Parameter redundancy in discrete state‐space and integrated models
McCrea, Rachel S.
2016-01-01
Discrete state‐space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state‐space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state‐space models using discrete analogues of methods for continuous state‐space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. PMID:27362826
Parameter redundancy in discrete state-space and integrated models.
Cole, Diana J; McCrea, Rachel S
2016-09-01
Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Joint Dynamics Modeling and Parameter Identification for Space Robot Applications
Directory of Open Access Journals (Sweden)
Adenilson R. da Silva
2007-01-01
Full Text Available Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS. The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Parameter and state estimator for state space models.
Ding, Ruifeng; Zhuang, Linfan
2014-01-01
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Exploring the Parameter Space of Warm-Inflation Models
Bastero-Gil, Mar; Kronberg, Nico
2015-01-01
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 $\\propto T^3/m_\\chi^2$ can sustain an era of inflation compatible with CMB observations. In this work, we extend these analyses by including the pole-dominated dissipation term $\\propto \\sqrt{m_\\chi T} \\exp(-m_\\chi/T)$. 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 ($O(10^4)$ for the quadratic hilltop potential) and smaller coupling constants, opening up interesting model-building possibilities. We also highlight the significan...
Exploring the parameter space of warm-inflation models
Energy Technology Data Exchange (ETDEWEB)
Bastero-Gil, Mar [Departamento de Física Teórica y del Cosmos, Universidad de Granada,Granada-18071 (Spain); Berera, Arjun; Kronberg, Nico [SUPA, School of Physics and Astronomy, University of Edinburgh,Edinburgh, EH9 3JZ (United Kingdom)
2015-12-22
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 ∝T{sup 3}/m{sub χ}{sup 2} can sustain an era of inflation compatible with CMB observations. In this work, we extend these analyses by including the pole-dominated dissipation term ∝√(m{sub χ}T)exp (−m{sub χ}/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 (O(10{sup 4}) 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.
Sun, Xiaodian; Jin, Li; Xiong, Momiao
2008-01-01
It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.
Trap configuration and spacing influences parameter estimates in spatial capture-recapture models.
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Catherine C Sun
Full Text Available An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation. We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.
Trap configuration and spacing influences parameter estimates in spatial capture-recapture models.
Sun, Catherine C; Fuller, Angela K; Royle, J Andrew
2014-01-01
An increasing number of studies employ spatial capture-recapture models to estimate population size, but there has been limited research on how different spatial sampling designs and trap configurations influence parameter estimators. Spatial capture-recapture models provide an advantage over non-spatial models by explicitly accounting for heterogeneous detection probabilities among individuals that arise due to the spatial organization of individuals relative to sampling devices. We simulated black bear (Ursus americanus) populations and spatial capture-recapture data to evaluate the influence of trap configuration and trap spacing on estimates of population size and a spatial scale parameter, sigma, that relates to home range size. We varied detection probability and home range size, and considered three trap configurations common to large-mammal mark-recapture studies: regular spacing, clustered, and a temporal sequence of different cluster configurations (i.e., trap relocation). We explored trap spacing and number of traps per cluster by varying the number of traps. The clustered arrangement performed well when detection rates were low, and provides for easier field implementation than the sequential trap arrangement. However, performance differences between trap configurations diminished as home range size increased. Our simulations suggest it is important to consider trap spacing relative to home range sizes, with traps ideally spaced no more than twice the spatial scale parameter. While spatial capture-recapture models can accommodate different sampling designs and still estimate parameters with accuracy and precision, our simulations demonstrate that aspects of sampling design, namely trap configuration and spacing, must consider study area size, ranges of individual movement, and home range sizes in the study population.
Humbird, Kelli; Peterson, J. Luc; Brandon, Scott; Field, John; Nora, Ryan; Spears, Brian
2016-10-01
Next-generation supercomputer architecture and in-transit data analysis have been used to create a large collection of 2-D ICF capsule implosion simulations. The database includes metrics for approximately 60,000 implosions, with x-ray images and detailed physics parameters available for over 20,000 simulations. To map and explore this large database, surrogate models for numerous quantities of interest are built using supervised machine learning algorithms. Response surfaces constructed using the predictive capabilities of the surrogates allow for continuous exploration of parameter space without requiring additional simulations. High performing regions of the input space are identified to guide the design of future experiments. In particular, a model for the yield built using a random forest regression algorithm has a cross validation score of 94.3% and is consistently conservative for high yield predictions. The model is used to search for robust volumes of parameter space where high yields are expected, even given variations in other input parameters. Surrogates for additional quantities of interest relevant to ignition are used to further characterize the high yield regions. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, Lawrence Livermore National Security, LLC. LLNL-ABS-697277.
She, M.; Jiang, L. P.
2014-12-01
In this paper, an oscillating dark energy model is presented in an isotropic but inhomogeneous plane symmetric space-time by considering a time periodic varying deceleration parameter. We find three different types of new solutions which describe different scenarios of oscillating universe. The first two solutions show an oscillating universe with singularities. For the third one, the universe is singularity-free during the whole evolution. Moreover, the Hubble parameter oscillates and keeps positive which explores an interesting possibility to unify the early inflation and late time acceleration of the universe.
Application of separable parameter space techniques to multi-tracer PET compartment modeling
Zhang, Jeff L.; Morey, A. Michael; Kadrmas, Dan J.
2016-02-01
Multi-tracer positron emission tomography (PET) can image two or more tracers in a single scan, characterizing multiple aspects of biological functions to provide new insights into many diseases. The technique uses dynamic imaging, resulting in time-activity curves that contain contributions from each tracer present. The process of separating and recovering separate images and/or imaging measures for each tracer requires the application of kinetic constraints, which are most commonly applied by fitting parallel compartment models for all tracers. Such multi-tracer compartment modeling presents challenging nonlinear fits in multiple dimensions. This work extends separable parameter space kinetic modeling techniques, previously developed for fitting single-tracer compartment models, to fitting multi-tracer compartment models. The multi-tracer compartment model solution equations were reformulated to maximally separate the linear and nonlinear aspects of the fitting problem, and separable least-squares techniques were applied to effectively reduce the dimensionality of the nonlinear fit. The benefits of the approach are then explored through a number of illustrative examples, including characterization of separable parameter space multi-tracer objective functions and demonstration of exhaustive search fits which guarantee the true global minimum to within arbitrary search precision. Iterative gradient-descent algorithms using Levenberg-Marquardt were also tested, demonstrating improved fitting speed and robustness as compared to corresponding fits using conventional model formulations. The proposed technique overcomes many of the challenges in fitting simultaneous multi-tracer PET compartment models.
The expanded triangular Kitaev–Heisenberg model in the full parameter space
Energy Technology Data Exchange (ETDEWEB)
Yao, Xiaoyan, E-mail: yaoxiaoyan@gmail.com
2014-06-13
The classical Kitaev–Heisenberg model on the triangular lattice is investigated by simulation in its full parameter space together with the next-nearest neighboring Heisenberg interaction or the single-ion anisotropy. The variation of the system is demonstrated directly by the joint density of states (DOS) depending on energy and magnetization obtained from Wang–Landau algorithm. The Metropolis Monte Carlo simulation and the zero-temperature Glauber dynamics are performed to show the internal energy, the correlation functions and spin configurations at zero temperature. It is revealed that two types of DOS (U and inverse U) divide the whole parameter range into two main parts with antiferromagnetic and ferromagnetic features respectively. In the parameter range of U type DOS, the mixed frustration from the triangular geometry and the Kitaev interaction produces rich phases, which are influenced in different ways by the next-nearest neighboring Heisenberg interaction and the single-ion anisotropy. - Highlights: • The expanded triangular Kitaev–Heisenberg model is investigated by simulation. • The density of states is shown in the full parameter space. • Rich low-temperature phases are induced by the mixed frustration. • The next nearest-neighboring Heisenberg interaction influences the phases. • The single-ion anisotropy modifies the shape of the density of states.
High Resolution Parameter Space from a Two Level Model on Semi-Insulating GaAs
da Silva, S L; de Oliveira, A G; Ribeiro, G M; da Silva, R L
2014-01-01
Semi-insulating Gallium Arsenide (SI-GaAs) samples experimentally show, under high electric fields and even at room temperature, negative differential conductivity in N-shaped form (NNDC). Since the most consolidated model for n-GaAs, namely, "the model", proposed by E. Scholl was not capable to generate the NNDC curve for SI-GaAs, in this work we proposed an alternative model. The model proposed, "the two-valley model" is based on the minimal set of generation recombination equations for two valleys inside of the conduction band, and an equation for the drift velocity as a function of the applied electric field, that covers the physical properties of the nonlinear electrical conduction of the SI-GaAs system. The "two valley model" was capable to generate theoretically the NNDC region for the first time, and with that, we were able to build a high resolution parameter-space of the periodicity (PSP) using a Periodicity-Detection (PD) routine. In the parameter space were observed self-organized periodic structu...
Sultan, E.; Manseta, K.; Khwaja, A.; Najafizadeh, L.; Gandjbakhche, A.; Pourrezaei, K.; Daryoush, A. S.
2011-02-01
Fiber based functional near infra-red (fNIR) spectroscopy has been considered as a cost effective imaging modality. To achieve a better spatial resolution and greater accuracy in extraction of the optical parameters (i.e., μa and μ's), broadband frequency modulated systems covering multi-octave frequencies of 10-1000MHz is considered. A helmet mounted broadband free space fNIR system is considered as significant improvement over bulky commercial fiber fNIR realizations that are inherently uncomfortable and dispersive for broadband operation. Accurate measurements of amplitude and phase of the frequency modulated NIR signals (670nm, 795nm, and 850nm) is reported here using free space optical transmitters and receivers realized in a small size and low cost modules. The tri-wavelength optical transmitter is based on vertical cavity semiconductor lasers (VCSEL), whereas the sensitive optical receiver is based on either PIN or APD photodiodes combined with transimpedance amplifiers. This paper also has considered brain phantoms to perform optical parameter extraction experiments using broadband modulated light for separations of up to 5cm. Analytical models for predicting forward (transmittance) and backward (reflectance) scattering of modulated photons in diffused media has been modeled using Diffusion Equation (DE). The robustness of the DE modeling and parameter extraction algorithm was studied by experimental verification of multi-layer diffused media phantoms. In particular, comparison between analytical and experimental models for narrow band and broadband has been performed to analyze the advantages of our broadband fNIR system.
Cellular parameters for track structure modelling of radiation hazard in space
Hollmark, M.; Lind, B.; Gudowska, I.; Waligorski, M.
Based on irradiation with 45 MeV/u N and B ions and with Co-60 gamma rays, track structure cellular parameters have been fitted for V 79-379A Chinese hamster lung fibroblasts and for human melanoma cells (AA wtp53). These sets of parameters will be used to develop a calculation of radiation hazard in deep space, based on the system for evaluating, summing and reporting occupational exposures proposed in 1967 by subcommittee of the NCRP, but never issued as an NCRP report. The key concepts of this system were: i) expression of the risk from all radiation exposures relative to that from a whole-body exposure to Co-60 radiation; ii) relating the risk from any exposure to that of the standard (Co-60) radiation through an "effectiveness factor" (ef), a product of sub-factors representing radiation quality, body region irradiated, and depth of penetration of radiation; the product of absorbed dose by ef being termed the "exposure record unit" (eru); iii) development of ef values and a cumulative eru record for external and internal emitters. Application of this concept should provide a better description of the Gy -equivalent presently in use by NASA for evaluating risk in deep space than the equivalent dose, following ICRP-60 recommendations. Dose and charged particle fluence levels encountered in space, particularly after Solar Particle Events, require that deterministic rather than stochastic effects be considered. Also, synergistic effects due to simultaneous multiple charged particle transfers, may have to be considered. Thus, models applicable in radiotherapy, where the Gy -equivalent is also applied, in conjunction with transport calculations performed using, e.g. the ADAM and EVA phantoms, along the concepts of the 1967 NCRP system, may be more appropriate for evaluating the radiation hazard from external fields with a large flux and a major high-LET component.
Identification of the parameters of a DC motor state space model
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Momir Ranislav Stanković
2013-06-01
Full Text Available A method for the identification of the DC state space model parameters based on the minimization of the error function using the least squares method is described in this paper. The algorithm is practically applied in the laboratory environment on an industrial DC motor. The verification of the results was performed by comparing the characteristic signals of real and modeled systems. The results show that the quality of the identification is satisfactory. Introduction The identification of system parameters is the first step in the analysis and synthesis of control systems. Identification Quality strongly impacts on the results of all other computations. In the theory of automatic control, many methods of identification are developed. Which method will be applied depends on the characteristics of the system. In this paper, we described an identification algorithm based on the least squares method. A practical test of this algorithm of estimation is done on a DC motor. parameter estimation with the least squares method A DC motor is a second-order system described with two differential equations: one which describes electrical and one which describes mechanical parts of the motor. The idea is to analyse the motor as two first-order systems. The main signals are responses of two first order sub-systems on appropriate inputs. Using a discrete state-space model of the motor and applying the least square method on the recorded signals, we get straightforward equations for the computation of all the necessary parameters: Rr, Lr , Je , Fe , Kme and Kem (Eykhoff, Wilsoons, 1974. Experimental results The practical application was realized in the laboratory where a DC middle-power motor was used as a control object. It is coupled with a DC generator which serves as a load. Generation of the input signals and measure of the responses were performed with the acquisition system based on the appropriate acquisition card and the MATLAB
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
DEFF Research Database (Denmark)
Koziel, Slawomir; Bandler, John W.; Madsen, Kaj
2006-01-01
We present a theoretical justification of a recently introduced surrogate modeling methodology based on space mapping that relies on an available data base and on-demand parameter extraction. Fine model data, the so-called base set, is assumed available in the region of interest. To evaluate the ...
Free flight in parameter space
DEFF Research Database (Denmark)
Dahlstedt, Palle; Nilsson, Per Anders
2008-01-01
The well-known difficulty of controlling many synthesis parameters in performance, for exploration and expression, is addressed. Inspired by interactive evolution, random vectors in parameter space are assigned to an array of pressure sensitive pads. Vectors are scaled with pressure and added to ...
DEFF Research Database (Denmark)
Lika, Konstadia; Augustine, Starrlight; Pecquerie, Laure
2014-01-01
The standard Dynamic Energy Budget (DEB) model assumes that food is converted to reserve and a fraction κ of mobilised reserve of an individual is allocated to somatic maintenance plus growth, while the rest is allocated to maturity maintenance plus maturation (in embryos and juveniles......) or reproduction (in adults). The add_my_pet collection of over 300 animal species from most larger phyla, and all chordate classes, shows that this model fits energy data very well. Nine parameters determine nine data points at abundant food: dry/wet weight ratio, age at birth, puberty, death, weight at birth...... maintenance and costs for structure, allocation fraction of mobilised reserve to soma, energy conductance, and ageing acceleration. We provide an efficient algorithm for mapping between data and parameter space in both directions and found expressions for the boundaries of the parameter and data spaces. One...
Rate control system algorithm developed in state space for models with parameter uncertainties
Directory of Open Access Journals (Sweden)
Adilson Jesus Teixeira
2011-09-01
Full Text Available Researching in weightlessness above the atmosphere needs a payload to carry the experiments. To achieve the weightlessness, the payload uses a rate control system (RCS in order to reduce the centripetal acceleration within the payload. The rate control system normally has actuators that supply a constant force when they are turned on. The development of an algorithm control for this rate control system will be based on the minimum-time problem method in the state space to overcome the payload and actuators dynamics uncertainties of the parameters. This control algorithm uses the initial conditions of optimal trajectories to create intermediate points or to adjust existing points of a switching function. It associated with inequality constraint will form a decision function to turn on or off the actuators. This decision function, for linear time-invariant systems in state space, needs only to test the payload state variables instead of spent effort in solving differential equations and it will be tuned in real time to the payload dynamic. It will be shown, through simulations, the results obtained for some cases of parameters uncertainties that the rate control system algorithm reduced the payload centripetal acceleration below μg level and keep this way with no limit cycle.
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.
White, J Wilson; Nickols, Kerry J; Malone, Daniel; Carr, Mark H; Starr, Richard M; Cordoleani, Flora; Baskett, Marissa L; Hastings, Alan; Botsford, Louis W
2016-12-01
Integral projection models (IPMs) have a number of advantages over matrix-model approaches for analyzing size-structured population dynamics, because the latter require parameter estimates for each age or stage transition. However, IPMs still require appropriate data. Typically they are parameterized using individual-scale relationships between body size and demographic rates, but these are not always available. We present an alternative approach for estimating demographic parameters from time series of size-structured survey data using a Bayesian state-space IPM (SSIPM). By fitting an IPM in a state-space framework, we estimate unknown parameters and explicitly account for process and measurement error in a dataset to estimate the underlying process model dynamics. We tested our method by fitting SSIPMs to simulated data; the model fit the simulated size distributions well and estimated unknown demographic parameters accurately. We then illustrated our method using nine years of annual surveys of the density and size distribution of two fish species (blue rockfish, Sebastes mystinus, and gopher rockfish, S. carnatus) at seven kelp forest sites in California. The SSIPM produced reasonable fits to the data, and estimated fishing rates for both species that were higher than our Bayesian prior estimates based on coast-wide stock assessment estimates of harvest. That improvement reinforces the value of being able to estimate demographic parameters from local-scale monitoring data. We highlight a number of key decision points in SSIPM development (e.g., open vs. closed demography, number of particles in the state-space filter) so that users can apply the method to their own datasets. © 2016 by the Ecological Society of America.
Bhalla, U S; Bower, J M
1993-06-01
1. Detailed compartmental computer simulations of single mitral and granule cells of the vertebrate olfactory bulb were constructed using previously published geometric data. Electrophysiological properties were determined by comparing model output to previously published experimental data, mainly current-clamp recordings. 2. The passive electrical properties of each model were explored by comparing model output with intracellular potential data from hyperpolarizing current injection experiments. The results suggest that membrane resistivity in both cells is nonuniform, with somatas having a substantially lower resistivity than the dendrites. 3. The active properties of these cells were explored by incorporating active ion channels into modeled compartments. On the basis of evidence from the literature, the mitral cell model included six channel types: fast sodium, fast delayed rectifier (Kfast), slow delayed rectifier (K), transient outward potassium current (KA), voltage- and calcium-dependent potassium current (KCa), and L-type calcium current. The granule cell model included four channel types: rat brain sodium, K, KA, and the non-inactivating muscarinic potassium current (KM). Modeled channels were based on the Hodgkin-Huxley formalism. 4. Representative kinetics for each of the channel classes above were obtained from the literature. The experimentally unknown spatial distributions of each included channel were obtained by systematic parameter searches. These were conducted in two ways: large-scale simulation series, in which each parameter was varied in turn, and an adaptation of a multidimensional conjugate gradient method. In each case, the simulated results were compared wtih experimental data using a curve-matching function evaluating mean squared differences of several aspects of the simulated and experimental voltage waveforms. 5. Systematic parameter variations revealed a single distinct region of parameter space in which the mitral cell model best
Quach, Minh; Brunel, Nicolas; d'Alché-Buc, Florence
2007-12-01
Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. Matlab code available on demand.
Constraining parameter space of the little Higgs model using data from tera-Z factory and ILC
Guo, Xing-Dao; Feng, Tai-Fu; Zhao, Shu-Min; Ke, Hong-Wei; Li, Xue-Qian
2015-02-01
The Standard Model (SM) prediction on the forward-backward asymmetry for bb¯ production (AbFB)is well consistent with the data of LEP I at the Z-pole, but deviates from the data at √s = 89.55 and 92.95 GeV which are slightly away from the pole. This deviation implies that there is still room for new physics. We calculate the AbFB at the vicinity of the Z-pole in the little Higgs model as well as other measurable parameters such as Rb and Rc, by which we may constrain the parameter space of the little Higgs model. This can be further tested in the newly proposed tera-Z factory. With the fitted parameters we further make predictions on AbFB and AtFB for tt¯ production at the International Linear Collider (ILC). Supported by National Natural Science Foundation of China (11275036, 11047002, 11375128), Fund of Natural Science Foundation of Hebei Province(A2011201118) and Natural Science Fund of Hebei University (2011JQ05, 2007113)
Parton Distributions in Impact Parameter Space
Dahiya, H; Ray, S
2007-01-01
Fourier transform of the generalized parton distributions (GPDs) at zero skewness with respect to the transverse momentum transfer gives the distribution of partons in the impact parameter space. We investigate the GPDs as well as the impact parameter dependent parton distributions (ipdpdfs) by expressing them in terms of overlaps of light front wave functions (LFWFs) and present a comparative study using three different model LFWFs.
Steingroever, Helen; Wetzels, Ruud; Wagenmakers, Eric-Jan
2013-01-01
The Iowa gambling task (IGT) is one of the most popular tasks used to study decision-making deficits in clinical populations. In order to decompose performance on the IGT in its constituent psychological processes, several cognitive models have been proposed (e.g., the Expectancy Valence (EV) and Prospect Valence Learning (PVL) models). Here we…
Li, Yun-He; Zhang, Xin
2014-01-01
Dark energy can modify the dynamics of dark matter if there exists a direct interaction between them. Thus a measurement of the structure growth, e.g., redshift-space distortions (RSD), can be a powerful tool to constrain the interacting dark energy (IDE) models. For the widely studied $Q=3\\beta H\\rho_{de}$ model, previous works showed that only a very small coupling ($\\beta\\sim\\mathcal{O}(10^{-3})$) can survive in current RSD data. However, all these analyses have to assume $w>-1$ and $\\beta>0$ due to the existence of the large-scale instability in the IDE scenario. In our recent work [Phys.\\ Rev.\\ D {\\bf 90}, 063005 (2014)], we successfully solved this large-scale instability problem by establishing a parametrized post-Friedmann (PPF) framework for the IDE scenario. So we, for the first time, have the ability to explore the full parameter space of the IDE models. In this work, we reexamine the observational constraints on the $Q=3\\beta H\\rho_{de}$ model within the PPF framework. By using the Planck data, th...
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)
Vavoulis, Dimitrios V; Straub, Volko A; Aston, John A D; Feng, Jianfeng
2012-01-01
Traditional approaches to the problem of parameter estimation in biophysical models of neurons and neural networks usually adopt a global search algorithm (for example, an evolutionary algorithm), often in combination with a local search method (such as gradient descent) in order to minimize the value of a cost function, which measures the discrepancy between various features of the available experimental data and model output. In this study, we approach the problem of parameter estimation in conductance-based models of single neurons from a different perspective. By adopting a hidden-dynamical-systems formalism, we expressed parameter estimation as an inference problem in these systems, which can then be tackled using a range of well-established statistical inference methods. The particular method we used was Kitagawa's self-organizing state-space model, which was applied on a number of Hodgkin-Huxley-type models using simulated or actual electrophysiological data. We showed that the algorithm can be used to estimate a large number of parameters, including maximal conductances, reversal potentials, kinetics of ionic currents, measurement and intrinsic noise, based on low-dimensional experimental data and sufficiently informative priors in the form of pre-defined constraints imposed on model parameters. The algorithm remained operational even when very noisy experimental data were used. Importantly, by combining the self-organizing state-space model with an adaptive sampling algorithm akin to the Covariance Matrix Adaptation Evolution Strategy, we achieved a significant reduction in the variance of parameter estimates. The algorithm did not require the explicit formulation of a cost function and it was straightforward to apply on compartmental models and multiple data sets. Overall, the proposed methodology is particularly suitable for resolving high-dimensional inference problems based on noisy electrophysiological data and, therefore, a potentially useful tool in
Pooley, C M; Bishop, S C; Marion, G
2015-06-06
Bayesian statistics provides a framework for the integration of dynamic models with incomplete data to enable inference of model parameters and unobserved aspects of the system under study. An important class of dynamic models is discrete state space, continuous-time Markov processes (DCTMPs). Simulated via the Doob-Gillespie algorithm, these have been used to model systems ranging from chemistry to ecology to epidemiology. A new type of proposal, termed 'model-based proposal' (MBP), is developed for the efficient implementation of Bayesian inference in DCTMPs using Markov chain Monte Carlo (MCMC). This new method, which in principle can be applied to any DCTMP, is compared (using simple epidemiological SIS and SIR models as easy to follow exemplars) to a standard MCMC approach and a recently proposed particle MCMC (PMCMC) technique. When measurements are made on a single-state variable (e.g. the number of infected individuals in a population during an epidemic), model-based proposal MCMC (MBP-MCMC) is marginally faster than PMCMC (by a factor of 2-8 for the tests performed), and significantly faster than the standard MCMC scheme (by a factor of 400 at least). However, when model complexity increases and measurements are made on more than one state variable (e.g. simultaneously on the number of infected individuals in spatially separated subpopulations), MBP-MCMC is significantly faster than PMCMC (more than 100-fold for just four subpopulations) and this difference becomes increasingly large. © 2015 The Author(s) Published by the Royal Society. All rights reserved.
MFV Reductions of MSSM Parameter Space
AbdusSalam, S.S.; Quevedo, F.
2015-01-01
The 100+ free parameters of the minimal supersymmetric standard model (MSSM) make it computationally difficult to compare systematically with data, motivating the study of specific parameter reductions such as the cMSSM and pMSSM. Here we instead study the reductions of parameter space implied by using minimal flavour violation (MFV) to organise the R-parity conserving MSSM, with a view towards systematically building in constraints on flavour-violating physics. Within this framework the space of parameters is reduced by expanding soft supersymmetry-breaking terms in powers of the Cabibbo angle, leading to a 24-, 30- or 42-parameter framework (which we call MSSM-24, MSSM-30, and MSSM-42 respectively), depending on the order kept in the expansion. We provide a Bayesian global fit to data of the MSSM-30 parameter set to show that this is manageable with current tools. We compare the MFV reductions to the 19-parameter pMSSM choice and show that the pMSSM is not contained as a subset. The MSSM-30 analysis favours...
MFV reductions of MSSM parameter space
Energy Technology Data Exchange (ETDEWEB)
AbdusSalam, S.S. [INFN - Sezione di Roma,P.le A. Moro 2, I-00185 Roma (Italy); The Abdus Salam ICTP,Trieste (Italy); Burgess, C.P. [Department of Physics & Astronomy, McMaster University,Hamilton ON (Canada); Perimeter Institute for Theoretical Physics,Waterloo, ON (Canada); Division PH -TH, CERN,CH-1211, Genève 23 (Switzerland); Quevedo, F. [The Abdus Salam ICTP,Trieste (Italy); DAMTP, Cambridge University,Cambridge (United Kingdom)
2015-02-11
The 100+ free parameters of the minimal supersymmetric standard model (MSSM) make it computationally difficult to compare systematically with data, motivating the study of specific parameter reductions such as the cMSSM and pMSSM. Here we instead study the reductions of parameter space implied by using minimal flavour violation (MFV) to organise the R-parity conserving MSSM, with a view towards systematically building in constraints on flavour-violating physics. Within this framework the space of parameters is reduced by expanding soft supersymmetry-breaking terms in powers of the Cabibbo angle, leading to a 24-, 30- or 42-parameter framework (which we call MSSM-24, MSSM-30, and MSSM-42 respectively), depending on the order kept in the expansion. We provide a Bayesian global fit to data of the MSSM-30 parameter set to show that this is manageable with current tools. We compare the MFV reductions to the 19-parameter pMSSM choice and show that the pMSSM is not contained as a subset. The MSSM-30 analysis favours a relatively lighter TeV-scale pseudoscalar Higgs boson and tan β∼10 with multi-TeV sparticles.
MFV reductions of MSSM parameter space
AbdusSalam, S. S.; Burgess, C. P.; Quevedo, F.
2015-02-01
The 100+ free parameters of the minimal supersymmetric standard model (MSSM) make it computationally difficult to compare systematically with data, motivating the study of specific parameter reductions such as the cMSSM and pMSSM. Here we instead study the reductions of parameter space implied by using minimal flavour violation (MFV) to organise the R-parity conserving MSSM, with a view towards systematically building in constraints on flavour-violating physics. Within this framework the space of parameters is reduced by expanding soft supersymmetry-breaking terms in powers of the Cabibbo angle, leading to a 24-, 30- or 42-parameter framework (which we call MSSM-24, MSSM-30, and MSSM-42 respectively), depending on the order kept in the expansion. We provide a Bayesian global fit to data of the MSSM-30 parameter set to show that this is manageable with current tools. We compare the MFV reductions to the 19-parameter pMSSM choice and show that the pMSSM is not contained as a subset. The MSSM-30 analysis favours a relatively lighter TeV-scale pseudoscalar Higgs boson and tan β ˜ 10 with multi-TeV sparticles.
Hsia, Wei-Shen
1987-01-01
A stochastic control model of the NASA/MSFC Ground Facility for Large Space Structures (LSS) control verification through Maximum Entropy (ME) principle adopted in Hyland's method was presented. Using ORACLS, a computer program was implemented for this purpose. Four models were then tested and the results presented.
A Tool for Parameter-space Explorations
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.
Khawli, Toufik Al; Gebhardt, Sascha; Eppelt, Urs; Hermanns, Torsten; Kuhlen, Torsten; Schulz, Wolfgang
2016-06-01
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.
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.
DEFF Research Database (Denmark)
Ibsen, Lars Bo; Liingaard, Morten
A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. The lumped-parameter model development have been reported by (Wolf 1991b; Wolf 1991a; Wolf and Paronesso 1991; Wolf and Paronesso 19...
Directory of Open Access Journals (Sweden)
Éric Fanchon
2012-08-01
Full Text Available This paper presents a novel framework for the modeling of biological networks. It makes use of recent tools analyzing the robust satisfaction of properties of (hybrid dynamical systems. The main challenge of this approach as applied to biological systems is to get access to the relevant parameter sets despite gaps in the available knowledge. An initial estimate of useful parameters was sought by formalizing the known behavior of the biological network in the STL logic using the tool Breach. Then, once a set of parameter values consistent with known biological properties was found, we tried to locally expand it into the largest possible valid region. We applied this methodology in an effort to model and better understand the complex network regulating iron homeostasis in mammalian cells. This system plays an important role in many biological functions, including erythropoiesis, resistance against infections, and proliferation of cancer cells.
A tool for parameter-space explorations
Murase, Yohsuke; Ito, Nobuyasu
2014-01-01
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....
Reynerson, Charles Martin
This research has been performed to create concept design and economic feasibility data for space business parks. A space business park is a commercially run multi-use space station facility designed for use by a wide variety of customers. Both space hardware and crew are considered as revenue producing payloads. Examples of commercial markets may include biological and materials research, processing, and production, space tourism habitats, and satellite maintenance and resupply depots. This research develops a design methodology and an analytical tool to create feasible preliminary design information for space business parks. The design tool is validated against a number of real facility designs. Appropriate model variables are adjusted to ensure that statistical approximations are valid for subsequent analyses. The tool is used to analyze the effect of various payload requirements on the size, weight and power of the facility. The approach for the analytical tool was to input potential payloads as simple requirements, such as volume, weight, power, crew size, and endurance. In creating the theory, basic principles are used and combined with parametric estimation of data when necessary. Key system parameters are identified for overall system design. Typical ranges for these key parameters are identified based on real human spaceflight systems. To connect the economics to design, a life-cycle cost model is created based upon facility mass. This rough cost model estimates potential return on investments, initial investment requirements and number of years to return on the initial investment. Example cases are analyzed for both performance and cost driven requirements for space hotels, microgravity processing facilities, and multi-use facilities. In combining both engineering and economic models, a design-to-cost methodology is created for more accurately estimating the commercial viability for multiple space business park markets.
Manifold parameter space and its applications
Sato, Atsushi
2004-11-01
We review the several features of the new parameter space which we presented in the previous paper, and show the differentiable manifold properties of this parameter space coordinate. Using this parameter coordinate we calculate three Feynman amplitudes of the vacuum polarization with a gluon loop, a quark loop and a ghost loop in QCD and show that the results are perfectly equal to those of the usual calculations by the Feynman parametrization technique in the scheme of the dimensional regularization. Then we try to calculate the anomalous magnetic moment of an on-shell quark in QCD by using the dimensional regularization, our new parametrization and integral method.
Parameter space for successful soccer kicks
Cook, Brandon G.; Goff, John Eric
2006-07-01
A computational model of two important types of soccer kicks, the free kick and the corner kick, is developed with the goal of determining the success rate for each type of kick. What is meant by 'success rate' is the probability of getting an unassisted goal via a free kick and the probability of having a corner kick reach an optimum location so that a teammate's chance of scoring a goal is increased. Success rates are determined through the use of four-dimensional parameter space volumes. A one-in-ten success rate is found for the free kick while the corner-kick success rate is found to be one in four.
Response model parameter linking
Barrett, Michelle Derbenwick
2015-01-01
With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of equating observed scores on different test forms. This thesis argues, however, that the use of item response models does not require
Energy Technology Data Exchange (ETDEWEB)
Newton, W. G.; Gearheart, M.; Li Baoan, E-mail: william.newton@tamuc.edu [Department of Physics and Astronomy, Texas A and M University-Commerce, Commerce, TX 75429-3011 (United States)
2013-01-15
We present a systematic survey of the range of predictions of the neutron star inner crust composition, crust-core transition densities and pressures, and density range of the nuclear 'pasta' phases at the bottom of the crust provided by the compressible liquid drop model in light of the current experimental and theoretical constraints on model parameters. Using a Skyrme-like model for nuclear matter, we construct baseline sequences of crust models by consistently varying the density dependence of the bulk symmetry energy at nuclear saturation density, L, under two conditions: (1) that the magnitude of the symmetry energy at saturation density J is held constant, and (2) J correlates with L under the constraint that the pure neutron matter (PNM) equation of state (EoS) satisfies the results of ab initio calculations at low densities. Such baseline crust models facilitate consistent exploration of the L dependence of crustal properties. The remaining surface energy and symmetric nuclear matter parameters are systematically varied around the baseline, and different functional forms of the PNM EoS at sub-saturation densities implemented, to estimate theoretical 'error bars' for the baseline predictions. Inner crust composition and transition densities are shown to be most sensitive to the surface energy at very low proton fractions and to the behavior of the sub-saturation PNM EoS. Recent calculations of the energies of neutron drops suggest that the low-proton-fraction surface energy might be higher than predicted in Skyrme-like models, which our study suggests may result in a greatly reduced volume of pasta in the crust than conventionally predicted.
Polarimetry for four Stockes parameters in space
Institute of Scientific and Technical Information of China (English)
张肇先; 王培纲
2002-01-01
Continuously growing attention has been paid to potential of polarimetry to provide additional information of remote sounding of the earth and other planets and to detect some special targets. In the present paper the polarimetric technique in space for all the four Stockes parameters is presented.
Distributed Parameter Modelling Applications
DEFF Research Database (Denmark)
2011-01-01
Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers and the d......Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers...... sands processing. The fertilizer granulation model considers the dynamics of MAP-DAP (mono and diammonium phosphates) production within an industrial granulator, that involves complex crystallisation, chemical reaction and particle growth, captured through population balances. A final example considers...
Koyama, Shinsuke; Paninski, Liam
2010-08-01
A number of important data analysis problems in neuroscience can be solved using state-space models. In this article, we describe fast methods for computing the exact maximum a posteriori (MAP) path of the hidden state variable in these models, given spike train observations. If the state transition density is log-concave and the observation model satisfies certain standard assumptions, then the optimization problem is strictly concave and can be solved rapidly with Newton-Raphson methods, because the Hessian of the loglikelihood is block tridiagonal. We can further exploit this block-tridiagonal structure to develop efficient parameter estimation methods for these models. We describe applications of this approach to neural decoding problems, with a focus on the classic integrate-and-fire model as a key example.
Visualization of Parameter Space for Image Analysis
Pretorius, A. Johannes; Bray, Mark-Anthony P.; Carpenter, Anne E.; Ruddle, Roy A.
2013-01-01
Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step - initialization of sampling - and the last step - visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler - a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach. PMID:22034361
Parameter space of general gauge mediation
Energy Technology Data Exchange (ETDEWEB)
Rajaraman, Arvind [Department of Physics and Astronomy, University of California, Irvine, CA 92697 (United States)], E-mail: arajaram@uci.edu; Shirman, Yuri [Department of Physics and Astronomy, University of California, Irvine, CA 92697 (United States)], E-mail: yshirman@uci.edu; Smidt, Joseph [Department of Physics and Astronomy, University of California, Irvine, CA 92697 (United States)], E-mail: jsmidt@uci.edu; Yu, Felix [Department of Physics and Astronomy, University of California, Irvine, CA 92697 (United States)], E-mail: felixy@uci.edu
2009-07-27
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.
