time coordinates should be expressed in terms of rational numbers. We show that this property is Lorentz invariant and may be used in the construction of discrete models of space-time different from the models of the lattice type constructed in the process of discretization of continuous models. (author)

Neutrinoless double-β decay matrix elements in large shell-model spaces with the generator-coordinate method

Science.gov (United States)

Jiao, C. F.; Engel, J.; Holt, J. D.

2017-11-01

We use the generator-coordinate method (GCM) with realistic shell-model interactions to closely approximate full shell-model calculations of the matrix elements for the neutrinoless double-β decay of 48Ca, 76Ge, and 82Se. We work in one major shell for the first isotope, in the f5 /2p g9 /2 space for the second and third, and finally in two major shells for all three. Our coordinates include not only the usual axial deformation parameter β , but also the triaxiality angle γ and neutron-proton pairing amplitudes. In the smaller model spaces our matrix elements agree well with those of full shell-model diagonalization, suggesting that our Hamiltonian-based GCM captures most of the important valence-space correlations. In two major shells, where exact diagonalization is not currently possible, our matrix elements are only slightly different from those in a single shell.

Formulating state space models in R with focus on longitudinal regression models

DEFF Research Database (Denmark)

Dethlefsen, Claus; Lundbye-Christensen, Søren

  We provide a language for formulating a range of state space models. The described methodology is implemented in the R -package sspir available from cran.r-project.org . A state space model is specified similarly to a generalized linear model in R , by marking the time-varying terms in the form......  We provide a language for formulating a range of state space models. The described methodology is implemented in the R -package sspir available from cran.r-project.org . A state space model is specified similarly to a generalized linear model in R , by marking the time-varying terms...

Local overfishing may be avoided by examining parameters of a spatio-temporal model.

Science.gov (United States)

Carson, Stuart; Shackell, Nancy; Mills Flemming, Joanna

2017-01-01

Spatial erosion of stock structure through local overfishing can lead to stock collapse because fish often prefer certain locations, and fisheries tend to focus on those locations. Fishery managers are challenged to maintain the integrity of the entire stock and require scientific approaches that provide them with sound advice. Here we propose a Bayesian hierarchical spatio-temporal modelling framework for fish abundance data to estimate key parameters that define spatial stock structure: persistence (similarity of spatial structure over time), connectivity (coherence of temporal pattern over space), and spatial variance (variation across the seascape). The consideration of these spatial parameters in the stock assessment process can help identify the erosion of structure and assist in preventing local overfishing. We use Atlantic cod (Gadus morhua) in eastern Canada as a case study an examine the behaviour of these parameters from the height of the fishery through its collapse. We identify clear signals in parameter behaviour under circumstances of destructive stock erosion as well as for recovery of spatial structure even when combined with a non-recovery in abundance. Further, our model reveals the spatial pattern of areas of high and low density persists over the 41 years of available data and identifies the remnant patches. Models of this sort are crucial to recovery plans if we are to identify and protect remaining sources of recolonization for Atlantic cod. Our method is immediately applicable to other exploited species.

Local overfishing may be avoided by examining parameters of a spatio-temporal model.

Directory of Open Access Journals (Sweden)

Stuart Carson

Full Text Available Spatial erosion of stock structure through local overfishing can lead to stock collapse because fish often prefer certain locations, and fisheries tend to focus on those locations. Fishery managers are challenged to maintain the integrity of the entire stock and require scientific approaches that provide them with sound advice. Here we propose a Bayesian hierarchical spatio-temporal modelling framework for fish abundance data to estimate key parameters that define spatial stock structure: persistence (similarity of spatial structure over time, connectivity (coherence of temporal pattern over space, and spatial variance (variation across the seascape. The consideration of these spatial parameters in the stock assessment process can help identify the erosion of structure and assist in preventing local overfishing. We use Atlantic cod (Gadus morhua in eastern Canada as a case study an examine the behaviour of these parameters from the height of the fishery through its collapse. We identify clear signals in parameter behaviour under circumstances of destructive stock erosion as well as for recovery of spatial structure even when combined with a non-recovery in abundance. Further, our model reveals the spatial pattern of areas of high and low density persists over the 41 years of available data and identifies the remnant patches. Models of this sort are crucial to recovery plans if we are to identify and protect remaining sources of recolonization for Atlantic cod. Our method is immediately applicable to other exploited species.

Space Science Cloud: a Virtual Space Science Research Platform Based on Cloud Model

Science.gov (United States)

Hu, Xiaoyan; Tong, Jizhou; Zou, Ziming

Through independent and co-operational science missions, Strategic Pioneer Program (SPP) on Space Science, the new initiative of space science program in China which was approved by CAS and implemented by National Space Science Center (NSSC), dedicates to seek new discoveries and new breakthroughs in space science, thus deepen the understanding of universe and planet earth. In the framework of this program, in order to support the operations of space science missions and satisfy the demand of related research activities for e-Science, NSSC is developing a virtual space science research platform based on cloud model, namely the Space Science Cloud (SSC). In order to support mission demonstration, SSC integrates interactive satellite orbit design tool, satellite structure and payloads layout design tool, payload observation coverage analysis tool, etc., to help scientists analyze and verify space science mission designs. Another important function of SSC is supporting the mission operations, which runs through the space satellite data pipelines. Mission operators can acquire and process observation data, then distribute the data products to other systems or issue the data and archives with the services of SSC. In addition, SSC provides useful data, tools and models for space researchers. Several databases in the field of space science are integrated and an efficient retrieve system is developing. Common tools for data visualization, deep processing (e.g., smoothing and filtering tools), analysis (e.g., FFT analysis tool and minimum variance analysis tool) and mining (e.g., proton event correlation analysis tool) are also integrated to help the researchers to better utilize the data. The space weather models on SSC include magnetic storm forecast model, multi-station middle and upper atmospheric climate model, solar energetic particle propagation model and so on. All the services above-mentioned are based on the e-Science infrastructures of CAS e.g. cloud storage and

Application of lumped-parameter models

DEFF Research Database (Denmark)

Ibsen, Lars Bo; Liingaard, Morten

This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil (section 1.1). Subse...

Mathematical model for adaptive control system of ASEA robot at Kennedy Space Center

Science.gov (United States)

Zia, Omar

1989-01-01

The dynamic properties and the mathematical model for the adaptive control of the robotic system presently under investigation at Robotic Application and Development Laboratory at Kennedy Space Center are discussed. NASA is currently investigating the use of robotic manipulators for mating and demating of fuel lines to the Space Shuttle Vehicle prior to launch. The Robotic system used as a testbed for this purpose is an ASEA IRB-90 industrial robot with adaptive control capabilities. The system was tested and it's performance with respect to stability was improved by using an analogue force controller. The objective of this research project is to determine the mathematical model of the system operating under force feedback control with varying dynamic internal perturbation in order to provide continuous stable operation under variable load conditions. A series of lumped parameter models are developed. The models include some effects of robot structural dynamics, sensor compliance, and workpiece dynamics.

