Piecewise Linear Model-Based Image Enhancement
Directory of Open Access Journals (Sweden)
Fabrizio Russo
2004-09-01
Full Text Available A novel technique for the sharpening of noisy images is presented. The proposed enhancement system adopts a simple piecewise linear (PWL function in order to sharpen the image edges and to reduce the noise. Such effects can easily be controlled by varying two parameters only. The noise sensitivity of the operator is further decreased by means of an additional filtering step, which resorts to a nonlinear model too. Results of computer simulations show that the proposed sharpening system is simple and effective. The application of the method to contrast enhancement of color images is also discussed.
3D Object Recognition Based on Linear Lie Algebra Model
Institute of Scientific and Technical Information of China (English)
LI Fang-xing; WU Ping-dong; SUN Hua-fei; PENG Lin-yu
2009-01-01
A surface model called the fibre bundle model and a 3D object model based on linear Lie algebra model are proposed.Then an algorithm of 3D object recognition using the linear Lie algebra models is presented.It is a convenient recognition method for the objects which are symmetric about some axis.By using the presented algorithm,the representation matrices of the fibre or the base curve from only finite points of the linear Lie algebra model can be obtained.At last some recognition results of practicalities are given.
Multivariate statistical modelling based on generalized linear models
Fahrmeir, Ludwig
1994-01-01
This book is concerned with the use of generalized linear models for univariate and multivariate regression analysis. Its emphasis is to provide a detailed introductory survey of the subject based on the analysis of real data drawn from a variety of subjects including the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account to have on their desks. "The basic aim of the authors is to bring together and review a large part of recent advances in statistical modelling of m...
Searle, Shayle R
2012-01-01
This 1971 classic on linear models is once again available--as a Wiley Classics Library Edition. It features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.
Model Based Adaptive Piecewise Linear Controller for Complicated Control Systems
Directory of Open Access Journals (Sweden)
Tain-Sou Tsay
2014-01-01
Full Text Available A model based adaptive piecewise linear control scheme for industry processes with specifications on peak overshoots and rise times is proposed. It is a gain stabilized control technique. Large gain is used for large tracking error to get fast response. Small gain is used between large and small tracking error for good performance. Large gain is used again for small tracking error to cope with large disturbance. Parameters of the three-segment piecewise linear controller are found by an automatic regulating time series which is function of output characteristics of the plant and reference model. The time series will be converged to steady values after the time response of the considered system matching that of the reference model. The proposed control scheme is applied to four numerical examples which have been compensated by PID controllers. Parameters of PID controllers are found by optimization method. It gives an almost command independent response and gives significant improvements for response time and performance.
Institute of Scientific and Technical Information of China (English)
Yuanyuan ZOU; Shaoyuan LI
2007-01-01
In this paper,a linear programming method is proposed to solve model predictive control for a class of hybrid systems.Firstly,using the(max,+)algebra,a typical subclass of hybrid systems called max-plus-linear(MPL)systems is obtained.And then,model predictive control(MPC)framework is extended to MPL systems.In general,the nonlinear optimization approach or extended linear complementarity problem(ELCP)were applied to solve the MPL-MPC optimization problem.A new optimization method based on canonical forms for max-min-plus-scaling(MMPS)functions (using the operations maximization,minimization,addition and scalar multiplication)with linear constraints on the inputs is presented.The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization.The validity of the algorithm is illustrated by an example.
Ripamonti, Francesco; Orsini, Lorenzo; Resta, Ferruccio
2015-04-01
Non-linear behavior is present in many mechanical system operating conditions. In these cases, a common engineering practice is to linearize the equation of motion around a particular operating point, and to design a linear controller. The main disadvantage is that the stability properties and validity of the controller are local. In order to improve the controller performance, non-linear control techniques represent a very attractive solution for many smart structures. The aim of this paper is to compare non-linear model-based and non-model-based control techniques. In particular the model-based sliding-mode-control (SMC) technique is considered because of its easy implementation and the strong robustness of the controller even under heavy model uncertainties. Among the non-model-based control techniques, the fuzzy control (FC), allowing designing the controller according to if-then rules, has been considered. It defines the controller without a system reference model, offering many advantages such as an intrinsic robustness. These techniques have been tested on the pendulum nonlinear system.
Pyemu: A Python-Based Framework for Linear-Based Model Uncertainty Analysis
White, J.
2014-12-01
pyEMU is an open-source python-based framework for model-independent linear-based parameter and predictive uncertainty analysis. The framework is designed to support the analysis of high-dimensional inverse problems that have thousands of parameters and hundreds of thousands of observations. The code is compatible with the PEST and PEST++ software suite, and implements several forms of linear analysis equations, such as Schur's complement for conditional uncertainty propagation and subspace error variance, including a form of error variance analysis of model structural error. These linear analysis equations are the most common and also the most applicable to large-scale environmental models. Several native python operators (such as multiplication, subtraction, addition, exponentiation) have been overloaded to make equation building more concise as well as to achieve speedup with operations involving diagonal matrices. To help ensure pyEMU is intuitive and easy to use, emphasis was placed on flexibility and concise object instantiation. As a result, several types of arguments can be handled elegantly.
Genetic algorithm-based multi-objective model for scheduling of linear construction projects
Senouci, Ahmed B.; Al-Derham, H.R.
2007-01-01
This paper presents a genetic algorithm-based multi-objective optimization model for the scheduling of linear construction projects. The model allows construction planners to generate and evaluate optimal/near-optimal construction scheduling plans that minimize both project time and cost. The computations in the present model are organized in three major modules. A scheduling module that develops practical schedules for linear construction projects. A cost module that computes the project's c...
Agent based reasoning for the non-linear stochastic models of long-range memory
Kononovicius, A.; Gontis, V.
2012-02-01
We extend Kirman's model by introducing variable event time scale. The proposed flexible time scale is equivalent to the variable trading activity observed in financial markets. Stochastic version of the extended Kirman's agent based model is compared to the non-linear stochastic models of long-range memory in financial markets. The agent based model providing matching macroscopic description serves as a microscopic reasoning of the earlier proposed stochastic model exhibiting power law statistics.
Energy Technology Data Exchange (ETDEWEB)
Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)
2005-10-01
In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)
Iterated non-linear model predictive control based on tubes and contractive constraints.
Murillo, M; Sánchez, G; Giovanini, L
2016-05-01
This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.
Directory of Open Access Journals (Sweden)
Mohammad Shahzad
2016-05-01
Full Text Available This study deals with the control of chaotic dynamics of tumor cells, healthy host cells, and effector immune cells in a chaotic Three Dimensional Cancer Model (TDCM by State Space Exact Linearization (SSEL technique based on Lie algebra. A non-linear feedback control law is designed which induces a coordinate transformation thereby changing the original chaotic TDCM system into a controlled one linear system. Numerical simulation has been carried using Mathematica that witness the robustness of the technique implemented on the chosen chaotic system.
Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models
Hiroki Yoshioka; Kenta Obata
2011-01-01
The fraction of vegetation cover (FVC) is often estimated by unmixing a linear mixture model (LMM) to assess the horizontal spread of vegetation within a pixel based on a remotely sensed reflectance spectrum. The LMM-based algorithm produces results that can vary to a certain degree, depending on the model assumptions. For example, the robustness of the results depends on the presence of errors in the measured reflectance spectra. The objective of this study was to derive a factor that could ...
Faraway, Julian J
2014-01-01
A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the first edition.New to the Second EditionReorganiz
Institute of Scientific and Technical Information of China (English)
Peixin ZHAO
2013-01-01
In this paper,we consider the variable selection for the parametric components of varying coefficient partially linear models with censored data.By constructing a penalized auxiliary vector ingeniously,we propose an empirical likelihood based variable selection procedure,and show that it is consistent and satisfies the sparsity.The simulation studies show that the proposed variable selection method is workable.
Vismara, S. O.; Ricci, S.; Bellini, M.; Trittoni, L.
2016-06-01
The objective of the present paper is to describe a procedure to identify and model the non-linear behaviour of structural elements. The procedure herein applied can be divided into two main steps: the system identification and the finite element model updating. The application of the restoring force surface method as a strategy to characterize and identify localized non-linearities has been investigated. This method, which works in the time domain, has been chosen because it has `built-in' characterization capabilities, it allows a direct non-parametric identification of non-linear single-degree-of-freedom systems and it can easily deal with sine-sweep excitations. Two different application examples are reported. At first, a numerical test case has been carried out to investigate the modelling techniques in the case of non-linear behaviour based on the presence of a free-play in the model. The second example concerns the flap of the Intermediate eXperimental Vehicle that successfully completed its 100-min mission on 11 February 2015. The flap was developed under the responsibility of Thales Alenia Space Italia, the prime contractor, which provided the experimental data needed to accomplish the investigation. The procedure here presented has been applied to the results of modal testing performed on the article. Once the non-linear parameters were identified, they were used to update the finite element model in order to prove its capability of predicting the flap behaviour for different load levels.
DEFF Research Database (Denmark)
Høskuldsson, Agnar
1996-01-01
Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four...... the basic problems in determining the dimension of linear models. Then each of the eight measures are treated. The results are illustrated by examples....
Foundations of linear and generalized linear models
Agresti, Alan
2015-01-01
A valuable overview of the most important ideas and results in statistical analysis Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications, and examples with crafted models to elucidate key ideas and promote practical model building. The book begins by illustrating the fundamentals of linear models,
Bin-objective shape optimization based on linear programming model of arch dam
Institute of Scientific and Technical Information of China (English)
JIN Hai; LIN Gao; YANG Ming-sheng
2007-01-01
Bin-objective shape optimization of arch dam based on linear programming model is discussed to minimize both dam volume and maximal tensile stress. The importance of weight coefficient of the above two objectives is chosen according to the value of importance ratio. The influence of weight coefficient to the optimization result is discussed in detail and the numerical example shows that both the model and method proposed is doable.
Modeling and Stability Analysis for Non-linear Network Control System Based on T-S Fuzzy Model
Institute of Scientific and Technical Information of China (English)
ZHANG Hong; FANG Huajing
2007-01-01
Based on the T-S fuzzy model, this paper presents a new model of non-linear network control system with stochastic transfer delay. Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model. Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model. All these results present a new approach for networked control system analysis and design.
Ravva, Patanjali; Karlsson, Mats O; French, Jonathan L
2014-04-30
The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization
An Entropy-Based Approach to Path Analysis of Structural Generalized Linear Models: A Basic Idea
Directory of Open Access Journals (Sweden)
Nobuoki Eshima
2015-07-01
Full Text Available A path analysis method for causal systems based on generalized linear models is proposed by using entropy. A practical example is introduced, and a brief explanation of the entropy coefficient of determination is given. Direct and indirect effects of explanatory variables are discussed as log odds ratios, i.e., relative information, and a method for summarizing the effects is proposed. The example dataset is re-analyzed by using the method.
Cogging force rejection method of linear motor based on internal model principle
Liu, Yang; Chen, Zhenyu; Yang, Tianbo
2015-02-01
The cogging force disturbance of linear motor is one of the main factors affecting the positioning accuracy of ultraprecision moving platform. And this drawback could not be completely overcome by improving the design of motor body, such as location modification of permanent magnet array, or optimization design of the shape of teeth-slot. So the active compensation algorithms become prevalent in cogging force rejection area. This paper proposed a control structure based on internal mode principle to attenuate the cogging force of linear motor which deteriorated the accuracy of position, and this structure could make tracking and anti-disturbing performance of close-loop designed respectively. In the first place, the cogging force was seen as the intrinsic property of linear motor and its model constituting controlled object with motor ontology model was obtained by data driven recursive identification method. Then, a control structure was designed to accommodate tracking and anti-interference ability separately by using internal model principle. Finally, the proposed method was verified in a long stroke moving platform driven by linear motor. The experiment results show that, by employing this control strategy, the positioning error caused by cogging force was decreased by 70%.
The SPH approach to the process of container filling based on non-linear constitutive models
Institute of Scientific and Technical Information of China (English)
Tao Jiang; Jie Ouyang; Lin Zhang; Jin-Lian Ren
2012-01-01
In this work,the transient free surface of container filling with non-linear constitutive equation's fluids is numerically investigated by the smoothed particle hydrodynamics (SPH) method.Specifically,the filling process of a square container is considered for non-linear polymer fluids based on the Cross model.The validity of the presented SPH is first verified by solving the Newtonian fluid and OldroydB fluid jet.Various phenomena in the filling process are shown,including the jet buckling,jet thinning,splashing or spluttering,steady filling.Moreover,a new phenomenon of vortex whirling is more evidently observed for the Cross model fluid compared with the Newtonian fluid case.
A novel crowd flow model based on linear fractional stable motion
Wei, Juan; Zhang, Hong; Wu, Zhenya; He, Junlin; Guo, Yangyong
2016-03-01
For the evacuation dynamics in indoor space, a novel crowd flow model is put forward based on Linear Fractional Stable Motion. Based on position attraction and queuing time, the calculation formula of movement probability is defined and the queuing time is depicted according to linear fractal stable movement. At last, an experiment and simulation platform can be used for performance analysis, studying deeply the relation among system evacuation time, crowd density and exit flow rate. It is concluded that the evacuation time and the exit flow rate have positive correlations with the crowd density, and when the exit width reaches to the threshold value, it will not effectively decrease the evacuation time by further increasing the exit width.
Generalized model of double random phase encoding based on linear algebra
Nakano, Kazuya; Takeda, Masafumi; Suzuki, Hiroyuki; Yamaguchi, Masahiro
2013-01-01
We propose a generalized model for double random phase encoding (DRPE) based on linear algebra. We defined the DRPE procedure in six steps. The first three steps form an encryption procedure, while the later three steps make up a decryption procedure. We noted that the first (mapping) and second (transform) steps can be generalized. As an example of this generalization, we used 3D mapping and a transform matrix, which is a combination of a discrete cosine transform and two permutation matrices. Finally, we investigated the sensitivity of the proposed model to errors in the decryption key.
A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels
Directory of Open Access Journals (Sweden)
Uttam Kumar
2012-09-01
Full Text Available Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM. HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations.
Sharifie, Javad; Lucas, Caro; Araabi, Babak N.
2006-06-01
Disturbance storm time index (Dst) is nonlinearly related to solar wind data. In this paper, Dst past values, Dst derivative, past values of southward interplanetary magnetic field, and the square root of dynamic pressure are used as inputs for modeling and prediction of the Dst index, especially during extreme events. The geoeffective solar wind parameters are selected depending on the physical background of the geomagnetic storm procedure and physical models. A locally linear neurofuzzy model with a progressive tree construction learning algorithm is applied as a powerful tool for nonlinear modeling of Dst index on the basis of its past values and solar wind parameters. The result for modeling and prediction of several intense storms shows that the geomagnetic disturbance Dst index based on geoeffective parameters is a nonlinear model that could be considered as the nonlinear extension of empirical linear physical models. The method is applied for prediction of some geomagnetic storms. Obtained results show that using the proposed method, the predicted values of several extreme storms are highly correlated with observed values. In addition, prediction of the main phase of many storms shows a good match with observed data, which constitutes an appropriate approach for solar storm alerting to vulnerable industries.
Prediction of Coal Consumption in China Based on the Partial Linear Model
Institute of Scientific and Technical Information of China (English)
Ying; XIE; Chunxiang; ZHAO
2015-01-01
China is one of the few countries using coal as the main energy and is the world’s second largest coal consumer. Researching the coal consumption is very necessary. At present,the prediction model of coal consumption is mainly based on time series analysis of price,and it rarely considers the influence of other factors. In this paper,on the basis of demand theory,we establish the multiple impact indicators,and use principal component analysis as well as partial linear model for multiple factors to establish coal consumption model. By using this model to forecast the coal consumption in 2011,we find that the predicted value is close to actual value,which means that the model is good.
Comparison of the Noise Robustness of FVC Retrieval Algorithms Based on Linear Mixture Models
Directory of Open Access Journals (Sweden)
Hiroki Yoshioka
2011-07-01
Full Text Available The fraction of vegetation cover (FVC is often estimated by unmixing a linear mixture model (LMM to assess the horizontal spread of vegetation within a pixel based on a remotely sensed reflectance spectrum. The LMM-based algorithm produces results that can vary to a certain degree, depending on the model assumptions. For example, the robustness of the results depends on the presence of errors in the measured reflectance spectra. The objective of this study was to derive a factor that could be used to assess the robustness of LMM-based algorithms under a two-endmember assumption. The factor was derived from the analytical relationship between FVC values determined according to several previously described algorithms. The factor depended on the target spectra, endmember spectra, and choice of the spectral vegetation index. Numerical simulations were conducted to demonstrate the dependence and usefulness of the technique in terms of robustness against the measurement noise.
Directory of Open Access Journals (Sweden)
Xiaoyi Wang
2015-01-01
Full Text Available In wastewater treatment plants (WWTPs, the dissolved oxygen is the key variable to be controlled in bioreactors. In this paper, linear active disturbance rejection control (LADRC is utilized to track the dissolved oxygen concentration based on benchmark simulation model number 1 (BSM1. Optimal LADRC parameters tuning approach for wastewater treatment processes is obtained by analyzing and simulations on BSM1. Moreover, by analyzing the estimation capacity of linear extended state observer (LESO in the control of dissolved oxygen, the parameter range of LESO is acquired, which is a valuable guidance for parameter tuning in simulation and even in practice. The simulation results show that LADRC can overcome the disturbance existing in the control of wastewater and improve the tracking accuracy of dissolved oxygen. LADRC provides another practical solution to the control of WWTPs.
Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm
Directory of Open Access Journals (Sweden)
Zhehuang Huang
2015-01-01
Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
Log-linear model based behavior selection method for artificial fish swarm algorithm.
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.
Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation
Directory of Open Access Journals (Sweden)
Chunqing Li
2012-01-01
Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.
A Fully Associative, Non-Linear Kinematic, Unified Viscoplastic Model for Titanium Based Matrices
Arnold, S. M.; Saleeb, A. F.; Castelli, M. G.
1994-01-01
Specific forms for both the Gibb's and complementary dissipation potentials are chosen such that a complete (i.e., fully associative) potential based multiaxial unified viscoplastic model is obtained. This model possesses one tensorial internal state variable that is associated with dislocation substructure, with an evolutionary law that has nonlinear kinematic hardening and both thermal and strain induced recovery mechanisms. A unique aspect of the present model is the inclusion of non-linear hardening through the use of a compliance operator, derived from the Gibb's potential, in the evolution law for the back stress. This non-linear tensorial operator is significant in that it allows both the flow and evolutionary laws to be fully associative (and therefore easily integrated) and greatly influences the multiaxial response under non-proportional loading paths. In addition to this nonlinear compliance operator, a new consistent, potential preserving, internal strain unloading criterion has been introduced to prevent abnormalities in the predicted stress-strain curves, which are present with nonlinear hardening formulations, during unloading and reversed loading of the external variables. Specification of an experimental program for the complete determination of the material functions and parameters for characterizing a metallic matrix, e.g., TIMETAL 21S, is given. The experiments utilized are tensile, creep, and step creep tests. Finally, a comparison of this model and a commonly used Bodner-Partom model is made on the basis of predictive accuracy and numerical efficiency.
Empirical likelihood-based inference in a partially linear model for longitudinal data
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A partially linear model with longitudinal data is considered, empirical likelihood to inference for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the parameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed.
Empirical likelihood-based inference in a partially linear model for longitudinal data
Institute of Scientific and Technical Information of China (English)
2008-01-01
A partially linear model with longitudinal data is considered, empirical likelihood to infer- ence for the regression coefficients and the baseline function is investigated, the empirical log-likelihood ratios is proven to be asymptotically chi-squared, and the corresponding confidence regions for the pa- rameters of interest are then constructed. Also by the empirical likelihood ratio functions, we can obtain the maximum empirical likelihood estimates of the regression coefficients and the baseline function, and prove the asymptotic normality. The numerical results are conducted to compare the performance of the empirical likelihood and the normal approximation-based method, and a real example is analysed.
Kohr, H.; Louis, A. K.
2011-04-01
We study single-axis electron tomography and present an improved version of the linear forward model given by Fanelli and Öktem (2008 Inverse Problems 24 013001) which accounts for inelastic scattering and image distortions caused by imperfect optics. Based on the concept of approximate inverse, we derive an algorithm of filtered backprojection type which is much faster than the frequently used iterative methods. Numerical tests with simulated and measured transmission electron microscope data and comparisons with other FBP-type methods reveal that our algorithm provides reconstructions with high contrast and resolution, while the noise level is significantly reduced.
Uddin, Md; Lee, J J; Kim, T S
2008-01-01
In proactive computing, human activity recognition from image sequences is an active research area. This paper presents a novel approach of human activity recognition based on Linear Discriminant Analysis (LDA) of Independent Component (IC) features from shape information. With extracted features, Hidden Markov Model (HMM) is applied for training and recognition. The recognition performance using LDA of IC features has been compared to other approaches including Principle Component Analysis (PCA), LDA of PC, and ICA. The preliminary results show much improved performance in the recognition rate with our proposed method.
Wang, Zhe; Li, Lizhi; Ni, Weidou; Li, Zheng
2011-01-01
A multivariate dominant factor based non-linearized PLS model is proposed. The intensities of different lines were taken to construct a multivariate dominant factor model, which describes the dominant concentration information of the measured species. In constructing such a multivariate model, non-linear transformation of multi characteristic line intensities according to the physical mechanisms of lased induced plasma spectrum were made, combined with linear-correlation-based PLS method, to model the nonlinear self-absorption and inter-element interference effects. This enables the linear PLS method to describe non-linear relationship more accurately and provides the statistics-based PLS method with physical backgrounds. Moreover, a secondary PLS is applied utilizing the whole spectra information to further correct the model results. Experiments were conducted using standard brass samples. Taylor expansion was applied to make the nonlinear transformation to describe the self-absorption effect of Cu. Then, li...
Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
1997-01-01
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Linear Model-Based Predictive Control of the LHC 1.8 K Cryogenic Loop
Blanco-Viñuela, E; De Prada-Moraga, C
1999-01-01
The LHC accelerator will employ 1800 superconducting magnets (for guidance and focusing of the particle beams) in a pressurized superfluid helium bath at 1.9 K. This temperature is a severely constrained control parameter in order to avoid the transition from the superconducting to the normal state. Cryogenic processes are difficult to regulate due to their highly non-linear physical parameters (heat capacity, thermal conductance, etc.) and undesirable peculiarities like non self-regulating process, inverse response and variable dead time. To reduce the requirements on either temperature sensor or cryogenic system performance, various control strategies have been investigated on a reduced-scale LHC prototype built at CERN (String Test). Model Based Predictive Control (MBPC) is a regulation algorithm based on the explicit use of a process model to forecast the plant output over a certain prediction horizon. This predicted controlled variable is used in an on-line optimization procedure that minimizes an approp...
Generalization of the ordinary state-based peridynamic model for isotropic linear viscoelasticity
Delorme, Rolland; Tabiai, Ilyass; Laberge Lebel, Louis; Lévesque, Martin
2017-02-01
This paper presents a generalization of the original ordinary state-based peridynamic model for isotropic linear viscoelasticity. The viscoelastic material response is represented using the thermodynamically acceptable Prony series approach. It can feature as many Prony terms as required and accounts for viscoelastic spherical and deviatoric components. The model was derived from an equivalence between peridynamic viscoelastic parameters and those appearing in classical continuum mechanics, by equating the free energy densities expressed in both frameworks. The model was simplified to a uni-dimensional expression and implemented to simulate a creep-recovery test. This implementation was finally validated by comparing peridynamic predictions to those predicted from classical continuum mechanics. An exact correspondence between peridynamics and the classical continuum approach was shown when the peridynamic horizon becomes small, meaning peridynamics tends toward classical continuum mechanics. This work provides a clear and direct means to researchers dealing with viscoelastic phenomena to tackle their problem within the peridynamic framework.
Wireless Positioning Based on a Segment-Wise Linear Approach for Modeling the Target Trajectory
DEFF Research Database (Denmark)
Figueiras, Joao; Pedersen, Troels; Schwefel, Hans-Peter
2008-01-01
measurements and the user mobility patterns. One class of typical human being movement patterns is the segment-wise linear approach, which is studied in this paper. Current tracking solutions, such as the Constant Velocity model, hardly handle such segment-wise linear patterns. In this paper we propose...
The Simulation and Correction to the Brain Deformation Based on the Linear Elastic Model in IGS
Institute of Scientific and Technical Information of China (English)
MU Xiao-lan; SONG Zhi-jian
2004-01-01
@@ The brain deformation is a vital factor affecting the precision of the IGS and it becomes a hotspot to simulate and correct the brain deformation recently.The research organizations, which firstly resolved the brain deformation with the physical models, have the Image Processing and Analysis department of Yale University, Biomedical Modeling Lab of Vanderbilt University and so on. The former uses the linear elastic model; the latter uses the consolidation model.The linear elastic model only needs to drive the model using the surface displacement of exposed brain cortex,which is more convenient to be measured in the clinic.
Radiobiological effect based treatment plan optimization with the linear quadratic model
Energy Technology Data Exchange (ETDEWEB)
Schell, Stefan; Wilkens, Jan J.; Oelfke, Uwe [German Cancer Research Center, Heidelberg (Germany). Dept. of Medical Physics in Radiation Oncology
2010-07-01
As an approach towards more biology-oriented treatment planning for external beam radiation therapy, we present the incorporation of local radiation damage models into three dimensional treatment planning. This allows effect based instead of dose based plan optimization which could potentially better match the biologically relevant tradeoff between target and normal tissues. In particular, our approach facilitates an effective comparison of different fractionation schemes. It is based on the linear quadratic model to describe the biological radiation effect. Effect based optimization was integrated into our inverse treatment planning software KonRad, and we demonstrate the resulting differences between conventional and biological treatment planning. Radiation damage can be analyzed both qualitatively and quantitatively in dependence of the fractionation scheme and tissue specific parameters in a three dimensional voxel based system. As an example the potential advantages as well as the associated risks of hypofractionation for prostate cancer are analyzed and visualized with the help of effective dose volume histograms. Our results suggest a very conservative view regarding alternative fractionation schemes since uncertainties in biological parameters are still too big to make reliable clinical predictions. (orig.)
Directory of Open Access Journals (Sweden)
Andrea Nobili
2015-01-01
Full Text Available Three generalizations of the Timoshenko beam model according to the linear theory of micropolar elasticity or its special cases, that is, the couple stress theory or the modified couple stress theory, recently developed in the literature, are investigated and compared. The analysis is carried out in a variational setting, making use of Hamilton’s principle. It is shown that both the Timoshenko and the (possibly modified couple stress models are based on a microstructural kinematics which is governed by kinosthenic (ignorable terms in the Lagrangian. Despite their difference, all models bring in a beam-plane theory only one microstructural material parameter. Besides, the micropolar model formally reduces to the couple stress model upon introducing the proper constraint on the microstructure kinematics, although the material parameter is generally different. Line loading on the microstructure results in a nonconservative force potential. Finally, the Hamiltonian form of the micropolar beam model is derived and the canonical equations are presented along with their general solution. The latter exhibits a general oscillatory pattern for the microstructure rotation and stress, whose behavior matches the numerical findings.
Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data
Lei Wang; Satoshi Uchida
2008-01-01
MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classificat...
An Ionospheric Index Model based on Linear Regression and Neural Network Approaches
Tshisaphungo, Mpho; McKinnell, Lee-Anne; Bosco Habarulema, John
2017-04-01
The ionosphere is well known to reflect radio wave signals in the high frequency (HF) band due to the present of electron and ions within the region. To optimise the use of long distance HF communications, it is important to understand the drivers of ionospheric storms and accurately predict the propagation conditions especially during disturbed days. This paper presents the development of an ionospheric storm-time index over the South African region for the application of HF communication users. The model will result into a valuable tool to measure the complex ionospheric behaviour in an operational space weather monitoring and forecasting environment. The development of an ionospheric storm-time index is based on a single ionosonde station data over Grahamstown (33.3°S,26.5°E), South Africa. Critical frequency of the F2 layer (foF2) measurements for a period 1996-2014 were considered for this study. The model was developed based on linear regression and neural network approaches. In this talk validation results for low, medium and high solar activity periods will be discussed to demonstrate model's performance.
Simple Cellular Automata-Based Linear Models for the Shrinking Generator
Fúster-Sabater, Amparo
2010-01-01
Structural properties of two well-known families of keystream generators, Shrinking Generators and Cellular Automata, have been analyzed. Emphasis is on the equivalence of the binary sequences obtained from both kinds of generators. In fact, Shrinking Generators (SG) can be identified with a subset of linear Cellular Automata (mainly rule 90, rule 150 or a hybrid combination of both rules). The linearity of these cellular models can be advantageously used in the cryptanalysis of those keystream generators.
The Simulation and Correction to the Brain Deformation Based on the Linear Elastic Model in IGS
Institute of Scientific and Technical Information of China (English)
MUXiao-lan; SONGZhi-jian
2004-01-01
The brain deformation is a vital factor affecting the precision of the IGS and it becomes a hotspot to simulate and correct the brain deformation recently.The research organizations, which firstly resolved the brain deformation with the physical models, have the Image Processing and Analysis department of Yale University, Biomedical Modeling Lab of Vanderbilt University and so on. The former uses the linear elastic model; the latter uses the consolidation model.
Lee, Bryan; Popescu, Dan C; Ourselin, Sébastien
2010-12-01
Surgical simulators provide another tool for training and practising surgical procedures, usually restricted to the use of cadavers. Our surgical simulator utilises Finite Element (FE) models based on linear elasticity. It is driven by displacements, as opposed to forces, allowing for realistic simulation of both deformation and haptic response at real-time rates. To achieve demanding computational requirements, the stiffness matrix K, which encompasses the geometrical and physical properties of the object, is precomputed, along with K⁻¹. Common to many surgical procedures is the requirement of cutting tissue. Introducing topology modifications, such as cutting, into these precomputed schemes does however come as a challenge, as the precomputed data needs to be modified, to reflect the new topology. In particular, recomputing K⁻¹ is too costly to be performed during the simulation. Our topology modification method is based upon updating K⁻¹ rather than entirely recomputing the matrix. By integrating condensation, we improve efficiency to allow for interaction with larger models. We can further enhance this by redistributing computational load to improve the system's real-time response. We exemplify our techniques with results from our surgical simulation system.
Sohrabi Mahboub, Mahdi; Farrokhpour, Hossein
2016-06-01
In this paper, we present the results of an extensive study on a novel approach to the molecular modeling of pure ionic liquids (ILs) that incorporates the perturbed thermodynamic linear Yukawa isotherm regularity (LYIR), which is derived based on an effective nearest neighboring pair attractive interaction of the Yukawa potential. The LYIR was used to model the densities of ILs up to high pressures (35 MPa) and in the temperature range 293.15 to 393.15 K. To use the LYIR for ILs, a simple molecular model was proposed to describe their molecular structure, in which they were considered as a liquid consisting of the ion pairs moving together in the fluid, and each ion pair was assumed to be a one-center spherical united atom. The ILs under consideration contained one of the IL cations [C2mim]+, [C4mim]+, [C7mim]+, [C8mim]+, [C3mpy]+, [C3mpip]+, [C3mpyr]+ or [C4mpyr]+, and one of the IL anions [BF4]-, [C(CN)3]-, [CF3SO4]- or [NTf2]-. The reliability and physical significance of the parameters as well as the proposed molecular model were tested by calculating the densities of pure imidazolium-, pyridinium-, piperidinium- and pyrrolidimium-based ILs. The results showed that the LYIR can be used to predict and reproduce the density of ILs in good agreement with the experimental data. In addition, the LYIR enabled us to determine the physical quantities, such as an effective Yukawa screening length, λ eff, the product of the effective energy well depth and the effective coordination number, (ɛ eff/k)z eff, the contribution of the non-reference thermal pressure and also the influence of the anionic and cationic structure on the λ eff parameter. The standard deviation of the IL densities predicted in this work is lower than those calculated by the one other important equation of state reported in the literature.
Fushimi, Akihiro; Kawashima, Hiroto; Kajihara, Hideo
Understanding the contribution of each emission source of air pollutants to ambient concentrations is important to establish effective measures for risk reduction. We have developed a source apportionment method based on an atmospheric dispersion model and multiple linear regression analysis (MLR) in conjunction with ambient concentrations simultaneously measured at points in a grid network. We used a Gaussian plume dispersion model developed by the US Environmental Protection Agency called the Industrial Source Complex model (ISC) in the method. Our method does not require emission amounts or source profiles. The method was applied to the case of benzene in the vicinity of the Keiyo Central Coastal Industrial Complex (KCCIC), one of the biggest industrial complexes in Japan. Benzene concentrations were simultaneously measured from December 2001 to July 2002 at sites in a grid network established in the KCCIC and the surrounding residential area. The method was used to estimate benzene emissions from the factories in the KCCIC and from automobiles along a section of a road, and then the annual average contribution of the KCCIC to the ambient concentrations was estimated based on the estimated emissions. The estimated contributions of the KCCIC were 65% inside the complex, 49% at 0.5-km sites, 35% at 1.5-km sites, 20% at 3.3-km sites, and 9% at a 5.6-km site. The estimated concentrations agreed well with the measured values. The estimated emissions from the factories and the road were slightly larger than those reported in the first Pollutant Release and Transfer Register (PRTR). These results support the reliability of our method. This method can be applied to other chemicals or regions to achieve reasonable source apportionments.
Monahan, John F
2008-01-01
Preface Examples of the General Linear Model Introduction One-Sample Problem Simple Linear Regression Multiple Regression One-Way ANOVA First Discussion The Two-Way Nested Model Two-Way Crossed Model Analysis of Covariance Autoregression Discussion The Linear Least Squares Problem The Normal Equations The Geometry of Least Squares Reparameterization Gram-Schmidt Orthonormalization Estimability and Least Squares Estimators Assumptions for the Linear Mean Model Confounding, Identifiability, and Estimability Estimability and Least Squares Estimators F
Endo, Vitor Takashi; de Carvalho Pereira, José Carlos
2017-05-01
Material properties description and understanding are essential aspects when computational solid mechanics is applied to product development. In order to promote injected fiber reinforced thermoplastic materials for structural applications, it is very relevant to develop material characterization procedures, considering mechanical properties variation in terms of fiber orientation and loading time. Therefore, a methodology considering sample manufacturing, mechanical tests and data treatment is described in this study. The mathematical representation of the material properties was solved by a linear viscoelastic constitutive model described by Prony series, which was properly adapted to orthotropic materials. Due to the large number of proposed constitutive model coefficients, a parameter identification method was employed to define mathematical functions. This procedure promoted good correlation among experimental tests, and analytical and numerical creep models. Such results encourage the use of numerical simulations for the development of structural components with the proposed linear viscoelastic orthotropic constitutive model. A case study was presented to illustrate an industrial application of proposed methodology.
Correction of TRMM 3B42V7 Based on Linear Regression Models over China
Directory of Open Access Journals (Sweden)
Shaohua Liu
2016-01-01
Full Text Available High temporal-spatial precipitation is necessary for hydrological simulation and water resource management, and remotely sensed precipitation products (RSPPs play a key role in supporting high temporal-spatial precipitation, especially in sparse gauge regions. TRMM 3B42V7 data (TRMM precipitation is an essential RSPP outperforming other RSPPs. Yet the utilization of TRMM precipitation is still limited by the inaccuracy and low spatial resolution at regional scale. In this paper, linear regression models (LRMs have been constructed to correct and downscale the TRMM precipitation based on the gauge precipitation at 2257 stations over China from 1998 to 2013. Then, the corrected TRMM precipitation was validated by gauge precipitation at 839 out of 2257 stations in 2014 at station and grid scales. The results show that both monthly and annual LRMs have obviously improved the accuracy of corrected TRMM precipitation with acceptable error, and monthly LRM performs slightly better than annual LRM in Mideastern China. Although the performance of corrected TRMM precipitation from the LRMs has been increased in Northwest China and Tibetan plateau, the error of corrected TRMM precipitation is still significant due to the large deviation between TRMM precipitation and low-density gauge precipitation.
Directory of Open Access Journals (Sweden)
Isabelle Naegelen
2015-01-01
Full Text Available Neutrophils participate in the maintenance of host integrity by releasing various cytotoxic proteins during degranulation. Due to recent advances, a major role has been attributed to neutrophil-derived cytokine secretion in the initiation, exacerbation, and resolution of inflammatory responses. Because the release of neutrophil-derived products orchestrates the action of other immune cells at the infection site and, thus, can contribute to the development of chronic inflammatory diseases, we aimed to investigate in more detail the spatiotemporal regulation of neutrophil-mediated release mechanisms of proinflammatory mediators. Purified human neutrophils were stimulated for different time points with lipopolysaccharide. Cells and supernatants were analyzed by flow cytometry techniques and used to establish secretion profiles of granules and cytokines. To analyze the link between cytokine release and degranulation time series, we propose an original strategy based on linear fitting, which may be used as a guideline, to (i define the relationship of granule proteins and cytokines secreted to the inflammatory site and (ii investigate the spatial regulation of neutrophil cytokine release. The model approach presented here aims to predict the correlation between neutrophil-derived cytokine secretion and degranulation and may easily be extrapolated to investigate the relationship between other types of time series of functional processes.
Naegelen, Isabelle; Beaume, Nicolas; Plançon, Sébastien; Schenten, Véronique; Tschirhart, Eric J.; Bréchard, Sabrina
2015-01-01
Neutrophils participate in the maintenance of host integrity by releasing various cytotoxic proteins during degranulation. Due to recent advances, a major role has been attributed to neutrophil-derived cytokine secretion in the initiation, exacerbation, and resolution of inflammatory responses. Because the release of neutrophil-derived products orchestrates the action of other immune cells at the infection site and, thus, can contribute to the development of chronic inflammatory diseases, we aimed to investigate in more detail the spatiotemporal regulation of neutrophil-mediated release mechanisms of proinflammatory mediators. Purified human neutrophils were stimulated for different time points with lipopolysaccharide. Cells and supernatants were analyzed by flow cytometry techniques and used to establish secretion profiles of granules and cytokines. To analyze the link between cytokine release and degranulation time series, we propose an original strategy based on linear fitting, which may be used as a guideline, to (i) define the relationship of granule proteins and cytokines secreted to the inflammatory site and (ii) investigate the spatial regulation of neutrophil cytokine release. The model approach presented here aims to predict the correlation between neutrophil-derived cytokine secretion and degranulation and may easily be extrapolated to investigate the relationship between other types of time series of functional processes. PMID:26579547
Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data
Directory of Open Access Journals (Sweden)
Lei Wang
2008-06-01
Full Text Available MODIS (Moderate Resolution Imaging Spectroradiometer is a key instrument aboard the Terra (EOS AM and Aqua (EOS PM satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.
Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data
Institute of Scientific and Technical Information of China (English)
2008-01-01
MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites.Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers.Shaoxing county of Zhcjiang Province in China was chosen to be the study site and early rice was selected as the study crop.The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day,which implies that MODIS data could be used as satellite data source for rice cultivation area estimation,possibly rice growth monitoring and yield forecasting on the regional scale.
Bilevel linear programming model of charging for effluent based on price control
Institute of Scientific and Technical Information of China (English)
LI Yu-hua; LI Lei; HU Yun-quan; SHAO Hai-hong
2007-01-01
For the optimum price problem of charging for effluent, this paper analyzes the optimal Pigovian Tax and the serious information asymmetry problem existing in the application process of optimal Pigovian Tax,which is predominant in theory. Then the bilevel system optimizing decision-making theory is applied to give bilevel linear programming decision-making model of charging for effluent, in which the government (environmental protection agency) acts as the upper level decision-making unit and the polluting enterprises act as the lower level decision-making unit. To some extent, the model avoids the serious information asymmetry between the government and the polluting enterprises on charging for effluent.
Campagnoli, Patrizia; Petris, Giovanni
2009-01-01
State space models have gained tremendous popularity in as disparate fields as engineering, economics, genetics and ecology. Introducing general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. It illustrates the fundamental steps needed to use dynamic linear models in practice, using R package.
Multicollinearity in hierarchical linear models.
Yu, Han; Jiang, Shanhe; Land, Kenneth C
2015-09-01
This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model.
Liu, Y.; Li, T.; Zhu, C.; Zhang, R.; Wu, Y.
2015-12-01
Three-dimensional (3-D) electromagnetic (EM) forward modelling and inversion continues to be an important issue for the correct interpretation of EM data.To this end,approximate solutions have been developed that allow the construction of relatively fast forward modelling and inversion schemes.We have developed an improved quasi-linear approximation which is more appropriate in solving the linear equation for greatly shortening calculation time.We achieved this by using green's function properties.Then we introduced the improved quasi-linear approximation to spectral induced polarization (SIP) to tackle the problem of the resolution and the efficiency.The localized quasi-linear (LQL) approximation theory is appropriate for multisource array-type surveys assuming that the normal field is slowly varying within the inhomogeneity domain.However,the normal field of attenuates severely which dose not satisfy the assumption of the LQL approximation.As a consenquence,the imaginary part is not accurate when LQL approximation is adopted for the simulation.The improved quasi-linear approximation provide a new approach with the same resolution of QL approximation and much less calculation time.We have also constructed three-dimensional SIP forward modeling based on improved quasi-linear approximation method.It only takes 0.8s for forward modeling when inhomogeneity domain is divided into 2000 blocks.Beyond that, we have introduced the Cole-Cole model to the algorithm and complete the three-dimensional complex resistivity conjugate gradient inversion with parameter restraint.The model trial results show that this method can obtain good inversion results in physical parameters such as zero frequency resistivity, polarization.The results demonstrate the stability and the efficiency of the improved quasi-linear approximation and the method may be a practical solution for3-D EM forward modelling and inversion of SIP.
Rust, H. W.; Vrac, M.; Lengaigne, M.; Sultan, B.
2012-04-01
Changes in precipitation patterns with potentially less precipitation and an increasing risk for droughts pose a threat to water resources and agricultural yields in Senegal. Precipitation in this region is dominated by the West-African Monsoon being active from May to October, a seasonal pattern with inter-annual to decadal variability in the 20th century which is likely to be affected by climate change. We built a generalized linear model for a full spatial description of rainfall in Senegal. The model uses season, location, and a discrete set of weather types as predictors and yields a spatially continuous description of precipitation occurrences and intensities. Weather types have been defined on NCEP/NCAR reanalysis using zonal and meridional winds, as well as relative humidity. This model is suitable for downscaling precipitation, particularly precipitation occurrences relevant for drough risk mapping.
Multi-Site Calibration of Linear Reservoir Based Geomorphologic Rainfall-Runoff Models
Directory of Open Access Journals (Sweden)
Bahram Saeidifarzad
2014-09-01
Full Text Available Multi-site optimization of two adapted event-based geomorphologic rainfall-runoff models was presented using Non-dominated Sorting Genetic Algorithm (NSGA-II method for the South Fork Eel River watershed, California. The first model was developed based on Unequal Cascade of Reservoirs (UECR and the second model was presented as a modified version of Geomorphological Unit Hydrograph based on Nash’s model (GUHN. Two calibration strategies were considered as semi-lumped and semi-distributed for imposing (or unimposing the geomorphology relations in the models. The results of models were compared with Nash’s model. Obtained results using the observed data of two stations in the multi-site optimization framework showed reasonable efficiency values in both the calibration and the verification steps. The outcomes also showed that semi-distributed calibration of the modified GUHN model slightly outperformed other models in both upstream and downstream stations during calibration. Both calibration strategies for the developed UECR model during the verification phase showed slightly better performance in the downstream station, but in the upstream station, the modified GUHN model in the semi-lumped strategy slightly outperformed the other models. The semi-lumped calibration strategy could lead to logical lag time parameters related to the basin geomorphology and may be more suitable for data-based statistical analyses of the rainfall-runoff process.
Energy Technology Data Exchange (ETDEWEB)
Siewert, C.E. [North Carolina State Univ., Dept. Mathematics, Raleigh, NC (United States)
2002-10-01
A synthetic-kernel model (CES model) of the linearized Boltzmann equation is used along with an analytical discrete-ordinates method (ADO) to solve three fundamental problems concerning flow of a rarefied gas in a plane channel. More specifically, the problems of Couette flow, Poiseuille flow and thermal-creep flow are solved in terms of the CES model equation for an arbitrary mixture of specular and diffuse reflection at the walls confining the flow, and numerical results for the basic quantities of interest are reported. The comparisons made with results derived from solutions based on computationally intensive methods applied to the linearized Boltzmann equation are used to conclude that the CES model can be employed with confidence to improve the accuracy of results available from simpler approximations such as the BGK model or the S model. (author)
System Reliability of Timber Trusses Based on Non-Linear Structural Modelling
DEFF Research Database (Denmark)
Hansson, Martin; Ellegaard, Peter
2006-01-01
Structural design is today concerned with single component performance where each limit state is related to a single mode of failure of a single component. Further, in limit state codes the strength variables are related to a deterministic value (usually the 5-percentile). However, in a structure...... spruce (Picea abies). Most studies found in the literature are based on linear-elastic theory and the variability within members is neglected mainly because of lack of data. The FE calculations are performed by TrussLab - a toolbox for MATLAB developed at Aalborg University. TrussLab considers contact...
van Ijsseldijk, E A; Valstar, E R; Stoel, B C; Nelissen, R G H H; Reiber, J H C; Kaptein, B L
2011-10-13
Accurate in vivo measurements methods of wear in total knee arthroplasty are required for a timely detection of excessive wear and to assess new implant designs. Component separation measurements based on model-based Roentgen stereophotogrammetric analysis (RSA), in which 3-dimensional reconstruction methods are used, have shown promising results, yet the robustness of these measurements is unknown. In this study, the accuracy and robustness of this measurement for clinical usage was assessed. The validation experiments were conducted in an RSA setup with a phantom setup of a knee in a vertical orientation. 72 RSA images were created using different variables for knee orientations, two prosthesis types (fixed-bearing Duracon knee and fixed-bearing Triathlon knee) and accuracies of the reconstruction models. The measurement error was determined for absolute and relative measurements and the effect of knee positioning and true seperation distance was determined. The measurement method overestimated the separation distance with 0.1mm on average. The precision of the method was 0.10mm (2*SD) for the Duracon prosthesis and 0.20mm for the Triathlon prosthesis. A slight difference in error was found between the measurements with 0° and 10° anterior tilt. (difference=0.08mm, p=0.04). The accuracy of 0.1mm and precision of 0.2mm can be achieved for linear wear measurements based on model-based RSA, which is more than adequate for clinical applications. The measurement is robust in clinical settings. Although anterior tilt seems to influence the measurement, the size of this influence is low and clinically irrelevant.
Multivariate covariance generalized linear models
DEFF Research Database (Denmark)
Bonat, W. H.; Jørgensen, Bent
2016-01-01
We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link...... function combined with a matrix linear predictor involving known matrices. The method is motivated by three data examples that are not easily handled by existing methods. The first example concerns multivariate count data, the second involves response variables of mixed types, combined with repeated...... are fitted by using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of types of response variables and covariance structures, including multivariate extensions...
Generalized functional linear models for gene-based case-control association studies.
Fan, Ruzong; Wang, Yifan; Mills, James L; Carter, Tonia C; Lobach, Iryna; Wilson, Alexander F; Bailey-Wilson, Joan E; Weeks, Daniel E; Xiong, Momiao
2014-11-01
By using functional data analysis techniques, we developed generalized functional linear models for testing association between a dichotomous trait and multiple genetic variants in a genetic region while adjusting for covariates. Both fixed and mixed effect models are developed and compared. Extensive simulations show that Rao's efficient score tests of the fixed effect models are very conservative since they generate lower type I errors than nominal levels, and global tests of the mixed effect models generate accurate type I errors. Furthermore, we found that the Rao's efficient score test statistics of the fixed effect models have higher power than the sequence kernel association test (SKAT) and its optimal unified version (SKAT-O) in most cases when the causal variants are both rare and common. When the causal variants are all rare (i.e., minor allele frequencies less than 0.03), the Rao's efficient score test statistics and the global tests have similar or slightly lower power than SKAT and SKAT-O. In practice, it is not known whether rare variants or common variants in a gene region are disease related. All we can assume is that a combination of rare and common variants influences disease susceptibility. Thus, the improved performance of our models when the causal variants are both rare and common shows that the proposed models can be very useful in dissecting complex traits. We compare the performance of our methods with SKAT and SKAT-O on real neural tube defects and Hirschsprung's disease datasets. The Rao's efficient score test statistics and the global tests are more sensitive than SKAT and SKAT-O in the real data analysis. Our methods can be used in either gene-disease genome-wide/exome-wide association studies or candidate gene analyses.
Quach, Minh; Brunel, Nicolas; d'Alché-Buc, Florence
2007-12-01
Statistical inference of biological networks such as gene regulatory networks, signaling pathways and metabolic networks can contribute to build a picture of complex interactions that take place in the cell. However, biological systems considered as dynamical, non-linear and generally partially observed processes may be difficult to estimate even if the structure of interactions is given. Using the same approach as Sitz et al. proposed in another context, we derive non-linear state-space models from ODEs describing biological networks. In this framework, we apply Unscented Kalman Filtering (UKF) to the estimation of both parameters and hidden variables of non-linear state-space models. We instantiate the method on a transcriptional regulatory model based on Hill kinetics and a signaling pathway model based on mass action kinetics. We successfully use synthetic data and experimental data to test our approach. This approach covers a large set of biological networks models and gives rise to simple and fast estimation algorithms. Moreover, the Bayesian tool used here directly provides uncertainty estimates on parameters and hidden states. Let us also emphasize that it can be coupled with structure inference methods used in Graphical Probabilistic Models. Matlab code available on demand.
Stable 1-Norm Error Minimization Based Linear Predictors for Speech Modeling
DEFF Research Database (Denmark)
Giacobello, Daniele; Christensen, Mads Græsbøll; Jensen, Tobias Lindstrøm;
2014-01-01
In linear prediction of speech, the 1-norm error minimization criterion has been shown to provide a valid alternative to the 2-norm minimization criterion. However, unlike 2-norm minimization, 1-norm minimization does not guarantee the stability of the corresponding all-pole filter and can generate...... of the shift operator associated with the particular prediction problem considered. The second method uses the alternative Cauchy bound to impose a convex constraint on the predictor in the 1-norm error minimization. These methods are compared with two existing methods: the Burg method, based on the 1-norm...... minimization of the forward and backward prediction error, and the iteratively reweighted 2-norm minimization known to converge to the 1-norm minimization with an appropriate selection of weights. The evaluation gives proof of the effectiveness of the new methods, performing as well as unconstrained 1-norm...
Explorative methods in linear models
DEFF Research Database (Denmark)
Høskuldsson, Agnar
2004-01-01
The author has developed the H-method of mathematical modeling that builds up the model by parts, where each part is optimized with respect to prediction. Besides providing with better predictions than traditional methods, these methods provide with graphic procedures for analyzing different feat...... features in data. These graphic methods extend the well-known methods and results of Principal Component Analysis to any linear model. Here the graphic procedures are applied to linear regression and Ridge Regression....
Explorative methods in linear models
DEFF Research Database (Denmark)
Høskuldsson, Agnar
2004-01-01
The author has developed the H-method of mathematical modeling that builds up the model by parts, where each part is optimized with respect to prediction. Besides providing with better predictions than traditional methods, these methods provide with graphic procedures for analyzing different...... features in data. These graphic methods extend the well-known methods and results of Principal Component Analysis to any linear model. Here the graphic procedures are applied to linear regression and Ridge Regression....
Institute of Scientific and Technical Information of China (English)
ZHANG Ren; HONG Mei; SUN Zhao-bo; NIU Sheng-jie; ZHU Wei-jun; MIN Jin-zhong; WAN Qi-lin
2006-01-01
Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF(empirical orthogonal function) temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results.A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.
Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara
2017-01-01
In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.
Wind turbine fatigue damage evaluation based on a linear model and a spectral method
DEFF Research Database (Denmark)
Tibaldi, Carlo; Henriksen, Lars Christian; Hansen, Morten Hartvig;
2015-01-01
to power spectral densities of wind turbine sensor responses to turbulent wind. In this paper, the model is validated both in time domain and frequency domain with a nonlinear aeroservoelastic model. The approach is compared quantitatively against fatigue damage obtained from the power spectra of time...... different wind speeds above rated and load dependency on the number of turbulence seeds used for their evaluation. For the full-range normal operation, the maximum difference between the two frequency domain-based estimates of the tower base lateral fatigue moments is 36%, whereas the differences...... for the other sensors are less than 15%. For the load variation evaluation, the maximum difference of the tower base longitudinal bending moment variation is 22%. Such large difference occurs only when the change in controller tuning has a low effect on the loads. Furthermore, results show that loads evaluated...
Generalized, Linear, and Mixed Models
McCulloch, Charles E; Neuhaus, John M
2011-01-01
An accessible and self-contained introduction to statistical models-now in a modernized new editionGeneralized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed m
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Compared with the traditional rigid-plastic/rigid-viscoplastic(RP/RVP) FEM(based on iteration solution),RP/RVP FEM based on linear programming (LP) has some remarkable advantages,such as it's free of convergence problem and its convenience in contact,rigid zone,and friction force treatment.The numerical model of RP/RVP FEM based on LP for axisymmetrical metal forming simulation is studied,and some related key factors and its treatment methods in formulation of constraint condition are proposed.Some solution examples are provided to validate its accuracy and efficiency.
Directory of Open Access Journals (Sweden)
Kohei Arai
2013-04-01
Full Text Available Comparative study on linear and nonlinear mixed pixel models of which pixels in remote sensing satellite images is composed with plural ground cover materials mixed together, is conducted for remote sensing satellite image analysis. The mixed pixel models are based on Cierniewski of ground surface reflectance model. The comparative study is conducted by using of Monte Carlo Ray Tracing: MCRT simulations. Through simulation study, the difference between linear and nonlinear mixed pixel models is clarified. Also it is found that the simulation model is validated.
Directory of Open Access Journals (Sweden)
Koichi Kobayashi
2016-01-01
Full Text Available A networked control system (NCS is a control system where components such as plants and controllers are connected through communication networks. Self-triggered control is well known as one of the control methods in NCSs and is a control method that for sampled-data control systems both the control input and the aperiodic sampling interval (i.e., the transmission interval are computed simultaneously. In this paper, a self-triggered model predictive control (MPC method for discrete-time linear systems with disturbances is proposed. In the conventional MPC method, the first one of the control input sequence obtained by solving the finite-time optimal control problem is sent and applied to the plant. In the proposed method, the first some elements of the control input sequence obtained are sent to the plant, and each element is sequentially applied to the plant. The number of elements is decided according to the effect of disturbances. In other words, transmission intervals can be controlled. Finally, the effectiveness of the proposed method is shown by numerical simulations.
Toward Model-Based Control of Non-linear Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Jensen, Tom Nørgaard; Kallesøe, Carsten;
2013-01-01
consumption. To have a better understanding of leakage in WSSs, to control pressure and leakage effectively, and for optimal design of WSSs, suitable modeling is an important prerequisite. In this paper a model with the main objective of pressure control and consequently leakage reduction is presented......Water leakage is an important component of water loss. Many methods have emerged from urban water supply systems (WSSs) for leakage control, but it still remains a challenge in many countries. Pressure management is an effective way to reduce the leakage in a system. It can also reduce the power....... Following an analogy to electric circuits, first the mathematical expression for pressure drop over each component of the pipe network (WSS) such as pipes, pumps, valves and water towers is presented. Then the network model is derived based on the circuit theory and subsequently used for pressure management...
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...... Bayesian hierarchy for sparse models using slab and spike priors (two-component δ-function and continuous mixtures), non-Gaussian latent factors and a stochastic search over the ordering of the variables. The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated...... and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable...
Decomposable log-linear models
DEFF Research Database (Denmark)
Eriksen, Poul Svante
can be characterized by a structured set of conditional independencies between some variables given some other variables. We term the new model class decomposable log-linear models, which is illustrated to be a much richer class than decomposable graphical models.It covers a wide range of non...
Pauwels, Valentijn R. N.; Verhoest, Niko E. C.; de Troch, FrançOis P.
2002-12-01
In hydrology the slow, subsurface component of the discharge is usually referred to as base flow. One method to model base flow is the conceptual approach, in which the complex physical reality is simplified using hypotheses and assumptions, and the various physical processes are described mathematically. The purpose of this paper is to develop and validate a conceptual method, based on hydraulic theory, to calculate the base flow of a catchment, under observed precipitation rates. The governing groundwater equation, the Boussinesq equation, valid for a unit width sloping aquifer, is linearized and solved for a temporally variable recharge rate. The solution allows the calculation of the transient water table profile in and the outflow from an aquifer under temporally variable recharge rates. When a catchment is considered a metahillslope, the solution can be used, when coupled to a routing model, to calculate the catchment base flow. The model is applied to the Zwalm catchment and four subcatchments in Belgium. The results suggest that it is possible to model base flow at the catchment scale, using a Boussinesq-based metahillslope model. The results further indicate that it is sufficient to use a relatively simple formulation of the infiltration, overland flow, and base flow processes to obtain reasonable estimates of the total catchment discharge.
Directory of Open Access Journals (Sweden)
Páez-Borrallo José M
2006-01-01
Full Text Available Location estimation is a recent interesting research area that 0exploits the possibilities of modern communication technology. In this paper, we present a new location system for wireless networks that is especially suitable for indoor terminal-based architectures, as it improves both the speed and the memory requirements. The algorithm is based on the application of linear discriminant functions and Markovian models and its performance has been compared with other systems presented in the literature. Simulation results show a very good performance in reducing the computing time and memory space and displaying an adequate behavior under conditions of few a priori calibration points per position.
Generalized Quadratic Linearization of Machine Models
Parvathy Ayalur Krishnamoorthy; Kamaraj Vijayarajan; Devanathan Rajagopalan
2011-01-01
In the exact linearization of involutive nonlinear system models, the issue of singularity needs to be addressed in practical applications. The approximate linearization technique due to Krener, based on Taylor series expansion, apart from being applicable to noninvolutive systems, allows the singularity issue to be circumvented. But approximate linearization, while removing terms up to certain order, also introduces terms of higher order than those removed into the system. To overcome th...
Directory of Open Access Journals (Sweden)
G.M. Golenkov
2015-12-01
Full Text Available Purpose. The research of the influence of value and direction of current on the equivalent spring magnetic force based on coaxial-linear motor (CLM – MS. Methodology. We carried out investigation of the equivalent harshness of magnetic spring with determination of electromechanical propulsion performance characteristics by the methods of computer modeling and experimental research of physical model of CLM – MS. The modeling of magnetic spring of CLM – MS is carried out by the finite-element method. The challenge is met as an axisymmetric challenge in cylindrical co-ordinates in magnetostatic approach. The experimental investigattion of the propulsion performance characteristics of magnetic spring is carried out on the test bench. Results. After the computer modeling and the experimental investigation of the electromechanical propulsion performance characteristics of magnetic spring the expressions of equivalent stiffness coefficient depending on the current in winding are obtained. The results of computer modeling are confirmed experimentally. Originality. The determination of equivalent stiffness coefficient of magnetic spring of vibration exciter based on coaxial-linear motor. Practical value. The obtained determination of equivalent stiffness coefficient of magnetic spring may be used in process of designing of vibration machines with devices for change of natural oscillation frequency.
Parameterized Linear Longitudinal Airship Model
Kulczycki, Eric; Elfes, Alberto; Bayard, David; Quadrelli, Marco; Johnson, Joseph
2010-01-01
A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamics
Inequality constrained normal linear models
Klugkist, I.G.
2005-01-01
This dissertation deals with normal linear models with inequality constraints among model parameters. It consists of an introduction and four chapters that are papers submitted for publication. The first chapter introduces the use of inequality constraints. Scientists often have one or more theories
Martian linear dunes : observation and modelling from the LMD GCM data base
allemand, pascal; Quiquerez, Amélie; Quantin, Cathy
2014-05-01
Dunes are common on Earth and Mars and have similar geometries on both planets. Martian dunes are larger than terrestrial one and are shaped by winds less efficient than terrestrial winds. Martian dunes move thus much more slowly than terrestrial dunes. Their characteristic time could be similar to the characteristic time of climate change on Mars. Their geometry could thus reflect past climate conditions. Linear dunes are a family of elongated dunes shaped by at least 2 winds that blow at an obtuse angle alternatively along the year. Contrary to simple dunes as barkhanes, it is therefore difficult to invert the shape of these dunes in term of wind direction and intensity. It is thus difficult to demonstrate if their geometry is coherent or not with the current wind regimes. We mapped 10 dune fields located inside impact craters of the southern hemisphere of Mars. Five fields are composed of barkhanes and 5 by linear dunes. For each dune field location, we extracted the annual wind velocity at 20m above the surface at a temporal resolution of 1 hour every 30 martian days from the Mars Climate Database of the LMD (www-mars.lmd.jussieu.fr/). The annual wind rose was calculated for each dune field. The sand flux along the year was also computed assuming a classical law of transport with threshold. Assuming that the avalanche face of barkhanes is perpendicular to the sand flux direction, we predicted the orientation of the avalanche face for each barkhane fields. These results are coherent with the observations. Assuming that linear dunes are aligned along the average sand flux direction, we predicted the orientation of the linear dunes and compared them to the observations. In 4 cases, the predicted dune orientation is consistent with observations. In one case, there is a strong discrepancy between the predicted and observed orientation that could indicate that this linear dune field is fossil.
Linear Parameter Varying Model Identification for Control of Rotorcraft-based UAV
Budiyono, Agus
2008-01-01
A rotorcraft-based unmanned aerial vehicle exhibits more complex properties compared to its full-size counterparts due to its increased sensitivity to control inputs and disturbances and higher bandwidth of its dynamics. As an aerial vehicle with vertical take-off and landing capability, the helicopter specifically poses a difficult problem of transition between forward flight and unstable hover and vice versa. The LPV control technique explicitly takes into account the change in performance due to the real-time parameter variations. The technique therefore theoretically guarantees the performance and robustness over the entire operating envelope. In this study, we investigate a new approach implementing model identification for use in the LPV control framework. The identification scheme employs recursive least square technique implemented on the LPV system represented by dynamics of helicopter during a transition. The airspeed as the scheduling of parameter trajectory is not assumed to vary slowly. The exclu...
Hakkarainen, Elina; Tähtinen, Matti
2016-05-01
Demonstrations of direct steam generation (DSG) in linear Fresnel collectors (LFC) have given promising results related to higher steam parameters compared to the current state-of-the-art parabolic trough collector (PTC) technology using oil as heat transfer fluid (HTF). However, DSG technology lacks feasible solution for long-term thermal energy storage (TES) system. This option is important for CSP technology in order to offer dispatchable power. Recently, molten salts have been proposed to be used as HTF and directly as storage medium in both line-focusing solar fields, offering storage capacity of several hours. This direct molten salt (DMS) storage concept has already gained operational experience in solar tower power plant, and it is under demonstration phase both in the case of LFC and PTC systems. Dynamic simulation programs offer a valuable effort for design and optimization of solar power plants. In this work, APROS dynamic simulation program is used to model a DMS linear Fresnel solar field with two-tank TES system, and example simulation results are presented in order to verify the functionality of the model and capability of APROS for CSP modelling and simulation.
Chen, G; de Figueiredo, R P
1993-01-01
The unified approach to optimal image interpolation problems presented provides a constructive procedure for finding explicit and closed-form optimal solutions to image interpolation problems when the type of interpolation can be either spatial or temporal-spatial. The unknown image is reconstructed from a finite set of sampled data in such a way that a mean-square error is minimized by first expressing the solution in terms of the reproducing kernel of a related Hilbert space, and then constructing this kernel using the fundamental solution of an induced linear partial differential equation, or the Green's function of the corresponding self-adjoint operator. It is proved that in most cases, closed-form fundamental solutions (or Green's functions) for the corresponding linear partial differential operators can be found in the general image reconstruction problem described by a first- or second-order linear partial differential operator. An efficient method for obtaining the corresponding closed-form fundamental solutions (or Green's functions) of the operators is presented. A computer simulation demonstrates the reconstruction procedure.
Yang, Zili
2017-07-01
Heart segmentation is an important auxiliary method in the diagnosis of many heart diseases, such as coronary heart disease and atrial fibrillation, and in the planning of tumor radiotherapy. Most of the existing methods for full heart segmentation treat the heart as a whole part and cannot accurately extract the bottom of the heart. In this paper, we propose a new method based on linear gradient model to segment the whole heart from the CT images automatically and accurately. Twelve cases were tested in order to test this method and accurate segmentation results were achieved and identified by clinical experts. The results can provide reliable clinical support.
Linear Logistic Test Modeling with R
Directory of Open Access Journals (Sweden)
Purya Baghaei
2014-01-01
Full Text Available The present paper gives a general introduction to the linear logistic test model (Fischer, 1973, an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014 functions to estimate the model and interpret its parameters. The applications of the model in test validation, hypothesis testing, cross-cultural studies of test bias, rule-based item generation, and investigating construct irrelevant factors which contribute to item difficulty are explained. The model is applied to an English as a foreign language reading comprehension test and the results are discussed.
Directory of Open Access Journals (Sweden)
Xiaoling Zhang
2013-01-01
Full Text Available The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers’ preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of “low risk and high return efficiency” in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.
Zhang, Xiaoling; Huang, Kai; Zou, Rui; Liu, Yong; Yu, Yajuan
2013-01-01
The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP) method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP) and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers' preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of "low risk and high return efficiency" in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.
Laloue, A.; Camiade, M.; Valenza, M; Vildeuil, J.C.; Nallatamby, J. C.; Prigent, M.; Obregon, J.; Quéré, R.
2000-01-01
For the first time, a fully measurement based extraction procedure of non linear and non uniform transmission line model of FET devices is proposed. This model describes accurately the distributed nature under the device gate which allows a good distortion prediction (IM3) and promises good perspectives for simulation of noise characteristics in non linear circuits.
Inferential Models for Linear Regression
Directory of Open Access Journals (Sweden)
Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
Energy Technology Data Exchange (ETDEWEB)
Burant, Aniela S.; Thompson, Christopher J.; Lowry, Gregory; Karamalidis, Athanasios
2016-05-17
Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch reactor system with dual spectroscopic detectors: a near infrared spectrometer for measuring the organic analyte in the CO2 phase, and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly-parameter linear free energy relationship and to develop five new linear free energy relationships for predicting water-sc-CO2 partitioning coefficients. Four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than the model built for the entire dataset.
Linearized Bekenstein Varying Alpha Models
Pina-Avelino, P; Oliveira, J C
2004-01-01
We study the simplest class of Bekenstein-type, varying $\\alpha$ models, in which the two available free functions (potential and gauge kinetic function) are Taylor-expanded up to linear order. Any realistic model of this type reduces to a model in this class for a certain time interval around the present day. Nevertheless, we show that no such model is consistent with all existing observational results. We discuss possible implications of these findings, and in particular clarify the ambiguous statement (often found in the literature) that ``the Webb results are inconsistent with Oklo''.
Linearized Bekenstein varying α models
Avelino, P. P.; Martins, C. J.; Oliveira, J. C.
2004-10-01
We study the simplest class of Bekenstein-type, varying α models, in which the two available free functions (potential and gauge kinetic function) are Taylor-expanded up to linear order. Any realistic model of this type reduces to a model in this class for a certain time interval around the present day. Nevertheless, we show that no such model is consistent with all existing observational results. We discuss possible implications of these findings, and, in particular, clarify the ambiguous statement (often found in the literature) that “the Webb results are inconsistent with Oklo.”
Toward Model-Based Control of Non-linear Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Jensen, Tom Nørgaard; Kallesøe, Carsten
2013-01-01
consumption. To have a better understanding of leakage in WSSs, to control pressure and leakage effectively, and for optimal design of WSSs, suitable modeling is an important prerequisite. In this paper a model with the main objective of pressure control and consequently leakage reduction is presented...
Avendaño-Valencia, L. D.; Fassois, S. D.
2015-07-01
The problem of damage detection in an operating wind turbine under normal operating conditions is addressed. This is characterized by difficulties associated with the lack of measurable excitation(s), the vibration response non-stationary nature, and its dependence on various types of uncertainties. To overcome these difficulties a stochastic approach based on Random Coefficient (RC) Linear Parameter Varying (LPV) AutoRegressive (AR) models is postulated. These models may effectively represent the non-stationary random vibration response under healthy conditions and subsequently used for damage detection through hypothesis testing. The performance of the method for damage and fault detection in an operating wind turbine is subsequently assessed via Monte Carlo simulations using the FAST simulation package.
Zukowska, Barbara; Breivik, Knut; Wania, Frank
2006-04-15
We recently proposed how to expand the applicability of multimedia models towards polar organic chemicals by expressing environmental phase partitioning with the help of poly-parameter linear free energy relationships (PP-LFERs). Here we elaborate on this approach by applying it to three pharmaceutical substances. A PP-LFER-based version of a Level III fugacity model calculates overall persistence, concentrations and intermedia fluxes of polar and non-polar organic chemicals between air, water, soil and sediments at steady-state. Illustrative modeling results for the pharmaceuticals within a defined coastal region are presented and discussed. The model results are highly sensitive to the degradation rate in water and the equilibrium partitioning between organic carbon and water, suggesting that an accurate description of this particular partitioning equilibrium is essential in order to obtain reliable predictions of environmental fate. The PP-LFER based modeling approach furthermore illustrates that the greatest mobility in aqueous phases may be experienced by pharmaceuticals that combines a small molecular size with strong H-acceptor properties.
Energy Technology Data Exchange (ETDEWEB)
Zukowska, Barbara [Department of Analytical Chemistry, Chemical Faculty, Gdansk University of Technology, 11/12 G. Narutowicza St., 80-952 Gdansk (Poland); Breivik, Knut [NILU- Norwegian Institute for Air Research, P.O. Box 100, NO-2027 Kjeller (Norway)]. E-mail: knut.breivik@nilu.no; Wania, Frank [Department of Physical and Environmental Sciences, University of Toronto at Scarborough, 1265 Military Trail, Scarborough, Ontario, M1C 1A4 (Canada)
2006-04-15
We recently proposed how to expand the applicability of multimedia models towards polar organic chemicals by expressing environmental phase partitioning with the help of poly-parameter linear free energy relationships (PP-LFERs). Here we elaborate on this approach by applying it to three pharmaceutical substances. A PP-LFER-based version of a Level III fugacity model calculates overall persistence, concentrations and intermedia fluxes of polar and non-polar organic chemicals between air, water, soil and sediments at steady-state. Illustrative modeling results for the pharmaceuticals within a defined coastal region are presented and discussed. The model results are highly sensitive to the degradation rate in water and the equilibrium partitioning between organic carbon and water, suggesting that an accurate description of this particular partitioning equilibrium is essential in order to obtain reliable predictions of environmental fate. The PP-LFER based modeling approach furthermore illustrates that the greatest mobility in aqueous phases may be experienced by pharmaceuticals that combines a small molecular size with strong H-acceptor properties.
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
with validation study, J. Nonparametric Statistics, 1995, 4: 365-394.［15］Wang, Q. H., Estimation of partial linear error-in-variables model, Jourmal of Multivariate Analysis, 1999, 69:30-64.［16］Wang, Q. H., Estimation of linear error-in-covariables models with validation data under random censorship,Journal of Multivariate Analysis, 2000, 74: 245-266.［17］Wang, Q. H., Estimation of partial linear error-in-response models with validation data, Ann. Inst. Statist. Math.,2003, 55(1): 21～39［18］Wang, Q. H., Dimension reduction in partly linear error-in-response model error-in-response models with validation data, Journal of Multivariate Analysis, 2003, 85(2): 234-252.［19］Wang, Q. H., Rao, J. N. K., Empirical likelihood-based in linear errors-in-covariables models with validation data, Biometrika, 2002, 89: 345-358.［20］Owen, A., Empirical likelihood for linear models, Ann. Statist., 1991, 19: 1725-1747.［21］Li, K. C., Sliced inverse regression for dimension reduction (with discussion), J. Amer. Statist. Assoc., 1991,86: 337-342.［22］Duan, N., Li, K. C., Slicing regression: a link-free regression method, Ann. Statist. 1991, 19, 505-530.［23］Zhu, L. X., Fang, K. T, Asymptotics for kernel estimator of sliced inverse regression, Ann. Statist., 1996, 24:1053-1068.［24］Carroll, R. J., Li, K. C., Errors in variables for nonlinear regression: dimension reduction and data visualization,J. Amer. Statist. Assoc., 1992, 87: 1040-1050.［25］Rosner, B., Willett, W. C., Spiegelman, D., Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error, Statist. Med., 1989, 8: 1075-1093.［26］H(a)rdle, W., Stoke, T M., Investigating smooth multiple regression by the method of average derivatives, J. Amer.Statist. Assoc., 1989, 84: 986-995.
Directory of Open Access Journals (Sweden)
Richard Ottermanns
Full Text Available In this study we present evidence that anthropogenic stressors can reduce the resilience of age-structured populations. Enhancement of disturbance in a model-based Daphnia population lead to a repression of chaotic population dynamics at the same time increasing the degree of synchrony between the population's age classes. Based on the theory of chaos-mediated survival an increased risk of extinction was revealed for this population exposed to high concentrations of a chemical stressor. The Lyapunov coefficient was supposed to be a useful indicator to detect disturbance thresholds leading to alterations in population dynamics. One possible explanation could be a discrete change in attractor orientation due to external disturbance. The statistical analysis of Lyapunov coefficient distribution is proposed as a methodology to test for significant non-linear effects of general disturbance on populations. Although many new questions arose, this study forms a theoretical basis for a dynamical definition of population recovery.
Zhang, Jilie; Zhang, Huaguang; Wang, Binrui; Cai, Tiaoyang
2016-05-01
In this paper, a nearly data-based optimal control scheme is proposed for linear discrete model-free systems with delays. The nearly optimal control can be obtained using only measured input/output data from systems, by reinforcement learning technology, which combines Q-learning with value iterative algorithm. First, we construct a state estimator by using the measured input/output data. Second, the quadratic functional is used to approximate the value function at each point in the state space, and the data-based control is designed by Q-learning method using the obtained state estimator. Then, the paper states the method, that is, how to solve the optimal inner kernel matrix ? in the least-square sense, by value iteration algorithm. Finally, the numerical examples are given to illustrate the effectiveness of our approach.
Directory of Open Access Journals (Sweden)
Eric H Y Lau
Full Text Available BACKGROUND: Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored. METHODS AND RESULTS: We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (ρ = 0.82, p≤0.02 for the inferred trend based on the multivariate model compared to other univariate models, while the inferred trend from the multivariate model performed as well as the best univariate model in the pre-pandemic and the pandemic period. The inferred trend and level from the multivariate model was able to match, if not outperform, the best univariate model albeit with missing data plus drop-in and drop-out of different surveillance data streams. An overall influenza index combining level and trend was constructed to demonstrate another potential use of the method. CONCLUSIONS: Our results demonstrate the
Methodology and applications in non-linear model-based geostatistics
DEFF Research Database (Denmark)
Christensen, Ole Fredslund
Today geostatistics is used in a number of research areas, among others agricultural and environmental sciences.This thesis concerns data and applications where the classical Gaussian spatial model is not appropriate. A transformation could be used in an attempt to obtain data that are approximat......Today geostatistics is used in a number of research areas, among others agricultural and environmental sciences.This thesis concerns data and applications where the classical Gaussian spatial model is not appropriate. A transformation could be used in an attempt to obtain data...
Directory of Open Access Journals (Sweden)
Xudong Yin
2014-02-01
Full Text Available The authors propose to implement conditional non-linear optimal perturbation related to model parameters (CNOP-P through an ensemble-based approach. The approach was first used in our earlier study and is improved to be suitable for calculating CNOP-P. Idealised experiments using the Lorenz-63 model are conducted to evaluate the performance of the improved ensemble-based approach. The results show that the maximum prediction error after optimisation has been multiplied manifold compared with the initial-guess prediction error, and is extremely close to, or greater than, the maximum value of the exhaustive attack method (a million random samples. The calculation of CNOP-P by the ensemble-based approach is capable of maintaining a high accuracy over a long prediction time under different constraints and initial conditions. Further, the CNOP-P obtained by the approach is applied to sensitivity analysis of the Lorenz-63 model. The sensitivity analysis indicates that when the prediction time is set to 0.2 time units, the Lorenz-63 model becomes extremely insensitive to one parameter, which leaves the other two parameters to affect the uncertainty of the model. Finally, a serial of parameter estimation experiments are performed to verify sensitivity analysis. It is found that when the three parameters are estimated simultaneously, the insensitive parameter is estimated much worse, but the Lorenz-63 model can still generate a very good simulation thanks to the relatively accurate values of the other two parameters. When only two sensitive parameters are estimated simultaneously and the insensitive parameter is left to be non-optimised, the outcome is better than the case when the three parameters are estimated simultaneously. With the increase of prediction time and observation, however, the model sensitivity to the insensitive parameter increases accordingly and the insensitive parameter can also be estimated successfully.