Cerruti, Matteo; Zech, Andreas
2013-01-01
The one-zone synchrotron-self-Compton (SSC) model aims to describe the spectral energy distribution (SED) of BL Lac objects via synchrotron emission by a non-thermal population of electrons and positrons in a single homogeneous emission region, partially upscattered to gamma-rays by the particles themselves. The model is usually considered as degenerate, given that the number of free parameters is higher than the number of observables. It is thus common to model the SED by choosing a single set of values for the SSC-model parameters that provide a good description of the data, without studying the entire parameter space. We present here a new numerical algorithm which permits us to find the complete set of solutions, using the information coming from the detection in the GeV and TeV energy bands. The algorithm is composed of three separate steps: we first prepare a grid of simulated SEDs and extract from each SED the values of the observables; we then parametrize each observable as a function of the SSC param...
Yedavalli, R. K.
1992-01-01
The problem of analyzing and designing controllers for linear systems subject to real parameter uncertainty is considered. An elegant, unified theory for robust eigenvalue placement is presented for a class of D-regions defined by algebraic inequalities by extending the nominal matrix root clustering theory of Gutman and Jury (1981) to linear uncertain time systems. The author presents explicit conditions for matrix root clustering for different D-regions and establishes the relationship between the eigenvalue migration range and the parameter range. The bounds are all obtained by one-shot computation in the matrix domain and do not need any frequency sweeping or parameter gridding. The method uses the generalized Lyapunov theory for getting the bounds.
Yedavalli, R. K.
1992-01-01
The problem of analyzing and designing controllers for linear systems subject to real parameter uncertainty is considered. An elegant, unified theory for robust eigenvalue placement is presented for a class of D-regions defined by algebraic inequalities by extending the nominal matrix root clustering theory of Gutman and Jury (1981) to linear uncertain time systems. The author presents explicit conditions for matrix root clustering for different D-regions and establishes the relationship between the eigenvalue migration range and the parameter range. The bounds are all obtained by one-shot computation in the matrix domain and do not need any frequency sweeping or parameter gridding. The method uses the generalized Lyapunov theory for getting the bounds.
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...
Stegen, Ronald; Gassmann, Matthias
2017-04-01
The use of a broad variation of agrochemicals is essential for the modern industrialized agriculture. During the last decades, the awareness of the side effects of their use has grown and with it the requirement to reproduce, understand and predict the behaviour of these agrochemicals in the environment, in order to optimize their use and minimize the side effects. The modern modelling has made great progress in understanding and predicting these chemicals with digital methods. While the behaviour of the applied chemicals is often investigated and modelled, most studies only simulate parent chemicals, considering total annihilation of the substance. However, due to a diversity of chemical, physical and biological processes, the substances are rather transformed into new chemicals, which themselves are transformed until, at the end of the chain, the substance is completely mineralized. During this process, the fate of each transformation product is determined by its own environmental characteristics and the pathway and results of transformation can differ largely by substance and environmental influences, that can occur in different compartments of the same site. Simulating transformation products introduces additional model uncertainties. Thus, the calibration effort increases compared to simulations of the transport and degradation of the primary substance alone. The simulation of the necessary physical processes needs a lot of calculation time. Due to that, few physically-based models offer the possibility to simulate transformation products at all, mostly at the field scale. The few models available for the catchment scale are not optimized for this duty, i.e. they are only able to simulate a single parent compound and up to two transformation products. Thus, for simulations of large physico-chemical parameter spaces, the enormous calculation time of the underlying hydrological model diminishes the overall performance. In this study, the structure of the model
Parameter Symmetry of the Interacting Boson Model
Shirokov, A M; Smirnov, Yu F; Shirokov, Andrey M.; Smirnov, Yu. F.
1998-01-01
We discuss the symmetry of the parameter space of the interacting boson model (IBM). It is shown that for any set of the IBM Hamiltonian parameters (with the only exception of the U(5) dynamical symmetry limit) one can always find another set that generates the equivalent spectrum. We discuss the origin of the symmetry and its relevance for physical applications.
Berman, A. L.
1976-01-01
In the last two decades, increasingly sophisticated deep space missions have placed correspondingly stringent requirements on navigational accuracy. As part of the effort to increase navigational accuracy, and hence the quality of radiometric data, much effort has been expended in an attempt to understand and compute the tropospheric effect on range (and hence range rate) data. The general approach adopted has been that of computing a zenith range refraction, and then mapping this refraction to any arbitrary elevation angle via an empirically derived function of elevation. The prediction of zenith range refraction derived from surface measurements of meteorological parameters is presented. Refractivity is separated into wet (water vapor pressure) and dry (atmospheric pressure) components. The integration of dry refractivity is shown to be exact. Attempts to integrate wet refractivity directly prove ineffective; however, several empirical models developed by the author and other researchers at JPL are discussed. The best current wet refraction model is here considered to be a separate day/night model, which is proportional to surface water vapor pressure and inversely proportional to surface temperature. Methods are suggested that might improve the accuracy of the wet range refraction model.
Sieve likelihood ratio inference on general parameter space
Institute of Scientific and Technical Information of China (English)
SHEN Xiaotong; SHI Jian
2005-01-01
In this paper,a theory on sieve likelihood ratio inference on general parameter spaces(including infinite dimensional) is studied.Under fairly general regularity conditions,the sieve log-likelihood ratio statistic is proved to be asymptotically x2 distributed,which can be viewed as a generalization of the well-known Wilks' theorem.As an example,a emiparametric partial linear model is investigated.
New explicit instantons and the geometry of the parameter space
Meyers, C.; Roo, M. de
1979-01-01
We obtain a geometrical description of the parameter space of instantons of topological charge k in an SU(n) gauge theory. We show how this space is related to a compact convex set of positive matrices. We give a characterization of points in the parameter space which correspond to embeddings. We de
Describing variations of the Fisher-matrix across parameter space
Schäfer, Björn Malte
2016-01-01
Forecasts in cosmology, both with Monte-Carlo Markov-chain methods and with the Fisher matrix formalism, depend on the choice of the fiducial model because both the signal strength of any observable as well as the model nonlinearities linking observables to cosmological parameters vary in the general case. In this paper we propose a method for extrapolating Fisher-forecasts across the space of cosmological parameters by constructing a suitable ba- sis. We demonstrate the validity of our method with constraints on a standard dark energy model extrapolated from a {\\Lambda}CDM-model, as can be expected from 2-bin weak lensing to- mography with a Euclid-like survey, in the parameter pairs $(\\Omega_\\text{m},\\sigma_8)$, $(\\Omega_\\text{m}, w_0)$ and $(w_0, w_\\text{a})$. Our numerical results include very accurate extrapolations across a wide range of cosmo- logical parameters in terms of shape, size and orientation of the parameter likelihood, and a decomposition of the change of the likelihood contours into modes, ...
Robust estimation of hydrological model parameters
Directory of Open Access Journals (Sweden)
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.
Sepehry-Fard, F.; Coulthard, Maurice H.
1995-01-01
The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.
Sepehry-Fard, F.; Coulthard, Maurice H.
1995-01-01
The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.
Photovoltaic module parameters acquisition model
Cibira, Gabriel; Koščová, Marcela
2014-09-01
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.
Mode choice model parameters estimation
Strnad, Irena
2010-01-01
The present work focuses on parameter estimation of two mode choice models: multinomial logit and EVA 2 model, where four different modes and five different trip purposes are taken into account. Mode choice model discusses the behavioral aspect of mode choice making and enables its application to a traffic model. Mode choice model includes mode choice affecting trip factors by using each mode and their relative importance to choice made. When trip factor values are known, it...
The Parameters of Common Information Spaces
DEFF Research Database (Denmark)
Bossen, Claus
2002-01-01
The paper proposes a refinement of the concept of 'Common Information Spaces' (CIS), which has been proposed as a conceptual framework for the CWCW field in order to provide analyses of cooperative work. The refinement is developed through an introductory discussion of previous analyses of CIS...
The Parameters of Common Information Spaces
DEFF Research Database (Denmark)
Bossen, Claus
2002-01-01
The paper proposes a refinement of the concept of 'Common Information Spaces' (CIS), which has been proposed as a conceptual framework for the CWCW field in order to provide analyses of cooperative work. The refinement is developed through an introductory discussion of previous analyses of CIS...
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em
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.
Cosmological Parameters from Redshift-Space Correlations
Matsubara, T; Matsubara, Takahiko; Szalay, Alexander S.
2002-01-01
We estimate how clustering in large-scale redshift surveys can constrain various cosmological parameters. Depth and sky coverage of modern redshift surveys are greater than ever, opening new possibilities for statistical analysis. We have constructed a novel maximum likelihood technique applicable to deep redshift surveys of wide sky coverage by taking into account the effects of both curvature and linear velocity distortions. The Fisher information matrix is evaluated numerically to show the bounds derived from a given redshift sample. We find that intermediate-redshift galaxies, such as the Luminous Red Galaxies (LRGs) in the Sloan Digital Sky Survey, can constrain cosmological parameters, including the cosmological constant, unexpectedly well. The importance of the dense as well as deep sampling in designing redshift surveys is emphasized.
Parameter Space of the Columbia River Estuarine Turbidity Maxima
McNeil, C. L.; Shcherbina, A.; Lopez, J.; Karna, T.; Baptista, A. M.; Crump, B. C.; Sanford, T. B.
2016-12-01
We present observations of estuarine turbidity maxima (ETM) in the North Channel of the Columbia River estuary (OR and WA, USA) covering different river discharge and flood tide conditions. Measurements were made using optical backscattering sensors on two REMUS-100 autonomous underwater vehicles (AUVs) during spring 2012, summer 2013, and fall 2012. Although significant short term variability in AUV measured optical backscatter was observed, some clustering of the data occurs around the estuarine regimes defined by a mixing parameter and a freshwater Froude number (Geyer & MacCready [2014]). Similar clustering is observed in long term time series of turbidity from the SATURN observatory. We will use available measurements and numerical model simulations of suspended sediment to further explore the variability of suspended sediment dynamics within a frame work of estuarine parameter space.
Wang, Wenyu; Xiong, Zhao-Hua; Zhao, Xin-Yan
2016-09-01
In models with vector-like quark doublets, the mass matrices of up and down type quarks are related. Precise diagonalization of the mass matrices has become an obstacle in numerical studies. In this work we first propose a diagonalization method. As its application, in the Standard Model with one vector-like quark doublet we present the quark mass spectrum and Feynman rules for the calculation of B → Xsγ. We find that i) under the constraints of the CKM matrix measurements, the mass parameters in the bilinear term are constrained to a small value by the small deviation from unitarity; ii) compared with the fourth generation extension of the Standard Model, there is an enhancement to the B → Xsγ process in the contribution of vector-like quarks, resulting in a non-decoupling effect in such models. Supported by Natural Science Foundation of China (11375001) and Talents Foundation of Education Department of Beijing
Wang, Wenyu; Zhao, Xin-Yan
2016-01-01
In the models with vector like quark doublets, the mass matrices of up and down type quarks are related. Precise diagonalization for the mass matrices became an obstacle in the numerical studies. In this work we propose a diagonalization method at first. As its application, in the standard model with one vector like quark doublet we present quark mass spectrum, Feynman rules for the calculation of $B\\to X_s\\gamma$. We find that i) under the constraints of the CKM matrix measurements, the mass parameters in the bilinear term are constrained to a small value by the small deviation from unitarity; ii) compared with the fourth generation extension of the standard model, there is an enhancement to $B\\to X_s\\gamma$ process in the contribution of vector like quark, resulting a non-decoupling effect in such models.
A unified minimax result for restricted parameter spaces
Marchand, Éric; 10.3150/10-BEJ336
2012-01-01
We provide a development that unifies, simplifies and extends considerably a number of minimax results in the restricted parameter space literature. Various applications follow, such as that of estimating location or scale parameters under a lower (or upper) bound restriction, location parameter vectors restricted to a polyhedral cone, scale parameters subject to restricted ratios or products, linear combinations of restricted location parameters, location parameters bounded to an interval with unknown scale, quantiles for location-scale families with parametric restrictions and restricted covariance matrices.
Delineating parameter unidentifiabilities in complex models
Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis
2017-03-01
Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.
An Optimization Model of Tunnel Support Parameters
Directory of Open Access Journals (Sweden)
Su Lijuan
2015-05-01
Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.
Search Space Calculation to Improve Parameter Estimation of Excitation Control Systems
Directory of Open Access Journals (Sweden)
Andrés J. Saavedra-Montes
2013-11-01
Full Text Available A method to calculate the search space for each parameter in an excitation control system is presented in this paper. The calculated search space is intended to reduce the number of parameter solution sets that can be found by an estimation algorithm, reducing its processing time. The method considers a synchronous generator time constant range between 4s and 10s, an excitation control system performance index, a controller design technique, and the excitation control system model structure. When the obtained search space is used to estimate the parameters, less processing time is used by the algorithm. Also the estimated parameters are closer to the reference ones.
Transformation of state space for two-parameter Markov processes
Institute of Scientific and Technical Information of China (English)
周健伟
1996-01-01
Let X=(X) be a two-parameter *-Markov process with a transition function (p1, p2, p), where X, takes values in the state space (Er,), T=[0,)2. For each r T, let f, be a measurable transformation of (E,) into the state space (E’r, ). Set Y,=f,(X,), r T. A sufficient condition is given for the process Y=(Yr) still to be a two-parameter *-Markov process with a transition function in terms of transition function (p1, p2, p) and fr. For *-Markov families of two-parameter processes with a transition function, a similar problem is also discussed.
Roe, Byron
2013-01-01
The effect of correlations between model parameters and nuisance parameters is discussed, in the context of fitting model parameters to data. Modifications to the usual $\\chi^2$ method are required. Fake data studies, as used at present, will not be optimum. Problems will occur for applications of the Maltoni-Schwetz \\cite{ms} theorem. Neutrino oscillations are used as examples, but the problems discussed here are general ones, which are often not addressed.
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.
Hsia, Wei Shen
1989-01-01
A validated technology data base is being developed in the areas of control/structures interaction, deployment dynamics, and system performance for Large Space Structures (LSS). A Ground Facility (GF), in which the dynamics and control systems being considered for LSS applications can be verified, was designed and built. One of the important aspects of the GF is to verify the analytical model for the control system design. The procedure is to describe the control system mathematically as well as possible, then to perform tests on the control system, and finally to factor those results into the mathematical model. The reduction of the order of a higher order control plant was addressed. The computer program was improved for the maximum entropy principle adopted in Hyland's MEOP method. The program was tested against the testing problem. It resulted in a very close match. Two methods of model reduction were examined: Wilson's model reduction method and Hyland's optimal projection (OP) method. Design of a computer program for Hyland's OP method was attempted. Due to the difficulty encountered at the stage where a special matrix factorization technique is needed in order to obtain the required projection matrix, the program was successful up to the finding of the Linear Quadratic Gaussian solution but not beyond. Numerical results along with computer programs which employed ORACLS are presented.
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.
Systematic parameter inference in stochastic mesoscopic modeling
Lei, Huan; Li, Zhen; Karniadakis, George
2016-01-01
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....
Parameter estimation in space systems using recurrent neural networks
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.
Determining Frequentist Confidence Limits Using a Directed Parameter Space Search
Daniel, Scott F.; Connolly, Andrew J.; Schneider, Jeff
2014-10-01
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.
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.
Held, Christian; Nattkemper, Tim; Palmisano, Ralf; Wittenberg, Thomas
2013-01-01
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. PMID:23766941
Estimating Illumination Parameters Using Spherical Harmonics Coefficients in Frequency Space
Institute of Scientific and Technical Information of China (English)
XIE Feng; TAO Linmi; XU Guangyou
2007-01-01
An algorithm is presented for estimating the direction and strength of point light with the strength of ambient illumination. Existing approaches evaluate these illumination parameters directly in the high dimensional image space, while we estimate the parameters in two steps:first by projecting the image to an orthogonal linear subspace based on spherical harmonic basis functions and then by calculating the parameters in the low dimensional subspace.The test results using the CMU PIE database and Yale Database B show the stability and effectiveness of the method.The resulting illumination information can be used to synthesize more realistic relighting images and to recognize objects under variable illumination.
Search Space Calculation to Improve Parameter Estimation of Excitation Control Systems
2013-01-01
A method to calculate the search space for each parameter in an excitation control system is presented in this paper. The calculated search space is intended to reduce the number of parameter solution sets that can be found by an estimation algorithm, reducing its processing time. The method considers a synchronous generator time constant range between 4s and 10s, an excitation control system performance index, a controller design technique, and the excitation control system model structure. ...
Space market model space industry input-output model
Hodgin, Robert F.; Marchesini, Roberto
1987-01-01
The goal of the Space Market Model (SMM) is to develop an information resource for the space industry. The SMM is intended to contain information appropriate for decision making in the space industry. The objectives of the SMM are to: (1) assemble information related to the development of the space business; (2) construct an adequate description of the emerging space market; (3) disseminate the information on the space market to forecasts and planners in government agencies and private corporations; and (4) provide timely analyses and forecasts of critical elements of the space market. An Input-Output model of market activity is proposed which are capable of transforming raw data into useful information for decision makers and policy makers dealing with the space sector.
Two space scatterer formalism calculation of bulk parameters of thunderclouds
Phanord, Dieudonne D.
1994-01-01
In a previous study, we used a modified two-space scatterer formalism of Twersky to establish for a cloud modeled as a statistically homogeneous distribution of spherical water droplets, the dispersion relations that determine its bulk propagation numbers and bulk indexes of refraction in terms of the vector equivalent scattering amplitude and the dyadic scattering amplitude of the single water droplet in isolation. The results were specialized to the forward direction of scattering while demanding that the scatterers preserve the incident polarization. We apply this approach to obtain specific numerical values for the macroscopic parameters of the cloud. We work with a cloud of density rho = 100 cm(exp -3), a wavelength lambda = 0.7774 microns, and with spherical water droplets of common radius alpha = 10 microns. In addition, the scattering medium is divided into three parts, the medium outside the cloud, moist air (the medium inside the cloud but outside the droplets), and the medium inside the spherical water droplets. The results of this report are applicable to a cloud of any geometry since the boundary does not interfere with the calculations. Also, it is important to notice the plane wave nature of the incidence wave in the moist atmosphere.
Mutagenesis by outer space parameters other than cosmic rays
Horneck, Gerda; Rabbow, Elke
We have studied the ability of microorganisms to cope with the complex interplay of the parameters of space in experiments in low Earth orbit and using space simulation facilities on ground. Emphasis was laid on space parameters other than cosmic rays. The studies are directed towards understanding prebiotic chemical evolution and biological evolution processes, and interplanetary transfer of life. Effects of space vacuum: Space experiments have shown that up to 70% of bacterial and fungal spores survived short-term exposure to space vacuum. The chances of survival in space were increased when spores were embedded in chemical protectants such as sugars, or salt crystals, or when they were exposed in multilayer. During the six years lasting LDEF mission up to 80% of bacterial spores survived exposure to space vacuum. A 10-fold increased mutation rate over the spontaneous rate has been observed in spores of Bacillus subtilis after exposure to space vacuum, which is probably based on a unique molecular signature of tandem-double base change at restricted sites in the DNA. In addition, DNA strand breaks have been observed to be induced by vacuum treatment. Effects of extraterrestrial solar UV radiation: Solar UV radiation has been found to be the most deleterious factor of space. The reason for this is the highly energetic UV-C and vacuum UV radiation that is directly absorbed by the DNA and which induces specific photoproducts in the DNA that are highly mutagenic and lethal. The damaging effect of extraterrestrial solar UV radiation was even aggravated, when the spores were simultaneously exposed to both, solar UV radiation and space vacuum. In order to investigate the mutagenic potential of solar UV radiation, DNA of the Escherichia coli plasmid pUC19 was exposed to selected wavebands of UV radiation (from vacuum UV to UV-A) by use of a solar simulator and space simulation facilities. Action spectra revealed that for vacuum UV different kinds of photochemical damage
Forecasts of non-Gaussian parameter spaces using Box-Cox transformations
Joachimi, B
2011-01-01
Forecasts of statistical constraints on model parameters using the Fisher matrix abound in many fields of astrophysics. The Fisher matrix formalism involves the assumption of Gaussianity in parameter space and hence fails to predict complex features of posterior probability distributions. Combining the standard Fisher matrix with Box-Cox transformations, we propose a novel method that accurately predicts arbitrary posterior shapes. The Box-Cox transformations are applied to parameter space to render it approximately multivariate Gaussian, performing the Fisher matrix calculation on the transformed parameters. We demonstrate that, after the Box-Cox parameters have been determined from an initial likelihood evaluation, the method correctly predicts changes in the posterior when varying various parameters of the experimental setup and the data analysis, with marginally higher computational cost than a standard Fisher matrix calculation. We apply the Box-Cox-Fisher formalism to forecast cosmological parameter con...
Naden, Levi N
2015-01-01
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 a thousand 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 o...
PARAMETER ESTIMATION OF ENGINEERING TURBULENCE MODEL
Institute of Scientific and Technical Information of China (English)
钱炜祺; 蔡金狮
2001-01-01
A parameter estimation algorithm is introduced and used to determine the parameters in the standard k-ε two equation turbulence model (SKE). It can be found from the estimation results that although the parameter estimation method is an effective method to determine model parameters, it is difficult to obtain a set of parameters for SKE to suit all kinds of separated flow and a modification of the turbulence model structure should be considered. So, a new nonlinear k-ε two-equation model (NNKE) is put forward in this paper and the corresponding parameter estimation technique is applied to determine the model parameters. By implementing the NNKE to solve some engineering turbulent flows, it is shown that NNKE is more accurate and versatile than SKE. Thus, the success of NNKE implies that the parameter estimation technique may have a bright prospect in engineering turbulence model research.
Simplicial models for trace spaces
DEFF Research Database (Denmark)
Raussen, Martin
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...... - as prodsimplicial complexes (with products of simplices as building blocks). This method makes use of certain poset categories of binary matrices and applies to a class of directed spaces that arise from a class of models of computation; still restricted but with a fair amount of generality. In the final section......, we outline a generalization to model spaces known as Higher Dimensional Automata. In particular, we describe algorithms that allow to determine not only the fundamental category of such amodel space, but all homological invariants of spaces of directed paths within it. The prodsimplical complexes...
Space Flight Cable Model Development
Spak, Kaitlin
2013-01-01
This work concentrates the modeling efforts presented in last year's VSGC conference paper, "Model Development for Cable-Harnessed Beams." The focus is narrowed to modeling of space-flight cables only, as a reliable damped cable model is not yet readily available and is necessary to continue modeling cable-harnessed space structures. New experimental data is presented, eliminating the low-frequency noise that plagued the first year's efforts. The distributed transfer function method is applied to a single section of space flight cable for Euler-Bernoulli and shear beams. The work presented here will be developed into a damped cable model that can be incorporated into an interconnected beam-cable system. The overall goal of this work is to accurately predict natural frequencies and modal damping ratios for cabled space structures.
The State Space Models Toolbox for MATLAB
Directory of Open Access Journals (Sweden)
Jyh-Ying Peng
2011-05-01
Full Text Available State Space Models (SSM is a MATLAB toolbox for time series analysis by state space methods. The software features fully interactive construction and combination of models, with support for univariate and multivariate models, complex time-varying (dy- namic models, non-Gaussian models, and various standard models such as ARIMA and structural time-series models. The software includes standard functions for Kalman fil- tering and smoothing, simulation smoothing, likelihood evaluation, parameter estimation, signal extraction and forecasting, with incorporation of exact initialization for filters and smoothers, and support for missing observations and multiple time series input with com- mon analysis structure. The software also includes implementations of TRAMO model selection and Hillmer-Tiao decomposition for ARIMA models. The software will provide a general toolbox for time series analysis on the MATLAB platform, allowing users to take advantage of its readily available graph plotting and general matrix computation capabilities.
Institute of Scientific and Technical Information of China (English)
HANG Li-hong; WANG Ai-guo; TIAN Nai-yuan; ZHANG Wei-cun; FAN Qiao-li
2011-01-01
The continuous casting technological parameters have a great influence on the secondary dendrite arm spacing of the slab, which determines the segregation behavior of materials. Therefore, the identification of technological parameters of continuous casting process directly impacts the property of slab. The relationships between continuous casting technological parameters and cooling rate of slab for spring steel were built using BP neural network model, based on which, the relevant secondary dendrite arm spacing was calculated. The simulation calculation was also carried out using the industrial data. The simulation results show that compared with that of the traditional method, the absolute error of calculation result obtained with BP neural network model reduced from 0. 015 to 0. 0005, and the relative error reduced from 6, 76 % to 0.22 %. BP neural network model had a more precise accuracy in the optimization of continuous casting technological parameters.
B -> tau nu: Opening up the Charged Higgs Parameter Space with R-parity Violation
Bose, Roshni
2011-01-01
The theoretically clean channel B+ -> tau+ nu shows a close to 3sigma 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.
Parameter optimization in S-system models
Directory of Open Access Journals (Sweden)
Vasconcelos Ana
2008-04-01
Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
Noncommutative spaces from matrix models
Lu, Lei
Noncommutative (NC) spaces commonly arise as solutions to matrix model equations of motion. They are natural generalizations of the ordinary commutative spacetime. Such spaces may provide insights into physics close to the Planck scale, where quantum gravity becomes relevant. Although there has been much research in the literature, aspects of these NC spaces need further investigation. In this dissertation, we focus on properties of NC spaces in several different contexts. In particular, we study exact NC spaces which result from solutions to matrix model equations of motion. These spaces are associated with finite-dimensional Lie-algebras. More specifically, they are two-dimensional fuzzy spaces that arise from a three-dimensional Yang-Mills type matrix model, four-dimensional tensor-product fuzzy spaces from a tensorial matrix model, and Snyder algebra from a five-dimensional tensorial matrix model. In the first part of this dissertation, we study two-dimensional NC solutions to matrix equations of motion of extended IKKT-type matrix models in three-space-time dimensions. Perturbations around the NC solutions lead to NC field theories living on a two-dimensional space-time. The commutative limit of the solutions are smooth manifolds which can be associated with closed, open and static two-dimensional cosmologies. One particular solution is a Lorentzian fuzzy sphere, which leads to essentially a fuzzy sphere in the Minkowski space-time. In the commutative limit, this solution leads to an induced metric that does not have a fixed signature, and have a non-constant negative scalar curvature, along with singularities at two fixed latitudes. The singularities are absent in the matrix solution which provides a toy model for resolving the singularities of General relativity. We also discussed the two-dimensional fuzzy de Sitter space-time, which has irreducible representations of su(1,1) Lie-algebra in terms of principal, complementary and discrete series. Field
Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models
Bates, Bryson C.
1992-01-01
A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.
Space debris: modeling and detectability
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
Space market model development project
Bishop, Peter C.
1987-01-01
The objectives of the research program, Space Market Model Development Project, (Phase 1) were: (1) to study the need for business information in the commercial development of space; and (2) to propose a design for an information system to meet the identified needs. Three simultaneous research strategies were used in proceeding toward this goal: (1) to describe the space business information which currently exists; (2) to survey government and business representatives on the information they would like to have; and (3) to investigate the feasibility of generating new economical information about the space industry.
Energy Technology Data Exchange (ETDEWEB)
Lee, B C; Schulz, M; de Supinski, B R
2006-09-28
Increasing system and algorithmic complexity, combined with a growing number of tunable application parameters, pose significant challenges for analytical performance modeling. This report outlines a series of robust techniques that enable efficient parameter space exploration based on empirical statistical modeling. In particular, this report applies statistical techniques such as clustering, association, correlation analyses to understand the parameter space better. Results from these statistical techniques guide the construction of piecewise polynomial regression models. Residual and significance tests ensure the resulting model is unbiased and efficient. We demonstrate these techniques in R, a statistical computing environment, for predicting the performance of semicoarsening multigrid. 50 and 75 percent of predictions achieve error rates of 5.5 and 10.0 percent or less, respectively.
Model space of economic events
Romanovsky, M. Yu.
A method for constructing the model or virtual space of economic events when economic objects can be considered as material ones is suggested. We describe change of share rates in time at stock markets as the potential difference of attracted bodies in time in this virtual space. Each share of each enterprise is displayed by a single particle with a unit “charge”. It is shown that the random value of potential difference at the origin of coordinates measured at a definite time interval has the probability density coinciding with the known distribution of “Levy flights” or “Levy walks”. A distribution of alteration in time of the “Standard and Poor” index value obtained by Mantegna and Stanley (they shown that it is the “Levy walks” distribution too) (Mantegna and Stanley, Nature 376 (1995) 46) is used for determination of the introduced potential dependence on coordinates in the model space. A simple phenomenological model of interaction potential is introduced. The potential law of each particle turns out to be closed to r-2.14 in the minimum possible three-dimensional model space. This model permits calculation of time of random potential correlations at a certain point of the model space. These correlations could characterize the time period of making a decision by an investor at stock exchange. It is shown that this time is notably shorter in unstable periods (1987). A “microscopical” model of interaction in the virtual space is also discussed.
Parameter space of experimental chaotic circuits with high-precision control parameters
de Sousa, Francisco F. G.; Rubinger, Rero M.; Sartorelli, José C.; Albuquerque, Holokx A.; Baptista, Murilo S.
2016-08-01
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.
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.
The determination of space parameters of the heliostatic collector field
Directory of Open Access Journals (Sweden)
Dušan Kudelas
2006-04-01
Full Text Available The assurance of perpetual perpendicular insolation of solar collector absorber surface may increase the insolation energy byca 42-45 %.. A consequence of theincrease in the energy production may be the reduction of the solar collectors’ surface area. For the large scale solar collector field conception is advantageous to build collector sections with several collectors in one heliostat. For the conception of the solar collector field with heliostat collectors is important to make a regular identification of space parameters of all parts of the solar system field. The placement of the heliostats is a basic condition for the optimal insolation conditions of heliostat solar collectors’ field.
Multi-parameter Tikhonov Regularisation in Topological Spaces
Grasmair, Markus
2011-01-01
We study the behaviour of Tikhonov regularisation on topological spaces with multiple regularisation terms. The main result of the paper shows that multi-parameter regularisation is well-posed in the sense that the results depend continuously on the data and converge to a true solution of the equation to be solved as the noise level decreases to zero. Moreover, we derive convergence rates in terms of a generalised Bregman distance using the method of variational inequalities. All the results in the paper, including the convergence rates, consider not only noise in the data, but also errors in the operator.
Dynamical Evolution of Young Embedded Clusters: A Parameter Space Survey
Proszkow, Eva-Marie
2009-01-01
This paper investigates the dynamical evolution of embedded stellar clusters from the protocluster stage, through the embedded star-forming phase, and out to ages of 10 Myr -- after the gas has been removed from the cluster. The relevant dynamical properties of young stellar clusters are explored over a wide range of possible star formation environments using N-body simulations. Many realizations of equivalent initial conditions are used to produce robust statistical descriptions of cluster evolution including the cluster bound fraction, radial probability distributions, as well as the distributions of close encounter distances and velocities. These cluster properties are presented as a function of parameters describing the initial configuration of the cluster, including the initial cluster membership N, initial stellar velocities, cluster radii, star formation efficiency, embedding gas dispersal time, and the degree of primordial mass segregation. The results of this parameter space survey, which includes ab...
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....
Parameter estimation of hydrologic models using data assimilation
Kaheil, Y. H.
2005-12-01
The uncertainties associated with the modeling of hydrologic systems sometimes demand that data should be incorporated in an on-line fashion in order to understand the behavior of the system. This paper represents a Bayesian strategy to estimate parameters for hydrologic models in an iterative mode. The paper presents a modified technique called localized Bayesian recursive estimation (LoBaRE) that efficiently identifies the optimum parameter region, avoiding convergence to a single best parameter set. The LoBaRE methodology is tested for parameter estimation for two different types of models: a support vector machine (SVM) model for predicting soil moisture, and the Sacramento Soil Moisture Accounting (SAC-SMA) model for estimating streamflow. The SAC-SMA model has 13 parameters that must be determined. The SVM model has three parameters. Bayesian inference is used to estimate the best parameter set in an iterative fashion. This is done by narrowing the sampling space by imposing uncertainty bounds on the posterior best parameter set and/or updating the "parent" bounds based on their fitness. The new approach results in fast convergence towards the optimal parameter set using minimum training/calibration data and evaluation of fewer parameter sets. The efficacy of the localized methodology is also compared with the previously used Bayesian recursive estimation (BaRE) algorithm.
Organizing the Parameter Space of the Global 21-cm Signal
Cohen, Aviad; Barkana, Rennan; Lotem, Matan
2016-01-01
The early star-forming Universe is still poorly constrained, with the properties of high-redshift stars, the first heating sources, and reionization highly uncertain. This leaves observers planning 21-cm experiments with little theoretical guidance. In this work we explore the possible range of high-redshift parameters including the star formation efficiency and the minimal mass of star-forming halos; the efficiency, spectral energy distribution, and redshift evolution of the first X-ray sources; and the history of reionization. These parameters are only weakly constrained by available observations, mainly the optical depth to the cosmic microwave background. We use realistic semi-numerical simulations to produce the global 21-cm signal over the redshift range $z = 6-40$ for each of 181 different combinations of the astrophysical parameters spanning the allowed range. We show that the expected signal fills a large parameter space, but with a fixed general shape for the global 21-cm curve. Even with our wide s...
Exploration of Parameter Spaces in a Virtual Observatory
Djorgovski, S G; Brunner, R J; Williams, R; Granat, R; Curkendall, D; Jacob, J; Stolorz, P
2001-01-01
Like every other field of intellectual endeavor, astronomy is being revolutionised by the advances in information technology. There is an ongoing exponential growth in the volume, quality, and complexity of astronomical data sets, mainly through large digital sky surveys and archives. The Virtual Observatory (VO) concept represents a scientific and technological framework needed to cope with this data flood. Systematic exploration of the observable parameter spaces, covered by large digital sky surveys spanning a range of wavelengths, will be one of the primary modes of research with a VO. This is where the truly new discoveries will be made, and new insights be gained about the already known astronomical objects and phenomena. We review some of the methodological challenges posed by the analysis of large and complex data sets expected in the VO-based research. The challenges are driven both by the size and the complexity of the data sets (billions of data vectors in parameter spaces of tens or hundreds of di...
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 us...
Running of soft parameters in extra space-time dimensions
Energy Technology Data Exchange (ETDEWEB)
Kobayashi, Tatsuo; Kubo, Jisuke; Mondragon, Myriam; Zoupanos, George
1999-06-14
The evolution of the parameters including those in the soft supersymmetry-breaking (SSB) sector is studied in the minimal supersymmetric standard model (MSSM) with a certain set of Kaluza-Klein towers which has been recently considered by Dienes et al. We use the continuous Wilson renormalization group technique to derive the one-loop matching condition between the effective, renormalizable and original, unrenormalizable theories. We investigate whether the assumption of a large compactification radius in the model is consistent with the gauge coupling unification, the b-{tau} unification and the radiative breaking of the electroweak gauge symmetry with the universal SSB terms. We calculate the superpartner spectrum under the assumption of the universal SSB parameters to find differences between the model and the MSSM.
Emergence and spread of antibiotic resistance: setting a parameter space.
Martínez, José Luis; Baquero, Fernando
2014-05-01
The emergence and spread of antibiotic resistance among human pathogens is a relevant problem for human health and one of the few evolution processes amenable to experimental studies. In the present review, we discuss some basic aspects of antibiotic resistance, including mechanisms of resistance, origin of resistance genes, and bottlenecks that modulate the acquisition and spread of antibiotic resistance among human pathogens. In addition, we analyse several parameters that modulate the evolution landscape of antibiotic resistance. Learning why some resistance mechanisms emerge but do not evolve after a first burst, whereas others can spread over the entire world very rapidly, mimicking a chain reaction, is important for predicting the evolution, and relevance for human health, of a given mechanism of resistance. Because of this, we propose that the emergence and spread of antibiotic resistance can only be understood in a multi-parameter space. Measuring the effect on antibiotic resistance of parameters such as contact rates, transfer rates, integration rates, replication rates, diversification rates, and selection rates, for different genes and organisms, growing under different conditions in distinct ecosystems, will allow for a better prediction of antibiotic resistance and possibilities of focused interventions.
PARAMETER ESTIMATION IN BREAD BAKING MODEL
Hadiyanto Hadiyanto; AJB van Boxtel
2012-01-01
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 pro...
Parameter counting in models with global symmetries
Energy Technology Data Exchange (ETDEWEB)
Berger, Joshua [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: jb454@cornell.edu; Grossman, Yuval [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: yuvalg@lepp.cornell.edu
2009-05-18
We present rules for determining the number of physical parameters in models with exact flavor symmetries. In such models the total number of parameters (physical and unphysical) needed to described a matrix is less than in a model without the symmetries. Several toy examples are studied in order to demonstrate the rules. The use of global symmetries in studying the minimally supersymmetric standard model (MSSM) is examined.
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...... method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented...
Cosmological models with constant deceleration parameter
Energy Technology Data Exchange (ETDEWEB)
Berman, M.S.; de Mello Gomide, F.
1988-02-01
Berman presented elsewhere a law of variation for Hubble's parameter that yields constant deceleration parameter models of the universe. By analyzing Einstein, Pryce-Hoyle and Brans-Dicke cosmologies, we derive here the necessary relations in each model, considering a perfect fluid.