A simulation of water pollution model parameter estimation

Science.gov (United States)

Kibler, J. F.

1976-01-01

A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.

Space Weather Models at the CCMC And Their Capabilities

Science.gov (United States)

Hesse, Michael; Rastatter, Lutz; MacNeice, Peter; Kuznetsova, Masha

2007-01-01

The Community Coordinated Modeling Center (CCMC) is a US inter-agency activity aiming at research in support of the generation of advanced space weather models. As one of its main functions, the CCMC provides to researchers the use of space science models, even if they are not model owners themselves. The second focus of CCMC activities is on validation and verification of space weather models, and on the transition of appropriate models to space weather forecast centers. As part of the latter activity, the CCMC develops real-time simulation systems that stress models through routine execution. A by-product of these real-time calculations is the ability to derive model products, which may be useful for space weather operators. In this presentation, we will provide an overview of the community-provided, space weather-relevant, model suite, which resides at CCMC. We will discuss current capabilities, and analyze expected future developments of space weather related modeling.

Simplicial models for trace spaces

DEFF Research Database (Denmark)

Raussen, Martin

Directed Algebraic Topology studies topological spaces in which certain directed paths (d-paths) - in general irreversible - are singled out. The main interest concerns the spaces of directed paths between given end points - and how those vary under variation of the end points. The original...... motivation stems from certain models for concurrent computation. So far, spaces of d-paths and their topological invariants have only been determined in cases that were elementary to overlook. In this paper, we develop a systematic approach describing spaces of directed paths - up to homotopy equivalence...

Equilibrium phase-space distributions and space charge limits in linacs

International Nuclear Information System (INIS)

Lysenko, W.P.

1977-10-01

Limits on beam current and emittance in proton and heavy ion linear accelerators resulting from space charge forces are calculated. The method involves determining equilibrium distributions in phase space using a continuous focusing, no acceleration, model in two degrees of freedom using the coordinates r and z. A nonlinear Poisson equation must be solved numerically. This procedure is a matching between the longitudinal and transverse directions to minimize the effect of longitudinal-transverse coupling which is believed to be the main problem in emittance growth due to space charge in linacs. Limits on the Clinton P. Anderson Meson Physics Facility (LAMPF) accelerator performance are calculated as an example. The beam physics is described by a few space charge parameters so that accelerators with different physical parameters can be compared in a natural way. The main result of this parameter study is that the requirement of a high-intensity beam is best fulfilled with a low-frequency accelerator whereas the requirement of a high-brightness beam is best fulfilled with a high-frequency accelerator

Identification of ecosystem parameters by SDE-modelling

DEFF Research Database (Denmark)

Stochastic differential equations (SDEs) for ecosystem modelling have attracted increasing attention during recent years. The modelling has mostly been through simulation experiments in order to analyse how system noise propagates through the ordinary differential equation formulation of ecosystem...... models. Estimation of parameters in SDEs is, however, possible by combining Kalman filter techniques and likelihood estimation. By modelling parameters as random walks it is possible to identify linear as well as non-linear interactions between ecosystem components. By formulating a simple linear SDE...

Housing land transaction data and structural econometric estimation of preference parameters for urban economic simulation models.

Science.gov (United States)

Caruso, Geoffrey; Cavailhès, Jean; Peeters, Dominique; Thomas, Isabelle; Frankhauser, Pierre; Vuidel, Gilles

2015-12-01

This paper describes a dataset of 6284 land transactions prices and plot surfaces in 3 medium-sized cities in France (Besançon, Dijon and Brest). The dataset includes road accessibility as obtained from a minimization algorithm, and the amount of green space available to households in the neighborhood of the transactions, as evaluated from a land cover dataset. Further to the data presentation, the paper describes how these variables can be used to estimate the non-observable parameters of a residential choice function explicitly derived from a microeconomic model. The estimates are used by Caruso et al. (2015) to run a calibrated microeconomic urban growth simulation model where households are assumed to trade-off accessibility and local green space amenities.

Housing land transaction data and structural econometric estimation of preference parameters for urban economic simulation models

Science.gov (United States)

Caruso, Geoffrey; Cavailhès, Jean; Peeters, Dominique; Thomas, Isabelle; Frankhauser, Pierre; Vuidel, Gilles

2015-01-01

This paper describes a dataset of 6284 land transactions prices and plot surfaces in 3 medium-sized cities in France (Besançon, Dijon and Brest). The dataset includes road accessibility as obtained from a minimization algorithm, and the amount of green space available to households in the neighborhood of the transactions, as evaluated from a land cover dataset. Further to the data presentation, the paper describes how these variables can be used to estimate the non-observable parameters of a residential choice function explicitly derived from a microeconomic model. The estimates are used by Caruso et al. (2015) to run a calibrated microeconomic urban growth simulation model where households are assumed to trade-off accessibility and local green space amenities. PMID:26958606

GMC COLLISIONS AS TRIGGERS OF STAR FORMATION. I. PARAMETER SPACE EXPLORATION WITH 2D SIMULATIONS

Energy Technology Data Exchange (ETDEWEB)

Wu, Benjamin [Department of Physics, University of Florida, Gainesville, FL 32611 (United States); Loo, Sven Van [School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT (United Kingdom); Tan, Jonathan C. [Departments of Astronomy and Physics, University of Florida, Gainesville, FL 32611 (United States); Bruderer, Simon, E-mail: benwu@phys.ufl.edu [Max Planck Institute for Extraterrestrial Physics, Giessenbachstrasse 1, D-85748 Garching (Germany)

2015-09-20

We utilize magnetohydrodynamic (MHD) simulations to develop a numerical model for giant molecular cloud (GMC)–GMC collisions between nearly magnetically critical clouds. The goal is to determine if, and under what circumstances, cloud collisions can cause pre-existing magnetically subcritical clumps to become supercritical and undergo gravitational collapse. We first develop and implement new photodissociation region based heating and cooling functions that span the atomic to molecular transition, creating a multiphase ISM and allowing modeling of non-equilibrium temperature structures. Then in 2D and with ideal MHD, we explore a wide parameter space of magnetic field strength, magnetic field geometry, collision velocity, and impact parameter and compare isolated versus colliding clouds. We find factors of ∼2–3 increase in mean clump density from typical collisions, with strong dependence on collision velocity and magnetic field strength, but ultimately limited by flux-freezing in 2D geometries. For geometries enabling flow along magnetic field lines, greater degrees of collapse are seen. We discuss observational diagnostics of cloud collisions, focussing on {sup 13}CO(J = 2–1), {sup 13}CO(J = 3–2), and {sup 12}CO(J = 8–7) integrated intensity maps and spectra, which we synthesize from our simulation outputs. We find that the ratio of J = 8–7 to lower-J emission is a powerful diagnostic probe of GMC collisions.

