Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.
Hahne, J M; Biessmann, F; Jiang, N; Rehbaum, H; Farina, D; Meinecke, F C; Muller, K-R; Parra, L C
2014-03-01
In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Robust nonlinear regression in applications
Lim, Changwon; Sen, Pranab K.; Peddada, Shyamal D.
2013-01-01
Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose gr...
Directory of Open Access Journals (Sweden)
Gurudeo Anand Tularam
2012-01-01
Full Text Available House price prediction continues to be important for government agencies insurance companies and real estate industry. This study investigates the performance of house sales price models based on linear and non-linear approaches to study the effects of selected variables. Linear stepwise Multivariate Regression (MR and nonlinear models of Neural Network (NN and Adaptive Neuro-Fuzzy (ANFIS are developed and compared. The GIS methods are used to integrate the data for the study area (Bathurst, Australia. While it was expected that the nonlinear methods would be much better the analysis shows NN and ANFIS are only slightly better than MR suggesting questions about high R2 often found in the literature. While structural data and macro-finance variables may contribute to higher R2 performance comparison was the goal of this study and besides the Australian data lacked structural elements. The results show that MR model could be improved. Also, the land value and location explained at best about 45% of the sale price variation. The analysis of price forecasts (within the 10% range of the actual prediction on average revealed that the non-linear models performed slightly better (29% than the linear (26%. The inclusion of social data improves the MR prediction in most of the suburbs. The suburbs analysis shows the importance of socially based locations and also variance due to types of housing dominant. In general terms of R2, the NN model (0.45 performed only slightly better than ANFIS 0.39 and better than MR (0.37; but the linear MRsoc performed better (0.42. In suburb level, the NN model (7/15 performed better than ANFIS (3/15 but the linear MR (5/15 was better than ANFIS. The improved linear MR (6/15 performed nearly as well as the non-linear NN. Linear methods appear to just as precise as the the more time consuming non linear methods in most cases for accounting for the differences and variation. However, when a much more in depth analysis is
Learning Inverse Rig Mappings by Nonlinear Regression.
Holden, Daniel; Saito, Jun; Komura, Taku
2016-11-11
We present a framework to design inverse rig-functions - functions that map low level representations of a character's pose such as joint positions or surface geometry to the representation used by animators called the animation rig. Animators design scenes using an animation rig, a framework widely adopted in animation production which allows animators to design character poses and geometry via intuitive parameters and interfaces. Yet most state-of-the-art computer animation techniques control characters through raw, low level representations such as joint angles, joint positions, or vertex coordinates. This difference often stops the adoption of state-of-the-art techniques in animation production. Our framework solves this issue by learning a mapping between the low level representations of the pose and the animation rig. We use nonlinear regression techniques, learning from example animation sequences designed by the animators. When new motions are provided in the skeleton space, the learned mapping is used to estimate the rig controls that reproduce such a motion. We introduce two nonlinear functions for producing such a mapping: Gaussian process regression and feedforward neural networks. The appropriate solution depends on the nature of the rig and the amount of data available for training. We show our framework applied to various examples including articulated biped characters, quadruped characters, facial animation rigs, and deformable characters. With our system, animators have the freedom to apply any motion synthesis algorithm to arbitrary rigging and animation pipelines for immediate editing. This greatly improves the productivity of 3D animation, while retaining the flexibility and creativity of artistic input.
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Bayesian nonlinear regression for large small problems
Chakraborty, Sounak
2012-07-01
Statistical modeling and inference problems with sample sizes substantially smaller than the number of available covariates are challenging. This is known as large p small n problem. Furthermore, the problem is more complicated when we have multiple correlated responses. We develop multivariate nonlinear regression models in this setup for accurate prediction. In this paper, we introduce a full Bayesian support vector regression model with Vapnik\\'s ε-insensitive loss function, based on reproducing kernel Hilbert spaces (RKHS) under the multivariate correlated response setup. This provides a full probabilistic description of support vector machine (SVM) rather than an algorithm for fitting purposes. We have also introduced a multivariate version of the relevance vector machine (RVM). Instead of the original treatment of the RVM relying on the use of type II maximum likelihood estimates of the hyper-parameters, we put a prior on the hyper-parameters and use Markov chain Monte Carlo technique for computation. We have also proposed an empirical Bayes method for our RVM and SVM. Our methods are illustrated with a prediction problem in the near-infrared (NIR) spectroscopy. A simulation study is also undertaken to check the prediction accuracy of our models. © 2012 Elsevier Inc.
Curvatures for Parameter Subsets in Nonlinear Regression
1986-01-01
The relative curvature measures of nonlinearity proposed by Bates and Watts (1980) are extended to an arbitrary subset of the parameters in a normal, nonlinear regression model. In particular, the subset curvatures proposed indicate the validity of linearization-based approximate confidence intervals for single parameters. The derivation produces the original Bates-Watts measures directly from the likelihood function. When the intrinsic curvature is negligible, the Bates-Watts parameter-effec...
Semiparametric maximum likelihood for nonlinear regression with measurement errors.
Suh, Eun-Young; Schafer, Daniel W
2002-06-01
This article demonstrates semiparametric maximum likelihood estimation of a nonlinear growth model for fish lengths using imprecisely measured ages. Data on the species corvina reina, found in the Gulf of Nicoya, Costa Rica, consist of lengths and imprecise ages for 168 fish and precise ages for a subset of 16 fish. The statistical problem may therefore be classified as nonlinear errors-in-variables regression with internal validation data. Inferential techniques are based on ideas extracted from several previous works on semiparametric maximum likelihood for errors-in-variables problems. The illustration of the example clarifies practical aspects of the associated computational, inferential, and data analytic techniques.
Robust Nonlinear Regression in Enzyme Kinetic Parameters Estimation
Directory of Open Access Journals (Sweden)
Maja Marasović
2017-01-01
Full Text Available Accurate estimation of essential enzyme kinetic parameters, such as Km and Vmax, is very important in modern biology. To this date, linearization of kinetic equations is still widely established practice for determining these parameters in chemical and enzyme catalysis. Although simplicity of linear optimization is alluring, these methods have certain pitfalls due to which they more often then not result in misleading estimation of enzyme parameters. In order to obtain more accurate predictions of parameter values, the use of nonlinear least-squares fitting techniques is recommended. However, when there are outliers present in the data, these techniques become unreliable. This paper proposes the use of a robust nonlinear regression estimator based on modified Tukey’s biweight function that can provide more resilient results in the presence of outliers and/or influential observations. Real and synthetic kinetic data have been used to test our approach. Monte Carlo simulations are performed to illustrate the efficacy and the robustness of the biweight estimator in comparison with the standard linearization methods and the ordinary least-squares nonlinear regression. We then apply this method to experimental data for the tyrosinase enzyme (EC 1.14.18.1 extracted from Solanum tuberosum, Agaricus bisporus, and Pleurotus ostreatus. The results on both artificial and experimental data clearly show that the proposed robust estimator can be successfully employed to determine accurate values of Km and Vmax.
Nonlinear wavelet estimation of regression function with random desigm
Institute of Scientific and Technical Information of China (English)
张双林; 郑忠国
1999-01-01
The nonlinear wavelet estimator of regression function with random design is constructed. The optimal uniform convergence rate of the estimator in a ball of Besov space Bp,q? is proved under quite genera] assumpations. The adaptive nonlinear wavelet estimator with near-optimal convergence rate in a wide range of smoothness function classes is also constructed. The properties of the nonlinear wavelet estimator given for random design regression and only with bounded third order moment of the error can be compared with those of nonlinear wavelet estimator given in literature for equal-spaced fixed design regression with i.i.d. Gauss error.
Implementing Variable Selection Techniques in Regression.
Thayer, Jerome D.
Variable selection techniques in stepwise regression analysis are discussed. In stepwise regression, variables are added or deleted from a model in sequence to produce a final "good" or "best" predictive model. Stepwise computer programs are discussed and four different variable selection strategies are described. These…
ASYMPTOTIC EFFICIENT ESTIMATION IN SEMIPARAMETRIC NONLINEAR REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
ZhuZhongyi; WeiBocheng
1999-01-01
In this paper, the estimation method based on the “generalized profile likelihood” for the conditionally parametric models in the paper given by Severini and Wong (1992) is extendedto fixed design semiparametrie nonlinear regression models. For these semiparametrie nonlinear regression models,the resulting estimator of parametric component of the model is shown to beasymptotically efficient and the strong convergence rate of nonparametric component is investigated. Many results (for example Chen (1988) ,Gao & Zhao (1993), Rice (1986) et al. ) are extended to fixed design semiparametric nonlinear regression models.
Nonlinear and Non Normal Regression Models in Physiological Research
1984-01-01
Applications of nonlinear and non normal regression models are in increasing order for appropriate interpretation of complex phenomenon of biomedical sciences. This paper reviews critically some applications of these models physiological research.
Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan
This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi......This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation...... of the predictive regression model is based on a shrinkage estimator to avoid overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by including lags of the dependent variable or other individual variables as predictors, as typically desired...... in macroeconomic and financial applications. Monte Carlo simulations as well as an empirical application to various key measures of real economic activity confirm that kernel ridge regression can produce more accurate forecasts than traditional linear and nonlinear methods for dealing with many predictors based...
Cardiovascular Response Identification Based on Nonlinear Support Vector Regression
Wang, Lu; Su, Steven W.; Chan, Gregory S. H.; Celler, Branko G.; Cheng, Teddy M.; Savkin, Andrey V.
This study experimentally investigates the relationships between central cardiovascular variables and oxygen uptake based on nonlinear analysis and modeling. Ten healthy subjects were studied using cycle-ergometry exercise tests with constant workloads ranging from 25 Watt to 125 Watt. Breath by breath gas exchange, heart rate, cardiac output, stroke volume and blood pressure were measured at each stage. The modeling results proved that the nonlinear modeling method (Support Vector Regression) outperforms traditional regression method (reducing Estimation Error between 59% and 80%, reducing Testing Error between 53% and 72%) and is the ideal approach in the modeling of physiological data, especially with small training data set.
Kernel Partial Least Squares for Nonlinear Regression and Discrimination
Rosipal, Roman; Clancy, Daniel (Technical Monitor)
2002-01-01
This paper summarizes recent results on applying the method of partial least squares (PLS) in a reproducing kernel Hilbert space (RKHS). A previously proposed kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.
Geometric Properties of AR（q） Nonlinear Regression Models
Institute of Scientific and Technical Information of China (English)
LIUYing-ar; WEIBo-cheng
2004-01-01
This paper is devoted to a study of geometric properties of AR(q) nonlinear regression models. We present geometric frameworks for regression parameter space and autoregression parameter space respectively based on the weighted inner product by fisher information matrix. Several geometric properties related to statistical curvatures are given for the models. The results of this paper extended the work of Bates & Watts(1980,1988)[1.2] and Seber & Wild (1989)[3].
An Excel Solver Exercise to Introduce Nonlinear Regression
Pinder, Jonathan P.
2013-01-01
Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a "black box" to the students. Thus, although correct models are…
An Excel Solver Exercise to Introduce Nonlinear Regression
Pinder, Jonathan P.
2013-01-01
Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a "black box" to the students. Thus, although correct models are…
Symmetric Nonlinear Regression. Research Report. ETS RR-07-13
Antal, Tamás
2007-01-01
An estimation tool for symmetric univariate nonlinear regression is presented. The method is based on introducing a nontrivial set of affine coordinates for diffeomorphisms of the real line. The main ingredient making the computations possible is the Connes-Moscovici Hopf algebra of these affine coordinates.
A Toolbox for Nonlinear Regression in R: The Package nlstools
Directory of Open Access Journals (Sweden)
Florent Baty
2015-08-01
Full Text Available Nonlinear regression models are applied in a broad variety of scientific fields. Various R functions are already dedicated to fitting such models, among which the function nls( has a prominent position. Unlike linear regression fitting of nonlinear models relies on non-trivial assumptions and therefore users are required to carefully ensure and validate the entire modeling. Parameter estimation is carried out using some variant of the least- squares criterion involving an iterative process that ideally leads to the determination of the optimal parameter estimates. Therefore, users need to have a clear understanding of the model and its parameterization in the context of the application and data considered, an a priori idea about plausible values for parameter estimates, knowledge of model diagnostics procedures available for checking crucial assumptions, and, finally, an under- standing of the limitations in the validity of the underlying hypotheses of the fitted model and its implication for the precision of parameter estimates. Current nonlinear regression modules lack dedicated diagnostic functionality. So there is a need to provide users with an extended toolbox of functions enabling a careful evaluation of nonlinear regression fits. To this end, we introduce a unified diagnostic framework with the R package nlstools. In this paper, the various features of the package are presented and exemplified using a worked example from pulmonary medicine.
Mirror Prescription Regression: A Differential Interferometric Technique
Directory of Open Access Journals (Sweden)
Brian M. Robinson
2010-01-01
Full Text Available We present a remote, differential method for measuring the prescription of aspheric mirrors using null interferometry in the center-of-curvature configuration. The method requires no equipment beyond that used in a basic interferometery setup (i.e., there are no shearing elements or absolute distance meters. We chose this configuration because of its widespread use. However, the method is generalizable to other configurations with an adjustment of the governing equation. The method involves taking a series of interferograms before and after small, known misalignments are applied to the mirror in the interferometry setup and calculating the prescription (e.g., radius of curvature and conic constant of the mirror, based on these differential measurements, using a nonlinear regression. We apply this method successfully to the testing of a Space Optics Research Lab off-axis parabola with a known focal length of 152.4 mm, a diameter of 76.2 mm, and an off-axis angle of 12°.
CONSERVATIVE ESTIMATING FUNCTIONIN THE NONLINEAR REGRESSION MODEL WITHAGGREGATED DATA
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.
On concurvity in nonlinear and nonparametric regression models
Directory of Open Access Journals (Sweden)
Sonia Amodio
2014-12-01
Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.
Regression based peak load forecasting using a transformation technique
Energy Technology Data Exchange (ETDEWEB)
Haida, Takeshi; Muto, Shoichi (Tokyo Electric Power Co. (Japan). Computer and Communication Research Center)
1994-11-01
This paper presents a regression based daily peak load forecasting method with a transformation technique. In order to forecast the load precisely through a year, the authors should consider seasonal load change, annual load growth and the latest daily load change. To deal with these characteristics in the load forecasting, a transformation technique is presented. This technique consists of a transformation function with translation and reflection methods. The transformation function is estimated with the previous year's data points, in order that the function converts the data points into a set of new data points with preserving the shape of temperature-load relationships in the previous year. Then, the function is slightly translated so that the transformed data points will fit the shape of temperature-load relationships in the year. Finally, multivariate regression analysis with the latest daily loads and weather observations estimates the forecasting model. Large forecasting errors caused by the weather-load nonlinear characteristic in the transitional seasons such as spring and fall are reduced. Performance of the technique which is verified with simulations on actual load data of Tokyo Electric Power Company is also described.
A nonlinear regression model-based predictive control algorithm.
Dubay, R; Abu-Ayyad, M; Hernandez, J M
2009-04-01
This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.
Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models
Bates, Bryson C.
1992-01-01
A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.
Fault Isolation for Nonlinear Systems Using Flexible Support Vector Regression
Directory of Open Access Journals (Sweden)
Yufang Liu
2014-01-01
Full Text Available While support vector regression is widely used as both a function approximating tool and a residual generator for nonlinear system fault isolation, a drawback for this method is the freedom in selecting model parameters. Moreover, for samples with discordant distributing complexities, the selection of reasonable parameters is even impossible. To alleviate this problem we introduce the method of flexible support vector regression (F-SVR, which is especially suited for modelling complicated sample distributions, as it is free from parameters selection. Reasonable parameters for F-SVR are automatically generated given a sample distribution. Lastly, we apply this method in the analysis of the fault isolation of high frequency power supplies, where satisfactory results have been obtained.
Modal Identification Using OMA Techniques: Nonlinearity Effect
Directory of Open Access Journals (Sweden)
E. Zhang
2015-01-01
Full Text Available This paper is focused on an assessment of the state of the art of operational modal analysis (OMA methodologies in estimating modal parameters from output responses of nonlinear structures. By means of the Volterra series, the nonlinear structure excited by random excitation is modeled as best linear approximation plus a term representing nonlinear distortions. As the nonlinear distortions are of stochastic nature and thus indistinguishable from the measurement noise, a protocol based on the use of the random phase multisine is proposed to reveal the accuracy and robustness of the linear OMA technique in the presence of the system nonlinearity. Several frequency- and time-domain based OMA techniques are examined for the modal identification of simulated and real nonlinear mechanical systems. Theoretical analyses are also provided to understand how the system nonlinearity degrades the performance of the OMA algorithms.
Drzewiecki, Wojciech
2016-12-01
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?
Directory of Open Access Journals (Sweden)
Jiangshan Lai
Full Text Available Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.
Nonlinear Identification Using Orthogonal Forward Regression With Nested Optimal Regularization.
Hong, Xia; Chen, Sheng; Gao, Junbin; Harris, Chris J
2015-12-01
An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.
Reduction of the curvature of a class of nonlinear regression models
Institute of Scientific and Technical Information of China (English)
吴翊; 易东云
2000-01-01
It is proved that the curvature of nonlinear model can be reduced to zero by increasing measured data for a class of nonlinear regression models. The result is important to actual problem and has obtained satisfying effect on data fusing.
Fast nonlinear regression method for CT brain perfusion analysis.
Bennink, Edwin; Oosterbroek, Jaap; Kudo, Kohsuke; Viergever, Max A; Velthuis, Birgitta K; de Jong, Hugo W A M
2016-04-01
Although computed tomography (CT) perfusion (CTP) imaging enables rapid diagnosis and prognosis of ischemic stroke, current CTP analysis methods have several shortcomings. We propose a fast nonlinear regression method with a box-shaped model (boxNLR) that has important advantages over the current state-of-the-art method, block-circulant singular value decomposition (bSVD). These advantages include improved robustness to attenuation curve truncation, extensibility, and unified estimation of perfusion parameters. The method is compared with bSVD and with a commercial SVD-based method. The three methods were quantitatively evaluated by means of a digital perfusion phantom, described by Kudo et al. and qualitatively with the aid of 50 clinical CTP scans. All three methods yielded high Pearson correlation coefficients ([Formula: see text]) with the ground truth in the phantom. The boxNLR perfusion maps of the clinical scans showed higher correlation with bSVD than the perfusion maps from the commercial method. Furthermore, it was shown that boxNLR estimates are robust to noise, truncation, and tracer delay. The proposed method provides a fast and reliable way of estimating perfusion parameters from CTP scans. This suggests it could be a viable alternative to current commercial and academic methods.
Printed Arabic Text Recognition using Linear and Nonlinear Regression
Directory of Open Access Journals (Sweden)
Ashraf A. Shahin
2017-01-01
Full Text Available Arabic language is one of the most popular languages in the world. Hundreds of millions of people in many countries around the world speak Arabic as their native speaking. However, due to complexity of Arabic language, recognition of printed and handwritten Arabic text remained untouched for a very long time compared with English and Chinese. Although, in the last few years, significant number of researches has been done in recognizing printed and handwritten Arabic text, it stills an open research field due to cursive nature of Arabic script. This paper proposes automatic printed Arabic text recognition technique based on linear and ellipse regression techniques. After collecting all possible forms of each character, unique code is generated to represent each character form. Each code contains a sequence of lines and ellipses. To recognize fonts, a unique list of codes is identified to be used as a fingerprint of font. The proposed technique has been evaluated using over 14000 different Arabic words with different fonts and experimental results show that average recognition rate of the proposed technique is 86%.
Regression Commonality Analysis: A Technique for Quantitative Theory Building
Nimon, Kim; Reio, Thomas G., Jr.
2011-01-01
When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…
Motulsky, Harvey J; Brown, Ronald E
2006-03-09
Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely) one or more outlier in only about 1-3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives.
Directory of Open Access Journals (Sweden)
Motulsky Harvey J
2006-03-01
Full Text Available Abstract Background Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads to the familiar goal of regression: to minimize the sum of the squares of the vertical or Y-value distances between the points and the curve. Outliers can dominate the sum-of-the-squares calculation, and lead to misleading results. However, we know of no practical method for routinely identifying outliers when fitting curves with nonlinear regression. Results We describe a new method for identifying outliers when fitting data with nonlinear regression. We first fit the data using a robust form of nonlinear regression, based on the assumption that scatter follows a Lorentzian distribution. We devised a new adaptive method that gradually becomes more robust as the method proceeds. To define outliers, we adapted the false discovery rate approach to handling multiple comparisons. We then remove the outliers, and analyze the data using ordinary least-squares regression. Because the method combines robust regression and outlier removal, we call it the ROUT method. When analyzing simulated data, where all scatter is Gaussian, our method detects (falsely one or more outlier in only about 1–3% of experiments. When analyzing data contaminated with one or several outliers, the ROUT method performs well at outlier identification, with an average False Discovery Rate less than 1%. Conclusion Our method, which combines a new method of robust nonlinear regression with a new method of outlier identification, identifies outliers from nonlinear curve fits with reasonable power and few false positives.
Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment
Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos
2013-01-01
In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…
Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment
Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos
2013-01-01
In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…
Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...
Robust Nonlinear Regression: A Greedy Approach Employing Kernels With Application to Image Denoising
Papageorgiou, George; Bouboulis, Pantelis; Theodoridis, Sergios
2017-08-01
We consider the task of robust non-linear regression in the presence of both inlier noise and outliers. Assuming that the unknown non-linear function belongs to a Reproducing Kernel Hilbert Space (RKHS), our goal is to estimate the set of the associated unknown parameters. Due to the presence of outliers, common techniques such as the Kernel Ridge Regression (KRR) or the Support Vector Regression (SVR) turn out to be inadequate. Instead, we employ sparse modeling arguments to explicitly model and estimate the outliers, adopting a greedy approach. The proposed robust scheme, i.e., Kernel Greedy Algorithm for Robust Denoising (KGARD), is inspired by the classical Orthogonal Matching Pursuit (OMP) algorithm. Specifically, the proposed method alternates between a KRR task and an OMP-like selection step. Theoretical results concerning the identification of the outliers are provided. Moreover, KGARD is compared against other cutting edge methods, where its performance is evaluated via a set of experiments with various types of noise. Finally, the proposed robust estimation framework is applied to the task of image denoising, and its enhanced performance in the presence of outliers is demonstrated.
Modern Multivariate Statistical Techniques Regression, Classification, and Manifold Learning
Izenman, Alan Julian
2006-01-01
Describes the advances in computation and data storage that led to the introduction of many statistical tools for high-dimensional data analysis. Focusing on multivariate analysis, this book discusses nonlinear methods as well as linear methods. It presents an integrated mixture of classical and modern multivariate statistical techniques.
Directory of Open Access Journals (Sweden)
Land Walker H
2011-01-01
Full Text Available Abstract Background When investigating covariate interactions and group associations with standard regression analyses, the relationship between the response variable and exposure may be difficult to characterize. When the relationship is nonlinear, linear modeling techniques do not capture the nonlinear information content. Statistical learning (SL techniques with kernels are capable of addressing nonlinear problems without making parametric assumptions. However, these techniques do not produce findings relevant for epidemiologic interpretations. A simulated case-control study was used to contrast the information embedding characteristics and separation boundaries produced by a specific SL technique with logistic regression (LR modeling representing a parametric approach. The SL technique was comprised of a kernel mapping in combination with a perceptron neural network. Because the LR model has an important epidemiologic interpretation, the SL method was modified to produce the analogous interpretation and generate odds ratios for comparison. Results The SL approach is capable of generating odds ratios for main effects and risk factor interactions that better capture nonlinear relationships between exposure variables and outcome in comparison with LR. Conclusions The integration of SL methods in epidemiology may improve both the understanding and interpretation of complex exposure/disease relationships.
Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
2007-01-01
This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...
CONFIDENCE REGIONS IN TERMS OF STATISTICAL CURVATURE FOR AR(q) NONLINEAR REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
刘应安; 韦博成
2004-01-01
This paper constructs a set of confidence regions of parameters in terms of statistical curvatures for AR(q) nonlinear regression models. The geometric frameworks are proposed for the model. Then several confidence regions for parameters and parameter subsets in terms of statistical curvatures are given based on the likelihood ratio statistics and score statistics. Several previous results, such as [1] and [2] are extended to AR(q)nonlinear regression models.
Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali
2014-05-01
Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.
Nonlinear dynamic macromodeling techniques for audio systems
Ogrodzki, Jan; Bieńkowski, Piotr
2015-09-01
This paper develops a modelling method and a models identification technique for the nonlinear dynamic audio systems. Identification is performed by means of a behavioral approach based on a polynomial approximation. This approach makes use of Discrete Fourier Transform and Harmonic Balance Method. A model of an audio system is first created and identified and then it is simulated in real time using an algorithm of low computational complexity. The algorithm consists in real time emulation of the system response rather than in simulation of the system itself. The proposed software is written in Python language using object oriented programming techniques. The code is optimized for a multithreads environment.
Two biased estimation techniques in linear regression: Application to aircraft
Klein, Vladislav
1988-01-01
Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.
Ahn, Kuk-Hyun; Palmer, Richard
2016-09-01
Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes 'spatial proximity' based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. 'Spatial proximity' based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.
Unbalance identification using the least angle regression technique
Chatzisavvas, Ioannis; Dohnal, Fadi
2015-01-01
The present investigation proposes a robust procedure for unbalance identification using the equivalent load method based on sparse vibration measurements. The procedure is demonstrated and benchmarked on an example rotor at constant speed. Since the number of measuring positions is much smaller than the number of possible fault locations, performing unbalance identification leads to an ill-posed problem. This problem was tried to be overcome previously with modal expansion in the time domain and with several linear regressions in the frequency domain. However, since the solution to the problem is a sparse equivalent force vector, these methods cannot provide a robust identification procedure. A robust identification can only be achieved by providing a-priori information on the number of unbalances to be identified. The presently proposed procedure achieves more precise unbalance identification without the need of a-priori information by incorporating a regularization technique. A well-known technique for producing sparse solutions is the Least Absolute Shrinkage and Selection Operator (LASSO). The proposed procedure is based on the generalized technique Least Angle Regression (LAR) which finds all the solutions of LASSO. A comparison of the time-domain approach, the frequency-domain approach and the proposed technique is made and the superiority of the latter technique in identifying the number of possible fault locations is highlighted. The selection of the threshold of the convergence algorithm of LAR as well as the selection of the value of the Lagrangian multiplier is discussed in some detail.
Röthig, Andreas; Chiarella, Carl
2006-01-01
This article explores nonlinearities in the response of speculators' trading activity to price changes in live cattle, corn, and lean hog futures markets. Analyzing weekly data from March 4, 1997 to December 27, 2005, we reject linearity in all of these markets. Using smooth transition regression models, we find a similar structure of nonlinearities with regard to the number of different regimes, the choice of the transition variable, and the value at which the transition occurs.
Hartmann, Armin; Van Der Kooij, Anita J; Zeeck, Almut
2009-07-01
In explorative regression studies, linear models are often applied without questioning the linearity of the relations between the predictor variables and the dependent variable, or linear relations are taken as an approximation. In this study, the method of regression with optimal scaling transformations is demonstrated. This method does not require predefined nonlinear functions and results in easy-to-interpret transformations that will show the form of the relations. The method is illustrated using data from a German multicenter project on the indication criteria for inpatient or day clinic psychotherapy treatment. The indication criteria to include in the regression model were selected with the Lasso, which is a tool for predictor selection that overcomes the disadvantages of stepwise regression methods. The resulting prediction model indicates that treatment status is (approximately) linearly related to some criteria and nonlinearly related to others.
Study on Solar Energetic proton (SEP) Prediction using Regression Technique
Yoon, KiChang; Kim, Jae-Hun; Kim, Young Yun; Kwon, Yongki; Wi, Gwan-sik
2016-07-01
It is well known that Solar Energetic Proton (SEP) can cause significant effects on electric devices in satellite such as displacement damage and single event effect. It can be dangerous to flight crew/passenger flying high altitude with polar route, and therefore, it is essential that it should be predicted in advance to mitigate radiation exposure risk. However, SEP has been hard to predict, because it is not well-connected solar activities such as solar flare, coronal mass ejection (CME). In this study, we analyzed the variation pattern of proton event from 2000 to 2015, and suggested optimum Gaussian function which can well describe the maximum value of previous event, after then, we finally adopted the regression technique to predict SEP value repetitively. This paper shows that the maximum value and duration of ongoing SEP events can be well predicted, but this model typically has large errors in case of predicting starting point and occurrence of SEP events.
On Calculating the Hougaard Measure of Skewness in a Nonlinear Regression Model with Two Parameters
Directory of Open Access Journals (Sweden)
S. A. EL-Shehawy
2009-01-01
Full Text Available Problem statement: This study presented an alternative computational algorithm for determining the values of the Hougaard measure of skewness as a nonlinearity measure in a Nonlinear Regression model (NLR-model with two parameters. Approach: These values indicated a degree of a nonlinear behavior in the estimator of the parameter in a NLR-model. Results: We applied the suggested algorithm on an example of a NLR-model in which there is a conditionally linear parameter. The algorithm is mainly based on many earlier studies in measures of nonlinearity. The algorithm was suited for implementation using computer algebra systems such as MAPLE, MATLAB and MATHEMATICA. Conclusion/Recommendations: The results with the corresponding output the same considering example will be compared with the results in some earlier studies.
REGRESSION TESTING : TABU SEARCH TECHNIQUE FOR CODE COVERAGE
Directory of Open Access Journals (Sweden)
T.Prem Jacob
2013-07-01
Full Text Available Software testing is one of the most expensive and critical activities which carries out every time in order to give a best quality of a software product. Here the regression testing which is based on testing mechanism is used to analyze the source code changes and also to make sure that the changes that does not establish new bugs in theearlier validated codes. Now a days many innovative methods are raised in performing the software testing, among them the unit testing which uses minimum time frame and gives more effort in performing a task. Under lots of schedule the unit testing mechanism is done by more developers as the software companies has an enough time to find cooperation among different operations like functionality, quality and time to market. There is an essential to reduce the unit testing time by making it as an automated one and also by making its process as more optimistic.Here, this paper propose a technique called Tabu search based technique for an effective code coverage to cyclomatic complexity which is used to measure the complexity of a program.
Multiple regression technique for Pth degree polynominals with and without linear cross products
Davis, J. W.
1973-01-01
A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.
Nonlinear continua fundaments for the computational techniques
Dvorkin, Eduardo N
2005-01-01
Offers a presentation of Continuum Mechanics, oriented towards numerical applications in the nonlinear analysis of solids, structures and fluid mechanics. This book develops general curvilinear coordinator kinematics of the continuum deformation using general curvilinear coordinates.
Nonlinear acoustic techniques for landmine detection.