Modeling of high power pulse generator based on the non-linear elements of pulsed facilities
Averyanov, G. P.; Dmitrieva, V. V.; Kobylyatskiy, A. V.
2017-01-01
The article considered the software implementation mathematical model of the voltage pulse generator with a hard switch. The interactive object-oriented software interface provides the choice of generator parameters and the type of its load, as well as pulses parameters analysis on the load at the generator switching.
Identification and inference in moments based analysis of linear dynamic panel data models
Bun, M.J.G.; Kleibergen, F.
2013-01-01
We show that Dif(ference), see Arellano and Bond (1991), Lev(el), see Arellano and Bover (1995) and Blundell and Bond (1998), or the N(on-)L(inear) moment conditions of Ahn and Schmidt (1995) do not identify the parameters of a first-order autoregressive panel data model when the autoregressive para
Fonville, Judith M; Bylesjö, Max; Coen, Muireann; Nicholson, Jeremy K; Holmes, Elaine; Lindon, John C; Rantalainen, Mattias
2011-10-31
Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of Trypanosoma brucei brucei infection. Automated and user-friendly procedures for the kernel-optimization have been incorporated into version 1.1.1 of the freely available K-OPLS software package for both R and Matlab to enable easy application of K-OPLS for non-linear prediction modeling.
Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe
2017-09-01
We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.
Modelling Loudspeaker Non-Linearities
DEFF Research Database (Denmark)
Agerkvist, Finn T.
2007-01-01
This paper investigates different techniques for modelling the non-linear parameters of the electrodynamic loudspeaker. The methods are tested not only for their accuracy within the range of original data, but also for the ability to work reasonable outside that range, and it is demonstrated...... that polynomial expansions are rather poor at this, whereas an inverse polynomial expansion or localized fitting functions such as the gaussian are better suited for modelling the Bl-factor and compliance. For the inductance the sigmoid function is shown to give very good results. Finally the time varying...
Directory of Open Access Journals (Sweden)
Amir R. Ali
2017-01-01
Full Text Available This paper presents and verifies the mathematical model of an electric field senor based on the whispering gallery mode (WGM. The sensing element is a dielectric microsphere, where the light is used to tune the optical modes of the microsphere. The light undergoes total internal reflection along the circumference of the sphere; then it experiences optical resonance. The WGM are monitored as sharp dips on the transmission spectrum. These modes are very sensitive to morphology changes of the sphere, such that, for every minute change in the sphere’s morphology, a shift in the transmission spectrum will happen and that is known as WGM shifts. Due to the electrostriction effect, the applied electric field will induce forces acting on the surface of the dielectric sphere. In turn, these forces will deform the sphere causing shifts in its WGM spectrum. The applied electric field can be obtained by calculating these shifts. Navier’s equation for linear elasticity is used to model the deformation of the sphere to find the WGM shift. The finite element numerical studies are performed to verify the introduced model and to study the behavior of the sensor at different values of microspheres’ Young’s modulus and dielectric constant. Furthermore, the sensitivity and resolution of the developed WGM electric filed sensor model will be presented in this paper.
A simulation-based goodness-of-fit test for random effects in generalized linear mixed models
DEFF Research Database (Denmark)
Waagepetersen, Rasmus
2006-01-01
The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...
A simulation-based goodness-of-fit test for random effects in generalized linear mixed models
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge
The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...
Li, Chung-I; Shyr, Yu
2016-12-01
As RNA-seq rapidly develops and costs continually decrease, the quantity and frequency of samples being sequenced will grow exponentially. With proteomic investigations becoming more multivariate and quantitative, determining a study's optimal sample size is now a vital step in experimental design. Current methods for calculating a study's required sample size are mostly based on the hypothesis testing framework, which assumes each gene count can be modeled through Poisson or negative binomial distributions; however, these methods are limited when it comes to accommodating covariates. To address this limitation, we propose an estimating procedure based on the generalized linear model. This easy-to-use method constructs a representative exemplary dataset and estimates the conditional power, all without requiring complicated mathematical approximations or formulas. Even more attractive, the downstream analysis can be performed with current R/Bioconductor packages. To demonstrate the practicability and efficiency of this method, we apply it to three real-world studies, and introduce our on-line calculator developed to determine the optimal sample size for a RNA-seq study.
Matrix algebra for linear models
Gruber, Marvin H J
2013-01-01
Matrix methods have evolved from a tool for expressing statistical problems to an indispensable part of the development, understanding, and use of various types of complex statistical analyses. This evolution has made matrix methods a vital part of statistical education. Traditionally, matrix methods are taught in courses on everything from regression analysis to stochastic processes, thus creating a fractured view of the topic. Matrix Algebra for Linear Models offers readers a unique, unified view of matrix analysis theory (where and when necessary), methods, and their applications. Written f
Soares Dos Santos, Marco P; Ferreira, Jorge A F; Simões, José A O; Pascoal, Ricardo; Torrão, João; Xue, Xiaozheng; Furlani, Edward P
2016-01-04
Magnetic levitation has been used to implement low-cost and maintenance-free electromagnetic energy harvesting. The ability of levitation-based harvesting systems to operate autonomously for long periods of time makes them well-suited for self-powering a broad range of technologies. In this paper, a combined theoretical and experimental study is presented of a harvester configuration that utilizes the motion of a levitated hard-magnetic element to generate electrical power. A semi-analytical, non-linear model is introduced that enables accurate and efficient analysis of energy transduction. The model predicts the transient and steady-state response of the harvester a function of its motion (amplitude and frequency) and load impedance. Very good agreement is obtained between simulation and experiment with energy errors lower than 14.15% (mean absolute percentage error of 6.02%) and cross-correlations higher than 86%. The model provides unique insight into fundamental mechanisms of energy transduction and enables the geometric optimization of harvesters prior to fabrication and the rational design of intelligent energy harvesters.
Soares Dos Santos, Marco P.; Ferreira, Jorge A. F.; Simões, José A. O.; Pascoal, Ricardo; Torrão, João; Xue, Xiaozheng; Furlani, Edward P.
2016-01-01
Magnetic levitation has been used to implement low-cost and maintenance-free electromagnetic energy harvesting. The ability of levitation-based harvesting systems to operate autonomously for long periods of time makes them well-suited for self-powering a broad range of technologies. In this paper, a combined theoretical and experimental study is presented of a harvester configuration that utilizes the motion of a levitated hard-magnetic element to generate electrical power. A semi-analytical, non-linear model is introduced that enables accurate and efficient analysis of energy transduction. The model predicts the transient and steady-state response of the harvester a function of its motion (amplitude and frequency) and load impedance. Very good agreement is obtained between simulation and experiment with energy errors lower than 14.15% (mean absolute percentage error of 6.02%) and cross-correlations higher than 86%. The model provides unique insight into fundamental mechanisms of energy transduction and enables the geometric optimization of harvesters prior to fabrication and the rational design of intelligent energy harvesters.
Institute of Scientific and Technical Information of China (English)
张珺; 郭宇锋; 徐跃; 林宏; 杨慧; 洪洋; 姚佳飞
2015-01-01
A novel one-dimensional (1D) analytical model is proposed for quantifying the breakdown voltage of reduced surface field (RESURF) lateral power device fabricated on silicon on an insulator (SOI) substrate. We assume that the charges in the depletion region contribute to the lateral PN junctions along the diagonal of the area shared by the lateral and vertical depletion regions. Based on the assumption, the lateral PN junction behaves as a linearly graded junction, thus resulting in a reduced surface electric field and high breakdown voltage. Using the proposed model, the breakdown voltage as a function of device parameters is investigated and compared with the numerical simulation by the TCAD tools. The analytical results are shown to be in fair agreement with the numerical results. Finally, a new RESURF criterion is derived which offers a useful scheme to optimize the structure parameters. This simple 1D model provides a clear physical insight into the RESURF effect and a new explanation on the improvement in breakdown voltage in an SOI RESURF device.
Carvajal, Gonzalo; Figueroa, Miguel
2014-07-01
Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods.
Model reduction of linear conservative mechanical systems
Schaft, van der A.J.; Oeloff, J.E.
1990-01-01
An approach for model reduction of linear conservative or weakly damped mechanical systems is proposed. It is based on the balancing of an associated gradient system. It uses the joint knowledge of the system matrix and the input and output matrices of the Hamiltonian system. The key idea is to asso
Linear systems a measurement based approach
Bhattacharyya, S P; Mohsenizadeh, D N
2014-01-01
This brief presents recent results obtained on the analysis, synthesis and design of systems described by linear equations. It is well known that linear equations arise in most branches of science and engineering as well as social, biological and economic systems. The novelty of this approach is that no models of the system are assumed to be available, nor are they required. Instead, a few measurements made on the system can be processed strategically to directly extract design values that meet specifications without constructing a model of the system, implicitly or explicitly. These new concepts are illustrated by applying them to linear DC and AC circuits, mechanical, civil and hydraulic systems, signal flow block diagrams and control systems. These applications are preliminary and suggest many open problems. The results presented in this brief are the latest effort in this direction and the authors hope these will lead to attractive alternatives to model-based design of engineering and other systems.
An R companion to linear statistical models
Hay-Jahans, Christopher
2011-01-01
Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cove
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Frison, Gianluca; Edlund, Kristian
2013-01-01
In this paper, we develop an efficient interior-point method (IPM) for the linear programs arising in economic model predictive control of linear systems. The novelty of our algorithm is that it combines a homogeneous and self-dual model, and a specialized Riccati iteration procedure. We test...... the algorithm in a conceptual study of power systems management. Simulations show that in comparison to state of the art software implementation of IPMs, our method is significantly faster and scales in a favourable way....
Ma, Jing; Yu, Jiong; Hao, Guangshu; Wang, Dan; Sun, Yanni; Lu, Jianxin; Cao, Hongcui; Lin, Feiyan
2017-02-20
The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.
Directory of Open Access Journals (Sweden)
Yu. B. Gimpilevich
2013-12-01
Full Text Available Introduction. Radio-frequency identification (RFID systems can be applied for a 2D spatial localization of objects in indoor spaces. For implementing the known localization method of trilateration one needs to build a model of ratio of distance between antenna and RFID tag versus power level of tag response signal. To maximize the accuracy of the model, one needs to collect measurement data from RFID tags placed at known positions. Due to the labor intensity of this process we aim to develop an alternative modification of elliptical trilateration method resulting in elimination of the preliminary data collecting stage. Theory part. We propose to use a linear model of distance vs power ratio for localization of passive RFID tags with small read ranges. Additionally, our model takes into account the possible ellipticity of position figures which happens because of antennas directivity. Also we propose some heuristics for solving the estimates ambiguity problem which arises when response signals from RFID tags are received by one or two antennas. Experimental part. We carried out the experimental analysis of the proposed trilateration variant using the previously developed RFID system in the 5 m ´ 5 m localization field. During the experiment, we compared our variant with a classical trilateration which was based on the polynomial model of distance vs power ratio formed by analyzing preliminarily gathered measurement data from RFID tags. The comparison indicated that our variant had a bigger by 1.6 cm mean localization error. Furthermore, taking into account ellipticity of position figures resulted in decrease of localization error for 18 of the 24 analyzed cases. Conclusions. It was determined that the proposed trilateration modification produced a slightly bigger mean localization error compared to the classical variant of trilateration. However, our approach allows one to eliminate the preliminary labor-intensive stage of collecting
Indian Academy of Sciences (India)
T S Jeyali Laseetha; R Sukanesh
2014-02-01
In this paper, we propose biogeography based optimization technique, with linear and sinusoidal migration models and simplified biogeography based optimization (S-BBO), for uniformly spaced linear antenna array synthesis to maximize the reduction of side lobe level (SLL). This paper explores biogeography theory. It generalizes two migration models in BBO namely, linear migration model and sinusoidal migration model. The performance of SLL reduction in ULA is investigated. Our performance study shows that among the two, sinusoidal migration model is a promising candidate for optimization. In our work, simplified – BBO algorithmis also deployed. This determines an optimum set value for amplitude excitations of antenna array elements that generate a radiation pattern with maximum side lobe level reduction. Our detailed investigation also shows that sinusoidal migration model of BBO performs better compared to the other evolutionary algorithms discussed in this paper.
Directory of Open Access Journals (Sweden)
L. L. Adams
2011-01-01
Full Text Available Problem statement: Management is generally easy to define and measure. And, good managers tend to have many of the same characteristics and skill sets. Great leaders, on the other hand, have fewer shared characteristics. Some great leaders are great orators, for example, and yet many other great leaders are terrible public speakers. Great leaders tend to be very intuitive, but other characteristics consistent with great leadership are few indeed. So, the authors of this study had a conversation over several years that led to the reduction of variables to two variables that immediately showed a pattern in individual leadership. Approach: This study presents a practical leadership matrix model based on non-linear dynamics and chaos theory. Specifically, the authors searched for two or more leadership variables (characteristics that would create a definite pattern. The researchers intuitively believed that some combination of variables would set up a pattern just as attractors (strange or otherwise create patterns in data and show some of the characteristics of the system being studied. Over time a set of two main variables, loosely labeled as ethics and energy at first, were identified that created a leadership pattern for individuals. This study describes the process that led to the identification of the two main variables and then to the matrix herein presented. Results: This model, called The WELL by the authors, was created at first to explain political leadership, yet is showing applicability to all kinds of leadership. The WELL as presented is a theoretical construct, with only experiential and qualitative evidence at present to support the patterns inferred from the model. Conclusion: In addition to the extensive political experience of the authors the experience of public safety, mental health, military and academic professionals has been sought to validate the main conclusions shown in this study and to improve the
Non-linear Loudspeaker Unit Modelling
DEFF Research Database (Denmark)
Pedersen, Bo Rohde; Agerkvist, Finn T.
2008-01-01
Simulations of a 6½-inch loudspeaker unit are performed and compared with a displacement measurement. The non-linear loudspeaker model is based on the major nonlinear functions and expanded with time-varying suspension behaviour and flux modulation. The results are presented with FFT plots of three...... frequencies and different displacement levels. The model errors are discussed and analysed including a test with loudspeaker unit where the diaphragm is removed....
Energy Technology Data Exchange (ETDEWEB)
Fonville, Judith M., E-mail: j.fonville07@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Bylesjoe, Max, E-mail: max.bylesjo@almacgroup.com [Almac Diagnostics, 19 Seagoe Industrial Estate, Craigavon BT63 5QD (United Kingdom); Coen, Muireann, E-mail: m.coen@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Nicholson, Jeremy K., E-mail: j.nicholson@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Holmes, Elaine, E-mail: elaine.holmes@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Lindon, John C., E-mail: j.lindon@imperial.ac.uk [Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ (United Kingdom); Rantalainen, Mattias, E-mail: rantalai@stats.ox.ac.uk [Department of Statistics, Oxford University, 1 South Parks Road, Oxford OX1 3TG (United Kingdom)
2011-10-31
Highlights: {yields} Non-linear modeling of metabonomic data using K-OPLS. {yields} automated optimization of the kernel parameter by simulated annealing. {yields} K-OPLS provides improved prediction performance for exemplar spectral data sets. {yields} software implementation available for R and Matlab under GPL v2 license. - Abstract: Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and
Energy Technology Data Exchange (ETDEWEB)
Ureba, A. [Dpto. Fisiología Médica y Biofísica. Facultad de Medicina, Universidad de Sevilla, E-41009 Sevilla (Spain); Salguero, F. J. [Nederlands Kanker Instituut, Antoni van Leeuwenhoek Ziekenhuis, 1066 CX Ámsterdam, The Nederlands (Netherlands); Barbeiro, A. R.; Jimenez-Ortega, E.; Baeza, J. A.; Leal, A., E-mail: alplaza@us.es [Dpto. Fisiología Médica y Biofísica, Facultad de Medicina, Universidad de Sevilla, E-41009 Sevilla (Spain); Miras, H. [Servicio de Radiofísica, Hospital Universitario Virgen Macarena, E-41009 Sevilla (Spain); Linares, R.; Perucha, M. [Servicio de Radiofísica, Hospital Infanta Luisa, E-41010 Sevilla (Spain)
2014-08-15
Purpose: The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. Methods: The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called “biophysical” map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process. Results: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A new rigid-plastic/rigid-viscoplastic (RP/RVP) FEM based on linear programming (LP) for plane-strain metal forming simulation is proposed. Compared with the traditional RP/RVP FEM based on iteration solution, it has some remarkable advantages, such as it's free of convergence problem and its convenience in contact, incompressibility constraint and rigid zone treatment. Two solution examples are provided to validate its accuracy and efficiency.
Directory of Open Access Journals (Sweden)
S. Mohammad Arabzad
2012-06-01
Full Text Available In recent years, numerous methods have been proposed to deal with supplier evaluation and selection problem, but a point which has been usually neglected by researchers is the role of purchasing items. The aim of this paper is to propose an integrated approach to select suppliers and allocate orders on the basis of the nature of the purchasing items which means that this issue plays an important role in supplier selection and order allocation. Therefore, items are first categorized according to the Kraljic’s model by the use of FMEA technique. Then, suppliers are categorized and evaluated in four phases with respect to different types of purchasing items (Strategic, Bottleneck, Leverage and Routine. Finally, an integer linear programming is utilized to allocate purchasing orders to suppliers. Furthermore, an empirical example is conducted to illustrate the stage of proposed approach. Results imply that ranking of suppliers and allocation of purchasing items based on the nature of purchasing items will create more capabilities in managing purchasing items and suppliers .
Nanda, Trushnamayee; Sahoo, Bhabagrahi; Beria, Harsh; Chatterjee, Chandranath
2016-08-01
Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall (AR) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the
From spiking neuron models to linear-nonlinear models.
Directory of Open Access Journals (Sweden)
Srdjan Ostojic
Full Text Available Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF, exponential integrate-and-fire (EIF and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.
Linear and non-linear perturbations in dark energy models
Escamilla-Rivera, Celia; Fabris, Julio C; Alcaniz, Jailson S
2016-01-01
In this work we discuss observational aspects of three time-dependent parameterisations of the dark energy equation of state $w(z)$. In order to determine the dynamics associated with these models, we calculate their background evolution and perturbations in a scalar field representation. After performing a complete treatment of linear perturbations, we also show that the non-linear contribution of the selected $w(z)$ parameterisations to the matter power spectra is almost the same for all scales, with no significant difference from the predictions of the standard $\\Lambda$CDM model.
Tian, Zhen; Li, Yongbao; Shi, Feng; Jiang, Steve B; Jia, Xun
2015-01-01
We recently built an analytical source model for GPU-based MC dose engine. In this paper, we present a sampling strategy to efficiently utilize this source model in GPU-based dose calculation. Our source model was based on a concept of phase-space-ring (PSR). This ring structure makes it effective to account for beam rotational symmetry, but not suitable for dose calculations due to rectangular jaw settings. Hence, we first convert PSR source model to its phase-space let (PSL) representation. Then in dose calculation, different types of sub-sources were separately sampled. Source sampling and particle transport were iterated. So that the particles being sampled and transported simultaneously are of same type and close in energy to alleviate GPU thread divergence. We also present an automatic commissioning approach to adjust the model for a good representation of a clinical linear accelerator . Weighting factors were introduced to adjust relative weights of PSRs, determined by solving a quadratic minimization ...
Multivariate generalized linear mixed models using R
Berridge, Damon Mark
2011-01-01
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model...
Carrasco, Josep L
2010-09-01
The classical concordance correlation coefficient (CCC) to measure agreement among a set of observers assumes data to be distributed as normal and a linear relationship between the mean and the subject and observer effects. Here, the CCC is generalized to afford any distribution from the exponential family by means of the generalized linear mixed models (GLMMs) theory and applied to the case of overdispersed count data. An example of CD34+ cell count data is provided to show the applicability of the procedure. In the latter case, different CCCs are defined and applied to the data by changing the GLMM that fits the data. A simulation study is carried out to explore the behavior of the procedure with a small and moderate sample size.
Directory of Open Access Journals (Sweden)
Fang-Rong Yan
Full Text Available This article provides a fully bayesian approach for modeling of single-dose and complete pharmacokinetic data in a population pharmacokinetic (PK model. To overcome the impact of outliers and the difficulty of computation, a generalized linear model is chosen with the hypothesis that the errors follow a multivariate Student t distribution which is a heavy-tailed distribution. The aim of this study is to investigate and implement the performance of the multivariate t distribution to analyze population pharmacokinetic data. Bayesian predictive inferences and the Metropolis-Hastings algorithm schemes are used to process the intractable posterior integration. The precision and accuracy of the proposed model are illustrated by the simulating data and a real example of theophylline data.
Linear Logistic Test Modeling with R
Baghaei, Purya; Kubinger, Klaus D.
2015-01-01
The present paper gives a general introduction to the linear logistic test model (Fischer, 1973), an extension of the Rasch model with linear constraints on item parameters, along with eRm (an R package to estimate different types of Rasch models; Mair, Hatzinger, & Mair, 2014) functions to estimate the model and interpret its parameters. The…
Ligier, Nicolas; Carter, John; Poulet, François; Langevin, Yves; Dumas, Christophe; Gourgeot, Florian
2016-04-01
Jupiter's moon Europa harbors a very young surface dated, based on cratering rates, to 10-50 M.y (Zahnle et al. 1998, Pappalardo et al. 1999). This young age implies rapid surface recycling and reprocessing, partially engendered by a global salty subsurface liquid ocean that could result in tectonic activity (Schmidt et al. 2011, Kattenhorn et al. 2014) and active plumes (Roth et al. 2014). The surface of Europa should contain important clues about the composition of this sub-surface briny ocean and about the potential presence of material of exobiological interest in it, thus reinforcing Europa as a major target of interest for upcoming space missions such as the ESA L-class mission JUICE. To perform the investigation of the composition of the surface of Europa, a global mapping campaign of the satellite was performed between October 2011 and January 2012 with the integral field spectrograph SINFONI on the Very Large Telescope (VLT) in Chile. The high spectral binning of this instrument (0.5 nm) is suitable to detect any narrow mineral signature in the wavelength range 1.45-2.45 μm. The spatially resolved spectra we obtained over five epochs nearly cover the entire surface of Europa with a pixel scale of 12.5 by 25 m.a.s (~35 by 70 km on Europa's surface), thus permitting a global scale study. Until recently, a large majority of studies only proposed sulfate salts along with sulfuric acid hydrate and water-ice to be present on Europa's surface. However, recent works based on Europa's surface coloration in the visible wavelength range and NIR spectral analysis support the hypothesis of the predominance of chlorine salts instead of sulfate salts (Hand & Carlson 2015, Fischer et al. 2015). Our linear spectral modeling supports this new hypothesis insofar as the use of Mg-bearing chlorines improved the fits whatever the region. As expected, the distribution of sulfuric acid hydrate is correlated to the Iogenic sulfur ion implantation flux distribution (Hendrix et al
Shabri, Ani; Samsudin, Ruhaidah
2014-01-01
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
Directory of Open Access Journals (Sweden)
Ani Shabri
2014-01-01
Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
Modeling hierarchical structures - Hierarchical Linear Modeling using MPlus
Jelonek, M
2006-01-01
The aim of this paper is to present the technique (and its linkage with physics) of overcoming problems connected to modeling social structures, which are typically hierarchical. Hierarchical Linear Models provide a conceptual and statistical mechanism for drawing conclusions regarding the influence of phenomena at different levels of analysis. In the social sciences it is used to analyze many problems such as educational, organizational or market dilemma. This paper introduces the logic of modeling hierarchical linear equations and estimation based on MPlus software. I present my own model to illustrate the impact of different factors on school acceptation level.
The General Linear Model as Structural Equation Modeling
Graham, James M.
2008-01-01
Statistical procedures based on the general linear model (GLM) share much in common with one another, both conceptually and practically. The use of structural equation modeling path diagrams as tools for teaching the GLM as a body of connected statistical procedures is presented. A heuristic data set is used to demonstrate a variety of univariate…
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2004-01-01
This paper presents a new sensorless vector control system for high performance induction motor drives fed by a matrix converter with non-linearity compensation. The nonlinear voltage distortion that is caused by commutation delay and on-state voltage drop in switching device is corrected by a new...... matrix converter model. Regulated Order Extended Luenberger Observer (ROELO) is employed to bring better response in the whole speed operation range and a method to select the observer gain is presented. Experimental results are shown to illustrate the performance of the proposed system...
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
Processing Approach of Non-linear Adjustment Models in the Space of Non-linear Models
Institute of Scientific and Technical Information of China (English)
LI Chaokui; ZHU Qing; SONG Chengfang
2003-01-01
This paper investigates the mathematic features of non-linear models and discusses the processing way of non-linear factors which contributes to the non-linearity of a nonlinear model. On the basis of the error definition, this paper puts forward a new adjustment criterion, SGPE.Last, this paper investigates the solution of a non-linear regression model in the non-linear model space and makes the comparison between the estimated values in non-linear model space and those in linear model space.
Bayesian Discovery of Linear Acyclic Causal Models
Hoyer, Patrik O
2012-01-01
Methods for automated discovery of causal relationships from non-interventional data have received much attention recently. A widely used and well understood model family is given by linear acyclic causal models (recursive structural equation models). For Gaussian data both constraint-based methods (Spirtes et al., 1993; Pearl, 2000) (which output a single equivalence class) and Bayesian score-based methods (Geiger and Heckerman, 1994) (which assign relative scores to the equivalence classes) are available. On the contrary, all current methods able to utilize non-Gaussianity in the data (Shimizu et al., 2006; Hoyer et al., 2008) always return only a single graph or a single equivalence class, and so are fundamentally unable to express the degree of certainty attached to that output. In this paper we develop a Bayesian score-based approach able to take advantage of non-Gaussianity when estimating linear acyclic causal models, and we empirically demonstrate that, at least on very modest size networks, its accur...
Comparing linear probability model coefficients across groups
DEFF Research Database (Denmark)
Holm, Anders; Ejrnæs, Mette; Karlson, Kristian Bernt
2015-01-01
This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more...... these limitations, and we suggest a restricted approach to using linear probability model coefficients in group comparisons....
Composite Linear Models | Division of Cancer Prevention
By Stuart G. Baker The composite linear models software is a matrix approach to compute maximum likelihood estimates and asymptotic standard errors for models for incomplete multinomial data. It implements the method described in Baker SG. Composite linear models for incomplete multinomial data. Statistics in Medicine 1994;13:609-622. The software includes a library of thirty examples from the literature. |
DEFF Research Database (Denmark)
Wiechowski, Wojciech Tomasz; Lykkegaard, Jan; Bak, Claus Leth
2007-01-01
In this paper two methods of validation of transmission network harmonic models are introduced. The methods were developed as a result of the work presented in [1]. The first method allows calculating the transfer harmonic impedance between two nodes of a network. Switching a linear, series network...... are used for calculation of the transfer harmonic impedance between the nodes. The determined transfer harmonic impedance can be used to validate a computer model of the network. The second method is an extension of the fist one. It allows switching a series element that contains a shunt branch......, as for example a transmission line. Both methods require that harmonic measurements performed at two ends of the disconnected element are precisely synchronized....
Directory of Open Access Journals (Sweden)
A. Kitipongwatana
2014-06-01
Full Text Available In this article, an excited oscillator which is analyzed by using a multi-time linear analytical model is proposed. An obtained closed-form solution can be exploited not only to explain phenomena in the beat and locked states that are mostly studied in literature but also in an additional state called the non-locked state. With the proposed analysis, it is found that the non-locked state of the oscillator behaves similarly to the up-conversion process. It provides a new point-of-view to the phase noise oscillator. Moreover, our principle indicates that the important factor defining the behavior in each state and state transition is the transfer function of the system. The proposed mathematical model is verified by the experimental and numerical results.
Zhang, Liping; Zhang, Shiwen; Huang, Yajie; Cao, Meng; Huang, Yuanfang; Zhang, Hongyan
2016-01-01
Understanding abandoned mine land (AML) changes during land reclamation is crucial for reusing damaged land resources and formulating sound ecological restoration policies. This study combines the linear programming (LP) model and the CLUE-S model to simulate land-use dynamics in the Mentougou District (Beijing, China) from 2007 to 2020 under three reclamation scenarios, that is, the planning scenario based on the general land-use plan in study area (scenario 1), maximal comprehensive benefit...
Bayes linear covariance matrix adjustment for multivariate dynamic linear models
Wilkinson, Darren J
2008-01-01
A methodology is developed for the adjustment of the covariance matrices underlying a multivariate constant time series dynamic linear model. The covariance matrices are embedded in a distribution-free inner-product space of matrix objects which facilitates such adjustment. This approach helps to make the analysis simple, tractable and robust. To illustrate the methods, a simple model is developed for a time series representing sales of certain brands of a product from a cash-and-carry depot. The covariance structure underlying the model is revised, and the benefits of this revision on first order inferences are then examined.
Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena
2013-01-01
The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models.
Exact linear modeling using Ore algebras
Schindelar, Kristina; Zerz, Eva
2010-01-01
Linear exact modeling is a problem coming from system identification: Given a set of observed trajectories, the goal is find a model (usually, a system of partial differential and/or difference equations) that explains the data as precisely as possible. The case of operators with constant coefficients is well studied and known in the systems theoretic literature, whereas the operators with varying coefficients were addressed only recently. This question can be tackled either using Gr\\"obner bases for modules over Ore algebras or by following the ideas from differential algebra and computing in commutative rings. In this paper, we present algorithmic methods to compute "most powerful unfalsified models" (MPUM) and their counterparts with variable coefficients (VMPUM) for polynomial and polynomial-exponential signals. We also study the structural properties of the resulting models, discuss computer algebraic techniques behind algorithms and provide several examples.
Lee, Hanna; Park, Eun Suk; Yu, Jae Kook; Yun, Eun Kyoung
2015-10-01
The purpose of this study was to develop a system dynamics model for adolescent obesity in Korea that could be used for obesity policy analysis. On the basis of the casual loop diagram, a model was developed by converting to stock and flow diagram. The Vensim DSS 5.0 program was used in the model development. We simulated method of moments to the calibration of this model with data from The Korea Youth Risk Behavior Web-based Survey 2005 to 2013. We ran the scenario simulation. This model can be used to understand the current adolescent obesity rate, predict the future obesity rate, and be utilized as a tool for controlling the risk factors. The results of the model simulation match well with the data. It was identified that a proper model, able to predict obesity probability, was established. These results of stock and flow diagram modeling in adolescent obesity can be helpful in development of obesity by policy planners and other stakeholders to better anticipate the multiple effects of interventions in both the short and the long term. In the future we suggest the development of an expanded model based on this adolescent obesity model.
DEFF Research Database (Denmark)
Yang, Z.; Izadi-Zamanabadi, Roozbeh; Blanke, M.
2000-01-01
Based on the model-matching strategy, an adaptive control reconfiguration method for a class of nonlinear control systems is proposed by using the multiple-model scheme. Instead of requiring the nominal and faulty nonlinear systems to match each other directly in some proper sense, three sets...... of LTI models are employed to approximate the faulty, reconfigured and nominal nonlinear systems respectively with respect to the on-line information of the operating system, and a set of compensating modules are proposed and designed so as to make the local LTI model approximating to the reconfigured...
DEFF Research Database (Denmark)
Yang, Z.; Izadi-Zamanabadi, Roozbeh; Blanke, M.
2000-01-01
Based on the model-matching strategy, an adaptive control reconfiguration method for a class of nonlinear control systems is proposed by using the multiple-model scheme. Instead of requiring the nominal and faulty nonlinear systems to match each other directly in some proper sense, three sets of ...... corresponding to the updating of local LTI models, which validations are determined by the model approximation errors and the optimal index of local design. The test on a nonlinear ship propulsion system shows the promising potential of this method for system reconfiguration...
A Note on the Identifiability of Generalized Linear Mixed Models
DEFF Research Database (Denmark)
Labouriau, Rodrigo
2014-01-01
I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity ...... conditions, and, therefore, is extensible to quasi-likelihood based generalized linear models. In particular, binomial and Poisson mixed models with dispersion parameter are identifiable when equipped with the standard parametrization......I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable. The proof is based on the assumptions of generalized linear mixed models on the first and second order moments and some general mild regularity...
An Inquiry-Based Linear Algebra Class
Wang, Haohao; Posey, Lisa
2011-01-01
Linear algebra is a standard undergraduate mathematics course. This paper presents an overview of the design and implementation of an inquiry-based teaching material for the linear algebra course which emphasizes discovery learning, analytical thinking and individual creativity. The inquiry-based teaching material is designed to fit the needs of a…
Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah
2017-08-01
In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.
Zhao, Haonan; Guo, Zhaojie; Yu, Xiangjiang
2017-02-01
This paper focuses on the strain modelling of extensional fault-propagation folds to reveal the effects of key factors on the strain accumulation and the relationship between the geometry and strain distribution of fault-related folds. A velocity-geometry-strain method is proposed for the analysis of the total strain and its accumulation process within the trishear zone of an extensional fault-propagation fold. This paper improves the non-linear trishear model proposed by Jin and Groshong (2006). Based on the improved model, the distribution of the strain rate within the trishear zone and the total strain are obtained. The numerical simulations of different parameters performed in this study indicate that the shape factor R, the total apical angle, and the P/S ratio control the final geometry and strain distribution of extensional fault-propagation folds. A small P/S ratio, a small apical angle, and an R value far greater or far smaller than 1 produce an asymmetric, narrow, and strongly deformed trishear zone. The velocity-geometry-strain analysis method is applied to two natural examples from Big Brushy Canyon in Texas and the northwestern Red Sea in Egypt. The strain distribution within the trishear zone is closely related to the geometry of the folds.
Comparing linear probability model coefficients across groups
DEFF Research Database (Denmark)
Holm, Anders; Ejrnæs, Mette; Karlson, Kristian Bernt
2015-01-01
This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more...... of the following three components: outcome truncation, scale parameters and distributional shape of the predictor variable. These results point to limitations in using linear probability model coefficients for group comparisons. We also provide Monte Carlo simulations and real examples to illustrate...... these limitations, and we suggest a restricted approach to using linear probability model coefficients in group comparisons....
Hiroki Yoshioka; Kenta Obata; Tomoaki Miura
2012-01-01
The spectral unmixing of a linear mixture model (LMM) with Normalized Difference Vegetation Index (NDVI) constraints was performed to estimate the fraction of vegetation cover (FVC) over the earth’s surface in an effort to facilitate long-term surface vegetation monitoring using a set of environmental satellites. Although the integrated use of multiple sensors improves the spatial and temporal quality of the data sets, area-averaged FVC values obtained using an LMM-based algorithm suffer from...
Model Reduction by Moment Matching for Linear Switched Systems
DEFF Research Database (Denmark)
Bastug, Mert; Petreczky, Mihaly; Wisniewski, Rafal;
2014-01-01
A moment-matching method for the model reduction of linear switched systems (LSSs) is developed. The method is based based upon a partial realization theory of LSSs and it is similar to the Krylov subspace methods used for moment matching for linear systems. The results are illustrated by numeric...
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable...
Spectral clustering based on local linear approximations
Arias-Castro, Ery; Lerman, Gilad
2010-01-01
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface, possibly contaminated with outliers. We consider a prototype for a higher-order spectral clustering method based on the residual from a local linear approximation. In an asymptotic setting where the number of points becomes large, we obtain theoretical guaranties for this algorithm and show that, both in terms of separation and robustness to outliers, it outperforms the standard spectral clustering algorithm based on pairwise distances of Ng, Jordan and Weiss (NIPS, 2001). Under some conditions on the dimension of, and the incidence angle at, an intersection, the algorithm is able to recover the intersecting clusters. The optimal choice for some of the tuning parameters depends on the dimension and thickness of the clusters. We provide estimators that come close enough for our purposes. We discuss the cases of clusters of mixed dimensions and of clus...
Non-linear finite element modeling
DEFF Research Database (Denmark)
Mikkelsen, Lars Pilgaard
The note is written for courses in "Non-linear finite element method". The note has been used by the author teaching non-linear finite element modeling at Civil Engineering at Aalborg University, Computational Mechanics at Aalborg University Esbjerg, Structural Engineering at the University...... on the governing equations and methods of implementing....
Correlations and Non-Linear Probability Models
DEFF Research Database (Denmark)
Breen, Richard; Holm, Anders; Karlson, Kristian Bernt
2014-01-01
the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models....
Linear feature detection based on ridgelet
Institute of Scientific and Technical Information of China (English)
HOU; Biao; (侯彪); LIU; Fang; (刘芳); JIAO; Licheng; (焦李成)
2003-01-01
Linear feature detection is very important in image processing. The detection efficiency will directly affect the perfomance of pattern recognition and pattern classification. Based on the idea of ridgelet, this paper presents a new discrete localized ridgelet transform and a new method for detecting linear feature in anisotropic images. Experimental results prove the efficiency of the proposed method.
Linear mixed models in sensometrics
DEFF Research Database (Denmark)
Kuznetsova, Alexandra
quality of decision making in Danish as well as international food companies and other companies using the same methods. The two open-source R packages lmerTest and SensMixed implement and support the methodological developments in the research papers as well as the ANOVA modelling part of the Consumer......Today’s companies and researchers gather large amounts of data of different kind. In consumer studies the objective is the collection of the data to better understand consumer acceptance of products. In such studies a number of persons (generally not trained) are selected in order to score products......, texture, sound - depending on the aim of a study. It is a common approach in both studies to consider persons coming from a larger population, which, from the statistical perspective, leads to the use of mixed effects models, where consumers/assessors enter as random effects (Lawless and Heymann, 1997...