Spatio-temporal modeling of nonlinear distributed parameter systems

CERN Document Server

Li, Han-Xiong

2011-01-01

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein s

Influence of simulation assumptions and input parameters on energy balance calculations of residential buildings

International Nuclear Information System (INIS)

Dodoo, Ambrose; Tettey, Uniben Yao Ayikoe; Gustavsson, Leif

2017-01-01

In this study, we modelled the influence of different simulation assumptions on energy balances of two variants of a residential building, comprising the building in its existing state and with energy-efficient improvements. We explored how selected parameter combinations and variations affect the energy balances of the building configurations. The selected parameters encompass outdoor microclimate, building thermal envelope and household electrical equipment including technical installations. Our modelling takes into account hourly as well as seasonal profiles of different internal heat gains. The results suggest that the impact of parameter interactions on calculated space heating of buildings is somewhat small and relatively more noticeable for an energy-efficient building in contrast to a conventional building. We find that the influence of parameters combinations is more apparent as more individual parameters are varied. The simulations show that a building's calculated space heating demand is significantly influenced by how heat gains from electrical equipment are modelled. For the analyzed building versions, calculated final energy for space heating differs by 9–14 kWh/m"2 depending on the assumed energy efficiency level for electrical equipment. The influence of electrical equipment on calculated final space heating is proportionally more significant for an energy-efficient building compared to a conventional building. This study shows the influence of different simulation assumptions and parameter combinations when varied simultaneously. - Highlights: • Energy balances are modelled for conventional and efficient variants of a building. • Influence of assumptions and parameter combinations and variations are explored. • Parameter interactions influence is apparent as more single parameters are varied. • Calculated space heating demand is notably affected by how heat gains are modelled.

Modeling volatility using state space models.

Science.gov (United States)

Timmer, J; Weigend, A S

1997-08-01

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used: Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).

Models for estimating photosynthesis parameters from in situ production profiles

Science.gov (United States)

Kovač, Žarko; Platt, Trevor; Sathyendranath, Shubha; Antunović, Suzana

2017-12-01

The rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

Science.gov (United States)

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Exploring the parameter space of warm-inflation models

Science.gov (United States)

Bastero-Gil, Mar; Berera, Arjun; Kronberg, Nico

2015-12-01

Warm inflation includes inflaton interactions with other fields throughout the inflationary epoch instead of confining such interactions to a distinct reheating era. Previous investigations have shown that, when certain constraints on the dynamics of these interactions and the resultant radiation bath are satisfied, a low-momentum-dominated dissipation coefficient propto T3/mχ2 can sustain an era of inflation compatible with CMB observations. In this work, we extend these analyses by including the pole-dominated dissipation term propto √mχ T exp(-mχ/T). We find that, with this enhanced dissipation, certain models, notably the quadratic hilltop potential, perform significantly better. Specifically, we can achieve 50 e-folds of inflation and a spectral index compatible with Planck data while requiring fewer mediator field (Script O(104) for the quadratic hilltop potential) and smaller coupling constants, opening up interesting model-building possibilities. We also highlight the significance of the specific parametric dependence of the dissipative coefficient which could prove useful in even greater reduction in field content.

GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling

International Nuclear Information System (INIS)

Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas

2015-01-01

Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and

PARAMETER COORDINATION AND ROBUST OPTIMIZATION FOR MULTIDISCIPLINARY DESIGN

Institute of Scientific and Technical Information of China (English)

HU Jie; PENG Yinghong; XIONG Guangleng

2006-01-01

A new parameter coordination and robust optimization approach for multidisciplinary design is presented. Firstly, the constraints network model is established to support engineering change, coordination and optimization. In this model, interval boxes are adopted to describe the uncertainty of design parameters quantitatively to enhance the design robustness. Secondly, the parameter coordination method is presented to solve the constraints network model, monitor the potential conflicts due to engineering changes, and obtain the consistency solution space corresponding to the given product specifications. Finally, the robust parameter optimization model is established, and genetic arithmetic is used to obtain the robust optimization parameter. An example of bogie design is analyzed to show the scheme to be effective.

Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

Science.gov (United States)

Janardhanan, S.; Datta, B.

2011-12-01

Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of

Identification of parameters of discrete-continuous models

International Nuclear Information System (INIS)

Cekus, Dawid; Warys, Pawel

2015-01-01

In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible

Identification of parameters of discrete-continuous models

Energy Technology Data Exchange (ETDEWEB)

Cekus, Dawid, E-mail: cekus@imipkm.pcz.pl; Warys, Pawel, E-mail: warys@imipkm.pcz.pl [Institute of Mechanics and Machine Design Foundations, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa (Poland)

2015-03-10

In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.

Identifying the connective strength between model parameters and performance criteria

Directory of Open Access Journals (Sweden)

B. Guse

2017-11-01

Full Text Available In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE, Kling–Gupta efficiency (KGE and its three components (alpha, beta and r as well as RSR (the ratio of the root mean square error to the standard deviation for different segments of the flow duration curve (FDC are calculated. With a joint analysis of two regression tree (RT approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria

Agricultural and Environmental Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Kaylie Rasmuson; Kurt Rautenstrauch

2003-01-01

This analysis is one of nine technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. It documents input parameters for the biosphere model, and supports the use of the model to develop Biosphere Dose Conversion Factors (BDCF). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in the biosphere Technical Work Plan (TWP, BSC 2003a). It should be noted that some documents identified in Figure 1-1 may be under development and therefore not available at the time this document is issued. The ''Biosphere Model Report'' (BSC 2003b) describes the ERMYN and its input parameters. This analysis report, ANL-MGR-MD-000006, ''Agricultural and Environmental Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. This report defines and justifies values for twelve parameters required in the biosphere model. These parameters are related to use of contaminated groundwater to grow crops. The parameter values recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN

Use of animal models for space flight physiology studies, with special focus on the immune system

Science.gov (United States)