Korman, Murray S; Sabatier, James M
2004-12-01
Measurements of the top surface vibration of a buried (inert) VS 2.2 anti-tank plastic landmine reveal significant resonances in the frequency range between 80 and 650 Hz. Resonances from measurements of the normal component of the acoustically induced soil surface particle velocity (due to sufficient acoustic-to-seismic coupling) have been used in detection schemes. Since the interface between the top plate and the soil responds nonlinearly to pressure fluctuations, characteristics of landmines, the soil, and the interface are rich in nonlinear physics and allow for a method of buried landmine detection not previously exploited. Tuning curve experiments (revealing "softening" and a back-bone curve linear in particle velocity amplitude versus frequency) help characterize the nonlinear resonant behavior of the soil-landmine oscillator. The results appear to exhibit the characteristics of nonlinear mesoscopic elastic behavior, which is explored. When two primary waves f1 and f2 drive the soil over the mine near resonance, a rich spectrum of nonlinearly generated tones is measured with a geophone on the surface over the buried landmine in agreement with Donskoy [SPIE Proc. 3392, 221-217 (1998); 3710, 239-246 (1999)]. In profiling, particular nonlinear tonals can improve the contrast ratio compared to using either primary tone in the spectrum.
Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.
Hu, Yi-Chung
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.
Multilayer Perceptron for Robust Nonlinear Interval Regression Analysis Using Genetic Algorithms
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets. PMID:25110755
Energy Technology Data Exchange (ETDEWEB)
Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States); Mahdi, Adam, E-mail: amahdi@ncsu.edu [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Majda, Andrew J., E-mail: jonjon@cims.nyu.edu [Department of Mathematics and Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 (United States)
2014-01-15
A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.
Scarneciu, Camelia C; Sangeorzan, Livia; Rus, Horatiu; Scarneciu, Vlad D; Varciu, Mihai S; Andreescu, Oana; Scarneciu, Ioan
2017-01-01
This study aimed at assessing the incidence of pulmonary hypertension (PH) at newly diagnosed hyperthyroid patients and at finding a simple model showing the complex functional relation between pulmonary hypertension in hyperthyroidism and the factors causing it. The 53 hyperthyroid patients (H-group) were evaluated mainly by using an echocardiographical method and compared with 35 euthyroid (E-group) and 25 healthy people (C-group). In order to identify the factors causing pulmonary hypertension the statistical method of comparing the values of arithmetical means is used. The functional relation between the two random variables (PAPs and each of the factors determining it within our research study) can be expressed by linear or non-linear function. By applying the linear regression method described by a first-degree equation the line of regression (linear model) has been determined; by applying the non-linear regression method described by a second degree equation, a parabola-type curve of regression (non-linear or polynomial model) has been determined. We made the comparison and the validation of these two models by calculating the determination coefficient (criterion 1), the comparison of residuals (criterion 2), application of AIC criterion (criterion 3) and use of F-test (criterion 4). From the H-group, 47% have pulmonary hypertension completely reversible when obtaining euthyroidism. The factors causing pulmonary hypertension were identified: previously known- level of free thyroxin, pulmonary vascular resistance, cardiac output; new factors identified in this study- pretreatment period, age, systolic blood pressure. According to the four criteria and to the clinical judgment, we consider that the polynomial model (graphically parabola- type) is better than the linear one. The better model showing the functional relation between the pulmonary hypertension in hyperthyroidism and the factors identified in this study is given by a polynomial equation of second
Aboveground biomass and carbon stocks modelling using non-linear regression model
Ain Mohd Zaki, Nurul; Abd Latif, Zulkiflee; Nazip Suratman, Mohd; Zainee Zainal, Mohd
2016-06-01
Aboveground biomass (AGB) is an important source of uncertainty in the carbon estimation for the tropical forest due to the variation biodiversity of species and the complex structure of tropical rain forest. Nevertheless, the tropical rainforest holds the most extensive forest in the world with the vast diversity of tree with layered canopies. With the usage of optical sensor integrate with empirical models is a common way to assess the AGB. Using the regression, the linkage between remote sensing and a biophysical parameter of the forest may be made. Therefore, this paper exemplifies the accuracy of non-linear regression equation of quadratic function to estimate the AGB and carbon stocks for the tropical lowland Dipterocarp forest of Ayer Hitam forest reserve, Selangor. The main aim of this investigation is to obtain the relationship between biophysical parameter field plots with the remotely-sensed data using nonlinear regression model. The result showed that there is a good relationship between crown projection area (CPA) and carbon stocks (CS) with Pearson Correlation (p < 0.01), the coefficient of correlation (r) is 0.671. The study concluded that the integration of Worldview-3 imagery with the canopy height model (CHM) raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the lowland Dipterocarp forest.
Fang, Sheng; Guo, Hua
2013-01-01
The parallel magnetic resonance imaging (parallel imaging) technique reduces the MR data acquisition time by using multiple receiver coils. Coil sensitivity estimation is critical for the performance of parallel imaging reconstruction. Currently, most coil sensitivity estimation methods are based on linear interpolation techniques. Such methods may result in Gibbs-ringing artifact or resolution loss, when the resolution of coil sensitivity data is limited. To solve the problem, we proposed a nonlinear coil sensitivity estimation method based on steering kernel regression, which performs a local gradient guided interpolation to the coil sensitivity. The in vivo experimental results demonstrate that this method can effectively suppress Gibbs ringing artifact in coil sensitivity and reduces both noise and residual aliasing artifact level in SENSE reconstruction.
Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.
Kawashima, Issaku; Kumano, Hiroaki
2017-01-01
Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.
Energy Technology Data Exchange (ETDEWEB)
Barus, R. P. P., E-mail: rismawan.ppb@gmail.com [Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung and Centre for Material and Technical Product, Jalan Sangkuriang No. 14 Bandung (Indonesia); Tjokronegoro, H. A.; Leksono, E. [Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung (Indonesia); Ismunandar [Chemistry Study, Faculty of Mathematics and Science, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung (Indonesia)
2014-09-25
Fuel cells are promising new energy conversion devices that are friendly to the environment. A set of control systems are required in order to operate a fuel cell based power plant system optimally. For the purpose of control system design, an accurate fuel cell stack model in describing the dynamics of the real system is needed. Currently, linear model are widely used for fuel cell stack control purposes, but it has limitations in narrow operation range. While nonlinear models lead to nonlinear control implemnetation whos more complex and hard computing. In this research, nonlinear cancellation technique will be used to transform a nonlinear model into a linear form while maintaining the nonlinear characteristics. The transformation is done by replacing the input of the original model by a certain virtual input that has nonlinear relationship with the original input. Then the equality of the two models is tested by running a series of simulation. Input variation of H2, O2 and H2O as well as disturbance input I (current load) are studied by simulation. The error of comparison between the proposed model and the original nonlinear model are less than 1 %. Thus we can conclude that nonlinear cancellation technique can be used to represent fuel cell nonlinear model in a simple linear form while maintaining the nonlinear characteristics and therefore retain the wide operation range.
Energy Technology Data Exchange (ETDEWEB)
Gunay, Ahmet [Deparment of Environmental Engineering, Faculty of Engineering and Architecture, Balikesir University (Turkey)], E-mail: ahmetgunay2@gmail.com
2007-09-30
The experimental data of ammonium exchange by natural Bigadic clinoptilolite was evaluated using nonlinear regression analysis. Three two-parameters isotherm models (Langmuir, Freundlich and Temkin) and three three-parameters isotherm models (Redlich-Peterson, Sips and Khan) were used to analyse the equilibrium data. Fitting of isotherm models was determined using values of standard normalization error procedure (SNE) and coefficient of determination (R{sup 2}). HYBRID error function provided lowest sum of normalized error and Khan model had better performance for modeling the equilibrium data. Thermodynamic investigation indicated that ammonium removal by clinoptilolite was favorable at lower temperatures and exothermic in nature.
Describing Adequacy of cure with maximum hardness ratios and non-linear regression.
Bouschlicher, Murray; Berning, Kristen; Qian, Fang
2008-01-01
Knoop Hardness (KH) ratios (HR) > or = 80% are commonly used as criteria for the adequate cure of a composite. These per-specimen HRs can be misleading, as both numerator and denominator may increase concurrently, prior to reaching an asymptotic, top-surface maximum hardness value (H(MAX)). Extended cure times were used to establish H(MAX) and descriptive statistics, and non-linear regression analysis were used to describe the relationship between exposure duration and HR and predict the time required for HR-H(MAX) = 80%. Composite samples 2.00 x 5.00 mm diameter (n = 5/grp) were cured for 10 seconds, 20 seconds, 40 seconds, 60 seconds, 90 seconds, 120 seconds, 180 seconds and 240 seconds in a 2-composite x 2-light curing unit design. A microhybrid (Point 4, P4) or microfill resin (Heliomolar, HM) composite was cured with a QTH or LED light curing unit and then stored in the dark for 24 hours prior to KH testing. Non-linear regression was calculated with: H = (H(MAX)-c)(1-e(-kt)) +c, H(MAX) = maximum hardness (a theoretical asymptotic value), c = constant (t = 0), k = rate constant and t = exposure duration describes the relationship between radiant exposure (irradiance x time) and HRs. Exposure durations for HR-H(MAX) = 80% were calculated. Two-sample t-tests for pairwise comparisons evaluated relative performance of the light curing units for similar surface x composite x exposure (10-90s). A good measure of goodness-of-fit of the non-linear regression, r2, ranged from 0.68-0.95. (mean = 0.82). Microhybrid (P4) exposure to achieve HR-H(MAX = 80% was 21 seconds for QTH and 34 seconds for the LED light curing unit. Corresponding values for microfill (HM) were 71 and 74 seconds, respectively. P4 HR-H(MAX) of LED vs QTH was statistically similar for 10 to 40 seconds, while HM HR-H(MAX) of LED was significantly lower than QTH for 10 to 40 seconds. It was concluded that redefined hardness ratios based on maximum hardness used in conjunction with non-linear regression
Heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions.
Lachos, Victor H; Bandyopadhyay, Dipankar; Garay, Aldo M
2011-08-01
An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. We derive a simple EM-type algorithm for iteratively computing maximum likelihood (ML) estimates and the observed information matrix is derived analytically. Simulation studies demonstrate the robustness of this flexible class against outlying and influential observations, as well as nice asymptotic properties of the proposed EM-type ML estimates. Finally, the methodology is illustrated using an ultrasonic calibration data.
Nonlinear plasmonic imaging techniques and their biological applications
Deka, Gitanjal; Sun, Chi-Kuang; Fujita, Katsumasa; Chu, Shi-Wei
2017-01-01
Nonlinear optics, when combined with microscopy, is known to provide advantages including novel contrast, deep tissue observation, and minimal invasiveness. In addition, special nonlinearities, such as switch on/off and saturation, can enhance the spatial resolution below the diffraction limit, revolutionizing the field of optical microscopy. These nonlinear imaging techniques are extremely useful for biological studies on various scales from molecules to cells to tissues. Nevertheless, in most cases, nonlinear optical interaction requires strong illumination, typically at least gigawatts per square centimeter intensity. Such strong illumination can cause significant phototoxicity or even photodamage to fragile biological samples. Therefore, it is highly desirable to find mechanisms that allow the reduction of illumination intensity. Surface plasmon, which is the collective oscillation of electrons in metal under light excitation, is capable of significantly enhancing the local field around the metal nanostructures and thus boosting up the efficiency of nonlinear optical interactions of the surrounding materials or of the metal itself. In this mini-review, we discuss the recent progress of plasmonics in nonlinear optical microscopy with a special focus on biological applications. The advancement of nonlinear imaging modalities (including incoherent/coherent Raman scattering, two/three-photon luminescence, and second/third harmonic generations that have been amalgamated with plasmonics), as well as the novel subdiffraction limit imaging techniques based on nonlinear behaviors of plasmonic scattering, is addressed.
Nonlinear plasmonic imaging techniques and their biological applications
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Deka Gitanjal
2016-07-01
Full Text Available Nonlinear optics, when combined with microscopy, is known to provide advantages including novel contrast, deep tissue observation, and minimal invasiveness. In addition, special nonlinearities, such as switch on/off and saturation, can enhance the spatial resolution below the diffraction limit, revolutionizing the field of optical microscopy. These nonlinear imaging techniques are extremely useful for biological studies on various scales from molecules to cells to tissues. Nevertheless, in most cases, nonlinear optical interaction requires strong illumination, typically at least gigawatts per square centimeter intensity. Such strong illumination can cause significant phototoxicity or even photodamage to fragile biological samples. Therefore, it is highly desirable to find mechanisms that allow the reduction of illumination intensity. Surface plasmon, which is the collective oscillation of electrons in metal under light excitation, is capable of significantly enhancing the local field around the metal nanostructures and thus boosting up the efficiency of nonlinear optical interactions of the surrounding materials or of the metal itself. In this mini-review, we discuss the recent progress of plasmonics in nonlinear optical microscopy with a special focus on biological applications. The advancement of nonlinear imaging modalities (including incoherent/coherent Raman scattering, two/three-photon luminescence, and second/third harmonic generations that have been amalgamated with plasmonics, as well as the novel subdiffraction limit imaging techniques based on nonlinear behaviors of plasmonic scattering, is addressed.
Poullis, Michael
2014-11-01
EuroSCORE II, despite improving on the original EuroSCORE system, has not solved all the calibration and predictability issues. Recursive, non-linear and mixed recursive and non-linear regression analysis were assessed with regard to sensitivity, specificity and predictability of the original EuroSCORE and EuroSCORE II systems. The original logistic EuroSCORE, EuroSCORE II and recursive, non-linear and mixed recursive and non-linear regression analyses of these risk models were assessed via receiver operator characteristic curves (ROC) and Hosmer-Lemeshow statistic analysis with regard to the accuracy of predicting in-hospital mortality. Analysis was performed for isolated coronary artery bypass grafts (CABGs) (n = 2913), aortic valve replacement (AVR) (n = 814), mitral valve surgery (n = 340), combined AVR and CABG (n = 517), aortic (n = 350), miscellaneous cases (n = 642), and combinations of the above cases (n = 5576). The original EuroSCORE had an ROC below 0.7 for isolated AVR and combined AVR and CABG. None of the methods described increased the ROC above 0.7. The EuroSCORE II risk model had an ROC below 0.7 for isolated AVR only. Recursive regression, non-linear regression, and mixed recursive and non-linear regression all increased the ROC above 0.7 for isolated AVR. The original EuroSCORE had a Hosmer-Lemeshow statistic that was above 0.05 for all patients and the subgroups analysed. All of the techniques markedly increased the Hosmer-Lemeshow statistic. The EuroSCORE II risk model had a Hosmer-Lemeshow statistic that was significant for all patients (P linear regression failed to improve on the original Hosmer-Lemeshow statistic. The mixed recursive and non-linear regression using the EuroSCORE II risk model was the only model that produced an ROC of 0.7 or above for all patients and procedures and had a Hosmer-Lemeshow statistic that was highly non-significant. The original EuroSCORE and the EuroSCORE II risk models do not have adequate ROC and Hosmer
Tiedeman, C.R.; Kernodle, J.M.; McAda, D.P.
1998-01-01
flow from adjacent regions; irrigation and septic field seepage; and leakage through the Rio Grande, canal, and Cochiti Reservoir beds. Ground water is discharged from the basin by withdrawal; evapotranspiration; subsurface flow; and flow to the Rio Grande, canals, and drains. The transient, three-dimensional numerical model of ground-water flow to which nonlinear-regression methods were applied simulates flow in the Albuquerque Basin from 1900 to March 1995. Six different basin subsurface configurations are considered in the model. These configurations are designed to test the effects of (1) varying the simulated basin thickness, (2) including a hypothesized hydrogeologic unit with large hydraulic conductivity in the western part of the basin (the west basin high-K zone), and (3) substantially lowering the simulated hydraulic conductivity of a fault in the western part of the basin (the low-K fault zone). The model with each of the subsurface configurations was calibrated using a nonlinear least- squares regression technique. The calibration data set includes 802 hydraulic-head measurements that provide broad spatial and temporal coverage of basin conditions, and one measurement of net flow from the Rio Grande and drains to the ground-water system in the Albuquerque area. Data are weighted on the basis of estimates of the standard deviations of measurement errors. The 10 to 12 parameters to which the calibration data as a whole are generally most sensitive were estimated by nonlinear regression, whereas the remaining model parameter values were specified. Results of model calibration indicate that the optimal parameter estimates as a whole are most reasonable in calibrations of the model with with configurations 3 (which contains 1,600-ft-thick basin deposits and the west basin high-K zone), 4 (which contains 5,000-ft-thick basin de
Regional flow duration curves: Geostatistical techniques versus multivariate regression
Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.
2016-10-01
A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.
Regional flow duration curves: Geostatistical techniques versus multivariate regression
Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.
2016-01-01
A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.
L2-gain and passivity techniques in nonlinear control
van der Schaft, Arjan
2017-01-01
This standard text gives a unified treatment of passivity and L2-gain theory for nonlinear state space systems, preceded by a compact treatment of classical passivity and small-gain theorems for nonlinear input-output maps. The synthesis between passivity and L2-gain theory is provided by the theory of dissipative systems. Specifically, the small-gain and passivity theorems and their implications for nonlinear stability and stabilization are discussed from this standpoint. The connection between L2-gain and passivity via scattering is detailed. Feedback equivalence to a passive system and resulting stabilization strategies are discussed. The passivity concepts are enriched by a generalised Hamiltonian formalism, emphasising the close relations with physical modeling and control by interconnection, and leading to novel control methodologies going beyond passivity. The potential of L2-gain techniques in nonlinear control, including a theory of all-pass factorizations of nonlinear systems, and of parametrization...
Nonlinear temporal pulse cleaning techniques and application
Institute of Scientific and Technical Information of China (English)
Yi; Xu; Jianzhou; Wang; Yansui; Huang; Yanyan; Li; Xiaomin; Lu; Yuxin; Leng
2013-01-01
Two different pulse cleaning techniques for ultra-high contrast laser systems are comparably analysed in this work.The first pulse cleaning technique is based on noncollinear femtosecond optical-parametric amplification(NOPA)and second-harmonic generation(SHG)processes.The other is based on cross-polarized wave(XPW)generation.With a double chirped pulse amplifier(double-CPA)scheme,although temporal contrast enhancement in a high-intensity femtosecond Ti:sapphire chirped pulse amplification(CPA)laser system can be achieved based on both of the techniques,the two different pulse cleaning techniques still have their own advantages and are suitable for different contrast enhancement requirements of different laser systems.
Imaging of contact acoustic nonlinearity using synthetic aperture technique.
Yun, Dongseok; Kim, Jongbeom; Jhang, Kyung-Young
2013-09-01
The angle beam incidence and reflection technique for the evaluation of contact acoustic nonlinearity (CAN) at solid-solid contact interfaces (e.g., closed cracks) has recently been developed to overcome the disadvantage of accessing both the inner and outer surfaces of structures for attaching pulsing and receiving transducers in the through-transmission of normal incidence technique. This paper proposes a technique for B-mode imaging of CAN based on the above reflection technique, which uses the synthetic aperture focusing technique (SAFT) and short-time Fourier transform (STFT) to visualize the distribution of the CAN-induced second harmonic magnitude as well as the nonlinear parameter. In order to verify the usefulness of the proposed method, a solid-solid contact interface was tested and the change of the contact acoustic nonlinearity according to the increasing contact pressure was visualized in images of the second harmonic magnitude and the relative nonlinear parameter. The experimental results showed good agreement with the previously developed theory identifying the dependence of the scattered second harmonics on the contact pressure. This technique can be used for the detection and improvement of the sizing accuracy of closed cracks that are difficult to detect using the conventional linear ultrasonic technique.
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Mir Mustafizur Rahman
2014-11-01
Full Text Available Thermal Infrared (TIR remote sensing images of urban environments are increasingly available from airborne and satellite platforms. However, limited access to high-spatial resolution (H-res: ~1 m TIR satellite images requires the use of TIR airborne sensors for mapping large complex urban surfaces, especially at micro-scales. A critical limitation of such H-res mapping is the need to acquire a large scene composed of multiple flight lines and mosaic them together. This results in the same scene components (e.g., roads, buildings, green space and water exhibiting different temperatures in different flight lines. To mitigate these effects, linear relative radiometric normalization (RRN techniques are often applied. However, the Earth’s surface is composed of features whose thermal behaviour is characterized by complexity and non-linearity. Therefore, we hypothesize that non-linear RRN techniques should demonstrate increased radiometric agreement over similar linear techniques. To test this hypothesis, this paper evaluates four (linear and non-linear RRN techniques, including: (i histogram matching (HM; (ii pseudo-invariant feature-based polynomial regression (PIF_Poly; (iii no-change stratified random sample-based linear regression (NCSRS_Lin; and (iv no-change stratified random sample-based polynomial regression (NCSRS_Poly; two of which (ii and iv are newly proposed non-linear techniques. When applied over two adjacent flight lines (~70 km2 of TABI-1800 airborne data, visual and statistical results show that both new non-linear techniques improved radiometric agreement over the previously evaluated linear techniques, with the new fully-automated method, NCSRS-based polynomial regression, providing the highest improvement in radiometric agreement between the master and the slave images, at ~56%. This is ~5% higher than the best previously evaluated linear technique (NCSRS-based linear regression.
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Roseane Cavalcanti dos Santos
2012-08-01
Full Text Available The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G, environments (E, and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.
A fast Stokes inversion technique based on quadratic regression
Teng, Fei; Deng, Yuan-Yong
2016-05-01
Stokes inversion calculation is a key process in resolving polarization information on radiation from the Sun and obtaining the associated vector magnetic fields. Even in the cases of simple local thermodynamic equilibrium (LTE) and where the Milne-Eddington approximation is valid, the inversion problem may not be easy to solve. The initial values for the iterations are important in handling the case with multiple minima. In this paper, we develop a fast inversion technique without iterations. The time taken for computation is only 1/100 the time that the iterative algorithm takes. In addition, it can provide available initial values even in cases with lower spectral resolutions. This strategy is useful for a filter-type Stokes spectrograph, such as SDO/HMI and the developed two-dimensional real-time spectrograph (2DS).
Describing Growth Pattern of Bali Cows Using Non-linear Regression Models
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Mohd. Hafiz A.W
2016-12-01
Full Text Available The objective of this study was to evaluate the best fit non-linear regression model to describe the growth pattern of Bali cows. Estimates of asymptotic mature weight, rate of maturing and constant of integration were derived from Brody, von Bertalanffy, Gompertz and Logistic models which were fitted to cross-sectional data of body weight taken from 74 Bali cows raised in MARDI Research Station Muadzam Shah Pahang. Coefficient of determination (R2 and residual mean squares (MSE were used to determine the best fit model in describing the growth pattern of Bali cows. Von Bertalanffy model was the best model among the four growth functions evaluated to determine the mature weight of Bali cattle as shown by the highest R2 and lowest MSE values (0.973 and 601.9, respectively, followed by Gompertz (0.972 and 621.2, respectively, Logistic (0.971 and 648.4, respectively and Brody (0.932 and 660.5, respectively models. The correlation between rate of maturing and mature weight was found to be negative in the range of -0.170 to -0.929 for all models, indicating that animals of heavier mature weight had lower rate of maturing. The use of non-linear model could summarize the weight-age relationship into several biologically interpreted parameters compared to the entire lifespan weight-age data points that are difficult and time consuming to interpret.
On diagrammatic technique for nonlinear dynamical systems
Semenyakin, Mykola
2014-01-01
In this paper we investigate phase flows over $\\mathbb{C}^n$ and $\\mathbb{R}^n$ generated by vector fields $V=\\sum P^{i}\\partial_i$ where $P^{i}$ are finite degree polynomials. With the convenient diagrammatic technique we get expressions for evolution operators $ev\\{V|t\\}: x(0)\\mapsto x(t)$ through the series in powers of $x(0)$ and $t$, represented as sum over all trees of particular type. Estimates are made for the radius of convergence in some particular cases. The phase flows behavior in the neighborhood of vector field fixed points are examined. Resonance cases are considered separately.
On diagrammatic technique for nonlinear dynamical systems
Semenyakin, Mykola
2014-01-01
In this paper we investigate phase flows over $\\mathbb{C}^n$ and $\\mathbb{R}^n$ generated by vector fields $V=\\sum P^{i}\\partial_i$ where $P^{i}$ are finite degree polynomials. With the convenient diagrammatic technique we get expressions for evolution operators $ev\\{V|t\\}: x(0)\\mapsto x(t)$ through the series in powers of $x(0)$ and $t$, represented as sum over all trees of particular type. Estimates are made for the radius of convergence in some particular cases. The phase flows behavior in...
Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza
2013-03-01
Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Dynamic structural correlation via nonlinear programming techniques
Ting, T.; Ojalvo, I. U.
1988-01-01
A solution to the correlation between structural dynamic test results and finite element analyses of the same components is presented in this paper. Basically, the method can be categorized as a Levenberg-Marquardt type Gauss-Newton method which requires only the differences between FE modal analyses and test results and their first derivatives with respect to preassigned design variables. With proper variable normalization and equation scaling, the method has been made numerically better-conditioned and the inclusion of the Levenberg-Marquardt technique overcomes any remaining difficulty encountered in inverting singular or near-singular matrices. An important feature is that each iteration requires only one function evaluation along with the associated design sensitivity analysis and so the procedure is computationally efficient.
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Hongjian Wang
2014-01-01
Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.
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Yi-Ming Chen
2017-01-01
Full Text Available Noninvasive medical procedures are usually preferable to their invasive counterparts in the medical community. Anemia examining through the palpebral conjunctiva is a convenient noninvasive procedure. The procedure can be automated to reduce the medical cost. We propose an anemia examining approach by using a Kalman filter (KF and a regression method. The traditional KF is often used in time-dependent applications. Here, we modified the traditional KF for the time-independent data in medical applications. We simply compute the mean value of the red component of the palpebral conjunctiva image as our recognition feature and use a penalty regression algorithm to find a nonlinear curve that best fits the data of feature values and the corresponding levels of hemoglobin (Hb concentration. To evaluate the proposed approach and several relevant approaches, we propose a risk evaluation scheme, where the entire Hb spectrum is divided into high-risk, low-risk, and doubtful intervals for anemia. The doubtful interval contains the Hb threshold, say 11 g/dL, separating anemia and nonanemia. A suspect sample is the sample falling in the doubtful interval. For the anemia screening purpose, we would like to have as less suspect samples as possible. The experimental results show that the modified KF reduces the number of suspect samples significantly for all the approaches considered here.
Braess, Dietrich; Dette, Holger
2004-01-01
We consider maximin and Bayesian D -optimal designs for nonlinear regression models. The maximin criterion requires the specification of a region for the nonlinear parameters in the model, while the Bayesian optimality criterion assumes that a prior distribution for these parameters is available. It was observed empirically by many authors that an increase of uncertainty in the prior information (i.e. a larger range for the parameter space in the maximin criterion or a larger variance of the ...
Lamb Wave Technique for Ultrasonic Nonlinear Characterization in Elastic Plates
Energy Technology Data Exchange (ETDEWEB)
Lee, Tae Hun; Kim, Chung Seok; Jhang, Kyung Young [Hanyang University, Seoul (Korea, Republic of)
2010-10-15
Since the acoustic nonlinearity is sensitive to the minute variation of material properties, the nonlinear ultrasonic technique(NUT) has been considered as a promising method to evaluate the material degradation or fatigue. However, there are certain limitations to apply the conventional NUT using the bulk wave to thin plates. In case of plates, the use of Lamb wave can be considered, however, the propagation characteristics of Lamb wave are completely different with the bulk wave, and thus the separate study for the nonlinearity of Lamb wave is required. For this work, this paper analyzed first the conditions of mode pair suitable for the practical application as well as for the cumulative propagation of quadratic harmonic frequency and summarized the result in for conditions: phase matching, non-zero power flux, group velocity matching, and non-zero out-of-plane displacement. Experimental results in aluminum plates showed that the amplitude of the secondary Lamb wave and nonlinear parameter grew up with increasing propagation distance at the mode pair satisfying the above all conditions and that the ration of nonlinear parameters measured in Al6061-T6 and Al1100-H15 was closed to the ratio of the absolute nonlinear parameters
APPLICATION OF NONLINEAR WATERMARK TECHNIQUES IN DIGITAL LIBRARIES
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A digital watermark is an invisible mark embedded in a digital image that may be used for a number of different purposes including copyright protection. Due to the urgent need for protecting the copyright of digital products in digital library, digital watermarking has been proposed as a solution to this problem. This letter describes potential situations that nonlinear theory can be used to enhance robustness and security of the watermark in digital library. Some nonlinear watermark techniques have been enumerated. Experimental results show that the proposed scheme is superior to the general watermark scheme both in security and robustness in digital library.
Non-linear regression model for spatial variation in precipitation chemistry for South India
Siva Soumya, B.; Sekhar, M.; Riotte, J.; Braun, Jean-Jacques
Chemical composition of rainwater changes from sea to inland under the influence of several major factors - topographic location of area, its distance from sea, annual rainfall. A model is developed here to quantify the variation in precipitation chemistry under the influence of inland distance and rainfall amount. Various sites in India categorized as 'urban', 'suburban' and 'rural' have been considered for model development. pH, HCO 3, NO 3 and Mg do not change much from coast to inland while, SO 4 and Ca change is subjected to local emissions. Cl and Na originate solely from sea salinity and are the chemistry parameters in the model. Non-linear multiple regressions performed for the various categories revealed that both rainfall amount and precipitation chemistry obeyed a power law reduction with distance from sea. Cl and Na decrease rapidly for the first 100 km distance from sea, then decrease marginally for the next 100 km, and later stabilize. Regression parameters estimated for different cases were found to be consistent ( R2 ˜ 0.8). Variation in one of the parameters accounted for urbanization. Model was validated using data points from the southern peninsular region of the country. Estimates are found to be within 99.9% confidence interval. Finally, this relationship between the three parameters - rainfall amount, coastline distance, and concentration (in terms of Cl and Na) was validated with experiments conducted in a small experimental watershed in the south-west India. Chemistry estimated using the model was in good correlation with observed values with a relative error of ˜5%. Monthly variation in the chemistry is predicted from a downscaling model and then compared with the observed data. Hence, the model developed for rain chemistry is useful in estimating the concentrations at different spatio-temporal scales and is especially applicable for south-west region of India.
Alves, Larissa A.; de Castro, Arthur H.; de Mendonça, Fernanda G.; de Mesquita, João P.
