Multi-dimensional model order selection
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Roemer Florian
2011-01-01
Full Text Available Abstract Multi-dimensional model order selection (MOS techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.
Model selection criteria : how to evaluate order restrictions
Kuiper, R.M.
2012-01-01
Researchers often have ideas about the ordering of model parameters. They frequently have one or more theories about the ordering of the group means, in analysis of variance (ANOVA) models, or about the ordering of coefficients corresponding to the predictors, in regression models.A researcher might
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
He, Zhangyi; Beaumont, Mark; Yu, Feng
2017-07-05
We explore the effect of different mechanisms of natural selection on the evolution of populations for one- and two-locus systems. We compare the effect of viability and fecundity selection in the context of the Wright-Fisher model with selection under the assumption of multiplicative fitness. We show that these two modes of natural selection correspond to different orderings of the processes of population regulation and natural selection in the Wright-Fisher model. We find that under the Wright-Fisher model these two different orderings can affect the distribution of trajectories of haplotype frequencies evolving with genetic recombination. However, the difference in the distribution of trajectories is only appreciable when the population is in significant linkage disequilibrium. We find that as linkage disequilibrium decays the trajectories for the two different models rapidly become indistinguishable. We discuss the significance of these findings in terms of biological examples of viability and fecundity selection, and speculate that the effect may be significant when factors such as gene migration maintain a degree of linkage disequilibrium. Copyright © 2017 He et al.
Vendor selection and order allocation using an integrated fuzzy mathematical programming model
Directory of Open Access Journals (Sweden)
Farzaneh Talebi
2015-09-01
Full Text Available In the context of supply chain management, supplier selection plays a key role in reaching desirable production planning. In today's competitive world, many enterprises have focused on selecting the appropriate suppliers in an attempt to reduce purchasing costs and improve quality products and services. Supplier selection is a multi-criteria decision problem, which includes different qualitative and quantitative criteria such as purchase cost, on time delivery, quality of service, etc. In this study, a fuzzy multi-objective mathematical programming model is presented to select appropriate supplier and assign desirable order to different supplies. The proposed model was implemented for an organization by considering 16 different scenarios and the results are compared with two other existing methods.
Modelling of Lime Kiln Using Subspace Method with New Order Selection Criterion
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Li Zhang
2014-01-01
Full Text Available This paper is taking actual control demand of rotary kiln as background and builds a calcining belt state space model using PO-Moesp subspace method. A novel order-delay double parameters error criterion (ODC is presented to reduce the modeling order. The proposed subspace order identification method takes into account the influence of order and delay on model error criterion simultaneously. For the introduction of the delay factors, the order is reduced dramatically in the system modeling. Also, in the data processing part sliding-window method is adopted for stripping delay factor from historical data. For this, the parameters can be changed flexibly. Some practical problems in industrial kiln process modeling are also solved. Finally, it is applied to an industrial kiln case.
Stochastic Pricing and Order Model with Transportation Mode Selection for Low-Carbon Retailers
Yi Zheng; Huchang Liao; Xue Yang
2016-01-01
More and more enterprises have begun to pay attention to their carbon footprint in the supply chain, of which transportation has become the second major source of carbon emissions. This paper aims to study both optimum pricing and order quantities, considering consumer demand and the selection of transportation modes by retailers, in terms of carbon emissions sensitivity and price sensitivity under the conditions of a cap-and-trade policy and uncertain market demand. Firstly, we analyze the e...
Arslan, Burcu; Taatgen, Niels A.; Verbrugge, Rineke
2017-01-01
The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback “Wrong,” they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children’s failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy. PMID:28293206
Arslan, Burcu; Taatgen, Niels A; Verbrugge, Rineke
2017-01-01
The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false belief tasks by constructing two computational cognitive models of this process: an instance-based learning model and a reinforcement learning model. Unlike the reinforcement learning model, the instance-based learning model predicted that children who fail second-order false belief tasks would give answers based on first-order theory of mind (ToM) reasoning as opposed to zero-order reasoning. This prediction was confirmed with an empirical study that we conducted with 72 5- to 6-year-old children. The results showed that 17% of the answers were correct and 83% of the answers were wrong. In line with our prediction, 65% of the wrong answers were based on a first-order ToM strategy, while only 29% of them were based on a zero-order strategy (the remaining 6% of subjects did not provide any answer). Based on our instance-based learning model, we propose that when children get feedback "Wrong," they explicitly revise their strategy to a higher level instead of implicitly selecting one of the available ToM strategies. Moreover, we predict that children's failures are due to lack of experience and that with exposure to second-order false belief reasoning, children can revise their wrong first-order reasoning strategy to a correct second-order reasoning strategy.
Objective ARX Model Order Selection for Multi-Channel Human Operator Identification
Roggenkämper, N; Pool, D.M.; Drop, F.M.; van Paassen, M.M.; Mulder, M.
2016-01-01
In manual control, the human operator primarily responds to visual inputs but may elect to make use of other available feedback paths such as physical motion, adopting a multi-channel control strategy. Hu- man operator identification procedures generally require a priori selection of the model
Directory of Open Access Journals (Sweden)
Zhi Li
2013-01-01
Full Text Available This paper presents a supplier selection and order allocation (SSOA model to solve the problem of a multiperiod supplier selection and then order allocation in the environment of short product life cycle and frequent material purchasing, for example, fast fashion environment in apparel industry. At the first stage, with consideration of multiple decision criteria and the fuzziness of the data involved in deciding the preferences of multiple decision variables in supplier selection, the fuzzy extent analytic hierarchy process (FEAHP is adopted. In the second stage, supplier ranks are inputted into an order allocation model that aims at minimizing the risk of material purchasing and minimizing the total material purchasing costs using a dynamic programming approach, subject to constraints on deterministic customer demand and deterministic supplier capacity. Numerical examples are presented, and computational results are reported.
Directory of Open Access Journals (Sweden)
Habibollah Mohamadi
2014-08-01
Full Text Available Recently, much attention has been given to Stochastic demand due to uncertainty in the real -world. In the literature, decision-making models and suppliers' selection do not often consider inventory management as part of shopping problems. On the other hand, the environmental sustainability of a supply chain depends on the shopping strategy of the supply chain members. The supplier selection plays an important role in the green chain. In this paper, a multi-objective nonlinear integer programming model for selecting a set of supplier considering Stochastic demand is proposed. while the cost of purchasing include the total cost, holding and stock out costs, rejected units, units have been delivered sooner, and total green house gas emissions are minimized, while the obtained total score from the supplier assessment process is maximized. It is assumed, the purchaser provides the different products from the number predetermined supplier to a with Stochastic demand and the uniform probability distribution function. The product price depends on the order quantity for each product line is intended. Multi-objective models using known methods, such as Lp-metric has become an objective function and then uses genetic algorithms and simulated annealing meta-heuristic is solved.
Zhang, Jiyuan; Cheong, Mun-Wai; Yu, Bin; Curran, Philip; Zhou, Weibiao
2014-09-04
The application of headspace-solid phase microextraction (HS-SPME) has been widely used in various fields as a simple and versatile method, yet challenging in quantification. In order to improve the reproducibility in quantification, a mathematical model with its root in psychological modeling and chemical reactor modeling was developed, describing the kinetic behavior of aroma active compounds extracted by SPME from two different food model systems, i.e., a semi-solid food and a liquid food. The model accounted for both adsorption and release of the analytes from SPME fiber, which occurred simultaneously but were counter-directed. The model had four parameters and their estimated values were found to be more reproducible than the direct measurement of the compounds themselves by instrumental analysis. With the relative standard deviations (RSD) of each parameter less than 5% and root mean square error (RMSE) less than 0.15, the model was proved to be a robust one in estimating the release of a wide range of low molecular weight acetates at three environmental temperatures i.e., 30, 40 and 60 °C. More insights of SPME behavior regarding the small molecule analytes were also obtained through the kinetic parameters and the model itself.
Directory of Open Access Journals (Sweden)
Jiyuan Zhang
2014-09-01
Full Text Available The application of headspace-solid phase microextraction (HS-SPME has been widely used in various fields as a simple and versatile method, yet challenging in quantification. In order to improve the reproducibility in quantification, a mathematical model with its root in psychological modeling and chemical reactor modeling was developed, describing the kinetic behavior of aroma active compounds extracted by SPME from two different food model systems, i.e., a semi-solid food and a liquid food. The model accounted for both adsorption and release of the analytes from SPME fiber, which occurred simultaneously but were counter-directed. The model had four parameters and their estimated values were found to be more reproducible than the direct measurement of the compounds themselves by instrumental analysis. With the relative standard deviations (RSD of each parameter less than 5% and root mean square error (RMSE less than 0.15, the model was proved to be a robust one in estimating the release of a wide range of low molecular weight acetates at three environmental temperatures i.e., 30, 40 and 60 °C. More insights of SPME behavior regarding the small molecule analytes were also obtained through the kinetic parameters and the model itself.
Arslan, Burcu; Taatgen, Niels A; Verbrugge, Rineke
2017-01-01
The focus of studies on second-order false belief reasoning generally was on investigating the roles of executive functions and language with correlational studies. Different from those studies, we focus on the question how 5-year-olds select and revise reasoning strategies in second-order false
Directory of Open Access Journals (Sweden)
Mohammad Mahdavi Mazdeh
2016-06-01
Full Text Available This paper analyzes a supply chain, which consists of a manufacturer, a retailer and several suppliers in which the retailer orders jobs to the manufacturer and the suppliers provide the requiring parts. The manufacturer schedules and processes the orders and dispatches them to the retailer either individually or collectively in batches. The manufacturer incurs a penalty cost for each tardy job and a transportation cost for every delivered batch and therefore, searches for a schedule that yields minimum number of tardy jobs and batches. Moreover, the manufacturer tries to optimize its supplying cost through locating the suppliers that offer appropriate release times and costs for manufacturing parts. Since the release times of parts directly affect scheduling of orders, in this research, we develop an integrated mathematical model for the manufacturer that incorporates suppliers' selection issue into the scheduling and batching decisions. Furthermore, we present a heuristic algorithm (greedy algorithm and also a local search to quickly determine the optimal or near-optimal solutions. The computational analysis shows the importance of the integrated model and also the superiority and effectiveness of the heuristic algorithms.
Fernandez, R.; Deveaux, V.
2010-01-01
We provide a formal definition and study the basic properties of partially ordered chains (POC). These systems were proposed to model textures in image processing and to represent independence relations between random variables in statistics (in the later case they are known as Bayesian networks).
Determining Reduced Order Models for Optimal Stochastic Reduced Order Models
Energy Technology Data Exchange (ETDEWEB)
Bonney, Matthew S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Brake, Matthew R.W. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2015-08-01
The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a Taylor series using finite difference, a proper orthogonal decomposition of the the output, a Craig-Bampton representation of the model, a method that uses Hyper-Dual numbers to determine the sensitivities, and a Meta-Model method that uses the Hyper-Dual results and constructs a polynomial curve to better represent the output data. The methods are compared against a parameter sweep and a distribution propagation where the first four statistical moments are used as a comparison. Each method produces very accurate results with the Craig-Bampton reduction having the least accurate results. The models are also compared based on time requirements for the evaluation of each model where the Meta- Model requires the least amount of time for computation by a significant amount. Each of the five models provided accurate results in a reasonable time frame. The determination of which model to use is dependent on the availability of the high-fidelity model and how many evaluations can be performed. Analysis of the output distribution is examined by using a large Monte-Carlo simulation along with a reduced simulation using Latin Hypercube and the stochastic reduced order model sampling technique. Both techniques produced accurate results. The stochastic reduced order modeling technique produced less error when compared to an exhaustive sampling for the majority of methods.
Second Order Mode Selective Phase-Matching
DEFF Research Database (Denmark)
Lassen, Mikael Østergaard; Delaubert, Vincent; Bachor, Hans. A-
2006-01-01
We exploit second order (χ(2)) nonlinear optical phase matching for the selection of individual high order transverse modes. The ratio between the generated components can be adjusted continuously via changes in the phase-matching condition. ©2007 Optical Society of America......We exploit second order (χ(2)) nonlinear optical phase matching for the selection of individual high order transverse modes. The ratio between the generated components can be adjusted continuously via changes in the phase-matching condition. ©2007 Optical Society of America...
Kuiper, Rebecca M; Nederhoff, Tim; Klugkist, Irene
2015-05-01
In this paper, the performance of six types of techniques for comparisons of means is examined. These six emerge from the distinction between the method employed (hypothesis testing, model selection using information criteria, or Bayesian model selection) and the set of hypotheses that is investigated (a classical, exploration-based set of hypotheses containing equality constraints on the means, or a theory-based limited set of hypotheses with equality and/or order restrictions). A simulation study is conducted to examine the performance of these techniques. We demonstrate that, if one has specific, a priori specified hypotheses, confirmation (i.e., investigating theory-based hypotheses) has advantages over exploration (i.e., examining all possible equality-constrained hypotheses). Furthermore, examining reasonable order-restricted hypotheses has more power to detect the true effect/non-null hypothesis than evaluating only equality restrictions. Additionally, when investigating more than one theory-based hypothesis, model selection is preferred over hypothesis testing. Because of the first two results, we further examine the techniques that are able to evaluate order restrictions in a confirmatory fashion by examining their performance when the homogeneity of variance assumption is violated. Results show that the techniques are robust to heterogeneity when the sample sizes are equal. When the sample sizes are unequal, the performance is affected by heterogeneity. The size and direction of the deviations from the baseline, where there is no heterogeneity, depend on the effect size (of the means) and on the trend in the group variances with respect to the ordering of the group sizes. Importantly, the deviations are less pronounced when the group variances and sizes exhibit the same trend (e.g., are both increasing with group number). © 2014 The British Psychological Society.
On nonlinear reduced order modeling
International Nuclear Information System (INIS)
Abdel-Khalik, Hany S.
2011-01-01
When applied to a model that receives n input parameters and predicts m output responses, a reduced order model estimates the variations in the m outputs of the original model resulting from variations in its n inputs. While direct execution of the forward model could provide these variations, reduced order modeling plays an indispensable role for most real-world complex models. This follows because the solutions of complex models are expensive in terms of required computational overhead, thus rendering their repeated execution computationally infeasible. To overcome this problem, reduced order modeling determines a relationship (often referred to as a surrogate model) between the input and output variations that is much cheaper to evaluate than the original model. While it is desirable to seek highly accurate surrogates, the computational overhead becomes quickly intractable especially for high dimensional model, n ≫ 10. In this manuscript, we demonstrate a novel reduced order modeling method for building a surrogate model that employs only 'local first-order' derivatives and a new tensor-free expansion to efficiently identify all the important features of the original model to reach a predetermined level of accuracy. This is achieved via a hybrid approach in which local first-order derivatives (i.e., gradient) of a pseudo response (a pseudo response represents a random linear combination of original model’s responses) are randomly sampled utilizing a tensor-free expansion around some reference point, with the resulting gradient information aggregated in a subspace (denoted by the active subspace) of dimension much less than the dimension of the input parameters space. The active subspace is then sampled employing the state-of-the-art techniques for global sampling methods. The proposed method hybridizes the use of global sampling methods for uncertainty quantification and local variational methods for sensitivity analysis. In a similar manner to
Randomized Local Model Order Reduction
Buhr, Andreas; Smetana, Kathrin
2017-01-01
In this paper we propose local approximation spaces for localized model order reduction procedures such as domain decomposition and multiscale methods. Those spaces are constructed from local solutions of the partial differential equation (PDE) with random boundary conditions, yield an approximation
Another Look at Some Information Order Selection Criteria in ...
African Journals Online (AJOL)
In this paper, the selection procedure of the order of an autoregressive process of order P, AR(p) using the information order selection criteria is considered. We compare the performance of three information criteria methods, the Akaike information criterion (AIC), Schwarz information criterion (BIC) and Hannan & Quinn ...
Generalized Reduced Order Model Generation, Phase I
National Aeronautics and Space Administration — M4 Engineering proposes to develop a generalized reduced order model generation method. This method will allow for creation of reduced order aeroservoelastic state...
Generalized Reduced Order Model Generation Project
National Aeronautics and Space Administration — M4 Engineering proposes to develop a generalized reduced order model generation method. This method will allow for creation of reduced order aeroservoelastic state...
Orderability and continuous selections for Wijsman and Vietoris hyperspaces
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Debora Di Caprio
2003-10-01
Full Text Available Bertacchi and Costantini obtained some conditions equivalent to the existence of continuous selections for the Wijsman hyperspace of ultrametric Polish spaces. We introduce a new class of hypertopologies, the macro-topologies. Both the Wijsman topology and the Vietoris topology belong to this class. We show that subject to natural conditions, the base space admits a closed order such that the minimum map is a continuous selection for every macro-topology. In the setting of Polish spaces, these conditions are substantially weaker than the ones given by Bertacchi and Costantini. In particular, we conclude that Polish spaces satisfying these conditions can be endowed with a compatible order and that the minimum function is a continuous selection for the Wijsman topology, just as it is for [0; 1]. This also solves a problem implicitely raised in Bertacchi and Costantini's paper.
Model selection for Gaussian kernel PCA denoising
DEFF Research Database (Denmark)
Jørgensen, Kasper Winther; Hansen, Lars Kai
2012-01-01
We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...
Generalized linear model for partially ordered data.
Zhang, Qiang; Ip, Edward Haksing
2012-01-13
Within the rich literature on generalized linear models, substantial efforts have been devoted to models for categorical responses that are either completely ordered or completely unordered. Few studies have focused on the analysis of partially ordered outcomes, which arise in practically every area of study, including medicine, the social sciences, and education. To fill this gap, we propose a new class of generalized linear models--the partitioned conditional model--that includes models for both ordinal and unordered categorical data as special cases. We discuss the specification of the partitioned conditional model and its estimation. We use an application of the method to a sample of the National Longitudinal Study of Youth to illustrate how the new method is able to extract from partially ordered data useful information about smoking youths that is not possible using traditional methods. Copyright © 2011 John Wiley & Sons, Ltd.
XY model with higher-order exchange.
Žukovič, Milan; Kalagov, Georgii
2017-08-01
An XY model, generalized by inclusion of up to an infinite number of higher-order pairwise interactions with an exponentially decreasing strength, is studied by spin-wave theory and Monte Carlo simulations. At low temperatures the model displays a quasi-long-range-order phase characterized by an algebraically decaying correlation function with the exponent η=T/[2πJ(p,α)], nonlinearly dependent on the parameters p and α that control the number of the higher-order terms and the decay rate of their intensity, respectively. At higher temperatures the system shows a crossover from the continuous Berezinskii-Kosterlitz-Thouless to the first-order transition for the parameter values corresponding to a highly nonlinear shape of the potential well. The role of topological excitations (vortices) in changing the nature of the transition is discussed.
International Nuclear Information System (INIS)
Martin Llorente, F.
1990-01-01
The models of atmospheric pollutants dispersion are based in mathematic algorithms that describe the transport, diffusion, elimination and chemical reactions of atmospheric contaminants. These models operate with data of contaminants emission and make an estimation of quality air in the area. This model can be applied to several aspects of atmospheric contamination
Bogiages, Christopher A.; Lotter, Christine
2011-01-01
In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…
Dynamical models of happiness with fractional order
Song, Lei; Xu, Shiyun; Yang, Jianying
2010-03-01
This present study focuses on a dynamical model of happiness described through fractional-order differential equations. By categorizing people of different personality and different impact factor of memory (IFM) with different set of model parameters, it is demonstrated via numerical simulations that such fractional-order models could exhibit various behaviors with and without external circumstance. Moreover, control and synchronization problems of this model are discussed, which correspond to the control of emotion as well as emotion synchronization in real life. This study is an endeavor to combine the psychological knowledge with control problems and system theories, and some implications for psychotherapy as well as hints of a personal approach to life are both proposed.
Nie, Huifen
The primary aim of this thesis is to investigate the phase behavior and kinetics of the disorder-order transition (ODT) and the order-order transition (OOT) in block copolymer solutions in selective solvents, using small-angle x-ray scattering (SAXS) and small-angle neutron scattering (SANS) techniques. The kinetics of the ODT and OOT in a triblock of polystyrene (PS) and poly(ethylene-co-butylene) (PEB), in mineral oil, a selective solvent for the middle PEB block, was followed by time-resolved SAXS using temperature ramp and rapid temperature quench techniques. In semi-dilute solutions, spherical micelles exhibited liquid-like ordering at elevated temperatures and ordered onto body-centered-cubic (BCC) lattice at intermediate temperatures. In concentrated solutions, both BCC ordering of spherical micelles and hexagonal packing (HEX) of cylindrical micelles were detected. The disorder to order transformation kinetics exhibit two-stage growth: the first stage reflecting the temperature equilibration and supercooling of the micellar fluid, and the second stage corresponding to the nucleation and growth of the ordered state. The order-order transformation from HEX to BCC was characterized by a continuous shift in the primary peak position and simultaneous growth of all higher order Bragg diffraction peaks in the scattering profiles. The data were interpreted in terms of a model, according to which cylindrical micelles exhibit fluctuations with a characteristic wavelength along the cylindrical axis before breaking up into spherical micelles. Our results show that the wavelength of the fluctuation remains unchanged while the amplitude of the fluctuations grows with time. The association behavior of triblock, pentablock, and heptablock copolymers of polystyrene and polybutadiene in selective solvents was examined by SANS measurement. Comparison of the scattering profiles shows that: (i) multiblock copolymers with the outer block in a poor solvent environment have a
Optimal inventory management and order book modeling
Baradel, Nicolas
2018-02-16
We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [14, 20, 21], the MM and the HFT define their trading strategy by optimizing the expected utility of terminal wealth, while the IB has a prescheduled task to sell or buy many shares of the considered asset. We derive the variational partial differential equations that characterize the value functions of the MM and HFT and explain how almost optimal control can be deduced from them. We then provide a first illustration of the interactions that can take place between these different market participants by simulating the dynamic of an order book in which each of them plays his own (optimal) strategy.
Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.
Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.
Optimal heavy tail estimation - Part 1: Order selection
Mudelsee, Manfred; Bermejo, Miguel A.
2017-12-01
The tail probability, P, of the distribution of a variable is important for risk analysis of extremes. Many variables in complex geophysical systems show heavy tails, where P decreases with the value, x, of a variable as a power law with a characteristic exponent, α. Accurate estimation of α on the basis of data is currently hindered by the problem of the selection of the order, that is, the number of largest x values to utilize for the estimation. This paper presents a new, widely applicable, data-adaptive order selector, which is based on computer simulations and brute force search. It is the first in a set of papers on optimal heavy tail estimation. The new selector outperforms competitors in a Monte Carlo experiment, where simulated data are generated from stable distributions and AR(1) serial dependence. We calculate error bars for the estimated α by means of simulations. We illustrate the method on an artificial time series. We apply it to an observed, hydrological time series from the River Elbe and find an estimated characteristic exponent of 1.48 ± 0.13. This result indicates finite mean but infinite variance of the statistical distribution of river runoff.
Optimal heavy tail estimation – Part 1: Order selection
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M. Mudelsee
2017-12-01
Full Text Available The tail probability, P, of the distribution of a variable is important for risk analysis of extremes. Many variables in complex geophysical systems show heavy tails, where P decreases with the value, x, of a variable as a power law with a characteristic exponent, α. Accurate estimation of α on the basis of data is currently hindered by the problem of the selection of the order, that is, the number of largest x values to utilize for the estimation. This paper presents a new, widely applicable, data-adaptive order selector, which is based on computer simulations and brute force search. It is the first in a set of papers on optimal heavy tail estimation. The new selector outperforms competitors in a Monte Carlo experiment, where simulated data are generated from stable distributions and AR(1 serial dependence. We calculate error bars for the estimated α by means of simulations. We illustrate the method on an artificial time series. We apply it to an observed, hydrological time series from the River Elbe and find an estimated characteristic exponent of 1.48 ± 0.13. This result indicates finite mean but infinite variance of the statistical distribution of river runoff.
Fractional-Order Nonlinear Systems Modeling, Analysis and Simulation
Petráš, Ivo
2011-01-01
"Fractional-Order Nonlinear Systems: Modeling, Analysis and Simulation" presents a study of fractional-order chaotic systems accompanied by Matlab programs for simulating their state space trajectories, which are shown in the illustrations in the book. Description of the chaotic systems is clearly presented and their analysis and numerical solution are done in an easy-to-follow manner. Simulink models for the selected fractional-order systems are also presented. The readers will understand the fundamentals of the fractional calculus, how real dynamical systems can be described using fractional derivatives and fractional differential equations, how such equations can be solved, and how to simulate and explore chaotic systems of fractional order. The book addresses to mathematicians, physicists, engineers, and other scientists interested in chaos phenomena or in fractional-order systems. It can be used in courses on dynamical systems, control theory, and applied mathematics at graduate or postgraduate level. ...
Exploring Several Methods of Groundwater Model Selection
Samani, Saeideh; Ye, Ming; Asghari Moghaddam, Asghar
2017-04-01
Selecting reliable models for simulating groundwater flow and solute transport is essential to groundwater resources management and protection. This work is to explore several model selection methods for avoiding over-complex and/or over-parameterized groundwater models. We consider six groundwater flow models with different numbers (6, 10, 10, 13, 13 and 15) of model parameters. These models represent alternative geological interpretations, recharge estimates, and boundary conditions at a study site in Iran. The models were developed with Model Muse, and calibrated against observations of hydraulic head using UCODE. Model selection was conducted by using the following four approaches: (1) Rank the models using their root mean square error (RMSE) obtained after UCODE-based model calibration, (2) Calculate model probability using GLUE method, (3) Evaluate model probability using model selection criteria (AIC, AICc, BIC, and KIC), and (4) Evaluate model weights using the Fuzzy Multi-Criteria-Decision-Making (MCDM) approach. MCDM is based on the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance, which is to identify the ideal solution by a gradual expansion from the local to the global scale of model parameters. The KIC and MCDM methods are superior to other methods, as they consider not only the fit between observed and simulated data and the number of parameter, but also uncertainty in model parameters. Considering these factors can prevent from occurring over-complexity and over-parameterization, when selecting the appropriate groundwater flow models. These methods selected, as the best model, one with average complexity (10 parameters) and the best parameter estimation (model 3).
Green supplier selection and order allocation in a low-carbon paper industry
DEFF Research Database (Denmark)
Govindan, Kannan; Sivakumar, R.
2016-01-01
is to engage the case company with their supplier networks to diminish the greenhouse gases (GHG) emissions and cost in their production process. It proposes a model to support the selection of the best green supplier and an allocation of order among the potential suppliers. The proposed model contains a two......-phase hybrid approach. The first phase presents the rating and selection of potential suppliers by considering economics (cost), operational factors (quality and delivery), and environmental criteria (recycle capability and GHG emission control) using Fuzzy Technique for Order of Preference by Similarity...... manufacturing industry is presented to elucidate the effectiveness of the proposed model. The results demonstrate a 26.2 % reduction of carbon emission by using recycle products in the production process. The firm benefits by forming a systematic methodology for green supplier evaluation and order allocation...
Marchenko, Yulia V.
2012-03-01
Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical Association.
Are Quantum Models for Order Effects Quantum?
Moreira, Catarina; Wichert, Andreas
2017-12-01
The application of principles of Quantum Mechanics in areas outside of physics has been getting increasing attention in the scientific community in an emergent disciplined called Quantum Cognition. These principles have been applied to explain paradoxical situations that cannot be easily explained through classical theory. In quantum probability, events are characterised by a superposition state, which is represented by a state vector in a N-dimensional vector space. The probability of an event is given by the squared magnitude of the projection of this superposition state into the desired subspace. This geometric approach is very useful to explain paradoxical findings that involve order effects, but do we really need quantum principles for models that only involve projections? This work has two main goals. First, it is still not clear in the literature if a quantum projection model has any advantage towards a classical projection. We compared both models and concluded that the Quantum Projection model achieves the same results as its classical counterpart, because the quantum interference effects play no role in the computation of the probabilities. Second, it intends to propose an alternative relativistic interpretation for rotation parameters that are involved in both classical and quantum models. In the end, instead of interpreting these parameters as a similarity measure between questions, we propose that they emerge due to the lack of knowledge concerned with a personal basis state and also due to uncertainties towards the state of world and towards the context of the questions.
Modeling Ability Differentiation in the Second-Order Factor Model
Molenaar, Dylan; Dolan, Conor V.; van der Maas, Han L. J.
2011-01-01
In this article we present factor models to test for ability differentiation. Ability differentiation predicts that the size of IQ subtest correlations decreases as a function of the general intelligence factor. In the Schmid-Leiman decomposition of the second-order factor model, we model differentiation by introducing heteroscedastic residuals,…
Decimative Spectral Estimation with Unconstrained Model Order
Directory of Open Access Journals (Sweden)
Stavroula-Evita Fotinea
2012-01-01
Full Text Available This paper presents a new state-space method for spectral estimation that performs decimation by any factor, it makes use of the full set of data and brings further apart the poles under consideration, while imposing almost no constraints to the size of the Hankel matrix (model order, as decimation increases. It is compared against two previously proposed techniques for spectral estimation (along with derived decimative versions, that lie among the most promising methods in the field of spectroscopy, where accuracy of parameter estimation is of utmost importance. Moreover, it is compared against a state-of-the-art purely decimative method proposed in literature. Experiments performed on simulated NMR signals prove the new method to be more robust, especially for low signal-to-noise ratio.
46 CFR 5.569 - Selection of an appropriate order.
2010-10-01
... an order and present argument in support of this suggestion during the presentation of aggravating or... should not affect the fair and impartial adjudication of each case on its individual facts and merits...
Reduced order model of draft tube flow
International Nuclear Information System (INIS)
Rudolf, P; Štefan, D
2014-01-01
Swirling flow with compact coherent structures is very good candidate for proper orthogonal decomposition (POD), i.e. for decomposition into eigenmodes, which are the cornerstones of the flow field. Present paper focuses on POD of steady flows, which correspond to different operating points of Francis turbine draft tube flow. Set of eigenmodes is built using a limited number of snapshots from computational simulations. Resulting reduced order model (ROM) describes whole operating range of the draft tube. ROM enables to interpolate in between the operating points exploiting the knowledge about significance of particular eigenmodes and thus reconstruct the velocity field in any operating point within the given range. Practical example, which employs axisymmetric simulations of the draft tube flow, illustrates accuracy of ROM in regions without vortex breakdown together with need for higher resolution of the snapshot database close to location of sudden flow changes (e.g. vortex breakdown). ROM based on POD interpolation is very suitable tool for insight into flow physics of the draft tube flows (especially energy transfers in between different operating points), for supply of data for subsequent stability analysis or as an initialization database for advanced flow simulations
The Optimal Economic Order: the simplest model
J. Tinbergen (Jan)
1992-01-01
textabstractIn the last five years humanity has become faced with the problem of the optimal socioeconomic order more clearly than ever. After the confrontation of capitalism and socialism, which was the core of the Marxist thesis, the fact transpired that capitalism was not the optimal order. It
The role of neighbours selection on cohesion and order of swarms.
Directory of Open Access Journals (Sweden)
Angelo M Calvão
Full Text Available We introduce a multi-agent model for exploring how selection of neighbours determines some aspects of order and cohesion in swarms. The model algorithm states that every agents' motion seeks for an optimal distance from the nearest topological neighbour encompassed in a limited attention field. Despite the great simplicity of the implementation, varying the amplitude of the attention landscape, swarms pass from cohesive and regular structures towards fragmented and irregular configurations. Interestingly, this movement rule is an ideal candidate for implementing the selfish herd hypothesis which explains aggregation of alarmed group of social animals.
Ordered Arrays of Size-Selected Oxide Nanoparticles
Gragnaniello, Luca; Ma, Teng; Barcaro, Giovanni; Sementa, Luca; Negreiros, Fabio R.; Fortunelli, Alessandro; Surnev, Svetlozar; Netzer, Falko P.
2012-05-01
A bottom-up approach to produce a long-range ordered superlattice of monodisperse and isomorphic metal-oxide nanoparticles (NP) supported onto an oxide substrate is demonstrated. The synthetic strategy consists of self-assembling metallic NP on an ultrathin nanopatterned aluminum oxide template followed by a morphology-conserving oxidation process, and is exemplified in the case of Ni, but is generally applicable to a wide range of metallic systems. Both fully oxidized and core-shell metal-metal-oxide particles are synthesized, up to 3-4 nm in diameter, and characterized via spectroscopic and theoretical tools. This opens up a new avenue for probing unit and ensemble effects on the properties of oxide materials in the nanoscale regime.
Voter models with heterozygosity selection
Czech Academy of Sciences Publication Activity Database
Sturm, A.; Swart, Jan M.
2008-01-01
Roč. 18, č. 1 (2008), s. 59-99 ISSN 1050-5164 R&D Projects: GA ČR GA201/06/1323; GA ČR GA201/07/0237 Institutional research plan: CEZ:AV0Z10750506 Keywords : Heterozygosity selection * rebellious voter model * branching * annihilation * survival * coexistence Subject RIV: BA - General Mathematics Impact factor: 1.285, year: 2008
Put the healthy item first: Order of ingredient listing influences consumer selection.
Policastro, Peggy; Smith, Zach; Chapman, Gretchen
2017-06-01
This study examined effects of an order form intervention on choice of healthy ingredients among college students ordering submarine sandwiches (subs). Over eight weekly sub-nights, college students submitted 9765 orders. The order form format was manipulated in one condition making the healthier selections more salient. Main outcome measures were selection of ingredients. Multi-level logistic regression, chi square, and analysis of variance were used to examine the effect of experimental conditions on selection of ingredients. The health-salient form increased selection of healthier and decreased selection of unhealthy ingredients. The manipulation increased fiber, decreased sodium, but did not impact calorie/fat levels of the subs served.
Model Order Reduction of Aeroservoelastic Model of Flexible Aircraft
Wang, Yi; Song, Hongjun; Pant, Kapil; Brenner, Martin J.; Suh, Peter
2016-01-01
This paper presents a holistic model order reduction (MOR) methodology and framework that integrates key technological elements of sequential model reduction, consistent model representation, and model interpolation for constructing high-quality linear parameter-varying (LPV) aeroservoelastic (ASE) reduced order models (ROMs) of flexible aircraft. The sequential MOR encapsulates a suite of reduction techniques, such as truncation and residualization, modal reduction, and balanced realization and truncation to achieve optimal ROMs at grid points across the flight envelope. The consistence in state representation among local ROMs is obtained by the novel method of common subspace reprojection. Model interpolation is then exploited to stitch ROMs at grid points to build a global LPV ASE ROM feasible to arbitrary flight condition. The MOR method is applied to the X-56A MUTT vehicle with flexible wing being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies demonstrated that relative to the fullorder model, our X-56A ROM can accurately and reliably capture vehicles dynamics at various flight conditions in the target frequency regime while the number of states in ROM can be reduced by 10X (from 180 to 19), and hence, holds great promise for robust ASE controller synthesis and novel vehicle design.
Model order reduction using eigen algorithm
African Journals Online (AJOL)
DR OKE
to use either for design or analysis. Hence, it is ... directly from the Eigen algorithm while the zeros are determined through factor division algorithm to obtain the reduced order system. ..... V. Singh, Chandra and H. Kar, “Improved Routh Pade approximationss: A computer aided approach”, IEEE Transaction on. Automat ...
Skewed factor models using selection mechanisms
Kim, Hyoung-Moon
2015-12-21
Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.
Order and disorder in product innovation models
Pina e Cunha, Miguel; Gomes, Jorge F.S.; Gomes, J.F.
2003-01-01
This article argues that the conceptual development of product innovation models goes hand in hand with paradigmatic changes in the field of organization science. Remarkable similarities in the change of organizational perspectives and product innovation models are noticeable. To illustrate how
Expatriates Selection: An Essay of Model Analysis
Directory of Open Access Journals (Sweden)
Rui Bártolo-Ribeiro
2015-03-01
Full Text Available The business expansion to other geographical areas with different cultures from which organizations were created and developed leads to the expatriation of employees to these destinations. Recruitment and selection procedures of expatriates do not always have the intended success leading to an early return of these professionals with the consequent organizational disorders. In this study, several articles published in the last five years were analyzed in order to identify the most frequently mentioned dimensions in the selection of expatriates in terms of success and failure. The characteristics in the selection process that may increase prediction of adaptation of expatriates to new cultural contexts of the some organization were studied according to the KSAOs model. Few references were found concerning Knowledge, Skills and Abilities dimensions in the analyzed papers. There was a strong predominance on the evaluation of Other Characteristics, and was given more importance to dispositional factors than situational factors for promoting the integration of the expatriates.
Model Order Reduction: Application to Electromagnetic Problems
Paquay, Yannick
2017-01-01
With the increase in computational resources, numerical modeling has grown expo- nentially these last two decades. From structural analysis to combustion modeling and electromagnetics, discretization methods–in particular the finite element method–have had a tremendous impact. Their main advantage consists in a correct representation of dynamical and nonlinear behaviors by solving equations at local scale, however the spatial discretization inherent to such approaches is also its main drawbac...
Pretreatment of wastewater: Optimal coagulant selection using Partial Order Scaling Analysis (POSA)
International Nuclear Information System (INIS)
Tzfati, Eran; Sein, Maya; Rubinov, Angelika; Raveh, Adi; Bick, Amos
2011-01-01
Jar-test is a well-known tool for chemical selection for physical-chemical wastewater treatment. Jar test results show the treatment efficiency in terms of suspended matter and organic matter removal. However, in spite of having all these results, coagulant selection is not an easy task because one coagulant can remove efficiently the suspended solids but at the same time increase the conductivity. This makes the final selection of coagulants very dependent on the relative importance assigned to each measured parameter. In this paper, the use of Partial Order Scaling Analysis (POSA) and multi-criteria decision analysis is proposed to help the selection of the coagulant and its concentration in a sequencing batch reactor (SBR). Therefore, starting from the parameters fixed by the jar-test results, these techniques will allow to weight these parameters, according to the judgments of wastewater experts, and to establish priorities among coagulants. An evaluation of two commonly used coagulation/flocculation aids (Alum and Ferric Chloride) was conducted and based on jar tests and POSA model, Ferric Chloride (100 ppm) was the best choice. The results obtained show that POSA and multi-criteria techniques are useful tools to select the optimal chemicals for the physical-technical treatment.
Model Order Reduction for Non Linear Mechanics
Pinillo, Rubén
2017-01-01
Context: Automotive industry is moving towards a new generation of cars. Main idea: Cars are furnished with radars, cameras, sensors, etc… providing useful information about the environment surrounding the car. Goals: Provide an efficient model for the radar input/output. Reducing computational costs by means of big data techniques.
Review and selection of unsaturated flow models
Energy Technology Data Exchange (ETDEWEB)
NONE
1993-09-10
Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer ground-water flow models; to conduct performance assessments; and to develop performance assessment models, where necessary. In the area of scientific modeling, the M&O CRWMS has the following responsibilities: To provide overall management and integration of modeling activities. To provide a framework for focusing modeling and model development. To identify areas that require increased or decreased emphasis. To ensure that the tools necessary to conduct performance assessment are available. These responsibilities are being initiated through a three-step process. It consists of a thorough review of existing models, testing of models which best fit the established requirements, and making recommendations for future development that should be conducted. Future model enhancement will then focus on the models selected during this activity. Furthermore, in order to manage future model development, particularly in those areas requiring substantial enhancement, the three-step process will be updated and reported periodically in the future.
Ordering dynamics of microscopic models with nonconserved order parameter of continuous symmetry
DEFF Research Database (Denmark)
Zhang, Z.; Mouritsen, Ole G.; Zuckermann, Martin J.
1993-01-01
from a disordered phase to an orientationally ordered phase of continuous symmetry. The Lebwohl-Lasher model accounts for the orientational ordering properties of the nematic-isotropic transition in liquid crystals and the Heisenberg model for the ferromagnetic-paramagnetic transition in magnetic...... crystals. For both models, which have a nonconserved order parameter, it is found that the linear scale, R(t), of the evolving order, following quenches to below the transition temperature, grows at late times in an effectively algebraic fashion, R(t)∼tn, with exponent values which are strongly temperature...
DEFF Research Database (Denmark)
Gupta, Pankaj; Govindan, Kannan; Mehlawat, Mukesh Kumar
2016-01-01
This paper focused on the analysis of imprecise information in terms of many critical parameters for a multi-objective multi-item vendor selection-order allocation problem with price-breaks. We used both quantitative and qualitative criteria taking into account the economic, technological, social......, environmental factors, and the price-breaks that were offered on order quantity following ‘all-unit discount schedule.’ We developed an optimization model that integrated fuzzy multi-objective integer linear programming and analytic hierarchy process techniques. A weighted possibilistic programming approach...... approach were tested on the data set of an industrial case study. A detailed performance analysis and comparisons were done to show superiority of the proposed methodology over the existing related fuzzy programming approaches....
Chen, Jonathan H; Goldstein, Mary K; Asch, Steven M; Mackey, Lester; Altman, Russ B
2017-05-01
Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets. The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders. Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% ( P sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability. Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.
5 CFR 330.606 - Order of selection for filling vacancies from within the agency.
2010-01-01
... Assistance Plans (CTAP) for Local Surplus and Displaced Employees § 330.606 Order of selection for filling... provide selection priority to surplus and displaced agency employees from another commuting area after it... employee for whom OPM has approved a rule 5.1 variation; (24) At the agency's discretion, the selection of...
Model selection for univariable fractional polynomials.
Royston, Patrick
2017-07-01
Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429-467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.
Multiscale Reduced Order Modeling of Complex Multi-Bay Structures
2013-07-01
modeled with 96,000 degrees of freedom within Nastran . Keywords: reduced order modeling, nonlinear geometric response, finite elements 2...deformations, i.e. exhibiting geometric nonlinearity, from finite element models generated using commercial codes (e.g. Nastran , Abaqus, DYNA3D), see...reduced order model of the 9-bay panel modeled within Nastran with 96,000 degrees of freedom. An excellent agreement between the nonlinear static
A reduced order model of a quadruped walking system
International Nuclear Information System (INIS)
Sano, Akihito; Furusho, Junji; Naganuma, Nobuyuki
1990-01-01
Trot walking has recently been studied by several groups because of its stability and realizability. In the trot, diagonally opposed legs form pairs. While one pair of legs provides support, the other pair of legs swings forward in preparation for the next step. In this paper, we propose a reduced order model for the trot walking. The reduced order model is derived by using two dominant modes of the closed loop system in which the local feedback at each joint is implemented. It is shown by numerical examples that the obtained reduced order model can well approximate the original higher order model. (author)
Fractional-order in a macroeconomic dynamic model
David, S. A.; Quintino, D. D.; Soliani, J.
2013-10-01
In this paper, we applied the Riemann-Liouville approach in order to realize the numerical simulations to a set of equations that represent a fractional-order macroeconomic dynamic model. It is a generalization of a dynamic model recently reported in the literature. The aforementioned equations have been simulated for several cases involving integer and non-integer order analysis, with some different values to fractional order. The time histories and the phase diagrams have been plotted to visualize the effect of fractional order approach. The new contribution of this work arises from the fact that the macroeconomic dynamic model proposed here involves the public sector deficit equation, which renders the model more realistic and complete when compared with the ones encountered in the literature. The results reveal that the fractional-order macroeconomic model can exhibit a real reasonable behavior to macroeconomics systems and might offer greater insights towards the understanding of these complex dynamic systems.