Sonnenfeld, Gerald

2005-01-01

Animal models have been used to study the effects of space flight on physiological systems. The animal models have been used because of the limited availability of human subjects for studies to be carried out in space as well as because of the need to carry out experiments requiring samples and experimental conditions that cannot be performed using humans. Experiments have been carried out in space using a variety of species, and included developmental biology studies. These species included rats, mice, non-human primates, fish, invertebrates, amphibians and insects. The species were chosen because they best fit the experimental conditions required for the experiments. Experiments with animals have also been carried out utilizing ground-based models that simulate some of the effects of exposure to space flight conditions. Most of the animal studies have generated results that parallel the effects of space flight on human physiological systems. Systems studied have included the neurovestibular system, the musculoskeletal system, the immune system, the neurological system, the hematological system, and the cardiovascular system. Hindlimb unloading, a ground-based model of some of the effects of space flight on the immune system, has been used to study the effects of space flight conditions on physiological parameters. For the immune system, exposure to hindlimb unloading has been shown to results in alterations of the immune system similar to those observed after space flight. This has permitted the development of experiments that demonstrated compromised resistance to infection in rodents maintained in the hindlimb unloading model as well as the beginning of studies to develop countermeasures to ameliorate or prevent such occurrences. Although there are limitations to the use of animal models for the effects of space flight on physiological systems, the animal models should prove very valuable in designing countermeasures for exploration class missions of the future.

A discrete-space urban model with environmental amenities

Science.gov (United States)

Liaila Tajibaeva; Robert G. Haight; Stephen Polasky

2008-01-01

This paper analyzes the effects of providing environmental amenities associated with open space in a discrete-space urban model and characterizes optimal provision of open space across a metropolitan area. The discrete-space model assumes distinct neighborhoods in which developable land is homogeneous within a neighborhood but heterogeneous across neighborhoods. Open...

Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization.

Energy Technology Data Exchange (ETDEWEB)

Buchheit, Thomas E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wilcox, Ian Zachary [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandoval, Andrew J [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Reza, Shahed [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-12-01

This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction and portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.

Scanning the parameter space of collapsing rotating thin shells

Science.gov (United States)

Rocha, Jorge V.; Santarelli, Raphael

2018-06-01

We present results of a comprehensive study of collapsing and bouncing thin shells with rotation, framing it in the context of the weak cosmic censorship conjecture. The analysis is based on a formalism developed specifically for higher odd dimensions that is able to describe the dynamics of collapsing rotating shells exactly. We analyse and classify a plethora of shell trajectories in asymptotically flat spacetimes. The parameters varied include the shell’s mass and angular momentum, its radial velocity at infinity, the (linear) equation-of-state parameter and the spacetime dimensionality. We find that plunges of rotating shells into black holes never produce naked singularities, as long as the matter shell obeys the weak energy condition, and so respects cosmic censorship. This applies to collapses of dust shells starting from rest or with a finite velocity at infinity. Not even shells with a negative isotropic pressure component (i.e. tension) lead to the formation of naked singularities, as long as the weak energy condition is satisfied. Endowing the shells with a positive isotropic pressure component allows for the existence of bouncing trajectories satisfying the dominant energy condition and fully contained outside rotating black holes. Otherwise any turning point occurs always inside the horizon. These results are based on strong numerical evidence from scans of numerous sections in the large parameter space available to these collapsing shells. The generalisation of the radial equation of motion to a polytropic equation-of-state for the matter shell is also included in an appendix.

Brownian motion model with stochastic parameters for asset prices

Science.gov (United States)

Ching, Soo Huei; Hin, Pooi Ah

2013-09-01

The Brownian motion model may not be a completely realistic model for asset prices because in real asset prices the drift μ and volatility σ may change over time. Presently we consider a model in which the parameter x = (μ,σ) is such that its value x (t + Δt) at a short time Δt ahead of the present time t depends on the value of the asset price at time t + Δt as well as the present parameter value x(t) and m-1 other parameter values before time t via a conditional distribution. The Malaysian stock prices are used to compare the performance of the Brownian motion model with fixed parameter with that of the model with stochastic parameter.

Study on Parameters Modeling of Wind Turbines Using SCADA Data

Directory of Open Access Journals (Sweden)

Yonglong YAN

2014-08-01

Full Text Available Taking the advantage of the current massive monitoring data from Supervisory Control and Data Acquisition (SCADA system of wind farm, it is of important significance for anomaly detection, early warning and fault diagnosis to build the data model of state parameters of wind turbines (WTs. The operational conditions and the relationships between the state parameters of wind turbines are complex. It is difficult to establish the model of state parameter accurately, and the modeling method of state parameters of wind turbines considering parameter selection is proposed. Firstly, by analyzing the characteristic of SCADA data, a reasonable range of data and monitoring parameters are chosen. Secondly, neural network algorithm is adapted, and the selection method of input parameters in the model is presented. Generator bearing temperature and cooling air temperature are regarded as target parameters, and the two models are built and input parameters of the models are selected, respectively. Finally, the parameter selection method in this paper and the method using genetic algorithm-partial least square (GA-PLS are analyzed comparatively, and the results show that the proposed methods are correct and effective. Furthermore, the modeling of two parameters illustrate that the method in this paper can applied to other state parameters of wind turbines.

Seven-parameter statistical model for BRDF in the UV band.

Science.gov (United States)

Bai, Lu; Wu, Zhensen; Zou, Xiren; Cao, Yunhua

2012-05-21

A new semi-empirical seven-parameter BRDF model is developed in the UV band using experimentally measured data. The model is based on the five-parameter model of Wu and the fourteen-parameter model of Renhorn and Boreman. Surface scatter, bulk scatter and retro-reflection scatter are considered. An optimizing modeling method, the artificial immune network genetic algorithm, is used to fit the BRDF measurement data over a wide range of incident angles. The calculation time and accuracy of the five- and seven-parameter models are compared. After fixing the seven parameters, the model can well describe scattering data in the UV band.

A General 2D Meshless Interpolating Boundary Node Method Based on the Parameter Space

Directory of Open Access Journals (Sweden)

Hongyin Yang

2017-01-01

Full Text Available The presented study proposed an improved interpolating boundary node method (IIBNM for 2D potential problems. The improved interpolating moving least-square (IIMLS method was applied to construct the shape functions, of which the delta function properties and boundary conditions were directly implemented. In addition, any weight function used in the moving least-square (MLS method was also applicable in the IIMLS method. Boundary cells were required in the computation of the boundary integrals, and additional discretization error was not avoided if traditional cells were used to approximate the geometry. The present study applied the parametric cells created in the parameter space to preserve the exact geometry, and the geometry was maintained due to the number of cells. Only the number of nodes on the boundary was required as additional information for boundary node construction. Most importantly, the IIMLS method can be applied in the parameter space to construct shape functions without the requirement of additional computations for the curve length.