Marrero-Ponce, Yovani; Martínez-Albelo, Eugenio R; Casañola-Martín, Gerardo M; Castillo-Garit, Juan A; Echevería-Díaz, Yunaimy; Zaldivar, Vicente Romero; Tygat, Jan; Borges, José E Rodriguez; García-Domenech, Ramón; Torrens, Francisco; Pérez-Giménez, Facundo
2010-11-01
Novel bond-level molecular descriptors are proposed, based on linear maps similar to the ones defined in algebra theory. The kth edge-adjacency matrix (E(k)) denotes the matrix of bond linear indices (non-stochastic) with regard to canonical basis set. The kth stochastic edge-adjacency matrix, ES(k), is here proposed as a new molecular representation easily calculated from E(k). Then, the kth stochastic bond linear indices are calculated using ES(k) as operators of linear transformations. In both cases, the bond-type formalism is developed. The kth non-stochastic and stochastic total linear indices are calculated by adding the kth non-stochastic and stochastic bond linear indices, respectively, of all bonds in molecule. First, the new bond-based molecular descriptors (MDs) are tested for suitability, for the QSPRs, by analyzing regressions of novel indices for selected physicochemical properties of octane isomers (first round). General performance of the new descriptors in this QSPR studies is evaluated with regard to the well-known sets of 2D/3D MDs. From the analysis, we can conclude that the non-stochastic and stochastic bond-based linear indices have an overall good modeling capability proving their usefulness in QSPR studies. Later, the novel bond-level MDs are also used for the description and prediction of the boiling point of 28 alkyl-alcohols (second round), and to the modeling of the specific rate constant (log k), partition coefficient (log P), as well as the antibacterial activity of 34 derivatives of 2-furylethylenes (third round). The comparison with other approaches (edge- and vertices-based connectivity indices, total and local spectral moments, and quantum chemical descriptors as well as E-state/biomolecular encounter parameters) exposes a good behavior of our method in this QSPR studies. Finally, the approach described in this study appears to be a very promising structural invariant, useful not only for QSPR studies but also for similarity
Linear mixed models for longitudinal data
Molenberghs, Geert
2000-01-01
This paperback edition is a reprint of the 2000 edition. This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Several variations to the conventional linear mixed model are discussed (a heterogeity model, conditional linear mixed models). This book will be of interest to applied statisticians and biomedical researchers in industry, public health organizations, contract research organizations, and academia. The book is explanatory rather than mathematically rigorous. Most analyses were done with the MIXED procedure of the SAS software package, and many of its features are clearly elucidated. However, some other commerc...
Correlations and Non-Linear Probability Models
DEFF Research Database (Denmark)
Breen, Richard; Holm, Anders; Karlson, Kristian Bernt
2014-01-01
Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations betwee...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models.......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under...
When to Use Hierarchical Linear Modeling
National Research Council Canada - National Science Library
Veronika Huta
2014-01-01
Previous publications on hierarchical linear modeling (HLM) have provided guidance on how to perform the analysis, yet there is relatively little information on two questions that arise even before analysis...
An introduction to hierarchical linear modeling
National Research Council Canada - National Science Library
Woltman, Heather; Feldstain, Andrea; MacKay, J. Christine; Rocchi, Meredith
2012-01-01
This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is provided that describes when to use this statistical technique and identifies key factors to consider before conducting this analysis...
Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.
2015-01-01
Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.
Linear causal modeling with structural equations
Mulaik, Stanley A
2009-01-01
Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal
Statistical Tests for Mixed Linear Models
Khuri, André I; Sinha, Bimal K
2011-01-01
An advanced discussion of linear models with mixed or random effects. In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component models, generating much research activity that relies on linear models with mixed and random effects. This volume covers the most important research of the past decade as well as the latest developments in hypothesis testing. It compiles all currently available results in the area of exact and optimum tests for variance component models and offers the only comprehensive treatment for these models a
Matrix Tricks for Linear Statistical Models
Puntanen, Simo; Styan, George PH
2011-01-01
In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple "tricks" which simplify and clarify the treatment of a problem - both for the student and
Implementation of neural network based non-linear predictive
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi...
Implementation of neural network based non-linear predictive control
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1999-01-01
of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...
Testing linearity against nonlinear moving average models
de Gooijer, J.G.; Brännäs, K.; Teräsvirta, T.
1998-01-01
Lagrange multiplier (LM) test statistics are derived for testing a linear moving average model against an additive smooth transition moving average model. The latter model is introduced in the paper. The small sample performance of the proposed tests are evaluated in a Monte Carlo study and compared
Exact Solutions in Nonlocal Linear Models
Vernov, S. Yu.
2008-01-01
A general class of cosmological models driven by a nonlocal scalar field inspired by the string field theory is studied. Using the fact that the considering linear nonlocal model is equivalent to an infinite number of local models we have found an exact special solution of the nonlocal Friedmann equations. This solution describes a monotonically increasing Universe with the phantom dark energy.
Transient Response Model of Standing Wave Piezoelectric Linear Ultrasonic Motor
Institute of Scientific and Technical Information of China (English)
SHI Yunlai; CHEN Chao; ZHAO Chunsheng
2012-01-01
A transient response model for describing the starting and stopping characteristics of the standing wave piezoelectric linear ultrasonic motor was presented.Based on the contact dynamic model,the kinetic equation of the motor was derived.The starting and stopping characteristics of the standing wave piezoelectric linear ultrasonic motor according to different loads,contact stiffness and inertia mass were described and analyzed,respectively.To validate the transient response model,a standing wave piezoelectric linear ultrasonic motor based on in-plane modes was used to carry out the simulation and experimental study.The corresponding results showed that the simulation of the motor performances based on the proposed model agreed well with the experimental results.This model will helpful to improve the stepping characteristics and the control flexibility of the standing wave piezoelectric linear ultrasonic motor.
Vuori, Kaarina; Strandén, Ismo; Sevón-Aimonen, Marja-Liisa; Mäntysaari, Esa A
2006-01-01
A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be done by common methods for linear mixed effects models, and thus existing programs can be used after small modifications. The applicability of this algorithm in animal breeding was studied with simulation using a Gompertz function growth model in pigs. Two growth data sets were analyzed: a full set containing observations from the entire growing period, and a truncated time trajectory set containing animals slaughtered prematurely, which is common in pig breeding. The results from the 50 simulation replicates with full data set indicate that the linearization approach was capable of estimating the original parameters satisfactorily. However, estimation of the parameters related to adult weight becomes unstable in the case of a truncated data set.
Global identifiability of linear structural equation models
Drton, Mathias; Sullivant, Seth
2010-01-01
Structural equation models are multivariate statistical models that are defined by specifying noisy functional relationships among random variables. We consider the classical case of linear relationships and additive Gaussian noise terms. We give a necessary and sufficient condition for global identifiability of the model in terms of a mixed graph encoding the linear structural equations and the correlation structure of the error terms. Global identifiability is understood to mean injectivity of the parametrization of the model and is fundamental in particular for applicability of standard statistical methodology.
Modeling digital switching circuits with linear algebra
Thornton, Mitchell A
2014-01-01
Modeling Digital Switching Circuits with Linear Algebra describes an approach for modeling digital information and circuitry that is an alternative to Boolean algebra. While the Boolean algebraic model has been wildly successful and is responsible for many advances in modern information technology, the approach described in this book offers new insight and different ways of solving problems. Modeling the bit as a vector instead of a scalar value in the set {0, 1} allows digital circuits to be characterized with transfer functions in the form of a linear transformation matrix. The use of transf
Identification and Modelling of Linear Dynamic Systems
Directory of Open Access Journals (Sweden)
Stanislav Kocur
2006-01-01
Full Text Available System identification and modelling are very important parts of system control theory. System control is only as good as good is created model of system. So this article deals with identification and modelling problems. There are simple classification and evolution of identification methods, and then the modelling problem is described. Rest of paper is devoted to two most known and used models of linear dynamic systems.
Non-Linear Sigma Model on Conifolds
Parthasarathy, R
2002-01-01
Explicit solutions to the conifold equations with complex dimension $n=3,4$ in terms of {\\it{complex coordinates (fields)}} are employed to construct the Ricci-flat K\\"{a}hler metrics on these manifolds. The K\\"{a}hler 2-forms are found to be closed. The complex realization of these conifold metrics are used in the construction of 2-dimensional non-linear sigma model with the conifolds as target spaces. The action for the sigma model is shown to be bounded from below. By a suitable choice of the 'integration constants', arising in the solution of Ricci flatness requirement, the metric and the equations of motion are found to be {\\it{non-singular}}. As the target space is Ricci flat, the perturbative 1-loop counter terms being absent, the model becomes topological. The inherent U(1) fibre over the base of the conifolds is shown to correspond to a gauge connection in the sigma model. The same procedure is employed to construct the metric for the resolved conifold, in terms of complex coordinates and the action ...
Linear thermal circulator based on Coriolis forces.
Li, Huanan; Kottos, Tsampikos
2015-02-01
We show that the presence of a Coriolis force in a rotating linear lattice imposes a nonreciprocal propagation of the phononic heat carriers. Using this effect we propose the concept of Coriolis linear thermal circulator which can control the circulation of a heat current. A simple model of three coupled harmonic masses on a rotating platform permits us to demonstrate giant circulating rectification effects for moderate values of the angular velocities of the platform.
Disc instantons in linear sigma models
Energy Technology Data Exchange (ETDEWEB)
Govindarajan, Suresh E-mail: suresh@chaos.iitm.ernet.in; Jayaraman, T. E-mail: jayaram@imsc.ernet.in; Sarkar, Tapobrata E-mail: tapo@theory.tifr.res.in
2002-12-16
We construct a linear sigma model for open-strings ending on special Lagrangian cycles of a Calabi-Yau manifold. We illustrate the construction for the cases considered by Aganagic and Vafa (AV). This leads naturally to concrete models for the moduli space of open-string instantons. These instanton moduli spaces can be seen to be intimately related to certain auxiliary boundary toric varieties. By considering the relevant Gelfand-Kapranov-Zelevinsky (GKZ) differential equations of the boundary toric variety, we obtain the contributions to the world volume superpotential on the A-branes from open-string instantons. By using an ansatz due to Aganagic, Klemm and Vafa (AKV), we obtain the relevant change of variables from the linear sigma model to the non-linear sigma model variables--the open-string mirror map. Using this mirror map, we obtain results in agreement with those of AV and AKV for the counting of holomorphic disc instantons.
Singh, Kunwar P; Basant, Nikita; Malik, Amrita; Jain, Gunja
2010-01-18
The paper describes linear and nonlinear modeling of the wastewater data for the performance evaluation of an up-flow anaerobic sludge blanket (UASB) reactor based wastewater treatment plant (WWTP). Partial least squares regression (PLSR), multivariate polynomial regression (MPR) and artificial neural networks (ANNs) modeling methods were applied to predict the levels of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the UASB reactor effluents using four input variables measured weekly in the influent wastewater during the peak (morning and evening) and non-peak (noon) hours over a period of 48 weeks. The performance of the models was assessed through the root mean squared error (RMSE), relative error of prediction in percentage (REP), the bias, the standard error of prediction (SEP), the coefficient of determination (R(2)), the Nash-Sutcliffe coefficient of efficiency (E(f)), and the accuracy factor (A(f)), computed from the measured and model predicted values of the dependent variables (BOD, COD) in the WWTP effluents. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and the model predicted values of BOD and COD. Although, the model predicted values of BOD and COD by all the three modeling approaches (PLSR, MPR, ANN) were in good agreement with their respective measured values in the WWTP effluents, the nonlinear models (MPR, ANNs) performed relatively better than the linear ones. These models can be used as a tool for the performance evaluation of the WWTPs. Copyright 2009 Elsevier B.V. All rights reserved.
Kannan, Govind; Milani, Ali A; Panahi, Issa M S; Briggs, Richard W
2011-12-01
Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.
Institute of Scientific and Technical Information of China (English)
Hasan ABBASI NOZARI; Hamed DEHGHAN BANADAKI; Mohammad MOKHTARE; Somaveh HEKMATI VAHED
2012-01-01
This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system.A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT),which is an incremental tree-based learning algorithm.The proposed NF models are compared with other known intelligent identifiers,namely multilayer perceptron (MLP) and radial basis function (RBF).Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system.Experimental results show the effectiveness of our proposed NF modelling approach.
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
线性混合模型的光谱解混算法综述%A review on spectral unmixing algorithms based on linear mixing model
Institute of Scientific and Technical Information of China (English)
李二森; 张保明; 宋丽华; 余文杰; 唐德瑾
2011-01-01
混合像元的存在不仅影响了基于高光谱图像的地物识别和分类精度,而且已经成为遥感科学向定量化发展的主要障碍.目前的混合像元分解算法大多采用线性混合模型,其关键步骤为端元提取.文中从线性混合模型的定义出发,总结了近年来提出的端元提取算法,并重点对SMACC、VCA、SGA等算法进行了深入的分析,最后总结了混合像元分解的发展趋势.%The mixels not only influence the accuracy of target detection and classification based on the hypersepectral images, but also greatly hinder the development of quantitative remote sensing. Now, most of the spectral unmixing algorithms are based on the linear spectral mixing model, and the model' s key procedure is endmember extraction. This paper firstly introduced the linear mixing model, then summarized the endmember extraction algorithms and deeply analyzed several endmember extraction algorithms ( SMACC, VCA, SGA, etc) . After that, the trends of the spectral unmixing were summarized.
Energy Technology Data Exchange (ETDEWEB)
Mohanty, Subhasish, E-mail: smohanty@anl.gov; Majumdar, Saurindranath
2015-10-15
Highlights: • High temperature gas cooled reactor. • Finite element based stress analysis. • H-451 graphite. • Irradiation creep model. • Graphite reflector stress analysis. - Abstract: Irradiation creep plays a major role in the structural integrity of the graphite components in high temperature gas cooled reactors. Finite element procedures combined with a suitable irradiation creep model can be used to simulate the time-integrated structural integrity of complex shapes, such as the reactor core graphite reflector and fuel bricks. In the present work a comparative study was undertaken to understand the effect of linear and nonlinear irradiation creep on results of finite element based stress analysis. Numerical results were generated through finite element simulations of a typical graphite reflector.
Introduction to general and generalized linear models
Madsen, Henrik
2010-01-01
IntroductionExamples of types of data Motivating examples A first view on the modelsThe Likelihood PrincipleIntroduction Point estimation theory The likelihood function The score function The information matrix Alternative parameterizations of the likelihood The maximum likelihood estimate (MLE) Distribution of the ML estimator Generalized loss-function and deviance Quadratic approximation of the log-likelihood Likelihood ratio tests Successive testing in hypothesis chains Dealing with nuisance parameters General Linear ModelsIntroduction The multivariate normal distribution General linear mod
Ruin Probability in Linear Time Series Model
Institute of Scientific and Technical Information of China (English)
ZHANG Lihong
2005-01-01
This paper analyzes a continuous time risk model with a linear model used to model the claim process. The time is discretized stochastically using the times when claims occur, using Doob's stopping time theorem and martingale inequalities to obtain expressions for the ruin probability as well as both exponential and non-exponential upper bounds for the ruin probability for an infinite time horizon. Numerical results are included to illustrate the accuracy of the non-exponential bound.
Are all Linear Paired Comparison Models Equivalent
1990-09-01
Previous authors (Jackson and Fleckenstein 1957, Mosteller 1958, Noether 1960) have found that different models of paired comparisons data lead to simi...ponential distribution with a location parameter (Mosteller 1958, Noether 1960). Formal statements describing the limiting behavior of the gamma...that are not convolu- tion type linear models (the uniform model considered by Smith (1956), Mosteller (1958), Noether (1960)) and other convolution
Estimation for the simple linear Boolean model
2006-01-01
We consider the simple linear Boolean model, a fundamental coverage process also known as the Markov/General/infinity queue. In the model, line segments of independent and identically distributed length are located at the points of a Poisson process. The segments may overlap, resulting in a pattern of "clumps"-regions of the line that are covered by one or more segments-alternating with uncovered regions or "spacings". Study and application of the model have been impeded by the difficult...
Orthogonal Nilpotent Superfields from Linear Models
Kallosh, Renata; Mosk, Benjamin; Murli, Divyanshu
2016-01-01
We derive supersymmetry/supergravity models with constrained orthogonal nilpotent superfields from the linear models in the formal limit when the masses of the sgoldstino, inflatino and sinflaton tend to infinity. The case when the sinflaton mass remains finite leads to a model with a `relaxed' constraint, where the sinflaton remains an independent field. Our procedure is equivalent to a requirement that some of the components of the curvature of the moduli space tend to infinity.
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.
Vaccination strategies for SEIR models using feedback linearization. Preliminary results
De la Sen, M; Alonso-Quesada, S
2011-01-01
A linearization-based feedback-control strategy for a SEIR epidemic model is discussed. The vaccination objective is the asymptotically tracking of the removed-by-immunity population to the total population while achieving simultaneously the remaining population (i.e. susceptible plus infected plus infectious) to asymptotically tend to zero. The disease controlpolicy is designed based on a feedback linearization technique which provides a general method to generate families of vaccination policies with sound technical background.
2015-01-01
During many years, battery models have been proposed with different levels of accuracy and complexity. In some cases, simple low-order aggregated battery pack models may be more appropriate and feasible than complex physic-chemical or high-order multi-cell battery pack models. For example: in early stages of the system design process, in non-focused battery applications, or whenever low configuration effort or low computational complexity is a requirement. The latter may be the case of Electr...
Hierarchical linear regression models for conditional quantiles
Institute of Scientific and Technical Information of China (English)
TIAN Maozai; CHEN Gemai
2006-01-01
The quantile regression has several useful features and therefore is gradually developing into a comprehensive approach to the statistical analysis of linear and nonlinear response models,but it cannot deal effectively with the data with a hierarchical structure.In practice,the existence of such data hierarchies is neither accidental nor ignorable,it is a common phenomenon.To ignore this hierarchical data structure risks overlooking the importance of group effects,and may also render many of the traditional statistical analysis techniques used for studying data relationships invalid.On the other hand,the hierarchical models take a hierarchical data structure into account and have also many applications in statistics,ranging from overdispersion to constructing min-max estimators.However,the hierarchical models are virtually the mean regression,therefore,they cannot be used to characterize the entire conditional distribution of a dependent variable given high-dimensional covariates.Furthermore,the estimated coefficient vector (marginal effects)is sensitive to an outlier observation on the dependent variable.In this article,a new approach,which is based on the Gauss-Seidel iteration and taking a full advantage of the quantile regression and hierarchical models,is developed.On the theoretical front,we also consider the asymptotic properties of the new method,obtaining the simple conditions for an n1/2-convergence and an asymptotic normality.We also illustrate the use of the technique with the real educational data which is hierarchical and how the results can be explained.
DEFF Research Database (Denmark)
Barreras, Jorge Varela; Pinto, Claudio; de Castro, Ricardo
2015-01-01
response in the range of interest of many applications, including electro-mobility. Model validation and performance evaluation is achieved in simulations by comparison with other low and high order EECM battery models over dynamic driving profiles. Significant improvements in terms of cell terminal...
Managing Clustered Data Using Hierarchical Linear Modeling
Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.
2012-01-01
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…
Managing Clustered Data Using Hierarchical Linear Modeling
Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.
2012-01-01
Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…
Controller design approach based on linear programming.
Tanaka, Ryo; Shibasaki, Hiroki; Ogawa, Hiromitsu; Murakami, Takahiro; Ishida, Yoshihisa
2013-11-01
This study explains and demonstrates the design method for a control system with a load disturbance observer. Observer gains are determined by linear programming (LP) in terms of the Routh-Hurwitz stability criterion and the final-value theorem. In addition, the control model has a feedback structure, and feedback gains are determined to be the linear quadratic regulator. The simulation results confirmed that compared with the conventional method, the output estimated by our proposed method converges to a reference input faster when a load disturbance is added to a control system. In addition, we also confirmed the effectiveness of the proposed method by performing an experiment with a DC motor.
Modelling Nonlinear Sequence Generators in terms of Linear Cellular Automata
Fúster-Sabater, Amparo; 10.1016/j.apm.2005.08.013
2010-01-01
In this work, a wide family of LFSR-based sequence generators, the so-called Clock-Controlled Shrinking Generators (CCSGs), has been analyzed and identified with a subset of linear Cellular Automata (CA). In fact, a pair of linear models describing the behavior of the CCSGs can be derived. The algorithm that converts a given CCSG into a CA-based linear model is very simple and can be applied to CCSGs in a range of practical interest. The linearity of these cellular models can be advantageously used in two different ways: (a) for the analysis and/or cryptanalysis of the CCSGs and (b) for the reconstruction of the output sequence obtained from this kind of generators.
On Bayes linear unbiased estimation of estimable functions for the singular linear model
Institute of Scientific and Technical Information of China (English)
ZHANG Weiping; WEI Laisheng
2005-01-01
The unique Bayes linear unbiased estimator (Bayes LUE) of estimable functions is derived for the singular linear model. The superiority of Bayes LUE over ordinary best linear unbiased estimator is investigated under mean square error matrix (MSEM)criterion.
Linear Regression Based Real-Time Filtering
Directory of Open Access Journals (Sweden)
Misel Batmend
2013-01-01
Full Text Available This paper introduces real time filtering method based on linear least squares fitted line. Method can be used in case that a filtered signal is linear. This constraint narrows a band of potential applications. Advantage over Kalman filter is that it is computationally less expensive. The paper further deals with application of introduced method on filtering data used to evaluate a position of engraved material with respect to engraving machine. The filter was implemented to the CNC engraving machine control system. Experiments showing its performance are included.
Linear Programming建模研讨%Modeling of Linear Programming
Institute of Scientific and Technical Information of China (English)
宋占奎; 於全收; 范光; 燕嬿; 胡杰军
2007-01-01
研究用图解法、simplexmethod和匈牙利法建立Linear Programming的数学模型并求得了最优解.结果表明:对仅有两个变量的Linear Programming,既可通过图解法求得最优解;也可用单纯形表简便地求得最优解;而对任务和人数不等的assignment problem,则用匈牙利法求最优解.
Estimation and variable selection for generalized additive partial linear models
Wang, Li
2011-08-01
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
Multivariate Generalized Linear Mixed Models Using R
Berridge, Damon M
2011-01-01
To provide researchers with the ability to analyze large and complex data sets using robust models, this book presents a unified framework for a broad class of models that can be applied using a dedicated R package (Sabre). The first five chapters cover the analysis of multilevel models using univariate generalized linear mixed models (GLMMs). The next few chapters extend to multivariate GLMMs and the last chapters address more specialized topics, such as parallel computing for large-scale analyses. Each chapter includes many real-world examples implemented using Sabre as well as exercises and
The linear model and hypothesis a general unifying theory
Seber, George
2015-01-01
This book provides a concise and integrated overview of hypothesis testing in four important subject areas, namely linear and nonlinear models, multivariate analysis, and large sample theory. The approach used is a geometrical one based on the concept of projections and their associated idempotent matrices, thus largely avoiding the need to involve matrix ranks. It is shown that all the hypotheses encountered are either linear or asymptotically linear, and that all the underlying models used are either exactly or asymptotically linear normal models. This equivalence can be used, for example, to extend the concept of orthogonality in the analysis of variance to other models, and to show that the asymptotic equivalence of the likelihood ratio, Wald, and Score (Lagrange Multiplier) hypothesis tests generally applies.
Welland, M. J.; Tenuta, E.; Prudil, A. A.
2017-06-01
This article describes a phase-field model for an isothermal multicomponent, multiphase system which avoids implicit interfacial energy contributions by starting from a grand potential formulation. A method is developed for incorporating arbitrary forms of the equilibrium thermodynamic potentials in all phases to determine an explicit relationship between chemical potentials and species concentrations. The model incorporates variable densities between adjacent phases, defect migration, and dependence of internal pressure on object dimensions ranging from the macro- to nanoscale. A demonstrative simulation of an overpressurized nanoscopic intragranular bubble in nuclear fuel migrating to a grain boundary under kinetically limited vacancy diffusion is shown.
Modelling female fertility traits in beef cattle using linear and non-linear models.
Naya, H; Peñagaricano, F; Urioste, J I
2017-06-01
Female fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h(2 ) 0.23 and r > 0.24, for non-linear models). While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. © 2017 Blackwell Verlag GmbH.
Misaligned Image Integration With Local Linear Model.
Baba, Tatsuya; Matsuoka, Ryo; Shirai, Keiichiro; Okuda, Masahiro
2016-05-01
We present a new image integration technique for a flash and long-exposure image pair to capture a dark scene without incurring blurring or noisy artifacts. Most existing methods require well-aligned images for the integration, which is often a burdensome restriction in practical use. We address this issue by locally transferring the colors of the flash images using a small fraction of the corresponding pixels in the long-exposure images. We formulate the image integration as a convex optimization problem with the local linear model. The proposed method makes it possible to integrate the color of the long-exposure image with the detail of the flash image without causing any harmful effects to its contrast, where we do not need perfect alignment between the images by virtue of our new integration principle. We show that our method successfully outperforms the state of the art in the image integration and reference-based color transfer for challenging misaligned data sets.
Directory of Open Access Journals (Sweden)
Margareta RACOVITA
2011-11-01
Full Text Available This work proposes to solve a problem related to maximizing hotels’ revenues through two methods established in operational research domain. In the first part of the paper, the approach involves formulating the objective function and problem’s constraints, as well as the expansion of the model, taking into consideration clients’ preferences and the opportunities of group reservations. In the second part of the paper, the problem is solved with the help of network flows model, which allows optimum allocation of the rooms in real time. At the end of the paper, there are highlighted the advantages of applying those two mathematic methods within the strategies of performances development within hotel industry.
Neural Network for Combining Linear and Non-Linear Modelling of Dynamic Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1994-01-01
The purpose of this paper is to develop a method to combine linear models with MLP networks. In other words to find a method to make a non-linear and multivariable model that performs at least as good as a linear model, when the training data lacks information.......The purpose of this paper is to develop a method to combine linear models with MLP networks. In other words to find a method to make a non-linear and multivariable model that performs at least as good as a linear model, when the training data lacks information....
Testing Parametric versus Semiparametric Modelling in Generalized Linear Models
Härdle, W.K.; Mammen, E.; Müller, M.D.
1996-01-01
We consider a generalized partially linear model E(Y|X,T) = G{X'b + m(T)} where G is a known function, b is an unknown parameter vector, and m is an unknown function.The paper introduces a test statistic which allows to decide between a parametric and a semiparametric model: (i) m is linear, i.e. m(
Using R In Generalized Linear Models
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Mihaela Covrig
2015-09-01
Full Text Available This paper aims to approach the estimation of generalized linear models (GLM on the basis of the glm routine package in R. Particularly, regression models will be analyzed for those cases in which the explained variable follows a Poisson or a Negative Binomial distribution. The paper will briefly present the GLM methodology for count data, while the practical part will revolve around estimating and comparing models in which the response variable shows the number of claims in a portfolio of automobile insurance policies.
Linear irreversible heat engines based on local equilibrium assumptions
Izumida, Yuki; Okuda, Koji
2015-08-01
We formulate an endoreversible finite-time Carnot cycle model based on the assumptions of local equilibrium and constant energy flux, where the efficiency and the power are expressed in terms of the thermodynamic variables of the working substance. By analyzing the entropy production rate caused by the heat transfer in each isothermal process during the cycle, and using the endoreversible condition applied to the linear response regime, we identify the thermodynamic flux and force of the present system and obtain a linear relation that connects them. We calculate the efficiency at maximum power in the linear response regime by using the linear relation, which agrees with the Curzon-Ahlborn (CA) efficiency known as the upper bound in this regime. This reason is also elucidated by rewriting our model into the form of the Onsager relations, where our model turns out to satisfy the tight-coupling condition leading to the CA efficiency.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
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E. Fallahi
2016-10-01
Full Text Available Introduction: Determining a suitable cropping pattern is an important task for planners and requires an exact and realistic decision-making process based on several goals and criteria corresponding to secure the interest of agricultural beneficiaries in long-term. Accordingly, this study reviews the current pattern operated by farmers in Sari, Iran, and intends to provide a cropping pattern that considers the multifold regional and agricultural sustainability criteria along with economic considerations. Materials and Methods: In order to achieve the study goals, a consolidated model of AHP and Linear Programming was applied. For this purpose, we constructed a three-level AHP, including a goal (determining the weight of each crop, seven criteria, and seven alternatives. Profitability, compatibility with regional and geographical conditions, water consumption, environmental effects of cropping, job creation opportunities, skill and proficiency required for producing a crop, and risk taken to cultivate a crop were considered as the criteria in the model. Seven alternative crops including rice, wheat, rapeseed, barley, soybean, clover, and vegetables were considered too. The next step is determining the weight of each criterion with regard to the goal and the weight of each alternative with regard to each criteria. By multiplying these weights, final weights for various crops were obtained from the model. Derived weights for each crop were then applied as objective function coefficients in the Linear Programming model and the model was solved subject to the constraints. Results and Discussion: Optimal cropping pattern determined based on the consolidated model of AHP and Linear Programming and the results compared to a scenario that only looks forward to maximizing the economic interests. Due to the low profitability of rapeseed and barley, these crops eliminated from the pattern in the profit-maximizing scenario. According to the results, the
Rethinking the Lintnerian Linear Valuation Model
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Shih-Cheng Lee
2014-09-01
Full Text Available This paper develops and tests a new valuation model. Callen and Morel (2000 apply the Lintner (1956 dividend model to the famous Ohlson (1995 valuation model and develop the Lintnerian linear accounting valuation model (henceforth, the CM model. However, Bauer and Bhattacharyya (2007 suggest that the Lintner dividend model does not fit firm dividend policy behaviour appropriately and decide to construct another dividend policy process. This study applies their dividend model to construct a new valuation model, which performs better than the original Ohlson and CM models empirically. Applying the Engle and Granger (1987 cointegration concepts, we examine the performance of the three models for the 1,564 firm-year panel data of US companies from 1973 to 2006. Our findings indicate that all tested variables are stationary after the first order difference process and that all three models exhibit long-run equilibrium relations among equity price and explanatory variables. However, our model has the highest cointegration ratio, which is almost 100 percent of sample firms. Hence, our model is more suitable to evaluate the equity value and provides improvement for the previous accounting valuation models.
Improved testing inference in mixed linear models
Melo, Tatiane F N; Cribari-Neto, Francisco; 10.1016/j.csda.2008.12.007
2011-01-01
Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Oftentimes, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test and also to a test obtained from a modified profile likelihood function. Our results generalize those in Zucker et al. (Journal of the Royal Statistical Society B, 2000, 62, 827-838) by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report numerical evidence which shows that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presente...
Decomposed Implicit Models of Piecewise - Linear Networks
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J. Brzobohaty
1992-05-01
Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.
Data perturbation analysis of a linear model
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The linear model features were carefully studied in the cases of data perturbation and mean shift perturbation.Some important features were also proved mathematically. The results show that the mean shift perturbation is equivalentto the data perturbation, that is, adding a parameter to an observation equation means that this set of data is deleted fromthe data set. The estimate of this parameter is its predicted residual in fact
Hippocampus Segmentation Based on Local Linear Mapping
Pang, Shumao; Jiang, Jun; Lu, Zhentai; Li, Xueli; Yang, Wei; Huang, Meiyan; Zhang, Yu; Feng, Yanqiu; Huang, Wenhua; Feng, Qianjin
2017-04-01
We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.
B-737 Linear Autoland Simulink Model
Belcastro, Celeste (Technical Monitor); Hogge, Edward F.
2004-01-01
The Linear Autoland Simulink model was created to be a modular test environment for testing of control system components in commercial aircraft. The input variables, physical laws, and referenced frames used are summarized. The state space theory underlying the model is surveyed and the location of the control actuators described. The equations used to realize the Dryden gust model to simulate winds and gusts are derived. A description of the pseudo-random number generation method used in the wind gust model is included. The longitudinal autopilot, lateral autopilot, automatic throttle autopilot, engine model and automatic trim devices are considered as subsystems. The experience in converting the Airlabs FORTRAN aircraft control system simulation to a graphical simulation tool (Matlab/Simulink) is described.
Dynamic generalized linear models for monitoring endemic diseases
DEFF Research Database (Denmark)
Lopes Antunes, Ana Carolina; Jensen, Dan Børge; Halasa, T.
The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control...... in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance...
Linear mixed models a practical guide using statistical software
West, Brady T; Galecki, Andrzej T
2006-01-01
Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-navigate reference details the use of procedures for fitting LMMs in five popular statistical software packages: SAS, SPSS, Stata, R/S-plus, and HLM. The authors introduce basic theoretical concepts, present a heuristic approach to fitting LMMs based on bo
Stochastic linear programming models, theory, and computation
Kall, Peter
2011-01-01
This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors’ SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. … T...
Directory of Open Access Journals (Sweden)
Hiroki Yoshioka
2012-07-01
Full Text Available The spectral unmixing of a linear mixture model (LMM with Normalized Difference Vegetation Index (NDVI constraints was performed to estimate the fraction of vegetation cover (FVC over the earth’s surface in an effort to facilitate long-term surface vegetation monitoring using a set of environmental satellites. Although the integrated use of multiple sensors improves the spatial and temporal quality of the data sets, area-averaged FVC values obtained using an LMM-based algorithm suffer from systematic biases caused by differences in the spatial resolutions of the sensors, known as scaling effects. The objective of this study is to investigate the scaling effects in area-averaged FVC values using analytical approaches by focusing on the monotonic behavior of the scaling effects as a function of the spatial resolution. The analysis was conducted based on a resolution transformation model introduced recently by the authors in the accompanying paper (Obata et al., 2012. The maximum value of the scaling effects present in FVC values was derived analytically and validated numerically. A series of derivations identified the error bounds (inherent uncertainties of the averaged FVC values caused by the scaling effect. The results indicate a fundamental difference between the NDVI and the retrieved FVC from NDVI, which should be noted for accuracy improvement of long-term observation datasets.
Linearized models for a new magnetic control in MAST
Energy Technology Data Exchange (ETDEWEB)
Artaserse, G., E-mail: giovanni.artaserse@enea.it [Associazione Euratom-ENEA sulla Fusione, Via Enrico Fermi 45, I-00044 Frascati (RM) (Italy); Maviglia, F.; Albanese, R. [Associazione Euratom-ENEA-CREATE sulla Fusione, Via Claudio 21, I-80125 Napoli (Italy); McArdle, G.J.; Pangione, L. [EURATOM/CCFE Fusion Association, Culham Science Centre, Abingdon, Oxon, OX14 3DB (United Kingdom)
2013-10-15
Highlights: ► We applied linearized models for a new magnetic control on MAST tokamak. ► A suite of procedures, conceived to be machine independent, have been used. ► We carried out model-based simulations, taking into account eddy currents effects. ► Comparison with the EFIT flux maps and the experimental magnetic signals are shown. ► A current driven model for the dynamic simulations of the experimental data have been performed. -- Abstract: The aim of this work is to provide reliable linearized models for the design and assessment of a new magnetic control system for MAST (Mega Ampère Spherical Tokamak) using rtEFIT, which can easily be exported to MAST Upgrade. Linearized models for magnetic control have been obtained using the 2D axisymmetric finite element code CREATE L. MAST linearized models include equivalent 2D axisymmetric schematization of poloidal field (PF) coils, vacuum vessel, and other conducting structures. A plasmaless and a double null configuration have been chosen as benchmark cases for the comparison with experimental data and EFIT reconstructions. Good agreement has been found with the EFIT flux map and the experimental signals coming from magnetic probes with only few mismatches probably due to broken sensors. A suite of procedures (equipped with a user friendly interface to be run even remotely) to provide linearized models for magnetic control is now available on the MAST linux machines. A new current driven model has been used to obtain a state space model having the PF coil currents as inputs. Dynamic simulations of experimental data have been carried out using linearized models, including modelling of the effects of the passive structures, showing a fair agreement. The modelling activity has been useful also to reproduce accurately the interaction between plasma current and radial position control loops.
When to Use Hierarchical Linear Modeling
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Veronika Huta
2014-04-01
Full Text Available Previous publications on hierarchical linear modeling (HLM have provided guidance on how to perform the analysis, yet there is relatively little information on two questions that arise even before analysis: Does HLM apply to ones data and research question? And if it does apply, how does one choose between HLM and other methods sometimes used in these circumstances, including multiple regression, repeated-measures or mixed ANOVA, and structural equation modeling or path analysis? The purpose of this tutorial is to briefly introduce HLM and then to review some of the considerations that are helpful in answering these questions, including the nature of the data, the model to be tested, and the information desired on the output. Some examples of how the same analysis could be performed in HLM, repeated-measures or mixed ANOVA, and structural equation modeling or path analysis are also provided. .
Zhang, Liping; Zhang, Shiwen; Huang, Yajie; Cao, Meng; Huang, Yuanfang; Zhang, Hongyan
2016-01-01
Understanding abandoned mine land (AML) changes during land reclamation is crucial for reusing damaged land resources and formulating sound ecological restoration policies. This study combines the linear programming (LP) model and the CLUE-S model to simulate land-use dynamics in the Mentougou District (Beijing, China) from 2007 to 2020 under three reclamation scenarios, that is, the planning scenario based on the general land-use plan in study area (scenario 1), maximal comprehensive benefits (scenario 2), and maximal ecosystem service value (scenario 3). Nine landscape-scale graph metrics were then selected to describe the landscape characteristics. The results show that the coupled model presented can simulate the dynamics of AML effectively and the spatially explicit transformations of AML were different. New cultivated land dominates in scenario 1, while construction land and forest land account for major percentages in scenarios 2 and 3, respectively. Scenario 3 has an advantage in most of the selected indices as the patches combined most closely. To conclude, reclaiming AML by transformation into more forest can reduce the variability and maintain the stability of the landscape ecological system in study area. These findings contribute to better mapping AML dynamics and providing policy support for the management of AML. PMID:27023575
Khanfar, Mohammad A; Taha, Mutasem O
2013-10-28
The mammalian target of rapamycin (mTOR) has an important role in cell growth, proliferation, and survival. mTOR is frequently hyperactivated in cancer, and therefore, it is a clinically validated target for cancer therapy. In this study, we combined exhaustive pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis to explore the structural requirements for potent mTOR inhibitors employing 210 known mTOR ligands. Genetic function algorithm (GFA) coupled with k nearest neighbor (kNN) and multiple linear regression (MLR) analyses were employed to build self-consistent and predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. Successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. Optimal QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of several new promising mTOR inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. The most potent hit illustrated an IC50 value of 48 nM.
Yu, Jianwei; Liu, Juan; An, Wei; Wang, Yongjing; Zhang, Junzhi; Wei, Wei; Su, Ming; Yang, Min
2015-01-01
A total of 86 source water samples from 38 cities across major watersheds of China were collected for a bromide (Br(-)) survey, and the bromate (BrO3 (-)) formation potentials (BFPs) of 41 samples with Br(-) concentration >20 μg L(-1) were evaluated using a batch ozonation reactor. Statistical analyses indicated that higher alkalinity, hardness, and pH of water samples could lead to higher BFPs, with alkalinity as the most important factor. Based on the survey data, a multiple linear regression (MLR) model including three parameters (alkalinity, ozone dose, and total organic carbon (TOC)) was established with a relatively good prediction performance (model selection criterion = 2.01, R (2) = 0.724), using logarithmic transformation of the variables. Furthermore, a contour plot was used to interpret the influence of alkalinity and TOC on BrO3 (-) formation with prediction accuracy as high as 71 %, suggesting that these two parameters, apart from ozone dosage, were the most important ones affecting the BFPs of source waters with Br(-) concentration >20 μg L(-1). The model could be a useful tool for the prediction of the BFPs of source water.
Bagherpoor, H M; Salmasi, Farzad R
2015-07-01
In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance.