2016-05-01
The oxygenated functional groups present on the surface of carbon dots with an average size of 2.7 ± 0.5 nm were characterized by a variety of techniques. In particular, we discussed the fit data of potentiometric titration curves using a nonlinear regression method based on the Levenberg-Marquardt algorithm. The results obtained by statistical treatment of the titration curve data showed that the best fit was obtained considering the presence of five Brønsted-Lowry acids on the surface of the carbon dots with constant ionization characteristics of carboxylic acids, cyclic ester, phenolic and pyrone-like groups. The total number of oxygenated acid groups obtained was 5 mmol g-1, with approximately 65% (∼2.9 mmol g-1) originating from groups with pKa < 6. The methodology showed good reproducibility and stability with standard deviations below 5%. The nature of the groups was independent of small variations in experimental conditions, i.e. the mass of carbon dots titrated and initial concentration of HCl solution. Finally, we believe that the methodology used here, together with other characterization techniques, is a simple, fast and powerful tool to characterize the complex acid-base properties of these so interesting and intriguing nanoparticles.
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Neela Deshpande
2014-12-01
Full Text Available In the recent past Artificial Neural Networks (ANN have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC along with two other data driven techniques namely Model Tree (MT and Non-linear Regression (NLR. Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data. The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cement ratio, Water–Cement ratio and Replacement ratio of natural aggregates by recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete.
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M. Srinivasan
2012-01-01
Full Text Available Problem statement: This study presents a novel method for the determination of average winding temperature rise of transformers under its predetermined field operating conditions. Rise in the winding temperature was determined from the estimated values of winding resistance during the heat run test conducted as per IEC standard. Approach: The estimation of hot resistance was modeled using Multiple Variable Regression (MVR, Multiple Polynomial Regression (MPR and soft computing techniques such as Artificial Neural Network (ANN and Adaptive Neuro Fuzzy Inference System (ANFIS. The modeled hot resistance will help to find the load losses at any load situation without using complicated measurement set up in transformers. Results: These techniques were applied for the hot resistance estimation for dry type transformer by using the input variables cold resistance, ambient temperature and temperature rise. The results are compared and they show a good agreement between measured and computed values. Conclusion: According to our experiments, the proposed methods are verified using experimental results, which have been obtained from temperature rise test performed on a 55 kVA dry-type transformer.
Regression techniques for estimating soil organic carbon contents from VIS/NIR reflectance spectra
Schwanghart, W.; Jarmer, T.; Bayer, A.; Hoffmann, U.; Hunziker, M.; Kuhn, N. J.; Ehlers, M.
2012-04-01
Soil reflectance spectroscopy is regarded as a promising approach to efficiently obtain densely sampled soil organic carbon (SOC) estimates at various spatial scales. The estimates are usually based on a statistical modeling approach since physical models are mostly not applicable owing to the manifold influences on soil spectra by different soil constituents and properties. Different multivariate statistical methods exist to estimate SOC concentrations in soil samples using visible and near infra-red (VIS/NIR) reflectance spectra. All these techniques face the challenge of generating accurate predictive models with a disproportionate large number of variables compared to the number of observations in such datasets, and in addition highly correlated independent variables. This often results in overfitting and may at the same time reduce the predictive power of such models. In this study, we conduct a rigorous assessment of the predictive ability of different regression techniques (stepwise regression, robust regression with feature selection, lasso, ridge regression, elastic net, principal component (PC) regression, partial least squares (PLS) regression). We apply datasets from different environments to include a wide variety of soils and to investigate the effects of different SOC variances and concentrations on model performance. Our hypothesis is that the predictive ability of regression techniques can be significantly improved by using more advanced techniques such as PLS regression. We discuss our findings with respect to the applicability of SOC estimation from VIS/NIR reflectance spectra in different environments.
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Boldizsar Nagy
2017-05-01
Full Text Available In the present study the biosorption characteristics of Cd (II and Zn (II ions from monocomponent aqueous solutions by Agaricus bisporus macrofungus were investigated. The initial metal ion concentrations, contact time, initial pH and temperature were parameters that influence the biosorption. Maximum removal efficiencies up to 76.10% and 70.09% (318 K for Cd (II and Zn (II, respectively and adsorption capacities up to 3.49 and 2.39 mg/g for Cd (II and Zn (II, respectively at the highest concentration, were calculated. The experimental data were analyzed using pseudo-first- and pseudo-second-order kinetic models, various isotherm models in linear and nonlinear (CMA-ES optimization algorithm regression and thermodynamic parameters were calculated. The results showed that the biosorption process of both studied metal ions, followed pseudo second-order kinetics, while equilibrium is best described by Sips isotherm. The changes in morphological structure after heavy metal-biomass interactions were evaluated by SEM analysis. Our results confirmed that macrofungus A. bisporus could be used as a cost effective, efficient biosorbent for the removal of Cd (II and Zn (II from aqueous synthetic solutions.
De Mello, Fernanda; Oliveira, Carlos A L; Ribeiro, Ricardo P; Resende, Emiko K; Povh, Jayme A; Fornari, Darci C; Barreto, Rogério V; McManus, Concepta; Streit, Danilo
2015-01-01
Was evaluated the pattern of growth among females and males of tambaqui by Gompertz nonlinear regression model. Five traits of economic importance were measured on 145 animals during the three years, totaling 981 morphometric data analyzed. Different curves were adjusted between males and females for body weight, height and head length and only one curve was adjusted to the width and body length. The asymptotic weight (a) and relative growth rate to maturity (k) were different between sexes in animals with ± 5 kg; slaughter weight practiced by a specific niche market, very profitable. However, there was no difference between males and females up to ± 2 kg; slaughter weight established to supply the bigger consumer market. Females showed weight greater than males (± 280 g), which are more suitable for fish farming purposes defined for the niche market to larger animals. In general, males had lower maximum growth rate (8.66 g / day) than females (9.34 g / day), however, reached faster than females, 476 and 486 days growth rate, respectively. The height and length body are the traits that contributed most to the weight at 516 days (P <0.001).
A fast nonlinear regression method for estimating permeability in CT perfusion imaging.
Bennink, Edwin; Riordan, Alan J; Horsch, Alexander D; Dankbaar, Jan Willem; Velthuis, Birgitta K; de Jong, Hugo W
2013-11-01
Blood-brain barrier damage, which can be quantified by measuring vascular permeability, is a potential predictor for hemorrhagic transformation in acute ischemic stroke. Permeability is commonly estimated by applying Patlak analysis to computed tomography (CT) perfusion data, but this method lacks precision. Applying more elaborate kinetic models by means of nonlinear regression (NLR) may improve precision, but is more time consuming and therefore less appropriate in an acute stroke setting. We propose a simplified NLR method that may be faster and still precise enough for clinical use. The aim of this study is to evaluate the reliability of in total 12 variations of Patlak analysis and NLR methods, including the simplified NLR method. Confidence intervals for the permeability estimates were evaluated using simulated CT attenuation-time curves with realistic noise, and clinical data from 20 patients. Although fixating the blood volume improved Patlak analysis, the NLR methods yielded significantly more reliable estimates, but took up to 12 × longer to calculate. The simplified NLR method was ∼4 × faster than other NLR methods, while maintaining the same confidence intervals (CIs). In conclusion, the simplified NLR method is a new, reliable way to estimate permeability in stroke, fast enough for clinical application in an acute stroke setting.
The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression
Directory of Open Access Journals (Sweden)
Chunxiao Zhang
2012-01-01
Full Text Available The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.
The Outlier Detection for Ordinal Data Using Scalling Technique of Regression Coefficients
Adnan, Arisman; Sugiarto, Sigit
2017-06-01
The aims of this study is to detect the outliers by using coefficients of Ordinal Logistic Regression (OLR) for the case of k category responses where the score from 1 (the best) to 8 (the worst). We detect them by using the sum of moduli of the ordinal regression coefficients calculated by jackknife technique. This technique is improved by scalling the regression coefficients to their means. R language has been used on a set of ordinal data from reference distribution. Furthermore, we compare this approach by using studentised residual plots of jackknife technique for ANOVA (Analysis of Variance) and OLR. This study shows that the jackknifing technique along with the proper scaling may lead us to reveal outliers in ordinal regression reasonably well.
Spatial 3-D nonlinear calibration technique for PSD
Guo, Lifeng; Zhang, Guoxiong; Zheng, Qi; Gong, Qiang; Liu, Wenyao
2006-11-01
A 3-D nonlinear calibration technique for Position sensitive detector (PSD) in long distance laser collimating measurement is proposed. An automatic calibration system was developed to measure the nonlinearity of a 2-D PSD in 3-D space. It is mainly composed of a high accurate 2-D motorized translational stage, a high precision distance measuring device, and a computer-based data acquisition and control system. With the aid of the calibration system, the nonlinear characteristic of 2-D PSD is checked in a long collimating distance up to 78 meters. The calibration experiment was carried out for a series of distance, e.g. every 15 meters. The results showed that the nonlinearity of 2-D PSD is different evidently when the PSD element is at different distance from the laser head. One calculating method is defined to evaluate the nonlinear errors. The spatial 3-D mapping relationship between the actual displacements of the incident light and the coordinates of 2-D PSD outputs is established using a multilayer feedforward neural network.
Nonlinear ultrasonic measurements based on cross-correlation filtering techniques
Yee, Andrew; Stewart, Dylan; Bunget, Gheorghe; Kramer, Patrick; Farinholt, Kevin; Friedersdorf, Fritz; Pepi, Marc; Ghoshal, Anindya
2017-02-01
Cyclic loading of mechanical components promotes the formation of dislocation dipoles in metals, which can serve as precursors to crack nucleation and ultimately lead to failure. In the laboratory setting, an acoustic nonlinearity parameter has been assessed as an effective indicator for characterizing the progression of fatigue damage precursors. However, the need to use monochromatic waves of medium-to-high acoustic energy has presented a constraint, making it problematic for use in field applications. This paper presents a potential approach for field measurement of acoustic nonlinearity by using general purpose ultrasonic pulser-receivers. Nonlinear ultrasonic measurements during fatigue testing were analyzed by the using contact and immersion pulse-through method. A novel cross-correlation filtering technique was developed to extract the fundamental and higher harmonic waves from the signals. As in the case of the classic harmonic generation, the nonlinearity parameters of the second and third harmonics indicate a strong correlation with fatigue cycles. Consideration was given to potential nonlinearities in the measurement system, and tests have confirmed that measured second harmonic signals exhibit a linear dependence on the input signal strength, further affirming the conclusion that this parameter relates to damage precursor formation from cyclic loading.
A non-linear regression method for CT brain perfusion analysis
Bennink, E.; Oosterbroek, J.; Viergever, M. A.; Velthuis, B. K.; de Jong, H. W. A. M.
2015-03-01
CT perfusion (CTP) imaging allows for rapid diagnosis of ischemic stroke. Generation of perfusion maps from CTP data usually involves deconvolution algorithms providing estimates for the impulse response function in the tissue. We propose the use of a fast non-linear regression (NLR) method that we postulate has similar performance to the current academic state-of-art method (bSVD), but that has some important advantages, including the estimation of vascular permeability, improved robustness to tracer-delay, and very few tuning parameters, that are all important in stroke assessment. The aim of this study is to evaluate the fast NLR method against bSVD and a commercial clinical state-of-art method. The three methods were tested against a published digital perfusion phantom earlier used to illustrate the superiority of bSVD. In addition, the NLR and clinical methods were also tested against bSVD on 20 clinical scans. Pearson correlation coefficients were calculated for each of the tested methods. All three methods showed high correlation coefficients (>0.9) with the ground truth in the phantom. With respect to the clinical scans, the NLR perfusion maps showed higher correlation with bSVD than the perfusion maps from the clinical method. Furthermore, the perfusion maps showed that the fast NLR estimates are robust to tracer-delay. In conclusion, the proposed fast NLR method provides a simple and flexible way of estimating perfusion parameters from CT perfusion scans, with high correlation coefficients. This suggests that it could be a better alternative to the current clinical and academic state-of-art methods.
Naumann, H D; Tedeschi, L O; Fonseca, M A
2015-11-01
Methane (CH) is a potent greenhouse gas that is normally produced by microbial fermentation in the rumen and released to the environment mainly during eructation. Prediction of ruminal CH production is important for ruminant nutrition, especially for the determination of ME intake to assess the amount of total GE available for metabolism by an animal. Equations have been developed to predict ruminal CH production based on dietary constituents, but none have considered condensed tannins (CT), which are known to impact CH production by ruminants. The objective was to develop an equation to predict ruminal CH, accounting for CT effects. Methane production data were acquired from 48-h in vitro fermentation of a diverse group of warm-season perennial forage legumes containing different concentrations of CT over the course of 3 yr ( = 113). The following nonlinear exponential decay regression equation was developed: CH₄ = 113.6 × exp (-0.1751 x CT) - 2.18), [corrected] in which CH is expressed in grams per kilogram of fermentable organic matter and CT is in percentage of the DM. This equation predicted that CH production could be reduced by approximately 50% when CT is 3.9% DM. This equation is likely more accurate when screening CT-containing forages for their potential ability to mitigate in vitro CH production by ruminants when the CT concentration is greater than 3% DM. Therefore, despite the degree of variability in ruminal CH production, this equation could be used as a tool for screening CT-containing forages for their potential to inhibit ruminal CH. Future research should focus on the development of predictive equations when other potential reducers of ruminal CH are used in conjunction with CT.
Energy Technology Data Exchange (ETDEWEB)
Deng, Yangyang; Parajuli, Prem B.
2011-08-10
Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.
Parappagoudar, Mahesh B.; Pratihar, Dilip K.; Datta, Gouranga L.
2008-08-01
A cement-bonded moulding sand system takes a fairly long time to attain the required strength. Hence, the moulds prepared with cement as a bonding material will have to wait a long time for the metal to be poured. In this work, an accelerator was used to accelerate the process of developing the bonding strength. Regression analysis was carried out on the experimental data collected as per statistical design of experiments (DOE) to establish input-output relationships of the process. The experiments were conducted to measure compression strength and hardness (output parameters) by varying the input variables, namely amount of cement, amount of accelerator, water in the form of cement-to-water ratio, and testing time. A two-level full-factorial design was used for linear regression model, whereas a three-level central composite design (CCD) had been utilized to develop non-linear regression model. Surface plots and main effects plots were used to study the effects of amount of cement, amount of accelerator, water and testing time on compression strength, and mould hardness. It was observed from both the linear as well as non-linear models that amount of cement, accelerator, and testing time have some positive contributions, whereas cement-to-water ratio has negative contribution to both the above responses. Compression strength was found to have linear relationship with the amount of cement and accelerator, and non-linear relationship with the remaining process parameters. Mould hardness was seen to vary linearly with testing time and non-linearly with the other parameters. Analysis of variance (ANOVA) was performed to test statistical adequacy of the models. Twenty random test cases were considered to test and compare their performances. Non-linear regression models were found to perform better than the linear models for both the responses. An attempt was also made to express compression strength of the moulding sand system as a function of mould hardness.
2002-06-01
IEEE TRANSACTIONS ON AUTOMATIC CONTROL , VOL. 47, NO. 6, JUNE 2002 1033 Application of Optimization Techniques to a Nonlinear Problem of Communication... IEEE TRANSACTIONS ON AUTOMATIC CONTROL , VOL. 47, NO. 6, JUNE 2002 We consider J source-destination pairs, each of which is assigned a fixed multihop...blocking probabilities are at the maximum permitted value. IEEE TRANSACTIONS ON AUTOMATIC CONTROL , VOL. 47, NO. 6, JUNE
Arc-length technique for nonlinear finite element analysis
Institute of Scientific and Technical Information of China (English)
MEMON Bashir-Ahmed; SU Xiao-zu(苏小卒)
2004-01-01
Nonlinear solution of reinforced concrete structures, particularly complete load-deflection response, requires tracing of the equilibrium path and proper treatment of the limit and bifurcation points. In this regard, ordinary solution techniques lead to instability near the limit points and also have problems in case of snap-through and snap-back. Thus they fail to predict the complete load-displacement response. The arc-length method serves the purpose well in principle, Received wide acceptance in finite element analysis, and has been used extensively. However modifications to the basic idea are vital to meet the particular needs of the analysis. This paper reviews some of the recent developments of the method in the last two decades, with particular emphasis on nonlinear finite element analysis of reinforced concrete structures.
STATISTICAL INFERENCES FOR VARYING-COEFFICINT MODELS BASED ON LOCALLY WEIGHTED REGRESSION TECHNIQUE
Institute of Scientific and Technical Information of China (English)
梅长林; 张文修; 梁怡
2001-01-01
Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fired by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.
Karadag, Dogan; Koc, Yunus; Turan, Mustafa; Ozturk, Mustafa
2007-06-01
Ammonium ion exchange from aqueous solution using clinoptilolite zeolite was investigated at laboratory scale. Batch experimental studies were conducted to evaluate the effect of various parameters such as pH, zeolite dosage, contact time, initial ammonium concentration and temperature. Freundlich and Langmuir isotherm models and pseudo-second-order model were fitted to experimental data. Linear and non-linear regression methods were compared to determine the best fitting of isotherm and kinetic model to experimental data. The rate limiting mechanism of ammonium uptake by zeolite was determined as chemical exchange. Non-linear regression has better performance for analyzing experimental data and Freundlich model was better than Langmuir to represent equilibrium data.
Nonlinear control techniques for an atomic force microscope system
Institute of Scientific and Technical Information of China (English)
Yongchun FANG; Matthew FEEMSTER; Darren DAWSON; Nader M.JALILI
2005-01-01
Two nonlinear control techniques are proposed for an atomic force microscope system.Initially,a learning-based control algorithm is developed for the microcantilever-sample system that achieves asymptotic cantilever tip tracking for periodic trajectories.Specifically,the control approach utilizes a learning-based feedforward term to compensate for periodic dynamics and high-gain terms to account for non-periodic dynamics.An adaptive control algorithm is then developed to achieve asymptotic cantilever tip tracking for bounded tip trajectories despite uncertainty throughout the system parameters.Simulation results are provided to illustrate the efficacy and performance of the control strategies.
Nonlinear Spline Kernel-based Partial Least Squares Regression Method and Its Application
Institute of Scientific and Technical Information of China (English)
JIA Jin-ming; WEN Xiang-jun
2008-01-01
Inspired by the traditional Wold's nonlinear PLS algorithm comprises of NIPALS approach and a spline inner function model,a novel nonlinear partial least squares algorithm based on spline kernel(named SK-PLS)is proposed for nonlinear modeling in the presence of multicollinearity.Based on the iuner-product kernel spanned by the spline basis functions with infinite numher of nodes,this method firstly maps the input data into a high dimensional feature space,and then calculates a linear PLS model with reformed NIPALS procedure in the feature space and gives a unified framework of traditional PLS"kernel"algorithms in consequence.The linear PLS in the feature space corresponds to a nonlinear PLS in the original input (primal)space.The good approximating property of spline kernel function enhances the generalization ability of the novel model,and two numerical experiments are given to illustrate the feasibility of the proposed method.
Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression
Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.
2013-01-01
Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…
Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Measuring the influence of networks on transaction costs using a non-parametric regression technique
DEFF Research Database (Denmark)
Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Kumar, K Vasanth
2007-04-02
Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.
Evaluating Non-Linear Regression Models in Analysis of Persian Walnut Fruit Growth
Directory of Open Access Journals (Sweden)
I. Karamatlou
2016-02-01
Full Text Available Introduction: Persian walnut (Juglans regia L. is a large, wind-pollinated, monoecious, dichogamous, long lived, perennial tree cultivated for its high quality wood and nuts throughout the temperate regions of the world. Growth model methodology has been widely used in the modeling of plant growth. Mathematical models are important tools to study the plant growth and agricultural systems. These models can be applied for decision-making anddesigning management procedures in horticulture. Through growth analysis, planning for planting systems, fertilization, pruning operations, harvest time as well as obtaining economical yield can be more accessible.Non-linear models are more difficult to specify and estimate than linear models. This research was aimed to studynon-linear regression models based on data obtained from fruit weight, length and width. Selecting the best models which explain that fruit inherent growth pattern of Persian walnut was a further goal of this study. Materials and Methods: The experimental material comprising 14 Persian walnut genotypes propagated by seed collected from a walnut orchard in Golestan province, Minoudasht region, Iran, at latitude 37◦04’N; longitude 55◦32’E; altitude 1060 m, in a silt loam soil type. These genotypes were selected as a representative sampling of the many walnut genotypes available throughout the Northeastern Iran. The age range of walnut trees was 30 to 50 years. The annual mean temperature at the location is16.3◦C, with annual mean rainfall of 690 mm.The data used here is the average of walnut fresh fruit and measured withgram/millimeter/day in2011.According to the data distribution pattern, several equations have been proposed to describesigmoidal growth patterns. Here, we used double-sigmoid and logistic–monomolecular models to evaluate fruit growth based on fruit weight and4different regression models in cluding Richards, Gompertz, Logistic and Exponential growth for evaluation
Exchange Rates and Monetary Fundamentals: What Do We Learn from Linear and Nonlinear Regressions?
Directory of Open Access Journals (Sweden)
Guangfeng Zhang
2014-01-01
Full Text Available This paper revisits the association between exchange rates and monetary fundamentals with the focus on both linear and nonlinear approaches. With the monthly data of Euro/US dollar and Japanese yen/US dollar, our linear analysis demonstrates the monetary model is a long-run description of exchange rate movements, and our nonlinear modelling suggests the error correction model describes the short-run adjustment of deviations of exchange rates, and monetary fundamentals are capable of explaining exchange rate dynamics under an unrestricted framework.
Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intraday Range
C.W.S. Chen (Cathy); R. Gerlach (Richard); B.B.K. Hwang (Bruce); M.J. McAleer (Michael)
2011-01-01
textabstractValue-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViar) models that incorporate intra-day pric
Sublinear Expectation Nonlinear Regression for the Financial Risk Measurement and Management
Directory of Open Access Journals (Sweden)
Yunquan Song
2013-01-01
normality of the estimation and the mini-max property of the prediction are obtained. Finally, simulation study and real data analysis are carried out to illustrate the new model and methods. In this paper, the notions and methodological developments are nonclassical and original, and the proposed modeling and inference methods establish the foundations for nonlinear expectation statistics.
Regressions by leaps and bounds and biased estimation techniques in yield modeling
Marquina, N. E. (Principal Investigator)
1979-01-01
The author has identified the following significant results. It was observed that OLS was not adequate as an estimation procedure when the independent or regressor variables were involved in multicollinearities. This was shown to cause the presence of small eigenvalues of the extended correlation matrix A'A. It was demonstrated that the biased estimation techniques and the all-possible subset regression could help in finding a suitable model for predicting yield. Latent root regression was an excellent tool that found how many predictive and nonpredictive multicollinearities there were.
Institute of Scientific and Technical Information of China (English)
Liu Yingan; Wei Bocheng
2008-01-01
Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity (Kantz and Schreiber (1997)). Tsai (1986) introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR(1) errors. Liu (2003) introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL(p, 0, 1) errors. Therefore, the important problems in regres- sion model are detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL(p, q, 1) random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedas-ticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai (1986), Wei, et al (1995), and Liu, et al (2003).
Parameter Estimation Technique of Nonlinear Prosthetic Hand System
Directory of Open Access Journals (Sweden)
M.H.Jali
2016-10-01
Full Text Available This paper illustrated the parameter estimation technique of motorized prosthetic hand system. Prosthetic hands have become importance device to help amputee to gain a normal functional hand. By integrating various types of actuators such as DC motor, hydraulic and pneumatic as well as mechanical part, a highly useful and functional prosthetic device can be produced. One of the first steps to develop a prosthetic device is to design a control system. Mathematical modeling is derived to ease the control design process later on. This paper explained the parameter estimation technique of a nonlinear dynamic modeling of the system using Lagrangian equation. The model of the system is derived by considering the energies of the finger when it is actuated by the DC motor. The parameter estimation technique is implemented using Simulink Design Optimization toolbox in MATLAB. All the parameters are optimized until it achieves a satisfactory output response. The results show that the output response of the system with parameter estimation value produces a better response compare to the default value
Nonlinear decoupling controller design based on least squares support vector regression
Institute of Scientific and Technical Information of China (English)
WEN Xiang-jun; ZHANG Yu-nong; YAN Wei-wu; XU Xiao-ming
2006-01-01
Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control ora general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is unknown or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.
Costiner, Sorin; Taasan, Shlomo
1994-01-01
This paper presents multigrid (MG) techniques for nonlinear eigenvalue problems (EP) and emphasizes an MG algorithm for a nonlinear Schrodinger EP. The algorithm overcomes the mentioned difficulties combining the following techniques: an MG projection coupled with backrotations for separation of solutions and treatment of difficulties related to clusters of close and equal eigenvalues; MG subspace continuation techniques for treatment of the nonlinearity; an MG simultaneous treatment of the eigenvectors at the same time with the nonlinearity and with the global constraints. The simultaneous MG techniques reduce the large number of self consistent iterations to only a few or one MG simultaneous iteration and keep the solutions in a right neighborhood where the algorithm converges fast.
Z-scan: A simple technique for determination of third-order optical nonlinearity
Energy Technology Data Exchange (ETDEWEB)
Singh, Vijender, E-mail: chahal-gju@rediffmail.com [Department of Applied Science, N.C. College of Engineering, Israna, Panipat-132107, Haryana (India); Aghamkar, Praveen, E-mail: p-aghamkar@yahoo.co.in [Department of Physics, Chaudhary Devi Lal University, Sirsa-125055, Haryana (India)
2015-08-28
Z-scan is a simple experimental technique to measure intensity dependent nonlinear susceptibilities of third-order nonlinear optical materials. This technique is used to measure the sign and magnitude of both real and imaginary part of the third order nonlinear susceptibility (χ{sup (3)}) of nonlinear optical materials. In this paper, we investigate third-order nonlinear optical properties of Ag-polymer composite film by using single beam z-scan technique with Q-switched, frequency doubled Nd: YAG laser (λ=532 nm) at 5 ns pulse. The values of nonlinear absorption coefficient (β), nonlinear refractive index (n{sub 2}) and third-order nonlinear optical susceptibility (χ{sup (3)}) of permethylazine were found to be 9.64 × 10{sup −7} cm/W, 8.55 × 10{sup −12} cm{sup 2}/W and 5.48 × 10{sup −10} esu, respectively.
Feng, Xin; Winters, Jack M
2011-01-01
Individualizing a neurorehabilitation training protocol requires understanding the performance of subjects with various capabilities under different task settings. We use multivariate regression to evaluate the performance of subjects with stroke-induced hemiparesis in trajectory tracking tasks using a force-reflecting joystick. A nonlinear effect was consistently shown in both dimensions of force field strength and impairment level for selected kinematic performance measures, with greatest sensitivity at lower force fields. This suggests that the form of a force field may play a different "role" for subjects with various impairment levels, and confirms that to achieve optimized therapeutic benefit, it is necessary to personalize interfaces.
Kamil Faisal; Ahmed Shaker
2017-01-01
Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to ...
Return-Volatility Relationship: Insights from Linear and Non-Linear Quantile Regression
D.E. Allen (David); A.K. Singh (Abhay); R.J. Powell (Robert); M.J. McAleer (Michael); J. Taylor (James); L. Thomas (Lyn)
2013-01-01
textabstractThe purpose of this paper is to examine the asymmetric relationship between price and implied volatility and the associated extreme quantile dependence using linear and non linear quantile regression approach. Our goal in this paper is to demonstrate that the relationship between the
Nonlinear auto-adjusting iterative reconstruction technique for interferometric tomography
Song, Yizhong; Sun, Tao; Qu, Peishu
2013-07-01
A new algebraic reconstruction technique (ART), nonlinear auto-adjusting iterative reconstruction technique (NAIRT), is proposed and applied to reconstruct a section of an actual thermal air flow field. With numerical simulation, NAIRT was tested to reconstruct a complicated field to demonstrate its superior reconstructive capability. In contrast, three typical ARTs, the basic ART, simultaneous ART (SART), and a modified SART (MSART), were simulated to demonstrate the reconstructive capability improvement attained through the use of the proposed NAIRT. The calculated results were discussed with mean square error (MSE) and peak error (PE). A thermal air flow field was produced with an alcohol burner and was detected by a laser beam. With laser beam projections, a cross-section of the field was reconstructed by NAIRT. As a result, the reconstructive capability was improved much by NAIRT. The MSE decreased by 95.5%, and PE by 97.2% from that of the basic ART. Only NAIRT converged without filters while its reconstructive accuracy improved. By increasing the projections from 42 to 84, the accuracy of NAIRT without filters was improved significantly. NAIRT could effectively reconstruct the section of the thermal field. The proposed NAIRT needed no filter for its convergence and it had the highest reconstructive accuracy and simplest iterative expression of those analyzed.
Creating a non-linear total sediment load formula using polynomial best subset regression model
Okcu, Davut; Pektas, Ali Osman; Uyumaz, Ali
2016-08-01
The aim of this study is to derive a new total sediment load formula which is more accurate and which has less application constraints than the well-known formulae of the literature. 5 most known stream power concept sediment formulae which are approved by ASCE are used for benchmarking on a wide range of datasets that includes both field and flume (lab) observations. The dimensionless parameters of these widely used formulae are used as inputs in a new regression approach. The new approach is called Polynomial Best subset regression (PBSR) analysis. The aim of the PBRS analysis is fitting and testing all possible combinations of the input variables and selecting the best subset. Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable. While selecting the best subset a multistep approach is used that depends on significance values and also the multicollinearity degrees of inputs. The new formula is compared to others in a holdout dataset and detailed performance investigations are conducted for field and lab datasets within this holdout data. Different goodness of fit statistics are used as they represent different perspectives of the model accuracy. After the detailed comparisons are carried out we figured out the most accurate equation that is also applicable on both flume and river data. Especially, on field dataset the prediction performance of the proposed formula outperformed the benchmark formulations.
Yadav, Manish; Singh, Nitin Kumar
2017-08-01
A comparison of the linear and non-linear regression method in selecting the optimum isotherm among three most commonly used adsorption isotherms (Langmuir, Freundlich, and Redlich-Peterson) was made to the experimental data of fluoride (F) sorption onto Bio-F at a solution temperature of 30 ± 1 °C. The coefficient of correlation (r2 ) was used to select the best theoretical isotherm among the investigated ones. A total of four Langmuir linear equations were discussed and out of which linear form of most popular Langmuir-1 and Langmuir-2 showed the higher coefficient of determination (0.976 and 0.989) as compared to other Langmuir linear equations. Freundlich and Redlich-Peterson isotherms showed a better fit to the experimental data in linear least-square method, while in non-linear method Redlich-Peterson isotherm equations showed the best fit to the tested data set. The present study showed that the non-linear method could be a better way to obtain the isotherm parameters and represent the most suitable isotherm. Redlich-Peterson isotherm was found to be the best representative (r2 = 0.999) for this sorption system. It is also observed that the values of β are not close to unity, which means the isotherms are approaching the Freundlich but not the Langmuir isotherm.