Embedding a State Space Model Into a Markov Decision Process
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Jørgensen, Erik; Højsgaard, Søren
2011-01-01
estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for modeling repeated measurements over time where the model parameters can evolve dynamically. In this paper we consider methods for embedding an SSM into an MDP with finite state and action space. Different...
Exploring Parameter Space Coverage of Various LISA Configurations
Katz, Michael L.
2017-01-01
With the success of LISA Pathfinder, the measurement of gravitational waves in space has taken an important step forward. We conduct an analysis of the measurement abilities of distinctive LISA detector designs, examining how the low-frequency band-edge behavior of the detector sensitivity curve affects measurement capabilities. We are particularly interested in LISA’s ability to measure massive black holes that are merging near the band-edge, with masses in the range of $\\sim 10^6-10^{10}M_\\odot$. We examine the ringdown and insprial detectability over a wide range of Massive Black Hole (MBH) binaries along with a broad palette of possible LISA design parameters.
Trait Characteristics of Diffusion Model Parameters
Directory of Open Access Journals (Sweden)
Anna-Lena Schubert
2016-07-01
Full Text Available Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence.
Parameter identification in the logistic STAR model
DEFF Research Database (Denmark)
Ekner, Line Elvstrøm; Nejstgaard, Emil
We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan D.; Stieglitz, Marc
2015-06-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal aspects of catchment hydrological variability.
Parameter Estimation of Partial Differential Equation Models
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.
Application of lumped-parameter models
Energy Technology Data Exchange (ETDEWEB)
Ibsen, Lars Bo; Liingaard, M.
2006-12-15
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. Subsequently, the assembly of the dynamic stiffness matrix for the foundation is considered, and the solution for obtaining the steady state response, when using lumped-parameter models is given. (au)
Parallel axes gear set optimization in two-parameter space
Theberge, Y.; Cardou, A.; Cloutier, L.
1991-05-01
This paper presents a method for optimal spur and helical gear transmission design that may be used in a computer aided design (CAD) approach. The design objective is generally taken as obtaining the most compact set for a given power input and gear ratio. A mixed design procedure is employed which relies both on heuristic considerations and computer capabilities. Strength and kinematic constraints are considered in order to define the domain of feasible designs. Constraints allowed include: pinion tooth bending strength, gear tooth bending strength, surface stress (resistance to pitting), scoring resistance, pinion involute interference, gear involute interference, minimum pinion tooth thickness, minimum gear tooth thickness, and profile or transverse contact ratio. A computer program was developed which allows the user to input the problem parameters, to select the calculation procedure, to see constraint curves in graphic display, to have an objective function level curve drawn through the design space, to point at a feasible design point and to have constraint values calculated at that point. The user can also modify some of the parameters during the design process.
Dynamical quantum Hall effect in the parameter space.
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.
Identification of slow molecular order parameters for Markov model construction
Perez-Hernandez, Guillermo; Giorgino, Toni; de Fabritiis, Gianni; Noé, Frank
2013-01-01
A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either g...
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
Variations of cosmic large-scale structure covariance matrices across parameter space
Reischke, Robert; Kiessling, Alina; Schäfer, Björn Malte
2017-03-01
The likelihood function for cosmological parameters, given by e.g. weak lensing shear measurements, depends on contributions to the covariance induced by the non-linear evolution of the cosmic web. As highly non-linear clustering to date has only been described by numerical N-body simulations in a reliable and sufficiently precise way, the necessary computational costs for estimating those covariances at different points in parameter space are tremendous. In this work, we describe the change of the matter covariance and the weak lensing covariance matrix as a function of cosmological parameters by constructing a suitable basis, where we model the contribution to the covariance from non-linear structure formation using Eulerian perturbation theory at third order. We show that our formalism is capable of dealing with large matrices and reproduces expected degeneracies and scaling with cosmological parameters in a reliable way. Comparing our analytical results to numerical simulations, we find that the method describes the variation of the covariance matrix found in the SUNGLASS weak lensing simulation pipeline within the errors at one-loop and tree-level for the spectrum and the trispectrum, respectively, for multipoles up to ℓ ≤ 1300. We show that it is possible to optimize the sampling of parameter space where numerical simulations should be carried out by minimizing interpolation errors and propose a corresponding method to distribute points in parameter space in an economical way.
Statefinder parameters in two dark energy models
Panotopoulos, Grigoris
2007-01-01
The statefinder parameters ($r,s$) in two dark energy models are studied. In the first, we discuss in four-dimensional General Relativity a two fluid model, in which dark energy and dark matter are allowed to interact with each other. In the second model, we consider the DGP brane model generalized by taking a possible energy exchange between the brane and the bulk into account. We determine the values of the statefinder parameters that correspond to the unique attractor of the system at hand. Furthermore, we produce plots in which we show $s,r$ as functions of red-shift, and the ($s-r$) plane for each model.
Self-similar structures in a 2D parameter-space of an inductorless Chua's circuit
Energy Technology Data Exchange (ETDEWEB)
Albuquerque, Holokx A. [Departamento de Fisica, Universidade do Estado de Santa Catarina, 89223-100 Joinville (Brazil)], E-mail: dfi2haa@joinville.udesc.br; Rubinger, Rero M. [Departamento de Fisica e Quimica, Universidade Federal de Itajuba, 37500-903 Itajuba (Brazil); Rech, Paulo C. [Departamento de Fisica, Universidade do Estado de Santa Catarina, 89223-100 Joinville (Brazil)
2008-06-30
In a 2D parameter-space of an inductorless Chua's circuit model, we carried out numerical investigations and observed self-similar stability structures embedded in a sea of chaos, known until recently just in discrete-time models, namely, shrimps. We showed that those structures are self-similar and organize themselves in a period-adding bifurcation cascade in a region of the parameter-space.
Robust PID Steering Control in Parameter Space for Highly Automated Driving
Directory of Open Access Journals (Sweden)
Mümin Tolga Emirler
2014-01-01
Full Text Available This paper is on the design of a parameter space based robust PID steering controller. This controller is used for automated steering in automated path following of a midsized sedan. Linear and nonlinear models of this midsized sedan are presented in the paper. Experimental results are used to validate the longitudinal and lateral dynamic models of this vehicle. This paper is on automated steering control and concentrates on the lateral direction of motion. The linear model is used to design a PID steering controller in parameter space that satisfies D-stability. The PID steering controller that is designed is used in a simulation study to illustrate the effectiveness of the proposed method. Simulation results for a circular trajectory and for a curved trajectory are presented and discussed in detail. This study is part of a larger research effort aimed at implementing highly automated driving in a midsized sedan.
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...
Setting Parameters for Biological Models With ANIMO
Schivo, Stefano; Scholma, Jetse; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole; van de Pol, Jan Cornelis; Langerak, Romanus; André, Étienne; Frehse, Goran
2014-01-01
ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions
Delineating Parameter Unidentifiabilities in Complex Models
Raman, Dhruva V; Papachristodoulou, Antonis
2016-01-01
Scientists use mathematical modelling to understand and predict the properties of complex physical systems. In highly parameterised models there often exist relationships between parameters over which model predictions are identical, or nearly so. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, and the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast timescale subsystems, as well as the regimes in which such approximations are valid. We base our algorithm on a novel quantification of regional parametric sensitivity: multiscale sloppiness. Traditional...
Correlators of Matrix Models on Homogeneous Spaces
Kitazawa, Y; Tomino, D; Kitazawa, Yoshihisa; Takayama, Yastoshi; Tomino, Dan
2004-01-01
We investigate the correlators of TrA_{mu}A_{nu} in matrix models on homogeneous spaces: S^2 and S^2 x S^2. Their expectation value is a good order parameter to measure the geometry of the space on which non-commutative gauge theory is realized. They also serve as the Wilson lines which carry the minimum momentum. We develop an efficient procedure to calculate them through 1PI diagrams. We determine the large N scaling behavior of the correlators. The order parameter shows that fuzzy S^2 x S^2 acquires a 4 dimensional fractal structure in contrast to fuzzy S^2. We also find that the two point functions exhibit logarithmic scaling violations.
Mirror symmetry for two-parameter models, 1
Candelas, Philip; Font, A; Katz, S; Morrison, Douglas Robert Ogston; Candelas, Philip; Ossa, Xenia de la; Font, Anamaria; Katz, Sheldon; Morrison, David R.
1994-01-01
We study, by means of mirror symmetry, the quantum geometry of the K\\"ahler-class parameters of a number of Calabi-Yau manifolds that have $b_{11}=2$. Our main interest lies in the structure of the moduli space and in the loci corresponding to singular models. This structure is considerably richer when there are two parameters than in the various one-parameter models that have been studied hitherto. We describe the intrinsic structure of the point in the (compactification of the) moduli space that corresponds to the large complex structure or classical limit. The instanton expansions are of interest owing to the fact that some of the instantons belong to families with continuous parameters. We compute the Yukawa couplings and their expansions in terms of instantons of genus zero. By making use of recent results of Bershadsky et al. we compute also the instanton numbers for instantons of genus one. For particular values of the parameters the models become birational to certain models with one parameter. The co...
Parameter Estimation, Model Reduction and Quantum Filtering
Chase, Bradley A
2009-01-01
This dissertation explores the topics of parameter estimation and model reduction in the context of quantum filtering. Chapters 2 and 3 provide a review of classical and quantum probability theory, stochastic calculus and filtering. Chapter 4 studies the problem of quantum parameter estimation and introduces the quantum particle filter as a practical computational method for parameter estimation via continuous measurement. Chapter 5 applies these techniques in magnetometry and studies the estimator's uncertainty scalings in a double-pass atomic magnetometer. Chapter 6 presents an efficient feedback controller for continuous-time quantum error correction. Chapter 7 presents an exact model of symmetric processes of collective qubit systems.
Mirror symmetry for two parameter models, 2
Candelas, Philip; Katz, S; Morrison, Douglas Robert Ogston; Philip Candelas; Anamaria Font; Sheldon Katz; David R Morrison
1994-01-01
We describe in detail the space of the two K\\"ahler parameters of the Calabi--Yau manifold \\P_4^{(1,1,1,6,9)}[18] by exploiting mirror symmetry. The large complex structure limit of the mirror, which corresponds to the classical large radius limit, is found by studying the monodromy of the periods about the discriminant locus, the boundary of the moduli space corresponding to singular Calabi--Yau manifolds. A symplectic basis of periods is found and the action of the Sp(6,\\Z) generators of the modular group is determined. From the mirror map we compute the instanton expansion of the Yukawa couplings and the generalized N=2 index, arriving at the numbers of instantons of genus zero and genus one of each degree. We also investigate an SL(2,\\Z) symmetry that acts on a boundary of the moduli space.
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.
Comparing spatial and temporal transferability of hydrological model parameters
Patil, Sopan; Stieglitz, Marc
2015-04-01
Operational use of hydrological models requires the transfer of calibrated parameters either in time (for streamflow forecasting) or space (for prediction at ungauged catchments) or both. Although the effects of spatial and temporal parameter transfer on catchment streamflow predictions have been well studied individually, a direct comparison of these approaches is much less documented. In our view, such comparison is especially pertinent in the context of increasing appeal and popularity of the "trading space for time" approaches that are proposed for assessing the hydrological implications of anthropogenic climate change. Here, we compare three different schemes of parameter transfer, viz., temporal, spatial, and spatiotemporal, using a spatially lumped hydrological model called EXP-HYDRO at 294 catchments across the continental United States. Results show that the temporal parameter transfer scheme performs best, with lowest decline in prediction performance (median decline of 4.2%) as measured using the Kling-Gupta efficiency metric. More interestingly, negligible difference in prediction performance is observed between the spatial and spatiotemporal parameter transfer schemes (median decline of 12.4% and 13.9% respectively). We further demonstrate that the superiority of temporal parameter transfer scheme is preserved even when: (1) spatial distance between donor and receiver catchments is reduced, or (2) temporal lag between calibration and validation periods is increased. Nonetheless, increase in the temporal lag between calibration and validation periods reduces the overall performance gap between the three parameter transfer schemes. Results suggest that spatiotemporal transfer of hydrological model parameters has the potential to be a viable option for climate change related hydrological studies, as envisioned in the "trading space for time" framework. However, further research is still needed to explore the relationship between spatial and temporal
Habitability Concept Models for Living in Space
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.
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...
Models and parameters for environmental radiological assessments
Energy Technology Data Exchange (ETDEWEB)
Miller, C W [ed.
1984-01-01
This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined 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. Chapters have been entered separately into the data base. (ACR)
Hadronic Total Cross-sections Through Soft Gluon Summation in Impact Parameter Space
1999-01-01
The 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 $\\alpha_s$ is discussed, both for finite as well as singular, but integrable, $\\alpha_s$. The formalism is applied to the prediction of total proton-proton and proton-antiproton cross-sections, where screening, due to soft gluon emission fro...
Li, Junlan; Huang, Hongzhou; Yan, Shaoze; Yang, Yunqiang
2017-07-01
Joint clearance and the uncertainty of geometric and physical parameters significantly influence the kinematic accuracy and dynamic response of space deployable mechanisms. Such mechanisms have been widely employed in astronautic missions to improve the capabilities of launchers. This paper proposes a methodology to investigate the kinematic accuracy and dynamic performance of space deployable mechanism with joint clearance while considering parameter uncertainty. The model of space deployable mechanism with a planar revolute joint is provided. With consideration of several uncertain parameters, the solving procedure of the dynamic equations is presented based on the Monte Carlo method. A case study is conducted to reveal the effect of parameter uncertainty on its kinematic accuracy and dynamic performance. The results indicate that parameter uncertainty should be considered to accurately evaluate the performance of long-term operating space deployable mechanisms, especially for such systems with clearance joints. According to the results, brief suggestions for design and evaluation of the mechanisms are provided.
Moving to continuous facial expression space using the MPEG-4 facial definition parameter (FDP) set
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.
Variations of cosmic large-scale structure covariance matrices across parameter space
Reischke, Robert; Schäfer, Björn Malte
2016-01-01
The likelihood function for cosmological parameters, given by e.g. weak lensing shear measurements, depends on contributions to the covariance induced by the nonlinear evolution of the cosmic web. As nonlinear clustering to date has only been described by numerical $N$-body simulations in a reliable and sufficiently precise way, the necessary computational costs for estimating those covariances at different points in parameter space are tremendous. In this work we describe the change of the matter covariance and of the weak lensing covariance matrix as a function of cosmological parameters by constructing a suitable basis, where we model the contribution to the covariance from nonlinear structure formation using Eulerian perturbation theory at third order. We show that our formalism is capable of dealing with large matrices and reproduces expected degeneracies and scaling with cosmological parameters in a reliable way. Comparing our analytical results to numerical simulations we find that the method describes...
Block-Nordsieck summation and partonic distributions in impact parameter space
Energy Technology Data Exchange (ETDEWEB)
Corsetti, A. [Rome Univ. `La Sapienza (Italy). INFN, Dept. of Physics; Grau, A. [Universidada de Granada (Spain). Dep. de Fisica Teorica y del Cosmos; Pancheri, G. [INFN, Laboratori nazionali di Frascati, Rome (Italy); Srivastava, Y.N. [Perugia Univ. (Italy). INFN, Dept. of Physics
1996-02-01
A model for the parton distributions of hadrons in impact parameter space has been constructed using soft gluon summation. This model incorporates the salient features of distributions obtained from the intrinsic transverse momentum behaviour of hadrons. Under the assumption that the intrinsic behaviour is dominated by soft gluon emission stimulated by the scattering process, the b-spectrum becomes softer and softer as the scattering energy increases. In minijet models for the inclusive cross-sections, this will counter the increase from {sigma}{sub j}et.
SpaceNet: Modeling and Simulating Space Logistics
Lee, Gene; Jordan, Elizabeth; Shishko, Robert; de Weck, Olivier; Armar, Nii; Siddiqi, Afreen
2008-01-01
This paper summarizes the current state of the art in interplanetary supply chain modeling and discusses SpaceNet as one particular method and tool to address space logistics modeling and simulation challenges. Fundamental upgrades to the interplanetary supply chain framework such as process groups, nested elements, and cargo sharing, enabled SpaceNet to model an integrated set of missions as a campaign. The capabilities and uses of SpaceNet are demonstrated by a step-by-step modeling and simulation of a lunar campaign.
SpaceNet: Modeling and Simulating Space Logistics
Lee, Gene; Jordan, Elizabeth; Shishko, Robert; de Weck, Olivier; Armar, Nii; Siddiqi, Afreen
2008-01-01
This paper summarizes the current state of the art in interplanetary supply chain modeling and discusses SpaceNet as one particular method and tool to address space logistics modeling and simulation challenges. Fundamental upgrades to the interplanetary supply chain framework such as process groups, nested elements, and cargo sharing, enabled SpaceNet to model an integrated set of missions as a campaign. The capabilities and uses of SpaceNet are demonstrated by a step-by-step modeling and simulation of a lunar campaign.
Benchmarking Ionizing Space Environment Models
Bourdarie, S.; Inguimbert, C.; Standarovski, D.; Vaillé, J.-R.; Sicard-Piet, A.; Falguere, D.; Ecoffet, R.; Poivey, C.; Lorfèvre, E.
2017-08-01
In-flight feedback data are collected, such as displacement damage doses, ionizing doses, and cumulated Single Event upset (SEU) on board various space vehicles and are compared to predictions performed with: 1) proton measurements performed with spectrometers data on board the same spacecraft if any and 2) protons spectrum predicted by the legacy AP8min model and the AP9 and Onera Proton Altitude Low models. When an accurate representation of the 3-D spacecraft shielding as well as appropriate ground calibrations are considered in the calculations, such comparisons provide powerful metrics to investigate engineering model accuracy. To describe >30 MeV trapped protons fluxes, the AP8 min model is found to provide closer predictions to observations than AP9 V1.30.001 (mean and perturbed mean).
Estimation of Model Parameters for Steerable Needles
Park, Wooram; Reed, Kyle B.; Okamura, Allison M.; Chirikjian, Gregory S.
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%. PMID:21643451
Estimation of Model Parameters for Steerable Needles.
Park, Wooram; Reed, Kyle B; Okamura, Allison M; Chirikjian, Gregory S
2010-01-01
Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.
Determining state-space models from sequential output data
Lin, Jiguan Gene
1988-01-01
This talk focuses on the determination of state-space models for large space systems using only the output data. The output data could be generated by the unknown or deliberate initial conditions of the space structure in question. We shall review some relevant fundamental work on the state-space modeling of sequential output data that is potentially applicable to large space structures. If formulated in terms of some generalized Markov parameters, this approach is in some sense similar to, but much simpler than, the Juang-Pappa Eigensystem Realization Algorithm (ERA) and the Ho-Kalman construction procedure.
Analysis of Modeling Parameters on Threaded Screws.
Energy Technology Data Exchange (ETDEWEB)
Vigil, Miquela S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vangoethem, Douglas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-06-01
Assembled mechanical systems often contain a large number of bolted connections. These bolted connections (joints) are integral aspects of the load path for structural dynamics, and, consequently, are paramount for calculating a structure's stiffness and energy dissipation prop- erties. However, analysts have not found the optimal method to model appropriately these bolted joints. The complexity of the screw geometry cause issues when generating a mesh of the model. This paper will explore different approaches to model a screw-substrate connec- tion. Model parameters such as mesh continuity, node alignment, wedge angles, and thread to body element size ratios are examined. The results of this study will give analysts a better understanding of the influences of these parameters and will aide in finding the optimal method to model bolted connections.
Exploring Replica-Exchange Wang-Landau sampling in higher-dimensional parameter space
Valentim, Alexandra; Rocha, Julio C. S.; Tsai, Shan-Ho; Li, Ying Wai; Eisenbach, Markus; Fiore, Carlos E.; Landau, David P.
2015-09-01
We considered a higher-dimensional extension for the replica-exchange Wang- Landau algorithm to perform a random walk in the energy and magnetization space of the two-dimensional Ising model. This hybrid scheme combines the advantages of Wang-Landau and Replica-Exchange algorithms, and the one-dimensional version of this approach has been shown to be very efficient and to scale well, up to several thousands of computing cores. This approach allows us to split the parameter space of the system to be simulated into several pieces and still perform a random walk over the entire parameter range, ensuring the ergodicity of the simulation. Previous work, in which a similar scheme of parallel simulation was implemented without using replica exchange and with a different way to combine the result from the pieces, led to discontinuities in the final density of states over the entire range of parameters. From our simulations, it appears that the replica-exchange Wang-Landau algorithm is able to overcome this difficulty, allowing exploration of higher parameter phase space by keeping track of the joint density of states.
Exploring Replica-Exchange Wang-Landau sampling in higher-dimensional parameter space
Energy Technology Data Exchange (ETDEWEB)
Valentim, Alexandra [University of Georgia, Athens, GA; Rocha, Julio C. S. [Universidade Federal de Minas Gerais; Tsai, Shan-Ho [University of Georgia, Athens, GA; Li, Ying Wai [ORNL; Eisenbach, Markus [ORNL; Fiore, Carlos E [University of Sao Paulo, BRAZIL; Landau, David P [University of Georgia, Athens, GA
2015-01-01
We considered a higher-dimensional extension for the replica-exchange Wang-Landau algorithm to perform a random walk in the energy and magnetization space of the two-dimensional Ising model. This hybrid scheme combines the advantages of Wang-Landau and Replica-Exchange algorithms, and the one-dimensional version of this approach has been shown to be very efficient and to scale well, up to several thousands of computing cores. This approach allows us to split the parameter space of the system to be simulated into several pieces and still perform a random walk over the entire parameter range, ensuring the ergodicity of the simulation. Previous work, in which a similar scheme of parallel simulation was implemented without using replica exchange and with a different way to combine the result from the pieces, led to discontinuities in the final density of states over the entire range of parameters. From our simulations, it appears that the replica-exchange Wang-Landau algorithm is able to overcome this diculty, allowing exploration of higher parameter phase space by keeping track of the joint density of states.
Linear Sigma Models With Strongly Coupled Phases -- One Parameter Models
Hori, Kentaro
2013-01-01
We systematically construct a class of two-dimensional $(2,2)$ supersymmetric gauged linear sigma models with phases in which a continuous subgroup of the gauge group is totally unbroken. We study some of their properties by employing a recently developed technique. The focus of the present work is on models with one K\\"ahler parameter. The models include those corresponding to Calabi-Yau threefolds, extending three examples found earlier by a few more, as well as Calabi-Yau manifolds of other dimensions and non-Calabi-Yau manifolds. The construction leads to predictions of equivalences of D-brane categories, systematically extending earlier examples. There is another type of surprise. Two distinct superconformal field theories corresponding to Calabi-Yau threefolds with different Hodge numbers, $h^{2,1}=23$ versus $h^{2,1}=59$, have exactly the same quantum K\\"ahler moduli space. The strong-weak duality plays a crucial r\\^ole in confirming this, and also is useful in the actual computation of the metric on t...
The Lund Model at Nonzero Impact Parameter
Janik, R A; Janik, Romuald A.; Peschanski, Robi
2003-01-01
We extend the formulation of the longitudinal 1+1 dimensional Lund model to nonzero impact parameter using the minimal area assumption. Complete formulae for the string breaking probability and the momenta of the produced mesons are derived using the string worldsheet Minkowskian helicoid geometry. For strings stretched into the transverse dimension, we find probability distribution with slope linear in m_T similar to the statistical models but without any thermalization assumptions.
IMPROVEMENT OF FLUID PIPE LUMPED PARAMETER MODEL
Institute of Scientific and Technical Information of China (English)
Kong Xiaowu; Wei Jianhua; Qiu Minxiu; Wu Genmao
2004-01-01
The traditional lumped parameter model of fluid pipe is introduced and its drawbacks are pointed out.Furthermore, two suggestions are put forward to remove these drawbacks.Firstly, the structure of equivalent circuit is modified, and then the evaluation of equivalent fluid resistance is change to take the frequency-dependent friction into account.Both simulation and experiment prove that this model is precise to characterize the dynamic behaviors of fluid in pipe.
Determination of the Parameter Sets for the Best Performance of IPS-driven ENLIL Model
Yun, Jongyeon; Choi, Kyu-Cheol; Yi, Jonghyuk; Kim, Jaehun; Odstrcil, Dusan
2016-12-01
Interplanetary scintillation-driven (IPS-driven) ENLIL model was jointly developed by University of California, San Diego (UCSD) and National Aeronaucics and Space Administration/Goddard Space Flight Center (NASA/GSFC). The model has been in operation by Korean Space Weather Cetner (KSWC) since 2014. IPS-driven ENLIL model has a variety of ambient solar wind parameters and the results of the model depend on the combination of these parameters. We have conducted researches to determine the best combination of parameters to improve the performance of the IPS-driven ENLIL model. The model results with input of 1,440 combinations of parameters are compared with the Advanced Composition Explorer (ACE) observation data. In this way, the top 10 parameter sets showing best performance were determined. Finally, the characteristics of the parameter sets were analyzed and application of the results to IPS-driven ENLIL model was discussed.
Modeling thermally active building components using space mapping
DEFF Research Database (Denmark)
Pedersen, Frank; Weitzmann, Peter; Svendsen, Svend
2005-01-01
simplified models of the components do not always provide useful solutions, since they are not always able to reproduce the correct thermal behavior. The space mapping technique transforms a simplified, but computationally inexpensive model, in order to align it with a detailed model or measurements....... This paper describes the principle of the space mapping technique, and introduces a simple space mapping technique. The technique is applied to a lumped parameter model of a thermo active component, which provides a model of the thermal performance of the component as a function of two design parameters......In order to efficiently implement thermally active building components in new buildings, it is necessary to evaluate the thermal interaction between them and other building components. Applying parameter investigation or numerical optimization methods to a differential-algebraic (DAE) model...
Primordial gravitational waves from the space-condensate inflation model
Koh, Seoktae; Tumurtushaa, Gansukh
2015-01-01
We consider the space-condensate inflation model to study the primordial gravitational waves generated in the early Universe. We calculate the energy spectrum of gravitational waves induced by the space-condensate inflation model for full frequency range with assumption that the phase transition between two consecutive regimes to be abrupt during evolution of the Universe. The suppression of energy spectrum is found in our model for the decreasing frequency of gravitational waves depending on the model parameter. To realize the suppression of energy spectrum of the primordial gravitational waves, we study an existence of the early phase transition during inflation for the space-condensate inflation model.
Consistent Stochastic Modelling of Meteocean Design Parameters
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Sterndorff, M. J.
2000-01-01
Consistent stochastic models of metocean design parameters and their directional dependencies are essential for reliability assessment of offshore structures. In this paper a stochastic model for the annual maximum values of the significant wave height, and the associated wind velocity, current...... velocity, and water level is presented. The stochastic model includes statistical uncertainty and dependency between the four stochastic variables. Further, a new stochastic model for annual maximum directional significant wave heights is presented. The model includes dependency between the maximum wave...... height from neighboring directional sectors. Numerical examples are presented where the models are calibrated using the Maximum Likelihood method to data from the central part of the North Sea. The calibration of the directional distributions is made such that the stochastic model for the omnidirectional...
The Space Laser Business Model
2005-01-01
Creating long-duration, high-powered lasers, for satellites, that can withstand the type of optical misalignment and damage dished out by the unforgiving environment of space, is work that is unique to NASA. It is complicated, specific work, where each step forward is into uncharted territory. In the 1990s, as this technology was first being created, NASA gave free reign to a group of "laser jocks" to develop their own business model and supply the Space Agency with the technology it needed. It was still to be a part of NASA as a division of Goddard Space Flight Center, but would operate independently out of a remote office. The idea for this satellite laboratory was based on the Skunk Works concept at Lockheed Martin Corporation. Formerly known as the Lockheed Corporation, in 1943, the aerospace firm, realizing that the type of advanced research it needed done could not be performed within the confines of a larger company, allowed a group of researchers and engineers to essentially run their own microbusiness without the corporate oversight. The Skunk Works project, in Burbank, California, produced America s first jet fighter, the world s most successful spy plane (U-2), the first 3-times-the-speed-of-sound surveillance aircraft, and the F-117A Nighthawk Stealth Fighter. Boeing followed suit with its Phantom Works, an advanced research and development branch of the company that operates independent of the larger unit and is responsible for a great deal of its most cutting-edge research. NASA s version of this advanced business model was the Space Lidar Technology Center (SLTC), just south of Goddard, in College Park, Maryland. Established in 1998 under a Cooperative Agreement between Goddard and the University of Maryland s A. James Clark School of Engineering, it was a high-tech laser shop where a small group of specialists, never more than 20 employees, worked all hours of the day and night to create the cutting- edge technology the Agency required of them. Drs
Automatic Determination of the Conic Coronal Mass Ejection Model Parameters
Pulkkinen, A.; Oates, T.; Taktakishvili, A.
2009-01-01
Characterization of the three-dimensional structure of solar transients using incomplete plane of sky data is a difficult problem whose solutions have potential for societal benefit in terms of space weather applications. In this paper transients are characterized in three dimensions by means of conic coronal mass ejection (CME) approximation. A novel method for the automatic determination of cone model parameters from observed halo CMEs is introduced. The method uses both standard image processing techniques to extract the CME mass from white-light coronagraph images and a novel inversion routine providing the final cone parameters. A bootstrap technique is used to provide model parameter distributions. When combined with heliospheric modeling, the cone model parameter distributions will provide direct means for ensemble predictions of transient propagation in the heliosphere. An initial validation of the automatic method is carried by comparison to manually determined cone model parameters. It is shown using 14 halo CME events that there is reasonable agreement, especially between the heliocentric locations of the cones derived with the two methods. It is argued that both the heliocentric locations and the opening half-angles of the automatically determined cones may be more realistic than those obtained from the manual analysis
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.
Order Parameters of the Dilute A Models
Warnaar, S O; Seaton, K A; Nienhuis, B
1993-01-01
The free energy and local height probabilities of the dilute A models with broken $\\Integer_2$ symmetry are calculated analytically using inversion and corner transfer matrix methods. These models possess four critical branches. The first two branches provide new realisations of the unitary minimal series and the other two branches give a direct product of this series with an Ising model. We identify the integrable perturbations which move the dilute A models away from the critical limit. Generalised order parameters are defined and their critical exponents extracted. The associated conformal weights are found to occur on the diagonal of the relevant Kac table. In an appropriate regime the dilute A$_3$ model lies in the universality class of the Ising model in a magnetic field. In this case we obtain the magnetic exponent $\\delta=15$ directly, without the use of scaling relations.
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...
Testing Linear Models for Ability Parameters in Item Response Models
Glas, Cees A.W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum like
Chu, Zhongyi; Ma, Ye; Hou, Yueyang; Wang, Fengwen
2017-02-01
This paper presents a novel identification method for the intact inertial parameters of an unknown object in space captured by a manipulator in a space robotic system. With strong dynamic and kinematic coupling existing in the robotic system, the inertial parameter identification of the unknown object is essential for the ideal control strategy based on changes in the attitude and trajectory of the space robot via capturing operations. Conventional studies merely refer to the principle and theory of identification, and an error analysis process of identification is deficient for a practical scenario. To solve this issue, an analysis of the effect of errors on identification is illustrated first, and the accumulation of measurement or estimation errors causing poor identification precision is demonstrated. Meanwhile, a modified identification equation incorporating the contact force, as well as the force/torque of the end-effector, is proposed to weaken the accumulation of errors and improve the identification accuracy. Furthermore, considering a severe disturbance condition caused by various measured noises, the hybrid immune algorithm, Recursive Least Squares and Affine Projection Sign Algorithm (RLS-APSA), is employed to decode the modified identification equation to ensure a stable identification property. Finally, to verify the validity of the proposed identification method, the co-simulation of ADAMS-MATLAB is implemented by multi-degree of freedom models of a space robotic system, and the numerical results show a precise and stable identification performance, which is able to guarantee the execution of aerospace operations and prevent failed control strategies.
Modelling spin Hamiltonian parameters of molecular nanomagnets.
Gupta, Tulika; Rajaraman, Gopalan
2016-07-12
Molecular nanomagnets encompass a wide range of coordination complexes possessing several potential applications. A formidable challenge in realizing these potential applications lies in controlling the magnetic properties of these clusters. Microscopic spin Hamiltonian (SH) parameters describe the magnetic properties of these clusters, and viable ways to control these SH parameters are highly desirable. Computational tools play a proactive role in this area, where SH parameters such as isotropic exchange interaction (J), anisotropic exchange interaction (Jx, Jy, Jz), double exchange interaction (B), zero-field splitting parameters (D, E) and g-tensors can be computed reliably using X-ray structures. In this feature article, we have attempted to provide a holistic view of the modelling of these SH parameters of molecular magnets. The determination of J includes various class of molecules, from di- and polynuclear Mn complexes to the {3d-Gd}, {Gd-Gd} and {Gd-2p} class of complexes. The estimation of anisotropic exchange coupling includes the exchange between an isotropic metal ion and an orbitally degenerate 3d/4d/5d metal ion. The double-exchange section contains some illustrative examples of mixed valance systems, and the section on the estimation of zfs parameters covers some mononuclear transition metal complexes possessing very large axial zfs parameters. The section on the computation of g-anisotropy exclusively covers studies on mononuclear Dy(III) and Er(III) single-ion magnets. The examples depicted in this article clearly illustrate that computational tools not only aid in interpreting and rationalizing the observed magnetic properties but possess the potential to predict new generation MNMs.
A six-parameter space to describe galaxy diversification
Fraix-Burnet, Didier; Chattopadhyay, Asis Kumar; Davoust, Emmanuel; Thuillard, Marc
2012-01-01
Galaxy diversification proceeds by transforming events like accretion, interaction or mergers. These explain the formation and evolution of galaxies that can now be described with many observables. Multivariate analyses are the obvious tools to tackle the datasets and understand the differences between different kinds of objects. However, depending on the method used, redundancies, incompatibilities or subjective choices of the parameters can void the usefulness of such analyses. The behaviour of the available parameters should be analysed before an objective reduction of dimensionality and subsequent clustering analyses can be undertaken, especially in an evolutionary context. We study a sample of 424 early-type galaxies described by 25 parameters, ten of which are Lick indices, to identify the most structuring parameters and determine an evolutionary classification of these objects. Four independent statistical methods are used to investigate the discriminant properties of the observables and the partitioni...
Do land parameters matter in large-scale hydrological modelling?
Gudmundsson, Lukas; Seneviratne, Sonia I.
2013-04-01
Many of the most pending issues in large-scale hydrology are concerned with predicting hydrological variability at ungauged locations. However, current-generation hydrological and land surface models that are used for their estimation suffer from large uncertainties. These models rely on mathematical approximations of the physical system as well as on mapped values of land parameters (e.g. topography, soil types, land cover) to predict hydrological variables (e.g. evapotranspiration, soil moisture, stream flow) as a function of atmospheric forcing (e.g. precipitation, temperature, humidity). Despite considerable progress in recent years, it remains unclear whether better estimates of land parameters can improve predictions - or - if a refinement of model physics is necessary. To approach this question we suggest scrutinizing our perception of hydrological systems by confronting it with the radical assumption that hydrological variability at any location in space depends on past and present atmospheric forcing only, and not on location-specific land parameters. This so called "Constant Land Parameter Hypothesis (CLPH)" assumes that variables like runoff can be predicted without taking location specific factors such as topography or soil types into account. We demonstrate, using a modern statistical tool, that monthly runoff in Europe can be skilfully estimated using atmospheric forcing alone, without accounting for locally varying land parameters. The resulting runoff estimates are used to benchmark state-of-the-art process models. These are found to have inferior performance, despite their explicit process representation, which accounts for locally varying land parameters. This suggests that progress in the theory of hydrological systems is likely to yield larger improvements in model performance than more precise land parameter estimates. The results also question the current modelling paradigm that is dominated by the attempt to account for locally varying land
Granger causality for state-space models.
Barnett, Lionel; Seth, Anil K
2015-04-01
Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations-commonplace in application domains as diverse as climate science, econometrics, and the neurosciences-induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.
Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations
Hanson, Andrea; Reed, Erik; Cavanagh, Peter
2011-01-01
Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.
Tropospheric Parameters and Subdaily EOP From Combinations of Independent Space Geodetic Data
Thaller, D.; Krügel, M.; Rothacher, M.; Angermann, D.; Schmid, R.; Tesmer, V.
