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.
Second-order model selection in mixture experiments
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Redgate, P.E.; Piepel, G.F.; Hrma, P.R.
1992-07-01
Full second-order models for q-component mixture experiments contain q(q+l)/2 terms, which increases rapidly as q increases. Fitting full second-order models for larger q may involve problems with ill-conditioning and overfitting. These problems can be remedied by transforming the mixture components and/or fitting reduced forms of the full second-order mixture model. Various component transformation and model reduction approaches are discussed. Data from a 10-component nuclear waste glass study are used to illustrate ill-conditioning and overfitting problems that can be encountered when fitting a full second-order mixture model. Component transformation, model term selection, and model evaluation/validation techniques are discussed and illustrated for the waste glass example.
Bayesian model evidence for order selection and correlation testing.
Johnston, Leigh A; Mareels, Iven M Y; Egan, Gary F
2011-01-01
Model selection is a critical component of data analysis procedures, and is particularly difficult for small numbers of observations such as is typical of functional MRI datasets. In this paper we derive two Bayesian evidence-based model selection procedures that exploit the existence of an analytic form for the linear Gaussian model class. Firstly, an evidence information criterion is proposed as a model order selection procedure for auto-regressive models, outperforming the commonly employed Akaike and Bayesian information criteria in simulated data. Secondly, an evidence-based method for testing change in linear correlation between datasets is proposed, which is demonstrated to outperform both the traditional statistical test of the null hypothesis of no correlation change and the likelihood ratio test.
Model Order Selection Rules for Covariance Structure Classification in Radar
Carotenuto, Vincenzo; De Maio, Antonio; Orlando, Danilo; Stoica, Petre
2017-10-01
The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.
Model Order Selection in Multi-baseline Interferometric Radar Systems
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Fulvio Gini
2005-12-01
Full Text Available Synthetic aperture radar interferometry (InSAR is a powerful technique to derive three-dimensional terrain images. Interest is growing in exploiting the advanced multi-baseline mode of InSAR to solve layover effects from complex orography, which generate reception of unexpected multicomponent signals that degrade imagery of both terrain radar reflectivity and height. This work addresses a few problems related to the implementation into interferometric processing of nonlinear algorithms for estimating the number of signal components, including a system trade-off analysis. Performance of various eigenvalues-based information-theoretic criteria (ITC algorithms is numerically investigated under some realistic conditions. In particular, speckle effects from surface and volume scattering are taken into account as multiplicative noise in the signal model. Robustness to leakage of signal power into the noise eigenvalues and operation with a small number of looks are investigated. The issue of baseline optimization for detection is also addressed. The use of diagonally loaded ITC methods is then proposed as a tool for robust operation in the presence of speckle decorrelation. Finally, case studies of a nonuniform array are studied and recommendations for a proper combination of ITC methods and system configuration are given.
A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios
Yue, Wei; Wang, Yuping
2017-01-01
Due to the important effect of the higher order moments to portfolio returns, the aim of this paper is to make use of the third and fourth moments for fuzzy multi-objective portfolio selection model. Firstly, in order to overcome the low diversity of the obtained solution set and lead to corner solutions for the conventional higher moment portfolio selection models, a new entropy function based on Minkowski measure is proposed as a new objective function and a novel fuzzy multi-objective weighted possibilistic higher order moment portfolio model is presented. Secondly, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Thirdly, several portfolio performance evaluation techniques are used to evaluate the performance of the portfolio models. Finally, some experiments are conducted by using the data of Shanghai Stock Exchange and the results indicate the efficiency and effectiveness of the proposed model and algorithm.
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.
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Zhangyi He
2017-07-01
Full Text Available 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.
Selection of higher order regression models in the analysis of multi-factorial transcription data.
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Olivia Prazeres da Costa
Full Text Available INTRODUCTION: Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control, and treatment/non-treatment with interferon-γ. RESULTS: We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction, alleviating (co-occurring effects are weaker than expected from the single effects, or aggravating (stronger than expected. We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. CONCLUSIONS: We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.
Vendor selection and order allocation using an integrated fuzzy mathematical programming model
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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.
Customer Order Decoupling Point Selection Model in Mass Customization Based on MAS
Institute of Scientific and Technical Information of China (English)
XU Xuanguo; LI Xiangyang
2006-01-01
Mass customization relates to the ability of providing individually designed products or services to customer with high process flexibility or integration. Literatures on mass customization have been focused on mechanism of MC, but little on customer order decoupling point selection. The aim of this paper is to present a model for customer order decoupling point selection of domain knowledge interactions between enterprises and customers in mass customization. Based on the analysis of other researchers' achievements combining the demand problems of customer and enterprise, a model of group decision for customer order decoupling point selection is constructed based on quality function deployment and multi-agent system. Considering relatively the decision makers of independent functional departments as independent decision agents, a decision agent set is added as the third dimensionality to house of quality, the cubic quality function deployment is formed. The decision-making can be consisted of two procedures: the first one is to build each plane house of quality in various functional departments to express each opinions; the other is to evaluate and gather the foregoing sub-decisions by a new plane quality function deployment. Thus, department decision-making can well use its domain knowledge by ontology, and total decision-making can keep simple by avoiding too many customer requirements.
Model Order Selection for Short Data: An Exponential Fitting Test (EFT
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Martin Haardt
2007-01-01
Full Text Available High-resolution methods for estimating signal processing parameters such as bearing angles in array processing or frequencies in spectral analysis may be hampered by the model order if poorly selected. As classical model order selection methods fail when the number of snapshots available is small, this paper proposes a method for noncoherent sources, which continues to work under such conditions, while maintaining low computational complexity. For white Gaussian noise and short data we show that the profile of the ordered noise eigenvalues is seen to approximately fit an exponential law. This fact is used to provide a recursive algorithm which detects a mismatch between the observed eigenvalue profile and the theoretical noise-only eigenvalue profile, as such a mismatch indicates the presence of a source. Moreover this proposed method allows the probability of false alarm to be controlled and predefined, which is a crucial point for systems such as RADARs. Results of simulations are provided in order to show the capabilities of the algorithm.
Dose-Response Modeling Under Simple Order Restrictions Using Bayesian Variable Selection Methods
Otava, Martin; Shkedy, Ziv; Lin, Dan; Goehlmann, Hinrich W. H.; Bijnens, Luc; Talloen, Willem; Kasim, Adetayo
2014-01-01
Bayesian modeling of dose–response data offers the possibility to establish the relationship between a clinical or a genomic response and increasing doses of a therapeutic compound and to determine the nature of the relationship wherever it exists. In this article, we focus on an order-restricted one-way ANOVA model which can be used to test the null hypothesis of no dose effect against an ordered alternative. Within the framework of the dose–response modeling, a model uncertainty can be addr...
Quilty, John; Adamowski, Jan; Khalil, Bahaa; Rathinasamy, Maheswaran
2016-03-01
The input variable selection problem has recently garnered much interest in the time series modeling community, especially within water resources applications, demonstrating that information theoretic (nonlinear)-based input variable selection algorithms such as partial mutual information (PMI) selection (PMIS) provide an improved representation of the modeled process when compared to linear alternatives such as partial correlation input selection (PCIS). PMIS is a popular algorithm for water resources modeling problems considering nonlinear input variable selection; however, this method requires the specification of two nonlinear regression models, each with parametric settings that greatly influence the selected input variables. Other attempts to develop input variable selection methods using conditional mutual information (CMI) (an analog to PMI) have been formulated under different parametric pretenses such as k nearest-neighbor (KNN) statistics or kernel density estimates (KDE). In this paper, we introduce a new input variable selection method based on CMI that uses a nonparametric multivariate continuous probability estimator based on Edgeworth approximations (EA). We improve the EA method by considering the uncertainty in the input variable selection procedure by introducing a bootstrap resampling procedure that uses rank statistics to order the selected input sets; we name our proposed method bootstrap rank-ordered CMI (broCMI). We demonstrate the superior performance of broCMI when compared to CMI-based alternatives (EA, KDE, and KNN), PMIS, and PCIS input variable selection algorithms on a set of seven synthetic test problems and a real-world urban water demand (UWD) forecasting experiment in Ottawa, Canada.
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Szymszal J.
2013-09-01
Full Text Available It has been found that the area where one can look for significant reserves in the procurement logistics is a rational management of the stock of raw materials. Currently, the main purpose of projects which increase the efficiency of inventory management is to rationalise all the activities in this area, taking into account and minimising at the same time the total inventory costs. The paper presents a method for optimising the inventory level of raw materials under a foundry plant conditions using two different control models. The first model is based on the estimate of an optimal level of the minimum emergency stock of raw materials, giving information about the need for an order to be placed immediately and about the optimal size of consignments ordered after the minimum emergency level has occurred. The second model is based on the estimate of a maximum inventory level of raw materials and an optimal order cycle. Optimisation of the presented models has been based on the previously done selection and use of rational methods for forecasting the time series of the delivery of a chosen auxiliary material (ceramic filters to a casting plant, including forecasting a mean size of the delivered batch of products and its standard deviation.
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
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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.
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Masoud parsaei
2014-06-01
Full Text Available The way orders are accepted or rejected is the most important factor in customer satisfaction and success of make-to-order systems. The incoming orders to such organizations have certain delivery date in which the customer expects the order to be fulfilled and delivered. In some cases, unexpectedly increased orders exceed the existing capacity for on time fulfillment. In addition to rejection of order, as a typical choice, other options like outsourcing and capacity expansion are available to compensate for capacity shortage and deliver incoming orders according to schedule. However, each of the proposed options is superior in one or more criteria and so selecting the best one is not simply possible. The main goal of this study is to provide managers with a comprehensive, systematic, and applicable approach to evaluate and select the best of the existing options. For this purpose, a model comprised of some multi-criteria techniques is delivered. Our proposed model is a blend of FAHP and FTOPSIS methods. In this model, FAHP is first used to determine the weight of criteria and then Fuzzy-TOPSIS (FTOPSIS is employed to rank the options. Finally, the proposed model is applied on an actual case to assess and examine its efficiency.
Goury, Olivier; Amsallem, David; Bordas, Stéphane Pierre Alain; Liu, Wing Kam; Kerfriden, Pierre
2016-08-01
In this paper, we present new reliable model order reduction strategies for computational micromechanics. The difficulties rely mainly upon the high dimensionality of the parameter space represented by any load path applied onto the representative volume element. We take special care of the challenge of selecting an exhaustive snapshot set. This is treated by first using a random sampling of energy dissipating load paths and then in a more advanced way using Bayesian optimization associated with an interlocked division of the parameter space. Results show that we can insure the selection of an exhaustive snapshot set from which a reliable reduced-order model can be built.
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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.
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 bel
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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.
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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.
Argyros, S A; Tyros, K
2012-01-01
We introduce the higher order spreading models associated to a Banach space $X$. Their definition is based on $\\ff$-sequences $(x_s)_{s\\in\\ff}$ with $\\ff$ a regular thin family and the plegma families. We show that the higher order spreading models of a Banach space $X$ form an increasing transfinite hierarchy $(\\mathcal{SM}_\\xi(X))_{\\xi<\\omega_1}$. Each $\\mathcal{SM}_\\xi (X)$ contains all spreading models generated by $\\ff$-sequences $(x_s)_{s\\in\\ff}$ with order of $\\ff$ equal to $\\xi$. We also provide a study of the fundamental properties of the hierarchy.
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Johnny Espin
2015-06-01
Full Text Available It is known, though not commonly, that one can describe fermions using a second order in derivatives Lagrangian instead of the first order Dirac one. In this description the propagator is scalar, and the complexity is shifted to the vertex, which contains a derivative operator. In this paper we rewrite the Lagrangian of the fermionic sector of the Standard Model in such second order form. The new Lagrangian is extremely compact, and is obtained from the usual first order Lagrangian by integrating out all primed (or dotted 2-component spinors. It thus contains just half of the 2-component spinors that appear in the usual Lagrangian, which suggests a new perspective on unification. We sketch a natural in this framework SU(2×SU(4⊂SO(9 unified theory.
Determining Reduced Order Models for Optimal Stochastic Reduced Order Models
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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.
Thibes, Ronaldo
2016-01-01
We perform the canonical and path integral quantizations of a lower-order derivatives model describing Podolsky's generalized electrodynamics. The physical content of the model shows an auxiliary massive vector field coupled to the usual electromagnetic field. The equivalence with Podolsky's original model is studied at classical and quantum levels. Concerning the dynamical time evolution we obtain a theory with two first-class and two second-class constraints in phase space. We calculate explicitly the corresponding Dirac brackets involving both vector fields. We use the Senjanovic procedure to implement the second-class constraints and the Batalin-Fradkin-Vilkovisky path integral quantization scheme to deal with the symmetries generated by the first-class constraints. The physical interpretation of the results turns out to be simpler due to the reduced derivatives order permeating the equations of motion, Dirac brackets and effective action.
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...
Regularized Reduced Order Models
Wells, David; Xie, Xuping; Iliescu, Traian
2015-01-01
This paper puts forth a regularization approach for the stabilization of proper orthogonal decomposition (POD) reduced order models (ROMs) for the numerical simulation of realistic flows. Two regularized ROMs (Reg-ROMs) are proposed: the Leray ROM (L-ROM) and the evolve-then-filter ROM (EF-ROM). These new Reg-ROMs use spatial filtering to smooth (regularize) various terms in the ROMs. Two spatial filters are used: a POD projection onto a POD subspace (Proj) and a new POD differential filter (DF). The four Reg-ROM/filter combinations are tested in the numerical simulation of the one-dimensional Burgers equation with a small diffusion coefficient and the three-dimensional flow past a circular cylinder at a low Reynolds number (Re = 100). Overall, the most accurate Reg-ROM/filter combination is EF-ROM-DF. Furthermore, the DF generally yields better results than Proj. Finally, the four Reg-ROM/filter combinations are computationally efficient and generally more accurate than the standard Galerkin ROM.
Model Selection for Geostatistical Models
Energy Technology Data Exchange (ETDEWEB)
Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.
2006-02-01
We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.
Join Order Selection - Good Enough is Easy
Waas, F.; Pellenkoft, A.J.
2000-01-01
Uniform sampling of join orders is known to be a competitive alternative to transformation-based optimization techniques. However, uniformity of the sampling process is difficult to establish and only for a restricted class of join queries techniques are known. In this paper, we investigate non-un
Ordered phases of diblock copolymers in selective solvent.
Grason, Gregory M
2007-03-21
The authors propose a mean-field model to explore the equilibrium coupling between micelle aggregation and lattice choice in neutral copolymer and selective solvent mixtures. They find both thermotropic and lyotropic transitions from face-centered cubic to body-centered cubic ordered phases of spherical micelles, in agreement with experimental observations of these systems over a broad range of conditions. The stability of the nonclosed packed phase can be attributed to two physical mechanisms: the large entropy of lattice phonons near crystal melting and the preference of the intermicelle repulsions for the body-centered cubic structure when the lattice becomes sufficiently dense at higher solution concentrations. Both mechanisms are controlled by the decrease of micelle aggregation and subsequent increase of lattice density as solvent selectivity is reduced. These results shed new light on the relationship between micelle structure--"crewcut" or "hairy"--and long-range order in micelle solutions.
Approximate Deconvolution Reduced Order Modeling
Xie, Xuping; Wang, Zhu; Iliescu, Traian
2015-01-01
This paper proposes a large eddy simulation reduced order model(LES-ROM) framework for the numerical simulation of realistic flows. In this LES-ROM framework, the proper orthogonal decomposition(POD) is used to define the ROM basis and a POD differential filter is used to define the large ROM structures. An approximate deconvolution(AD) approach is used to solve the ROM closure problem and develop a new AD-ROM. This AD-ROM is tested in the numerical simulation of the one-dimensional Burgers equation with a small diffusion coefficient(10^{-3})
2006-05-01
interests include feature selection, statistical learning, multivariate statistics, market research, and classification. He may be contacted at...current youth market , and reducing barriers to Army enlistment. Part of the Army Recruiting Initiatives was the creation of a recruiter selection...Selection Model DevelPed by the Openuier Reseach Crate of E...lneSstm Erapseeeng Depce-teo, WViitd Ntt. siliec Academy, NW..t Point, 271 Weau/’itt 21M
ORDERED WEIGHTED AVERAGING AGGREGATION METHOD FOR PORTFOLIO SELECTION
Institute of Scientific and Technical Information of China (English)
LIU Shancun; QIU Wanhua
2004-01-01
Portfolio management is a typical decision making problem under incomplete,sometimes unknown, informationThis paper considers the portfolio selection problemsunder a general setting of uncertain states without probabilityThe investor's preferenceis based on his optimum degree about the nature, and his attitude can be described by anOrdered Weighted Averaging Aggregation functionWe construct the OWA portfolio selec-tion model, which is a nonlinear programming problemThe problem can be equivalentlytransformed into a mixed integer linear programmingA numerical example is given andthe solutions imply that the investor's strategies depend not only on his optimum degreebut also on his preference weight vectorThe general game-theoretical portfolio selectionmethod, max-min method and competitive ratio method are all the special settings of thismodel.
ICA Model Order Estimation Using Clustering Method
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P. Sovka
2007-12-01
Full Text Available In this paper a novel approach for independent component analysis (ICA model order estimation of movement electroencephalogram (EEG signals is described. The application is targeted to the brain-computer interface (BCI EEG preprocessing. The previous work has shown that it is possible to decompose EEG into movement-related and non-movement-related independent components (ICs. The selection of only movement related ICs might lead to BCI EEG classification score increasing. The real number of the independent sources in the brain is an important parameter of the preprocessing step. Previously, we used principal component analysis (PCA for estimation of the number of the independent sources. However, PCA estimates only the number of uncorrelated and not independent components ignoring the higher-order signal statistics. In this work, we use another approach - selection of highly correlated ICs from several ICA runs. The ICA model order estimation is done at significance level ÃŽÂ± = 0.05 and the model order is less or more dependent on ICA algorithm and its parameters.
Interference of selective higher-order modes in optical fibers
Institute of Scientific and Technical Information of China (English)
Li Enbang; Peng Gangding
2007-01-01
The interference of selective higher-order modes in optical fibers is investigated both theoretically and experimentally.It has been demonstrated that by coupling the LP01 mode in a step-index single-mode fiber(SMF)to the LPom modes in step-index muhimode fibers(MMFs)with different parameters,one can selectively generate higher-order modes and construct all-fiber interferometers.The research presented in this paper forms a basis of a new type of fiber devices with potential applications in fiber sensing,optical fiber communications,and optical signal processing.
Entropic criterion for model selection
Tseng, Chih-Yuan
2006-10-01
Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.
Orderability and continuous selections for Wijsman and Vietoris hyperspaces
Directory of Open Access Journals (Sweden)
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.
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...
Complexity regularized hydrological model selection
Pande, S.; Arkesteijn, L.; Bastidas, L.A.
2014-01-01
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.
Complexity regularized hydrological model selection
Pande, S.; Arkesteijn, L.; Bastidas, L.A.
2014-01-01
This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.
Individual Influence on Model Selection
Sterba, Sonya K.; Pek, Jolynn
2012-01-01
Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit…
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...
Birth order and selected work-related personality variables.
Phillips, A S; Bedeian, A G; Mossholder, K W; Touliatos, J
1988-12-01
A possible link between birth order and various individual characteristics (e. g., intelligence, potential eminence, need for achievement, sociability) has been suggested by personality theorists such as Adler for over a century. The present study examines whether birth order is associated with selected personality variables that may be related to various work outcomes. 3 of 7 hypotheses were supported and the effect sizes for these were small. Firstborns scored significantly higher than later borns on measures of dominance, good impression, and achievement via conformity. No differences between firstborns and later borns were found in managerial potential, work orientation, achievement via independence, and sociability. The study's sample consisted of 835 public, government, and industrial accountants responding to a national US survey of accounting professionals. The nature of the sample may have been partially responsible for the results obtained. Its homogeneity may have caused any birth order effects to wash out. It can be argued that successful membership in the accountancy profession requires internalization of a set of prescribed rules and standards. It may be that accountants as a group are locked in to a behavioral framework. Any differentiation would result from spurious interpersonal differences, not from predictable birth-order related characteristics. A final interpretation is that birth order effects are nonexistent or statistical artifacts. Given the present data and particularistic sample, however, the authors have insufficient information from which to draw such a conclusion.
Designing lipids for selective partitioning into liquid ordered membrane domains.
Momin, Noor; Lee, Stacey; Gadok, Avinash K; Busch, David J; Bachand, George D; Hayden, Carl C; Stachowiak, Jeanne C; Sasaki, Darryl Y
2015-04-28
Self-organization of lipid molecules into specific membrane phases is key to the development of hierarchical molecular assemblies that mimic cellular structures. While the packing interaction of the lipid tails should provide the major driving force to direct lipid partitioning to ordered or disordered membrane domains, numerous examples show that the headgroup and spacer play important but undefined roles. We report here the development of several new biotinylated lipids that examine the role of spacer chemistry and structure on membrane phase partitioning. The new lipids were prepared with varying lengths of low molecular weight polyethylene glycol (EGn) spacers to examine how spacer hydrophilicity and length influence their partitioning behavior following binding with FITC-labeled streptavidin in liquid ordered (Lo) and liquid disordered (Ld) phase coexisting membranes. Partitioning coefficients (Kp Lo/Ld) of the biotinylated lipids were determined using fluorescence measurements in studies with giant unilamellar vesicles (GUVs). Compared against DPPE-biotin, DPPE-cap-biotin, and DSPE-PEG2000-biotin lipids, the new dipalmityl-EGn-biotin lipids exhibited markedly enhanced partitioning into liquid ordered domains, achieving Kp of up to 7.3 with a decaethylene glycol spacer (DP-EG10-biotin). We further demonstrated biological relevance of the lipids with selective partitioning to lipid raft-like domains observed in giant plasma membrane vesicles (GPMVs) derived from mammalian cells. Our results found that the spacer group not only plays a pivotal role for designing lipids with phase selectivity but may also influence the structural order of the domain assemblies.
Fractional Order Models of Industrial Pneumatic Controllers
Directory of Open Access Journals (Sweden)
Abolhassan Razminia
2014-01-01
Full Text Available This paper addresses a new approach for modeling of versatile controllers in industrial automation and process control systems such as pneumatic controllers. Some fractional order dynamical models are developed for pressure and pneumatic systems with bellows-nozzle-flapper configuration. In the light of fractional calculus, a fractional order derivative-derivative (FrDD controller and integral-derivative (FrID are remodeled. Numerical simulations illustrate the application of the obtained theoretical results in simple examples.
Partial order of frustrated Potts model
Energy Technology Data Exchange (ETDEWEB)
Igarashi, Ryo [CCSE, Japan Atomic Energy Agency, Higashi-Ueno, Taito, Tokyo 110-0015 (Japan); Ogata, Masao, E-mail: igarashi.ryo@jaea.go.j [Deaprtment of Physics, University of Tokyo, Hongo, Bunkyo, Tokyo 133-0033 (Japan)
2010-01-01
We investigate a 4-state ferromagnetic Potts model with a special type of geometrical frustration on a three dimensional diamond lattice. We find that the model undergoes unconventional phase transition; half of the spins in the system order in a two dimensional hexagonal-sheet-like structure while the remaining half of the spins stay disordered. The ordered sheets and the disordered sheets stack one after another. We obtain fairly large residual entropy using the Wang-Landau Monte Carlo simulation.
Mathematical modelling of fractional order circuits
Moreles, Miguel Angel
2016-01-01
In this work a classical derivation of fractional order circuits models is presented. Generalized constitutive equations in terms of fractional Riemann-Liouville derivatives are introduced in the Maxwell's equations. Next the Kirchhoff voltage law is applied in a RCL circuit configuration. A fractional differential equation model is obtained with Caputo derivatives. Thus standard initial conditions apply.
Methods in Model Order Reduction (MOR) field
Institute of Scientific and Technical Information of China (English)
刘志超
2014-01-01
Nowadays, the modeling of systems may be quite large, even up to tens of thousands orders. In spite of the increasing computational powers, direct simulation of these large-scale systems may be impractical. Thus, to industry requirements, analytically tractable and computationally cheap models must be designed. This is the essence task of Model Order Reduction (MOR). This article describes the basics of MOR optimization, various way of designing MOR, and gives the conclusion about existing methods. In addition, it proposed some heuristic footpath.
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.
Launch vehicle selection model
Montoya, Alex J.
1990-01-01
Over the next 50 years, humans will be heading for the Moon and Mars to build scientific bases to gain further knowledge about the universe and to develop rewarding space activities. These large scale projects will last many years and will require large amounts of mass to be delivered to Low Earth Orbit (LEO). It will take a great deal of planning to complete these missions in an efficient manner. The planning of a future Heavy Lift Launch Vehicle (HLLV) will significantly impact the overall multi-year launching cost for the vehicle fleet depending upon when the HLLV will be ready for use. It is desirable to develop a model in which many trade studies can be performed. In one sample multi-year space program analysis, the total launch vehicle cost of implementing the program reduced from 50 percent to 25 percent. This indicates how critical it is to reduce space logistics costs. A linear programming model has been developed to answer such questions. The model is now in its second phase of development, and this paper will address the capabilities of the model and its intended uses. The main emphasis over the past year was to make the model user friendly and to incorporate additional realistic constraints that are difficult to represent mathematically. We have developed a methodology in which the user has to be knowledgeable about the mission model and the requirements of the payloads. We have found a representation that will cut down the solution space of the problem by inserting some preliminary tests to eliminate some infeasible vehicle solutions. The paper will address the handling of these additional constraints and the methodology for incorporating new costing information utilizing learning curve theory. The paper will review several test cases that will explore the preferred vehicle characteristics and the preferred period of construction, i.e., within the next decade, or in the first decade of the next century. Finally, the paper will explore the interaction
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.
A high-order electromagnetic gyrokinetic model
Miyato, N
2013-01-01
A high-order extension is presented for the electromagnetic gyrokinetic formulation in which the parallel canonical momentum is taken as one of phase space coordinates. The high-order displacement vector associated with the guiding-center transformation should be considered in the long wavelength regime. This yields addtional terms in the gyrokinetic Hamiltonian which lead to modifications to the gyrokinetic Poisson and Amp\\`ere equations. In addition, the high-order piece of the guiding-center transformation for the parallel canonical momentum should be also kept in the electromagnetic model. The high-order piece contains the Ba\\~nos drift effect and further modifies the gyrokinetic Amp\\`ere equation.
Model Selection Principles in Misspecified Models
Lv, Jinchi
2010-01-01
Model selection is of fundamental importance to high dimensional modeling featured in many contemporary applications. Classical principles of model selection include the Kullback-Leibler divergence principle and the Bayesian principle, which lead to the Akaike information criterion and Bayesian information criterion when models are correctly specified. Yet model misspecification is unavoidable when we have no knowledge of the true model or when we have the correct family of distributions but miss some true predictor. In this paper, we propose a family of semi-Bayesian principles for model selection in misspecified models, which combine the strengths of the two well-known principles. We derive asymptotic expansions of the semi-Bayesian principles in misspecified generalized linear models, which give the new semi-Bayesian information criteria (SIC). A specific form of SIC admits a natural decomposition into the negative maximum quasi-log-likelihood, a penalty on model dimensionality, and a penalty on model miss...
Bayesian Model Selection and Statistical Modeling
Ando, Tomohiro
2010-01-01
Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model. Realizing how crucial this issue is, many researchers and practitioners have been extensively investigating the Bayesian model selection problem. This book provides comprehensive explanations of the concepts and derivations of the Bayesian approach for model selection and related criteria, including the Bayes factor, the Bayesian information criterion (BIC), the generalized BIC, and the pseudo marginal lik
Order Parameters of the Dilute A Models
Warnaar, S O; Seaton, K A; Nienhuis, B
1993-01-01
The free energy and local height probabilities of the dilute A models with broken $\\Integer_2$ symmetry are calculated analytically using inversion and corner transfer matrix methods. These models possess four critical branches. The first two branches provide new realisations of the unitary minimal series and the other two branches give a direct product of this series with an Ising model. We identify the integrable perturbations which move the dilute A models away from the critical limit. Generalised order parameters are defined and their critical exponents extracted. The associated conformal weights are found to occur on the diagonal of the relevant Kac table. In an appropriate regime the dilute A$_3$ model lies in the universality class of the Ising model in a magnetic field. In this case we obtain the magnetic exponent $\\delta=15$ directly, without the use of scaling relations.
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.
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. ...
NEW METHOD FOR LOW ORDER SPECTRAL MODEL AND ITS APPLICATION
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In order to overcome the deficiency in classical method of low order spectral model, a new method for low order spectral model was advanced. Through calculating the multiple correlation coefficients between combinations of different functions and the recorded data under the least square criterion, the truncated functions which can mostly reflect the studied physical phenomenon were objectively distilled from these data. The new method overcomes the deficiency of artificially selecting the truncated functions in the classical low order spectral model. The new method being applied to study the inter-annual variation of summer atmospheric circulation over Northern Hemisphere, the truncated functions were obtained with the atmospheric circulation data of June 1994 and June 1998. The mechanisms for the two-summer atmospheric circulation variations over Northern Hemisphere were obtained with two-layer quasi-geostrophic baroclinic equation.
Introduction. Modelling natural action selection.
Prescott, Tony J; Bryson, Joanna J; Seth, Anil K
2007-09-29
Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder.
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,…
Modeling ability differentiation in the second-order factor model
Molenaar, D.; Dolan, C.V.; van der Maas, H.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
Bayesian Evidence and Model Selection
Knuth, Kevin H; Malakar, Nabin K; Mubeen, Asim M; Placek, Ben
2014-01-01
In this paper we review the concept of the Bayesian evidence and its application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Application to several practical examples within the context of signal processing are discussed.
Quantum selection of order in an XXZ antiferromagnet on a Kagome lattice.
Chernyshev, A L; Zhitomirsky, M E
2014-12-05
Selection of the ground state of the kagome-lattice XXZ antiferromagnet by quantum fluctuations is investigated by combining nonlinear spin-wave and real-space perturbation theories. The two methods unanimously favor q=0 over sqrt[3]×sqrt[3] magnetic order in a wide range of the anisotropy parameter 0≤Δ≲0.72. Both approaches are also in accord on the magnitude of the quantum order-by-disorder effect generated by topologically nontrivial, looplike spin-flip processes. A tentative S-Δ phase diagram of the model is proposed.