Temporal mode selectivity by frequency conversion in second-order nonlinear optical waveguides
DEFF Research Database (Denmark)
Reddy, D. V.; Raymer, M. G.; McKinstrie, C. J.
2013-01-01
We explore theoretically the feasibility of using frequency conversion by sum- or difference-frequency generation, enabled by three-wave-mixing, for selectively multiplexing orthogonal input waveforms that overlap in time and frequency. Such a process would enable a drop device for use in a trans......We explore theoretically the feasibility of using frequency conversion by sum- or difference-frequency generation, enabled by three-wave-mixing, for selectively multiplexing orthogonal input waveforms that overlap in time and frequency. Such a process would enable a drop device for use...... in a transparent optical network using temporally orthogonal waveforms to encode different channels. We model the process using coupled-mode equations appropriate for wave mixing in a uniform second-order nonlinear optical medium pumped by a strong laser pulse. We find Green functions describing the process......, and employ Schmidt (singular-value) decompositions thereof to quantify its viability in functioning as a coherent waveform discriminator. We define a selectivity figure of merit in terms of the Schmidt coefficients, and use it to compare and contrast various parameter regimes via extensive numerical...
Higher-order RANS turbulence models for separated flows
National Aeronautics and Space Administration — Higher-order Reynolds-averaged Navier-Stokes (RANS) models are developed to overcome the shortcomings of second-moment RANS models in predicting separated flows....
Orbital Order in Two-Orbital Hubbard Model
Honkawa, Kojiro; Onari, Seiichiro
2018-03-01
In strongly correlated multiorbital systems, various ordered phases appear. In particular, the orbital order in iron-based superconductors attracts much attention since it is considered to be the origin of the nematic state. To clarify the essential conditions for realizing orbital orders, we study the simple two-orbital (dxz,dyz) Hubbard model. We find that the orbital order, which corresponds to the nematic order, appears due to the vertex corrections even in the two-orbital model. Thus, the dxy orbital is not essential to realize the nematic orbital order. The obtained orbital order is determined by the orbital dependence and the topology of Fermi surfaces. We also find that another type of orbital order, which is rotated 45°, appears in a heavily hole-doped case.
Spiking and bursting patterns of fractional-order Izhikevich model
Teka, Wondimu W.; Upadhyay, Ranjit Kumar; Mondal, Argha
2018-03-01
Bursting and spiking oscillations play major roles in processing and transmitting information in the brain through cortical neurons that respond differently to the same signal. These oscillations display complex dynamics that might be produced by using neuronal models and varying many model parameters. Recent studies have shown that models with fractional order can produce several types of history-dependent neuronal activities without the adjustment of several parameters. We studied the fractional-order Izhikevich model and analyzed different kinds of oscillations that emerge from the fractional dynamics. The model produces a wide range of neuronal spike responses, including regular spiking, fast spiking, intrinsic bursting, mixed mode oscillations, regular bursting and chattering, by adjusting only the fractional order. Both the active and silent phase of the burst increase when the fractional-order model further deviates from the classical model. For smaller fractional order, the model produces memory dependent spiking activity after the pulse signal turned off. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. On the network level, the response of the neuronal network shifts from random to scale-free spiking. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.
Investigation of Effectiveness of Order Review and Release Models in Make to Order Supply Chain
Directory of Open Access Journals (Sweden)
Kundu Kaustav
2016-01-01
Full Text Available Nowadays customisation becomes more common due to vast requirement from the customers for which industries are trying to use make-to-order (MTO strategy. Due to high variation in the process, workload control models are extensively used for jobshop companies which usually adapt MTO strategy. Some authors tried to implement workload control models, order review and release systems, in non-repetitive manufacturing companies, where there is a dominant flow in production. Those models are better in shop floor but their performances are never been investigated in high variation situations like MTO supply chain. This paper starts with the introduction of particular issues in MTO companies and a general overview of order review and release systems widely used in the industries. Two order review and release systems, the Limited and Balanced models, particularly suitable for flow shop system are applied to MTO supply chain, where the processing times are difficult to estimate due to high variation. Simulation results show that the Balanced model performs much better than the Limited model if the processing times can be estimated preciously.
Model order reduction techniques with applications in finite element analysis
Qu, Zu-Qing
2004-01-01
Despite the continued rapid advance in computing speed and memory the increase in the complexity of models used by engineers persists in outpacing them. Even where there is access to the latest hardware, simulations are often extremely computationally intensive and time-consuming when full-blown models are under consideration. The need to reduce the computational cost involved when dealing with high-order/many-degree-of-freedom models can be offset by adroit computation. In this light, model-reduction methods have become a major goal of simulation and modeling research. Model reduction can also ameliorate problems in the correlation of widely used finite-element analyses and test analysis models produced by excessive system complexity. Model Order Reduction Techniques explains and compares such methods focusing mainly on recent work in dynamic condensation techniques: - Compares the effectiveness of static, exact, dynamic, SEREP and iterative-dynamic condensation techniques in producing valid reduced-order mo...
Model selection and comparison for independents sinusoids
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2014-01-01
In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve this me....... Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online.......In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve...... this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion...
Selected sports talent development models
Michal Vičar
2017-01-01
Background: Sports talent in the Czech Republic is generally viewed as a static, stable phenomena. It stands in contrast with widespread praxis carried out in Anglo-Saxon countries that emphasise its fluctuant nature. This is reflected in the current models describing its development. Objectives: The aim is to introduce current models of talent development in sport. Methods: Comparison and analysing of the following models: Balyi - Long term athlete development model, Côté - Developmen...
Modelling Limit Order Execution Times from Market Data
Kim, Adlar; Farmer, Doyne; Lo, Andrew
2007-03-01
Although the term ``liquidity'' is widely used in finance literatures, its meaning is very loosely defined and there is no quantitative measure for it. Generally, ``liquidity'' means an ability to quickly trade stocks without causing a significant impact on the stock price. From this definition, we identified two facets of liquidity -- 1.execution time of limit orders, and 2.price impact of market orders. The limit order is an order to transact a prespecified number of shares at a prespecified price, which will not cause an immediate execution. On the other hand, the market order is an order to transact a prespecified number of shares at a market price, which will cause an immediate execution, but are subject to price impact. Therefore, when the stock is liquid, market participants will experience quick limit order executions and small market order impacts. As a first step to understand market liquidity, we studied the facet of liquidity related to limit order executions -- execution times. In this talk, we propose a novel approach of modeling limit order execution times and show how they are affected by size and price of orders. We used q-Weibull distribution, which is a generalized form of Weibull distribution that can control the fatness of tail to model limit order execution times.
MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS
International Nuclear Information System (INIS)
Asensio Ramos, A.; Manso Sainz, R.; Martínez González, M. J.; Socas-Navarro, H.; Viticchié, B.; Orozco Suárez, D.
2012-01-01
Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.
VEMAP 1: Selected Model Results
National Aeronautics and Space Administration — The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and...
Multi-Criteria Model for Determining Order Size
Directory of Open Access Journals (Sweden)
Katarzyna Jakowska-Suwalska
2013-01-01
Full Text Available A multi-criteria model for determining the order size for materials used in production has been presented. It was assumed that the consumption rate of each material is a random variable with a known probability distribution. Using such a model, in which the purchase cost of materials ordered is limited, three criteria were considered: order size, probability of a lack of materials in the production process, and deviations in the order size from the consumption rate in past periods. Based on an example, it has been shown how to use the model to determine the order sizes for polyurethane adhesive and wood in a hard-coal mine. (original abstract
Atmospheric Turbulence Modeling for Aerospace Vehicles: Fractional Order Fit
Kopasakis, George (Inventor)
2015-01-01
An improved model for simulating atmospheric disturbances is disclosed. A scale Kolmogorov spectral may be scaled to convert the Kolmogorov spectral into a finite energy von Karman spectral and a fractional order pole-zero transfer function (TF) may be derived from the von Karman spectral. Fractional order atmospheric turbulence may be approximated with an integer order pole-zero TF fit, and the approximation may be stored in memory.
A variable-order fractal derivative model for anomalous diffusion
Directory of Open Access Journals (Sweden)
Liu Xiaoting
2017-01-01
Full Text Available This paper pays attention to develop a variable-order fractal derivative model for anomalous diffusion. Previous investigations have indicated that the medium structure, fractal dimension or porosity may change with time or space during solute transport processes, results in time or spatial dependent anomalous diffusion phenomena. Hereby, this study makes an attempt to introduce a variable-order fractal derivative diffusion model, in which the index of fractal derivative depends on temporal moment or spatial position, to characterize the above mentioned anomalous diffusion (or transport processes. Compared with other models, the main advantages in description and the physical explanation of new model are explored by numerical simulation. Further discussions on the dissimilitude such as computational efficiency, diffusion behavior and heavy tail phenomena of the new model and variable-order fractional derivative model are also offered.
An improved second-order continuum traffic model
Marques, W., Jr.; Velasco, R. M.
2010-02-01
We construct a second-order continuum traffic model by using an iterative procedure in order to derive a constitutive relation for the traffic pressure which is similar to the Navier-Stokes equation for ordinary fluids. Our second-order traffic model represents an improvement on the traffic model suggested by Kerner and Konhäuser since the iterative procedure introduces, in the constitutive relation for the traffic pressure, a density-dependent viscosity coefficient. By using a finite-difference scheme based on the Steger-Warming flux splitting, we investigate the solution of our improved second-order traffic model for specific problems like shock fronts in traffic and freeway-lane drop.
An improved second-order continuum traffic model
International Nuclear Information System (INIS)
Marques, W Jr; Velasco, R M
2010-01-01
We construct a second-order continuum traffic model by using an iterative procedure in order to derive a constitutive relation for the traffic pressure which is similar to the Navier–Stokes equation for ordinary fluids. Our second-order traffic model represents an improvement on the traffic model suggested by Kerner and Konhäuser since the iterative procedure introduces, in the constitutive relation for the traffic pressure, a density-dependent viscosity coefficient. By using a finite-difference scheme based on the Steger–Warming flux splitting, we investigate the solution of our improved second-order traffic model for specific problems like shock fronts in traffic and freeway-lane drop
The linear utility model for optimal selection
Mellenbergh, Gideon J.; van der Linden, Willem J.
A linear utility model is introduced for optimal selection when several subpopulations of applicants are to be distinguished. Using this model, procedures are described for obtaining optimal cutting scores in subpopulations in quota-free as well as quota-restricted selection situations. The cutting
Zhang, Guobo; Chen, Min; Liu, Feng; Yuan, Xiaohui; Weng, Suming; Zheng, Jun; Ma, Yanyun; Shao, Fuqiu; Sheng, Zhengming; Zhang, Jie
2017-10-02
Relativistically intense laser solid target interaction has been proved to be a promising way to generate high-order harmonics, which can be used to diagnose ultrafast phenomena. However, their emission direction and spectra still lack tunability. Based upon two-dimensional particle-in-cell simulations, we show that directional enhancement of selected high-order-harmonics can be realized using blazed grating targets. Such targets can select harmonics with frequencies being integer times of the grating frequency. Meanwhile, the radiation intensity and emission area of the harmonics are increased. The emission direction is controlled by tailoring the local blazed structure. Theoretical and electron dynamics analysis for harmonics generation, selection and directional enhancement from the interaction between multi-cycle laser and grating target are carried out. These studies will benefit the generation and application of laser plasma-based high order harmonics.
Efficient Modal Basis Selection Criteria for Reduced-Order Nonlinear Simulation
Przekop, Adam; Rizzi, Stephen A.
2008-01-01
A modal basis selection technique for a reduced-order nonlinear numerical simulation with application to two-dimensional structures is presented as a two-step procedure. A system identification analysis is first performed using proper orthogonal decomposition. Using these results, a set of load-invariant bases consisting of the normal modes is next selected. Two criteria for making the basis selection are offered; one using the modal assurance criterion and the other using the modal expansion theorem. The quality of the subsequent reduced-order analyses are examined through comparison with computationally intensive finite element nonlinear simulations in physical degrees-of-freedom. A clamped flat isotropic plate under a random acoustic loading is considered to demonstrate the procedure. It is found that the subject procedure enables formation of an accurate and computationally efficient reduced-order system applicable to a broad range of loading conditions.
VEMAP 1: Selected Model Results
National Aeronautics and Space Administration — ABSTRACT: The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) was a multi-institutional, international effort addressing the response of biogeography and...
Order reduction for a model of marine bacteriophage evolution
Pagliarini, Silvia; Korobeinikov, Andrei
2017-02-01
A typical mechanistic model of viral evolution necessary includes several time scales which can differ by orders of magnitude. Such a diversity of time scales makes analysis of these models difficult. Reducing the order of a model is highly desirable when handling such a model. A typical approach applied to such slow-fast (or singularly perturbed) systems is the time scales separation technique. Constructing the so-called quasi-steady-state approximation is the usual first step in applying the technique. While this technique is commonly applied, in some cases its straightforward application can lead to unsatisfactory results. In this paper we construct the quasi-steady-state approximation for a model of evolution of marine bacteriophages based on the Beretta-Kuang model. We show that for this particular model the quasi-steady-state approximation is able to produce only qualitative but not quantitative fit.
Selection of classification models from repository of model for water ...
African Journals Online (AJOL)
This paper proposes a new technique, Model Selection Technique (MST) for selection and ranking of models from the repository of models by combining three performance measures (Acc, TPR and TNR). This technique provides weightage to each performance measure to find the most suitable model from the repository of ...
Testing static tradeoff theory against pecking order models of capital ...
African Journals Online (AJOL)
We test two models with the purpose of finding the best empirical explanation for corporate financing choice of a cross section of 27 Nigerian quoted companies. The models were developed to represent the Static tradeoff Theory and the Pecking order Theory of capital structure with a view to make comparison between ...
Bayesian variable order Markov models: Towards Bayesian predictive state representations
Dimitrakakis, C.
2009-01-01
We present a Bayesian variable order Markov model that shares many similarities with predictive state representations. The resulting models are compact and much easier to specify and learn than classical predictive state representations. Moreover, we show that they significantly outperform a more
Latent Partially Ordered Classification Models and Normal Mixtures
Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith
2013-01-01
Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for…
Testing static tradeoff theiry against pecking order models of capital ...
African Journals Online (AJOL)
We test two models with the purpose of finding the best empirical explanation for corporate financing choice of a cross section of 27 Nigerian quoted companies. The models were developed to represent the Static tradeoff Theory and the Pecking order Theory of capital structure with a view to make comparison between ...
Partial-Order Reduction for GPU Model Checking
Neele, T.; Wijs, A.; Bosnacki, D.; van de Pol, Jan Cornelis; Artho, C; Legay, A.; Peled, D.
2016-01-01
Model checking using GPUs has seen increased popularity over the last years. Because GPUs have a limited amount of memory, only small to medium-sized systems can be verified. For on-the-fly explicit-state model checking, we improve memory efficiency by applying partial-order reduction. We propose
Data-Driven Model Order Reduction for Bayesian Inverse Problems
Cui, Tiangang
2014-01-06
One of the major challenges in using MCMC for the solution of inverse problems is the repeated evaluation of computationally expensive numerical models. We develop a data-driven projection- based model order reduction technique to reduce the computational cost of numerical PDE evaluations in this context.
Mendelson, Tamra C; Martin, Michael D; Flaxman, Samuel M
2014-09-01
The origin of species remains a central question, and recent research focuses on the role of ecological differences in promoting speciation. Ecological differences create opportunities for divergent selection (i.e. 'ecological' speciation), a Darwinian hypothesis that hardly requires justification. In contrast, 'mutation-order' speciation proposes that, instead of adapting to different environments, populations find different ways to adapt to similar environments, implying that speciation does not require ecological differences. This distinction is critical as it provides an alternative hypothesis to the prevailing view that ecological differences drive speciation. Speciation by sexual selection lies at the centre of debates about the importance of ecological differences in promoting speciation; here, we present verbal and mathematical models of mutation-order divergence by sexual selection. We develop three general cases and provide a two-locus population genetic model for each. Results indicate that alternative secondary sexual traits can fix in populations that initially experience similar natural and sexual selection and that divergent traits and preferences can remain stable in the face of low gene flow. This stable divergence can facilitate subsequent divergence that completes or reinforces speciation. We argue that a mutation-order process could explain widespread diversity in secondary sexual traits among closely related, allopatric species. © 2014 John Wiley & Sons Ltd/CNRS.
Mathematical modelling of fractional order circuit elements and bioimpedance applications
Moreles, Miguel Angel; Lainez, Rafael
2017-05-01
In this work a classical derivation of fractional order circuits models is presented. Generalised constitutive equations in terms of fractional Riemann-Liouville derivatives are introduced in the Maxwell's equations for each circuit element. Next the Kirchhoff voltage law is applied in a RCL circuit configuration. It is shown that from basic properties of Fractional Calculus, a fractional differential equation model with Caputo derivatives is obtained. Thus standard initial conditions apply. Finally, models for bioimpedance are revisited.
Measurement Error in Designed Experiments for Second Order Models
McMahan, Angela Renee
1997-01-01
Measurement error (ME) in the factor levels of designed experiments is often overlooked in the planning and analysis of experimental designs. A familiar model for this type of ME, called the Berkson error model, is discussed at length. Previous research has examined the effect of Berkson error on two-level factorial and fractional factorial designs. This dissertation extends the examination to designs for second order models. The results are used to suggest ...
Modeling Human Behaviour with Higher Order Logic: Insider Threats
DEFF Research Database (Denmark)
Boender, Jaap; Ivanova, Marieta Georgieva; Kammuller, Florian
2014-01-01
it to the sociological process of logical explanation. As a case study on modeling human behaviour, we present the modeling and analysis of insider threats as a Higher Order Logic theory in Isabelle/HOL. We show how each of the three step process of sociological explanation can be seen in our modeling of insider’s state......, its context within an organisation and the effects on security as outcomes of a theorem proving analysis....
Alternative modal basis selection procedures for reduced-order nonlinear random response simulation
Przekop, Adam; Guo, Xinyun; Rizzi, Stephen A.
2012-08-01
Three procedures to guide selection of an efficient modal basis in a nonlinear random response analysis are examined. One method is based only on proper orthogonal decomposition, while the other two additionally involve smooth orthogonal decomposition. Acoustic random response problems are employed to assess the performance of the three modal basis selection approaches. A thermally post-buckled beam exhibiting snap-through behavior, a shallowly curved arch in the auto-parametric response regime and a plate structure are used as numerical test articles. The results of a computationally taxing full-order analysis in physical degrees of freedom are taken as the benchmark for comparison with the results from the three reduced-order analyses. For the cases considered, all three methods are shown to produce modal bases resulting in accurate and computationally efficient reduced-order nonlinear simulations.
Abnormal Waves Modelled as Second-order Conditional Waves
DEFF Research Database (Denmark)
Jensen, Jørgen Juncher
2005-01-01
The paper presents results for the expected second order short-crested wave conditional of a given wave crest at a specific point in time and space. The analysis is based on the second order Sharma and Dean shallow water wave theory. Numerical results showing the importance of the spectral density......, the water depth and the directional spreading on the conditional mean wave profile are presented. Application of conditional waves to model and explain abnormal waves, e.g. the well-known New Year Wave measured at the Draupner platform January 1st 1995, is discussed. Whereas the wave profile can be modelled...... quite well by the second order conditional wave including directional spreading and finite water depth the probability to encounter such a wave is still, however, extremely rare. The use of the second order conditional wave as initial condition to a fully non-linear three-dimensional analysis...
Group-ICA model order highlights patterns of functional brain connectivity
Directory of Open Access Journals (Sweden)
Ahmed eAbou Elseoud
2011-06-01
Full Text Available Resting-state networks (RSNs can be reliably and reproducibly detected using independent component analysis (ICA at both individual subject and group levels. Altering ICA dimensionality (model order estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual-regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders.
Terminology Modeling for an Enterprise Laboratory Orders Catalog
Zhou, Li; Goldberg, Howard; Pabbathi, Deepika; Wright, Adam; Goldman, Debora S.; Van Putten, Cheryl; Barley, Amanda; Rocha, Roberto A.
2009-01-01
Laboratory test orders are used in a variety of clinical information systems at Partners HealthCare. At present, each site at Partners manages its own set of laboratory orders with locally defined codes. Our current plan is to implement an enterprise catalog, where laboratory test orders are mapped to reference terminologies and codes from different sites are mapped to each other. This paper describes the terminology modeling effort that preceded the implementation of the enterprise laboratory orders catalog. In particular, we present our experience in adapting HL7’s “Common Terminology Services 2 – Upper Level Class Model” as a terminology metamodel for guiding the development of fully specified laboratory orders and related services. PMID:20351950
A Dynamic Model for Limb Selection
Cox, R.F.A; Smitsman, A.W.
2008-01-01
Two experiments and a model on limb selection are reported. In Experiment 1 left-handed and right-handed participants (N = 36) repeatedly used one hand for grasping a small cube. After a clear switch in the cube’s location, perseverative limb selection was revealed in both handedness groups. In
AN OVERVIEW OF REDUCED ORDER MODELING TECHNIQUES FOR SAFETY APPLICATIONS
Energy Technology Data Exchange (ETDEWEB)
Mandelli, D.; Alfonsi, A.; Talbot, P.; Wang, C.; Maljovec, D.; Smith, C.; Rabiti, C.; Cogliati, J.
2016-10-01
The RISMC project is developing new advanced simulation-based tools to perform Computational Risk Analysis (CRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermal-hydraulic behavior of the reactors primary and secondary systems, but also external event temporal evolution and component/system ageing. Thus, this is not only a multi-physics problem being addressed, but also a multi-scale problem (both spatial, µm-mm-m, and temporal, seconds-hours-years). As part of the RISMC CRA approach, a large amount of computationally-expensive simulation runs may be required. An important aspect is that even though computational power is growing, the overall computational cost of a RISMC analysis using brute-force methods may be not viable for certain cases. A solution that is being evaluated to assist the computational issue is the use of reduced order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RISMC analysis computational cost by decreasing the number of simulation runs; for this analysis improvement we used surrogate models instead of the actual simulation codes. This article focuses on the use of reduced order modeling techniques that can be applied to RISMC analyses in order to generate, analyze, and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (microseconds instead of hours/days).
Reduced order modeling of some fluid flows of industrial interest
International Nuclear Information System (INIS)
Alonso, D; Terragni, F; Velazquez, A; Vega, J M
2012-01-01
Some basic ideas are presented for the construction of robust, computationally efficient reduced order models amenable to be used in industrial environments, combined with somewhat rough computational fluid dynamics solvers. These ideas result from a critical review of the basic principles of proper orthogonal decomposition-based reduced order modeling of both steady and unsteady fluid flows. In particular, the extent to which some artifacts of the computational fluid dynamics solvers can be ignored is addressed, which opens up the possibility of obtaining quite flexible reduced order models. The methods are illustrated with the steady aerodynamic flow around a horizontal tail plane of a commercial aircraft in transonic conditions, and the unsteady lid-driven cavity problem. In both cases, the approximations are fairly good, thus reducing the computational cost by a significant factor. (review)
Reduced order modeling of some fluid flows of industrial interest
Energy Technology Data Exchange (ETDEWEB)
Alonso, D; Terragni, F; Velazquez, A; Vega, J M, E-mail: josemanuel.vega@upm.es [E.T.S.I. Aeronauticos, Universidad Politecnica de Madrid, 28040 Madrid (Spain)
2012-06-01
Some basic ideas are presented for the construction of robust, computationally efficient reduced order models amenable to be used in industrial environments, combined with somewhat rough computational fluid dynamics solvers. These ideas result from a critical review of the basic principles of proper orthogonal decomposition-based reduced order modeling of both steady and unsteady fluid flows. In particular, the extent to which some artifacts of the computational fluid dynamics solvers can be ignored is addressed, which opens up the possibility of obtaining quite flexible reduced order models. The methods are illustrated with the steady aerodynamic flow around a horizontal tail plane of a commercial aircraft in transonic conditions, and the unsteady lid-driven cavity problem. In both cases, the approximations are fairly good, thus reducing the computational cost by a significant factor. (review)
In-Situ Residual Tracking in Reduced Order Modelling
Directory of Open Access Journals (Sweden)
Joseph C. Slater
2002-01-01
Full Text Available Proper orthogonal decomposition (POD based reduced-order modelling is demonstrated to be a weighted residual technique similar to Galerkin's method. Estimates of weighted residuals of neglected modes are used to determine relative importance of neglected modes to the model. The cumulative effects of neglected modes can be used to estimate error in the reduced order model. Thus, once the snapshots have been obtained under prescribed training conditions, the need to perform full-order simulations for comparison is eliminates. This has the potential to allow the analyst to initiate further training when the reduced modes are no longer sufficient to accurately represent the predominant phenomenon of interest. The response of a fluid moving at Mach 1.2 above a panel to a forced localized oscillation of the panel at and away from the training operating conditions is used to demonstrate the evaluation method.
Composite symmetry-protected topological order and effective models
Nietner, A.; Krumnow, C.; Bergholtz, E. J.; Eisert, J.
2017-12-01
Strongly correlated quantum many-body systems at low dimension exhibit a wealth of phenomena, ranging from features of geometric frustration to signatures of symmetry-protected topological order. In suitable descriptions of such systems, it can be helpful to resort to effective models, which focus on the essential degrees of freedom of the given model. In this work, we analyze how to determine the validity of an effective model by demanding it to be in the same phase as the original model. We focus our study on one-dimensional spin-1 /2 systems and explain how nontrivial symmetry-protected topologically ordered (SPT) phases of an effective spin-1 model can arise depending on the couplings in the original Hamiltonian. In this analysis, tensor network methods feature in two ways: on the one hand, we make use of recent techniques for the classification of SPT phases using matrix product states in order to identify the phases in the effective model with those in the underlying physical system, employing Künneth's theorem for cohomology. As an intuitive paradigmatic model we exemplify the developed methodology by investigating the bilayered Δ chain. For strong ferromagnetic interlayer couplings, we find the system to transit into exactly the same phase as an effective spin-1 model. However, for weak but finite coupling strength, we identify a symmetry broken phase differing from this effective spin-1 description. On the other hand, we underpin our argument with a numerical analysis making use of matrix product states.
Review and selection of unsaturated flow models
Energy Technology Data Exchange (ETDEWEB)
Reeves, M.; Baker, N.A.; Duguid, J.O. [INTERA, Inc., Las Vegas, NV (United States)
1994-04-04
Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.
Review and selection of unsaturated flow models
International Nuclear Information System (INIS)
Reeves, M.; Baker, N.A.; Duguid, J.O.
1994-01-01
Since the 1960's, ground-water flow models have been used for analysis of water resources problems. In the 1970's, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970's and well into the 1980's focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M ampersand O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M ampersand O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing
Research on Modeling of Hydropneumatic Suspension Based on Fractional Order
Directory of Open Access Journals (Sweden)
Junwei Zhang
2015-01-01
Full Text Available With such excellent performance as nonlinear stiffness, adjustable vehicle height, and good vibration resistance, hydropneumatic suspension (HS has been more and more applied to heavy vehicle and engineering vehicle. Traditional modeling methods are still confined to simple models without taking many factors into consideration. A hydropneumatic suspension model based on fractional order (HSM-FO is built with the advantage of fractional order (FO in viscoelastic material modeling considering the mechanics property of multiphase medium of HS. Then, the detailed calculation method is proposed based on Oustaloup filtering approximation algorithm. The HSM-FO is implemented in Matlab/Simulink, and the results of comparison among the simulation curve of fractional order, integral order, and the curve of real experiment prove the feasibility and validity of HSM-FO. The damping force property of the suspension system under different fractional orders is also studied. In the end of this paper, several conclusions concerning HSM-FO are drawn according to analysis of simulation.
Graphical tools for model selection in generalized linear models.
Murray, K; Heritier, S; Müller, S
2013-11-10
Model selection techniques have existed for many years; however, to date, simple, clear and effective methods of visualising the model building process are sparse. This article describes graphical methods that assist in the selection of models and comparison of many different selection criteria. Specifically, we describe for logistic regression, how to visualize measures of description loss and of model complexity to facilitate the model selection dilemma. We advocate the use of the bootstrap to assess the stability of selected models and to enhance our graphical tools. We demonstrate which variables are important using variable inclusion plots and show that these can be invaluable plots for the model building process. We show with two case studies how these proposed tools are useful to learn more about important variables in the data and how these tools can assist the understanding of the model building process. Copyright © 2013 John Wiley & Sons, Ltd.
Bayesian Variable Selection on Model Spaces Constrained by Heredity Conditions.
Taylor-Rodriguez, Daniel; Womack, Andrew; Bliznyuk, Nikolay
2016-01-01
This paper investigates Bayesian variable selection when there is a hierarchical dependence structure on the inclusion of predictors in the model. In particular, we study the type of dependence found in polynomial response surfaces of orders two and higher, whose model spaces are required to satisfy weak or strong heredity conditions. These conditions restrict the inclusion of higher-order terms depending upon the inclusion of lower-order parent terms. We develop classes of priors on the model space, investigate their theoretical and finite sample properties, and provide a Metropolis-Hastings algorithm for searching the space of models. The tools proposed allow fast and thorough exploration of model spaces that account for hierarchical polynomial structure in the predictors and provide control of the inclusion of false positives in high posterior probability models.
Methods for model selection in applied science and engineering.
Energy Technology Data Exchange (ETDEWEB)
Field, Richard V., Jr.
2004-10-01
Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be
Reverse time migration by Krylov subspace reduced order modeling
Basir, Hadi Mahdavi; Javaherian, Abdolrahim; Shomali, Zaher Hossein; Firouz-Abadi, Roohollah Dehghani; Gholamy, Shaban Ali
2018-04-01
Imaging is a key step in seismic data processing. To date, a myriad of advanced pre-stack depth migration approaches have been developed; however, reverse time migration (RTM) is still considered as the high-end imaging algorithm. The main limitations associated with the performance cost of reverse time migration are the intensive computation of the forward and backward simulations, time consumption, and memory allocation related to imaging condition. Based on the reduced order modeling, we proposed an algorithm, which can be adapted to all the aforementioned factors. Our proposed method benefit from Krylov subspaces method to compute certain mode shapes of the velocity model computed by as an orthogonal base of reduced order modeling. Reverse time migration by reduced order modeling is helpful concerning the highly parallel computation and strongly reduces the memory requirement of reverse time migration. The synthetic model results showed that suggested method can decrease the computational costs of reverse time migration by several orders of magnitudes, compared with reverse time migration by finite element method.
Impact of Physics Parameterization Ordering in a Global Atmosphere Model
Donahue, Aaron S.; Caldwell, Peter M.
2018-02-01
Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of subgrid-scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the Energy Exascale Earth System Model (E3SM) General Circulation Model (GCM), a close relative of version 1 of the Community Earth System Model (CESM1). Like most GCMs, parameterizations in E3SM are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effect of the preceding processes. This coupling strategy is noncommutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 24 simulations with deep convection, shallow convection, macrophysics/microphysics, and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are as big as the intermodel spread in phase 5 of the Coupled Model Intercomparison Project. One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Where output is written is therefore an important control on apparent model behavior. Application of k-means clustering demonstrates that the positioning of macro/microphysics and shallow convection plays a critical role on the model solution.
Genetic search feature selection for affective modeling
DEFF Research Database (Denmark)
Martínez, Héctor P.; Yannakakis, Georgios N.
2010-01-01
Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...
Psyche Mission: Scientific Models and Instrument Selection
Polanskey, C. A.; Elkins-Tanton, L. T.; Bell, J. F., III; Lawrence, D. J.; Marchi, S.; Park, R. S.; Russell, C. T.; Weiss, B. P.
2017-12-01
NASA has chosen to explore (16) Psyche with their 14th Discovery-class mission. Psyche is a 226-km diameter metallic asteroid hypothesized to be the exposed core of a planetesimal that was stripped of its rocky mantle by multiple hit and run collisions in the early solar system. The spacecraft launch is planned for 2022 with arrival at the asteroid in 2026 for 21 months of operations. The Psyche investigation has five primary scientific objectives: A. Determine whether Psyche is a core, or if it is unmelted material. B. Determine the relative ages of regions of Psyche's surface. C. Determine whether small metal bodies incorporate the same light elements as are expected in the Earth's high-pressure core. D. Determine whether Psyche was formed under conditions more oxidizing or more reducing than Earth's core. E. Characterize Psyche's topography. The mission's task was to select the appropriate instruments to meet these objectives. However, exploring a metal world, rather than one made of ice, rock, or gas, requires development of new scientific models for Psyche to support the selection of the appropriate instruments for the payload. If Psyche is indeed a planetary core, we expect that it should have a detectable magnetic field. However, the strength of the magnetic field can vary by orders of magnitude depending on the formational history of Psyche. The implications of both the extreme low-end and the high-end predictions impact the magnetometer and mission design. For the imaging experiment, what can the team expect for the morphology of a heavily impacted metal body? Efforts are underway to further investigate the differences in crater morphology between high velocity impacts into metal and rock to be prepared to interpret the images of Psyche when they are returned. Finally, elemental composition measurements at Psyche using nuclear spectroscopy encompass a new and unexplored phase space of gamma-ray and neutron measurements. We will present some end
The Use of Evolution in a Central Action Selection Model
Directory of Open Access Journals (Sweden)
F. Montes-Gonzalez
2007-01-01
Full Text Available The use of effective central selection provides flexibility in design by offering modularity and extensibility. In earlier papers we have focused on the development of a simple centralized selection mechanism. Our current goal is to integrate evolutionary methods in the design of non-sequential behaviours and the tuning of specific parameters of the selection model. The foraging behaviour of an animal robot (animat has been modelled in order to integrate the sensory information from the robot to perform selection that is nearly optimized by the use of genetic algorithms. In this paper we present how selection through optimization finally arranges the pattern of presented behaviours for the foraging task. Hence, the execution of specific parts in a behavioural pattern may be ruled out by the tuning of these parameters. Furthermore, the intensive use of colour segmentation from a colour camera for locating a cylinder sets a burden on the calculations carried out by the genetic algorithm.
A Hybrid Multiple Criteria Decision Making Model for Supplier Selection
Directory of Open Access Journals (Sweden)
Chung-Min Wu
2013-01-01
Full Text Available The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain their weights. To avoid calculation and additional pairwise comparisons of ANP, a technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. The use of a combination of the fuzzy Delphi method, ANP, and TOPSIS, proposing an MCDM model for supplier selection, and applying these to a real case are the unique features of this study.
Ordering kinetics in model systems with inhibited interfacial adsorption
DEFF Research Database (Denmark)
Willart, J.-F.; Mouritsen, Ole G.; Naudts, J.
1992-01-01
. The results are related to experimental work on ordering processes in orientational glasses. It is suggested that the experimental observation of very slow ordering kinetics in, e.g., glassy crystals of cyanoadamantane may be a consequence of low-temperature activated processes which ultimately lead......The ordering kinetics in two-dimensional Ising-like spin moels with inhibited interfacial adsorption are studied by computer-simulation calculations. The inhibited interfacial adsorption is modeled by a particular interfacial adsorption condition on the structure of the domain wall between......, of the algebraic growth law, R(t)∼Atn, whereas the growth exponent, n, remains close to the value n=1/2 predicted by the classical Lifshitz-Allen-Cahn growth law for systems with nonconserved order parameter. At very low temperatures there is, however, an effective crossover to a much slower algebraic growth...
A fractional-order model for MINMOD Millennium.
Cho, Yongjin; Kim, Imbunm; Sheen, Dongwoo
2015-04-01
MINMOD Millennium has been widely used to estimate insulin sensitivity (SI) in glucose-insulin dynamics. In order to explain the rheological behavior of glucose-insulin we attempt to modify MINMOD Millennium with fractional-order differentiation of order α ∈ (0, 1]. We show that the new modified model has non-negative, bounded solutions and a stable equilibrium point. Quasi-optimal fractional orders and parameters are estimated by using a nonlinear weighted least-squares method, the Levenberg-Marquardt algorithm, and the fractional Adams-Bashforth-Moulton method for several subjects (normal subjects and type 2 diabetic patients). The numerical results confirm that SI is significantly lower in diabetics than in non-diabetics. In addition, we explain the new factor (τ(1 - α)) determining glucose tolerance and the relation between SI and τ(1 - α). Copyright © 2015 Elsevier Inc. All rights reserved.
Robust simulation of buckled structures using reduced order modeling
International Nuclear Information System (INIS)
Wiebe, R.; Perez, R.A.; Spottswood, S.M.
2016-01-01
Lightweight metallic structures are a mainstay in aerospace engineering. For these structures, stability, rather than strength, is often the critical limit state in design. For example, buckling of panels and stiffeners may occur during emergency high-g maneuvers, while in supersonic and hypersonic aircraft, it may be induced by thermal stresses. The longstanding solution to such challenges was to increase the sizing of the structural members, which is counter to the ever present need to minimize weight for reasons of efficiency and performance. In this work we present some recent results in the area of reduced order modeling of post- buckled thin beams. A thorough parametric study of the response of a beam to changing harmonic loading parameters, which is useful in exposing complex phenomena and exercising numerical models, is presented. Two error metrics that use but require no time stepping of a (computationally expensive) truth model are also introduced. The error metrics are applied to several interesting forcing parameter cases identified from the parametric study and are shown to yield useful information about the quality of a candidate reduced order model. Parametric studies, especially when considering forcing and structural geometry parameters, coupled environments, and uncertainties would be computationally intractable with finite element models. The goal is to make rapid simulation of complex nonlinear dynamic behavior possible for distributed systems via fast and accurate reduced order models. This ability is crucial in allowing designers to rigorously probe the robustness of their designs to account for variations in loading, structural imperfections, and other uncertainties. (paper)
Atmospheric Turbulence Modeling for Aero Vehicles: Fractional Order Fits
Kopasakis, George
2015-01-01
Atmospheric turbulence models are necessary for the design of both inlet/engine and flight controls, as well as for studying coupling between the propulsion and the vehicle structural dynamics for supersonic vehicles. Models based on the Kolmogorov spectrum have been previously utilized to model atmospheric turbulence. In this paper, a more accurate model is developed in its representative fractional order form, typical of atmospheric disturbances. This is accomplished by first scaling the Kolmogorov spectral to convert them into finite energy von Karman forms and then by deriving an explicit fractional circuit-filter type analog for this model. This circuit model is utilized to develop a generalized formulation in frequency domain to approximate the fractional order with the products of first order transfer functions, which enables accurate time domain simulations. The objective of this work is as follows. Given the parameters describing the conditions of atmospheric disturbances, and utilizing the derived formulations, directly compute the transfer function poles and zeros describing these disturbances for acoustic velocity, temperature, pressure, and density. Time domain simulations of representative atmospheric turbulence can then be developed by utilizing these computed transfer functions together with the disturbance frequencies of interest.
Time Ordering in Frontal Lobe Patients: A Stochastic Model Approach
Magherini, Anna; Saetti, Maria Cristina; Berta, Emilia; Botti, Claudio; Faglioni, Pietro
2005-01-01
Frontal lobe patients reproduced a sequence of capital letters or abstract shapes. Immediate and delayed reproduction trials allowed the analysis of short- and long-term memory for time order by means of suitable Markov chain stochastic models. Patients were as proficient as healthy subjects on the immediate reproduction trial, thus showing spared…
Reduced order modelling and predictive control of multivariable ...
Indian Academy of Sciences (India)
Anuj Abraham
2018-03-16
Mar 16, 2018 ... The performance of constraint generalized predictive control scheme is found to be superior to that of the conventional PID controller in terms of overshoot, settling time and performance indices, mainly ISE, IAE and MSE. Keywords. Predictive control; distillation column; reduced order model; dominant pole; ...
An Almost Integration-free Approach to Ordered Response Models
van Praag, B.M.S.; Ferrer-i-Carbonell, A.
2006-01-01
'In this paper we propose an alternative approach to the estimation of ordered response models. We show that the Probit-method may be replaced by a simple OLS-approach, called P(robit)OLS, without any loss of efficiency. This method can be generalized to the analysis of panel data. For large-scale
Selecting model complexity in learning problems
Energy Technology Data Exchange (ETDEWEB)
Buescher, K.L. [Los Alamos National Lab., NM (United States); Kumar, P.R. [Illinois Univ., Urbana, IL (United States). Coordinated Science Lab.
1993-10-01
To learn (or generalize) from noisy data, one must resist the temptation to pick a model for the underlying process that overfits the data. Many existing techniques solve this problem at the expense of requiring the evaluation of an absolute, a priori measure of each model`s complexity. We present a method that does not. Instead, it uses a natural, relative measure of each model`s complexity. This method first creates a pool of ``simple`` candidate models using part of the data and then selects from among these by using the rest of the data.
Bilinear reduced order approximate model of parabolic distributed solar collectors
Elmetennani, Shahrazed
2015-07-01
This paper proposes a novel, low dimensional and accurate approximate model for the distributed parabolic solar collector, by means of a modified gaussian interpolation along the spatial domain. The proposed reduced model, taking the form of a low dimensional bilinear state representation, enables the reproduction of the heat transfer dynamics along the collector tube for system analysis. Moreover, presented as a reduced order bilinear state space model, the well established control theory for this class of systems can be applied. The approximation efficiency has been proven by several simulation tests, which have been performed considering parameters of the Acurex field with real external working conditions. Model accuracy has been evaluated by comparison to the analytical solution of the hyperbolic distributed model and its semi discretized approximation highlighting the benefits of using the proposed numerical scheme. Furthermore, model sensitivity to the different parameters of the gaussian interpolation has been studied.
Melody Track Selection Using Discriminative Language Model
Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong
In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.
Reduced order modeling of fluid/structure interaction.
Energy Technology Data Exchange (ETDEWEB)
Barone, Matthew Franklin; Kalashnikova, Irina; Segalman, Daniel Joseph; Brake, Matthew Robert
2009-11-01
This report describes work performed from October 2007 through September 2009 under the Sandia Laboratory Directed Research and Development project titled 'Reduced Order Modeling of Fluid/Structure Interaction.' This project addresses fundamental aspects of techniques for construction of predictive Reduced Order Models (ROMs). A ROM is defined as a model, derived from a sequence of high-fidelity simulations, that preserves the essential physics and predictive capability of the original simulations but at a much lower computational cost. Techniques are developed for construction of provably stable linear Galerkin projection ROMs for compressible fluid flow, including a method for enforcing boundary conditions that preserves numerical stability. A convergence proof and error estimates are given for this class of ROM, and the method is demonstrated on a series of model problems. A reduced order method, based on the method of quadratic components, for solving the von Karman nonlinear plate equations is developed and tested. This method is applied to the problem of nonlinear limit cycle oscillations encountered when the plate interacts with an adjacent supersonic flow. A stability-preserving method for coupling the linear fluid ROM with the structural dynamics model for the elastic plate is constructed and tested. Methods for constructing efficient ROMs for nonlinear fluid equations are developed and tested on a one-dimensional convection-diffusion-reaction equation. These methods are combined with a symmetrization approach to construct a ROM technique for application to the compressible Navier-Stokes equations.