2004-12-01
The space geodetic techniques GPS, VLBI, SLR and DORIS contribute to the determination of several geodetic parameters (e.g. site positions, Earth orientation parameters (EOP), tropospheric parameters) providing valuable information to study various geophysical processes. Due to the different strengths of the techniques it can be expected that the parameters benefit from a combination. The VLBI campaign CONT02, initiated by the IVS, provides 15~days of continuous VLBI measurements. Therefore, this data set is well-suited for the combination with other techniques. Especially the combination with other microwave techniques like GPS provides the opportunity to estimate common tropospheric parameters in addition to station coordinates and EOP. For the studies presented here, free daily normal equations were generated for GPS and VLBI using identical models and the same parameterization to avoid any inconsistencies. Additionally, the normal equation of a 14-day SLR solution is included to investigate primarily reference frame related aspects. The work focusses on the combination of tropospheric parameters and EOP with a high resolution in time: solutions with one and two hour resolution of the parameters were compared to decide whether a higher time resolution is more appropriate to describe the time-dependent behavior of these parameters. For the validation of the tropospheric parameters independent data sets of water vapor radiometers are used, and the EOP are compared with a subdaily model derived from altimetry. Special attention has to be addressed to the tropospheric parameters from GPS, because they are sensitive to the physical characteristics of the antenna and the antenna environment. The comparison with VLBI-derived tropospheric parameters shows that absolute antenna phase center corrections should be used instead of relative models. Similarly, if a radome is installed at the antenna, the tropospheric zenith delay estimates change significantly. As no phase
SP_Ace: a new code to derive stellar parameters and elemental abundances
Boeche, C
2015-01-01
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=2,000-20,000). Methods: After the astrophysical calibration of the oscillator strengths of 4643 absorption lines covering the wavelength ranges 5212-6860\\AA\\ and 8400-8924\\AA, 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 s...
GMC Collisions as Triggers of Star Formation. I. Parameter Space Exploration with 2D Simulations
Wu, Benjamin; Tan, Jonathan C; Bruderer, Simon
2015-01-01
We utilize magnetohydrodynamic (MHD) simulations to develop a numerical model for 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 (PDR) 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 a...
Modelling tourists arrival using time varying parameter
Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.
2017-06-01
The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.
Probabilistic Constraint Programming for Parameters Optimisation of Generative Models
Zanin, Massimiliano; Sousa, Pedro A C; Cruz, Jorge
2015-01-01
Complex networks theory has commonly been used for modelling and understanding the interactions taking place between the elements composing complex systems. More recently, the use of generative models has gained momentum, as they allow identifying which forces and mechanisms are responsible for the appearance of given structural properties. In spite of this interest, several problems remain open, one of the most important being the design of robust mechanisms for finding the optimal parameters of a generative model, given a set of real networks. In this contribution, we address this problem by means of Probabilistic Constraint Programming. By using as an example the reconstruction of networks representing brain dynamics, we show how this approach is superior to other solutions, in that it allows a better characterisation of the parameters space, while requiring a significantly lower computational cost.
The Transform between the space of observed values and the space of possible values of the parameter
Bityukov, S; Smirnova, V; Taperechkina, V
2013-01-01
In ref [math.ST/0411462] the notion of statistically dual distributions is introduced. The reconstruction of confidence density [AIP Conference Proceedings 803 (2005) 398] for the location parameter for several pairs of statistically dual distributions (Poisson and Gamma, normal and normal, Cauchy and Cauchy, Laplace and Laplace) in the case of single observation of the random variable is a unique. It allows to introduce the Transform between the space of observed values and the space of possible values of the parameter.
Grid-based exploration of cosmological parameter space with Snake
Mikkelsen, K; Eriksen, H K
2012-01-01
We present a fully parallelized grid-based parameter estimation algorithm for investigating multidimensional likelihoods called Snake, and apply it to cosmological parameter estimation. The basic idea is to map out the likelihood grid-cell by grid-cell according to decreasing likelihood, and stop when a certain threshold has been reached. This approach improves vastly on the "curse of dimensionality" problem plaguing standard grid-based parameter estimation simply by disregarding grid-cells with negligible likelihood. The main advantages of this method compared to standard Metropolis-Hastings MCMC methods include 1) trivial extraction of arbitrary conditional distributions; 2) direct access to Bayesian evidences; 3) better sampling of the tails of the distribution; and 4) nearly perfect parallelization scaling. The main disadvantage is, as in the case of brute-force grid-based evaluation, a dependency on the number of parameters, N_par. One of the main goals of the present paper is to determine how large N_pa...
Parameter estimation, model reduction and quantum filtering
Chase, Bradley A.
This thesis explores the topics of parameter estimation and model reduction in the context of quantum filtering. The last is a mathematically rigorous formulation of continuous quantum measurement, in which a stream of auxiliary quantum systems is used to infer the state of a target quantum system. Fundamental quantum uncertainties appear as noise which corrupts the probe observations and therefore must be filtered in order to extract information about the target system. This is analogous to the classical filtering problem in which techniques of inference are used to process noisy observations of a system in order to estimate its state. Given the clear similarities between the two filtering problems, I devote the beginning of this thesis to a review of classical and quantum probability theory, stochastic calculus and filtering. This allows for a mathematically rigorous and technically adroit presentation of the quantum filtering problem and solution. Given this foundation, I next consider the related problem of quantum parameter estimation, in which one seeks to infer the strength of a parameter that drives the evolution of a probe quantum system. By embedding this problem in the state estimation problem solved by the quantum filter, I present the optimal Bayesian estimator for a parameter when given continuous measurements of the probe system to which it couples. For cases when the probe takes on a finite number of values, I review a set of sufficient conditions for asymptotic convergence of the estimator. For a continuous-valued parameter, I present a computational method called quantum particle filtering for practical estimation of the parameter. Using these methods, I then study the particular problem of atomic magnetometry and review an experimental method for potentially reducing the uncertainty in the estimate of the magnetic field beyond the standard quantum limit. The technique involves double-passing a probe laser field through the atomic system, giving
Modeling thermally active building components using space mapping
DEFF Research Database (Denmark)
Pedersen, Frank; Weitzmann, Peter; Svendsen, Svend
2005-01-01
In order to efficiently implement thermally active building components in new buildings, it is necessary to evaluate the thermal interaction between them and other building components. Applying parameter investigation or numerical optimization methods to a differential-algebraic (DAE) model....... This paper describes the principle of the space mapping technique, and introduces a simple space mapping technique. The technique is applied to a lumped parameter model of a thermo active component, which provides a model of the thermal performance of the component as a function of two design parameters...... of a building provides a systematic way of estimating efficient building designs. However, using detailed numerical calculations of the components in the building is a time consuming process, which may become prohibitive if the DAE model is to be used for parameter variation or optimization. Unfortunately...
Dynamic models in space and time
Elhorst, J.P.
2001-01-01
This paper presents a first-order autoregressive distributed lag model in both space and time. It is shown that this model encompasses a wide series of simpler models frequently used in the analysis of space-time data as well as models that better fit the data and have never been used before. A fram
Kuznetsova, Maria
The Community Coordinated Modeling Center (CCMC, http://ccmc.gsfc.nasa.gov) was established at the dawn of the new millennium as a long-term flexible solution to the problem of transition of progress in space environment modeling to operational space weather forecasting. CCMC hosts an expanding collection of state-of-the-art space weather models developed by the international space science community. Over the years the CCMC acquired the unique experience in preparing complex models and model chains for operational environment and developing and maintaining custom displays and powerful web-based systems and tools ready to be used by researchers, space weather service providers and decision makers. In support of space weather needs of NASA users CCMC is developing highly-tailored applications and services that target specific orbits or locations in space and partnering with NASA mission specialists on linking CCMC space environment modeling with impacts on biological and technological systems in space. Confidence assessment of model predictions is an essential element of space environment modeling. CCMC facilitates interaction between model owners and users in defining physical parameters and metrics formats relevant to specific applications and leads community efforts to quantify models ability to simulate and predict space environment events. Interactive on-line model validation systems developed at CCMC make validation a seamless part of model development circle. The talk will showcase innovative solutions for space weather research, validation, anomaly analysis and forecasting and review on-going community-wide model validation initiatives enabled by CCMC applications.
Ahmady, M R
2007-01-01
The contributions of supersymmetric particles in the isospin symmetry violation in B -> K^* gamma decay mode are investigated. The model parameters are adopted from minimal Supergravity with minimal flavor violation. A complete scan of the mSUGRA parameter space has been performed, using the next to leading supersymmetric contributions to the relevant Wilson coefficients. The results are compared to recent experimental data in order to obtain constraints on the parameter space. We point out that isospin asymmetry can prove to be an interesting observable and imposes severe restrictions on the allowed parameter space, in particular for large values of tan(beta). The constraints obtained with isospin asymmetry also appear as more restricting than the ones from the branching ratio of B -> X_s gamma.
Emergence and spread of antibiotic resistance: setting a parameter space
Martínez, José Luis; Baquero, Fernando
2014-01-01
The emergence and spread of antibiotic resistance among human pathogens is a relevant problem for human health and one of the few evolution processes amenable to experimental studies. In the present review, we discuss some basic aspects of antibiotic resistance, including mechanisms of resistance, origin of resistance genes, and bottlenecks that modulate the acquisition and spread of antibiotic resistance among human pathogens. In addition, we analyse several parameters that modulate the evol...
Solar parameters for modeling interplanetary background
Bzowski, M; Tokumaru, M; Fujiki, K; Quemerais, E; Lallement, R; Ferron, S; Bochsler, P; McComas, D J
2011-01-01
The goal of the Fully Online Datacenter of Ultraviolet Emissions (FONDUE) Working Team of the International Space Science Institute in Bern, Switzerland, was to establish a common calibration of various UV and EUV heliospheric observations, both spectroscopic and photometric. Realization of this goal required an up-to-date model of spatial distribution of neutral interstellar hydrogen in the heliosphere, and to that end, a credible model of the radiation pressure and ionization processes was needed. This chapter describes the solar factors shaping the distribution of neutral interstellar H in the heliosphere. Presented are the solar Lyman-alpha flux and the solar Lyman-alpha resonant radiation pressure force acting on neutral H atoms in the heliosphere, solar EUV radiation and the photoionization of heliospheric hydrogen, and their evolution in time and the still hypothetical variation with heliolatitude. Further, solar wind and its evolution with solar activity is presented in the context of the charge excha...
Modeling of Parameters of Subcritical Assembly SAD
Petrochenkov, S; Puzynin, I
2005-01-01
The accepted conceptual design of the experimental Subcritical Assembly in Dubna (SAD) is based on the MOX core with a nominal unit capacity of 25 kW (thermal). This corresponds to the multiplication coefficient $k_{\\rm eff} =0.95$ and accelerator beam power 1 kW. A subcritical assembly driven with the existing 660 MeV proton accelerator at the Joint Institute for Nuclear Research has been modelled in order to make choice of the optimal parameters for the future experiments. The Monte Carlo method was used to simulate neutron spectra, energy deposition and doses calculations. Some of the calculation results are presented in the paper.
Exploring Replica-Exchange Wang-Landau sampling in higher-dimensional parameter space
Valentim, Alexandra; Tsai, Shan-Ho; Li, Ying Wai; Eisenbach, Markus; Fiore, Carlos E; Landau, David P
2015-01-01
We considered a higher-dimensional extension for the replica-exchange Wang-Landau algorithm to perform a random walk in the energy and magnetization space of the two-dimensional Ising model. This hybrid scheme combines the advantages of Wang-Landau and Replica-Exchange algorithms, and the one-dimensional version of this approach has been shown to be very efficient and to scale well, up to several thousands of computing cores. This approach allows us to split the parameter space of the system to be simulated into several pieces and still perform a random walk over the entire parameter range, ensuring the ergodicity of the simulation. Previous work, in which a similar scheme of parallel simulation was implemented without using replica exchange and with a different way to combine the result from the pieces, led to discontinuities in the final density of states over the entire range of parameters. From our simulations, it appears that the replica-exchange Wang-Landau algorithm is able to overcome this difficulty,...
5-Dimensional Extended Space Model
Tsipenyuk, D. Yu.; Andreev, V. A.
2006-01-01
We put forward an idea that physical phenomena have to be treated in 5-dimensional space where the fifth coordinate is the interval S. Thus, we considered the (1+4) extended space G(T;X,Y,Z,S). In addition to Lorentz transformations (T;X), (T;Y), (T;Z) which are in (1+3)-dimensional Minkowski space, in the proposed (1+4)d extended space two other types of transformations exist in planes (T,S); (X,S), (Y,S), (Z,S) that converts massive particles into massless and vice versa. We also consider e...
Baker Syed; Poskar C; Junker Björn
2011-01-01
Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. Wh...
Moose models with vanishing $S$ parameter
Casalbuoni, R; Dominici, Daniele
2004-01-01
In the linear moose framework, which naturally emerges in deconstruction models, we show that there is a unique solution for the vanishing of the $S$ parameter at the lowest order in the weak interactions. We consider an effective gauge theory based on $K$ SU(2) gauge groups, $K+1$ chiral fields and electroweak groups $SU(2)_L$ and $U(1)_Y$ at the ends of the chain of the moose. $S$ vanishes when a link in the moose chain is cut. As a consequence one has to introduce a dynamical non local field connecting the two ends of the moose. Then the model acquires an additional custodial symmetry which protects this result. We examine also the possibility of a strong suppression of $S$ through an exponential behavior of the link couplings as suggested by Randall Sundrum metric.
Model parameters for simulation of physiological lipids
McGlinchey, Nicholas
2016-01-01
Coarse grain simulation of proteins in their physiological membrane environment can offer insight across timescales, but requires a comprehensive force field. Parameters are explored for multicomponent bilayers composed of unsaturated lipids DOPC and DOPE, mixed‐chain saturation POPC and POPE, and anionic lipids found in bacteria: POPG and cardiolipin. A nonbond representation obtained from multiscale force matching is adapted for these lipids and combined with an improved bonding description of cholesterol. Equilibrating the area per lipid yields robust bilayer simulations and properties for common lipid mixtures with the exception of pure DOPE, which has a known tendency to form nonlamellar phase. The models maintain consistency with an existing lipid–protein interaction model, making the force field of general utility for studying membrane proteins in physiologically representative bilayers. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26864972
The supersymmetric parameter space in light of B-physics observables and electroweak precision data
Ellis, John; Heinemeyer, Sven; Olive, Keith A.; Weber, Arne M.; Weiglein, Georg
2007-08-01
Indirect information about the possible scale of supersymmetry (SUSY) breaking is provided by B-physics observables (BPO) as well as electroweak precision observables (EWPO). We combine the constraints imposed by recent measurements of the BPO BR(b → sγ), BR(Bs → μ+μ-), BR(Bu → τντ) and ΔMBs with those obtained from the experimental measurements of the EWPO MW, sin2 θeff, ΓZ, (g-2)μ and Mh, incorporating the latest theoretical calculations of these observables within the Standard Model and supersymmetric extensions. We perform a χ2 fit to the parameters of the constrained minimal supersymmetric extension of the Standard Model (CMSSM), in which the SUSY-breaking parameters are universal at the GUT scale, and the non-universal Higgs model (NUHM), in which this constraint is relaxed for the soft SUSY-breaking contributions to the Higgs masses. Assuming that the lightest supersymmetric particle (LSP) provides the cold dark matter density preferred by WMAP and other cosmological data, we scan over the remaining parameter space. Within the CMSSM, we confirm the preference found previously for a relatively low SUSY-breaking scale, though there is some slight tension between the EWPO and the BPO. In studies of some specific NUHM scenarios compatible with the cold dark matter constraint we investigate (MA, tan β) planes and find preferred regions that have values of χ2 somewhat lower than in the CMSSM.
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),...
Simplicial models of trace spaces
DEFF Research Database (Denmark)
Raussen, Martin
2010-01-01
Directed algebraic topology studies topological spaces in which certain directed paths (d-paths) are singled out; in most cases of interest, the reverse path of a d-path is no longer a d-path. We are mainly concerned with spaces of directed paths between given end points, and how those vary under...
Mean Shift Detection for State Space Models
Kuhn, J.; Mandjes, M.; Taimre, T.; Weber, T.; McPhee, M.J.; Anderssen, R.S.
2015-01-01
In this paper we develop and validate a procedure for testing against a shift in mean in the observations and hidden state sequence of state space models with Gaussian noise. State space models are popular for modelling stochastic networks as they allow to take into account that observations of the
A Hybrid 3D Indoor Space Model
Jamali, Ali; Rahman, Alias Abdul; Boguslawski, Pawel
2016-10-01
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.
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.
Public space patterns: Modelling the language of urban space
Montenegro, N.; Beirao, J.N.; Duarte, J.P.
2011-01-01
This paper describes the “Public Space Patterns” ontology including its related rule-based model, used as a basic structure of a “City Information Modelling” (CIM). This model was developed within a larger research project aimed at developing a tool for urban planning and design. The main purpose is
Geometric Parameter Identification of a 6-DOF Space Robot Using a Laser-Ranger
Directory of Open Access Journals (Sweden)
Yu Liu
2012-01-01
Full Text Available The geometric parameters of a space robot change with the terrible temperature change in orbit, which will cause the end-effector pose (position and orientation error of a space robot, and so weakens its operability. With this in consideration, a new geometric parameter identification method is presented based on a laser-ranger attached to the end-effector. Then, independence of the geometric parameters is analyzed, and their identification equations are derived. With the derived identification Jacobian matrix, the optimal identification configurations are chosen according to the observability index O3. Subsequently, through simulation the geometric parameter identification of a 6-DOF space robot is implemented for these identification configurations, and the identified parameters are verified in a set of independent reference configurations. The result shows that in spite of distance measurement alone, pose accuracy of the space robot still has a greater improvement, so the identification method is practical and valid.
An Evolution Model of Space Debris Environment
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Various types of models including engineering models andevolution models have been developed to understand space debris environment since 1960s. Evolution model, consisting of a set of supporting models such as Launch Model, Breakup Model and Atmosphere Model, can reliably predicts the evolution of space debris environment. Of these supporting models, Breakup Model is employed to describe the distribution of debris and debris cloud during a explosion or collision case which is one of the main factors affecting the amount of total space debris. An analytical orbit debris environment model referred to as the “Particles-In-Boxes" model has been introduced. By regarding the orbit debris as the freedom particles running in the huge volume, the sources and sinks mechanism is established. Then the PIB model is expanded to the case of multiple-species in multiple-tier system. Combined with breakup model, the evolution of orbit debris environment is predicted.
Modeling the long-term evolution of space debris
Nikolaev, Sergei; De Vries, Willem H.; Henderson, John R.; Horsley, Matthew A.; Jiang, Ming; Levatin, Joanne L.; Olivier, Scot S.; Pertica, Alexander J.; Phillion, Donald W.; Springer, Harry K.
2017-03-07
A space object modeling system that models the evolution of space debris is provided. The modeling system simulates interaction of space objects at simulation times throughout a simulation period. The modeling system includes a propagator that calculates the position of each object at each simulation time based on orbital parameters. The modeling system also includes a collision detector that, for each pair of objects at each simulation time, performs a collision analysis. When the distance between objects satisfies a conjunction criterion, the modeling system calculates a local minimum distance between the pair of objects based on a curve fitting to identify a time of closest approach at the simulation times and calculating the position of the objects at the identified time. When the local minimum distance satisfies a collision criterion, the modeling system models the debris created by the collision of the pair of objects.
Modeling the long-term evolution of space debris
Energy Technology Data Exchange (ETDEWEB)
Nikolaev, Sergei; De Vries, Willem H.; Henderson, John R.; Horsley, Matthew A.; Jiang, Ming; Levatin, Joanne L.; Olivier, Scot S.; Pertica, Alexander J.; Phillion, Donald W.; Springer, Harry K.
2017-03-07
A space object modeling system that models the evolution of space debris is provided. The modeling system simulates interaction of space objects at simulation times throughout a simulation period. The modeling system includes a propagator that calculates the position of each object at each simulation time based on orbital parameters. The modeling system also includes a collision detector that, for each pair of objects at each simulation time, performs a collision analysis. When the distance between objects satisfies a conjunction criterion, the modeling system calculates a local minimum distance between the pair of objects based on a curve fitting to identify a time of closest approach at the simulation times and calculating the position of the objects at the identified time. When the local minimum distance satisfies a collision criterion, the modeling system models the debris created by the collision of the pair of objects.
The Deuterium Fractionation Timescale in Dense Cloud Cores: A Parameter Space Exploration
Kong, Shuo; Tan, Jonathan C; Wakelam, Valentine
2013-01-01
The deuterium fraction of simple species such as N$_2$H$^+$ can be easily measured and can provide information about the age of dense and cold material, important to compare with dynamical models of cloud core formation and evolution. Here we perform a parameter space exploration using a gas-phase chemical model which includes deuterium chemistry and the spin states of H$_2$ and H$_3^+$ isotopologues. This allows us to study the effect of various poorly known parameters on the timescale to achieve the deuterium fractions observed in starless cores and clumps in various star-forming regions. We conclude that for a broad range of parameters, the relatively large deuterium fractions ($\\gtrsim$ 0.1) observed towards both low- and high-mass starless cores require core ages to be at least a few times longer than the free-fall timescale. This condition could be relaxed if cosmic ray ionization rates are very high $\\gtrsim 10^{-16}\\:{\\rm s}^{-1}$ or initial ortho-to-para ratios of $\\rm H_2$ are very low ($\\lesssim 10...
Kieran, Kathleen; Hall, Timothy L.; Parsons, Jessica E.; Wolf, J. Stuart; Fowlkes, J. Brian; Cain, Charles A.; Roberts, William W.
2007-05-01
Focused ultrasound energy is capable of noninvasively, nonthermally ablating tissue. However, the relative contributions of thermal and cavitational effects in the therapeutic use of ultrasound are poorly understood. We sought to identify the ultrasound parameter space within which tissue can be ablated by solely mechanical means (cavitation), without a significant thermal component. Methods: Ultrasound energy (750 kHz, 20 microsecond pulses) was applied sequentially in a 3×3 grid configuration to the cortical tissue of ex vivo porcine kidneys submerged in degassed water. While maintaining constant energy density, intensity (0.11-211 kW/cm2) and duty cycle (0.04%-CW) were varied widely. A thermocouple co-localized with the center of each grid provided continuous temperature measurements. Following ablations, the kidneys were examined grossly and histologically. Results: Ablated tissue was classified into one of four discrete morphologic categories: blanched (firm, pale, desiccated tissue), disrupted (cavity containing thin, isochromatic liquid; no blanching), mixed blanched/disrupted (cavity containing pale, thick liquid; minimal blanching), and no grossly visible effect. Morphologically similar lesions clustered together within the ultrasound parameter space. Disrupted lesions had significantly lower maximal temperatures (44.2 °C) than desiccated (67.5 °C; p<0.0001) or mixed (59.4 °C; p<0.0001) lesions. Conclusions: In an ex vivo model, we have defined the ultrasound parameters within which mechanical tissue ablation, with minimal thermal components, is possible. Future research in vivo is directed toward optimizing the parameters for cavitational tissue ablation, and better understanding the impact of tissue perfusion on lesion generation and intralesional temperature rise.
Uncertainty Quantification for Optical Model Parameters
Lovell, A E; Sarich, J; Wild, S M
2016-01-01
Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of this work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fit and create corresponding 95\\% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. We study a number of reactions involving neutron and deuteron p...
Numerical modeling of partial discharges parameters
Directory of Open Access Journals (Sweden)
Kartalović Nenad M.
2016-01-01
Full Text Available In recent testing of the partial discharges or the use for the diagnosis of insulation condition of high voltage generators, transformers, cables and high voltage equipment develops rapidly. It is a result of the development of electronics, as well as, the development of knowledge about the processes of partial discharges. The aim of this paper is to contribute the better understanding of this phenomenon of partial discharges by consideration of the relevant physical processes in isolation materials and isolation systems. Prebreakdown considers specific processes, and development processes at the local level and their impact on specific isolation material. This approach to the phenomenon of partial discharges needed to allow better take into account relevant discharge parameters as well as better numerical model of partial discharges.
Jakobsen, Sofie; Jensen, Frank
2014-12-09
We assess the accuracy of force field (FF) electrostatics at several levels of approximation from the standard model using fixed partial charges to conformational specific multipole fits including up to quadrupole moments. Potential-derived point charges and multipoles are calculated using least-squares methods for a total of ∼1000 different conformations of the 20 natural amino acids. Opposed to standard charge fitting schemes the procedure presented in the current work employs fitting points placed on a single isodensity surface, since the electrostatic potential (ESP) on such a surface determines the ESP at all points outside this surface. We find that the effect of multipoles beyond partial atomic charges is of the same magnitude as the effect due to neglecting conformational dependency (i.e., polarizability), suggesting that the two effects should be included at the same level in FF development. The redundancy at both the partial charge and multipole levels of approximation is quantified. We present an algorithm which stepwise reduces or increases the dimensionality of the charge or multipole parameter space and provides an upper limit of the ESP error that can be obtained at a given truncation level. Thereby, we can identify a reduced set of multipole moments corresponding to ∼40% of the total number of multipoles. This subset of parameters provides a significant improvement in the representation of the ESP compared to the simple point charge model and close to the accuracy obtained using the complete multipole parameter space. The selection of the ∼40% most important multipole sites is highly transferable among different conformations, and we find that quadrupoles are of high importance for atoms involved in π-bonding, since the anisotropic electric field generated in such regions requires a large degree of flexibility.
Parameter-space metric of semicoherent searches for continuous gravitational waves
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 year-long 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 additio...
Mapping of ionospheric parameters for space weather predictions: A concise review
Institute of Scientific and Technical Information of China (English)
Y. KAMIDE; A. IEDA
2008-01-01
Reviewing brieflythe recent progress in a joint program of specifying the polar ionosphere primarily on the basis of ground magnetometer data, this paper em-phasizes the importance of processing data from around the world in real time for space weather predictions. The output parameters from the program include ionospheric electric fields and currents and field-aligned currents. These real-time records are essential for running computer simulations under realistic boundary conditions and thus for making numerical predictions of space weather efficient as reliable as possible. Data from individual ground magnetometers as well as from the solar wind are collected and are used as input for the KRM and AMIE mag-netogram-inversion algorithms, through which the two-dimensional distribution of the ionospheric parameters is calculated. One of the goals of the program is to specify the solar-terrestrial environment in terms of ionospheric processes and to provide the scientific community with more than what geomagnetic activity Indices and statistical models indicate.
Mapping of ionospheric parameters for space weather predictions: A concise review
Institute of Scientific and Technical Information of China (English)
Y.; KAMIDE; A.; IEDA
2008-01-01
Reviewing briefly the recent progress in a joint program of specifying the polar ionosphere primarily on the basis of ground magnetometer data, this paper em-phasizes the importance of processing data from around the world in real time for space weather predictions. The output parameters from the program include ionospheric electric fields and currents and field-aligned currents. These real-time records are essential for running computer simulations under realistic boundary conditions and thus for making numerical predictions of space weather efficient as reliable as possible. Data from individual ground magnetometers as well as from the solar wind are collected and are used as input for the KRM and AMIE mag-netogram-inversion algorithms, through which the two-dimensional distribution of the ionospheric parameters is calculated. One of the goals of the program is to specify the solar-terrestrial environment in terms of ionospheric processes and to provide the scientific community with more than what geomagnetic activity indices and statistical models indicate.
Fast estimation of space-robots inertia parameters: A modular mathematical formulation
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.
Weighted Multi-Parameter Non-Isotropic Flag Triebel-Lizorkin and Besov Spaces
Liao, F; Liu, Z.
2014-01-01
In this paper, the authors use the discrete Littlewood-Paley-Stein theory to introduce weighted multi-parameter Triebel-Lizorkin and Besov spaces associated with non-isotropic flag singular integrals under a rather weak weight condition $(w\\in A_\\infty)$. They also obtain the boundedness of flag singular integrals on these spaces.
Parameter Optimisation for the Behaviour of Elastic Models over Time
DEFF Research Database (Denmark)
Mosegaard, Jesper
2004-01-01
Optimisation of parameters for elastic models is essential for comparison or finding equivalent behaviour of elastic models when parameters cannot simply be transferred or converted. This is the case with a large range of commonly used elastic models. In this paper we present a general method...... that will optimise parameters based on the behaviour of the elastic models over time....
Model Identification of Linear Parameter Varying Aircraft Systems
Fujimore, Atsushi; Ljung, Lennart
2007-01-01
This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...
More Efficient Bayesian-based Optimization and Uncertainty Assessment of Hydrologic Model Parameters
2012-02-01
is more objective, repeatable, and better capitalizes on the computational capacity of the modern computer) is an active area of research and...existence of multiple local optima , non-smooth objective function surfaces, and long valleys in parameter space that are a result of excessive parameter...outputs, structural aspects of the model, as well as its input dataset, model parameters that are adjustable through the calibration process, and the
State-Space Modelling of Loudspeakers using Fractional Derivatives
DEFF Research Database (Denmark)
King, Alexander Weider; Agerkvist, Finn T.
2015-01-01
This work investigates the use of fractional order derivatives in modeling moving-coil loudspeakers. A fractional order state-space solution is developed, leading the way towards incorporating nonlinearities into a fractional order system. The method is used to calculate the response....... It is shown that the identified parameters can be used in a linear fractional order state-space model to simulate the loudspeakers’ time domain response...... of a fractional harmonic oscillator, representing the mechanical part of a loudspeaker, showing the effect of the fractional derivative and its relationship to viscoelasticity. Finally, a loudspeaker model with a fractional order viscoelastic suspension and fractional order voice coil is fit to measurement data...
Accelerated gravitational wave parameter estimation with reduced order modeling.
Canizares, Priscilla; Field, Scott E; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2015-02-20
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ∼30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ∼70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.
Institute of Scientific and Technical Information of China (English)
徐芳; 张文旗; 窦松江; 江艳平; 周连敏
2015-01-01
大港滩海地区为平台式钻井，以大斜度/水平井进行开发，钻、测井资料少。该区断层多而复杂，构造起伏大，储层横向变化快，钻井风险大。本文基于研究区构造特征、沉积特征等，利用地震解释成果及井数据依次建立断层模型、构造模型；将地震属性、地震反演等成果作为第二变量，充分应用有限的井资料，采取逐级约束、逐级控制的方法，从泥质含量模型到岩相模型，再到沉积微相模型，进一步到孔隙度模型，最后到渗透率模型，最终完成了地质模型的建立。同时利用地震数据体空间检验、剖面检验等多种方法检验地质模型，确保了模型的准确性。该套少井多参数联合地质建模技术解决了井资料匮乏带来的难题，所建立的地质模型为提高钻井成功率提供保障，为油藏数值模拟提供基础。%Well drilling is based on the platform,and the type of wells is mainly of high angle of shallow sea area in Dagang oilfield. Therefore,the drilling and logging data is limited. There are many faults,which leaded to the com-plicated structure. The reservoir changes so fast that the risk of drilling is increased. Based on the structure and sed-imentary features,the fault and structure model are set up with the seismic interpretation and well data. The step by step controlling method is adopted. VSH model is set up controlled by seismic data or seismic inversion result,and lithology model controlled by VSH model,microfacies model controlled by lithology model,and porosity model con-trolled by microfacies model,and permeability model controlled by porosity model. In the process limited well data is applied adequately. Meanwhile,the model is examined by seismic data in 3D space,etc. This method of multi-pa-rameters combination geological modeling technique solves the problem caused by lack of well data. The geological model can increase the success rate of
State-Space Modeling, System Identification and Control of a 4th Order Rotational Mechanical System
2009-12-01
state-space form. Identification of the state-space parameters was accomplished using the parameter estimation function in Matlab’s System ... Identification Toolbox utilizing experimental input/output data. The identified model was then constructed in Simulink and the accuracy of the identified model
Modeling the reconstructed BAO in Fourier space
Seo, Hee-Jong; Beutler, Florian; Ross, Ashley J.; Saito, Shun
2016-08-01
The density field reconstruction technique, which partially reverses the non-linear degradation of the Baryon acoustic oscillation (BAO) feature in the galaxy redshift surveys, has been successful in substantially improving the cosmology constraints from recent surveys such as Baryon Oscillation Spectroscopic Survey (BOSS). We estimate the efficiency of the method as a function of various reconstruction details. To directly quantify the BAO information in non-linear density fields before and after reconstruction, we calculate the cross-correlations (i.e. propagators) of the pre(post)-reconstructed density field with the initial linear field using a mock sample that mimics the clustering of the BOSS galaxies. The results directly provide the BAO damping as a function of wavenumber that can be implemented into the Fisher matrix analysis. We focus on investigating the dependence of the propagator on a choice of smoothing filters and on two major different conventions of the redshift-space density field reconstruction that have been used in literature. By estimating the BAO signal to noise for each case, we predict constraints on the angular diameter distance and Hubble parameter using the Fisher matrix analysis. We thus determine an optimal Gaussian smoothing filter scale for the signal-to-noise level of the BOSS CMASS. We also present appropriate BAO fitting models for different reconstruction methods based on the first- and second-order Lagrangian perturbation theory in Fourier space. Using the mock data, we show that the modified BAO fitting model can substantially improve the accuracy of the BAO position in the best fits as well as the goodness of the fits.
[Calculation of parameters in forest evapotranspiration model].
Wang, Anzhi; Pei, Tiefan
2003-12-01
Forest evapotranspiration is an important component not only in water balance, but also in energy balance. It is a great demand for the development of forest hydrology and forest meteorology to simulate the forest evapotranspiration accurately, which is also a theoretical basis for the management and utilization of water resources and forest ecosystem. Taking the broadleaved Korean pine forest on Changbai Mountain as an example, this paper constructed a mechanism model for estimating forest evapotranspiration, based on the aerodynamic principle and energy balance equation. Using the data measured by the Routine Meteorological Measurement System and Open-Path Eddy Covariance Measurement System mounted on the tower in the broadleaved Korean pine forest, the parameters displacement height d, stability functions for momentum phi m, and stability functions for heat phi h were ascertained. The displacement height of the study site was equal to 17.8 m, near to the mean canopy height, and the functions of phi m and phi h changing with gradient Richarson number R i were constructed.
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 phase-space model for Pleistocene ice volume
Imbrie, John Z; Lisiecki, Lorraine E
2011-01-01
We present a phase-space model that simulates Pleistocene ice volume changes based on Earth's orbital parameters. Terminations in the model are triggered by a combination of ice volume and orbital forcing and agree well with age estimates for Late Pleistocene terminations. The average phase at which model terminations begin is approximately 90 +/- 90 degrees before the maxima in all three orbital cycles. The large variability in phase is likely caused by interactions between the three cycles and ice volume. Unlike previous ice volume models, this model produces an orbitally driven increase in 100-kyr power during the mid-Pleistocene transition without any change in model parameters. This supports the hypothesis that Pleistocene variations in the 100-kyr power of glacial cycles could be caused, at least in part, by changes in Earth's orbital parameters, such as amplitude modulation of the 100-kyr eccentricity cycle, rather than changes within the climate system.
Integrated Space Asset Management Database and Modeling
Gagliano, L.; MacLeod, T.; Mason, S.; Percy, T.; Prescott, J.
Orbit (LEO) region of the space object catalogue, and the Space Surveillance Telescope to maintain watch over the relatively crowded Geosynchronous sector above the Indian Ocean. The Australian government has also encouraged and supported increased investment by commercial and academic interests in SSA Research and Development capabilities. Nevertheless, as Australia operates virtually no space systems itself, the Australian government has limited understanding of SSA. This can impact the ability to make informed decisions about participation in systems such as the SSN or further investments in Australian capability. Therefore Defence in Australia has sponsored ongoing work by the Defence Science and Technology (DST) Group to build up necessary understanding to support such decisions. This paper describes some of the operational analyses carried out to date in this program. The program has centred on high-level modelling and simulation of the potential contribution sensors in Australia might make to maintain the unclassified LEO catalogue. This has involved calculating the ability of generic sensors to observe LEO objects, as a function of the sensors locations and key coverage parameters such as range, elevation limits and operating hours which in turn depend on whether the sensors are active or passive. It has also required identification, computation and refinement of appropriate performance metrics to summarise the output of the simulations. This paper will outline work done, the results obtained and the conclusions drawn to date. In particular it notes findings so far and outstanding issues in carrying out perhaps the most difficult part of this work: assessing the difference new Australian systems might make to the overall performance of an enlarged SSN.
Modelling and Design of a Microstrip Band-Pass Filter Using Space Mapping Techniques
Tavakoli, Saeed; Mohanna, Shahram
2010-01-01
Determination of design parameters based on electromagnetic simulations of microwave circuits is an iterative and often time-consuming procedure. Space mapping is a powerful technique to optimize such complex models by efficiently substituting accurate but expensive electromagnetic models, fine models, with fast and approximate models, coarse models. In this paper, we apply two space mapping, an explicit space mapping as well as an implicit and response residual space mapping, techniques to a case study application, a microstrip band-pass filter. First, we model the case study application and optimize its design parameters, using explicit space mapping modelling approach. Then, we use implicit and response residual space mapping approach to optimize the filter's design parameters. Finally, the performance of each design methods is evaluated. It is shown that the use of above-mentioned techniques leads to achieving satisfactory design solutions with a minimum number of computationally expensive fine model eval...
Suriza, A. Z.; Md Rafiqul, Islam; Wajdi, A. K.; Naji, A. W.