Institute of Scientific and Technical Information of China (English)
刘鹿幸; 周永务
2011-01-01
Manufacturers in selecting vendors must measure all aspects of the performance of candidate vendors. From the view of supply chain risk, this paper constructed the fuzzy multi - objective programming model to select the supplier with the selection criteria such as cost, quality, on - time delivery and risk control. Through the use of chance - constrained programming and fuzzy set theory, the former model became the nonlinear programming model. Finally, the mathematical example was given to testify the effectiveness of the proposed model by using the mini - max method and Monte Carlo simulation method.%制造商时于供应商的选择必须综合考虑待选的供应商在各个方面的表现.以供应链风险为视角,考虑价格、质量、准时供货、风险控制为选择准则,建立供应商选择的模糊多目标规划模型.通过机会约束规划方法和模糊集理论,进一步转化成非线性规划模型.最后采用数学算例,使用极小极大法、蒙特卡罗仿真法验证该模型的有效性.
Model Selection for Pion Photoproduction
Landay, J; Fernández-Ramírez, C; Hu, B; Molina, R
2016-01-01
Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the $S$-matrix are implemented to different degree in different approaches, but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the Least Absolute Shrinkage and Selection Operator (LASSO) in combination with criteria from information theory and $K$-fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data, then, its feasibility for real data is demonstrated by analyzing the latest available measu...
Using the Neumann series expansion for assembling Reduced Order Models
Directory of Open Access Journals (Sweden)
Nasisi S.
2014-06-01
Full Text Available An efficient method to remove the limitation in selecting the master degrees of freedom in a finite element model by means of a model order reduction is presented. A major difficulty of the Guyan reduction and IRS method (Improved Reduced System is represented by the need of appropriately select the master and slave degrees of freedom for the rate of convergence to be high. This study approaches the above limitation by using a particular arrangement of the rows and columns of the assembled matrices K and M and employing a combination between the IRS method and a variant of the analytical selection of masters presented in (Shah, V. N., Raymund, M., Analytical selection of masters for the reduced eigenvalue problem, International Journal for Numerical Methods in Engineering 18 (1 1982 in case first lowest frequencies had to be sought. One of the most significant characteristics of the approach is the use of the Neumann series expansion that motivates this particular arrangement of the matrices’ entries. The method shows a higher rate of convergence when compared to the standard IRS and very accurate results for the lowest reduced frequencies. To show the effectiveness of the proposed method two testing structures and the human vocal tract model employed in (Vampola, T., Horacek, J., Svec, J. G., FE modeling of human vocal tract acoustics. Part I: Prodution of Czech vowels, Acta Acustica United with Acustica 94 (3 2008 are presented.
A Selective Review of Group Selection in High Dimensional Models
Huang, Jian; Ma, Shuangge
2012-01-01
Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties, and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.
Selected soil thermal conductivity models
Directory of Open Access Journals (Sweden)
Rerak Monika
2017-01-01
Full Text Available The paper presents collected from the literature models of soil thermal conductivity. This is a very important parameter, which allows one to assess how much heat can be transferred from the underground power cables through the soil. The models are presented in table form, thus when the properties of the soil are given, it is possible to select the most accurate method of calculating its thermal conductivity. Precise determination of this parameter results in designing the cable line in such a way that it does not occur the process of cable overheating.
Supplier selection and order splitting in multiple-sourcing inventory systems
Institute of Scientific and Technical Information of China (English)
Guicong WANG; Zhaoliang JIANG; Zhaoqian LI; Wenping LIU
2008-01-01
Supplier selection and inventory control are critical decision processes in single-item multiple-supplier systems. An integer linear programming model is proposed to help managers determine the reorder level, choose the best suppliers, and place the optimum order quantities so that the total average inventory cost is minimum, and constraints of supplier ability, quality, and demand are considered. An algorithm combining the branch-bound algorithm and enumeration algorithm is developed to solve the problems. Application of the proposed model in an automobile industry shows that it is effective.
Coherent selection of invisible high-order electromagnetic excitations
Tseng, Ming Lun; Fang, Xu; Savinov, Vassili; Wu, Pin Chieh; Ou, Jun-Yu; Zheludev, Nikolay I.; Tsai, Din Ping
2017-03-01
Far-field spectroscopy and mapping of electromagnetic near-field distribution are the two dominant tools for analysis and characterization of the electromagnetic response in nanophotonics. Despite the widespread use, these methods can fail at identifying weak electromagnetic excitations masked by stronger neighboring excitations. This is particularly problematic in ultrafast nanophotonics, including optical sensing, nonlinear optics and nanolasers, where the broad resonant modes can overlap to a significant degree. Here, using plasmonic metamaterials, we demonstrate that coherent spectroscopy can conveniently isolate and detect such hidden high-order photonic excitations. Our results establish that the coherent spectroscopy is a powerful new tool. It complements the conventional methods for analysis of the electromagnetic response, and provides a new route to designing and characterizing novel photonic devices and materials.
Modeling Interconnect Variability Using Efficient Parametric Model Order Reduction
Li, Peng; Li, Xin; Pileggi, Lawrence T; Nassif, Sani R
2011-01-01
Assessing IC manufacturing process fluctuations and their impacts on IC interconnect performance has become unavoidable for modern DSM designs. However, the construction of parametric interconnect models is often hampered by the rapid increase in computational cost and model complexity. In this paper we present an efficient yet accurate parametric model order reduction algorithm for addressing the variability of IC interconnect performance. The efficiency of the approach lies in a novel combination of low-rank matrix approximation and multi-parameter moment matching. The complexity of the proposed parametric model order reduction is as low as that of a standard Krylov subspace method when applied to a nominal system. Under the projection-based framework, our algorithm also preserves the passivity of the resulting parametric models.
Modal contribution and state space order selection in operational modal analysis
Cara, F. Javier; Juan, Jesús; Alarcón, Enrique; Reynders, Edwin; De Roeck, Guido
2013-07-01
The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.
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 wa
Multi-order Arnoldi-based model order reduction of second-order time-delay systems
Xiao, Zhi-Hua; Jiang, Yao-Lin
2016-09-01
In this paper, we discuss the Krylov subspace-based model order reduction methods of second-order systems with time delays, and present two structure-preserving methods for model order reduction of these second-order systems, which avoid to convert the second-order systems into first-order ones. One method is based on a Krylov subspace by using the Taylor series expansion, the other method is based on the Laguerre series expansion. These two methods are used in the multi-order Arnoldi algorithm to construct the projection matrices. The resulting reduced models can not only preserve the structure of the original systems, but also can match a certain number of approximate moments or Laguerre expansion coefficients. The effectiveness of the proposed methods is demonstrated by two numerical examples.
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.
Model selection for pion photoproduction
Landay, J.; Döring, M.; Fernández-Ramírez, C.; Hu, B.; Molina, R.
2017-01-01
Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory and K -fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (d σ /d Ω ), photon-beam asymmetries (Σ ), and target asymmetry differential cross sections (d σT/d ≡T d σ /d Ω ) in the low-energy regime.
Model selection and comparison for independents sinusoids
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2014-01-01
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......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....... 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....
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
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...... in this optimal regime. We also find an operating regime in which high-efficiency frequency conversion without temporal-shape selectivity can be achieved while preserving the shapes of a wide class of input pulses. The results are applicable to both classical and quantum frequency conversion....
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...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models....
Pretreatment of wastewater: Optimal coagulant selection using Partial Order Scaling Analysis (POSA)
Energy Technology Data Exchange (ETDEWEB)
Tzfati, Eran, E-mail: etzfati@walla.com [Department of Chemical Engineering, Shenkar College of Engineering and Design, 12 Anna Frank street, Ramat Gan 52526 (Israel); Sein, Maya, E-mail: maya_shine@hotmail.com [Department of Chemical Engineering, Shenkar College of Engineering and Design, 12 Anna Frank street, Ramat Gan 52526 (Israel); Rubinov, Angelika, E-mail: angel@mail.shenkar.ac.il [Department of Chemical Engineering, Shenkar College of Engineering and Design, 12 Anna Frank street, Ramat Gan 52526 (Israel); Raveh, Adi, E-mail: msraveh@mscc.huji.ac.il [The School of Business Administration, Hebrew University, Jerusalem (Israel); Bick, Amos, E-mail: amosbick@gmail.com [Department of Chemical Engineering, Shenkar College of Engineering and Design, 12 Anna Frank street, Ramat Gan 52526 (Israel); Department of Industrial Engineering and Management, Jerusalem College of Technology, 21 Havaad Haleumi St., Jerusalem 91160 (Israel)
2011-06-15
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.
Dynamical numerical model for nematic order reconstruction
Lombardo, G.; Ayeb, H.; Barberi, R.
2008-05-01
In highly frustrated calamitic nematic liquid crystals, a strong elastic distortion can be confined on a few nanometers. The classical elastic theory fails to describe such systems and a more complete description based on the tensor order parameter Q is required. A finite element method is used to implement the Q dynamics by a variational principle and it is shown that a uniaxial nematic configuration can evolve passing through transient biaxial states. This solution, which connects two competing uniaxial nematic textures, is known as “nematic order reconstruction.”
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
Numerical Monte Carlo temperature-quenching experiments have been performed on two three-dimensional classical lattice models with continuous ordering symmetry: the Lebwohl-Lasher model [Phys. Rev. A 6, 426 (1972)] and the ferromagnetic isotropic Heisenberg model. Both models describe a transition...... 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...
Order and Disorder in Product Innovation Models
Pina e Cunha, Miguel; Gomes, Jorge F.S.
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 chan
Green supplier selection and order allocation in a low-carbon paper industry
DEFF Research Database (Denmark)
Govindan, Kannan; Sivakumar, R.
2016-01-01
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......The low-carbon supply chain is one of the predominant topics towards a green economy and it establishes the opportunity to reduce carbon emissions across the product value chain. This paper focuses on recycling and optimized sourcing in the paper industry as a case company. The main objective......-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...
A Decision Model for Selecting Participants in Supply Chain
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In order to satisfy the rapid changing requirements of customers, enterprises must cooperate with each other to form supply chain. The first and the most important stage in the forming of supply chain is the selection of participants. The article proposes a two-staged decision model to select partners. The first stage is the inter company comparison in each business process to select highefficiency candidate based on inside variables. The next stage is to analyse the combination of different candidates in order to select the most perfect partners according to a goal-programming model.
INTRUSION DETECTION BASED ON THE SECOND-ORDER STOCHASTIC MODEL
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper presents a new method based on a second-order stochastic model for computer intrusion detection. The results show that the performance of the second-order stochastic model is better than that of a first-order stochastic model. In this study, different window sizes are also used to test the performance of the model. The detection results show that the second-order stochastic model is not so sensitive to the window size, comparing with the first-order stochastic model and other previous researches. The detection result of window sizes 6 and 10 is the same.
Selective Maintenance Model Considering Time Uncertainty
Le Chen; Zhengping Shu; Yuan Li; Xuezhi Lv
2012-01-01
This study proposes a selective maintenance model for weapon system during mission interval. First, it gives relevant definitions and operational process of material support system. Then, it introduces current research on selective maintenance modeling. Finally, it establishes numerical model for selecting corrective and preventive maintenance tasks, considering time uncertainty brought by unpredictability of maintenance procedure, indetermination of downtime for spares and difference of skil...
Model Order Reduction for Electronic Circuits:
DEFF Research Database (Denmark)
Hjorth, Poul G.; Shontz, Suzanne
Electronic circuits are ubiquitous; they are used in numerous industries including: the semiconductor, communication, robotics, auto, and music industries (among many others). As products become more and more complicated, their electronic circuits also grow in size and complexity. This increased ...... in the semiconductor industry. Circuit simulation proceeds by using Maxwell’s equations to create a mathematical model of the circuit. The boundary element method is then used to discretize the equations, and the variational form of the equations are then solved on the graph network....
Ordered mesoporous carbon/Nafion as a versatile and selective solid-phase microextraction coating.
Zeng, Jingbin; Zhao, Cuiying; Chen, Jingjing; Subhan, Fazle; Luo, Liwen; Yu, Jianfeng; Cui, Bingwen; Xing, Wei; Chen, Xi; Yan, Zifeng
2014-10-24
In this study, ordered mesoporous carbon (OMC) with large surface area (1019m(2)g(-1)), uniform mesoporous structure (pore size distribution centering at 4.2nm) and large pore volume (1.46cm(3)g(-1)) was synthesized using 2D hexagonally mesoporous silica MSU-H as the hard template and sucrose as the carbon precursor. The as-synthesized OMC was immobilized onto a stainless steel wire using Nafion as a binder to prepare an OMC/Nafion solid-phase microextraction (SPME) coating. The extraction characteristics of the OMC/Nafion coating were extensively investigated using a wide range of analytes including non-polar (light petroleum and benzene homologues) and polar compounds (amines and phenols). The OMC/Nafion coating exhibited much better extraction efficiency towards all selected analytes than that of a multi-walled carbon nanotubes/Nafion coating with similar length and thickness, which is ascribed to its high surface area, well-ordered mesoporous structure and large pore volume. When the OMC/Nafion coating was used to extract a mixture containing various kinds of analytes, it possessed excellent extraction selectivity towards aromatic non-polar compounds. In addition, the feasibility of the OMC/Nafion coating for application in electrochemically enhanced SPME was demonstrated using protonated amines as model analytes. Copyright © 2014 Elsevier B.V. All rights reserved.
Partial Orders and Fully Abstract Models for Concurrency
DEFF Research Database (Denmark)
Engberg, Uffe Henrik
1990-01-01
In this thesis sets of labelled partial orders are employed as fundamental mathematical entities for modelling nondeterministic and concurrent processes thereby obtaining so-called noninterleaving semantics. Based on different closures of sets of labelled partial orders, simple algebraic languages...
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 < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). Existing order 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.
Bayesian Constrained-Model Selection for Factor Analytic Modeling
Peeters, Carel F.W.
2016-01-01
My dissertation revolves around Bayesian approaches towards constrained statistical inference in the factor analysis (FA) model. Two interconnected types of restricted-model selection are considered. These types have a natural connection to selection problems in the exploratory FA (EFA) and confirmatory FA (CFA) model and are termed Type I and Type II model selection. Type I constrained-model selection is taken to mean the determination of the appropriate dimensionality of a model. This type ...
Sliding Mode Control Design via Reduced Order Model Approach
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper presents a design of continuous-time sliding mode control for the higher order systems via reduced order model. It is shown that a continuous-time sliding mode control designed for the reduced order model gives similar performance for the higher order system. The method is illustrated by numerical examples. The paper also introduces a technique for design of a sliding surface such that the system satisfies a cost-optimality condition when on the sliding surface.
Model selection bias and Freedman's paradox
Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.
2010-01-01
In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.
Selected Logistics Models and Techniques.
1984-09-01
ACCESS PROCEDURE: On-Line System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease...System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease arrangement. • SPONSOR: ASD/ACCC
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.
Innovative first order elimination kinetics working model for easy learning
Directory of Open Access Journals (Sweden)
Navin Budania
2016-06-01
Conclusions: First order elimination kinetics is easily understood with the help of above working model. More and more working models could be developed for teaching difficult topics. [Int J Basic Clin Pharmacol 2016; 5(3.000: 862-864
The second-order decomposition model of nonlinear irregular waves
DEFF Research Database (Denmark)
Yang, Zhi Wen; Bingham, Harry B.; Li, Jin Xuan;
2013-01-01
into the first- and the second-order super-harmonic as well as the second-order sub-harmonic components by transferring them into an identical Fourier frequency-space and using a Newton-Raphson iteration method. In order to evaluate the present model, a variety of monochromatic waves and the second...
Research on Modeling of Hydropneumatic Suspension Based on Fractional Order
Junwei Zhang; Sizhong Chen; Yuzhuang Zhao; Jianbo Feng; Chang Liu; Ying Fan
2015-01-01
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 considerin...
Exponential order statistic models of software reliability growth
Miller, D. R.
1986-01-01
Failure times of a software reliability growth process are modeled as order statistics of independent, nonidentically distributed exponential random variables. The Jelinsky-Moranda, Goel-Okumoto, Littlewood, Musa-Okumoto Logarithmic, and Power Law models are all special cases of Exponential Order Statistic Models, but there are many additional examples also. Various characterizations, properties and examples of this class of models are developed and presented.
On the Economic Order Quantity Model With Transportation Costs
S.I. Birbil (Ilker); K. Bulbul; J.B.G. Frenk (Hans); H.M. Mulder (Henry)
2009-01-01
textabstractWe consider an economic order quantity type model with unit out-of-pocket holding costs, unit opportunity costs of holding, fixed ordering costs and general transportation costs. For these models, we analyze the associated optimization problem and derive an easy procedure for determining
Model-order reduction of biochemical reaction networks
Rao, Shodhan; Schaft, Arjan van der; Eunen, Karen van; Bakker, Barbara M.; Jayawardhana, Bayu
2013-01-01
In this paper we propose a model-order reduction method for chemical reaction networks governed by general enzyme kinetics, including the mass-action and Michaelis-Menten kinetics. The model-order reduction method is based on the Kron reduction of the weighted Laplacian matrix which describes the gr
Reduced order modeling of steady flows subject to aerodynamic constraints
DEFF Research Database (Denmark)
Zimmermann, Ralf; Vendl, Alexander; Goertz, Stefan
2014-01-01
A novel reduced-order modeling method based on proper orthogonal decomposition for predicting steady, turbulent flows subject to aerodynamic constraints is introduced. Model-order reduction is achieved by replacing the governing equations of computational fluid dynamics with a nonlinear weighted ...
MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS
Energy Technology Data Exchange (ETDEWEB)
Asensio Ramos, A.; Manso Sainz, R.; Martinez Gonzalez, M. J.; Socas-Navarro, H. [Instituto de Astrofisica de Canarias, E-38205, La Laguna, Tenerife (Spain); Viticchie, B. [ESA/ESTEC RSSD, Keplerlaan 1, 2200 AG Noordwijk (Netherlands); Orozco Suarez, D., E-mail: aasensio@iac.es [National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588 (Japan)
2012-04-01
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.
On Local Homogeneity and Stochastically Ordered Mixed Rasch Models
Kreiner, Svend; Hansen, Mogens; Hansen, Carsten Rosenberg
2006-01-01
Mixed Rasch models add latent classes to conventional Rasch models, assuming that the Rasch model applies within each class and that relative difficulties of items are different in two or more latent classes. This article considers a family of stochastically ordered mixed Rasch models, with ordinal latent classes characterized by increasing total…
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...
Four Order Electrostatic Discharge Circuit Model and its Simulation
Directory of Open Access Journals (Sweden)
Xiaodong Wang
2012-12-01
Full Text Available According to the international electrotechnical commission issued IEC61000-4-2 test standard, through the electrostatic discharge current waveform characteristics analysis and numerical experiment method, and construct a new ESD current expression. Using Laplasse transform, established the ESD system mathematical model. According to the mathematical model, construction of passive four order ESD system circuit model and active four order ESD system circuit model, and simulation. The simulation results meet the IEC61000-4-2 standard, and verify the consistency of the ESD current expression, the mathematical model and the circuit model.
Order reduction of large-scale linear oscillatory system models
Energy Technology Data Exchange (ETDEWEB)
Trudnowksi, D.J. (Pacific Northwest Lab., Richland, WA (United States))
1994-02-01
Eigen analysis and signal analysis techniques of deriving representations of power system oscillatory dynamics result in very high-order linear models. In order to apply many modern control design methods, the models must be reduced to a more manageable order while preserving essential characteristics. Presented in this paper is a model reduction method well suited for large-scale power systems. The method searches for the optimal subset of the high-order model that best represents the system. An Akaike information criterion is used to define the optimal reduced model. The method is first presented, and then examples of applying it to Prony analysis and eigenanalysis models of power systems are given.
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.
First and Higher Order Effects on Zero Order Radiative Transfer Model
Neelam, M.; Mohanty, B.
2014-12-01
Microwave radiative transfer model are valuable tool in understanding the complex land surface interactions. Past literature has largely focused on local sensitivity analysis for factor priotization and ignoring the interactions between the variables and uncertainties around them. Since land surface interactions are largely nonlinear, there always exist uncertainties, heterogeneities and interactions thus it is important to quantify them to draw accurate conclusions. In this effort, we used global sensitivity analysis to address the issues of variable uncertainty, higher order interactions, factor priotization and factor fixing for zero-order radiative transfer (ZRT) model. With the to-be-launched Soil Moisture Active Passive (SMAP) mission of NASA, it is very important to have a complete understanding of ZRT for soil moisture retrieval to direct future research and cal/val field campaigns. This is a first attempt to use GSA technique to quantify first order and higher order effects on brightness temperature from ZRT model. Our analyses reflect conditions observed during the growing agricultural season for corn and soybeans in two different regions in - Iowa, U.S.A and Winnipeg, Canada. We found that for corn fields in Iowa, there exist significant second order interactions between soil moisture, surface roughness parameters (RMS height and correlation length) and vegetation parameters (vegetation water content, structure and scattering albedo), whereas in Winnipeg, second order interactions are mainly due to soil moisture and vegetation parameters. But for soybean fields in both Iowa and Winnipeg, we found significant interactions only to exist between soil moisture and surface roughness parameters.
Stochastic Models for Phylogenetic Trees on Higher-order Taxa
Aldous, David; Popovic, Lea
2007-01-01
Simple stochastic models for phylogenetic trees on species have been well studied. But much paleontology data concerns time series or trees on higher-order taxa, and any broad picture of relationships between extant groups requires use of higher-order taxa. A coherent model for trees on (say) genera should involve both a species-level model and a model for the classification scheme by which species are assigned to genera. We present a general framework for such models, and describe three alternate classification schemes. Combining with the species-level model of Aldous-Popovic (2005), one gets models for higher-order trees, and we initiate analytic study of such models. In particular we derive formulas for the lifetime of genera, for the distribution of number of species per genus, and for the offspring structure of the tree on genera.
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...
Modeling of higher order systems using artificial bee colony algorithm
Directory of Open Access Journals (Sweden)
Aytekin Bağış
2016-05-01
Full Text Available In this work, modeling of the higher order systems based on the use of the artificial bee colony (ABC algorithm were examined. Proposed model parameters for the sample systems in the literature were obtained by using the algorithm, and its performance was presented comparatively with the other methods. Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.
Dual equivalence in models with higher-order derivatives
Bazeia, D; Nascimento, J R S; Ribeiro, R F; Wotzasek, C
2003-01-01
We introduce a class of higher-order derivative models in (2,1) space-time dimensions. The models are described by a vector field, and contain a Proca-like mass term which prevents gauge invariance. We use the gauge embedding procedure to generate another class of higher-order derivative models, gauge-invariant and dual to the former class. We also show that the gauge embedding approach works appropriately when the vector field couples with fermionic matter.
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 amplitude analysis
Guegan, Baptiste; Stevens, Justin; Williams, Mike
2015-01-01
Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of amplitudes to contribute to most physical processes. The use of an overly complex model results in reduced predictive power and worse resolution on unknown parameters of interest. Therefore, it is common to reduce the complexity by removing from consideration some subset of the allowed amplitudes. This paper studies a data-driven method for limiting model complexity through regularization during regression in the context of a multivariate (Dalitz-plot) analysis. The regularization technique applied greatly improves the performance. A method is also proposed for obtaining the significance of a resonance in a multivariate amplitude analysis.
Cosmic Acceleration in a Model of Fourth Order Gravity
Banerjee, Shreya; Singh, Tejinder P
2015-01-01
We investigate a fourth order model of gravity, having a free length parameter, and no cosmological constant or dark energy. We consider cosmological evolution of a flat Friedmann universe in this model for the case that the length parameter is of the order of present Hubble radius. By making a suitable choice for the present value of the Hubble parameter, and value of third derivative of the scale factor (the jerk) we find that the model can explain cosmic acceleration to the same degree of accuracy as the standard concordance model. If the free length parameter is assumed to be time-dependent, and of the order of the Hubble parameter of the corresponding epoch, the model can still explain cosmic acceleration, and provides a possible resolution of the cosmic coincidence problem. We also compare redshift drift in this model, with that in the standard model.
Symmetry and partial order reduction techniques in model checking Rebeca
Jaghouri, M.M.; Sirjani, M.; Mousavi, M.R.; Movaghar, A.
2007-01-01
Rebeca is an actor-based language with formal semantics that can be used in modeling concurrent and distributed software and protocols. In this paper, we study the application of partial order and symmetry reduction techniques to model checking dynamic Rebeca models. Finding symmetry based equivalen
Building Higher-Order Markov Chain Models with EXCEL
Ching, Wai-Ki; Fung, Eric S.; Ng, Michael K.
2004-01-01
Categorical data sequences occur in many applications such as forecasting, data mining and bioinformatics. In this note, we present higher-order Markov chain models for modelling categorical data sequences with an efficient algorithm for solving the model parameters. The algorithm can be implemented easily in a Microsoft EXCEL worksheet. We give a…
Two-step variable selection in quantile regression models
Directory of Open Access Journals (Sweden)
FAN Yali
2015-06-01
Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions,in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform l1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.
Model and Controller Order Reduction for Infinite Dimensional Systems
Directory of Open Access Journals (Sweden)
Fatmawati
2010-05-01
Full Text Available This paper presents a reduced order model problem using reciprocal transformation and balanced truncation followed by low order controller design of infinite dimensional systems. The class of systems considered is that of an exponentially stable state linear systems (A, B, C, where operator A has a bounded inverse, and the operator B and C are of finite-rank and bounded. We can connect the system (A, B, C with its reciprocal system via the solutions of the Lyapunov equations. The realization of the reciprocal system is reduced by balanced truncation. This result is further translated using reciprocal transformation as the reduced-order model for the systems (A, B, C. Then the low order controller is designed based on the reduced order model. The numerical examples are studied using simulations of Euler-Bernoulli beam to show the closed-loop performance.
The Ouroboros Model, selected facets.
Thomsen, Knud
2011-01-01
The Ouroboros Model features a biologically inspired cognitive architecture. At its core lies a self-referential recursive process with alternating phases of data acquisition and evaluation. Memory entries are organized in schemata. The activation at a time of part of a schema biases the whole structure and, in particular, missing features, thus triggering expectations. An iterative recursive monitor process termed 'consumption analysis' is then checking how well such expectations fit with successive activations. Mismatches between anticipations based on previous experience and actual current data are highlighted and used for controlling the allocation of attention. A measure for the goodness of fit provides feedback as (self-) monitoring signal. The basic algorithm works for goal directed movements and memory search as well as during abstract reasoning. It is sketched how the Ouroboros Model can shed light on characteristics of human behavior including attention, emotions, priming, masking, learning, sleep and consciousness.
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.
Second-Order Model Reduction Based on Gramians
Directory of Open Access Journals (Sweden)
Cong Teng
2012-01-01
Full Text Available Some new and simple Gramian-based model order reduction algorithms are presented on second-order linear dynamical systems, namely, SVD methods. Compared to existing Gramian-based algorithms, that is, balanced truncation methods, they are competitive and more favorable for large-scale systems. Numerical examples show the validity of the algorithms. Error bounds on error systems are discussed. Some observations are given on structures of Gramians of second order linear systems.
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.
High order fluid model for ionization fronts in streamer discharges
Markosyan, A.; Dujko, S.; Ebert, U.; Almeida, P.G.C.; Alves, L.L.; Guerra, V.
2012-01-01
A high order fluid model for streamer dynamics is developed by closing the system after the 4th mo- ment of the Boltzmann equation in local mean energy approximation. This is done by approximating the high order pressure tensor in the heat flux equation through the previous moments. The electric fi
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.
Reduced-order models for vertical human-structure interaction
Van Nimmen, Katrien; Lombaert, Geert; De Roeck, Guido; Van den Broeck, Peter
2016-09-01
For slender and lightweight structures, the vibration serviceability under crowd- induced loading is often critical in design. Currently, designers rely on equivalent load models, upscaled from single-person force measurements. Furthermore, it is important to consider the mechanical interaction with the human body as this can significantly reduce the structural response. To account for these interaction effects, the contact force between the pedestrian and the structure can be modelled as the superposition of the force induced by the pedestrian on a rigid floor and the force resulting from the mechanical interaction between the structure and the human body. For the case of large crowds, however, this approach leads to models with a very high system order. In the present contribution, two equivalent reduced-order models are proposed to approximate the dynamic behaviour of the full-order coupled crowd-structure system. A numerical study is performed to evaluate the impact of the modelling assumptions on the structural response to pedestrian excitation. The results show that the full-order moving crowd model can be well approximated by a reduced-order model whereby the interaction with the pedestrians in the crowd is modelled using a single (equivalent) SDOF system.
5 CFR 330.705 - Order of selection in filling vacancies from outside the agency's workforce.
2010-01-01
... Career Transition Assistance Plan for Displaced Employees § 330.705 Order of selection in filling... Career Transition Assistance Plan or Reemployment Priority List as described in subparts F and B of this... into the competitive service, e.g., conversion of a veterans' recruitment appointee to a...
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...
Random Effect and Latent Variable Model Selection
Dunson, David B
2008-01-01
Presents various methods for accommodating model uncertainty in random effects and latent variable models. This book focuses on frequentist likelihood ratio and score tests for zero variance components. It also focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models
Modeling Business Processes with Azzurra: Order Fulﬁlment
Canobbio, Giulia; Dalpiaz, Fabiano
2012-01-01
Azzurra is a specification language for modeling and enacting business processes. Azzurra is founded on social concepts, such as roles, agents and commitments among them, and Azzurra models are social models consisting of networks of commitments. As such, Azzurra models support the flexible enactment of business processes, and provide a semantic notion of actor accountability and business process compliance. In this technical report, we apply Azzurra to the order fulfilment exemplar from ...
An integrated model for supplier selection process
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
In today's highly competitive manufacturing environment, the supplier selection process becomes one of crucial activities in supply chain management. In order to select the best supplier(s) it is not only necessary to continuously tracking and benchmarking performance of suppliers but also to make a tradeoff between tangible and intangible factors some of which may conflict. In this paper an integration of case-based reasoning (CBR), analytical network process (ANP) and linear programming (LP) is proposed to solve the supplier selection problem.
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.
A High-Order Multiscale Global Atmospheric Model
Nair, Ram
2016-04-01
The High-Order Method Modeling Environment (HOMME), developed at NCAR, is a petascale hydrostatic framework, which employs the cubed-sphere grid system and high-order continuous or discontinuous Galerkin (DG) methods. Recently, the HOMME framework is being extended to a non-hydrostatic dynamical core, named as the "High-Order Multiscale Atmospheric Model (HOMAM)." The spatial discretization is based on DG or high-order finite-volume methods. Orography is handled by the terrain-following height-based coordinate system. To alleviate the stringent CFL stability requirement resulting from the vertical aspects of the dynamics, an operator-splitting time integration scheme based on the horizontally explicit and vertically implicit (HEVI) philosophy is adopted for HOMAM. Preliminary results with the benchmark test cases proposed in the Dynamical Core Model Intercomparison project (DCMIP) test-suite will be presented in the seminar.