Integrable higher order deformations of Heisenberg supermagnetic model
International Nuclear Information System (INIS)
Guo Jiafeng; Yan Zhaowen; Wang Shikun; Wu Ke; Zhao Weizhong
2009-01-01
The Heisenberg supermagnet model is an integrable supersymmetric system and has a close relationship with the strong electron correlated Hubbard model. In this paper, we investigate the integrable higher order deformations of Heisenberg supermagnet models with two different constraints: (i) S 2 =3S-2I for S is an element of USPL(2/1)/S(U(2)xU(1)) and (ii) S 2 =S for S is an element of USPL(2/1)/S(L(1/1)xU(1)). In terms of the gauge transformation, their corresponding gauge equivalent counterparts are derived.
Economic assessment model architecture for AGC/AVLIS selection
International Nuclear Information System (INIS)
Hoglund, R.L.
1984-01-01
The economic assessment model architecture described provides the flexibility and completeness in economic analysis that the selection between AGC and AVLIS demands. Process models which are technology-specific will provide the first-order responses of process performance and cost to variations in process parameters. The economics models can be used to test the impacts of alternative deployment scenarios for a technology. Enterprise models provide global figures of merit for evaluating the DOE perspective on the uranium enrichment enterprise, and business analysis models compute the financial parameters from the private investor's viewpoint
Accelerating transient simulation of linear reduced order models.
Energy Technology Data Exchange (ETDEWEB)
Thornquist, Heidi K.; Mei, Ting; Keiter, Eric Richard; Bond, Brad
2011-10-01
Model order reduction (MOR) techniques have been used to facilitate the analysis of dynamical systems for many years. Although existing model reduction techniques are capable of providing huge speedups in the frequency domain analysis (i.e. AC response) of linear systems, such speedups are often not obtained when performing transient analysis on the systems, particularly when coupled with other circuit components. Reduced system size, which is the ostensible goal of MOR methods, is often insufficient to improve transient simulation speed on realistic circuit problems. It can be shown that making the correct reduced order model (ROM) implementation choices is crucial to the practical application of MOR methods. In this report we investigate methods for accelerating the simulation of circuits containing ROM blocks using the circuit simulator Xyce.
Directory of Open Access Journals (Sweden)
Thanoon Y. Thanoon
2016-03-01
Full Text Available In this paper, ordered categorical variables are used to compare between linear and nonlinear interactions of fixed covariate and latent variables Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Hidden continuous normal distribution (censored normal distribution is used to handle the problem of ordered categorical data. Statistical inferences, which involve estimation of parameters and their standard deviations, and residuals analyses for testing the selected model, are discussed. The proposed procedure is illustrated by a simulation data obtained from R program. Analysis are done by using OpenBUGS program.
Aeroelastic simulation using CFD based reduced order models
International Nuclear Information System (INIS)
Zhang, W.; Ye, Z.; Li, H.; Yang, Q.
2005-01-01
This paper aims at providing an accurate and efficient method for aeroelastic simulation. System identification is used to get the reduced order models of unsteady aerodynamics. Unsteady Euler codes are used to compute the output signals while 3211 multistep input signals are utilized. LS(Least Squares) method is used to estimate the coefficients of the input-output difference model. The reduced order models are then used in place of the unsteady CFD code for aeroelastic simulation. The aeroelastic equations are marched by an improved 4th order Runge-Kutta method that only needs to compute the aerodynamic loads one time at every time step. The computed results agree well with that of the direct coupling CFD/CSD methods. The computational efficiency is improved 1∼2 orders while still retaining the high accuracy. A standard aeroelastic computing example (isogai wing) with S type flutter boundary is computed and analyzed. It is due to the system has more than one neutral points at the Mach range of 0.875∼0.9. (author)
Anisotropic Third-Order Regularization for Sparse Digital Elevation Models
Lellmann, Jan
2013-01-01
We consider the problem of interpolating a surface based on sparse data such as individual points or level lines. We derive interpolators satisfying a list of desirable properties with an emphasis on preserving the geometry and characteristic features of the contours while ensuring smoothness across level lines. We propose an anisotropic third-order model and an efficient method to adaptively estimate both the surface and the anisotropy. Our experiments show that the approach outperforms AMLE and higher-order total variation methods qualitatively and quantitatively on real-world digital elevation data. © 2013 Springer-Verlag.
Computational Analysis of Complex Population Dynamical Model with Arbitrary Order
Directory of Open Access Journals (Sweden)
Fazal Haq
2018-01-01
Full Text Available This paper considers the approximation of solution for a fractional order biological population model. The fractional derivative is considered in the Caputo sense. By using Laplace Adomian decomposition method (LADM, we construct a base function and provide deformation equation of higher order in a simple equation. The considered scheme gives us a solution in the form of rapidly convergent infinite series. Some examples are used to show the efficiency of the method. The results show that LADM is efficient and accurate for solving such types of nonlinear problems.
Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond
Directory of Open Access Journals (Sweden)
Johannes Neubauer
2013-09-01
Full Text Available We present a graphical and dynamic framework for binding and execution of business process models. It is tailored to integrate 1 ad hoc processes modeled graphically, 2 third party services discovered in the (Internet, and 3 (dynamically synthesized process chains that solve situation-specific tasks, with the synthesis taking place not only at design time, but also at runtime. Key to our approach is the introduction of type-safe stacked second-order execution contexts that allow for higher-order process modeling. Tamed by our underlying strict service-oriented notion of abstraction, this approach is tailored also to be used by application experts with little technical knowledge: users can select, modify, construct and then pass (component processes during process execution as if they were data. We illustrate the impact and essence of our framework along a concrete, realistic (business process modeling scenario: the development of Springer's browser-based Online Conference Service (OCS. The most advanced feature of our new framework allows one to combine online synthesis with the integration of the synthesized process into the running application. This ability leads to a particularly flexible way of implementing self-adaption, and to a particularly concise and powerful way of achieving variability not only at design time, but also at runtime.
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: 'Are we actually dealing with a convolutive mixture?'. We try to answer this question for EEG data....
A Model of Gear Transmission: Fractional Order System Dynamics
Directory of Open Access Journals (Sweden)
Katica (Stevanović Hedrih
2010-01-01
Full Text Available A theoretical model of multistep gear transmission dynamics is presented. This model is based on the assumption that the connection between the teeth of the gears is with properties within the range from ideal clasic to viscoelastic so that a new model of connection between the teeth was expressed by means of derivative of fractional order. For this model a two-step gear transmision with three degrees of freedom of motion has been used. The obtained solutions are in the analytic form of the expansion according to time. As boundary cases this model gives results for the case of ideally elastic connection of the gear teeth and for the case of viscoelastic connection of the gear teeth, as well. Eigen fractional modes are obtained and a vizualization is done.
Empirical Reduced-Order Modeling for Boundary Feedback Flow Control
Directory of Open Access Journals (Sweden)
Seddik M. Djouadi
2008-01-01
Full Text Available This paper deals with the practical and theoretical implications of model reduction for aerodynamic flow-based control problems. Various aspects of model reduction are discussed that apply to partial differential equation- (PDE- based models in general. Specifically, the proper orthogonal decomposition (POD of a high dimension system as well as frequency domain identification methods are discussed for initial model construction. Projections on the POD basis give a nonlinear Galerkin model. Then, a model reduction method based on empirical balanced truncation is developed and applied to the Galerkin model. The rationale for doing so is that linear subspace approximations to exact submanifolds associated with nonlinear controllability and observability require only standard matrix manipulations utilizing simulation/experimental data. The proposed method uses a chirp signal as input to produce the output in the eigensystem realization algorithm (ERA. This method estimates the system's Markov parameters that accurately reproduce the output. Balanced truncation is used to show that model reduction is still effective on ERA produced approximated systems. The method is applied to a prototype convective flow on obstacle geometry. An H∞ feedback flow controller is designed based on the reduced model to achieve tracking and then applied to the full-order model with excellent performance.
Identification of the reduced order models of a BWR reactor
International Nuclear Information System (INIS)
Hernandez S, A.
2004-01-01
The present work has as objective to analyze the relative stability of a BWR type reactor. It is analyzed that so adaptive it turns out to identify the parameters of a model of reduced order so that this it reproduces a condition of given uncertainty. This will take of a real fact happened in the La Salle plant under certain operation conditions of power and flow of coolant. The parametric identification is carried out by means of an algorithm of recursive least square and an Output Error model (Output Error), measuring the output power of the reactor when the instability is present, and considering that it is produced by a change in the reactivity of the system in the same way that a sign of type step. Also it is carried out an analytic comparison of the relative stability, analyzing two types of answers: the original answer of the uncertainty of the reactor vs. the obtained response identifying the parameters of the model of reduced order, reaching the conclusion that it is very viable to adapt a model of reduced order to study the stability of a reactor, under the only condition to consider that the dynamics of the reactivity is of step type. (Author)
Advanced Fluid Reduced Order Models for Compressible Flow.
Energy Technology Data Exchange (ETDEWEB)
Tezaur, Irina Kalashnikova [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Fike, Jeffrey A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Carlberg, Kevin Thomas [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Barone, Matthew F. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Maddix, Danielle [Stanford Univ., CA (United States); Mussoni, Erin E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Balajewicz, Maciej [Univ. of Illinois, Urbana-Champaign, IL (United States)
2017-09-01
This report summarizes fiscal year (FY) 2017 progress towards developing and implementing within the SPARC in-house finite volume flow solver advanced fluid reduced order models (ROMs) for compressible captive-carriage flow problems of interest to Sandia National Laboratories for the design and qualification of nuclear weapons components. The proposed projection-based model order reduction (MOR) approach, known as the Proper Orthogonal Decomposition (POD)/Least- Squares Petrov-Galerkin (LSPG) method, can substantially reduce the CPU-time requirement for these simulations, thereby enabling advanced analyses such as uncertainty quantification and de- sign optimization. Following a description of the project objectives and FY17 targets, we overview briefly the POD/LSPG approach to model reduction implemented within SPARC . We then study the viability of these ROMs for long-time predictive simulations in the context of a two-dimensional viscous laminar cavity problem, and describe some FY17 enhancements to the proposed model reduction methodology that led to ROMs with improved predictive capabilities. Also described in this report are some FY17 efforts pursued in parallel to the primary objective of determining whether the ROMs in SPARC are viable for the targeted application. These include the implemen- tation and verification of some higher-order finite volume discretization methods within SPARC (towards using the code to study the viability of ROMs on three-dimensional cavity problems) and a novel structure-preserving constrained POD/LSPG formulation that can improve the accuracy of projection-based reduced order models. We conclude the report by summarizing the key takeaways from our FY17 findings, and providing some perspectives for future work.
MODELING THE SELF-ASSEMBLY OF ORDERED NANOPOROUS MATERIALS
Energy Technology Data Exchange (ETDEWEB)
Monson, Peter [University of Massachusetts; Auerbach, Scott [University of Massachusetts
2017-11-13
This report describes progress on a collaborative project on the multiscale modeling of the assembly processes in the synthesis of nanoporous materials. Such materials are of enormous importance in modern technology with application in the chemical process industries, biomedicine and biotechnology as well as microelectronics. The project focuses on two important classes of materials: i) microporous crystalline materials, such as zeolites, and ii) ordered mesoporous materials. In the first case the pores are part of the crystalline structure, while in the second the structures are amorphous on the atomistic length scale but where surfactant templating gives rise to order on the length scale of 2 - 20 nm. We have developed a modeling framework that encompasses both these kinds of materials. Our models focus on the assembly of corner sharing silica tetrahedra in the presence of structure directing agents. We emphasize a balance between sufficient realism in the models and computational tractibility given the complex many-body phenomena. We use both on-lattice and off-lattice models and the primary computational tools are Monte Carlo simulations with sampling techniques and ensembles appropriate to specific situations. Our modeling approach is the first to capture silica polymerization, nanopore crystallization, and mesopore formation through computer-simulated self assembly.
Variable selection in Logistic regression model with genetic algorithm.
Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi
2018-02-01
Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.
HOKF: High Order Kalman Filter for Epilepsy Forecasting Modeling.
Nguyen, Ngoc Anh Thi; Yang, Hyung-Jeong; Kim, Sunhee
2017-08-01
Epilepsy forecasting has been extensively studied using high-order time series obtained from scalp-recorded electroencephalography (EEG). An accurate seizure prediction system would not only help significantly improve patients' quality of life, but would also facilitate new therapeutic strategies to manage epilepsy. This paper thus proposes an improved Kalman Filter (KF) algorithm to mine seizure forecasts from neural activity by modeling three properties in the high-order EEG time series: noise, temporal smoothness, and tensor structure. The proposed High-Order Kalman Filter (HOKF) is an extension of the standard Kalman filter, for which higher-order modeling is limited. The efficient dynamic of HOKF system preserves the tensor structure of the observations and latent states. As such, the proposed method offers two main advantages: (i) effectiveness with HOKF results in hidden variables that capture major evolving trends suitable to predict neural activity, even in the presence of missing values; and (ii) scalability in that the wall clock time of the HOKF is linear with respect to the number of time-slices of the sequence. The HOKF algorithm is examined in terms of its effectiveness and scalability by conducting forecasting and scalability experiments with a real epilepsy EEG dataset. The results of the simulation demonstrate the superiority of the proposed method over the original Kalman Filter and other existing methods. Copyright © 2017 Elsevier B.V. All rights reserved.
Model predictive control based on reduced order models applied to belt conveyor system.
Chen, Wei; Li, Xin
2016-11-01
In the paper, a model predictive controller based on reduced order model is proposed to control belt conveyor system, which is an electro-mechanics complex system with long visco-elastic body. Firstly, in order to design low-degree controller, the balanced truncation method is used for belt conveyor model reduction. Secondly, MPC algorithm based on reduced order model for belt conveyor system is presented. Because of the error bound between the full-order model and reduced order model, two Kalman state estimators are applied in the control scheme to achieve better system performance. Finally, the simulation experiments are shown that balanced truncation method can significantly reduce the model order with high-accuracy and model predictive control based on reduced-model performs well in controlling the belt conveyor system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
On spatial mutation-selection models
Energy Technology Data Exchange (ETDEWEB)
Kondratiev, Yuri, E-mail: kondrat@math.uni-bielefeld.de [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Kutoviy, Oleksandr, E-mail: kutoviy@math.uni-bielefeld.de, E-mail: kutovyi@mit.edu [Fakultät für Mathematik, Universität Bielefeld, Postfach 100131, 33501 Bielefeld (Germany); Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Minlos, Robert, E-mail: minl@iitp.ru; Pirogov, Sergey, E-mail: pirogov@proc.ru [IITP, RAS, Bolshoi Karetnyi 19, Moscow (Russian Federation)
2013-11-15
We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.
Modeling the assembly order of multimeric heteroprotein complexes.
Peterson, Lenna X; Togawa, Yoichiro; Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Roy, Amitava; Shin, Woong-Hee; Kihara, Daisuke
2018-01-01
Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be
Modeling the assembly order of multimeric heteroprotein complexes.
Directory of Open Access Journals (Sweden)
Lenna X Peterson
2018-01-01
Full Text Available Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure
Knowledge of the Semantic Constraints on Adjective Order Can Be Selectively Impaired.
Kemmerer, David; Tranel, Daniel; Zdanczyk, Cynthia
2009-01-01
When multiple adjectives are used to modify a noun, they tend to be sequenced in the following way according to semantic class: value > size > dimension > various physical properties > color. To investigate the neural substrates of these semantic constraints on adjective order, we administered a battery of three tests to 34 brain-damaged patients and 19 healthy participants. Six patients manifested the following performance profile. First, they failed a test that required them to discriminate between semantically determined correct and incorrect sequences of adjectives-e.g., thick blue towel vs. *blue thick towel. Second, they passed a test that assessed their knowledge of two purely syntactic aspects of adjective order-specifically, that adjectives can precede nouns, and that adjectives can precede other adjectives. Finally, they also passed a test that assessed their knowledge of the categorical (i.e., class-level) features of adjective meanings that interact with the semantic constraints underlying adjective order-e.g., that thick is a dimensional adjective and that blue is a color adjective. Taken together, these behavioral findings suggest that the six patients have selectively impaired knowledge of the abstract principles that determine how different semantic classes of adjectives are typically mapped onto different syntactic positions in NPs. To identify the neuroanatomical lesion patterns that tend to correlate with defective processing of adjective order, we combined lesion data from the six patients just described with lesion data from six other patients who we reported in a previous study as having similar impairments [Kemmerer, D. (2000). Selective impairment of knowledge underlying adjective order: Evidence for the autonomy of grammatical semantics. Journal of Neurolinguistics, 13, 57-82.] We found that the most common areas of damage included the left posterior inferior frontal gyrus and the left inferior parietal lobule. Overall, these results shed
Multivariable robust adaptive controller using reduced-order model
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Wei Wang
1990-04-01
Full Text Available In this paper a multivariable robust adaptive controller is presented for a plant with bounded disturbances and unmodeled dynamics due to plant-model order mismatches. The robust stability of the closed-loop system is achieved by using the normalization technique and the least squares parameter estimation scheme with dead zones. The weighting polynomial matrices are incorporated into the control law, so that the open-loop unstable or/and nonminimum phase plants can be handled.
Multicriteria framework for selecting a process modelling language
Scanavachi Moreira Campos, Ana Carolina; Teixeira de Almeida, Adiel
2016-01-01
The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.
Sparse model selection via integral terms
Schaeffer, Hayden; McCalla, Scott G.
2017-08-01
Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations. In this work, we develop a learning approach for the selection and identification of a dynamical system directly from noisy data. The learning is performed by extracting a small subset of important features from an overdetermined set of possible features using a nonconvex sparse regression model. The sparse regression model is constructed to fit the noisy data to the trajectory of the dynamical system while using the smallest number of active terms. Computational experiments detail the model's stability, robustness to noise, and recovery accuracy. Examples include nonlinear equations, population dynamics, chaotic systems, and fast-slow systems.
Adverse selection model regarding tobacco consumption
Directory of Open Access Journals (Sweden)
Dumitru MARIN
2006-01-01
Full Text Available The impact of introducing a tax on tobacco consumption can be studied trough an adverse selection model. The objective of the model presented in the following is to characterize the optimal contractual relationship between the governmental authorities and the two type employees: smokers and non-smokers, taking into account that the consumers’ decision to smoke or not represents an element of risk and uncertainty. Two scenarios are run using the General Algebraic Modeling Systems software: one without taxes set on tobacco consumption and another one with taxes set on tobacco consumption, based on an adverse selection model described previously. The results of the two scenarios are compared in the end of the paper: the wage earnings levels and the social welfare in case of a smoking agent and in case of a non-smoking agent.
Ecohydrological model parameter selection for stream health evaluation.
Woznicki, Sean A; Nejadhashemi, A Pouyan; Ross, Dennis M; Zhang, Zhen; Wang, Lizhu; Esfahanian, Abdol-Hossein
2015-04-01
Variable selection is a critical step in development of empirical stream health prediction models. This study develops a framework for selecting important in-stream variables to predict four measures of biological integrity: total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa, family index of biotic integrity (FIBI), Hilsenhoff biotic integrity (HBI), and fish index of biotic integrity (IBI). Over 200 flow regime and water quality variables were calculated using the Hydrologic Index Tool (HIT) and Soil and Water Assessment Tool (SWAT). Streams of the River Raisin watershed in Michigan were grouped using the Strahler stream classification system (orders 1-3 and orders 4-6), k-means clustering technique (two clusters: C1 and C2), and all streams (one grouping). For each grouping, variable selection was performed using Bayesian variable selection, principal component analysis, and Spearman's rank correlation. Following selection of best variable sets, models were developed to predict the measures of biological integrity using adaptive-neuro fuzzy inference systems (ANFIS), a technique well-suited to complex, nonlinear ecological problems. Multiple unique variable sets were identified, all which differed by selection method and stream grouping. Final best models were mostly built using the Bayesian variable selection method. The most effective stream grouping method varied by health measure, although k-means clustering and grouping by stream order were always superior to models built without grouping. Commonly selected variables were related to streamflow magnitude, rate of change, and seasonal nitrate concentration. Each best model was effective in simulating stream health observations, with EPT taxa validation R2 ranging from 0.67 to 0.92, FIBI ranging from 0.49 to 0.85, HBI from 0.56 to 0.75, and fish IBI at 0.99 for all best models. The comprehensive variable selection and modeling process proposed here is a robust method that extends our
Long range ordering in model Ni-Cr-X alloys
International Nuclear Information System (INIS)
Young, G.A.; Eno, D.R.
2015-01-01
Nickel-chromium alloys are used throughout commercial nuclear power systems due to their desirable combination of corrosion resistance and mechanical properties. However, some Ni-Cr alloys can undergo long range ordering (LRO), forming the Ni 2 Cr phase when exposed to temperatures < 590 C. degrees. LRO results in lattice contraction, hardening, and a change in slip mode, which, in turn, can cause dimensional changes, internal stress, and appreciable embrittlement. Despite the technological importance of this alloy system, the variables that influence LRO are not well understood and the time-temperature-transformation kinetics poorly defined. In order to assess the risk of LRO in nuclear power systems, the present research uses model Ni-Cr alloys and ageing times up to 10000 hours to define the kinetics of LRO and to assess the effects of cold work, quench rate, and alloying additions. Results show that the hardening caused by ordering is well described by the Kolmogorov-Johnson-Mehl-Avrami (KJMA) equation with an Avrami exponent, n near 0.65 and an apparent activation energy that depends on the starting condition of the alloy. Furnace cooled samples displayed a Q ∼ 244 kJ/mol, which suggests bulk diffusional growth of the ordered phase, while water quenched samples exhibited a Q ∼ 147 kJ/mol, indicating that excess vacancies accelerate ordering. Cold work (10% or 20%) acts to disrupt any ordering that forms on furnace cooling but has no apparent effect on the apparent activation energy or Avrami exponent. Iron additions decrease the temperature below which the ordered phase is stable but do not appear to affect the rate of ordering. Investigation of other alloying suggest that molybdenum (∼ 2.47 wt.%) may accelerate ordering but other alloying elements studied (Si up to 0.28 wt.%, Mn up to 0.19 wt.%, and Nb up to 2.38 wt.%) have little influence. These findings, combined with a review of LRO in commercial alloys indicate that LRO can develop over a wide
Modeling and Selection of Software Service Variants
Wittern, John Erik
2015-01-01
Providers and consumers have to deal with variants, meaning alternative instances of a service?s design, implementation, deployment, or operation, when developing or delivering software services. This work presents service feature modeling to deal with associated challenges, comprising a language to represent software service variants and a set of methods for modeling and subsequent variant selection. This work?s evaluation includes a POC implementation and two real-life use cases.
Selective suppression of high-order harmonics within phase-matched spectral regions.
Lerner, Gavriel; Diskin, Tzvi; Neufeld, Ofer; Kfir, Ofer; Cohen, Oren
2017-04-01
Phase matching in high-harmonic generation leads to enhancement of multiple harmonics. It is sometimes desired to control the spectral structure within the phase-matched spectral region. We propose a scheme for selective suppression of high-order harmonics within the phase-matched spectral region while weakly influencing the other harmonics. The method is based on addition of phase-mismatched segments within a phase-matched medium. We demonstrate the method numerically in two examples. First, we show that one phase-mismatched segment can significantly suppress harmonic orders 9, 15, and 21. Second, we show that two phase-mismatched segments can efficiently suppress circularly polarized harmonics with one helicity over the other when driven by a bi-circular field. The new method may be useful for various applications, including the generation of highly helical bright attosecond pulses.
POD/MAC-Based Modal Basis Selection for a Reduced Order Nonlinear Response Analysis
Rizzi, Stephen A.; Przekop, Adam
2007-01-01
A feasibility study was conducted to explore the applicability of a POD/MAC basis selection technique to a nonlinear structural response analysis. For the case studied the application of the POD/MAC technique resulted in a substantial improvement of the reduced order simulation when compared to a classic approach utilizing only low frequency modes present in the excitation bandwidth. Further studies are aimed to expand application of the presented technique to more complex structures including non-planar and two-dimensional configurations. For non-planar structures the separation of different displacement components may not be necessary or desirable.
Model Selection in Data Analysis Competitions
DEFF Research Database (Denmark)
Wind, David Kofoed; Winther, Ole
2014-01-01
The use of data analysis competitions for selecting the most appropriate model for a problem is a recent innovation in the field of predictive machine learning. Two of the most well-known examples of this trend was the Netflix Competition and recently the competitions hosted on the online platfor...
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...
Mahachie John, Jestinah M; Cattaert, Tom; Lishout, François Van; Gusareva, Elena S; Steen, Kristel Van
2012-01-01
Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on "top" findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using "on-the-fly" lower-order effects adjusting when screening for SNP-SNP interactions
An Ordered Regression Model to Predict Transit Passengers’ Behavioural Intentions
Energy Technology Data Exchange (ETDEWEB)
Oña, J. de; Oña, R. de; Eboli, L.; Forciniti, C.; Mazzulla, G.
2016-07-01
Passengers’ behavioural intentions after experiencing transit services can be viewed as signals that show if a customer continues to utilise a company’s service. Users’ behavioural intentions can depend on a series of aspects that are difficult to measure directly. More recently, transit passengers’ behavioural intentions have been just considered together with the concepts of service quality and customer satisfaction. Due to the characteristics of the ways for evaluating passengers’ behavioural intentions, service quality and customer satisfaction, we retain that this kind of issue could be analysed also by applying ordered regression models. This work aims to propose just an ordered probit model for analysing service quality factors that can influence passengers’ behavioural intentions towards the use of transit services. The case study is the LRT of Seville (Spain), where a survey was conducted in order to collect the opinions of the passengers about the existing transit service, and to have a measure of the aspects that can influence the intentions of the users to continue using the transit service in the future. (Author)
Reduced order methods for modeling and computational reduction
Rozza, Gianluigi
2014-01-01
This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics. Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This...
Probabilistic Rotor Life Assessment Using Reduced Order Models
Directory of Open Access Journals (Sweden)
Brian K. Beachkofski
2009-01-01
Full Text Available Probabilistic failure assessments for integrally bladed disks are system reliability problems where a failure in at least one blade constitutes a rotor system failure. Turbine engine fan and compressor blade life is dominated by High Cycle Fatigue (HCF initiated either by pure HCF or Foreign Object Damage (FOD. To date performing an HCF life assessment for the entire rotor system has been too costly in analysis time to be practical. Although the substantial run-time has previously precluded a full-rotor probabilistic analysis, reduced order models make this process tractable as demonstrated in this work. The system model includes frequency prediction, modal stress variation, mistuning amplification, FOD effect, and random material capability. The model has many random variables which are most easily handled through simple random sampling.
Topological order in an exactly solvable 3D spin model
International Nuclear Information System (INIS)
Bravyi, Sergey; Leemhuis, Bernhard; Terhal, Barbara M.
2011-01-01
Research highlights: RHtriangle We study exactly solvable spin model with six-qubit nearest neighbor interactions on a 3D face centered cubic lattice. RHtriangle The ground space of the model exhibits topological quantum order. RHtriangle Elementary excitations can be geometrically described as the corners of rectangular-shaped membranes. RHtriangle The ground space can encode 4g qubits where g is the greatest common divisor of the lattice dimensions. RHtriangle Logical operators acting on the encoded qubits are described in terms of closed strings and closed membranes. - Abstract: We study a 3D generalization of the toric code model introduced recently by Chamon. This is an exactly solvable spin model with six-qubit nearest-neighbor interactions on an FCC lattice whose ground space exhibits topological quantum order. The elementary excitations of this model which we call monopoles can be geometrically described as the corners of rectangular-shaped membranes. We prove that the creation of an isolated monopole separated from other monopoles by a distance R requires an operator acting on Ω(R 2 ) qubits. Composite particles that consist of two monopoles (dipoles) and four monopoles (quadrupoles) can be described as end-points of strings. The peculiar feature of the model is that dipole-type strings are rigid, that is, such strings must be aligned with face-diagonals of the lattice. For periodic boundary conditions the ground space can encode 4g qubits where g is the greatest common divisor of the lattice dimensions. We describe a complete set of logical operators acting on the encoded qubits in terms of closed strings and closed membranes.
Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistant States
Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter M.; Brenner, Martin J.
2017-01-01
This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and weak fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design.
A MATHEMATICAL MODELLING APPROACH TO ONE-DAY CRICKET BATTING ORDERS
Directory of Open Access Journals (Sweden)
Matthews Ovens1
2006-12-01
Full Text Available While scoring strategies and player performance in cricket have been studied, there has been little published work about the influence of batting order with respect to One-Day cricket. We apply a mathematical modelling approach to compute efficiently the expected performance (runs distribution of a cricket batting order in an innings. Among other applications, our method enables one to solve for the probability of one team beating another or to find the optimal batting order for a set of 11 players. The influence of defence and bowling ability can be taken into account in a straightforward manner. In this presentation, we outline how we develop our Markov Chain approach to studying the progress of runs for a batting order of non- identical players along the lines of work in baseball modelling by Bukiet et al., 1997. We describe the issues that arise in applying such methods to cricket, discuss ideas for addressing these difficulties and note limitations on modelling batting order for One-Day cricket. By performing our analysis on a selected subset of the possible batting orders, we apply the model to quantify the influence of batting order in a game of One Day cricket using available real-world data for current players
Model selection criterion in survival analysis
Karabey, Uǧur; Tutkun, Nihal Ata
2017-07-01
Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.
Towards a Wind Turbine Wake Reduced-Order Model
Hamilton, Nicholas; Viggiano, Bianca; Calaf, Marc; Tutkun, Murat; Cal, Raúl Bayoán
2017-11-01
A reduced-order model for a wind turbine wake is sought for prediction and control. Basis functions from the proper orthogonal decomposition (POD) represent the spatially coherent turbulence structures in the wake; eigenvalues delineate the turbulence kinetic energy associated with each mode. Back-projecting the POD modes onto the velocity snapshots produces coefficients that express the amplitude of each mode in time. A reduced-order model of the wind turbine wake (wakeROM) is defined through a series of polynomial parameters that quantify mode interaction and the evolution of each mode coefficient. Tikhonov regularization is employed to recalibrate the dynamical system, reducing error in the modeled mode coefficients and adding stability to the system. The wakeROM is periodically reinitialized by relating the incoming turbulent velocity to the POD mode coefficients. A high-level view of the wakeROM provides as a platform to discuss promising research direction, alternate processes that will enhance stability, and portability to control methods. NSF- ECCS-1032647, NSF-CBET-1034581, Research Council of Norway Project Number 231491.
On Using Selection Procedures with Binomial Models.
1983-10-01
eds.), Shinko Tsusho Co. Ltd., Tokyo, Japan , pp. 501-533. Gupta, S. S. and Sobel, M. (1960). Selecting a subset containing the best of several...IA_____3_6r__I____ *TITLE food A$ieweI L TYPE of 09PORT 6 PERIOD COVERED ON USING SELECTION PROCEDURES WITH BINOMIAL MODELS Technical 6. PeSPRFeauS1 ONG. REPORT...ontoedis stoc toeSI. to Ei.,..,t&* toemR.,. 14. SUPPOLEMENTARY MOCTES 19. Rey WORDS (Coatiou. 40 ow.oa* edo if Necesary and #do""&a by block number
Aerosol model selection and uncertainty modelling by adaptive MCMC technique
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M. Laine
2008-12-01
Full Text Available We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way.
The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ. It uses Markov chain Monte Carlo (MCMC technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use.
We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.
Temporal aggregation in first order cointegrated vector autoregressive models
DEFF Research Database (Denmark)
La Cour, Lisbeth Funding; Milhøj, Anders
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline...
Fundamental Frequency and Model Order Estimation Using Spatial Filtering
DEFF Research Database (Denmark)
Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll
2014-01-01
In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal...... extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment...... on a trumpet signal show the applicability on real signals....
Highly Active and Selective Hydrogenation of CO2to Ethanol by Ordered Pd-Cu Nanoparticles.
Bai, Shuxing; Shao, Qi; Wang, Pengtang; Dai, Qiguang; Wang, Xingyi; Huang, Xiaoqing
2017-05-24
Carbon dioxide (CO 2 ) hydrogenation to ethanol (C 2 H 5 OH) is considered a promising way for CO 2 conversion and utilization, whereas desirable conversion efficiency remains a challenge. Herein, highly active, selective and stable CO 2 hydrogenation to C 2 H 5 OH was enabled by highly ordered Pd-Cu nanoparticles (NPs). By tuning the composition of the Pd-Cu NPs and catalyst supports, the efficiency of CO 2 hydrogenation to C 2 H 5 OH was well optimized with Pd 2 Cu NPs/P25 exhibiting high selectivity to C 2 H 5 OH of up to 92.0% and the highest turnover frequency of 359.0 h -1 . Diffuse reflectance infrared Fourier transform spectroscopy results revealed the high C 2 H 5 OH production and selectivity of Pd 2 Cu NPs/P25 can be ascribed to boosting *CO (adsorption CO) hydrogenation to *HCO, the rate-determining step for the CO 2 hydrogenation to C 2 H 5 OH.
Post-model selection inference and model averaging
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2011-07-01
Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....
Uniform design based SVM model selection for face recognition
Li, Weihong; Liu, Lijuan; Gong, Weiguo
2010-02-01
Support vector machine (SVM) has been proved to be a powerful tool for face recognition. The generalization capacity of SVM depends on the model with optimal hyperparameters. The computational cost of SVM model selection results in application difficulty in face recognition. In order to overcome the shortcoming, we utilize the advantage of uniform design--space filling designs and uniformly scattering theory to seek for optimal SVM hyperparameters. Then we propose a face recognition scheme based on SVM with optimal model which obtained by replacing the grid and gradient-based method with uniform design. The experimental results on Yale and PIE face databases show that the proposed method significantly improves the efficiency of SVM model selection.
Chemical identification using Bayesian model selection
Energy Technology Data Exchange (ETDEWEB)
Burr, Tom; Fry, H. A. (Herbert A.); McVey, B. D. (Brian D.); Sander, E. (Eric)
2002-01-01
Remote detection and identification of chemicals in a scene is a challenging problem. We introduce an approach that uses some of the image's pixels to establish the background characteristics while other pixels represent the target for which we seek to identify all chemical species present. This leads to a generalized least squares problem in which we focus on 'subset selection' to identify the chemicals thought to be present. Bayesian model selection allows us to approximate the posterior probability that each chemical in the library is present by adding the posterior probabilities of all the subsets which include the chemical. We present results using realistic simulated data for the case with 1 to 5 chemicals present in each target and compare performance to a hybrid of forward and backward stepwise selection procedure using the F statistic.
Antiferromagnetic order in the Hubbard model on the Penrose lattice
Koga, Akihisa; Tsunetsugu, Hirokazu
2017-12-01
We study an antiferromagnetic order in the ground state of the half-filled Hubbard model on the Penrose lattice and investigate the effects of quasiperiodic lattice structure. In the limit of infinitesimal Coulomb repulsion U →+0 , the staggered magnetizations persist to be finite, and their values are determined by confined states, which are strictly localized with thermodynamics degeneracy. The magnetizations exhibit an exotic spatial pattern, and have the same sign in each of cluster regions, the size of which ranges from 31 sites to infinity. With increasing U , they continuously evolve to those of the corresponding spin model in the U =∞ limit. In both limits of U , local magnetizations exhibit a fairly intricate spatial pattern that reflects the quasiperiodic structure, but the pattern differs between the two limits. We have analyzed this pattern change by a mode analysis by the singular value decomposition method for the fractal-like magnetization pattern projected into the perpendicular space.
Pairing of parafermions of order 2: seniority model
International Nuclear Information System (INIS)
Nelson, Charles A
2004-01-01
As generalizations of the fermion seniority model, four multi-mode Hamiltonians are considered to investigate some of the consequences of the pairing of parafermions of order 2. Two- and four-particle states are explicitly constructed for H A ≡ -GA†A with A† ≡ 1/2 Σ m>0 c† m c† -m and the distinct H C ≡ -GC†C with C† ≡ 1/2 Σ m>0 c† -m c† m , and for the time-reversal invariant H (-) ≡ -G(A† - C†)(A - C) and H (+) ≡ -G(A† + C†)(A + C), which has no analogue in the fermion case. The spectra and degeneracies are compared with those of the usual fermion seniority model
Quantifying and modeling birth order effects in autism.
Directory of Open Access Journals (Sweden)
Tychele Turner
Full Text Available Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models. We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies.
Roof planes detection via a second-order variational model
Benciolini, Battista; Ruggiero, Valeria; Vitti, Alfonso; Zanetti, Massimo
2018-04-01
The paper describes a unified automatic procedure for the detection of roof planes in gridded height data. The procedure exploits the Blake-Zisserman (BZ) model for segmentation in both 2D and 1D, and aims to detect, to model and to label roof planes. The BZ model relies on the minimization of a functional that depends on first- and second-order derivatives, free discontinuities and free gradient discontinuities. During the minimization, the relative strength of each competitor is controlled by a set of weight parameters. By finding the minimum of the approximated BZ functional, one obtains: (1) an approximation of the data that is smoothed solely within regions of homogeneous gradient, and (2) an explicit detection of the discontinuities and gradient discontinuities of the approximation. Firstly, input data is segmented using the 2D BZ. The maps of data and gradient discontinuities are used to isolate building candidates and planar patches (i.e. regions with homogeneous gradient) that correspond to roof planes. Connected regions that can not be considered as buildings are filtered according to both patch dimension and distribution of the directions of the normals to the boundary. The 1D BZ model is applied to the curvilinear coordinates of boundary points of building candidates in order to reduce the effect of data granularity when the normals are evaluated. In particular, corners are preserved and can be detected by means of gradient discontinuity. Lastly, a total least squares model is applied to estimate the parameters of the plane that best fits the points of each planar patch (orthogonal regression with planar model). Refinement of planar patches is performed by assigning those points that are close to the boundaries to the planar patch for which a given proximity measure assumes the smallest value. The proximity measure is defined to account for the variance of a fitting plane and a weighted distance of a point from the plane. The effectiveness of the
Reserve selection using nonlinear species distribution models.
Moilanen, Atte
2005-06-01
Reserve design is concerned with optimal selection of sites for new conservation areas. Spatial reserve design explicitly considers the spatial pattern of the proposed reserve network and the effects of that pattern on reserve cost and/or ability to maintain species there. The vast majority of reserve selection formulations have assumed a linear problem structure, which effectively means that the biological value of a potential reserve site does not depend on the pattern of selected cells. However, spatial population dynamics and autocorrelation cause the biological values of neighboring sites to be interdependent. Habitat degradation may have indirect negative effects on biodiversity in areas neighboring the degraded site as a result of, for example, negative edge effects or lower permeability for animal movement. In this study, I present a formulation and a spatial optimization algorithm for nonlinear reserve selection problems in grid-based landscapes that accounts for interdependent site values. The method is demonstrated using habitat maps and nonlinear habitat models for threatened birds in the Netherlands, and it is shown that near-optimal solutions are found for regions consisting of up to hundreds of thousands grid cells, a landscape size much larger than those commonly attempted even with linear reserve selection formulations.
Wang, Shiyu; Yang, Yan; Culpepper, Steven Andrew; Douglas, Jeffrey A.
2018-01-01
A family of learning models that integrates a cognitive diagnostic model and a higher-order, hidden Markov model in one framework is proposed. This new framework includes covariates to model skill transition in the learning environment. A Bayesian formulation is adopted to estimate parameters from a learning model. The developed methods are…
Behavioral optimization models for multicriteria portfolio selection
Directory of Open Access Journals (Sweden)
Mehlawat Mukesh Kumar
2013-01-01
Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.
Financial applications of a Tabu search variable selection model
Directory of Open Access Journals (Sweden)
Zvi Drezner
2001-01-01
Full Text Available We illustrate how a comparatively new technique, a Tabu search variable selection model [Drezner, Marcoulides and Salhi (1999], can be applied efficiently within finance when the researcher must select a subset of variables from among the whole set of explanatory variables under consideration. Several types of problems in finance, including corporate and personal bankruptcy prediction, mortgage and credit scoring, and the selection of variables for the Arbitrage Pricing Model, require the researcher to select a subset of variables from a larger set. In order to demonstrate the usefulness of the Tabu search variable selection model, we: (1 illustrate its efficiency in comparison to the main alternative search procedures, such as stepwise regression and the Maximum R2 procedure, and (2 show how a version of the Tabu search procedure may be implemented when attempting to predict corporate bankruptcy. We accomplish (2 by indicating that a Tabu Search procedure increases the predictability of corporate bankruptcy by up to 10 percentage points in comparison to Altman's (1968 Z-Score model.
Topological order in an exactly solvable 3D spin model
Bravyi, Sergey; Leemhuis, Bernhard; Terhal, Barbara M.
2011-04-01
We study a 3D generalization of the toric code model introduced recently by Chamon. This is an exactly solvable spin model with six-qubit nearest-neighbor interactions on an FCC lattice whose ground space exhibits topological quantum order. The elementary excitations of this model which we call monopoles can be geometrically described as the corners of rectangular-shaped membranes. We prove that the creation of an isolated monopole separated from other monopoles by a distance R requires an operator acting on Ω( R2) qubits. Composite particles that consist of two monopoles (dipoles) and four monopoles (quadrupoles) can be described as end-points of strings. The peculiar feature of the model is that dipole-type strings are rigid, that is, such strings must be aligned with face-diagonals of the lattice. For periodic boundary conditions the ground space can encode 4 g qubits where g is the greatest common divisor of the lattice dimensions. We describe a complete set of logical operators acting on the encoded qubits in terms of closed strings and closed membranes.
A simple parametric model selection test
Susanne M. Schennach; Daniel Wilhelm
2014-01-01
We propose a simple model selection test for choosing among two parametric likelihoods which can be applied in the most general setting without any assumptions on the relation between the candidate models and the true distribution. That is, both, one or neither is allowed to be correctly speci fied or misspeci fied, they may be nested, non-nested, strictly non-nested or overlapping. Unlike in previous testing approaches, no pre-testing is needed, since in each case, the same test statistic to...
Robust inference in sample selection models
Zhelonkin, Mikhail
2015-11-20
The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.
Novel metrics for growth model selection.
Grigsby, Matthew R; Di, Junrui; Leroux, Andrew; Zipunnikov, Vadim; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William
2018-01-01
Literature surrounding the statistical modeling of childhood growth data involves a diverse set of potential models from which investigators can choose. However, the lack of a comprehensive framework for comparing non-nested models leads to difficulty in assessing model performance. This paper proposes a framework for comparing non-nested growth models using novel metrics of predictive accuracy based on modifications of the mean squared error criteria. Three metrics were created: normalized, age-adjusted, and weighted mean squared error (MSE). Predictive performance metrics were used to compare linear mixed effects models and functional regression models. Prediction accuracy was assessed by partitioning the observed data into training and test datasets. This partitioning was constructed to assess prediction accuracy for backward (i.e., early growth), forward (i.e., late growth), in-range, and on new-individuals. Analyses were done with height measurements from 215 Peruvian children with data spanning from near birth to 2 years of age. Functional models outperformed linear mixed effects models in all scenarios tested. In particular, prediction errors for functional concurrent regression (FCR) and functional principal component analysis models were approximately 6% lower when compared to linear mixed effects models. When we weighted subject-specific MSEs according to subject-specific growth rates during infancy, we found that FCR was the best performer in all scenarios. With this novel approach, we can quantitatively compare non-nested models and weight subgroups of interest to select the best performing growth model for a particular application or problem at hand.