2013-03-01
As the demand for higher and unlimited bandwidth for communication channel is increased, Free Space Optics (FSO) is a good alternative solution. As it is protocol transparent, easy to install, cost effective and have capabilities like fiber optics, its demand rises very fast. Weather condition, however is the limiting factor for FSO link. In the temperate region the major blockage for FSO link feasibility is fog. In the tropical region high rainfall rate is expected to be the major drawback of FSO link availability. Rain attenuation is the most significant to influence FSO link availability in tropical region. As for now the available k and α values are developed using data from temperate regions. Therefore, the objective of this paper is to propose new parameters for specific rain attenuation prediction model that represents tropical weather condition. The proposed values are derived from data measured in Malaysia and using methods recommended by ITU-R.
A variational approach for dissipative quantum transport in a wide parameter space
Energy Technology Data Exchange (ETDEWEB)
Zhang, Yu, E-mail: zhy@yangtze.hku.hk; Kwok, YanHo; Chen, GuanHua, E-mail: ghc@everest.hku.hk [Department of Chemistry, The University of Hong Kong, Pokfluam Road (Hong Kong); Yam, ChiYung [Department of Chemistry, The University of Hong Kong, Pokfluam Road (Hong Kong); Beijing Computational Science Research Center, Beijing 100094 (China)
2015-09-14
Recent development of theoretical method for dissipative quantum transport has achieved notable progresses in the weak or strong electron-phonon coupling regime. However, a generalized theory for dissipative quantum transport in a wide parameter space had not been established. In this work, a variational polaron theory for dissipative quantum transport in a wide range of electron-phonon coupling is developed. The optimal polaron transformation is determined by the optimization of the Feynman-Bogoliubov upper bound of free energy. The free energy minimization ends up with an optimal mean-field Hamiltonian and a minimal interaction Hamiltonian. Hence, second-order perturbation can be applied to the transformed system, resulting in an accurate and efficient method for the treatment of dissipative quantum transport with different electron-phonon coupling strength. Numerical benchmark calculation on a single site model coupled to one phonon mode is presented.
A hybrid method of estimating pulsating flow parameters in the space-time domain
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.
A variational approach for dissipative quantum transport in a wide parameter space.
Zhang, Yu; Yam, ChiYung; Chen, GuanHua
2015-09-14
Recent development of theoretical method for dissipative quantum transport has achieved notable progresses in the weak or strong electron-phonon coupling regime. However, a generalized theory for dissipative quantum transport in a wide parameter space had not been established. In this work, a variational polaron theory for dissipative quantum transport in a wide range of electron-phonon coupling is developed. The optimal polaron transformation is determined by the optimization of the Feynman-Bogoliubov upper bound of free energy. The free energy minimization ends up with an optimal mean-field Hamiltonian and a minimal interaction Hamiltonian. Hence, second-order perturbation can be applied to the transformed system, resulting in an accurate and efficient method for the treatment of dissipative quantum transport with different electron-phonon coupling strength. Numerical benchmark calculation on a single site model coupled to one phonon mode is presented.
Optimal vibration control of curved beams using distributed parameter models
Liu, Fushou; Jin, Dongping; Wen, Hao
2016-12-01
The design of linear quadratic optimal controller using spectral factorization method is studied for vibration suppression of curved beam structures modeled as distributed parameter models. The equations of motion for active control of the in-plane vibration of a curved beam are developed firstly considering its shear deformation and rotary inertia, and then the state space model of the curved beam is established directly using the partial differential equations of motion. The functional gains for the distributed parameter model of curved beam are calculated by extending the spectral factorization method. Moreover, the response of the closed-loop control system is derived explicitly in frequency domain. Finally, the suppression of the vibration at the free end of a cantilevered curved beam by point control moment is studied through numerical case studies, in which the benefit of the presented method is shown by comparison with a constant gain velocity feedback control law, and the performance of the presented method on avoidance of control spillover is demonstrated.
Space Vehicle Reliability Modeling in DIORAMA
Energy Technology Data Exchange (ETDEWEB)
Tornga, Shawn Robert [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-07-12
When modeling system performance of space based detection systems it is important to consider spacecraft reliability. As space vehicles age the components become prone to failure for a variety of reasons such as radiation damage. Additionally, some vehicles may lose the ability to maneuver once they exhaust fuel supplies. Typically failure is divided into two categories: engineering mistakes and technology surprise. This document will report on a method of simulating space vehicle reliability in the DIORAMA framework.
International Space Station Radiation Shielding Model Development
Qualls, G. D.; Wilson, J. W.; Sandridge, C.; Cucinotta, F. A.; Nealy, J. E.; Heinbockel, J. H.; Hugger, C. P.; Verhage, J.; Anderson, B. M.; Atwell, W.
2001-01-01
The projected radiation levels within the International Space Station (ISS) have been criticized by the Aerospace Safety Advisory Panel in their report to the NASA Administrator. Methods for optimal reconfiguration and augmentation of the ISS shielding are now being developed. The initial steps are to develop reconfigurable and realistic radiation shield models of the ISS modules, develop computational procedures for the highly anisotropic radiation environment, and implement parametric and organizational optimization procedures. The targets of the redesign process are the crew quarters where the astronauts sleep and determining the effects of ISS shadow shielding of an astronaut in a spacesuit. The ISS model as developed will be reconfigurable to follow the ISS. Swapping internal equipment rack assemblies via location mapping tables will be one option for shield optimization. Lightweight shield augmentation materials will be optimally fit to crew quarter areas using parametric optimization procedures to minimize the augmentation shield mass. The optimization process is being integrated into the Intelligence Synthesis Environment s (ISE s) immersive simulation facility at the Langley Research Center and will rely on High Performance Computing and Communication (HPCC) for rapid evaluation of shield parameter gradients.
Development, validation and application of numerical space environment models
Honkonen, Ilja
2013-10-01
Currently the majority of space-based assets are located inside the Earth's magnetosphere where they must endure the effects of the near-Earth space environment, i.e. space weather, which is driven by the supersonic flow of plasma from the Sun. Space weather refers to the day-to-day changes in the temperature, magnetic field and other parameters of the near-Earth space, similarly to ordinary weather which refers to changes in the atmosphere above ground level. Space weather can also cause adverse effects on the ground, for example, by inducing large direct currents in power transmission systems. The performance of computers has been growing exponentially for many decades and as a result the importance of numerical modeling in science has also increased rapidly. Numerical modeling is especially important in space plasma physics because there are no in-situ observations of space plasmas outside of the heliosphere and it is not feasible to study all aspects of space plasmas in a terrestrial laboratory. With the increasing number of computational cores in supercomputers, the parallel performance of numerical models on distributed memory hardware is also becoming crucial. This thesis consists of an introduction, four peer reviewed articles and describes the process of developing numerical space environment/weather models and the use of such models to study the near-Earth space. A complete model development chain is presented starting from initial planning and design to distributed memory parallelization and optimization, and finally testing, verification and validation of numerical models. A grid library that provides good parallel scalability on distributed memory hardware and several novel features, the distributed cartesian cell-refinable grid (DCCRG), is designed and developed. DCCRG is presently used in two numerical space weather models being developed at the Finnish Meteorological Institute. The first global magnetospheric test particle simulation based on the
When the optimal is not the best: parameter estimation in complex biological models.
Directory of Open Access Journals (Sweden)
Diego Fernández Slezak
Full Text Available BACKGROUND: The vast computational resources that became available during the past decade enabled the development and simulation of increasingly complex mathematical models of cancer growth. These models typically involve many free parameters whose determination is a substantial obstacle to model development. Direct measurement of biochemical parameters in vivo is often difficult and sometimes impracticable, while fitting them under data-poor conditions may result in biologically implausible values. RESULTS: We discuss different methodological approaches to estimate parameters in complex biological models. We make use of the high computational power of the Blue Gene technology to perform an extensive study of the parameter space in a model of avascular tumor growth. We explicitly show that the landscape of the cost function used to optimize the model to the data has a very rugged surface in parameter space. This cost function has many local minima with unrealistic solutions, including the global minimum corresponding to the best fit. CONCLUSIONS: The case studied in this paper shows one example in which model parameters that optimally fit the data are not necessarily the best ones from a biological point of view. To avoid force-fitting a model to a dataset, we propose that the best model parameters should be found by choosing, among suboptimal parameters, those that match criteria other than the ones used to fit the model. We also conclude that the model, data and optimization approach form a new complex system and point to the need of a theory that addresses this problem more generally.
Momentum-space Harper-Hofstadter model
Ozawa, Tomoki; Price, Hannah M.; Carusotto, Iacopo
2015-08-01
We show how the weakly trapped Harper-Hofstadter model can be mapped onto a Harper-Hofstadter model in momentum space. In this momentum-space model, the band dispersion plays the role of the periodic potential, the Berry curvature plays the role of an effective magnetic field, the real-space harmonic trap provides the momentum-space kinetic energy responsible for the hopping, and the trap position sets the boundary conditions around the magnetic Brillouin zone. Spatially local interactions translate into nonlocal interactions in momentum space: within a mean-field approximation, we show that increasing interparticle interactions leads to a structural change of the ground state, from a single rotationally symmetric ground state to degenerate ground states that spontaneously break rotational symmetry.
The Cosmological Constant for the Crystalline Vacuum Cosmic Space Model
Montemayor-Aldrete, J A; Morales-Mori, A; Mendoza-Allende, A; Montemayor-Varela, A; Castillo-Mussot, M; Vazquez, G J
2005-01-01
The value of the cosmological constant arising from a crystalline model for vacuum cosmic space with lattice parameter of the order of the neutron radius [1] has been calculated. The model allows to solve, in an easy way, the problem of the cosmological constant giving the right order of magnitude, which corresponds very well with the mean value of matter density in the universe. The obtained value is about 10-48 Km-2. Diffraction experiments with non-thermal neutron beam in cosmic space are proposed to search for the possibility of crystalline structure of vacuum space and to measure the lattice parameter. PACS numbers: 98.80.Es, 04.20.-q, 03.65.-w, 61.50.-f, 98.80.Ft
Transfer function modeling of damping mechanisms in distributed parameter models
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
3D space analysis of dental models
Chuah, Joon H.; Ong, Sim Heng; Kondo, Toshiaki; Foong, Kelvin W. C.; Yong, Than F.
2001-05-01
Space analysis is an important procedure by orthodontists to determine the amount of space available and required for teeth alignment during treatment planning. Traditional manual methods of space analysis are tedious and often inaccurate. Computer-based space analysis methods that work on 2D images have been reported. However, as the space problems in the dental arch exist in all three planes of space, a full 3D analysis of the problems is necessary. This paper describes a visualization and measurement system that analyses 3D images of dental plaster models. Algorithms were developed to determine dental arches. The system is able to record the depths of the Curve of Spee, and quantify space liabilities arising from a non-planar Curve of Spee, malalignment and overjet. Furthermore, the difference between total arch space available and the space required to arrange the teeth in ideal occlusion can be accurately computed. The system for 3D space analysis of the dental arch is an accurate, comprehensive, rapid and repeatable method of space analysis to facilitate proper orthodontic diagnosis and treatment planning.
On the modeling of internal parameters in hyperelastic biological materials
Giantesio, Giulia
2016-01-01
This paper concerns the behavior of hyperelastic energies depending on an internal parameter. First, the situation in which the internal parameter is a function of the gradient of the deformation is presented. Second, two models where the parameter describes the activation of skeletal muscle tissue are analyzed. In those models, the activation parameter depends on the strain and it is important to consider the derivative of the parameter with respect to the strain in order to capture the proper behavior of the stress.
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
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...... (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......, and initial velocity), all remaining parameters are varied, resulting in more than 20 runs investigated here. The output parameters considered are velocity, density, magnetic field strength, and temperature. We find that the largest effects on the model output are the input parameters of upper limit...
Determining extreme parameter correlation in ground water models
DEFF Research Database (Denmark)
Hill, Mary Cole; Østerby, Ole
2003-01-01
In ground water flow system models with hydraulic-head observations but without significant imposed or observed flows, extreme parameter correlation generally exists. As a result, hydraulic conductivity and recharge parameters cannot be uniquely estimated. In complicated problems, such correlation...... correlation coefficients, but it required sensitivities that were one to two significant digits less accurate than those that required using parameter correlation coefficients; and (3) both the SVD and parameter correlation coefficients identified extremely correlated parameters better when the parameters...
Multiobjective Automatic Parameter Calibration of a Hydrological Model
Directory of Open Access Journals (Sweden)
Donghwi Jung
2017-03-01
Full Text Available This study proposes variable balancing approaches for the exploration (diversification and exploitation (intensification of the non-dominated sorting genetic algorithm-II (NSGA-II with simulated binary crossover (SBX and polynomial mutation (PM in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives—minimizing the percent bias and minimizing three peak flow differences—are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization in a benchmark hydrological calibration problem for the Leaf River (1950 km2 near Collins, Mississippi. Three performance metrics—solution quality, spacing, and convergence—are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.
Model comparisons and genetic and environmental parameter ...
African Journals Online (AJOL)
arc
South African Journal of Animal Science 2005, 35 (1) ... Genetic and environmental parameters were estimated for pre- and post-weaning average daily gain ..... and BWT (and medium maternal genetic correlations) indicates that these traits ...
Lag space estimation in time series modelling
DEFF Research Database (Denmark)
Goutte, Cyril
1997-01-01
The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...
NEW DOCTORAL DEGREE Parameter estimation problem in the Weibull model
Marković, Darija
2009-01-01
In this dissertation we consider the problem of the existence of best parameters in the Weibull model, one of the most widely used statistical models in reliability theory and life data theory. Particular attention is given to a 3-parameter Weibull model. We have listed some of the many applications of this model. We have described some of the classical methods for estimating parameters of the Weibull model, two graphical methods (Weibull probability plot and hazard plot), and two analyt...
A hypocentral version of the space-time ETAS model
Guo, Yicun; Zhuang, Jiancang; Zhou, Shiyong
2015-10-01
The space-time Epidemic-Type Aftershock Sequence (ETAS) model is extended by incorporating the depth component of earthquake hypocentres. The depths of the direct offspring produced by an earthquake are assumed to be independent of the epicentre locations and to follow a beta distribution, whose shape parameter is determined by the depth of the parent event. This new model is verified by applying it to the Southern California earthquake catalogue. The results show that the new model fits data better than the original epicentre ETAS model and that it provides the potential for modelling and forecasting seismicity with higher resolutions.
Parameter optimization model in electrical discharge machining process
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.
Outdoor-indoor Space: Unified Modeling and Shortest Path Search
DEFF Research Database (Denmark)
Jensen, Søren Kejser; Nielsen, Jens Thomas Vejlby; Lu, Hua;
2016-01-01
Graph models are widely used for representing the topology of outdoor space (O-Space) and indoor space (I-Space). However, existing models neglect the intersection between O-Space and I-Space, only allowing for computations such as shortest path and nearest neighbor queries in either O-Space or I...
Schaefer, Andreas; Wenzel, Friedemann
2017-04-01
Subduction zones are generally the sources of the earthquakes with the highest magnitudes. Not only in Japan or Chile, but also in Pakistan, the Solomon Islands or for the Lesser Antilles, subduction zones pose a significant hazard for the people. To understand the behavior of subduction zones, especially to identify their capabilities to produce maximum magnitude earthquakes, various physical models have been developed leading to a large number of various datasets, e.g. from geodesy, geomagnetics, structural geology, etc. There have been various studies to utilize this data for the compilation of a subduction zone parameters database, but mostly concentrating on only the major zones. Here, we compile the largest dataset of subduction zone parameters both in parameter diversity but also in the number of considered subduction zones. In total, more than 70 individual sources have been assessed and the aforementioned parametric data have been combined with seismological data and many more sources have been compiled leading to more than 60 individual parameters. Not all parameters have been resolved for each zone, since the data completeness depends on the data availability and quality for each source. In addition, the 3D down-dip geometry of a majority of the subduction zones has been resolved using historical earthquake hypocenter data and centroid moment tensors where available and additionally compared and verified with results from previous studies. With such a database, a statistical study has been undertaken to identify not only correlations between those parameters to estimate a parametric driven way to identify potentials for maximum possible magnitudes, but also to identify similarities between the sources themselves. This identification of similarities leads to a classification system for subduction zones. Here, it could be expected if two sources share enough common characteristics, other characteristics of interest may be similar as well. This concept
Calibration of imaging parameters for space-borne airglow photography using city light positions
Hozumi, Yuta; Saito, Akinori; Ejiri, Mitsumu K.
2016-09-01
A new method for calibrating imaging parameters of photographs taken from the International Space Station (ISS) is presented in this report. Airglow in the mesosphere and the F-region ionosphere was captured on the limb of the Earth with a digital single-lens reflex camera from the ISS by astronauts. To utilize the photographs as scientific data, imaging parameters, such as the angle of view, exact position, and orientation of the camera, should be determined because they are not measured at the time of imaging. A new calibration method using city light positions shown in the photographs was developed to determine these imaging parameters with high accuracy suitable for airglow study. Applying the pinhole camera model, the apparent city light positions on the photograph are matched with the actual city light locations on Earth, which are derived from the global nighttime stable light map data obtained by the Defense Meteorological Satellite Program satellite. The correct imaging parameters are determined in an iterative process by matching the apparent positions on the image with the actual city light locations. We applied this calibration method to photographs taken on August 26, 2014, and confirmed that the result is correct. The precision of the calibration was evaluated by comparing the results from six different photographs with the same imaging parameters. The precisions in determining the camera position and orientation are estimated to be ±2.2 km and ±0.08°, respectively. The 0.08° difference in the orientation yields a 2.9-km difference at a tangential point of 90 km in altitude. The airglow structures in the photographs were mapped to geographical points using the calibrated imaging parameters and compared with a simultaneous observation by the Visible and near-Infrared Spectral Imager of the Ionosphere, Mesosphere, Upper Atmosphere, and Plasmasphere mapping mission installed on the ISS. The comparison shows good agreements and supports the validity
Sensitivity of a Shallow-Water Model to Parameters
Kazantsev, Eugene
2011-01-01
An adjoint based technique is applied to a shallow water model in order to estimate the influence of the model's parameters on the solution. Among parameters the bottom topography, initial conditions, boundary conditions on rigid boundaries, viscosity coefficients Coriolis parameter and the amplitude of the wind stress tension are considered. Their influence is analyzed from three points of view: 1. flexibility of the model with respect to a parameter that is related to the lowest value of the cost function that can be obtained in the data assimilation experiment that controls this parameter; 2. possibility to improve the model by the parameter's control, i.e. whether the solution with the optimal parameter remains close to observations after the end of control; 3. sensitivity of the model solution to the parameter in a classical sense. That implies the analysis of the sensitivity estimates and their comparison with each other and with the local Lyapunov exponents that characterize the sensitivity of the mode...
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.
Reconciling Planck with the local value of H0 in extended parameter space
Di Valentino, Eleonora; Melchiorri, Alessandro; Silk, Joseph
2016-10-01
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.
Reconciling Planck with the local value of $H_0$ in extended parameter space
Di Valentino, Eleonora; Silk, Joseph
2016-01-01
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 LCDM 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 $\\Lambda$CDM 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 ...
Smith, David; Schuldt, Carsten; Lorenz, Jessica; Tschirner, Teresa; Moebius-Winkler, Maximilian; Kaes, Josef; Glaser, Martin; Haendler, Tina; Schnauss, Joerg
2015-03-01
Biologically evolved materials are often used as inspiration in the development of new materials as well as examinations into the underlying physical principles governing their behavior. For instance, the biopolymer constituents of the highly dynamic cellular cytoskeleton such as actin have inspired a deep understanding of soft polymer-based materials. However, the molecular toolbox provided by biological systems has been evolutionarily optimized to carry out the necessary functions of cells, and the inability modify basic properties such as biopolymer stiffness hinders a meticulous examination of parameter space. Using actin as inspiration, we circumvent these limitations using model systems assembled from programmable materials such as DNA. Nanorods with comparable, but controllable dimensions and mechanical properties as actin can be constructed from small sets of specially designed DNA strands. In entangled gels, these allow us to systematically determine the dependence of network mechanical properties on parameters such as persistence length and crosslink strength. At higher concentrations in the presence of local attractive forces, we see a transition to highly-ordered bundled and ``aster'' phases similar to those previously characterized in systems of actin or microtubules.
The Parameter Space of Magnetized Target Fusion (aka Magneto-Inertial Fusion)
Lindemuth, Irvin
2016-10-01
Magnetized Target Fusion (MTF), aka Magneto-Inertial Fusion (MIF), is an approach to fusion that compresses a preformed, magnetized (but not necessarily magnetically confined) plasma with an imploding liner or pusher. MTF/MIF operates in a density regime in between the eleven orders of magnitude (1011) in density that separate inertial confinement fusion (ICF) from magnetic confinement fusion MCF. Compared to MCF, the higher density, shorter confinement times, and compressional heating as the dominant heating mechanism potentially reduce the impact of magnetic instabilities. Compared to ICF, the magnetically reduced thermal transport and lower density leads to orders-of-magnitude reduction in the difficult-to-achieve areal-density parameter and a significant reduction in required implosion velocity and radial convergence, potentially reducing the deleterious effects of implosion hydrodynamic instabilities. This tutorial presents fundamental analysis and simple time-dependent modeling to show where significant fusion gain might be achieved in the intermediate-density regime. The analysis shows that the fusion design space is potentially a continuum between ICF and MCF but practical considerations limit the space in which ignition might be obtained. Generic time-dependent modeling addresses the key physics requirements and defines ``ball-park'' values needed for target-plasma initial density, temperature, and magnetic field and implosion system size, energy, and velocity. The modeling shows energy gains greater than 30 can potentially be achieved and that high gain may be obtained at low convergence ratios, e.g., less than 15. A non-exhaustive review of past and present MTF/MIF efforts is presented and the renewed interest in MTF/MIF within the US (e.g., ARPA-E's ALPHA program) and abroad is noted.
Observational modeling of topological spaces
Energy Technology Data Exchange (ETDEWEB)
Molaei, M.R. [Department of Mathematics, Shahid Bahonar University of Kerman, Kerman 76169-14111 (Iran, Islamic Republic of)], E-mail: mrmolaei@mail.uk.ac.ir
2009-10-15
In this paper a model for a multi-dimensional observer by using of the fuzzy theory is presented. Relative form of Tychonoff theorem is proved. The notion of topological entropy is extended. The persistence of relative topological entropy under relative conjugate relation is proved.
A Modeler's Perspective on Space Weather Forecasting (Invited)
Wiltberger, M. J.
2010-12-01
Space physics is moving into a new era where numerical models originally developed for answering science questions are used as the basis for making operational space weather forecasts. Answering this challenge requires developments on multiple fronts requiring collaborations across space physics disciplines and between the research and operations communities. Since space weather in geospace is driven by the solar wind conditions a natural solution to improving the forecast lead time is to couple geospace models to heliospheric models. The quality of these forecast is dependent upon the ability of the heliospheric models to accurately model IMF Bz. Another challenge presented by moving into the forecasting arena is preparing the models for real-time operation which includes both computational performance and data redundancy issues. Moving into operations also presents modelers with an opportunity to assess their models performance over calculation intervals unprecedented duration. A key collaboration in the transition of models to operation is the discussion between forecasters and developers on what forecast parameters can accurately be predicted by the current generation of numerical models. This collaboration naturally includes a discussion of the definition of the best metrics to be used in quantitatively assessing performance.
AX-5 space suit reliability model
Reinhardt, AL; Magistad, John
1990-01-01
The AX-5 is an all metal Extra-vehicular (EVA) space suit currently under consideration for use on Space Station Freedom. A reliability model was developed based on the suit's unique design and on projected joint cycle requirements. Three AX-5 space suit component joints were cycled under simulated load conditions in accordance with NASA's advanced space suit evaluation plan. This paper will describe the reliability model developed, the results of the cycle testing, and an interpretation of the model and test results in terms of projected Mean Time Between Failure for the AX-5. A discussion of the maintenance implications and life cycle for the AX-5 based on this projection is also included.
Modeling individual effects in the Cormack-Jolly-Seber Model: A state-space formulation
Royle, J. Andrew
2008-01-01
In population and evolutionary biology, there exists considerable interest in individual heterogeneity in parameters of demographic models for open populations. However, flexible and practical solutions to the development of such models have proven to be elusive. In this article, I provide a state-space formulation of open population capture-recapture models with individual effects. The state-space formulation provides a generic and flexible framework for modeling and inference in models with individual effects, and it yields a practical means of estimation in these complex problems via contemporary methods of Markov chain Monte Carlo. A straightforward implementation can be achieved in the software package WinBUGS. I provide an analysis of a simple model with constant parameter detection and survival probability parameters. A second example is based on data from a 7-year study of European dippers, in which a model with year and individual effects is fitted.
Selection of Phase Space Reconstruction Parameters for EMG Signals of the Uterus
Directory of Open Access Journals (Sweden)
Brzozowska Ewelina
2016-12-01
Full Text Available Biological time series have a finite number of samples with noise included in them. Because of this fact, it is not possible to reconstruct phase space in an ideal manner. One kind of biomedical signals are electrohisterographical (EHG datasets, which represent uterine muscle contractile activity. In the process of phase space reconstruction, the most important thing is suitable choice of the method for calculating the time delay τ and embedding dimension d, which will reliably reconstruct the original signal. The parameters used in digital signal processing are key to arranging adequate parameters of the analysed attractor embedded in the phase space. The aim of this paper is to present a method employed for phase space reconstruction for EHG signals that will make it possible for their further analysis to be carried out.
An introduction to Space Weather Integrated Modeling
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.
A growing social network model in geographical space
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.
The STAMP Software for State Space Models
Directory of Open Access Journals (Sweden)
Roy Mendelssohn
2011-05-01
Full Text Available This paper reviews the use of STAMP (Structural Time Series Analyser, Modeler and Predictor for modeling time series data using state-space methods with unobserved components. STAMP is a commercial, GUI-based program that runs on Windows, Linux and Macintosh computers as part of the larger OxMetrics System. STAMP can estimate a wide-variety of both univariate and multivariate state-space models, provides a wide array of diagnostics, and has a batch mode capability. The use of STAMP is illustrated for the Nile river data which is analyzed throughout this issue, as well as by modeling a variety of oceanographic and climate related data sets. The analyses of the oceanographic and climate data illustrate the breadth of models available in STAMP, and that state-space methods produce results that provide new insights into important scientific problems.
Estimation of shape model parameters for 3D surfaces
DEFF Research Database (Denmark)
Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen;
2008-01-01
Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D s...
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.
Reconsidering seismological constraints on the available parameter space of macroscopic dark matter
Cyncynates, David; Sidhu, Jagjit; Starkman, Glenn D
2016-01-01
Using lunar seismological data, constraints have been proposed on the available parameter space of macroscopic dark matter (macros). We show that actual limits are considerably weaker by considering in greater detail the mechanism through which macro impacts generate detectable seismic waves, which have wavelengths considerably longer than the diameter of the macro. We show that the portion of the macro parameter space that can be ruled out by current seismological evidence is considerably smaller than previously reported, and specifically that candidates with greater than or equal to nuclear density are not excluded by lunar seismology.
Characterization in bi-parameter space of a non-ideal oscillator
de Souza, S. L. T.; Batista, A. M.; Baptista, M. S.; Caldas, I. L.; Balthazar, J. M.
2017-01-01
We investigate the dynamical behavior of a non-ideal Duffing oscillator, a system composed of a mass-spring-pendulum driven by a DC motor with limited power supply. To identify new features on Duffing oscillator parameter space due to the limited power supply, we provide an extensive numerical characterization in the bi-parameter space by using Lyapunov exponents. Following this procedure, we identify remarkable new organized distribution of periodic windows, the ones known as Arnold tongues and also shrimp-shaped structures. In addition, we also identify intertwined basins of attraction for coexisting multiple attractors connected with tongues.
The magnetically driven imploding liner parameter space of the ATLAS capacitor bank
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.
Cusp Points in the Parameter Space of Degenerate 3-RPR Planar Parallel Manipulators
Manubens, Montserrat; Chablat, Damien; Wenger, Philippe; Rouillier, Fabrice
2012-01-01
This paper investigates the conditions in the design parameter space for the existence and distribution of the cusp locus for planar parallel manipulators. Cusp points make possible non-singular assembly-mode changing motion, which increases the maximum singularity-free workspace. An accurate algorithm for the determination is proposed amending some imprecisions done by previous existing algorithms. This is combined with methods of Cylindric Algebraic Decomposition, Gr\\"obner bases and Discriminant Varieties in order to partition the parameter space into cells with constant number of cusp points. These algorithms will allow us to classify a family of degenerate 3-RPR manipulators.
Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects
2017-02-22
AFRL-AFOSR-UK-TR-2017-0023 Linear and Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects Marco Martorella... UNIVERSITY DI PISA, DEPARTMENT DI INGEGNERIA Final Report 02/22/2017 DISTRIBUTION A: Distribution approved for public release. AF Office Of Scientific Research...Nonlinear Time-Frequency Analysis for Parameter Estimation of Resident Space Objects 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-14-1-0183 5c. PROGRAM
HBT Parameters and Space-Momentum Correlations in Relativistic Heavy-Ion Collisions
Institute of Scientific and Technical Information of China (English)
张景波; 霍雷; 张卫宁; 李新华; 许怒; 刘亦铭
2001-01-01
Using the relativistic quantum molecular dynamics approach, with a correlation after-burner, the physics of the transverse momentum dependence of the Hanbury-Brown and Twiss parameters is studied for Au t Au, Si + Si and p + p collisions at the centre-of-mass energy v s = 200 AGeV. The results indicate that the space-momentum correlations would affect such dependence in both heavy-ion and elementary collisions. The size parameters as a function of the transverse mass mt are sensitive to the degree of space-momentum correlations.
Quantum homogeneous spaces and special functions with a dimensional deformation parameter
Bonechi, F.; Giachetti, R.; del Olmo, M. A.; Sorace, E.; Tarlini, M.
1996-12-01
We study the most elementary aspects of harmonic analysis on a homogeneous space of a deformation of the two-dimensional Euclidean group, admitting generalizations to dimensions three and four, whose quantum parameter has the physical dimensions of length. The homogeneous space is recognized as a new quantum plane and the action of the Euclidean quantum group is used to determine an eigenvalue problem for the Casimir operator, which constitutes the analogue of the Schrödinger equation in the presence of such a deformation. The solutions are given in the plane-wave and angular-momentum bases and are expressed in terms of hypergeometric series with non-commuting parameters.
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...
Continuous-time discrete-space models for animal movement
Hanks, Ephraim M.; Hooten, Mevin B.; Alldredge, Mat W.
2015-01-01
The processes influencing animal movement and resource selection are complex and varied. Past efforts to model behavioral changes over time used Bayesian statistical models with variable parameter space, such as reversible-jump Markov chain Monte Carlo approaches, which are computationally demanding and inaccessible to many practitioners. We present a continuous-time discrete-space (CTDS) model of animal movement that can be fit using standard generalized linear modeling (GLM) methods. This CTDS approach allows for the joint modeling of location-based as well as directional drivers of movement. Changing behavior over time is modeled using a varying-coefficient framework which maintains the computational simplicity of a GLM approach, and variable selection is accomplished using a group lasso penalty. We apply our approach to a study of two mountain lions (Puma concolor) in Colorado, USA.
Acoustic omni meta-atom for decoupled access to all octants of a wave parameter space
Koo, Sukmo; Cho, Choonlae; Jeong, Jun-ho; Park, Namkyoo
2016-01-01
The common behaviour of a wave is determined by wave parameters of its medium, which are generally associated with the characteristic oscillations of its corresponding elementary particles. In the context of metamaterials, the decoupled excitation of these fundamental oscillations would provide an ideal platform for top–down and reconfigurable access to the entire constitutive parameter space; however, this has remained as a conceivable problem that must be accomplished, after being pointed out by Pendry. Here by focusing on acoustic metamaterials, we achieve the decoupling of density ρ, modulus B−1 and bianisotropy ξ, by separating the paths of particle momentum to conform to the characteristic oscillations of each macroscopic wave parameter. Independent access to all octants of wave parameter space (ρ, B−1, ξ)=(+/−,+/−,+/−) is thus realized using a single platform that we call an omni meta-atom; as a building block that achieves top–down access to the target properties of metamaterials. PMID:27687689
Parameter-space metric of semicoherent searches for continuous gravitational waves
Pletsch, Holger J.
2010-08-01
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.
Compositional modelling of distributed-parameter systems
Maschke, Bernhard; Schaft, van der Arjan; Lamnabhi-Lagarrigue, F.; Loría, A.; Panteley, E.
2005-01-01
The Hamiltonian formulation of distributed-parameter systems has been a challenging reserach area for quite some time. (A nice introduction, especially with respect to systems stemming from fluid dynamics, can be found in [26], where also a historical account is provided.) The identification of the
Parameter Estimation and Experimental Design in Groundwater Modeling
Institute of Scientific and Technical Information of China (English)
SUN Ne-zheng
2004-01-01
This paper reviews the latest developments on parameter estimation and experimental design in the field of groundwater modeling. Special considerations are given when the structure of the identified parameter is complex and unknown. A new methodology for constructing useful groundwater models is described, which is based on the quantitative relationships among the complexity of model structure, the identifiability of parameter, the sufficiency of data, and the reliability of model application.
Preliminary Multivariable Cost Model for Space Telescopes
Stahl, H. Philip
2010-01-01
Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored
Bayesian approach to decompression sickness model parameter estimation.
Howle, L E; Weber, P W; Nichols, J M
2017-03-01
We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.
Hornik, Kurt; Grün, Bettina
2014-01-01
Diaconis and Ylvisaker (1979) give necessary conditions for conjugate priors for distributions from the natural exponential family to be proper as well as to have the property of linear posterior expectation of the mean parameter of the family. Their conditions for propriety and linear posterior expectation are also sufficient if the natural parameter space is equal to the set of all d-dimensional real numbers. In this paper their results are extended to characterize when conjugate priors ...
On standard conjugate families for natural exponential families with bounded natural parameter space
Hornik, Kurt; Grün, Bettina
2014-01-01
Diaconis and Ylvisaker (1979) give necessary conditions for conjugate priors for distributions from the natural exponential family to be proper as well as to have the property of linear posterior expectation of the mean parameter of the family. Their conditions for propriety and linear posterior expectation are also sufficient if the natural parameter space is equal to the set of all d -dimensional real numbers. In this paper their results are extended to characterize when conjugate priors ar...
Rejoinder: Sifting through model space
Heisey, Dennis M.; Osnas, Erik E.; Cross, Paul C.; Joly, Damien O.; Langenberg, Julia A.; Miller, Michael W.
2010-01-01
Observational data sets generated by complex processes are common in ecology. Traditionally these have been very challenging to analyze because of the limitations of available statistical tools. This seems to be changing, and these are exciting times to be involved with ecological statistics, not just because of the neo-Bayesian revival but also because of the proliferation of computationally intensive methods in general. It is now possible to fit much richer models to observational data than in the relatively recent past, which in turn has stimulated much interest in how to evaluate and compare such models. In such an immature, vibrant, and rapidly growing field, not everyone is going to agree on the best way to do things. This is reflected in the contrast of opinions offered by the discussants. Each offers a thoughtful and thought-provoking critique of our work that reflects the current thinking in a non-negligible segment of the ecological data analysis community. We want to thank them for their insights.
A new parameter of geomagnetic storms for the severity of space weather
Balan, N.; Batista, I. S.; Tulasi Ram, S.; Rajesh, P. K.
2016-12-01
Using the continuous Dst data available since 1957 and H component data for the Carrington space weather event of 1859, the paper shows that the mean value of Dst during the main phase of geomagnetic storms, called mean DstMP, is a unique parameter that can indicate the severity of space weather. All storms having high mean DstMP (≤-250 nT), which corresponds to high amount of energy input in the magnetosphere-ionosphere system in short duration, are found associated with severe space weather events that caused all known electric power outages and telegraph system failures.
Modeling diurnal hormone profiles by hierarchical state space models.
Liu, Ziyue; Guo, Wensheng
2015-10-30
Adrenocorticotropic hormone (ACTH) diurnal patterns contain both smooth circadian rhythms and pulsatile activities. How to evaluate and compare them between different groups is a challenging statistical task. In particular, we are interested in testing (1) whether the smooth ACTH circadian rhythms in chronic fatigue syndrome and fibromyalgia patients differ from those in healthy controls and (2) whether the patterns of pulsatile activities are different. In this paper, a hierarchical state space model is proposed to extract these signals from noisy observations. The smooth circadian rhythms shared by a group of subjects are modeled by periodic smoothing splines. The subject level pulsatile activities are modeled by autoregressive processes. A functional random effect is adopted at the pair level to account for the matched pair design. Parameters are estimated by maximizing the marginal likelihood. Signals are extracted as posterior means. Computationally efficient Kalman filter algorithms are adopted for implementation. Application of the proposed model reveals that the smooth circadian rhythms are similar in the two groups but the pulsatile activities in patients are weaker than those in the healthy controls. Copyright © 2015 John Wiley & Sons, Ltd.