The Renormalization of the Electroweak Standard Model to All Orders
Kraus, E
1998-01-01
We give the renormalization of the standard model of electroweak interactions to all orders of perturbation theory by using the method of algebraic renormalization, which is based on general properties of renormalized perturbation theory and not on a specific regularization scheme. The Green functions of the standard model are uniquely constructed to all orders, if one defines the model by the Slavnov-Taylor identity, Ward-identities of rigid symmetry and a specific form of the abelian local gauge Ward-identity, which continues the Gell-Mann Nishijima relation to higher orders. Special attention is directed to the mass diagonalization of massless and massive neutral vectors and ghosts. For obtaining off-shell infrared finite expressions it is required to take into account higher order corrections into the functional symmetry operators. It is shown, that the normalization conditions of the on-shell schemes are in agreement with the most general symmetry transformations allowed by the algebraic constraints.
Short-Run Asset Selection using a Logistic Model
Directory of Open Access Journals (Sweden)
Walter Gonçalves Junior
2011-06-01
Full Text Available Investors constantly look for significant predictors and accurate models to forecast future results, whose occasional efficacy end up being neutralized by market efficiency. Regardless, such predictors are widely used for seeking better (and more unique perceptions. This paper aims to investigate to what extent some of the most notorious indicators have discriminatory power to select stocks, and if it is feasible with such variables to build models that could anticipate those with good performance. In order to do that, logistical regressions were conducted with stocks traded at Bovespa using the selected indicators as explanatory variables. Investigated in this study were the outputs of Bovespa Index, liquidity, the Sharpe Ratio, ROE, MB, size and age evidenced to be significant predictors. Also examined were half-year, logistical models, which were adjusted in order to check the potential acceptable discriminatory power for the asset selection.
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.
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
Modeling Human Behaviour with Higher Order Logic: Insider Threats
DEFF Research Database (Denmark)
Boender, Jaap; Ivanova, Marieta Georgieva; Kammuller, Florian
2014-01-01
In this paper, we approach the problem of modeling the human component in technical systems with a view on the difference between the use of model and theory in sociology and computer science. One aim of this essay is to show that building of theories and models for sociology can be compared...... 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...
Sullivan, D L; Schommer, J C
1993-01-01
The purpose of this study was to investigate empirically the potential cost savings to a pharmaceutical wholesaler using the Economic Order Quantity (EOQ) model. This model allows for calculating the order quantity that minimizes both ordering and holding costs. A regional pharmaceutical wholesaler was selected for a case analysis study using the EOQ model. Eleven brand name products were randomly selected for the analysis. The average yearly cost savings using EOQ was $31.92 per product. The potential yearly cost savings based on 8500 brand name stock-keeping units was $271,320. Using EOQ can therefore assist pharmaceutical wholesalers in minimizing holding and ordering costs and improve efficiency for pharmaceutical distribution channels.
Next-to-leading order corrections to the valon model
Indian Academy of Sciences (India)
G R Bouroun; E Esfandyari
2016-01-01
A seminumerical solution to the valon model at next-to-leading order (NLO) in the Laguerre polynomials is presented. We used the valon model to generate the structure of proton with respect to the Laguerre polynomials method. The results are compared with H1 data and other parametrizations.
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.
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...
Cross-validation criteria for SETAR model selection
de Gooijer, J.G.
2001-01-01
Three cross-validation criteria, denoted C, C_c, and C_u are proposed for selecting the orders of a self-exciting threshold autoregressive SETAR) model when both the delay and the threshold value are unknown. The derivatioon of C is within a natural cross-validation framework. The crietion C_c is si
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.
A Fractional Order Recovery SIR Model from a Stochastic Process.
Angstmann, C N; Henry, B I; McGann, A V
2016-03-01
Over the past several decades, there has been a proliferation of epidemiological models with ordinary derivatives replaced by fractional derivatives in an ad hoc manner. These models may be mathematically interesting, but their relevance is uncertain. Here we develop an SIR model for an epidemic, including vital dynamics, from an underlying stochastic process. We show how fractional differential operators arise naturally in these models whenever the recovery time from the disease is power-law distributed. This can provide a model for a chronic disease process where individuals who are infected for a long time are unlikely to recover. The fractional order recovery model is shown to be consistent with the Kermack-McKendrick age-structured SIR model, and it reduces to the Hethcote-Tudor integral equation SIR model. The derivation from a stochastic process is extended to discrete time, providing a stable numerical method for solving the model equations. We have carried out simulations of the fractional order recovery model showing convergence to equilibrium states. The number of infecteds in the endemic equilibrium state increases as the fractional order of the derivative tends to zero.
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.
Projection-Based Reduced Order Modeling for Spacecraft Thermal Analysis
Qian, Jing; Wang, Yi; Song, Hongjun; Pant, Kapil; Peabody, Hume; Ku, Jentung; Butler, Charles D.
2015-01-01
This paper presents a mathematically rigorous, subspace projection-based reduced order modeling (ROM) methodology and an integrated framework to automatically generate reduced order models for spacecraft thermal analysis. Two key steps in the reduced order modeling procedure are described: (1) the acquisition of a full-scale spacecraft model in the ordinary differential equation (ODE) and differential algebraic equation (DAE) form to resolve its dynamic thermal behavior; and (2) the ROM to markedly reduce the dimension of the full-scale model. Specifically, proper orthogonal decomposition (POD) in conjunction with discrete empirical interpolation method (DEIM) and trajectory piece-wise linear (TPWL) methods are developed to address the strong nonlinear thermal effects due to coupled conductive and radiative heat transfer in the spacecraft environment. Case studies using NASA-relevant satellite models are undertaken to verify the capability and to assess the computational performance of the ROM technique in terms of speed-up and error relative to the full-scale model. ROM exhibits excellent agreement in spatiotemporal thermal profiles (<0.5% relative error in pertinent time scales) along with salient computational acceleration (up to two orders of magnitude speed-up) over the full-scale analysis. These findings establish the feasibility of ROM to perform rational and computationally affordable thermal analysis, develop reliable thermal control strategies for spacecraft, and greatly reduce the development cycle times and costs.
An Order Statistics Approach to the Halo Model for Galaxies
Paul, Niladri; Paranjape, Aseem; Sheth, Ravi K.
2017-01-01
We use the Halo Model to explore the implications of assuming that galaxy luminosities in groups are randomly drawn from an underlying luminosity function. We show that even the simplest of such order statistics models - one in which this luminosity function p(L) is universal - naturally produces a number of features associated with previous analyses based on the `central plus Poisson satellites' hypothesis. These include the monotonic relation of mean central luminosity with halo mass, the Lognormal distribution around this mean, and the tight relation between the central and satellite mass scales. In stark contrast to observations of galaxy clustering, however, this model predicts no luminosity dependence of large scale clustering. We then show that an extended version of this model, based on the order statistics of a halo mass dependent luminosity function p(L|m), is in much better agreement with the clustering data as well as satellite luminosities, but systematically under-predicts central luminosities. This brings into focus the idea that central galaxies constitute a distinct population that is affected by different physical processes than are the satellites. We model this physical difference as a statistical brightening of the central luminosities, over and above the order statistics prediction. The magnitude gap between the brightest and second brightest group galaxy is predicted as a by-product, and is also in good agreement with observations. We propose that this order statistics framework provides a useful language in which to compare the Halo Model for galaxies with more physically motivated galaxy formation models.
Reduced Order Internal Models in the Frequency Domain
Laakkonen, Petteri; Paunonen, Lassi
2016-01-01
The internal model principle states that all robustly regulating controllers must contain a suitably reduplicated internal model of the signal to be regulated. Using frequency domain methods, we show that the number of the copies may be reduced if the class of perturbations in the problem is restricted. We present a two step design procedure for a simple controller containing a reduced order internal model achieving robust regulation. The results are illustrated with an example of a five tank...
On the verification of PGD reduced-order models
Pled, Florent; Chamoin, Ludovic; Ladevèze, Pierre
2014-01-01
International audience; In current computational mechanics practice, multidimensional as well as multiscale or parametric models encountered in a wide variety of scientific and engineering fields often require either the resolution of significantly large complexity problems or the direct calculation of very numerous solutions of such complex models. In this framework, the use of model order reduction allows to dramatically reduce the computational requirements engendered by the increasing mod...
Optimal ordering policies for continuous review perishable inventory models.
Weiss, H J
1980-01-01
This paper extends the notions of perishable inventory models to the realm of continuous review inventory systems. The traditional perishable inventory costs of ordering, holding, shortage or penalty, disposal and revenue are incorporated into the continuous review framework. The type of policy that is optimal with respect to long run average expected cost is presented for both the backlogging and lost-sales models. In addition, for the lost-sales model the cost function is presented and analyzed.
A first-order thermal model for building design
Energy Technology Data Exchange (ETDEWEB)
Mathews, E.H. [Centre for Experimental and Numerical Thermoflow, Univ. of Pretoria (South Africa); Richards, P.G. [Centre for Experimental and Numerical Thermoflow, Univ. of Pretoria (South Africa); Lombard, C. [Centre for Experimental and Numerical Thermoflow, Univ. of Pretoria (South Africa)
1994-12-31
Simplified thermal models of buildings can successfully be applied in building design. This paper describes the derivation and validation of a first-order thermal model which has a clear physical interpretation, is based on uncomplicated calculation procedures and requires limited input information. Because extensive simplifications and assumptions are inherent in the development of the model, a comprehensive validation study is reported. The validity of the thermal model was proven with 70 validation studies in 32 buildings comprising a wide range of thermal characteristics. The accuracy of predictions compares well with other sophisticated programs. The proposed model is considered to be eminently suitable for incorporation in an efficient design tool. (orig.)
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...
Cardinality constrained portfolio selection via factor models
Monge, Juan Francisco
2017-01-01
In this paper we propose and discuss different 0-1 linear models in order to solve the cardinality constrained portfolio problem by using factor models. Factor models are used to build portfolios to track indexes, together with other objectives, also need a smaller number of parameters to estimate than the classical Markowitz model. The addition of the cardinality constraints limits the number of securities in the portfolio. Restricting the number of securities in the portfolio allows us to o...
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.
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).
Identification of slow molecular order parameters for Markov model construction
Perez-Hernandez, Guillermo; Giorgino, Toni; de Fabritiis, Gianni; Noé, Frank
2013-01-01
A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either g...
Using of "pseudo-second-order model" in adsorption.
Ho, Yuh-Shan
2014-01-01
A research paper's contribution exists not only in its originality and creativity but also in its continuity and development for research that follows. However, the author easily ignores it. Citation error and quotation error occurred very frequently in a scientific paper. Numerous researchers use secondary references without knowing the original idea from authors. Sulaymon et al. (Environ Sci Pollut Res 20:3011-3023, 2013) and Spiridon et al. (Environ Sci Pollut Res 20:6367-6381, 2013) presented wrong pseudo-second-order models in Environmental Science and Pollution Research, vol. 20. This comment pointed the errors of the kinetic models and offered information for citing original idea of pseudo-second-order kinetic expression. In order to stop the proliferation of the mistake, it is suggested to cite the original paper for the kinetic model which provided greater accuracy and more details about the kinetic expression.
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)
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.
Low Order Empirical Galerkin Models for Feedback Flow Control
Tadmor, Gilead; Noack, Bernd
2005-11-01
Model-based feedback control restrictions on model order and complexity stem from several generic considerations: real time computation, the ability to either measure or reliably estimate the state in real time and avoiding sensitivity to noise, uncertainty and numerical ill-conditioning are high on that list. Empirical POD Galerkin models are attractive in the sense that they are simple and (optimally) efficient, but are notoriously fragile, and commonly fail to capture transients and control effects. In this talk we review recent efforts to enhance empirical Galerkin models and make them suitable for feedback design. Enablers include `subgrid' estimation of turbulence and pressure representations, tunable models using modes from multiple operating points, and actuation models. An invariant manifold defines the model's dynamic envelope. It must be respected and can be exploited in observer and control design. These ideas are benchmarked in the cylinder wake system and validated by a systematic DNS investigation of a 3-dimensional Galerkin model of the controlled wake.
Nonlocal order parameters for the 1D Hubbard model.
Montorsi, Arianna; Roncaglia, Marco
2012-12-07
We characterize the Mott-insulator and Luther-Emery phases of the 1D Hubbard model through correlators that measure the parity of spin and charge strings along the chain. These nonlocal quantities order in the corresponding gapped phases and vanish at the critical point U(c)=0, thus configuring as hidden order parameters. The Mott insulator consists of bound doublon-holon pairs, which in the Luther-Emery phase turn into electron pairs with opposite spins, both unbinding at U(c). The behavior of the parity correlators is captured by an effective free spinless fermion model.
Eimer, Martin; Grubert, Anna
2015-09-01
Previous electrophysiological studies have shown that attentional selection processes are highly sensitive to the temporal order of task-relevant visual events. When two successively presented colour-defined target stimuli are separated by a stimulus onset asynchrony (SOA) of only 10 ms, the onset latencies of N2pc components to these stimuli (which reflect their attentional selection) precisely match their objective temporal separation. We tested whether such small onset differences are accessible to conscious awareness by instructing participants to report the category (letter or digit) of the first of two target-colour items that were separated by an SOA of 10, 20, or 30 ms. Performance was at chance level for the 10 ms SOA, demonstrating that temporal order information which is available to attentional control processes cannot be utilized for conscious temporal order judgments. These results provide new evidence that selective attention and conscious awareness are functionally separable, and support the hypothesis that attention and awareness operate at different stages of cognitive processing.
Directory of Open Access Journals (Sweden)
Dilek Teker
2013-01-01
Full Text Available The aim of this research is to compose a new rating methodology and provide credit notches to 23 countries which of 13 are developed and 10 are emerging. There are various literature that explains the determinants of credit ratings. Following the literature, we select 11 variables for our model which of 5 are eliminated by the factor analysis. We use specific dummies to investigate the structural breaks in time and cross section such as pre crises, post crises, BRIC membership, EU membership, OPEC membership, shipbuilder country and platinum reserved country. Then we run an ordered probit model and give credit notches to the countries. We use FITCH ratings as benchmark. Thus, at the end we compare the notches of FITCH with the ones we derive out of our estimated model.
Das, Saptarshi; Saha, Suman; Das, Shantanu; Gupta, Amitava
2011-07-01
In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modelling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.
Expert System Model for Educational Personnel Selection
Directory of Open Access Journals (Sweden)
Héctor A. Tabares-Ospina
2013-06-01
Full Text Available The staff selection is a difficult task due to the subjectivity that the evaluation means. This process can be complemented using a system to support decision. This paper presents the implementation of an expert system to systematize the selection process of professors. The management of software development is divided into 4 parts: requirements, design, implementation and commissioning. The proposed system models a specific knowledge through relationships between variables evidence and objective.
Fuzzy Economic Order Quantity Inventory Models Without Backordering
Institute of Scientific and Technical Information of China (English)
WANG Xiaobin; TANG Wansheng; ZHAO Ruiqing
2007-01-01
In economic order quantity models without backordering, both the stock cost of each unit quantity and the order cost of each cycle are characterized as independent fuzzy variables rather than fuzzy numbers as in previous studies. Based on an expected value criterion or a credibility criterion, a fuzzy expected value model and a fuzzy dependent hance programming (DCP) model are constructed. The purpose of the fuzzy expected value model is to find the optimal order quantity such that the fuzzy expected value of the total cost is minimal. The fuzzy DCP model is used to find the optimal order quantity for maximizing the credibility of an event such that the total cost in the planning periods does not exceed a certain budget level.Fuzzy simulations are designed to calculate the expected value of the fuzzy objective function and the credibility of each fuzzy event. A particle swarm optimization (PSO) algorithm based on a fuzzy simulation is designed, by integrating the fuzzy simulation and the PSO algorithm. Finally, a numerical example is given to illustrate the feasibility and validity of the proposed algorithm.
Model Order Reduction for Fluid Dynamics with Moving Solid Boundary
Gao, Haotian; Wei, Mingjun
2016-11-01
We extended the application of POD-Galerkin projection for model order reduction from usual fixed-domain problems to more general fluid-solid systems when moving boundary/interface is involved. The idea is similar to numerical simulation approaches using embedded forcing terms to represent boundary motion and domain change. However, such a modified approach will not get away with the unsteadiness of boundary terms which appear as time-dependent coefficients in the new Galerkin model. These coefficients need to be pre-computed for prescribed motion, or worse, to be computed at each time step for non-prescribed motion. The extra computational cost gets expensive in some cases and eventually undermines the value of using reduced-order models. One solution is to decompose the moving boundary/domain to orthogonal modes and derive another low-order model with fixed coefficients for boundary motion. Further study shows that the most expensive integrations resulted from the unsteady motion (in both original and domain-decomposition approaches) have almost negligible impact on the overall dynamics. Dropping these expensive terms reduces the computation cost by at least one order while no obvious effect on model accuracy is noticed. Supported by ARL.
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 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...... neighboring domains. This condition can be either hard, as modeled by a singularity in the domain-boundary potential, or soft, as modeled by a version of the Blume-Capel model. The results show that the effect of the steric hindrance, be it hard or soft, is only manifested in the amplitude, A...
Parameterized reduced-order models using hyper-dual numbers.
Energy Technology Data Exchange (ETDEWEB)
Fike, Jeffrey A.; Brake, Matthew Robert
2013-10-01
The goal of most computational simulations is to accurately predict the behavior of a real, physical system. Accurate predictions often require very computationally expensive analyses and so reduced order models (ROMs) are commonly used. ROMs aim to reduce the computational cost of the simulations while still providing accurate results by including all of the salient physics of the real system in the ROM. However, real, physical systems often deviate from the idealized models used in simulations due to variations in manufacturing or other factors. One approach to this issue is to create a parameterized model in order to characterize the effect of perturbations from the nominal model on the behavior of the system. This report presents a methodology for developing parameterized ROMs, which is based on Craig-Bampton component mode synthesis and the use of hyper-dual numbers to calculate the derivatives necessary for the parameterization.
An Order Statistics Approach to the Halo Model for Galaxies
Paul, Niladri; Sheth, Ravi K
2016-01-01
We use the Halo Model to explore the implications of assuming that galaxy luminosities in groups are randomly drawn from an underlying luminosity function. We show that even the simplest of such order statistics models -- one in which this luminosity function $p(L)$ is universal -- naturally produces a number of features associated with previous analyses based on the `central plus Poisson satellites' hypothesis. These include the monotonic relation of mean central luminosity with halo mass, the Lognormal distribution around this mean, and the tight relation between the central and satellite mass scales. In stark contrast to observations of galaxy clustering, however, this model predicts $\\textit{no}$ luminosity dependence of large scale clustering. We then show that an extended version of this model, based on the order statistics of a $\\textit{halo mass dependent}$ luminosity function $p(L|m)$, is in much better agreement with the clustering data as well as satellite luminosities, but systematically under-pre...
Reduced order modeling of grid-connected photovoltaic inverter systems
Wasynczuk, O.; Krause, P. C.; Anwah, N. A.
1988-04-01
This report summarizes the development of reduced order models of three-phase, line- and self-commutated inverter systems. This work was performed as part of the National Photovoltaics Program within the United States Department of Energy and was supervised by Sandia National Laboratories. The overall objective of the national program is to promote the development of low cost, reliable terrestrial photovoltaic systems for widespread use in residential, commercial and utility applications. The purpose of the effort reported herein is to provide reduced order models of three-phase, line- and self-commutated PV systems suitable for implementation into transient stability programs, which are commonly used to predict the stability characteristics of large-scale power systems. The accuracy of the reduced models is verified by comparing the response characteristics predicted therefrom with the response established using highly detailed PV system models in which the inverter switching is represented in detail.
Numerical modeling of higher order magnetic moments in UXO discrimination
Sanchez, V.; Yaoguo, L.; Nabighian, M.N.; Wright, D.L.
2008-01-01
The surface magnetic anomaly observed in unexploded ordnance (UXO) clearance is mainly dipolar, and consequently, the dipole is the only magnetic moment regularly recovered in UXO discrimination. The dipole moment contains information about the intensity of magnetization but lacks information about the shape of the target. In contrast, higher order moments, such as quadrupole and octupole, encode asymmetry properties of the magnetization distribution within the buried targets. In order to improve our understanding of magnetization distribution within UXO and non-UXO objects and to show its potential utility in UXO clearance, we present a numerical modeling study of UXO and related metallic objects. The tool for the modeling is a nonlinear integral equation describing magnetization within isolated compact objects of high susceptibility. A solution for magnetization distribution then allows us to compute the magnetic multipole moments of the object, analyze their relationships, and provide a depiction of the anomaly produced by different moments within the object. Our modeling results show the presence of significant higher order moments for more asymmetric objects, and the fields of these higher order moments are well above the noise level of magnetic gradient data. The contribution from higher order moments may provide a practical tool for improved UXO discrimination. ?? 2008 IEEE.
A reduced order model for nonlinear vibroacoustic problems
Directory of Open Access Journals (Sweden)
Ouisse Morvan
2012-07-01
Full Text Available This work is related to geometrical nonlinearities applied to thin plates coupled with fluid-filled domain. Model reduction is performed to reduce the computation time. Reduced order model (ROM is issued from the uncoupled linear problem and enriched with residues to describe the nonlinear behavior and coupling effects. To show the efficiency of the proposed method, numerical simulations in the case of an elastic plate closing an acoustic cavity are presented.
Robust simulation of buckled structures using reduced order modeling
Wiebe, R.; Perez, R. A.; Spottswood, S. M.
2016-09-01
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.
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.
Bayesian variable selection for latent class models.
Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria
2011-09-01
In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.
A multi agent model for the limit order book dynamics
Bartolozzi, M.
2010-01-01
In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book.aEuro (c) The agents follow a noise decision making process where the
Higher-Order Item Response Models for Hierarchical Latent Traits
Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming
2013-01-01
Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…
A Hybrid Program Projects Selection Model for Nonprofit TV Stations
Directory of Open Access Journals (Sweden)
Kuei-Lun Chang
2015-01-01
Full Text Available This study develops a hybrid multiple criteria decision making (MCDM model to select program projects for nonprofit TV stations on the basis of managers’ perceptions. By the concept of balanced scorecard (BSC and corporate social responsibility (CSR, we collect criteria for selecting the best program project. Fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Next, considering the interdependence among the selection criteria, analytic network process (ANP is then used to obtain the weights of them. To avoid calculation and additional pairwise comparisons of ANP, technique for order preference by similarity to ideal solution (TOPSIS is used to rank the alternatives. A case study is presented to demonstrate the applicability of the proposed model.
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.
Update rules and interevent time distributions: Slow ordering vs. no ordering in the Voter Model
Fernández-Gracia, Juan; Miguel, M San
2011-01-01
We introduce a general methodology of update rules accounting for arbitrary interevent time distributions in simulations of interacting agents. In particular we consider update rules that depend on the state of the agent, so that the update becomes part of the dynamical model. As an illustration we consider the voter model in fully-connected, random and scale free networks with an update probability inversely proportional to the persistence, that is, the time since the last event. We find that in the thermodynamic limit, at variance with standard updates, the system orders slowly. The approach to the absorbing state is characterized by a power law decay of the density of interfaces, observing that the mean time to reach the absorbing state might be not well defined.
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 Analysi...... might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian.......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...
Order-Induced Selectivity Increase of Cu60Pd40 in the Semi-Hydrogenation of Acetylene
Directory of Open Access Journals (Sweden)
Marc Armbrüster
2013-07-01
Full Text Available The two structural modifications of Cu60Pd40 were synthesized as bulk powders and tested as unsupported model catalysts in the semi-hydrogenation of acetylene. The partly ordered low-temperature modification (CsCl type of structure showed an outstanding ethylene selectivity of >90% over 20 h on stream while the disordered high-temperature modification (Cu type of structure was 20% less selective, indicating an influence of the degree of order in the crystal structure on the catalytic properties. The results are supported by XRD and in situ XPS experiments. The latter suggest the existence of partly isolated Pd sites on the surface. In situ PGAA investigations proved the absence of metal hydride formation during reaction. Quantum chemical calculations of the electronic structure of both modifications using the CPA-FPLO framework revealed significant differences in their respective density of states, thus still leaving open the question of whether the degree of structural order or/and the electronic hybridization is the decisive factor for the observed difference in selectivity.
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.
MODEL SELECTION FOR LOG-LINEAR MODELS OF CONTINGENCY TABLES
Institute of Scientific and Technical Information of China (English)
ZHAO Lincheng; ZHANG Hong
2003-01-01
In this paper, we propose an information-theoretic-criterion-based model selection procedure for log-linear model of contingency tables under multinomial sampling, and establish the strong consistency of the method under some mild conditions. An exponential bound of miss detection probability is also obtained. The selection procedure is modified so that it can be used in practice. Simulation shows that the modified method is valid. To avoid selecting the penalty coefficient in the information criteria, an alternative selection procedure is given.
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.
A first order system model of fracture healing
Institute of Scientific and Technical Information of China (English)
WANG Xiao-ping; ZHANG Xian-long; LI Zhu-guo; YU Xin-gang
2005-01-01
A first order system model is proposed for simulating the influence of stress stimulation on fracture strength during fracture healing. To validate the model, the diaphyses of bilateral tibiae in 70 New Zealand rabbits were osteotomized and fixed with rigid plates and stress-relaxation plates, respectively. Stress shielding rate and ultimate bending strength of the healing bone were measured at 2 to 48 weeks postoperatively. Ratios of stress stimulation and fracture strength of the healing bone to those of intact bone were taken as the system input and output. The assumed first order system model can approximate the experimental data on fracture strength from the input of stress stimulation over time, both for the rigid plate group and the stress-relaxation plate group, with different system parameters of time constant and gain. The fitting curve indicates that the effect of mechanical stimulus occurs mainly in late stages of healing. First order system can model the stress adaptation process of fracture healing. This approach presents a simple bio-mathematical model of the relationship between stress stimulation and fracture strength, and has the potential to optimize planning of functional exercises and conduct parametric studies.
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.
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.
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.
Adaptive Covariance Estimation with model selection
Biscay, Rolando; Loubes, Jean-Michel
2012-01-01
We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud.
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.
A Theoretical Model for Selective Exposure Research.
Roloff, Michael E.; Noland, Mark
This study tests the basic assumptions underlying Fishbein's Model of Attitudes by correlating an individual's selective exposure to types of television programs (situation comedies, family drama, and action/adventure) with the attitudinal similarity between individual attitudes and attitudes characterized on the programs. Twenty-three college…
An improved model for reduced-order physiological fluid flows
San, Omer; 10.1142/S0219519411004666
2012-01-01
An improved one-dimensional mathematical model based on Pulsed Flow Equations (PFE) is derived by integrating the axial component of the momentum equation over the transient Womersley velocity profile, providing a dynamic momentum equation whose coefficients are smoothly varying functions of the spatial variable. The resulting momentum equation along with the continuity equation and pressure-area relation form our reduced-order model for physiological fluid flows in one dimension, and are aimed at providing accurate and fast-to-compute global models for physiological systems represented as networks of quasi one-dimensional fluid flows. The consequent nonlinear coupled system of equations is solved by the Lax-Wendroff scheme and is then applied to an open model arterial network of the human vascular system containing the largest fifty-five arteries. The proposed model with functional coefficients is compared with current classical one-dimensional theories which assume steady state Hagen-Poiseuille velocity pro...
Directory of Open Access Journals (Sweden)
Kristian M. Lien
1990-01-01
Full Text Available This paper presents a new algorithm based on the heuristic tearing algorithm by Gundersen and Hertzberg (1983. The basic idea in both the original and the proposed algorithm is sequential tearing of strong components which have been identified by an algorithm proposed by Targan (1972. The new algorithm has two alternative options for selection of tear streams, and alternative precedence orderings may be generated for the selected set of tear streams. The algorithm has been tested on several problems. It has identified minimal (optimal tear sets for all of them, including the four problems presented in Gundersen and Hertzberg (1983 where the original algorithm could not find a minimal tear set. A Lisp implementation of the algorithm is described, and example problems arc presented.
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.
Second order closure modeling of turbulent buoyant wall plumes
Zhu, Gang; Lai, Ming-Chia; Shih, Tsan-Hsing
1992-01-01
Non-intrusive measurements of scalar and momentum transport in turbulent wall plumes, using a combined technique of laser Doppler anemometry and laser-induced fluorescence, has shown some interesting features not present in the free jet or plumes. First, buoyancy-generation of turbulence is shown to be important throughout the flow field. Combined with low-Reynolds-number turbulence and near-wall effect, this may raise the anisotropic turbulence structure beyond the prediction of eddy-viscosity models. Second, the transverse scalar fluxes do not correspond only to the mean scalar gradients, as would be expected from gradient-diffusion modeling. Third, higher-order velocity-scalar correlations which describe turbulent transport phenomena could not be predicted using simple turbulence models. A second-order closure simulation of turbulent adiabatic wall plumes, taking into account the recent progress in scalar transport, near-wall effect and buoyancy, is reported in the current study to compare with the non-intrusive measurements. In spite of the small velocity scale of the wall plumes, the results showed that low-Reynolds-number correction is not critically important to predict the adiabatic cases tested and cannot be applied beyond the maximum velocity location. The mean and turbulent velocity profiles are very closely predicted by the second-order closure models. but the scalar field is less satisfactory, with the scalar fluctuation level underpredicted. Strong intermittency of the low-Reynolds-number flow field is suspected of these discrepancies. The trends in second- and third-order velocity-scalar correlations, which describe turbulent transport phenomena, are also predicted in general, with the cross-streamwise correlations better than the streamwise one. Buoyancy terms modeling the pressure-correlation are shown to improve the prediction slightly. The effects of equilibrium time-scale ratio and boundary condition are also discussed.
Advanced Fluid Reduced Order Models for Compressible Flow.
Energy Technology Data Exchange (ETDEWEB)
Tezaur, Irina Kalashnikova; Fike, Jeffrey A.; Carlberg, Kevin Thomas; Barone, Matthew F.; Maddix, Danielle; Mussoni, Erin E.; Balajewicz, Maciej (UIUC)
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.
Selective detection of ordered sodium signals by a jump-and-return pulse sequence
Lee, Jae-Seung; Regatte, Ravinder R.; Jerschow, Alexej
2009-09-01
A simple pulse sequence, derived from the shaped pulse optimally exciting the central transition of a spin 3/2, can be used to selectively detect ordered sodium with a given quadrupolar coupling. The pulse sequence consists of two pulses with opposite phases and separated by a delay, called a quadrupolar jump-and-return (QJR) sequence. This QJR sequence is tested with a phantom made of sodium ions in bacteriophage and in aqueous solution and its feasibility for contrast modification based on the quadrupolar coupling is demonstrated.