Theory and Low-Order Modeling of Unsteady Airfoil Flows
Ramesh, Kiran
Unsteady flow phenomena are prevalent in a wide range of problems in nature and engineering. These include, but are not limited to, aerodynamics of insect flight, dynamic stall in rotorcraft and wind turbines, leading-edge vortices in delta wings, micro-air vehicle (MAV) design, gust handling and flow control. The most significant characteristics of unsteady flows are rapid changes in the circulation of the airfoil, apparent-mass effects, flow separation and the leading-edge vortex (LEV) phenomenon. Although experimental techniques and computational fluid dynamics (CFD) methods have enabled the detailed study of unsteady flows and their underlying features, a reliable and inexpensive loworder method for fast prediction and for use in control and design is still required. In this research, a low-order methodology based on physical principles rather than empirical fitting is proposed. The objective of such an approach is to enable insights into unsteady phenomena while developing approaches to model them. The basis of the low-order model developed here is unsteady thin-airfoil theory. A time-stepping approach is used to solve for the vorticity on an airfoil camberline, allowing for large amplitudes and nonplanar wakes. On comparing lift coefficients from this method against data from CFD and experiments for some unsteady test cases, it is seen that the method predicts well so long as LEV formation does not occur and flow over the airfoil is attached. The formation of leading-edge vortices (LEVs) in unsteady flows is initiated by flow separation and the formation of a shear layer at the airfoil's leading edge. This phenomenon has been observed to have both detrimental (dynamic stall in helicopters) and beneficial (high-lift flight in insects) effects. To predict the formation of LEVs in unsteady flows, a Leading Edge Suction Parameter (LESP) is proposed. This parameter is calculated from inviscid theory and is a measure of the suction at the airfoil's leading edge. It
A Study of Enhanced, Higher Order Boussinesq-Type Equations and Their Numerical Modelling
DEFF Research Database (Denmark)
Banijamali, Babak
model is designated for the solution of higher-order Boussinesq-type equations, formulated in terms of the horizontal velocity at an arbitrary depth vector. Various discretisation techniques and grid definitions have been considered in this endeavour, undertaking a detailed analysis of the selected......This project has encompassed efforts in two separate veins: on the one hand, the acquiring of highly accurate model equations of the Boussinesq-type, and on the other hand, the theoretical and practical work in implementing such equations in the form of conventional numerical models, with obvious...... potential for applications to the realm of numerical modelling in coastal engineering. The derivation and analysis of several forms of higher-order in dispersion and non-linearity Boussinesq-type equations have been undertaken, obtaining and investigating the properties of a new and generalised class...
Stochastic reduced order models for inverse problems under uncertainty.
Warner, James E; Aquino, Wilkins; Grigoriu, Mircea D
2015-03-01
This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well.
Vortex network community based reduced-order force model
Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya; Taira, Kunihiko
2017-11-01
We characterize the vortical wake interactions by utilizing network theory and cluster-based approaches, and develop a data-inspired unsteady force model. In the present work, the vortical interaction network is defined by nodes representing vortical elements and the edges quantified by induced velocity measures amongst the vortices. The full vorticity field is reduced to a finite number of vortical clusters based on network community detection algorithm, which serves as a basis for a skeleton network that captures the essence of the wake dynamics. We use this reduced representation of the wake to develop a data-inspired reduced-order force model that can predict unsteady fluid forces on the body. The overall formulation is demonstrated for laminar flows around canonical bluff body wake and stalled flow over an airfoil. We also show the robustness of the present network-based model against noisy data, which motivates applications towards turbulent flows and experimental measurements. Supported by the National Science Foundation (Grant 1632003).
McNeish, Daniel; Dumas, Denis
2017-01-01
Recent methodological work has highlighted the promise of nonlinear growth models for addressing substantive questions in the behavioral sciences. In this article, we outline a second-order nonlinear growth model in order to measure a critical notion in development and education: potential. Here, potential is conceptualized as having three components-ability, capacity, and availability-where ability is the amount of skill a student is estimated to have at a given timepoint, capacity is the maximum amount of ability a student is predicted to be able to develop asymptotically, and availability is the difference between capacity and ability at any particular timepoint. We argue that single timepoint measures are typically insufficient for discerning information about potential, and we therefore describe a general framework that incorporates a growth model into the measurement model to capture these three components. Then, we provide an illustrative example using the public-use Early Childhood Longitudinal Study-Kindergarten data set using a Michaelis-Menten growth function (reparameterized from its common application in biochemistry) to demonstrate our proposed model as applied to measuring potential within an educational context. The advantage of this approach compared to currently utilized methods is discussed as are future directions and limitations.
Najhan Mohd Nagib, Ahmad; Naufal Adnan, Ahmad; Ismail, Azianti; Halim, Nurul Hayati Abdul; Syuhadah Khusaini, Nurul
2016-11-01
The inventory model had been utilized since the early 1900s. The implementation of the inventory management model is generally to ensure that an organisation is able to fulfil customer's demand at the lowest possible cost to improve profitability. This paper focuses on reviewing previous published papers regarding inventory control model mainly in the food and beverage processing industry. The author discusses four inventory models, which are the make-to-stock (MTS), make-to-order (MTO), economic order quantity (EOQ), and hybrid of MTS-MTO models. The issues raised by the researchers on the above techniques as well as the elements need to be considered upon selection have been discussed in this paper. The main objective of the study is to highlight the important role played by these inventory control models in the food and beverage processing industry.
In vitro biological models in order to study BNCT
International Nuclear Information System (INIS)
Dagrosa, Maria A.; Kreimann, Erica L.; Schwint, Amanda E.; Juvenal, Guillermo J.; Pisarev, Mario A.; Farias, Silvia S.; Garavaglia, Ricardo N.; Batistoni, Daniel A.
1999-01-01
Undifferentiated thyroid carcinoma (UTC) lacks an effective treatment. Boron neutron capture therapy (BNCT) is based on the selective uptake of 10 B-boronated compounds by some tumours, followed by irradiation with an appropriate neutron beam. The radioactive boron originated ( 11 B) decays releasing 7 Li, gamma rays and alpha particles, and these latter will destroy the tumour. In order to explore the possibility of applying BNCT to UTC we have studied the biodistribution of BPA. In vitro studies: the uptake of p- 10 borophenylalanine (BPA) by the UTC cell line ARO, primary cultures of normal bovine thyroid cells (BT) and human follicular adenoma (FA) thyroid was studied. No difference in BPA uptake was observed between proliferating and quiescent ARO cells. The uptake by quiescent ARO, BT and FA showed that the ARO/BT and ARO/FA ratios were 4 and 5, respectively (p< 0.001). The present experimental results open the possibility of applying BNCT for the treatment of UTC. (author)
Reduced order modeling in topology optimization of vibroacoustic problems
DEFF Research Database (Denmark)
Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Brunskog, Jonas
2017-01-01
There is an interest in introducing topology optimization techniques in the design process of structural-acoustic systems. In topology optimization, the design space must be finely meshed in order to obtain an accurate design, which results in large numbers of degrees of freedom when designing...... or size optimization in large vibroacoustic models; however, new challenges are encountered when dealing with topology optimization. Since a design parameter per element is considered, the total number of design variables becomes very large; this poses a challenge to most existing pMOR techniques, which...... suffer from the curse of dimensionality. Moreover, the fact that the nature of the elements changes throughout the optimization (material to void or material to air) makes it more difficult to create a global basis that is accurate throughout the whole design space. In this work, these challenges...
Basic first-order model theory in Mizar
Directory of Open Access Journals (Sweden)
Marco Bright Caminati
2010-01-01
Full Text Available The author has submitted to Mizar Mathematical Library a series of five articles introducing a framework for the formalization of classical first-order model theory.In them, Goedel's completeness and Lowenheim-Skolem theorems have also been formalized for the countable case, to offer a first application of it and to showcase its utility.This is an overview and commentary on some key aspects of this setup.It features exposition and discussion of a new encoding of basic definitions and theoretical gears needed for the task, remarks about the design strategies and approaches adopted in their implementation, and more general reflections about proof checking induced by the work done.
Dynamical analysis of fractional order model of immunogenic tumors
Directory of Open Access Journals (Sweden)
Sadia Arshad
2016-07-01
Full Text Available In this article, we examine the fractional order model of the cytotoxic T lymphocyte response to a growing tumor cell population. We investigate the long-term behavior of tumor growth and explore the conditions of tumor elimination analytically. We establish the conditions for the tumor-free equilibrium and tumor-infection equilibrium to be asymptotically stable and provide the expression of the basic reproduction number. Existence of physical significant tumor-infection equilibrium points is investigated analytically. We show that tumor growth rate, source rate of immune cells, and death rate of immune cells play vital role in tumor dynamics and system undergoes saddle-node and transcritical bifurcation based on these parameters. Furthermore, the effect of cancer treatment is discussed by varying the values of relevant parameters. Numerical simulations are presented to illustrate the analytical results.
The Origins of Order: Self-Organization and Selection in Evolution
Kauffman, Stuart A.
The following sections are included: * Introduction * Fitness Landscapes in Sequence Space * The NK Model of Rugged Fitness Landscapes * The NK Model of Random Epistatic Interactions * The Rank Order Statistics on K = N - 1 Random Landscapes * The number of local optima is very large * The expected fraction of fitter 1-mutant neighbors dwindles by 1/2 on each improvement step * Walks to local optima are short and vary as a logarithmic function of N * The expected time to reach an optimum is proportional to the dimensionality of the space * The ratio of accepted to tried mutations scales as lnN/N * Any genotype can only climb to a small fraction of the local optima * A small fraction of the genotypes can climb to any one optimum * Conflicting constraints cause a "complexity catastrophe": as complexity increase accessible adaptive peaks fall toward the mean fitness * The "Tunable" NK Family of Correlated Landscapes * Other Combinatorial Optimization Problems and Their Landscapes * Summary * References
Snodgrass, Michael; Kalaida, Natasha; Winer, E Samuel
2009-06-01
Access can either be first-order or second-order. First order access concerns whether contents achieve representation in phenomenal consciousness at all; second-order access concerns whether phenomenally conscious contents are selected for metacognitive, higher order processing by reflective consciousness. When the optional and flexible nature of second-order access is kept in mind, there remain strong reasons to believe that exclusion failure can indeed isolate phenomenally conscious stimuli that are not so accessed. Irvine's [Irvine, E. (2009). Signal detection theory, the exclusion failure paradigm and weak consciousness-Evidence for the access/phenomenal distinction? Consciousness and Cognition.] partial access argument fails because exclusion failure is indeed due to lack of second-order access, not insufficient phenomenally conscious information. Further, the enable account conforms with both qualitative differences and subjective report, and is simpler than the endow account. Finally, although first-order access may be a distinct and important process, second-order access arguably reflects the core meaning of access generally.
Sparsity enabled cluster reduced-order models for control
Kaiser, Eurika; Morzyński, Marek; Daviller, Guillaume; Kutz, J. Nathan; Brunton, Bingni W.; Brunton, Steven L.
2018-01-01
Characterizing and controlling nonlinear, multi-scale phenomena are central goals in science and engineering. Cluster-based reduced-order modeling (CROM) was introduced to exploit the underlying low-dimensional dynamics of complex systems. CROM builds a data-driven discretization of the Perron-Frobenius operator, resulting in a probabilistic model for ensembles of trajectories. A key advantage of CROM is that it embeds nonlinear dynamics in a linear framework, which enables the application of standard linear techniques to the nonlinear system. CROM is typically computed on high-dimensional data; however, access to and computations on this full-state data limit the online implementation of CROM for prediction and control. Here, we address this key challenge by identifying a small subset of critical measurements to learn an efficient CROM, referred to as sparsity-enabled CROM. In particular, we leverage compressive measurements to faithfully embed the cluster geometry and preserve the probabilistic dynamics. Further, we show how to identify fewer optimized sensor locations tailored to a specific problem that outperform random measurements. Both of these sparsity-enabled sensing strategies significantly reduce the burden of data acquisition and processing for low-latency in-time estimation and control. We illustrate this unsupervised learning approach on three different high-dimensional nonlinear dynamical systems from fluids with increasing complexity, with one application in flow control. Sparsity-enabled CROM is a critical facilitator for real-time implementation on high-dimensional systems where full-state information may be inaccessible.
Second-order closure PBL model with new third-order moments: Comparison with LES data
Canuto, V. M.; Minotti, F.; Ronchi, C.; Ypma, R. M.; Zeman, O.
1994-01-01
This paper contains two parts. In the first part, a new set of diagnostic equations is derived for the third-order moments for a buoyancy-driven flow, by exact inversion of the prognostic equations for the third-order moment equations in the stationary case. The third-order moments exhibit a universal structure: they all are a linear combination of the derivatives of all the second-order moments, bar-w(exp 2), bar-w theta, bar-theta(exp 2), and bar-q(exp 2). Each term of the sum contains a turbulent diffusivity D(sub t), which also exhibits a universal structure of the form D(sub t) = a nu(sub t) + b bar-w theta. Since the sign of the convective flux changes depending on stable or unstable stratification, D(sub t) varies according to the type of stratification. Here nu(sub t) approximately equal to wl (l is a mixing length and w is an rms velocity) represents the 'mechanical' part, while the 'buoyancy' part is represented by the convective flux bar-w theta. The quantities a and b are functions of the variable N(sub tau)(exp 2), where N(exp 2) = g alpha derivative of Theta with respect to z and tau is the turbulence time scale. The new expressions for the third-order moments generalize those of Zeman and Lumley, which were subsequently adopted by Sun and Ogura, Chen and Cotton, and Finger and Schmidt in their treatments of the convective boundary layer. In the second part, the new expressions for the third-order moments are used to solve the ensemble average equations describing a purely convective boundary laye r heated from below at a constant rate. The computed second- and third-order moments are then compared with the corresponding Large Eddy Simulation (LES) results, most of which are obtained by running a new LES code, and part of which are taken from published results. The ensemble average results compare favorably with the LES data.
Kopasakis, George
2014-01-01
The presentation covers a recently developed methodology to model atmospheric turbulence as disturbances for aero vehicle gust loads and for controls development like flutter and inlet shock position. The approach models atmospheric turbulence in their natural fractional order form, which provides for more accuracy compared to traditional methods like the Dryden model, especially for high speed vehicle. The presentation provides a historical background on atmospheric turbulence modeling and the approaches utilized for air vehicles. This is followed by the motivation and the methodology utilized to develop the atmospheric turbulence fractional order modeling approach. Some examples covering the application of this method are also provided, followed by concluding remarks.
PROPOSAL OF AN EMPIRICAL MODEL FOR SUPPLIERS SELECTION
Directory of Open Access Journals (Sweden)
Paulo Ávila
2015-03-01
Full Text Available The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, trough the literature review, there were identified five broad suppliers selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. Thereafter, a survey was elaborated and companies were contacted in order to answer which factors have more relevance in their decisions to choose the suppliers. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP method or Simple Multi-Attribute Rating Technique (SMART. The result of the research undertaken by the authors is a reference model that represents a decision making support for the suppliers/partners selection process.
Stabilization Approaches for Linear and Nonlinear Reduced Order Models
Rezaian, Elnaz; Wei, Mingjun
2017-11-01
It has been a major concern to establish reduced order models (ROMs) as reliable representatives of the dynamics inherent in high fidelity simulations, while fast computation is achieved. In practice it comes to stability and accuracy of ROMs. Given the inviscid nature of Euler equations it becomes more challenging to achieve stability, especially where moving discontinuities exist. Originally unstable linear and nonlinear ROMs are stabilized here by two approaches. First, a hybrid method is developed by integrating two different stabilization algorithms. At the same time, symmetry inner product is introduced in the generation of ROMs for its known robust behavior for compressible flows. Results have shown a notable improvement in computational efficiency and robustness compared to similar approaches. Second, a new stabilization algorithm is developed specifically for nonlinear ROMs. This method adopts Particle Swarm Optimization to enforce a bounded ROM response for minimum discrepancy between the high fidelity simulation and the ROM outputs. Promising results are obtained in its application on the nonlinear ROM of an inviscid fluid flow with discontinuities. Supported by ARL.
Genetic Algorithm-Guided, Adaptive Model Order Reduction of Flexible Aircrafts
Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter; Brenner, Martin J.
2017-01-01
This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM) for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design.
Construction of energy-stable Galerkin reduced order models.
Energy Technology Data Exchange (ETDEWEB)
Kalashnikova, Irina; Barone, Matthew Franklin; Arunajatesan, Srinivasan; van Bloemen Waanders, Bart Gustaaf
2013-05-01
This report aims to unify several approaches for building stable projection-based reduced order models (ROMs). Attention is focused on linear time-invariant (LTI) systems. The model reduction procedure consists of two steps: the computation of a reduced basis, and the projection of the governing partial differential equations (PDEs) onto this reduced basis. Two kinds of reduced bases are considered: the proper orthogonal decomposition (POD) basis and the balanced truncation basis. The projection step of the model reduction can be done in two ways: via continuous projection or via discrete projection. First, an approach for building energy-stable Galerkin ROMs for linear hyperbolic or incompletely parabolic systems of PDEs using continuous projection is proposed. The idea is to apply to the set of PDEs a transformation induced by the Lyapunov function for the system, and to build the ROM in the transformed variables. The resulting ROM will be energy-stable for any choice of reduced basis. It is shown that, for many PDE systems, the desired transformation is induced by a special weighted L2 inner product, termed the %E2%80%9Csymmetry inner product%E2%80%9D. Attention is then turned to building energy-stable ROMs via discrete projection. A discrete counterpart of the continuous symmetry inner product, a weighted L2 inner product termed the %E2%80%9CLyapunov inner product%E2%80%9D, is derived. The weighting matrix that defines the Lyapunov inner product can be computed in a black-box fashion for a stable LTI system arising from the discretization of a system of PDEs in space. It is shown that a ROM constructed via discrete projection using the Lyapunov inner product will be energy-stable for any choice of reduced basis. Connections between the Lyapunov inner product and the inner product induced by the balanced truncation algorithm are made. Comparisons are also made between the symmetry inner product and the Lyapunov inner product. The performance of ROMs constructed
Bayesian Model Selection in Geophysics: The evidence
Vrugt, J. A.
2016-12-01
Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model output, and inference of the model parameters. Per Bayes theorem, the posterior probability, , P(H|D), of a hypothesis, H, given the data D, is equivalent to the product of its prior probability, P(H), and likelihood, L(H|D), divided by a normalization constant, P(D). In geophysics, the hypothesis, H, often constitutes a description (parameterization) of the subsurface for some entity of interest (e.g. porosity, moisture content). The normalization constant, P(D), is not required for inference of the subsurface structure, yet of great value for model selection. Unfortunately, it is not particularly easy to estimate P(D) in practice. Here, I will introduce the various building blocks of a general purpose method which provides robust and unbiased estimates of the evidence, P(D). This method uses multi-dimensional numerical integration of the posterior (parameter) distribution. I will then illustrate this new estimator by application to three competing subsurface models (hypothesis) using GPR travel time data from the South Oyster Bacterial Transport Site, in Virginia, USA. The three subsurface models differ in their treatment of the porosity distribution and use (a) horizontal layering with fixed layer thicknesses, (b) vertical layering with fixed layer thicknesses and (c) a multi-Gaussian field. The results of the new estimator are compared against the brute force Monte Carlo method, and the Laplace-Metropolis method.
Ding, Yi; Peng, Kai; Yu, Miao; Lu, Lei; Zhao, Kun
2017-08-01
The performance of the two selected spatial frequency phase unwrapping methods is limited by a phase error bound beyond which errors will occur in the fringe order leading to a significant error in the recovered absolute phase map. In this paper, we propose a method to detect and correct the wrong fringe orders. Two constraints are introduced during the fringe order determination of two selected spatial frequency phase unwrapping methods. A strategy to detect and correct the wrong fringe orders is also described. Compared with the existing methods, we do not need to estimate the threshold associated with absolute phase values to determine the fringe order error, thus making it more reliable and avoiding the procedure of search in detecting and correcting successive fringe order errors. The effectiveness of the proposed method is validated by the experimental results.
High-dimensional model estimation and model selection
CERN. Geneva
2015-01-01
I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.
Acoustic model order reduction for the lowest condition number in inverse method
Madoliat, Reza; Nouri, Nowrouz Mohammad; Rahrovi, Ali
2017-06-01
Acoustic sources with wide surfaces can be broken down in a fluid environment into smaller acoustic sources. In this study, a general model is presented, indicating the type, number, direction, position and strength of these sources in such a way that the main sound and the sound of the equivalent sources match each other acceptably. When the position and direction of the source is determined, the strength of the source can be found using the inverse method. However, since the solution is not unique in the inverse method, a different acoustic strength is obtained for the sources if different positions are selected. By selecting an arrangement of general sources and using an optimization algorithm, the least possible mismatch between the main sound and the sound of equivalent sources can be achieved. In the inverse method, it is important to reduce the effects of measurement errors. The sensor placement and acoustic model order reduction (AMOR) are studied for reducing these effects.
International Nuclear Information System (INIS)
Gupta, S.S.; Panchapakesan, S.
1975-01-01
A quantile selection procedure in reliability problems pertaining to a restricted family of probability distributions is discussed. This family is assumed to be star-ordered with respect to the standard normal distribution folded at the origin. Motivation for this formulation of the problem is described. Both exact and asymptotic results dealing with the distribution of the maximum of ratios of order statistics from such a family are obtained and tables of the appropriate constants, percentiles of this statistic, are given in order to facilitate the use of the selection procedure
State reduced order models for the modelling of the thermal behavior of buildings
Energy Technology Data Exchange (ETDEWEB)
Menezo, Christophe; Bouia, Hassan; Roux, Jean-Jacques; Depecker, Patrick [Institute National de Sciences Appliquees de Lyon, Villeurbanne Cedex, (France). Centre de Thermique de Lyon (CETHIL). Equipe Thermique du Batiment]. E-mail: menezo@insa-cethil-etb.insa-lyon.fr; bouia@insa-cethil-etb.insa-lyon.fr; roux@insa-cethil-etb.insa-lyon.fr; depecker@insa-cethil-etb.insa-lyon.fr
2000-07-01
This work is devoted to the field of building physics and related to the reduction of heat conduction models. The aim is to enlarge the model libraries of heat and mass transfer codes through limiting the considerable dimensions reached by the numerical systems during the modelling process of a multizone building. We show that the balanced realization technique, specifically adapted to the coupling of reduced order models with the other thermal phenomena, turns out to be very efficient. (author)
Permutation forests for modeling word order in machine translation
Stanojević, M.
2017-01-01
In natural language, there is only a limited space for variation in the word order of linguistic productions. From a linguistic perspective, word order is the result of multiple application of syntactic recursive functions. These syntactic operations produce hierarchical syntactic structures, as
Selecting a model of supersymmetry breaking mediation
International Nuclear Information System (INIS)
AbdusSalam, S. S.; Allanach, B. C.; Dolan, M. J.; Feroz, F.; Hobson, M. P.
2009-01-01
We study the problem of selecting between different mechanisms of supersymmetry breaking in the minimal supersymmetric standard model using current data. We evaluate the Bayesian evidence of four supersymmetry breaking scenarios: mSUGRA, mGMSB, mAMSB, and moduli mediation. The results show a strong dependence on the dark matter assumption. Using the inferred cosmological relic density as an upper bound, minimal anomaly mediation is at least moderately favored over the CMSSM. Our fits also indicate that evidence for a positive sign of the μ parameter is moderate at best. We present constraints on the anomaly and gauge mediated parameter spaces and some previously unexplored aspects of the dark matter phenomenology of the moduli mediation scenario. We use sparticle searches, indirect observables and dark matter observables in the global fit and quantify robustness with respect to prior choice. We quantify how much information is contained within each constraint.
A Novel Method for Decoding Any High-Order Hidden Markov Model
Directory of Open Access Journals (Sweden)
Fei Ye
2014-01-01
Full Text Available This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.
Reduced Order Aeroservoelastic Models with Rigid Body Modes, Phase II
National Aeronautics and Space Administration — Complex aeroelastic and aeroservoelastic phenomena can be modeled on complete aircraft configurations generating models with millions of degrees of freedom. Starting...
Modelling Trends in Ordered Correspondence Analysis Using Orthogonal Polynomials.
Lombardo, Rosaria; Beh, Eric J; Kroonenberg, Pieter M
2016-06-01
The core of the paper consists of the treatment of two special decompositions for correspondence analysis of two-way ordered contingency tables: the bivariate moment decomposition and the hybrid decomposition, both using orthogonal polynomials rather than the commonly used singular vectors. To this end, we will detail and explain the basic characteristics of a particular set of orthogonal polynomials, called Emerson polynomials. It is shown that such polynomials, when used as bases for the row and/or column spaces, can enhance the interpretations via linear, quadratic and higher-order moments of the ordered categories. To aid such interpretations, we propose a new type of graphical display-the polynomial biplot.
Model order reduction using eigen algorithm | Singh | International ...
African Journals Online (AJOL)
-scale dynamic systems where denominator polynomial determined through Eigen algorithm and numerator polynomial via factor division algorithm. In Eigen algorithm, the most dominant Eigen value of both original and reduced order ...
Hidden Markov Model for Stock Selection
Directory of Open Access Journals (Sweden)
Nguyet Nguyen
2015-10-01
Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.
How Many Separable Sources? Model Selection In Independent Components Analysis
DEFF Research Database (Denmark)
Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen
2015-01-01
Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis....../Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from...... among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though...
Directory of Open Access Journals (Sweden)
Christer Dalen
2017-10-01
Full Text Available A model reduction technique based on optimization theory is presented, where a possible higher order system/model is approximated with an unstable DIPTD model by using only step response data. The DIPTD model is used to tune PD/PID controllers for the underlying possible higher order system. Numerous examples are used to illustrate the theory, i.e. both linear and nonlinear models. The Pareto Optimal controller is used as a reference controller.
Auditory-model based robust feature selection for speech recognition.
Koniaris, Christos; Kuropatwinski, Marcin; Kleijn, W Bastiaan
2010-02-01
It is shown that robust dimension-reduction of a feature set for speech recognition can be based on a model of the human auditory system. Whereas conventional methods optimize classification performance, the proposed method exploits knowledge implicit in the auditory periphery, inheriting its robustness. Features are selected to maximize the similarity of the Euclidean geometry of the feature domain and the perceptual domain. Recognition experiments using mel-frequency cepstral coefficients (MFCCs) confirm the effectiveness of the approach, which does not require labeled training data. For noisy data the method outperforms commonly used discriminant-analysis based dimension-reduction methods that rely on labeling. The results indicate that selecting MFCCs in their natural order results in subsets with good performance.
Application of random coherence order selection in gradient-enhanced multidimensional NMR
International Nuclear Information System (INIS)
Bostock, Mark J.; Nietlispach, Daniel
2016-01-01
Development of multidimensional NMR is essential to many applications, for example in high resolution structural studies of biomolecules. Multidimensional techniques enable separation of NMR signals over several dimensions, improving signal resolution, whilst also allowing identification of new connectivities. However, these advantages come at a significant cost. The Fourier transform theorem requires acquisition of a grid of regularly spaced points to satisfy the Nyquist criterion, while frequency discrimination and acquisition of a pure phase spectrum require acquisition of both quadrature components for each time point in every indirect (non-acquisition) dimension, adding a factor of 2 N -1 to the number of free- induction decays which must be acquired, where N is the number of dimensions. Compressed sensing (CS) ℓ 1 -norm minimisation in combination with non-uniform sampling (NUS) has been shown to be extremely successful in overcoming the Nyquist criterion. Previously, maximum entropy reconstruction has also been used to overcome the limitation of frequency discrimination, processing data acquired with only one quadrature component at a given time interval, known as random phase detection (RPD), allowing a factor of two reduction in the number of points for each indirect dimension (Maciejewski et al. 2011 PNAS 108 16640). However, whilst this approach can be easily applied in situations where the quadrature components are acquired as amplitude modulated data, the same principle is not easily extended to phase modulated (P-/N-type) experiments where data is acquired in the form exp (iωt) or exp (-iωt), and which make up many of the multidimensional experiments used in modern NMR. Here we demonstrate a modification of the CS ℓ 1 -norm approach to allow random coherence order selection (RCS) for phase modulated experiments; we generalise the nomenclature for RCS and RPD as random quadrature detection (RQD). With this method, the power of RQD can be extended
Serial recall of colors: Two models of memory for serial order applied to continuous visual stimuli.
Peteranderl, Sonja; Oberauer, Klaus
2018-01-01
This study investigated the effects of serial position and temporal distinctiveness on serial recall of simple visual stimuli. Participants observed lists of five colors presented at varying, unpredictably ordered interitem intervals, and their task was to reproduce the colors in their order of presentation by selecting colors on a continuous-response scale. To control for the possibility of verbal labeling, articulatory suppression was required in one of two experimental sessions. The predictions were derived through simulation from two computational models of serial recall: SIMPLE represents the class of temporal-distinctiveness models, whereas SOB-CS represents event-based models. According to temporal-distinctiveness models, items that are temporally isolated within a list are recalled more accurately than items that are temporally crowded. In contrast, event-based models assume that the time intervals between items do not affect recall performance per se, although free time following an item can improve memory for that item because of extended time for the encoding. The experimental and the simulated data were fit to an interference measurement model to measure the tendency to confuse items with other items nearby on the list-the locality constraint-in people as well as in the models. The continuous-reproduction performance showed a pronounced primacy effect with no recency, as well as some evidence for transpositions obeying the locality constraint. Though not entirely conclusive, this evidence favors event-based models over a role for temporal distinctiveness. There was also a strong detrimental effect of articulatory suppression, suggesting that verbal codes can be used to support serial-order memory of simple visual stimuli.
Comparing plasma fluid models of different order for 1D streamer ionization fronts
A. Markosyan (Aram); H.J. Teunissen (Jannis); S. Dujko (Sasa); U. M. Ebert (Ute)
2015-01-01
htmlabstractWe evaluate the performance of three plasma fluid models: the first order reaction-drift-diffusion model based on the local field approximation; the second order reaction-drift-diffusion model based on the local energy approximation and a recently developed high order fluid model by
A Network Analysis Model for Selecting Sustainable Technology
Directory of Open Access Journals (Sweden)
Sangsung Park
2015-09-01
Full Text Available Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.
Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It
DEFF Research Database (Denmark)
Mengistu, Henok; Lehman, Joel Anthony; Clune, Jeff
2016-01-01
of evolvable digital phenotypes. Although some types of selection in evolutionary computation indirectly encourage evolvability, one unexplored possibility is to directly select for evolvability. To do so, we estimate an individual's future potential for diversity by calculating the behavioral diversity of its...... immediate offspring, and select organisms with increased offspring variation. While the technique is computationally expensive, we hypothesized that direct selection would better encourage evolvability than indirect methods. Experiments in two evolutionary robotics domains confirm this hypothesis: in both...... domains, such Evolvability Search produces solutions with higher evolvability than those produced with Novelty Search or traditional objective-based search algorithms. Further experiments demonstrate that the higher evolvability produced by Evolvability Search in a training environment also generalizes...
Calculus for cognitive scientists higher order models and their analysis
Peterson, James K
2016-01-01
This book offers a self-study program on how mathematics, computer science and science can be profitably and seamlessly intertwined. This book focuses on two variable ODE models, both linear and nonlinear, and highlights theoretical and computational tools using MATLAB to explain their solutions. It also shows how to solve cable models using separation of variables and the Fourier Series.
Temporal Aggregation in First Order Cointegrated Vector Autoregressive models
DEFF Research Database (Denmark)
Milhøj, Anders; la Cour, Lisbeth Funding
2011-01-01
with the frequency of the data. We also introduce a graphical representation that will prove useful as an additional informational tool for deciding the appropriate cointegration rank of a model. In two examples based on models of time series of different grades of gasoline, we demonstrate the usefulness of our...
Abnormal Waves Modelled as Second-order Conditional Waves
DEFF Research Database (Denmark)
Jensen, Jørgen Juncher
2005-01-01
, the water depth and the directional spreading on the conditional mean wave profile are presented. Application of conditional waves to model and explain abnormal waves, e.g. the well-known New Year Wave measured at the Draupner platform January 1st 1995, is discussed. Whereas the wave profile can be modelled...
LQG controller designs from reduced order models for a launch ...
Indian Academy of Sciences (India)
This paper describes the effort of a multivariable control approach applied to the Geosynchronous Satellite Launch Vehicle (GSLV) of the Indian Space Research Organization (ISRO) during a certain stage of its launch. The fuel slosh dynamics are modelled using a pendulum model analogy. We describe two design ...
Higher-Order Hamiltonian Model for Unidirectional Water Waves
Bona, J. L.; Carvajal, X.; Panthee, M.; Scialom, M.
2018-04-01
Formally second-order correct, mathematical descriptions of long-crested water waves propagating mainly in one direction are derived. These equations are analogous to the first-order approximations of KdV- or BBM-type. The advantage of these more complex equations is that their solutions corresponding to physically relevant initial perturbations of the rest state may be accurate on a much longer timescale. The initial value problem for the class of equations that emerges from our derivation is then considered. A local well-posedness theory is straightforwardly established by a contraction mapping argument. A subclass of these equations possess a special Hamiltonian structure that implies the local theory can be continued indefinitely.
Order parameter model for unstable multilane traffic flow
Lubashevsky, Ihor A.; Mahnke, Reinhard
1999-01-01
We discuss a phenomenological approach to the description of unstable vehicle motion on multilane highways that explains in a simple way the observed sequence of the phase transitions "free flow -> synchronized motion -> jam" as well as the hysteresis in the transition "free flow synchronized motion". We introduce a new variable called order parameter that accounts for possible correlations in the vehicle motion at different lanes. So, it is principally due to the "many-body" effects in the ...
Neutrino masses and their ordering: global data, priors and models
Gariazzo, S.; Archidiacono, M.; de Salas, P. F.; Mena, O.; Ternes, C. A.; Tórtola, M.
2018-03-01
We present a full Bayesian analysis of the combination of current neutrino oscillation, neutrinoless double beta decay and Cosmic Microwave Background observations. Our major goal is to carefully investigate the possibility to single out one neutrino mass ordering, namely Normal Ordering or Inverted Ordering, with current data. Two possible parametrizations (three neutrino masses versus the lightest neutrino mass plus the two oscillation mass splittings) and priors (linear versus logarithmic) are exhaustively examined. We find that the preference for NO is only driven by neutrino oscillation data. Moreover, the values of the Bayes factor indicate that the evidence for NO is strong only when the scan is performed over the three neutrino masses with logarithmic priors; for every other combination of parameterization and prior, the preference for NO is only weak. As a by-product of our Bayesian analyses, we are able to (a) compare the Bayesian bounds on the neutrino mixing parameters to those obtained by means of frequentist approaches, finding a very good agreement; (b) determine that the lightest neutrino mass plus the two mass splittings parametrization, motivated by the physical observables, is strongly preferred over the three neutrino mass eigenstates scan and (c) find that logarithmic priors guarantee a weakly-to-moderately more efficient sampling of the parameter space. These results establish the optimal strategy to successfully explore the neutrino parameter space, based on the use of the oscillation mass splittings and a logarithmic prior on the lightest neutrino mass, when combining neutrino oscillation data with cosmology and neutrinoless double beta decay. We also show that the limits on the total neutrino mass ∑ mν can change dramatically when moving from one prior to the other. These results have profound implications for future studies on the neutrino mass ordering, as they crucially state the need for self-consistent analyses which explore the
Differential geometry of viscoelastic models with fractional-order derivatives
International Nuclear Information System (INIS)
Yajima, Takahiro; Nagahama, Hiroyuki
2010-01-01
Viscoelastic materials with memory effect are studied based on the fractional rheonomic geometry. The geometric objects are regarded as basic quantities of fractional viscoelastic models, i.e. the metric tensor and torsion tensor are interpreted as the strain and the fractional strain rate, respectively. The generalized viscoelastic equations are expressed by the geometric objects. Especially, the basic constitutive equations such as Voigt and Maxwell models can be derived geometrically from the generalized equation. This leads to the fact that various viscoelastic models can be unified into one geometric expression.
Reduced Order Modeling of Combustion Instability in a Gas Turbine Model Combustor
Arnold-Medabalimi, Nicholas; Huang, Cheng; Duraisamy, Karthik
2017-11-01
Hydrocarbon fuel based propulsion systems are expected to remain relevant in aerospace vehicles for the foreseeable future. Design of these devices is complicated by combustion instabilities. The capability to model and predict these effects at reduced computational cost is a requirement for both design and control of these devices. This work focuses on computational studies on a dual swirl model gas turbine combustor in the context of reduced order model development. Full fidelity simulations are performed utilizing URANS and Hybrid RANS-LES with finite rate chemistry. Following this, data decomposition techniques are used to extract a reduced basis representation of the unsteady flow field. These bases are first used to identify sensor locations to guide experimental interrogations and controller feedback. Following this, initial results on developing a control-oriented reduced order model (ROM) will be presented. The capability of the ROM will be further assessed based on different operating conditions and geometric configurations.
Stripe order from the perspective of the Hubbard model
Energy Technology Data Exchange (ETDEWEB)
Devereaux, Thomas Peter
2018-03-01
A microscopic understanding of the strongly correlated physics of the cuprates must account for the translational and rotational symmetry breaking that is present across all cuprate families, commonly in the form of stripes. Here we investigate emergence of stripes in the Hubbard model, a minimal model believed to be relevant to the cuprate superconductors, using determinant quantum Monte Carlo (DQMC) simulations at finite temperatures and density matrix renormalization group (DMRG) ground state calculations. By varying temperature, doping, and model parameters, we characterize the extent of stripes throughout the phase diagram of the Hubbard model. Our results show that including the often neglected next-nearest-neighbor hopping leads to the absence of spin incommensurability upon electron-doping and nearly half-filled stripes upon hole-doping. The similarities of these findings to experimental results on both electron and hole-doped cuprate families support a unified description across a large portion of the cuprate phase diagram.
A new Russell model for selecting suppliers
Azadi, Majid; Shabani, Amir; Farzipoor Saen, Reza
2014-01-01
Recently, supply chain management (SCM) has been considered by many researchers. Supplier evaluation and selection plays a significant role in establishing an effective SCM. One of the techniques that can be used for selecting suppliers is data envelopment analysis (DEA). In some situations, to
Heterogeneous traffic flow modelling using second-order macroscopic continuum model
Mohan, Ranju; Ramadurai, Gitakrishnan
2017-01-01
Modelling heterogeneous traffic flow lacking in lane discipline is one of the emerging research areas in the past few years. The two main challenges in modelling are: capturing the effect of varying size of vehicles, and the lack in lane discipline, both of which together lead to the 'gap filling' behaviour of vehicles. The same section length of the road can be occupied by different types of vehicles at the same time, and the conventional measure of traffic concentration, density (vehicles per lane per unit length), is not a good measure for heterogeneous traffic modelling. First aim of this paper is to have a parsimonious model of heterogeneous traffic that can capture the unique phenomena of gap filling. Second aim is to emphasize the suitability of higher-order models for modelling heterogeneous traffic. Third, the paper aims to suggest area occupancy as concentration measure of heterogeneous traffic lacking in lane discipline. The above mentioned two main challenges of heterogeneous traffic flow are addressed by extending an existing second-order continuum model of traffic flow, using area occupancy for traffic concentration instead of density. The extended model is calibrated and validated with field data from an arterial road in Chennai city, and the results are compared with those from few existing generalized multi-class models.
Selective experimental review of the Standard Model
International Nuclear Information System (INIS)
Bloom, E.D.
1985-02-01
Before disussing experimental comparisons with the Standard Model, (S-M) it is probably wise to define more completely what is commonly meant by this popular term. This model is a gauge theory of SU(3)/sub f/ x SU(2)/sub L/ x U(1) with 18 parameters. The parameters are α/sub s/, α/sub qed/, theta/sub W/, M/sub W/ (M/sub Z/ = M/sub W//cos theta/sub W/, and thus is not an independent parameter), M/sub Higgs/; the lepton masses, M/sub e/, Mμ, M/sub r/; the quark masses, M/sub d/, M/sub s/, M/sub b/, and M/sub u/, M/sub c/, M/sub t/; and finally, the quark mixing angles, theta 1 , theta 2 , theta 3 , and the CP violating phase delta. The latter four parameters appear in the quark mixing matrix for the Kobayashi-Maskawa and Maiani forms. Clearly, the present S-M covers an enormous range of physics topics, and the author can only lightly cover a few such topics in this report. The measurement of R/sub hadron/ is fundamental as a test of the running coupling constant α/sub s/ in QCD. The author will discuss a selection of recent precision measurements of R/sub hadron/, as well as some other techniques for measuring α/sub s/. QCD also requires the self interaction of gluons. The search for the three gluon vertex may be practically realized in the clear identification of gluonic mesons. The author will present a limited review of recent progress in the attempt to untangle such mesons from the plethora q anti q states of the same quantum numbers which exist in the same mass range. The electroweak interactions provide some of the strongest evidence supporting the S-M that exists. Given the recent progress in this subfield, and particularly with the discovery of the W and Z bosons at CERN, many recent reviews obviate the need for further discussion in this report. In attempting to validate a theory, one frequently searches for new phenomena which would clearly invalidate it. 49 references, 28 figures
The Complexity of Model Checking Higher-Order Fixpoint Logic
DEFF Research Database (Denmark)
Axelsson, Roland; Lange, Martin; Somla, Rafal
2007-01-01
Higher Order Fixpoint Logic (HFL) is a hybrid of the simply typed λ-calculus and the modal μ-calculus. This makes it a highly expressive temporal logic that is capable of expressing various interesting correctness properties of programs that are not expressible in the modal μ-calculus. This paper...... of solving rather large parity games of small index. As a consequence of this we obtain an ExpTime upper bound on the expression complexity of each HFLk,m. The lower bound is established by a reduction from the word problem for alternating (k-1)-fold exponential space bounded Turing Machines. As a corollary...
Fuzzy Goal Programming Approach in Selective Maintenance Reliability Model
Directory of Open Access Journals (Sweden)
Neha Gupta
2013-12-01
Full Text Available 800x600 In the present paper, we have considered the allocation problem of repairable components for a parallel-series system as a multi-objective optimization problem and have discussed two different models. In first model the reliability of subsystems are considered as different objectives. In second model the cost and time spent on repairing the components are considered as two different objectives. These two models is formulated as multi-objective Nonlinear Programming Problem (MONLPP and a Fuzzy goal programming method is used to work out the compromise allocation in multi-objective selective maintenance reliability model in which we define the membership functions of each objective function and then transform membership functions into equivalent linear membership functions by first order Taylor series and finally by forming a fuzzy goal programming model obtain a desired compromise allocation of maintenance components. A numerical example is also worked out to illustrate the computational details of the method. Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4
3D Higher Order Modeling in the BEM/FEM Hybrid Formulation
Fink, P. W.; Wilton, D. R.