Physical quantities and spatial parameters in the complex octonion curved space
Weng, Zi-Hua
2016-01-01
The paper focuses on finding out several physical quantities to exert an influence on the spatial parameters of complex-octonion curved space, including the metric coefficient, connection coefficient, and curvature tensor. In the flat space described with the complex octonions, the radius vector is combined with the integrating function of field potential to become a composite radius vector. And the latter can be considered as the radius vector in a flat composite-space (a function space). Further it is able to deduce some formulae between the physical quantity and spatial parameter, in the complex-octonion curved composite-space. Under the condition of weak field approximation, these formulae infer a few results accordant with the General Theory of Relativity. The study reveals that it is capable of ascertaining which physical quantities are able to result in the warping of space, in terms of the curved composite-space described with the complex octonions. Moreover, the method may be expanded into some curve...
Nonlinear state space model identification of synchronous generators
Energy Technology Data Exchange (ETDEWEB)
Dehghani, M.; Nikravesh, S.K.Y. [Electrical Engineering Department, Amirkabir University of Technology, Tehran (Iran)
2008-05-15
A method for identification of a synchronous generator is suggested in this paper. The method uses the theoretical relations of machine parameters and the Prony method to find the state space model of the system. Such models are useful for controller design and stability tests. The proposed identification method is applied to a third order model of a synchronous generator. In this study, the field voltage is considered as the input and the active output power and the rotor angle are considered as the outputs of the synchronous generator. Simulation results show good accuracy of the identified model. (author)
Developing Viable Financing Models for Space Tourism
Eilingsfeld, F.; Schaetzler, D.
2002-01-01
Increasing commercialization of space services and the impending release of government's control of space access promise to make space ventures more attractive. Still, many investors shy away from going into the space tourism market as long as they do not feel secure that their return expectations will be met. First and foremost, attracting investors from the capital markets requires qualifying financing models. Based on earlier research on the cost of capital for space tourism, this paper gives a brief run-through of commercial, technical and financial due diligence aspects. After that, a closer look is taken at different valuation techniques as well as alternative ways of streamlining financials. Experience from earlier ventures has shown that the high cost of capital represents a significant challenge. Thus, the sophistication and professionalism of business plans and financial models needs to be very high. Special emphasis is given to the optimization of the debt-to-equity ratio over time. The different roles of equity and debt over a venture's life cycle are explained. Based on the latter, guidelines for the design of an optimized loan structure are given. These are then applied to simulating the financial performance of a typical space tourism venture over time, including the calculation of Weighted Average Cost of Capital (WACC) and Net Present Value (NPV). Based on a concluding sensitivity analysis, the lessons learned are presented. If applied properly, these will help to make space tourism economically viable.
Sigma-Model Solitons on Noncommutative Spaces
Dabrowski, Ludwik; Landi, Giovanni; Luef, Franz
2015-12-01
We use results from time-frequency analysis and Gabor analysis to construct new classes of sigma-model solitons over the Moyal plane and over noncommutative tori, taken as source spaces, with a target space made of two points. A natural action functional leads to self-duality equations for projections in the source algebra. Solutions, having nontrivial topological content, are constructed via suitable Morita duality bimodules.
X-ray Pulsars Across the Parameter Space of Luminosity, Accretion Mode, and Spin
Laycock, Silas; Yang, Jun; Christodoulou, Dimitris; Coe, Malcolm; Cappallo, Rigel; Zezas, Andreas; Ho, Wynn C. G.; Hong, JaeSub; Fingerman, Samuel; Drake, Jeremy J.; Kretschmar, Peter; Antoniou, Vallia
2017-08-01
We present our multi-satellite library of X-ray Pulsar observations to the community, and highlight recent science results. Available at www.xraypulsars.space the library provides a range of high-level data products, including: activity histories, pulse-profiles, phased event files, and a unique pulse-profile modeling interface. The initial release (v1.0) contains some 15 years of RXTE-PCA, Chandra ACIS-I, and XMM-PN observations of the Small Magellanic Cloud, creating a valuable record of pulsar behavior. Our library is intended to enable new progress on fundamental NS parameters and accretion physics. The major motivations are (1) Assemble a large homogeneous sample to enable population statistics. This has so far been used to map the propeller transition, and explore the role of retrograde and pro-grade accretion disks. (2) Obtain pulse-profiles for the same pulsars on many different occasions, at different luminosities and states in order to break model degeneracies. This effort has led to preliminary measurements of the offsets between magnetic and spin axes. With the addition of other satellites, and Galactic pulsars, the library will cover the entire available range of luminosity, variability timescales and accretion regimes.
A new switching parameter varying optoelectronic delayed feedback model with computer simulation
Liu, Lingfeng; Miao, Suoxia; Cheng, Mengfan; Gao, Xiaojing
2016-02-01
In this paper, a new switching parameter varying optoelectronic delayed feedback model is proposed and analyzed by computer simulation. This model is switching between two parameter varying optoelectronic delayed feedback models based on chaotic pseudorandom sequences. Complexity performance results show that this model has a high complexity compared to the original model. Furthermore, this model can conceal the time delay effectively against the auto-correlation function, delayed mutual information and permutation information analysis methods, and can extent the key space, which greatly improve its security.
A Theory and Method for Modeling of Structures with Stochastic Parameters
Institute of Scientific and Technical Information of China (English)
ZHANG Bei; YIN Xue-gang; WANG Fu-ming; ZHONG Yan-hui; CAI Ying-chun
2004-01-01
In order to reflect the stochastic characteristics of structures more comprehensively and accurately, a theory and method for modeling of structures with stochastic parameters is presented by using probability finite element method and stochastic experiment data of structures based on the modeling of structures with deterministic parameters. Double-decker space frame is taken as an example to validate this theory and method, good results are gained.
Single Parameter Model for Free Recall And the Nature of Partially Filled Working Memory
Tarnow, Dr Eugen
2009-01-01
I present a single parameter model of free recall and fit the one parameter, the probability per time unit of an item in working memory entering the next memory store (similar to Atkinson and Shiffrin, 1968), to the original Murdock (1962) data. Working memory is modeled as having space for a maximum of 4 items (Cowan, 2001). The first four probability values convey precise information about how items in the partially filled working memory enter the next memory store. In particular, one ...
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... and uncertainty estimation. Essential issues relating to calibration are discussed. The classical regression methods are described; however, the main focus is on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The next two chapters describe case studies in which the GLUE methodology...
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-11-01
simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.
Space Weather Parameters Computed on the Basis of the Magnetogram Inversion Technique
Institute of Scientific and Technical Information of China (English)
V. M. Mishin; M. F(o)rster; A.D. Bazarzhapov; T.I. Saifudinova; Y.A. Karavaev; P. Stauning; J. Watermann; V. Golovkov; S. Solovyev
2005-01-01
In this paper is given short description of the magnetogram inversion technique, MIT2, and of methods of calculation of some parameters of space weather. Are given also examples of new results, obtained using the MIT2 and solar wind data.
AN IMPROVED GENETIC ALGORITHM FOR SEARCHING OPTIMAL PARAMETERS IN n—DIMENSIONAL SPACE
Institute of Scientific and Technical Information of China (English)
TangBin; HuGuangrui
2002-01-01
An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented,which encodes movement direction and distance and searches from coarse to precise.The algorithm can realize global optimization and improve the search efficiency,and can be applied effectively in industrial optimization ,data mining and pattern recognition.
AN IMPROVED GENETIC ALGORITHM FOR SEARCHING OPTIMAL PARAMETERS IN n-DIMENSIONAL SPACE
Institute of Scientific and Technical Information of China (English)
Tang Bin; Hu Guangrui
2002-01-01
An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.
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.
Ternary interaction parameters in calphad solution models
Energy Technology Data Exchange (ETDEWEB)
Eleno, Luiz T.F., E-mail: luizeleno@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica; Schön, Claudio G., E-mail: schoen@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Computational Materials Science Laboratory. Department of Metallurgical and Materials Engineering
2014-07-01
For random, diluted, multicomponent solutions, the excess chemical potentials can be expanded in power series of the composition, with coefficients that are pressure- and temperature-dependent. For a binary system, this approach is equivalent to using polynomial truncated expansions, such as the Redlich-Kister series for describing integral thermodynamic quantities. For ternary systems, an equivalent expansion of the excess chemical potentials clearly justifies the inclusion of ternary interaction parameters, which arise naturally in the form of correction terms in higher-order power expansions. To demonstrate this, we carry out truncated polynomial expansions of the excess chemical potential up to the sixth power of the composition variables. (author)
A Model of Emergent Universe in Inhomogeneous Space-Time
Bhattacharya, Subhra
2016-01-01
A scenario of an emergent universe is constructed in the background of an inhomogeneous space-time model which is asymptotically (at spatial infinity) FRW space-time. The cosmic substratum consists of non-interacting two components, namely {\\bf a)} homogeneous and isotropic fluid but dissipative in nature and {\\bf b)} an inhomogeneous and anisotropic barotropic fluid. In non-equilibrium thermodynamic prescription (second order deviations), particle creation mechanism is considered the cause for the dissipative phenomena. It is found that for constant value of the particle creation rate parameter there exists a scenario of emergent universe.
Synthetic Weyl points in generalized parameter space and their topological properties
Wang, Qiang; Liu, Hui; Wan, Xiangang; Zhu, Shining; Chan, C T
2016-01-01
Weyl fermions1 do not appear in nature as elementary particles, but they are now found to exist as nodal points in the band structure of electronic and classical wave crystals. Novel phenomena such as Fermi arcs and chiral anomaly have fueled the interest of these topological points which are frequently perceived as monopoles in momentum space. Here, we demonstrate that generalized Weyl points can exist in a parameter space and we report the first observation of such nodal points in one-dimensional photonic crystals in the optical range. The reflection phase inside the band gap of a truncated photonic crystal exhibits vortexes in the parameter space where the Weyl points are defined and they share the same topological charges as the Weyl points. These vortexes guarantee the existence of interface states, the trajectory of which can be understood as "Fermi arcs" emerging from the Weyl nodes.
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.
Variation ranges of motion parameters for space debris in the geosynchronous ring
Zhao, Chang-Yin; Zhang, Ming-Jiang; Yu, Sheng-Xian; Xiong, Jian-Ning; Zhang, Wei; Zhu, Ting-Lei
2016-06-01
We propose a method that uses only one set of known orbital elements to directly determine the motion state and variation ranges of motion parameters, including the inclination, right ascension of the ascending node (RAAN), evolution period of the orbital plane, maximum libration amplitude of the semi-major axis, commensurable angle, libration period and drift period, for space debris in the geosynchronous ring. These variation ranges of motion parameters characterize the evolution of debris quantitatively and illustrate the three-dimensional (3D) variations. Employing the proposed method, we study the motion state and variation ranges of motion parameters for catalogued and uncontrolled space debris with existing two-line element (TLE) data in the geosynchronous ring, and present specific results. We also compare our results with actual observational results derived from long-term TLE historical data, and find that, in the vast majority of cases, our proposed method of determining the motion state and variation ranges of motion parameters via only one set of known orbital elements is effective. In addition, before the elaboration of the variation ranges of motion parameters stated above, we obtain the statistical distribution of space debris in the orbital plane and the daily motion from the TLE historical data. We then derive two mathematical formulae that explain the statistical distribution and daily motion on the basis of the essence of dynamics, which contributes to the characterization of the evolution of debris.
Parameter estimation and error analysis in environmental modeling and computation
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
Directory of Open Access Journals (Sweden)
Houda Salhi
2016-01-01
Full Text Available This paper deals with the parameter estimation problem for multivariable nonlinear systems described by MIMO state-space Wiener models. Recursive parameters and state estimation algorithms are presented using the least squares technique, the adjustable model, and the Kalman filter theory. The basic idea is to estimate jointly the parameters, the state vector, and the internal variables of MIMO Wiener models based on a specific decomposition technique to extract the internal vector and avoid problems related to invertibility assumption. The effectiveness of the proposed algorithms is shown by an illustrative simulation example.
Features of human skin in HSV color space and new recognition parameter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Features of human skin in HSV color space are widely applied in the area of image retrieval based on content. H is selected as the basic recognition parameter because its value has a narrow range for the skin color and can keep stable while the illumination intensity or the curvature of skin surface is changing. Rules of parameters with the change of illumination in HSV color space are studied. It is firstly found that the mean of saturation and value (S+V)/2 can keep stable when the illumination intensity is changed or the skin surface is inflected, and (S+V)/2 changes with skin color, but the tendency of change is contrary to that of H. Therefore, (S+V)/H can be used as a new recognition parameter which can enhance HSV ability to recognize human skin.
Ejiri, Shinji; Yamada, Norikazu
2016-01-01
Aiming to understand the phase structure of lattice QCD at nonzero temperature and density, we study the phase transitions of QCD in an extended parameter space, where the number of flavor and quark masses are considered as parameters. Performing simulations of 2 flavor QCD and using the reweighting method, we investigate (2+Nf) flavor QCD at finite density, where two light flavors and Nf massive flavors exist. Calculating probability distribution functions, we determine the critical surface terminating first order phase transitions in the parameter space of the light quark mass, the heavy quark mass and the chemical potential. Through the study of the many flavor system, we discuss the phase structure of QCD at finite density.
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.
Directory of Open Access Journals (Sweden)
Solène MARRY
2012-04-01
Full Text Available The research referred to in the article concerns the factors influencing the perception of ordinary sonic public space and everyday sounds. Sound perception parameters, such as vegetation or sound sources, are analysed in urban public spaces. This research, which is based on my PhD project, tries to understand how urban people perceive their sonic environment and try to contribute to sonic ambiance knowledge. The research is based on a qualitative investigation conducted among 29 people. It is, on the one hand, based on questionnaires and focus groups in situ and, on the other hand, on individual interviews (in-depth interviews, sonic mind maps, and it illustrates different parameters (temporal, spatial, sensitive and individual that influence a person’s assessment of the sound environment. This qualitative investigation is correlated with acoustic measures in two seasons. The results show, among other things, the impact of vegetation and urban fittings on sonic perception, and they underline the influence of city planning and urban fittings on sound perception in public urban spaces.
GIS-Based Hydrogeological-Parameter Modeling
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A regression model is proposed to relate the variation of water well depth with topographic properties (area and slope), the variation of hydraulic conductivity and vertical decay factor. The implementation of this model in GIS environment (ARC/TNFO) based on known water data and DEM is used to estimate the variation of hydraulic conductivity and decay factor of different lithoiogy units in watershed context.
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens;
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests, or p...
Xu, Wenfu; Hu, Zhonghua; Zhang, Yu; Liang, Bin
2017-03-01
After being launched into space to perform some tasks, the inertia parameters of a space robotic system may change due to fuel consumption, hardware reconfiguration, target capturing, and so on. For precision control and simulation, it is required to identify these parameters on orbit. This paper proposes an effective method for identifying the complete inertia parameters (including the mass, inertia tensor and center of mass position) of a space robotic system. The key to the method is to identify two types of simple dynamics systems: equivalent single-body and two-body systems. For the former, all of the joints are locked into a designed configuration and the thrusters are used for orbital maneuvering. The object function for optimization is defined in terms of acceleration and velocity of the equivalent single body. For the latter, only one joint is unlocked and driven to move along a planned (exiting) trajectory in free-floating mode. The object function is defined based on the linear and angular momentum equations. Then, the parameter identification problems are transformed into non-linear optimization problems. The Particle Swarm Optimization (PSO) algorithm is applied to determine the optimal parameters, i.e. the complete dynamic parameters of the two equivalent systems. By sequentially unlocking the 1st to nth joints (or unlocking the nth to 1st joints), the mass properties of body 0 to n (or n to 0) are completely identified. For the proposed method, only simple dynamics equations are needed for identification. The excitation motion (orbit maneuvering and joint motion) is also easily realized. Moreover, the method does not require prior knowledge of the mass properties of any body. It is general and practical for identifying a space robotic system on-orbit.
Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.
Kim, Seock-Ho; Cohen, Allan S.
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
On linear models and parameter identifiability in experimental biological systems.
Lamberton, Timothy O; Condon, Nicholas D; Stow, Jennifer L; Hamilton, Nicholas A
2014-10-07
A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.
CHAMP: Changepoint Detection Using Approximate Model Parameters
2014-06-01
positions as a Markov chain in which the transition probabilities are defined by the time since the last changepoint: p(τi+1 = t|τi = s) = g(t− s), (1...experimentally verified using artifi- cially generated data and are compared to those of Fearnhead and Liu [5]. 2 Related work Hidden Markov Models (HMMs) are...length α, and maximum number of particles M . Output: Viterbi path of changepoint times and models // Initialize data structures 1: max path, prev queue
WINKLER'S SINGLE-PARAMETER SUBGRADE MODEL FROM ...
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[3, 9]. However, mainly due to the simplicity of Winkler's model in practical applications and .... this case, the coefficient B takes the dimension of a ... In plane-strain problems, the assumption of ... loaded circular region; s is the radial coordinate.
A Bayesian state-space formulation of dynamic occupancy models.
Royle, J Andrew; Kéry, Marc
2007-07-01
Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by non-detection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and WinBUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site
A Bayesian state-space formulation of dynamic occupancy models
Royle, J. Andrew; Kery, M.
2007-01-01
Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by nondetection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and Win BUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site
A simulation of water pollution model parameter estimation
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.
Hornik, Kurt; Grün, Bettina
2014-04-01
Diaconis and Ylvisaker (1979) give necessary conditions for conjugate priors for distributions from the natural exponential family to be proper as well as to have the property of linear posterior expectation of the mean parameter of the family. Their conditions for propriety and linear posterior expectation are also sufficient if the natural parameter space is equal to the set of all [Formula: see text]-dimensional real numbers. In this paper their results are extended to characterize when conjugate priors are proper if the natural parameter space is bounded. For the special case where the natural exponential family is through a spherical probability distribution [Formula: see text], we show that the proper conjugate priors can be characterized by the behavior of the moment generating function of [Formula: see text] at the boundary of the natural parameter space, or the second-order tail behavior of [Formula: see text]. In addition, we show that if these families are non-regular, then linear posterior expectation never holds. The results for this special case are also extended to natural exponential families through elliptical probability distributions.
On retrial queueing model with fuzzy parameters
Ke, Jau-Chuan; Huang, Hsin-I.; Lin, Chuen-Horng
2007-01-01
This work constructs the membership functions of the system characteristics of a retrial queueing model with fuzzy customer arrival, retrial and service rates. The α-cut approach is used to transform a fuzzy retrial-queue into a family of conventional crisp retrial queues in this context. By means of the membership functions of the system characteristics, a set of parametric non-linear programs is developed to describe the family of crisp retrial queues. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the system characteristics are expressed and governed by the membership functions, more information is provided for use by management. By extending this model to the fuzzy environment, fuzzy retrial-queue is represented more accurately and analytic results are more useful for system designers and practitioners.
A model for configuration spaces of points
Campos, Ricardo; Willwacher, Thomas
2016-01-01
The configuration space of points on a $D$-dimensional smooth framed manifold may be compactified so as to admit a right action over the framed little $D$-disks operad. We construct a real combinatorial model for these modules, for compact smooth manifolds without boundary.
Modelling Complex Relevance Spaces with Copulas
C. Eickhoff (Carsten); A.P. de Vries (Arjen)
2014-01-01
htmlabstractModern relevance models consider a wide range of criteria in order to identify those documents that are expected to satisfy the user's information need. With growing dimensionality of the underlying relevance spaces the need for sophisticated score combination and estimation schemes
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; ten Brummelhuis, P.G.J.; ten Brummelhuis, Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The
Exploring the interdependencies between parameters in a material model.
Energy Technology Data Exchange (ETDEWEB)
Silling, Stewart Andrew; Fermen-Coker, Muge
2014-01-01
A method is investigated to reduce the number of numerical parameters in a material model for a solid. The basis of the method is to detect interdependencies between parameters within a class of materials of interest. The method is demonstrated for a set of material property data for iron and steel using the Johnson-Cook plasticity model.
An Alternative Three-Parameter Logistic Item Response Model.
Pashley, Peter J.
Birnbaum's three-parameter logistic function has become a common basis for item response theory modeling, especially within situations where significant guessing behavior is evident. This model is formed through a linear transformation of the two-parameter logistic function in order to facilitate a lower asymptote. This paper discusses an…
Parameter identification in tidal models with uncertain boundaries
Bagchi, Arunabha; Brummelhuis, ten Paul
1994-01-01
In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The hyperboli
A compact cyclic plasticity model with parameter evolution
DEFF Research Database (Denmark)
Krenk, Steen; Tidemann, L.
2017-01-01
, and it is demonstrated that this simple formulation enables very accurate representation of experimental results. An extension of the theory to account for model parameter evolution effects, e.g. in the form of changing yield level, is included in the form of extended evolution equations for the model parameters...
Directory of Open Access Journals (Sweden)
Patarawan Sangnawakij
2017-02-01
Full Text Available The problem of estimating parameters in a gamma distribution has been widely studied with respect to both theories and applications. In special cases, when the parameter space is bounded, the construction of the confidence interval based on the classical Neyman procedure is unsatisfactory because the information regarding the restriction of the parameter is disregarded. In order to develop the estimator for this issue, the confidence intervals for the coefficient of variation for the case of a gamma distribution were proposed. Extending to two populations, the confidence intervals for the difference and the ratio of coefficients of variation with restricted parameters were presented. Monte Carlo simulations were used to investigate the performance of the proposed estimators. The results showed that the proposed confidence intervals performed better than the compared estimators in terms of expected length, especially when the coefficients of variation were close to the boundary. Additionally, two examples using real data were analyzed to illustrate the findings of the paper.
Regionalization of SWAT Model Parameters for Use in Ungauged Watersheds
Directory of Open Access Journals (Sweden)
Indrajeet Chaubey
2010-11-01
Full Text Available There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and therefore BMP representation. This is, however, only possible for gauged watersheds. There are many watersheds for which there are very little or no monitoring data available, thus the question as to whether it would be possible to extend and/or generalize model parameters obtained through calibration of gauged watersheds to ungauged watersheds within the same region. This study explored the possibility of developing regionalized model parameter sets for use in ungauged watersheds. The study evaluated two regionalization methods: global averaging, and regression-based parameters, on the SWAT model using data from priority watersheds in Arkansas. Resulting parameters were tested and model performance determined on three gauged watersheds. Nash-Sutcliffe efficiencies (NS for stream flow obtained using regression-based parameters (0.53–0.83 compared well with corresponding values obtained through model calibration (0.45–0.90. Model performance obtained using global averaged parameter values was also generally acceptable (0.4 ≤ NS ≤ 0.75. Results from this study indicate that regionalized parameter sets for the SWAT model can be obtained and used for making satisfactory hydrologic response predictions in ungauged watersheds.
Concepts of disability: the Activity Space Model.
Kopec, J A
1995-03-01
This paper describes a new conceptual framework for functional assessment, the Activity Space Model (ASM). According to this model, functional impairments may lead to restrictions in an individual's activity space, a multidimensional space that represents human potential for activity. For each elementary ability, restrictions in the corresponding dimension of the activity space can be evaluated by deriving a difficulty curve that depicts the relationship between the level of performance and the psychophysical cost of activity. The effect of disease on daily functioning is explained in terms of a tradeoff between the psychophysical cost and the value of each act of behavior to the disabled individual. These two constructs are measured on the same scale and expressed in units of difficulty. The location of each task within the activity space in relation to the difficulty curve determines whether it will be performed or avoided at a given point in time. The ASM has both theoretical and practical implications. It offers a new, integrated perspective on disability and suggests new strategies for developing and evaluating functional assessment measures.
Effects of Space Weather on Biomedical Parameters during the Solar Activity Cycles 23-24.
Ragul'skaya, M V; Rudenchik, E A; Chibisov, S M; Gromozova, E N
2015-06-01
The results of long-term (1998-2012) biomedical monitoring of the biotropic effects of space weather are discussed. A drastic change in statistical distribution parameters in the middle of 2005 was revealed that did not conform to usual sinusoidal distribution of the biomedical data reflecting changes in the number of solar spots over a solar activity cycle. The dynamics of space weather of 2001-2012 is analyzed. The authors hypothesize that the actual change in statistical distributions corresponds to the adaptation reaction of the biosphere to nonstandard geophysical characteristics of the 24th solar activity cycle and the probable long-term decrease in solar activity up to 2067.
Exploring the triplet parameters space to optimise the final focus of the FCC-hh
Van Riesen-Haupt, Leon; Seryi, Andrei; Cruz Alaniz, Emilia
2017-01-01
One of the main challenges when designing final focus systems of particle accelerators is maximising the beam stay clear in the strong quadrupole magnets of the inner triplet. Moreover it is desirable to keep the quadrupoles in the triplet as short as possible for space and costs reasons but also to reduce chromaticity and simplify corrections schemes. An algorithm that explores the triplet parameter space to optimise both these aspects was written. It uses thin lenses as a first approximation and MADX for more precise calculations. In cooperation with radiation studies, this algorithm was then applied to design an alternative triplet for the final focus of the Future Circular Collider (FCC-hh).
NWP model forecast skill optimization via closure parameter variations
Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.
2012-04-01
We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.
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
Nonlinear regime-switching state-space (RSSS) models.
Chow, Sy-Miin; Zhang, Guangjian
2013-10-01
Nonlinear dynamic factor analysis models extend standard linear dynamic factor analysis models by allowing time series processes to be nonlinear at the latent level (e.g., involving interaction between two latent processes). In practice, it is often of interest to identify the phases--namely, latent "regimes" or classes--during which a system is characterized by distinctly different dynamics. We propose a new class of models, termed nonlinear regime-switching state-space (RSSS) models, which subsumes regime-switching nonlinear dynamic factor analysis models as a special case. In nonlinear RSSS models, the change processes within regimes, represented using a state-space model, are allowed to be nonlinear. An estimation procedure obtained by combining the extended Kalman filter and the Kim filter is proposed as a way to estimate nonlinear RSSS models. We illustrate the utility of nonlinear RSSS models by fitting a nonlinear dynamic factor analysis model with regime-specific cross-regression parameters to a set of experience sampling affect data. The parallels between nonlinear RSSS models and other well-known discrete change models in the literature are discussed briefly.
Alexeev, A D; Kolosnitsyn, N I; Konstantinov, M Yu; Melnikov, V N; Sanders, A J
2000-01-01
We describe some new estimates concerning the recently proposed SEE (Satellite Energy Exchange) experiment for measuring the gravitational interaction parameters in space. The experiment entails precision tracking of the relative motion of two test bodies (a heavy "Shepherd", and a light "Particle") on board a drag-free space capsule. The new estimates include (i) the sensitivity of Particle trajectories and G measurement to the Shepherd quadrupole moment uncertainties; (ii) the measurement errors of G and the strength of a putative Yukawa-type force whose range parameter \\lambda may be either of the order of a few meters or close to the Earth radius; (iii) a possible effect of the Van Allen radiation belts on the SEE experiment due to test body electric charging. The main conclusions are that (i) the SEE concept may allow one to measure G with an uncertainty smaller than 10^{-7} and a progress up to 2 orders of magnitude is possible in the assessment of the hypothetic Yukawa forces and (ii) van Allen chargin...
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......'. 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...... 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....
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. ...
Spatio-temporal modeling of nonlinear distributed parameter systems
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
Directory of Open Access Journals (Sweden)
Baker Syed
2011-01-01
Full Text Available Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF, rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Baker, Syed Murtuza; Poskar, C Hart; Junker, Björn H
2011-10-11
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.
Approximate Methods for State-Space Models
Koyama, Shinsuke; Shalizi, Cosma Rohilla; Kass, Robert E; 10.1198/jasa.2009.tm08326
2010-01-01
State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models, which uses Laplace's method, an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution. This {\\em Laplace-Gaussian filter} (LGF) gives fast, recursive, deterministic state estimates, with an error which is set by the stochastic characteristics of the model and is, we show, stable over time. We illustrate the estimation ability of the LGF by applying it to the problem of neural decoding and compare it to sequential Monte Carlo both in simulat...
A systematic study of Lyman-Alpha transfer through outflowing shells: Model parameter estimation
Gronke, Max; Dijkstra, Mark
2015-01-01
Outflows promote the escape of Lyman-$\\alpha$ (Ly$\\alpha$) photons from dusty interstellar media. The process of radiative transfer through interstellar outflows is often modelled by a spherically symmetric, geometrically thin shell of gas that scatters photons emitted by a central Ly$\\alpha$ source. Despite its simplified geometry, this `shell model' has been surprisingly successful at reproducing observed Ly$\\alpha$ line shapes. In this paper we perform automated line fitting on a set of noisy simulated shell model spectra, in order to determine whether degeneracies exist between the different shell model parameters. While there are some significant degeneracies, we find that most parameters are accurately recovered, especially the HI column density ($N_{\\rm HI}$) and outflow velocity ($v_{\\rm exp}$). This work represents an important first step in determining how the shell model parameters relate to the actual physical properties of Ly$\\alpha$ sources. To aid further exploration of the parameter space, we ...
Weigand, M.; Kemna, A.
2016-06-01
Spectral induced polarization (SIP) data are commonly analysed using phenomenological models. Among these models the Cole-Cole (CC) model is the most popular choice to describe the strength and frequency dependence of distinct polarization peaks in the data. More flexibility regarding the shape of the spectrum is provided by decomposition schemes. Here the spectral response is decomposed into individual responses of a chosen elementary relaxation model, mathematically acting as kernel in the involved integral, based on a broad range of relaxation times. A frequently used kernel function is the Debye model, but also the CC model with some other a priorly specified frequency dispersion (e.g. Warburg model) has been proposed as kernel in the decomposition. The different decomposition approaches in use, also including conductivity and resistivity formulations, pose the question to which degree the integral spectral parameters typically derived from the obtained relaxation time distribution are biased by the approach itself. Based on synthetic SIP data sampled from an ideal CC response, we here investigate how the two most important integral output parameters deviate from the corresponding CC input parameters. We find that the total chargeability may be underestimated by up to 80 per cent and the mean relaxation time may be off by up to three orders of magnitude relative to the original values, depending on the frequency dispersion of the analysed spectrum and the proximity of its peak to the frequency range limits considered in the decomposition. We conclude that a quantitative comparison of SIP parameters across different studies, or the adoption of parameter relationships from other studies, for example when transferring laboratory results to the field, is only possible on the basis of a consistent spectral analysis procedure. This is particularly important when comparing effective CC parameters with spectral parameters derived from decomposition results.
Identification of hydrological model parameter variation using ensemble Kalman filter
Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao
2016-12-01
Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
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.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Directory of Open Access Journals (Sweden)
Guanqun eZhang
2011-11-01
Full Text Available A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel while being defined by only a few parameters (unlike comprehensive distributed-parameter models. As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Zhang, Guanqun; Hahn, Jin-Oh; Mukkamala, Ramakrishna
2011-01-01
A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel) while being defined by only a few parameters (unlike comprehensive distributed-parameter models). As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications. PMID:22053157
The inelastic differential cross section in impact parameter space at ISR energies
Henzi, R
1974-01-01
Implications of increasing total cross sections and diffractive structures at CERN-ISR on the inelastic differential cross section in impact parameter space are discussed. It is a Gaussian plus a small 'edge' correction and its increase through the ISR energies is peripheral as compared to the overall region of inelastic collisions, while inside this region it remains relatively constant and below the unitarity bound. (25 refs).
2016-06-07
Characterization Of Aerosols And Atmospheric Parameters From Space-Borne And Surface-Based Remote Sensing Si-Chee Tsay Yoram J. Kaufman 301-614-6188...term goal for this project is threefold: (i) to develop remote sensing procedures for determinng aerosol loading and optical properties over land and...can lead to the best results. OBJECTIVES In preparation for the era of hyperspectral sensors in remote sensing , we need to establish a climatology of
Spatial variability of the parameters of a semi-distributed hydrological model
de Lavenne, Alban; Thirel, Guillaume; Andréassian, Vazken; Perrin, Charles; Ramos, Maria-Helena
2016-05-01
Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).
Parameter estimation and investigation of a bolted joint model
Shiryayev, O. V.; Page, S. M.; Pettit, C. L.; Slater, J. C.
2007-11-01
Mechanical joints are a primary source of variability in the dynamics of built-up structures. Physical phenomena in the joint are quite complex and therefore too impractical to model at the micro-scale. This motivates the development of lumped parameter joint models with discrete interfaces so that they can be easily implemented in finite element codes. Among the most important considerations in choosing a model for dynamically excited systems is its ability to model energy dissipation. This translates into the need for accurate and reliable methods to measure model parameters and estimate their inherent variability from experiments. The adjusted Iwan model was identified as a promising candidate for representing joint dynamics. Recent research focused on this model has exclusively employed impulse excitation in conjunction with neural networks to identify the model parameters. This paper presents an investigation of an alternative parameter estimation approach for the adjusted Iwan model, which employs data from oscillatory forcing. This approach is shown to produce parameter estimates with precision similar to the impulse excitation method for a range of model parameters.
Modeling and Parameter Estimation of a Small Wind Generation System
Directory of Open Access Journals (Sweden)
Carlos A. Ramírez Gómez
2013-11-01
Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.
Statistical modeling of space shuttle environmental data
Tubbs, J. D.; Brewer, D. W.
1983-01-01
Statistical models which use a class of bivariate gamma distribution are examined. Topics discussed include: (1) the ratio of positively correlated gamma varieties; (2) a method to determine if unequal shape parameters are necessary in bivariate gamma distribution; (3) differential equations for modal location of a family of bivariate gamma distribution; and (4) analysis of some wind gust data using the analytical results developed for modeling application.
Parameter estimation of hidden periodic model in random fields
Institute of Scientific and Technical Information of China (English)
何书元
1999-01-01
Two-dimensional hidden periodic model is an important model in random fields. The model is used in the field of two-dimensional signal processing, prediction and spectral analysis. A method of estimating the parameters for the model is designed. The strong consistency of the estimators is proved.
Modeling Space Radiation with Radiomimetic Agent Bleomycin
Lu, Tao
2017-01-01
Space radiation consists of proton and helium from solar particle events (SPE) and high energy heavy ions from galactic cosmic ray (GCR). This mixture of radiation with particles at different energy levels has different effects on biological systems. Currently, majority studies of radiation effects on human were based on single-source radiation due to the limitation of available method to model effects of space radiation on living organisms. While NASA Space Radiation Laboratory is working on advanced switches to make it possible to have a mixed field radiation with particles of different energies, the radiation source will be limited. Development of an easily available experimental model for studying effects of mixed field radiation could greatly speed up our progress in our understanding the molecular mechanisms of damage and responses from exposure to space radiation, and facilitate the discovery of protection and countermeasures against space radiation, which is critical for the mission to Mars. Bleomycin, a radiomimetic agent, has been widely used to study radiation induced DNA damage and cellular responses. Previously, bleomycin was often compared to low low Linear Energy Transfer (LET) gamma radiation without defined characteristics. Our recent work demonstrated that bleomycin could induce complex clustered DNA damage in human fibroblasts that is similar to DNA damage induced by high LET radiation. These type of DNA damage is difficult to repair and can be visualized by gamma-H2Ax staining weeks after the initial insult. The survival ratio between early and late plating of human fibroblasts after bleomycin treatment is between low LET and high LET radiation. Our results suggest that bleomycin induces DNA damage and other cellular stresses resembling those resulted from mixed field radiation with both low and high LET particles. We hypothesize that bleomycin could be used to mimic space radiation in biological systems. Potential advantages and limitations of
Improving the Performance of the Space Surveillance Telescope as a Function of Seeing Parameter
2015-03-26
M. Morton and T. Roberts , "Joint space operations center (JSpOC) mission system (JMS)," DTIC, AIR FORCE SPACE COMMAND PETERSON AFB CO, 2011. [12...Orbiting Asteroids," in IEEE Aerospace Conference, 2009. [33] J. W. Goodman, Introduction to Fourier Optics, 3rd ed., Englewood, Co,: Roberts ...Institute of Technology, Wright-Patterson Air Force Base, OH, 2012. [43] I. B. Putnam and S. C. Cain, "Modeling a Temporally Evolving Atmosphere with
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.
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.