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.
Magnetic moments in chemically ordered mass-selected CoPt and FePt clusters
Energy Technology Data Exchange (ETDEWEB)
Dupuis, V., E-mail: Veronique.Dupuis@univ-lyon1.fr [Institut Lumière Matière, UMR5306 Université Lyon 1-CNRS, Université de Lyon, 69622 Villeurbanne cedex (France); Khadra, G.; Linas, S.; Hillion, A. [Institut Lumière Matière, UMR5306 Université Lyon 1-CNRS, Université de Lyon, 69622 Villeurbanne cedex (France); Gragnaniello, L. [Institute of Condensed Matter Physics, EPFL, CH-1015 Lausanne (Switzerland); Tamion, A.; Tuaillon-Combes, J.; Bardotti, L.; Tournus, F. [Institut Lumière Matière, UMR5306 Université Lyon 1-CNRS, Université de Lyon, 69622 Villeurbanne cedex (France); Otero, E.; Ohresser, P. [Synchrotron SOLEIL, L’Orme des Merisiers, Saint-Aubin BP 48, F-91192 Gif-sur-Yvette Cedex (France); Rogalev, A.; Wilhelm, F. [European Synchrotron Radiation Facility, BP 220, F-38043 Grenoble Cedex (France)
2015-06-01
By combining high photon flux and chemical selectivity, X-ray absorption spectroscopy and X-ray magnetic circular dichroism (XMCD) have been used to study the magnetism of CoPt and FePt clusters before and after their transition to the chemically ordered L1{sub 0}-like phase. Compared to the bulk, we find larger magnetic spin and orbital moments of Fe, Co and Pt atoms in nanoalloys. - Highlights: • Study of magnetism on well-defined CoPt and FePt clusters embedded in carbon matrix • X-ray magnetic circular dichroism (XMCD) at each specific Fe, Co and Pt edges, before and after annealing to induce transition to the chemically L1{sub 0}-like phase. • Quantitative values of the spin and orbital magnetic moments of Co (resp. Fe) and Pt after the chemical ordering transition. • Specific nanoalloy effects.
Analytical Higher-Order Model for Flexible and Stretchable Sensors
Institute of Scientific and Technical Information of China (English)
ZHANG Yongfang; ZHU Hongbin; LIU Cheng; LIU Xu; LIU Fuxi; L Yanjun
2015-01-01
The stretchable sensor wrapped around a foldable airfoil or embedded inside of it has great potential for use in the monitoring of the structural status of the foldable airfoil. The design methodology is important to the development of the stretchable sensor for status monitoring on the foldable airfoil. According to the requirement of mechanical flexibility of the sensor, the combined use of a layered flexible structural formation and a strain isolation layer is implemented. An analytical higher-order model is proposed to predict the stresses of the strain-isolation layer based on the shear-lag model for the safe design of the flexible and stretchable sensors. The normal stress and shear stress equations in the constructed structure of the sensors are obtained by the proposed model. The stress distribution in the structure is investigated when bending load is applied to the structures. The numerical results show that the proposed model can predict the variation of normal stress and shear stress along the thickness of the strain-isolation (polydimethylsiloxane) layer accurately. The results by the proposed model are in good agreement with the finite element method, in which the normal stress is variable while the shear stress is invariable along the thickness direction of strain-isolation layer. The high-order model is proposed to predict the stresses of the layered structure of the flexible and stretchable sensor for monitoring the status of the foldable airfoil.
An ergodic configurational thermostat using selective control of higher order temperatures.
Patra, Puneet Kumar; Bhattacharya, Baidurya
2015-05-21
The conventional Nosé-Hoover type deterministic thermostat scheme for controlling temperature by configurational variables (Braga-Travis (BT) thermostat) is non-ergodic for systems with a few degrees of freedom. While for the original Nosé-Hoover kinetic thermostat ergodicity has been achieved by controlling the higher order moments of kinetic energy, the issues of nonergodicity of BT thermostat persists. In this paper, we introduce two new measures of configurational temperature (second and third order) based on the generalized temperature-curvature relationship and obtain a family of deterministic thermostatting schemes by selectively (and simultaneously) controlling the different orders of temperatures through pseudo-friction terms. The ergodic characteristics of the proposed thermostats are tested using a single harmonic oscillator through statistical (normality of joint distributions at different Poincare sections) as well as dynamical tests (difference of the minimum and maximum largest Lyapunov exponent). Our results indicate that simultaneously controlling the first and the second order configurational temperatures (C(1,2) thermostat) is sufficient to make the dynamics ergodic. A 2000 particle Lennard-Jones system is subjected to (i) equilibrium and (ii) sudden temperature change under BT and C(1,2) thermostatting schemes. The C(1,2) thermostat is found to be more robust than the BT thermostat without increasing computational costs.
Model selection for radiochromic film dosimetry
Méndez, Ignasi
2015-01-01
The purpose of this study was to find the most accurate model for radiochromic film dosimetry by comparing different channel independent perturbation models. A model selection approach based on (algorithmic) information theory was followed, and the results were validated using gamma-index analysis on a set of benchmark test cases. Several questions were addressed: (a) whether incorporating the information of the non-irradiated film, by scanning prior to irradiation, improves the results; (b) whether lateral corrections are necessary when using multichannel models; (c) whether multichannel dosimetry produces better results than single-channel dosimetry; (d) which multichannel perturbation model provides more accurate film doses. It was found that scanning prior to irradiation and applying lateral corrections improved the accuracy of the results. For some perturbation models, increasing the number of color channels did not result in more accurate film doses. Employing Truncated Normal perturbations was found to...
The good, the bad, and the timely: how temporal order and moral judgment influence causal selection.
Reuter, Kevin; Kirfel, Lara; van Riel, Raphael; Barlassina, Luca
2014-01-01
Causal selection is the cognitive process through which one or more elements in a complex causal structure are singled out as actual causes of a certain effect. In this paper, we report on an experiment in which we investigated the role of moral and temporal factors in causal selection. Our results are as follows. First, when presented with a temporal chain in which two human agents perform the same action one after the other, subjects tend to judge the later agent to be the actual cause. Second, the impact of temporal location on causal selection is almost canceled out if the later agent did not violate a norm while the former did. We argue that this is due to the impact that judgments of norm violation have on causal selection-even if the violated norm has nothing to do with the obtaining effect. Third, moral judgments about the effect influence causal selection even in the case in which agents could not have foreseen the effect and did not intend to bring it about. We discuss our findings in connection to recent theories of the role of moral judgment in causal reasoning, on the one hand, and to probabilistic models of temporal location, on the other.
Portfolio Selection Model with Derivative Securities
Institute of Scientific and Technical Information of China (English)
王春峰; 杨建林; 蒋祥林
2003-01-01
Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is developed based on utility function which can capture asymmetries in random variable distributions. Other realistic conditions are also considered, such as liabilities and integer decision variables. Since the resulting model is a complex mixed-integer nonlinear programming problem, simulated annealing algorithm is applied for its solution. A numerical example is given and sensitivity analysis is conducted for the model.
A multi agent model for the limit order book dynamics
Bartolozzi, M.
2010-11-01
In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book. The agents follow a noise decision making process where their actions are related to a stochastic variable, the market sentiment, which we define as a mixture of public and private information. The model, despite making just few basic assumptions over the trading strategies of the agents, is able to reproduce several empirical features of the high-frequency dynamics of the market microstructure not only related to the price movements but also to the deposition of the orders in the book.
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
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......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...... parameters. In this paper, we present an estimation procedure for harmonic-structured signals in situations with strong interference using spatial filtering, or beamforming. We jointly estimate the fundamental frequency and the constrained model order through the output of the beamformers. Besides that, we...
Wave Transformation Modeling with Effective Higher-Order Finite Elements
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Tae-Hwa Jung
2016-01-01
Full Text Available This study introduces a finite element method using a higher-order interpolation function for effective simulations of wave transformation. Finite element methods with a higher-order interpolation function usually employ a Lagrangian interpolation function that gives accurate solutions with a lesser number of elements compared to lower order interpolation function. At the same time, it takes a lot of time to get a solution because the size of the local matrix increases resulting in the increase of band width of a global matrix as the order of the interpolation function increases. Mass lumping can reduce computation time by making the local matrix a diagonal form. However, the efficiency is not satisfactory because it requires more elements to get results. In this study, the Legendre cardinal interpolation function, a modified Lagrangian interpolation function, is used for efficient calculation. Diagonal matrix generation by applying direct numerical integration to the Legendre cardinal interpolation function like conducting mass lumping can reduce calculation time with favorable accuracy. Numerical simulations of regular, irregular and solitary waves using the Boussinesq equations through applying the interpolation approaches are carried out to compare the higher-order finite element models on wave transformation and examine the efficiency of calculation.
Regularization method for calibrated POD reduced-order models
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El Majd Badr Abou
2014-01-01
Full Text Available In this work we present a regularization method to improve the accuracy of reduced-order models based on Proper Orthogonal Decomposition. The bench mark configuration retained corresponds to a case of relatively simple dynamics: a two-dimensional flow around a cylinder for a Reynolds number of 200. Finally, we show for this flow configuration that this procedure is efficient in term of reduction of errors.
The Complexity of Model Checking Higher-Order Fixpoint Logic
DEFF Research Database (Denmark)
Axelsson, Roland; Lange, Martin; Somla, Rafal
2007-01-01
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...... provides complexity results for its model checking problem. In particular we consider its fragments HFLk,m which are formed using types of bounded order k and arity m only. We establish k-ExpTime-completeness for model checking each HFLk,m fragment. For the upper bound we reduce the problem to the problem...
On fractional order composite model reference adaptive control
Wei, Yiheng; Sun, Zhenyuan; Hu, Yangsheng; Wang, Yong
2016-08-01
This paper presents a novel composite model reference adaptive control approach for a class of fractional order linear systems with unknown constant parameters. The method is extended from the model reference adaptive control. The parameter estimation error of our method depends on both the tracking error and the prediction error, whereas the existing method only depends on the tracking error, which makes our method has better transient performance in the sense of generating smooth system output. By the aid of the continuous frequency distributed model, stability of the proposed approach is established in the Lyapunov sense. Furthermore, the convergence property of the model parameters estimation is presented, on the premise that the closed-loop control system is stable. Finally, numerical simulation examples are given to demonstrate the effectiveness of the proposed schemes.
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.
Robust model selection and the statistical classification of languages
García, J. E.; González-López, V. A.; Viola, M. L. L.
2012-10-01
In this paper we address the problem of model selection for the set of finite memory stochastic processes with finite alphabet, when the data is contaminated. We consider m independent samples, with more than half of them being realizations of the same stochastic process with law Q, which is the one we want to retrieve. We devise a model selection procedure such that for a sample size large enough, the selected process is the one with law Q. Our model selection strategy is based on estimating relative entropies to select a subset of samples that are realizations of the same law. Although the procedure is valid for any family of finite order Markov models, we will focus on the family of variable length Markov chain models, which include the fixed order Markov chain model family. We define the asymptotic breakdown point (ABDP) for a model selection procedure, and we show the ABDP for our procedure. This means that if the proportion of contaminated samples is smaller than the ABDP, then, as the sample size grows our procedure selects a model for the process with law Q. We also use our procedure in a setting where we have one sample conformed by the concatenation of sub-samples of two or more stochastic processes, with most of the subsamples having law Q. We conducted a simulation study. In the application section we address the question of the statistical classification of languages according to their rhythmic features using speech samples. This is an important open problem in phonology. A persistent difficulty on this problem is that the speech samples correspond to several sentences produced by diverse speakers, corresponding to a mixture of distributions. The usual procedure to deal with this problem has been to choose a subset of the original sample which seems to best represent each language. The selection is made by listening to the samples. In our application we use the full dataset without any preselection of samples. We apply our robust methodology estimating
On Model Selection Criteria in Multimodel Analysis
Energy Technology Data Exchange (ETDEWEB)
Ye, Ming; Meyer, Philip D.; Neuman, Shlomo P.
2008-03-21
Hydrologic systems are open and complex, rendering them prone to multiple conceptualizations and mathematical descriptions. There has been a growing tendency to postulate several alternative hydrologic models for a site and use model selection criteria to (a) rank these models, (b) eliminate some of them and/or (c) weigh and average predictions and statistics generated by multiple models. This has led to some debate among hydrogeologists about the merits and demerits of common model selection (also known as model discrimination or information) criteria such as AIC [Akaike, 1974], AICc [Hurvich and Tsai, 1989], BIC [Schwartz, 1978] and KIC [Kashyap, 1982] and some lack of clarity about the proper interpretation and mathematical representation of each criterion. In particular, whereas we [Neuman, 2003; Ye et al., 2004, 2005; Meyer et al., 2007] have based our approach to multimodel hydrologic ranking and inference on the Bayesian criterion KIC (which reduces asymptotically to BIC), Poeter and Anderson [2005] and Poeter and Hill [2007] have voiced a preference for the information-theoretic criterion AICc (which reduces asymptotically to AIC). Their preference stems in part from a perception that KIC and BIC require a "true" or "quasi-true" model to be in the set of alternatives while AIC and AICc are free of such an unreasonable requirement. We examine the model selection literature to find that (a) all published rigorous derivations of AIC and AICc require that the (true) model having generated the observational data be in the set of candidate models; (b) though BIC and KIC were originally derived by assuming that such a model is in the set, BIC has been rederived by Cavanaugh and Neath [1999] without the need for such an assumption; (c) KIC reduces to BIC as the number of observations becomes large relative to the number of adjustable model parameters, implying that it likewise does not require the existence of a true model in the set of alternatives; (d) if a true
Second Order Model for Strongly Sheared Compressible Turbulence
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marzougui hamed
2015-01-01
Full Text Available In this paper, we propose a model designed to describe a strongly sheared compressible homogeneous turbulent flows. Such flows are far from equilibrium and are well represented by the A3 and A4 cases of the DNS of Sarkar. Speziale and Xu developed a relaxation model in incompressible turbulence able to take into account significant departures from equilibrium. In a previous paper, Radhia et al. proposed a relaxation model similar to that of Speziale and Xu .This model is based on an algebraic representation of the Reynolds stress tensor, much simpler than that of Speziale and Xu and it gave a good result for rapid axisymetric contraction. In this work, we propose to extend the Radhia et al’s. model to compressible homogenous turbulence. This model is based on the pressure-strain model of Launder et al., where we incorporate turbulent Mach number in order to take into account compressibility effects. To assess this model, two numerical simulations were performed which are similar to the cases A3 and A4 of the DNS of Sarkar.
A Neurodynamical Model for Selective Visual Attention
Institute of Scientific and Technical Information of China (English)
QU Jing-Yi; WANG Ru-Bin; ZHANG Yuan; DU Ying
2011-01-01
A neurodynamical model for selective visual attention considering orientation preference is proposed. Since orientation preference is one of the most important properties of neurons in the primary visual cortex, it should be fully considered besides external stimuli intensity. By tuning the parameter of orientation preference, the regimes of synchronous dynamics associated with the development of the attention focus are studied. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics correspond to the partial synchronization mode. Simulation results show that the model can sequentially select objects with different orientation preferences and has a reliable shift of attention from one object to another, which are consistent with the experimental results that neurons with different orientation preferences are laid out in pinwheel patterns.%A neurodynamical model for selective visual attention considering orientation preference is proposed.Since orientation preference is one of the most important properties of neurons in the primary visual cortex,it should be fully considered besides external stimuli intensity.By tuning the parameter of orientation preference,the regimes of synchronous dynamics associated with the development of the attention focus are studied.The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed.Such dynamics correspond to the partial synchronization mode.Simulation results show that the model can sequentially select objects with different orientation preferences and has a reliable shift of attention from one object to another,which are consistent with the experimental results that neurons with different orientation preferences are laid out in pinwheel patterns.Selective visual
Post-model selection inference and model averaging
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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.
Update rules and interevent time distributions: slow ordering versus no ordering in the voter model.
Fernández-Gracia, J; Eguíluz, V M; San Miguel, M
2011-07-01
We introduce a general methodology of update rules accounting for arbitrary interevent time (IET) distributions in simulations of interacting agents. We consider in particular update rules that depend on the state of the agent, so that the update becomes part of the dynamical model. As an illustration we consider the voter model in fully connected, random, and scale-free networks with an activation probability inversely proportional to the time since the last action, where an action can be an update attempt (an exogenous update) or a change of state (an endogenous update). We find that in the thermodynamic limit, at variance with standard updates and the exogenous update, the system orders slowly for the endogenous update. The approach to the absorbing state is characterized by a power-law decay of the density of interfaces, observing that the mean time to reach the absorbing state might be not well defined. The IET distributions resulting from both update schemes show power-law tails.
Update rules and interevent time distributions: Slow ordering versus no ordering in the voter model
Fernández-Gracia, J.; Eguíluz, V. M.; San Miguel, M.
2011-07-01
We introduce a general methodology of update rules accounting for arbitrary interevent time (IET) distributions in simulations of interacting agents. We consider in particular update rules that depend on the state of the agent, so that the update becomes part of the dynamical model. As an illustration we consider the voter model in fully connected, random, and scale-free networks with an activation probability inversely proportional to the time since the last action, where an action can be an update attempt (an exogenous update) or a change of state (an endogenous update). We find that in the thermodynamic limit, at variance with standard updates and the exogenous update, the system orders slowly for the endogenous update. The approach to the absorbing state is characterized by a power-law decay of the density of interfaces, observing that the mean time to reach the absorbing state might be not well defined. The IET distributions resulting from both update schemes show power-law tails.
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 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 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....
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....
Financial applications of a Tabu search variable selection model
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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.
Behavioral optimization models for multicriteria portfolio selection
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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.
A MATHEMATICAL MODELLING APPROACH TO ONE-DAY CRICKET BATTING ORDERS
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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
Order-parameter model for unstable multilane traffic flow
Lubashevsky; Mahnke
2000-11-01
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 "free flow synchronized mode jam" phase transitions as well as the hysteresis in these transitions. We introduce a variable called an 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 car interaction in contrast to such variables as the mean car density and velocity being actually the zeroth and first moments of the "one-particle" distribution function. Therefore, we regard the order parameter as an additional independent state variable of traffic flow. We assume that these correlations are due to a small group of "fast" drivers and by taking into account the general properties of the driver behavior we formulate a governing equation for the order parameter. In this context we analyze the instability of homogeneous traffic flow that manifested itself in the above-mentioned phase transitions and gave rise to the hysteresis in both of them. Besides, the jam is characterized by the vehicle flows at different lanes which are independent of one another. We specify a certain simplified model in order to study the general features of the car cluster self-formation under the "free flow synchronized motion" phase transition. In particular, we show that the main local parameters of the developed cluster are determined by the state characteristics of vehicle motion only.
Tracking Models for Optioned Portfolio Selection
Liang, Jianfeng
In this paper we study a target tracking problem for the portfolio selection involving options. In particular, the portfolio in question contains a stock index and some European style options on the index. A refined tracking-error-variance methodology is adopted to formulate this problem as a multi-stage optimization model. We derive the optimal solutions based on stochastic programming and optimality conditions. Attention is paid to the structure of the optimal payoff function, which is shown to possess rich properties.
New insights in portfolio selection modeling
Zareei, Abalfazl
2016-01-01
Recent advancements in the field of network theory commence a new line of developments in portfolio selection techniques that stands on the ground of perceiving financial market as a network with assets as nodes and links accounting for various types of relationships among financial assets. In the first chapter, we model the shock propagation mechanism among assets via network theory and provide an approach to construct well-diversified portfolios that are resilient to shock propagation and c...
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...
Cross gramian approximation with Laguerre polynomials for model order reduction
Perev, Kamen
2015-11-01
This paper considers the problem of model order reduction by approximate balanced truncation with Laguerre polynomials approximation of the system cross gramian. The cross gramian contains information both for the reachability of the system as well as for its observability. The main property of the cross gramian for a square symmetric stable linear system is that its square is equal to the product of the reachability and observability gramians and therefore, the absolute values of its eigenvalues are equal to the Hankel singular values. This is the reason to use the cross gramian for computing balancing transformations for model reduction. Laguerre polynomial series representations are used to approximate the cross gramian of the system at infinity. The orthogonal polynomials of Laguerre possess good convergence properties and allow to reduce the computational complexity of the model reduction problem. Numerical experiments are performed confirming the effectiveness of the proposed method.
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.
Bayesian model selection in Gaussian regression
Abramovich, Felix
2009-01-01
We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized least squares estimation with a complexity penalty associated with a prior on the model size. We investigate the optimality properties of the resulting estimator. We establish the oracle inequality and specify conditions on the prior that imply its asymptotic minimaxity within a wide range of sparse and dense settings for "nearly-orthogonal" and "multicollinear" designs.
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 platform...... Kaggle. In this paper, we will state and try to verify a set of qualitative hypotheses about predictive modelling, both in general and in the scope of data analysis competitions. To verify our hypotheses we will look at previous competitions and their outcomes, use qualitative interviews with top...
Electroviscoelasticity of liquid/liquid interfaces: fractional-order model.
Spasic, Aleksandar M; Lazarevic, Mihailo P
2005-02-01
A number of theories that describe the behavior of liquid-liquid interfaces have been developed and applied to various dispersed systems, e.g., Stokes, Reiner-Rivelin, Ericksen, Einstein, Smoluchowski, and Kinch. A new theory of electroviscoelasticity describes the behavior of electrified liquid-liquid interfaces in fine dispersed systems and is based on a new constitutive model of liquids. According to this model liquid-liquid droplet or droplet-film structure (collective of particles) is considered as a macroscopic system with internal structure determined by the way the molecules (ions) are tuned (structured) into the primary components of a cluster configuration. How the tuning/structuring occurs depends on the physical fields involved, both potential (elastic forces) and nonpotential (resistance forces). All these microelements of the primary structure can be considered as electromechanical oscillators assembled into groups, so that excitation by an external physical field may cause oscillations at the resonant/characteristic frequency of the system itself (coupling at the characteristic frequency). Up to now, three possible mathematical formalisms have been discussed related to the theory of electroviscoelasticity. The first is the tension tensor model, where the normal and tangential forces are considered, only in mathematical formalism, regardless of their origin (mechanical and/or electrical). The second is the Van der Pol derivative model, presented by linear and nonlinear differential equations. Finally, the third model presents an effort to generalize the previous Van der Pol equation: the ordinary time derivative and integral are now replaced with the corresponding fractional-order time derivative and integral of order p<1.
Low-order models of biogenic ocean mixing
Dabiri, J. O.; Rosinelli, D.; Koumoutsakos, P.
2009-12-01
Biogenic ocean mixing, the process whereby swimming animals may affect ocean circulation, has primarily been studied using order-of-magnitude theoretical estimates and a small number of field observations. We describe numerical simulations of arrays of simplified animal shapes migrating in inviscid fluid and at finite Reynolds numbers. The effect of density stratification is modeled in the fluid dynamic equations of motion by a buoyancy acceleration term, which arises due to perturbations to the density field by the migrating bodies. The effects of fluid viscosity, body spacing, and array configuration are investigated to identify scenarios in which a meaningful contribution to ocean mixing by swimming animals is plausible.
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...
Second order kinetic Kohn-Sham lattice model
Solorzano, Sergio; Herrmann, Hans
2016-01-01
In this work we introduce a new semi-implicit second order correction scheme to the kinetic Kohn-Sham lattice model. The new approach is validated by performing realistic exchange-correlation energy calculations of atoms and dimers of the first two rows of the periodic table finding good agreement with the expected values. Additionally we simulate the ethane molecule where we recover the bond lengths and compare the results with standard methods. Finally, we discuss the current applicability of pseudopotentials within the lattice kinetic Kohn-Sham approach.
Higher Order Mean Squared Error of Generalized Method of Moments Estimators for Nonlinear Models
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Yi Hu
2014-01-01
Full Text Available Generalized method of moments (GMM has been widely applied for estimation of nonlinear models in economics and finance. Although generalized method of moments has good asymptotic properties under fairly moderate regularity conditions, its finite sample performance is not very well. In order to improve the finite sample performance of generalized method of moments estimators, this paper studies higher-order mean squared error of two-step efficient generalized method of moments estimators for nonlinear models. Specially, we consider a general nonlinear regression model with endogeneity and derive the higher-order asymptotic mean square error for two-step efficient generalized method of moments estimator for this model using iterative techniques and higher-order asymptotic theories. Our theoretical results allow the number of moments to grow with sample size, and are suitable for general moment restriction models, which contains conditional moment restriction models as special cases. The higher-order mean square error can be used to compare different estimators and to construct the selection criteria for improving estimator’s finite sample performance.
Inflation model selection meets dark radiation
Tram, Thomas; Vallance, Robert; Vennin, Vincent
2017-01-01
We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species Neff. Using a minimal dataset (Planck low-l polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptions are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when Neff is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H0 makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H0 tension would have deep consequences for inflation model selection.
Phase-field-crystal model for fcc ordering.
Wu, Kuo-An; Adland, Ari; Karma, Alain
2010-06-01
We develop and analyze a two-mode phase-field-crystal model to describe fcc ordering. The model is formulated by coupling two different sets of crystal density waves corresponding to and reciprocal lattice vectors, which are chosen to form triads so as to produce a simple free-energy landscape with coexistence of crystal and liquid phases. The feasibility of the approach is demonstrated with numerical examples of polycrystalline and (111) twin growth. We use a two-mode amplitude expansion to characterize analytically the free-energy landscape of the model, identifying parameter ranges where fcc is stable or metastable with respect to bcc. In addition, we derive analytical expressions for the elastic constants for both fcc and bcc. Those expressions show that a nonvanishing amplitude of [200] density waves is essential to obtain mechanically stable fcc crystals with a nonvanishing tetragonal shear modulus (C11-C12)/2. We determine the model parameters for specific materials by fitting the peak liquid structure factor properties and solid-density wave amplitudes following the approach developed for bcc [K.-A. Wu and A. Karma, Phys. Rev. B 76, 184107 (2007)]. This procedure yields reasonable predictions of elastic constants for both bcc Fe and fcc Ni using input parameters from molecular dynamics simulations. The application of the model to two-dimensional square lattices is also briefly examined.
Naugler, Christopher T; Guo, Maggie
2016-04-01
There is a need to develop and validate new metrics to access the appropriateness of laboratory test requests. The mean abnormal result rate (MARR) is a proposed measure of ordering selectivity, the premise being that higher mean abnormal rates represent more selective test ordering. As a validation of this metric, we compared the abnormal rate of lab tests with the number of tests ordered on the same requisition. We hypothesized that requisitions with larger numbers of requested tests represent less selective test ordering and therefore would have a lower overall abnormal rate. We examined 3,864,083 tests ordered on 451,895 requisitions and found that the MARR decreased from about 25% if one test was ordered to about 7% if nine or more tests were ordered, consistent with less selectivity when more tests were ordered. We then examined the MARR for community-based testing for 1,340 family physicians and found both a wide variation in MARR as well as an inverse relationship between the total tests ordered per year per physician and the physician-specific MARR. The proposed metric represents a new utilization metric for benchmarking relative selectivity of test orders among physicians. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Ordered LOGIT Model approach for the determination of financial distress.
Kinay, B
2010-01-01
Nowadays, as a result of the global competition encountered, numerous companies come up against financial distresses. To predict and take proactive approaches for those problems is quite important. Thus, the prediction of crisis and financial distress is essential in terms of revealing the financial condition of companies. In this study, financial ratios relating to 156 industrial firms that are quoted in the Istanbul Stock Exchange are used and probabilities of financial distress are predicted by means of an ordered logit regression model. By means of Altman's Z Score, the dependent variable is composed by scaling the level of risk. Thus, a model that can compose an early warning system and predict financial distress is proposed.
Low-order modelling of droplets on hydrophobic surfaces
Matar, Omar; Wray, Alex; Kahouadji, Lyes; Davis, Stephen
2015-11-01
We consider the behaviour of a droplet deposited onto a hydrophobic substrate. This and associated problems have garnered a wide degree of attention due to their significance in industrial and experimental settings, such as the post-rupture dewetting problem. These problems have generally defied low-order analysis due to the multi-valued nature of the interface, but we show here how to overcome this in this instance. We first discuss the static problem: when the droplet is stationary, its shape is prescribed by an ordinary differential equation (ODE) given by balancing gravitational and capillary stresses at the interface. This is dependent on the contact angle, the Bond number and the volume of the drop. In the high Bond number limit, we derive several low-order models of varying complexity to predict the shape of such drops. These are compared against numerical calculations of the ODE. We then approach the dynamic problem: in this case, the full Stokes equations throughout the drop must be considered. A low-order approach is used by solving the biharmonic equation in a coordinate system naturally mapping to the droplet shape. The results are compared against direct numerical simulations. EPSRC Programme Grant, MEMPHIS, EP/K0039761/1, EPSRC Doctoral Prize Fellowship (AWW).
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing...... cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss......Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent...
The Markowitz model for portfolio selection
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MARIAN ZUBIA ZUBIAURRE
2002-06-01
Full Text Available Since its first appearance, The Markowitz model for portfolio selection has been a basic theoretical reference, opening several new development options. However, practically it has not been used among portfolio managers and investment analysts in spite of its success in the theoretical field. With our paper we would like to show how The Markowitz model may be of great help in real stock markets. Through an empirical study we want to verify the capability of Markowitz’s model to present portfolios with higher profitability and lower risk than the portfolio represented by IBEX-35 and IGBM indexes. Furthermore, we want to test suggested efficiency of these indexes as representatives of market theoretical-portfolio.
Model selection for Poisson processes with covariates
Sart, Mathieu
2011-01-01
We observe $n$ inhomogeneous Poisson processes with covariates and aim at estimating their intensities. To handle this problem, we assume that the intensity of each Poisson process is of the form $s (\\cdot, x)$ where $x$ is the covariate and where $s$ is an unknown function. We propose a model selection approach where the models are used to approximate the multivariate function $s$. We show that our estimator satisfies an oracle-type inequality under very weak assumptions both on the intensities and the models. By using an Hellinger-type loss, we establish non-asymptotic risk bounds and specify them under various kind of assumptions on the target function $s$ such as being smooth or composite. Besides, we show that our estimation procedure is robust with respect to these assumptions.
Information criteria for astrophysical model selection
Liddle, A R
2007-01-01
Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from WMAP3 data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.