2000-01-01
Higher order divergence- and curl-conforming bases have been shown to provide significant benefits, in both convergence rate and accuracy, in the 2D hybrid finite element/boundary element formulation (P. Fink and D. Wilton, National Radio Science Meeting, Boulder, CO, Jan. 2000). A critical issue in achieving the potential for accuracy of the approach is the accurate evaluation of all matrix elements. These involve products of high order polynomials and, in some instances, singular Green's functions. In the 2D formulation, the use of a generalized Gaussian quadrature method was found to greatly facilitate the computation and to improve the accuracy of the boundary integral equation self-terms. In this paper, a 3D, hybrid electric field formulation employing higher order bases and higher order elements is presented. The improvements in convergence rate and accuracy, compared to those resulting from lower order modeling, are established. Techniques developed to facilitate the computation of the boundary integral self-terms are also shown to improve the accuracy of these terms. Finally, simple preconditioning techniques are used in conjunction with iterative solution procedures to solve the resulting linear system efficiently. In order to handle the boundary integral singularities in the 3D formulation, the parent element- either a triangle or rectangle-is subdivided into a set of sub-triangles with a common vertex at the singularity. The contribution to the integral from each of the sub-triangles is computed using the Duffy transformation to remove the singularity. This method is shown to greatly facilitate t'pe self-term computation when the bases are of higher order. In addition, the sub-triangles can be further divided to achieve near arbitrary accuracy in the self-term computation. An efficient method for subdividing the parent element is presented. The accuracy obtained using higher order bases is compared to that obtained using lower order bases when the number
Age-ordered shirt numbering reduces the selection bias associated with the relative age effect.
Mann, David L; van Ginneken, Pleun J M A
2017-04-01
When placed into age groups for junior sporting competition, the relative differences in age between children leads to a bias in who is evaluated as being talented. While the impact of this relative age effect (RAE) is clear, until now there has been no evidence to show how to reduce it. The aim of this study was to determine whether the selection bias associated with the RAE could be reduced. Talent scouts from an elite football club watched junior games and ranked players on the basis of their potential. Scouts were allocated to one of three groups provided with contrasting information about the age of the players: (1) no age information, (2) players' birthdates or (3) knowledge that the numbers on the playing shirts corresponded to the relative age of the players. Results revealed a significant selection bias for the scouts in the no-age information group, and that bias remained when scouts knew the players' dates-of-birth. Strikingly though, the selection bias was eliminated when scouts watched the games knowing the shirt numbers corresponded to the relative ages of the players. The selection bias associated with the RAE can be reduced if information about age is presented appropriately.
Directory of Open Access Journals (Sweden)
M. Imron Mustajib
2010-01-01
Full Text Available This paper discusses the development of simultaneous optimization model to determine component tolerance of assembly product and plant for manufacturing processes by considering quality tolerance limits, and delivery time constraint to minimize total cost in collaboration environment of make-to-order manufacturing systems. Total cost of the system consists of manufacturing costs and quality loss costs as the tolerance function, operational costs for multi-plant manufacturing collaboration which includes: setup costs, material handling costs, operating costs of assembly, manual operations costs, and transportation costs. Formulation of the model developed uses mixed integer non linear programming as a method of solution search. In the numerical examples presented, the optimization process results an optimal solution. Optimal solution is not sensitive if the changes in quality tolerance constraint and delivery time constraint is not large. While the addition of an alternative plant for producing a component can changes the alternative plant selected
Multilevel Higher-Order Item Response Theory Models
Huang, Hung-Yu; Wang, Wen-Chung
2014-01-01
In the social sciences, latent traits often have a hierarchical structure, and data can be sampled from multiple levels. Both hierarchical latent traits and multilevel data can occur simultaneously. In this study, we developed a general class of item response theory models to accommodate both hierarchical latent traits and multilevel data. The…
Proposed higher order continuum-based models for an elastic ...
African Journals Online (AJOL)
Three new variants of continuum-based models for an elastic subgrade are proposed. The subgrade is idealized as a homogenous, isotropic elastic layer of thickness H overlying a firm stratum. All components of the stress tensor in the subgrade are taken into account. Reasonable assumptions are made regarding the ...
Next-to-leading order corrections to the valon model
Indian Academy of Sciences (India)
To obtain the proton structure function in valon model with respect to the Laguerre polynomials one needs to use an elegant and fast numerical method at LO up to NLO. Therefore, we concentrate on the Laguerre polynomials in our determinations. In the. Laguerre method [13,14], the Laguerre polynomials are defined as.
A generalized cellular automata approach to modeling first order ...
Indian Academy of Sciences (India)
Cellular automata; enzyme kinetics; extended von-Neumann neighborhood. 1. Introduction. Over the past two decades, there has been a significant growth in the use of computer-generated models to study dynamic phenomena in biochemical systems (Kier et al 2005). The need to include greater details about biochemical ...
Mixed Higher Order Variational Model for Image Recovery
Directory of Open Access Journals (Sweden)
Pengfei Liu
2014-01-01
Full Text Available A novel mixed higher order regularizer involving the first and second degree image derivatives is proposed in this paper. Using spectral decomposition, we reformulate the new regularizer as a weighted L1-L2 mixed norm of image derivatives. Due to the equivalent formulation of the proposed regularizer, an efficient fast projected gradient algorithm combined with monotone fast iterative shrinkage thresholding, called, FPG-MFISTA, is designed to solve the resulting variational image recovery problems under majorization-minimization framework. Finally, we demonstrate the effectiveness of the proposed regularization scheme by the experimental comparisons with total variation (TV scheme, nonlocal TV scheme, and current second degree methods. Specifically, the proposed approach achieves better results than related state-of-the-art methods in terms of peak signal to ratio (PSNR and restoration quality.
Optimization of power rationing order based on fuzzy evaluation model
Zhang, Siyuan; Liu, Li; Xie, Peiyuan; Tang, Jihong; Wang, Canlin
2018-04-01
With the development of production and economic growth, China's electricity load has experienced a significant increase. Over the years, in order to alleviate the contradiction of power shortage, a series of policies and measures to speed up electric power construction have been made in china, which promotes the rapid development of the power industry and the power construction has made great achievements. For China, after large-scale power facilities, power grid long-term power shortage situation has been improved to some extent, but in a certain period of time, the power development still exists uneven development. On the whole, it is still in the state of insufficient power, and the situation of power restriction is still severe in some areas, so it is necessary to study on the power rationing.
Narlikar, Leelavati; Mehta, Nidhi; Galande, Sanjeev; Arjunwadkar, Mihir
2013-01-01
The structural simplicity and ability to capture serial correlations make Markov models a popular modeling choice in several genomic analyses, such as identification of motifs, genes and regulatory elements. A critical, yet relatively unexplored, issue is the determination of the order of the Markov model. Most biological applications use a predetermined order for all data sets indiscriminately. Here, we show the vast variation in the performance of such applications with the order. To identify the ‘optimal’ order, we investigated two model selection criteria: Akaike information criterion and Bayesian information criterion (BIC). The BIC optimal order delivers the best performance for mammalian phylogeny reconstruction and motif discovery. Importantly, this order is different from orders typically used by many tools, suggesting that a simple additional step determining this order can significantly improve results. Further, we describe a novel classification approach based on BIC optimal Markov models to predict functionality of tissue-specific promoters. Our classifier discriminates between promoters active across 12 different tissues with remarkable accuracy, yielding 3 times the precision expected by chance. Application to the metagenomics problem of identifying the taxum from a short DNA fragment yields accuracies at least as high as the more complex mainstream methodologies, while retaining conceptual and computational simplicity. PMID:23267010
Behave or Be Banned? Banning Orders and Selective Exclusion from Public Space
Schuilenburg, M.B.
2015-01-01
By describing the practice of banning orders in the Netherlands, we investigate how the involved parties are dealing with their new responsibilities. We describe the way the changes in security management exert further influence in the spatial environment of city centers, and we analyze what this
Han, Suyue; Chang, Gary Han; Schirmer, Clemens; Modarres-Sadeghi, Yahya
2016-11-01
We construct a reduced-order model (ROM) to study the Wall Shear Stress (WSS) distributions in image-based patient-specific aneurysms models. The magnitude of WSS has been shown to be a critical factor in growth and rupture of human aneurysms. We start the process by running a training case using Computational Fluid Dynamics (CFD) simulation with time-varying flow parameters, such that these parameters cover the range of parameters of interest. The method of snapshot Proper Orthogonal Decomposition (POD) is utilized to construct the reduced-order bases using the training CFD simulation. The resulting ROM enables us to study the flow patterns and the WSS distributions over a range of system parameters computationally very efficiently with a relatively small number of modes. This enables comprehensive analysis of the model system across a range of physiological conditions without the need to re-compute the simulation for small changes in the system parameters.
A model selection support system for numerical simulations of nuclear thermal-hydraulics
International Nuclear Information System (INIS)
Gofuku, Akio; Shimizu, Kenji; Sugano, Keiji; Yoshikawa, Hidekazu; Wakabayashi, Jiro
1990-01-01
In order to execute efficiently a dynamic simulation of a large-scaled engineering system such as a nuclear power plant, it is necessary to develop intelligent simulation support system for all phases of the simulation. This study is concerned with the intelligent support for the program development phase and is engaged in the adequate model selection support method by applying AI (Artificial Intelligence) techniques to execute a simulation consistent with its purpose and conditions. A proto-type expert system to support the model selection for numerical simulations of nuclear thermal-hydraulics in the case of cold leg small break loss-of-coolant accident of PWR plant is now under development on a personal computer. The steps to support the selection of both fluid model and constitutive equations for the drift flux model have been developed. Several cases of model selection were carried out and reasonable model selection results were obtained. (author)
Thomas, D.L.; Johnson, D.; Griffith, B.
2006-01-01
Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a
Computer modeling: from sports to spaceflight ... from order to chaos.
Danby, J. M. A.
In this volume the problems of concern are those that can be modeled by systems of ordinary differential equations and cannot be solved by mathematical formulae. The subjects covered are diverse and include chaotic systems; population growth and ecology; sickness and health; competition and economics; sports; travel and reaction; space travel and astronomy; pendulums; springs; chemical and other reacting systems. Accompanying the book is software on a CD-ROM which includes over 50 projects from this book.
LQG controller designs from reduced order models for a launch ...
Indian Academy of Sciences (India)
Performing the indicated operations, assuming τpi and ˙τpi are small quantities and noting that .... Sensors in Rate path. GR = ω2. 2 s2 + 2ξ2ω2s + ω2. 2. , where ω2 = 12·5 Hz = 78·54 rad/sec and ξ2 = 0·7. 3. Formulation of the state space model. The vehicle dynamics including rigid body, slosh and actuators can be written ...
Behave or Be Banned? Banning Orders and Selective Exclusion from Public Space
Schuilenburg, M.B.
2015-01-01
By describing the practice of banning orders in the Netherlands, we investigate how the involved parties are dealing with their new responsibilities. We describe the way the changes in security management exert further influence in the spatial environment of city centers, and we analyze what this means in terms of equality of rights for all citizens. With this analysis, an attempt to explain the exercise of power beyond the State in policing space in Western countries is provided.
Ullah, Asmat; Chen, Wen; Khan, Mushtaq Ahmad
2017-07-01
This paper introduces a fractional order total variation (FOTV) based model with three different weights in the fractional order derivative definition for multiplicative noise removal purpose. The fractional-order Euler Lagrange equation which is a highly non-linear partial differential equation (PDE) is obtained by the minimization of the energy functional for image restoration. Two numerical schemes namely an iterative scheme based on the dual theory and majorization- minimization algorithm (MMA) are used. To improve the restoration results, we opt for an adaptive parameter selection procedure for the proposed model by applying the trial and error method. We report numerical simulations which show the validity and state of the art performance of the fractional-order model in visual improvement as well as an increase in the peak signal to noise ratio comparing to corresponding methods. Numerical experiments also demonstrate that MMAbased methodology is slightly better than that of an iterative scheme.
Uncertainty associated with selected environmental transport models
International Nuclear Information System (INIS)
Little, C.A.; Miller, C.W.
1979-11-01
A description is given of the capabilities of several models to predict accurately either pollutant concentrations in environmental media or radiological dose to human organs. The models are discussed in three sections: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations. This procedure is infeasible for food chain models and, therefore, the uncertainty embodied in the models input parameters, rather than the model output, is estimated. Aquatic transport models are divided into one-dimensional, longitudinal-vertical, and longitudinal-horizontal models. Several conclusions were made about the ability of the Gaussian plume atmospheric dispersion model to predict accurately downwind air concentrations from releases under several sets of conditions. It is concluded that no validation study has been conducted to test the predictions of either aquatic or terrestrial food chain models. Using the aquatic pathway from water to fish to an adult for 137 Cs as an example, a 95% one-tailed confidence limit interval for the predicted exposure is calculated by examining the distributions of the input parameters. Such an interval is found to be 16 times the value of the median exposure. A similar one-tailed limit for the air-grass-cow-milk-thyroid for 131 I and infants was 5.6 times the median dose. Of the three model types discussed in this report,the aquatic transport models appear to do the best job of predicting observed concentrations. However, this conclusion is based on many fewer aquatic validation data than were availaable for atmospheric model validation
Developing a conceptual model for selecting and evaluating online markets
Directory of Open Access Journals (Sweden)
Sadegh Feizollahi
2013-04-01
Full Text Available There are many evidences, which emphasis on the benefits of using new technologies of information and communication in international business and many believe that E-Commerce can help satisfy customer explicit and implicit requirements. Internet shopping is a concept developed after the introduction of electronic commerce. Information technology (IT and its applications, specifically in the realm of the internet and e-mail promoted the development of e-commerce in terms of advertising, motivating and information. However, with the development of new technologies, credit and financial exchange on the internet websites were constructed so to facilitate e-commerce. The proposed study sends a total of 200 questionnaires to the target group (teachers - students - professionals - managers of commercial web sites and it manages to collect 130 questionnaires for final evaluation. Cronbach's alpha test is used for measuring reliability and to evaluate the validity of measurement instruments (questionnaires, and to assure construct validity, confirmatory factor analysis is employed. In addition, in order to analyze the research questions based on the path analysis method and to determine markets selection models, a regular technique is implemented. In the present study, after examining different aspects of e-commerce, we provide a conceptual model for selecting and evaluating online marketing in Iran. These findings provide a consistent, targeted and holistic framework for the development of the Internet market in the country.
Quality Quandaries- Time Series Model Selection and Parsimony
DEFF Research Database (Denmark)
Bisgaard, Søren; Kulahci, Murat
2009-01-01
Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....
Quantization of second-order Lagrangians: Model problem
Moore, R. A.; Scott, T. C.
1991-08-01
Many aspects of a model problem, the Lagrangian of which contains a term depending quadratically on the acceleration, are examined in the regime where the classical solution consists of two independent normal modes. It is shown that the techniques of conversion to a problem of Lagrange, generalized mechanics, and Dirac's method for constrained systems all yield the same canonical form for the Hamiltonian. It is also seen that the resultant canonical equations of motion are equivalent to the Euler-Lagrange equations. In canonical form, all of the standard results apply, quantization follows in the usual way, and the interpretation of the results is straightforward. It is also demonstrated that perturbative methods fail, both classically and quantum mechanically, indicating the need for the nonperturbative techniques applied herein. Finally, it is noted that this result may have fundamental implications for certain relativistic theories.
Analytical Modelling Of Milling For Tool Design And Selection
Fontaine, M.; Devillez, A.; Dudzinski, D.
2007-05-01
This paper presents an efficient analytical model which allows to simulate a large panel of milling operations. A geometrical description of common end mills and of their engagement in the workpiece material is proposed. The internal radius of the rounded part of the tool envelope is used to define the considered type of mill. The cutting edge position is described for a constant lead helix and for a constant local helix angle. A thermomechanical approach of oblique cutting is applied to predict forces acting on the tool and these results are compared with experimental data obtained from milling tests on a 42CrMo4 steel for three classical types of mills. The influence of some tool's geometrical parameters on predicted cutting forces is presented in order to propose optimisation criteria for design and selection of cutting tools.
Analytical Modelling Of Milling For Tool Design And Selection
International Nuclear Information System (INIS)
Fontaine, M.; Devillez, A.; Dudzinski, D.
2007-01-01
This paper presents an efficient analytical model which allows to simulate a large panel of milling operations. A geometrical description of common end mills and of their engagement in the workpiece material is proposed. The internal radius of the rounded part of the tool envelope is used to define the considered type of mill. The cutting edge position is described for a constant lead helix and for a constant local helix angle. A thermomechanical approach of oblique cutting is applied to predict forces acting on the tool and these results are compared with experimental data obtained from milling tests on a 42CrMo4 steel for three classical types of mills. The influence of some tool's geometrical parameters on predicted cutting forces is presented in order to propose optimisation criteria for design and selection of cutting tools
The Meaning of Higher-Order Factors in Reflective-Measurement Models
Eid, Michael; Koch, Tobias
2014-01-01
Higher-order factor analysis is a widely used approach for analyzing the structure of a multidimensional test. Whenever first-order factors are correlated researchers are tempted to apply a higher-order factor model. But is this reasonable? What do the higher-order factors measure? What is their meaning? Willoughby, Holochwost, Blanton, and Blair…
Artistic foundations of the Carmelite Order in Silesia in early modern times. Selected issues
Directory of Open Access Journals (Sweden)
Dariusz Galewski
2016-12-01
Full Text Available The Order of The Brothers of The Most Blessed Virgin Mary of Mount Carmel arrived in Silesia late in the 14th century, when its first monastery was established in Strzegom. It was however liquidated during the Reformation. Only after the end of the Thirty Years’ War was it successfully regained, and three new locations were opened in Głębowice, Wołów, and Kożuchów. Baron Johann Adam de Garnier, a colonel of the imperial army hailing from Alsace, owner of the estate in Głębowice, played a major role in their design and development. The first two centres are impressive Baroque projects with lavishly furnished churches, while the third is a mediaeval castle entrusted to the monks late in the 17th century and adapted into a monastery, which, besides the Jesuit Otyń, is considered a singularity in Silesia. The essay presents the artistic achievement of Silesian Carmelites based on the state-of-the-art research and emphasises the role of the founder. It is also an attempt at assessing the contribution of the Order to the development of Baroque culture and art in Silesia, which is modest, especially when compared to the neighbouring regions of the Commonwealth of Poland–Lithuania. It also emphasised the absence of the reformed branch of the Order, that is the Discalced Carmelites, in the area. Although the achievements of the Carmelites would be difficult to compare with the impressive heritage of Cistercians and Jesuits who dominated the religious and artistic realm of post-Tridentine Catholic Church in Silesia, it nonetheless merits the interest of researchers in various fields of culture and art.
Structure and selection in an autocatalytic binary polymer model
DEFF Research Database (Denmark)
Tanaka, Shinpei; Fellermann, Harold; Rasmussen, Steen
2014-01-01
a pool of monomers, highly ordered populations with particular sequence patterns are dynamically selected out of a vast number of possible states. The interplay between the selected microscopic sequence patterns and the macroscopic cooperative structures is examined both analytically and in simulation...
A parametric model order reduction technique for poroelastic finite element models.
Lappano, Ettore; Polanz, Markus; Desmet, Wim; Mundo, Domenico
2017-10-01
This research presents a parametric model order reduction approach for vibro-acoustic problems in the frequency domain of systems containing poroelastic materials (PEM). The method is applied to the Finite Element (FE) discretization of the weak u-p integral formulation based on the Biot-Allard theory and makes use of reduced basis (RB) methods typically employed for parametric problems. The parametric reduction is obtained rewriting the Biot-Allard FE equations for poroelastic materials using an affine representation of the frequency (therefore allowing for RB methods) and projecting the frequency-dependent PEM system on a global reduced order basis generated with the proper orthogonal decomposition instead of standard modal approaches. This has proven to be better suited to describe the nonlinear frequency dependence and the strong coupling introduced by damping. The methodology presented is tested on two three-dimensional systems: in the first experiment, the surface impedance of a PEM layer sample is calculated and compared with results of the literature; in the second, the reduced order model of a multilayer system coupled to an air cavity is assessed and the results are compared to those of the reference FE model.
Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota.
Energy Technology Data Exchange (ETDEWEB)
Portone, Teresa; Niederhaus, John Henry; Sanchez, Jason James; Swiler, Laura Painton
2018-02-01
This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.
DEFF Research Database (Denmark)
Hejlesen, Mads Mølholm
A regularisation method for solving the Poisson equation using Green’s functions is presented.The method is shown to obtain a convergence rate which corresponds to the design of the regularised Green’s function and a spectral-like convergence rate is obtained using a spectrally ideal regularisation....... It is shown that the regularised Poisson solver can be extended to handle mixed periodic and free-space boundary conditions. This is done by solving the equation spectrally in the periodic directions which yields a modified Helmholtz equation for the free-space directions which in turn is solved by deriving...... the appropriate regularised Green’s functions. Using an analogy to the particle-particle particle-mesh method, a framework for calculating multi-resolution solutions using local refinement patches is presented. The regularised Poisson solver is shown to maintain a high order converging solution for different...
Multiphysics modeling of selective laser sintering/melting
Ganeriwala, Rishi Kumar
A significant percentage of total global employment is due to the manufacturing industry. However, manufacturing also accounts for nearly 20% of total energy usage in the United States according to the EIA. In fact, manufacturing accounted for 90% of industrial energy consumption and 84% of industry carbon dioxide emissions in 2002. Clearly, advances in manufacturing technology and efficiency are necessary to curb emissions and help society as a whole. Additive manufacturing (AM) refers to a relatively recent group of manufacturing technologies whereby one can 3D print parts, which has the potential to significantly reduce waste, reconfigure the supply chain, and generally disrupt the whole manufacturing industry. Selective laser sintering/melting (SLS/SLM) is one type of AM technology with the distinct advantage of being able to 3D print metals and rapidly produce net shape parts with complicated geometries. In SLS/SLM parts are built up layer-by-layer out of powder particles, which are selectively sintered/melted via a laser. However, in order to produce defect-free parts of sufficient strength, the process parameters (laser power, scan speed, layer thickness, powder size, etc.) must be carefully optimized. Obviously, these process parameters will vary depending on material, part geometry, and desired final part characteristics. Running experiments to optimize these parameters is costly, energy intensive, and extremely material specific. Thus a computational model of this process would be highly valuable. In this work a three dimensional, reduced order, coupled discrete element - finite difference model is presented for simulating the deposition and subsequent laser heating of a layer of powder particles sitting on top of a substrate. Validation is provided and parameter studies are conducted showing the ability of this model to help determine appropriate process parameters and an optimal powder size distribution for a given material. Next, thermal stresses upon
Selvamony, Subash Chandra Bose
2013-01-01
This literature compares the performance of second order competitive consecutive reaction in Fed-Batch Reactor with that in continuous Plug Flow Reactor. In a kinetic sense, this simulation study aims to develop a case for continuous Plug Flow Reactor in pharmaceutical, fine chemical, and related other chemical industries. MATLAB is used to find solutions for the differential equations. The simulation results show that, for certain cases of nonelementary scenario, product selectivity is highe...
Energy Technology Data Exchange (ETDEWEB)
Zhou, Shenghai; Wu, Hongmin; Wu, Ying; Shi, Hongyan; Feng, Xun; Jiang, Shang; Chen, Jian; Song, Wenbo, E-mail: wbsong@jlu.edu.cn
2014-08-01
Hemi-ordered nanoporous carbon (HONC) was obtained from a mesoporous silica template through a nano-replication method using furfuryl alcohol as the carbon source. The structure and morphology of HONC were characterized and analyzed in detail by X-ray diffraction, N{sub 2}-sorption, Raman spectroscopy and transmission electron microscopy. HONC was then demonstrated as active electrode material for selective determination of nitrite in either physiological or environmental system. Well separated oxidation peaks of ascorbic acid, dopamine, uric acid and nitrite were observed in physiological system, and simultaneous discrimination of catechol, hydroquinone, resorcinol and nitrite in environmental system was also accomplished. Distinctly improved performances for selective determination of nitrite (such as significantly fast and sensitive current response with especially high selectivity) coexisted with ascorbic acid, dopamine and uric acid in the physiological system, as well as with catechol, hydroquinone and resorcinol in the environmental system were achieved at HONC electrode material. The excellent discriminating ability and high selectivity for NO{sub 2}{sup −} determination were ascribed to the good electronic conductivity, unique hemi-ordered porous structure, large surface area and large number of edge plane defect sites contained on the surface of nanopore walls of HONC. Results in this work demonstrated that HONC is one of the promising catalytic electrode materials for nitrite sensor fabrication. - Highlights: • Hemi-ordered nanoporous carbon as an active electrode material • Good discriminating ability towards NO{sub 2}{sup −} from physiological or environmental system • Highly selective determination of nitrite with fast and sensitive current response.
An Optimization Model For Strategy Decision Support to Select Kind of CPO’s Ship
Suaibah Nst, Siti; Nababan, Esther; Mawengkang, Herman
2018-01-01
The selection of marine transport for the distribution of crude palm oil (CPO) is one of strategy that can be considered in reducing cost of transport. The cost of CPO’s transport from one area to CPO’s factory located at the port of destination may affect the level of CPO’s prices and the number of demands. In order to maintain the availability of CPO a strategy is required to minimize the cost of transporting. In this study, the strategy used to select kind of charter ships as barge or chemical tanker. This study aims to determine an optimization model for strategy decision support in selecting kind of CPO’s ship by minimizing costs of transport. The select of ship was done randomly, so that two-stage stochastic programming model was used to select the kind of ship. Model can help decision makers to select either barge or chemical tanker to distribute CPO.
Directory of Open Access Journals (Sweden)
Sigit Saptono
2017-10-01
Full Text Available ABSTRACTHigher order thinking skills are needed to understand the problem and the essence of the lecture material Biology Sel. Study design Research and Development aims to develop reasoning skills and analytic thinking biology student teachers through the application of learning models Integration Attributes Formative Assessment (IAAF. Some 61 students of Biology Education Semarang State University who is doing his third semester courses Cell Biology is the subject of research. Analytical reasoning and thinking ability of students is measured through individual assignments, group assignments concept map creation and preparation of the Review articles, and 30 items about the shape of the selected response and constructed response questions, validated questions. The result showed that the ability of reasoning and analytical thinking of students can be expanded significantly, although the development of the ability of argumentation, one category of analytic thinking skills, they need serious attention.
A Hybrid Multiple Criteria Decision Making Model for Supplier Selection
Wu, Chung-Min; Hsieh, Ching-Lin; Chang, Kuei-Lun
2013-01-01
The sustainable supplier selection would be the vital part in the management of a sustainable supply chain. In this study, a hybrid multiple criteria decision making (MCDM) model is applied to select optimal supplier. The fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP) is then used to obtain their weights. To avoid calculation and additional pairwise compa...
Astrophysical Model Selection in Gravitational Wave Astronomy
Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.
2012-01-01
Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.
Modeling and Analysis of Supplier Selection Method Using ...
African Journals Online (AJOL)
However, in these parts of the world the application of tools and models for supplier selection problem is yet to surface and the banking and finance industry here in Ethiopia is no exception. Thus, the purpose of this research was to address supplier selection problem through modeling and application of analytical hierarchy ...
Dealing with selection bias in educational transition models
DEFF Research Database (Denmark)
Holm, Anders; Jæger, Mads Meier
2011-01-01
This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational tr...
Directory of Open Access Journals (Sweden)
Sigit Saptono
2017-02-01
Full Text Available Abstrak ___________________________________________________________________ Higher order thinking skills sangat dibutuhkan untuk memahami permasalahan dan esensi materi perkuliahan Biologi Sel. Studi dengan desain Research and Development ini bertujuan untuk mengembangkan kemampuan penalaran dan berpikir analitik mahasiswa calon guru biologi melalui penerapan model pembelajaran Integrasi Atribut Asesmen Formatif (IAAF. Sejumlah 61 mahasiswa program studi Pendidikan Biologi Universitas Negeri Semarang semester tiga yang sedang menempuh mata kuliah Biologi Sel menjadi subjek penelitian. Kemampuan penalaran dan berpikir analitik mahasiswa diukur melalui tugas individu, tugas kelompok pembuatan peta konsep dan penyusunan laporan reviu artikel, dan 30 item soal berbentuk selected response questions dan constructed response questions tervalidasi. Hasil analisis data menunjukkan bahwa kemampuan penalaran dan berpikir analitik mahasiswa dapat berkembang secara signifikan, meskipun perkembangan kemampuan argumentasi, salah satu kategori kemampuan berpikir analitik, masih perlu perhatian yang cukup serius. Abstract ___________________________________________________________________ Higher order thinking skills are needed to understand the problem and the essence of the lecture material Biology Sel. Study design Research and Development aims to develop reasoning skills and analytic thinking biology student teachers through the application of learning models Integration Attributes Formative Assessment (IAAF. Some 61 students of Biology Education Semarang State University who is doing his third semester courses Cell Biology is the subject of research. Analytical reasoning and thinking ability of students is measured through individual assignments, group assignments concept map creation and preparation of the Review articles, and 30 items about the shape of the selected response and constructed response questions, validated questions. The result showed
Isotope-selective high-order interferometry with large organic molecules in free fall
Rodewald, Jonas; Dörre, Nadine; Grimaldi, Andrea; Geyer, Philipp; Felix, Lukas; Mayor, Marcel; Shayeghi, Armin; Arndt, Markus
2018-03-01
Interferometry in the time domain has proven valuable for matter-wave based measurements. This concept has recently been generalized to cold molecular clusters using short-pulse standing light waves which realized photo-depletion gratings, arranged in a time-domain Talbot–Lau interferometer (OTIMA). Here we extend this idea further to large organic molecules and demonstrate a new scheme to scan the emerging molecular interferogram in position space. The capability of analyzing different isotopes of the same monomer under identical conditions opens perspectives for studying the interference fringe shift as a function of time in gravitational free fall. The universality of OTIMA interferometry allows one to handle a large variety of particles. In our present work, quasi-continuous laser evaporation allows transferring fragile organic molecules into the gas phase, covering more than an order of magnitude in mass between 614 amu and 6509 amu, i.e. 300% more massive than in previous OTIMA experiments. For all masses, we find about 30% fringe visibility.
Order of magnitude cost appraisal for selected aspects of clad waste management
Energy Technology Data Exchange (ETDEWEB)
Zima, G.E.
1977-02-01
A simple formula, incorporating the fixed charge rate principle, is applied to a clad waste management exercise involving densification, canning, transportation and salt disposal. For the purpose of comparison with the bulk of published nuclear waste management costs, cost and fixed charge rate data appropriate to roughly the period 1970 to 1973 are used. Within the context of this order of magnitude appraisal, densification displays some cost advantage, reflected principally in the transportation cost. Dependent on the degree of densification, above a certain clad waste generation rate the transportation savings may be expected to exceed reasonable densification costs. There is no explicit consideration of the decontamination step in this appraisal. The limited accessibility of surface effect decontamination to internal transuranic and activation product contamination suggests a quite small influence of decontamination on the transportation and disposal costs. Decontamination may, however, have a significant effect on the ease of establishing a practicable containment envelope of high reliability throughout the clad waste history. A brief comparison is made of clad waste management costs with the major costs of the nuclear power economy. This comparison implies a virtually unlimited technical latitude for clad waste treatment in accommodating the public safety without significant perturbation of nuclear power costs. It is submitted that clad waste management optimization will be under the primal constraint of maximizing thelong term public safety, with economic analysis useful only as a discriminator between waste handling alternatives of sensibly equivalent containment qualities. Some areas of clad waste treatment meriting increased attention are noted.
On Optimal Input Design and Model Selection for Communication Channels
Energy Technology Data Exchange (ETDEWEB)
Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Python Program to Select HII Region Models
Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.
2016-01-01
HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.
Ground-water transport model selection and evaluation guidelines
International Nuclear Information System (INIS)
Simmons, C.S.; Cole, C.R.
1983-01-01
Guidelines are being developed to assist potential users with selecting appropriate computer codes for ground-water contaminant transport modeling. The guidelines are meant to assist managers with selecting appropriate predictive models for evaluating either arid or humid low-level radioactive waste burial sites. Evaluation test cases in the form of analytical solutions to fundamental equations and experimental data sets have been identified and recommended to ensure adequate code selection, based on accurate simulation of relevant physical processes. The recommended evaluation procedures will consider certain technical issues related to the present limitations in transport modeling capabilities. A code-selection plan will depend on identifying problem objectives, determining the extent of collectible site-specific data, and developing a site-specific conceptual model for the involved hydrology. Code selection will be predicated on steps for developing an appropriate systems model. This paper will review the progress in developing those guidelines. 12 references
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Directory of Open Access Journals (Sweden)
Risa Kato
2009-01-01
Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
Testing Static Trade-off Against Pecking Order Models of Capital Structure
Lakshmi Shyam-Sunder; Stewart C. Myers
1994-01-01
This paper tests traditional capital structure models against the alternative of a pecking order model of corporate financing. The basic pecking order model, which predicts external debt financing driven by the internal financial deficit, has much greater explanatory power than a static trade-off model which predicts that each firm adjusts toward an optimal debt ratio. We show that the power of some usual tests of the trade-off model is virtually nil. We question whether the available empiric...
Random effect selection in generalised linear models
DEFF Research Database (Denmark)
Denwood, Matt; Houe, Hans; Forkman, Björn
We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...
Directory of Open Access Journals (Sweden)
mahmoud ahmadpour borazjani
2016-02-01
Full Text Available In parallel with the increasing global population, food security or increasing agricultural products is one of the biggest challenges that human society is faced with during the recent decades, this necessity led to overuse of pesticides and fertilizers which cause serious and sometimes irreversible dangers. In the resent years, several measures have been taken to return to organic agriculture. The current study attempted to represent a agronomic program in which decision makers in addition to their economic goals, pay more attention to environment by producing organic products. To achieve this goal, goal programming and its combination with Extended Lexicographic Goal Programming framework, based on multiple criteria decision making methods were used in Bardsir and Orzoyeh regions. The required data were collected using 567 questionnaires and doing interview with beneficiaries using random sampling method in 2013. In the current study, six different goals included profit maximization, employment of labor maximization, minimization of irrigation water, working machinery, fertilizer and pesticides at difference priorities were put in the model. The results indicated that by optimizing the cropping pattern; we can reduce consumption of harmful inputs without any reduction in yield or profit. Therefore, informing farmers about advantages of organic agriculture and government’s supports could be an effective step towards sustainable production.
Model-order reduction of lumped parameter systems via fractional calculus
Hollkamp, John P.; Sen, Mihir; Semperlotti, Fabio
2018-04-01
This study investigates the use of fractional order differential models to simulate the dynamic response of non-homogeneous discrete systems and to achieve efficient and accurate model order reduction. The traditional integer order approach to the simulation of non-homogeneous systems dictates the use of numerical solutions and often imposes stringent compromises between accuracy and computational performance. Fractional calculus provides an alternative approach where complex dynamical systems can be modeled with compact fractional equations that not only can still guarantee analytical solutions, but can also enable high levels of order reduction without compromising on accuracy. Different approaches are explored in order to transform the integer order model into a reduced order fractional model able to match the dynamic response of the initial system. Analytical and numerical results show that, under certain conditions, an exact match is possible and the resulting fractional differential models have both a complex and frequency-dependent order of the differential operator. The implications of this type of approach for both model order reduction and model synthesis are discussed.
The genealogy of samples in models with selection.
Neuhauser, C; Krone, S M
1997-02-01
We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.
Bayesian Modeling of ChIP-chip Data Through a High-Order Ising Model
Mo, Qianxing
2010-01-29
ChIP-chip experiments are procedures that combine chromatin immunoprecipitation (ChIP) and DNA microarray (chip) technology to study a variety of biological problems, including protein-DNA interaction, histone modification, and DNA methylation. The most important feature of ChIP-chip data is that the intensity measurements of probes are spatially correlated because the DNA fragments are hybridized to neighboring probes in the experiments. We propose a simple, but powerful Bayesian hierarchical approach to ChIP-chip data through an Ising model with high-order interactions. The proposed method naturally takes into account the intrinsic spatial structure of the data and can be used to analyze data from multiple platforms with different genomic resolutions. The model parameters are estimated using the Gibbs sampler. The proposed method is illustrated using two publicly available data sets from Affymetrix and Agilent platforms, and compared with three alternative Bayesian methods, namely, Bayesian hierarchical model, hierarchical gamma mixture model, and Tilemap hidden Markov model. The numerical results indicate that the proposed method performs as well as the other three methods for the data from Affymetrix tiling arrays, but significantly outperforms the other three methods for the data from Agilent promoter arrays. In addition, we find that the proposed method has better operating characteristics in terms of sensitivities and false discovery rates under various scenarios. © 2010, The International Biometric Society.
Modeling shape selection of buckled dielectric elastomers
Langham, Jacob; Bense, Hadrien; Barkley, Dwight
2018-02-01
A dielectric elastomer whose edges are held fixed will buckle, given a sufficiently applied voltage, resulting in a nontrivial out-of-plane deformation. We study this situation numerically using a nonlinear elastic model which decouples two of the principal electrostatic stresses acting on an elastomer: normal pressure due to the mutual attraction of oppositely charged electrodes and tangential shear ("fringing") due to repulsion of like charges at the electrode edges. These enter via physically simplified boundary conditions that are applied in a fixed reference domain using a nondimensional approach. The method is valid for small to moderate strains and is straightforward to implement in a generic nonlinear elasticity code. We validate the model by directly comparing the simulated equilibrium shapes with the experiment. For circular electrodes which buckle axisymetrically, the shape of the deflection profile is captured. Annular electrodes of different widths produce azimuthal ripples with wavelengths that match our simulations. In this case, it is essential to compute multiple equilibria because the first model solution obtained by the nonlinear solver (Newton's method) is often not the energetically favored state. We address this using a numerical technique known as "deflation." Finally, we observe the large number of different solutions that may be obtained for the case of a long rectangular strip.
Traditional and robust vector selection methods for use with similarity based models
International Nuclear Information System (INIS)
Hines, J. W.; Garvey, D. R.
2006-01-01
Vector selection, or instance selection as it is often called in the data mining literature, performs a critical task in the development of nonparametric, similarity based models. Nonparametric, similarity based modeling (SBM) is a form of 'lazy learning' which constructs a local model 'on the fly' by comparing a query vector to historical, training vectors. For large training sets the creation of local models may become cumbersome, since each training vector must be compared to the query vector. To alleviate this computational burden, varying forms of training vector sampling may be employed with the goal of selecting a subset of the training data such that the samples are representative of the underlying process. This paper describes one such SBM, namely auto-associative kernel regression (AAKR), and presents five traditional vector selection methods and one robust vector selection method that may be used to select prototype vectors from a larger data set in model training. The five traditional vector selection methods considered are min-max, vector ordering, combination min-max and vector ordering, fuzzy c-means clustering, and Adeli-Hung clustering. Each method is described in detail and compared using artificially generated data and data collected from the steam system of an operating nuclear power plant. (authors)
Shao, C G; Liu, Z Z; Wang, J F; Luo, J
2003-07-01
The L-state Potts model for rumor is the N-spin chain describing how a simple rumor transmitted by N recreant rumormongers is aggrandized. The studied rumor is represented mathematically by a simple proposition with the universal quantifier, which again is represented geometrically by a point in a proposition space. During the transmission, such a proposition is changed with the change of the rumor, which has individual number N0 at the beginning of the transmission. Correspondingly, the point expressing the proposition may start from an arbitrary site at the proposition space, and then it shifts in the space. Thus, a spin sum of the Potts model corresponds to a walk of a point in the proposition space and spin configuration's numbers is given by enumerating the corresponding walks. The concept of the lattice path in combinatorial mathematics is introduced and the exact series representation of the configuration's numbers is given. The partition function exhibits the transition of the chain and critical equivalent inverse temperature beta(c) is determined. Moreover, there is a crossover value of the individual number, N00. The model has a first-order transition when N0N00.
Dynamics of a Fractional Order HIV Infection Model with Specific Functional Response and Cure Rate
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Adnane Boukhouima
2017-01-01
Full Text Available We propose a fractional order model in this paper to describe the dynamics of human immunodeficiency virus (HIV infection. In the model, the infection transmission process is modeled by a specific functional response. First, we show that the model is mathematically and biologically well posed. Second, the local and global stabilities of the equilibria are investigated. Finally, some numerical simulations are presented in order to illustrate our theoretical results.
Wentworth, Mami Tonoe
techniques for model calibration. For Bayesian model calibration, we employ adaptive Metropolis algorithms to construct densities for input parameters in the heat model and the HIV model. To quantify the uncertainty in the parameters, we employ two MCMC algorithms: Delayed Rejection Adaptive Metropolis (DRAM) [33] and Differential Evolution Adaptive Metropolis (DREAM) [66, 68]. The densities obtained using these methods are compared to those obtained through the direct numerical evaluation of the Bayes' formula. We also combine uncertainties in input parameters and measurement errors to construct predictive estimates for a model response. A significant emphasis is on the development and illustration of techniques to verify the accuracy of sampling-based Metropolis algorithms. We verify the accuracy of DRAM and DREAM by comparing chains, densities and correlations obtained using DRAM, DREAM and the direct evaluation of Bayes formula. We also perform similar analysis for credible and prediction intervals for responses. Once the parameters are estimated, we employ energy statistics test [63, 64] to compare the densities obtained by different methods for the HIV model. The energy statistics are used to test the equality of distributions. We also consider parameter selection and verification techniques for models having one or more parameters that are noninfluential in the sense that they minimally impact model outputs. We illustrate these techniques for a dynamic HIV model but note that the parameter selection and verification framework is applicable to a wide range of biological and physical models. To accommodate the nonlinear input to output relations, which are typical for such models, we focus on global sensitivity analysis techniques, including those based on partial correlations, Sobol indices based on second-order model representations, and Morris indices, as well as a parameter selection technique based on standard errors. A significant objective is to provide
Modeling HIV-1 drug resistance as episodic directional selection.
Directory of Open Access Journals (Sweden)
Ben Murrell
Full Text Available The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.
Wang, Xinsheng; Wang, Chenxu; Yu, Mingyan
2016-07-01
In this paper, we propose a generalised sub-block structure preservation interconnect model order reduction (MOR) technique based on the swarm intelligence method, that is, particle swarm optimisation (PSO). The swarm intelligence-based structure preservation MOR can be used for a standard model as a criterion for different structure preservation interconnect MOR methods. In the proposed technique, the PSO method is used for predicting the unknown elements of structure-preserving reduced-order modelling of interconnect circuits. The prediction is based on minimising the difference of transform function between the original full-order and desired reduced-order systems maintaining the full-order structure in the reduced-order model. The proposed swarm-intelligence-based structure-preserving MOR method is compared with published work on structure preservation MOR SPRIM techniques. Simulation and synthesis results verify the accuracy and validity of the new structure-preserving MOR technique.