Structural break detection method based on the Adaptive Regression Splines technique
Kucharczyk, Daniel; Wyłomańska, Agnieszka; Zimroz, Radosław
2017-04-01
For many real data, long term observation consists of different processes that coexist or occur one after the other. Those processes very often exhibit different statistical properties and thus before the further analysis the observed data should be segmented. This problem one can find in different applications and therefore new segmentation techniques have been appeared in the literature during last years. In this paper we propose a new method of time series segmentation, i.e. extraction from the analysed vector of observations homogeneous parts with similar behaviour. This method is based on the absolute deviation about the median of the signal and is an extension of the previously proposed techniques also based on the simple statistics. In this paper we introduce the method of structural break point detection which is based on the Adaptive Regression Splines technique, one of the form of regression analysis. Moreover we propose also the statistical test which allows testing hypothesis of behaviour related to different regimes. First, the methodology we apply to the simulated signals with different distributions in order to show the effectiveness of the new technique. Next, in the application part we analyse the real data set that represents the vibration signal from a heavy duty crusher used in a mineral processing plant.
Nonlinear programming technique for analyzing flocculent settling data.
Rashid, Md Mamunur; Hayes, Donald F
2014-04-01
The traditional graphical approach for drawing iso-concentration curves to analyze flocculent settling data and design sedimentation basins poses difficulties for computer-based design methods. Thus, researchers have developed empirical approaches to analyze settling data. In this study, the ability of five empirical approaches to fit flocculent settling test data is compared. Particular emphasis is given to compare rule-based SETTLE and rule-based nonlinear programming (NLP) techniques as a viable alternative to the modeling methods of Berthouex and Stevens (1982), San (1989), and Ozer (1994). Published flocculent settling data are used to test the suitability of these empirical approaches. The primary objective, however, is to determine if the results of a NLP optimization technique are more reliable than those of other approaches. For this, mathematical curve fitting is conducted and the modeled concentration data are graphically compared to the observed data. The design results in terms of average solid removal efficiency as a function of detention times are also compared. Finally, the sum of squared errors values from these approaches are compared. The results indicate a strong correlation between observed and NLP modeled concentration data. The SETTLE and NLP approaches tend to be more conservative at lower retention times and less conservative at longer retention times. The SETTLE approach appears to be the most conservative. In terms of sum of squared errors values, NLP appears to be rank number one (i.e., best model) for eight data sets and number two for six data sets among 15 data sets. Therefore, NLP is recommended for analyzing flocculent settling data as a logical extension of other approaches. The NLP approach is further recommended as it is an optimization technique and uses conventional mathematical algorithms that can be solved using widely available software such as EXCEL and LINGO.
Early cost estimating for road construction projects using multiple regression techniques
Directory of Open Access Journals (Sweden)
Ibrahim Mahamid
2011-12-01
Full Text Available The objective of this study is to develop early cost estimating models for road construction projects using multiple regression techniques, based on 131 sets of data collected in the West Bank in Palestine. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for the required regression model could be easily extracted from sketches or scope definition of the project. 11 regression models are developed to estimate the total cost of road construction project in US dollar; 5 of them include bid quantities as input variables and 6 include road length and road width. The coefficient of determination r2 for the developed models is ranging from 0.92 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE of the developed regression models are ranging from 13% to 31%, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% and ±50%.
Strand, L. D.; Schultz, A. L.; Reedy, G. K.
1972-01-01
A microwave Doppler shift system, with increased resolution over earlier microwave techniques, was developed for the purpose of measuring the regression rates of solid propellants during rapid pressure transients. A continuous microwave beam is transmitted to the base of a burning propellant sample cast in a metal waveguide tube. A portion of the wave is reflected from the regressing propellant-flame zone interface. The phase angle difference between the incident and reflected signals and its time differential are continuously measured using a high resolution microwave network analyzer and related instrumentation. The apparent propellant regression rate is directly proportional to this latter differential measurement. Experiments were conducted to verify the (1) spatial and time resolution of the system, (2) effect of propellant surface irregularities and compressibility on the measurements, and (3) accuracy of the system for quasi-steady-state regression rate measurements. The microwave system was also used in two different transient combustion experiments: in a rapid depressurization bomb, and in the high-frequency acoustic pressure environment of a T-burner.
Baum, J. D.; Levine, J. N.
1980-01-01
The selection of a satisfactory numerical method for calculating the propagation of steep fronted shock life waveforms in a solid rocket motor combustion chamber is discussed. A number of different numerical schemes were evaluated by comparing the results obtained for three problems: the shock tube problems; the linear wave equation, and nonlinear wave propagation in a closed tube. The most promising method--a combination of the Lax-Wendroff, Hybrid and Artificial Compression techniques, was incorporated into an existing nonlinear instability program. The capability of the modified program to treat steep fronted wave instabilities in low smoke tactical motors was verified by solving a number of motor test cases with disturbance amplitudes as high as 80% of the mean pressure.
Khan, Saad Ahmad; Thakore, Vaibhav; Behal, Aman; Bölöni, Ladislau; Hickman, James J
2013-03-01
Applications of non-invasive neuroelectronic interfacing in the fields of whole-cell biosensing, biological computation and neural prosthetic devices depend critically on an efficient decoding and processing of information retrieved from a neuron-electrode junction. This necessitates development of mathematical models of the neuron-electrode interface that realistically represent the extracellular signals recorded at the neuroelectronic junction without being computationally expensive. Extracellular signals recorded using planar microelectrode or field effect transistor arrays have, until now, primarily been represented using linear equivalent circuit models that fail to reproduce the correct amplitude and shape of the signals recorded at the neuron-microelectrode interface. In this paper, to explore viable alternatives for a computationally inexpensive and efficient modeling of the neuron-electrode junction, input-output data from the neuron-electrode junction is modeled using a parametric Wiener model and a Nonlinear Auto-Regressive network with eXogenous input trained using a dynamic Neural Network model (NARX-NN model). Results corresponding to a validation dataset from these models are then employed to compare and contrast the computational complexity and efficiency of the aforementioned modeling techniques with the Lee-Schetzen technique of cross-correlation for estimating a nonlinear dynamic model of the neuroelectronic junction.
Fitting Nonlinear Curves by use of Optimization Techniques
Hill, Scott A.
2005-01-01
MULTIVAR is a FORTRAN 77 computer program that fits one of the members of a set of six multivariable mathematical models (five of which are nonlinear) to a multivariable set of data. The inputs to MULTIVAR include the data for the independent and dependent variables plus the user s choice of one of the models, one of the three optimization engines, and convergence criteria. By use of the chosen optimization engine, MULTIVAR finds values for the parameters of the chosen model so as to minimize the sum of squares of the residuals. One of the optimization engines implements a routine, developed in 1982, that utilizes the Broydon-Fletcher-Goldfarb-Shanno (BFGS) variable-metric method for unconstrained minimization in conjunction with a one-dimensional search technique that finds the minimum of an unconstrained function by polynomial interpolation and extrapolation without first finding bounds on the solution. The second optimization engine is a faster and more robust commercially available code, denoted Design Optimization Tool, that also uses the BFGS method. The third optimization engine is a robust and relatively fast routine that implements the Levenberg-Marquardt algorithm.
Omura, J. K.; Simon, M. K.
1982-01-01
A theory for deducing and predicting the performance of transmitter/receivers for bandwidth efficient modulations suitable for use on the nonlinear satellite channel is presented. The underlying principle used throughout is the development of receiver structures based on the maximum likelihood decision rule and aproximations to it. The bit error probability transfer function bounds developed in great detail in Part 4 is applied to these modulation/demodulation techniques. The effects of the various degrees of receiver mismatch are considered both theoretically and by numerous illustrative examples.
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A practical regression method of saturation exponential in pre-dose technique is proposed. The method is mainly applied for porcelain dating. To test, the method, some simulated paleodoses of the imitations of ancient porcelain were used. The measured results are in good agreement with the simulated values of the paleodoses, and the average ratios of the two values by using the two ways are 1.05 and 0.99 with standard deviations (±lσ) of 19% and 15% respectively. Such errors can be accepted in porcelain dating.
Harding, Brian J; Gehrels, Thomas W; Makela, Jonathan J
2014-02-01
The Earth's thermosphere plays a critical role in driving electrodynamic processes in the ionosphere and in transferring solar energy to the atmosphere, yet measurements of thermospheric state parameters, such as wind and temperature, are sparse. One of the most popular techniques for measuring these parameters is to use a Fabry-Perot interferometer to monitor the Doppler width and breadth of naturally occurring airglow emissions in the thermosphere. In this work, we present a technique for estimating upper-atmospheric winds and temperatures from images of Fabry-Perot fringes captured by a CCD detector. We estimate instrument parameters from fringe patterns of a frequency-stabilized laser, and we use these parameters to estimate winds and temperatures from airglow fringe patterns. A unique feature of this technique is the model used for the laser and airglow fringe patterns, which fits all fringes simultaneously and attempts to model the effects of optical defects. This technique yields accurate estimates for winds, temperatures, and the associated uncertainties in these parameters, as we show with a Monte Carlo simulation.
Malfense Fierro, Gian Piero; Meo, Michele
2017-02-01
Recently, there has been high interest in the capabilities of nonlinear ultrasound techniques for damage/defect detection as these techniques have been shown to be quite accurate in imaging some particular type of damage. This paper presents a Constructive Nonlinear Array (CNA) method, for the detection and imaging of material defects/damage in a complex composite stiffened panel. CNA requires the construction of an ultrasound array in a similar manner to standard phased arrays systems, which require multiple transmitting and receiving elements. The method constructively phase-match multiple captured signals at a particular position given multiple transmit positions, similar to the total focusing method (TFM) method. Unlike most of the ultrasonic linear techniques, a longer excitation signal was used to achieve a steady-state excitation at each capturing position, so that compressive and tensile stress at defect/crack locations increases the likelihood of the generation of nonlinear elastic waves. Moreover, the technique allows the reduction of instrumentation nonlinear wave generation by relying on signal attenuation to naturally filter these errors. Experimental tests were carried out on a stiffened panel with manufacturing defects. Standard industrial linear ultrasonic test were carried out for comparison. The proposed new method allows to image damages/defects in a reliable and reproducible manner and overcomes some of the main limitations of nonlinear ultrasound techniques. In particular, the effectiveness and robustness of CNA and the advantages over linear ultrasonic were clearly demonstrated allowing a better resolution and imaging of complex and realistic flaws.
Constrained Sparse Galerkin Regression
Loiseau, Jean-Christophe
2016-01-01
In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.
Validation of Two Nonlinear System Identification Techniques Using an Experimental Testbed
Directory of Open Access Journals (Sweden)
V. Lenaerts
2004-01-01
Full Text Available The identification of a nonlinear system is performed using experimental data and two different techniques, i.e. a method based on the Wavelet transform and the Restoring Force Surface method. Both techniques exploit the system free response and result in the estimation of linear and nonlinear physical parameters.
Chen, Wen-Yuan; Wang, Mei; Fu, Zhou-Xing
2014-06-16
Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1) we use a terrain drop compensation (TDC) technique to solve the problem of the concavity of railway crossings; (2) we use a linear regression technique to predict the position and length of an object from image processing; (3) we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP) to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas.
Directory of Open Access Journals (Sweden)
Wen-Yuan Chen
2014-06-01
Full Text Available Most railway accidents happen at railway crossings. Therefore, how to detect humans or objects present in the risk area of a railway crossing and thus prevent accidents are important tasks. In this paper, three strategies are used to detect the risk area of a railway crossing: (1 we use a terrain drop compensation (TDC technique to solve the problem of the concavity of railway crossings; (2 we use a linear regression technique to predict the position and length of an object from image processing; (3 we have developed a novel strategy called calculating local maximum Y-coordinate object points (CLMYOP to obtain the ground points of the object. In addition, image preprocessing is also applied to filter out the noise and successfully improve the object detection. From the experimental results, it is demonstrated that our scheme is an effective and corrective method for the detection of railway crossing risk areas.
Hussain, Mirza Zahid; Li, Fuguo; Wang, Jing; Yuan, Zhanwei; Li, Pan; Wu, Tao
2015-07-01
The present study comprises the determination of constitutive relationship for thermo-mechanical processing of INCONEL 718 through double multivariate nonlinear regression, a newly developed approach which not only considers the effect of strain, strain rate, and temperature on flow stress but also explains the interaction effect of these thermo-mechanical parameters on flow behavior of the alloy. Hot isothermal compression experiments were performed on Gleeble-3500 thermo-mechanical testing machine in the temperature range of 1153 to 1333 K within the strain rate range of 0.001 to 10 s-1. The deformation behavior of INCONEL 718 is analyzed and summarized by establishing the high temperature deformation constitutive equation. The calculated correlation coefficient ( R) and average absolute relative error ( AARE) underline the precision of proposed constitutive model.
Directory of Open Access Journals (Sweden)
Adnane El Hamidi
2012-01-01
Full Text Available Interactions of Cu(II ions with calcium phosphate Brushite (DCPD in aqueous solutions were investigated by batch conditions and under several sorption parameters like contact time, pH of solution and initial metal concentration. The retention of copper was found maximum and dominated by exchange reaction process in the pH range 4-6. The reaction process was found initially fast and more than 98% was removed at equilibrium. The kinetics data of batch interaction was analyzed with various kinetic models. It was found that the pseudo-first order model using the non-linear regression method predicted best the experimental data. Furthermore, the adsorption process was modeled by Langmuir isotherm and the removal capacity was 331.64 mg.g-1. Consequently, Cu2+ concentration independent kinetics and single surface layer sorption isotherm are then suggested as appropriate mechanisms for the whole process.
Application of nonlinear dynamic techniques to high pressure plasma jets
Ghorui, S.; Das, A. K.
2010-02-01
Arcs and arc plasmas have been known and used for welding, cutting, chemical synthesis and multitude of other industrial applications for more than hundred years. Though a copious source of heat, light and active species, plasma arc is inherently unstable, turbulent and difficult to control. During recent years, primarily driven by the need of new and energy efficient materials processing, various research groups around the world have been studying new and innovative ways of looking at the issues related to arc dynamics, arc stabilization, species non equilibrium, flow and heat transfer in a stabilized arc plasma device. In this context, experimental determination of nature of arc instabilities using tools of non-linear dynamics, theoretical model formulation, prediction of instability behavior under given operating conditions and possible control methods for the observed instabilities in arcs are reviewed. Space selective probing of the zones inside arc plasma devices without disturbing the system is probably the best way to identify the originating zone of instabilities inside such devices. Existence of extremely high temperature and inaccessibility to direct experimentations due to mechanical obstructions make this task extremely difficult. Probing instabilities in otherwise inaccessible inner regions of the torches, using binary gas mixture as plasma gas is a novel technique that primarily rests on a process known as demixing in arcs. Once a binary gas mixture enters the constricted plasma column, the demixing process sets in causing spatial variations for each of the constituent gases depending on the diffusion coefficients and the gradient of the existing temperature field. By varying concentrations of the constituent gases in the feeding line, it is possible to obtain spatial variations of the plasma composition in a desired manner, enabling spatial probing of the associated zones. Detailed compositional description of different zones inside the torch may be
Higher-order techniques for some problems of nonlinear control
Directory of Open Access Journals (Sweden)
Sarychev Andrey V.
2002-01-01
Full Text Available A natural first step when dealing with a nonlinear problem is an application of some version of linearization principle. This includes the well known linearization principles for controllability, observability and stability and also first-order optimality conditions such as Lagrange multipliers rule or Pontryagin's maximum principle. In many interesting and important problems of nonlinear control the linearization principle fails to provide a solution. In the present paper we provide some examples of how higher-order methods of differential geometric control theory can be used for the study nonlinear control systems in such cases. The presentation includes: nonlinear systems with impulsive and distribution-like inputs; second-order optimality conditions for bang–bang extremals of optimal control problems; methods of high-order averaging for studying stability and stabilization of time-variant control systems.
Techniques in Linear and Nonlinear Partial Differential Equations
1991-10-21
nonlinear partial differential equations , elliptic 15. NUMBER OF PAGES hyperbolic and parabolic. Variational methods. Vibration problems. Ordinary Five...NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS FINAL TECHNICAL REPORT PROFESSOR LOUIS NIRENBERG OCTOBER 21, 1991 NT)S CRA&I D FIC ,- U.S. ARMY RESEARCH OFFICE...Analysis and partial differential equations . ed. C. Sadowsky. Marcel Dekker (1990) 567-619. [7] Lin, Fanghua, Asymptotic behavior of area-minimizing
Energy Technology Data Exchange (ETDEWEB)
Torello, David [GW Woodruff School of Mechanical Engineering, Georgia Tech (United States); Kim, Jin-Yeon [School of Civil and Environmental Engineering, Georgia Tech (United States); Qu, Jianmin [Department of Civil and Environmental Engineering, Northwestern University (United States); Jacobs, Laurence J. [School of Civil and Environmental Engineering, Georgia Tech and GW Woodruff School of Mechanical Engineering, Georgia Tech (United States)
2015-03-31
This research considers the effects of diffraction, attenuation, and the nonlinearity of generating sources on measurements of nonlinear ultrasonic Rayleigh wave propagation. A new theoretical framework for correcting measurements made with air-coupled and contact piezoelectric receivers for the aforementioned effects is provided based on analytical models and experimental considerations. A method for extracting the nonlinearity parameter β{sub 11} is proposed based on a nonlinear least squares curve-fitting algorithm that is tailored for Rayleigh wave measurements. Quantitative experiments are conducted to confirm the predictions for the nonlinearity of the piezoelectric source and to demonstrate the effectiveness of the curve-fitting procedure. These experiments are conducted on aluminum 2024 and 7075 specimens and a β{sub 11}{sup 7075}/β{sub 11}{sup 2024} measure of 1.363 agrees well with previous literature and earlier work.
DEFF Research Database (Denmark)
Sharif, Behzad; Makowski, David; Plauborg, Finn;
2017-01-01
-validation. The regression methods leading to the most accurate yield predictions were Lasso and Elastic Net, and the least accurate methods were ordinary least squares and stepwise regression. Partial least squares and ridge regression methods gave intermediate results. The estimated relative yield change for a +1°C......Statistical regression models represent alternatives to process-based dynamic models for predicting the response of crop yields to variation in climatic conditions. Regression models can be used to quantify the effect of change in temperature and precipitation on yields. However, it is difficult...... to identify the most relevant input variables that should be included in regression models due to the high number of candidate variables and to their correlations. This paper compares several regression techniques for modeling response of winter oilseed rape yield to a high number of correlated input...
Nonlinear systems techniques for dynamical analysis and control
Lefeber, Erjen; Arteaga, Ines
2017-01-01
This treatment of modern topics related to the control of nonlinear systems is a collection of contributions celebrating the work of Professor Henk Nijmeijer and honoring his 60th birthday. It addresses several topics that have been the core of Professor Nijmeijer’s work, namely: the control of nonlinear systems, geometric control theory, synchronization, coordinated control, convergent systems and the control of underactuated systems. The book presents recent advances in these areas, contributed by leading international researchers in systems and control. In addition to the theoretical questions treated in the text, particular attention is paid to a number of applications including (mobile) robotics, marine vehicles, neural dynamics and mechanical systems generally. This volume provides a broad picture of the analysis and control of nonlinear systems for scientists and engineers with an interest in the interdisciplinary field of systems and control theory. The reader will benefit from the expert participan...
Reconstructing the Nonlinear Dynamical Systems by Evolutionary Computation Techniques
Institute of Scientific and Technical Information of China (English)
LIU Minzhong; KANG Lishan
2006-01-01
We introduce a new dynamical evolutionary algorithm(DEA) based on the theory of statistical mechanics and investigate the reconstruction problem for the nonlinear dynamical systems using observation data. The convergence of the algorithm is discussed. We make the numerical experiments and test our model using the two famous chaotic systems (mainly the Lorenz and Chen systems ). The results show the relatively accurate reconstruction of these chaotic systems based on observational data can be obtained. Therefore we may conclude that there are broad prospects using our method to model the nonlinear dynamical systems.
Directory of Open Access Journals (Sweden)
vahid Rezaverdinejad
2017-01-01
important models to estimate ETc in greenhouse. The inputs of these models are net radiation, temperature, day after planting and air vapour pressure deficit (or relative humidity. Materials and Methods: In this study, daily ETc of reference crop, greenhouse tomato and cucumber crops were measured using lysimeter method in Urmia region. Several linear, nonlinear regressions and artificial neural networks were considered for ETc modelling in greenhouse. For this purpose, the effective meteorological parameters on ETc process includes: air temperature (T, air humidity (RH, air pressure (P, air vapour pressure deficit (VPD, day after planting (N and greenhouse net radiation (SR were considered and measured. According to the goodness of fit, different models of artificial neural networks and regression were compared and evaluated. Furthermore, based on partial derivatives of regression models, sensitivity analysis was conducted. The accuracy and performance of the employed models was judged by ten statistical indices namely root mean square error (RMSE, normalized root mean square error (NRMSE and coefficient of determination (R2. Results and Discussion: Based on the results, the most accurate regression model to reference ETc prediction was obtained three variables exponential function of VPD, RH and SR with RMSE=0.378 mm day-1. The RMSE of optimal artificial neural network to reference ET prediction for train and test data sets were obtained 0.089 and 0.365 mm day-1, respectively. The performance of logarithmic and exponential functions to prediction of cucumber ETc were proper, with high dependent variables especially, and the most accurate regression model to cucumber ET prediction was obtained for exponential function of five variables: VPD, N, T, RH and SR with RMSE=0.353 mm day-1. In addition, for tomato ET prediction, the most accurate regression model was obtained for exponential function of four variables: VPD, N, RH and SR with RMSE= 0.329 mm day-1. The best
Ncibi, Mohamed Chaker
2008-05-01
In any single component isotherm study, determining the best-fitting model is a key analysis to mathematically describe the involved sorption system and, therefore, to explore the related theoretical assumptions. Hence, several error calculation functions have been widely used to estimate the error deviations between experimental and theoretically predicted equilibrium adsorption values (Q(e,exp)vs.Q(e,theo) as X- and Y-axis, respectively), including the average relative error deviation, the Marquardt's percent standard error deviation, the hybrid fractional error function, the sum of the squares of the errors, the correlation coefficient and the residuals. In this study, five other statistical functions are analysed to investigate their applicability as suitable tools to evaluate isotherm model fitness, namely the Pearson correlation coefficient, the coefficient of determination, the Chi-square test, the F-test and the Student's T-test, using the commonly-used functions as references. The adsorption of textile dye onto Posidonia oceanica seagrass fibres was carried out, as study case, in batch mode at 20 degrees C. Besides, and in order to get an overall approach of the possible utilization of these statistical functions within the studied item, the examination was realized for both linear and non-linear regression analysis. The related results showed that, among the five studied statistical tools, the chi(2) and Student's T-tests were suitable to determine the best-fitting isotherm model for the case of linear modelling approach. On the other hand, dealing with the non-linear analysis, despite the Student's T-test, all the other functions gave satisfactorily results, by agreeing the commonly-used error functions calculation.
The constructive technique and its application in solving a nonlinear reaction diffusion equation
Institute of Scientific and Technical Information of China (English)
Lai Shao-Yong; Guo Yun-Xi; Qing Yin; Wu Yong-Hong
2009-01-01
A mathematical technique based on the consideration of a nonlinear partial differential equation together with an additional condition in the form of an ordinary differential equation is employed to study a nonlinear reaction diffusion equation which describes a real process in physics and in chemistry. Several exact solutions for the equation are acquired under certain circumstances.
A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis
Taneja, Abhishek
2011-01-01
The growing volume of data usually creates an interesting challenge for the need of data analysis tools that discover regularities in these data. Data mining has emerged as disciplines that contribute tools for data analysis, discovery of hidden knowledge, and autonomous decision making in many application domains. The purpose of this study is to compare the performance of two data mining techniques viz., factor analysis and multiple linear regression for different sample sizes on three unique sets of data. The performance of the two data mining techniques is compared on following parameters like mean square error (MSE), R-square, R-Square adjusted, condition number, root mean square error(RMSE), number of variables included in the prediction model, modified coefficient of efficiency, F-value, and test of normality. These parameters have been computed using various data mining tools like SPSS, XLstat, Stata, and MS-Excel. It is seen that for all the given dataset, factor analysis outperform multiple linear re...
Linear iterative technique for solution of nonlinear thermal network problems
Energy Technology Data Exchange (ETDEWEB)
Seabourn, C.M.
1976-11-01
A method for rapid and accurate solution of linear and/or nonlinear thermal network problems is described. It is a matrix iterative process that converges for nodal temperatures and variations of thermal conductivity with temperature. The method is computer oriented and can be changed easily for design studies.
具有AR(q)误差非线性回归模型的几何性质%Geometric Properties of AR(q) Nonlinear Regression Models
Institute of Scientific and Technical Information of China (English)
刘应安; 韦博成
2004-01-01
This paper is devoted to a study of geometric properties of AR(q) nonlinear regression models. We present geometric frameworks for regression parameter space and autoregression parameter space respectively based on the weighted inner product by fisher information matrix. Several geometric properties related to statistical curvatures are given for the models. The results of this paper extended the work of Bates & Watts(1980,1988) [1,2] and Seber & Wild(1989) [3].
Indian Academy of Sciences (India)
S Venugopal Rao; P T Anusha; L Giribabu; Surya P Tewari
2010-11-01
We present our experimental results on the picosecond nonlinear optical (NLO) studies of symmetrical and unsymmetrical phthalocyanines, examined using the Z-scan technique. Both the open-aperture and closed-aperture Z-scan curves for three samples were recorded and the nonlinear coefficients were extracted from the theoretical fits. The nonlinear absorption/refraction contribution from the solvent was also identified. The observed open aperture behaviour for these molecules is understood in terms of the absorption coefficients of these molecules near 800 nm and the peak intensities used. It is established that these phthalocyanines exhibit large optical nonlinearities and, hence, are suitable for optical limiting applications.
Shabani, Farzin; Kumar, Lalit; Solhjouy-fard, Samaneh
2016-05-01
The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm (Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations
Shabani, Farzin; Kumar, Lalit; Solhjouy-fard, Samaneh
2017-08-01
The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm ( Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations
Application of nonlinear forecasting techniques for meteorological modeling
Directory of Open Access Journals (Sweden)
V. Pérez-Muñuzuri
Full Text Available A nonlinear forecasting method was used to predict the behavior of a cloud coverage time series several hours in advance. The method is based on the reconstruction of a chaotic strange attractor using four years of cloud absorption data obtained from half-hourly Meteosat infrared images from Northwestern Spain. An exhaustive nonlinear analysis of the time series was carried out to reconstruct the phase space of the underlying chaotic attractor. The forecast values are used by a non-hydrostatic meteorological model ARPS for daily weather prediction and their results compared with surface temperature measurements from a meteorological station and a vertical sounding. The effect of noise in the time series is analyzed in terms of the prediction results.
Key words: Meterology and atmospheric dynamics (mesoscale meteorology; general – General (new fields
Data Analysis Techniques for Resolving Nonlinear Processes in Plasmas : a Review
de Wit, T. Dudok
1996-01-01
The growing need for a better understanding of nonlinear processes in plasma physics has in the last decades stimulated the development of new and more advanced data analysis techniques. This review lists some of the basic properties one may wish to infer from a data set and then presents appropriate analysis techniques with some recent applications. The emphasis is put on the investigation of nonlinear wave phenomena and turbulence in space plasmas.
A SELF-ADAPTIVE TECHNIQUE FOR A KIND OF NONLINEAR CONJUGATE GRADIENT METHODS
Institute of Scientific and Technical Information of China (English)
王丽平
2004-01-01
Conjugate gradient methods. are a class of important methods for unconstrained optimization, especially when the dimension is large. In 2001, Dai and Liao have proposed a new conjugate condition, based on it two nonlinear conjugate gradient methods are constructed. With trust region idea, this paper gives a self-adaptive technique for the two methods. The numerical results show that this technique works well for the given nonlinear optimization test problems.
Characterization of nonlinear ultrasonic effects using the dynamic wavelet fingerprint technique
Lv, Hongtao; Jiao, Jingpin; Meng, Xiangji; He, Cunfu; Wu, Bin
2017-02-01
An improved dynamic wavelet fingerprint (DWFP) technique was developed to characterize nonlinear ultrasonic effects. The white area in the fingerprint was used as the nonlinear feature to quantify the degree of damage. The performance of different wavelet functions, the effect of scale factor and white subslice ratio on the nonlinear feature extraction were investigated, and the optimal wavelet function, scale factor and white subslice ratio for maximum damage sensitivity were determined. The proposed DWFP method was applied to the analysis of experimental signals obtained from nonlinear ultrasonic harmonic and wave-mixing experiments. It was demonstrated that the proposed DWFP method can be used to effectively extract nonlinear features from the experimental signals. Moreover, the proposed nonlinear fingerprint coefficient was sensitive to micro cracks and correlated well with the degree of damage.
Energy Technology Data Exchange (ETDEWEB)
Cloutier, J.R.; D`Souza, C.N.; Mracek, C.P. [Air Force Armament Directorate, Eglin, FL (United States)
1994-12-31
A little known technique for systematically designing nonlinear regulators is analyzed. The technique consists of first using direct parameterization to bring the nonlinear system to a linear structure having state-dependent coefficients (SDC). A state-dependent Riccati equation (SDRE) is then solved at each point x along the trajectory to obtain a nonlinear feedback controller of the form u = -R{sup -1}(x)B{sup T}(x)P(x)x, where P(x) is the solution of the SDRE. In the case of scalar x, it is shown that the SDRE approach yields a control solution which satisfies all of the necessary conditions for optimality even when the state and control weightings are functions of the state. It is also shown that the solution is globally asymptotically stable. In the multivariable case, the optimality, suboptimality and stability properties of the SDRE method are investigated. Under various mild assumptions of controllability and observability, the following is shown: (a) concerning the necessary conditions for optimality, where H is the Hamiltonian of the system, H{sub u} = 0 is always satisfied and, under stability, {lambda} = -H{sub x} is asymptotically satisfied at a quadratic rate as the states are driven toward the origin, (b) if it exists, a parameter-dependent SDC parameterization can be computed such that the multivariable SDRE closed loop solution satisfies all of the necessary conditions for optimality for a given initial condition, and (c) the method is locally asymptotically stable. A general nonlinear minimum-energy (nonlinear H{sub {infinity}}) problem is then posed. For this problem, the SDRF, method involves the solution of two coupled state-dependent Riccati equations at each point x along the trajectory. In the case of full state information, again under mild assumptions of controllability and observability, it is shown that the SDRE non-linear H{sub {infinity}} controller is internally locally asymptotically stable.