Entropic Priors and Bayesian Model Selection
Brewer, Brendon J
2009-01-01
We demonstrate that the principle of maximum relative entropy (ME), used judiciously, can ease the specification of priors in model selection problems. The resulting effect is that models that make sharp predictions are disfavoured, weakening the usual Bayesian "Occam's Razor". This is illustrated with a simple example involving what Jaynes called a "sure thing" hypothesis. Jaynes' resolution of the situation involved introducing a large number of alternative "sure thing" hypotheses that were possible before we observed the data. However, in more complex situations, it may not be possible to explicitly enumerate large numbers of alternatives. The entropic priors formalism produces the desired result without modifying the hypothesis space or requiring explicit enumeration of alternatives; all that is required is a good model for the prior predictive distribution for the data. This idea is illustrated with a simple rigged-lottery example, and we outline how this idea may help to resolve a recent debate amongst ...
Bayesian model selection applied to artificial neural networks used for water resources modeling
Kingston, Greer B.; Maier, Holger R.; Lambert, Martin F.
2008-04-01
Artificial neural networks (ANNs) have proven to be extremely valuable tools in the field of water resources engineering. However, one of the most difficult tasks in developing an ANN is determining the optimum level of complexity required to model a given problem, as there is no formal systematic model selection method. This paper presents a Bayesian model selection (BMS) method for ANNs that provides an objective approach for comparing models of varying complexity in order to select the most appropriate ANN structure. The approach uses Markov Chain Monte Carlo posterior simulations to estimate the evidence in favor of competing models and, in this study, three known methods for doing this are compared in terms of their suitability for being incorporated into the proposed BMS framework for ANNs. However, it is acknowledged that it can be particularly difficult to accurately estimate the evidence of ANN models. Therefore, the proposed BMS approach for ANNs incorporates a further check of the evidence results by inspecting the marginal posterior distributions of the hidden-to-output layer weights, which unambiguously indicate any redundancies in the hidden layer nodes. The fact that this check is available is one of the greatest advantages of the proposed approach over conventional model selection methods, which do not provide such a test and instead rely on the modeler's subjective choice of selection criterion. The advantages of a total Bayesian approach to ANN development, including training and model selection, are demonstrated on two synthetic and one real world water resources case study.
Rank-based model selection for multiple ions quantum tomography
Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian
2012-10-01
The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the ‘sparsity’ properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods—the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)—two models consisting of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of four ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a four ions experiment aimed at creating a Smolin state of rank 4. By applying the two methods together with the Pearson χ2 test we conclude that the data can be suitably described with a model whose rank is between 7 and 9. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements.
Desriac, Noémie; Coroller, Louis; Jannic, Frederic; Postollec, Florence; Sohier, Danièle
2015-02-01
In order to integrate omics data to quantitative microbiological risk assessment in foods, gene expressions may serve as bacterial behaviour biomarkers. In this study an integrative approach encompassing predictive modelling and mRNAs quantifications, was followed to select molecular biomarkers to further predict the acid resistance of Bacillus weihenstephanensis. A multivariate analysis was performed to correlate the acid bacterial resistance and the gene expression of vegetative cells with or without exposure to stressing conditions. This mathematical method provides the advantage to take gene expressions and their interactions into account. The use of the Partial Least Squares algorithm allowed the selection of nine genes as acid resistance biomarkers among thirty targeted genes. According to their involvement in the general acid stress response of Bacillus, these genes were assigned to three different biological modules namely, metabolic rearrangements, general stress response and oxidative stress response. The oxidative stress response appeared as the major activated biological module in B. weihenstephanensis cells submitted to acid stress conditions. Furthermore, as a firstly described model, the developed concept showed promising results to further be used to predict bacterial resistance using gene expression. Thus, this study underlines the possibility to integrate the bacterial physiology state, using omics biomarkers, into bacterial behaviour modelling and provide mechanistic understanding in acid bacterial resistance mechanisms.
Falk, Richard A.
The monograph examines the relationship of nuclear power to world order. The major purpose of the document is to stimulate research, education, dialogue, and political action for a just and peaceful world order. The document is presented in five chapters. Chapter I stresses the need for a system of global security to counteract dangers brought…
Appropriate model selection methods for nonstationary generalized extreme value models
Kim, Hanbeen; Kim, Sooyoung; Shin, Hongjoon; Heo, Jun-Haeng
2017-04-01
Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.
Reduced order modeling in iTOUGH2
Pau, George Shu Heng; Zhang, Yingqi; Finsterle, Stefan; Wainwright, Haruko; Birkholzer, Jens
2014-04-01
The inverse modeling and uncertainty quantification capabilities of iTOUGH2 are augmented with reduced order models (ROMs) that act as efficient surrogates for computationally expensive high fidelity models (HFMs). The implementation of the ROM capabilities involves integration of three main computational components. The first component is the ROM itself. Two response surface approximations are currently implemented: Gaussian process regression (GPR) and radial basis function (RBF) interpolation. The second component is a multi-output adaptive sampling procedure that determines the sample points used to construct the ROMs. The third component involves defining appropriate error measures for the adaptive sampling procedure, allowing ROMs to be constructed efficiently with limited user intervention. Details in all three components must complement one another to obtain an accurate approximation. The new capability and its integration with other analysis tools within iTOUGH2 are demonstrated in two examples. The results from using the ROMs in an uncertainty quantification analysis and a global sensitivity analysis compare favorably with the results obtained using the HFMs. GPR is more accurate than RBF, but the difference can be small and similar conclusion can be deduced from the analyses. In the second example involving a realistic numerical model for a hypothetical industrial-scale carbon storage project in the Southern San Joaquin Basin, California, USA, significant reduction in computational effort can be achieved when ROMs are used to perform a rigorous global sensitivity analysis.
QOS Aware Formalized Model for Semantic Web Service Selection
Directory of Open Access Journals (Sweden)
Divya Sachan
2014-10-01
Full Text Available Selecting the most relevant Web Service according to a client requirement is an onerous task, as innumerous number of functionally same Web Services(WS are listed in UDDI registry. WS are functionally same but their Quality and performance varies as per service providers. A web Service Selection Process involves two major points: Recommending the pertinent Web Service and avoiding unjustifiable web service. The deficiency in keyword based searching is that it doesn’t handle the client request accurately as keyword may have ambiguous meaning on different scenarios. UDDI and search engines all are based on keyword search, which are lagging behind on pertinent Web service selection. So the search mechanism must be incorporated with the Semantic behavior of Web Services. In order to strengthen this approach, the proposed model is incorporated with Quality of Services (QoS based Ranking of semantic web services.
Ancestral process and diffusion model with selection
Mano, Shuhei
2008-01-01
The ancestral selection graph in population genetics introduced by Krone and Neuhauser (1997) is an analogue to the coalescent genealogy. The number of ancestral particles, backward in time, of a sample of genes is an ancestral process, which is a birth and death process with quadratic death and linear birth rate. In this paper an explicit form of the number of ancestral particle is obtained, by using the density of the allele frequency in the corresponding diffusion model obtained by Kimura (1955). It is shown that fixation is convergence of the ancestral process to the stationary measure. The time to fixation of an allele is studied in terms of the ancestral process.
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.
Capillary wave approach to order-order fluid interfaces in the 3D three-state Potts model
Provero, P
1994-01-01
The physics of fluid interfaces between domains of different magnetization in the ordered phase of the 3D three-state Potts model is studied by means of a Monte Carlo simulation. It is shown that finite--size effects in the interface free energy are well described by the capillary wave model at two loop order, supporting the idea of the universality of this description of fluid interfaces in 3D statistical models.
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.
Improving randomness characterization through Bayesian model selection
R., Rafael Díaz-H; Martínez, Alí M Angulo; U'Ren, Alfred B; Hirsch, Jorge G; Marsili, Matteo; Castillo, Isaac Pérez
2016-01-01
Nowadays random number generation plays an essential role in technology with important applications in areas ranging from cryptography, which lies at the core of current communication protocols, to Monte Carlo methods, and other probabilistic algorithms. In this context, a crucial scientific endeavour is to develop effective methods that allow the characterization of random number generators. However, commonly employed methods either lack formality (e.g. the NIST test suite), or are inapplicable in principle (e.g. the characterization derived from the Algorithmic Theory of Information (ATI)). In this letter we present a novel method based on Bayesian model selection, which is both rigorous and effective, for characterizing randomness in a bit sequence. We derive analytic expressions for a model's likelihood which is then used to compute its posterior probability distribution. Our method proves to be more rigorous than NIST's suite and the Borel-Normality criterion and its implementation is straightforward. We...
Directory of Open Access Journals (Sweden)
Salvador Lucas
2015-12-01
Full Text Available Recent developments in termination analysis for declarative programs emphasize the use of appropriate models for the logical theory representing the program at stake as a generic approach to prove termination of declarative programs. In this setting, Order-Sorted First-Order Logic provides a powerful framework to represent declarative programs. It also provides a target logic to obtain models for other logics via transformations. We investigate the automatic generation of numerical models for order-sorted first-order logics and its use in program analysis, in particular in termination analysis of declarative programs. We use convex domains to give domains to the different sorts of an order-sorted signature; we interpret the ranked symbols of sorted signatures by means of appropriately adapted convex matrix interpretations. Such numerical interpretations permit the use of existing algorithms and tools from linear algebra and arithmetic constraint solving to synthesize the models.
How many separable sources? Model selection in independent components analysis.
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 computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian.
Asset pricing model selection: Indonesian Stock Exchange
Pasaribu, Rowland Bismark Fernando
2010-01-01
The Capital Asset Pricing Model (CAPM) has dominated finance theory for over thirty years; it suggests that the market beta alone is sufficient to explain stock returns. However evidence shows that the cross-section of stock returns cannot be described solely by the one-factor CAPM. Therefore, the idea is to add other factors in order to complete the beta in explaining the price movements in the stock exchange. The Arbitrage Pricing Theory (APT) has been proposed as the first multifactor succ...
Accelerated gravitational wave parameter estimation with reduced order modeling.
Canizares, Priscilla; Field, Scott E; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2015-02-20
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ∼30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ∼70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.
High order discretization schemes for stochastic volatility models
Jourdain, Benjamin
2009-01-01
In usual stochastic volatility models, the process driving the volatility of the asset price evolves according to an autonomous one-dimensional stochastic differential equation. We assume that the coefficients of this equation are smooth. Using It\\^o's formula, we get rid, in the asset price dynamics, of the stochastic integral with respect to the Brownian motion driving this SDE. Taking advantage of this structure, we propose - a scheme, based on the Milstein discretization of this SDE, with order one of weak trajectorial convergence for the asset price, - a scheme, based on the Ninomiya-Victoir discretization of this SDE, with order two of weak convergence for the asset price. We also propose a specific scheme with improved convergence properties when the volatility of the asset price is driven by an Orstein-Uhlenbeck process. We confirm the theoretical rates of convergence by numerical experiments and show that our schemes are well adapted to the multilevel Monte Carlo method introduced by Giles [2008a,b].
Computational design of patterned interfaces using reduced order models
Vattré, A. J.; Abdolrahim, N.; Kolluri, K.; Demkowicz, M. J.
2014-01-01
Patterning is a familiar approach for imparting novel functionalities to free surfaces. We extend the patterning paradigm to interfaces between crystalline solids. Many interfaces have non-uniform internal structures comprised of misfit dislocations, which in turn govern interface properties. We develop and validate a computational strategy for designing interfaces with controlled misfit dislocation patterns by tailoring interface crystallography and composition. Our approach relies on a novel method for predicting the internal structure of interfaces: rather than obtaining it from resource-intensive atomistic simulations, we compute it using an efficient reduced order model based on anisotropic elasticity theory. Moreover, our strategy incorporates interface synthesis as a constraint on the design process. As an illustration, we apply our approach to the design of interfaces with rapid, 1-D point defect diffusion. Patterned interfaces may be integrated into the microstructure of composite materials, markedly improving performance. PMID:25169868
Basic first-order model theory in Mizar
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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.
Inflationary scenarios in Starobinsky model with higher order corrections
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Artymowski, Michał [Institute of Physics, Jagiellonian University,Łojasiewicza 11, 30-348 Kraków (Poland); Lalak, Zygmunt [Institute of Theoretical Physics, Faculty of Physics, University of Warsaw,ul. Pasteura 5, 02-093 Warsaw (Poland); Lewicki, Marek [Institute of Theoretical Physics, Faculty of Physics, University of Warsaw,ul. Pasteura 5, 02-093 Warsaw (Poland); Michigan Center for Theoretical Physics, University of Michigan,450 Church Street, Ann Arbor MI 48109 (United States)
2015-06-17
We consider the Starobinsky inflation with a set of higher order corrections parametrised by two real coefficients λ{sub 1} ,λ{sub 2}. In the Einstein frame we have found a potential with the Starobinsky plateau, steep slope and possibly with an additional minimum, local maximum or a saddle point. We have identified three types of inflationary behaviour that may be generated in this model: i) inflation on the plateau, ii) at the local maximum (topological inflation), iii) at the saddle point. We have found limits on parameters λ{sub i} and initial conditions at the Planck scale which enable successful inflation and disable eternal inflation at the plateau. We have checked that the local minimum away from the GR vacuum is stable and that the field cannot leave it neither via quantum tunnelling nor via thermal corrections.
PROPOSAL OF AN EMPIRICAL MODEL FOR SUPPLIERS SELECTION
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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.
Inflation Model Selection meets Dark Radiation
Tram, Thomas; Vennin, Vincent
2016-01-01
We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard $\\Lambda\\mathrm{CDM}$ model and an extension including dark radiation parametrised by its effective number of relativistic species $N_\\mathrm{eff}$. We find that the observational status of most inflationary models is unchanged, with the exception of potentials such as power-law inflation that predict a value for the scalar spectral index that is too large in $\\Lambda\\mathrm{CDM}$ but which can be accommodated when $N_\\mathrm{eff}$ is allowed to vary. In this case, cosmic microwave background data indicate that power-law inflation is one of the best models together with plateau potentials. However, contrary to plateau p...
Gain Enhancement of a Wide Slot Antenna Using a Second-Order Bandpass Frequency Selective Surface
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A. Chatterjee
2015-06-01
Full Text Available Gain enhancement of a wide slot antenna over a wide frequency band using a low profile, second order bandpass frequency selective surface (FSS as a superstrate is presented in this paper. The proposed multilayered FSS with non-resonant unit cells in each layer allows in-phase transmission of waves radiated from the antenna over a 3dB bandwidth of about 50%. The design allows an enhancement of upto 4dBi in the antenna gain over the entire frequency band (5-8GHz of operation. The FSS provides a very low insertion loss between the two transmission poles along with a linearly decreasing transmission phase over the band. The composite structure shows an impedance bandwidth (-10dB of 65% with an average gain between 6-8dBi over the frequency band with a peak gain of 9dBi. Measurement results of the fabricated prototype matches well with the predicted values.
Selecting CO2 Sources for CO2 Utilization by Environmental-Merit-Order Curves.
von der Assen, Niklas; Müller, Leonard J; Steingrube, Annette; Voll, Philip; Bardow, André
2016-02-01
Capture and utilization of CO2 as alternative carbon feedstock for fuels, chemicals, and materials aims at reducing greenhouse gas emissions and fossil resource use. For capture of CO2, a large variety of CO2 sources exists. Since they emit much more CO2 than the expected demand for CO2 utilization, the environmentally most favorable CO2 sources should be selected. For this purpose, we introduce the environmental-merit-order (EMO) curve to rank CO2 sources according to their environmental impacts over the available CO2 supply. To determine the environmental impacts of CO2 capture, compression and transport, we conducted a comprehensive literature study for the energy demands of CO2 supply, and constructed a database for CO2 sources in Europe. Mapping these CO2 sources reveals that CO2 transport distances are usually small. Thus, neglecting transport in a first step, we find that environmental impacts are minimized by capturing CO2 first from chemical plants and natural gas processing, then from paper mills, power plants, and iron and steel plants. In a second step, we computed regional EMO curves considering transport and country-specific impacts for energy supply. Building upon regional EMO curves, we identify favorable locations for CO2 utilization with lowest environmental impacts of CO2 supply, so-called CO2 oases.
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.
Higher-order models versus direct hierarchical models: g as superordinate or breadth factor?
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GILLES E. GIGNAC
2008-03-01
Full Text Available Intelligence research appears to have overwhelmingly endorsed a superordinate (higher-order model conceptualization of g, in comparison to the relatively less well-known breadth conceptualization of g, as represented by the direct hierarchical model. In this paper, several similarities and distinctions between the indirect and direct hierarchical models are delineated. Based on the re-analysis of five correlation matrices, it was demonstrated via CFA that the conventional conception of g as a higher-order superordinate factor was likely not as plausible as a first-order breadth factor. The results are discussed in light of theoretical advantages of conceptualizing g as a first-order factor. Further, because the associations between group-factors and g are constrained to zero within a direct hierarchical model, previous observations of isomorphic associations between a lower-order group factor and g are questioned.
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.
Fuzzy modelling for selecting headgear types.
Akçam, M Okan; Takada, Kenji
2002-02-01
The purpose of this study was to develop a computer-assisted inference model for selecting appropriate types of headgear appliance for orthodontic patients and to investigate its clinical versatility as a decision-making aid for inexperienced clinicians. Fuzzy rule bases were created for degrees of overjet, overbite, and mandibular plane angle variables, respectively, according to subjective criteria based on the clinical experience and knowledge of the authors. The rules were then transformed into membership functions and the geometric mean aggregation was performed to develop the inference model. The resultant fuzzy logic was then tested on 85 cases in which the patients had been diagnosed as requiring headgear appliances. Eight experienced orthodontists judged each of the cases, and decided if they 'agreed', 'accepted', or 'disagreed' with the recommendations of the computer system. Intra-examiner agreements were investigated using repeated judgements of a set of 30 orthodontic cases and the kappa statistic. All of the examiners exceeded a kappa score of 0.7, allowing them to participate in the test run of the validity of the proposed inference model. The examiners' agreement with the system's recommendations was evaluated statistically. The average satisfaction rate of the examiners was 95.6 per cent and, for 83 out of the 85 cases, 97.6 per cent. The majority of the examiners (i.e. six or more out of the eight) were satisfied with the recommendations of the system. Thus, the usefulness of the proposed inference logic was confirmed.
SLAM: A Connectionist Model for Attention in Visual Selection Tasks.
Phaf, R. Hans; And Others
1990-01-01
The SeLective Attention Model (SLAM) performs visual selective attention tasks and demonstrates that object selection and attribute selection are both necessary and sufficient for visual selection. The SLAM is described, particularly with regard to its ability to represent an individual subject performing filtering tasks. (TJH)
Formal modeling and verification of fractional order linear systems.
Zhao, Chunna; Shi, Likun; Guan, Yong; Li, Xiaojuan; Shi, Zhiping
2016-05-01
This paper presents a formalization of a fractional order linear system in a higher-order logic (HOL) theorem proving system. Based on the formalization of the Grünwald-Letnikov (GL) definition, we formally specify and verify the linear and superposition properties of fractional order systems. The proof provides a rigor and solid underpinnings for verifying concrete fractional order linear control systems. Our implementation in HOL demonstrates the effectiveness of our approach in practical applications.
Estimation of a multivariate mean under model selection uncertainty
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Georges Nguefack-Tsague
2014-05-01
Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty. When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.
Selection between foreground models for global 21-cm experiments
Harker, Geraint
2015-01-01
The precise form of the foregrounds for sky-averaged measurements of the 21-cm line during and before the epoch of reionization is unknown. We suggest that the level of complexity in the foreground models used to fit global 21-cm data should be driven by the data, under a Bayesian model selection methodology. A first test of this approach is carried out by applying nested sampling to simplified models of global 21-cm data to compute the Bayesian evidence for the models. If the foregrounds are assumed to be polynomials of order n in log-log space, we can infer the necessity to use n=4 rather than n=3 with <2h of integration with limited frequency coverage, for reasonable values of the n=4 coefficient. Using a higher-order polynomial does not necessarily prevent a significant detection of the 21-cm signal. Even for n=8, we can obtain very strong evidence distinguishing a reasonable model for the signal from a null model with 128h of integration. More subtle features of the signal may, however, be lost if the...
Fourth order phase-field model for local max-ent approximants applied to crack propagation
Amiri, Fatemeh; Millán, Daniel; Arroyo Balaguer, Marino; Silani, Mohammad; Rabczuk, Timon
2016-01-01
We apply a fourth order phase-field model for fracture based on local maximum entropy (LME) approximants. The higher order continuity of the meshfree LME approximants allows to directly solve the fourth order phase-field equations without splitting the fourth order differential equation into two second order differential equations. We will first show that the crack surface can be captured more accurately in the fourth order model. Furthermore, less nodes are needed for the fourth order model ...
Measuring the spatial frequency selectivity of second-order texture mechanisms.
Sutter, A; Sperling, G; Chubb, C
1995-04-01
Recent investigations of texture and motion perception suggest two early filtering stages: an initial stage of selective linear filtering followed by rectification and a second stage of linear filtering. Here we demonstrate that there are differently scaled second-stage filters, and we measure their contrast modulation sensitivity as a function of spatial frequency. Our stimuli are Gabor modulations of a suprathreshold, bandlimited, isotropic carrier noise. The subjects' task is to discriminate between two possible orientations of the Gabor. Carrier noises are filtered into four octave-wide bands, centered at m = 2, 4, 8, and 16 c/deg. The Gabor test signals are w = 0.5, 1, 2, 4 and 8 c/deg. The threshold modulation of the test signal is measured for all 20 combinations of m and w. For each carrier frequency m, the Gabor test frequency w to which subjects are maximally sensitive appears to be approximately 3-4 octaves below m. The consistent m x w interaction suggests that each second-stage spatial filter may be differentially tuned to a particular first-stage spatial frequency. The most sensitive combination is a second-stage filter of 1 c/deg with first-stage inputs of 8-16 c/deg. We conclude that second-order texture perception appears to utilize multiple channels tuned to spatial frequency and orientation, with channels tuned to low modulation frequencies appearing to be best served by carrier frequencies 8 to 16 times higher than the modulations they are tuned to detect.
Model Selection Through Sparse Maximum Likelihood Estimation
Banerjee, Onureena; D'Aspremont, Alexandre
2007-01-01
We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l_1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l_1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright & Jordan (2006)), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for...
Hidden Markov Model for Stock Selection
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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.
Construction of energy-stable Galerkin reduced order models.
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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
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.
A supplier-selection model with classification and joint replenishment of inventory items
Mohammaditabar, Davood; Hassan Ghodsypour, Seyed
2016-06-01
Since inventory costs are closely related to suppliers, many models in the literature have selected the suppliers and also allocated orders, simultaneously. Such models usually consider either a single inventory item or multiple inventory items which have independent holding and ordering costs. However, in practice, ordering multiple items from the same supplier leads to a reduction in ordering costs. This paper presents a model in capacity-constrained supplier-selection and order-allocation problem, which considers the joint replenishment of inventory items with a direct grouping approach. In such supplier-selection problems, the following items are considered: a fixed major ordering cost to each supplier, which is independent from the items in the order; a minor ordering cost for each item ordered to each supplier; and the inventory holding and purchasing costs. To solve the developed NP-hard problem, a simulated annealing algorithm was proposed and then compared to a modified genetic algorithm of the literature. The numerical example represented that the number of groups and selected suppliers were reduced when the major ordering cost increased in comparison to other costs. There were also more savings when the number of groups was determined by the model in comparison to predetermined number of groups or no grouping scenarios.
Numerical simulation for SI model with variable-order fractional
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mohamed mohamed
2016-04-01
Full Text Available In this paper numerical studies for the variable-order fractional delay differential equations are presented. Adams-Bashforth-Moulton algorithm has been extended to study this problem, where the derivative is defined in the Caputo variable-order fractional sense. Special attention is given to prove the error estimate of the proposed method. Numerical test examples are presented to demonstrate utility of the method. Chaotic behaviors are observed in variable-order one dimensional delayed systems.
State reduced order models for the modelling of the thermal behavior of buildings
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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)
Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation
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Guodong Wang
2014-01-01
Full Text Available Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.
A Network Analysis Model for Selecting Sustainable Technology
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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.
Wind Farm Flow Modeling using an Input-Output Reduced-Order Model
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Annoni, Jennifer; Gebraad, Pieter; Seiler, Peter
2016-08-01
Wind turbines in a wind farm operate individually to maximize their own power regardless of the impact of aerodynamic interactions on neighboring turbines. There is the potential to increase power and reduce overall structural loads by properly coordinating turbines. To perform control design and analysis, a model needs to be of low computational cost, but retains the necessary dynamics seen in high-fidelity models. The objective of this work is to obtain a reduced-order model that represents the full-order flow computed using a high-fidelity model. A variety of methods, including proper orthogonal decomposition and dynamic mode decomposition, can be used to extract the dominant flow structures and obtain a reduced-order model. In this paper, we combine proper orthogonal decomposition with a system identification technique to produce an input-output reduced-order model. This technique is used to construct a reduced-order model of the flow within a two-turbine array computed using a large-eddy simulation.
Unifying models for X-ray selected and Radio selected BL Lac Objects
Fossati, G; Ghisellini, G; Maraschi, L; Brera-Merate, O A
1997-01-01
We discuss alternative interpretations of the differences in the Spectral Energy Distributions (SEDs) of BL Lacs found in complete Radio or X-ray surveys. A large body of observations in different bands suggests that the SEDs of BL Lac objects appearing in X-ray surveys differ from those appearing in radio surveys mainly in having a (synchrotron) spectral cut-off (or break) at much higher frequency. In order to explain the different properties of radio and X-ray selected BL Lacs Giommi and Padovani proposed a model based on a common radio luminosity function. At each radio luminosity, objects with high frequency spectral cut-offs are assumed to be a minority. Nevertheless they dominate the X-ray selected population due to the larger X-ray-to-radio-flux ratio. An alternative model explored here (reminiscent of the orientation models previously proposed) is that the X-ray luminosity function is "primary" and that at each X-ray luminosity a minority of objects has larger radio-to-X-ray flux ratio. The prediction...
Reduced Order Aeroservoelastic Models with Rigid Body Modes Project
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...
Dispersion-free monochromatization method for selecting a single-order harmonic beam
Takahashi, Eiji J; Ichimaru, Satoshi; Midorikawa, Katsumi
2015-01-01
We propose a method to monochromatize multiple orders of high harmonics by using a proper designed multilayer mirror. Multilayer mirrors designed by our concept realize the perfect extraction of a single-order harmonic from multiple-order harmonic beam, and exhibit broadband tenability and high reflectivity in the soft-x-ray region. Furthermore, the proposed monochromatization method can preserve the femtosecond to attosecond pulse duration for the reflected beam. This device is very useful for ultrafast soft x-ray experiments that require high-order harmonic beams, such as femtosecond/attosecond, time-resolved, pump-probe spectroscopy.
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 of linear time invariant systems
Directory of Open Access Journals (Sweden)
Lj. Radić-Weissenfeld
2008-05-01
Full Text Available This paper addresses issues related to the order reduction of systems with multiple input/output ports. The order reduction is divided up into two steps. The first step is the standard order reduction method based on the multipoint approximation of system matrices by applying Krylov subspace. The second step is based on the rejection of the weak part of a system. To recognise the weak system part, Lyapunov equations are used. Thus, this paper introduces efficient solutions of the Lyapunov equations for port to port subsystems.
Strategic Competence as a Fourth-Order Factor Model: A Structural Equation Modeling Approach
Phakiti, Aek
2008-01-01
This article reports on an empirical study that tests a fourth-order factor model of strategic competence through the use of structural equation modeling (SEM). The study examines the hierarchical relationship of strategic competence to (a) strategic knowledge of cognitive and metacognitive strategy use in general (i.e., trait) and (b) strategic…
Distributed-order diffusion equations and multifractality: Models and solutions
Sandev, Trifce; Chechkin, Aleksei V.; Korabel, Nickolay; Kantz, Holger; Sokolov, Igor M.; Metzler, Ralf
2015-10-01
We study distributed-order time fractional diffusion equations characterized by multifractal memory kernels, in contrast to the simple power-law kernel of common time fractional diffusion equations. Based on the physical approach to anomalous diffusion provided by the seminal Scher-Montroll-Weiss continuous time random walk, we analyze both natural and modified-form distributed-order time fractional diffusion equations and compare the two approaches. The mean squared displacement is obtained and its limiting behavior analyzed. We derive the connection between the Wiener process, described by the conventional Langevin equation and the dynamics encoded by the distributed-order time fractional diffusion equation in terms of a generalized subordination of time. A detailed analysis of the multifractal properties of distributed-order diffusion equations is provided.
Two -Dimensional Wavelength Selective Diffraction by High-Order Three-Dimensional Composite Grating
Institute of Scientific and Technical Information of China (English)
Kohji; Furuhashi; Hideaki; Okayama; Hirochika; Nakajima
2003-01-01
We propose a wavelength selective diffraction using reflectors placed on three-dimensional grid cross points. Different wavelengths are separated into spots distributed in two-dimensional plane. Compact device with high port counts is attainable.
The detection of observations possibly influential for model selection
Ph.H.B.F. Franses (Philip Hans)
1991-01-01
textabstractModel selection can involve several variables and selection criteria. A simple method to detect observations possibly influential for model selection is proposed. The potentials of this method are illustrated with three examples, each of which is taken from related studies.
Evolvability Search: Directly Selecting for Evolvability in order to Study and Produce It
Mengistu, Henok; Lehman, Joel Anthony; Clune, Jeff
2016-01-01
One hallmark of natural organisms is their significant evolvability, i.e.,their increased potential for further evolution. However, reproducing such evolvability in artificial evolution remains a challenge, which both reduces the performance of evolutionary algorithms and inhibits the study 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,...
Directory of Open Access Journals (Sweden)
Kotb A.E.H.M. Kotb
2011-01-01
Full Text Available Problem statement: In this study, we provide a simple method to determine the inventory policy of probabilistic single-item Economic Order Quantity (EOQ model, that has varying order cost and zero lead time. The model is restricted to the expected holding cost and the expected available limited storage space. Approach: The annual expected total cost is composed of three components (expected purchase cost, expected ordering cost and expected holding cost. The problem is then solved using a modified Geometric Programming method (GP. Results: Using the annual expected total cost to determine the optimal solutions, number of periods, maximum inventory level and minimum expected total cost per period. A classical model is derived and numerical example is solved to confirm the model. Conclusion/Recommendations: The results indicated the total cost decreased with changes in optimal solutions. Possible future extension of this model was include continuous decreasing ordering function of the number of periods and introducing expected annual demand rate as a decision variable.
Ordering in Two-Dimensional Ising Models with Competing Interactions
2004-01-01
We study the 2D Ising model on a square lattice with additional non-equal diagonal next-nearest neighbor interactions. The cases of classical and quantum (transverse) models are considered. Possible phases and their locations in the space of three Ising couplings are analyzed. In particular, incommensurate phases occurring only at non-equal diagonal couplings, are predicted. We also analyze a spin-pseudospin model comprised of the quantum Ising model coupled to XY spin chains in a particular ...