Estimating parameters for generalized mass action models with connectivity information
Directory of Open Access Journals (Sweden)
Voit Eberhard O
2009-05-01
Full Text Available Abstract Background Determining the parameters of a mathematical model from quantitative measurements is the main bottleneck of modelling biological systems. Parameter values can be estimated from steady-state data or from dynamic data. The nature of suitable data for these two types of estimation is rather different. For instance, estimations of parameter values in pathway models, such as kinetic orders, rate constants, flux control coefficients or elasticities, from steady-state data are generally based on experiments that measure how a biochemical system responds to small perturbations around the steady state. In contrast, parameter estimation from dynamic data requires time series measurements for all dependent variables. Almost no literature has so far discussed the combined use of both steady-state and transient data for estimating parameter values of biochemical systems. Results In this study we introduce a constrained optimization method for estimating parameter values of biochemical pathway models using steady-state information and transient measurements. The constraints are derived from the flux connectivity relationships of the system at the steady state. Two case studies demonstrate the estimation results with and without flux connectivity constraints. The unconstrained optimal estimates from dynamic data may fit the experiments well, but they do not necessarily maintain the connectivity relationships. As a consequence, individual fluxes may be misrepresented, which may cause problems in later extrapolations. By contrast, the constrained estimation accounting for flux connectivity information reduces this misrepresentation and thereby yields improved model parameters. Conclusion The method combines transient metabolic profiles and steady-state information and leads to the formulation of an inverse parameter estimation task as a constrained optimization problem. Parameter estimation and model selection are simultaneously carried out
Adaptive Numerical Algorithms in Space Weather Modeling
Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav
2010-01-01
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical
Study on Strand Space Model Theory
Institute of Scientific and Technical Information of China (English)
JI QingGuang(季庆光); QING SiHan(卿斯汉); ZHOU YongBin(周永彬); FENG DengGuo(冯登国)
2003-01-01
The growing interest in the application of formal methods of cryptographic pro-tocol analysis has led to the development of a number of different ways for analyzing protocol. Inthis paper, it is strictly proved that if for any strand, there exists at least one bundle containingit, then an entity authentication protocol is secure in strand space model (SSM) with some smallextensions. Unfortunately, the results of attack scenario demonstrate that this protocol and the Yahalom protocol and its modification are de facto insecure. By analyzing the reasons of failure offormal inference in strand space model, some deficiencies in original SSM are pointed out. In orderto break through these limitations of analytic capability of SSM, the generalized strand space model(GSSM) induced by some protocol is proposed. In this model, some new classes of strands, oraclestrands, high order oracle strands etc., are developed, and some notions are formalized strictly in GSSM, such as protocol attacks, valid protocol run and successful protocol run. GSSM can thenbe used to further analyze the entity authentication protocol. This analysis sheds light on why thisprotocol would be vulnerable while it illustrates that GSSM not only can prove security protocolcorrect, but also can be efficiently used to construct protocol attacks. It is also pointed out thatusing other protocol to attack some given protocol is essentially the same as the case of using themost of protocol itself.
Growth-Parameter Spaces and Optical Properties of Cubic Boron Nitride Films on Si(001)
Institute of Scientific and Technical Information of China (English)
FAN Ya-Ming; ZHANG Xing-Wang; YOU Jing-Bi; YING Jie; TAN Hai-Ren; CHEN Nuo-Fu
2009-01-01
Cubic boron nitride (c-BN) films were deposited on Si(O01) substrates in an ion beam assisted deposition (IBAD)system under various conditions, and the growth parameter spaces and optical properties of c-BN films have been investigated systematically. The results indicate that suitable ion bombardment is necessary for the growth of c-BN films, and a well defined parameter space can be established by using the P/a-parameter. The refractive index of BN films keeps a constant of 1.8 for the c-BN content lower than 50%, while for c-BN films with higher cubic phase the refractive index increases with the c-BN content from 1.8 at χc = 50% to 2.1 at χc = 90%.Furthermore, the relationship between n and p for BN films can be described by the Anderzon-Schreiber equation,and the overlap field parameter γ is determined to be 2.05.
Uncertainty in the relationship between flow and parameters in models of pollutant transport
Romanowicz, R.; Osuch, M.; Wallis, S.; Napiórkowski, J. J.
2009-04-01
Fluorescent dye-tracer studies are usually performed under steady-state flow conditions. However, the model parameters, estimated using the tracer data, depend on the discharges. This paper investigates uncertainties in the relationship between discharges and parameters of a transient storage (TS) and an aggregated dead zone (ADZ) models. We apply a Bayesian statistical approach to derive the cumulative distribution of a range of model parameters conditioned on discharges. The data consist of eighteen tracer concentration profiles taken at different flow values at two cross-sections from the Murray Burn, a stream flowing through the Heriot-Watt University Campus at Riccarton in Edinburgh, Scotland. A number of studies have been reported of the dependence of TS and ADZ model parameters on discharge but there are very few studies on the uncertainty related to that parameterization, which is the aim of this work. As the TS model is purely deterministic and the ADZ model is stochastic, different approaches are required to estimate the uncertainty in the dependence of their parameters on flow. The Generalised Likelihood Uncertainty Estimation (GLUE) approach is suitable for the deterministic models and is therefore applied to the TS model. The method applies Monte Carlo sampling of parameter space used in multiple simulations of a deterministic transient storage model. The relationship between model parameters and flow has the form of a nonlinear regression model based on multiple random realizations of the deterministic transport model. The parameterization of that relationship and its introduction into the TS model allow for the conditioning of parameter estimates and as a result, also model predictions on the whole set of available observations. In the case of the ADZ model, the approach is based on Monte Carlo sampling of ADZ model parameters, taking into account heteroscedastic variance of the observations and estimates of the covariance of the model parameters
Differential geometry of the space of Ising models
Machta, Benjamin; Chachra, Ricky; Transtrum, Mark; Sethna, James
2012-02-01
We use information geometry to understand the emergence of simple effective theories, using an Ising model perturbed with terms coupling non-nearest-neighbor spins as an example. The Fisher information is a natural metric of distinguishability for a parameterized space of probability distributions, applicable to models in statistical physics. Near critical points both the metric components (four-point susceptibilities) and the scalar curvature diverge with corresponding critical exponents. However, connections to Renormalization Group (RG) ideas have remained elusive. Here, rather than looking at RG flows of parameters, we consider the reparameterization-invariant flow of the manifold itself. To do this we numerically calculate the metric in the original parameters, taking care to use only information available after coarse-graining. We show that under coarse-graining the metric contracts very anisotropically, leading to a ``sloppy'' spectrum with the metric's Eigenvalues spanning many orders of magnitude. Our results give a qualitative explanation for the success of simple models: most directions in parameter space become fundamentally indistinguishable after coarse-graining.
Towards predictive food process models: A protocol for parameter estimation.
Vilas, Carlos; Arias-Méndez, Ana; Garcia, Miriam R; Alonso, Antonio A; Balsa-Canto, E
2016-05-31
Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.
Estimation of the input parameters in the Feller neuronal model
Ditlevsen, Susanne; Lansky, Petr
2006-06-01
The stochastic Feller neuronal model is studied, and estimators of the model input parameters, depending on the firing regime of the process, are derived. Closed expressions for the first two moments of functionals of the first-passage time (FTP) through a constant boundary in the suprathreshold regime are derived, which are used to calculate moment estimators. In the subthreshold regime, the exponentiality of the FTP is utilized to characterize the input parameters. The methods are illustrated on simulated data. Finally, approximations of the first-passage-time moments are suggested, and biological interpretations and comparisons of the parameters in the Feller and the Ornstein-Uhlenbeck models are discussed.
Ward, Adam S.; Kelleher, Christa A.; Mason, Seth J. K.; Wagener, Thorsten; McIntyre, Neil; McGlynn, Brian L.; Runkel, Robert L.; Payn, Robert A.
2017-01-01
Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.
Modelling of Patterns in Space and Time
Murray, James
1984-01-01
This volume contains a selection of papers presented at the work shop "Modelling of Patterns in Space and Time", organized by the 80nderforschungsbereich 123, "8tochastische Mathematische Modelle", in Heidelberg, July 4-8, 1983. The main aim of this workshop was to bring together physicists, chemists, biologists and mathematicians for an exchange of ideas and results in modelling patterns. Since the mathe matical problems arising depend only partially on the particular field of applications the interdisciplinary cooperation proved very useful. The workshop mainly treated phenomena showing spatial structures. The special areas covered were morphogenesis, growth in cell cultures, competition systems, structured populations, chemotaxis, chemical precipitation, space-time oscillations in chemical reactors, patterns in flames and fluids and mathematical methods. The discussions between experimentalists and theoreticians were especially interesting and effective. The editors hope that these proceedings reflect ...
Access Nets: Modeling Access to Physical Spaces
Frohardt, Robert; Chang, Bor-Yuh Evan; Sankaranarayanan, Sriram
Electronic, software-managed mechanisms using, for example, radio-frequency identification (RFID) cards, enable great flexibility in specifying access control policies to physical spaces. For example, access rights may vary based on time of day or could differ in normal versus emergency situations. With such fine-grained control, understanding and reasoning about what a policy permits becomes surprisingly difficult requiring knowledge of permission levels, spatial layout, and time. In this paper, we present a formal modeling framework, called AccessNets, suitable for describing a combination of access permissions, physical spaces, and temporal constraints. Furthermore, we provide evidence that model checking techniques are effective in reasoning about physical access control policies. We describe our results from a tool that uses reachability analysis to validate security policies.
MCMC for non-linear state space models using ensembles of latent sequences
2013-01-01
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no straightforward solution. We take a Bayesian approach to the inference of unknown parameters of a non-linear state model; this, in turn, requires the availability of efficient Markov Chain Monte Carlo (MCMC) sampling methods for the latent (hidden) variables and model parameters. Using the ensemble ...
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-05-01
Full Text Available Physical parameterizations in General Circulation Models (GCMs, having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
Optimal parameters for the FFA-Beddoes dynamic stall model
Energy Technology Data Exchange (ETDEWEB)
Bjoerck, A.; Mert, M. [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H.A. [Risoe National Lab., Roskilde (Denmark)
1999-03-01
Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)
Data Model Management for Space Information Systems
Hughes, J. Steven; Crichton, Daniel J.; Ramirez, Paul; Mattmann, chris
2006-01-01
The Reference Architecture for Space Information Management (RASIM) suggests the separation of the data model from software components to promote the development of flexible information management systems. RASIM allows the data model to evolve independently from the software components and results in a robust implementation that remains viable as the domain changes. However, the development and management of data models within RASIM are difficult and time consuming tasks involving the choice of a notation, the capture of the model, its validation for consistency, and the export of the model for implementation. Current limitations to this approach include the lack of ability to capture comprehensive domain knowledge, the loss of significant modeling information during implementation, the lack of model visualization and documentation capabilities, and exports being limited to one or two schema types. The advent of the Semantic Web and its demand for sophisticated data models has addressed this situation by providing a new level of data model management in the form of ontology tools. In this paper we describe the use of a representative ontology tool to capture and manage a data model for a space information system. The resulting ontology is implementation independent. Novel on-line visualization and documentation capabilities are available automatically, and the ability to export to various schemas can be added through tool plug-ins. In addition, the ingestion of data instances into the ontology allows validation of the ontology and results in a domain knowledge base. Semantic browsers are easily configured for the knowledge base. For example the export of the knowledge base to RDF/XML and RDFS/XML and the use of open source metadata browsers provide ready-made user interfaces that support both text- and facet-based search. This paper will present the Planetary Data System (PDS) data model as a use case and describe the import of the data model into an ontology tool
Data Model Management for Space Information Systems
Hughes, J. Steven; Crichton, Daniel J.; Ramirez, Paul; Mattmann, chris
2006-01-01
The Reference Architecture for Space Information Management (RASIM) suggests the separation of the data model from software components to promote the development of flexible information management systems. RASIM allows the data model to evolve independently from the software components and results in a robust implementation that remains viable as the domain changes. However, the development and management of data models within RASIM are difficult and time consuming tasks involving the choice of a notation, the capture of the model, its validation for consistency, and the export of the model for implementation. Current limitations to this approach include the lack of ability to capture comprehensive domain knowledge, the loss of significant modeling information during implementation, the lack of model visualization and documentation capabilities, and exports being limited to one or two schema types. The advent of the Semantic Web and its demand for sophisticated data models has addressed this situation by providing a new level of data model management in the form of ontology tools. In this paper we describe the use of a representative ontology tool to capture and manage a data model for a space information system. The resulting ontology is implementation independent. Novel on-line visualization and documentation capabilities are available automatically, and the ability to export to various schemas can be added through tool plug-ins. In addition, the ingestion of data instances into the ontology allows validation of the ontology and results in a domain knowledge base. Semantic browsers are easily configured for the knowledge base. For example the export of the knowledge base to RDF/XML and RDFS/XML and the use of open source metadata browsers provide ready-made user interfaces that support both text- and facet-based search. This paper will present the Planetary Data System (PDS) data model as a use case and describe the import of the data model into an ontology tool
Spreading Models in Banach Space Theory
Argyros, S A; Tyros, K
2010-01-01
We extend the classical Brunel-Sucheston definition of the spreading model by introducing the $\\mathcal{F}$-sequences $(x_s)_{s\\in\\mathcal{F}}$ in a Banach space and the plegma families in $\\mathcal{F}$ where $\\mathcal{F}$ is a regular thin family. The new concept yields a transfinite increasing hierarchy of classes of 1-subsymmetric sequences. We explore the corresponding theory and we present examples establishing this hierarchy and illustrating the limitation of the theory.
Energy Technology Data Exchange (ETDEWEB)
Dinca, Laurian; Aldemir, Tunc; Rizzoni, Giorgio
1999-06-01
A probabilistic approach is presented which can be used for the estimation of system parameters and unmonitored state variables towards model-based fault diagnosis in dynamic systems. The method can be used with any type of input-output model and can accommodate noisy data and/or parameter/modeling uncertainties. The methodology is based on Markovian representation of system dynamics in discretized state space. The example system used for the illustration of the methodology focuses on the intake, fueling, combustion and exhaust components of internal combustion engines. The results show that the methodology is capable of estimating the system parameters and tracking the unmonitored dynamic variables within user-specified magnitude intervals (which may reflect noise in the monitored data, random changes in the parameters or modeling uncertainties in general) within data collection time and hence has potential for on-line implementation.
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation of struct......This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation...... response during excitation and the geometrical damping related to free vibrations of a hexagonal footing. The optimal order of a lumped-parameter model is determined for each degree of freedom, i.e. horizontal and vertical translation as well as torsion and rocking. In particular, the necessity of coupling...... between horizontal sliding and rocking is discussed....
A New Approach for Parameter Optimization in Land Surface Model
Institute of Scientific and Technical Information of China (English)
LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin
2011-01-01
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
The dynamics of blood biochemical parameters in cosmonauts during long-term space flights
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.
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
. Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied...
Estimating winter wheat phenological parameters: Implications for crop modeling
Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...
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
Numerical modelling of elastic space tethers
DEFF Research Database (Denmark)
Kristiansen, Kristian Uldall; Palmer, P. L.; Roberts, R. M.
2012-01-01
In this paper the importance of the ill-posedness of the classical, non-dissipative massive tether model on an orbiting tether system is studied numerically. The computations document that via the regularisation of bending resistance a more reliable numerical integrator can be produced. Furthermore......, the numerical experiments of an orbiting tether system show that bending may introduce significant forces in some regions of phase space. Finally, numerical evidence for the existence of an almost invariant slow manifold of the singularly perturbed, regularised, non-dissipative massive tether model is provided...
Retrospective forecast of ETAS model with daily parameters estimate
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Parameter Estimates in Differential Equation Models for Population Growth
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
Dynamic Modeling and Parameter Identification of Power Systems
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
@@ The generator, the excitation system, the steam turbine and speed governor, and the load are the so called four key models of power systems. Mathematical modeling and parameter identification for the four key models are of great importance as the basis for designing, operating, and analyzing power systems.
Dynamic Load Model using PSO-Based Parameter Estimation
Taoka, Hisao; Matsuki, Junya; Tomoda, Michiya; Hayashi, Yasuhiro; Yamagishi, Yoshio; Kanao, Norikazu
This paper presents a new method for estimating unknown parameters of dynamic load model as a parallel composite of a constant impedance load and an induction motor behind a series constant reactance. An adequate dynamic load model is essential for evaluating power system stability, and this model can represent the behavior of actual load by using appropriate parameters. However, the problem of this model is that a lot of parameters are necessary and it is not easy to estimate a lot of unknown parameters. We propose an estimating method based on Particle Swarm Optimization (PSO) which is a non-linear optimization method by using the data of voltage, active power and reactive power measured at voltage sag.
Parameter Estimation for the Thurstone Case III Model.
Mackay, David B.; Chaiy, Seoil
1982-01-01
The ability of three estimation criteria to recover parameters of the Thurstone Case V and Case III models from comparative judgment data was investigated via Monte Carlo techniques. Significant differences in recovery are shown to exist. (Author/JKS)
Institute of Scientific and Technical Information of China (English)
Youlong XIA; Zong-Liang YANG; Paul L. STOFFA; Mrinal K. SEN
2005-01-01
Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.
On vector autoregressive modeling in space and time
di Giacinto, Valter
2010-06-01
Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until lately the VAR approach had received little attention in regional science and spatial economic analysis. This paper aims to contribute in this field by dealing with the issues of parameter identification and estimation and of structural impulse response analysis. In particular, there is a discussion of the adaptation of the recursive identification scheme (which represents one of the more common approaches in the time series VAR literature) to a space-time environment. Parameter estimation is subsequently based on the Full Information Maximum Likelihood (FIML) method, a standard approach in structural VAR analysis. As a convenient tool to summarize the information conveyed by regional dynamic multipliers with a specific emphasis on the scope of spatial spillover effects, a synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space. Asymptotic confidence bands for the STIR estimates are also derived from bootstrap estimates of the standard errors. Finally, to provide a basic illustration of the methodology, the paper presents an application of a simple bivariate fiscal model fitted to data for Italian NUTS 2 regions.
Representations of coherent and squeezed states in an extended two-parameter Fock space
Institute of Scientific and Technical Information of China (English)
M. K. Tavassoly; M. H. Lake
2012-01-01
Recently an f-deformed Fock space which is spanned by ｜n〉λ was introduced.These bases are the eigenstates of a deformed non-Hermitian Hamiltonian.In this contribution,we will use rather new nonorthogonal basis vectors for the construction of coherent and squeezed states,which in special case lead to the earlier known states.For this purpose,we first generalize the previously introduced Fock space spanned by ｜n〉λ bases,to a new one,spanned by extended two-parameters bases ｜n〉λ1,λ2.These bases are now the eigenstates of a non-Hermitian Hamiltonian Hλ1,λ2 =a(+)1,λ2a +1/2,where a(+)λ1,λ2 =a(+) + λ1a + λ2 and a are,respectively,the deformed creation and ordinary bosonic annihilation operators.The bases ｜n〉λ1,λ2 are nonorthogonal (squeezed states),but normalizable.Then,we deduce the new representations of coherent and squeezed states in our two-parameter Fock space.Finally,we discuss the quantum statistical properties,as well as the non-classical properties of the obtained states numerically.
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
Cities in Space: Three Simple Models
Paul Krugman
1991-01-01
Urban agglomerations arise at least in part out of the interaction between economies of scale in production and market size effects. This paper develops a simple spatial framework to develop illustrative models of the determinants of urban location, of the number and size of cities, and of the degree of urbanization. A Central theme is the probable existence of multiple equilibria, and the dependence of the range of potential outcomes on a few key parameters.
Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.
Demissie, Yonas; Valocchi, Albert; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Parameter estimation for groundwater models under uncertain irrigation data
Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
Parameter estimation in stochastic rainfall-runoff models
DEFF Research Database (Denmark)
Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur
2006-01-01
the parameters, including the noise terms. The parameter estimation method is a maximum likelihood method (ML) where the likelihood function is evaluated using a Kalman filter technique. The ML method estimates the parameters in a prediction error settings, i.e. the sum of squared prediction error is minimized....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... 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...
Transformations among CE–CVM model parameters for multicomponent systems
Indian Academy of Sciences (India)
B Nageswara Sarma; Shrikant Lele
2005-06-01
In the development of thermodynamic databases for multicomponent systems using the cluster expansion–cluster variation methods, we need to have a consistent procedure for expressing the model parameters (CECs) of a higher order system in terms of those of the lower order subsystems and to an independent set of parameters which exclusively represent interactions of the higher order systems. Such a procedure is presented in detail in this communication. Furthermore, the details of transformations required to express the model parameters in one basis from those defined in another basis for the same system are also presented.
Calibration of back-analysed model parameters for landslides using classification statistics
Cepeda, Jose; Henderson, Laura
2016-04-01
Back-analyses are useful for characterizing the geomorphological and mechanical processes and parameters involved in the initiation and propagation of landslides. These processes and parameters can in turn be used for improving forecasts of scenarios and hazard assessments in areas or sites which have similar settings to the back-analysed cases. The selection of the modeled landslide that produces the best agreement with the actual observations requires running a number of simulations by varying the type of model and the sets of input parameters. The comparison of the simulated and observed parameters is normally performed by visual comparison of geomorphological or dynamic variables (e.g., geometry of scarp and final deposit, maximum velocities and depths). Over the past six years, a method developed by NGI has been used by some researchers for a more objective selection of back-analysed input model parameters. That method includes an adaptation of the equations for calculation of classifiers, and a comparative evaluation of classifiers of the selected parameter sets in the Receiver Operating Characteristic (ROC) space. This contribution presents an updating of the methodology. The proposed procedure allows comparisons between two or more "clouds" of classifiers. Each cloud represents the performance of a model over a range of input parameters (e.g., samples of probability distributions). Considering the fact that each cloud does not necessarily produce a full ROC curve, two new normalised ROC-space parameters are introduced for characterizing the performance of each cloud. The first parameter is representative of the cloud position relative to the point of perfect classification. The second parameter characterizes the position of the cloud relative to the theoretically perfect ROC curve and the no-discrimination line. The methodology is illustrated with back-analyses of slope stability and landslide runout of selected case studies. This research activity has been
Estimating input parameters from intracellular recordings in the Feller neuronal model
Bibbona, Enrico; Lansky, Petr; Sirovich, Roberta
2010-03-01
We study the estimation of the input parameters in a Feller neuronal model from a trajectory of the membrane potential sampled at discrete times. These input parameters are identified with the drift and the infinitesimal variance of the underlying stochastic diffusion process with multiplicative noise. The state space of the process is restricted from below by an inaccessible boundary. Further, the model is characterized by the presence of an absorbing threshold, the first hitting of which determines the length of each trajectory and which constrains the state space from above. We compare, both in the presence and in the absence of the absorbing threshold, the efficiency of different known estimators. In addition, we propose an estimator for the drift term, which is proved to be more efficient than the others, at least in the explored range of the parameters. The presence of the threshold makes the estimates of the drift term biased, and two methods to correct it are proposed.
SPOTting Model Parameters Using a Ready-Made Python Package.
Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz
2015-01-01
The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
SPOTting Model Parameters Using a Ready-Made Python Package.
Directory of Open Access Journals (Sweden)
Tobias Houska
Full Text Available The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool, an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI. We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.
Numerical modeling of piezoelectric transducers using physical parameters.
Cappon, Hans; Keesman, Karel J
2012-05-01
Design of ultrasonic equipment is frequently facilitated with numerical models. These numerical models, however, need a calibration step, because usually not all characteristics of the materials used are known. Characterization of material properties combined with numerical simulations and experimental data can be used to acquire valid estimates of the material parameters. In our design application, a finite element (FE) model of an ultrasonic particle separator, driven by an ultrasonic transducer in thickness mode, is required. A limited set of material parameters for the piezoelectric transducer were obtained from the manufacturer, thus preserving prior physical knowledge to a large extent. The remaining unknown parameters were estimated from impedance analysis with a simple experimental setup combined with a numerical optimization routine using 2-D and 3-D FE models. Thus, a full set of physically interpretable material parameters was obtained for our specific purpose. The approach provides adequate accuracy of the estimates of the material parameters, near 1%. These parameter estimates will subsequently be applied in future design simulations, without the need to go through an entire series of characterization experiments. Finally, a sensitivity study showed that small variations of 1% in the main parameters caused changes near 1% in the eigenfrequency, but changes up to 7% in the admittance peak, thus influencing the efficiency of the system. Temperature will already cause these small variations in response; thus, a frequency control unit is required when actually manufacturing an efficient ultrasonic separation system.
Parameter estimation and model selection in computational biology.
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Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Geant4 models for space radiation environment.
Ivantchenko, Anton; Nieminen, Petteri; Incerti, Sebastien; Santin, Giovanni; Ivantchenko, Vladimir; Grichine, Vladimir; Allison, John
The space radiation environment includes wide varieties of particles from electrons to heavy ions. In order to correctly predict the dose received by astronauts and devices the simulation models must have good applicability and produce accurate results from 10 MeV/u up to 10 GeV/u, where the most radioactive hazardous particles are present in the spectra. Appropriate models should also provide a good description of electromagnetic interactions down to very low energies (10 eV/u - 10 MeV/u) for understanding the damage mechanisms due to long-term low doses. Predictions of biological dose during long interplanetary journeys also need models for hadronic interactions of energetic heavy ions extending higher energies (10 GeV/u - 100 GeV/u, but possibly up to 1 TeV/u). Geant4 is a powerful toolkit, which in some areas well surpasses the needs from space radiation studies, while in other areas is being developed and/or validated to properly cover the modelling requirements outlined above. Our activities in ESA projects deal with the research and development of both Geant4 hadronic and electromagnetic physics. Recently the scope of verification tests and benchmarks has been extended. Hadronic tests and benchmarks run proton, pion, and ion interactions with matter at various energies. In the Geant4 hadronic sub-libraries, the most accurate cross sections have been identified and selected as a default for all particle types relevant to space applications. Significant developments were carried out for ion/ion interaction models. These now allow one to perform Geant4 simulations for all particle types and energies relevant to space applications. For the validation of ion models the hadronic testing suite for ion interactions was significantly extended. In this work the results of benchmarking versus data in a wide energy range for projectile protons and ions will be shown and discussed. Here we show results of the tests runs and their precision. Recommendations for Geant4
SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology
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Alexander Dörr
2014-12-01
Full Text Available The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.
An Effective Parameter Screening Strategy for High Dimensional Watershed Models
Khare, Y. P.; Martinez, C. J.; Munoz-Carpena, R.
2014-12-01
Watershed simulation models can assess the impacts of natural and anthropogenic disturbances on natural systems. These models have become important tools for tackling a range of water resources problems through their implementation in the formulation and evaluation of Best Management Practices, Total Maximum Daily Loads, and Basin Management Action Plans. For accurate applications of watershed models they need to be thoroughly evaluated through global uncertainty and sensitivity analyses (UA/SA). However, due to the high dimensionality of these models such evaluation becomes extremely time- and resource-consuming. Parameter screening, the qualitative separation of important parameters, has been suggested as an essential step before applying rigorous evaluation techniques such as the Sobol' and Fourier Amplitude Sensitivity Test (FAST) methods in the UA/SA framework. The method of elementary effects (EE) (Morris, 1991) is one of the most widely used screening methodologies. Some of the common parameter sampling strategies for EE, e.g. Optimized Trajectories [OT] (Campolongo et al., 2007) and Modified Optimized Trajectories [MOT] (Ruano et al., 2012), suffer from inconsistencies in the generated parameter distributions, infeasible sample generation time, etc. In this work, we have formulated a new parameter sampling strategy - Sampling for Uniformity (SU) - for parameter screening which is based on the principles of the uniformity of the generated parameter distributions and the spread of the parameter sample. A rigorous multi-criteria evaluation (time, distribution, spread and screening efficiency) of OT, MOT, and SU indicated that SU is superior to other sampling strategies. Comparison of the EE-based parameter importance rankings with those of Sobol' helped to quantify the qualitativeness of the EE parameter screening approach, reinforcing the fact that one should use EE only to reduce the resource burden required by FAST/Sobol' analyses but not to replace it.
Model reduction for Space Station Freedom
Williams, Trevor
1992-01-01
Model reduction is an important practical problem in the control of flexible spacecraft, and a considerable amount of work has been carried out on this topic. Two of the best known methods developed are modal truncation and internal balancing. Modal truncation is simple to implement but can give poor results when the structure possesses clustered natural frequencies, as often occurs in practice. Balancing avoids this problem but has the disadvantages of high computational cost, possible numerical sensitivity problems, and no physical interpretation for the resulting balanced 'modes'. The purpose of this work is to examine the performance of the subsystem balancing technique developed by the investigator when tested on a realistic flexible space structure, in this case a model of the Permanently Manned Configuration (PMC) of Space Station Freedom. This method retains the desirable properties of standard balancing while overcoming the three difficulties listed above. It achieves this by first decomposing the structural model into subsystems of highly correlated modes. Each subsystem is approximately uncorrelated from all others, so balancing them separately and then combining yields comparable results to balancing the entire structure directly. The operation count reduction obtained by the new technique is considerable: a factor of roughly r(exp 2) if the system decomposes into r equal subsystems. Numerical accuracy is also improved significantly, as the matrices being operated on are of reduced dimension, and the modes of the reduced-order model now have a clear physical interpretation; they are, to first order, linear combinations of repeated-frequency modes.
Edgeworth streaming model for redshift space distortions
Uhlemann, Cora
2015-01-01
We derive the Edgeworth streaming model (ESM) for the redshift space correlation function starting from an arbitrary distribution function for biased tracers of dark matter by considering its two-point statistics and show that it reduces to the Gaussian streaming model (GSM) when neglecting non-Gaussianities. We test the accuracy of the GSM and ESM independent of perturbation theory using the Horizon Run 2 N-body halo catalog. While the monopole of the redshift space halo correlation function is well described by the GSM, higher multipoles improve upon including the leading order non-Gaussian correction in the ESM: the GSM quadrupole breaks down on scales below 30 Mpc/h whereas the ESM stays accurate to 2% within statistical errors down to 10 Mpc/h. To predict the scale dependent functions entering the streaming model we employ Convolution Lagrangian perturbation theory (CLPT) based on the dust model and local Lagrangian bias. Since dark matter halos carry an intrinsic length scale given by their Lagrangian r...
Modeling utilization distributions in space and time.
Keating, Kim A; Cherry, Steve
2009-07-01
W. Van Winkle defined the utilization distribution (UD) as a probability density that gives an animal's relative frequency of occurrence in a two-dimensional (x, y) plane. We extend Van Winkle's work by redefining the UD as the relative frequency distribution of an animal's occurrence in all four dimensions of space and time. We then describe a product kernel model estimation method, devising a novel kernel from the wrapped Cauchy distribution to handle circularly distributed temporal covariates, such as day of year. Using Monte Carlo simulations of animal movements in space and time, we assess estimator performance. Although not unbiased, the product kernel method yields models highly correlated (Pearson's r = 0.975) with true probabilities of occurrence and successfully captures temporal variations in density of occurrence. In an empirical example, we estimate the expected UD in three dimensions (x, y, and t) for animals belonging to each of two distinct bighorn sheep (Ovis canadensis) social groups in Glacier National Park, Montana, USA. Results show the method can yield ecologically informative models that successfully depict temporal variations in density of occurrence for a seasonally migratory species. Some implications of this new approach to UD modeling are discussed.
Nonlinear State Space Modeling and System Identification for Electrohydraulic Control
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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.
Energy Technology Data Exchange (ETDEWEB)
Hamimid, M., E-mail: Hamimid_mourad@hotmail.com [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Mimoune, S.M., E-mail: s.m.mimoune@mselab.org [Laboratoire de modelisation des systemes energetiques LMSE, Universite de Biskra, BP 145, 07000 Biskra (Algeria); Feliachi, M., E-mail: mouloud.feliachi@univ-nantes.fr [IREENA-IUT, CRTT, 37 Boulevard de l' Universite, BP 406, 44602 Saint Nazaire Cedex (France)
2012-07-01
In this present work, the minor hysteresis loops model based on parameters scaling of the modified Jiles-Atherton model is evaluated by using judicious expressions. These expressions give the minor hysteresis loops parameters as a function of the major hysteresis loop ones. They have exponential form and are obtained by parameters identification using the stochastic optimization method 'simulated annealing'. The main parameters influencing the data fitting are three parameters, the pinning parameter k, the mean filed parameter {alpha} and the parameter which characterizes the shape of anhysteretic magnetization curve a. To validate this model, calculated minor hysteresis loops are compared with measured ones and good agreements are obtained.
Forest biophysical parameter estimation using space-borne bistatic PolInSAR measurements
Khati, Unmesh; Singh, Gulab; Mohanty, Shradha
2016-05-01
Forest height is an important indicator of the health of the forest ecosystem and can be utilized for accurate estimation of important parameters such as forest above-ground biomass. PolInSAR techniques have been utilized for forest height estimation using airborne and space-borne platforms. However, temporal decorrelation severely limits the ability of space-borne PolInSAR observations for meaningful height inversion. With the launch of the TerraSAR-X/TanDEM-X platforms, acquisition of Polarimetric SAR data in bistatic mode, without the undesired effects of temporal decorrelation, is possible. Full-PolInSAR bistatic data is acquired over Indian tropical forests and the height inversion results are presented in this research article. The inverted height shows a good correlation with field measured height, with r = 0.8. The inversion shows over-estimation over low height forests, while providing an accurate estimation for tall forested areas.
MODELING OF FUEL SPRAY CHARACTERISTICS AND DIESEL COMBUSTION CHAMBER PARAMETERS
Directory of Open Access Journals (Sweden)
G. M. Kukharonak
2011-01-01
Full Text Available The computer model for coordination of fuel spray characteristics with diesel combustion chamber parameters has been created in the paper. The model allows to observe fuel sprays develоpment in diesel cylinder at any moment of injection, to calculate characteristics of fuel sprays with due account of a shape and dimensions of a combustion chamber, timely to change fuel injection characteristics and supercharging parameters, shape and dimensions of a combustion chamber. Moreover the computer model permits to determine parameters of holes in an injector nozzle that provides the required fuel sprays characteristics at the stage of designing a diesel engine. Combustion chamber parameters for 4ЧН11/12.5 diesel engine have been determined in the paper.
Mathematically Modeling Parameters Influencing Surface Roughness in CNC Milling
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Engin Nas
2012-01-01
Full Text Available In this study, steel AISI 1050 is subjected to process of face milling in CNC milling machine and such parameters as cutting speed, feed rate, cutting tip, depth of cut influencing the surface roughness are investigated experimentally. Four different experiments are conducted by creating different combinations for parameters. In conducted experiments, cutting tools, which are coated by PVD method used in forcing steel and spheroidal graphite cast iron are used. Surface roughness values, which are obtained by using specified parameters with cutting tools, are measured and correlation between measured surface roughness values and parameters is modeled mathematically by using curve fitting algorithm. Mathematical models are evaluated according to coefficients of determination (R2 and the most ideal one is suggested for theoretical works. Mathematical models, which are proposed for each experiment, are estipulated.
Regionalization parameters of conceptual rainfall-runoff model
Osuch, M.
2003-04-01
Main goal of this study was to develop techniques for the a priori estimation parameters of hydrological model. Conceptual hydrological model CLIRUN was applied to around 50 catchment in Poland. The size of catchments range from 1 000 to 100 000 km2. The model was calibrated for a number of gauged catchments with different catchment characteristics. The parameters of model were related to different climatic and physical catchment characteristics (topography, land use, vegetation and soil type). The relationships were tested by comparing observed and simulated runoff series from the gauged catchment that were not used in the calibration. The model performance using regional parameters was promising for most of the calibration and validation catchments.
Cognitive engineering models in space systems
Mitchell, Christine M.
1993-01-01
NASA space systems, including mission operations on the ground and in space, are complex, dynamic, predominantly automated systems in which the human operator is a supervisory controller. Models of cognitive functions in complex systems are needed to describe human performance and form the theoretical basis of operator workstation design, including displays, controls, and decision aids. Currently, there several candidate modeling methodologies. They include the Rasmussen abstraction/aggregation hierarchy and decision ladder, the goal-means network, the problem behavior graph, and the operator function model. The research conducted under the sponsorship of this grant focuses on the extension of the theoretical structure of the operator function model and its application to NASA Johnson mission operations and space station applications. The initial portion of this research consists of two parts. The first is a series of technical exchanges between NASA Johnson and Georgia Tech researchers. The purpose is to identify candidate applications for the current operator function model; prospects include mission operations and the Data Management System Testbed. The second portion will address extensions of the operator function model to tailor it to the specific needs of Johnson applications. At this point, we have accomplished two things. During a series of conversations with JSC researchers, we have defined the technical goal of the research supported by this grant to be the structural definition of the operator function model and its computer implementation, OFMspert. Both the OFM and OFMspert have matured to the point that they require infrastructure to facilitate use by researchers not involved in the evolution of the tools. The second accomplishment this year was the identification of the Payload Deployment and Retrieval System (PDRS) as a candidate system for the case study. In conjunction with government and contractor personnel in the Human-Computer Interaction Lab
Space physiology IV: mathematical modeling of the cardiovascular system in space exploration.