Lumped-parameter Model of a Bucket Foundation

DEFF Research Database (Denmark)

Andersen, Lars; Ibsen, Lars Bo; Liingaard, Morten

2009-01-01

efficient model that can be applied in aero-elastic codes for fast evaluation of the dynamic structural response of wind turbines. The target solutions, utilised for calibration of the lumped-parameter models, are obtained by a coupled finite-element/boundaryelement scheme in the frequency domain......, and the quality of the models are tested in the time and frequency domains. It is found that precise results are achieved by lumped-parameter models with two to four internal degrees of freedom per displacement or rotation of the foundation. Further, coupling between the horizontal sliding and rocking cannot...

Extended Hamiltonian formalism of the pure space-like axial gauge Schwinger model

International Nuclear Information System (INIS)

Nakawaki, Yuji; Mccartor, Gary

2001-01-01

We demonstrate that pure space-like axial gauge quantizations of gauge fields can be constructed in ways that are free from infrared divergences. To do so, we must extend the Hamiltonian formalism to include residual gauge fields. We construct an operator solution and an extended Hamiltonian of the pure space-like axial gauge Schwinger model. We begin by constructing an axial gauge formation in auxiliary coordinates, x μ =(x + , x - ), where x + =x 0 sinθ + x 1 cosθ, x - =x 0 cosθ - x 1 sinθ, and we take A=A 0 cosθ + A 1 sin θ=0 as the gauge fixing condition. In the region 0 - as the evolution parameter and construct a traditional canonical formulation of the temporal gauge Schwinger model in which residual gauge fields dependent only on x + are static canonical variables. Then we extrapolate the temporal gauge operator solution into the axial region, π / 4 + is taken as the evolution parameter. In the axial region we find that we have to take the representation of the residual gauge fields realizing the Mandelstam-Leibbrandt prescription in order for the infrared divergences resulting from (∂) -1 to be canceled by corresponding ones resulting from the inverse of the hyperbolic Laplace operator. We overcome the difficulty of constructing the Hamiltonian for the residual gauge fields by employing McCartor and Robertson's method, which gives us a term integrated over x - =constant. Finally, by taking the limit θ→π / 2 - 0, we obtain an operator solution and the Hamiltonian of the axial gauge (Coulomb gauge) Schwinger model in ordinary coordinates. That solution includes auxiliary fields, and the representation space is of indefinite metric, providing further evidence that 'physical' gauges are no more physical than 'unphysical' gauges. (author)

Source term modelling parameters for Project-90

International Nuclear Information System (INIS)

Shaw, W.; Smith, G.; Worgan, K.; Hodgkinson, D.; Andersson, K.

1992-04-01

This document summarises the input parameters for the source term modelling within Project-90. In the first place, the parameters relate to the CALIBRE near-field code which was developed for the Swedish Nuclear Power Inspectorate's (SKI) Project-90 reference repository safety assessment exercise. An attempt has been made to give best estimate values and, where appropriate, a range which is related to variations around base cases. It should be noted that the data sets contain amendments to those considered by KBS-3. In particular, a completely new set of inventory data has been incorporated. The information given here does not constitute a complete set of parameter values for all parts of the CALIBRE code. Rather, it gives the key parameter values which are used in the constituent models within CALIBRE and the associated studies. For example, the inventory data acts as an input to the calculation of the oxidant production rates, which influence the generation of a redox front. The same data is also an initial value data set for the radionuclide migration component of CALIBRE. Similarly, the geometrical parameters of the near-field are common to both sub-models. The principal common parameters are gathered here for ease of reference and avoidance of unnecessary duplication and transcription errors. (au)

The role of extreme orbits in the global organization of periodic regions in parameter space for one dimensional maps

Energy Technology Data Exchange (ETDEWEB)

Costa, Diogo Ricardo da, E-mail: diogo_cost@hotmail.com [Departamento de Física, UNESP – Universidade Estadual Paulista, Av. 24A, 1515, Bela Vista, 13506-900, Rio Claro, SP (Brazil); Hansen, Matheus [Departamento de Física, UNESP – Universidade Estadual Paulista, Av. 24A, 1515, Bela Vista, 13506-900, Rio Claro, SP (Brazil); Instituto de Física, Univ. São Paulo, Rua do Matão, Cidade Universitária, 05314-970, São Paulo – SP (Brazil); Guarise, Gustavo [Departamento de Física, UNESP – Universidade Estadual Paulista, Av. 24A, 1515, Bela Vista, 13506-900, Rio Claro, SP (Brazil); Medrano-T, Rene O. [Departamento de Ciências Exatas e da Terra, UNIFESP – Universidade Federal de São Paulo, Rua São Nicolau, 210, Centro, 09913-030, Diadema, SP (Brazil); Department of Mathematics, Imperial College London, London SW7 2AZ (United Kingdom); Leonel, Edson D. [Departamento de Física, UNESP – Universidade Estadual Paulista, Av. 24A, 1515, Bela Vista, 13506-900, Rio Claro, SP (Brazil); Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34151 Trieste (Italy)

2016-04-22

We show that extreme orbits, trajectories that connect local maximum and minimum values of one dimensional maps, play a major role in the parameter space of dissipative systems dictating the organization for the windows of periodicity, hence producing sets of shrimp-like structures. Here we solve three fundamental problems regarding the distribution of these sets and give: (i) their precise localization in the parameter space, even for sets of very high periods; (ii) their local and global distributions along cascades; and (iii) the association of these cascades to complicate sets of periodicity. The extreme orbits are proved to be a powerful indicator to investigate the organization of windows of periodicity in parameter planes. As applications of the theory, we obtain some results for the circle map and perturbed logistic map. The formalism presented here can be extended to many other different nonlinear and dissipative systems. - Highlights: • Extreme orbits and the organization of periodic regions in parameter space. • One-dimensional dissipative mappings. • The circle map and also a time perturbed logistic map were studied.

The role of extreme orbits in the global organization of periodic regions in parameter space for one dimensional maps

International Nuclear Information System (INIS)

Costa, Diogo Ricardo da; Hansen, Matheus; Guarise, Gustavo; Medrano-T, Rene O.; Leonel, Edson D.

2016-01-01

We show that extreme orbits, trajectories that connect local maximum and minimum values of one dimensional maps, play a major role in the parameter space of dissipative systems dictating the organization for the windows of periodicity, hence producing sets of shrimp-like structures. Here we solve three fundamental problems regarding the distribution of these sets and give: (i) their precise localization in the parameter space, even for sets of very high periods; (ii) their local and global distributions along cascades; and (iii) the association of these cascades to complicate sets of periodicity. The extreme orbits are proved to be a powerful indicator to investigate the organization of windows of periodicity in parameter planes. As applications of the theory, we obtain some results for the circle map and perturbed logistic map. The formalism presented here can be extended to many other different nonlinear and dissipative systems. - Highlights: • Extreme orbits and the organization of periodic regions in parameter space. • One-dimensional dissipative mappings. • The circle map and also a time perturbed logistic map were studied.