Energy Technology Data Exchange (ETDEWEB)
Lewin, M.D.; Sarasua, S.; Jones, P.A. (Agency for Toxic Substances and Disease Registry, Atlanta, GA (United States). Div. of Health Studies)
1999-07-01
For the purpose of examining the association between blood lead levels and household-specific soil lead levels, the authors used a multivariate linear regression model to find a slope factor relating soil lead levels to blood lead levels. They used previously collected data from the Agency for Toxic Substances and Disease Registry's (ATSDR's) multisite lead and cadmium study. The data included in the blood lead measurements of 1,015 children aged 6--71 months, and corresponding household-specific environmental samples. The environmental samples included lead in soil, house dust, interior paint, and tap water. After adjusting for income, education or the parents, presence of a smoker in the household, sex, and dust lead, and using a double log transformation, they found a slope factor of 0.1388 with a 95% confidence interval of 0.09--0.19 for the dose-response relationship between the natural log of the soil lead level and the natural log of the blood lead level. The predicted blood lead level corresponding to a soil lead level of 500 mg/kg was 5.99 [micro]g/kg with a 95% prediction interval of 2.08--17.29. Predicted values and their corresponding prediction intervals varied by covariate level. The model shows that increased soil lead level is associated with elevated blood leads in children, but that predictions based on this regression model are subject to high levels of uncertainty and variability.
Zhang, Liping; Zhang, Shiwen; Huang, Yajie; Cao, Meng; Huang, Yuanfang; Zhang, Hongyan
2016-03-24
Understanding abandoned mine land (AML) changes during land reclamation is crucial for reusing damaged land resources and formulating sound ecological restoration policies. This study combines the linear programming (LP) model and the CLUE-S model to simulate land-use dynamics in the Mentougou District (Beijing, China) from 2007 to 2020 under three reclamation scenarios, that is, the planning scenario based on the general land-use plan in study area (scenario 1), maximal comprehensive benefits (scenario 2), and maximal ecosystem service value (scenario 3). Nine landscape-scale graph metrics were then selected to describe the landscape characteristics. The results show that the coupled model presented can simulate the dynamics of AML effectively and the spatially explicit transformations of AML were different. New cultivated land dominates in scenario 1, while construction land and forest land account for major percentages in scenarios 2 and 3, respectively. Scenario 3 has an advantage in most of the selected indices as the patches combined most closely. To conclude, reclaiming AML by transformation into more forest can reduce the variability and maintain the stability of the landscape ecological system in study area. These findings contribute to better mapping AML dynamics and providing policy support for the management of AML.
Confirming the Lanchestrian linear-logarithmic model of attrition
Energy Technology Data Exchange (ETDEWEB)
Hartley, D.S. III.
1990-12-01
This paper is the fourth in a series of reports on the breakthrough research in historical validation of attrition in conflict. Significant defense policy decisions, including weapons acquisition and arms reduction, are based in part on models of conflict. Most of these models are driven by their attrition algorithms, usually forms of the Lanchester square and linear laws. None of these algorithms have been validated. The results of this paper confirm the results of earlier papers, using a large database of historical results. The homogeneous linear-logarithmic Lanchestrian attrition model is validated to the extent possible with current initial and final force size data and is consistent with the Iwo Jima data. A particular differential linear-logarithmic model is described that fits the data very well. A version of Helmbold's victory predicting parameter is also confirmed, with an associated probability function. 37 refs., 73 figs., 68 tabs.
Directory of Open Access Journals (Sweden)
Mäntysaari Esa A
2006-06-01
Full Text Available Abstract A method based on Taylor series expansion for estimation of location parameters and variance components of non-linear mixed effects models was considered. An attractive property of the method is the opportunity for an easily implemented algorithm. Estimation of non-linear mixed effects models can be done by common methods for linear mixed effects models, and thus existing programs can be used after small modifications. The applicability of this algorithm in animal breeding was studied with simulation using a Gompertz function growth model in pigs. Two growth data sets were analyzed: a full set containing observations from the entire growing period, and a truncated time trajectory set containing animals slaughtered prematurely, which is common in pig breeding. The results from the 50 simulation replicates with full data set indicate that the linearization approach was capable of estimating the original parameters satisfactorily. However, estimation of the parameters related to adult weight becomes unstable in the case of a truncated data set.
Applied Research of Enterprise Cost Control Based on Linear Programming
Directory of Open Access Journals (Sweden)
Yu Shuo
2015-01-01
This paper researches the enterprise cost control through the linear programming model, and analyzes the restriction factors of the labor of enterprise production, raw materials, processing equipment, sales price, and other factors affecting the enterprise income, so as to obtain an enterprise cost control model based on the linear programming. This model can calculate rational production mode in the case of limited resources, and acquire optimal enterprise income. The production guiding program and scheduling arrangement of the enterprise can be obtained through calculation results, so as to provide scientific and effective guidance for the enterprise production. This paper adds the sensitivity analysis in the linear programming model, so as to learn about the stability of the enterprise cost control model based on linear programming through the sensitivity analysis, and verify the rationality of the model, and indicate the direction for the enterprise cost control. The calculation results of the model can provide a certain reference for the enterprise planning in the market economy environment, which have strong reference and practical significance in terms of the enterprise cost control.
Running vacuum cosmological models: linear scalar perturbations
Perico, E. L. D.; Tamayo, D. A.
2017-08-01
In cosmology, phenomenologically motivated expressions for running vacuum are commonly parameterized as linear functions typically denoted by Λ(H2) or Λ(R). Such models assume an equation of state for the vacuum given by bar PΛ = - bar rhoΛ, relating its background pressure bar PΛ with its mean energy density bar rhoΛ ≡ Λ/8πG. This equation of state suggests that the vacuum dynamics is due to an interaction with the matter content of the universe. Most of the approaches studying the observational impact of these models only consider the interaction between the vacuum and the transient dominant matter component of the universe. We extend such models by assuming that the running vacuum is the sum of independent contributions, namely bar rhoΛ = Σibar rhoΛi. Each Λ i vacuum component is associated and interacting with one of the i matter components in both the background and perturbation levels. We derive the evolution equations for the linear scalar vacuum and matter perturbations in those two scenarios, and identify the running vacuum imprints on the cosmic microwave background anisotropies as well as on the matter power spectrum. In the Λ(H2) scenario the vacuum is coupled with every matter component, whereas the Λ(R) description only leads to a coupling between vacuum and non-relativistic matter, producing different effects on the matter power spectrum.
CP Violation in the Linear Sigma Model
Mizher, Ana Júlia
2009-01-01
Motivated by the possibility of the formation of CP-odd domains in heavy ion collisions, we investigate the effects of CP violation on the chiral transition within the linear sigma model with two flavors of quarks. We also study how the CP-odd system is affected by the presence of a strong magnetic field, that is presumably generated in a non-central heavy ion collision. We find that both ingredients play an important role, influencing drastically the nature of the phase transition and the critical temperature.
CP Violation in the Linear Sigma Model
Mizher, Ana Júlia; Fraga, Eduardo S.
2008-01-01
Motivated by the possibility of the formation of CP-odd domains in heavy ion collisions, we investigate the effects of CP violation on the chiral transition within the linear sigma model with two flavors of quarks. We also study how the CP-odd system is affected by the presence of a strong magnetic field, that is presumably generated in a non-central heavy ion collision. We find that both ingredients play an important role, influencing drastically the nature of the phase transition and the cr...
CP Violation in the Linear Sigma Model
Energy Technology Data Exchange (ETDEWEB)
Mizher, Ana Julia; Fraga, Eduardo S. [Instituto de Fisica, Universidade Federal do Rio de Janeiro, Caixa Postal 68528, Rio de Janeiro, RJ 21941-972 (Brazil)
2009-04-01
Motivated by the possibility of the formation of CP-odd domains in heavy ion collisions, we investigate the effects of CP violation on the chiral transition within the linear sigma model with two flavors of quarks. We also study how the CP-odd system is affected by the presence of a strong magnetic field, that is presumably generated in a non-central heavy ion collision. We find that both ingredients play an important role, influencing drastically the nature of the phase transition and the critical temperature.
Linear Amplifier Model for Optomechanical Systems
Botter, Thierry; Brahms, Nathan; Schreppler, Sydney; Stamper-Kurn, Dan M
2011-01-01
We model optomechanical systems as linear optical amplifiers. This provides a unified treatment of diverse optomechanical phenomena. We emphasize, in particular, the relationship between ponderomotive squeezing and optomechanically induced transparency, two foci of current research. We characterize the amplifier response to quantum and deliberately applied fluctuations, both optical and mechanical. Further, we apply these results to establish quantum limits on external force sensing both on and off cavity resonance. We find that the maximum sensitivity attained on resonance constitutes an absolute upper limit, not surpassed when detuning off cavity resonance. The theory can be extended to a two-sided cavity with losses and limited detection efficiency.
An introduction to hierarchical linear modeling
Directory of Open Access Journals (Sweden)
Heather Woltman
2012-02-01
Full Text Available This tutorial aims to introduce Hierarchical Linear Modeling (HLM. A simple explanation of HLM is provided that describes when to use this statistical technique and identifies key factors to consider before conducting this analysis. The first section of the tutorial defines HLM, clarifies its purpose, and states its advantages. The second section explains the mathematical theory, equations, and conditions underlying HLM. HLM hypothesis testing is performed in the third section. Finally, the fourth section provides a practical example of running HLM, with which readers can follow along. Throughout this tutorial, emphasis is placed on providing a straightforward overview of the basic principles of HLM.
Linear Parametric Model Checking of Timed Automata
DEFF Research Database (Denmark)
Hune, Tohmas Seidelin; Romijn, Judi; Stoelinga, Mariëlle
2001-01-01
of a subclass of parametric timed automata (L/U automata), for which the emptiness problem is decidable, contrary to the full class where it is know to be undecidable. Also we present a number of lemmas enabling the verication eort to be reduced for L/U automata in some cases. We illustrate our approach......We present an extension of the model checker Uppaal capable of synthesize linear parameter constraints for the correctness of parametric timed automata. The symbolic representation of the (parametric) state-space is shown to be correct. A second contribution of this paper is the identication...
F-theory and linear sigma models
Bershadsky, M; Greene, Brian R; Johansen, A; Lazaroiu, C I
1998-01-01
We present an explicit method for translating between the linear sigma model and the spectral cover description of SU(r) stable bundles over an elliptically fibered Calabi-Yau manifold. We use this to investigate the 4-dimensional duality between (0,2) heterotic and F-theory compactifications. We indirectly find that much interesting heterotic information must be contained in the `spectral bundle' and in its dual description as a gauge theory on multiple F-theory 7-branes. A by-product of these efforts is a method for analyzing semistability and the splitting type of vector bundles over an elliptic curve given as the sheaf cohomology of a monad.
Feuer, Ronny; Vlaic, Sebastian; Arlt, Janine; Sawodny, Oliver; Dahmen, Uta; Zanger, Ulrich M; Thomas, Maria
2015-01-01
Gene expression analysis is an essential part of biological and medical investigations. Quantitative real-time PCR (qPCR) is characterized with excellent sensitivity, dynamic range, reproducibility and is still regarded to be the gold standard for quantifying transcripts abundance. Parallelization of qPCR such as by microfluidic Taqman Fluidigm Biomark Platform enables evaluation of multiple transcripts in samples treated under various conditions. Despite advanced technologies, correct evaluation of the measurements remains challenging. Most widely used methods for evaluating or calculating gene expression data include geNorm and ΔΔCt, respectively. They rely on one or several stable reference genes (RGs) for normalization, thus potentially causing biased results. We therefore applied multivariable regression with a tailored error model to overcome the necessity of stable RGs. We developed a RG independent data normalization approach based on a tailored linear error model for parallel qPCR data, called LEMming. It uses the assumption that the mean Ct values within samples of similarly treated groups are equal. Performance of LEMming was evaluated in three data sets with different stability patterns of RGs and compared to the results of geNorm normalization. Data set 1 showed that both methods gave similar results if stable RGs are available. Data set 2 included RGs which are stable according to geNorm criteria, but became differentially expressed in normalized data evaluated by a t-test. geNorm-normalized data showed an effect of a shifted mean per gene per condition whereas LEMming-normalized data did not. Comparing the decrease of standard deviation from raw data to geNorm and to LEMming, the latter was superior. In data set 3 according to geNorm calculated average expression stability and pairwise variation, stable RGs were available, but t-tests of raw data contradicted this. Normalization with RGs resulted in distorted data contradicting literature, while
Practical likelihood analysis for spatial generalized linear mixed models
DEFF Research Database (Denmark)
Bonat, W. H.; Ribeiro, Paulo Justiniano
2016-01-01
We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are, respectiv......We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...
Institute of Scientific and Technical Information of China (English)
张纪勇
2013-01-01
According to the working principle of a rotary motor developed the cylindrical linear lifting model based on the linear induction motor. And established the equivalent circuit which calculates the model structure sizes and starting characteristic parameters considering the effect of side end from the relationship of energy equilibrium, This will lay on the technical foundation for the practical of the lifting model.% 根据旋转电动机工作原理，研发了基于直线感应电动机的圆筒形直线举升模型，并从能量平衡的角度建立了考虑边端效应的等效电路，计算了模型结构参数和起动特性参数，为举升模型的实用化奠定了技术基础。
Injecting Abstract Interpretations into Linear Cost Models
Directory of Open Access Journals (Sweden)
David Cachera
2010-06-01
Full Text Available We present a semantics based framework for analysing the quantitative behaviour of programs with regard to resource usage. We start from an operational semantics equipped with costs. The dioid structure of the set of costs allows for defining the quantitative semantics as a linear operator. We then present an abstraction technique inspired from abstract interpretation in order to effectively compute global cost information from the program. Abstraction has to take two distinct notions of order into account: the order on costs and the order on states. We show that our abstraction technique provides a correct approximation of the concrete cost computations.
Tests of risk premia in linear factor models
Kleibergen, F.
2009-01-01
We show that statistical inference on the risk premia in linear factor models that is based on the Fama-MacBeth (FM) and generalized least squares (GLS) two-pass risk premia estimators is misleading when the β’s are small and/or the number of assets is large. We propose novel statistics, that are ba
Tests of risk premia in linear factor models
Kleibergen, F.R.
2005-01-01
We show that inference on risk premia in linear factor models that is based on the Fama-MacBeth and GLS risk premia estimators is misleading when the ß’s are small and/or the number of assets is large. We propose some novel statistics that remain trustworthy in these cases. The inadequacy of Fama-Ma
Tests of risk premia in linear factor models
Kleibergen, F.
2009-01-01
We show that statistical inference on the risk premia in linear factor models that is based on the Fama-MacBeth (FM) and generalized least squares (GLS) two-pass risk premia estimators is misleading when the β’s are small and/or the number of assets is large. We propose novel statistics, that are
Linear Modeling of Multiple Sclerosis and Its Subgroubs
Directory of Open Access Journals (Sweden)
Yeliz Karaca
2015-03-01
Full Text Available OBJECTIVE: In this study,120 patients diagnosed with clinical multiple sclerosis (MS of relapsing remitting type (RRMS, secondary progressive type (SPMS and primary progressive type (PPMS were examined, as well as 19 healthy subjects. All subjects were between the ages of 20 and 55. Disability levels of MS symptoms were determined using Expanded Disability Status Scale (EDSS. We focused on three regions in the brain, brain stem, periventricular corpus callosum, and upper cervical regions EDSS scores and number of lesions in these three regions are considered as the parameters of the linear mathematical model to determine the subgroups of the disease. METHODS: Initially, the distinction between healthy subjects and patients was made. Then, if the subject was determined to have MS, the distinction of type, i.e. RRMS, SPMS/PPMS, and later, RRMS/SPMS distinction was made. In all determinations linear models were used and number of lesions in the specified three regions and EDSS scores were assumed as the parameters of the model. The coefficients of the models were obtained by least squares method. RESULTS: In the linear model attached to MR parameters, there was 100% success for distinction of patients and healthy subjects. Success for distinction of RRMS and SPMS/PPMS patients and RRMS/SPMS patients was 94% and 78.94%, respectively is attained. Based on EDSS scores, linear model provides 99% success in the distinction between patients and healthy subjects. In the models created for the distinction between groups, success rate was 94% was for RRMS-SPMS/PPMS and 64% for RRMS/SPMS. CONCLUSION: The correlation of MS diagnosis using various features obtained from MR images and EDSS scores with subgroups of the disease and the possibility of developing a linear model were determined. Using the features having the highest correlation rate, various linear models were developed and high success was achieved.
Enabling linear model for the IMGC-02 absolute gravimeter
Nagornyi, V D; Svitlov, S
2013-01-01
Measurement procedures of most rise-and-fall absolute gravimeters has to resolve singularity at the apex of the trajectory caused by the discrete fringe counting in the Michelson-type interferometers. Traditionally the singularity is addressed by implementing non-linear models of the trajectory, but they introduce problems of their own, such as biasness, non-uniqueness, and instability of the gravity estimates. Using IMGC-02 gravimeter as example, we show that the measurement procedure of the rise-and-fall gravimeters can be based on the linear models which successfully resolve the singularity and provide rigorous estimates of the gravity value. The linear models also facilitate further enhancements of the instrument, such as accounting for new types of disturbances and active compensation for the vibrations.
Ira Remsen, saccharin, and the linear model.
Warner, Deborah J
2008-03-01
While working in the chemistry laboratory at Johns Hopkins University, Constantin Fahlberg oxidized the 'ortho-sulfamide of benzoic acid' and, by chance, found the result to be incredibly sweet. Several years later, now working on his own, he termed this stuff saccharin, developed methods of making it in quantity, obtained patents on these methods, and went into production. As the industrial and scientific value of saccharin became apparent, Ira Remsen pointed out that the initial work had been done in his laboratory and at his suggestion. The ensuing argument, carried out in the courts of law and public opinion, illustrates the importance of the linear model to scientists who staked their identities on the model of disinterested research but who also craved credit for important practical results.
Train repathing in emergencies based on fuzzy linear programming.
Meng, Xuelei; Cui, Bingmou
2014-01-01
Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.
Kenta Obata; Hiroki Yoshioka
2010-01-01
Measurements of the fraction of vegetation cover (FVC), retrieved from remotely sensed reflectance spectra, serves as a useful measure of land cover changes on the regional and global scales. A linear mixture model (LMM) is frequently employed to analytically estimate the FVC using the spectral vegetation index (VI) as a constraint. Variations in the application of this algorithm arise due to differences in the choice of endmember spectra and VI model assumptions. As a result, the retrieved F...
Richly parameterized linear models additive, time series, and spatial models using random effects
Hodges, James S
2013-01-01
A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects.Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. The aut
Lewin, M D; Sarasua, S; Jones, P A
1999-07-01
For the purpose of examining the association between blood lead levels and household-specific soil lead levels, we used a multivariate linear regression model to find a slope factor relating soil lead levels to blood lead levels. We used previously collected data from the Agency for Toxic Substances and Disease Registry's (ATSDR's) multisite lead and cadmium study. The data included the blood lead measurements (0.5 to 40.2 microg/dL) of 1015 children aged 6-71 months, and corresponding household-specific environmental samples. The environmental samples included lead in soil (18.1-9980 mg/kg), house dust (5.2-71,000 mg/kg), interior paint (0-16.5 mg/cm2), and tap water (0.3-103 microg/L). After adjusting for income, education of the parents, presence of a smoker in the household, sex, and dust lead, and using a double log transformation, we found a slope factor of 0.1388 with a 95% confidence interval of 0.09-0.19 for the dose-response relationship between the natural log of the soil lead level and the natural log of the blood lead level. The predicted blood lead level corresponding to a soil lead level of 500 mg/kg was 5.99 microg/kg with a 95% prediction interval of 2. 08-17.29. Predicted values and their corresponding prediction intervals varied by covariate level. The model shows that increased soil lead level is associated with elevated blood leads in children, but that predictions based on this regression model are subject to high levels of uncertainty and variability.
ERF Based Precision Linear Drive System
Tönshoff, Hans Kurt; Stegmann, Axel
The design of clutches using electrorheological fluids (ERF) or magnetorheological suspensions (MRS) offers a new approach to precision linear drive systems based on the ‘inch-worm’ principle. This principle needs two clutches or controllable clamps and one (or more) elements to vary the distance between these clamps. To perform a caterpillar-like motion the actuators are controlled in a cyclic mode. This paper presents design elements, system structure and first results of motion performance of a prototype drive system. The presented linear drive combines the high resolution of piezo actuators, the unlimited travel of the ‘inch-worm’ principle and the clutch effect of ERF without a substantial mechanical excitation. The motion of the two clamps is controlled to move a stage in point to point mode or to follow a given velocity profile. Using two piezo actuators the stage can move continuously. Pre-loaded piezo actuators control the distances between the two clamps and the central slide. Each ERF clamp is designed as a set of thin parallel steel plate electrodes which interlock with a set of ground plates. Drive force limits are given by the maximum yield force of the clamps as well as by the relation of the actuator expansion and the ERF/MRS shear stiffness in “solid” mode.
Bayesian Subset Modeling for High-Dimensional Generalized Linear Models
Liang, Faming
2013-06-01
This article presents a new prior setting for high-dimensional generalized linear models, which leads to a Bayesian subset regression (BSR) with the maximum a posteriori model approximately equivalent to the minimum extended Bayesian information criterion model. The consistency of the resulting posterior is established under mild conditions. Further, a variable screening procedure is proposed based on the marginal inclusion probability, which shares the same properties of sure screening and consistency with the existing sure independence screening (SIS) and iterative sure independence screening (ISIS) procedures. However, since the proposed procedure makes use of joint information from all predictors, it generally outperforms SIS and ISIS in real applications. This article also makes extensive comparisons of BSR with the popular penalized likelihood methods, including Lasso, elastic net, SIS, and ISIS. The numerical results indicate that BSR can generally outperform the penalized likelihood methods. The models selected by BSR tend to be sparser and, more importantly, of higher prediction ability. In addition, the performance of the penalized likelihood methods tends to deteriorate as the number of predictors increases, while this is not significant for BSR. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Confirming the Lanchestrian linear-logarithmic model of attrition
Energy Technology Data Exchange (ETDEWEB)
Hartley, D.S. III.
1990-12-01
This paper is the fourth in a series of reports on the breakthrough research in historical validation of attrition in conflict. Significant defense policy decisions, including weapons acquisition and arms reduction, are based in part on models of conflict. Most of these models are driven by their attrition algorithms, usually forms of the Lanchester square and linear laws. None of these algorithms have been validated. The results of this paper confirm the results of earlier papers, using a large database of historical results. The homogeneous linear-logarithmic Lanchestrian attrition model is validated to the extent possible with current initial and final force size data and is consistent with the Iwo Jima data. A particular differential linear-logarithmic model is described that fits the data very well. A version of Helmbold's victory predicting parameter is also confirmed, with an associated probability function. The implications of these findings are potentially far-reaching. Two-sided daily attrition data on a large number of battles is needed to absolutely confirm these results. Such a confirmation will require that numerous computer conflict models containing square and linear law based attrition algorithms be reexamined. It is conceivable that complex mixed, heterogeneous, square plus linear law algorithms may produce the same results as a homogeneous mixed linear-logarithmic law algorithm; however, such an occurrence is by no means assured. Even without such absolute confirmation, the results of this research allow the analysis of combat data for the effects of training, weather, leadership, and other human factors, unencumbered by the force size effects.
Linear models in the mathematics of uncertainty
Mordeson, John N; Clark, Terry D; Pham, Alex; Redmond, Michael A
2013-01-01
The purpose of this book is to present new mathematical techniques for modeling global issues. These mathematical techniques are used to determine linear equations between a dependent variable and one or more independent variables in cases where standard techniques such as linear regression are not suitable. In this book, we examine cases where the number of data points is small (effects of nuclear warfare), where the experiment is not repeatable (the breakup of the former Soviet Union), and where the data is derived from expert opinion (how conservative is a political party). In all these cases the data is difficult to measure and an assumption of randomness and/or statistical validity is questionable. We apply our methods to real world issues in international relations such as nuclear deterrence, smart power, and cooperative threat reduction. We next apply our methods to issues in comparative politics such as successful democratization, quality of life, economic freedom, political stability, and fail...
Marginal linearization method in modeling on fuzzy control systems
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Marginal linearization method in modeling on fuzzy control systems is proposed, which is to deal with the nonlinear model with variable coefficients. The method can turn a nonlinear model with variable coefficients into a linear model with variable coefficients in the way that the membership functions of the fuzzy sets in fuzzy partitions of the universes are changed from triangle waves into rectangle waves. However, the linearization models are incomplete in their forms because of their lacking some items. For solving this problem, joint approximation by using linear models is introduced. The simulation results show that marginal linearization models are of higher approximation precision than their original nonlinear models.
Interacting Dark Energy Models -- Scalar Linear Perturbations
Perico, E L D
2016-01-01
We extend the dark sector interacting models assuming the dark energy as the sum of independent contributions $\\rho_{\\Lambda} =\\sum_i\\rho_{\\Lambda i}$, associated with (and interacting with) each of the $i$ material species. We derive the linear scalar perturbations for two interacting dark energy scenarios, modeling its cosmic evolution and identifying their different imprints in the CMB and matter power spectrum. Our treatment was carried out for two phenomenological motivated expressions of the dark energy density, $\\rho_\\Lambda(H^2)$ and $\\rho_\\Lambda(R)$. The $\\rho_\\Lambda(H^2)$ description turned out to be a full interacting model, i.e., the dark energy interacts with everyone material species in the universe, whereas the $\\rho_\\Lambda(R)$ description only leads to interactions between dark energy and the non-relativistic matter components; which produces different imprints of the two models on the matter power spectrum. A comparison with the Planck 2015 data was made in order to constrain the free para...
Linearizing Control of Induction Motor Based on Networked Control Systems
Institute of Scientific and Technical Information of China (English)
Jun Ren; Chun-Wen Li; De-Zong Zhao
2009-01-01
A new approach to speed control of induction motors is developed by introducing networked control systems (NCSs) into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities (LMIs). Simulation results verify the efficiency of the proposed scheme.
Król, Piotr; Lechowicz, Jaromir B; Król, Bożena
2013-04-01
Polyurethane elastomers coating were synthesised by using typical diisocyanates, polyether and polyester polyols and HO-tertiary amines or diols as a chain extenders. Mole fractions of structural fragments (κexp) responsible for the polar interactions within polyurethane chains were calculated by (1)H NMR method. Obtained results were confronted with the analogous parameter values (κtheor) calculated on the basis of process stoichiometry, considering the stage of the production of isocyanate prepolymers and reaction of their extension for polyurethanes. Trials of linear correlation between the κexp parameters and surface free energy (SFE) values of investigated coatings were presented. SFE values were determined by Owens-Wendt method, using contact angles measured with the goniometric method. Based on achieved results, another empirical models, allowing for prediction the influence of the kind of polyurethane raw materials on SFE values of received coatings were determined. It was found that it is possible to regulate the SFE in the range millijoules per cubic metre by the selection of appropriate substrates. It has been found that use of 2,2,3,3-tetrafluoro-1,4-butanediol as a fluorinated extender of prepolymer chains is essential to obtain coatings with increased hydrophobicity, applied among others as biomaterials-next to diphenylmethane diisocyanate and polyoxyethylene glycol.
Iris Segmentation Based upon Linear Basis Function Model%基于线性基函数模型的虹膜定位
Institute of Scientific and Technical Information of China (English)
王凯; 钱沄涛
2011-01-01
To improve accuracy of non-circular iris segmentation, an iris segmentation algorithm based on linear basis function model is proposed. The proposed algorithm regards iris segmentation problem as a machine learning problem which derives iris boundary curve from iris boundary points. First, boundary points are located by coarse segmentation.Second, a linear basis function model is constructed to derive boundary curve from boundary points, thus the segmentation is complete. Compared with traditional iris segmentation algorithms, the proposed method locates non-circular iris with high accuracy, with the expense of a few more time. Experiment results on the dataset of CASIA-3.0 IRIS-Interval reveal that the accuracy of proposed method is 99.92％, and the time cost is lms more, which is capable for state-of-the-art iris recognition systems.%针对非圆虹膜定位困难的问题,本文提出了一种基于线性基函数模型的虹膜定位算法.该算法将虹膜定位问题看作从虹膜边缘点求虹膜边缘曲线的机器学习问题,先粗定位找到边缘点,再建立线性基函数模型从边缘点求得边缘曲线,从而实现了虹膜的精确定位.与传统的虹膜定位算法相比,所提算法能很好的定位非圆虹膜,而且花费时间只有极少的增加.在CASIA 3.0虹膜数据库上的仿真实验表明,所提算法定位准确率达到99.92%,定位时间只增加了1 ms,具有良好的实时性.
Numerical modelling in non linear fracture mechanics
Directory of Open Access Journals (Sweden)
Viggo Tvergaard
2007-07-01
Full Text Available Some numerical studies of crack propagation are based on using constitutive models that accountfor damage evolution in the material. When a critical damage value has been reached in a materialpoint, it is natural to assume that this point has no more carrying capacity, as is done numerically in the elementvanish technique. In the present review this procedure is illustrated for micromechanically based materialmodels, such as a ductile failure model that accounts for the nucleation and growth of voids to coalescence, and a model for intergranular creep failure with diffusive growth of grain boundary cavities leading to micro-crack formation. The procedure is also illustrated for low cycle fatigue, based on continuum damage mechanics. In addition, the possibility of crack growth predictions for elastic-plastic solids using cohesive zone models to represent the fracture process is discussed.
Conditional likelihood inference in generalized linear mixed models.
Sartori, Nicola; Severini , T.A
2002-01-01
Consider a generalized linear model with a canonical link function, containing both fixed and random effects. In this paper, we consider inference about the fixed effects based on a conditional likelihood function. It is shown that this conditional likelihood function is valid for any distribution of the random effects and, hence, the resulting inferences about the fixed effects are insensitive to misspecification of the random effects distribution. Inferences based on the conditional likelih...
Watermark Resistance Analysis Based On Linear Transformation
Directory of Open Access Journals (Sweden)
N.Karthika Devi
2012-06-01
Full Text Available Generally, digital watermark can be embedded in any copyright image whose size is not larger than it. The watermarking schemes can be classified into two categories: spatial domain approach or transform domain approach. Previous works have shown that the transform domain scheme is typically more robust to noise, common image processing, and compression when compared with the spatial transform scheme. Improvements in performance of watermarking schemes can be obtained by exploiting the characteristics of the human visual system (HVS in the watermarking process. We propose a linear transformation based watermarking algorithm. The watermarking bits are embedded into cover image to produce watermarked image. The efficiency of watermark is checked using pre-defined attacks. Attack resistance analysis is done using BER (Bit Error Rate calculation. Finally, the Quality of the watermarked image can be obtained.
F-theory and linear sigma models
Energy Technology Data Exchange (ETDEWEB)
Bershadsky, M.; Johansen, A. [Harvard Univ., Cambridge, MA (United States). Lyman Lab. of Physics; Chiang, T.M. [Newman Laboratory of Nuclear Studies, Cornell University, Ithaca, NY 14850 (United States); Greene, B.R.; Lazaroiu, C.I. [Departments of Physics and Mathematics, Columbia University, New York, NY 10027 (United States)
1998-09-07
We present an explicit method for translating between the linear sigma model and the spectral cover description of SU(r) stable bundles over an elliptically fibered Calabi-Yau manifold. We use this to investigate the four-dimensional duality between (0,2) heterotic and F-theory compactifications. We indirectly find that much interesting heterotic information must be contained in the `spectral bundle` and in its dual description as a gauge theory on multiple F-theory 7-branes. A by-product of these efforts is a method for analyzing semistability and the splitting type of vector bundles over an elliptic curve given as the sheaf cohomology of a monad. (orig.) 24 refs.
Distributed static linear Gaussian models using consensus.
Belanovic, Pavle; Valcarcel Macua, Sergio; Zazo, Santiago
2012-10-01
Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance trade-off.
Testing Linear Models for Ability Parameters in Item Response Models
Glas, Cees A.W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum like
Neural Network Hydrological Modelling: Linear Output Activation Functions?
Abrahart, R. J.; Dawson, C. W.
2005-12-01
obvious advantages related to the use of a linear activation function in the output unit: (i) to restrict potential impacts and distortions associated with upper limit and lower limit saturation effects; and (ii) to address potential deficiencies and ceilings associated with undershoots or requirements to extrapolate beyond the range of the training dataset. The harmful side effects of using linear as opposed to non-linear activation functions in the output unit will be reported in this paper based on an investigation of six-hour timestep operational river level forecasting for the Skelton Gauging Station [Station No: 027009; Grid Ref: SE 568 554] on the River Ouse in England. The power to develop simple near-linear one-step-ahead forecasts remained more or less unchanged; whereas the challenge to develop more demanding non-linear four-step-ahead forecasts revealed major shortcomings related to the implementation of a linear activation function in the output unit of a parsimonious neural network model.
Support vector classification algorithm based on variable parameter linear programming
Institute of Scientific and Technical Information of China (English)
Xiao Jianhua; Lin Jian
2007-01-01
To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed.In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model.The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given.An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.
Chabanas, M; Marecaux, C; Swider, P; Boutault, F; Chabanas, Matthieu; Payan, Yohan; Marecaux, Christophe; Swider, Pascal; Boutault, Franck
2004-01-01
A Finite Element model of the face soft tissue is proposed to simulate the morphological outcomes of maxillofacial surgery. Three modelling options are implemented: a linear elastic model with small and large deformation hypothesis, and an hyperelastic Mooney-Rivlin model. An evaluation procedure based on a qualitative and quantitative comparison of the simulations with a post-operative CT scan is detailed. It is then applied to one clinical case to evaluate the differences between the three models, and with the actual patient morphology. First results shows in particular that for a "simple" clinical procedure where stress is less than 20%, a linear model seams sufficient for a correct modelling.
Nakano, M.; Kumagai, H.; Kumazawa, M.; Yamaoka, K.; Chouet, B.A.
1998-01-01
We present a method to quantify the source excitation function and characteristic frequencies of long-period volcanic events. The method is based on an inhomogeneous autoregressive (AR) model of a linear dynamic system, in which the excitation is assumed to be a time-localized function applied at the beginning of the event. The tail of an exponentially decaying harmonic waveform is used to determine the characteristic complex frequencies of the event by the Sompi method. The excitation function is then derived by operating an AR filter constructed from the characteristic frequencies to the entire seismogram of the event, including the inhomogeneous part of the signal. We apply this method to three long-period events at Kusatsu-Shirane Volcano, central Japan, whose waveforms display simple decaying monochromatic oscillations except for the beginning of the events. We recover time-localized excitation functions lasting roughly 1 s at the start of each event and find that the estimated functions are very similar to each other at all the stations of the seismic network for each event. The phases of the characteristic oscillations referred to the estimated excitation function fall within a narrow range for almost all the stations. These results strongly suggest that the excitation and mode of oscillation are both dominated by volumetric change components. Each excitation function starts with a pronounced dilatation consistent with a sudden deflation of the volumetric source which may be interpreted in terms of a choked-flow transport mechanism. The frequency and Q of the characteristic oscillation both display a temporal evolution from event to event. Assuming a crack filled with bubbly water as seismic source for these events, we apply the Van Wijngaarden-Papanicolaou model to estimate the acoustic properties of the bubbly liquid and find that the observed changes in the frequencies and Q are consistently explained by a temporal change in the radii of the bubbles
Boolean Variables in Economic Models Solved by Linear Programming
Directory of Open Access Journals (Sweden)
Lixandroiu D.
2014-12-01
Full Text Available The article analyses the use of logical variables in economic models solved by linear programming. Focus is given to the presentation of the way logical constraints are obtained and of the definition rules based on predicate logic. Emphasis is also put on the possibility to use logical variables in constructing a linear objective function on intervals. Such functions are encountered when costs or unitary receipts are different on disjunct intervals of production volumes achieved or sold. Other uses of Boolean variables are connected to constraint systems with conditions and the case of a variable which takes values from a finite set of integers.
Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots
Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.
2013-01-01
Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…
Thurstonian models for sensory discrimination tests as generalized linear models
DEFF Research Database (Denmark)
Brockhoff, Per B.; Christensen, Rune Haubo Bojesen
2010-01-01
Sensory discrimination tests such as the triangle, duo-trio, 2-AFC and 3-AFC tests produce binary data and the Thurstonian decision rule links the underlying sensory difference 6 to the observed number of correct responses. In this paper it is shown how each of these four situations can be viewed...... as a so-called generalized linear model. The underlying sensory difference 6 becomes directly a parameter of the statistical model and the estimate d' and it's standard error becomes the "usual" output of the statistical analysis. The d' for the monadic A-NOT A method is shown to appear as a standard...... linear contrast in a generalized linear model using the probit link function. All methods developed in the paper are implemented in our free R-package sensR (http://www.cran.r-project.org/package=sensR/). This includes the basic power and sample size calculations for these four discrimination tests...
Institute of Scientific and Technical Information of China (English)
Pascale KULISA; Cédric DANO
2006-01-01
Three linear two-equation turbulence models k- ε, k- ω and k- 1 and a non-linear k- l model are used for aerodynamic and thermal turbine flow prediction. The pressure profile in the wake and the heat transfer coefficient on the blade are compared with experimental data. Good agreement is obtained with the linear k- l model. No significant modifications are observed with the non-linear model. The balance of transport equation terms in the blade wake is also presented. Linear and non-linear k- l models are evaluated to predict the threedimensional vortices characterising the turbine flows. The simulations show that the passage vortex is the main origin of the losses.
Zangiabadi, M.; H. R. MALEKI
2007-01-01
In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters based on those for multiobjective linear programming problems. Then by using the concept of comparison of fuzzy numbers, we transform a linear programming problem with fuzzy parameters to a multiobjective linear programming problem. To this end, w...
Solving linear integer programming problems by a novel neural model.
Cavalieri, S
1999-02-01
The paper deals with integer linear programming problems. As is well known, these are extremely complex problems, even when the number of integer variables is quite low. Literature provides examples of various methods to solve such problems, some of which are of a heuristic nature. This paper proposes an alternative strategy based on the Hopfield neural network. The advantage of the strategy essentially lies in the fact that hardware implementation of the neural model allows for the time required to obtain a solution so as not depend on the size of the problem to be solved. The paper presents a particular class of integer linear programming problems, including well-known problems such as the Travelling Salesman Problem and the Set Covering Problem. After a brief description of this class of problems, it is demonstrated that the original Hopfield model is incapable of supplying valid solutions. This is attributed to the presence of constant bias currents in the dynamic of the neural model. A demonstration of this is given and then a novel neural model is presented which continues to be based on the same architecture as the Hopfield model, but introduces modifications thanks to which the integer linear programming problems presented can be solved. Some numerical examples and concluding remarks highlight the solving capacity of the novel neural model.