Keith Sharp, M; Batzel, Jerry Joseph; Montani, Jean-Pierre
2013-08-01
Mathematical modeling represents an important tool for analyzing cardiovascular function during spaceflight. This review describes how modeling of the cardiovascular system can contribute to space life science research and illustrates this process via modeling efforts to study postflight orthostatic intolerance (POI), a key issue for spaceflight. Examining this application also provides a context for considering broader applications of modeling techniques to the challenges of bioastronautics. POI, which affects a large fraction of astronauts in stand tests upon return to Earth, presents as dizziness, fainting and other symptoms, which can diminish crew performance and cause safety hazards. POI on the Moon or Mars could be more critical. In the field of bioastronautics, POI has been the dominant application of cardiovascular modeling for more than a decade, and a number of mechanisms for POI have been investigated. Modeling approaches include computational models with a range of incorporated factors and hemodynamic sophistication, and also physical models tested in parabolic and orbital flight. Mathematical methods such as parameter sensitivity analysis can help identify key system mechanisms. In the case of POI, this could lead to more effective countermeasures. Validation is a persistent issue in modeling efforts, and key considerations and needs for experimental data to synergistically improve understanding of cardiovascular responses are outlined. Future directions in cardiovascular modeling include subject-specific assessment of system status, as well as research on integrated physiological responses, leading, for instance, to assessment of subject-specific susceptibility to POI or effects of cardiovascular alterations on muscular, vision and cognitive function.
Hamilton's Equations with Euler Parameters for Rigid Body Dynamics Modeling. Chapter 3
Shivarama, Ravishankar; Fahrenthold, Eric P.
2004-01-01
A combination of Euler parameter kinematics and Hamiltonian mechanics provides a rigid body dynamics model well suited for use in strongly nonlinear problems involving arbitrarily large rotations. The model is unconstrained, free of singularities, includes a general potential energy function and a minimum set of momentum variables, and takes an explicit state space form convenient for numerical implementation. The general formulation may be specialized to address particular applications, as illustrated in several three dimensional example problems.
Creating and Exploring Huge Parameter Spaces: Interactive Evolution as a Tool for Sound Generation
DEFF Research Database (Denmark)
Dahlstedt, Palle
2001-01-01
of huge synthesis parameter spaces, and presents a possibility for the sound artist to create new sound engines customized for this kind of creation and exploration – sound engines too complex to control in any other way. Different sound engines are presented, together with a discussion of compositional......In this paper, a program is presented that applies interactive evolution to sound generation, i.e., preferred individuals are repeatedly selected from a population of genetically bred sound objects, created with various synthesis and pattern generation algorithms. This simplifies aural exploration...
Cusp points in the parameter space of RPR-2PRR parallel manipulator
Moroz, Guillaume Inria; Wenger, Philippe; Rouiller, Fabrice; 10.1007/978-90-481-9689-0
2010-01-01
This paper investigates the existence conditions of cusp points in the design parameter space of the R\\underline{P}R-2P\\underline{R}R parallel manipulators. Cusp points make possible non-singular assembly-mode changing motion, which can possibly increase the size of the aspect, i.e. the maximum singularity free workspace. The method used is based on the notion of discriminant varieties and Cylindrical Algebraic Decomposition, and resorts to Gr\\"obner bases for the solutions of systems of equations.
From High Dimensional Chaos to Stable Periodic Orbits: The Structure of Parameter Space
Energy Technology Data Exchange (ETDEWEB)
Barreto, E.; Hunt, B.R.; Grebogi, C.; Yorke, J.A. [University of Maryland, College Park, Maryland 20742 (United States)
1997-06-01
Regions in the parameter space of chaotic systems that correspond to stable behavior are often referred to as windows. In this Letter, we elucidate the occurrence of such regions in higher dimensional chaotic systems. We describe the fundamental structure of these windows, and also indicate under what circumstances one can expect to find them. These results are applicable to systems that exhibit several positive Lyapunov exponents, and are of importance to both the theoretical and the experimental understanding of dynamical systems. {copyright} {ital 1997} {ital The American Physical Society}
Directory of Open Access Journals (Sweden)
M. Jauhari
1982-07-01
Full Text Available Evaluation of certain parameters of the trajectory of a small arm projectile on the basis of Siacci approximation requires the values of space (S and Time (T functions as tabulated in the Ingalls and Hodsock ballistic tables. The development is reported of a computerized system, whereby the necessity of referring to these tables has been completely obviated. Programme flow-char has been presented and the logic behind the flow of programme has been made explicit. The programme has been executed successfully on the DCM Microsystem 1121.
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... distribution. Methods from structural reliability analysis are used to model the uncertainties and to assess the reliability for fatigue failure. Maximum Likelihood and Least Square estimation techniques are used to estimate fatigue life distribution parameters....
A discrete-space urban model with environmental amenities
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...
Introduction of a valence space in QRPA: Impact on vibrational mass parameters and spectroscopy
Energy Technology Data Exchange (ETDEWEB)
Lechaftois, F., E-mail: francois.lechaftois@cea.fr; Péru, S. [CEA, DAM, DIF F-91297 Arpajon (France); Deloncle, I. [CEA, DAM, DIF F-91297 Arpajon (France); CSNSM, IN2P3/CNRS, F-91405 Orsay Campus (France)
2015-10-15
For the first time, using a unique finite range interaction (D1M Gogny force), a fully coherent and time-feasible calculation of the Bohr Hamiltonian vibrational mass is envisioned in a Hartree-Fock-Bogoliubov + Quasiparticle Random Phase Approximation (QRPA) framework. In order to reach handable computation time, we evaluate the feasibility of this method by considering the insertion of a valence space for QRPA. We validate our approach in the even-even tin isotopes comparing the convergence scheme of the mass parameter with those of built-in QRPA outputs: excited state energy and reduced transition probability. The seeming convergence of these intrinsic quantities is shown to be misleading and the difference with the theoretical expected value is quantified. This work is a primary step towards the systematic calculation of mass parameters.
MODELING PARAMETERS OF ARC OF ELECTRIC ARC FURNACE
Directory of Open Access Journals (Sweden)
R.N. Khrestin
2015-08-01
Full Text Available Purpose. The aim is to build a mathematical model of the electric arc of arc furnace (EAF. The model should clearly show the relationship between the main parameters of the arc. These parameters determine the properties of the arc and the possibility of optimization of melting mode. Methodology. We have built a fairly simple model of the arc, which satisfies the above requirements. The model is designed for the analysis of electromagnetic processes arc of varying length. We have compared the results obtained when testing the model with the results obtained on actual furnaces. Results. During melting in real chipboard under the influence of changes in temperature changes its properties arc plasma. The proposed model takes into account these changes. Adjusting the length of the arc is the main way to regulate the mode of smelting chipboard. The arc length is controlled by the movement of the drive electrode. The model reflects the dynamic changes in the parameters of the arc when changing her length. We got the dynamic current-voltage characteristics (CVC of the arc for the different stages of melting. We got the arc voltage waveform and identified criteria by which possible identified stage of smelting. Originality. In contrast to the previously known models, this model clearly shows the relationship between the main parameters of the arc EAF: arc voltage Ud, amperage arc id and length arc d. Comparison of the simulation results and experimental data obtained from real particleboard showed the adequacy of the constructed model. It was found that character of change of magnitude Md, helps determine the stage of melting. Practical value. It turned out that the model can be used to simulate smelting in EAF any capacity. Thus, when designing the system of control mechanism for moving the electrode, the model takes into account changes in the parameters of the arc and it can significantly reduce electrode material consumption and energy consumption
Environmental Transport Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
M. Wasiolek
2004-09-10
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 for the license application (TSPA-LA) 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-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was 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 in volcanic ash). The analysis
Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters.
Liu, Fei; Heiner, Monika; Yang, Ming
2016-01-01
Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.
Approximate Methods for State-Space Models.
Koyama, Shinsuke; Pérez-Bolde, Lucia Castellanos; Shalizi, Cosma Rohilla; Kass, Robert E
2010-03-01
State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate or slow. In this paper, we study a nonlinear filter for nonlinear/non-Gaussian state-space models, which uses Laplace's method, an asymptotic series expansion, to approximate the state's conditional mean and variance, together with a Gaussian conditional distribution. This Laplace-Gaussian filter (LGF) gives fast, recursive, deterministic state estimates, with an error which is set by the stochastic characteristics of the model and is, we show, stable over time. We illustrate the estimation ability of the LGF by applying it to the problem of neural decoding and compare it to sequential Monte Carlo both in simulations and with real data. We find that the LGF can deliver superior results in a small fraction of the computing time.
Microblog Sentiment Analysis with Emoticon Space Model
Institute of Scientific and Technical Information of China (English)
姜飞; 刘奕群; 孙甲申; 朱璇; 张敏; 马少平
2015-01-01
Emoticons have been widely employed to express different types of moods, emotions, and feelings in microblog environments. They are therefore regarded as one of the most important signals for microblog sentiment analysis. Most existing studies use several emoticons that convey clear emotional meanings as noisy sentiment labels or similar sentiment indicators. However, in practical microblog environments, tens or even hundreds of emoticons are frequently adopted and all emoticons have their own unique emotional meanings. Besides, a considerable number of emoticons do not have clear emotional meanings. An improved sentiment analysis model should not overlook these phenomena. Instead of manually assigning sentiment labels to several emoticons that convey relatively clear meanings, we propose the emoticon space model (ESM) that leverages more emoticons to construct word representations from a massive amount of unlabeled data. By projecting words and microblog posts into an emoticon space, the proposed model helps identify subjectivity, polarity, and emotion in microblog environments. The experimental results for a public microblog benchmark corpus (NLP&CC 2013) indicate that ESM effectively leverages emoticon signals and outperforms previous state-of-the-art strategies and benchmark best runs.
Inhalation Exposure Input Parameters for the Biosphere Model
Energy Technology Data Exchange (ETDEWEB)
K. Rautenstrauch
2004-09-10
This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.
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
Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies
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.
Indirect detection constraints on the model space of dark matter effective theories
Carpenter, Linda M.; Colburn, Russell; Goodman, Jessica
2015-11-01
Using limits on photon flux from dwarf spheroidal galaxies, we place bounds on the parameter space of models in which dark matter annihilates into multiple final state particle pair channels. We derive constraints on effective operator models with dark matter couplings to third generation fermions and to pairs of standard model vector bosons. We present limits in various slices of model parameter space along with estimations of the region of maximal validity of the effective operator approach for indirect detection. We visualize our bounds for models with multiple final state annihilations by projecting parameter space constraints onto triangles, a technique familiar from collider physics; and we compare our bounds to collider limits on equivalent models.
Indirect Detection Constraints on the Model Space of Dark Matter Effective Theories
Carpenter, Linda M; Goodman, Jessica
2015-01-01
Using limits on photon flux from Dwarf Spheroidal galaxies, we place bounds on the parameter space of models in which Dark Matter annihilates into multiple final state particle pair channels. We derive constraints on effective operator models with Dark Matter couplings to third generation fermions and to pairs of Standard Model vector bosons. We present limits in various slices of model parameter space along with estimations of the region of maximal validity of the effective operator approach for indirect detection. We visualize our bounds for models with multiple final state annihilations by projecting parameter space constraints onto triangles, a technique familiar from collider physics; and we compare our bounds to collider limits on equivalent models.
Construction of constant-Q viscoelastic model with three parameters
Institute of Scientific and Technical Information of China (English)
SUN Cheng-yu; YIN Xing-yao
2007-01-01
The popularly used viscoelastic models have some shortcomings in describing relationship between quality factor (Q) and frequency, which is not consistent with the observation data. Based on the theory of viscoelasticity, a new approach to construct constant-Q viscoelastic model in given frequency band with three parameters is developed. The designed model describes the frequency-independence feature of quality factor very well, and the effect of viscoelasticity on seismic wave field can be studied relatively accurate in theory with this model. Furthermore, the number of required parameters in this model has been reduced fewer than that of other constant-Q models, this can simplify the solution of the viscoelastic problems to some extent. At last, the accuracy and application range have been analyzed through numerical tests. The effect of viscoelasticity on wave propagation has been briefly illustrated through the change of frequency spectra and waveform in several different viscoelastic models.
Global-scale regionalization of hydrologic model parameters
Beck, Hylke E.; van Dijk, Albert I. J. M.; de Roo, Ad; Miralles, Diego G.; McVicar, Tim R.; Schellekens, Jaap; Bruijnzeel, L. Adrian
2016-05-01
Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10,000 km2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Köppen-Geiger climate types and even for evaluation catchments > 5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via www.gloh2o.org.
Bayesian parameter estimation for nonlinear modelling of biological pathways
Directory of Open Access Journals (Sweden)
Ghasemi Omid
2011-12-01
Full Text Available Abstract Background The availability of temporal measurements on biological experiments has significantly promoted research areas in systems biology. To gain insight into the interaction and regulation of biological systems, mathematical frameworks such as ordinary differential equations have been widely applied to model biological pathways and interpret the temporal data. Hill equations are the preferred formats to represent the reaction rate in differential equation frameworks, due to their simple structures and their capabilities for easy fitting to saturated experimental measurements. However, Hill equations are highly nonlinearly parameterized functions, and parameters in these functions cannot be measured easily. Additionally, because of its high nonlinearity, adaptive parameter estimation algorithms developed for linear parameterized differential equations cannot be applied. Therefore, parameter estimation in nonlinearly parameterized differential equation models for biological pathways is both challenging and rewarding. In this study, we propose a Bayesian parameter estimation algorithm to estimate parameters in nonlinear mathematical models for biological pathways using time series data. Results We used the Runge-Kutta method to transform differential equations to difference equations assuming a known structure of the differential equations. This transformation allowed us to generate predictions dependent on previous states and to apply a Bayesian approach, namely, the Markov chain Monte Carlo (MCMC method. We applied this approach to the biological pathways involved in the left ventricle (LV response to myocardial infarction (MI and verified our algorithm by estimating two parameters in a Hill equation embedded in the nonlinear model. We further evaluated our estimation performance with different parameter settings and signal to noise ratios. Our results demonstrated the effectiveness of the algorithm for both linearly and nonlinearly
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
2007-01-01
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil with focus on the horizontal sliding and rocking. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines...
Muscle parameters for musculoskeletal modelling of the human neck
Borst, J.; Forbes, P.A.; Happee, R.; Veeger, H.E.J.
2011-01-01
Background: To study normal or pathological neuromuscular control, a musculoskeletal model of the neck has great potential but a complete and consistent anatomical dataset which comprises the muscle geometry parameters to construct such a model is not yet available. Methods: A dissection experiment
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
2007-01-01
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil with focus on the horizontal sliding and rocking. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines...
Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies
Smith, Carrie E.; Cribbie, Robert A.
2013-01-01
When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate ([alpha]). Despite the fact that many significance tests are often conducted in SEM, rarely is…
Muscle parameters for musculoskeletal modelling of the human neck
Borst, J.; Forbes, P.A.; Happee, R.; Veeger, H.E.J.
2011-01-01
Background: To study normal or pathological neuromuscular control, a musculoskeletal model of the neck has great potential but a complete and consistent anatomical dataset which comprises the muscle geometry parameters to construct such a model is not yet available. Methods: A dissection experiment
Geometry parameters for musculoskeletal modelling of the shoulder system
Van der Helm, F C; Veeger, DirkJan (H. E. J.); Pronk, G M; Van der Woude, L H; Rozendal, R H
1992-01-01
A dynamical finite-element model of the shoulder mechanism consisting of thorax, clavicula, scapula and humerus is outlined. The parameters needed for the model are obtained in a cadaver experiment consisting of both shoulders of seven cadavers. In this paper, in particular, the derivation of geomet
Precise correction to parameter ρ in the littlest Higgs model
Institute of Scientific and Technical Information of China (English)
Farshid Tabbak; F.Farnoudi
2008-01-01
In this paper tree-level violation of weak isospin parameter,ρ in the flame of the littlest Higgs model is studied.The potentially large deviation from the standard model prediction for the ρ in terms of the littlest Higgs model parameters is calculated.The maximum value for ρ for f ＝ 1 TeV,c ＝ 0.05,c'＝ 0.05and v'= 1.5 GeV is ρ = 1.2973 which means a large enhancement than the SM.
Comparative Analysis of Visco-elastic Models with Variable Parameters
Directory of Open Access Journals (Sweden)
Silviu Nastac
2010-01-01
Full Text Available The paper presents a theoretical comparative study for computational behaviour analysis of vibration isolation elements based on viscous and elastic models with variable parameters. The changing of elastic and viscous parameters can be produced by natural timed evolution demo-tion or by heating developed into the elements during their working cycle. It was supposed both linear and non-linear numerical viscous and elastic models, and their combinations. The results show the impor-tance of numerical model tuning with the real behaviour, as such the characteristics linearity, and the essential parameters for damping and rigidity. Multiple comparisons between linear and non-linear simulation cases dignify the basis of numerical model optimization regarding mathematical complexity vs. results reliability.
Maximum likelihood estimation in constrained parameter spaces for mixtures of factor analyzers
Greselin, Francesca; Ingrassia, Salvatore
2013-01-01
Mixtures of factor analyzers are becoming more and more popular in the area of model based clustering of high-dimensional data. According to the likelihood approach in data modeling, it is well known that the unconstrained log-likelihood function may present spurious maxima and singularities and this is due to specific patterns of the estimated covariance structure, when their determinant approaches 0. To reduce such drawbacks, in this paper we introduce a procedure for the parameter estimati...
Improvement of Continuous Hydrologic Models and HMS SMA Parameters Reduction
Rezaeian Zadeh, Mehdi; Zia Hosseinipour, E.; Abghari, Hirad; Nikian, Ashkan; Shaeri Karimi, Sara; Moradzadeh Azar, Foad
2010-05-01
Hydrological models can help us to predict stream flows and associated runoff volumes of rainfall events within a watershed. There are many different reasons why we need to model the rainfall-runoff processes of for a watershed. However, the main reason is the limitation of hydrological measurement techniques and the costs of data collection at a fine scale. Generally, we are not able to measure all that we would like to know about a given hydrological systems. This is very particularly the case for ungauged catchments. Since the ultimate aim of prediction using models is to improve decision-making about a hydrological problem, therefore, having a robust and efficient modeling tool becomes an important factor. Among several hydrologic modeling approaches, continuous simulation has the best predictions because it can model dry and wet conditions during a long-term period. Continuous hydrologic models, unlike event based models, account for a watershed's soil moisture balance over a long-term period and are suitable for simulating daily, monthly, and seasonal streamflows. In this paper, we describe a soil moisture accounting (SMA) algorithm added to the hydrologic modeling system (HEC-HMS) computer program. As is well known in the hydrologic modeling community one of the ways for improving a model utility is the reduction of input parameters. The enhanced model developed in this study is applied to Khosrow Shirin Watershed, located in the north-west part of Fars Province in Iran, a data limited watershed. The HMS SMA algorithm divides the potential path of rainfall onto a watershed into five zones. The results showed that the output of HMS SMA is insensitive with the variation of many parameters such as soil storage and soil percolation rate. The study's objective is to remove insensitive parameters from the model input using Multi-objective sensitivity analysis. Keywords: Continuous Hydrologic Modeling, HMS SMA, Multi-objective sensitivity analysis, SMA Parameters
A software for parameter estimation in dynamic models
Directory of Open Access Journals (Sweden)
M. Yuceer
2008-12-01
Full Text Available A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.
Ferranti, Francesco; Rolain, Yves
2017-01-01
This paper proposes a novel state-space matrix interpolation technique to generate linear parameter-varying (LPV) models starting from a set of local linear time-invariant (LTI) models estimated at fixed operating conditions. Since the state-space representation of LTI models is unique up to a similarity transformation, the state-space matrices need to be represented in a common state-space form. This is needed to avoid potentially large variations as a function of the scheduling parameters of the state-space matrices to be interpolated due to underlying similarity transformations, which might degrade the accuracy of the interpolation significantly. Underlying linear state coordinate transformations for a set of local LTI models are extracted by the computation of similarity transformation matrices by means of linear least-squares approximations. These matrices are then used to transform the local LTI state-space matrices into a form suitable to achieve accurate interpolation results. The proposed LPV modeling technique is validated by pertinent numerical results.
Condition Parameter Modeling for Anomaly Detection in Wind Turbines
Directory of Open Access Journals (Sweden)
Yonglong Yan
2014-05-01
Full Text Available Data collected from the supervisory control and data acquisition (SCADA system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs, is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified.
Parameter Estimation of Photovoltaic Models via Cuckoo Search
Directory of Open Access Journals (Sweden)
Jieming Ma
2013-01-01
Full Text Available Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE value. The proposed method outperforms other algorithms applied in this study.
[A dynamic model of the extravehicular (correction of extravehicuar) activity space suit].
Yang, Feng; Yuan, Xiu-gan
2002-12-01
Objective. To establish a dynamic model of the space suit base on the particular configuration of the space suit. Method. The mass of the space suit components, moment of inertia, mobility of the joints of space suit, as well as the suit-generated torques, were considered in this model. The expressions to calculate the moment of inertia were developed by simplifying the geometry of the space suit. A modified Preisach model was used to mathematically describe the hysteretic torque characteristics of joints in a pressurized space suit, and it was implemented numerically basing on the observed suit parameters. Result. A dynamic model considering mass, moment of inertia and suit-generated torques was established. Conclusion. This dynamic model provides some elements for the dynamic simulation of the astronaut extravehicular activity.
Segmentation of complex objects' sonar images using parameter-fixed MRF model
Institute of Scientific and Technical Information of China (English)
YAO Bin; LI Hai-sen; ZHOU Tian; SUN SHENG-he
2006-01-01
The effective method of the recognition of underwater complex objects in sonar image is to segment sonar image into target, shadow and sea-bottom reverberation regions and then extract the edge of the object. Because of the time-varying and space-varying characters of underwater acoustics environment, the sonar images have poor quality and serious speckle noise, so traditional image segmentation is unable to achieve precise segmentation. In the paper, the image segmentation process based on MRF (Markov random field) model is studied, and a practical method of estimating model parameters is proposed. Through analyzing the impact of chosen model parameters, a sonar imagery segmentation algorithm based on fixed parameters' MRF model is proposed. Both of the segmentation effect and the low computing load are gained. By applying the algorithm to the synthesized texture image and actual side-scan sonar image, the algorithm can be achieved with precise segmentation result.
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Distributed Systems Integration Dept.
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Estimation of the parameters of ETAS models by Simulated Annealing
Lombardi, Anna Maria
2015-01-01
This paper proposes a new algorithm to estimate the maximum likelihood parameters of an Epidemic Type Aftershock Sequences (ETAS) model. It is based on Simulated Annealing, a versatile method that solves problems of global optimization and ensures convergence to a global optimum. The procedure is tested on both simulated and real catalogs. The main conclusion is that the method performs poorly as the size of the catalog decreases because the effect of the correlation of the ETAS parameters is...
CADLIVE optimizer: web-based parameter estimation for dynamic models
Directory of Open Access Journals (Sweden)
Inoue Kentaro
2012-08-01
Full Text Available Abstract Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models.
Extraction of Spatial Parameters from Classified LIDAR Data and Aerial Photograph for Sound Modeling
Biswas, S.; Lohani, B.
2012-07-01
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.
A mathematical space mapping model for ballistic carbon nanotube field-effect transistors
Emamifar, Farnousha; Yousefi, Reza
2016-11-01
In this study, a mathematical model is presented based on mathematical space mapping for ballistic carbon nanotube field-effect transistors. This model is generalized from another model that was based on the concept of neural space mapping to calculate the three parameters of a coarse model. These parameters were the threshold voltage, the Early voltage, and assumed constant k of a modified "level 1" MOSFET model in simulation program with integrated circuit emphasis (SPICE). In this work, three analytical relations are introduced to replace the neural networks of the main model. The comparisons between the proposed model and a well-known reference model, named FETToy, show that the proposed model had reasonable accuracy in terms of different biases and physical parameters.
Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.
Liu, Ziyue; Cappola, Anne R; Crofford, Leslie J; Guo, Wensheng
2014-01-01
The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.
Reference physiological parameters for pharmacodynamic modeling of liver cancer
Energy Technology Data Exchange (ETDEWEB)
Travis, C.C.; Arms, A.D.
1988-01-01
This document presents a compilation of measured values for physiological parameters used in pharamacodynamic modeling of liver cancer. The physiological parameters include body weight, liver weight, the liver weight/body weight ratio, and number of hepatocytes. Reference values for use in risk assessment are given for each of the physiological parameters based on analyses of valid measurements taken from the literature and other reliable sources. The proposed reference values for rodents include sex-specific measurements for the B6C3F{sub 1}, mice and Fishcer 344/N, Sprague-Dawley, and Wistar rats. Reference values are also provided for humans. 102 refs., 65 tabs.
Uncertainty of Modal Parameters Estimated by ARMA Models
DEFF Research Database (Denmark)
Jensen, Jacob Laigaard; Brincker, Rune; Rytter, Anders
1990-01-01
In this paper the uncertainties of identified modal parameters such as eidenfrequencies and damping ratios are assed. From the measured response of dynamic excited structures the modal parameters may be identified and provide important structural knowledge. However the uncertainty of the parameters...... by simulation study of a lightly damped single degree of freedom system. Identification by ARMA models has been choosen as system identification method. It is concluded that both the sampling interval and number of sampled points may play a significant role with respect to the statistical errors. Furthermore...
X-Parameter Based Modelling of Polar Modulated Power Amplifiers
DEFF Research Database (Denmark)
Wang, Yelin; Nielsen, Troels Studsgaard; Sira, Daniel
2013-01-01
X-parameters are developed as an extension of S-parameters capable of modelling non-linear devices driven by large signals. They are suitable for devices having only radio frequency (RF) and DC ports. In a polar power amplifier (PA), phase and envelope of the input modulated signal are applied...... at separate ports and the envelope port is neither an RF nor a DC port. As a result, X-parameters may fail to characterise the effect of the envelope port excitation and consequently the polar PA. This study introduces a solution to the problem for a commercial polar PA. In this solution, the RF-phase path...
Multivariable Wind Modeling in State Space
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Pedersen, B. J.
2011-01-01
Turbulence of the incoming wind field is of paramount importance to the dynamic response of wind turbines. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper an empirical...... cross-spectral density function for the along-wind turbulence component over the rotor plane is taken as the starting point. The spectrum is spatially discretized in terms of a Hermitian cross-spectral density matrix for the turbulence state vector which turns out not to be positive definite. Since...... the succeeding state space and ARMA modeling of the turbulence rely on the positive definiteness of the cross-spectral density matrix, the problem with the non-positive definiteness of such matrices is at first addressed and suitable treatments regarding it are proposed. From the adjusted positive definite cross...
Modeling Physarum space exploration using memristors
Ntinas, V.; Vourkas, I.; Sirakoulis, G. Ch; Adamatzky, A. I.
2017-05-01
Slime mold Physarum polycephalum optimizes its foraging behaviour by minimizing the distances between the sources of nutrients it spans. When two sources of nutrients are present, the slime mold connects the sources, with its protoplasmic tubes, along the shortest path. We present a two-dimensional mesh grid memristor based model as an approach to emulate Physarum’s foraging strategy, which includes space exploration and reinforcement of the optimally formed interconnection network in the presence of multiple aliment sources. The proposed algorithmic approach utilizes memristors and LC contours and is tested in two of the most popular computational challenges for Physarum, namely maze and transportation networks. Furthermore, the presented model is enriched with the notion of noise presence, which positively contributes to a collective behavior and enables us to move from deterministic to robust results. Consequently, the corresponding simulation results manage to reproduce, in a much better qualitative way, the expected transportation networks.
A probabilistic model of RNA conformational space
DEFF Research Database (Denmark)
Frellsen, Jes; Moltke, Ida; Thiim, Martin
2009-01-01
The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling...... procedure. Both are only partly solved problems. Here, we focus on the problem of conformational sampling. The current state of the art solution is based on fragment assembly methods, which construct plausible conformations by stringing together short fragments obtained from experimental structures. However...... efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D...
A Bayesian framework for parameter estimation in dynamical models.
Directory of Open Access Journals (Sweden)
Flávio Codeço Coelho
Full Text Available Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.
Modelling of Water Turbidity Parameters in a Water Treatment Plant
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A. S. KOVO
2005-01-01
Full Text Available The high cost of chemical analysis of water has necessitated various researches into finding alternative method of determining portable water quality. This paper is aimed at modelling the turbidity value as a water quality parameter. Mathematical models for turbidity removal were developed based on the relationships between water turbidity and other water criteria. Results showed that the turbidity of water is the cumulative effect of the individual parameters/factors affecting the system. A model equation for the evaluation and prediction of a clarifier’s performance was developed:Model: T = T0(-1.36729 + 0.037101∙10λpH + 0.048928t + 0.00741387∙alkThe developed model will aid the predictive assessment of water treatment plant performance. The limitations of the models are as a result of insufficient variable considered during the conceptualization.
Simultaneous estimation of parameters in the bivariate Emax model.
Magnusdottir, Bergrun T; Nyquist, Hans
2015-12-10
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.
Mazoyer, Johan; Pueyo, Laurent; Norman, Colin; N'Diaye, Mamadou; Mawet, Dimitri; Soummer, Rémi; Perrin, Marshall; Choquet, Élodie; Carlotti, Alexis
2015-09-01
As the performance of coronagraphs improves, the achievable contrast is more and more dependent of the shape of the pupil. The future generation of space and ground based coronagraphic instruments will have to achieve high contrast levels on on-axis and/or segmented telescopes. To correct for the high amplitude aberrations introduced by secondary mirror structures and segmentation of the primary mirror, we explore a two deformable mirror (DM) method. The major difficulty of several DM methods is the non-linear relation linking actuator strokes to the point spread function in the coronagraph focal plane. The Active Compensation of Aperture Discontinuities (ACAD) method is achieving this minimization by solving a non linear differential Monge Ampere equation. Once this open loop method have reached the minimum, a close-loop stroke minimization method can be applied to correct for phase and amplitude aberrations to achieve the ultimate contrast. In this paper, I describe the results of the parametric analysis that that I have undertaken on this method. After recalling the principle of the method, I will described the explored parameter space (deformable mirror set-up, shape of the pupil, bandwidth, coronagraph designs). I will precisely described the way I simulated the Vortex coronagraph for this numerical simulation. Finally I will present the preliminary results of this parametric analysis for space telescope pupils only.
Institute of Scientific and Technical Information of China (English)
Guo Zhen LU; Ya Yuan XIAO
2012-01-01
The main purpose of this paper is to derive a new (p,q)-atomic decomposition on the multi-parameter Hardy space Hp(X1 × X2) for 0 ＜ p0 ＜ p ≤ 1 for some po and all 1 ＜ q ＜ ∞,where X1 × X2 is the product of two spaces of homogeneous type in the sense of Coifman and Weiss.This decomposition converges in both Lq(x1× X2) (for 1 ＜ q ＜ ∞) and Hardy space Hp(X1 × X2) (for 0 ＜ p ≤ 1).As an application,we prove that an operator T,which is bounded on Lq(X1 × X2) for some 1 ＜ q ＜ ∞,is bounded from Hp(X1 × X2) to Lp(X1 × X2) if and only ifT is bounded uniformly on all (p,q)-product atoms in Lp(X1 × X2).The similar boundedness criterion from Hp(X1 × X2) to Hp(X1 × X2) is also obtained.
Shape parameter estimate for a glottal model without time position
Degottex, Gilles; Roebel, Axel; Rodet, Xavier
2009-01-01
cote interne IRCAM: Degottex09a; None / None; National audience; From a recorded speech signal, we propose to estimate a shape parameter of a glottal model without estimating his time position. Indeed, the literature usually propose to estimate the time position first (ex. by detecting Glottal Closure Instants). The vocal-tract filter estimate is expressed as a minimum-phase envelope estimation after removing the glottal model and a standard lips radiation model. Since this filter is mainly b...
Light-Front Spin-1 Model: Parameters Dependence
Mello, Clayton S; de Melo, J P B C; Frederico, T
2015-01-01
We study the structure of the $\\rho$-meson within a light-front model with constituent quark degrees of freedom. We calculate electroweak static observables: magnetic and quadrupole moments, decay constant and charge radius. The prescription used to compute the electroweak quantities is free of zero modes, which makes the calculation implicitly covariant. We compare the results of our model with other ones found in the literature. Our model parameters give a decay constant close to the experimental one.
Cosmological Models with Variable Deceleration Parameter in Lyra's Manifold
Pradhan, A; Singh, C B
2006-01-01
FRW models of the universe have been studied in the cosmological theory based on Lyra's manifold. A new class of exact solutions has been obtained by considering a time dependent displacement field for variable deceleration parameter from which three models of the universe are derived (i) exponential (ii) polynomial and (iii) sinusoidal form respectively. The behaviour of these models of the universe are also discussed. Finally some possibilities of further problems and their investigations have been pointed out.
Solar Model Parameters and Direct Measurements of Solar Neutrino Fluxes
Bandyopadhyay, A; Goswami, S; Petcov, S T; Bandyopadhyay, Abhijit; Choubey, Sandhya; Goswami, Srubabati
2006-01-01
We explore a novel possibility of determining the solar model parameters, which serve as input in the calculations of the solar neutrino fluxes, by exploiting the data from direct measurements of the fluxes. More specifically, we use the rather precise value of the $^8B$ neutrino flux, $\\phi_B$ obtained from the global analysis of the solar neutrino and KamLAND data, to derive constraints on each of the solar model parameters on which $\\phi_B$ depends. We also use more precise values of $^7Be$ and $pp$ fluxes as can be obtained from future prospective data and discuss whether such measurements can help in reducing the uncertainties of one or more input parameters of the Standard Solar Model.
IP-Sat: Impact-Parameter dependent Saturation model; revised
Rezaeian, Amir H; Van de Klundert, Merijn; Venugopalan, Raju
2013-01-01
In this talk, we present a global analysis of available small-x data on inclusive DIS and exclusive diffractive processes, including the latest data from the combined HERA analysis on reduced cross sections within the Impact-Parameter dependent Saturation (IP-Sat) Model. The impact-parameter dependence of dipole amplitude is crucial in order to have a unified description of both inclusive and exclusive diffractive processes. With the parameters of model fixed via a fit to the high-precision reduced cross-section, we compare model predictions to data for the structure functions, the longitudinal structure function, the charm structure function, exclusive vector mesons production and Deeply Virtual Compton Scattering (DVCS). Excellent agreement is obtained for the processes considered at small x in a wide range of Q^2.
QCD-inspired determination of NJL model parameters
Springer, Paul; Rechenberger, Stefan; Rennecke, Fabian
2016-01-01
The QCD phase diagram at finite temperature and density has attracted considerable interest over many decades now, not least because of its relevance for a better understanding of heavy-ion collision experiments. Models provide some insight into the QCD phase structure but usually rely on various parameters. Based on renormalization group arguments, we discuss how the parameters of QCD low-energy models can be determined from the fundamental theory of the strong interaction. We particularly focus on a determination of the temperature dependence of these parameters in this work and comment on the effect of a finite quark chemical potential. We present first results and argue that our findings can be used to improve the predictive power of future model calculations.
SPOTting model parameters using a ready-made Python package
Houska, Tobias; Kraft, Philipp; Breuer, Lutz
2015-04-01
The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for
Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms
Berhausen, Sebastian; Paszek, Stefan
2016-01-01
In recent years, there have occurred system failures in many power systems all over the world. They have resulted in a lack of power supply to a large number of recipients. To minimize the risk of occurrence of power failures, it is necessary to perform multivariate investigations, including simulations, of power system operating conditions. To conduct reliable simulations, the current base of parameters of the models of generating units, containing the models of synchronous generators, is necessary. In the paper, there is presented a method for parameter estimation of a synchronous generator nonlinear model based on the analysis of selected transient waveforms caused by introducing a disturbance (in the form of a pseudorandom signal) in the generator voltage regulation channel. The parameter estimation was performed by minimizing the objective function defined as a mean square error for deviations between the measurement waveforms and the waveforms calculated based on the generator mathematical model. A hybrid algorithm was used for the minimization of the objective function. In the paper, there is described a filter system used for filtering the noisy measurement waveforms. The calculation results of the model of a 44 kW synchronous generator installed on a laboratory stand of the Institute of Electrical Engineering and Computer Science of the Silesian University of Technology are also given. The presented estimation method can be successfully applied to parameter estimation of different models of high-power synchronous generators operating in a power system.
Modelling of intermittent microwave convective drying: parameter sensitivity
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Zhang Zhijun
2017-06-01
Full Text Available The reliability of the predictions of a mathematical model is a prerequisite to its utilization. A multiphase porous media model of intermittent microwave convective drying is developed based on the literature. The model considers the liquid water, gas and solid matrix inside of food. The model is simulated by COMSOL software. Its sensitivity parameter is analysed by changing the parameter values by ±20%, with the exception of several parameters. The sensitivity analysis of the process of the microwave power level shows that each parameter: ambient temperature, effective gas diffusivity, and evaporation rate constant, has significant effects on the process. However, the surface mass, heat transfer coefficient, relative and intrinsic permeability of the gas, and capillary diffusivity of water do not have a considerable effect. The evaporation rate constant has minimal parameter sensitivity with a ±20% value change, until it is changed 10-fold. In all results, the temperature and vapour pressure curves show the same trends as the moisture content curve. However, the water saturation at the medium surface and in the centre show different results. Vapour transfer is the major mass transfer phenomenon that affects the drying process.