Assessing composition and structure of soft biphasic media from Kelvin-Voigt fractional derivative model parameters

Science.gov (United States)

Zhang, Hongmei; Wang, Yue; Fatemi, Mostafa; Insana, Michael F.

2017-03-01

Kelvin-Voigt fractional derivative (KVFD) model parameters have been used to describe viscoelastic properties of soft tissues. However, translating model parameters into a concise set of intrinsic mechanical properties related to tissue composition and structure remains challenging. This paper begins by exploring these relationships using a biphasic emulsion materials with known composition. Mechanical properties are measured by analyzing data from two indentation techniques—ramp-stress relaxation and load-unload hysteresis tests. Material composition is predictably correlated with viscoelastic model parameters. Model parameters estimated from the tests reveal that elastic modulus E 0 closely approximates the shear modulus for pure gelatin. Fractional-order parameter α and time constant τ vary monotonically with the volume fraction of the material’s fluid component. α characterizes medium fluidity and the rate of energy dissipation, and τ is a viscous time constant. Numerical simulations suggest that the viscous coefficient η is proportional to the energy lost during quasi-static force-displacement cycles, E A . The slope of E A versus η is determined by α and the applied indentation ramp time T r. Experimental measurements from phantom and ex vivo liver data show close agreement with theoretical predictions of the η -{{E}A} relation. The relative error is less than 20% for emulsions 22% for liver. We find that KVFD model parameters form a concise features space for biphasic medium characterization that described time-varying mechanical properties. The experimental work was carried out at the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. Methodological development, including numerical simulation and all data analysis, were carried out at the school of Life Science and Technology, Xi’an JiaoTong University, 710049, China.

Halogenation of Hydraulic Fracturing Additives in the Shale Well Parameter Space

Science.gov (United States)

Sumner, A. J.; Plata, D.

2017-12-01

Horizontal Drilling and Hydraulic fracturing (HDHF) involves the deep-well injection of a `fracking fluid' composed of diverse and numerous chemical additives designed to facilitate the release and collection of natural gas from shale plays. The potential impacts of HDHF operations on water resources and ecosystems are numerous, and analyses of flowback samples revealed organic compounds from both geogenic and anthropogenic sources. Furthermore, halogenated chemicals were also detected, and these compounds are rarely disclosed, suggesting the in situ halogenation of reactive additives. To test this transformation hypothesis, we designed and operated a novel high pressure and temperature reactor system to simulate the shale well parameter space and investigate the chemical reactivity of twelve commonly disclosed and functionally diverse HDHF additives. Early results revealed an unanticipated halogenation pathway of α-β unsaturated aldehyde, Cinnamaldehyde, in the presence of oxidant and concentrated brine. Ongoing experiments over a range of parameters informed a proposed mechanism, demonstrating the role of various shale-well specific parameters in enabling the demonstrated halogenation pathway. Ultimately, these results will inform a host of potentially unintended interactions of HDHF additives during the extreme conditions down-bore of a shale well during HDHF activities.

Modeling of exoplanets interiors in the framework of future space missions

Science.gov (United States)

Brugger, B.; Mousis, O.; Deleuil, M.

2017-12-01

Probing the interior of exoplanets with known masses and radii is possible via the use of models of internal structure. Here we present a model able to handle various planetary compositions, from terrestrial bodies to ocean worlds or carbon-rich planets, and its application to the case of CoRoT-7b. Using the elemental abundances of an exoplanet’s host star, we significantly reduce the degeneracy limiting such models. This further constrains the type and state of material present at the surface, and helps estimating the composition of a secondary atmosphere that could form in these conditions through potential outgassing. Upcoming space missions dedicated to exoplanet characterization, such as PLATO, will provide accurate fundamental parameters of Earth-like planets orbiting in the habitable zone, for which our model is well adapted.

Effect of alloy deformation on the average spacing parameters of non-deforming particles

International Nuclear Information System (INIS)

Fisher, J.; Gurland, J.

1980-02-01

It is shown on the basis of stereological definitions and a few simple experiments that the commonly used average dispersion parameters, area fraction (A/sub A/)/sub β/, areal particle density N/sub Aβ/ and mean free path lambda/sub α/, remain invariant during plastic deformation in the case of non-deforming equiaxed particles. Directional effects on the spacing parameters N/sub Aβ/ and lambda/sub α/ arise during uniaxial deformation by rotation and preferred orientation of nonequiaxed particles. Particle arrangement in stringered or layered structures and the effect of deformation on nearest neighbor distances of particles and voids are briefly discussed in relation to strength and fracture theories

Agricultural and Environmental Input Parameters for the Biosphere Model

Energy Technology Data Exchange (ETDEWEB)

Kaylie Rasmuson; Kurt Rautenstrauch

2003-06-20

This analysis is one of nine technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. It documents input parameters for the biosphere model, and supports the use of the model to develop Biosphere Dose Conversion Factors (BDCF). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in the biosphere Technical Work Plan (TWP, BSC 2003a). It should be noted that some documents identified in Figure 1-1 may be under development and therefore not available at the time this document is issued. The ''Biosphere Model Report'' (BSC 2003b) describes the ERMYN and its input parameters. This analysis report, ANL-MGR-MD-000006, ''Agricultural and Environmental Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. This report defines and justifies values for twelve parameters required in the biosphere model. These parameters are related to use of contaminated groundwater to grow crops. The parameter values recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN.

Near-Earth Space Radiation Models

Science.gov (United States)

Xapsos, Michael A.; O'Neill, Patrick M.; O'Brien, T. Paul

2012-01-01

Review of models of the near-Earth space radiation environment is presented, including recent developments in trapped proton and electron, galactic cosmic ray and solar particle event models geared toward spacecraft electronics applications.

The mobilisation model and parameter sensitivity

International Nuclear Information System (INIS)

Blok, B.M.