Non linear behaviour of cell tensegrity models
Alippi, A.; Bettucci, A.; Biagioni, A.; Conclusio, D.; D'Orazio, A.; Germano, M.; Passeri, D.
2012-05-01
Tensegrity models for the cytoskeleton structure of living cells is largely used nowadays for interpreting the biochemical response of living tissues to mechanical stresses. Microtubules, microfilaments and filaments are the microscopic cell counterparts of struts (microtubules) and cables (microfilaments and filaments) in the macroscopic world: the formers oppose to compression, the latters to tension, thus yielding an overall structure, light and highly deformable. Specific cell surface receptors, such as integrins, act as the coupling elements that transmit the outside mechanical stress state into the cell body. Reversible finite deformations of tensegrity structures have been widely demonstrated experimentally and in a number of living cell simulations. In the present paper, the bistability behaviour of two general models, the linear bar oscillator and the icosahedron, is studied, as they are both obtained from mathematical simulation, the former, and from larger scale experiments, the latter. The discontinuity in the frequency response of the oscillation amplitude and the lateral bending of the resonance curves are put in evidence, as it grows larger as the driving amplitude increases, respectively.
DEFF Research Database (Denmark)
Holst, René; Jørgensen, Bent
2015-01-01
The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains...... a marginal as well as a conditional interpretation. The estimation procedure is based on a computationally efficient quasi-score method for the regression parameters combined with a REML-like bias-corrected Pearson estimating function for the dispersion and correlation parameters. This avoids...... the multidimensional integral of the conventional GLMM likelihood and allows an extension of the robust empirical sandwich estimator for use with both association and regression parameters. The method is applied to a set of otholit data, used for age determination of fish....
Linear Strategy for Boolean Ring Based Theorem Proving
Institute of Scientific and Technical Information of China (English)
WU Jinzhao; LIU Zhuojun
2000-01-01
Two inference rules are discussed in boolean ring based theorem proving, and linear strategy is developed. It is shown that both of them are complete for linear strategy. Moreover, by introducing a partial ordering on atoms, pseudo O-linear and O-linear strategies are presented. The former is complete, the latter, however, is complete for clausal theorem proving.
Design of model-based disturbance attenuation for linear motor%直线电机的基于模型扰动抑制设计
Institute of Scientific and Technical Information of China (English)
张凤; 郭庆鼎; 勾丽丽; 王飞
2001-01-01
This paper adopts Model- Based Disturbance Attenuation (MBDA) method to attenuates external disturbances such as frictional forces, cutting forces, load variations for a permanent magnet linear AC synchronous motor (PMLSM) with high precision and micro -feed. The MBDA uses the nominal plant which is in parallel form with the plant and the output feedback. The error signal, which is the difference between the plant output and the nominal plant output, is fed back through a compensator. (IP) controller is designed in velocity loop so as to meet the requirement of fast tracking and resist disturbance greatly. Simulation results verify that this control method has fast response speed and strong disturbance attenuation, it also can effectively increase processing precision.%针对高精度、微进给永磁直线交流同步电机驱动系统，采用基于模型的扰动抑制方法，对诸如摩擦力、切削力、负载变动之类的扰动进行抑制.同时针对速度环设计了积分-比例(IP)控制器，以满足系统快速跟踪指令的要求，并具有较强的抗扰动能力.仿真结果表明，该控制方法响应速度快，抗干扰能力强，且能有效地提高加工精度.
Contact Analysis and Modeling of Standing Wave Linear Ultrasonic Motor
Institute of Scientific and Technical Information of China (English)
SHI Yunlai; ZHAO Chunsheng; ZHANG Jianhui
2011-01-01
A contact model for describing the contact mechanics between the stator and slider of the standing wave linear ultrasonic motor was presented.The proposed model starts from the assumption that the vibration characteristics of the stator is not affected by the contact process.A modified friction models was used to analyze the contact problems.Firstly,the dynamic normal contact force,interface friction force,and steady-state characteristics were analyzed.Secondly,the influences of the contact layer material,the dynamic characteristics of the stator,and the pre-load on motor performance were simulated.Finally,to validate the contact model,a linear ultrasonic motor based on in-plane modes was used as an example.The corresponding results show that a set of simulation of motor performances based on the proposed contact mechanism is in good agreement with experimental results.This model is helpful to understanding the operation principle of the standing wave linear motor and thus contributes to the design of these tvpes of motor.
Robust linear parameter varying induction motor control with polytopic models
Directory of Open Access Journals (Sweden)
Dalila Khamari
2013-01-01
Full Text Available This paper deals with a robust controller for an induction motor which is represented as a linear parameter varying systems. To do so linear matrix inequality (LMI based approach and robust Lyapunov feedback controller are associated. This new approach is related to the fact that the synthesis of a linear parameter varying (LPV feedback controller for the inner loop take into account rotor resistance and mechanical speed as varying parameter. An LPV flux observer is also synthesized to estimate rotor flux providing reference to cited above regulator. The induction motor is described as a polytopic model because of speed and rotor resistance affine dependence their values can be estimated on line during systems operations. The simulation results are presented to confirm the effectiveness of the proposed approach where robustness stability and high performances have been achieved over the entire operating range of the induction motor.
Modeling and analysis of linear hyperbolic systems of balance laws
Bartecki, Krzysztof
2016-01-01
This monograph focuses on the mathematical modeling of distributed parameter systems in which mass/energy transport or wave propagation phenomena occur and which are described by partial differential equations of hyperbolic type. The case of linear (or linearized) 2 x 2 hyperbolic systems of balance laws is considered, i.e., systems described by two coupled linear partial differential equations with two variables representing physical quantities, depending on both time and one-dimensional spatial variable. Based on practical examples of a double-pipe heat exchanger and a transportation pipeline, two typical configurations of boundary input signals are analyzed: collocated, wherein both signals affect the system at the same spatial point, and anti-collocated, in which the input signals are applied to the two different end points of the system. The results of this book emerge from the practical experience of the author gained during his studies conducted in the experimental installation of a heat exchange cente...
Axioms and Models of Linear Logic
Hesselink, Wim H.
1990-01-01
Girard's recent system of linear logic is presented in a way that avoids the two-level structure of formulae and sequents, and that minimises the number of primitive function symbols. A deduction theorem is proved concerning the classical implication as embedded in linear logic. The Hilbert-style ax
Linearized Functional Minimization for Inverse Modeling
Energy Technology Data Exchange (ETDEWEB)
Wohlberg, Brendt [Los Alamos National Laboratory; Tartakovsky, Daniel M. [University of California, San Diego; Dentz, Marco [Institute of Environmental Assessment and Water Research, Barcelona, Spain
2012-06-21
Heterogeneous aquifers typically consist of multiple lithofacies, whose spatial arrangement significantly affects flow and transport. The estimation of these lithofacies is complicated by the scarcity of data and by the lack of a clear correlation between identifiable geologic indicators and attributes. We introduce a new inverse-modeling approach to estimate both the spatial extent of hydrofacies and their properties from sparse measurements of hydraulic conductivity and hydraulic head. Our approach is to minimize a functional defined on the vectors of values of hydraulic conductivity and hydraulic head fields defined on regular grids at a user-determined resolution. This functional is constructed to (i) enforce the relationship between conductivity and heads provided by the groundwater flow equation, (ii) penalize deviations of the reconstructed fields from measurements where they are available, and (iii) penalize reconstructed fields that are not piece-wise smooth. We develop an iterative solver for this functional that exploits a local linearization of the mapping from conductivity to head. This approach provides a computationally efficient algorithm that rapidly converges to a solution. A series of numerical experiments demonstrates the robustness of our approach.
Comparison of Linear Prediction Models for Audio Signals
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.
Comparison of Linear Prediction Models for Audio Signals
Directory of Open Access Journals (Sweden)
van Waterschoot Toon
2008-01-01
Full Text Available While linear prediction (LP has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.
Modeling a Linear Generator for Energy Harvesting Applications
2014-12-01
their potential uses. A flexible model of a linear generator created in MATLAB Simulink is presented. The model is a three-phase, 12-pole, non-salient...attention to linear generators and their potential uses. A flexible model of a linear generator created in MATLAB Simulink is presented. The model...Chapter III and the full MATLAB and Simulink code can be found in the Appendix. Several adaptations of the model have been created, with the results
Category-theoretic models of linear Abadi & Plotkin Logic
DEFF Research Database (Denmark)
Birkedal, Lars; Møgelberg, Rasmus Ejlers; Lerchedahl Petersen, Rasmus
2008-01-01
This paper presents a sound and complete category-theoretic notion of models for Linear Abadi & Plotkin Logic [Birkedal et al., 2006], a logic suitable for reasoning about parametricity in combination with recursion. A subclass of these called parametric LAPL structures can be seen as an axiomati......This paper presents a sound and complete category-theoretic notion of models for Linear Abadi & Plotkin Logic [Birkedal et al., 2006], a logic suitable for reasoning about parametricity in combination with recursion. A subclass of these called parametric LAPL structures can be seen...... as an axiomatization of domain theoretic models of parametric polymorphism, and we show how to solve general (nested) recursive domain equations in these. parametric LAPL structures constitute a general notion of model of parametricity in a setting with recursion. In future papers we will demonstrate this by showing...... how many different models of parametricity and recursion give rise to parametric LAPL structures, including Simpson and Rosolini’s set theoretic models [Rosolini and Simpson, 2004], a syntactic model based on Lily [Pitts, 2000, Bierman et al., 2000] and a model based on admissible pers over...
Modeling and optimization of ultrasonic linear motors
Fernandez Lopez, José; Perriard, Yves
2007-01-01
Ultrasonic motors have received much attention these last years, in particular with regard to their modeling and their design principle. Their operating principle is based on piezoelectric ceramics that convert electrical energy into mechanical energy in the form of vibrations of an elastic body whose surface points perform an elliptic motion with a frequency in the ultrasonic range (≥ 20 kHz). The moving part, which is pressed against the vibrating body by a prestressing force, can move than...
Modeling hierarchical structures - Hierarchical Linear Modeling using MPlus
Jelonek, Magdalena
2006-01-01
The aim of this paper is to present the technique (and its linkage with physics) of overcoming problems connected to modeling social structures, which are typically hierarchical. Hierarchical Linear Models provide a conceptual and statistical mechanism for drawing conclusions regarding the influence of phenomena at different levels of analysis. In the social sciences it is used to analyze many problems such as educational, organizational or market dilemma. This paper introduces the logic of m...
Vavilin, Vasily A; Rytov, Sergey V; Shim, Natalia; Vogt, Carsten
2016-06-01
The non-linear dynamics of stable carbon and hydrogen isotope signatures during methane oxidation by the methanotrophic bacteria Methylosinus sporium strain 5 (NCIMB 11126) and Methylocaldum gracile strain 14 L (NCIMB 11912) under copper-rich (8.9 µM Cu(2+)), copper-limited (0.3 µM Cu(2+)) or copper-regular (1.1 µM Cu(2+)) conditions has been described mathematically. The model was calibrated by experimental data of methane quantities and carbon and hydrogen isotope signatures of methane measured previously in laboratory microcosms reported by Feisthauer et al. [ 1 ] M. gracile initially oxidizes methane by a particulate methane monooxygenase and assimilates formaldehyde via the ribulose monophosphate pathway, whereas M. sporium expresses a soluble methane monooxygenase under copper-limited conditions and uses the serine pathway for carbon assimilation. The model shows that during methane solubilization dominant carbon and hydrogen isotope fractionation occurs. An increase of biomass due to growth of methanotrophs causes an increase of particulate or soluble monooxygenase that, in turn, decreases soluble methane concentration intensifying methane solubilization. The specific maximum rate of methane oxidation υm was proved to be equal to 4.0 and 1.3 mM mM(-1) h(-1) for M. sporium under copper-rich and copper-limited conditions, respectively, and 0.5 mM mM(-1) h(-1) for M. gracile. The model shows that methane oxidation cannot be described by traditional first-order kinetics. The kinetic isotope fractionation ceases when methane concentrations decrease close to the threshold value. Applicability of the non-linear model was confirmed by dynamics of carbon isotope signature for carbon dioxide that was depleted and later enriched in (13)C. Contrasting to the common Rayleigh linear graph, the dynamic curves allow identifying inappropriate isotope data due to inaccurate substrate concentration analyses. The non-linear model pretty adequately described experimental
DEFF Research Database (Denmark)
Dashab, Golam Reza; Kadri, Naveen Kumar; Mahdi Shariati, Mohammad;
2012-01-01
) Mixed model analysis (MMA), 2) Random haplotype model (RHM), 3) Genealogy-based mixed model (GENMIX), and 4) Bayesian variable selection (BVS). The data consisted of phenotypes of 2000 animals from 20 sire families and were genotyped with 9990 SNPs on five chromosomes. Results: Out of the eight...
Nicholas A. Povak; Paul F. Hessburg; Todd C. McDonnell; Keith M. Reynolds; Timothy J. Sullivan; R. Brion Salter; Bernard J. Crosby
2014-01-01
Accurate estimates of soil mineral weathering are required for regional critical load (CL) modeling to identify ecosystems at risk of the deleterious effects from acidification. Within a correlative modeling framework, we used modeled catchment-level base cation weathering (BCw) as the response variable to identify key environmental correlates and predict a continuous...
Nonrigid Registration of Monomodal MRI Using Linear Viscoelastic Model
Directory of Open Access Journals (Sweden)
Jian Yang
2014-01-01
Full Text Available This paper describes a method for nonrigid registration of monomodal MRI based on physical laws. The proposed method assumes that the properties of image deformations are like those of viscoelastic matter, which exhibits the properties of both an elastic solid and a viscous fluid. Therefore, the deformation fields of the deformed image are constrained by both sets of properties. After global registration, the local shape variations are assumed to have the properties of the Maxwell model of linear viscoelasticity, and the deformation fields are constrained by the corresponding partial differential equations. To speed up the registration, an adaptive force is introduced according to the maximum displacement of each iteration. Both synthetic datasets and real datasets are used to evaluate the proposed method. We compare the results of the linear viscoelastic model with those of the fluid model on the basis of both the standard and adaptive forces. The results demonstrate that the adaptive force increases in both models and that the linear viscoelastic model improves the registration accuracy.
Maleki, Afshin; Daraei, Hiua; Alaei, Loghman; Faraji, Aram
2014-01-01
Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.
Analysis of linear trade models and relation to scale economies.
Gomory, R E; Baumol, W J
1997-09-01
We discuss linear Ricardo models with a range of parameters. We show that the exact boundary of the region of equilibria of these models is obtained by solving a simple integer programming problem. We show that there is also an exact correspondence between many of the equilibria resulting from families of linear models and the multiple equilibria of economies of scale models.
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.
On the relation between the linear factor model and the latent profile model
Halpin, P.F.; Dolan, C.V.; Grasman, R.P.P.P.; de Boeck, P.
2011-01-01
The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do
On the Relation between the Linear Factor Model and the Latent Profile Model
Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul
2011-01-01
The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…
Validating a quasi-linear transport model versus nonlinear simulations
Casati, A.; Bourdelle, C.; Garbet, X.; Imbeaux, F.; Candy, J.; Clairet, F.; Dif-Pradalier, G.; Falchetto, G.; Gerbaud, T.; Grandgirard, V.; Gürcan, Ö. D.; Hennequin, P.; Kinsey, J.; Ottaviani, M.; Sabot, R.; Sarazin, Y.; Vermare, L.; Waltz, R. E.
2009-08-01
In order to gain reliable predictions on turbulent fluxes in tokamak plasmas, physics based transport models are required. Nonlinear gyrokinetic electromagnetic simulations for all species are still too costly in terms of computing time. On the other hand, interestingly, the quasi-linear approximation seems to retain the relevant physics for fairly reproducing both experimental results and nonlinear gyrokinetic simulations. Quasi-linear fluxes are made of two parts: (1) the quasi-linear response of the transported quantities and (2) the saturated fluctuating electrostatic potential. The first one is shown to follow well nonlinear numerical predictions; the second one is based on both nonlinear simulations and turbulence measurements. The resulting quasi-linear fluxes computed by QuaLiKiz (Bourdelle et al 2007 Phys. Plasmas 14 112501) are shown to agree with the nonlinear predictions when varying various dimensionless parameters, such as the temperature gradients, the ion to electron temperature ratio, the dimensionless collisionality, the effective charge and ranging from ion temperature gradient to trapped electron modes turbulence.
Model averaging for semiparametric additive partial linear models
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
To improve the prediction accuracy of semiparametric additive partial linear models(APLM) and the coverage probability of confidence intervals of the parameters of interest,we explore a focused information criterion for model selection among ALPM after we estimate the nonparametric functions by the polynomial spline smoothing,and introduce a general model average estimator.The major advantage of the proposed procedures is that iterative backfitting implementation is avoided,which thus results in gains in computational simplicity.The resulting estimators are shown to be asymptotically normal.A simulation study and a real data analysis are presented for illustrations.
On linear models and parameter identifiability in experimental biological systems.
Lamberton, Timothy O; Condon, Nicholas D; Stow, Jennifer L; Hamilton, Nicholas A
2014-10-07
A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.
A Parallel Encryption Algorithm Based on Piecewise Linear Chaotic Map
Directory of Open Access Journals (Sweden)
Xizhong Wang
2013-01-01
Full Text Available We introduce a parallel chaos-based encryption algorithm for taking advantage of multicore processors. The chaotic cryptosystem is generated by the piecewise linear chaotic map (PWLCM. The parallel algorithm is designed with a master/slave communication model with the Message Passing Interface (MPI. The algorithm is suitable not only for multicore processors but also for the single-processor architecture. The experimental results show that the chaos-based cryptosystem possesses good statistical properties. The parallel algorithm provides much better performance than the serial ones and would be useful to apply in encryption/decryption file with large size or multimedia.
NEW VERSATILE CAMERA CALIBRATION TECHNIQUE BASED ON LINEAR RECTIFICATION
Institute of Scientific and Technical Information of China (English)
Pan Feng; Wang Xuanyin
2004-01-01
A new versatile camera calibration technique for machine vision using off-the-shelf cameras is described. Aimed at the large distortion of the off-the-shelf cameras, a new camera distortion rectification technology based on line-rectification is proposed. A full-camera-distortion model is introduced and a linear algorithm is provided to obtain the solution. After the camera rectification intrinsic and extrinsic parameters are obtained based on the relationship between the homograph and absolute conic. This technology needs neither a high-accuracy three-dimensional calibration block, nor a complicated translation or rotation platform. Both simulations and experiments show that this method is effective and robust.
Linear regression model selection using p-values when the model dimension grows
Pokarowski, Piotr; Teisseyre, Paweł
2012-01-01
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied for families of linear regression models. We prove that such procedures are consistent i.e. the minimal true model is chosen with probability tending to 1 even when the number of models under consideration slowly increases with a sample size. The simulation study indicates that introduced methods perform promisingly when compared with Akaike and Bayesian Information Criteria.
Model-Checking Linear-Time Properties of Quantum Systems
Ying, Mingsheng; Yu, Nengkun; Feng, Yuan
2011-01-01
We define a formal framework for reasoning about linear-time properties of quantum systems in which quantum automata are employed in the modeling of systems and certain closed subspaces of state (Hilbert) spaces are used as the atomic propositions about the behavior of systems. We provide an algorithm for verifying invariants of quantum automata. Then automata-based model-checking technique is generalized for the verification of safety properties recognizable by reversible automata and omega-properties recognizable by reversible Buechi automata.
H∞ /H2 model reduction through dilated linear matrix inequalities
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Stoustrup, Jakob
2012-01-01
This paper presents sufficient dilated linear matrix inequalities (LMI) conditions to the $H_{infty}$ and $H_{2}$ model reduction problem. A special structure of the auxiliary (slack) variables allows the original model of order $n$ to be reduced to an order $r=n/s$ where $n,r,s in field...... not satisfactorily approximates the original system, an iterative algorithm based on dilated LMIs is proposed to significantly improve the approximation bound. The effectiveness of the method is accessed by numerical experiments. The method is also applied to the $H_2$ order reduction of a flexible wind turbine...
Admissibilities of linear estimator in a class of linear models with a multivariate t error variable
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This paper discusses admissibilities of estimators in a class of linear models,which include the following common models:the univariate and multivariate linear models,the growth curve model,the extended growth curve model,the seemingly unrelated regression equations,the variance components model,and so on.It is proved that admissible estimators of functions of the regression coefficient β in the class of linear models with multivariate t error terms,called as Model II,are also ones in the case that error terms have multivariate normal distribution under a strictly convex loss function or a matrix loss function.It is also proved under Model II that the usual estimators of β are admissible for p 2 with a quadratic loss function,and are admissible for any p with a matrix loss function,where p is the dimension of β.
Linear models for joint association and linkage QTL mapping
Directory of Open Access Journals (Sweden)
Fernando Rohan L
2009-09-01
Full Text Available Abstract Background Populational linkage disequilibrium and within-family linkage are commonly used for QTL mapping and marker assisted selection. The combination of both results in more robust and accurate locations of the QTL, but models proposed so far have been either single marker, complex in practice or well fit to a particular family structure. Results We herein present linear model theory to come up with additive effects of the QTL alleles in any member of a general pedigree, conditional to observed markers and pedigree, accounting for possible linkage disequilibrium among QTLs and markers. The model is based on association analysis in the founders; further, the additive effect of the QTLs transmitted to the descendants is a weighted (by the probabilities of transmission average of the substitution effects of founders' haplotypes. The model allows for non-complete linkage disequilibrium QTL-markers in the founders. Two submodels are presented: a simple and easy to implement Haley-Knott type regression for half-sib families, and a general mixed (variance component model for general pedigrees. The model can use information from all markers. The performance of the regression method is compared by simulation with a more complex IBD method by Meuwissen and Goddard. Numerical examples are provided. Conclusion The linear model theory provides a useful framework for QTL mapping with dense marker maps. Results show similar accuracies but a bias of the IBD method towards the center of the region. Computations for the linear regression model are extremely simple, in contrast with IBD methods. Extensions of the model to genomic selection and multi-QTL mapping are straightforward.
Linear control theory for gene network modeling.
Shin, Yong-Jun; Bleris, Leonidas
2010-09-16
Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.
Wavefront Sensing for WFIRST with a Linear Optical Model
Jurling, Alden S.; Content, David A.
2012-01-01
In this paper we develop methods to use a linear optical model to capture the field dependence of wavefront aberrations in a nonlinear optimization-based phase retrieval algorithm for image-based wavefront sensing. The linear optical model is generated from a ray trace model of the system and allows the system state to be described in terms of mechanical alignment parameters rather than wavefront coefficients. This approach allows joint optimization over images taken at different field points and does not require separate convergence of phase retrieval at individual field points. Because the algorithm exploits field diversity, multiple defocused images per field point are not required for robustness. Furthermore, because it is possible to simultaneously fit images of many stars over the field, it is not necessary to use a fixed defocus to achieve adequate signal-to-noise ratio despite having images with high dynamic range. This allows high performance wavefront sensing using in-focus science data. We applied this technique in a simulation model based on the Wide Field Infrared Survey Telescope (WFIRST) Intermediate Design Reference Mission (IDRM) imager using a linear optical model with 25 field points. We demonstrate sub-thousandth-wave wavefront sensing accuracy in the presence of noise and moderate undersampling for both monochromatic and polychromatic images using 25 high-SNR target stars. Using these high-quality wavefront sensing results, we are able to generate upsampled point-spread functions (PSFs) and use them to determine PSF ellipticity to high accuracy in order to reduce the systematic impact of aberrations on the accuracy of galactic ellipticity determination for weak-lensing science.
Wavefront sensing for WFIRST with a linear optical model
Jurling, Alden S.; Content, David A.
2012-09-01
In this paper we develop methods to use a linear optical model to capture the field dependence of wavefront aberrations in a nonlinear optimization-based phase retrieval algorithm for image-based wavefront sensing. The linear optical model is generated from a ray trace model of the system and allows the system state to be described in terms of mechanical alignment parameters rather than wavefront coefficients. This approach allows joint optimization over images taken at different field points and does not require separate convergence of phase retrieval at individual field points. Because the algorithm exploits field diversity, multiple defocused images per field point are not required for robustness. Furthermore, because it is possible to simultaneously fit images of many stars over the field, it is not necessary to use a fixed defocus to achieve adequate signal-to-noise ratio despite having images with high dynamic range. This allows high performance wavefront sensing using in-focus science data. We applied this technique in a simulation model based on the Wide Field Infrared Survey Telescope (WFIRST) Intermediate Design Reference Mission (IDRM) imager using a linear optical model with 25 field points. We demonstrate sub-thousandth-wave wavefront sensing accuracy in the presence of noise and moderate undersampling for both monochromatic and polychromatic images using 25 high-SNR target stars. Using these high-quality wavefront sensing results, we are able to generate upsampled point-spread functions (PSFs) and use them to determine PSF ellipticity to high accuracy in order to reduce the systematic impact of aberrations on the accuracy of galactic ellipticity determination for weak-lensing science.
Linear Factor Models and the Estimation of Expected Returns
Sarisoy, Cisil; de Goeij, Peter; Werker, Bas
2016-01-01
Linear factor models of asset pricing imply a linear relationship between expected returns of assets and exposures to one or more sources of risk. We show that exploiting this linear relationship leads to statistical gains of up to 31% in variances when estimating expected returns on individual asse
Finite element modeling of nanotube structures linear and non-linear models
Awang, Mokhtar; Muhammad, Ibrahim Dauda
2016-01-01
This book presents a new approach to modeling carbon structures such as graphene and carbon nanotubes using finite element methods, and addresses the latest advances in numerical studies for these materials. Based on the available findings, the book develops an effective finite element approach for modeling the structure and the deformation of grapheme-based materials. Further, modeling processing for single-walled and multi-walled carbon nanotubes is demonstrated in detail.
DEFF Research Database (Denmark)
Andersen, Per Kragh; Klein, John P.; Rosthøj, Susanne
2003-01-01
Generalised estimating equation; Generalised linear model; Jackknife pseudo-value; Logistic regression; Markov Model; Multi-state model......Generalised estimating equation; Generalised linear model; Jackknife pseudo-value; Logistic regression; Markov Model; Multi-state model...
Error Control of Iterative Linear Solvers for Integrated Groundwater Models
Dixon, Matthew; Brush, Charles; Chung, Francis; Dogrul, Emin; Kadir, Tariq
2010-01-01
An open problem that arises when using modern iterative linear solvers, such as the preconditioned conjugate gradient (PCG) method or Generalized Minimum RESidual method (GMRES) is how to choose the residual tolerance in the linear solver to be consistent with the tolerance on the solution error. This problem is especially acute for integrated groundwater models which are implicitly coupled to another model, such as surface water models, and resolve both multiple scales of flow and temporal interaction terms, giving rise to linear systems with variable scaling. This article uses the theory of 'forward error bound estimation' to show how rescaling the linear system affects the correspondence between the residual error in the preconditioned linear system and the solution error. Using examples of linear systems from models developed using the USGS GSFLOW package and the California State Department of Water Resources' Integrated Water Flow Model (IWFM), we observe that this error bound guides the choice of a prac...
Linear peristaltic pump based on electromagnetic actuators
Directory of Open Access Journals (Sweden)
Maddoui Lotfi
2014-01-01
Full Text Available In this paper a study and design of a linear peristaltic pump are presented. A set of electromagnetic (solenoid actuators is used as the active tools to drag the liquid by crushing an elastic tube. The pump consists of six serially-connected electromagnetic actuators controlled via an electronic board. This may be considered as a simulated peristalsis action of intestines. The dynamic performances of the pump are investigated analytically and experimentally.
An analytically linearized helicopter model with improved modeling accuracy
Jensen, Patrick T.; Curtiss, H. C., Jr.; Mckillip, Robert M., Jr.
1991-01-01
An analytically linearized model for helicopter flight response including rotor blade dynamics and dynamic inflow, that was recently developed, was studied with the objective of increasing the understanding, the ease of use, and the accuracy of the model. The mathematical model is described along with a description of the UH-60A Black Hawk helicopter and flight test used to validate the model. To aid in utilization of the model for sensitivity analysis, a new, faster, and more efficient implementation of the model was developed. It is shown that several errors in the mathematical modeling of the system caused a reduction in accuracy. These errors in rotor force resolution, trim force and moment calculation, and rotor inertia terms were corrected along with improvements to the programming style and documentation. Use of a trim input file to drive the model is examined. Trim file errors in blade twist, control input phase angle, coning and lag angles, main and tail rotor pitch, and uniform induced velocity, were corrected. Finally, through direct comparison of the original and corrected model responses to flight test data, the effect of the corrections on overall model output is shown.
Non-linear rheology in a model biological tissue
Matoz-Fernandez, D A; Barrat, Jean-Louis; Bertin, Eric; Martens, Kirsten
2016-01-01
Mechanical signaling plays a key role in biological processes like embryo development and cancer growth. One prominent way to probe mechanical properties of tissues is to study their response to externally applied forces. Using a particle-based model featuring random apoptosis and environment-dependent division rates, we evidence a crossover from linear flow to a shear-thinning regime with increasing shear rate. To rationalize this non-linear flow we derive a theoretical mean-field scenario that accounts for the interplay of mechanical and active noise in local stresses. These noises are respectively generated by the elastic response of the cell matrix to cell rearrangements and by the internal activity.
General linear matrix model, Minkowski spacetime and the Standard Model
Belyea, Chris
2010-01-01
The Hermitian matrix model with general linear symmetry is argued to decouple into a finite unitary matrix model that contains metastable multidimensional lattice configurations and a fermion determinant. The simplest metastable state is a Hermitian Weyl kinetic operator of either handedness on a 3+1 D lattice with general nonlocal interactions. The Hermiticity produces 16 effective Weyl fermions by species doubling, 8 left- and 8 right-handed. These are identified with a Standard Model generation. Only local non-anomalous gauge fields within the soup of general fluctuations can survive at long distances, and the degrees of freedom for gauge fields of an $SU(8)_L X SU(8)_R$ GUT are present. Standard Model gauge symmetries associate with particular species symmetries, for example change of QCD color associates with permutation of doubling status amongst space directions. Vierbein gravity is probably also generated. While fundamental Higgs fields are not possible, low fermion current masses can arise from chira...
Repopulation Kinetics and the Linear-Quadratic Model
O'Rourke, S. F. C.; McAneney, H.; Starrett, C.; O'Sullivan, J. M.
2009-08-01
The standard Linear-Quadratic (LQ) survival model for radiotherapy is used to investigate different schedules of radiation treatment planning for advanced head and neck cancer. We explore how these treament protocols may be affected by different tumour repopulation kinetics between treatments. The laws for tumour cell repopulation include the logistic and Gompertz models and this extends the work of Wheldon et al. [1], which was concerned with the case of exponential repopulation between treatments. Treatment schedules investigated include standarized and accelerated fractionation. Calculations based on the present work show, that even with growth laws scaled to ensure that the repopulation kinetics for advanced head and neck cancer are comparable, considerable variation in the survival fraction to orders of magnitude emerged. Calculations show that application of the Gompertz model results in a significantly poorer prognosis for tumour eradication. Gaps in treatment also highlight the differences in the LQ model with the effect of repopulation kinetics included.
Linear latent variable models: the lava-package
DEFF Research Database (Denmark)
Holst, Klaus Kähler; Budtz-Jørgensen, Esben
2013-01-01
An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are...... interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain....
Model Identification of Linear Parameter Varying Aircraft Systems
Fujimore, Atsushi; Ljung, Lennart
2007-01-01
This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...
Remote sensing image fusion based on Bayesian linear estimation
Institute of Scientific and Technical Information of China (English)
GE ZhiRong; WANG Bin; ZHANG LiMing
2007-01-01
A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spatial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM+ demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation,PCA-based method and wavelet-based method.
A linear model of population dynamics
Lushnikov, A. A.; Kagan, A. I.
2016-08-01
The Malthus process of population growth is reformulated in terms of the probability w(n,t) to find exactly n individuals at time t assuming that both the birth and the death rates are linear functions of the population size. The master equation for w(n,t) is solved exactly. It is shown that w(n,t) strongly deviates from the Poisson distribution and is expressed in terms either of Laguerre’s polynomials or a modified Bessel function. The latter expression allows for considerable simplifications of the asymptotic analysis of w(n,t).
Modelling point patterns with linear structures
DEFF Research Database (Denmark)
Møller, Jesper; Rasmussen, Jakob Gulddahl
Many observed spatial point patterns contain points placed roughly on line segments. Point patterns exhibiting such structures can be found for example in archaeology (locations of bronze age graves in Denmark) and geography (locations of mountain tops). We consider a particular class of point...... processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...
Modelling point patterns with linear structures
DEFF Research Database (Denmark)
Møller, Jesper; Rasmussen, Jakob Gulddahl
2009-01-01
Many observed spatial point patterns contain points placed roughly on line segments. Point patterns exhibiting such structures can be found for example in archaeology (locations of bronze age graves in Denmark) and geography (locations of mountain tops). We consider a particular class of point...... processes whose realizations contain such linear structures. Such a point process is constructed sequentially by placing one point at a time. The points are placed in such a way that new points are often placed close to previously placed points, and the points form roughly line shaped structures. We...
Computing Gröbner Bases within Linear Algebra
Suzuki, Akira
In this paper, we present an alternative algorithm to compute Gröbner bases, which is based on computations on sparse linear algebra. Both of S-polynomial computations and monomial reductions are computed in linear algebra simultaneously in this algorithm. So it can be implemented to any computational system which can handle linear algebra. For a given ideal in a polynomial ring, it calculates a Gröbner basis along with the corresponding term order appropriately.
DEFF Research Database (Denmark)
Dashab, Golam Reza; Kadri, Naveen Kumar; Mahdi Shariati, Mohammad
2012-01-01
Background: Despite many success stories of genome wide association studies (GWAS), challenges exist in QTL detection especially in datasets with many levels of relatedness. In this study we compared four methods of GWA on a dataset simulated for the 15th QTL-MAS workshop. The four methods were 1......) Mixed model analysis (MMA), 2) Random haplotype model (RHM), 3) Genealogy-based mixed model (GENMIX), and 4) Bayesian variable selection (BVS). The data consisted of phenotypes of 2000 animals from 20 sire families and were genotyped with 9990 SNPs on five chromosomes. Results: Out of the eight...
Hsieh, Chi-Hsuan; Chiu, Yu-Fang; Shen, Yi-Hsiang; Chu, Ta-Shun; Huang, Yuan-Hao
2016-02-01
This paper presents an ultra-wideband (UWB) impulse-radio radar signal processing platform used to analyze human respiratory features. Conventional radar systems used in human detection only analyze human respiration rates or the response of a target. However, additional respiratory signal information is available that has not been explored using radar detection. The authors previously proposed a modified raised cosine waveform (MRCW) respiration model and an iterative correlation search algorithm that could acquire additional respiratory features such as the inspiration and expiration speeds, respiration intensity, and respiration holding ratio. To realize real-time respiratory feature extraction by using the proposed UWB signal processing platform, this paper proposes a new four-segment linear waveform (FSLW) respiration model. This model offers a superior fit to the measured respiration signal compared with the MRCW model and decreases the computational complexity of feature extraction. In addition, an early-terminated iterative correlation search algorithm is presented, substantially decreasing the computational complexity and yielding negligible performance degradation. These extracted features can be considered the compressed signals used to decrease the amount of data storage required for use in long-term medical monitoring systems and can also be used in clinical diagnosis. The proposed respiratory feature extraction algorithm was designed and implemented using the proposed UWB radar signal processing platform including a radar front-end chip and an FPGA chip. The proposed radar system can detect human respiration rates at 0.1 to 1 Hz and facilitates the real-time analysis of the respiratory features of each respiration period.
Applications of the Linear Logistic Test Model in Psychometric Research
Kubinger, Klaus D.
2009-01-01
The linear logistic test model (LLTM) breaks down the item parameter of the Rasch model as a linear combination of some hypothesized elementary parameters. Although the original purpose of applying the LLTM was primarily to generate test items with specified item difficulty, there are still many other potential applications, which may be of use…
Modeling local item dependence with the hierarchical generalized linear model.
Jiao, Hong; Wang, Shudong; Kamata, Akihito
2005-01-01
Local item dependence (LID) can emerge when the test items are nested within common stimuli or item groups. This study proposes a three-level hierarchical generalized linear model (HGLM) to model LID when LID is due to such contextual effects. The proposed three-level HGLM was examined by analyzing simulated data sets and was compared with the Rasch-equivalent two-level HGLM that ignores such a nested structure of test items. The results demonstrated that the proposed model could capture LID and estimate its magnitude. Also, the two-level HGLM resulted in larger mean absolute differences between the true and the estimated item difficulties than those from the proposed three-level HGLM. Furthermore, it was demonstrated that the proposed three-level HGLM estimated the ability distribution variance unaffected by the LID magnitude, while the two-level HGLM with no LID consideration increasingly underestimated the ability variance as the LID magnitude increased.
Statistical Modelling of Cardiovascular Data. An Introduction to Linear Mixed Models
Gonçalves, Paulo; Lenoir, Christophe; Heymes, Christophe; Swynghedauw, Bernard; Lavergne, Christian
2005-01-01
Most of statistical approaches in cardiovascular research were based on variance analysis (ANOVA). However, most of the time, the assumption that data are independent is violated since several measures are performed on the same subject (repeated measures). In addition, the presence of intra- and inter-observers variability can potentially obscure significant differences. The linear mixed model (LMM) is an extended multivariate linear regression method of analysis that accounts for both fixed ...
Employment of CB models for non-linear dynamic analysis
Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.
1990-01-01
The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.
Grammar Based Genetic Programming Using Linear Representations
Institute of Scientific and Technical Information of China (English)
ZHANGHong; LUYinan; WANGFei
2003-01-01
In recent years,there has been a great interest in genetic programming(GP),which is used to solve many applications such as data mining,electronic engineering and pattern recognition etc.. Genetic programming paradigm as a from of adaptive learning is a functional approach to many problems that require a nonfixed representation and GP typically operates on a population of parse which usually represent computer programs whose nodes have single data type.In this paper GP using context-free grammars(CFGs) is described.This technique separates search space from solution space through a genotype to phenotype mapping.The genotypes and phenotypes of the individuals both act on different linear representations.A phenotype is postfix expression,a new method of representing which is described by making use of the definition and related features of a context-free grammar,i.e.a genotype is a variable length,linear valid genome determined by a simplifled derivation tree(SDT) generated from a context-free grammar.A CFG is used to specify how the possible solutions are created according to experiential knowledge and to direct legal crossover(ormutation)operations without any explicit reference to the process of program generation and parsing,and automatically ensuring typing and syntax correctness.Some related definitions involving genetic operators are described.Fitness evaluation is given.This technique is applied to a symbol regression problem-the identification of nonlinear dynamic characteristics of cushioning packaging.Experimental results show this method can flnd good relations between variables and is better than basic GP without a grammar.Future research on it is outlined.