A Novel Analog-to-digital conversion Technique using nonlinear duty-cycle modulation
Directory of Open Access Journals (Sweden)
Jean Mbihi
2012-06-01
Full Text Available A new type of analog-to-digital conversion technique is presented in this paper. The interfacing hardware is a very simple nonlinear circuit with 1-bit modulated output. As a implication, behind the hardware simplicity retained is hidden a dreadful nonlinear duty-cycle modulation ratio. However, the overall nonlinear behavior embeds a sufficiently wide linear range, for a rigorous digital reconstitution of the analog input signal using a standard linear filter. Simulation and experimental results obtained using a well tested prototyping system, show the feasibility and good quality of the proposed conversion technique.
Nondestructive evaluation of notched cracks in mortars by nonlinear ultrasonic technique
Chen, Jun; Ren, Jun; Yin, Tingyuan
2016-04-01
In this paper, a nonlinear ultrasonic technique is used to nondestructively characterise concentrated defects in cement-based materials. Cracks are artificially notched in mortar samples and five different crack widths are used to simulate increased damage of samples. The relative ratio of second harmonic amplitude to the square of fundamental ultrasonic signal amplitude is defined as the damage indicator of the nonlinear ultrasonic technique, which is measured for mortar samples in conjunction with a typical linear nondestructive evaluation parameter - ultrasonic pulse velocity. It is found that both linear and nonlinear damage parameters have a good correlation with the change of crack width, while the nonlinearity parameter shows a better sensitivity to the width increase. In addition, the nonlinearity parameter presents an exponential increase with the crack growth, indicating an accelerating nonlinear ultrasonic response of materials to increased internal damage in the late phase. The results demonstrate that the nonlinear ultrasonic technique based on the second harmonic principle keeps the high sensitivity to the isolated cracks in cement-based materials, similarly to the case of distributed cracks in previous studies. The developed technique could thus be a useful experimental tool for the assessment of concentrated damage of concrete structures.
Isingizwe Nturambirwe, J. Frédéric; Perold, Willem J.; Opara, Umezuruike L.
2016-02-01
Near infrared (NIR) spectroscopy has gained extensive use in quality evaluation. It is arguably one of the most advanced spectroscopic tools in non-destructive quality testing of food stuff, from measurement to data analysis and interpretation. NIR spectral data are interpreted through means often involving multivariate statistical analysis, sometimes associated with optimisation techniques for model improvement. The objective of this research was to explore the extent to which genetic algorithms (GA) can be used to enhance model development, for predicting fruit quality. Apple fruits were used, and NIR spectra in the range from 12000 to 4000 cm-1 were acquired on both bruised and healthy tissues, with different degrees of mechanical damage. GAs were used in combination with partial least squares regression methods to develop bruise severity prediction models, and compared to PLS models developed using the full NIR spectrum. A classification model was developed, which clearly separated bruised from unbruised apple tissue. GAs helped improve prediction models by over 10%, in comparison with full spectrum-based models, as evaluated in terms of error of prediction (Root Mean Square Error of Cross-validation). PLS models to predict internal quality, such as sugar content and acidity were developed and compared to the versions optimized by genetic algorithm. Overall, the results highlighted the potential use of GA method to improve speed and accuracy of fruit quality prediction.
DEFF Research Database (Denmark)
Sharifzadeh, Sara; Clemmensen, Line Katrine Harder; Borggaard, Claus
2014-01-01
feature selection method outperforms the PCA for both linear and non-linear methods. The highest performance was obtained by linear ridge regression applied on the selected features from the proposed Elastic net (EN) -based feature selection strategy. All the best models use a reduced number...... of meat samples (430–970 nm) were used for training and testing of the L⁎a⁎b prediction models. Finding a sparse solution or the use of a minimum number of bands is of particular interest to make an industrial vision set-up simpler and cost effective. In this paper, a wide range of linear, non-linear......, kernel-based regression and sparse regression methods are compared. In order to improve the prediction results of these models, we propose a supervised feature selection strategy which is compared with the Principal component analysis (PCA) as a pre-processing step. The results showed that the proposed...
An analysis of a new nonlinear estimation technique: The state-dependent Ricatti equation method
Ewing, Craig Michael
1999-10-01
Research into nonlinear estimation techniques for terminal homing missiles has been conducted for many decades. The terminal state estimator, also called the guidance filter, is responsible for providing accurate estimates of target motion for use in guiding the missile to a collision course with the target. Some form of the extended-Kalman filter (EKF) has become the standard estimation technique employed in most modern weapon guidance systems. EKF linearization of nonlinear dynamics and/or measurements can cause problems of divergence when confronted by highly nonlinear conditions. The objective of this dissertation is to analyze a new nonlinear estimation technique that is based on the parameterization of the nonlinearities. This parameterization converts the nonlinear estimation problem into the form of a steady-state continuous Kalman filtering problem with state-dependent coefficients. This new technique, called the state-dependent Ricatti equation filter (SDREF), allows the nonlinearities of the system to be fully incorporated into the filter design, before stochastic uncertainties are imposed, without the need for linearization. The SDREF was investigated in three problems: an exoatmospheric, terminal homing, ballistic-missile intercept problem; a highly nonlinear pendulum example; and an algorithmic loss of observability problem. The exoatmospheric guidance problem examined nonlinear measurements with linear dynamics. To investigate the SDREF when used with a combination of nonlinear dynamics and nonlinear measurements, a highly nonlinear, two-state pendulum problem was also examined. While these problems were useful in gaining insight into the performance characteristics of the SDREF, no formal proof of stability could be determined for the original formulation of the estimator. The original SDREF solved an algebraic SDRE that arose from an infinite-time horizon formulation of the nonlinear filtering problem. A modification to the SDREF formulation was
Differential geometry techniques for sets of nonlinear partial differential equations
Estabrook, Frank B.
1990-01-01
An attempt is made to show that the Cartan theory of partial differential equations can be a useful technique for applied mathematics. Techniques for finding consistent subfamilies of solutions that are generically rich and well-posed and for introducing potentials or other usefully consistent auxiliary fields are introduced. An extended sample calculation involving the Korteweg-de Vries equation is given.
Nonlinear Filtering Techniques Comparison for Battery State Estimation
Directory of Open Access Journals (Sweden)
Aspasia Papazoglou
2014-09-01
Full Text Available The performance of estimation algorithms is vital for the correct functioning of batteries in electric vehicles, as poor estimates will inevitably jeopardize the operations that rely on un-measurable quantities, such as State of Charge and State of Health. This paper compares the performance of three nonlinear estimation algorithms: the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter, where a lithium-ion cell model is considered. The effectiveness of these algorithms is measured by their ability to produce accurate estimates against their computational complexity in terms of number of operations and execution time required. The trade-offs between estimators' performance and their computational complexity are analyzed.
Institute of Scientific and Technical Information of China (English)
苗宇; 苏宏业; 禇健
2009-01-01
The quality of process data in a chemical plant significantly affects the performance and benefits gained from activities like performance monitoring, online optimization, and control. Since many chemical processes often show nonlinear dynamics, techniques like extended Kalman filter (EKF) and nonlinear dynamic data reconciliation (NDDR) have been developed to improve the data quality. Recently, the recursive nonlinear dynamic data reconciliation (RNDDR) technique has been proposed, which combines the merits of EKF and NDDR techniques. However, the RNDDR technique cannot handle measurements with gross errors. In this paper, a support vector (SV) regression approach for recursive simultaneous data reconciliation and gross error detection in nonlinear dynamical systems is proposed. SV regression is a compromise between the empirical risk and the model complexity, and for data reconciliation it is robust to random and gross errors. By minimizing the regularized risk instead of the maximum likelihood in the RNDDR, our approach could achieve not only recursive nonlinear dynamic data reconciliation but also gross error detection simultaneously. The nonlinear dynamic system simulation results in this paper show that the proposed approach is robust, efficient, stable, and accurate for simultaneous data reconciliation and gross error detection in nonlinear dynamic systems within a recursive real-time estimation framework. It can also give better performance of control.
Measurements of nonlinear optical properties of PVDF/ZnO using Z-scan technique
Energy Technology Data Exchange (ETDEWEB)
Shanshool, Haider Mohammed, E-mail: haidshan62@gmail.com [Ministry of Science and Technology, Baghdad (Iraq); Yahaya, Muhammad [School of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Selangor (Malaysia); Yunus, Wan Mahmood Mat [Department of Physics, Faculty of Science, University Putra Malaysia, Serdang (Malaysia); Abdullah, Ibtisam Yahya [Department of Physics, College of Science, University of Mosul, Mosul (Iraq)
2015-10-15
The nonlinear optical properties of ZnO nanoparticles dispersed in poly (vinylidene fluoride) (PVDF) polymer are investigated. PVDF/ZnO nanocomposites were prepared by mixing different concentrations of ZnO nanoparticles, as the filler, with PVDF, as the polymer matrix, using casting method. Acetone was used as a solvent for the polymer. FTIR spectra of the samples were analyzed thus confirming the formation of α and β phases. The absorbance spectra of the samples were obtained, thereby showing high absorption in the UV region. The linear absorption coefficient was calculated. The single-beam Z-scan technique was used to measure the nonlinear refractive index and the nonlinear absorption coefficient of the PVDF/ZnO nanocomposite samples. We observed that the nonlinear refractive index is in the order of 10{sup -13} cm{sup 2}/W with the negative sign, whereas the nonlinear absorption coefficient is in the order of 10{sup -8} cm/W. (author)
Compensation techniques for non-linearities in H-bridge inverters
Directory of Open Access Journals (Sweden)
Daniel Zammit
2016-12-01
Full Text Available This paper presents compensation techniques for component non-linearities in H-bridge inverters as those used in grid-connected photovoltaic (PV inverters. Novel compensation techniques depending on the switching device current were formulated to compensate for the non-linearities in inverter circuits caused by the voltage drops on the switching devices. Both simulation and experimental results will be presented. Testing was carried out on a PV inverter which was designed and constructed for this research. Very satisfactory results were obtained from all the compensation techniques presented, however the exact compensation method was the most effective, providing the highest reduction in harmonics.
Kounadis, A. N.
1992-05-01
An efficient and easily applicable, approximate analytic technique for the solution of nonlinear initial and boundary-value problems associated with nonlinear ordinary differential equations (O.D.E.) of any order and variable coefficients, is presented. Convergence, uniqueness and upper bound error estimates of solutions, obtained by the successive approximations scheme of the proposed technique, are thoroughly established. Important conclusions regarding the improvement of convergence for large time and large displacement solutions in case of nonlinear initial-value problems are also assessed. The proposed technique is much more efficient than the perturbations schemes for establishing the large postbuckling response of structural systems. The efficiency, simplicity and reliability of the proposed technique is demonstrated by two illustrative examples for which available numerical results exist.
Sarhadi, Ali; Burn, Donald H.; Johnson, Fiona; Mehrotra, Raj; Sharma, Ashish
2016-05-01
Accurate projection of global warming on the probabilistic behavior of hydro-climate variables is one of the main challenges in climate change impact assessment studies. Due to the complexity of climate-associated processes, different sources of uncertainty influence the projected behavior of hydro-climate variables in regression-based statistical downscaling procedures. The current study presents a comprehensive methodology to improve the predictive power of the procedure to provide improved projections. It does this by minimizing the uncertainty sources arising from the high-dimensionality of atmospheric predictors, the complex and nonlinear relationships between hydro-climate predictands and atmospheric predictors, as well as the biases that exist in climate model simulations. To address the impact of the high dimensional feature spaces, a supervised nonlinear dimensionality reduction algorithm is presented that is able to capture the nonlinear variability among projectors through extracting a sequence of principal components that have maximal dependency with the target hydro-climate variables. Two soft-computing nonlinear machine-learning methods, Support Vector Regression (SVR) and Relevance Vector Machine (RVM), are engaged to capture the nonlinear relationships between predictand and atmospheric predictors. To correct the spatial and temporal biases over multiple time scales in the GCM predictands, the Multivariate Recursive Nesting Bias Correction (MRNBC) approach is used. The results demonstrate that this combined approach significantly improves the downscaling procedure in terms of precipitation projection.
A new approach of binary addition and subtraction by non-linear material based switching technique
Indian Academy of Sciences (India)
Archan Kumar Das; Partha Partima Das; Sourangshu Mukhopadhyay
2005-02-01
Here, we refer a new proposal of binary addition as well as subtraction in all-optical domain by exploitation of proper non-linear material-based switching technique. In this communication, the authors extend this technique for both adder and subtractor accommodating the spatial input encoding system.
Indirect techniques for adaptive input-output linearization of non-linear systems
Teel, Andrew; Kadiyala, Raja; Kokotovic, Peter; Sastry, Shankar
1991-01-01
A technique of indirect adaptive control based on certainty equivalence for input output linearization of nonlinear systems is proven convergent. It does not suffer from the overparameterization drawbacks of the direct adaptive control techniques on the same plant. This paper also contains a semiindirect adaptive controller which has several attractive features of both the direct and indirect schemes.
Non-Linearity in Wide Dynamic Range CMOS Image Sensors Utilizing a Partial Charge Transfer Technique
Directory of Open Access Journals (Sweden)
Izhal Abdul Halin
2009-11-01
Full Text Available The partial charge transfer technique can expand the dynamic range of a CMOS image sensor by synthesizing two types of signal, namely the long and short accumulation time signals. However the short accumulation time signal obtained from partial transfer operation suffers of non-linearity with respect to the incident light. In this paper, an analysis of the non-linearity in partial charge transfer technique has been carried, and the relationship between dynamic range and the non-linearity is studied. The results show that the non-linearity is caused by two factors, namely the current diffusion, which has an exponential relation with the potential barrier, and the initial condition of photodiodes in which it shows that the error in the high illumination region increases as the ratio of the long to the short accumulation time raises. Moreover, the increment of the saturation level of photodiodes also increases the error in the high illumination region.
Non-Linearity in Wide Dynamic Range CMOS Image Sensors Utilizing a Partial Charge Transfer Technique
Shafie, Suhaidi; Kawahito, Shoji; Halin, Izhal Abdul; Hasan, Wan Zuha Wan
2009-01-01
The partial charge transfer technique can expand the dynamic range of a CMOS image sensor by synthesizing two types of signal, namely the long and short accumulation time signals. However the short accumulation time signal obtained from partial transfer operation suffers of non-linearity with respect to the incident light. In this paper, an analysis of the non-linearity in partial charge transfer technique has been carried, and the relationship between dynamic range and the non-linearity is studied. The results show that the non-linearity is caused by two factors, namely the current diffusion, which has an exponential relation with the potential barrier, and the initial condition of photodiodes in which it shows that the error in the high illumination region increases as the ratio of the long to the short accumulation time raises. Moreover, the increment of the saturation level of photodiodes also increases the error in the high illumination region. PMID:22303133
Sudheesh, P.; Rao, D. Mallikharjuna; Chandrasekharan, K.
2014-01-01
The third-order nonlinear optical properties of newly synthesized phenylhydrazone derivatives and the influence of noble metal nanoparticles (Ag & Au) on their nonlinear optical responses were investigated by employing Degenerate Four wave Mixing (DFWM) technique with a 7 nanosecond, 10Hz Nd: YAG laser pulses at 532nm. Metal nanoparticles were prepared by laser ablation and the particle formation was confirmed using UV-Visible spectroscopy, Transmission Electron Microscopy (TEM). The nonlinear optical susceptibility were measured and found to be of the order 10-13esu. The results are encouraging and conclude that the materials are promising candidate for future optical device applications.
Directory of Open Access Journals (Sweden)
Omholt Stig W
2011-06-01
Full Text Available Abstract Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs to variation in features of the trajectories of the state variables (outputs throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR, where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR and ordinary least squares (OLS regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback
Development of a nonlinear ultrasonic NDE technique for detection of kissing bonds in composites
Alston, Jonathan; Croxford, Anthony; Potter, Jack; Blanloeuil, Philippe
2017-04-01
The development of low-cost bonded assembly of composite aerospace structures ideally requires an NDE method to detect the presence of poor quality, weak bonds or kissing bonds. Such interfaces can introduce nonlinearity as a result of contact nonlinearity where an ultrasonic wave is distorted when it interacts with the interface. In general, the nonlinear elastic behaviour of these interfaces will generate harmonics but they can be lost among the harmonics generated by other nonlinearities present in the experimental system. The technique developed in this research is a non-collinear method; this involves the interaction of two ultrasonic beams, and it allows the removal of virtually all system nonlinearity except for that produced in the region where the two beams overlap. The frequencies of the two beams and the angle between are varied during the experiment. By measuring the nonlinear mixing response as these two parameters are swept through a `fingerprint' of the nonlinear properties in the interaction region can be obtained. This fingerprint has been shown to contain information about the bulk material and the interface status. Work is ongoing to understand which features in the fingerprints reliably correlate with particular material or interface properties. To build this understanding a greatly simplified kissing bond, a compression loaded aluminium-aluminium interface, has been tested. Modelling of the nonlinear behaviour of the aluminium interface has also been conducted.
Directory of Open Access Journals (Sweden)
Ahmad A. Saifan
2016-04-01
Full Text Available Regression testing is a safeguarding procedure to validate and verify adapted software, and guarantee that no errors have emerged. However, regression testing is very costly when testers need to re-execute all the test cases against the modified software. This paper proposes a new approach in regression test selection domain. The approach is based on meta-models (test models and structured models to decrease the number of test cases to be used in the regression testing process. The approach has been evaluated using three Java applications. To measure the effectiveness of the proposed approach, we compare the results using the re-test to all approaches. The results have shown that our approach reduces the size of test suite without negative impact on the effectiveness of the fault detection.
Correction of Phase Distortion by Nonlinear Optical Techniques
1981-05-01
ward wave oscillators and distributed feedback lasers, occur even in the presence of pump attenuation. It is obvious that pump depletion effects...a*. Efl v* Z* ^iCVb^^f-V VEfl> (4-3-2) Ik -VE +^ V,2 E - n— p p 2k T p 2nc W {M[(I +1 )En - (E -E*) t...offset techniques. (1) Since the pumps may be arranged to be non-counterpropagating with angle offset techniques, feedback of the pump into the
Directory of Open Access Journals (Sweden)
Ying-Ying Wang
2015-06-01
Full Text Available The identification difficulties for a dual-rate Hammerstein system lie in two aspects. First, the identification model of the system contains the products of the parameters of the nonlinear block and the linear block, and a standard least squares method cannot be directly applied to the model; second, the traditional single-rate discrete-time Hammerstein model cannot be used as the identification model for the dual-rate sampled system. In order to solve these problems, by combining the polynomial transformation technique with the key variable separation technique, this paper converts the Hammerstein system into a dual-rate linear regression model about all parameters (linear-in-parameter model and proposes a recursive least squares algorithm to estimate the parameters of the dual-rate system. The simulation results verify the effectiveness of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Marcel Schwieder
2014-04-01
Full Text Available Anthropogenic interventions in natural and semi-natural ecosystems often lead to substantial changes in their functioning and may ultimately threaten ecosystem service provision. It is, therefore, necessary to monitor these changes in order to understand their impacts and to support management decisions that help ensuring sustainability. Remote sensing has proven to be a valuable tool for these purposes, and especially hyperspectral sensors are expected to provide valuable data for quantitative characterization of land change processes. In this study, simulated EnMAP data were used for mapping shrub cover fractions along a gradient of shrub encroachment, in a study region in southern Portugal. We compared three machine learning regression techniques: Support Vector Regression (SVR; Random Forest Regression (RF; and Partial Least Squares Regression (PLSR. Additionally, we compared the influence of training sample size on the prediction performance. All techniques showed reasonably good results when trained with large samples, while SVR always outperformed the other algorithms. The best model was applied to produce a fractional shrub cover map for the whole study area. The predicted patterns revealed a gradient of shrub cover between regions affected by special agricultural management schemes for nature protection and areas without land use incentives. Our results highlight the value of EnMAP data in combination with machine learning regression techniques for monitoring gradual land change processes.
Dávila Pintle, José A; Lara, Edmundo Reynoso; Iturbe Castillo, Marcelo D
2013-07-01
It is presented a criteria for selecting the optimum aperture radius for the one beam Z-scan technique (OBZT), based on the analysis of the transmittance of the aperture. It is also presented a modification to the OBZT by directly measuring the beam radius in the far field with a rotating disk, which allows to determine simultaneously the non-linear absorptive coefficient and non-linear refractive index, much less sensitive to wave front distortions caused by inhomogeneities of the sample with a negligible loss of signal to noise ratio. It is demonstrated its equivalence to the OBZT.
Advanced Phase noise modeling techniques of nonlinear microwave devices
Prigent, M.; J. C. Nallatamby; R. Quere
2004-01-01
In this paper we present a coherent set of tools allowing an accurate and predictive design of low phase noise oscillators. Advanced phase noise modelling techniques in non linear microwave devices must be supported by a proven combination of the following : - Electrical modeling of low-frequency noise of semiconductor devices, oriented to circuit CAD . The local noise sources will be either cyclostationary noise sources or quasistationary noise sources. - Theoretic...
Extending the perturbation technique to the modal representation of nonlinear systems
Energy Technology Data Exchange (ETDEWEB)
Soltani, S. [Department of Electrical Engineering, Science and Research Branch, Islamic Azad University (IAU), 1477893855-14515775, Tehran (Iran); Pariz, N.; Ghazi, R. [Department of Electrical Engineering, ferdowsi University, 9177948944-1111, Mashhad (Iran)
2009-08-15
After a brief review of perturbation technique, using this method an approach is developed to represent and study the behavior of nonlinear dynamic power systems. For the first time in this field, perturbation technique is applied to obtain an approximate closed form expression for the zero input response of stressed power systems. In order to show the superiority of the proposed method, it has been applied to a typical nonlinear system which is a single machine infinite bus (SMIB) power system with unified power flow controller (UPFC). The accuracy and competency of this method in comparison with Modal Series method will also be validated. (author)
Guermond, Jean-Luc
2014-01-01
© 2014 Society for Industrial and Applied Mathematics. This paper proposes an explicit, (at least) second-order, maximum principle satisfying, Lagrange finite element method for solving nonlinear scalar conservation equations. The technique is based on a new viscous bilinear form introduced in Guermond and Nazarov [Comput. Methods Appl. Mech. Engrg., 272 (2014), pp. 198-213], a high-order entropy viscosity method, and the Boris-Book-Zalesak flux correction technique. The algorithm works for arbitrary meshes in any space dimension and for all Lipschitz fluxes. The formal second-order accuracy of the method and its convergence properties are tested on a series of linear and nonlinear benchmark problems.
Menendez, P.; Eilers, P.; Tikunov, Y.M.; Bovy, A.G.; Eeuwijk, van F.
2012-01-01
The search for models which link tomato taste attributes to their metabolic profiling, is a main challenge within the breeding programs that aim to enhance tomato flavor. In this paper, we compared such models calculated by the traditional statistical approach, stepwise regression, with models obtai
Haroon, Muhammad; Adams, Douglas E.
2007-04-01
Fatigue tests on a stabilizer bar link of an automotive suspension system are used to initiate a crack and grow the crack size. During these tests, slow sine sweeps are used to extract narrowband restoring forces across the stabilizer bar link. The restoring forces are shown to characterize the nonlinear changes in component internal forces due to crack growth. Broadband frequency response domain techniques are used to analyze the durability response data. Nonlinear frequency domain models of the dynamic transmissibility across the cracked region are shown to change as a function of crack growth. Higher order spectra are used to show the increase in nonlinear coupling of response frequency components with the appearance and growth of the crack. It is shown that crack growth can be detected and characterized by the changes in nonlinear indicators.
Nonlinear optical properties of natural laccaic acid dye studied using Z-scan technique
Zongo, S.; Sanusi, K.; Britton, J.; Mthunzi, P.; Nyokong, T.; Maaza, M.; Sahraoui, B.
2015-08-01
We have investigated the nonlinear optical properties, including the optical limiting behaviour for five different concentrations of laccaic acid dye in solution and a thin film obtained through doping in poly (methyl methacrylate) (PMMA) polymer. The experiments were performed by using single beam Z-scan technique at 532 nm with 10 ns, 10 Hz Nd:YAG laser pulses excitation. From the open-aperture Z-scan data, we derived that the laccaic dye samples exhibit strong two photon absorption (2PA). The nonlinear refractive index was determined through the closed aperture Z-scan data. The estimated absorption coefficient β2, nonlinear refractive index n2 and second order hyperpolarizability γ were found to be of the order of 10-10 m/W, 10-9 esu and 10-32 esu, respectively. The Z-scan study reveals that the natural laccaic acid dye emerges as a promising material for third order nonlinear optical devices application.
APPLICATION OF REGRESSION MODELLING TECHNIQUES IN DESALINATION OF SEA WATER BY MEMBRANE DISTILLATION
Directory of Open Access Journals (Sweden)
SELVI S. R
2015-08-01
Full Text Available The objective of this work is to gain an idea about the statistical significance of experimental parameters on the performance of membrane distillation. In this work the raw sea water sample without pretreatment was collected from Puducherry and desalinated using direct contact membrane distillation method. Experimental data analysis was carried out using statistical methods. The experimental data involves the effects of feed temperature, feed flow rate and feed concentration on the permeate flux. In statistical methods, regression model was developed to correlate the significance of input parameters like feed temperature, feed concentration and feed flow rate with the output parameter like permeate flux in the process of membrane distillation. Since the performance of the membrane distillation in the desalination of water is characterised by permeate flux, regression model using simple linear method was carried out. Goodness of model fitting should always has to be validated. Regression model was validated using ANOVA. Estimates of ANOVA for the parameter study was given and the coefficient obtained by regression analysis was specified in the regression equation and concluded that the highest coefficient of input parameter is significant, highly influences the response. Feed flow rate and feed temperature has higher influence on permeate flux than that of feed concentration. The coefficient of feed concentration was found to be negative which indicates less significant factor on permeate flux. The chemical composition of sea water was given by water quality analysis . TDS of membrane distilled water was found to be 18ppm than the initial feed TDS of sea water 27,720 ppm. From the experimental work it was found, salt rejection as 99% and water analysis report confirms the quality of distillate obtained by this desalination process as potable water.
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access to no...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
HMM Speaker Identification Using Linear and Non-linear Merging Techniques
Mahola, Unathi; Marwala, Tshilidzi
2007-01-01
Speaker identification is a powerful, non-invasive and in-expensive biometric technique. The recognition accuracy, however, deteriorates when noise levels affect a specific band of frequency. In this paper, we present a sub-band based speaker identification that intends to improve the live testing performance. Each frequency sub-band is processed and classified independently. We also compare the linear and non-linear merging techniques for the sub-bands recognizer. Support vector machines and Gaussian Mixture models are the non-linear merging techniques that are investigated. Results showed that the sub-band based method used with linear merging techniques enormously improved the performance of the speaker identification over the performance of wide-band recognizers when tested live. A live testing improvement of 9.78% was achieved
Optical nonlinearity of organic dyes as studied by Z-scan and transient grating techniques
Indian Academy of Sciences (India)
Umakanta Tripathy; R Justin Rajesh; Prem B Bisht; A Subrahamanyam
2002-12-01
The excited state absorption cross-section of 5,5′-dichloro-11-diphenylamino- 3,3′-diethyl-10,12-ethylinethiatricarbocyanine perchlorate (IR140) have been measured by using a single beam transmission technique. Z-scan experiments have been used to find out a few nonlinear parameters. The excited state relaxation times have also been measured by using laser induced transient grating (LITG) technique.
GDTM-Padé technique for the non-linear differential-difference equation
Directory of Open Access Journals (Sweden)
Lu Jun-Feng
2013-01-01
Full Text Available This paper focuses on applying the GDTM-Padé technique to solve the non-linear differential-difference equation. The bell-shaped solitary wave solution of Belov-Chaltikian lattice equation is considered. Comparison between the approximate solutions and the exact ones shows that this technique is an efficient and attractive method for solving the differential-difference equations.
Adaptive, Small-Rotation-Based, Corotational Technique for Analysis of 2D Nonlinear Elastic Frames
Directory of Open Access Journals (Sweden)
Jaroon Rungamornrat
2014-01-01
Full Text Available This paper presents an efficient and accurate numerical technique for analysis of two-dimensional frames accounted for both geometric nonlinearity and nonlinear elastic material behavior. An adaptive remeshing scheme is utilized to optimally discretize a structure into a set of elements where the total displacement can be decomposed into the rigid body movement and one possessing small rotations. This, therefore, allows the force-deformation relationship for the latter part to be established based on small-rotation-based kinematics. Nonlinear elastic material model is integrated into such relation via the prescribed nonlinear moment-curvature relationship. The global force-displacement relation for each element can be derived subsequently using corotational formulations. A final system of nonlinear algebraic equations along with its associated gradient matrix for the whole structure is obtained by a standard assembly procedure and then solved numerically by Newton-Raphson algorithm. A selected set of results is then reported to demonstrate and discuss the computational performance including the accuracy and convergence of the proposed technique.
Wang, X.; Zheng, G. T.
2016-02-01
A simple and general Equivalent Dynamic Stiffness Mapping technique is proposed for identifying the parameters or the mathematical model of a nonlinear structural element with steady-state primary harmonic frequency response functions (FRFs). The Equivalent Dynamic Stiffness is defined as the complex ratio between the internal force and the displacement response of unknown element. Obtained with the test data of responses' frequencies and amplitudes, the real and imaginary part of Equivalent Dynamic Stiffness are plotted as discrete points in a three dimensional space over the displacement amplitude and the frequency, which are called the real and the imaginary Equivalent Dynamic Stiffness map, respectively. These points will form a repeatable surface as the Equivalent Dynamic stiffness is only a function of the corresponding data as derived in the paper. The mathematical model of the unknown element can then be obtained by surface-fitting these points with special functions selected by priori knowledge of the nonlinear type or with ordinary polynomials if the type of nonlinearity is not pre-known. An important merit of this technique is its capability of dealing with strong nonlinearities owning complicated frequency response behaviors such as jumps and breaks in resonance curves. In addition, this technique could also greatly simplify the test procedure. Besides there is no need to pre-identify the underlying linear parameters, the method uses the measured data of excitation forces and responses without requiring a strict control of the excitation force during the test. The proposed technique is demonstrated and validated with four classical single-degree-of-freedom (SDOF) numerical examples and one experimental example. An application of this technique for identification of nonlinearity from multiple-degree-of-freedom (MDOF) systems is also illustrated.
Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E
2016-07-19
Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning.
Kukush, Alexander; Schneeweiss, Hans
2004-01-01
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error model to the asymptotic covariance matrices of the CS and SQS estimators studied in Kukush et al (2002). For small measurement error variances they are equal up to the order of the measurement error variance and thus nearly equally efficient.
RF Calibration of On-Chip DfT Chain by DC Stimuli and Statistical Multivariate Regression Technique
Ramzan, Rashad; Dabrowski, Jerzy
2015-01-01
The problem of parameter variability in RF and analog circuits is escalating with CMOS scaling. Consequently every RF chip produced in nano-meter CMOS technologies needs to be tested. On-chip Design for Testability (DfT) features, which are meant to reduce test time and cost also suffer from parameter variability. Therefore, RF calibration of all on-chip test structures is mandatory. In this paper, Artificial Neural Networks (ANN) are employed as a multivariate regression technique to archite...