Variable selection for mixture and promotion time cure rate models.
Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng
2016-11-16
Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.
Qi, D.; Majda, A.
2017-12-01
A low-dimensional reduced-order statistical closure model is developed for quantifying the uncertainty in statistical sensitivity and intermittency in principal model directions with largest variability in high-dimensional turbulent system and turbulent transport models. Imperfect model sensitivity is improved through a recent mathematical strategy for calibrating model errors in a training phase, where information theory and linear statistical response theory are combined in a systematic fashion to achieve the optimal model performance. The idea in the reduced-order method is from a self-consistent mathematical framework for general systems with quadratic nonlinearity, where crucial high-order statistics are approximated by a systematic model calibration procedure. Model efficiency is improved through additional damping and noise corrections to replace the expensive energy-conserving nonlinear interactions. Model errors due to the imperfect nonlinear approximation are corrected by tuning the model parameters using linear response theory with an information metric in a training phase before prediction. A statistical energy principle is adopted to introduce a global scaling factor in characterizing the higher-order moments in a consistent way to improve model sensitivity. Stringent models of barotropic and baroclinic turbulence are used to display the feasibility of the reduced-order methods. Principal statistical responses in mean and variance can be captured by the reduced-order models with accuracy and efficiency. Besides, the reduced-order models are also used to capture crucial passive tracer field that is advected by the baroclinic turbulent flow. It is demonstrated that crucial principal statistical quantities like the tracer spectrum and fat-tails in the tracer probability density functions in the most important large scales can be captured efficiently with accuracy using the reduced-order tracer model in various dynamical regimes of the flow field with
An isotonic partial credit model for ordering subjects on the basis of their sum scores
Ligtvoet, R.
2012-01-01
In practice, the sum of the item scores is often used as a basis for comparing subjects. For items that have more than two ordered score categories, only the partial credit model (PCM) and special cases of this model imply that the subjects are stochastically ordered on the common latent variable.
Comparing higher order models for the EORTC QLQ-C30
DEFF Research Database (Denmark)
Gundy, Chad M; Fayers, Peter M; Grønvold, Mogens
2012-01-01
To investigate the statistical fit of alternative higher order models for summarizing the health-related quality of life profile generated by the EORTC QLQ-C30 questionnaire.......To investigate the statistical fit of alternative higher order models for summarizing the health-related quality of life profile generated by the EORTC QLQ-C30 questionnaire....
Directory of Open Access Journals (Sweden)
Renxin Xiao
2016-03-01
Full Text Available In order to properly manage lithium-ion batteries of electric vehicles (EVs, it is essential to build the battery model and estimate the state of charge (SOC. In this paper, the fractional order forms of Thevenin and partnership for a new generation of vehicles (PNGV models are built, of which the model parameters including the fractional orders and the corresponding resistance and capacitance values are simultaneously identified based on genetic algorithm (GA. The relationships between different model parameters and SOC are established and analyzed. The calculation precisions of the fractional order model (FOM and integral order model (IOM are validated and compared under hybrid test cycles. Finally, extended Kalman filter (EKF is employed to estimate the SOC based on different models. The results prove that the FOMs can simulate the output voltage more accurately and the fractional order EKF (FOEKF can estimate the SOC more precisely under dynamic conditions.
Roşu, M. M.; Tarbă, C. I.; Neagu, C.
2016-11-01
The current models for inventory management are complementary, but together they offer a large pallet of elements for solving complex problems of companies when wanting to establish the optimum economic order quantity for unfinished products, row of materials, goods etc. The main objective of this paper is to elaborate an automated decisional model for the calculus of the economic order quantity taking into account the price regressive rates for the total order quantity. This model has two main objectives: first, to determine the periodicity when to be done the order n or the quantity order q; second, to determine the levels of stock: lighting control, security stock etc. In this way we can provide the answer to two fundamental questions: How much must be ordered? When to Order? In the current practice, the business relationships with its suppliers are based on regressive rates for price. This means that suppliers may grant discounts, from a certain level of quantities ordered. Thus, the unit price of the products is a variable which depends on the order size. So, the most important element for choosing the optimum for the economic order quantity is the total cost for ordering and this cost depends on the following elements: the medium price per units, the stock cost, the ordering cost etc.
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Feipeng Guo; Qibei Lu
2013-01-01
With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic met...
Effect of Model Selection on Computed Water Balance Components
Jhorar, R.K.; Smit, A.A.M.F.R.; Roest, C.W.J.
2009-01-01
Soil water flow modelling approaches as used in four selected on-farm water management models, namely CROPWAT. FAIDS, CERES and SWAP, are compared through numerical experiments. The soil water simulation approaches used in the first three models are reformulated to incorporate ail evapotranspiration
Ensembling Variable Selectors by Stability Selection for the Cox Model
Directory of Open Access Journals (Sweden)
Qing-Yan Yin
2017-01-01
Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.
Elementary Teachers' Selection and Use of Visual Models
Lee, Tammy D.; Gail Jones, M.
2018-02-01
As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.
Zheng, Guo; Wang, Jue; Wang, Lin; Zhou, Muchun; Xin, Yu; Song, Minmin
2017-11-15
The general formulae for second-order moments of Schell-model beams with various correlation functions in atmospheric turbulence are derived and validated by the Bessel-Gaussian Schell-model beams and cosine-Gaussian-correlated Schell-model beams. Our finding shows that the second-order moments of partially coherent Schell-model beams are related to the second-order partial derivatives of source spectral degree of coherence at the origin. The formulae we provide are much more convenient to analyze and research propagation problems in turbulence.
Probabilistic wind power forecasting with online model selection and warped gaussian process
International Nuclear Information System (INIS)
Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin
2014-01-01
Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms
Validation of elk resource selection models with spatially independent data
Priscilla K. Coe; Bruce K. Johnson; Michael J. Wisdom; John G. Cook; Marty Vavra; Ryan M. Nielson
2011-01-01
Knowledge of how landscape features affect wildlife resource use is essential for informed management. Resource selection functions often are used to make and validate predictions about landscape use; however, resource selection functions are rarely validated with data from landscapes independent of those from which the models were built. This problem has severely...
A Working Model of Natural Selection Illustrated by Table Tennis
Dinc, Muhittin; Kilic, Selda; Aladag, Caner
2013-01-01
Natural selection is one of the most important topics in biology and it helps to clarify the variety and complexity of organisms. However, students in almost every stage of education find it difficult to understand the mechanism of natural selection and they can develop misconceptions about it. This article provides an active model of natural…
Augmented Self-Modeling as an Intervention for Selective Mutism
Kehle, Thomas J.; Bray, Melissa A.; Byer-Alcorace, Gabriel F.; Theodore, Lea A.; Kovac, Lisa M.
2012-01-01
Selective mutism is a rare disorder that is difficult to treat. It is often associated with oppositional defiant behavior, particularly in the home setting, social phobia, and, at times, autism spectrum disorder characteristics. The augmented self-modeling treatment has been relatively successful in promoting rapid diminishment of selective mutism…
Robust Decision-making Applied to Model Selection
Energy Technology Data Exchange (ETDEWEB)
Hemez, Francois M. [Los Alamos National Laboratory
2012-08-06
The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define each of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.
Target Selection Models with Preference Variation Between Offenders
Townsley, Michael; Birks, Daniel; Ruiter, Stijn; Bernasco, Wim; White, Gentry
2016-01-01
Objectives: This study explores preference variation in location choice strategies of residential burglars. Applying a model of offender target selection that is grounded in assertions of the routine activity approach, rational choice perspective, crime pattern and social disorganization theories,
Akaike information criterion to select well-fit resist models
Burbine, Andrew; Fryer, David; Sturtevant, John
2015-03-01
In the field of model design and selection, there is always a risk that a model is over-fit to the data used to train the model. A model is well suited when it describes the physical system and not the stochastic behavior of the particular data collected. K-fold cross validation is a method to check this potential over-fitting to the data by calibrating with k-number of folds in the data, typically between 4 and 10. Model training is a computationally expensive operation, however, and given a wide choice of candidate models, calibrating each one repeatedly becomes prohibitively time consuming. Akaike information criterion (AIC) is an information-theoretic approach to model selection based on the maximized log-likelihood for a given model that only needs a single calibration per model. It is used in this study to demonstrate model ranking and selection among compact resist modelforms that have various numbers and types of terms to describe photoresist behavior. It is shown that there is a good correspondence of AIC to K-fold cross validation in selecting the best modelform, and it is further shown that over-fitting is, in most cases, not indicated. In modelforms with more than 40 fitting parameters, the size of the calibration data set benefits from additional parameters, statistically validating the model complexity.
Debnath, M; Santoni, C; Leonardi, S; Iungo, G V
2017-04-13
The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator.This article is part of the themed issue 'Wind energy in complex terrains'. © 2017 The Author(s).
Debnath, M.; Santoni, C.; Leonardi, S.; Iungo, G. V.
2017-03-01
The dynamics of the velocity field resulting from the interaction between the atmospheric boundary layer and a wind turbine array can affect significantly the performance of a wind power plant and the durability of wind turbines. In this work, dynamics in wind turbine wakes and instabilities of helicoidal tip vortices are detected and characterized through modal decomposition techniques. The dataset under examination consists of snapshots of the velocity field obtained from large-eddy simulations (LES) of an isolated wind turbine, for which aerodynamic forcing exerted by the turbine blades on the atmospheric boundary layer is mimicked through the actuator line model. Particular attention is paid to the interaction between the downstream evolution of the helicoidal tip vortices and the alternate vortex shedding from the turbine tower. The LES dataset is interrogated through different modal decomposition techniques, such as proper orthogonal decomposition and dynamic mode decomposition. The dominant wake dynamics are selected for the formulation of a reduced order model, which consists in a linear time-marching algorithm where temporal evolution of flow dynamics is obtained from the previous temporal realization multiplied by a time-invariant operator. This article is part of the themed issue 'Wind energy in complex terrains'.
Fast Simulating High Order Models Application to Micro Electro-Mechanical Systems (MEMS)
International Nuclear Information System (INIS)
Yacine, Z.; Benfdila, A.; Djennoune, S.
2009-01-01
The approximation of high order systems by low order models is one of the important problems in system theory. The use of a reduced order model makes it easier to implement analysis, simulations and control system designs. Numerous methods are available in the literature for order reduction of linear continuous systems in time domain as well as in frequency domain. But, this is not the case for non linear systems. The well known Trajectory Piece-Wise Linear approach (TPWL) elaborated to nonlinear model order reduction guarantees a simplification and an accurate representation of the behaviour of strongly non linear systems handling local and global approximation. The present attempt is towards evolving an improvement for the TPWL order reduction technique, which ensures a good quality of approximation combining the advantages of the Krylov subspaces method and the local linearization. We illustrate the technique on a MEMS circuit (Micro Electro-Mechanical System).
A risk assessment model for selecting cloud service providers
Cayirci, Erdal; Garaga, Alexandr; Santana de Oliveira, Anderson; Roudier, Yves
2016-01-01
The Cloud Adoption Risk Assessment Model is designed to help cloud customers in assessing the risks that they face by selecting a specific cloud service provider. It evaluates background information obtained from cloud customers and cloud service providers to analyze various risk scenarios. This facilitates decision making an selecting the cloud service provider with the most preferable risk profile based on aggregated risks to security, privacy, and service delivery. Based on this model we ...
SELECTION MOMENTS AND GENERALIZED METHOD OF MOMENTS FOR HETEROSKEDASTIC MODELS
Directory of Open Access Journals (Sweden)
Constantin ANGHELACHE
2016-06-01
Full Text Available In this paper, the authors describe the selection methods for moments and the application of the generalized moments method for the heteroskedastic models. The utility of GMM estimators is found in the study of the financial market models. The selection criteria for moments are applied for the efficient estimation of GMM for univariate time series with martingale difference errors, similar to those studied so far by Kuersteiner.
Hamdan, Sadeque; Cheaitou, Ali
2017-08-01
This data article provides detailed optimization input and output datasets and optimization code for the published research work titled "Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability" (Hamdan and Cheaitou, 2017, In press) [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the green supplier selection and order allocation problem with all-unit quantity discount and varying number of suppliers. More particularly, the datasets presented in this article allow researchers to generate the exact optimization outputs obtained by the authors of Hamdan and Cheaitou (2017, In press) [1] using the provided optimization code and then to use them for comparison with the outputs of other techniques or methodologies such as heuristic approaches. Moreover, this article includes the randomly generated optimization input data and the related outputs that are used as input data for the statistical analysis presented in Hamdan and Cheaitou (2017 In press) [1] in which two different approaches for ranking potential suppliers are compared. This article also provides the time analysis data used in (Hamdan and Cheaitou (2017, In press) [1] to study the effect of the problem size on the computation time as well as an additional time analysis dataset. The input data for the time study are generated randomly, in which the problem size is changed, and then are used by the optimization problem to obtain the corresponding optimal outputs as well as the corresponding computation time.
Directory of Open Access Journals (Sweden)
Sadeque Hamdan
2017-08-01
Full Text Available This data article provides detailed optimization input and output datasets and optimization code for the published research work titled “Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability” (Hamdan and Cheaitou, 2017, In press [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the green supplier selection and order allocation problem with all-unit quantity discount and varying number of suppliers. More particularly, the datasets presented in this article allow researchers to generate the exact optimization outputs obtained by the authors of Hamdan and Cheaitou (2017, In press [1] using the provided optimization code and then to use them for comparison with the outputs of other techniques or methodologies such as heuristic approaches. Moreover, this article includes the randomly generated optimization input data and the related outputs that are used as input data for the statistical analysis presented in Hamdan and Cheaitou (2017 In press [1] in which two different approaches for ranking potential suppliers are compared. This article also provides the time analysis data used in (Hamdan and Cheaitou (2017, In press [1] to study the effect of the problem size on the computation time as well as an additional time analysis dataset. The input data for the time study are generated randomly, in which the problem size is changed, and then are used by the optimization problem to obtain the corresponding optimal outputs as well as the corresponding computation time.
Model Selection in Continuous Test Norming With GAMLSS.
Voncken, Lieke; Albers, Casper J; Timmerman, Marieke E
2017-06-01
To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box-Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box-Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.
Selection Criteria in Regime Switching Conditional Volatility Models
Directory of Open Access Journals (Sweden)
Thomas Chuffart
2015-05-01
Full Text Available A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and loss functions can lead to misspecification ; BIC sometimes indicates the wrong regime switching framework. Depending on the Data Generating Process used in the experiments, great care is needed when choosing a criterion.
A seventh-order model for dynamic response of an electro-hydraulic servo valve
Directory of Open Access Journals (Sweden)
Liu Changhai
2014-12-01
Full Text Available In this paper, taking two degrees of freedom on the armature–flapper assembly into account, a seventh-order model is deduced and proposed for the dynamic response of a two-stage electro-hydraulic servo valve from nonlinear equations. These deductions are based on fundamental laws of electromagnetism, fluid, and general mechanics. The coefficients of the proposed seventh-order model are derived in terms of servo valve physical parameters and fluid properties explicitly. For validating the results of the proposed model, an AMESim simulation model based on physical laws and the existing low-order models validated by other researchers through experiments are used to compare with the seventh-order model. The results show that the seventh-order model can reflect the physical behavior of the servo valve more explicitly than the existing low-order models and it could provide guidance more easily for a linear control design approach and sensitivity analysis than the AMESim simulation model.
A guide to Bayesian model selection for ecologists
Hooten, Mevin B.; Hobbs, N.T.
2015-01-01
The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.
SOLVING FRACTIONAL-ORDER COMPETITIVE LOTKA-VOLTERRA MODEL BY NSFD SCHEMES
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S.ZIBAEI
2016-12-01
Full Text Available In this paper, we introduce fractional-order into a model competitive Lotka- Volterra prey-predator system. We will discuss the stability analysis of this fractional system. The non-standard nite difference (NSFD scheme is implemented to study the dynamic behaviors in the fractional-order Lotka-Volterra system. Proposed non-standard numerical scheme is compared with the forward Euler and fourth order Runge-Kutta methods. Numerical results show that the NSFD approach is easy and accurate for implementing when applied to fractional-order Lotka-Volterra model.
Empirical analyses of a choice model that captures ordering among attribute values
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2017-01-01
an alternative additionally because it has the highest price. In this paper, we specify a discrete choice model that takes into account the ordering of attribute values across alternatives. This model is used to investigate the effect of attribute value ordering in three case studies related to alternative-fuel...... vehicles, mode choice, and route choice. In our application to choices among alternative-fuel vehicles, we see that especially the price coefficient is sensitive to changes in ordering. The ordering effect is also found in the applications to mode and route choice data where both travel time and cost...
Edwards, Mark S; Burt, Jennifer S; Lipp, Ottmar V
2010-05-01
We investigated selective attention for masked and unmasked, threat, and positively valenced words, in high trait anxious (HTA) and low trait anxious (LTA) individuals using the emotional Stroop colour-naming task. State anxiety was varied within participants through the threat of electric shock. To investigate whether the sequencing of the state anxiety manipulation affected colour-naming latencies, the ordering of the shock threat and shock safe conditions was counterbalanced across participants. The results indicated that the ordering of the state anxiety manipulation moderated masked and unmasked threat bias effects. Specifically, relative to LTA individuals, HTA individuals showed a threat interference effect, but this effect was limited to those who performed under the threat of shock in the later stages of the experiment. Irrespective of exposure mode and state anxiety status, all individuals showed interference for threat in the early stages of the experiment, relative to a threat facilitation effect in the later stages of the experiment. For the unmasked trials alone, the data also revealed a significant threat interference effect for the HTA group relative to the LTA group in the shock threat condition, and this effect was evident irrespective of shock threat order. The results are discussed with respect to the automatic nature of emotional processing in anxiety.
Identification and non-integer order modelling of synchronous machines operating as generator
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Szymon Racewicz
2012-09-01
Full Text Available This paper presents an original mathematical model of a synchronous generator using derivatives of fractional order. In contrast to classical models composed of a large number of R-L ladders, it comprises half-order impedances, which enable the accurate description of the electromagnetic induction phenomena in a wide frequency range, while minimizing the order and number of model parameters. The proposed model takes into account the skin eff ect in damper cage bars, the eff ects of eddy currents in rotor solid parts, and the saturation of the machine magnetic circuit. The half-order transfer functions used for modelling these phenomena were verifi ed by simulation of ferromagnetic sheet impedance using the fi nite elements method. The analysed machine’s parameters were identified on the basis of SSFR (StandStill Frequency Response characteristics measured on a gradually magnetised synchronous machine.
Statistical model selection with “Big Data”
Directory of Open Access Journals (Sweden)
Jurgen A. Doornik
2015-12-01
Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.
PID controller tuning for the first-order-plus-dead-time process model via Hermite-Biehler theorem.
Roy, Anindo; Iqbal, Kamran
2005-07-01
This paper discusses PID stabilization of a first-order-plus-dead-time (FOPDT) process model using the stability framework of the Hermite-Biehler theorem. The FOPDT model approximates many processes in the chemical and petroleum industries. Using a PID controller and first-order Padé approximation for the transport delay, the Hermite-Biehler theorem allows one to analytically study the stability of the closed-loop system. We derive necessary and sufficient conditions for stability and develop an algorithm for selection of stabilizing feedback gains. The results are given in terms of stability bounds that are functions of plant parameters. Sensitivity and disturbance rejection characteristics of the proposed PID controller are studied. The results are compared with established tuning methods such as Ziegler-Nichols, Cohen-Coon, and internal model control.
Reduced-order LPV model of flexible wind turbines from high fidelity aeroelastic codes
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Sønderby, Ivan Bergquist; Hansen, Morten Hartvig
2013-01-01
of high-order linear time invariant (LTI) models. Firstly, the high-order LTI models are locally approximated using modal and balanced truncation and residualization. Then, an appropriate coordinate transformation is applied to allow interpolation of the model matrices between points on the parameter...... space. The obtained LPV model is of suitable size for designing modern gain-scheduling controllers based on recently developed LPV control design techniques. Results are thoroughly assessed on a set of industrial wind turbine models generated by the recently developed aeroelastic code HAWCStab2....
An algorithm to use higher order invariants for modelling potential energy surface of nanoclusters
Jindal, Shweta; Bulusu, Satya S.
2018-02-01
In order to fit potential energy surface (PES) of gold nanoclusters, we have integrated bispectrum features with artificial neural network (ANN) learning technique in this work. We have also devised an algorithm for selecting the frequencies that need to be coupled for extracting the phase information between different frequency bands. We have found that higher order invariant like bispectrum is highly efficient in exploring the PES as compared to other invariants. The sensitivity of bispectrum can also be exploited in acting as an order parameter for calculating many thermodynamic properties of nanoclusters.
The fractional-order modeling and synchronization of electrically coupled neuron systems
Moaddy, K.
2012-11-01
In this paper, we generalize the integer-order cable model of the neuron system into the fractional-order domain, where the long memory dependence of the fractional derivative can be a better fit for the neuron response. Furthermore, the chaotic synchronization with a gap junction of two or multi-coupled-neurons of fractional-order are discussed. The circuit model, fractional-order state equations and the numerical technique are introduced in this paper for individual and multiple coupled neuron systems with different fractional-orders. Various examples are introduced with different fractional orders using the non-standard finite difference scheme together with the Grünwald-Letnikov discretization process which is easily implemented and reliably accurate. © 2011 Elsevier Ltd. All rights reserved.
Integrated model for pricing, delivery time setting, and scheduling in make-to-order environments
Garmdare, Hamid Sattari; Lotfi, M. M.; Honarvar, Mahboobeh
2018-03-01
Usually, in make-to-order environments which work only in response to the customer's orders, manufacturers for maximizing the profits should offer the best price and delivery time for an order considering the existing capacity and the customer's sensitivity to both the factors. In this paper, an integrated approach for pricing, delivery time setting and scheduling of new arrival orders are proposed based on the existing capacity and accepted orders in system. In the problem, the acquired market demands dependent on the price and delivery time of both the manufacturer and its competitors. A mixed-integer non-linear programming model is presented for the problem. After converting to a pure non-linear model, it is validated through a case study. The efficiency of proposed model is confirmed by comparing it to both the literature and the current practice. Finally, sensitivity analysis for the key parameters is carried out.
Optimal experiment design for model selection in biochemical networks.
Vanlier, Joep; Tiemann, Christian A; Hilbers, Peter A J; van Riel, Natal A W
2014-02-20
Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors.
Quantile hydrologic model selection and model structure deficiency assessment : 1. Theory
Pande, S.
2013-01-01
A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies
Applications of Reduced Order Models for Groundwater Impacts Due to Leaking Brine or Carbon Dioxide
Bacon, D. H.; Keating, E. H.; Carroll, S.; Mansoor, K.; Sun, Y.; Zheng, L.; Harp, D. R.; Dai, Z.
2015-12-01
The National Risk Assessment Partnership has developed a suite of reduced-order models (ROMs) that can be used to predict the impact of CO2 and brine leaks on overlying aquifers. The these computationally-efficient models are based on field-scale reactive transport simulations. The ROMs reproduce the ensemble behavior of large numbers of simulations very well and thus are well-suited to applications that consider a large number of scenarios such as sensitivity analysis, risk assessment, and uncertainty analysis. In this presentation, we seek to demonstrate applicability of ROM-based ensemble analysis. We consider two questions. First, what types of decisions could these analyses support? Second, what types of aquifers could these ROMs be applied to? Four examples are presented for applying these ROMs, in ensemble mode, to supporting decisions in the early stages in a hypothetical geologic CO2 sequestration project. These decisions pertain to site selection, site characterization, monitoring network evaluation, and health impacts. In all these cases, we consider potential brine/CO2 leak rates at the base of the aquifer to be uncertain. We show that derived probabilities provide information relevant to the decision at hand. Although the ROMs were developed using site-specific data from two aquifers (the High Plains, and the unconfined, carbonate portion of the Edwards), the models accept aquifer characteristics as variable inputs and so they may have more broad applicability. Of the nine water quality metrics the ROMs can predict (pH, TDS, 4 trace metals, 3 organic compounds) we conclude that pH and TDS predictions are the most transferable to other aquifers. Guidelines are presented for determining the aquifer types for which the ROMs should be applicable.
Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach
Directory of Open Access Journals (Sweden)
Haifeng Guo
2012-01-01
Full Text Available This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.
A Low Order Model for Analyzing effects of Blade Fatigue Load Control
DEFF Research Database (Denmark)
Kallesøe, Bjarne Skovmose
2006-01-01
A new low order mathematical model is introduced to analyse blade dynamics and blade load reducing control strategies for wind turbines. The model consists of a typical wing section model combined with a rotor speed model, leading to four structural degrees of freedom (flapwise, edgewise, and tor......A new low order mathematical model is introduced to analyse blade dynamics and blade load reducing control strategies for wind turbines. The model consists of a typical wing section model combined with a rotor speed model, leading to four structural degrees of freedom (flapwise, edgewise......, and torsional blade oscillations, and rotor speed). The aerodynamics is described by a model of unsteady aerodynamic. The equations of motion are derived in nonlinear and linear form. The linear equations of motion are used for stability analysis and control design. The nonlinear equations of motion are used...
John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models
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A. Alexander Beaujean
2015-10-01
Full Text Available The development of factor models is inextricably tied to the history of intelligence research. One of the most commonly-cited scholars in the field is John Carroll, whose three-stratum theory of cognitive ability has been one of the most influential models of cognitive ability in the past 20 years. Nonetheless, there is disagreement about how Carroll conceptualized the factors in his model. Some argue that his model is best represented through a higher-order model, while others argue that a bi-factor model is a better representation. Carroll was explicit about what he perceived the best way to represent his model, but his writings are not always easy to understand. In this article, I clarify his position by first describing the details and implications of bi-factor and higher-order models then show that Carroll’s published views are better represented by a bi-factor model.
Development of an Environment for Software Reliability Model Selection
1992-09-01
now is directed to other related problems such as tools for model selection, multiversion programming, and software fault tolerance modeling... multiversion programming, 7. Hlardware can be repaired by spare modules, which is not. the case for software, 2-6 N. Preventive maintenance is very important
Directory of Open Access Journals (Sweden)
Yang Xiao-Jun
2017-01-01
Full Text Available In this paper, we address a class of the fractional derivatives of constant and variable orders for the first time. Fractional-order relaxation equations of constants and variable orders in the sense of Caputo type are modeled from mathematical view of point. The comparative results of the anomalous relaxation among the various fractional derivatives are also given. They are very efficient in description of the complex phenomenon arising in heat transfer.
Xiang, Hong-Jun; Zhang, Zhi-Wei; Shi, Zhi-Fei; Li, Hong
2018-04-01
A fully coupled modeling approach is developed for piezoelectric energy harvesters in this work based on the use of available robust finite element packages and efficient reducing order modeling techniques. At first, the harvester is modeled using finite element packages. The dynamic equilibrium equations of harvesters are rebuilt by extracting system matrices from the finite element model using built-in commands without any additional tools. A Krylov subspace-based scheme is then applied to obtain a reduced-order model for improving simulation efficiency but preserving the key features of harvesters. Co-simulation of the reduced-order model with nonlinear energy harvesting circuits is achieved in a system level. Several examples in both cases of harmonic response and transient response analysis are conducted to validate the present approach. The proposed approach allows to improve the simulation efficiency by several orders of magnitude. Moreover, the parameters used in the equivalent circuit model can be conveniently obtained by the proposed eigenvector-based model order reduction technique. More importantly, this work establishes a methodology for modeling of piezoelectric energy harvesters with any complicated mechanical geometries and nonlinear circuits. The input load may be more complex also. The method can be employed by harvester designers to optimal mechanical structures or by circuit designers to develop novel energy harvesting circuits.
Testing exclusion restrictions and additive separability in sample selection models
DEFF Research Database (Denmark)
Huber, Martin; Mellace, Giovanni
2014-01-01
Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction of these......Standard sample selection models with non-randomly censored outcomes assume (i) an exclusion restriction (i.e., a variable affecting selection, but not the outcome) and (ii) additive separability of the errors in the selection process. This paper proposes tests for the joint satisfaction...... of these assumptions by applying the approach of Huber and Mellace (Testing instrument validity for LATE identification based on inequality moment constraints, 2011) (for testing instrument validity under treatment endogeneity) to the sample selection framework. We show that the exclusion restriction and additive...... separability imply two testable inequality constraints that come from both point identifying and bounding the outcome distribution of the subpopulation that is always selected/observed. We apply the tests to two variables for which the exclusion restriction is frequently invoked in female wage regressions: non...
Selection Bias in Educational Transition Models: Theory and Empirical Evidence
DEFF Research Database (Denmark)
Holm, Anders; Jæger, Mads
Most studies using Mare’s (1980, 1981) seminal model of educational transitions find that the effect of family background decreases across transitions. Recently, Cameron and Heckman (1998, 2001) have argued that the “waning coefficients” in the Mare model are driven by selection on unobserved...... the United States, United Kingdom, Denmark, and the Netherlands shows that when we take selection into account the effect of family background variables on educational transitions is largely constant across transitions. We also discuss several difficulties in estimating educational transition models which...
Novel web service selection model based on discrete group search.
Zhai, Jie; Shao, Zhiqing; Guo, Yi; Zhang, Haiteng
2014-01-01
In our earlier work, we present a novel formal method for the semiautomatic verification of specifications and for describing web service composition components by using abstract concepts. After verification, the instantiations of components were selected to satisfy the complex service performance constraints. However, selecting an optimal instantiation, which comprises different candidate services for each generic service, from a large number of instantiations is difficult. Therefore, we present a new evolutionary approach on the basis of the discrete group search service (D-GSS) model. With regard to obtaining the optimal multiconstraint instantiation of the complex component, the D-GSS model has competitive performance compared with other service selection models in terms of accuracy, efficiency, and ability to solve high-dimensional service composition component problems. We propose the cost function and the discrete group search optimizer (D-GSO) algorithm and study the convergence of the D-GSS model through verification and test cases.
Constructing set-valued fundamental diagrams from jamiton solutions in second order traffic models
Seibold, Benjamin
2013-09-01
Fundamental diagrams of vehicular traiic ow are generally multivalued in the congested ow regime. We show that such set-valued fundamental diagrams can be constructed systematically from simple second order macroscopic traiic models, such as the classical Payne-Whitham model or the inhomogeneous Aw-Rascle-Zhang model. These second order models possess nonlinear traveling wave solutions, called jamitons, and the multi-valued parts in the fundamental diagram correspond precisely to jamiton-dominated solutions. This study shows that transitions from function-valued to set-valued parts in a fundamental diagram arise naturally in well-known second order models. As a particular consequence, these models intrinsically reproduce traiic phases. © American Institute of Mathematical Sciences.
Low-order aeroelastic models of wind turbines for controller design
DEFF Research Database (Denmark)
Sønderby, Ivan Bergquist
Wind turbine controllers are used to optimize the performance of wind turbines such as to reduce power variations and fatigue and extreme loads on wind turbine components. Accurate tuning and design of modern controllers must be done using low-order models that accurately captures the aeroelastic...... response of the wind turbine. The purpose of this thesis is to investigate the necessary model complexity required in aeroelastic models used for controller design and to analyze and propose methods to design low-order aeroelastic wind turbine models that are suited for model-based control design...... stall using only few states. A set of reduced-order models obtained at various operating points are shown to be easily connected by interpolation and are thereby suited for gain-scheduling control design. A new method is proposed to reduce separately the number of structural and aerodynamic states...
Teaching Higher Order Thinking in the Introductory MIS Course: A Model-Directed Approach
Wang, Shouhong; Wang, Hai
2011-01-01
One vision of education evolution is to change the modes of thinking of students. Critical thinking, design thinking, and system thinking are higher order thinking paradigms that are specifically pertinent to business education. A model-directed approach to teaching and learning higher order thinking is proposed. An example of application of the…
A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means
Polak, Marike; De Rooij, Mark; Heiser, Willem J.
2012-01-01
In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) "criterion…
Exact Sampling and Decoding in High-Order Hidden Markov Models
Carter, S.; Dymetman, M.; Bouchard, G.
2012-01-01
We present a method for exact optimization and sampling from high order Hidden Markov Models (HMMs), which are generally handled by approximation techniques. Motivated by adaptive rejection sampling and heuristic search, we propose a strategy based on sequentially refining a lower-order language
Reardon, Sean F.; Shear, Benjamin R.; Castellano, Katherine E.; Ho, Andrew D.
2017-01-01
Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups' test score distributions from such data. Because…
van der Linden, Willem J.
1995-01-01
Dichotomous item response theory (IRT) models can be viewed as families of stochastically ordered distributions of responses to test items. This paper explores several properties of such distributiom. The focus is on the conditions under which stochastic order in families of conditional
Large-order behavior of nondecoupling effects in the standard model and triviality
International Nuclear Information System (INIS)
Aoki, K.
1994-01-01
We compute some nondecoupling effects in the standard model, such as the ρ parameter, to all orders in the coupling constant expansion. We analyze their large order behavior and explicitly show how they are related to the nonperturbative cutoff dependence of these nondecoupling effects due to the triviality of the theory
Nonlinear AeroServoElastic Reduced Order Model for Active Structural Control Project
National Aeronautics and Space Administration — The overall goal of the proposed effort is to develop and demonstrate rigorous model order reduction (MOR) technologies to automatically generate fully coupled,...
The formulation and estimation of a spatial skew-normal generalized ordered-response model.
2016-06-01
This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an : associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal : distribut...
Novel Reduced Order in Time Models for Problems in Nonlinear Aeroelasticity, Phase I
National Aeronautics and Space Administration — Research is proposed for the development and implementation of state of the art, reduced order models for problems in nonlinear aeroelasticity. Highly efficient and...
Universal block diagram based modeling and simulation schemes for fractional-order control systems.
Bai, Lu; Xue, Dingyü
2017-05-08
Universal block diagram based schemes are proposed for modeling and simulating the fractional-order control systems in this paper. A fractional operator block in Simulink is designed to evaluate the fractional-order derivative and integral. Based on the block, the fractional-order control systems with zero initial conditions can be modeled conveniently. For modeling the system with nonzero initial conditions, the auxiliary signal is constructed in the compensation scheme. Since the compensation scheme is very complicated, therefore the integrator chain scheme is further proposed to simplify the modeling procedures. The accuracy and effectiveness of the schemes are assessed in the examples, the computation results testify the block diagram scheme is efficient for all Caputo fractional-order ordinary differential equations (FODEs) of any complexity, including the implicit Caputo FODEs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Xiaoxiao Meng
2018-01-01
Full Text Available AC microgrid mainly comprise inverter-interfaced distributed generators (IIDGs, which are nonlinear complex systems with multiple time scales, including frequency control, time delay measurements, and electromagnetic transients. The droop control-based IIDG in an AC microgrid is selected as the research object in this study, which comprises power droop controller, voltage- and current-loop controllers, and filter and line. The multi-time scale characteristics of the detailed IIDG model are divided based on singular perturbation theory. In addition, the IIDG model order is reduced by neglecting the system fast dynamics. The static and transient stability consistency of the IIDG model order reduction are demonstrated by extracting features of the IIDG small signal model and using the quadratic approximation method of the stability region boundary, respectively. The dynamic response consistencies of the IIDG model order reduction are evaluated using the frequency, damping and amplitude features extracted by the Prony transformation. Results are applicable to provide a simplified model for the dynamic characteristic analysis of IIDG systems in AC microgrid. The accuracy of the proposed method is verified by using the eigenvalue comparison, the transient stability index comparison and the dynamic time-domain simulation.
A seventh-order model for dynamic response of an electro-hydraulic servo valve
Liu Changhai; Jiang Hongzhou
2014-01-01
In this paper, taking two degrees of freedom on the armature–flapper assembly into account, a seventh-order model is deduced and proposed for the dynamic response of a two-stage electro-hydraulic servo valve from nonlinear equations. These deductions are based on fundamental laws of electromagnetism, fluid, and general mechanics. The coefficients of the proposed seventh-order model are derived in terms of servo valve physical parameters and fluid properties explicitly. For validating the resu...
A fourth order spline collocation approach for a business cycle model
Sayfy, A.; Khoury, S.; Ibdah, H.
2013-10-01
A collocation approach, based on a fourth order cubic B-splines is presented for the numerical solution of a Kaleckian business cycle model formulated by a nonlinear delay differential equation. The equation is approximated and the nonlinearity is handled by employing an iterative scheme arising from Newton's method. It is shown that the model exhibits a conditionally dynamical stable cycle. The fourth-order rate of convergence of the scheme is verified numerically for different special cases.
Selection of climate change scenario data for impact modelling
DEFF Research Database (Denmark)
Sloth Madsen, M; Fox Maule, C; MacKellar, N
2012-01-01
Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study...... illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make...... the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented...
Generalized Gramian Framework for Model/Controller Order Reduction of Switched Systems
DEFF Research Database (Denmark)
Shaker, Hamid Reza; Wisniewski, Rafal
2011-01-01
In this article, a general method for model/controller order reduction of switched linear dynamical systems is presented. The proposed technique is based on the generalised gramian framework for model reduction. It is shown that different classical reduction methods can be developed into a genera......In this article, a general method for model/controller order reduction of switched linear dynamical systems is presented. The proposed technique is based on the generalised gramian framework for model reduction. It is shown that different classical reduction methods can be developed...
Adverse Selection Models with Three States of Nature
Directory of Open Access Journals (Sweden)
Daniela MARINESCU
2011-02-01
Full Text Available In the paper we analyze an adverse selection model with three states of nature, where both the Principal and the Agent are risk neutral. When solving the model, we use the informational rents and the efforts as variables. We derive the optimal contract in the situation of asymmetric information. The paper ends with the characteristics of the optimal contract and the main conclusions of the model.
A SUPPLIER SELECTION MODEL FOR SOFTWARE DEVELOPMENT OUTSOURCING
Directory of Open Access Journals (Sweden)
Hancu Lucian-Viorel
2010-12-01
Full Text Available This paper presents a multi-criteria decision making model used for supplier selection for software development outsourcing on e-marketplaces. This model can be used in auctions. The supplier selection process becomes complex and difficult on last twenty years since the Internet plays an important role in business management. Companies have to concentrate their efforts on their core activities and the others activities should be realized by outsourcing. They can achieve significant cost reduction by using e-marketplaces in their purchase process and by using decision support systems on supplier selection. In the literature were proposed many approaches for supplier evaluation and selection process. The performance of potential suppliers is evaluated using multi criteria decision making methods rather than considering a single factor cost.
Modeling quality attributes and metrics for web service selection
Oskooei, Meysam Ahmadi; Daud, Salwani binti Mohd; Chua, Fang-Fang
2014-06-01
Since the service-oriented architecture (SOA) has been designed to develop the system as a distributed application, the service selection has become a vital aspect of service-oriented computing (SOC). Selecting the appropriate web service with respect to quality of service (QoS) through using mathematical solution for optimization of problem turns the service selection problem into a common concern for service users. Nowadays, number of web services that provide the same functionality is increased and selection of services from a set of alternatives which differ in quality parameters can be difficult for service consumers. In this paper, a new model for QoS attributes and metrics is proposed to provide a suitable solution for optimizing web service selection and composition with low complexity.
[On selection criteria in spatially distributed models of competition].
Il'ichev, V G; Il'icheva, O A
2014-01-01
Discrete models of competitors (initial population and mutants) are considered in which reproduction is set by increasing and concave function, and migration in the space consisting of a set of areas, is described by a Markov matrix. This allows the use of the theory of monotonous operators to study problems of selection, coexistence and stability. It is shown that the higher is the number of areas, more and more severe constraints of selective advantage to initial population are required.
Comparing the staffing models of outsourcing in selected companies
Chaloupková, Věra
2010-01-01
This thesis deals with problems of takeover of employees in outsourcing. The capital purpose is to compare the staffing model of outsourcing in selected companies. To compare in selected companies I chose multi-criteria analysis. This thesis is dividend into six chapters. The first charter is devoted to the theoretical part. In this charter describes the basic concepts as outsourcing, personal aspects, phase of the outsourcing projects, communications and culture. The rest of thesis is devote...
SOME USES OF MODELS OF QUANTITATIVE GENETIC SELECTION IN SOCIAL SCIENCE.
Weight, Michael D; Harpending, Henry
2017-01-01
The theory of selection of quantitative traits is widely used in evolutionary biology, agriculture and other related fields. The fundamental model known as the breeder's equation is simple, robust over short time scales, and it is often possible to estimate plausible parameters. In this paper it is suggested that the results of this model provide useful yardsticks for the description of social traits and the evaluation of transmission models. The differences on a standard personality test between samples of Old Order Amish and Indiana rural young men from the same county and the decline of homicide in Medieval Europe are used as illustrative examples of the overall approach. It is shown that the decline of homicide is unremarkable under a threshold model while the differences between rural Amish and non-Amish young men are too large to be a plausible outcome of simple genetic selection in which assortative mating by affiliation is equivalent to truncation selection.
Flexible implementation of the Ensemble Model with arbitrary order of moments
Energy Technology Data Exchange (ETDEWEB)
Ackermann, W. [Technische Universitaet Darmstadt, Institut fuer Theorie Elektromagnetischer Felder (TEMF), Schlossgartenstrasse 8, D 64289 Darmstadt (Germany)]. E-mail: ackermann@temf.tu-darmstadt.de; Weiland, T. [Technische Universitaet Darmstadt, Institut fuer Theorie Elektromagnetischer Felder (TEMF), Schlossgartenstrasse 8, D 64289 Darmstadt (Germany)]. E-mail: thomas.weiland@temf.tu-darmstadt.de
2006-03-01
The Ensemble Model takes advantage of an approach to express the phase space particle distribution function in terms of the first, second and higher order moments instead of considering individual particles. Based on a new flexible implementation, an arbitrary number of orders can be processed and automatically converted into proper update equations for the simulation program V-Code. In this paper the influence of the introduction of higher order moments on the beam dynamics simulation is investigated. The achievable accuracy and the numerical efforts are compared with the ones obtained from the lower order calculations.
Modeling 3D PCMI using the Extended Finite Element Method with higher order elements
Energy Technology Data Exchange (ETDEWEB)
Jiang, W. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Spencer, Benjamin W. [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2017-03-31
This report documents the recent development to enable XFEM to work with higher order elements. It also demonstrates the application of higher order (quadratic) elements to both 2D and 3D models of PCMI problems, where discrete fractures in the fuel are represented using XFEM. The modeling results demonstrate the ability of the higher order XFEM to accurately capture the effects of a crack on the response in the vicinity of the intersecting surfaces of cracked fuel and cladding, as well as represent smooth responses in the regions away from the crack.
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2011-01-01
, propagated exponentially, can lead to severely sub-optimal plans. Modern optimizers typically maintain one-dimensional statistical summaries and make the attribute value independence and join uniformity assumptions for efficiently estimating selectivities. Therefore, selectivity estimation errors in today......’s optimizers are frequently caused by missed correlations between attributes. We present a selectivity estimation approach that does not make the independence assumptions. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution of all...