Latent log-linear models for handwritten digit classification.
Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann
2012-06-01
We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.
DEFF Research Database (Denmark)
Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian
2014-01-01
differences and differences between the solution found by each optimisation method. One of the investigated approaches utilises LP (linear programming) for optimisation, one uses LP with binary operation constraints, while the third approach uses NLP (non-linear programming). The LP model is used...... of selected units by 23%, while for a non-linear approach the increase can be higher than 39%. The results indicate a higher coherence between the two latter approaches, and that the MLP (mixed integer programming) optimisation is most appropriate from a viewpoint of accuracy and runtime. © 2014 Elsevier Ltd...
The Optimal Linear Combination of Multiple Predictors Under the Generalized Linear Models.
Jin, Hua; Lu, Ying
2009-11-15
Multiple alternative diagnostic tests for one disease are commonly available to clinicians. It's important to use all the good diagnostic predictors simultaneously to establish a new predictor with higher statistical utility. Under the generalized linear model for binary outcomes, the linear combination of multiple predictors in the link function is proved optimal in the sense that the area under the receiver operating characteristic (ROC) curve of this combination is the largest among all possible linear combination. The result was applied to analysis of the data from the Study of Osteoporotic Fractures (SOF) with comparison to Su and Liu's approach.
Analysis of Robust Quasi-deviances for Generalized Linear Models
Directory of Open Access Journals (Sweden)
Eva Cantoni
2004-04-01
Full Text Available Generalized linear models (McCullagh and Nelder 1989 are a popular technique for modeling a large variety of continuous and discrete data. They assume that the response variables Yi , for i = 1, . . . , n, come from a distribution belonging to the exponential family, such that E[Yi ] = ?i and V[Yi ] = V (?i , and that ?i = g(?i = xiT?, where ? ? IR p is the vector of parameters, xi ? IR p, and g(. is the link function. The non-robustness of the maximum likelihood and the maximum quasi-likelihood estimators has been studied extensively in the literature. For model selection, the classical analysis-of-deviance approach shares the same bad robustness properties. To cope with this, Cantoni and Ronchetti (2001 propose a robust approach based on robust quasi-deviance functions for estimation and variable selection. We refer to that paper for a deeper discussion and the review of the literature.
General expression for linear and nonlinear time series models
Institute of Scientific and Technical Information of China (English)
Ren HUANG; Feiyun XU; Ruwen CHEN
2009-01-01
The typical time series models such as ARMA, AR, and MA are founded on the normality and stationarity of a system and expressed by a linear difference equation; therefore, they are strictly limited to the linear system. However, some nonlinear factors are within the practical system; thus, it is difficult to fit the model for real systems with the above models. This paper proposes a general expression for linear and nonlinear auto-regressive time series models (GNAR). With the gradient optimization method and modified AIC information criteria integrated with the prediction error, the parameter estimation and order determination are achieved. The model simulation and experiments show that the GNAR model can accurately approximate to the dynamic characteristics of the most nonlinear models applied in academics and engineering. The modeling and prediction accuracy of the GNAR model is superior to the classical time series models. The proposed GNAR model is flexible and effective.
Dynamic Model of Linear Induction Motor Considering the End Effects
Directory of Open Access Journals (Sweden)
H. A. Hairik
2009-01-01
Full Text Available In this paper the dynamic behavior of linear induction motor is described by a mathematical model taking into account the end effects and the core losses. The need for such a model rises due to the complexity of linear induction motors electromagnetic field theory. The end affects by introducing speed dependent scale factor to the magnetizing inductance and series resistance in the d-axis equivalent circuit. Simulation results are presented to show the validity of the model during both no-load and sudden load change intervals. This model can also be used directly in simulation researches for linear induction motor vector control drive systems.
Institute of Scientific and Technical Information of China (English)
Bao Xue ZHANG; Bai Sen LIU; Chang Yu LU
2004-01-01
Consider the partitioned linear regression model A = (y, X1β1 + X2β2, σ2V) and its four reduced linear models, where y is an n × 1 observable random vector with E(y) = Xβ and dispersion matrix Var(y) = σ2V, where σ2 is an unknown positive scalar, V is an n × n known symmetric nonnegative definite matrix, X = (X1: X2) is an n× (p+q) known design matrix with rank(X) = r ≤ (p+q),andβ = (β'1:β'2)' withβ1 andβ2 being p × 1 and q × 1 vectors of unknown parameters, respectively. In this article the formulae for the differences between the best linear unbiased estimators of M2X1β1under the model A and its best linear unbiased estimators under the reduced linear models of A are given,where M2 = I - X2X2+. Furthermore, the necessary and sufficient conditions for the equalities between the best linear unbiased estimators of M2X1β1 under the model A and those under its reduced linear models are established. Lastly, we also study the connections between the model A and its linear transformation model.
Generalized linear models with coarsened covariates: a practical Bayesian approach.
Johnson, Timothy R; Wiest, Michelle M
2014-06-01
Coarsened covariates are a common and sometimes unavoidable phenomenon encountered in statistical modeling. Covariates are coarsened when their values or categories have been grouped. This may be done to protect privacy or to simplify data collection or analysis when researchers are not aware of their drawbacks. Analyses with coarsened covariates based on ad hoc methods can compromise the validity of inferences. One valid method for accounting for a coarsened covariate is to use a marginal likelihood derived by summing or integrating over the unknown realizations of the covariate. However, algorithms for estimation based on this approach can be tedious to program and can be computationally expensive. These are significant obstacles to their use in practice. To overcome these limitations, we show that when expressed as a Bayesian probability model, a generalized linear model with a coarsened covariate can be posed as a tractable missing data problem where the missing data are due to censoring. We also show that this model is amenable to widely available general-purpose software for simulation-based inference for Bayesian probability models, providing researchers a very practical approach for dealing with coarsened covariates.
Linear Latent Force Models using Gaussian Processes
Álvarez, Mauricio A; Lawrence, Neil D
2011-01-01
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data driven modelling with a physical model of the system. We show how different, physically-inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology and geostatistics.
Foundation stiffness in the linear modeling of wind turbines
Chiang, Chih-Hung; Yu, Chih-Peng; Chen, Yan-Hao; Lai, Jiunnren; Hsu, Keng-Tsang; Cheng, Chia-Chi
2017-04-01
Effects of foundation stiffness on the linear vibrations of wind turbine systems are of concerns for both planning and construction of wind turbine systems. Current study performed numerical modeling for such a problem using linear spectral finite elements. The effects of foundation stiffness were investigated for various combinations of shear wave velocity of soil, size of tower base plate, and pile length. Multiple piles are also included in the models such that the foundation stiffness can be analyzed more realistically. The results indicate that the shear wave velocity of soil and the size of tower base plate have notable effects on the dominant frequency of the turbine-tower system. The larger the lateral dimension, the stiffer the foundation. Large pile cap and multiple spaced piles result in higher stiffness than small pile cap and a mono-pile. The lateral stiffness of a mono-pile mainly depends on the shear wave velocity of soil with the exception for a very short pile that the end constraints may affect the lateral vibration of the superstructure. Effective pile length may be determined by comparing the simulation results of the frictional pile to those of the end-bearing pile.
Directory of Open Access Journals (Sweden)
Wenlan Ke
2016-02-01
Full Text Available Water resource and environment capacity have become two of the most important restrictions for sustainable development in resource-based cities whose leading industries are the exploitation and processing of resources. Taking Ordos in China as an example, this article constructs an integrated model combining a multi-objective optimization model with input–output analysis to achieve the tradeoffs between economic growth, water utilization and environmental protection. This dynamic model includes socioeconomic, water supply–demand, water quality control, air quality control, energy consumption control and integrated policy sub-models. These six sub-models interact with each other. After simulation, this article proposes efficient solutions on industrial restructuring by maximizing the Gross Regional Product of Ordos from 394.3 in 2012 to 785.1 billion RMB in 2025 with a growth rate of 6.4% annually; and presents a water supply plan by maximizing the proportion of reclaimed water from 2% to 6.3% through sewage treatment technology selection and introduction, and effective water allocation. Meanwhile, the environmental impacts are all in line with the planning targets. This study illustrates that the integrated modeling is generic and can be applied to any region suffering uncoordinated development issues and can serve as a pre-evaluation approach for conducting early warning research to offer suggestions for government decision-making.
Measurement of spatial object's exterior attitude based on linear CCD
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
It is difficult to realize real-time measurement of exterior attitude by the traditional systems based on the area image sensor which have conflict between speed and accuracy.The subsystem for three-dimensional (3D) coordinate rcconstruction of point target (S3DCRPT) which is composed of three one-dimensional (1D) cameras based on linear charge-coupled device (CCD) can determine the distant light spots' spatial position. The attitude angle of the measured object is determined by the spatial solution while the coordinate reconstruction is separately carried on by the S3DCRPT with some point cooperation targets (PCTs) on the measured object. A new optical system is designed to solve the interference problem with one-to-one relationship between the PCTs and the S3DCRPT optical subsystems,which improves the measurement accuracy and saves space. The mathematical model of the attitude measurement is established,and partial and global calibrations are realized for the multi-camera attitude measurement system.The test results show the feasibility of the exterior attitude measurement based on linear CCD.
Linear latent variable models: the lava-package
DEFF Research Database (Denmark)
Holst, Klaus Kähler; Budtz-Jørgensen, Esben
2013-01-01
An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features...... are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation...
On-line control models for the Stanford Linear Collider
Energy Technology Data Exchange (ETDEWEB)
Sheppard, J.C.; Helm, R.H.; Lee, M.J.; Woodley, M.D.
1983-03-01
Models for computer control of the SLAC three-kilometer linear accelerator and damping rings have been developed as part of the control system for the Stanford Linear Collider. Some of these models have been tested experimentally and implemented in the control program for routine linac operations. This paper will describe the development and implementation of these models, as well as some of the operational results.
Identification of Influential Points in a Linear Regression Model
Directory of Open Access Journals (Sweden)
Jan Grosz
2011-03-01
Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.
A comparative study of linear and region based diagrams
Directory of Open Access Journals (Sweden)
Björn Gottfried
2015-06-01
Full Text Available There are two categories of objects spatial information science investigates: actual objects and their spatial properties, such as in geography, and abstract objects which are employed metaphorically, as for visual languages. A prominent example of the latter are diagrams that model knowledge of some domain. Different aspects of diagrams are of interest, including their formal properties or how human users work with them, for example, with diagrams representing sets. The literature about diagrammatic systems for the representation of sets shows a dominance of region-based diagrams like Euler circles and Venn diagrams. The effectiveness of these diagrams, however, is limited because region-based diagrams become quite complex for more then three sets. By contrast, linear diagrams are not equally prevalent but enable the representation of a greater number of sets without getting cluttered. Cluttered diagrams exhibit inherent complexity due to overlapping objects, irrelevant details, or other reasons that impinge upon their legibility. This study contrasts both types of diagrammatic systems and investigates whether the performance of users differs for both kinds of diagrams. A significant difference can be shown regarding the number of diagrams that can be drawn within a fixed period of time and regarding the number of errors made. The results indicate that linear diagrams are more effective by being more restrictive and because region based diagrams show much clutter due to overlapping, coincident, and tangentially touching contours, as well as an overwhelming number of empty zones. Linear diagrams are less prone to errors and do not suffer from clutter.
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.
A Non-linear Stochastic Model for an Office Building with Air Infiltration
DEFF Research Database (Denmark)
Thavlov, Anders; Madsen, Henrik
2015-01-01
This paper presents a non-linear heat dynamic model for a multi-room office building with air infiltration. Several linear and non-linear models, with and without air infiltration, are investigated and compared. The models are formulated using stochastic differential equations and the model...... parameters are estimated using a maximum likelihood technique. Based on the maximum likelihood value, the different models are statistically compared to each other using Wilk's likelihood ratio test. The model showing the best performance is finally verified in both the time domain and the frequency domain...
Extended Linear Models with Gaussian Priors
DEFF Research Database (Denmark)
Quinonero, Joaquin
2002-01-01
on the parameters. The Relevance Vector Machine, introduced by Tipping, is a particular case of such a model. I give the detailed derivations of the expectation-maximisation (EM) algorithm used in the training. These derivations are not found in the literature, and might be helpful for newcomers....
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...
Generalized Linear Models with Applications in Engineering and the Sciences
Myers, Raymond H; Vining, G Geoffrey; Robinson, Timothy J
2012-01-01
Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities."-Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Ma
Linear Factor Models and the Estimation of Expected Returns
Sarisoy, Cisil; de Goeij, Peter; Werker, Bas
2015-01-01
Estimating expected returns on individual assets or portfolios is one of the most fundamental problems of finance research. The standard approach, using historical averages,produces noisy estimates. Linear factor models of asset pricing imply a linear relationship between expected returns and exposu
Linear and Nonlinear Thinking: A Multidimensional Model and Measure
Groves, Kevin S.; Vance, Charles M.
2015-01-01
Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…
Linear and Nonlinear Thinking: A Multidimensional Model and Measure
Groves, Kevin S.; Vance, Charles M.
2015-01-01
Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…
Sikora, M; Dohm, O; Alber, M
2007-08-07
A dedicated, efficient Monte Carlo (MC) accelerator head model for intensity modulated stereotactic radiosurgery treatment planning is needed to afford a highly accurate simulation of tiny IMRT fields. A virtual source model (VSM) of a mini multi-leaf collimator (MLC) (the Elekta Beam Modulator (EBM)) is presented, allowing efficient generation of particles even for small fields. The VSM of the EBM is based on a previously published virtual photon energy fluence model (VEF) (Fippel et al 2003 Med. Phys. 30 301) commissioned with large field measurements in air and in water. The original commissioning procedure of the VEF, based on large field measurements only, leads to inaccuracies for small fields. In order to improve the VSM, it was necessary to change the VEF model by developing (1) a method to determine the primary photon source diameter, relevant for output factor calculations, (2) a model of the influence of the flattening filter on the secondary photon spectrum and (3) a more realistic primary photon spectrum. The VSM model is used to generate the source phase space data above the mini-MLC. Later the particles are transmitted through the mini-MLC by a passive filter function which significantly speeds up the time of generation of the phase space data after the mini-MLC, used for calculation of the dose distribution in the patient. The improved VSM model was commissioned for 6 and 15 MV beams. The results of MC simulation are in very good agreement with measurements. Less than 2% of local difference between the MC simulation and the diamond detector measurement of the output factors in water was achieved. The X, Y and Z profiles measured in water with an ion chamber (V = 0.125 cm(3)) and a diamond detector were used to validate the models. An overall agreement of 2%/2 mm for high dose regions and 3%/2 mm in low dose regions between measurement and MC simulation for field sizes from 0.8 x 0.8 cm(2) to 16 x 21 cm(2) was achieved. An IMRT plan film verification
Quantitative Analysis of Yu Ebao Based on Linear Regression Model%基于线性回归模型的余额宝价值分析
Institute of Scientific and Technical Information of China (English)
刘冬青
2014-01-01
余额宝以其较低的门槛让更多的人接触到货币基金。通过介绍余额宝的主体框架以及2013年余额宝的收益和费用情况，建立线性回归模型，定量分析了余额宝的收益。经过研究发现：可以怀疑在高额利益的背后余额宝公司可能存在前期的贴息问题以吸引客户；余额宝通过协议存款将利润从银行转给客户的同时，并且在一定程度上也把风险转嫁给了客户；余额宝给金融行业带来了革命性的创新是不可否认的，同时也带来了潜在的系统性风险。%Yu Ebao,with its low threshold, let more people access to the monetary fund.Through detailed introduction of the main frame of Yu Ebao ,and the income and expenses of Yu Ebao.Establish a linear regression model,to analysis the Yu Ebao quantitatively.Through the study found that: in order to attract customers,Yu Ebao may have the problem of interests behind the high profits;Yu Ebao transfer the profit to customers from bank through agreement deposits ,and to a certain extent also the risk on to their customers at the same time ;Yu Ebao has brought the revolutionary innovation to the financial industry is undeniable,but also brought the potential systemic risk.
Multivariate linear models and repeated measurements revisited
DEFF Research Database (Denmark)
Dalgaard, Peter
2009-01-01
Methods for generalized analysis of variance based on multivariate normal theory have been known for many years. In a repeated measurements context, it is most often of interest to consider transformed responses, typically within-subject contrasts or averages. Efficiency considerations leads...
DEFF Research Database (Denmark)
Jørgensen, John Bagterp; Jørgensen, Sten Bay
2007-01-01
model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model......A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space...
A linear algebra model for quasispecies
García-Pelayo, Ricardo
2002-06-01
In the present work we present a simple model of the population genetics of quasispecies. We show that the error catastrophe arises because in Biology the mutation rates are almost zero and the mutations themselves are almost neutral. We obtain and discuss previously known results from the point of view of this model. New results are: the fitness of a sequence in terms of its abundance in the quasispecies, a formula for the stable distribution of a quasispecies in which the fitness depends only on the Hamming distance to the master sequence, the time it takes the master sequence to generate a stable quasispecies (such as in the infection by a virus) and the fitness of quasispecies.
Forecasting telecommunications data with linear models
Madden, Gary G; Tan, Joachim
2007-01-01
For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in telecommunications data and impinges on a company’s’ ability to properly evaluate and plan their business. ITU Recommendation E.507 provides a selection of econometric models for forecasting these trends. However, no specific guidance is given....
Statistical Inference for Partially Linear Regression Models with Measurement Errors
Institute of Scientific and Technical Information of China (English)
Jinhong YOU; Qinfeng XU; Bin ZHOU
2008-01-01
In this paper, the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors. Firstly,a bandwidth selection procedure is proposed, which is a combination of the difference-based technique and GCV method. Secondly, a goodness-of-fit test procedure is proposed,which is an extension of the generalized likelihood technique. Thirdly, a variable selection procedure for the parametric part is provided based on the nonconcave penalization and corrected profile least squares. Same as "Variable selection via nonconcave penalized like-lihood and its oracle properties" (J. Amer. Statist. Assoc., 96, 2001, 1348-1360), it is shown that the resulting estimator has an oracle property with a proper choice of regu-larization parameters and penalty function. Simulation studies are conducted to illustrate the finite sample performances of the proposed procedures.
Circular and linear modeling of female sexual desire and arousal.
Hayes, Richard D
2011-03-01
There is debate in the scientific literature regarding which model most accurately represents the female sexual response. Traditionally, sex research has been conducted within the framework of the linear sexual response model proposed by Masters and Johnson, modified by Kaplan, then Robinson. Criticism of linear models prompted the development of a composite model of the female sexual response by Basson et al., which included linear and circular pathways. This review aims to systematically assess published studies to determine the extent to which circular and linear models or pathways reflect the female sexual response. Medline, EMBASE, and PsychINFO databases were searched for original data papers and review articles. Inclusion criteria were that articles were published since 1990, in English, and compared linear and circular sexual response models or addressed aspects of these models. Reviews were required to be systematic and include a meta-analysis. Of the 898 studies identified through the initial literature search, 13 original studies and one review met the inclusion criteria. Two studies directly compared models providing limited evidence that most women identify with linear sexual response pathways. However, there is increasing evidence that circular pathways may accurately reflect some aspects of the female sexual response. Further comparative studies are encouraged.
Directory of Open Access Journals (Sweden)
Seyed Abolghasem Mortazavi
2014-03-01
Full Text Available Water resources sustainability is one of the major issues in the agricultural sustainability. In this study sustainability of water resources has been investigated by use of linear and non-linear models in six models based on optimal utilization of water resources in the north parts farms of Iran because of incorrect use of agricultural water resources, from 2011 to 2012. Also “gross margin per a unit of water consumption” and “employment per a unit of water consumption” are used as indicators for assessing the sustainability of cropping patterns. The results show that cropping pattern of fractional goal programming (FGP model has been near to current situation and has shown realistic conditions according to expertise and advantage of this area in cultivation of certain crops. So the FGP model has desirability in each of indicators than other five models.
Linear Sigma Models With Strongly Coupled Phases -- One Parameter Models
Hori, Kentaro
2013-01-01
We systematically construct a class of two-dimensional $(2,2)$ supersymmetric gauged linear sigma models with phases in which a continuous subgroup of the gauge group is totally unbroken. We study some of their properties by employing a recently developed technique. The focus of the present work is on models with one K\\"ahler parameter. The models include those corresponding to Calabi-Yau threefolds, extending three examples found earlier by a few more, as well as Calabi-Yau manifolds of other dimensions and non-Calabi-Yau manifolds. The construction leads to predictions of equivalences of D-brane categories, systematically extending earlier examples. There is another type of surprise. Two distinct superconformal field theories corresponding to Calabi-Yau threefolds with different Hodge numbers, $h^{2,1}=23$ versus $h^{2,1}=59$, have exactly the same quantum K\\"ahler moduli space. The strong-weak duality plays a crucial r\\^ole in confirming this, and also is useful in the actual computation of the metric on t...
Low-energy limit of the extended Linear Sigma Model
Divotgey, Florian; Giacosa, Francesco; Rischke, Dirk H
2016-01-01
The extended Linear Sigma Model (eLSM) is an effective hadronic model based on the linear realization of chiral symmetry $SU(N_f)_L \\times SU(N_f)_R$, with (pseudo)scalar and (axial-)vector mesons as degrees of freedom. In this paper, we study the low-energy limit of the eLSM for $N_f=2$ flavors by integrating out all fields except for the pions, the (pseudo-)Nambu--Goldstone bosons of chiral symmetry breaking. We only keep terms entering at tree level and up to fourth order in powers of derivatives of the pion fields. Up to this order, there are four low-energy coupling constants in the resulting low-energy effective action. We show that the latter is formally identical to Chiral Perturbation Theory (ChPT), after choosing a representative for the coset space generated by chiral symmetry breaking and expanding up to fourth order in powers of derivatives of the pion fields. Two of the low-energy coupling constants of the eLSM are uniquely determined by a fit to hadron masses and decay widths. We find that thei...
Acoustic FMRI noise: linear time-invariant system model.
Rizzo Sierra, Carlos V; Versluis, Maarten J; Hoogduin, Johannes M; Duifhuis, Hendrikus Diek
2008-09-01
Functional magnetic resonance imaging (fMRI) enables sites of brain activation to be localized in human subjects. For auditory system studies, however, the acoustic noise generated by the scanner tends to interfere with the assessments of this activation. Understanding and modeling fMRI acoustic noise is a useful step to its reduction. To study acoustic noise, the MR scanner is modeled as a linear electroacoustical system generating sound pressure signals proportional to the time derivative of the input gradient currents. The transfer function of one MR scanner is determined for two different input specifications: 1) by using the gradient waveform calculated by the scanner software and 2) by using a recording of the gradient current. Up to 4 kHz, the first method is shown as reliable as the second one, and its use is encouraged when direct measurements of gradient currents are not possible. Additionally, the linear order and average damping properties of the gradient coil system are determined by impulse response analysis. Since fMRI is often based on echo planar imaging (EPI) sequences, a useful validation of the transfer function prediction ability can be obtained by calculating the acoustic output for the EPI sequence. We found a predicted sound pressure level (SPL) for the EPI sequence of 104 dB SPL compared to a measured value of 102 dB SPL. As yet, the predicted EPI pressure waveform shows similarity as well as some differences with the directly measured EPI pressure waveform.
Adaptive Error Estimation in Linearized Ocean General Circulation Models
Chechelnitsky, Michael Y.
1999-01-01
Data assimilation methods are routinely used in oceanography. The statistics of the model and measurement errors need to be specified a priori. This study addresses the problem of estimating model and measurement error statistics from observations. We start by testing innovation based methods of adaptive error estimation with low-dimensional models in the North Pacific (5-60 deg N, 132-252 deg E) to TOPEX/POSEIDON (TIP) sea level anomaly data, acoustic tomography data from the ATOC project, and the MIT General Circulation Model (GCM). A reduced state linear model that describes large scale internal (baroclinic) error dynamics is used. The methods are shown to be sensitive to the initial guess for the error statistics and the type of observations. A new off-line approach is developed, the covariance matching approach (CMA), where covariance matrices of model-data residuals are "matched" to their theoretical expectations using familiar least squares methods. This method uses observations directly instead of the innovations sequence and is shown to be related to the MT method and the method of Fu et al. (1993). Twin experiments using the same linearized MIT GCM suggest that altimetric data are ill-suited to the estimation of internal GCM errors, but that such estimates can in theory be obtained using acoustic data. The CMA is then applied to T/P sea level anomaly data and a linearization of a global GFDL GCM which uses two vertical modes. We show that the CMA method can be used with a global model and a global data set, and that the estimates of the error statistics are robust. We show that the fraction of the GCM-T/P residual variance explained by the model error is larger than that derived in Fukumori et al.(1999) with the method of Fu et al.(1993). Most of the model error is explained by the barotropic mode. However, we find that impact of the change in the error statistics on the data assimilation estimates is very small. This is explained by the large
Optimization for decision making linear and quadratic models
Murty, Katta G
2010-01-01
While maintaining the rigorous linear programming instruction required, Murty's new book is unique in its focus on developing modeling skills to support valid decision-making for complex real world problems, and includes solutions to brand new algorithms.
Linear Characteristic Graphical Models: Representation, Inference and Applications
Bickson, Danny
2010-01-01
Heavy-tailed distributions naturally occur in many real life problems. Unfortunately, it is typically not possible to compute inference in closed-form in graphical models which involve such heavy-tailed distributions. In this work, we propose a novel simple linear graphical model for independent latent random variables, called linear characteristic model (LCM), defined in the characteristic function domain. Using stable distributions, a heavy-tailed family of distributions which is a generalization of Cauchy, L\\'evy and Gaussian distributions, we show for the first time, how to compute both exact and approximate inference in such a linear multivariate graphical model. LCMs are not limited to stable distributions, in fact LCMs are always defined for any random variables (discrete, continuous or a mixture of both). We provide a realistic problem from the field of computer networks to demonstrate the applicability of our construction. Other potential application is iterative decoding of linear channels with non-...
Linear model applied to the evaluation of pharmaceutical stability data
Directory of Open Access Journals (Sweden)
Renato Cesar Souza
2013-09-01
Full Text Available The expiry date on the packaging of a product gives the consumer the confidence that the product will retain its identity, content, quality and purity throughout the period of validity of the drug. The definition of this term in the pharmaceutical industry is based on stability data obtained during the product registration. By the above, this work aims to apply the linear regression according to the guideline ICH Q1E, 2003, to evaluate some aspects of a product undergoing in a registration phase in Brazil. With this propose, the evaluation was realized with the development center of a multinational company in Brazil, with samples of three different batches composed by two active principal ingredients in two different packages. Based on the preliminary results obtained, it was possible to observe the difference of degradation tendency of the product in two different packages and the relationship between the variables studied, added knowledge so new models of linear equations can be applied and developed for other products.
Mining Distance-Based Outliers in Near Linear Time
National Aeronautics and Space Administration — Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to...
Directory of Open Access Journals (Sweden)
M. Kalyan Chakravarthi
2015-05-01
Full Text Available The aim of this study is to design and implement an Adaptive Model Based Gain Scheduled (AMBGS Controller using classical controller tuning techniques for a Single Spherical Nonlinear Tank System (SSTLLS. A varying range of development in the control mechanisms have been evidently seen in the last two decades. The control of level has always been a topic of discussion in the process control scenario. In this study a real time SSTLLS has been chosen for investigation. System identification of these different regions of nonlinear process is done using black box model, which is identified to be nonlinear and approximated to be a First Order plus Dead Time (FOPDT model. A proportional and integral controller is designed using LabVIEW and Skogestad’s and Ziegler Nichols (ZN tuning methods are implemented. The paper will provide details about the data acquisition unit, shows the implementation of the controller and compare the results of PI tuning methods used for an AMBGS Controller.
Observer-based linear parameter varying H∞ tracking control for hypersonic vehicles
Directory of Open Access Journals (Sweden)
Yiqing Huang
2016-11-01
Full Text Available This article aims to develop observer-based linear parameter varying output feedback H∞ tracking controller for hypersonic vehicles. Due to the complexity of an original nonlinear model of the hypersonic vehicle dynamics, a slow–fast loop linear parameter varying polytopic model is introduced for system stability analysis and controller design. Then, a state observer is developed by linear parameter varying technique in order to estimate the unmeasured attitude angular for slow loop system. Also, based on the designed linear parameter varying state observer, a kind of attitude tracking controller is presented to reduce tracking errors for all bounded reference attitude angular inputs. The closed-loop linear parameter varying system is proved to be quadratically stable by Lypapunov function technique. Finally, simulation results show that the developed linear parameter varying H∞ controller has good tracking capability for reference commands.
Linear logical relations and observational equivalences for session-based concurrency
Perez, Jorge A.; Caires, Luis; Pfenning, Frank; Toninho, Bernardo
2014-01-01
We investigate strong normalization, confluence, and behavioral equality in the realm of session-based concurrency. These interrelated issues underpin advanced correctness analysis in models of structured communications. The starting point for our study is an interpretation of linear logic
Filtering nonlinear dynamical systems with linear stochastic models
Harlim, J.; Majda, A. J.
2008-06-01
An important emerging scientific issue is the real time filtering through observations of noisy signals for nonlinear dynamical systems as well as the statistical accuracy of spatio-temporal discretizations for filtering such systems. From the practical standpoint, the demand for operationally practical filtering methods escalates as the model resolution is significantly increased. For example, in numerical weather forecasting the current generation of global circulation models with resolution of 35 km has a total of billions of state variables. Numerous ensemble based Kalman filters (Evensen 2003 Ocean Dyn. 53 343-67 Bishop et al 2001 Mon. Weather Rev. 129 420-36 Anderson 2001 Mon. Weather Rev. 129 2884-903 Szunyogh et al 2005 Tellus A 57 528-45 Hunt et al 2007 Physica D 230 112-26) show promising results in addressing this issue; however, all these methods are very sensitive to model resolution, observation frequency, and the nature of the turbulent signals when a practical limited ensemble size (typically less than 100) is used. In this paper, we implement a radical filtering approach to a relatively low (40) dimensional toy model, the L-96 model (Lorenz 1996 Proc. on Predictability (ECMWF, 4-8 September 1995) pp 1-18) in various chaotic regimes in order to address the 'curse of ensemble size' for complex nonlinear systems. Practically, our approach has several desirable features such as extremely high computational efficiency, filter robustness towards variations of ensemble size (we found that the filter is reasonably stable even with a single realization) which makes it feasible for high dimensional problems, and it is independent of any tunable parameters such as the variance inflation coefficient in an ensemble Kalman filter. This radical filtering strategy decouples the problem of filtering a spatially extended nonlinear deterministic system to filtering a Fourier diagonal system of parametrized linear stochastic differential equations (Majda and Grote
Generalized linear mixed models modern concepts, methods and applications
Stroup, Walter W
2012-01-01
PART I The Big PictureModeling BasicsWhat Is a Model?Two Model Forms: Model Equation and Probability DistributionTypes of Model EffectsWriting Models in Matrix FormSummary: Essential Elements for a Complete Statement of the ModelDesign MattersIntroductory Ideas for Translating Design and Objectives into ModelsDescribing ""Data Architecture"" to Facilitate Model SpecificationFrom Plot Plan to Linear PredictorDistribution MattersMore Complex Example: Multiple Factors with Different Units of ReplicationSetting the StageGoals for Inference with Models: OverviewBasic Tools of InferenceIssue I: Data
A BEHAVIORAL-APPROACH TO LINEAR EXACT MODELING
ANTOULAS, AC; WILLEMS, JC
1993-01-01
The behavioral approach to system theory provides a parameter-free framework for the study of the general problem of linear exact modeling and recursive modeling. The main contribution of this paper is the solution of the (continuous-time) polynomial-exponential time series modeling problem. Both re
Scaling and linear response in the GOY model
Kadanoff, Leo; Lohse, Detlef; Schörghofer, Norbert
1997-01-01
The GOY model is a model for turbulence in which two conserved quantities cascade up and down a linear array of shells. When the viscosity parameter, small nu, Greek, is small the model has a qualitative behavior which is similar to the Kolmogorov theories of turbulence. Here a static solution to th
A hierarchical linear model for tree height prediction.
Vicente J. Monleon
2003-01-01
Measuring tree height is a time-consuming process. Often, tree diameter is measured and height is estimated from a published regression model. Trees used to develop these models are clustered into stands, but this structure is ignored and independence is assumed. In this study, hierarchical linear models that account explicitly for the clustered structure of the data...
DEFF Research Database (Denmark)
Garde, Henrik
2017-01-01
Detecting inhomogeneities in the electrical conductivity is a special case of the inverse problem in electrical impedance tomography, that leads to fast direct reconstruction methods. One such method can, under reasonable assumptions, exactly characterize the inhomogeneities based on monotonicity....... For a fair comparison, exact matrix characterizations are used when probing the monotonicity relations to avoid errors from numerical solution to PDEs and numerical integration. Using a special factorization of the Neumann-to-Dirichlet map also makes the non-linear method as fast as the linear method...
Model Predictive Control of PMSM Based on Feedback Linearization%基于反馈线性化的永磁同步电机模型预测控制
Institute of Scientific and Technical Information of China (English)
林辉; 王永宾; 计宏
2011-01-01
A control scheme with feedback linearization and model predictive control (MPC) for permanent magnet synchronous motor (PMSM) is presented. The necessary and sufficient conditions of feedback linearization of the non-linear PMSM model are discussed with differential geometry method and it is transformed into linear one. The principle of MPC and the treatment of system constraints are analyzed. The controllers are respectively designed for linear PMSM model with MPC and the method of state feedback pole assignment. In the case of constraint and unconstraint condition, the control effect of above-mentioned both schemes are compared. The simulation results show that the control method based on feedback linearization and MPC can simplify the design of the controller and reduce the error generated as the model mismatch; it can overcome the effect of constraints condition and ensure the ideal steady state and dynamic performance.%提出一种基于反馈线性化和模型预测控制(MPC)策略的永磁同步电机(PMSM)控制方案.运用微分几何理论讨论了非线性PMSM模型可进行反馈线性化的充分必要条件,并将其转换为新坐标空间中的线性模型;分析了MPC原理和对系统约束条件的处理方法.针对获得的PMSM线性模型,分别采用MPC和状态反馈极点配置方法设计了控制器.在有约束条件和无约束条件的情况下,对上述两种控制方法进行了控制效果比较.仿真实验结果表明,基于反馈线性化和MPC的控制方法可以简化系统的控制器设计,减小因模型失配而产生的误差;可以解决约束条件对系统性能的影响,保证系统具有理想的稳态和动态性能.
An Improved Piecewise Linear Chaotic Map Based Image Encryption Algorithm
Directory of Open Access Journals (Sweden)
Yuping Hu
2014-01-01
Full Text Available An image encryption algorithm based on improved piecewise linear chaotic map (MPWLCM model was proposed. The algorithm uses the MPWLCM to permute and diffuse plain image simultaneously. Due to the sensitivity to initial key values, system parameters, and ergodicity in chaotic system, two pseudorandom sequences are designed and used in the processes of permutation and diffusion. The order of processing pixels is not in accordance with the index of pixels, but it is from beginning or end alternately. The cipher feedback was introduced in diffusion process. Test results and security analysis show that not only the scheme can achieve good encryption results but also its key space is large enough to resist against brute attack.
An Improved Piecewise Linear Chaotic Map Based Image Encryption Algorithm
Hu, Yuping; Wang, Zhijian
2014-01-01
An image encryption algorithm based on improved piecewise linear chaotic map (MPWLCM) model was proposed. The algorithm uses the MPWLCM to permute and diffuse plain image simultaneously. Due to the sensitivity to initial key values, system parameters, and ergodicity in chaotic system, two pseudorandom sequences are designed and used in the processes of permutation and diffusion. The order of processing pixels is not in accordance with the index of pixels, but it is from beginning or end alternately. The cipher feedback was introduced in diffusion process. Test results and security analysis show that not only the scheme can achieve good encryption results but also its key space is large enough to resist against brute attack. PMID:24592159
1979-01-01
The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.
dglars: An R Package to Estimate Sparse Generalized Linear Models
Directory of Open Access Journals (Sweden)
Luigi Augugliaro
2014-09-01
Full Text Available dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013, developed to study the sparse structure of a generalized linear model. This method, called dgLARS, is based on a differential geometrical extension of the least angle regression method proposed in Efron, Hastie, Johnstone, and Tibshirani (2004. The core of the dglars package consists of two algorithms implemented in Fortran 90 to efficiently compute the solution curve: a predictor-corrector algorithm, proposed in Augugliaro et al. (2013, and a cyclic coordinate descent algorithm, proposed in Augugliaro, Mineo, and Wit (2012. The latter algorithm, as shown here, is significantly faster than the predictor-corrector algorithm. For comparison purposes, we have implemented both algorithms.
Distributed Lag Linear and Non-Linear Models in R: The Package dlnm
Directory of Open Access Journals (Sweden)
Antonio Gasparrini
2011-08-01
Full Text Available Distributed lag non-linear models (DLNMs represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.
Non-linear Growth Models in Mplus and SAS.
Grimm, Kevin J; Ram, Nilam
2009-10-01
Non-linear growth curves or growth curves that follow a specified non-linear function in time enable researchers to model complex developmental patterns with parameters that are easily interpretable. In this paper we describe how a variety of sigmoid curves can be fit using the Mplus structural modeling program and the non-linear mixed-effects modeling procedure NLMIXED in SAS. Using longitudinal achievement data collected as part of a study examining the effects of preschool instruction on academic gain we illustrate the procedures for fitting growth models of logistic, Gompertz, and Richards functions. Brief notes regarding the practical benefits, limitations, and choices faced in the fitting and estimation of such models are included.
A quasi-linear gyrokinetic transport model for tokamak plasmas
Casati, Alessandro
2012-01-01
The development of a quasi-linear gyrokinetic transport model for tokamak plasmas, ultimately designed to provide physically comprehensive predictions of the time evolution of the thermodynamic relevant quantities, is a task that requires tight links among theoretical, experimental and numerical studies. The framework of the model here proposed, which operates a reduction of complexity on the nonlinear self-organizing plasma dynamics, allows in fact multiple validations of the current understanding of the tokamak micro-turbulence. The main outcomes of this work stem from the fundamental steps involved by the formulation of such a reduced transport model, namely: (1) the verification of the quasi-linear plasma response against the nonlinearly computed solution, (2) the improvement of the turbulent saturation model through an accurate validation of the nonlinear codes against the turbulence measurements, (3) the integration of the quasi-linear model within an integrated transport solver.