Sarhadi, Ali; Burn, Donald H.; Yang, Ge; Ghodsi, Ali
2017-02-01
One of the main challenges in climate change studies is accurate projection of the global warming impacts on the probabilistic behaviour of hydro-climate processes. Due to the complexity of climate-associated processes, identification of predictor variables from high dimensional atmospheric variables is considered a key factor for improvement of climate change projections in statistical downscaling approaches. For this purpose, the present paper adopts a new approach of supervised dimensionality reduction, which is called "Supervised Principal Component Analysis (Supervised PCA)" to regression-based statistical downscaling. This method is a generalization of PCA, extracting a sequence of principal components of atmospheric variables, which have maximal dependence on the response hydro-climate variable. To capture the nonlinear variability between hydro-climatic response variables and projectors, a kernelized version of Supervised PCA is also applied for nonlinear dimensionality reduction. The effectiveness of the Supervised PCA methods in comparison with some state-of-the-art algorithms for dimensionality reduction is evaluated in relation to the statistical downscaling process of precipitation in a specific site using two soft computing nonlinear machine learning methods, Support Vector Regression and Relevance Vector Machine. The results demonstrate a significant improvement over Supervised PCA methods in terms of performance accuracy.
Directory of Open Access Journals (Sweden)
Matthias Schmid
Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.
Directory of Open Access Journals (Sweden)
K. R. Subhashini
2014-01-01
synthesis is termed as the variation in the element excitation amplitude and nonlinear synthesis is process of variation in element angular position. Both ADE and AFA are a high-performance stochastic evolutionary algorithm used to solve N-dimensional problems. These methods are used to determine a set of parameters of antenna elements that provide the desired radiation pattern. The effectiveness of the algorithms for the design of conformal antenna array is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that nonlinear synthesis, aided by the discussed techniques, provides considerable enhancements compared to linear synthesis.
Interferometric and nonlinear-optical spectral-imaging techniques for outer space and live cells
Itoh, Kazuyoshi
2015-12-01
Multidimensional signals such as the spectral images allow us to have deeper insights into the natures of objects. In this paper the spectral imaging techniques that are based on optical interferometry and nonlinear optics are presented. The interferometric imaging technique is based on the unified theory of Van Cittert-Zernike and Wiener-Khintchine theorems and allows us to retrieve a spectral image of an object in the far zone from the 3D spatial coherence function. The retrieval principle is explained using a very simple object. The promising applications to space interferometers for astronomy that are currently in progress will also be briefly touched on. An interesting extension of interferometric spectral imaging is a 3D and spectral imaging technique that records 4D information of objects where the 3D and spectral information is retrieved from the cross-spectral density function of optical field. The 3D imaging is realized via the numerical inverse propagation of the cross-spectral density. A few techniques suggested recently are introduced. The nonlinear optical technique that utilizes stimulated Raman scattering (SRS) for spectral imaging of biomedical targets is presented lastly. The strong signals of SRS permit us to get vibrational information of molecules in the live cell or tissue in real time. The vibrational information of unstained or unlabeled molecules is crucial especially for medical applications. The 3D information due to the optical nonlinearity is also the attractive feature of SRS spectral microscopy.
A Construction of String 2-Group Models using a Transgression-Regression Technique
Waldorf, Konrad
2012-01-01
In this note we present a new construction of the string group that ends optionally in two different contexts: strict diffeological 2-groups or finite-dimensional Lie 2-groups. It is canonical in the sense that no choices are involved; all the data is written down and can be looked up (at least somewhere). The basis of our construction is the basic gerbe of Gawedzki-Reis and Meinrenken. The main new insight is that under a transgression-regression procedure, the basic gerbe picks up a multiplicative structure coming from the Mickelsson product over the loop group. The conclusion of the construction is a relation between multiplicative gerbes and 2-group extensions for which we use recent work of Schommer-Pries.
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access...... to nonpublic information. Our analysis shows that information networks have an impact on the level of TAC. Many resources that are sacrificed for TAC are inputs that also enter the technical production process. As most production data do not separate between these two usages of inputs, high transaction costs...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Ghazali, Nurul Adyani; Ramli, Nor Azam; Yahaya, Ahmad Shukri; Yusof, Noor Faizah Fitri M D; Sansuddin, Nurulilyana; Al Madhoun, Wesam Ahmed
2010-06-01
Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.
Directory of Open Access Journals (Sweden)
Noor Zaitun Yahaya
2017-01-01
Full Text Available This paper investigated the use of boosted regression trees (BRTs to draw an inference about daytime and nighttime ozone formation in a coastal environment. Hourly ground-level ozone data for a full calendar year in 2010 were obtained from the Kemaman (CA 002 air quality monitoring station. A BRT model was developed using hourly ozone data as a response variable and nitric oxide (NO, Nitrogen Dioxide (NO2 and Nitrogen Dioxide (NOx and meteorological parameters as explanatory variables. The ozone BRT algorithm model was constructed from multiple regression models, and the 'best iteration' of BRT model was performed by optimizing prediction performance. Sensitivity testing of the BRT model was conducted to determine the best parameters and good explanatory variables. Using the number of trees between 2,500-3,500, learning rate of 0.01, and interaction depth of 5 were found to be the best setting for developing the ozone boosting model. The performance of the O3 boosting models were assessed, and the fraction of predictions within two factor (FAC2, coefficient of determination (R2 and the index of agreement (IOA of the model developed for day and nighttime are 0.93, 0.69 and 0.73 for daytime and 0.79, 0.55 and 0.69 for nighttime respectively. Results showed that the model developed was within the acceptable range and could be used to understand ozone formation and identify potential sources of ozone for estimating O3 concentrations during daytime and nighttime. Results indicated that the wind speed, wind direction, relative humidity, and temperature were the most dominant variables in terms of influencing ozone formation. Finally, empirical evidence of the production of a high ozone level by wind blowing from coastal areas towards the interior region, especially from industrial areas, was obtained.
Institute of Scientific and Technical Information of China (English)
无
2012-01-01
The iterative technique of sign-changing solution is studied for a nonlinear third-order two-point boundary value problem, where the nonlinear term has the time sin-gularity. By applying the monotonically iterative technique, an existence theorem is established and two useful iterative schemes are obtained.
Study of nonlinear absorption properties of reduced graphene oxide by Z-scan technique
Sreeja, V. G.; Vinitha, G.; Reshmi, R.; Anila, E. I.; Jayaraj, M. K.
2017-05-01
Graphene has generated enormous research interest during the last decade due to its significant unique properties and wide applications in the field of optoelectronics and photonics. This research studied the structural and nonlinear absorption properties of reduced graphene oxide (rGO) synthesized by Modified Hummer's method. Structural and physiochemical properties of the rGO were explored with the help of Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy (Raman). Nonlinear absorption property in rGO, was investigated by open aperture Z-scan technique by using a continuous wave (CW) laser. The Z-scan results demonstrate saturable absorption property of rGO with a nonlinear absorption coefficient, β, of -2.62 × 10-4 cm/W, making it suitable for applications in Q switching, generation of ultra-fast high energy pulses in laser cavity and mode lockers.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
We introduce a new method to derive the orbital parameters of spectroscopic binary stars by nonlinear least squares of (o - c). Using the measured radial velocity data of the four double lined spectroscopic binary systems,AI Phe,GM Dra,HD 93917 and V502 Oph,we derived both the orbital and combined spectroscopic elements of these systems.Our numerical results are in good agreement with the those obtained using the method of Lehmann-Filhés.
Use of binary logistic regression technique with MODIS data to estimate wild fire risk
Fan, Hong; Di, Liping; Yang, Wenli; Bonnlander, Brian; Li, Xiaoyan
2007-11-01
Many forest fires occur across the globe each year, which destroy life and property, and strongly impact ecosystems. In recent years, wildland fires and altered fire disturbance regimes have become a significant management and science problem affecting ecosystems and wildland/urban interface cross the United States and global. In this paper, we discuss the estimation of 504 probability models for forecasting fire risk for 14 fuel types, 12 months, one day/week/month in advance, which use 19 years of historical fire data in addition to meteorological and vegetation variables. MODIS land products are utilized as a major data source, and a logistical binary regression was adopted to solve fire forecast probability. In order to better modeling the change of fire risk along with the transition of seasons, some spatial and temporal stratification strategies were applied. In order to explore the possibilities of real time prediction, the Matlab distributing computing toolbox was used to accelerate the prediction. Finally, this study give an evaluation and validation of predict based on the ground truth collected. Validating results indicate these fire risk models have achieved nearly 70% accuracy of prediction and as well MODIS data are potential data source to implement near real-time fire risk prediction.
Study of nonlinear refraction of organic dye by Z-scan technique using He-Ne laser
Medhekar, S.; Kumar, R.; Mukherjee, S.; Choubey, R. K.
2013-02-01
Laser induced third-order nonlinear optical responses of Brilliant Green solution has been investigated by utilizing single beam Z-scan technique with a continuous-wave He-Ne laser radiation at 632.8 nm. It was observed that the material exhibits self-defocusing type optical nonlinearity. The measurements of nonlinear refraction were carried out at different dye concentrations and found that the increase in solution concentration leads to the linear increase of the nonlinear refractive index. The experimental results confirm great potential of the Brilliant Green for the application in nonlinear optical devices.
Study of nonlinear refraction of organic dye by Z-scan technique using He-Ne laser
Energy Technology Data Exchange (ETDEWEB)
Medhekar, S.; Kumar, R.; Mukherjee, S.; Choubey, R. K. [Dept. of Applied Physics, Birla Institute of Technology, Mesra, Ranchi - 835215, Jharkhand (India)
2013-02-05
Laser induced third-order nonlinear optical responses of Brilliant Green solution has been investigated by utilizing single beam Z-scan technique with a continuous-wave He-Ne laser radiation at 632.8 nm. It was observed that the material exhibits self-defocusing type optical nonlinearity. The measurements of nonlinear refraction were carried out at different dye concentrations and found that the increase in solution concentration leads to the linear increase of the nonlinear refractive index. The experimental results confirm great potential of the Brilliant Green for the application in nonlinear optical devices.
Measurement of Temperature Change in Nonlinear Optical Materials by Using the Z-Scan Technique
Institute of Scientific and Technical Information of China (English)
DONG Shu-Guang; YANG Jun-Yi; SHUI Min; YI Chuan-Xiang; LI Zhong-Guo; SONG Ying-Lin
2011-01-01
@@ Spatial and temporal changes of temperature in a novel polymer are investigated by using the Z-scan technique under ns laser pulse excitation.According to the open aperture Z-scan experimental results, the nonlinear absorption coefficient of the polymer is determined.By solving the diffusion equation of heat conduction induced by optical absorption, the spatial and temporal changes in temperature are obtained.This change in temperature drives the photo-acoustic and electromagnetic wave propagating in the polymer and induces the change in refractive index, which serves as a negative lens, and the closed aperture Z-scan shows a peak and valley profile.Based on the numerical calculation, we achieve a good fit to the closed-aperture Z-scan curve with an optimized nonlinear refractive index.This consistency attests the existence of temperature change in the solution, and the Z-scan technique is suitable to investigate this change in temperature.
Roul, Pradip
2016-06-01
This paper presents a new iterative technique for solving nonlinear singular two-point boundary value problems with Neumann and Robin boundary conditions. The method is based on the homotopy perturbation method and the integral equation formalism in which a recursive scheme is established for the components of the approximate series solution. This method does not involve solution of a sequence of nonlinear algebraic or transcendental equations for the unknown coefficients as in some other iterative techniques developed for singular boundary value problems. The convergence result for the proposed method is established in the paper. The method is illustrated by four numerical examples, two of which have physical significance: The first problem is an application of the reaction-diffusion process in a porous spherical catalyst and the second problem arises in the study of steady-state oxygen-diffusion in a spherical cell with Michaelis-Menten uptake kinetics.
Nonlinear filtering techniques for noisy geophysical data: Using big data to predict the future
Moore, J. M.
2014-12-01
Chaos is ubiquitous in physical systems. Within the Earth sciences it is readily evident in seismology, groundwater flows and drilling data. Models and workflows have been applied successfully to understand and even to predict chaotic systems in other scientific fields, including electrical engineering, neurology and oceanography. Unfortunately, the high levels of noise characteristic of our planet's chaotic processes often render these frameworks ineffective. This contribution presents techniques for the reduction of noise associated with measurements of nonlinear systems. Our ultimate aim is to develop data assimilation techniques for forward models that describe chaotic observations, such as episodic tremor and slip (ETS) events in fault zones. A series of nonlinear filters are presented and evaluated using classical chaotic systems. To investigate whether the filters can successfully mitigate the effect of noise typical of Earth science, they are applied to sunspot data. The filtered data can be used successfully to forecast sunspot evolution for up to eight years (see figure).
A technique using a nonlinear helicopter model for determining trims and derivatives
Ostroff, A. J.; Downing, D. R.; Rood, W. J.
1976-01-01
A technique is described for determining the trims and quasi-static derivatives of a flight vehicle for use in a linear perturbation model; both the coupled and uncoupled forms of the linear perturbation model are included. Since this technique requires a nonlinear vehicle model, detailed equations with constants and nonlinear functions for the CH-47B tandem rotor helicopter are presented. Tables of trims and derivatives are included for airspeeds between -40 and 160 knots and rates of descent between + or - 10.16 m/sec (+ or - 200 ft/min). As a verification, the calculated and referenced values of comparable trims, derivatives, and linear model poles are shown to have acceptable agreement.
Analytical vs. Simulation Solution Techniques for Pulse Problems in Non-linear Stochastic Dynamics
DEFF Research Database (Denmark)
Iwankiewicz, R.; Nielsen, Søren R. K.
-numerical techniques suitable for Markov response problems such as moments equation, Petrov-Galerkin and cell-to-cell mapping techniques are briefly discussed. Usefulness of these techniques is limited by the fact that effectiveness of each of them depends on the mean rate of impulses. Another limitation is the size...... of the problem, i.e. the number of state variables of the dynamical systems. In contrast, the application of the simulation techniques is not limited to Markov problems, nor is it dependent on the mean rate of impulses. Moreover their use is straightforward for a large class of point processes, at least......Advantages and disadvantages of available analytical and simulation techniques for pulse problems in non-linear stochastic dynamics are discussed. First, random pulse problems, both those which do and do not lead to Markov theory, are presented. Next, the analytical and analytically...
Nonlinear techniques for forecasting solar activity directly from its time series
Ashrafi, S.; Roszman, L.; Cooley, J.
1993-01-01
This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.
Weeratunga, Sisira K.; Kamath, Chandrika
2002-05-01
Removing noise from data is often the first step in data analysis. Denoising techniques should not only reduce the noise, but do so without blurring or changing the location of the edges. Many approaches have been proposed to accomplish this; in this paper, we focus on one such approach, namely the use of non-linear diffusion operators. This approach has been studied extensively from a theoretical viewpoint ever since the 1987 work of Perona and Malik showed that non-linear filters outperformed the more traditional linear Canny edge detector. We complement this theoretical work by investigating the performance of several isotropic diffusion operators on test images from scientific domains. We explore the effects of various parameters such as the choice of diffusivity function, explicit and implicit methods for the discretization of the PDE, and approaches for the spatial discretization of the non-linear operator etc. We also compare these schemes with simple spatial filters and the more complex wavelet-based shrinkage techniques. Our empirical results show that, with an appropriate choice of parameters, diffusion-based schemes can be as effective as competitive techniques.
PERFORMANCE OF PID CONTROLLER OF NONLINEAR SYSTEM USING SWARM INTELLIGENCE TECHNIQUES
Directory of Open Access Journals (Sweden)
Neeraj Jain
2016-07-01
Full Text Available In this paper swarm intelligence based PID controller tuning is proposed for a nonlinear ball and hoop system. Particle swarm optimization (PSO, Artificial bee colony (ABC, Bacterial foraging optimization (BFO is some example of swarm intelligence techniques which are focused for PID controller tuning. These algorithms are also tested on perturbed ball and hoop model. Integral square error (ISE based performance index is used for finding the best possible value of controller parameters. Matlab software is used for designing the ball and hoop model. It is found that these swarm intelligence techniques have easy implementation & lesser settling & rise time compare to conventional methods.
Yu, Lijing; Zhou, Lingling; Tan, Li; Jiang, Hongbo; Wang, Ying; Wei, Sheng; Nie, Shaofa
2014-01-01
Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012. The best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend. The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.
Assessment of Alkali-Silica Reaction Damage in Mortars with Nonlinear Ultrasonic Techniques
Chen, J.; Jayapalan, A. R.; Kurtis, K. E.; Kim, J.-Y.; Jacobs, L. J.
2008-02-01
In this work, a nonlinear ultrasonic modulation technique is employed to assess the damage state of portland cement mortar samples induced by alkali-silica reaction (ASR). Due to the nonlinear interaction of propagating waves caused by distributed microcracks that are agitated from its equilibrium state, the ultrasonic responses of samples produce sideband frequencies around the frequency of propagating waves. The amplitude of the sidebands depends on the amplitude of the input signals and is particularly sensitive to the state of damage evolved in the sample. Therefore, the development of internal microcracks with increasing duration of exposure to aggressive conditions can be quantitatively related to the variation of external ultrasonic measurements. The ultrasonic results are compared with results from standard ASR expansion measurements (ASTM C 1260), and a proportionally increasing relation was found in the early stages. In addition, aggregates with different alkali-reactivity (i.e., low reactivity or high reactivity) were examined in a similar manner. The results indicate that the nonlinear parameter obtained from ultrasonic tests directly reflects the difference of aggregate reactivity. This clearly indicates that the developed nonlinear ultrasonic method is potentially a good alternative for a more rapid and still reliable assessment of aggregate alkali-reactivity.
Enhanced nonlinear iterative techniques applied to a non-equilibrium plasma flow
Energy Technology Data Exchange (ETDEWEB)
Knoll, D.A.; McHugh, P.R. [Idaho National Engineering Lab., Idaho Falls, ID (United States)
1996-12-31
We study the application of enhanced nonlinear iterative methods to the steady-state solution of a system of two-dimensional convection-diffusion-reaction partial differential equations that describe the partially-ionized plasma flow in the boundary layer of a tokamak fusion reactor. This system of equations is characterized by multiple time and spatial scales, and contains highly anisotropic transport coefficients due to a strong imposed magnetic field. We use Newton`s method to linearize the nonlinear system of equations resulting from an implicit, finite volume discretization of the governing partial differential equations, on a staggered Cartesian mesh. The resulting linear systems are neither symmetric nor positive definite, and are poorly conditioned. Preconditioned Krylov iterative techniques are employed to solve these linear systems. We investigate both a modified and a matrix-free Newton-Krylov implementation, with the goal of reducing CPU cost associated with the numerical formation of the Jacobian. A combination of a damped iteration, one-way multigrid and a pseudo-transient continuation technique are used to enhance global nonlinear convergence and CPU efficiency. GMRES is employed as the Krylov method with Incomplete Lower-Upper(ILU) factorization preconditioning. The goal is to construct a combination of nonlinear and linear iterative techniques for this complex physical problem that optimizes trade-offs between robustness, CPU time, memory requirements, and code complexity. It is shown that a one-way multigrid implementation provides significant CPU savings for fine grid calculations. Performance comparisons of the modified Newton-Krylov and matrix-free Newton-Krylov algorithms will be presented.
Indian Academy of Sciences (India)
P Shivakumara; G Hemantha Kumar; D S Guru; P Nagabhushan
2005-02-01
When a document is scanned either mechanically or manually for digitization, it often suffers from some degree of skew or tilt. Skew-angle detection plays an important role in the ﬁeld of document analysis systems and OCR in achieving the expected accuracy. In this paper, we consider skew estimation of Roman script. The method uses the boundary growing approach to extract the lowermost and uppermost coordinates of pixels of characters of text lines present in the document, which can be subjected to linear regression analysis (LRA) to determine the skew angle of a skewed document. Further, the proposed technique works ﬁne for scaled text binary documents also. The technique works based on the assumption that the space between the text lines is greater than the space between the words and characters. Finally, in order to evaluate the performance of the proposed methodology we compare the experimental results with those of well-known existing methods.
Directory of Open Access Journals (Sweden)
Mohamed A. El-Beltagy
2013-01-01
Full Text Available This paper introduces higher-order solutions of the stochastic nonlinear differential equations with the Wiener-Hermite expansion and perturbation (WHEP technique. The technique is used to study the quadratic nonlinear stochastic oscillatory equation with different orders, different number of corrections, and different strengths of the nonlinear term. The equivalent deterministic equations are derived up to third order and fourth correction. A model numerical integral solver is developed to solve the resulting set of equations. The numerical solver is tested and validated and then used in simulating the stochastic quadratic nonlinear oscillatory motion with different parameters. The solution ensemble average and variance are computed and compared in all cases. The current work extends the use of WHEP technique in solving stochastic nonlinear differential equations.
Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele
2015-11-01
The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225 mm of cumulative rainfall in 7 h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire
A Comparison of PDE-based Non-Linear Anisotropic Diffusion Techniques for Image Denoising
Energy Technology Data Exchange (ETDEWEB)
Weeratunga, S K; Kamath, C
2003-01-06
PDE-based, non-linear diffusion techniques are an effective way to denoise images. In a previous study, we investigated the effects of different parameters in the implementation of isotropic, non-linear diffusion. Using synthetic and real images, we showed that for images corrupted with additive Gaussian noise, such methods are quite effective, leading to lower mean-squared-error values in comparison with spatial filters and wavelet-based approaches. In this paper, we extend this work to include anisotropic diffusion, where the diffusivity is a tensor valued function which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. We consider several anisotropic diffusivity functions as well as approaches for discretizing the diffusion operator that minimize the mesh orientation effects. We investigate how these tensor-valued diffusivity functions compare in image quality, ease of use, and computational costs relative to simple spatial filters, the more complex bilateral filters, wavelet-based methods, and isotropic non-linear diffusion based techniques.
Comparison of PDE-based non-linear anistropic diffusion techniques for image denoising
Weeratunga, Sisira K.; Kamath, Chandrika
2003-05-01
PDE-based, non-linear diffusion techniques are an effective way to denoise images.In a previous study, we investigated the effects of different parameters in the implementation of isotropic, non-linear diffusion. Using synthetic and real images, we showed that for images corrupted with additive Gaussian noise, such methods are quite effective, leading to lower mean-squared-error values in comparison with spatial filters and wavelet-based approaches. In this paper, we extend this work to include anisotropic diffusion, where the diffusivity is a tensor valued function which can be adapted to local edge orientation. This allows smoothing along the edges, but not perpendicular to it. We consider several anisotropic diffusivity functions as well as approaches for discretizing the diffusion operator that minimize the mesh orientation effects. We investigate how these tensor-valued diffusivity functions compare in image quality, ease of use, and computational costs relative to simple spatial filters, the more complex bilateral filters, wavelet-based methods, and isotropic non-linear diffusion based techniques.
Inverse solution technique of steady-state responses for local nonlinear structures
Wang, Xing; Guan, Xin; Zheng, Gangtie
2016-03-01
An inverse solution technique with the ability of obtaining complete steady-state primary harmonic responses of local nonlinear structures in the frequency domain is proposed in the present paper. In this method, the nonlinear dynamic equations of motion is first condensed from many to only one algebraic amplitude-frequency equation of relative motion. Then this equation is transformed into a polynomial form, and with its frequency as the unknown variable, the polynomial equation is solved by tracing all the solutions of frequency with the increase of amplitude. With this solution technique, some complicated dynamic behaviors such as sharp tuning, anomalous jumps, breaks in responses and detached resonance curves could be obtained. The proposed method is demonstrated and validated through a finite element beam under force excitations and a lumped parameter model with a local nonlinear element under base excitations. The phenomenon of detached resonance curves in the frequency response and its coupling effects with multiple linear modes in the latter example are observed.
Institute of Scientific and Technical Information of China (English)
丁先文; 徐亮; 林金官
2012-01-01
经验似然方法已经被广泛用于线性模型和广义线性模型.本文基于经验似然方法对非线性回归模型进行统计诊断.首先得到模型参数的极大经验似然估计；其次基于经验似然研究了三种不同的影响曲率度量；最后通过一个实际例子,说明了诊断方法的有效性.%The empirical likelihood method has been extensively applied to linear regression and generalized linear regression models. In this paper, the diagnostic measures for nonlinear regression models are studied based on the empirical likelihood method. First, the maximum empirical likelihood estimate of the parameters are obtained. Then, three different measures of influence curvatures are studied. Last, real data analysis are given to illustrate the validity of statistical diagnostic measures.
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
Owodunni, Damilola S.
2014-04-01
In this paper, compressed sensing techniques are proposed to linearize commercial power amplifiers driven by orthogonal frequency division multiplexing signals. The nonlinear distortion is considered as a sparse phenomenon in the time-domain, and three compressed sensing based algorithms are presented to estimate and compensate for these distortions at the receiver using a few and, at times, even no frequency-domain free carriers (i.e. pilot carriers). The first technique is a conventional compressed sensing approach, while the second incorporates a priori information about the distortions to enhance the estimation. Finally, the third technique involves an iterative data-aided algorithm that does not require any pilot carriers and hence allows the system to work at maximum bandwidth efficiency. The performances of all the proposed techniques are evaluated on a commercial power amplifier and compared. The error vector magnitude and symbol error rate results show the ability of compressed sensing to compensate for the amplifier\\'s nonlinear distortions. © 2013 Elsevier B.V.
Lambert, Ronald J W; Mytilinaios, Ioannis; Maitland, Luke; Brown, Angus M
2012-08-01
This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. However, a disadvantage of using the Excel method was the inability to return confidence intervals for the computed parameters or the correlations between them. Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve.
Energy Technology Data Exchange (ETDEWEB)
Dyar, M.D., E-mail: mdyar@mtholyoke.edu [Dept. of Astronomy, Mount Holyoke College, 50 College St., South Hadley, MA 01075 (United States); Carmosino, M.L.; Breves, E.A.; Ozanne, M.V. [Dept. of Astronomy, Mount Holyoke College, 50 College St., South Hadley, MA 01075 (United States); Clegg, S.M.; Wiens, R.C. [Los Alamos National Laboratory, P.O. Box 1663, MS J565, Los Alamos, NM 87545 (United States)
2012-04-15
A remote laser-induced breakdown spectrometer (LIBS) designed to simulate the ChemCam instrument on the Mars Science Laboratory Rover Curiosity was used to probe 100 geologic samples at a 9-m standoff distance. ChemCam consists of an integrated remote LIBS instrument that will probe samples up to 7 m from the mast of the rover and a remote micro-imager (RMI) that will record context images. The elemental compositions of 100 igneous and highly-metamorphosed rocks are determined with LIBS using three variations of multivariate analysis, with a goal of improving the analytical accuracy. Two forms of partial least squares (PLS) regression are employed with finely-tuned parameters: PLS-1 regresses a single response variable (elemental concentration) against the observation variables (spectra, or intensity at each of 6144 spectrometer channels), while PLS-2 simultaneously regresses multiple response variables (concentrations of the ten major elements in rocks) against the observation predictor variables, taking advantage of natural correlations between elements. Those results are contrasted with those from the multivariate regression technique of the least absolute shrinkage and selection operator (lasso), which is a penalized shrunken regression method that selects the specific channels for each element that explain the most variance in the concentration of that element. To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units. Results demonstrate that the lasso, PLS-1, and PLS-2 all yield comparable results in terms of accuracy for this dataset. However, the interpretability of these methods differs greatly in terms of fundamental understanding of LIBS emissions. PLS techniques generate principal components, linear combinations of intensities at any number of spectrometer channels, which explain as much variance in the
Dyar, M. D.; Carmosino, M. L.; Breves, E. A.; Ozanne, M. V.; Clegg, S. M.; Wiens, R. C.
2012-04-01
A remote laser-induced breakdown spectrometer (LIBS) designed to simulate the ChemCam instrument on the Mars Science Laboratory Rover Curiosity was used to probe 100 geologic samples at a 9-m standoff distance. ChemCam consists of an integrated remote LIBS instrument that will probe samples up to 7 m from the mast of the rover and a remote micro-imager (RMI) that will record context images. The elemental compositions of 100 igneous and highly-metamorphosed rocks are determined with LIBS using three variations of multivariate analysis, with a goal of improving the analytical accuracy. Two forms of partial least squares (PLS) regression are employed with finely-tuned parameters: PLS-1 regresses a single response variable (elemental concentration) against the observation variables (spectra, or intensity at each of 6144 spectrometer channels), while PLS-2 simultaneously regresses multiple response variables (concentrations of the ten major elements in rocks) against the observation predictor variables, taking advantage of natural correlations between elements. Those results are contrasted with those from the multivariate regression technique of the least absolute shrinkage and selection operator (lasso), which is a penalized shrunken regression method that selects the specific channels for each element that explain the most variance in the concentration of that element. To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units. Results demonstrate that the lasso, PLS-1, and PLS-2 all yield comparable results in terms of accuracy for this dataset. However, the interpretability of these methods differs greatly in terms of fundamental understanding of LIBS emissions. PLS techniques generate principal components, linear combinations of intensities at any number of spectrometer channels, which explain as much variance in the
Analytical approximate technique for strongly nonlinear oscillators problem arising in engineering
Directory of Open Access Journals (Sweden)
Y. Khan
2012-12-01
Full Text Available In this paper, a novel method called generalized of the variational iteration method is presented to obtain an approximate analytical solution for strong nonlinear oscillators problem associated in engineering phenomena. This approach resulted in the frequency of the motion as a function of the amplitude of oscillation. It is determined that the method works very well for the whole range of parameters and an excellent agreement is demonstrated and discussed between the approximate frequencies and the exact one. The most significant features of this method are its simplicity and excellent accuracy for the whole range of oscillation amplitude values. Also, the results reveal that this technique is very effective and convenient for solving conservative oscillatory systems with complex nonlinearities. Results obtained by the proposed method are compared with Energy Balance Method (EBM and exact solution showed that, contrary to EBM, simply one or two iterations are enough for obtaining highly accurate results.
Miniaci, M.; Gliozzi, A. S.; Morvan, B.; Krushynska, A.; Bosia, F.; Scalerandi, M.; Pugno, N. M.
2017-05-01
The appearance of nonlinear effects in elastic wave propagation is one of the most reliable and sensitive indicators of the onset of material damage. However, these effects are usually very small and can be detected only using cumbersome digital signal processing techniques. Here, we propose and experimentally validate an alternative approach, using the filtering and focusing properties of phononic crystals to naturally select and reflect the higher harmonics generated by nonlinear effects, enabling the realization of time-reversal procedures for nonlinear elastic source detection. The proposed device demonstrates its potential as an efficient, compact, portable, passive apparatus for nonlinear elastic wave sensing and damage detection.