Modeling Human Behaviour with Higher Order Logic: Insider Threats
Boender, Jaap; Ivanova, Marieta Georgieva; Kammüller, Florian; Primierio, Giuseppe
2014-01-01
In this paper, we approach the problem of modeling the human component in technical systems with a view on the difference between the use of model and theory in sociology and computer science. One aim of this essay is to show that building of theories and models for sociology can be compared and imp
Selective experimental review of the Standard Model
Energy Technology Data Exchange (ETDEWEB)
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 ..cap alpha../sub s/, ..cap alpha../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/, M..mu.., 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/sub 1/, theta/sub 2/, theta/sub 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 ..cap alpha../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 ..cap alpha../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.
Domain minimization and beyond: Modeling prepositional phrase ordering
Wiechmann, D.; Lohmann, A.
2013-01-01
An important account of linear ordering in syntax is John A. Hawkins' (2004) theory of cognitive efficiency and the principles of domain minimization formulated therein. In its latest formulation, the theory postulates syntactic and semantic minimization principles. With regard to the relative stren
Higher Order Hierarchical Legendre Basis Functions for Electromagnetic Modeling
DEFF Research Database (Denmark)
Jørgensen, Erik; Volakis, John L.; Meincke, Peter
2004-01-01
This paper presents a new hierarchical basis of arbitrary order for integral equations solved with the Method of Moments (MoM). The basis is derived from orthogonal Legendre polynomials which are modified to impose continuity of vector quantities between neighboring elements while maintaining mos...
Higher Order Hierarchical Legendre Basis Functions for Electromagnetic Modeling
DEFF Research Database (Denmark)
Jørgensen, Erik; Volakis, John L.; Meincke, Peter
2004-01-01
This paper presents a new hierarchical basis of arbitrary order for integral equations solved with the Method of Moments (MoM). The basis is derived from orthogonal Legendre polynomials which are modified to impose continuity of vector quantities between neighboring elements while maintaining mos...
High order fluid model for ionization fronts in streamer discharges
Markosyan, A.; Dujko, S.; Hundsdorfer, W.; Ebert, U.
2011-01-01
When non-ionized or lowly ionized matter is exposed to high electric fields, non-equilibrium ionization processes, streamer discharges, can develop. Streamers occur in nature and as well in many industrial applications such as the treatment of exhaust gasses, polluted water or biogas. A third order
Institute of Scientific and Technical Information of China (English)
许恒周; 金晶
2011-01-01
农地流转市场的发展,有利于市场机制配置土地资源,但由于土地生产、社会保障功能的双重作用,农地流转市场的发展必然会对农民养老保障方式选择产生重要影响。本文利用农户调查问卷,在测算农地市场发育程度的基础上,运用有序Probit模型实证分析了农地流转市场发育对农民养老保障模式选择的影响程度。实证分析结果表明：在影响农民养老保障模式选择的因素中,年龄、婚姻状况、文化程度、家庭劳动力数量、是否有儿子、农地流转市场发育程度、农地流转类型等都有着显著影响,并且农地流转市场发育程度每提高一个单位,农民愿意选择社会养老保障%The development of farmland transfer market is beneficial to the market allocation of land resource, however, because of the dual function of farmland resource, the development of farmland transfer market necessarily affect the mode of old-age security for farmers. With the development of farmland transfer market, the security function of farmland is gradually infirm because of change of policy environment and development of market economy, and represent the multiplicity of avail for farmers on the macro-environment, this change affect the intrinsic old-security system, gradually form the coexist condition of family old-security, self old-security and social old-security. So this paper bring forward the following hypothesis： the higher the degree of farmland transfer market, the farmers are more inclined to choose the social old-security, and there is correlativity between the development of farmland transfer market and the mode of old-security. Based on the measurement of farmland transfer market development and investigation questionnaire of farmers, this paper employ the ordered probit model to empirically analyze the influence of farmland transfer market development on selection of old-age security. Results show that age, marriage
High order overlay modeling and APC simulation with Zernike-Legendre polynomials
Ju, JawWuk; Kim, MinGyu; Lee, JuHan; Sherwin, Stuart; Hoo, George; Choi, DongSub; Lee, Dohwa; Jeon, Sanghuck; Lee, Kangsan; Tien, David; Pierson, Bill; Robinson, John C.; Levy, Ady; Smith, Mark D.
2015-03-01
Feedback control of overlay errors to the scanner is a well-established technique in semiconductor manufacturing [1]. Typically, overlay errors are measured, and then modeled by least-squares fitting to an overlay model. Overlay models are typically Cartesian polynomial functions of position within the wafer (Xw, Yw), and of position within the field (Xf, Yf). The coefficients from the data fit can then be fed back to the scanner to reduce overlay errors in future wafer exposures, usually via a historically weighted moving average. In this study, rather than using the standard Cartesian formulation, we examine overlay models using Zernike polynomials to represent the wafer-level terms, and Legendre polynomials to represent the field-level terms. Zernike and Legendre polynomials can be selected to have the same fitting capability as standard polynomials (e.g., second order in X and Y, or third order in X and Y). However, Zernike polynomials have the additional property of being orthogonal over the unit disk, which makes them appropriate for the wafer-level model, and Legendre polynomials are orthogonal over the unit square, which makes them appropriate for the field-level model. We show several benefits of Zernike/Legendre-based models in this investigation in an Advanced Process Control (APC) simulation using highly-sampled fab data. First, the orthogonality property leads to less interaction between the terms, which makes the lot-to-lot variation in the fitted coefficients smaller than when standard polynomials are used. Second, the fitting process itself is less coupled - fitting to a lower-order model, and then fitting the residuals to a higher order model gives very similar results as fitting all of the terms at once. This property makes fitting techniques such as dual pass or cascading [2] unnecessary, and greatly simplifies the options available for the model recipe. The Zernike/Legendre basis gives overlay performance (mean plus 3 sigma of the residuals
Model for personal computer system selection.
Blide, L
1987-12-01
Successful computer software and hardware selection is best accomplished by following an organized approach such as the one described in this article. The first step is to decide what you want to be able to do with the computer. Secondly, select software that is user friendly, well documented, bug free, and that does what you want done. Next, you select the computer, printer and other needed equipment from the group of machines on which the software will run. Key factors here are reliability and compatibility with other microcomputers in your facility. Lastly, you select a reliable vendor who will provide good, dependable service in a reasonable time. The ability to correctly select computer software and hardware is a key skill needed by medical record professionals today and in the future. Professionals can make quality computer decisions by selecting software and systems that are compatible with other computers in their facility, allow for future net-working, ease of use, and adaptability for expansion as new applications are identified. The key to success is to not only provide for your present needs, but to be prepared for future rapid expansion and change in your computer usage as technology and your skills grow.
A Framework Model for an Order Fulfillment System Based on Service Oriented Architecture
Institute of Scientific and Technical Information of China (English)
YANG Li-xi; LI Shi-qi
2008-01-01
To effectively implement order fulfillment, we present an integrated framework model focusing on the whole process of order fulfillment. Firstly, five aims of the OFS (order fulfillment system) are built. Then after discussing three major processes of order fulfillment, we summarize functional and quality attributes of the OFS. Subsequently, we investigate SOA (Service Oriented Architecture) and present a SOA meta-model to be an integrated framework and to fulfill quality requirements. Moreover, based on the SOA meta-model, we construct a conceptual framework model that aims to conveniently integrate other functions from different systems into the order fulfillment system. This model offers enterprises a new approach to implementing order fulfillment.
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; van den Berg, Stéphanie Martine
2017-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; Berg, van den Stephanie M.
2016-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update
Lubke, Gitta H.; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; Berg, van den Stephanie M.
2017-01-01
Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indexes such as Akaike’s information criterion (AIC) or Bayesian information criterion (BIC), and inference is done based on the
Methods for eigenvalue problems with applications in model order reduction
Rommes, J.
2007-01-01
Physical structures and processes are modeled by dynamical systems in a wide range of application areas. The increasing demand for complex components and large structures, together with an increasing demand for detail and accuracy, makes the models larger and more complicated. To be able to simulate
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...
Lopuszynski, Michal; Majewski, Jacek A.
2007-01-01
We present theoretical studies for the third-order elastic constants $C_{ijk}$ in zinc-blende nitrides AlN, GaN, and InN. Our predictions for these compounds are based on detailed ab initio calculations of strain-energy and strain-stress relations in the framework of the density functional theory. To judge the computational accuracy, we compare the ab initio calculated results for $C_{ijk}$ with experimental data available for Si and GaAs. We also underline the relation of the third-order ela...
A Processing Model for Free Word Order Languages
Rambow, O; Rambow, Owen; Joshi, Aravind K.
1995-01-01
Like many verb-final languages, Germn displays considerable word-order freedom: there is no syntactic constraint on the ordering of the nominal arguments of a verb, as long as the verb remains in final position. This effect is referred to as ``scrambling'', and is interpreted in transformational frameworks as leftward movement of the arguments. Furthermore, arguments from an embedded clause may move out of their clause; this effect is referred to as ``long-distance scrambling''. While scrambling has recently received considerable attention in the syntactic literature, the status of long-distance scrambling has only rarely been addressed. The reason for this is the problematic status of the data: not only is long-distance scrambling highly dependent on pragmatic context, it also is strongly subject to degradation due to processing constraints. As in the case of center-embedding, it is not immediately clear whether to assume that observed unacceptability of highly complex sentences is due to grammatical restric...
Prediction of Farmers’ Income and Selection of Model ARIMA
Institute of Scientific and Technical Information of China (English)
2010-01-01
Based on the research technology of scholars’ prediction of farmers’ income and the data of per capita annual net income in rural households in Henan Statistical Yearbook from 1979 to 2009,it is found that time series of farmers’ income is in accordance with I(2)non-stationary process.The order-determination and identification of the model are achieved by adopting the correlogram-based analytical method of Box-Jenkins.On the basis of comparing a group of model properties with different parameters,model ARIMA(4,2,2)is built up.The testing result shows that the residual error of the selected model is white noise and accords with the normal distribution,which can be used to predict farmers’ income.The model prediction indicates that income in rural households will continue to increase from 2009 to 2012 and will reach the value of 2 282.4,2 502.9,2 686.9 and 2 884.5 respectively.The growth speed will go down from fast to slow with weak sustainability.
Simultaneous confidence bands for Yule-Walker estimators and order selection
Jirak, Moritz
2012-01-01
Let $\\{X_k,k\\in{\\mathbb{Z}}\\}$ be an autoregressive process of order $q$. Various estimators for the order $q$ and the parameters ${\\bolds \\Theta}_q=(\\theta_1,...,\\theta_q)^T$ are known; the order is usually determined with Akaike's criterion or related modifications, whereas Yule-Walker, Burger or maximum likelihood estimators are used for the parameters ${\\bolds\\Theta}_q$. In this paper, we establish simultaneous confidence bands for the Yule--Walker estimators $\\hat{\\theta}_i$; more precisely, it is shown that the limiting distribution of ${\\max_{1\\leq i\\leq d_n}}|\\hat{\\theta}_i-\\theta_i|$ is the Gumbel-type distribution $e^{-e^{-z}}$, where $q\\in\\{0,...,d_n\\}$ and $d_n=\\mathcal {O}(n^{\\delta})$, $\\delta >0$. This allows to modify some of the currently used criteria (AIC, BIC, HQC, SIC), but also yields a new class of consistent estimators for the order $q$. These estimators seem to have some potential, since they outperform most of the previously mentioned criteria in a small simulation study. In particul...
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 inves- tigate 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
The Impact of Wireless Technology on Order Selection Audits at an Auto Parts Distribution Center
Goomas, David T.
2012-01-01
Audits of store order pallets or totes performed by auditors at five distribution centers (two experimental and three comparison distribution centers) were used to check for picking accuracy prior to being loaded onto a truck for store delivery. Replacing the paper audits with wireless handheld computers that included immediate auditory and visual…
Selective growth and ordering of SiGe nanowires for band gap engineering.
Benkouider, A; Ronda, A; Gouyé, A; Herrier, C; Favre, L; Lockwood, D J; Rowell, N L; Delobbe, A; Sudraud, P; Berbezier, I
2014-08-22
Selective growth and self-organization of silicon-germanium (SiGe) nanowires (NWs) on focused ion beam (FIB) patterned Si(111) substrates is reported. In its first step, the process involves the selective synthesis of Au catalysts in SiO₂-free areas; its second step involves the preferential nucleation and growth of SiGe NWs on the catalysts. The selective synthesis process is based on a simple, room-temperature reduction of gold salts (Au³⁺Cl₄⁻) in aqueous solution, which provides well-organized Au catalysts. By optimizing the reduction process, we are able to generate a bidimensional regular array of Au catalysts with self-limited sizes positioned in SiO₂-free windows opened in a SiO₂/Si(111) substrate by FIB patterning. Such Au catalysts subsequently serve as preferential nucleation and growth sites of well-organized NWs. Furthermore, these NWs with tunable position and size exhibit the relevant features and bright luminescence that would find several applications in optoelectronic nanodevices.
General fabrication of ordered nanocone arrays by one-step selective plasma etching.
Wang, Qiang; Tian, Zhaoshuo; Li, Yunlong; Tian, Shibing; Li, Yunming; Ren, Shoutian; Gu, Changzhi; Li, Junjie
2014-03-21
One-step selective direct current (DC) plasma etching technology is employed to fabricate large-area well-aligned nanocone arrays on various functional materials including semiconductor, insulator and metal. The cones have nanoscale apexes (∼2 nm) with high aspect ratios, which were achieved by a selective plasma etching process using only CH4 and H2 in a bias-assisted hot filament chemical vapor deposition (HFCVD) system without any masked process. The CH(3)(+) ions play a major role to etch the roughened surface into a conical structure under the auxiliary of H(+) ions. Randomly formed nano-carbon may act as an original mask on the smooth surface to initiate the following selective ions sputtering. Physical impinging of energetic ions onto the concave regions is predominant in comparison with the etching of convex parts on the surface, which is identified as the key mechanism for the formation of conical nanostructures. This one-step maskless plasma etching technology enables the universal formation of uniform nanocone structures on versatile substrates for many promising applications.
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....
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....
Modeling Pluto-Charon mutual eclipse events. I. First-order models
Energy Technology Data Exchange (ETDEWEB)
Dunbar, R.S.; Tedesco, E.F.
1986-11-01
The present first order analytical and numerical models of light curves due to mutual events between close planetary binaries, the effects of shadowing are included. Attention is given to the case of the Pluto-Charon system. The results of the analytical and numerical approaches agree to well within the expected light curve measurement error. The model predicts that the current mutual eclipse event series will end by November 1990. 12 references.
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.
Reduced Order Models for Dynamic Behavior of Elastomer Damping Devices
Morin, B.; Legay, A.; Deü, J.-F.
2016-09-01
In the context of passive damping, various mechanical systems from the space industry use elastomer components (shock absorbers, silent blocks, flexible joints...). The material of these devices has frequency, temperature and amplitude dependent characteristics. The associated numerical models, using viscoelastic and hyperelastic constitutive behaviour, may become computationally too expensive during a design process. The aim of this work is to propose efficient reduced viscoelastic models of rubber devices. The first step is to choose an accurate material model that represent the viscoelasticity. The second step is to reduce the rubber device finite element model to a super-element that keeps the frequency dependence. This reduced model is first built by taking into account the fact that the device's interfaces are much more rigid than the rubber core. To make use of this difference, kinematical constraints enforce the rigid body motion of these interfaces reducing the rubber device model to twelve dofs only on the interfaces (three rotations and three translations per face). Then, the superelement is built by using a component mode synthesis method. As an application, the dynamic behavior of a structure supported by four hourglass shaped rubber devices under harmonic loads is analysed to show the efficiency of the proposed approach.
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.
High order fluid model for streamer discharges: I. Derivation of model and transport data
Dujko, S; White, R D; Ebert, U
2013-01-01
Streamer discharges pose basic problems in plasma physics, as they are very transient, far from equilibrium and have high ionization density gradients; they appear in diverse areas of science and technology. The present paper focuses on the derivation of a high order fluid model for streamers. Using momentum transfer theory, the fluid equations are obtained as velocity moments of the Boltzmann equation; they are closed in the local mean energy approximation and coupled to the Poisson equation for the space charge generated electric field. The high order tensor in the energy flux equation is approximated by the product of two lower order moments to close the system. The average collision frequencies for momentum and energy transfer in elastic and inelastic collisions for electrons in molecular nitrogen are calculated from a multi term Boltzmann equation solution. We then discuss, in particular, (1) the correct implementation of transport data in streamer models; (2) the accuracy of the two term approximation f...
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
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...... 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...... configurations of the refinement patches.The regularised Poisson solver has been implemented in a high order particle-mesh based vortex method for simulating incompressible fluid flow. A re-meshing of the vortex particlesis used to ensure the convergence of the method and a re-projection of the vorticity field...
Kruidhof, H.; Bouwmeester, H.J.M.; Doorn, van R.H.E.; Burggraaf, A.J.
1993-01-01
New results on the oxygen permeability of perovskite-type oxides SrCo0.8B'0.2O3¿¿ (with B'=Cr, Fe, Co and Cu) and La0.6Sr0.4CoO3¿¿ are presented. The occurrence of order-disorder transitions at elevated temperatures (790¿940°C) in these phases has been confirmed by DSC measurements and, in some case
Living ordered neural networks as model systems for signal processing
Villard, C.; Amblard, P. O.; Becq, G.; Gory-Fauré, S.; Brocard, J.; Roth, S.
2007-06-01
Neural circuit architecture is a fundamental characteristic of the brain, and how architecture is bound to biological functions is still an open question. Some neuronal geometries seen in the retina or the cochlea are intriguing: information is processed in parallel by several entities like in "pooling" networks which have recently drawn the attention of signal processing scientists. These systems indeed exhibit the noise-enhanced processing effect, which is also actively discussed in the neuroscience community at the neuron scale. The aim of our project is to use in-vitro ordered neuron networks as living paradigms to test ideas coming from the computational science. The different technological bolts that have to be solved are enumerated and the first results are presented. A neuron is a polarised cell, with an excitatory axon and a receiving dendritic tree. We present how soma confinement and axon differentiation can be induced by surface functionalization techniques. The recording of large neuron networks, ordered or not, is also detailed and biological signals shown. The main difficulty to access neural noise in the case of weakly connected networks grown on micro electrode arrays is explained. This open the door to a new detection technology suitable for sub-cellular analysis and stimulation, whose development will constitute the next step of this project.
Evidence accumulation as a model for lexical selection
Anders, R.; Riès, S.; van Maanen, L.; Alario, F.-X.
2015-01-01
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
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…
Passivity Preserving Model Order Reduction For the SMIB
Ionescu, Tudor C.; Scherpen, Jacquelien M. A.
2008-01-01
We apply (linear) positive real balancing to the model of a single machine connected to an infinite bus. For that we compute the available storage and the required supply using Taylor approximation and define axis positive real singular value functions. Furthermore, we apply linear positive real bal
Comparative modelling of chemical ordering in palladium-iridium nanoalloys
Energy Technology Data Exchange (ETDEWEB)
Davis, Jack B. A.; Johnston, Roy L., E-mail: r.l.johnston@bham.ac.uk [School of Chemistry, University of Birmingham, Birmingham B15 2TT (United Kingdom); Rubinovich, Leonid; Polak, Micha, E-mail: mpolak@bgu.ac.il [Department of Chemistry, Ben-Gurion University of the Negev, Beer-Sheva 84105 (Israel)
2014-12-14
Chemical ordering in “magic-number” palladium-iridium nanoalloys has been studied by means of density functional theory (DFT) computations, and compared to those obtained by the Free Energy Concentration Expansion Method (FCEM) using derived coordination dependent bond energy variations (CBEV), and by the Birmingham Cluster Genetic Algorithm using the Gupta potential. Several compositions have been studied for 38- and 79-atom particles as well as the site preference for a single Ir dopant atom in the 201-atom truncated octahedron (TO). The 79- and 38-atom nanoalloy homotops predicted for the TO by the FCEM/CBEV are shown to be, respectively, the global minima and competitive low energy minima. Significant reordering of minima predicted by the Gupta potential is seen after reoptimisation at the DFT level.
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.
Higher-Order Approximation of Cubic-Quintic Duffing Model
DEFF Research Database (Denmark)
Ganji, S. S.; Barari, Amin; Babazadeh, H.
2011-01-01
We apply an Artificial Parameter Lindstedt-Poincaré Method (APL-PM) to find improved approximate solutions for strongly nonlinear Duffing oscillations with cubic-quintic nonlinear restoring force. This approach yields simple linear algebraic equations instead of nonlinear algebraic equations...... without analytical solution which makes it a unique solution. It is demonstrated that this method works very well for the whole range of parameters in the case of the cubic-quintic oscillator, and excellent agreement of the approximate frequencies with the exact one has been observed and discussed....... Moreover, it is not limited to the small parameter such as in the classical perturbation method. Interestingly, this study revealed that the relative error percentage in the second-order approximate analytical period is less than 0.042% for the whole parameter values. In addition, we compared...
The Optimal Selection for Restricted Linear Models with Average Estimator
Directory of Open Access Journals (Sweden)
Qichang Xie
2014-01-01
Full Text Available The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC, which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
High-Order Entropy Compressed Bit Vectors with Rank/Select
Directory of Open Access Journals (Sweden)
Kai Beskers
2014-11-01
Full Text Available We design practical implementations of data structures for compressing bit-vectors to support efficient rank-queries (counting the number of ones up to a given point. Unlike previous approaches, which either store the bit vectors plainly, or focus on compressing bit-vectors with low densities of ones or zeros, we aim at low entropies of higher order, for example 101010...10. Our implementations achieve very good compression ratios, while showing only a modest increase in query time.
Directory of Open Access Journals (Sweden)
Wang Kang Kang
2013-06-01
Full Text Available In this paper, our aim is to establish a class of Shannon-McMillan theorems for $m$th-order nonhomogeneous Markov information source on the generalized random selection system by constructing the consistent distribution functions. As corollaries, we obtain some Shannon-McMillan theorems for $m$th-order nonhomogeneous Markov information source and the general nonhomogeneous Markov information source. Some results which have been obtained are extended. In the proof, a new technique for studying Shannon-McMillan theorems in information theory is applied.
A Reduced Order, One Dimensional Model of Joint Response
Energy Technology Data Exchange (ETDEWEB)
DOHNER,JEFFREY L.
2000-11-06
As a joint is loaded, the tangent stiffness of the joint reduces due to slip at interfaces. This stiffness reduction continues until the direction of the applied load is reversed or the total interface slips. Total interface slippage in joints is called macro-slip. For joints not undergoing macro-slip, when load reversal occurs the tangent stiffness immediately rebounds to its maximum value. This occurs due to stiction effects at the interface. Thus, for periodic loads, a softening and rebound hardening cycle is produced which defines a hysteretic, energy absorbing trajectory. For many jointed sub-structures, this hysteretic trajectory can be approximated using simple polynomial representations. This allows for complex joint substructures to be represented using simple non-linear models. In this paper a simple one dimensional model is discussed.
A Refinement of the Classical Order Point Model
Farhad Moeeni; Stephen Replogle; Zariff Chaudhury; Ahmad Syamil
2012-01-01
Factors such as demand volume and replenishment lead time that influence production and inventory control systems are random variables. Existing inventory models incorporate the parameters (e.g., mean and standard deviation) of these statistical quantities to formulate inventory policies. In practice, only sample estimates of these parameters are available. The estimates are subject to sampling variation and hence are random variables. Whereas the effect of sampling variability on estimates o...
A first-order dynamical model of hierarchical triple stars and its application
Xu, Xingbo; Fu, Yanning
2015-01-01
For most hierarchical triple stars, the classical double two-body model of zeroth-order cannot describe the motions of the components under the current observational accuracy. In this paper, Marchal's first-order analytical solution is implemented and a more efficient simplified version is applied to real triple stars. The results show that, for most triple stars, the proposed first-order model is preferable to the zeroth-order model either in fitting observational data or in predicting component positions.
Selection of Temporal Lags When Modeling Economic and Financial Processes.
Matilla-Garcia, Mariano; Ojeda, Rina B; Marin, Manuel Ruiz
2016-10-01
This paper suggests new nonparametric statistical tools and procedures for modeling linear and nonlinear univariate economic and financial processes. In particular, the tools presented help in selecting relevant lags in the model description of a general linear or nonlinear time series; that is, nonlinear models are not a restriction. The tests seem to be robust to the selection of free parameters. We also show that the test can be used as a diagnostic tool for well-defined models.
Moldenhauer, Jacob
2009-01-01
We compare higher order gravity models to observational constraints from magnitude-redshift supernova data, distance to the last scattering surface of the CMB, and Baryon Acoustic Oscillations. We follow a recently proposed systematic approach to higher order gravity models based on minimal sets of curvature invariants, and select models that pass some physical acceptability conditions (free of ghost instabilities, real and positive propagation speeds, and free of separatrices). Models that satisfy these physical and observational constraints are found in this analysis and do provide fits to the data that are very close to those of the LCDM concordance model. However, we find that the limitation of the models considered here comes from the presence of superluminal mode propagations for the constrained parameter space of the models.
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
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...
The Critical Infrastructure Portfolio Selection Model
2008-06-13
bastardized version of Vilfredo Pareto’s actual observation, this assumption is commonly understood and generally accepted across multiple domains...order to generate sets of “efficient,” or Pareto optimal, portfolios of projects which best satisfy customer needs ( )rkŷ via the most effective...urban environment. However, before continuing it is important to note that Figure 44 represents a “ Pareto Analysis” graph of the same information
Order and chaos in hydrodynamic BL Her models
Smolec, R
2013-01-01
Many dynamical systems of different complexity, e.g. 1D logistic map, the Lorentz equations, or real phenomena, like turbulent convection, show chaotic behaviour. Despite huge differences, the dynamical scenarios for these systems are strikingly similar: chaotic bands are born through the series of period doubling bifurcations and merge through interior crises. Within chaotic bands periodic windows are born through the tangent bifurcations, preceded by the intermittent behaviour. This is a universal behaviour of dynamical systems (Feigenbaum 1983). We demonstrate such behaviour in models of pulsating stars.
On the use of fractional order PK-PD models
Ionescu, Clara; Copot, Dana
2017-01-01
Quantifying and controlling depth of anesthesia is a challenging process due to lack of measurement technology for direct effects of drug supply into the body. Efforts are being made to develop new sensor techniques and new horizons are explored for modeling this intricate process. This paper introduces emerging tools available on the ‘engineering market’ imported from the area of fractional calculus. A novel interpretation of the classical drug-effect curve is given, enabling linear control. This enables broadening the horizon of signal processing and control techniques and suggests future research lines.
2009-01-01
Solid-contact ion-selective electrodes (SC-ISEs) can exhibit very low detection limits and, in contrast to conventional ISEs, do not require an optimization of the inner filling solution. This work shows that subnanomolar detection limits can also be achieved with SC-ISEs with three-dimensionally ordered macroporous (3DOM) carbon contacts, which have been shown recently to exhibit excellent long-term stabilities and good resistance to the interferences from oxygen and light. The detection lim...
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.
REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling
Energy Technology Data Exchange (ETDEWEB)
Agarwal, Khushbu; Sharma, Poorva; Ma, Jinliang; Lo, Chaomei; Gorton, Ian; Liu, Yan
2013-04-30
Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations. These computationally efficient versions are known as reduced-order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods that can be used to generate a ROM, automatically quantifies the ROM accuracy, and provides support for an iterative approach to improve ROM accuracy. REVEAL is designed to be extensible in order to utilize the core functionality with any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can ‘mix and match’ mathematical techniques to best suit the characteristics of their model. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.
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%.
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.
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.
A proposed fractional-order Gompertz model and its application to tumour growth data.
Bolton, Larisse; Cloot, Alain H J J; Schoombie, Schalk W; Slabbert, Jacobus P
2015-06-01
A fractional-order Gompertz model of orders between 0 and 2 is proposed. The main purpose of this investigation is to determine whether the ordinary or proposed fractional Gompertz model would best fit our experimental dataset. The solutions for the proposed model are obtained using fundamental concepts from fractional calculus. The closed-form equations of both the proposed model and the ordinary Gompertz model are calibrated using an experimental dataset containing tumour growth volumes of a Rhabdomyosarcoma tumour in a mouse. With regard to the proposed model, the order, within the interval mentioned, that resulted in the best fit to the data was used in a further investigation into the prediction capability of the model. This was compared to the prediction capability of the ordinary Gompertz model. The result of the investigation was that a fractional-order Gompertz model of order 0.68 produced a better fit to our experimental dataset than the well-known ordinary Gompertz model.
Using multilevel models to quantify heterogeneity in resource selection
Wagner, T.; Diefenbach, D.R.; Christensen, S.A.; Norton, A.S.
2011-01-01
Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection. ?? The Wildlife Society, 2011.
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.
POD/DEIM Nonlinear model order reduction of an ADI implicit shallow water equations model
Stefanescu, Razvan
2012-01-01
In the present paper we consider a 2-D shallow-water equations (SWE) model on a $\\beta$-plane solved using an alternating direction fully implicit (ADI) finite-difference scheme on a rectangular domain. The scheme was shown to be unconditionally stable for the linearized equations. The discretization yields a number of nonlinear systems of algebraic equations. We then use a proper orthogonal decomposition (POD) to reduce the dimension of the SWE model. Due to the model nonlinearities, the computational complexity of the reduced model still depends on the number of variables of the full shallow - water equations model. By employing the discrete empirical interpolation method (DEIM) we reduce the computational complexity of the reduced order model due to its depending on the nonlinear full dimension model and regain the full model reduction expected from the POD model. To emphasize the CPU gain in performance due to use of POD/DEIM, we also propose testing an explicit Euler finite difference scheme (EE) as an a...
Energy Technology Data Exchange (ETDEWEB)
Hong, J.H. [Kyungwon University, Songnam (Korea, Republic of)
1995-07-01
This paper describes a method of obtaining transmission network equivalents from the network`s response to a impulse excitation signal. Proposed method is based on the modal decomposition representation for the large-scale interconnected system. For this framework we use Prony analysis to identify the network function of the system and to decompose the large system into a parallel combination of simple first-order systems. As a result, rational network function of optimal low order can be obtained in a direct and simple way. And Thevenin-type of discrete-time filter model can be generated. It can reproduce the driving-point impedance characteristic of the network. Furthermore proposed model can be implemented into the EMTP in a direct manner. The simulation results with the full system representation and the developed equivalent system showed a good agreement. (author). 14 refs., 11 figs.