Genetic signatures of natural selection in a model invasive ascidian
Lin, Yaping; Chen, Yiyong; Yi, Changho; Fong, Jonathan J.; Kim, Won; Rius, Marc; Zhan, Aibin
2017-03-01
Invasive species represent promising models to study species’ responses to rapidly changing environments. Although local adaptation frequently occurs during contemporary range expansion, the associated genetic signatures at both population and genomic levels remain largely unknown. Here, we use genome-wide gene-associated microsatellites to investigate genetic signatures of natural selection in a model invasive ascidian, Ciona robusta. Population genetic analyses of 150 individuals sampled in Korea, New Zealand, South Africa and Spain showed significant genetic differentiation among populations. Based on outlier tests, we found high incidence of signatures of directional selection at 19 loci. Hitchhiking mapping analyses identified 12 directional selective sweep regions, and all selective sweep windows on chromosomes were narrow (~8.9 kb). Further analyses indentified 132 candidate genes under selection. When we compared our genetic data and six crucial environmental variables, 16 putatively selected loci showed significant correlation with these environmental variables. This suggests that the local environmental conditions have left significant signatures of selection at both population and genomic levels. Finally, we identified “plastic” genomic regions and genes that are promising regions to investigate evolutionary responses to rapid environmental change in C. robusta.
Liu, Weihua; Yang, Yi; Wang, Shuqing; Liu, Yang
2014-01-01
Order insertion often occurs in the scheduling process of logistics service supply chain (LSSC), which disturbs normal time scheduling especially in the environment of mass customization logistics service. This study analyses order similarity coefficient and order insertion operation process and then establishes an order insertion scheduling model of LSSC with service capacity and time factors considered. This model aims to minimize the average unit volume operation cost of logistics service integrator and maximize the average satisfaction degree of functional logistics service providers. In order to verify the viability and effectiveness of our model, a specific example is numerically analyzed. Some interesting conclusions are obtained. First, along with the increase of completion time delay coefficient permitted by customers, the possible inserting order volume first increases and then trends to be stable. Second, supply chain performance reaches the best when the volume of inserting order is equal to the surplus volume of the normal operation capacity in mass service process. Third, the larger the normal operation capacity in mass service process is, the bigger the possible inserting order's volume will be. Moreover, compared to increasing the completion time delay coefficient, improving the normal operation capacity of mass service process is more useful.
Modelling stock order flows with non-homogeneous intensities from high-frequency data
Gorshenin, Andrey K.; Korolev, Victor Yu.; Zeifman, Alexander I.; Shorgin, Sergey Ya.; Chertok, Andrey V.; Evstafyev, Artem I.; Korchagin, Alexander Yu.
2013-10-01
A micro-scale model is proposed for the evolution of such information system as the limit order book in financial markets. Within this model, the flows of orders (claims) are described by doubly stochastic Poisson processes taking account of the stochastic character of intensities of buy and sell orders that determine the price discovery mechanism. The proposed multiplicative model of stochastic intensities makes it possible to analyze the characteristics of the order flows as well as the instantaneous proportion of the forces of buyers and sellers, that is, the imbalance process, without modelling the external information background. The proposed model gives the opportunity to link the micro-scale (high-frequency) dynamics of the limit order book with the macro-scale models of stock price processes of the form of subordinated Wiener processes by means of limit theorems of probability theory and hence, to use the normal variance-mean mixture models of the corresponding heavy-tailed distributions. The approach can be useful in different areas with similar properties (e.g., in plasma physics).
[Research on high-order Windkessel model for assessing vascular compliance].
Ren, Yinzi; Xu, Jing; Gong, Shijin; Li, Li; Hu, Qijun; Yan, Jing; Ning, Gangmin
2011-04-01
In this paper, we propose the construction of a fifth-order Windkessel model, and give complete mathematical solutions for this model. Utilizing the diastolic pulse wave analytical methods, we derived the parameters of the mathematical model. The parameters were further applied to estimate arterial compliance, blood flow inertia, peripheral resistance and other indices. With simulation tools we assess the validity of the model, and built a simulation circuit with the model parameters R, C and L. The model parameters were obtained from the high-order Windkessel model. The stroke volume of left ventricle is employed as the input of the simulation circuit. At the end of the circuit, the responding signal was gained. And it in turn was compared with the measured pulse waveform. The results show that the fifth-order Windkessel model is superior to the third-order Windkessel model in the pulse wave fitting and stability, and thus better reflects the role of microvessles in the circulatory system.
Directory of Open Access Journals (Sweden)
Lubna Moin
2009-04-01
Full Text Available This research paper basically explores and compares the different modeling and analysis techniques and than it also explores the model order reduction approach and significance. The traditional modeling and simulation techniques for dynamic systems are generally adequate for single-domain systems only, but the Bond Graph technique provides new strategies for reliable solutions of multi-domain system. They are also used for analyzing linear and non linear dynamic production system, artificial intelligence, image processing, robotics and industrial automation. This paper describes a unique technique of generating the Genetic design from the tree structured transfer function obtained from Bond Graph. This research work combines bond graphs for model representation with Genetic programming for exploring different ideas on design space tree structured transfer function result from replacing typical bond graph element with their impedance equivalent specifying impedance lows for Bond Graph multiport. This tree structured form thus obtained from Bond Graph is applied for generating the Genetic Tree. Application studies will identify key issues and importance for advancing this approach towards becoming on effective and efficient design tool for synthesizing design for Electrical system. In the first phase, the system is modeled using Bond Graph technique. Its system response and transfer function with conventional and Bond Graph method is analyzed and then a approach towards model order reduction is observed. The suggested algorithm and other known modern model order reduction techniques are applied to a 11th order high pass filter [1], with different approach. The model order reduction technique developed in this paper has least reduction errors and secondly the final model retains structural information. The system response and the stability analysis of the system transfer function taken by conventional and by Bond Graph method is compared and
DEFF Research Database (Denmark)
Ramin, Elham; Flores Alsina, Xavier; Sin, Gürkan
2014-01-01
This study investigates the sensitivity of wastewater treatment plant (WWTP) model performance to the selection of one-dimensional secondary settling tanks (1-D SST) models with first-order and second-order mathematical structures. We performed a global sensitivity analysis (GSA) on the benchmark......, the settling parameters were found to be as influential as the biokinetic parameters on the uncertainty of WWTP model predictions, particularly for biogas production and treated water quality. However, the sensitivity measures were found to be dependent on the 1-D SST models selected. Accordingly, we suggest...... a different optimum parameter selection for the calibration of WWTP models when either of the 1-D SST models is used. Using first-order models, the calibration should give equal importance to the adjustment of the hindered settling and slow settling parameter values. The adjusted hindered settling parameters...
The production-distribution problem with order acceptance and package delivery: models and algorithm
Directory of Open Access Journals (Sweden)
Khalili Majid
2016-01-01
Full Text Available The production planning and distribution are among the most important decisions in the supply chain. Classically, in this problem, it is assumed that all orders have to produced and separately delivered; while, in practice, an order may be rejected if the cost that it brings to the supply chain exceeds its revenue. Moreover, orders can be delivered in a batch to reduce the related costs. This paper considers the production planning and distribution problem with order acceptance and package delivery to maximize the profit. At first, a new mathematical model based on mixed integer linear programming is developed. Using commercial optimization software, the model can optimally solve small or even medium sized instances. For large instances, a solution method, based on imperialist competitive algorithms, is also proposed. Using numerical experiments, the proposed model and algorithm are evaluated.
Directory of Open Access Journals (Sweden)
C.G. Ozoegwu
2016-01-01
Full Text Available The general least squares model for milling process state term is presented. A discrete map for milling stability analysis that is based on the third-order case of the presented general least squares milling state term model is first studied and compared with its third-order counterpart that is based on the interpolation theory. Both numerical rate of convergence and chatter stability results of the two maps are compared using the single degree of freedom (1DOF milling model. The numerical rate of convergence of the presented third-order model is also studied using the two degree of freedom (2DOF milling process model. Comparison gave that stability results from the two maps agree closely but the presented map demonstrated reduction in number of needed calculations leading to about 30% savings in computational time (CT. It is seen in earlier works that accuracy of milling stability analysis using the full-discretization method rises from first-order theory to second-order theory and continues to rise to the third-order theory. The present work confirms this trend. In conclusion, the method presented in this work will enable fast and accurate computation of stability diagrams for use by machinists.
Selecting an appropriate genetic evaluation model for selection in a developing dairy sector
McGill, D.M.; Mulder, H.A.; Thomson, P.C.; Lievaart, J.J.
2014-01-01
This study aimed to identify genetic evaluation models (GEM) to accurately select cattle for milk production when only limited data are available. It is based on a data set from the Pakistani Sahiwal progeny testing programme which includes records from five government herds, each consisting of 100
Qi, Di
Turbulent dynamical systems are ubiquitous in science and engineering. Uncertainty quantification (UQ) in turbulent dynamical systems is a grand challenge where the goal is to obtain statistical estimates for key physical quantities. In the development of a proper UQ scheme for systems characterized by both a high-dimensional phase space and a large number of instabilities, significant model errors compared with the true natural signal are always unavoidable due to both the imperfect understanding of the underlying physical processes and the limited computational resources available. One central issue in contemporary research is the development of a systematic methodology for reduced order models that can recover the crucial features both with model fidelity in statistical equilibrium and with model sensitivity in response to perturbations. In the first part, we discuss a general mathematical framework to construct statistically accurate reduced-order models that have skill in capturing the statistical variability in the principal directions of a general class of complex systems with quadratic nonlinearity. A systematic hierarchy of simple statistical closure schemes, which are built through new global statistical energy conservation principles combined with statistical equilibrium fidelity, are designed and tested for UQ of these problems. Second, the capacity of imperfect low-order stochastic approximations to model extreme events in a passive scalar field advected by turbulent flows is investigated. The effects in complicated flow systems are considered including strong nonlinear and non-Gaussian interactions, and much simpler and cheaper imperfect models with model error are constructed to capture the crucial statistical features in the stationary tracer field. Several mathematical ideas are introduced to improve the prediction skill of the imperfect reduced-order models. Most importantly, empirical information theory and statistical linear response theory are
A Predictive Model of Multi-Stage Production Planning for Fixed Time Orders
Directory of Open Access Journals (Sweden)
Kozłowski Edward
2014-09-01
Full Text Available The traditional production planning model based upon a deterministic approach is well described in the literature. Due to the uncertain nature of manufacturing processes, such model can however incorrectly represent actual situations on the shop floor. This study develops a mathematical modeling framework for generating production plans in a multistage manufacturing process. The devised model takes into account the stochastic model for predicting the occurrence of faulty products. The aim of the control model is to determine the number of products which should be manufactured in each planning period to minimize both manufacturing costs and potential financial penalties for failing to fulfill the order completely.
The Properties of Model Selection when Retaining Theory Variables
DEFF Research Database (Denmark)
Hendry, David F.; Johansen, Søren
Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....
Time-Frequency Analysis Using Warped-Based High-Order Phase Modeling
Directory of Open Access Journals (Sweden)
Ioana Cornel
2005-01-01
Full Text Available The high-order ambiguity function (HAF was introduced for the estimation of polynomial-phase signals (PPS embedded in noise. Since the HAF is a nonlinear operator, it suffers from noise-masking effects and from the appearance of undesired cross-terms when multicomponents PPS are analyzed. In order to improve the performances of the HAF, the multi-lag HAF concept was proposed. Based on this approach, several advanced methods (e.g., product high-order ambiguity function (PHAF have been recently proposed. Nevertheless, performances of these new methods are affected by the error propagation effect which drastically limits the order of the polynomial approximation. This phenomenon acts especially when a high-order polynomial modeling is needed: representation of the digital modulation signals or the acoustic transient signals. This effect is caused by the technique used for polynomial order reduction, common for existing approaches: signal multiplication with the complex conjugated exponentials formed with the estimated coefficients. In this paper, we introduce an alternative method to reduce the polynomial order, based on the successive unitary signal transformation, according to each polynomial order. We will prove that this method reduces considerably the effect of error propagation. Namely, with this order reduction method, the estimation error at a given order will depend only on the performances of the estimation method.
Higher Order Moments Generation by Mellin Transform for Compound Models of Clutter
Bhattacharya, C
2008-01-01
The compound models of clutter statistics are found suitable to describe the nonstationary nature of radar backscattering from high-resolution observations. In this letter, we show that the properties of Mellin transform can be utilized to generate higher order moments of simple and compound models of clutter statistics in a compact manner.
Estimation of Higher Order Moments for Compound Models of Clutter by Mellin Transform
Bhattacharya, C
2008-01-01
The compound models of clutter statistics are found suitable to describe the nonstationary nature of radar backscattering from high-resolution observations. In this letter, we show that the properties of Mellin transform can be utilized to generate higher order moments of simple and compound models of clutter statistics in a compact manner
Short-Term Memory for Serial Order: A Recurrent Neural Network Model
Botvinick, Matthew M.; Plaut, David C.
2006-01-01
Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according…
A Fuel-Sensitive Reduced-Order Model (ROM) for Piston Engine Scaling Analysis
2017-09-29
computational fluid dynamics simulations resolving the transient 3-D spray behavior have been performed to validate the model... computational fluid dynamics , fuels 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 56...from the work of Naber and Siebers (Naber and Siebers 1996; Siebers 1999) and a full-order model (FOM) based on 3-D– computational fluid dynamics
An efficient flexible-order model for coastal and ocean water waves
DEFF Research Database (Denmark)
Engsig-Karup, Allan Peter; Bingham, Harry B.; Lindberg, Ole
Current work are directed toward the development of an improved numerical 3D model for fully nonlinear potential water waves over arbitrary depths. The model is high-order accurate, robust and efficient for large-scale problems, and support will be included for flexibility in the description of s...
First Order Fire Effects Model: FOFEM 4.0, user's guide
Elizabeth D. Reinhardt; Robert E. Keane; James K. Brown
1997-01-01
A First Order Fire Effects Model (FOFEM) was developed to predict the direct consequences of prescribed fire and wildfire. FOFEM computes duff and woody fuel consumption, smoke production, and fire-caused tree mortality for most forest and rangeland types in the United States. The model is available as a computer program for PC or Data General computer.
Towards medium-term (order of months) morphodynamic modelling of the Teign estuary, UK
Bernardes, Marcos E. C.; Davidson, Mark A.; Dyer, Keith R.; George, Ken J.
2006-07-01
The main objective of this paper is to address the principal mechanisms involved in the medium-term (order of months to years) morphodynamic evolution of estuaries through the application of a process-based numerical modelling. The Teign estuary (Teignmouth, UK) is the selected site. The system is forced by the macrotidal semi-diurnal tide in the English Channel and is perturbed to a minor extent by high river discharge events (freshets). Although waves have a definite influence on the adjacent coastal area, Wells (Teignmouth Quay Development Environmental Statement: Changes to Physical Processes. Report R.984c:140. ABP Marine Environmental Research Ltd., Southampton, 2002b) suggested that swell waves do not enter the estuary. Hence, wave effects are neglected in this study, as only tides and the river discharge are taken into account. The sediment grain size is highly variable, but mainly sandy. Within the frame of the COAST3D project ( http://www.hrwallingford.co.uk/projects/COAST3D ), four bathymetric surveys of the adjacent coastal area were carried out at a nearly weekly intervals. The outer estuary and the adjacent coastal area were also surveyed every 6 months as part of the COASTVIEW project ( http://thecoastviewproject.org ). Based on these data and on continuously measured parameters, such as water level, waves, wind and river discharge, numerical modelling of the morphodynamic processes can be tested. To replicate the morphological changes in the medium-term within a feasible simulation time, forcing conditions are reduced through the use of an input reduction method (called ensemble technique). In this study, simulations are based on the coupling between Telemac-2D and its non-cohesive sediment transport module, Sisyphe (version 5.3 for both modules). Three different sediment transport formulae were tested: (1) Engelund and Hansen (A monograph on sediment transport in alluvial streams, 3rd edn. Technological University of Denmark, Copenhagen, 1967
Directory of Open Access Journals (Sweden)
Bin Wang
2016-01-01
Full Text Available This paper studies the application of frequency distributed model for finite time control of a fractional order nonlinear hydroturbine governing system (HGS. Firstly, the mathematical model of HGS with external random disturbances is introduced. Secondly, a novel terminal sliding surface is proposed and its stability to origin is proved based on the frequency distributed model and Lyapunov stability theory. Furthermore, based on finite time stability and sliding mode control theory, a robust control law to ensure the occurrence of the sliding motion in a finite time is designed for stabilization of the fractional order HGS. Finally, simulation results show the effectiveness and robustness of the proposed scheme.
Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2007-01-01
We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...
Effective high-order solver with thermally perfect gas model for hypersonic heating prediction
International Nuclear Information System (INIS)
Jiang, Zhenhua; Yan, Chao; Yu, Jian; Qu, Feng; Ma, Libin
2016-01-01
Highlights: • Design proper numerical flux for thermally perfect gas. • Line-implicit LUSGS enhances efficiency without extra memory consumption. • Develop unified framework for both second-order MUSCL and fifth-order WENO. • The designed gas model can be applied to much wider temperature range. - Abstract: Effective high-order solver based on the model of thermally perfect gas has been developed for hypersonic heat transfer computation. The technique of polynomial curve fit coupling to thermodynamics equation is suggested to establish the current model and particular attention has been paid to the design of proper numerical flux for thermally perfect gas. We present procedures that unify five-order WENO (Weighted Essentially Non-Oscillatory) scheme in the existing second-order finite volume framework and a line-implicit method that improves the computational efficiency without increasing memory consumption. A variety of hypersonic viscous flows are performed to examine the capability of the resulted high order thermally perfect gas solver. Numerical results demonstrate its superior performance compared to low-order calorically perfect gas method and indicate its potential application to hypersonic heating predictions for real-life problem.
Directory of Open Access Journals (Sweden)
Weihua Liu
2014-01-01
Full Text Available Order insertion often occurs in the scheduling process of logistics service supply chain (LSSC, which disturbs normal time scheduling especially in the environment of mass customization logistics service. This study analyses order similarity coefficient and order insertion operation process and then establishes an order insertion scheduling model of LSSC with service capacity and time factors considered. This model aims to minimize the average unit volume operation cost of logistics service integrator and maximize the average satisfaction degree of functional logistics service providers. In order to verify the viability and effectiveness of our model, a specific example is numerically analyzed. Some interesting conclusions are obtained. First, along with the increase of completion time delay coefficient permitted by customers, the possible inserting order volume first increases and then trends to be stable. Second, supply chain performance reaches the best when the volume of inserting order is equal to the surplus volume of the normal operation capacity in mass service process. Third, the larger the normal operation capacity in mass service process is, the bigger the possible inserting order’s volume will be. Moreover, compared to increasing the completion time delay coefficient, improving the normal operation capacity of mass service process is more useful.
Ordering Cost Reduction in Inventory Model with Defective Items and Backorder Price Discount
Directory of Open Access Journals (Sweden)
Karuppuchamy Annadurai
2014-01-01
Full Text Available In the real market, as unsatisfied demands occur, the longer the length of lead time is, the smaller the proportion of backorder would be. In order to make up for the inconvenience and even the losses of royal and patient customers, the supplier may offer a backorder price discount to secure orders during the shortage period. Also, ordering policies determined by conventional inventory models may be inappropriate for the situation in which an arrival lot contains some defective items. To compensate for the inconvenience of backordering and to secure orders, the supplier may offer a price discount on the stockout item. The purpose of this study is to explore a coordinated inventory model including defective arrivals by allowing the backorder price discount and ordering cost as decision variables. There are two inventory models proposed in this paper, one with normally distributed demand and another with distribution free demand. A computer code using the software Matlab 7.0 is developed to find the optimal solution and present numerical examples to illustrate the models. The results in the numerical examples indicate that the savings of the total cost are realized through ordering cost reduction and backorder price discount.
Frequency Weighted Model Order Reduction Technique and Error Bounds for Discrete Time Systems
Directory of Open Access Journals (Sweden)
Muhammad Imran
2014-01-01
for whole frequency range. However, certain applications (like controller reduction require frequency weighted approximation, which introduce the concept of using frequency weights in model reduction techniques. Limitations of some existing frequency weighted model reduction techniques include lack of stability of reduced order models (for two sided weighting case and frequency response error bounds. A new frequency weighted technique for balanced model reduction for discrete time systems is proposed. The proposed technique guarantees stable reduced order models even for the case when two sided weightings are present. Efficient technique for frequency weighted Gramians is also proposed. Results are compared with other existing frequency weighted model reduction techniques for discrete time systems. Moreover, the proposed technique yields frequency response error bounds.
Dynamical building simulation: A low order model for thermal bridges losses
Energy Technology Data Exchange (ETDEWEB)
Gao, Y. [School of Environment and Energy, Beijing University of Civil Engineering and Architecture, Beijing 100044 (China); Roux, J.J. [Centre de thermique de Lyon, CNRS-UMR 5008 - UCBL - INSA de LYON, Domaine scientifique de la Doua, Bat. Reyssinet, 40 rue des arts, Villeurbanne 69100 (France); Zhao, L.H. [Department of Architecture, South China University of Technology, GuangZhou 510640 (China); Jiang, Y. [School of Architecture, Tsinghua University, Beijing 100084 (China)
2008-07-01
Thermal bridges losses represent an increasing part of heat losses owing to significant three-dimensional heat transfer characteristics in modern buildings, but one-dimensional models are used in most simulation software for thermal analyses to simplify the calculations. State model reduction techniques were used to develop low-order three-dimensional heat transfer model for additional losses of thermal bridges, which is efficient and accuracy. Coupling this technique with traditional one-dimensional model for walls losses, it is possible to reduce a large amount of time simulations. Low-order model was validated from frequency response and time-domain output. And the effect of this model was shown with its implementation in software ''TRNSYS''. (author)
Model Order Reductions for Stability Analysis of Islanded Microgrids With Droop Control
DEFF Research Database (Denmark)
Mariani, Valerio; Vasca, Francesco; Vásquez, Juan C.
2015-01-01
the stability properties of the original closed loop model. The analysis shows that the currents dynamics influence the stability of the microgrid particularly for high values of the frequency droop control parameters. It is also shown that a further reduction of the model order leads to the typical oscillator......Three-phase inverters subject to droop control are widely used in islanded microgrids to interface distributed energy resources to the network and to properly share the loads among different units. In this paper, a mathematical model for islanded microgrids with linear loads and inverters under...... frequency and voltage droop control is proposed. The model is constructed by introducing a suitable state space transformation which allows to write the closed loop model in an explicit state space form. Then, the singular perturbations technique is used to obtain reduced order models which reproduce...
High-order state space simulation models of helicopter flight mechanics
Kim, Frederick D.; Celi, Roberto; Tischler, Mark B.
1993-01-01
This paper describes the formulation and validation of a high-order linearized mathematical model of helicopter flight mechanics, which includes rotor flap and lag degrees of freedom as well as inflow dynamics. The model is extracted numerically from an existing nonlinear, blade element, real-time simulation model. Extensive modifications in the formulation and solution process of the nonlinear model, required for a theoetically rigorous linearization, are described in detail. The validation results show that the linearized model successfully captures the coupled rotor-fuselage dynamics in the frequency band most critical for the design of advanced flight control systems. Additional results quantify the extent to which the order of the model can be reduced without loss of fidelity.
Selecting an optimal mixed products using grey relationship model
Directory of Open Access Journals (Sweden)
Farshad Faezy Razi
2013-06-01
Full Text Available This paper presents an integrated supplier selection and inventory management using grey relationship model (GRM as well as multi-objective decision making process. The proposed model of this paper first ranks different suppliers based on GRM technique and then determines the optimum level of inventory by considering different objectives. To show the implementation of the proposed model, we use some benchmark data presented by Talluri and Baker [Talluri, S., & Baker, R. C. (2002. A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3, 544-558.]. The preliminary results indicate that the proposed model of this paper is capable of handling different criteria for supplier selection.
Sample selection and taste correlation in discrete choice transport modelling
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2008-01-01
of taste correlation in willingness-to-pay estimation are presented. The first contribution addresses how to incorporate taste correlation in the estimation of the value of travel time for public transport. Given a limited dataset the approach taken is to use theory on the value of travel time as guidance...... many issues that deserve attention. This thesis investigates how sample selection can affect estimation of discrete choice models and how taste correlation should be incorporated into applied mixed logit estimation. Sampling in transport modelling is often based on an observed trip. This may cause...... a sample to be choice-based or governed by a self-selection mechanism. In both cases, there is a possibility that sampling affects the estimation of a population model. It was established in the seventies how choice-based sampling affects the estimation of multinomial logit models. The thesis examines...
A Reduced-Order Model of Transport Phenomena for Power Plant Simulation
Energy Technology Data Exchange (ETDEWEB)
Paul Cizmas; Brian Richardson; Thomas Brenner; Raymond Fontenot
2009-09-30
A reduced-order model based on proper orthogonal decomposition (POD) has been developed to simulate transient two- and three-dimensional isothermal and non-isothermal flows in a fluidized bed. Reduced-order models of void fraction, gas and solids temperatures, granular energy, and z-direction gas and solids velocity have been added to the previous version of the code. These algorithms are presented and their implementation is discussed. Verification studies are presented for each algorithm. A number of methods to accelerate the computations performed by the reduced-order model are presented. The errors associated with each acceleration method are computed and discussed. Using a combination of acceleration methods, a two-dimensional isothermal simulation using the reduced-order model is shown to be 114 times faster than using the full-order model. In the pursue of achieving the objectives of the project and completing the tasks planned for this program, several unplanned and unforeseen results, methods and studies have been generated. These additional accomplishments are also presented and they include: (1) a study of the effect of snapshot sampling time on the computation of the POD basis functions, (2) an investigation of different strategies for generating the autocorrelation matrix used to find the POD basis functions, (3) the development and implementation of a bubble detection and tracking algorithm based on mathematical morphology, (4) a method for augmenting the proper orthogonal decomposition to better capture flows with discontinuities, such as bubbles, and (5) a mixed reduced-order/full-order model, called point-mode proper orthogonal decomposition, designed to avoid unphysical due to approximation errors. The limitations of the proper orthogonal decomposition method in simulating transient flows with moving discontinuities, such as bubbling flows, are discussed and several methods are proposed to adapt the method for future use.
Spatial Fleming-Viot models with selection and mutation
Dawson, Donald A
2014-01-01
This book constructs a rigorous framework for analysing selected phenomena in evolutionary theory of populations arising due to the combined effects of migration, selection and mutation in a spatial stochastic population model, namely the evolution towards fitter and fitter types through punctuated equilibria. The discussion is based on a number of new methods, in particular multiple scale analysis, nonlinear Markov processes and their entrance laws, atomic measure-valued evolutions and new forms of duality (for state-dependent mutation and multitype selection) which are used to prove ergodic theorems in this context and are applicable for many other questions and renormalization analysis for a variety of phenomena (stasis, punctuated equilibrium, failure of naive branching approximations, biodiversity) which occur due to the combination of rare mutation, mutation, resampling, migration and selection and make it necessary to mathematically bridge the gap (in the limit) between time and space scales.
Eluru, Naveen; Chakour, Vincent; Chamberlain, Morgan; Miranda-Moreno, Luis F
2013-10-01
Vehicle operating speed measured on roadways is a critical component for a host of analysis in the transportation field including transportation safety, traffic flow modeling, roadway geometric design, vehicle emissions modeling, and road user route decisions. The current research effort contributes to the literature on examining vehicle speed on urban roads methodologically and substantively. In terms of methodology, we formulate a new econometric model framework for examining speed profiles. The proposed model is an ordered response formulation of a fractional split model. The ordered nature of the speed variable allows us to propose an ordered variant of the fractional split model in the literature. The proposed formulation allows us to model the proportion of vehicles traveling in each speed interval for the entire segment of roadway. We extend the model to allow the influence of exogenous variables to vary across the population. Further, we develop a panel mixed version of the fractional split model to account for the influence of site-specific unobserved effects. The paper contributes substantively by estimating the proposed model using a unique dataset from Montreal consisting of weekly speed data (collected in hourly intervals) for about 50 local roads and 70 arterial roads. We estimate separate models for local roads and arterial roads. The model estimation exercise considers a whole host of variables including geometric design attributes, roadway attributes, traffic characteristics and environmental factors. The model results highlight the role of various street characteristics including number of lanes, presence of parking, presence of sidewalks, vertical grade, and bicycle route on vehicle speed proportions. The results also highlight the presence of site-specific unobserved effects influencing the speed distribution. The parameters from the modeling exercise are validated using a hold-out sample not considered for model estimation. The results indicate
Evidence accumulation as a model for lexical selection.
Anders, R; Riès, S; van Maanen, L; Alario, F X
2015-11-01
We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.
Integrated model for supplier selection and performance evaluation
Directory of Open Access Journals (Sweden)
Borges de Araújo, Maria Creuza
2015-08-01
Full Text Available This paper puts forward a model for selecting suppliers and evaluating the performance of those already working with a company. A simulation was conducted in a food industry. This sector has high significance in the economy of Brazil. The model enables the phases of selecting and evaluating suppliers to be integrated. This is important so that a company can have partnerships with suppliers who are able to meet their needs. Additionally, a group method is used to enable managers who will be affected by this decision to take part in the selection stage. Finally, the classes resulting from the performance evaluation are shown to support the contractor in choosing the most appropriate relationship with its suppliers.
Accuracy Analysis of the Zero-Order Hold Model for Digital Pulsewidth Modulation
DEFF Research Database (Denmark)
Ma, Junpeng; Wang, Xiongfei; Blaabjerg, Frede
2018-01-01
This paper analyzes the accuracy of the zero-order hold (ZOH) model for the digital pulsewidth modulator (DPWM) in the s-domain. The s-domain model and the exact z-domain model for the control loop of the single-phase inverter with L-type filter is elaborated for quantifying the deviation...... of the ZOH model for DPWM. The influence of the different computational delay and duty-cycle update modes on this deviation is analyzed in detail. The compensation method for this deviation of the ZOH model is proposed for accurately predicting the stability region of the control system in the s...
The Selection of ARIMA Models with or without Regressors
DEFF Research Database (Denmark)
Johansen, Søren; Riani, Marco; Atkinson, Anthony C.
We develop a $C_{p}$ statistic for the selection of regression models with stationary and nonstationary ARIMA error term. We derive the asymptotic theory of the maximum likelihood estimators and show they are consistent and asymptotically Gaussian. We also prove that the distribution of the sum o...
Selecting candidate predictor variables for the modelling of post ...
African Journals Online (AJOL)
Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project. Afri. Health Sci. .... Initial list of candidate predictor variables, N=17. Clinical. Laboratory. Social/Demographic. Vital signs (HR, RR, BP, T). Hemoglobin. Age. Oxygen saturation. Blood culture. Sex.
Computationally efficient thermal-mechanical modelling of selective laser melting
Yang, Y.; Ayas, C.; Brabazon, Dermot; Naher, Sumsun; Ul Ahad, Inam
2017-01-01
The Selective laser melting (SLM) is a powder based additive manufacturing (AM) method to produce high density metal parts with complex topology. However, part distortions and accompanying residual stresses deteriorates the mechanical reliability of SLM products. Modelling of the SLM process is
Multivariate time series modeling of selected childhood diseases in ...
African Journals Online (AJOL)
This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2004-01-01
This paper presents a new sensorless vector control system for high performance induction motor drives fed by a matrix converter with non-linearity compensation. The nonlinear voltage distortion that is caused by commutation delay and on-state voltage drop in switching device is corrected by a new...... matrix converter model. Regulated Order Extended Luenberger Observer (ROELO) is employed to bring better response in the whole speed operation range and a method to select the observer gain is presented. Experimental results are shown to illustrate the performance of the proposed system...
Approaches for Reduced Order Modeling of Electrically Actuated von Karman Microplates
Saghir, Shahid
2016-07-25
This article presents and compares different approaches to develop reduced order models for the nonlinear von Karman rectangular microplates actuated by nonlinear electrostatic forces. The reduced-order models aim to investigate the static and dynamic behavior of the plate under small and large actuation forces. A fully clamped microplate is considered. Different types of basis functions are used in conjunction with the Galerkin method to discretize the governing equations. First we investigate the convergence with the number of modes retained in the model. Then for validation purpose, a comparison of the static results is made with the results calculated by a nonlinear finite element model. The linear eigenvalue problem for the plate under the electrostatic force is solved for a wide range of voltages up to pull-in. Results among the various reduced-order modes are compared and are also validated by comparing to results of the finite-element model. Further, the reduced order models are employed to capture the forced dynamic response of the microplate under small and large vibration amplitudes. Comparison of the different approaches are made for this case. Keywords: electrically actuated microplates, static analysis, dynamics of microplates, diaphragm vibration, large amplitude vibrations, nonlinear dynamics
A High Order Filter with Galerkin Finite Element Method for the Spherical Local domain Model
Lee, C. H.; Cheong, H. B.; Kang, H. G.
2017-12-01
A High Order Filter with Galerkin Finite Element Method for the Spherical Local domain ModelChung-Hui Lee1 and Hyeong-Bin Cheong and Hyun-Gyu KangDepartment of Environmental Atmospheric Sciences, Pukyong National University, Busan, Korea (1 chlee@pukyong.ac.kr) A high-order filter with Galerkin finite element method is constructed by applying a two dimensional finite element method with quadrilateral basis functions to the spherical limited area domain. The quadrilateral basis function is defined as four shape-functions on separate four grid-boxes which share the same gridpoint. A first-order derivative is represented with an algebraic equation consisting of nine point stencil. Helmholtz equation on a sphere is the basic component of the high order filter and the filtering is performed by solving this equation with two dimensional finite element method. As the theory describes, for spherical Laplacian operator and first-order derivative, the convergence rates of the error were revealed to be second-order and fourth-order, respectively. In addition, since the convergence rate of errors for the filter in this study was the same as the filter with Fourier finite element method, the accuracy of the filter is comparable to the filter based on the Fourier finite element method. The high-order filter was applied to the WRF (Weather Research and Forecasting) as hyper-viscosity and its performance was compared with those of the built-in viscosity scheme of the WRF model. As a result of the tropical cyclone simulation, the forecast error for the high-order filter and the built-in viscosity were similar for the minimum pressure and track prediction. However, for the precipitation and rainfall distribution, the prediction with high-order filter appeared closer to observations than those with built-in viscosity.
Evaluation of the Component Chemical Potentials in Analytical Models for Ordered Alloy Phases
Directory of Open Access Journals (Sweden)
W. A. Oates
2011-01-01
Full Text Available The component chemical potentials in models of solution phases with a fixed number of sites can be evaluated easily when the Helmholtz energy is known as an analytical function of composition. In the case of ordered phases, however, the situation is less straightforward, because the Helmholtz energy is a functional involving internal order parameters. Because of this, the chemical potentials are usually obtained numerically from the calculated integral Helmholtz energy. In this paper, we show how the component chemical potentials can be obtained analytically in ordered phases via the use of virtual cluster chemical potentials. Some examples are given which illustrate the simplicity of the method.
Ferroquadrupolar Order in the Spin-1 Bilinear-Biquadratic Model up to the Second Nearest Neighbor
Pires, A. S. T.
2017-10-01
We have studied some ferroquadrupolar phases of the S = 1 Heisenberg model with bilinear and biquadratic exchange interactions on the square lattice up to the second nearest neighbor, using the SU(3) Schwinger bosons formalism in a mean field approximation. This technique is very convenient to treat nematic order. This technique has the advantage of using the fundamental representation of the SU(N) group instead of SU(2), designed to capture spin-quadrupolar order in addition to the dipolar magnetic order. We also present quadrupole structure factors that can be measured in future experiments. Our calculations can have implications in the study of iron-based superconductors.
Study on the Business Cycle Model with Fractional-Order Time Delay under Random Excitation
Directory of Open Access Journals (Sweden)
Zifei Lin
2017-07-01
Full Text Available Time delay of economic policy and memory property in a real economy system is omnipresent and inevitable. In this paper, a business cycle model with fractional-order time delay which describes the delay and memory property of economic control is investigated. Stochastic averaging method is applied to obtain the approximate analytical solution. Numerical simulations are done to verify the method. The effects of the fractional order, time delay, economic control and random excitation on the amplitude of the economy system are investigated. The results show that time delay, fractional order and intensity of random excitation can all magnify the amplitude and increase the volatility of the economy system.
Directory of Open Access Journals (Sweden)
Alok Dhaundiyal
2016-10-01
Full Text Available This article focuses on the influence of relevant parameters of biomass pyrolysis on the numerical solution of the isothermal nth-order distributed activation energy model (DAEM using the Rayleigh distribution as the initial distribution function F(E of the activation energies. In this study, the integral upper limit, the frequency factor, the reaction order and the scale parameters are investigated. This paper also derived the asymptotic approximation for the DAEM. The influence of these parameters is used to calculate the kinetic parameters of the isothermal nth-order DAEM with the help of thermo-analytical results of TGA/DTG analysis.
Gómez-Aguilar, J. F.
2018-03-01
In this paper, we analyze an alcoholism model which involves the impact of Twitter via Liouville-Caputo and Atangana-Baleanu-Caputo fractional derivatives with constant- and variable-order. Two fractional mathematical models are considered, with and without delay. Special solutions using an iterative scheme via Laplace and Sumudu transform were obtained. We studied the uniqueness and existence of the solutions employing the fixed point postulate. The generalized model with variable-order was solved numerically via the Adams method and the Adams-Bashforth-Moulton scheme. Stability and convergence of the numerical solutions were presented in details. Numerical examples of the approximate solutions are provided to show that the numerical methods are computationally efficient. Therefore, by including both the fractional derivatives and finite time delays in the alcoholism model studied, we believe that we have established a more complete and more realistic indicator of alcoholism model and affect the spread of the drinking.
Viable inflationary models ending with a first-order phase transition
International Nuclear Information System (INIS)
Cortes, Marina; Liddle, Andrew R.
2009-01-01
We investigate the parameter space of two-field inflation models where inflation terminates via a first-order phase transition causing nucleation of bubbles. Such models experience a tension from the need to ensure nearly scale-invariant density perturbations, while avoiding a near scale-invariant bubble size distribution which would conflict observations. We perform an exact analysis of the different regimes of the models, where the energy density of the inflaton field ranges from being negligible as compared to the vacuum energy to providing most of the energy for inflation. Despite recent microwave anisotropy results favoring a spectral index less than 1, we find that there are still viable models that end with bubble production and can match all available observations. As a by-product of our analysis, we also provide an up-to-date assessment of the viable parameter space of Linde's original second-order hybrid model across its full parameter range.
Enhanced regime predictability in atmospheric low-order models due to stochastic forcing.
Kwasniok, Frank
2014-06-28
Regime predictability in atmospheric low-order models augmented with stochastic forcing is studied. Atmospheric regimes are identified as persistent or metastable states using a hidden Markov model analysis. A somewhat counterintuitive, coherence resonance-like effect is observed: regime predictability increases with increasing noise level up to an intermediate optimal value, before decreasing when further increasing the noise level. The enhanced regime predictability is due to increased persistence of the regimes. The effect is found in the Lorenz '63 model and a low-order model of barotropic flow over topography. The increased predictability is only present in the regime dynamics, that is, in a coarse-grained view of the system; predictability of individual trajectories decreases monotonically with increasing noise level. A possible explanation for the phenomenon is given and implications of the finding for weather and climate modelling and prediction are discussed. © 2014 The Author(s) Published by the Royal Society. All rights reserved.
Determination of Economic Order Quantity in a fuzzy EOQ Model using of GMIR Deffuzification
Directory of Open Access Journals (Sweden)
Hamidreza Salmani Mojaveri
2017-03-01
Full Text Available Inappropriate inventory control policies and its incorrect implementation can cause improper operation and uncompetitive advantage of organization logistic operation in the market. Therefore, analysis inventory control policies are important to be understood, including carrying cost, ordering cost, warehouse renting cost, and buying cost. In this research, Economic Order Quantity (EOQ problem in fuzzy condition is reviewed in two different situations. The first model concerned to costs (carrying cost, ordering cost, warehouse renting cost and buying cost, which is considered as triangular fuzzy numbers. The second model was in addition to inventory the cost system, in which annual demand is also reviewed as fuzzy numbers. In each model, graded mean integration representation (GMIR deffuzification was used for parameters deffuzification. Then, the final objective from this analysis was to obtain economic quantity formula through derivation.
Directory of Open Access Journals (Sweden)
Lin Wen Feng
2016-01-01
Full Text Available In this article, we study inventory models to determine the optimal special order and maximum saving cost of imperfective items when the supplier offers a temporary discount. The received items are not all perfect and the defectives can be screened out by the end of 100% screening process. Three models are considered according to the special order occurs at regular replenishment time, non-regular replenishment time, and screening time of economic order quantity cycle. Each model has two sub-cases to be discussed. In temporary discount problems, in general, there are integer operators in objective functions. We suggest theorems to find the closed-form solutions to these kinds of problems. Furthermore, numerical examples and sensitivity analysis are given to illustrate the results of the proposed properties and theorems.
[Biomass dynamics of tree branches of higher order. A model analysis].
Galitskiĭ, V V
2012-01-01
The sectional model of biomass dynamics of freely growing tree brahcnes of all orders is presented. The model is an extension of the sectional tree biomass model proposed earlier. The branches model showed bell-shaped dynamics of a branches biomass and, accordingly, boundedness of branch orders number. The important element of the model of branches system is the inter-verticil green biomass. The model is parameterized on the basis of published data on lifespan of branches of different orders and age in which the biomass of skeletal branches of spruce, Picea abies (L.) Karst, reaches the maximum. When adding known peculiarities of spruce growth (such as the initial growth inhibiton and presence of the inter-verticil branches) to the model of biomass dynamics of regular branches system, good appproximation of all natural data by model values is obtained. The possible mechanism of inter-verticil branches appearance in response to improvement of a tree growth conditions, and also their function in a tree growth process, namely replacement of regular branches incapable of appropriate response, is described. Initiation of appearing and/or waking of the sleeping (adventive) buds which give rise to inter-verticil branches is probably caused by rise of pressure of photosynthates in a tree phloem what the published results of experiments on a decapitaion of branches of Wollemia nobilis (Araucariaceae) also testify.
Model selection for the extraction of movement primitives
Directory of Open Access Journals (Sweden)
Dominik M Endres
2013-12-01
Full Text Available A wide range of blind source separation methods have been used in motor control research for the extraction of movement primitives from EMG and kinematic data. Popular examples are principal component analysis (PCA,independent component analysis (ICA, anechoic demixing, and the time-varying synergy model. However, choosing the parameters of these models, or indeed choosing the type of model, is often done in a heuristic fashion, driven by result expectations as much as by the data. We propose an objective criterion which allows to select the model type, number of primitives and the temporal smoothness prior. Our approach is based on a Laplace approximation to the posterior distribution of the parameters of a given blind source separation model, re-formulated as a Bayesian generative model.We first validate our criterion on ground truth data, showing that it performs at least as good as traditional model selection criteria (Bayesian information criterion, BIC and the Akaike Information Criterion (AIC. Then, we analyze human gait data, finding that an anechoic mixture model with a temporal smoothness constraint on the sources can best account for the data.