An efficient numerical technique for the solution of nonlinear singular boundary value problems
Singh, Randhir; Kumar, Jitendra
2014-04-01
In this work, a new technique based on Green's function and the Adomian decomposition method (ADM) for solving nonlinear singular boundary value problems (SBVPs) is proposed. The technique relies on constructing Green's function before establishing the recursive scheme for the solution components. In contrast to the existing recursive schemes based on the ADM, the proposed technique avoids solving a sequence of transcendental equations for the undetermined coefficients. It approximates the solution in the form of a series with easily computable components. Additionally, the convergence analysis and the error estimate of the proposed method are supplemented. The reliability and efficiency of the proposed method are demonstrated by several numerical examples. The numerical results reveal that the proposed method is very efficient and accurate.
Chopped nonlinear magneto-optic rotation: a technique for precision measurements
Ravishankar, Harish; Natarajan, Vasant
2011-01-01
We have developed a technique for precise measurement of small magnetic fields using nonlinear magneto-optic rotation (NMOR). The technique relies on the resonant laser beam being chopped. During the on time, the atoms are optically pumped into an aligned ground state ($\\Delta m=2$ coherence). During the off time, they freely precess around the magnetic field at the Larmor frequency. If the on-off modulation frequency matches (twice) the Larmor precession frequency, the rotation is resonantly enhanced in every cycle, thereby making the process like a repeated Ramsey measurement of the Larmor frequency. We study chopped-NMOR in a paraffin-coated Cs vapor cell. The out-of-phase demodulated rotation shows a Lorentzian peak of linewidth 85 $\\mu$G, corresponding to a sensitivity of 0.15 nG/$\\sqrt{{\\rm Hz}}$. We discuss the potential of this technique for the measurement of an atomic electric-dipole moment.
Huttunen, Jani; Kokkola, Harri; Mielonen, Tero; Esa Juhani Mononen, Mika; Lipponen, Antti; Reunanen, Juha; Vilhelm Lindfors, Anders; Mikkonen, Santtu; Erkki Juhani Lehtinen, Kari; Kouremeti, Natalia; Bais, Alkiviadis; Niska, Harri; Arola, Antti
2016-07-01
In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during
Ramoelo, A.; Skidmore, A. K.; Cho, M. A.; Mathieu, R.; Heitkönig, I. M. A.; Dudeni-Tlhone, N.; Schlerf, M.; Prins, H. H. T.
2013-08-01
Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems.
Institute of Scientific and Technical Information of China (English)
WANG; Weida; XIA; Junding; ZHOU; Zhixin
2006-01-01
This paper studies the thermoluminescence (TL) dating of the ancient porcelain using a regression method of saturation exponential in the pre-dose technique. The experimental results show that the measured errors are 15% (±1σ) for the paleodose and 17% (±1σ) for the annual dose respectively, and the TL age error is 23% (±1σ) in this method. The larger Chinese porcelains from the museum and the nation-wide collectors have been dated by this method. The results show that the certainty about the authenticity testing is larger than 95%, and the measurable porcelains make up about 95% of the porcelain dated. It is very successful in discrimination for the imitations of ancient Chinese porcelains. This paper describes the measured principle and method for the paleodose of porcelains. The TL ages are dated by this method for the 39 shards and porcelains from past dynasties of China and the detailed data in the measurement are reported.
Biyanto, Totok R.
2016-06-01
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO2 emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Energy Technology Data Exchange (ETDEWEB)
Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)
2016-06-03
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Calibration of Vector Magnetogram with the Nonlinear Least-squares Fitting Technique
Institute of Scientific and Technical Information of China (English)
Jiang-Tao Su; Hong-Qi Zhang
2004-01-01
To acquire Stokes profiles from observations of a simple sunspot with the Video Vector Magnetograph at Huairou Solar Observing Station(HSOS),we scanned the FeIλ5324.19 A line over the wavelength interval from 150mA redward of the line center to 150mA blueward,in steps of 10mA.With the technique of analytic inversion of Stokes profiles via nonlinear least-squares,we present the calibration coefficients for the HSOS vector magnetic magnetogram.We obtained the theoretical calibration error with linear expressions derived from the Unno-Becker equation under weak-field approximation.
Nonlinear Second-Order Partial Differential Equation-Based Image Smoothing Technique
Directory of Open Access Journals (Sweden)
Tudor Barbu
2016-09-01
Full Text Available A second-order nonlinear parabolic PDE-based restoration model is provided in this article. The proposed anisotropic diffusion-based denoising approach is based on some robust versions of the edge-stopping function and of the conductance parameter. Two stable and consistent approximation schemes are then developed for this differential model. Our PDE-based filtering technique achieves an efficient noise removal while preserving the edges and other image features. It outperforms both the conventional filters and also many PDE-based denoising approaches, as it results from the successful experiments and method comparison applied.
Parker, Matthew D; Jones, Lynette A; Hunter, Ian W; Taberner, A J; Nash, M P; Nielsen, P M F
2017-01-01
A triaxial force-sensitive microrobot was developed to dynamically perturb skin in multiple deformation modes, in vivo. Wiener static nonlinear identification was used to extract the linear dynamics and static nonlinearity of the force-displacement behavior of skin. Stochastic input forces were applied to the volar forearm and thenar eminence of the hand, producing probe tip perturbations in indentation and tangential extension. Wiener static nonlinear approaches reproduced the resulting displacements with variances accounted for (VAF) ranging 94-97%, indicating a good fit to the data. These approaches provided VAF improvements of 0.1-3.4% over linear models. Thenar eminence stiffness measures were approximately twice those measured on the forearm. Damping was shown to be significantly higher on the palm, whereas the perturbed mass typically was lower. Coefficients of variation (CVs) for nonlinear parameters were assessed within and across individuals. Individual CVs ranged from 2% to 11% for indentation and from 2% to 19% for extension. Stochastic perturbations with incrementally increasing mean amplitudes were applied to the same test areas. Differences between full-scale and incremental reduced-scale perturbations were investigated. Different incremental preloading schemes were investigated. However, no significant difference in parameters was found between different incremental preloading schemes. Incremental schemes provided depth-dependent estimates of stiffness and damping, ranging from 300 N/m and 2 Ns/m, respectively, at the surface to 5 kN/m and 50 Ns/m at greater depths. The device and techniques used in this research have potential applications in areas, such as evaluating skincare products, assessing skin hydration, or analyzing wound healing.
Nonlinear Ultrasonic Techniques to Monitor Radiation Damage in RPV and Internal Components
Energy Technology Data Exchange (ETDEWEB)
Jacobs, Laurence [Georgia Inst. of Technology, Atlanta, GA (United States); Kim, Jin-Yeon [Georgia Inst. of Technology, Atlanta, GA (United States); Qu, Jisnmin [Northwestern Univ., Evanston, IL (United States); Ramuhalli, Pradeep [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Wall, Joe [Electric Power Research Inst. (EPRI), Knoxville, TN (United States)
2015-11-02
The objective of this research is to demonstrate that nonlinear ultrasonics (NLU) can be used to directly and quantitatively measure the remaining life in radiation damaged reactor pressure vessel (RPV) and internal components. Specific damage types to be monitored are irradiation embrittlement and irradiation assisted stress corrosion cracking (IASCC). Our vision is to develop a technique that allows operators to assess damage by making a limited number of NLU measurements in strategically selected critical reactor components during regularly scheduled outages. This measured data can then be used to determine the current condition of these key components, from which remaining useful life can be predicted. Methods to unambiguously characterize radiation related damage in reactor internals and RPVs remain elusive. NLU technology has demonstrated great potential to be used as a material sensor – a sensor that can continuously monitor a material’s damage state. The physical effect being monitored by NLU is the generation of higher harmonic frequencies in an initially monochromatic ultrasonic wave. The degree of nonlinearity is quantified with the acoustic nonlinearity parameter, β, which is an absolute, measurable material constant. Recent research has demonstrated that nonlinear ultrasound can be used to characterize material state and changes in microscale characteristics such as internal stress states, precipitate formation and dislocation densities. Radiation damage reduces the fracture toughness of RPV steels and internals, and can leave them susceptible to IASCC, which may in turn limit the lifetimes of some operating reactors. The ability to characterize radiation damage in the RPV and internals will enable nuclear operators to set operation time thresholds for vessels and prescribe and schedule replacement activities for core internals. Such a capability will allow a more clear definition of reactor safety margins. The research consists of three tasks: (1
Tomlinson, Sean
2016-04-01
The calculation and comparison of physiological characteristics of thermoregulation has provided insight into patterns of ecology and evolution for over half a century. Thermoregulation has typically been explored using linear techniques; I explore the application of non-linear scaling to more accurately calculate and compare characteristics and thresholds of thermoregulation, including the basal metabolic rate (BMR), peak metabolic rate (PMR) and the lower (Tlc) and upper (Tuc) critical limits to the thermo-neutral zone (TNZ) for Australian rodents. An exponentially-modified logistic function accurately characterised the response of metabolic rate to ambient temperature, while evaporative water loss was accurately characterised by a Michaelis-Menten function. When these functions were used to resolve unique parameters for the nine species studied here, the estimates of BMR and TNZ were consistent with the previously published estimates. The approach resolved differences in rates of metabolism and water loss between subfamilies of Australian rodents that haven't been quantified before. I suggest that non-linear scaling is not only more effective than the established segmented linear techniques, but also is more objective. This approach may allow broader and more flexible comparison of characteristics of thermoregulation, but it needs testing with a broader array of taxa than those used here.
Directory of Open Access Journals (Sweden)
Co Jan Miles
2016-01-01
Full Text Available Predicting links between the nodes of a graph has become an important Data Mining task because of its direct applications to biology, social networking, communication surveillance, and other domains. Recent literature in time-series link prediction has shown that the Vector Auto Regression (VAR technique is one of the most accurate for this problem. In this study, we apply Support Vector Machine (SVM to improve the VAR technique that uses an unweighted adjacency matrix along with 5 matrices: Common Neighbor (CN, Adamic-Adar (AA, Jaccard’s Coefficient (JC, Preferential Attachment (PA, and Research Allocation Index (RA. A DBLP dataset covering the years from 2003 until 2013 was collected and transformed into time-sliced graph representations. The appropriate matrices were computed from these graphs, mapped to the feature space, and then used to build baseline VAR models with lag of 2 and some corresponding SVM classifiers. Using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC as the main fitness metric, the average result of 82.04% for the VAR was improved to 84.78% with SVM. Additional experiments to handle the highly imbalanced dataset by oversampling with SMOTE and undersampling with K-means clusters, however, did not improve the average AUC-ROC of the baseline SVM.
Robust Control Synthesis of Polynomial Nonlinear Systems Using Sum of Squares Technique
Institute of Scientific and Technical Information of China (English)
HUANG Wen-Chao; SUN Hong-Fei; ZENG Jian-Ping
2013-01-01
In this paper,sum of squares (SOS) technique is used to analyze the robust state feedback synthesis problem for a class of uncertain affine nonlinear systems with polynomial vector fields.Sufficient conditions are given to obtain the solutions to the above control problem either without or with guaranteed cost or H∞ performance objectives.Moreover,such solvable conditions can be formulated as SOS programming problems in terms of state dependent linear matrix inequalities (LMIs) which can be dealt with by the SOS technique directly.Besides,an idea is provided to describe the inverse of polynomial or even rational matrices by introducing some extra polynomials.A numerical example is presented to illustrate the effectiveness of the approach.
Higher-order Solution of Stochastic Diffusion equation with Nonlinear Losses Using WHEP technique
El-Beltagy, Mohamed A.
2014-01-06
Using Wiener-Hermite expansion with perturbation (WHEP) technique in the solution of the stochastic partial differential equations (SPDEs) has the advantage of converting the problem to a system of deterministic equations that can be solved efficiently using the standard deterministic numerical methods [1]. The Wiener-Hermite expansion is the only known expansion that handles the white/colored noise exactly. The main statistics, such as the mean, covariance, and higher order statistical moments, can be calculated by simple formulae involving only the deterministic Wiener-Hermite coefficients. In this poster, the WHEP technique is used to solve the 2D diffusion equation with nonlinear losses and excited with white noise. The solution will be obtained numerically and will be validated and compared with the analytical solution that can be obtained from any symbolic mathematics package such as Mathematica.
Motiei, H.; Jafari, A.; Naderali, R.
2017-02-01
In this paper, two chemically synthesized organic azo dyes, 2-(2,5-Dichloro-phenyazo)-5,5-dimethyl-cyclohexane-1,3-dione (azo dye (i)) and 5,5-Dimethyl-2-tolylazo-cyclohexane-1,3-dione (azo dye (ii)), have been studied from optical Kerr nonlinearity point of view. These materials were characterized by Ultraviolet-visible spectroscopy. Experiments were performed using a continous wave diode-pumped laser at 532 nm wavelength in three intensities of the laser beam. Nonlinear absorption (β), refractive index (n2) and third-order susceptibility (χ (3)) of dyes, were calculated. Nonlinear absorption coefficient of dyes have been calculated from two methods; 1) using theoretical fits and experimental data in the Z-scan technique, 2) using the strength of nonlinearity curves. The values of β obtained from both of the methods were approximately the same. The results demonstrated that azo dye (ii) displays better nonlinearity and has a lower two-photon absorption threshold than azo dye (i). Calculated parameter related to strength of nonlinearity for azo dye (ii) was higher than azo dye (i), It may be due to presence of methyl in azo dye (ii) instead of chlorine in azo dye (i). Furthermore, The measured values of third order susceptibility of azo dyes were from the order of 10-9 esu . These azo dyes can be suitable candidate for optical switching devices.
Cicone, Antonio; Zhou, Haomin; Piersanti, Mirko; Materassi, Massimo; Spogli, Luca
2017-04-01
Nonlinear and nonstationary signals are ubiquitous in real life. Their decomposition and analysis is of crucial importance in many research fields. Traditional techniques, like Fourier and wavelet Transform have been proved to be limited in this context. In the last two decades new kind of nonlinear methods have been developed which are able to unravel hidden features of these kinds of signals. In this talk we will review the state of the art and present a new method, called Adaptive Local Iterative Filtering (ALIF). This method, developed originally to study mono-dimensional signals, unlike any other technique proposed so far, can be easily generalized to study two or higher dimensional signals. Furthermore, unlike most of the similar methods, it does not require any a priori assumption on the signal itself, so that the method can be applied as it is to any kind of signals. Applications of ALIF algorithm to real life signals analysis will be presented. Like, for instance, the behavior of the water level near the coastline in presence of a Tsunami, the length of the day signal, the temperature and pressure measured at ground level on a global grid, and the radio power scintillation from GNSS signals.
On large-scale nonlinear programming techniques for solving optimal control problems
Energy Technology Data Exchange (ETDEWEB)
Faco, J.L.D.
1994-12-31
The formulation of decision problems by Optimal Control Theory allows the consideration of their dynamic structure and parameters estimation. This paper deals with techniques for choosing directions in the iterative solution of discrete-time optimal control problems. A unified formulation incorporates nonlinear performance criteria and dynamic equations, time delays, bounded state and control variables, free planning horizon and variable initial state vector. In general they are characterized by a large number of variables, mostly when arising from discretization of continuous-time optimal control or calculus of variations problems. In a GRG context the staircase structure of the jacobian matrix of the dynamic equations is exploited in the choice of basic and super basic variables and when changes of basis occur along the process. The search directions of the bound constrained nonlinear programming problem in the reduced space of the super basic variables are computed by large-scale NLP techniques. A modified Polak-Ribiere conjugate gradient method and a limited storage quasi-Newton BFGS method are analyzed and modifications to deal with the bounds on the variables are suggested based on projected gradient devices with specific linesearches. Some practical models are presented for electric generation planning and fishery management, and the application of the code GRECO - Gradient REduit pour la Commande Optimale - is discussed.
Benhammouda, Brahim
2016-01-01
Since 1980, the Adomian decomposition method (ADM) has been extensively used as a simple powerful tool that applies directly to solve different kinds of nonlinear equations including functional, differential, integro-differential and algebraic equations. However, for differential-algebraic equations (DAEs) the ADM is applied only in four earlier works. There, the DAEs are first pre-processed by some transformations like index reductions before applying the ADM. The drawback of such transformations is that they can involve complex algorithms, can be computationally expensive and may lead to non-physical solutions. The purpose of this paper is to propose a novel technique that applies the ADM directly to solve a class of nonlinear higher-index Hessenberg DAEs systems efficiently. The main advantage of this technique is that; firstly it avoids complex transformations like index reductions and leads to a simple general algorithm. Secondly, it reduces the computational work by solving only linear algebraic systems with a constant coefficient matrix at each iteration, except for the first iteration where the algebraic system is nonlinear (if the DAE is nonlinear with respect to the algebraic variable). To demonstrate the effectiveness of the proposed technique, we apply it to a nonlinear index-three Hessenberg DAEs system with nonlinear algebraic constraints. This technique is straightforward and can be programmed in Maple or Mathematica to simulate real application problems.
Jafari, A.; Naderali, R.; Motiei, H.
2017-02-01
The studies on the third-order nonlinear optical properties of carboxymethyl cellulose nanocomposite in the absence and presence of inorganic acid as a dopant was reported. The Z-scan technique was used to measure the nonlinear refraction n2, and absorption β, indexes and the third-order nonlinear susceptibility χ3. Characterization of this nanocomposite was performed by using scanning electron microscopy and Ultraviolet-Visible absorption spectroscopy in two different solvents; Dimethylformamide and N-Methylpyrrolidone. Additionally X-ray diffraction was used to study their crystal structure. The measured values of the nonlinear refraction of each sample in both of the solutions were in the order of 10-9m2/w and the corresponding third-order nonlinear susceptibilities were in the order 10-4 esu.
Energy Technology Data Exchange (ETDEWEB)
Delaune, X.; Piteau, Ph.; Borsoi, L. [CEA Saclay, Laboratoire d' Etudes de Dynamique, CEA, DEN, DM2S, SEMT, 91 - Gif-sur-Yvette (France); Antunes, J.; Debut, V. [Applied Dynamics Laboratory, Instituto Tecnologico e Nuclear, ITN/ADL, Estrada Nacional 10, 2686 Sacavem Codex (Portugal)
2010-06-15
Predictive computation of the nonlinear dynamical responses of gap-supported tubes subjected to flow excitation has been the subject of very active research. Nevertheless, there is a need for robust techniques capable of extracting, from the actual vibratory response data, information which is relevant for asserting the components integrity. The dynamical contact/impact (vibro-impact) forces are of paramount significance, as are the tube/support gaps. Following our previous studies in this field using wave-propagation techniques, we apply modal methods in the present paper for extracting such information. The dynamical support forces, as well as the vibratory responses at the support locations, are identified from one or several vibratory response measurements at remote transducers, from which the support gaps can be inferred. As for most inverse problems, the identification results prove quite sensitive to noise and modeling error problems. Therefore, topics discussed in the paper include regularization techniques to mitigate the effects of non-measured noise perturbations. In particular, a method is proposed to improve the identification of contact forces at the supports when the system is excited by an unknown distributed turbulence force field. The important topic of dealing with the imperfect knowledge of the modal parameters used to build the inverted transfer functions is addressed elsewhere. Here, the extensive identifications presented are based on the exact modal parameters and performed on realistic numerical simulations of gap-supported tubes subjected to flow excitation. We can thus confront the identified dynamical support contact forces and vibratory motions at the gap-support with the actual values stemming from the original nonlinear computations, with overall satisfying results. (authors)
Dikaios, Nikolaos; Atkinson, David; Tudisca, Chiara; Purpura, Pierpaolo; Forster, Martin; Ahmed, Hashim; Beale, Timothy; Emberton, Mark; Punwani, Shonit
2017-03-01
The aim of this work is to compare Bayesian Inference for nonlinear models with commonly used traditional non-linear regression (NR) algorithms for estimating tracer kinetics in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The algorithms are compared in terms of accuracy, and reproducibility under different initialization settings. Further it is investigated how a more robust estimation of tracer kinetics affects cancer diagnosis. The derived tracer kinetics from the Bayesian algorithm were validated against traditional NR algorithms (i.e. Levenberg-Marquardt, simplex) in terms of accuracy on a digital DCE phantom and in terms of goodness-of-fit (Kolmogorov-Smirnov test) on ROI-based concentration time courses from two different patient cohorts. The first cohort consisted of 76 men, 20 of whom had significant peripheral zone prostate cancer (any cancer-core-length (CCL) with Gleason>3+3 or any-grade with CCL>=4mm) following transperineal template prostate mapping biopsy. The second cohort consisted of 9 healthy volunteers and 24 patients with head and neck squamous cell carcinoma. The diagnostic ability of the derived tracer kinetics was assessed with receiver operating characteristic area under curve (ROC AUC) analysis. The Bayesian algorithm accurately recovered the ground-truth tracer kinetics for the digital DCE phantom consistently improving the Structural Similarity Index (SSIM) across the 50 different initializations compared to NR. For optimized initialization, Bayesian did not improve significantly the fitting accuracy on both patient cohorts, and it only significantly improved the ve ROC AUC on the HN population from ROC AUC=0.56 for the simplex to ROC AUC=0.76. For both cohorts, the values and the diagnostic ability of tracer kinetic parameters estimated with the Bayesian algorithm weren't affected by their initialization. To conclude, the Bayesian algorithm led to a more accurate and reproducible quantification of tracer kinetic
Nonlinear adaptive control based on fuzzy sliding mode technique and fuzzy-based compensator.
Nguyen, Sy Dzung; Vo, Hoang Duy; Seo, Tae-Il
2017-09-01
It is difficult to efficiently control nonlinear systems in the presence of uncertainty and disturbance (UAD). One of the main reasons derives from the negative impact of the unknown features of UAD as well as the response delay of the control system on the accuracy rate in the real time of the control signal. In order to deal with this, we propose a new controller named CO-FSMC for a class of nonlinear control systems subjected to UAD, which is constituted of a fuzzy sliding mode controller (FSMC) and a fuzzy-based compensator (CO). Firstly, the FSMC and CO are designed independently, and then an adaptive fuzzy structure is discovered to combine them. Solutions for avoiding the singular cases of the fuzzy-based function approximation and reducing the calculating cost are proposed. Based on the solutions, fuzzy sliding mode technique, lumped disturbance observer and Lyapunov stability analysis, a closed-loop adaptive control law is formulated. Simulations along with a real application based on a semi-active train-car suspension are performed to fully evaluate the method. The obtained results reflected that vibration of the chassis mass is insensitive to UAD. Compared with the other fuzzy sliding mode control strategies, the CO-FSMC can provide the best control ability to reduce unwanted vibrations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Tofighi, Elham; Mahdizadeh, Amin
2016-09-01
This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.
Use of stochastic optimization techniques for damage detection in complex nonlinear systems
Directory of Open Access Journals (Sweden)
Jafarkhani R.
2012-07-01
Full Text Available In this study, the performance of stochastic optimization techniques in the finite element model updating approach was investigated for damage detection in a quarter-scale two-span reinforced concrete bridge system which was tested experimentally at the University of Nevada, Reno. The damage sequence in the structure was induced by a range of progressively increasing excitations in the transverse direction of the specimen. Intermediate non-destructive white noise excitations and response measurements were used for system identification and damage detection purposes. It is shown that, when evaluated together with the strain gauge measurements and visual inspection results, the applied finite element model updating algorithm on this complex nonlinear system could accurately detect, localize, and quantify the damage in the tested bridge columns throughout the different phases of the experiment.
Active control and parameter updating techniques for nonlinear thermal network models
Papalexandris, M. V.; Milman, M. H.
The present article reports on active control and parameter updating techniques for thermal models based on the network approach. Emphasis is placed on applications where radiation plays a dominant role. Examples of such applications are the thermal design and modeling of spacecrafts and space-based science instruments. Active thermal control of a system aims to approximate a desired temperature distribution or to minimize a suitably defined temperature-dependent functional. Similarly, parameter updating aims to update the values of certain parameters of the thermal model so that the output approximates a distribution obtained through direct measurements. Both problems are formulated as nonlinear, least-square optimization problems. The proposed strategies for their solution are explained in detail and their efficiency is demonstrated through numerical tests. Finally, certain theoretical results pertaining to the characterization of solutions of the problems of interest are also presented.
Assimilation of ERBE data with a nonlinear programming technique to improve cloud-cover diagnosis
Wu, Xiangqian; Smith, William L.
1992-01-01
A method is developed to assimilate satellite data for the purpose of improving the diagnosis of fractional cloud cover within a numerical weather prediction model. The method makes use of a nonlinear programming technique to find a set of parameters for the cloud diagnosis that minimizes the difference between the observed and model-produced outgoing longwave radiation (OLR). The algorithm and theoretical basis of the method are presented. The method has been applied in two forecast experiments using a numerical weather prediction model. The results from a winter case demonstrate that the root-mean-square (rms) difference between the observed and forecasted OLR can be reduced by 50 percent when the optimized cloud diagnosis is used, with the remaining rms difference within the background noise.
Directory of Open Access Journals (Sweden)
Chein-Shan Liu
2014-01-01
Full Text Available To solve an unconstrained nonlinear minimization problem, we propose an optimal algorithm (OA as well as a globally optimal algorithm (GOA, by deflecting the gradient direction to the best descent direction at each iteration step, and with an optimal parameter being derived explicitly. An invariant manifold defined for the model problem in terms of a locally quadratic function is used to derive a purely iterative algorithm and the convergence is proven. Then, the rank-two updating techniques of BFGS are employed, which result in several novel algorithms as being faster than the steepest descent method (SDM and the variable metric method (DFP. Six numerical examples are examined and compared with exact solutions, revealing that the new algorithms of OA, GOA, and the updated ones have superior computational efficiency and accuracy.
High-resolution fiber optic temperature sensors using nonlinear spectral curve fitting technique
Su, Z. H.; Gan, J.; Yu, Q. K.; Zhang, Q. H.; Liu, Z. H.; Bao, J. M.
2013-04-01
A generic new data processing method is developed to accurately calculate the absolute optical path difference of a low-finesse Fabry-Perot cavity from its broadband interference fringes. The method combines Fast Fourier Transformation with nonlinear curve fitting of the entire spectrum. Modular functions of LabVIEW are employed for fast implementation of the data processing algorithm. The advantages of this technique are demonstrated through high performance fiber optic temperature sensors consisting of an infrared superluminescent diode and an infrared spectrometer. A high resolution of 0.01 °C is achieved over a large dynamic range from room temperature to 800 °C, limited only by the silica fiber used for the sensor.
A Beam-Fourier Technique for the Numerical Investigation of 2D Nonlinear Convective Flows
Papanicolaou, N. C.
2011-11-01
In the current work, we develop a numerical method suitable for treating the problem of nonlinear two-dimensional flows in rectangular domains. For the spatial approximation we employ the Fourier-Galerkin approach. More specifically, our basis functions are products of trigonometric and Beam functions. This choice means that the solutions automatically satisfy the boundary and periodic conditions in the x and y directions respectively. The accuracy of the method is assessed by applying it to model problems which admit exact analytical solutions. The numerical and analytic solutions are found to be in good agreement. The convergence rate of the spectral coefficients is found to be fifth-order algebraic in the x-direction and y-direction, confirming the efficiency and speed of our technique.
Bhowmick, Arup; Sahoo, Sushree S.; Mohapatra, Ashok K.
2016-08-01
We discuss the optical-heterodyne-detection technique to study the absorption and dispersion of a probe beam propagating through a medium with a narrow resonance. The technique has been demonstrated for Rydberg electromagnetically induced transparency in rubidium thermal vapor and the optical nonlinearity of a probe beam with variable intensity has been studied. A quantitative comparison of the experimental result with a suitable theoretical model is presented. The limitations and the working regime of the technique are discussed.
Directory of Open Access Journals (Sweden)
Zvezdelina Lyubenova Yaneva
2013-01-01
Full Text Available The study assessed the applicability of Rhizopus oryzae dead fungi as a biosorbent medium for p-nitrophenol (p-NP removal from aqueous phase. The extent of biosorption was measured through five equilibrium sorption isotherms represented by the Langmuir, Freundlich, Redlich-Peterson, multilayer and Fritz-Schlunder models. Linear and nonlinear regression methods were compared to determine the best-fitting equilibrium model to the experimental data. A detailed error analysis was undertaken to investigate the effect of applying seven error criteria for the determination of the single-component isotherm parameters. According to the comparison of the error functions and to the estimation of the corrected Akaike information criterion (, the Freundlich equation was ranked as the first and the Fritz-Schlunder as the second best-fitting models describing the experimental data. The present investigations proved the high efficiency (94% of Rhizopus Oryzae as an alternative adsorbent for p-NP removal from aqueous phase and revealed the mechanism of the separation process.
Institute of Scientific and Technical Information of China (English)
冯三营; 薛留根
2012-01-01
考虑非参数协变量带有测量误差(EV)的非线性半参数模型,在测量误差分布为普通光滑分布时,利用经验似然方法,给出了回归系数,光滑函数以及误差方差的最大经验似然估计.在一定条件下证明了所得估计量的渐近正态性和相合性.最后通过数值模拟研究了所提估计方法在有限样本下的实际表现.%In this paper, we consider the nonlinear semiparametric models with measurement error in the nonparametric part. When the error is ordinarily smooth, we obtain the maximum empirical likelihood estimators of regression coefficient, smooth function and error variance by using the empirical likelihood method. The asymptotic normality and consistency of the proposed estimators are proved under some appropriate conditions. Finite sample performance of the proposed method is illustrated in a simulation study.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
Identification of a Class of Non-linear State Space Models using RPE Techniques
DEFF Research Database (Denmark)
Zhou, Wei-Wu; Blanke, Mogens
1989-01-01
The RPE (recursive prediction error) method in state-space form is developed in the nonlinear systems and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions...... of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows a quite convincing performance of the filter as combined parameter and state estimator...