Bayesian Model Selection for LISA Pathfinder
Karnesis, Nikolaos; Sopuerta, Carlos F; Gibert, Ferran; Armano, Michele; Audley, Heather; Congedo, Giuseppe; Diepholz, Ingo; Ferraioli, Luigi; Hewitson, Martin; Hueller, Mauro; Korsakova, Natalia; Plagnol, Eric; Vitale, and Stefano
2013-01-01
The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the LISA/eLISA concept. The Data Analysis (DA) team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment on-board LPF. These models are used for simulations, but more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the DA team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching to this problem is to recover the essential parameters of the LTP which describe the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes Factor between two competing models. In our analysis, we use three main different methods to estimate...
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Development of SPAWM: selection program for available watershed models.
Cho, Yongdeok; Roesner, Larry A
2014-01-01
A selection program for available watershed models (also known as SPAWM) was developed. Thirty-three commonly used watershed models were analyzed in depth and classified in accordance to their attributes. These attributes consist of: (1) land use; (2) event or continuous; (3) time steps; (4) water quality; (5) distributed or lumped; (6) subsurface; (7) overland sediment; and (8) best management practices. Each of these attributes was further classified into sub-attributes. Based on user selected sub-attributes, the most appropriate watershed model is selected from the library of watershed models. SPAWM is implemented using Excel Visual Basic and is designed for use by novices as well as by experts on watershed modeling. It ensures that the necessary sub-attributes required by the user are captured and made available in the selected watershed model.
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Gerretzen, Jan; Szymańska, Ewa; Bart, Jacob; Davies, Antony N; van Manen, Henk-Jan; van den Heuvel, Edwin R; Jansen, Jeroen J; Buydens, Lutgarde M C
2016-09-28
The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of
Quantile hydrologic model selection and model structure deficiency assessment: 2. Applications
Pande, S.
2013-01-01
Quantile hydrologic model selection and structure deficiency assessment is applied in three case studies. The performance of quantile model selection problem is rigorously evaluated using a model structure on the French Broad river basin data set. The case study shows that quantile model selection
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.
Adapting AIC to conditional model selection
M. van Ommen (Matthijs)
2012-01-01
textabstractIn statistical settings such as regression and time series, we can condition on observed information when predicting the data of interest. For example, a regression model explains the dependent variables $y_1, \\ldots, y_n$ in terms of the independent variables $x_1, \\ldots, x_n$.
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...
Wind farm density and harvested power in very large wind farms: A low-order model
Cortina, G.; Sharma, V.; Calaf, M.
2017-07-01
In this work we create new understanding of wind turbine wakes recovery process as a function of wind farm density using large-eddy simulations of an atmospheric boundary layer diurnal cycle. Simulations are forced with a constant geostrophic wind and a time varying surface temperature extracted from a selected period of the Cooperative Atmospheric Surface Exchange Study field experiment. Wind turbines are represented using the actuator disk model with rotation and yaw alignment. A control volume analysis around each turbine has been used to evaluate wind turbine wake recovery and corresponding harvested power. Results confirm the existence of two dominant recovery mechanisms, advection and flux of mean kinetic energy, which are modulated by the background thermal stratification. For the low-density arrangements advection dominates, while for the highly loaded wind farms the mean kinetic energy recovers through fluxes of mean kinetic energy. For those cases in between, a smooth balance of both mechanisms exists. From the results, a low-order model for the wind farms' harvested power as a function of thermal stratification and wind farm density has been developed, which has the potential to be used as an order-of-magnitude assessment tool.
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
Model selection in systems biology depends on experimental design.
Silk, Daniel; Kirk, Paul D W; Barnes, Chris P; Toni, Tina; Stumpf, Michael P H
2014-06-01
Experimental design attempts to maximise the information available for modelling tasks. An optimal experiment allows the inferred models or parameters to be chosen with the highest expected degree of confidence. If the true system is faithfully reproduced by one of the models, the merit of this approach is clear - we simply wish to identify it and the true parameters with the most certainty. However, in the more realistic situation where all models are incorrect or incomplete, the interpretation of model selection outcomes and the role of experimental design needs to be examined more carefully. Using a novel experimental design and model selection framework for stochastic state-space models, we perform high-throughput in-silico analyses on families of gene regulatory cascade models, to show that the selected model can depend on the experiment performed. We observe that experimental design thus makes confidence a criterion for model choice, but that this does not necessarily correlate with a model's predictive power or correctness. Finally, in the special case of linear ordinary differential equation (ODE) models, we explore how wrong a model has to be before it influences the conclusions of a model selection analysis.
Markosyan, A H; Ebert, U
2013-01-01
The high order fluid model developed in the preceding paper is employed here to study the propagation of negative planar streamer fronts in pure nitrogen. The model consists of the balance equations for electron density, average electron velocity, average electron energy and average electron energy flux. These balance equations have been obtained as velocity moments of Boltzmann's equation and are here coupled to the Poisson equation for the space charge electric field. Here the results of simulations with the high order model, with a PIC/MC (Particle in cell/Monte Carlo) model and with the first order fluid model based on the hydrodynamic drift-diffusion approximation are presented and compared. The comparison with the MC model clearly validates our high order fluid model, thus supporting its correct theoretical derivation and numerical implementation. The results of the first order fluid model with local field approximation, as usually used for streamer discharges, show considerable deviations. Furthermore,...
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.
Ability, Breadth, and Parsimony in Computational Models of Higher-Order Cognition
Cassimatis, Nicholas L.; Bello, Paul; Langley, Pat
2008-01-01
Computational models will play an important role in our understanding of human higher-order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher-order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do;…
Stochastic modelling and predictability: analysis of a low-order coupled ocean-atmosphere model.
Vannitsem, Stéphane
2014-06-28
There is a growing interest in developing stochastic schemes for the description of processes that are poorly represented in atmospheric and climate models, in order to increase their variability and reduce the impact of model errors. The use of such noise could however have adverse effects by modifying in undesired ways a certain number of moments of their probability distributions. In this work, the impact of developing a stochastic scheme (based on stochastic averaging) for the ocean is explored in the context of a low-order coupled (deterministic) ocean-atmosphere system. After briefly analysing its variability, its ability in predicting the oceanic flow generated by the coupled system is investigated. Different phases in the error dynamics are found: for short lead times, an initial overdispersion of the ensemble forecast is present while the ensemble mean follows a dynamics reminiscent of the combined amplification of initial condition and model errors for deterministic systems; for longer lead times, a reliable diffusive ensemble spread is observed. These different phases are also found for ensemble-oriented skill measures like the Brier score and the rank histogram. The implications of these features on building stochastic models are then briefly discussed.
Hao, Zhihao; Javanparast, Behnam; Enjalran, Matthew; Gingras, Michel
2014-03-01
We study the problem of partially ordered phases with periodically arranged disordered sites on the pyrochlore lattice. The periodicity of the phases is characterized by one or more wave vectors k = {1/21/21/2 } . Starting from a general microscopic Hamiltonian including anisotropic nearest-neighbor exchange, long-range dipolar interactions and second- and third-nearest neighbor exchange, we identify using standard mean-field theory (s-MFT) an extended range of interaction parameters that support partially ordered phases. We demonstrate that thermal fluctuations beyond s-MFT are responsible for the selection of one particular partially ordered phase, e.g. the ``4- k'' phase over the ``1- k'' phase. We suggest that the transition into the 4- k phase is continuous with its critical properties controlled by the cubic fixed point of a Ginzburg-Landau theory with a 4-component vector order-parameter. By combining an extension of the Thouless-Anderson-Palmer method originally used to study fluctuations in spin glasses with parallel-tempering Monte-Carlo simulations, we establish the phase diagram for different types of partially ordered phases. Our result reveals the origin of 4- k phase observed bellow 1K in Gd2Ti2O7. Funded by NSERC of Canada. M. G. acknowledge funding from Canadian Research Chair program (Tier 1).
Validation of a RANS transition model using a high-order weighted compact nonlinear scheme
Tu, GuoHua; Deng, XiaoGang; Mao, MeiLiang
2013-04-01
A modified transition model is given based on the shear stress transport (SST) turbulence model and an intermittency transport equation. The energy gradient term in the original model is replaced by flow strain rate to saving computational costs. The model employs local variables only, and then it can be conveniently implemented in modern computational fluid dynamics codes. The fifth-order weighted compact nonlinear scheme and the fourth-order staggered scheme are applied to discrete the governing equations for the purpose of minimizing discretization errors, so as to mitigate the confusion between numerical errors and transition model errors. The high-order package is compared with a second-order TVD method on simulating the transitional flow of a flat plate. Numerical results indicate that the high-order package give better grid convergence property than that of the second-order method. Validation of the transition model is performed for transitional flows ranging from low speed to hypersonic speed.
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
A mixed model reduction method for preserving selected physical information
Zhang, Jing; Zheng, Gangtie
2017-03-01
A new model reduction method in the frequency domain is presented. By mixedly using the model reduction techniques from both the time domain and the frequency domain, the dynamic model is condensed to selected physical coordinates, and the contribution of slave degrees of freedom is taken as a modification to the model in the form of effective modal mass of virtually constrained modes. The reduced model can preserve the physical information related to the selected physical coordinates such as physical parameters and physical space positions of corresponding structure components. For the cases of non-classical damping, the method is extended to the model reduction in the state space but still only contains the selected physical coordinates. Numerical results are presented to validate the method and show the effectiveness of the model reduction.
Regression model for tuning the PID controller with fractional order time delay system
S.P. Agnihotri; Laxman Madhavrao Waghmare
2014-01-01
In this paper a regression model based for tuning proportional integral derivative (PID) controller with fractional order time delay system is proposed. The novelty of this paper is that tuning parameters of the fractional order time delay system are optimally predicted using the regression model. In the proposed method, the output parameters of the fractional order system are used to derive the regression function. Here, the regression model depends on the weights of the exponential function...
Model-order reduction of nonlinear models of electromagnetic phased-array hyperthermia.
Kowalski, Marc E; Jin, Jian-Ming
2003-11-01
A method based on the Karhunen-Loéve (KL) transform is proposed for the reduction of large-scale, nonlinear ordinary differential equations such as those arising from the finite difference modeling of biological heat transfer. The method of snapshots is used to expedite computation of the required quantities in the KL procedure. Guidelines are presented and validated for snapshot selection and resultant basis series truncation, emphasizing the special physical features of the electromagnetic phased-array heat transfer physics. Applications to fast temperature prediction are presented.
Ma, Lu; Wang, Guan; Yan, Xuedong; Weng, Jinxian
2016-04-01
Debates on the ordering patterns of crash injury severity are ongoing in the literature. Models without proper econometrical structures for accommodating the complex ordering patterns of injury severity could result in biased estimations and misinterpretations of factors. This study proposes a hybrid finite mixture (HFM) model aiming to capture heterogeneous ordering patterns of driver injury severity while enhancing modeling flexibility. It attempts to probabilistically partition samples into two groups in which one group represents an unordered/nominal data-generating process while the other represents an ordered data-generating process. Conceptually, the newly developed model offers flexible coefficient settings for mining additional information from crash data, and more importantly it allows the coexistence of multiple ordering patterns for the dependent variable. A thorough modeling performance comparison is conducted between the HFM model, and the multinomial logit (MNL), ordered logit (OL), finite mixture multinomial logit (FMMNL) and finite mixture ordered logit (FMOL) models. According to the empirical results, the HFM model presents a strong ability to extract information from the data, and more importantly to uncover heterogeneous ordering relationships between factors and driver injury severity. In addition, the estimated weight parameter associated with the MNL component in the HFM model is greater than the one associated with the OL component, which indicates a larger likelihood of the unordered pattern than the ordered pattern for driver injury severity.
Selection of probability based weighting models for Boolean retrieval system
Energy Technology Data Exchange (ETDEWEB)
Ebinuma, Y. (Japan Atomic Energy Research Inst., Tokai, Ibaraki. Tokai Research Establishment)
1981-09-01
Automatic weighting models based on probability theory were studied if they can be applied to boolean search logics including logical sum. The INIS detabase was used for searching of one particular search formula. Among sixteen models three with good ranking performance were selected. These three models were further applied to searching of nine search formulas in the same database. It was found that two models among them show slightly better average ranking performance while the other model, the simplest one, seems also practical.
Sensitivity of resource selection and connectivity models to landscape definition
Katherine A. Zeller; Kevin McGarigal; Samuel A. Cushman; Paul Beier; T. Winston Vickers; Walter M. Boyce
2017-01-01
Context: The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention. Objectives: We evaluated the sensitivity of resource selection and connectivity models to four landscape...
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…
Elementary Teachers' Selection and Use of Visual Models
Lee, Tammy D.; Gail Jones, M.
2017-07-01
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.
Fluctuating selection models and McDonald-Kreitman type analyses.
Directory of Open Access Journals (Sweden)
Toni I Gossmann
Full Text Available It is likely that the strength of selection acting upon a mutation varies through time due to changes in the environment. However, most population genetic theory assumes that the strength of selection remains constant. Here we investigate the consequences of fluctuating selection pressures on the quantification of adaptive evolution using McDonald-Kreitman (MK style approaches. In agreement with previous work, we show that fluctuating selection can generate evidence of adaptive evolution even when the expected strength of selection on a mutation is zero. However, we also find that the mutations, which contribute to both polymorphism and divergence tend, on average, to be positively selected during their lifetime, under fluctuating selection models. This is because mutations that fluctuate, by chance, to positive selected values, tend to reach higher frequencies in the population than those that fluctuate towards negative values. Hence the evidence of positive adaptive evolution detected under a fluctuating selection model by MK type approaches is genuine since fixed mutations tend to be advantageous on average during their lifetime. Never-the-less we show that methods tend to underestimate the rate of adaptive evolution when selection fluctuates.
The Optimal Portfolio Selection Model under g -Expectation
National Research Council Canada - National Science Library
Li Li
2014-01-01
This paper solves the optimal portfolio selection model under the framework of the prospect theory proposed by Kahneman and Tversky in the 1970s with decision rule replaced by the g -expectation introduced by Peng...
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.
Information-theoretic model selection applied to supernovae data
Biesiada, M
2007-01-01
There are several different theoretical ideas invoked to explain the dark energy with relatively little guidance of which one of them might be right. Therefore the emphasis of ongoing and forthcoming research in this field shifts from estimating specific parameters of cosmological model to the model selection. In this paper we apply information-theoretic model selection approach based on Akaike criterion as an estimator of Kullback-Leibler entropy. In particular, we present the proper way of ranking the competing models based on Akaike weights (in Bayesian language - posterior probabilities of the models). Out of many particular models of dark energy we focus on four: quintessence, quintessence with time varying equation of state, brane-world and generalized Chaplygin gas model and test them on Riess' Gold sample. As a result we obtain that the best model - in terms of Akaike Criterion - is the quintessence model. The odds suggest that although there exist differences in the support given to specific scenario...
Sensor Optimization Selection Model Based on Testability Constraint
Institute of Scientific and Technical Information of China (English)
YANG Shuming; QIU Jing; LIU Guanjun
2012-01-01
Sensor selection and optimization is one of the important parts in design for testability.To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability,a novel sensor optimization selection model is proposed.Firstly,a universal architecture for sensor selection and optimization is provided.Secondly,a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability.Thirdly,a sensor selection and optimization model for prognostics and health management is constructed,which takes sensor cost as objective finction and the defined testability indexes as constraint conditions.Due to NP-hard property of the model,a generic algorithm is designed to obtain the optimal solution.At last,a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform.The application results and comparison analysis show the proposed model and algorithm are effective and feasible.This approach can be used to select sensors for prognostics and health management of any system.
A Model for Selection of Eyespots on Butterfly Wings.
Directory of Open Access Journals (Sweden)
Toshio Sekimura
Full Text Available The development of eyespots on the wing surface of butterflies of the family Nympalidae is one of the most studied examples of biological pattern formation.However, little is known about the mechanism that determines the number and precise locations of eyespots on the wing. Eyespots develop around signaling centers, called foci, that are located equidistant from wing veins along the midline of a wing cell (an area bounded by veins. A fundamental question that remains unsolved is, why a certain wing cell develops an eyespot, while other wing cells do not.We illustrate that the key to understanding focus point selection may be in the venation system of the wing disc. Our main hypothesis is that changes in morphogen concentration along the proximal boundary veins of wing cells govern focus point selection. Based on previous studies, we focus on a spatially two-dimensional reaction-diffusion system model posed in the interior of each wing cell that describes the formation of focus points. Using finite element based numerical simulations, we demonstrate that variation in the proximal boundary condition is sufficient to robustly select whether an eyespot focus point forms in otherwise identical wing cells. We also illustrate that this behavior is robust to small perturbations in the parameters and geometry and moderate levels of noise. Hence, we suggest that an anterior-posterior pattern of morphogen concentration along the proximal vein may be the main determinant of the distribution of focus points on the wing surface. In order to complete our model, we propose a two stage reaction-diffusion system model, in which an one-dimensional surface reaction-diffusion system, posed on the proximal vein, generates the morphogen concentrations that act as non-homogeneous Dirichlet (i.e., fixed boundary conditions for the two-dimensional reaction-diffusion model posed in the wing cells. The two-stage model appears capable of generating focus point distributions
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.
Modeling Suspicious Email Detection using Enhanced Feature Selection
2013-01-01
The paper presents a suspicious email detection model which incorporates enhanced feature selection. In the paper we proposed the use of feature selection strategies along with classification technique for terrorists email detection. The presented model focuses on the evaluation of machine learning algorithms such as decision tree (ID3), logistic regression, Na\\"ive Bayes (NB), and Support Vector Machine (SVM) for detecting emails containing suspicious content. In the literature, various algo...
RUC at TREC 2014: Select Resources Using Topic Models
2014-11-01
them being observed (i.e. sampled). To infer the topic Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the...Selection. In CIKM 2009, pages 1277-1286. [10] M. Baillie, M. Carmen, and F. Crestani. A Multiple- Collection Latent Topic Model for Federated...RUC at TREC 2014: Select Resources Using Topic Models Qiuyue Wang, Shaochen Shi, Wei Cao School of Information Renmin University of China Beijing
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.
Evans, Jason; Sullivan, Jack
2011-01-01
A priori selection of models for use in phylogeny estimation from molecular sequence data is increasingly important as the number and complexity of available models increases. The Bayesian information criterion (BIC) and the derivative decision-theoretic (DT) approaches rely on a conservative approximation to estimate the posterior probability of a given model. Here, we extended the DT method by using reversible jump Markov chain Monte Carlo approaches to directly estimate model probabilities for an extended candidate pool of all 406 special cases of the general time reversible + Γ family. We analyzed 250 diverse data sets in order to evaluate the effectiveness of the BIC approximation for model selection under the BIC and DT approaches. Model choice under DT differed between the BIC approximation and direct estimation methods for 45% of the data sets (113/250), and differing model choice resulted in significantly different sets of trees in the posterior distributions for 26% of the data sets (64/250). The model with the lowest BIC score differed from the model with the highest posterior probability in 30% of the data sets (76/250). When the data indicate a clear model preference, the BIC approximation works well enough to result in the same model selection as with directly estimated model probabilities, but a substantial proportion of biological data sets lack this characteristic, which leads to selection of underparametrized models.
Multi-Level Reduced Order Modeling Equipped with Probabilistic Error Bounds
Abdo, Mohammad Gamal Mohammad Mostafa
model parameters, responses, or state variables, are projected onto lower dimensional subspaces, referred to as the "active subspaces", which are selected to capture a user-defined portion of the snapshots variations. Once determined, the ROM model application involves constraining the variables to the active subspaces. In doing so, the contribution from the variables discarded components can be estimated using a fundamental theorem from random matrix theory which has its roots in Dixon's theory, developed in 1983. This theory was initially presented for linear matrix operators. The thesis extends this theorem's results to allow reduction of general smooth nonlinear operators. The result is an approach by which the adequacy of a given active subspace determined using a given set of snapshots, generated either using the full high fidelity model, or other models with lower fidelity, can be assessed, which provides insight to the analyst on the type of snapshots required to reach a reduction that can satisfy user-defined preset tolerance limits on the reduction errors. Reactor physics calculations are employed as a test bed for the proposed developments. The focus will be on reducing the effective dimensionality of the various data streams such as the cross-section data and the neutron flux. The developed methods will be applied to representative assembly level calculations, where the size of the cross-section and flux spaces are typically large, as required by downstream core calculations, in order to capture the broad range of conditions expected during reactor operation. (Abstract shortened by ProQuest.).
Selection of the optimal model of integrated sustainable management system in the mining companies
Miletić, Slavica; Bogdanović, Dejan; Paunković, Jane
2015-01-01
The multi-criteria analysis for the selection of the optimal model of integrated management system was conducted in this paper in order to improve the performance of mining companies. Integrated management system is the process of integrating of different management systems in a contemporary business as a requirement for each company in order to survive in the market. Modern companies that have implemented integrated management system work better, have arranged processes, improve its structur...
Van Enter, A C D
2003-01-01
We consider various sufficiently nonlinear sigma models for nematic ordering of RP^{N-1} type and of lattice gauge type with continous symmetries. We rigorously show that they exhibit a first-order transition in the temperature. The result holds in dimension 2 or more for the RP{N-1} models and in dimension 3 or more for the lattice gauge models. In the two-dimensional case our results clarify and solve a recent controversy about the possibilty of such transitions. For lattice gauge models our methods provide the first prof of a first-order transition in a model with a continous gauge symmetry.
Selection Criteria in Regime Switching Conditional Volatility Models
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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.
Directory of Open Access Journals (Sweden)
Yi Xu
2013-01-01
Full Text Available We propose a fourth-order total bounded variation regularization model which could reduce undesirable effects effectively. Based on this model, we introduce an improved split Bregman iteration algorithm to obtain the optimum solution. The convergence property of our algorithm is provided. Numerical experiments show the more excellent visual quality of the proposed model compared with the second-order total bounded variation model which is proposed by Liu and Huang (2010.
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.
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
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Feipeng Guo
2013-10-01
Full Text Available 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 method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.
Advances of Model Order Reduction Research in Large-scale System Simulation
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Model Order Reduction (MOR) plays more and more imp or tant role in complex system simulation, design and control recently. For example , for the large-size space structures, VLSI and MEMS (Micro-ElectroMechanical Systems) etc., in order to shorten the development cost, increase the system co ntrolling accuracy and reduce the complexity of controllers, the reduced order model must be constructed. Even in Virtual Reality (VR), the simulation and d isplay must be in real-time, the model order must be red...
Basic principles and aims of model order reduction in compliant mechanisms
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M. Rösner
2011-10-01
Full Text Available Model order reduction appears to be beneficial for the synthesis and simulation of compliant mechanisms due to computational costs. Model order reduction is an established method in many technical fields for the approximation of large-scale linear time-invariant dynamical systems described by ordinary differential equations. Based on system theory, underlying representations of the dynamical system are introduced from which the general reduced order model is derived by projection. During the last years, numerous new procedures were published and investigated appropriate to simulation, optimization and control. Singular value decomposition, condensation-based and Krylov subspace methods representing three order reduction methods are reviewed and their advantages and disadvantages are outlined in this paper. The convenience of applying model order reduction in compliant mechanisms is quoted. Moreover, the requested attributes for order reduction as a future research direction meeting the characteristics of compliant mechanisms are commented.
Higgs boson mass in the Standard Model at two-loop order and beyond
Energy Technology Data Exchange (ETDEWEB)
Martin, Stephen P. [Northern Illinois U.; Robertson, David G. [Otterbein Coll.
2014-10-23
We calculate the mass of the Higgs boson in the standard model in terms of the underlying Lagrangian parameters at complete 2-loop order with leading 3-loop corrections. A computer program implementing the results is provided. The program also computes and minimizes the standard model effective potential in Landau gauge at 2-loop order with leading 3-loop corrections.
BAYESIAN PARAMETER ESTIMATION IN A MIXED-ORDER MODEL OF BOD DECAY. (U915590)
We describe a generalized version of the BOD decay model in which the reaction is allowed to assume an order other than one. This is accomplished by making the exponent on BOD concentration a free parameter to be determined by the data. This "mixed-order" model may be ...
Necessary and Sufficient Conditions on Partial Orders for Modeling Concurrent Computations
Chauhan, Himanshu; Vijay K Garg
2014-01-01
Partial orders are used extensively for modeling and analyzing concurrent computations. In this paper, we define two properties of partially ordered sets: width-extensibility and interleaving-consistency, and show that a partial order can be a valid state based model: (1) of some synchronous concurrent computation iff it is width-extensible, and (2) of some asynchronous concurrent computation iff it is width-extensible and interleaving-consistent. We also show a duality between the event base...
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......-frequency non-minimum phase zeros. To correctly predict the non-minimum phase zeros, it is shown to be essential to include lateral tower and blade flap degrees of freedom. The thesis describes and analyzes various methods to design low-order aeroelastic models of wind turbines. Low-order models are designed...
Statistical model selection with “Big Data”
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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.
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...... variables. This paper, first, explains theoretically how selection on unobserved variables leads to waning coefficients and, second, illustrates empirically how selection leads to biased estimates of the effect of family background on educational transitions. Our empirical analysis using data from...
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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.
Estimating seabed scattering mechanisms via Bayesian model selection.
Steininger, Gavin; Dosso, Stan E; Holland, Charles W; Dettmer, Jan
2014-10-01
A quantitative inversion procedure is developed and applied to determine the dominant scattering mechanism (surface roughness and/or volume scattering) from seabed scattering-strength data. The classification system is based on trans-dimensional Bayesian inversion with the deviance information criterion used to select the dominant scattering mechanism. Scattering is modeled using first-order perturbation theory as due to one of three mechanisms: Interface scattering from a rough seafloor, volume scattering from a heterogeneous sediment layer, or mixed scattering combining both interface and volume scattering. The classification system is applied to six simulated test cases where it correctly identifies the true dominant scattering mechanism as having greater support from the data in five cases; the remaining case is indecisive. The approach is also applied to measured backscatter-strength data where volume scattering is determined as the dominant scattering mechanism. Comparison of inversion results with core data indicates the method yields both a reasonable volume heterogeneity size distribution and a good estimate of the sub-bottom depths at which scatterers occur.
Modeling neuron selectivity over simple midlevel features for image classification.
Shu Kong; Zhuolin Jiang; Qiang Yang
2015-08-01
We now know that good mid-level features can greatly enhance the performance of image classification, but how to efficiently learn the image features is still an open question. In this paper, we present an efficient unsupervised midlevel feature learning approach (MidFea), which only involves simple operations, such as k-means clustering, convolution, pooling, vector quantization, and random projection. We show this simple feature can also achieve good performance in traditional classification task. To further boost the performance, we model the neuron selectivity (NS) principle by building an additional layer over the midlevel features prior to the classifier. The NS-layer learns category-specific neurons in a supervised manner with both bottom-up inference and top-down analysis, and thus supports fast inference for a query image. Through extensive experiments, we demonstrate that this higher level NS-layer notably improves the classification accuracy with our simple MidFea, achieving comparable performances for face recognition, gender classification, age estimation, and object categorization. In particular, our approach runs faster in inference by an order of magnitude than sparse coding-based feature learning methods. As a conclusion, we argue that not only do carefully learned features (MidFea) bring improved performance, but also a sophisticated mechanism (NS-layer) at higher level boosts the performance further.
Discrete Methods Based on First Order Reversal Curves to Identify Preisach Model of Smart Materials
Institute of Scientific and Technical Information of China (English)
LI Fan; ZHAO Jian-hui
2007-01-01
Preisach model is widely used in modeling of smart materials. Although first order reversal curves (FORCs) have often found applications in the fields of physics and geology, they are able to serve to identify Preisach model. In order to clarify the relationship between the Preisach model and the first order reversal curves, this paper is directed towards: (1) giving the reason a first order reversal curve is introduced; (2) presenting, for identifying Preisach model, two discrete methods, which are analytically based on first order reversal curves. Herein also is indicated the solution's uniqueness of these two identifying methods. At last, the validity of these two methods is verified by simulating a real smart actuator both methods have been applied to.
Mean-value second-order uncertainty analysis method: application to water quality modelling
Mailhot, Alain; Villeneuve, Jean-Pierre
Uncertainty analysis in hydrology and water quality modelling is an important issue. Various methods have been proposed to estimate uncertainties on model results based on given uncertainties on model parameters. Among these methods, the mean-value first-order second-moment (MFOSM) method and the advanced mean-value first-order second-moment (AFOSM) method are the most common ones. This paper presents a method based on a second-order approximation of a model output function. The application of this method requires the estimation of first- and second-order derivatives at a mean-value point in the parameter space. Application to a Streeter-Phelps prototype model is presented. Uncertainties on two and six parameters are considered. Exceedance probabilities (EP) of dissolved oxygen concentrations are obtained and compared with EP computed using Monte Carlo, AFOSM and MFOSM methods. These results show that the mean-value second-order method leads to better estimates of EP.
Ramesh, K; Nirmalkumar, A; Gurusamy, G
2010-01-01
In this paper, design of current controller for a two quadrant DC motor drive was proposed with the help of model order reduction technique. The calculation of current controller gain with some approximations in the conventional design process is replaced by proposed model order reduction method. The model order reduction technique proposed in this paper gives the better controller gain value for the DC motor drive. The proposed model order reduction method is a mixed method, where the numerator polynomial of reduced order model is obtained by using stability equation method and the denominator polynomial is obtained by using some approximation technique preceded in this paper. The designed controllers responses were simulated with the help of MATLAB to show the validity of the proposed method.
Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romanach, Stephanie; Watling, James I.; Mazzotti, Frank J.
2017-01-01
Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (models had high performance metrics (>0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using
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.
A novel wideband, low-profile and second-order miniaturized band-pass frequency selective surfaces
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Nianxi Xu
2015-07-01
Full Text Available A novel wideband, low-profile and second-order miniaturized band-pass frequency selective surface (FSS made of metallic mesh and its complementary structures with skewed arrays of modified triples is presented in this paper. Compared with traditional second-order bandpass FSSs obtained using λ/4 apart from one another, the novel FSS with an overall thickness of λ/18 is composed of three metallic layers (the outside and middle layers are complementary separated by two electric thin dielectric substrates. This arrangement can shorten the inter-element spacing and increase the bandwidth, while the up and bottom metallic layers can constitute a symmetric biplanar FSS and thus realize ability of maximally flat second-order bandpass response. The novel FSS has a −3 dB bandwidth about 8.2 GHz (6.9 -15.1 GHz and a fractional bandwidth exceeds 75%. Moreover, such an FSS has the merits of stable performance for incident angles within 50° and different polarizations. The principles of operation along with guidelines for the design of the proposed FSS, the simulated results by vector modal matching method, and the experimental values of the fabricated prototype are also presented and discussed.