Order Matters: Sequencing Scale-Realistic Versus Simplified Models to Improve Science Learning
Chen, Chen; Schneps, Matthew H.; Sonnert, Gerhard
2016-10-01
Teachers choosing between different models to facilitate students' understanding of an abstract system must decide whether to adopt a model that is simplified and striking or one that is realistic and complex. Only recently have instructional technologies enabled teachers and learners to change presentations swiftly and to provide for learning based on multiple models, thus giving rise to questions about the order of presentation. Using disjoint individual growth modeling to examine the learning of astronomical concepts using a simulation of the solar system on tablets for 152 high school students (age 15), the authors detect both a model effect and an order effect in the use of the Orrery, a simplified model that exaggerates the scale relationships, and the True-to-scale, a proportional model that more accurately represents the realistic scale relationships. Specifically, earlier exposure to the simplified model resulted in diminution of the conceptual gain from the subsequent realistic model, but the realistic model did not impede learning from the following simplified model.
A POD reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows
Fang, F.; Pain, C. C.; Navon, I. M.; Gorman, G. J.; Piggott, M. D.; Allison, P. A.; Farrell, P. E.; Goddard, A. J. H.
Herein a new approach to enhance the accuracy of a novel Proper Orthogonal Decomposition (POD) model applied to moderate Reynolds number flows (of the type typically encountered in ocean models) is presented. This approach develops the POD model of Fang et al. [Fang, F., Pain, C.C., Navon, I.M., Piggott, M.D., Gorman, G.J., Allison, P., Goddard, A.J.H., 2008. Reduced-order modelling of an adaptive mesh ocean model. International Journal for Numerical Methods in Fluids. doi:10.1002/fld.1841] used in conjunction with the Imperial College Ocean Model (ICOM), an adaptive, non-hydrostatic finite element model. Both the velocity and vorticity results of the POD reduced order model (ROM) exhibit an overall good agreement with those obtained from the full model. The accuracy of the POD-Galerkin model with the use of adaptive meshes is first evaluated using the Munk gyre flow test case with Reynolds numbers ranging between 400 and 2000. POD models using the L2 norm become oscillatory when the Reynolds number exceeds Re=400. This is because the low-order truncation of the POD basis inhibits generally all the transfers between the large and the small (unresolved) scales of the fluid flow. Accuracy is improved by using the H1 POD projector in preference to the L2 POD projector. The POD bases are constructed by incorporating gradients as well as function values in the H1 Sobolev norm. The accuracy of numerical results is further enhanced by increasing the number of snapshots and POD bases. Error estimation was used to assess the effect of truncation (involved in the POD-Galerkin approach) when adaptive meshes are used in conjunction with POD/ROM. The RMSE of velocity results between the full model and POD-Galerkin model is reduced by as much as 50% by using the H1 norm and increasing the number of snapshots and POD bases.
Directory of Open Access Journals (Sweden)
Rodrigo Alves Silva
2017-09-01
Full Text Available This paper aims to show the importance of the use of financial metrics in decision-making of credit scoring models selection. In order to achieve such, we considered an automatic approval system approach and we carried out a performance analysis of the financial metrics on the theoretical portfolios generated by seven credit scoring models based on main statistical learning techniques. The models were estimated on German Credit dataset and the results were analyzed based on four metrics: total accuracy, error cost, risk adjusted return on capital and Sharpe index. The results show that total accuracy, widely used as a criterion for selecting credit scoring models, is unable to select the most profitable model for the company, indicating the need to incorporate financial metrics into the credit scoring model selection process. Keywords Credit risk; Model’s selection; Statistical learning.
On selection of optimal stochastic model for accelerated life testing
International Nuclear Information System (INIS)
Volf, P.; Timková, J.
2014-01-01
This paper deals with the problem of proper lifetime model selection in the context of statistical reliability analysis. Namely, we consider regression models describing the dependence of failure intensities on a covariate, for instance, a stressor. Testing the model fit is standardly based on the so-called martingale residuals. Their analysis has already been studied by many authors. Nevertheless, the Bayes approach to the problem, in spite of its advantages, is just developing. We shall present the Bayes procedure of estimation in several semi-parametric regression models of failure intensity. Then, our main concern is the Bayes construction of residual processes and goodness-of-fit tests based on them. The method is illustrated with both artificial and real-data examples. - Highlights: • Statistical survival and reliability analysis and Bayes approach. • Bayes semi-parametric regression modeling in Cox's and AFT models. • Bayes version of martingale residuals and goodness-of-fit test
Model building strategy for logistic regression: purposeful selection.
Zhang, Zhongheng
2016-03-01
Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.
Statistical modelling in biostatistics and bioinformatics selected papers
Peng, Defen
2014-01-01
This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and fu...
Bakker, Eric
2010-02-15
A generalized description of the response behavior of potentiometric polymer membrane ion-selective electrodes is presented on the basis of ion-exchange equilibrium considerations at the sample-membrane interface. This paper includes and extends on previously reported theoretical advances in a more compact yet more comprehensive form. Specifically, the phase boundary potential model is used to derive the origin of the Nernstian response behavior in a single expression, which is valid for a membrane containing any charge type and complex stoichiometry of ionophore and ion-exchanger. This forms the basis for a generalized expression of the selectivity coefficient, which may be used for the selectivity optimization of ion-selective membranes containing electrically charged and neutral ionophores of any desired stoichiometry. It is shown to reduce to expressions published previously for specialized cases, and may be effectively applied to problems relevant in modern potentiometry. The treatment is extended to mixed ion solutions, offering a comprehensive yet formally compact derivation of the response behavior of ion-selective electrodes to a mixture of ions of any desired charge. It is compared to predictions by the less accurate Nicolsky-Eisenman equation. The influence of ion fluxes or any form of electrochemical excitation is not considered here, but may be readily incorporated if an ion-exchange equilibrium at the interface may be assumed in these cases.
A model for the sustainable selection of building envelope assemblies
International Nuclear Information System (INIS)
Huedo, Patricia; Mulet, Elena; López-Mesa, Belinda
2016-01-01
The aim of this article is to define an evaluation model for the environmental impacts of building envelopes to support planners in the early phases of materials selection. The model is intended to estimate environmental impacts for different combinations of building envelope assemblies based on scientifically recognised sustainability indicators. These indicators will increase the amount of information that existing catalogues show to support planners in the selection of building assemblies. To define the model, first the environmental indicators were selected based on the specific aims of the intended sustainability assessment. Then, a simplified LCA methodology was developed to estimate the impacts applicable to three types of dwellings considering different envelope assemblies, building orientations and climate zones. This methodology takes into account the manufacturing, installation, maintenance and use phases of the building. Finally, the model was validated and a matrix in Excel was created as implementation of the model. - Highlights: • Method to assess the envelope impacts based on a simplified LCA • To be used at an earlier phase than the existing methods in a simple way. • It assigns a score by means of known sustainability indicators. • It estimates data about the embodied and operating environmental impacts. • It compares the investment costs with the costs of the consumed energy.
2010-07-01
... select travel service providers for which my agency is not a mandatory user in order to maximize my... frequent traveler benefits. You must use the travel service provider for which your agency is a mandatory... 41 Public Contracts and Property Management 4 2010-07-01 2010-07-01 false May I select travel...
Energy Technology Data Exchange (ETDEWEB)
Honerkamp, Carsten [Institute for Theoretical Solid State Physics, RWTH Aachen University (Germany); JARA - Fundamentals of Future Information Technology, Aachen (Germany)
2017-11-15
We investigate the impact of electron self-energy corrections on potential antiferromagnetic ordering instabilities in mono- and bilayer graphene, modeled by a Hubbard-type lattice model with onsite interactions among the electrons, using a self-consistent random phase approximation (RPA). In qualitative agreement with earlier studies we find that the electronic interactions cause non-Fermi liquid behavior at low energies. In self-consistent RPA, the transition scales for antiferromagnetic ordering are renormalized significantly by these self-energy effects, both for interaction-driven and temperature-driven cases. (copyright 2017 by WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)
Kerfriden, P.; Goury, O.; Rabczuk, T.; Bordas, S.P.A.
2013-01-01
We propose in this paper a reduced order modelling technique based on domain partitioning for parametric problems of fracture. We show that coupling domain decomposition and projection-based model order reduction permits to focus the numerical effort where it is most needed: around the zones where damage propagates. No a priori knowledge of the damage pattern is required, the extraction of the corresponding spatial regions being based solely on algebra. The efficiency of the proposed approach is demonstrated numerically with an example relevant to engineering fracture. PMID:23750055
Dyjas, Oliver; Ulrich, Rolf
2014-01-01
In typical discrimination experiments, participants are presented with a constant standard and a variable comparison stimulus and their task is to judge which of these two stimuli is larger (comparative judgement). In these experiments, discrimination sensitivity depends on the temporal order of these stimuli (Type B effect) and is usually higher when the standard precedes rather than follows the comparison. Here, we outline how two models of stimulus discrimination can account for the Type B effect, namely the weighted difference model (or basic Sensation Weighting model) and the Internal Reference Model. For both models, the predicted psychometric functions for comparative judgements as well as for equality judgements, in which participants indicate whether they perceived the two stimuli to be equal or not equal, are derived and it is shown that the models also predict a Type B effect for equality judgements. In the empirical part, the models' predictions are evaluated. To this end, participants performed a duration discrimination task with comparative judgements and with equality judgements. In line with the models' predictions, a Type B effect was observed for both judgement types. In addition, a time-order error, as indicated by shifts of the psychometric functions, and differences in response times were observed only for the equality judgement. Since both models entail distinct additional predictions, it seems worthwhile for future research to unite the two models into one conceptual framework.
A model of the nerve impulse using two first-order differential equations
Hindmarsh, J. L.; Rose, R. M.
1982-03-01
The Hodgkin-Huxley model1 of the nerve impulse consists of four coupled nonlinear differential equations, six functions and seven constants. Because of the complexity of these equations and the necessity for numerical solution, it is difficult to use them in simulations of interactions in small neural networks. Thus, it would be useful to have a second-order differential equation which predicted correctly properties such as the frequency-current relationship. Fitzhugh2 introduced a second-order model of the nerve impulse, but his equations predict an action potential duration which is similar to the inter-spike interval3 and they do not give a reasonable frequency-current relationship. To develop a second-order model having few parameters but which does not have these disadvantages, we have generalized the second-order Fitzhugh equations2, and based the form of the functions in the new equations on voltage-clamp data obtained from a snail neurone. We report here an unexpected property of the resulting equations-the x and y null clines in the phase plane lie close together when the phase point is on the recovery side of the phase plane. The resulting slow movement along the phase path gives a long inter-spike interval, a property not shown clearly by previous models2,4. The model also predicts the linearity of the frequency-current relationship, and may be useful for studying detailed interactions in networks containing small numbers of neurones.
Directory of Open Access Journals (Sweden)
Philipp Singer
Full Text Available One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
Higher-order factors of the big five model of personality: a reanalysis of Digman (1997).
Mutch, Christopher
2005-02-01
Based on the results from factor analyses conducted on 14 different data sets, Digman proposed a model of two higher-order factors, or metatraits, that subsumed the Big Five personality traits. In the current article, problems in Digman's analyses were explicated, and more appropriate analyses were then conducted using the same 14 correlation matrices from Digman's study. The resultant two-factor model produced improper solutions, poor model fit indices, or both, in almost all of the 14 data sets and thus raised serious doubts about the veracity of Digman's proposed model.
Lie Symmetry Analysis of a First-Order Feedback Model of Option Pricing
Directory of Open Access Journals (Sweden)
Winter Sinkala
2015-01-01
Full Text Available A first-order feedback model of option pricing consisting of a coupled system of two PDEs, a nonliner generalised Black-Scholes equation and the classical Black-Scholes equation, is studied using Lie symmetry analysis. This model arises as an extension of the classical Black-Scholes model when liquidity is incorporated into the market. We compute the admitted Lie point symmetries of the system and construct an optimal system of the associated one-dimensional subalgebras. We also construct some invariant solutions of the model.
A Stable Clock Error Model Using Coupled First and Second Order Gauss-Markov Processes
Carpenter, Russell; Lee, Taesul
2008-01-01
Long data outages may occur in applications of global navigation satellite system technology to orbit determination for missions that spend significant fractions of their orbits above the navigation satellite constellation(s). Current clock error models based on the random walk idealization may not be suitable in these circumstances, since the covariance of the clock errors may become large enough to overflow flight computer arithmetic. A model that is stable, but which approximates the existing models over short time horizons is desirable. A coupled first- and second-order Gauss-Markov process is such a model.
Energy Technology Data Exchange (ETDEWEB)
Meeks, E.; Chou, C. -P.; Garratt, T.
2013-03-31
Engineering simulations of coal gasifiers are typically performed using computational fluid dynamics (CFD) software, where a 3-D representation of the gasifier equipment is used to model the fluid flow in the gasifier and source terms from the coal gasification process are captured using discrete-phase model source terms. Simulations using this approach can be very time consuming, making it difficult to imbed such models into overall system simulations for plant design and optimization. For such system-level designs, process flowsheet software is typically used, such as Aspen Plus® [1], where each component where each component is modeled using a reduced-order model. For advanced power-generation systems, such as integrated gasifier/gas-turbine combined-cycle systems (IGCC), the critical components determining overall process efficiency and emissions are usually the gasifier and combustor. Providing more accurate and more computationally efficient reduced-order models for these components, then, enables much more effective plant-level design optimization and design for control. Based on the CHEMKIN-PRO and ENERGICO software, we have developed an automated methodology for generating an advanced form of reduced-order model for gasifiers and combustors. The reducedorder model offers representation of key unit operations in flowsheet simulations, while allowing simulation that is fast enough to be used in iterative flowsheet calculations. Using high-fidelity fluiddynamics models as input, Reaction Design’s ENERGICO® [2] software can automatically extract equivalent reactor networks (ERNs) from a CFD solution. For the advanced reduced-order concept, we introduce into the ERN a much more detailed kinetics model than can be included practically in the CFD simulation. The state-of-the-art chemistry solver technology within CHEMKIN-PRO allows that to be accomplished while still maintaining a very fast model turn-around time. In this way, the ERN becomes the basis for
Long-lifetime Martian orbit selection using a time-dependent model of the Martian atmosphere
Culp, R. D.; Stewart, A. I.; Chow, C.-C.; Uphoff, C.
1984-01-01
A mathematical model of the time-dependent Martian atmosphere has been developed in order to accurately calculate the effects of aerodynamic drag on a low altitude satellite. The time-dependent properties of the model include solar activity effects, dust storm effects, seasonal and diurnal variations, and annual motion effects. Position effects are accounted for through Martian latitude and longitude. Expected values of mass density, temperature, scale height, and the estimated standard deviation of the mass density are provided. An example of the use of the model in selecting an orbit for the Mars Geochemical/Climatology Orbiter is given.
Long-lifetime Martian orbit selection using a time-dependent model of the Martian atmosphere
Culp, R. D.; Stewart, A. I.; Chow, C.-C.; Uphoff, C.
1984-08-01
A mathematical model of the time-dependent Martian atmosphere has been developed in order to accurately calculate the effects of aerodynamic drag on a low altitude satellite. The time-dependent properties of the model include solar activity effects, dust storm effects, seasonal and diurnal variations, and annual motion effects. Position effects are accounted for through Martian latitude and longitude. Expected values of mass density, temperature, scale height, and the estimated standard deviation of the mass density are provided. An example of the use of the model in selecting an orbit for the Mars Geochemical/Climatology Orbiter is given.
Directory of Open Access Journals (Sweden)
Henry de-Graft Acquah
2013-01-01
Full Text Available Information Criteria provides an attractive basis for selecting the best model from a set of competing asymmetric price transmission models or theories. However, little is understood about the sensitivity of the model selection methods to model complexity. This study therefore fits competing asymmetric price transmission models that differ in complexity to simulated data and evaluates the ability of the model selection methods to recover the true model. The results of Monte Carlo experimentation suggest that in general BIC, CAIC and DIC were superior to AIC when the true data generating process was the standard error correction model, whereas AIC was more successful when the true model was the complex error correction model. It is also shown that the model selection methods performed better in large samples for a complex asymmetric data generating process than with a standard asymmetric data generating process. Except for complex models, AIC's performance did not make substantial gains in recovery rates as sample size increased. The research findings demonstrate the influence of model complexity in asymmetric price transmission model comparison and selection.
Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P
2010-06-01
The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it
Mixed Lp Estimators Variety for Model Order Reduction in Control Oriented System Identification
Directory of Open Access Journals (Sweden)
Christophe Corbier
2015-01-01
Full Text Available A new family of MLE type Lp estimators for model order reduction in dynamical systems identification is presented in this paper. A family of Lp distributions proposed in this work combines Lp2 (1
A Comparison of Reduced Order Modeling Techniques Used in Dynamic Substructuring [PowerPoint
Energy Technology Data Exchange (ETDEWEB)
Roettgen, Dan [Wisc; Seeger, Benjamin [Stuttgart; Tai, Wei Che [Washington; Baek, Seunghun [Michigan; Dossogne, Tilan [Liege; Allen, Matthew S [Wisc; Kuether, Robert J.; Brake, Matthew Robert; Mayes, Randall L.
2016-01-01
Experimental dynamic substructuring is a means whereby a mathematical model for a substructure can be obtained experimentally and then coupled to a model for the rest of the assembly to predict the response. Recently, various methods have been proposed that use a transmission simulator to overcome sensitivity to measurement errors and to exercise the interface between the substructures; including the Craig-Bampton, Dual Craig-Bampton, and Craig-Mayes methods. This work compares the advantages and disadvantages of these reduced order modeling strategies for two dynamic substructuring problems. The methods are first used on an analytical beam model to validate the methodologies. Then they are used to obtain an experimental model for structure consisting of a cylinder with several components inside connected to the outside case by foam with uncertain properties. This represents an exceedingly difficult structure to model and so experimental substructuring could be an attractive way to obtain a model of the system.
A Comparison of Reduced Order Modeling Techniques Used in Dynamic Substructuring.
Energy Technology Data Exchange (ETDEWEB)
Roettgen, Dan; Seegar, Ben; Tai, Wei Che; Baek, Seunghun; Dossogne, Tilan; Allen, Matthew; Kuether, Robert J.; Brake, Matthew Robert; Mayes, Randall L.
2015-10-01
Experimental dynamic substructuring is a means whereby a mathematical model for a substructure can be obtained experimentally and then coupled to a model for the rest of the assembly to predict the response. Recently, various methods have been proposed that use a transmission simulator to overcome sensitivity to measurement errors and to exercise the interface between the substructures; including the Craig-Bampton, Dual Craig-Bampton, and Craig-Mayes methods. This work compares the advantages and disadvantages of these reduced order modeling strategies for two dynamic substructuring problems. The methods are first used on an analytical beam model to validate the methodologies. Then they are used to obtain an experimental model for structure consisting of a cylinder with several components inside connected to the outside case by foam with uncertain properties. This represents an exceedingly difficult structure to model and so experimental substructuring could be an attractive way to obtain a model of the system.
Hastatic order in the two-channel Kondo-Heisenberg model
Zhang, Guanghua; Flint, Rebecca
Understanding Kondo physics in materials with non-Kramers doublets requires understanding the two channel Kondo effect, as valence fluctuations are from a non-Kramers doublet ground state to an excited Kramers doublet. Here, the development of a heavy Fermi liquid requires a channel symmetry breaking hybridization. This order, which breaks both single and double time-reversal symmetry was recently introduced as hastatic order. Here we employ an SU(N) fermionic mean-field treatment of the two-channel Kondo-Heisenberg model on a square lattice to explore properties of hastatic order and particularly the competition between the hastatic order and magnetism, as embodied by a spin liquid phase in our model. For simplicity, only a momentum independent hybrization between the non-Kramers f2 states and conduction electrons is considered. Upon varying the RKKY coupling and conduction electron density, we find both uniform and staggered [ Q = (π , π) ] hastatic order, in addition to the spin liquid phase, with metal-insulator transitions, including Lifshitz transitions inside the staggered phase. As the band degeneracy of the conduction electron bands is broken, the uniform hastatic order is partially suppressed compared to the staggered phase.
Broken selection rule in the quantum Rabi model.
Forn-Díaz, P; Romero, G; Harmans, C J P M; Solano, E; Mooij, J E
2016-06-07
Understanding the interaction between light and matter is very relevant for fundamental studies of quantum electrodynamics and for the development of quantum technologies. The quantum Rabi model captures the physics of a single atom interacting with a single photon at all regimes of coupling strength. We report the spectroscopic observation of a resonant transition that breaks a selection rule in the quantum Rabi model, implemented using an LC resonator and an artificial atom, a superconducting qubit. The eigenstates of the system consist of a superposition of bare qubit-resonator states with a relative sign. When the qubit-resonator coupling strength is negligible compared to their own frequencies, the matrix element between excited eigenstates of different sign is very small in presence of a resonator drive, establishing a sign-preserving selection rule. Here, our qubit-resonator system operates in the ultrastrong coupling regime, where the coupling strength is 10% of the resonator frequency, allowing sign-changing transitions to be activated and, therefore, detected. This work shows that sign-changing transitions are an unambiguous, distinctive signature of systems operating in the ultrastrong coupling regime of the quantum Rabi model. These results pave the way to further studies of sign-preserving selection rules in multiqubit and multiphoton models.
Models of cultural niche construction with selection and assortative mating.
Creanza, Nicole; Fogarty, Laurel; Feldman, Marcus W
2012-01-01
Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Models of cultural niche construction with selection and assortative mating.
Directory of Open Access Journals (Sweden)
Nicole Creanza
Full Text Available Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.
Model selection for integrated pest management with stochasticity.
Akman, Olcay; Comar, Timothy D; Hrozencik, Daniel
2018-04-07
In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model. Copyright © 2017 Elsevier Ltd. All rights reserved.
Selection of Models for Ingestion Pathway and Relocation Radii Determination
International Nuclear Information System (INIS)
Blanchard, A.
1998-01-01
The distance at which intermediate phase protective actions (such as food interdiction and relocation) may be needed following postulated accidents at three Savannah River Site nonreactor nuclear facilities will be determined by modeling. The criteria used to select dispersion/deposition models are presented. Several models were considered, including ARAC, MACCS, HOTSPOT, WINDS (coupled with PUFF-PLUME), and UFOTRI. Although ARAC and WINDS are expected to provide more accurate modeling of atmospheric transport following an actual release, analyses consistent with regulatory guidance for planning purposes may be accomplished with comparatively simple dispersion models such as HOTSPOT and UFOTRI. A recommendation is made to use HOTSPOT for non-tritium facilities and UFOTRI for tritium facilities
2015-03-16
with logical rules to simulate an archetype biochemical network, the human coagulation cascade. The model consisted of five differential equations...coagulation system. Coagulation is an archetype proteolytic cascade involving both positive and negative feedback [10–12]. Coagulation is mediated by a...purely ODE models in the literature . We estimated the model parameters from in vitro extrinsic coagulation data sets, in the presence of ATIII, with and
Liu, Qun; Jiang, Daqing; Hayat, Tasawar; Ahmad, Bashir
2017-09-01
In this paper, we investigate two stochastic SIR epidemic models with higher order perturbation. For the nonautonomous periodic case of the model, by using Has'minskii's theory of periodic solution, we show that the system has at least one nontrivial positive T-periodic solution. For the system disturbed by both the white noise and telephone noise, we establish sufficient conditions for positive recurrence and the existence of ergodic stationary distribution of the positive solution.
Response errors explain the failure of independent-channels models of perception of temporal order
Directory of Open Access Journals (Sweden)
Miguel A García-Pérez
2012-04-01
Full Text Available Independent-channels models of perception of temporal order (also referred to as threshold models or perceptual latency models have been ruled out because two formal properties of these models (monotonicity and parallelism are not borne out by data from ternary tasks in which observers must judge whether stimulus A was presented before, after, or simultaneously with stimulus B. These models generally assume that observed responses are authentic indicators of unobservable judgments, but blinks, lapses of attention, or errors in pressing the response keys (maybe, but not only, motivated by time pressure when reaction times are being recorded may make observers misreport their judgments or simply guess a response. We present an extension of independent-channels models that considers response errors and we show that the model produces psychometric functions that do not satisfy monotonicity and parallelism. The model is illustrated by fitting it to data from a published study in which the ternary task was used. The fitted functions describe very accurately the absence of monotonicity and parallelism shown by the data. These characteristics of empirical data are thus consistent with independent-channels models when response errors are taken into consideration. The implications of these results for the analysis and interpretation of temporal-order judgment data are discussed.
Response errors explain the failure of independent-channels models of perception of temporal order.
García-Pérez, Miguel A; Alcalá-Quintana, Rocío
2012-01-01
Independent-channels models of perception of temporal order (also referred to as threshold models or perceptual latency models) have been ruled out because two formal properties of these models (monotonicity and parallelism) are not borne out by data from ternary tasks in which observers must judge whether stimulus A was presented before, after, or simultaneously with stimulus B. These models generally assume that observed responses are authentic indicators of unobservable judgments, but blinks, lapses of attention, or errors in pressing the response keys (maybe, but not only, motivated by time pressure when reaction times are being recorded) may make observers misreport their judgments or simply guess a response. We present an extension of independent-channels models that considers response errors and we show that the model produces psychometric functions that do not satisfy monotonicity and parallelism. The model is illustrated by fitting it to data from a published study in which the ternary task was used. The fitted functions describe very accurately the absence of monotonicity and parallelism shown by the data. These characteristics of empirical data are thus consistent with independent-channels models when response errors are taken into consideration. The implications of these results for the analysis and interpretation of temporal order judgment data are discussed.
Linear stability analysis of first-order delayed car-following models on a ring
Lassarre, Sylvain; Roussignol, Michel; Tordeux, Antoine
2012-09-01
The evolution of a line of vehicles on a ring is modeled by means of first-order car-following models. Three generic models describe the speed of a vehicle as a function of the spacing ahead and the speed of the predecessor. The first model is a basic one with no delay. The second is a delayed car-following model with a strictly positive parameter for the driver and vehicle reaction time. The last model includes a reaction time parameter with an anticipation process by which the delayed position of the predecessor is estimated. Explicit conditions for the linear stability of homogeneous configurations are calculated for each model. Two methods of calculus are compared: an exact one via Hopf bifurcations and an approximation by second-order models. The conditions describe stable areas for the parameters of the models that we interpret. The results notably show that the impact of the reaction time on the stability can be palliated by the anticipation process.
Numerical Model based Reliability Estimation of Selective Laser Melting Process
DEFF Research Database (Denmark)
Mohanty, Sankhya; Hattel, Jesper Henri
2014-01-01
Selective laser melting is developing into a standard manufacturing technology with applications in various sectors. However, the process is still far from being at par with conventional processes such as welding and casting, the primary reason of which is the unreliability of the process. While...... of the selective laser melting process. A validated 3D finite-volume alternating-direction-implicit numerical technique is used to model the selective laser melting process, and is calibrated against results from single track formation experiments. Correlation coefficients are determined for process input...... parameters such as laser power, speed, beam profile, etc. Subsequently, uncertainties in the processing parameters are utilized to predict a range for the various outputs, using a Monte Carlo method based uncertainty analysis methodology, and the reliability of the process is established....
Modelling Technical and Economic Parameters in Selection of Manufacturing Devices
Directory of Open Access Journals (Sweden)
Naqib Daneshjo
2017-11-01
Full Text Available Sustainable science and technology development is also conditioned by continuous development of means of production which have a key role in structure of each production system. Mechanical nature of the means of production is complemented by controlling and electronic devices in context of intelligent industry. A selection of production machines for a technological process or technological project has so far been practically resolved, often only intuitively. With regard to increasing intelligence, the number of variable parameters that have to be considered when choosing a production device is also increasing. It is necessary to use computing techniques and decision making methods according to heuristic methods and more precise methodological procedures during the selection. The authors present an innovative model for optimization of technical and economic parameters in the selection of manufacturing devices for industry 4.0.
Averaging principle for second-order approximation of heterogeneous models with homogeneous models.
Fibich, Gadi; Gavious, Arieh; Solan, Eilon
2012-11-27
Typically, models with a heterogeneous property are considerably harder to analyze than the corresponding homogeneous models, in which the heterogeneous property is replaced by its average value. In this study we show that any outcome of a heterogeneous model that satisfies the two properties of differentiability and symmetry is O(ε(2)) equivalent to the outcome of the corresponding homogeneous model, where ε is the level of heterogeneity. We then use this averaging principle to obtain new results in queuing theory, game theory (auctions), and social networks (marketing).
Directory of Open Access Journals (Sweden)
Branka Marasović
2009-03-01
Full Text Available In this paper we select an optimal portfolio on the Croatian capital market by using the multicriterial programming. In accordance with the modern portfolio theory maximisation of returns at minimal risk should be the investment goal of any successful investor. However, contrary to the expectations of the modern portfolio theory, the tests carried out on a number of financial markets reveal the existence of other indicators important in portfolio selection. Considering the importance of variables other than return and risk, selection of the optimal portfolio becomes a multicriterial problem which should be solved by using the appropriate techniques.In order to select an optimal portfolio, absolute values of criteria, like return, risk, price to earning value ratio (P/E, price to book value ratio (P/B and price to sale value ratio (P/S are included in our multicriterial model. However the problem might occur as the mean values of some criteria are significantly different for different sectors and because financial managers emphasize that comparison of the same criteria for different sectors could lead us to wrong conclusions. In the second part of the paper, relative values of previously stated criteria (in relation to mean value of sector are included in model for selecting optimal portfolio. Furthermore, the paper shows that if relative values of criteria are included in multicriterial model for selecting optimal portfolio, return in subsequent period is considerably higher than if absolute values of the same criteria were used.
Directory of Open Access Journals (Sweden)
Rytz Andreas
2002-06-01
Full Text Available Abstract Background The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. Results The gene selection model presented herein is based on the observation that: (1 variance of gene expression is a function of absolute expression; (2 one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3 this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SDwithin confidence level of 99.9%. Real time-PCR (RT-PCR analysis demonstrated 85.7% concordance with microarray data selected by the limit function. Conclusion The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence.
Spin model for nontrivial types of magnetic order in inverse-perovskite antiferromagnets
Mochizuki, Masahito; Kobayashi, Masaya; Okabe, Reoya; Yamamoto, Daisuke
2018-02-01
Nontrivial magnetic orders in the inverse-perovskite manganese nitrides are theoretically studied by constructing a classical spin model describing the magnetic anisotropy and frustrated exchange interactions inherent in specific crystal and electronic structures of these materials. With a replica-exchange Monte Carlo technique, a theoretical analysis of this model reproduces the experimentally observed triangular Γ5 g and Γ4 g spin-ordered patterns and the systematic evolution of magnetic orders. Our Rapid Communication solves a 40-year-old problem of nontrivial magnetism for the inverse-perovskite manganese nitrides and provides a firm basis for clarifying the magnetism-driven negative thermal expansion phenomenon discovered in this class of materials.
Ordering in the quenched two-dimensional axial next-nearest-neighbor Ising model
International Nuclear Information System (INIS)
Hassold, G.N.; Srolovitz, D.J.
1988-01-01
Monte Carlo simulations of ordering in the two-dimensional axial next-nearest-neighbor Ising model following a quench were performed using nonconserved dynamics for a wide range of frustration parameters, κ, and temperatures. It was found that in quenches from T>>T/sub c/ to T 1 2 kinetics. Similar results are found for quenches at κ≥1, where the ordered structure is striped. However, for 0 phase (i.e., striped phase). Quenches to higher temperatures show the presence of a finite glass-transition temperature. Discontinuous changes in the value of the frustration parameter from the ferromagnetic to the -phase region of the phase diagram at low temperature yields a phase change which occurs via classical nucleation and growth. A simple energetic or growth model is proposed which accounts for all of the temperatures at which the ordering kinetics undergoes transitions
Vascular stents: Coupling full 3-D with reduced-order structural models
International Nuclear Information System (INIS)
Avdeev, I; Shams, M
2010-01-01
Self-expanding nitinol stents are used to treat peripheral arterial disease. The peripheral arteries are subjected to a combination of mechanical forces such as compression, torsion, bending, and contraction. Most commercially available peripheral self-expanding stents are composed of a series of sub-millimeter V-shaped struts, which are laser-cut from a nitinol tube and surface-treated for better fatigue performance. The numerical stent models must accurately predict location and distribution of local stresses and strains caused by large arterial deformations. Full 3-D finite element non-linear analysis of an entire stent is computationally expensive to the point of being prohibitive, especially for longer stents. Reduced-order models based on beam or shell elements are fairly accurate in capturing global deformations, but are not very helpful in predicting stent failure. We propose a mixed approach that combines the full 3-D model and reduced-order models. Several global-local, full 3-D/reduced-order finite element models of a peripheral self-expanding stent were validated and compared with experimental data. The kinematic constraint method used to couple various elements together was found to be very efficient and easily applicable to commercial FEA codes. The proposed mixed models can be used to accurately predict stent failure based on realistic (patient-specific), non-linear kinematic behavior of peripheral arteries.
Vascular stents: Coupling full 3-D with reduced-order structural models
Avdeev, I.; Shams, M.
2010-06-01
Self-expanding nitinol stents are used to treat peripheral arterial disease. The peripheral arteries are subjected to a combination of mechanical forces such as compression, torsion, bending, and contraction. Most commercially available peripheral self-expanding stents are composed of a series of sub-millimeter V-shaped struts, which are laser-cut from a nitinol tube and surface-treated for better fatigue performance. The numerical stent models must accurately predict location and distribution of local stresses and strains caused by large arterial deformations. Full 3-D finite element non-linear analysis of an entire stent is computationally expensive to the point of being prohibitive, especially for longer stents. Reduced-order models based on beam or shell elements are fairly accurate in capturing global deformations, but are not very helpful in predicting stent failure. We propose a mixed approach that combines the full 3-D model and reduced-order models. Several global-local, full 3-D/reduced-order finite element models of a peripheral self-expanding stent were validated and compared with experimental data. The kinematic constraint method used to couple various elements together was found to be very efficient and easily applicable to commercial FEA codes. The proposed mixed models can be used to accurately predict stent failure based on realistic (patient-specific), non-linear kinematic behavior of peripheral arteries.
Using Count Data and Ordered Models in National Forest Recreation Demand Analysis
Simões, Paula; Barata, Eduardo; Cruz, Luis
2013-11-01
This research addresses the need to improve our knowledge on the demand for national forests for recreation and offers an in-depth data analysis supported by the complementary use of count data and ordered models. From a policy-making perspective, while count data models enable the estimation of monetary welfare measures, ordered models allow for the wider use of the database and provide a more flexible analysis of data. The main purpose of this article is to analyse the individual forest recreation demand and to derive a measure of its current use value. To allow a more complete analysis of the forest recreation demand structure the econometric approach supplements the use of count data models with ordered category models using data obtained by means of an on-site survey in the Bussaco National Forest (Portugal). Overall, both models reveal that travel cost and substitute prices are important explanatory variables, visits are a normal good and demographic variables seem to have no influence on demand. In particular, estimated price and income elasticities of demand are quite low. Accordingly, it is possible to argue that travel cost (price) in isolation may be expected to have a low impact on visitation levels.
Trimming a hazard logic tree with a new model-order-reduction technique
Porter, Keith; Field, Edward; Milner, Kevin R
2017-01-01
The size of the logic tree within the Uniform California Earthquake Rupture Forecast Version 3, Time-Dependent (UCERF3-TD) model can challenge risk analyses of large portfolios. An insurer or catastrophe risk modeler concerned with losses to a California portfolio might have to evaluate a portfolio 57,600 times to estimate risk in light of the hazard possibility space. Which branches of the logic tree matter most, and which can one ignore? We employed two model-order-reduction techniques to simplify the model. We sought a subset of parameters that must vary, and the specific fixed values for the remaining parameters, to produce approximately the same loss distribution as the original model. The techniques are (1) a tornado-diagram approach we employed previously for UCERF2, and (2) an apparently novel probabilistic sensitivity approach that seems better suited to functions of nominal random variables. The new approach produces a reduced-order model with only 60 of the original 57,600 leaves. One can use the results to reduce computational effort in loss analyses by orders of magnitude.
Vague Sets Security Measure for Steganographic System Based on High-Order Markov Model
Directory of Open Access Journals (Sweden)
Chun-Juan Ouyang
2017-01-01
Full Text Available Security measure is of great importance in both steganography and steganalysis. Considering that statistical feature perturbations caused by steganography in an image are always nondeterministic and that an image is considered nonstationary, in this paper, the steganography is regarded as a fuzzy process. Here a steganographic security measure is proposed. This security measure evaluates the similarity between two vague sets of cover images and stego images in terms of n-order Markov chain to capture the interpixel correlation. The new security measure has proven to have the properties of boundedness, commutativity, and unity. Furthermore, the security measures of zero order, first order, second order, third order, and so forth are obtained by adjusting the order value of n-order Markov chain. Experimental results indicate that the larger n is, the better the measuring ability of the proposed security measure will be. The proposed security measure is more sensitive than other security measures defined under a deterministic distribution model, when the embedding is low. It is expected to provide a helpful guidance for designing secure steganographic algorithms or reliable steganalytic methods.
Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection
DEFF Research Database (Denmark)
Bork, Lasse; Møller, Stig Vinther
2015-01-01
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia...
Partially ordered mixed hidden Markov model for the disablement process of older adults.
Ip, Edward H; Zhang, Qiang; Rejeski, W Jack; Harris, Tamara B; Kritchevsky, Stephen
2013-06-01
At both the individual and societal levels, the health and economic burden of disability in older adults is enormous in developed countries, including the U.S. Recent studies have revealed that the disablement process in older adults often comprises episodic periods of impaired functioning and periods that are relatively free of disability, amid a secular and natural trend of decline in functioning. Rather than an irreversible, progressive event that is analogous to a chronic disease, disability is better conceptualized and mathematically modeled as states that do not necessarily follow a strict linear order of good-to-bad. Statistical tools, including Markov models, which allow bidirectional transition between states, and random effects models, which allow individual-specific rate of secular decline, are pertinent. In this paper, we propose a mixed effects, multivariate, hidden Markov model to handle partially ordered disability states. The model generalizes the continuation ratio model for ordinal data in the generalized linear model literature and provides a formal framework for testing the effects of risk factors and/or an intervention on the transitions between different disability states. Under a generalization of the proportional odds ratio assumption, the proposed model circumvents the problem of a potentially large number of parameters when the number of states and the number of covariates are substantial. We describe a maximum likelihood method for estimating the partially ordered, mixed effects model and show how the model can be applied to a longitudinal data set that consists of N = 2,903 older adults followed for 10 years in the Health Aging and Body Composition Study. We further statistically test the effects of various risk factors upon the probabilities of transition into various severe disability states. The result can be used to inform geriatric and public health science researchers who study the disablement process.
Directory of Open Access Journals (Sweden)
Kang-Wook Lee
2017-05-01
Full Text Available An important issue for international businesses and academia is selecting countries in which to expand in order to achieve entrepreneurial sustainability. This study develops a country selection model for sustainable construction businesses using both objective and subjective information. The objective information consists of 14 variables related to country risk and project performance in 32 countries over 25 years. This hybrid model applies subjective weighting from industrial experts to objective information using a fuzzy LinPreRa-based Analytic Hierarchy Process. The hybrid model yields a more accurate country selection compared to a purely objective information-based model in experienced countries. Interestingly, the hybrid model provides some different predictions with only subjective opinions in unexperienced countries, which implies that expert opinion is not always reliable. In addition, feedback from five experts in top international companies is used to validate the model’s completeness, effectiveness, generality, and applicability. The model is expected to aid decision makers in selecting better candidate countries that lead to sustainable business success.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Directory of Open Access Journals (Sweden)
Jun-He Yang
2017-01-01
Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
Interacting gaps model, dynamics of order book, and stock-market fluctuations
Czech Academy of Sciences Publication Activity Database
Svorenčík, A.; Slanina, František
2007-01-01
Roč. 57, - (2007), s. 453-462 ISSN 1434-6028 R&D Projects: GA MŠk 1P04OCP10.001 Institutional research plan: CEZ:AV0Z10100520 Keywords : interacting gaps model * dynamics of order book * stock - market fluctuations Subject RIV: BE - Theoretical Physics Impact factor: 1.356, year: 2007
Compound waves in a higher order nonlinear model of thermoviscous fluids
DEFF Research Database (Denmark)
Rønne Rasmussen, Anders; Sørensen, Mads Peter; Gaididei, Yuri B.
2016-01-01
A generalized traveling wave ansatz is used to investigate compound shock waves in a higher order nonlinear model of a thermoviscous fluid. The fluid velocity potential is written as a traveling wave plus a linear function of space and time. The latter offers the possibility of predicting...
Algebraic Specifications, Higher-order Types and Set-theoretic Models
DEFF Research Database (Denmark)
Kirchner, Hélène; Mosses, Peter David
2001-01-01
, and power-sets. This paper presents a simple framework for algebraic specifications with higher-order types and set-theoretic models. It may be regarded as the basis for a Horn-clause approximation to the Z framework, and has the advantage of being amenable to prototyping and automated reasoning. Standard...
Effect of random field disorder on the first order transition in p-spin interaction model
Sumedha; Singh, Sushant K.
2016-01-01
We study the random field p-spin model with Ising spins on a fully connected graph using the theory of large deviations in this paper. This is a good model to study the effect of quenched random field on systems which have a sharp first order transition in the pure state. For p = 2, the phase-diagram of the model, for bimodal distribution of the random field, has been well studied and is known to undergo a continuous transition for lower values of the random field (h) and a first order transition beyond a threshold, htp(≈ 0.439) . We find the phase diagram of the model, for all p ≥ 2, with bimodal random field distribution, using large deviation techniques. We also look at the fluctuations in the system by calculating the magnetic susceptibility. For p = 2, beyond the tricritical point in the regime of first order transition, we find that for htp ho = 1 / p!), the system does not show ferromagnetic order even at zero temperature. We find that the magnetic susceptibility for p ≥ 3 is discontinuous at the transition point for h
Probabilistic modelling of combined sewer overflow using the First Order Reliability Method
DEFF Research Database (Denmark)
Thorndahl, Søren; Schaarup-Jensen, Kjeld; Jensen, Jacob Birk
2007-01-01
uncertainties on an application of the commercial urban drainage model MOUSE combined with the probabilistic First Order Reliability Method (FORM). Applying statistical characteristics on several years of rainfall, it is possible to derive a parameterization of the rainfall input and the failure probability...
Numerical computation of the optimal control model of higher-order ...
African Journals Online (AJOL)
The paper implemented the optimal control problem of higher-order nondispersive wave. The Extended Conjugate Gradient Method [1], was used to compute the optimal values of the control and state variables of the model while the analytical expressions of the state and control variables generated the analytical values.
Saragih, Sahat; Napitupulu, E. Elvis; Fauzi, Amin
2017-01-01
This research aims to develop a student-centered learning model based on local culture and instrument of mathematical higher order thinking of junior high school students in the frame of the 2013-Curriculum in North Sumatra, Indonesia. The subjects of the research are seventh graders which are taken proportionally random consisted of three public…