Linear approximation model network and its formation via evolutionary computation
Indian Academy of Sciences (India)
Yun Li; Kay Chen Tan
2000-04-01
To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked through output or parameter interpolation. The linear models are valid for the entire operating trajectory and hence overcome the local validity of LMN models, which impose the predetermination of a scheduling variable that predicts characteristic changes of the nonlinear system. LAMs can be evolved fromsampled step response data directly, eliminating the need forlocal linearisation upon a pre-model using derivatives of the nonlinear system. The structural difference between a LAM network and an LMN isthat the overall model of the latteris a parameter-varying system and hence nonlinear,while the formerremains linear time-invariant (LTI). Hence, existing LTI and transfer function theory applies to a LAM network, which is therefore easy to use for control system design. Validation results show that the proposed method offers a simple, transparent and accurate multivariable modelling technique for nonlinear systems.
Mathematical modelling in engineering: A proposal to introduce linear algebra concepts
Andrea Dorila Cárcamo; Joan Vicenç Gómez; Josep María Fortuny
2016-01-01
The modern dynamic world requires that basic science courses for engineering, including linear algebra, emphasize the development of mathematical abilities primarily associated with modelling and interpreting, which aren´t limited only to calculus abilities. Considering this, an instructional design was elaborated based on mathematic modelling and emerging heuristic models for the construction of specific linear algebra concepts: span and spanning set. This was applied to first year e...
Mathematical modelling in engineering: a proposal to introduce linear algebra concepts
Cárcamo Bahamonde, Andrea Dorila; Gómez Urgellés, Joan Vicenç; Fortuny Aymeni, José María
2016-01-01
The modern dynamic world requires that basic science courses for engineering, including linear algebra, emphasize the development of mathematical abilities primarily associated with modelling and interpreting, which aren´t limited only to calculus abilities. Considering this, an instructional design was elaborated based on mathematic modelling and emerging heuristic models for the construction of specific linear algebra concepts: span and spanning set. This was applied to first year engineeri...
Mathematical modelling in engineering: a proposal to introduce linear algebra concepts
Cárcamo Bahamonde, Andrea; Gómez Urgellés, Joan Vicenç; Fortuny Aymemi, Josep Maria
2016-01-01
The modern dynamic world requires that basic science courses for engineering, including linear algebra, emphasize the development of mathematical abilities primarily associated with modelling and interpreting, which aren´t limited only to calculus abilities. Considering this, an instructional design was elaborated based on mathematic modelling and emerging heuristic models for the construction of specific linear algebra concepts: span and spanning set. This was applied to first year engineer...
Model Checking for a General Linear Model with Nonignorable Missing Covariates
Institute of Scientific and Technical Information of China (English)
Zhi-hua SUN; Wai-Cheung IP; Heung WONG
2012-01-01
In this paper,we investigate the model checking problem for a general linear model with nonignorable missing covariates.We show that,without any parametric model assumption for the response probability,the least squares method yields consistent estimators for the linear model even if only the complete data are applied.This makes it feasible to propose two testing procedures for the corresponding model checking problem:a score type lack-of-fit test and a test based on the empirical process.The asymptotic properties of the test statistics are investigated.Both tests are shown to have asymptotic power 1 for local alternatives converging to the null at the rate n-(r),0 ≤ (r) ＜ 1/2.Simulation results show that both tests perform satisfactorily.
Directory of Open Access Journals (Sweden)
Kesavan.E
2013-04-01
Full Text Available This paper suggests an idea to design an adaptive PID controller for Non-linear liquid tank System and is implemented in PLC. Online estimation of linear parameters (Time constant and Gain brings an exact model of the process to take perfect control action. Based on these estimated values, the controller parameters will be well tuned by internal model control. Internal model control is an unremarkably used technique and provides well tuned controller in order to have a good controlling process. PLC with its ability to have both continues control for PID Control and digital control for fault diagnosis which ascertains faults in the system and provides alerts about the status of the entire process.
Phylogenetic mixtures and linear invariants for equal input models.
Casanellas, Marta; Steel, Mike
2017-04-01
The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the 'equal input model'. This model generalizes the 'Felsenstein 1981' model (and thereby the Jukes-Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a 'random cluster' process. We describe the structure and dimension of the vector spaces of phylogenetic mixtures and of linear invariants for any fixed phylogenetic tree (and for all trees-the so called 'model invariants'), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of [Formula: see text] leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake (Mol Biol Evol 4:167-191, 1987).
A Linearization Approach for Rational Nonlinear Models in Mathematical Physics
Institute of Scientific and Technical Information of China (English)
Robert A. Van Gorder
2012-01-01
In this paper, a novel method for linearization of rational second order nonlinear models is discussed. In particular, we discuss an application of the 5 expansion method （created to deal with problems in Quantum Field Theory） which will enable both the linearization and perturbation expansion of such equations. Such a method allows for one to quickly obtain the order zero perturbation theory in terms of certain special functions which are governed by linear equations. Higher order perturbation theories can then be obtained in terms of such special functions. One benefit to such a method is that it may be applied even to models without small physical parameters, as the perturbation is given in terms of the degree of nonlinearity, rather than any physical parameter. As an application, we discuss a method of linearizing the six Painlev~ equations by an application of the method. In addition to highlighting the benefits of the method, we discuss certain shortcomings of the method.
Virtual estimator for piecewise linear systems based on observability analysis.
Morales-Morales, Cornelio; Adam-Medina, Manuel; Cervantes, Ilse; Vela-Valdés, Luis G; Beltrán, Carlos Daniel García
2013-02-27
This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system's outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results.
Virtual Estimator for Piecewise Linear Systems Based on Observability Analysis
Morales-Morales, Cornelio; Adam-Medina, Manuel; Cervantes, Ilse; Vela-Valdés and, Luis G.; García Beltrán, Carlos Daniel
2013-01-01
This article proposes a virtual sensor for piecewise linear systems based on observability analysis that is in function of a commutation law related with the system's outpu. This virtual sensor is also known as a state estimator. Besides, it presents a detector of active mode when the commutation sequences of each linear subsystem are arbitrary and unknown. For the previous, this article proposes a set of virtual estimators that discern the commutation paths of the system and allow estimating their output. In this work a methodology in order to test the observability for piecewise linear systems with discrete time is proposed. An academic example is presented to show the obtained results. PMID:23447007
Performance modeling and prediction for linear algebra algorithms
Iakymchuk, Roman
2012-01-01
This dissertation incorporates two research projects: performance modeling and prediction for dense linear algebra algorithms, and high-performance computing on clouds. The first project is focused on dense matrix computations, which are often used as computational kernels for numerous scientific applications. To solve a particular mathematical operation, linear algebra libraries provide a variety of algorithms. The algorithm of choice depends, obviously, on its performance. Performance of su...
Probabilistic prediction of fatigue damage based on linear fracture mechanics
Directory of Open Access Journals (Sweden)
M. Krejsa
2017-01-01
Full Text Available Paper describes in detail and gives example of the probabilistic assessment of a steel structural element subject to fatigue load, particular attention being paid to cracks from the edge and those from surface. Fatigue crack damage depends on a number of stress range cycles. Three sizes are important for the characteristics of the propagation of fatigue cracks - the initial size, detectable size and acceptable size. The theoretical model of fatigue crack progression in paper is based on a linear fracture mechanics. When determining the required degree of reliability, it is possible to specify the time of the first inspection of the construction which will focus on the fatigue damage. Using a conditional probability, times for subsequent inspections can be determined. For probabilistic calculation of fatigue crack progression was used the original and new probabilistic methods - the Direct Optimized Probabilistic Calculation (“DOProC”, which is based on optimized numerical integration. The algorithm of the probabilistic calculation was applied in the FCProbCalc code (“Fatigue Crack Probabilistic Calculation”, using which is possible to carry out the probabilistic modelling of propagation of fatigue cracks in a user friendly environment very effectively.
Resource-oriented Programming Based on Linear Logic
Directory of Open Access Journals (Sweden)
Valerie Novitzká
2007-08-01
Full Text Available In our research we consider programming as logical reasoning over types.Linear logic with its resource-oriented features yields a proper means for our approachbecause it enables to consider about resources as in real life: after their use they areexhausted. Computation then can be regarded as proof search. In our paper we presenthow space and time can be introduced into this logic and we discuss several programminglanguages based on linear logic.
Commutator-based linearization of $N = 1$ nonlinear supersymmetry
Tsuda, Motomu
2016-01-01
We consider the linearization of $N = 1$ nonlinear supersymmetry (NLSUSY) based on a commutator algebra in Volkov-Akulov NLSUSY theory. We show explicitly that $U(1)$ gauge and scalar supermultiplets in addition to a vector supermultiplet with general auxiliary fields in linear SUSY theories are obtained from a same set of bosonic and fermionic functionals (composites) which are expressed as simple products of the powers of a Nambu-Goldstone fermion and a fundamental determinant in the NLSUSY theory.
MODELLING AND CONTROLLING OF INDUCTION MOTOR BY USING LINEAR ADRC
Directory of Open Access Journals (Sweden)
CH. NAGA KOTI KUMAR,
2011-04-01
Full Text Available In this paper we present a new novel approach for the speed control of an IM using Linear Active Disturbance Rejection Controller [LADRC]. The field oriented control of IM needs the accuratemathematical model of IM, but it is very difficult to develop an accurate mathematical model. The LADRC does depend on the mathematical model so it is very robust to changes in plant parameters. This controller can also estimate and compensate the general disturbances which include the unknown internal dynamics and external disturbances by using the Extended State Observer, which can reduce the system to a linear one.
Optimizing Biorefinery Design and Operations via Linear Programming Models
Energy Technology Data Exchange (ETDEWEB)
Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric
2017-03-28
The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for
A random effects generalized linear model for reliability compositive evaluation
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
This paper first proposes a random effects generalized linear model to evaluate the storage life of one kind of high reliable and small sample-sized products by combining multi-sources information of products coming from the same population but stored at different environments. The relevant algorithms are also provided. Simulation results manifest the soundness and effectiveness of the proposed model.
CONTRIBUTIONS TO THE FINITE ELEMENT MODELING OF LINEAR ULTRASONIC MOTORS
Directory of Open Access Journals (Sweden)
Oana CHIVU
2013-05-01
Full Text Available The present paper is concerned with the main modeling elements as produced by means of thefinite element method of linear ultrasonic motors. Hence, first the model is designed and then a modaland harmonic analysis are carried out in view of outlining the main outcomes
CONSISTENCY OF LS ESTIMATOR IN SIMPLE LINEAR EV REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
Liu Jixue; Chen Xiru
2005-01-01
Consistency of LS estimate of simple linear EV model is studied. It is shown that under some common assumptions of the model, both weak and strong consistency of the estimate are equivalent but it is not so for quadratic-mean consistency.
Inverse Modelling Problems in Linear Algebra Undergraduate Courses
Martinez-Luaces, Victor E.
2013-01-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…
Locally supersymmetric D=3 non-linear sigma models
Wit, B. de; Tollsten, A. K.; Nicolai, H.
1992-01-01
We study non-linear sigma models with N local supersymmetries in three space-time dimensions. For N=1 and 2 the target space of these models is Riemannian or Kahler, respectively. All N>2 theories are associated with Einstein spaces. For N=3 the target space is quaternionic, while for N=4 it general
Applying the General Linear Model to Repeated Measures Problems.
Pohlmann, John T.; McShane, Michael G.
The purpose of this paper is to demonstrate the use of the general linear model (GLM) in problems with repeated measures on a dependent variable. Such problems include pretest-posttest designs, multitrial designs, and groups by trials designs. For each of these designs, a GLM analysis is demonstrated wherein full models are formed and restrictions…
Inverse Modelling Problems in Linear Algebra Undergraduate Courses
Martinez-Luaces, Victor E.
2013-01-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…
Optical linear algebra processors: noise and error-source modeling.
Casasent, D; Ghosh, A
1985-06-01
The modeling of system and component noise and error sources in optical linear algebra processors (OLAP's) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
Optical linear algebra processors - Noise and error-source modeling
Casasent, D.; Ghosh, A.
1985-01-01
The modeling of system and component noise and error sources in optical linear algebra processors (OLAPs) are considered, with attention to the frequency-multiplexed OLAP. General expressions are obtained for the output produced as a function of various component errors and noise. A digital simulator for this model is discussed.
A random effects generalized linear model for reliability compositive evaluation
Institute of Scientific and Technical Information of China (English)
ZHAO Hui; YU Dan
2009-01-01
This paper first proposes a random effects generalized linear model to evaluate the storage life of one kind of high reliable and small sample-sized products by combining multi-sources information of products coming from the same population but stored at different environments.The relevant algorithms are also provided.Simulation results manifest the soundness and effectiveness of the proposed model.
Logical consistency and sum-constrained linear models
van Perlo -ten Kleij, Frederieke; Steerneman, A.G.M.; Koning, Ruud H.
2006-01-01
A topic that has received quite some attention in the seventies and eighties is logical consistency of sum-constrained linear models. Loosely defined, a sum-constrained model is logically consistent if the restrictions on the parameters and explanatory variables are such that the sum constraint is a
Non-linear Constitutive Model for the Oligocarbonate Polyurethane Material
Institute of Scientific and Technical Information of China (English)
Marek Pawlikowski
2014-01-01
The polyurethane,which was the subject of the constitutive research presented in the paper,was based on oligocarbonate diols Desmophen C2100 produced by Bayer@.The constitutive modelling was performed with a view to applying the material as the inlay of intervertebral disc prostheses.The polyurethane was assumed to be non-linearly viscohyperelastic,isotropic and incompressible.The constitutive equation was derived from the postulated strain energy function.The elastic and rheological constants were identified on the basis of experimental tests,i.e.relaxation tests and monotonic uniaxial tests at two different strain rates,i.e.λ =0.1 min-1 and λ =1.0 min-1.The stiffness tensor was derived and introduced to Abaqus@finite element (FE) software in order to numerically validate the constitutive model.The results of the constants identification and numerical implementation show that the derived constitutive equation is fully adequate to model stress-strain behavior of the polyurethane material.
A family of quantization based piecewise linear filter networks
DEFF Research Database (Denmark)
Sørensen, John Aasted
1992-01-01
A family of quantization-based piecewise linear filter networks is proposed. For stationary signals, a filter network from this family is a generalization of the classical Wiener filter with an input signal and a desired response. The construction of the filter network is based on quantization of...
Linear Power-Flow Models in Multiphase Distribution Networks: Preprint
Energy Technology Data Exchange (ETDEWEB)
Bernstein, Andrey; Dall' Anese, Emiliano
2017-05-26
This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- from advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.
Institute of Scientific and Technical Information of China (English)
孙超平; 杨善林; 施敏加
2012-01-01
文章构建了基于非线性主成分分析法(NLCPA)的SWOT战略定位模型,实现了SWOT定性分析与非线性定量分析的有机结合,充分考虑了影响战略定位SWOT子因素间的关联性.该模型通过引入非线性主成分分析法,避免了AHP-SWOT等基于线性均值SWOT战略定位模型的缺点,提高了分析结果的科学性和可信度.运用该模型对中航工业某飞机装备有限公司战略定位的SWOT因素进行综合评估,采用得到的综合主成分的指标代替原来较多的指标,将主成分的方差贡献率作为权重,以主成分的线性加权值作为SWOT因素的综合评价结果,提高了该公司战略绩效管理定位的效度,并与基于线性均值分析法的SWOT战略定位模型应用结果进行了验证比较.%The SWOT strategic model based on non-linear principal component analysis(NLPCA) is established, which combines the SWOT qualitative analysis and non-linear quantitative analysis. The correlation between the strategic factors is taken into consideration. By introducing the NLPCA method into the model?the deficiency that lies in SWOT strategic model based on linear mean value analysis such as AHP-SWOT can be avoided, which improves the scientificity and reliability of the analytical results. The model is applied to a certain Aircraft Equipment Co. , Ltd. from China Aviation Industry, and the original indicators are replaced by the obtained ones of comprehensive principal component. The variance contribution rate of principal component is used as the weight, and the linearity and weight of the principal component as the comprehensive evaluation results of SWOT factors, which improve the efficiency of the strategic performance management of the company. The application result of new NLPCA-SWOT analysis is superior to that of LMV-SWOT Analysis.
Change-Of-Bases Abstractions for Non-Linear Systems
Sankaranarayanan, Sriram
2012-01-01
We present abstraction techniques that transform a given non-linear dynamical system into a linear system or an algebraic system described by polynomials of bounded degree, such that, invariant properties of the resulting abstraction can be used to infer invariants for the original system. The abstraction techniques rely on a change-of-basis transformation that associates each state variable of the abstract system with a function involving the state variables of the original system. We present conditions under which a given change of basis transformation for a non-linear system can define an abstraction. Furthermore, the techniques developed here apply to continuous systems defined by Ordinary Differential Equations (ODEs), discrete systems defined by transition systems and hybrid systems that combine continuous as well as discrete subsystems. The techniques presented here allow us to discover, given a non-linear system, if a change of bases transformation involving degree-bounded polynomials yielding an alge...
Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables
Henson, Robert A.; Templin, Jonathan L.; Willse, John T.
2009-01-01
This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Frison, Gianluca; Skajaa, Anders
2015-01-01
We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...... is significantly faster than several state-of-the-art IPMs based on sparse linear algebra, and 2) warm-start reduces the average number of iterations by 35-40%.......We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...
Institute of Scientific and Technical Information of China (English)
林穗; 赵菲
2016-01-01
针对在线广告投放中对实时性和高精确度的要求，对比了Hadoop和Spark两种主流平台在实现流程及效率方面的差异，提出了将线性模型结合Spark技术应用在广告投放系统中，并从数值特征、迭代和步长等方面对模型进行优化。经测试表明，调优后的精确度有5％至10％的提升。%The online advertisement is the most important model of Internet profit.It makes the compu-ting advertising a hot point of current research.For the real-time requirement and the high accuracy re-quirement in the online advertising system, the difference between the implementation and efficiency is compared based on the two popular platforms of Hadoop and Spark.And then a linear model is proposed combined with the technology of Spark to be applied in the advertising system.Finally the linear model is optimized from the aspect of numerical characteristics, iteration and step size.The testing result shows that the accuracy has a 5%to 10%increase after the optimization.
Efficient estimation of moments in linear mixed models
Wu, Ping; Zhu, Li-Xing; 10.3150/10-BEJ330
2012-01-01
In the linear random effects model, when distributional assumptions such as normality of the error variables cannot be justified, moments may serve as alternatives to describe relevant distributions in neighborhoods of their means. Generally, estimators may be obtained as solutions of estimating equations. It turns out that there may be several equations, each of them leading to consistent estimators, in which case finding the efficient estimator becomes a crucial problem. In this paper, we systematically study estimation of moments of the errors and random effects in linear mixed models.
A new estimate of the parameters in linear mixed models
Institute of Scientific and Technical Information of China (English)
王松桂; 尹素菊
2002-01-01
In linear mixed models, there are two kinds of unknown parameters: one is the fixed effect, theother is the variance component. In this paper, new estimates of these parameters, called the spectral decom-position estimates, are proposed, Some important statistical properties of the new estimates are established,in particular the linearity of the estimates of the fixed effects with many statistical optimalities. A new methodis applied to two important models which are used in economics, finance, and mechanical fields. All estimatesobtained have good statistical and practical meaning.
Precise Asymptotics of Error Variance Estimator in Partially Linear Models
Institute of Scientific and Technical Information of China (English)
Shao-jun Guo; Min Chen; Feng Liu
2008-01-01
In this paper, we focus our attention on the precise asymptoties of error variance estimator in partially linear regression models, yi = xTi β + g(ti) +εi, 1 ≤i≤n, {εi,i = 1,... ,n } are i.i.d random errors with mean 0 and positive finite variance q2. Following the ideas of Allan Gut and Aurel Spataru[7,8] and Zhang[21],on precise asymptotics in the Baum-Katz and Davis laws of large numbers and precise rate in laws of the iterated logarithm, respectively, and subject to some regular conditions, we obtain the corresponding results in partially linear regression models.
Regularization Paths for Generalized Linear Models via Coordinate Descent
Directory of Open Access Journals (Sweden)
Jerome Friedman
2010-02-01
Full Text Available We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso, ℓ2 (ridge regression and mixtures of the two (the elastic net. The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
A Mathematical Theory of the Gauged Linear Sigma Model
Fan, Huijun; Ruan, Yongbin
2015-01-01
We construct a rigorous mathematical theory of Witten's Gauged Linear Sigma Model (GLSM). Our theory applies to a wide range of examples, including many cases with non-Abelian gauge group. Both the Gromov-Witten theory of a Calabi-Yau complete intersection X and the Landau-Ginzburg dual (FJRW-theory) of X can be expressed as gauged linear sigma models. Furthermore, the Landau-Ginzburg/Calabi-Yau correspondence can be interpreted as a variation of the moment map or a deformation of GIT in the GLSM. This paper focuses primarily on the algebraic theory, while a companion article will treat the analytic theory.
The minimal linear sigma model for the Goldstone Higgs
Feruglio, Ferruccio; Kanshin, Kirill; Machado, Pedro Accioly Nogueira; Rigolin, Stefano; Saa, Sara
2016-01-01
In the context of the minimal SO(5) linear {\\sigma}-model, a complete renormalizable Lagrangian -including gauge bosons and fermions- is considered, with the symmetry softly broken to SO(4). The scalar sector describes both the electroweak Higgs doublet and the singlet {\\sigma}. Varying the {\\sigma} mass would allow to sweep from the regime of perturbative ultraviolet completion to the non-linear one assumed in models in which the Higgs particle is a low-energy remnant of some strong dynamics. We analyze the phenomenological implications and constraints from precision observables and LHC data. Furthermore, we derive the d <= 6 effective Lagrangian in the limit of heavy exotic fermions.
An Example of Competence-Based Learning: Use of Maxima in Linear Algebra for Engineers
Diaz, Ana; Garcia, Alfonsa; de la Villa, Agustin
2011-01-01
This paper analyses the role of Computer Algebra Systems (CAS) in a model of learning based on competences. The proposal is an e-learning model Linear Algebra course for Engineering, which includes the use of a CAS (Maxima) and focuses on problem solving. A reference model has been taken from the Spanish Open University. The proper use of CAS is…
Mathematical Modelling and the Learning Trajectory: Tools to Support the Teaching of Linear Algebra
Cárcamo Bahamonde, Andrea Dorila; Fortuny Aymemí, Josep Maria; Gómez i Urgellés, Joan Vicenç
2017-01-01
In this article we present a didactic proposal for teaching linear algebra based on two compatible theoretical models: emergent models and mathematical modelling. This proposal begins with a problematic situation related to the creation and use of secure passwords, which leads students toward the construction of the concepts of spanning set and…
Mathematical Modelling and the Learning Trajectory: Tools to Support the Teaching of Linear Algebra
Cárcamo Bahamonde, Andrea Dorila; Fortuny Aymemí, Josep Maria; Gómez i Urgellés, Joan Vicenç
2017-01-01
In this article we present a didactic proposal for teaching linear algebra based on two compatible theoretical models: emergent models and mathematical modelling. This proposal begins with a problematic situation related to the creation and use of secure passwords, which leads students toward the construction of the concepts of spanning set and…
Gage, Nicholas A.; Lewis, Timothy J.; Stichter, Janine P.
2012-01-01
Of the myriad practices currently utilized for students with disabilities, particularly students with or at risk for emotional and/or behavioral disorder (EBD), functional behavior assessment (FBA) is a practice with an emerging solid research base. However, the FBA research base relies on single-subject design (SSD) and synthesis has relied on…
Linear System Models for Ultrasonic Imaging: Application to Signal Statistics
Zemp, Roger J.; Abbey, Craig K.; Insana, Michael F.
2009-01-01
Linear equations for modeling echo signals from shift-variant systems forming ultrasonic B-mode, Doppler, and strain images are analyzed and extended. The approach is based on a solution to the homogeneous wave equation for random inhomogeneous media. When the system is shift-variant, the spatial sensitivity function—defined as a spatial weighting function that determines the scattering volume for a fixed point of time—has advantages over the point-spread function traditionally used to analyze ultrasound systems. Spatial sensitivity functions are necessary for determining statistical moments in the context of rigorous image quality assessment, and they are time-reversed copies of point-spread functions for shift variant systems. A criterion is proposed to assess the validity of a local shift-invariance assumption. The analysis reveals realistic situations in which in-phase signals are correlated to the corresponding quadrature signals, which has strong implications for assessing lesion detectability. Also revealed is an opportunity to enhance near- and far-field spatial resolution by matched filtering unfocused beams. The analysis connects several well-known approaches to modeling ultrasonic echo signals. PMID:12839176
Generalized Functional Linear Models With Semiparametric Single-Index Interactions
Li, Yehua
2010-06-01
We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.
Directory of Open Access Journals (Sweden)
Muayad Al-Qaisy
2013-04-01
Full Text Available In this article, multi-input multi-output (MIMO linear model predictive controller (LMPC based on state space model and nonlinear model predictive controller based on neural network (NNMPC are applied on a continuous stirred tank reactor (CSTR. The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA and reactor temperature (T. NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.
Piecewise linear and Boolean models of chemical reaction networks
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-01-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions (xn/(Jn + xn)). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent n is large. However, while the case of small constant J appears in practice, it is not well understood. We provide a mathematical analysis of this limit, and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator. PMID:25412739
Piecewise linear and Boolean models of chemical reaction networks.
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-12-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions ([Formula: see text]). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent [Formula: see text] is large. However, while the case of small constant [Formula: see text] appears in practice, it is not well understood. We provide a mathematical analysis of this limit and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed-form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator.
Design of a novel integrated position sensor based on Hall effects for linear oscillating actuator
Wang, Tianyi; Yan, Liang; Jiao, Zongxia
2015-07-01
Linear oscillating actuator provides linear reciprocate motion directly without other auxiliary components, which is suitable for high integration applications in aerospace industry. Accurate position control is essential for linear oscillating motor and relies on concise measurement of mover position. However, most position measurements are dependent on external complicated sensors, which hinders further integration of linear oscillating actuation system. In this paper, a novel position sensing system for linear oscillating actuator based on Hall effects is proposed to achieve accurate and high integration measurement simultaneously. Axial sensing magnetic field with approximately linear relationship with position is created for direct and convenient measurement. Analytical model of sensing magnetic field is set up for optimization and validated by finite element method and experimental results. Finally, sensing magnets are integrated into motor prototype for experiments. Dynamic position results are tested in experiments and prove to be effective and accurate for position sensing with short-stroke.
Non-linear calibration models for near infrared spectroscopy.
Ni, Wangdong; Nørgaard, Lars; Mørup, Morten
2014-02-27
Different calibration techniques are available for spectroscopic applications that show nonlinear behavior. This comprehensive comparative study presents a comparison of different nonlinear calibration techniques: kernel PLS (KPLS), support vector machines (SVM), least-squares SVM (LS-SVM), relevance vector machines (RVM), Gaussian process regression (GPR), artificial neural network (ANN), and Bayesian ANN (BANN). In this comparison, partial least squares (PLS) regression is used as a linear benchmark, while the relationship of the methods is considered in terms of traditional calibration by ridge regression (RR). The performance of the different methods is demonstrated by their practical applications using three real-life near infrared (NIR) data sets. Different aspects of the various approaches including computational time, model interpretability, potential over-fitting using the non-linear models on linear problems, robustness to small or medium sample sets, and robustness to pre-processing, are discussed. The results suggest that GPR and BANN are powerful and promising methods for handling linear as well as nonlinear systems, even when the data sets are moderately small. The LS-SVM is also attractive due to its good predictive performance for both linear and nonlinear calibrations.
Dynamic Response of Linear Mechanical Systems Modeling, Analysis and Simulation
Angeles, Jorge
2012-01-01
Dynamic Response of Linear Mechanical Systems: Modeling, Analysis and Simulation can be utilized for a variety of courses, including junior and senior-level vibration and linear mechanical analysis courses. The author connects, by means of a rigorous, yet intuitive approach, the theory of vibration with the more general theory of systems. The book features: A seven-step modeling technique that helps structure the rather unstructured process of mechanical-system modeling A system-theoretic approach to deriving the time response of the linear mathematical models of mechanical systems The modal analysis and the time response of two-degree-of-freedom systems—the first step on the long way to the more elaborate study of multi-degree-of-freedom systems—using the Mohr circle Simple, yet powerful simulation algorithms that exploit the linearity of the system for both single- and multi-degree-of-freedom systems Examples and exercises that rely on modern computational toolboxes for both numerical and symbolic compu...
RF Circuit linearity optimization using a general weak nonlinearity model
Cheng, W.; Oude Alink, M.S.; Annema, Anne J.; Croon, Jeroen A.; Nauta, Bram
2012-01-01
This paper focuses on optimizing the linearity in known RF circuits, by exploring the circuit design space that is usually available in today’s deep submicron CMOS technologies. Instead of using brute force numerical optimizers we apply a generalized weak nonlinearity model that only involves AC
EMPIRICAL LIKELIHOOD FOR LINEAR MODELS UNDER m-DEPENDENT ERRORS
Institute of Scientific and Technical Information of China (English)
QinYongsong; JiangBo; LiYufang
2005-01-01
In this paper，the empirical likelihood confidence regions for the regression coefficient in a linear model are constructed under m-dependent errors. It is shown that the blockwise empirical likelihood is a good way to deal with dependent samples.
Mathematical modelling and linear stability analysis of laser fusion cutting
Hermanns, Torsten; Schulz, Wolfgang; Vossen, Georg; Thombansen, Ulrich
2016-06-01
A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process' amount of dynamic behavior.
Plane answers to complex questions the theory of linear models
Christensen, Ronald
1987-01-01
This book was written to rigorously illustrate the practical application of the projective approach to linear models. To some, this may seem contradictory. I contend that it is possible to be both rigorous and illustrative and that it is possible to use the projective approach in practical applications. Therefore, unlike many other books on linear models, the use of projections and sub spaces does not stop after the general theory. They are used wherever I could figure out how to do it. Solving normal equations and using calculus (outside of maximum likelihood theory) are anathema to me. This is because I do not believe that they contribute to the understanding of linear models. I have similar feelings about the use of side conditions. Such topics are mentioned when appropriate and thenceforward avoided like the plague. On the other side of the coin, I just as strenuously reject teaching linear models with a coordinate free approach. Although Joe Eaton assures me that the issues in complicated problems freq...
A least squares estimation method for the linear learning model
B. Wierenga (Berend)
1978-01-01
textabstractThe author presents a new method for estimating the parameters of the linear learning model. The procedure, essentially a least squares method, is easy to carry out and avoids certain difficulties of earlier estimation procedures. Applications to three different data sets are reported, a
Combined forecasts from linear and nonlinear time series models
N. Terui (Nobuhiko); H.K. van Dijk (Herman)
1999-01-01
textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally (non)line
Tableaux and Systemes : Early French Contributions to Linear Production Models
Steenge, Albertus; van den Berg, Richard
2017-01-01
Properly speaking the history of linear production modelling begins in the second half of the 18th century. A comparison between Francois Quesnay's Tableaux economiques and Achilles-Nicolas Isnard’s systèmes des richesses provides insights into the various possible directions within this nascent bra
The Moduli Space in the Gauged Linear Sigma Model
Fan, Huijun; Ruan, Yongbin
2016-01-01
This is a survey article for the mathematical theory of Witten's Gauged Linear Sigma Model, as developed recently by the authors. Instead of developing the theory in the most general setting, in this paper we focus on the description of the moduli.
S-AMP for non-linear observation models
DEFF Research Database (Denmark)
Cakmak, Burak; Winther, Ole; Fleury, Bernard H.
2015-01-01
Recently we presented the S-AMP approach, an extension of approximate message passing (AMP), to be able to handle general invariant matrix ensembles. In this contribution we extend S-AMP to non-linear observation models. We obtain generalized AMP (GAMP) as the special case when the measurement...
A Hierarchy of Linear Threshold Models for the Spread of Political Revolutions on Social Networks
Lang, John C
2015-01-01
We study a linear threshold agent-based model (ABM) for the spread of political revolutions on social networks using empirical network data. We propose new techniques for building a hierarchy of simplified ordinary differential equation (ODE) based models that aim to capture essential features of the ABM, including effects of the actual networks, and give insight in the parameter regime transitions of the ABM. We relate the ABM and the hierarchy of models to a population-level compartmental ODE model that we proposed previously for the spread of political revolutions [1], which is shown to be mathematically consistent with the proposed ABM and provides a way to analyze the global behaviour of the ABM. This consistency with the linear threshold ABM also provides further justification a posteriori for the compartmental model of [1]. Extending concepts from epidemiological modelling, we define a basic reproduction number $R_0$ for the linear threshold ABM and apply it to predict ABM behaviour on empirical networ...
Intuitionistic Fuzzy Weighted Linear Regression Model with Fuzzy Entropy under Linear Restrictions.
Kumar, Gaurav; Bajaj, Rakesh Kumar
2014-01-01
In fuzzy set theory, it is well known that a triangular fuzzy number can be uniquely determined through its position and entropies. In the present communication, we extend this concept on triangular intuitionistic fuzzy number for its one-to-one correspondence with its position and entropies. Using the concept of fuzzy entropy the estimators of the intuitionistic fuzzy regression coefficients have been estimated in the unrestricted regression model. An intuitionistic fuzzy weighted linear regression (IFWLR) model with some restrictions in the form of prior information has been considered. Further, the estimators of regression coefficients have been obtained with the help of fuzzy entropy for the restricted/unrestricted IFWLR model by assigning some weights in the distance function.
Institute of Scientific and Technical Information of China (English)
程东升; 张建武; 叶晓峰; 黄维纲
2003-01-01
A sliding mode control approach based on the feedback linearization is proposed for the electrically controllable clutch of AMT vehicles. The nonlinear dynamic model for the hydraulic actuator associated with clutch is established. By means of the exact feedback linearization procedure of differential geometry, an equivalent, fully controllable and linear model is derived via a homomorphic transformation for the AMT clutch system.Furthermore, a sliding mode control is introduced to improve robustness. The tracking tests are performed using the sliding mode control on a Santana LX passenger car, and the experimental results prove that this nonlinear controller is of fine robustness and high degree of tracking accuracy.
The Fast Linear Accelerator Modeling Engine for FRIB Online Model Service
He, Z; Davidsaver, M; Fukushima, K; Shen, G; Ikegami, M
2016-01-01
Commissioning of a large accelerator facility like FRIB needs support from an online beam dynamics model. Considering the new physics challenges of FRIB such as modeling of non-axisymmetric superconducting RF cavities and multi-charge state acceleration, there is no readily available online beam tuning code. The design code of FRIB super-conducting linac, IMPACT-Z, is not suitable for online tuning because of its code design and running speed. Therefore, the Fast Linear Accelerator Modeling Engine (FLAME), specifically designed to fulfill FRIB's online modeling challenges, is proposed. The physics model of FLAME, especially its novel way of modeling non-axisymmetric superconducting RF cavities using a multipole expansion based thin-lens kick model, is discussed in detail, and the benchmark results against FRIB design code is presented, after which the software design strategy of FLAME and its execution speed is presented.
Defects in the discrete non-linear Schroedinger model
Energy Technology Data Exchange (ETDEWEB)
Doikou, Anastasia, E-mail: adoikou@upatras.gr [University of Patras, Department of Engineering Sciences, Physics Division, GR-26500 Patras (Greece)
2012-01-01
The discrete non-linear Schroedinger (NLS) model in the presence of an integrable defect is examined. The problem is viewed from a purely algebraic point of view, starting from the fundamental algebraic relations that rule the model. The first charges in involution are explicitly constructed, as well as the corresponding Lax pairs. These lead to sets of difference equations, which include particular terms corresponding to the impurity point. A first glimpse regarding the corresponding continuum limit is also provided.
Deterministic operations research models and methods in linear optimization
Rader, David J
2013-01-01
Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear
Linear Sigma Model Toolshed for D-brane Physics
Energy Technology Data Exchange (ETDEWEB)
Hellerman, Simeon
2001-08-23
Building on earlier work, we construct linear sigma models for strings on curved spaces in the presence of branes. Our models include an extremely general class of brane-worldvolume gauge field configurations. We explain in an accessible manner the mathematical ideas which suggest appropriate worldsheet interactions for generating a given open string background. This construction provides an explanation for the appearance of the derived category in D-brane physic complementary to that of recent work of Douglas.
Prediction of Typhoon Tracks Using Dynamic Linear Models
Institute of Scientific and Technical Information of China (English)
Keon-Tae SOHN; H. Joe KWON; Ae-Sook SUH
2003-01-01
This paper presents a study on the statistical forecasts of typhoon tracks. Numerical models havetheir own systematic errors, like a bias. In order to improve the accuracy of track forecasting, a statisticalmodel called DLM (dynamic linear model) is applied to remove the systematic error. In the analysis oftyphoons occurring over the western North Pacific in 1997 and 2000, DLM is useful as an adaptive modelfor the prediction of typhoon tracks.
Estimation linear model using block generalized inverse of a matrix
Jasińska, Elżbieta; Preweda, Edward
2013-01-01
The work shows the principle of generalized linear model, point estimation, which can be used as a basis for determining the status of movements and deformations of engineering objects. The structural model can be put on any boundary conditions, for example, to ensure the continuity of the deformations. Estimation by the method of least squares was carried out taking into account the terms and conditions of the Gauss- Markov for quadratic forms stored using Lagrange function. The original sol...
Linear lattice modeling of the recycler ring at Fermilab
Energy Technology Data Exchange (ETDEWEB)
Xiao, Meiqin; Valishev, Alexander; Nagaslaev, Vladimir P.; /Fermilab; Sajaev, Vadim; /Argonne
2006-06-01
Substantial differences are found in tunes and beta functions between the existing linear model and the real storage ring. They result in difficulties when tuning the machine to new lattice conditions. We are trying to correct the errors by matching the model into the real machine using Orbit Response Matrix (ORM) Fit method. The challenges with ORM particularly in the Recycler ring and the results are presented in this paper.
Model Reduction of Linear Switched Systems by Restricting Discrete Dynamics
DEFF Research Database (Denmark)
Bastug, Mert; Petreczky, Mihaly; Wisniewski, Rafal
2014-01-01
We present a procedure for reducing the number of continuous states of discrete-time linear switched systems, such that the reduced system has the same behavior as the original system for a subset of switching sequences. The proposed method is expected to be useful for abstraction based control s...