Selection of proper objective lens for the higher-order multiphoton microscopy at the 1700-nm window
Wen, Wenhui; Qiu, Ping
2017-02-01
The 1700-nm window has emerged as a promising excitation window for multiphoton microscopy (MPM). On one hand, the combined low tissue absorption and scattering make this window well suited for deep-tissue MPM; on the other hand, the long excitation wavelength makes higher-order MPM in biological tissues feasible, e.g., recently 4-photon fluorescence MPM in mouse brain has been demonstrated. Objective lens is a key optical component in the entire MPM setup. Multiphoton signal levels are largely dependent on the transmittance of objective lens. Here we demonstrate experimental results of transmittance measurement of two water immersion objective lenses commonly used for MPM at the 1700-nm window, covering both the excitation and the signal window. Our target application is MPM of even higher order excited at this window, i.e., 4th harmonic generation (FHG) imaging and 5-photon fluorescence generation. Our results show that, although the customized objective lens offers higher transmittance at the excitation window, it suffers from dramatically degraded transmittance at the signal window, compared with the non-customized objective lens. These results will offer guidelines for selection of proper objective lens for higher-order MPM at the 1700-nm window.
Index-aware model order reduction methods applications to differential-algebraic equations
Banagaaya, N; Schilders, W H A
2016-01-01
The main aim of this book is to discuss model order reduction (MOR) methods for differential-algebraic equations (DAEs) with linear coefficients that make use of splitting techniques before applying model order reduction. The splitting produces a system of ordinary differential equations (ODE) and a system of algebraic equations, which are then reduced separately. For the reduction of the ODE system, conventional MOR methods can be used, whereas for the reduction of the algebraic systems new methods are discussed. The discussion focuses on the index-aware model order reduction method (IMOR) and its variations, methods for which the so-called index of the original model is automatically preserved after reduction.
Models of microbiome evolution incorporating host and microbial selection.
Zeng, Qinglong; Wu, Steven; Sukumaran, Jeet; Rodrigo, Allen
2017-09-25
Numerous empirical studies suggest that hosts and microbes exert reciprocal selective effects on their ecological partners. Nonetheless, we still lack an explicit framework to model the dynamics of both hosts and microbes under selection. In a previous study, we developed an agent-based forward-time computational framework to simulate the neutral evolution of host-associated microbial communities in a constant-sized, unstructured population of hosts. These neutral models allowed offspring to sample microbes randomly from parents and/or from the environment. Additionally, the environmental pool of available microbes was constituted by fixed and persistent microbial OTUs and by contributions from host individuals in the preceding generation. In this paper, we extend our neutral models to allow selection to operate on both hosts and microbes. We do this by constructing a phenome for each microbial OTU consisting of a sample of traits that influence host and microbial fitnesses independently. Microbial traits can influence the fitness of hosts ("host selection") and the fitness of microbes ("trait-mediated microbial selection"). Additionally, the fitness effects of traits on microbes can be modified by their hosts ("host-mediated microbial selection"). We simulate the effects of these three types of selection, individually or in combination, on microbiome diversities and the fitnesses of hosts and microbes over several thousand generations of hosts. We show that microbiome diversity is strongly influenced by selection acting on microbes. Selection acting on hosts only influences microbiome diversity when there is near-complete direct or indirect parental contribution to the microbiomes of offspring. Unsurprisingly, microbial fitness increases under microbial selection. Interestingly, when host selection operates, host fitness only increases under two conditions: (1) when there is a strong parental contribution to microbial communities or (2) in the absence of a strong
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...
Directory of Open Access Journals (Sweden)
Weerachai Jaratlerdsiri
Full Text Available Major Histocompatibility Complex (MHC class II genes encode for molecules that aid in the presentation of antigens to helper T cells. MHC characterisation within and between major vertebrate taxa has shed light on the evolutionary mechanisms shaping the diversity within this genomic region, though little characterisation has been performed within the Order Crocodylia. Here we investigate the extent and effect of selective pressures and trans-species polymorphism on MHC class II α and β evolution among 20 extant species of Crocodylia. Selection detection analyses showed that diversifying selection influenced MHC class II β diversity, whilst diversity within MHC class II α is the result of strong purifying selection. Comparison of translated sequences between species revealed the presence of twelve trans-species polymorphisms, some of which appear to be specific to the genera Crocodylus and Caiman. Phylogenetic reconstruction clustered MHC class II α sequences into two major clades representing the families Crocodilidae and Alligatoridae. However, no further subdivision within these clades was evident and, based on the observation that most MHC class II α sequences shared the same trans-species polymorphisms, it is possible that they correspond to the same gene lineage across species. In contrast, phylogenetic analyses of MHC class II β sequences showed a mixture of subclades containing sequences from Crocodilidae and/or Alligatoridae, illustrating orthologous relationships among those genes. Interestingly, two of the subclades containing sequences from both Crocodilidae and Alligatoridae shared specific trans-species polymorphisms, suggesting that they may belong to ancient lineages pre-dating the divergence of these two families from the common ancestor 85-90 million years ago. The results presented herein provide an immunogenetic resource that may be used to further assess MHC diversity and functionality in Crocodylia.
Jaratlerdsiri, Weerachai; Isberg, Sally R; Higgins, Damien P; Miles, Lee G; Gongora, Jaime
2014-01-01
Major Histocompatibility Complex (MHC) class II genes encode for molecules that aid in the presentation of antigens to helper T cells. MHC characterisation within and between major vertebrate taxa has shed light on the evolutionary mechanisms shaping the diversity within this genomic region, though little characterisation has been performed within the Order Crocodylia. Here we investigate the extent and effect of selective pressures and trans-species polymorphism on MHC class II α and β evolution among 20 extant species of Crocodylia. Selection detection analyses showed that diversifying selection influenced MHC class II β diversity, whilst diversity within MHC class II α is the result of strong purifying selection. Comparison of translated sequences between species revealed the presence of twelve trans-species polymorphisms, some of which appear to be specific to the genera Crocodylus and Caiman. Phylogenetic reconstruction clustered MHC class II α sequences into two major clades representing the families Crocodilidae and Alligatoridae. However, no further subdivision within these clades was evident and, based on the observation that most MHC class II α sequences shared the same trans-species polymorphisms, it is possible that they correspond to the same gene lineage across species. In contrast, phylogenetic analyses of MHC class II β sequences showed a mixture of subclades containing sequences from Crocodilidae and/or Alligatoridae, illustrating orthologous relationships among those genes. Interestingly, two of the subclades containing sequences from both Crocodilidae and Alligatoridae shared specific trans-species polymorphisms, suggesting that they may belong to ancient lineages pre-dating the divergence of these two families from the common ancestor 85-90 million years ago. The results presented herein provide an immunogenetic resource that may be used to further assess MHC diversity and functionality in Crocodylia.
Periodic Integration: Further Results on Model Selection and Forecasting
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1996-01-01
textabstractThis paper considers model selection and forecasting issues in two closely related models for nonstationary periodic autoregressive time series [PAR]. Periodically integrated seasonal time series [PIAR] need a periodic differencing filter to remove the stochastic trend. On the other
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
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 structur
AN EXPERT SYSTEM MODEL FOR THE SELECTION OF TECHNICAL PERSONNEL
Directory of Open Access Journals (Sweden)
Emine COŞGUN
2005-03-01
Full Text Available In this study, a model has been developed for the selection of the technical personnel. In the model Visual Basic has been used as user interface, Microsoft Access has been utilized as database system and CLIPS program has been used as expert system program. The proposed model has been developed by utilizing expert system technology. In the personnel selection process, only the pre-evaluation of the applicants has been taken into consideration. Instead of replacing the expert himself, a decision support program has been developed to analyze the data gathered from the job application forms. The attached study will assist the expert to make faster and more accurate decisions.
Adamowski, J. F.; Quilty, J.; Khalil, B.; Rathinasamy, M.
2014-12-01
This paper explores forecasting short-term urban water demand (UWD) (using only historical records) through a variety of machine learning techniques coupled with a novel input variable selection (IVS) procedure. The proposed IVS technique termed, bootstrap rank-ordered conditional mutual information for real-valued signals (brCMIr), is multivariate, nonlinear, nonparametric, and probabilistic. The brCMIr method was tested in a case study using water demand time series for two urban water supply system pressure zones in Ottawa, Canada to select the most important historical records for use with each machine learning technique in order to generate forecasts of average and peak UWD for the respective pressure zones at lead times of 1, 3, and 7 days ahead. All lead time forecasts are computed using Artificial Neural Networks (ANN) as the base model, and are compared with Least Squares Support Vector Regression (LSSVR), as well as a novel machine learning method for UWD forecasting: the Extreme Learning Machine (ELM). Results from one-way analysis of variance (ANOVA) and Tukey Honesty Significance Difference (HSD) tests indicate that the LSSVR and ELM models are the best machine learning techniques to pair with brCMIr. However, ELM has significant computational advantages over LSSVR (and ANN) and provides a new and promising technique to explore in UWD forecasting.
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.
Dynamics and predictability of a low-order wind-driven ocean - atmosphere model
Vannitsem, Stéphane
2013-04-01
Prediction Errors under the Combined Effect of Initial Condition and Model Errors. J Atmos Sci 66: 766-778. Pierini S (2011) Low-Frequency Variability, Coherence Resonance, and Phase Selection in a Low-Order Model of the Wind-Driven Ocean Circulation. J Phys Oceanogr 41: 1585-1604.
Generating dynamic higher-order Markov models in web usage mining
Borges, J; Levene, Mark
2005-01-01
Markov models have been widely used for modelling users’ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation ...
Improved Nonlinear Model of a Second-Order Charge-Pump Pll
Gillespie, Diarmaid; Kennedy, Michael Peter; Kolumbán, Géza
An improved model of a second-order Charge-Pump Phase-Locked Loop (CP-PLL) is proposed. An event-driven second-order CP-PLL o-model is further developed from that described by Hedayat [1]. This model is made practical by taking account of VCO overload. Transient simulations are shown which illustrate the nature of phase-locking.
First Versus Second Order Latent Growth Curve Models: Some Insights From Latent State-Trait Theory
Geiser, Christian; Keller, Brian; Lockhart, Ginger
2013-01-01
First order latent growth curve models (FGMs) estimate change based on a single observed variable and are widely used in longitudinal research. Despite significant advantages, second order latent growth curve models (SGMs), which use multiple indicators, are rarely used in practice, and not all aspects of these models are widely understood. In this article, our goal is to contribute to a deeper understanding of theoretical and practical differences between FGMs and SGMs. We define the latent ...
Comments on the present state of second-order closure models for incompressible flows
Speziale, Charles G.
1992-01-01
Second-order closure models account for history and nonlocal effects of the mean velocity gradients on the Reynolds stress tensor. Turbulent flows involving body forces or curvature, Reynolds stress relaxational effects, and counter-gradient transport are usually better described. The topics are presented in viewgraph form and include: (1) the Reynolds stress transport equation; (2) issues in second-order closure modeling; and (3) near wall models.
The confluence model: birth order as a within-family or between-family dynamic?
Zajonc, R B; Sulloway, Frank J
2007-09-01
The confluence model explains birth-order differences in intellectual performance by quantifying the changing dynamics within the family. Wichman, Rodgers, and MacCallum (2006) claimed that these differences are a between-family phenomenon--and hence are not directly related to birth order itself. The study design and analyses presented by Wichman et al. nevertheless suffer from crucial shortcomings, including their use of unfocused tests, which cause statistically significant trends to be overlooked. In addition, Wichman et al. treated birth-order effects as a linear phenomenon thereby ignoring the confluence model's prediction that these two samples may manifest opposing results based on age. This article cites between- and within-family data that demonstrate systematic birth-order effects as predicted by the confluence model. The corpus of evidence invoked here offers strong support for the assumption of the confluence model that birth-order differences in intellectual performance are primarily a within-family phenomenon.
Energy Technology Data Exchange (ETDEWEB)
Zheng Yongai, E-mail: zhengyongai@163.co [Department of Computer, Yangzhou University, Yangzhou, 225009 (China); Nian Yibei [School of Energy and Power Engineering, Yangzhou University, Yangzhou, 225009 (China); Wang Dejin [Department of Computer, Yangzhou University, Yangzhou, 225009 (China)
2010-12-01
In this Letter, a kind of novel model, called the generalized Takagi-Sugeno (T-S) fuzzy model, is first developed by extending the conventional T-S fuzzy model. Then, a simple but efficient method to control fractional order chaotic systems is proposed using the generalized T-S fuzzy model and adaptive adjustment mechanism (AAM). Sufficient conditions are derived to guarantee chaos control from the stability criterion of linear fractional order systems. The proposed approach offers a systematic design procedure for stabilizing a large class of fractional order chaotic systems from the literature about chaos research. The effectiveness of the approach is tested on fractional order Roessler system and fractional order Lorenz system.
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...
A seventh-order model for dynamic response of an electro-hydraulic servo valve
Institute of Scientific and Technical Information of China (English)
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 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.
Fuzzy MCDM Model for Risk Factor Selection in Construction Projects
Directory of Open Access Journals (Sweden)
Pejman Rezakhani
2012-11-01
Full Text Available Risk factor selection is an important step in a successful risk management plan. There are many risk factors in a construction project and by an effective and systematic risk selection process the most critical risks can be distinguished to have more attention. In this paper through a comprehensive literature survey, most significant risk factors in a construction project are classified in a hierarchical structure. For an effective risk factor selection, a modified rational multi criteria decision making model (MCDM is developed. This model is a consensus rule based model and has the optimization property of rational models. By applying fuzzy logic to this model, uncertainty factors in group decision making such as experts` influence weights, their preference and judgment for risk selection criteria will be assessed. Also an intelligent checking process to check the logical consistency of experts` preferences will be implemented during the decision making process. The solution inferred from this method is in the highest degree of acceptance of group members. Also consistency of individual preferences is checked by some inference rules. This is an efficient and effective approach to prioritize and select risks based on decisions made by group of experts in construction projects. The applicability of presented method is assessed through a case study.
SOLVING FRACTIONAL-ORDER COMPETITIVE LOTKA-VOLTERRA MODEL BY NSFD SCHEMES
Directory of Open Access Journals (Sweden)
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.
Reduced density matrices and topological order in a quantum dimer model
Energy Technology Data Exchange (ETDEWEB)
Furukawa, Shunsuke [Laboratoire de Physique Theorique de la Matiere Condensee, UMR 7600 of CNRS, Universite P et M Curie, case 121, 4 Place Jussieu, 75252 Paris Cedex (France); Misguich, Gregoire [Service de Physique Theorique, CEA Saclay, 91191 Gif-sur-Yvette Cedex (France); Oshikawa, Masaki [Institute for Solid State Physics, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8581 (Japan)
2007-04-11
Resonating valence bond (RVB) liquids in two dimensions are believed to exhibit topological order and to admit no local order parameter of any kind. This is a defining property of 'liquids', but it has been confirmed explicitly only in a few exactly solvable models. In this paper, we investigate the quantum dimer model on the triangular lattice. This possesses an RVB-type liquid phase, however, for which the absence of a local order parameter has not been proved. We examine the question numerically with a measure based on reduced density matrices. We find a scaling of the measure which strongly supports the absence of any local order parameter.
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.
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.
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.
Bayesian model selection for constrained multivariate normal linear models
Mulder, J.
2010-01-01
The expectations that researchers have about the structure in the data can often be formulated in terms of equality constraints and/or inequality constraints on the parameters in the model that is used. In a (M)AN(C)OVA model, researchers have expectations about the differences between the
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
2017-01-01
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. PMID:28266616
Order Reduction of the Radiative Heat Transfer Model for the Simulation of Plasma Arcs
Fagiano, Lorenzo
2015-01-01
An approach to derive low-complexity models describing thermal radiation for the sake of simulating the behavior of electric arcs in switchgear systems is presented. The idea is to approximate the (high dimensional) full-order equations, modeling the propagation of the radiated intensity in space, with a model of much lower dimension, whose parameters are identified by means of nonlinear system identification techniques. The low-order model preserves the main structural aspects of the full-order one, and its parameters can be straightforwardly used in arc simulation tools based on computational fluid dynamics. In particular, the model parameters can be used together with the common approaches to resolve radiation in magnetohydrodynamic simulations, including the discrete-ordinate method, the P-N methods and photohydrodynamics. The proposed order reduction approach is able to systematically compute the partitioning of the electromagnetic spectrum in frequency bands, and the related absorption coefficients, tha...
Identification and non-integer order modelling of synchronous machines operating as generator
Directory of Open Access Journals (Sweden)
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.
IT vendor selection model by using structural equation model & analytical hierarchy process
Maitra, Sarit; Dominic, P. D. D.
2012-11-01
Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.
On the relation between hydrogen bonds, tetrahedral order and molecular mobility in model water
Pereyra, R G; Malaspina, D C; Carignano, M A
2013-01-01
We studied by molecular dynamics simulations the relation existing between the lifetime of hydrogen bonds, the tetrahedral order and the diffusion coefficient of model water. We tested four different models: SPC/E, TIP4P-Ew, TIP5P-Ew and Six-site, these last two having sites explicitly resembling the water lone pairs. While all the models perform reasonably well at ambient conditions, their behavior is significantly different for temperatures below 270 K. The models with explicit lone-pairs have a longer hydrogen bond lifetime, a better tetrahedral order and a smaller diffusion coefficient than the models without them.
On the equivalence between standard and sequentially ordered hidden Markov models
Chopin, Nicolas
2012-01-01
Chopin (2007) introduced a sequentially ordered hidden Markov model, for which states are ordered according to their order of appearance, and claimed that such a model is a re-parametrisation of a standard Markov model. This note gives a formal proof that this equivalence holds in Bayesian terms, as both formulations generate equivalent posterior distributions, but does not hold in Frequentist terms, as both formulations generate incompatible likelihood functions. Perhaps surprisingly, this shows that Bayesian re-parametrisation and Frequentist re-parametrisation are not identical concepts.
Selecting Optimal Subset of Features for Student Performance Model
Directory of Open Access Journals (Sweden)
Hany M. Harb
2012-09-01
Full Text Available Educational data mining (EDM is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the student behavior in the learning process. Classification methods like decision trees, rule mining, and Bayesian network, can be applied on the educational data for predicting the student behavior like performance in an examination. This prediction may help in student evaluation. As the feature selection influences the predictive accuracy of any performance model, it is essential to study elaborately the effectiveness of student performance model in connection with feature selection techniques. The main objective of this work is to achieve high predictive performance by adopting various feature selection techniques to increase the predictive accuracy with least number of features. The outcomes show a reduction in computational time and constructional cost in both training and classification phases of the student performance model.
Sample selection and taste correlation in discrete choice transport modelling
DEFF Research Database (Denmark)
Mabit, Stefan Lindhard
2008-01-01
the question for a broader class of models. It is shown that the original result may be somewhat generalised. Another question investigated is whether mode choice operates as a self-selection mechanism in the estimation of the value of travel time. The results show that self-selection can at least partly...... explain counterintuitive results in value of travel time estimation. However, the results also point at the difficulty of finding suitable instruments for the selection mechanism. Taste heterogeneity is another important aspect of discrete choice modelling. Mixed logit models are designed to capture...... 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...
Tuning algorithms for fractional order internal model controllers for time delay processes
Muresan, Cristina I.; Dutta, Abhishek; Dulf, Eva H.; Pinar, Zehra; Maxim, Anca; Ionescu, Clara M.
2016-03-01
This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.
Economic Order Quantity Model with Two Levels of Delayed Payment and Bad Debt
Directory of Open Access Journals (Sweden)
Qin Juanjuan
2012-08-01
Full Text Available The purpose of this study is to determine the optimal retailer’s replenishment policies considering the customers’ bad debt and delayed payment in the three-stage supply chain with the dominant retailer. The effect of bad debt is analyzed on the interest earned and interest charged to build the models of the retailer’s decision in two cases. By analyzing the model, the retailer’s optimal replenishment time and the optimal order quantity are obtained. Furthermore, analyze the effect of parameters on the retailer’s optimal order policies. Finally, the numerical analysis is presented to demonstrate the conclusions. The results show that the delayed payment offered by the manufacturer becomes large, the retailer's optimal order cycle and the optimal order quantity increases or remains the same; When the delayed payment time offered by the retailer decreases, the retailer's optimal order cycle and the optimal order quantity increases or remains the same. When the fixed ordering cost is reduced, the retailer's optimal order cycle and the optimal order quantity decreases or remains the same. When the charged interest is greater than the earned interest, with the bad debt rate increasing, the retailer's optimal order cycle and optimal order quantity is converged to a certain value.
Subsymmetry and asymmetry models for multiway square contingency tables with ordered categories
Directory of Open Access Journals (Sweden)
Aktaş Serpil
2016-01-01
Full Text Available This paper suggests several models that describe the symmetry and asymmetry structure of each subdimension for the multiway square contingency table with ordered categories. A classical three-way categorical example is examined to illustrate the model results. These models analyze the subsymmetric and asymetric structure of the table.
Gong, Maozhen
Selecting an appropriate prior distribution is a fundamental issue in Bayesian Statistics. In this dissertation, under the framework provided by Berger and Bernardo, I derive the reference priors for several models which include: Analysis of Variance (ANOVA)/Analysis of Covariance (ANCOVA) models with a categorical variable under common ordering constraints, the conditionally autoregressive (CAR) models and the simultaneous autoregressive (SAR) models with a spatial autoregression parameter rho considered. The performances of reference priors for ANOVA/ANCOVA models are evaluated by simulation studies with comparisons to Jeffreys' prior and Least Squares Estimation (LSE). The priors are then illustrated in a Bayesian model of the "Risk of Type 2 Diabetes in New Mexico" data, where the relationship between the type 2 diabetes risk (through Hemoglobin A1c) and different smoking levels is investigated. In both simulation studies and real data set modeling, the reference priors that incorporate internal order information show good performances and can be used as default priors. The reference priors for the CAR and SAR models are also illustrated in the "1999 SAT State Average Verbal Scores" data with a comparison to a Uniform prior distribution. Due to the complexity of the reference priors for both CAR and SAR models, only a portion (12 states in the Midwest) of the original data set is considered. The reference priors can give a different marginal posterior distribution compared to a Uniform prior, which provides an alternative for prior specifications for areal data in Spatial statistics.
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.
He, Xingrong; Yang, Yongqiang; Wu, Weihui; Wang, Di; Ding, Huanwen; Huang, Weihong
2010-06-01
In order to simplify the distal femoral comminuted fracture surgery and improve the accuracy of the parts to be reset, a kind of surgery orienting model for the surgery operation was designed according to the scanning data of computer tomography and the three-dimensional reconstruction image. With the use of DiMetal-280 selective laser melting rapid prototyping system, the surgery orienting model of 316L stainless steel was made through orthogonal experiment for processing parameter optimization. The technology of direct manufacturing of surgery orienting model by selective laser melting was noted to have obvious superiority with high speed, precise profile and good accuracy in size when compared with the conventional one. The model was applied in a real surgical operation for thighbone replacement; it worked well. The successful development of the model provides a new method for the automatic manufacture of customized surgery model, thus building a foundation for more clinical applications in the future.
Zhai, Yi; Wang, Yan; Wang, Zhaoqi; Liu, Yongji; Zhang, Lin; He, Yuanqing; Chang, Shengjiang
2014-01-01
An achromatic element eliminating only longitudinal chromatic aberration (LCA) while maintaining transverse chromatic aberration (TCA) is established for the eye model, which involves the angle formed by the visual and optical axis. To investigate the impacts of higher-order aberrations on vision, the actual data of higher-order aberrations of human eyes with three typical levels are introduced into the eye model along visual axis. Moreover, three kinds of individual eye models are established to investigate the impacts of higher-order aberrations, chromatic aberration (LCA+TCA), LCA and TCA on vision under the photopic condition, respectively. Results show that for most human eyes, the impact of chromatic aberration on vision is much stronger than that of higher-order aberrations, and the impact of LCA in chromatic aberration dominates. The impact of TCA is approximately equal to that of normal level higher-order aberrations and it can be ignored when LCA exists.
Numerical Models of Higher-Order Boussinesq Equations and Comparisons with Laboratory Measurement
Institute of Scientific and Technical Information of China (English)
邹志利; 张晓莉
2001-01-01
Nonlinear water wave propagation passing a submerged shelf is studied experimentally and numerically. The applicability of two different wave propagation models has been investigated. One is higher-order Boussinesq equationsderived by Zou (1999) and the other is the classic Boussinesq equations. Physical experiments are conducted, three differ-ent front slopes (1:10, 1:5 and 1:2) of the shelf are set up in the experiment and their effects on wave propagation are in-vestigated. Comparisons of numerical results with test data are made, the model of higher-order Boussinesq equationsagrees much better with the measurements than the model of the classical Boussinesq equations. The results show thatthe higher-order Boussinesq equations can also be applied to the steeper slope case although the mild slope assumption isemployed in the derivation of the higher order terms of higher order Boussinesq equations.
Integrated model for pricing, delivery time setting, and scheduling in make-to-order environments
Garmdare, Hamid Sattari; Lotfi, M. M.; Honarvar, Mahboobeh
2017-05-01
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.
New second order Mumford-Shah model based on Γ-convergence approximation for image processing
Duan, Jinming; Lu, Wenqi; Pan, Zhenkuan; Bai, Li
2016-05-01
In this paper, a second order variational model named the Mumford-Shah total generalized variation (MSTGV) is proposed for simultaneously image denoising and segmentation, which combines the original Γ-convergence approximated Mumford-Shah model with the second order total generalized variation (TGV). For image denoising, the proposed MSTGV can eliminate both the staircase artefact associated with the first order total variation and the edge blurring effect associated with the quadratic H1 regularization or the second order bounded Hessian regularization. For image segmentation, the MSTGV can obtain clear and continuous boundaries of objects in the image. To improve computational efficiency, the implementation of the MSTGV does not directly solve its high order nonlinear partial differential equations and instead exploits the efficient split Bregman algorithm. The algorithm benefits from the fast Fourier transform, analytical generalized soft thresholding equation, and Gauss-Seidel iteration. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the proposed model.
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....
John Carroll’s Views on Intelligence: Bi-Factor vs. Higher-Order Models
Directory of Open Access Journals (Sweden)
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.
Fractional-order mathematical model of an irrigation main canal pool
Directory of Open Access Journals (Sweden)
Shlomi N. Calderon-Valdez
2015-09-01
Full Text Available In this paper a fractional order model for an irrigation main canal is proposed. It is based on the experiments developed in a laboratory prototype of a hydraulic canal and the application of a direct system identification methodology. The hydraulic processes that take place in this canal are equivalent to those that occur in real main irrigation canals and the results obtained here can therefore be easily extended to real canals. The accuracy of the proposed fractional order model is compared by deriving two other integer-order models of the canal of a complexity similar to that proposed here. The parameters of these three mathematical models have been identified by minimizing the Integral Square Error (ISE performance index existing between the models and the real-time experimental data obtained from the canal prototype. A comparison of the performances of these three models shows that the fractional-order model has the lowest error and therefore the higher accuracy. Experiments showed that our model outperformed the accuracy of the integer-order models by about 25%, which is a significant improvement as regards to capturing the canal dynamics.
First-order phase transition in $1d$ Potts model with long-range interactions
Uzelac, K.; Glumac, Z.
1998-01-01
The first-order phase transition in the one-dimensional $q$-state Potts model with long-range interactions decaying with distance as $1/r^{1+\\sigma}$ has been studied by Monte Carlo numerical simulations for $0 2$. On the basis of finite-size scaling analysis of interface free energy $\\Delta F_L$, specific heat and Binder's fourth order cumulant, we obtain the first-order transition which occurs for $\\sigma$ below a threshold value $\\sigma_c(q)$.
A Reduced-Order Model for Evaluating the Dynamic Response of Multilayer Plates to Impulsive Loads
2016-04-12
distribution is unlimited SAE INTERNATIONAL • Motivation • Technical Approach • Reduced Order Model (ROM) • Validation – by Spectrum Finite Element ...also be paramount factors for Army vehicles in order for faster transport, greater fuel conservation, higher payload and higher mobility. • Develop...Matrix • The departure wave vectors ?̃?∗ and ?̃? contain same elements but in different order. It can be expressed in equivalence through a global
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.
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…
Variable-fidelity and reduced-order models for aero data for loads predictions
DEFF Research Database (Denmark)
Goertz, Stefan; Zimmermann, Ralf; Han, Zhong Hua
2013-01-01
This paper summarizes recent progress in developing metamodels for efficiently predicting the aerodynamic loads acting on industrial aircraft configurations. We introduce a physics-based approach to reduced-order modeling based on proper orthogonal decomposition of snapshots of the full-order CFD...
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 mod
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…
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…
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 distribution
Novel Reduced Order in Time Models for Problems in Nonlinear Aeroelasticity Project
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...
Fractional order Buck-Boost converter in CCM: modelling, analysis and simulations
Wang, Faqiang; Ma, Xikui
2014-12-01
In this paper, the modelling, analysis and the power electronics simulator (PSIM) simulations of the fractional order Buck-Boost converter operating in continuous conduction mode (CCM) operation are investigated. Based on the three-terminal switch device method, the average circuit model of the fractional order Buck-Boost converter is established, and the corresponding DC equivalent circuit model and AC small signal equivalent circuit model are presented. And then, the equilibrium point and the transfer functions are derived. It is found that the equilibrium point is not influenced by the inductor's or the capacitor's order, but both these orders are included in the derived transfer functions. Finally, the comparisons between the theoretical analysis and the PSIM simulations are given for confirmation.
Neel order in the two-dimensional S=1/2 Heisenberg Model
Löw, Ute
2007-01-01
The existence of Neel order in the S=1/2 Heisenberg model on the square lattice at T=0 is shown using inequalities set up by Kennedy, Lieb and Shastry in combination with high precision Quantum Monte Carlo data.
Equilibrium pricing in an order book environment: Case study for a spin model
Meudt, Frederik; Schmitt, Thilo A.; Schäfer, Rudi; Guhr, Thomas
2016-07-01
When modeling stock market dynamics, the price formation is often based on an equilibrium mechanism. In real stock exchanges, however, the price formation is governed by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more generally, are compatible with the order book environment. We tackle this issue in the framework of a case study by embedding the Bornholdt-Kaizoji-Fujiwara spin model into the order book dynamics. To this end, we use a recently developed agent based model that realistically incorporates the order book. We find realistic stylized facts. We conclude for the studied case that equilibrium pricing is not needed and that the corresponding assumption of a "fundamental" price may be abandoned.