1993-12-01

In the PRObabillistic Safety Assessment (PROSA) of radioactive waste in a salt repository one of the nuclide release scenario's is the subrosion scenario. A new subrosion model SUBRECN has been developed. In this model the combined effect of a depth-dependent subrosion, glass dissolution, and salt rise has been taken into account. The subrosion model SUBRECN and the implementation of this model in the German computer program EMOS4 is presented. A new computer program PANTER is derived from EMOS4. PANTER models releases of radionuclides via subrosion from a disposal site in a salt pillar into the biosphere. For uncertainty and sensitivity analyses the new subrosion model Latin Hypercube Sampling has been used for determine the different values for the uncertain parameters. The influence of the uncertainty in the parameters on the dose calculations has been investigated by the following sensitivity techniques: Spearman Rank Correlation Coefficients, Partial Rank Correlation Coefficients, Standardised Rank Regression Coefficients, and the Smirnov Test. (orig./HP)

The effect of environmental parameters to dust concentration in air-conditioned space

Science.gov (United States)

Ismail, A. M. M.; Manssor, N. A. S.; Nalisa, A.; Yahaya, N.

2017-08-01

Malaysia has a wet and hot climate, therefore most of the spaces are air conditioned. The environment might affect dust concentration inside a space and affect the indoor air quality (IAQ). The main objective of this study is to study the dust concentration collected inside enclosed air-conditioned space. The measurement was done physically at four selected offices and two classrooms using a number of equipment to measure the dust concentration and environmental parameters which are temperature and relative air humidity. It was found that the highest dust concentration produced in office (temperature of 24.7°C, relative humidity of 66.5%) is 0.075 mg/m3, as compared to classroom, the highest dust concentration produced is 0.060 mg/m3 office (temperature of 25.9°C, relative humidity of 64.0%). However, both measurements show that value still within the safety level set by DOSH Malaysia (2005-2010) and ASHRAE 62.2 2016. The office contained higher dust concentration compared to classroom because of frequent movement transpires daily due to the functional of the offices.

A logistics model for large space power systems

Science.gov (United States)

Koelle, H. H.

Space Power Systems (SPS) have to overcome two hurdles: (1) to find an attractive design, manufacturing and assembly concept and (2) to have available a space transportation system that can provide economical logistic support during the construction and operational phases. An initial system feasibility study, some five years ago, was based on a reference system that used terrestrial resources only and was based partially on electric propulsion systems. The conclusion was: it is feasible but not yet economically competitive with other options. This study is based on terrestrial and extraterrestrial resources and on chemical (LH 2/LOX) propulsion systems. These engines are available from the Space Shuttle production line and require small changes only. Other so-called advanced propulsion systems investigated did not prove economically superior if lunar LOX is available! We assume that a Shuttle derived Heavy Lift Launch Vehicle (HLLV) will become available around the turn of the century and that this will be used to establish a research base on the lunar surface. This lunar base has the potential to grow into a lunar factory producing LOX and construction materials for supporting among other projects also the construction of space power systems in geostationary orbit. A model was developed to simulate the logistics support of such an operation for a 50-year life cycle. After 50 years 111 SPS units with 5 GW each and an availability of 90% will produce 100 × 5 = 500 GW. The model comprises 60 equations and requires 29 assumptions of the parameter involved. 60-state variables calculated with the 60 equations mentioned above are given on an annual basis and as averages for the 50-year life cycle. Recycling of defective parts in geostationary orbit is one of the features of the model. The state-of-the-art with respect to SPS technology is introduced as a variable Mg mass/MW electric power delivered. If the space manufacturing facility, a maintenance and repair facility

Bayesian estimation of parameters in a regional hydrological model

Directory of Open Access Journals (Sweden)

K. Engeland

2002-01-01

Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

Study on TVD parameters sensitivity of a crankshaft using multiple scale and state space method considering quadratic and cubic non-linearities

Directory of Open Access Journals (Sweden)

R. Talebitooti

Full Text Available In this paper the effect of quadratic and cubic non-linearities of the system consisting of the crankshaft and torsional vibration damper (TVD is taken into account. TVD consists of non-linear elastomer material used for controlling the torsional vibration of crankshaft. The method of multiple scales is used to solve the governing equations of the system. Meanwhile, the frequency response of the system for both harmonic and sub-harmonic resonances is extracted. In addition, the effects of detuning parameters and other dimensionless parameters for a case of harmonic resonance are investigated. Moreover, the external forces including both inertia and gas forces are simultaneously applied into the model. Finally, in order to study the effectiveness of the parameters, the dimensionless governing equations of the system are solved, considering the state space method. Then, the effects of the torsional damper as well as all corresponding parameters of the system are discussed.

Application of a rat hindlimb model: a prediction of force spaces reachable through stimulation of nerve fascicles.

Science.gov (United States)

Johnson, Will L; Jindrich, Devin L; Zhong, Hui; Roy, Roland R; Edgerton, V Reggie

2011-12-01

A device to generate standing or locomotion through chronically placed electrodes has not been fully developed due in part to limitations of clinical experimentation and the high number of muscle activation inputs of the leg. We investigated the feasibility of functional electrical stimulation paradigms that minimize the input dimensions for controlling the limbs by stimulating at nerve fascicles, utilizing a model of the rat hindlimb, which combined previously collected morphological data with muscle physiological parameters presented herein. As validation of the model, we investigated the suitability of a lumped-parameter model for the prediction of muscle activation during dynamic tasks. Using the validated model, we found that the space of forces producible through activation of muscle groups sharing common nerve fascicles was nonlinearly dependent on the number of discrete muscle groups that could be individually activated (equivalently, the neuroanatomical level of activation). Seven commonly innervated muscle groups were sufficient to produce 78% of the force space producible through individual activation of the 42 modeled hindlimb muscles. This novel, neuroanatomically derived reduction in input dimension emphasizes the potential to simplify controllers for functional electrical stimulation to improve functional recovery after a neuromuscular injury.

Remote sensing of refractivity from space for global observations of atmospheric parameters

International Nuclear Information System (INIS)

Gorbunov, M.E.; Sokolovskiy, S.V.

1993-01-01

This report presents the first results of computational simulations on the retrieval of meteorological parameters from space refractometric data on the basis of the ECHAM 3 model developed at the Max Planck Institute for Meteorology (Roeckner et al. 1992). For this purpose the grid fields of temperature, geopotential and humidity available from the model were interpolated and a continuous spatial field of refractivity (together with its first derivative) was generated. This field was used for calculating the trajectories of electromagnetic rays for the given orbits of transmitting and receiving satellites and for the determination of the quantities (incident angles or Doppler frequency shifts) being measured at receiving satellite during occultation. These quantities were then used for solving the inverse problem - retrieving the distribution of refractivity in the vicinity of the ray perigees. The retrieved refractivity was used to calculate pressure and temperature (using the hydrostatic equation and the equation of state). The results were compared with initial data, and the retrieval errors were evaluated. The study shows that the refractivity can be retrieved with very high accuracy in particular if a tomographic reconstruction is applied. Effects of humidity and temperature are not separable. Stratospheric temperatures globally and upper tropospheric temperatures at middle and high latitudes can be accurately retrieved, other areas require humidity data. Alternatively humidity data can be retrieved if the temperature fields are known. (orig.)

Models and parameters for environmental radiological assessments

International Nuclear Information System (INIS)

Miller, C.W.

1983-01-01

This article reviews the forthcoming book Models and Parameters for Environmental Radiological Assessments, which presents a unified compilation of models and parameters for assessing the impact on man of radioactive discharges, both routine and accidental, into the environment. Models presented in this book include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Summaries are presented for each of the transport and dosimetry areas previously for each of the transport and dosimetry areas previously mentioned, and details are available in the literature cited. A chapter of example problems illustrates many of the methodologies presented throughout the text. Models and parameters presented are based on the results of extensive literature reviews and evaluations performed primarily by the staff of the Health and Safety Research Division of Oak Ridge National Laboratory

An introduction to Space Weather Integrated Modeling

Science.gov (United States)

Zhong, D.; Feng, X.

2012-12-01

The need for a software toolkit that integrates space weather models and data is one of many challenges we are facing with when applying the models to space weather forecasting. To meet this challenge, we have developed Space Weather Integrated Modeling (SWIM) that is capable of analysis and visualizations of the results from a diverse set of space weather models. SWIM has a modular design and is written in Python, by using NumPy, matplotlib, and the Visualization ToolKit (VTK). SWIM provides data management module to read a variety of spacecraft data products and a specific data format of Solar-Interplanetary Conservation Element/Solution Element MHD model (SIP-CESE MHD model) for the study of solar-terrestrial phenomena. Data analysis, visualization and graphic user interface modules are also presented in a user-friendly way to run the integrated models and visualize the 2-D and 3-D data sets interactively. With these tools we can locally or remotely analysis the model result rapidly, such as extraction of data on specific location in time-sequence data sets, plotting interplanetary magnetic field lines, multi-slicing of solar wind speed, volume rendering of solar wind density, animation of time-sequence data sets, comparing between model result and observational data. To speed-up the analysis, an in-situ visualization interface is used to support visualizing the data 'on-the-fly'. We also modified some critical time-consuming analysis and visualization methods with the aid of GPU and multi-core CPU. We have used this tool to visualize the data of SIP-CESE MHD model in real time, and integrated the Database Model of shock arrival, Shock Propagation Model, Dst forecasting model and SIP-CESE MHD model developed by SIGMA Weather Group at State Key Laboratory of Space Weather/CAS.

Space weather modeling using artificial neural network. (Slovak Title: Modelovanie kozmického počasia umelou neurónovou sietou)

Science.gov (United States)

Valach, F.; Revallo, M.; Hejda, P.; Bochníček, J.

2010-12-01

Our modern society with its advanced technology is becoming increasingly vulnerable to the Earth's system disorders originating in explosive processes on the Sun. Coronal mass ejections (CMEs) blasted into interplanetary space as gigantic clouds of ionized gas can hit Earth within a few hours or days and cause, among other effects, geomagnetic storms - perhaps the best known manifestation of solar wind interaction with Earth's magnetosphere. Solar energetic particles (SEP), accelerated to near relativistic energy during large solar storms, arrive at the Earth's orbit even in few minutes and pose serious risk to astronauts traveling through the interplanetary space. These and many other threats are the reason why experts pay increasing attention to space weather and its predictability. For research on space weather, it is typically necessary to examine a large number of parameters which are interrelated in a complex non-linear way. One way to cope with such a task is to use an artificial neural network for space weather modeling, a tool originally developed for artificial intelligence. In our contribution, we focus on practical aspects of the neural networks application to modeling and forecasting selected space weather parameters.

Parameter Estimation of Nonlinear Models in Forestry.

OpenAIRE

Fekedulegn, Desta; Mac Siúrtáin, Máirtín Pádraig; Colbert, Jim J.

1999-01-01

Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinnin...

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

Science.gov (United States)

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

2018-05-01

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

Learning about physical parameters: the importance of model discrepancy

International Nuclear Information System (INIS)

Brynjarsdóttir, Jenný; O'Hagan, Anthony

2014-01-01

Science-based simulation models are widely used to predict the behavior of complex physical systems. It is also common to use observations of the physical system to solve the inverse problem, that is, to learn about the values of parameters within the model, a process which is often called calibration. The main goal of calibration is usually to improve the predictive performance of the simulator but the values of the parameters in the model may also be of intrinsic scientific interest in their own right. In order to make appropriate use of observations of the physical system it is important to recognize model discrepancy, the difference between reality and the simulator output. We illustrate through a simple example that an analysis that does not account for model discrepancy may lead to biased and over-confident parameter estimates and predictions. The challenge with incorporating model discrepancy in statistical inverse problems is being confounded with calibration parameters, which will only be resolved with meaningful priors. For our simple example, we model the model-discrepancy via a Gaussian process and demonstrate that through accounting for model discrepancy our prediction within the range of data is correct. However, only with realistic priors on the model discrepancy do we uncover the true parameter values. Through theoretical arguments we show that these findings are typical of the general problem of learning about physical parameters and the underlying physical system using science-based mechanistic models. (paper)

Importance-truncated shell model for multi-shell valence spaces

Energy Technology Data Exchange (ETDEWEB)

Stumpf, Christina; Vobig, Klaus; Roth, Robert [Institut fuer Kernphysik, TU Darmstadt (Germany)

2016-07-01

The valence-space shell model is one of the work horses in nuclear structure theory. In traditional applications, shell-model calculations are carried out using effective interactions constructed in a phenomenological framework for rather small valence spaces, typically spanned by one major shell. We improve on this traditional approach addressing two main aspects. First, we use new effective interactions derived in an ab initio approach and, thus, establish a connection to the underlying nuclear interaction providing access to single- and multi-shell valence spaces. Second, we extend the shell model to larger valence spaces by applying an importance-truncation scheme based on a perturbative importance measure. In this way, we reduce the model space to the relevant basis states for the description of a few target eigenstates and solve the eigenvalue problem in this physics-driven truncated model space. In particular multi-shell valence spaces are not tractable otherwise. We combine the importance-truncated shell model with refined extrapolation schemes to approximately recover the exact result. We present first results obtained in the importance-truncated shell model with the newly derived ab initio effective interactions for multi-shell valence spaces, e.g., the sdpf shell.

Wind Farm Decentralized Dynamic Modeling With Parameters

DEFF Research Database (Denmark)

Soltani, Mohsen; Shakeri, Sayyed Mojtaba; Grunnet, Jacob Deleuran

2010-01-01

Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...... local models. The results of this report are especially useful, but not limited, to design a decentralized wind farm controller, since in centralized controller design one can also use the model and update it in a central computing node.......Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...