Jamali, Jamshid; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman
2016-01-01
Background: Measurement equivalence is an essential prerequisite for making valid comparisons in mental health questionnaires across groups. In most methods used for assessing measurement equivalence, which is known as Differential Item Functioning (DIF), latent variables are assumed to be continuous. Objective: To compare a new method called Latent Class Regression (LCR) designed for discrete latent variable with the multiple indicators multiple cause (MIMIC) as a continuous latent variable technique to assess the measurement equivalence of the 12-item General Health Questionnaire (GHQ-12), which is a cross deferent subgroup of Iranian nurses. Methods: A cross-sectional survey was conducted in 2014 among 771 nurses working in the hospitals of Fars and Bushehr provinces of southern Iran. To identify the Minor Psychiatric Disorders (MPD), the nurses completed self-report GHQ-12 questionnaires and sociodemographic questions. Two uniform-DIF detection methods, LCR and MIMIC, were applied for comparability when the GHQ-12 score was assumed to be discrete and continuous, respectively. Results: The result of fitting LCR with 2 classes indicated that 27.4% of the nurses had MPD. Gender was identified as an influential factor of the level of MPD.LCR and MIMIC agree with detection of DIF and DIF-free items by gender, age, education and marital status in 83.3, 100.0, 91.7 and 83.3% cases, respectively. Conclusions: The results indicated that the GHQ-12 is to a great degree, an invariant measure for the assessment of MPD among nurses. High convergence between the two methods suggests using the LCR approach in cases of discrete latent variable, e.g. GHQ-12 and adequate sample size. PMID:27482129
Institute of Scientific and Technical Information of China (English)
钟志威
2016-01-01
针对稀疏角度投影数据CT图像重建问题,TV-ART算法将图像的梯度稀疏先验知识引入代数重建法( ART)中,对分段平滑的图像具有较好的重建效果。但是,该算法在边界重建时会产生阶梯效应,影响重建质量。因此,本文提出自适应核回归函数结合代数重建法的重建算法( LAKR-ART),不仅在边界重建时不会产生阶梯效应,而且对细节纹理重建具有更好的重建效果。最后对shepp-logan标准CT图像和实际CT头颅图像进行仿真实验,并与ART、TV-ART算法进行比较,实验结果表明本文算法有效。%To the problem of sparse angular projection data of CT image reconstruction, TV-ART algorithm introduces the gradient sparse prior knowledge of image to algebraic reconstruction, and the local smooth image gets a better reconstruction effect. How-ever, the algorithm generates step effect when the borders are reconstructed, affecting the quality of the reconstruction. Therefore, this paper proposes an adaptive kernel regression function combined with Algebraic Reconstruction Technique reconstruction algo-rithm ( LAKR-ART) , it does not produce the step effect on the border reconstruction, and has a better effect to detail reconstruc-tion. Finally we use the shepp-logan CT image and the actual CT image to make the simulation experiment, and compare with ART and TV-ART algorithm. The experimental results show the algorithm is of effectiveness.
Hagberg, Gisela E; Bianciardi, Marta; Brainovich, Valentina; Cassara, Antonino Mario; Maraviglia, Bruno
2012-02-15
Although the majority of fMRI studies exploit magnitude changes only, there is an increasing interest regarding the potential additive information conveyed by the phase signal. This integrated part of the complex number furnished by the MR scanners can also be used for exploring direct detection of neuronal activity and for thermography. Few studies have explicitly addressed the issue of the available signal stability in the context of phase time-series, and therefore we explored the spatial pattern of frequency specific phase fluctuations, and evaluated the effect of physiological noise components (heart beat and respiration) on the phase signal. Three categories of retrospective noise reduction techniques were explored and the temporal signal stability was evaluated in terms of a physiologic noise model, for seven fMRI measurement protocols in eight healthy subjects at 3T, for segmented CSF, gray and white matter voxels. We confirmed that for most processing methods, an efficient use of the phase information is hampered by the fact that noise from physiological and instrumental sources contributes significantly more to the phase than to the magnitude instability. Noise regression based on the phase evolution of the central k-space point, RETROICOR, or an orthonormalized combination of these were able to reduce their impact, but without bringing phase stability down to levels expected from the magnitude signal. Similar results were obtained after targeted removal of scan-to-scan variations in the bulk magnetic field by the dynamic off-resonance in k-space (DORK) method and by the temporal off-resonance alignment of single-echo time series technique (TOAST). We found that spatial high-pass filtering was necessary, and in vivo a Gaussian filter width of 20mm was sufficient to suppress physiological noise and bring the phase fluctuations to magnitude levels. Stronger filters brought the fluctuations down to levels dictated by thermal noise contributions, and for 62
Whitlock, C. H., III
1977-01-01
Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.
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.
Hosen, Md. Alal; Chowdhury, M. S. H.; Ali, Mohammad Yeakub; Ismail, Ahmad Faris
In the present paper, a novel analytical approximation technique has been proposed based on the energy balance method (EBM) to obtain approximate periodic solutions for the focus generalized highly nonlinear oscillators. The expressions of the natural frequency-amplitude relationship are obtained using a novel analytical way. The accuracy of the proposed method is investigated on three benchmark oscillatory problems, namely, the simple relativistic oscillator, the stretched elastic wire oscillator (with a mass attached to its midpoint) and the Duffing-relativistic oscillator. For an initial oscillation amplitude A0 = 100, the maximal relative errors of natural frequency found in three oscillators are 2.1637%, 0.0001% and 1.201%, respectively, which are much lower than the errors found using the existing methods. It is highly remarkable that an excellent accuracy of the approximate natural frequency has been found which is valid for the whole range of large values of oscillation amplitude as compared with the exact ones. Very simple solution procedure and high accuracy that is found in three benchmark problems reveal the novelty, reliability and wider applicability of the proposed analytical approximation technique.
Adaptive Neural Control of Pure-Feedback Nonlinear Time-Delay Systems via Dynamic Surface Technique.
Min Wang; Xiaoping Liu; Peng Shi
2011-12-01
This paper is concerned with robust stabilization problem for a class of nonaffine pure-feedback systems with unknown time-delay functions and perturbed uncertainties. Novel continuous packaged functions are introduced in advance to remove unknown nonlinear terms deduced from perturbed uncertainties and unknown time-delay functions, which avoids the functions with control law to be approximated by radial basis function (RBF) neural networks. This technique combining implicit function and mean value theorems overcomes the difficulty in controlling the nonaffine pure-feedback systems. Dynamic surface control (DSC) is used to avoid "the explosion of complexity" in the backstepping design. Design difficulties from unknown time-delay functions are overcome using the function separation technique, the Lyapunov-Krasovskii functionals, and the desirable property of hyperbolic tangent functions. RBF neural networks are employed to approximate desired virtual controls and desired practical control. Under the proposed adaptive neural DSC, the number of adaptive parameters required is reduced significantly, and semiglobal uniform ultimate boundedness of all of the signals in the closed-loop system is guaranteed. Simulation studies are given to demonstrate the effectiveness of the proposed design scheme.
Evaluation of frost damage in cement-based materials by a nonlinear elastic wave technique
Eiras, J. N.; Kundu, T.; Popovics, J. S.; Monzó, J.; Soriano, L.; Payá, J.
2014-03-01
Frost resistance of concrete is a major concern in cold regions. RILEM (International union of laboratories and experts in construction materials, systems and structures) recommendations provide two alternatives for evaluating frost damage by nondestructive evaluation methods for concrete like materials. The first method is based on the ultrasonic pulse velocity measurement, while the second alternative technique is based on the resonant vibration test. In this study, we monitor the frost damage in Portland cement mortar samples with water to cement ratio of 0.5 and aggregate to cement ratio of 3. The samples are completely saturated by water and are frozen for 24 hours at -25°C. The frost damage is monitored after 0, 5, 10, 15 and 20 freezing-thawing cycles by nonlinear impact resonance acoustic spectroscopy (NIRAS). The results obtained are compared with those obtained by resonant vibration tests, the second alternative technique recommended by RILEM. The obtained results show that NIRAS is more sensitive to early stages of damage than the standard resonant vibration tests.
Flight control design using a blend of modern nonlinear adaptive and robust techniques
Yang, Xiaolong
In this dissertation, the modern control techniques of feedback linearization, mu synthesis, and neural network based adaptation are used to design novel control laws for two specific applications: F/A-18 flight control and reusable launch vehicle (an X-33 derivative) entry guidance. For both applications, the performance of the controllers is assessed. As a part of a NASA Dryden program to develop and flight test experimental controllers for an F/A-18 aircraft, a novel method of combining mu synthesis and feedback linearization is developed to design longitudinal and lateral-directional controllers. First of all, the open-loop and closed-loop dynamics of F/A-18 are investigated. The production F/A-18 controller as well as the control distribution mechanism are studied. The open-loop and closed-loop handling qualities of the F/A-18 are evaluated using low order transfer functions. Based on this information, a blend of robust mu synthesis and feedback linearization is used to design controllers for a low dynamic pressure envelope of flight conditions. For both the longitudinal and the lateral-directional axes, a robust linear controller is designed for a trim point in the center of the envelope. Then by including terms to cancel kinematic nonlinearities and variations in the aerodynamic forces and moments over the flight envelope, a complete nonlinear controller is developed. In addition, to compensate for the model uncertainty, linearization error and variations between operating points, neural network based adaptation is added to the designed longitudinal controller. The nonlinear simulations, robustness and handling qualities analysis indicate that the performance is similar to or better than that for the production F/A-18 controllers. When the dynamic pressure is very low, the performance of both the experimental and the production flight controllers is degraded, but Level I handling qualities are still achieved. A new generation of Reusable Launch Vehicles
AUTO-EXTRACTING TECHNIQUE OF DYNAMIC CHAOS FEATURES FOR NONLINEAR TIME SERIES
Institute of Scientific and Technical Information of China (English)
CHEN Guo
2006-01-01
The main purpose of nonlinear time series analysis is based on the rebuilding theory of phase space, and to study how to transform the response signal to rebuilt phase space in order to extract dynamic feature information, and to provide effective approach for nonlinear signal analysis and fault diagnosis of nonlinear dynamic system. Now, it has already formed an important offset of nonlinear science. But, traditional method cannot extract chaos features automatically, and it needs man's participation in the whole process. A new method is put forward, which can implement auto-extracting of chaos features for nonlinear time series. Firstly, to confirm time delay τ by autocorrelation method; Secondly, to compute embedded dimension m and correlation dimension D;Thirdly, to compute the maximum Lyapunov index λmax; Finally, to calculate the chaos degree Dch of features extracting has important meaning to fault diagnosis of nonlinear system based on nonlinear chaos features. Examples show validity of the proposed method.
Sridhar, Upasana Manimegalai; Govindarajan, Anand; Rhinehart, R Russell
2016-01-01
This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques.
Nonlinear optical techniques for imaging and manipulating the mouse central nervous system
Farrar, Matthew John
The spinal cord of vertebrates serves as the conduit for somatosensory information and motor control, as well as being the locus of neural circuits that govern fast reflexes and patterned behaviors, such as walking in mammals or swimming in fish. Consequently, pathologies of the spinal cord -such as spinal cord injury (SCI)- lead to loss of motor control and sensory perception, with accompanying decline in life expectancy and quality of life. Despite the devastating effects of these diseases, few therapies exist to substantially ameliorate patient outcome. In part, studies of spinal cord pathology have been limited by the inability to perform in vivo imaging at the level of cellular processes. The focus of this thesis is to present the underlying theory for and demonstration of novel multi-photon microscopy (MPM) and optical manipulation techniques as they apply to studies the mouse central nervous system (CNS), with an emphasis on the spinal cord. The scientific findings which have resulted from the implementation of these techniques are also presented. In particular, we have demonstrated that third harmonic generation is a dye-free method of imaging CNS myelin, a fundamental constituent of the spinal cord that is difficult to label using exogenous dyes and/or transgenic constructs. Since gaining optical access to the spinal cord is a prerequisite for spinal cord imaging, we review our development of a novel spinal cord imaging chamber and surgical procedure which allowed us to image for multiple weeks following implantation without the need for repeated surgeries. We also have used MPM to characterize spinal venous blood flow before and after point occlusions. We review a novel nonlinear microscopy technique that may serve to show optical interfaces in three dimensions inside scattering tissue. Finally, we discuss a model and show results of optoporation, a means of transfecting cells with genetic constructs. Brief reviews of MPM and SCI are also presented.
Liu, Tong-Zu; Xu, Chang; Rota, Matteo; Cai, Hui; Zhang, Chao; Shi, Ming-Jun; Yuan, Rui-Xia; Weng, Hong; Meng, Xiang-Yu; Kwong, Joey S W; Sun, Xin
2017-04-01
Approximately 27-37% of the general population experience prolonged sleep duration and 12-16% report shortened sleep duration. However, prolonged or shortened sleep duration may be associated with serious health problems. A comprehensive, flexible, non-linear meta-regression with restricted cubic spline (RCS) was used to investigate the dose-response relationship between sleep duration and all-cause mortality in adults. Medline (Ovid), Embase, EBSCOhost-PsycINFO, and EBSCOhost-CINAHL Plus databases, reference lists of relevant review articles, and included studies were searched up to Nov. 29, 2015. Prospective cohort studies investigating the association between sleep duration and all-cause mortality in adults with at least three categories of sleep duration were eligible for inclusion. We eventually included in our study 40 cohort studies enrolling 2,200,425 participants with 271,507 deaths. A J-shaped association between sleep duration and all-cause mortality was present: compared with 7 h of sleep (reference for 24-h sleep duration), both shortened and prolonged sleep durations were associated with increased risk of all-cause mortality (4 h: relative risk [RR] = 1.05; 95% confidence interval [CI] = 1.02-1.07; 5 h: RR = 1.06; 95% CI = 1.03-1.09; 6 h: RR = 1.04; 95% CI = 1.03-1.06; 8 h: RR = 1.03; 95% CI = 1.02-1.05; 9 h: RR = 1.13; 95% CI = 1.10-1.16; 10 h: RR = 1.25; 95% CI = 1.22-1.28; 11 h: RR = 1.38; 95% CI = 1.33-1.44; n = 29; P < 0.01 for non-linear test). With regard to the night-sleep duration, prolonged night-sleep duration was associated with increased all-cause mortality (8 h: RR = 1.01; 95% CI = 0.99-1.02; 9 h: RR = 1.08; 95% CI = 1.05-1.11; 10 h: RR = 1.24; 95% CI = 1.21-1.28; n = 13; P < 0.01 for non-linear test). Subgroup analysis showed females with short sleep duration a day (<7 h) were at high risk of all-cause mortality (4 h: RR = 1.07; 95% CI = 1.02-1.13; 5 h: RR = 1.08; 95
Li, Zhong-Yu; Xu, Song; Chen, Zi-Hui; Zhang, Fu-Shi; Kasatani, Kazuo
2011-08-01
Third-order optical nonlinearities of two squarylium dyes with benzothiazole donor groups (BSQ1 and BSQ2) in chloroform solution are measured by a picosecond Z-scan technique at 532 nm. It is found that the two compounds show the saturation absorption and nonlinear self-focus refraction effect. The molecular second hyperpolarizabilities are calculated to be 7.46 × 10-31 esu and 5.01 × 10-30 esu for BSQ1 and BSQ2, respectively. The large optical nonlinearities of squarylium dyes can be attributed to their rigid and intramolecular charge transfer structure. The difference in γ values is attributed to the chloro group of benzene rings of BSQ2 and the one-photon resonance effect. It is found that the third-order nonlinear susceptibilities of two squarylium dyes are mainly determined by the real parts of χ(3), and the large optical nonlinearities of studied squarylium dyes can be attributed to the nonlinear refraction.
Institute of Scientific and Technical Information of China (English)
LI Zhong-Yu; XU Song; CHEN Zi-Hui; ZHANG Fu-Shi; KASATANI Kazuo
2011-01-01
@@ Third-order optical nonlinearities of two squarylium dyes with benzothiazole donor groups (BSQ1 and BSQ2)in chloroform solution are measured by a picosecond Z-scan technique at 532 nm.It is found that the two compounds show the saturation absorption and nonlinear self-focus refraction effect.The molecular second hyperpolarizabilities are calculated to be 7.46×10-31 esu and 5.01×10-30 esu for BSQ1 and BSQ2, respectively.The large optical nonlinearities of squarylium dyes can be attributed to their rigid and intramolecular charge transfer structure.The difference in γvalues is attributed to the chloro group of benzene rings of BSQ2 and the one-photon resonance effect.It is found that the third-order nonlinear susceptibilities of two squarylium dyes are mainly determined by the real parts of X(3), and the large optical nonlinearities of studied squarylium dyes can be attributed to the nonlinear refraction.
Arivazhagan, T.; Siva Bala Solanki, S.; Rajesh, Narayana Perumal
2017-02-01
The butyl 4-hydroxybenzoate single crystal has been grown by vertical Bridgman technique using single wall ampoule. The cell parameters of the grown crystal are verified by single crystal X-ray diffraction analysis. The functional groups of the grown crystal were identified by Fourier transform infrared analysis. The melting, decomposition and crystallization point of the compound are determined by thermo gravimetric analysis and differential scanning calorimetric analysis. The mechanical properties of the grown crystal has been analyzed by Vickers microhardness method. The optical behavior of the grown crystal has been observed by UV-vis-NIR transmission spectroscopic analysis which shows that the lower cut-off wavelength lying at 293 nm and found that the energy band gap value is 4.05 eV. The blue light emission of the crystal was identified by photoluminescence studies. The positive third order nonlinear optical parameters like nonlinear refractive index (n2), nonlinear absorption co-efficient (β) and third order nonlinear susceptibility (χ3) of the grown crystal was calculated by Z-scan studies. The positive sign of nonlinear refractive index (n2) indicates that the crystal exhibits self focusing optical nonlinearity. The crystal exhibits good optical power limiting behavior.
Z-scan technique for investigation of the noninstantaneous optical Kerr nonlinearity.
Gu, Bing; Wang, Hui-Tian; Ji, Wei
2009-09-15
By treating laser-induced optical Kerr nonlinearity as a noninstantaneous decaying process, we present the pulse-duration-dependent Z-scan analytical expressions for an arbitrary aperture and an arbitrary nonlinear magnitude. This theory has the capacity to characterize the third-order nonlinear refraction induced by a laser pulse with its temporal duration being much longer than or comparable to the recovery time of the nonlinear effect. Through Z-scan measurements at different pulse durations, the nonlinear refractive coefficient and the recovery time could be determined unambiguously and simultaneously. Furthermore, the theory can be utilized to confirm whether the measured optical Kerr nonlinearity is instantaneous or noninstantaneous with respect to the given pulse duration.
Identification of a class of nonlinear state-space models using RPE techniques
DEFF Research Database (Denmark)
Zhou, W. W.; Blanke, Mogens
1986-01-01
The recursive prediction error methods in state-space form have been efficiently used as parameter identifiers for linear systems, and especially Ljung's innovations filter using a Newton search direction has proved to be quite ideal. In this paper, the RPE method in state-space form is developed...... to the nonlinear case and extended to include the exact form of a nonlinearity, thus enabling structure preservation for certain classes of nonlinear systems. Both the discrete and the continuous-discrete versions of the algorithm in an innovations model are investigated, and a nonlinear simulation example shows...... a quite convincing performance of the filter as combined parameter and state estimator....
Neural Feedback Passivity of Unknown Nonlinear Systems via Sliding Mode Technique.
Yu, Wen
2015-07-01
Passivity method is very effective to analyze large-scale nonlinear systems with strong nonlinearities. However, when most parts of the nonlinear system are unknown, the published neural passivity methods are not suitable for feedback stability. In this brief, we propose a novel sliding mode learning algorithm and sliding mode feedback passivity control. We prove that for a wide class of unknown nonlinear systems, this neural sliding mode control can passify and stabilize them. This passivity method is validated with a simulation and real experiment tests.
Nonlinear photoacoustic microscopy via a loss modulation technique: from detection to imaging.
Lai, Yu-Hung; Lee, Szu-Yu; Chang, Chieh-Feng; Cheng, Yu-Hsiang; Sun, Chi-Kuang
2014-01-13
In order to achieve high-resolution deep-tissue imaging, multi-photon fluorescence microscopy and photoacoustic tomography had been proposed in the past two decades. However, combining the advantages of these two imaging systems to achieve optical-spatial resolution with an ultrasonic-penetration depth is still a field with challenges. In this paper, we investigate the detection of the two-photon photoacoustic ultrasound, and first demonstrate background-free two-photon photoacoustic imaging in a phantom sample. To generate the background-free two-photon photoacoustic signals, we used a high-repetition rate femtosecond laser to induce narrowband excitation. Combining a loss modulation technique, we successfully created a beating on the light intensity, which not only provides pure sinusoidal modulation, but also ensures the spectrum sensitivity and frequency selectivity. By using the lock-in detection, the power dependency experiment validates our methodology to frequency-select the source of the nonlinearity. This ensures our capability of measuring the background-free two-photon photoacoustic waves by detecting the 2nd order beating signal directly. Furthermore, by mixing the nanoparticles and fluorescence dyes as contrast agents, the two-photon photoacoustic signal was found to be enhanced and detected. In the end, we demonstrate subsurface two-photon photoacoustic bio-imaging based on the optical scanning mechanism inside phantom samples.
Directory of Open Access Journals (Sweden)
Peng Guo
2012-12-01
Full Text Available With appropriate vibration modeling and analysis the incipient failure of key components such as the tower, drive train and rotor of a large wind turbine can be detected. In this paper, the Nonlinear State Estimation Technique (NSET has been applied to model turbine tower vibration to good effect, providing an understanding of the tower vibration dynamic characteristics and the main factors influencing these. The developed tower vibration model comprises two different parts: a sub-model used for below rated wind speed; and another for above rated wind speed. Supervisory control and data acquisition system (SCADA data from a single wind turbine collected from March to April 2006 is used in the modeling. Model validation has been subsequently undertaken and is presented. This research has demonstrated the effectiveness of the NSET approach to tower vibration; in particular its conceptual simplicity, clear physical interpretation and high accuracy. The developed and validated tower vibration model was then used to successfully detect blade angle asymmetry that is a common fault that should be remedied promptly to improve turbine performance and limit fatigue damage. The work also shows that condition monitoring is improved significantly if the information from the vibration signals is complemented by analysis of other relevant SCADA data such as power performance, wind speed, and rotor loads.
Development of experimental verification techniques for non-linear deformation and fracture.
Energy Technology Data Exchange (ETDEWEB)
Moody, Neville Reid; Bahr, David F. (Washington State University, Pullman, WA)
2003-12-01
This project covers three distinct features of thin film fracture and deformation in which the current experimental technique of nanoindentation demonstrates limitations. The first feature is film fracture, which can be generated either by nanoindentation or bulge testing thin films. Examples of both tests will be shown, in particular oxide films on metallic or semiconductor substrates. Nanoindentations were made into oxide films on aluminum and titanium substrates for two cases; one where the metal was a bulk (effectively single crystal) material and the other where the metal was a 1 pm thick film grown on a silica or silicon substrate. In both cases indentation was used to produce discontinuous loading curves, which indicate film fracture after plastic deformation of the metal. The oxides on bulk metals fractures occurred at reproducible loads, and the tensile stress in the films at fracture were approximately 10 and 15 GPa for the aluminum and titanium oxides respectively. Similarly, bulge tests of piezoelectric oxide films have been carried out and demonstrate film fracture at stresses of only 100's of MPa, suggesting the importance of defects and film thickness in evaluating film strength. The second feature of concern is film adhesion. Several qualitative and quantitative tests exist today that measure the adhesion properties of thin films. A relatively new technique that uses stressed overlayers to measure adhesion has been proposed and extensively studied. Delamination of thin films manifests itself in the form of either telephone cord or straight buckles. The buckles are used to calculate the interfacial fracture toughness of the film-substrate system. Nanoindentation can be utilized if more energy is needed to initiate buckling of the film system. Finally, deformation in metallic systems can lead to non-linear deformation due to 'bursts' of dislocation activity during nanoindentation. An experimental study to examine the structure of
Shih, Kao-Shang; Hsu, Ching-Chi; Hsu, Tzu-Pin; Hou, Sheng-Mou; Liaw, Chen-Kun
2014-02-01
Femoral shaft fractures can be treated using retrograde interlocking nailing systems; however, fracture nonunion still occurs. Dynamic fixation techniques, which remove either the proximal or distal locking screws, have been used to solve the problem of nonunion. In addition, a surgical rule for dynamic fixation techniques has been defined based on past clinical reports. However, the biomechanical performance of the retrograde interlocking nailing systems with either the traditional static fixation technique or the dynamic fixation techniques has not been investigated by using nonlinear numerical modeling. Three-dimensional nonlinear finite element models were developed, and the implant strength, fixation stability, and contact area of the fracture surfaces were evaluated. Three types of femoral shaft fractures (a proximal femoral shaft fracture, a middle femoral shaft fracture, and a distal femoral shaft fracture) fixed by three fixation techniques (insertion of all the locking screws, removal of the proximal locking screws, or removal of the distal locking screws) were analyzed. The results showed that the static fixation technique resulted in sufficient fixation stability and that the dynamic fixation techniques decreased the failure risk of the implant and produced a larger contact area of the fracture surfaces. The outcomes of the current study could assist orthopedic surgeons in comprehending the biomechanical performances of both static and dynamic fixation techniques. In addition, the surgeons could also select a fixation technique based on the specific patient situation using the numerical outcomes of this study.
Monakhova, Yulia B; Diehl, Bernd W K
2015-11-10
(1)H NMR spectroscopy was used to distinguish pure porcine heparin and porcine heparin blended with bovine species and to quantify the degree of such adulteration. For multivariate modelling several statistical methods such as partial least squares regression (PLS), ridge regression (RR), stepwise regression with variable selection (SR), stepwise principal component regression (SPCR) were utilized for modeling NMR data of in-house prepared blends (n=80). The models were exhaustively validated using independent test and prediction sets. PLS and RR showed the best performance for estimating heparin falsification regarding its animal origin with the limit of detection (LOD) and root mean square error of validation (RMSEV) below 2% w/w and 1% w/w, respectively. Reproducibility expressed in coefficients of variation was estimated to be below 10% starting from approximately 5% w/w of bovine adulteration. Acceptable calibration model was obtained by SPCR, by its application range was limited, whereas SR is least recommended for heparin matrix. The developed method was found to be applicable also to heparinoid matrix (not purified heparin). In this case root mean square of prediction (RMSEP) and LOD were approximately 7% w/w and 8% w/w, respectively. The simple and cheap NMR method is recommended for screening of heparin animal origin in parallel with official NMR test of heparin authenticity and purity.
Khaki, Mehdi; Forootan, Ehsan; Kuhn, Michael; Awange, Joseph; Pattiaratchi, Charitha
2016-04-01
Quantifying large-scale (basin/global) water storage changes is essential to understand the Earth's hydrological water cycle. Hydrological models have usually been used to simulate variations in storage compartments resulting from changes in water fluxes (i.e., precipitation, evapotranspiration and runoff) considering physical or conceptual frameworks. Models however represent limited skills in accurately simulating the storage compartments that could be the result of e.g., the uncertainty of forcing parameters, model structure, etc. In this regards, data assimilation provides a great chance to combine observational data with a prior forecast state to improve both the accuracy of model parameters and to improve the estimation of model states at the same time. Various methods exist that can be used to perform data assimilation into hydrological models. The one more frequently used particle-based algorithms suitable for non-linear systems high-dimensional systems is the Ensemble Kalman Filtering (EnKF). Despite efficiency and simplicity (especially in EnKF), this method indicate some drawbacks. To implement EnKF, one should use the sample covariance of observations and model state variables to update a priori estimates of the state variables. The sample covariance can be suboptimal as a result of small ensemble size, model errors, model nonlinearity, and other factors. Small ensemble can also lead to the development of correlations between state components that are at a significant distance from one another where there is no physical relation. To investigate the under-sampling issue raise by EnKF, covariance inflation technique in conjunction with localization was implemented. In this study, a comparison between latest methods used in the data assimilation framework, to overcome the mentioned problem, is performed. For this, in addition to implementing EnKF, we introduce and apply the Local Ensemble Kalman Filter (LEnKF) utilizing covariance localization to remove
Zuev, Vladimir V.; Gerasimov, Vladislav V.; Pravdin, Vladimir L.; Pavlinskiy, Aleksei V.; Nakhtigalova, Daria P.
2017-01-01
Among lidar techniques, the pure rotational Raman (PRR) technique is the best suited for tropospheric and lower stratospheric temperature measurements. Calibration functions are required for the PRR technique to retrieve temperature profiles from lidar remote sensing data. Both temperature retrieval accuracy and number of calibration coefficients depend on the selected function. The commonly used calibration function (linear in reciprocal temperature 1/T with two calibration coefficients) ignores all types of broadening of individual PRR lines of atmospheric N2 and O2 molecules. However, the collisional (pressure) broadening dominates over other types of broadening of PRR lines in the troposphere and can differently affect the accuracy of tropospheric temperature measurements depending on the PRR lidar system. We recently derived the calibration function in the general analytical form that takes into account the collisional broadening of all N2 and O2 PRR lines (Gerasimov and Zuev, 2016). This general calibration function represents an infinite series and, therefore, cannot be directly used in the temperature retrieval algorithm. For this reason, its four simplest special cases (calibration functions nonlinear in 1/T with three calibration coefficients), two of which have not been suggested before, were considered and analyzed. All the special cases take the collisional PRR lines broadening into account in varying degrees and the best function among them was determined via simulation. In this paper, we use the special cases to retrieve tropospheric temperature from real PRR lidar data. The calibration function best suited for tropospheric temperature retrievals is determined from the comparative analysis of temperature uncertainties yielded by using these functions. The absolute and relative statistical uncertainties of temperature retrieval are given in an analytical form assuming Poisson statistics of photon counting. The vertical tropospheric temperature
Tiffany, S. H.; Adams, W. M., Jr.
1984-01-01
A technique which employs both linear and nonlinear methods in a multilevel optimization structure to best approximate generalized unsteady aerodynamic forces for arbitrary motion is described. Optimum selection of free parameters is made in a rational function approximation of the aerodynamic forces in the Laplace domain such that a best fit is obtained, in a least squares sense, to tabular data for purely oscillatory motion. The multilevel structure and the corresponding formulation of the objective models are presented which separate the reduction of the fit error into linear and nonlinear problems, thus enabling the use of linear methods where practical. Certain equality and inequality constraints that may be imposed are identified; a brief description of the nongradient, nonlinear optimizer which is used is given; and results which illustrate application of the method are presented.
Rebechi, S R; Vélez, M A; Vaira, S; Perotti, M C
2016-02-01
The aims of the present study were to test the accuracy of the fatty acid ratios established by the Argentinean Legislation to detect adulterations of milk fat with animal fats and to propose a regression model suitable to evaluate these adulterations. For this purpose, 70 milk fat, 10 tallow and 7 lard fat samples were collected and analyzed by gas chromatography. Data was utilized to simulate arithmetically adulterated milk fat samples at 0%, 2%, 5%, 10% and 15%, for both animal fats. The fatty acids ratios failed to distinguish adulterated milk fats containing less than 15% of tallow or lard. For each adulterant, Multiple Linear Regression (MLR) was applied, and a model was chosen and validated. For that, calibration and validation matrices were constructed employing genuine and adulterated milk fat samples. The models were able to detect adulterations of milk fat at levels greater than 10% for tallow and 5% for lard. Copyright © 2015 Elsevier Ltd. All rights reserved.
Fruitet, Joan; McFarland, Dennis J.; Wolpaw, Jonathan R.
2010-02-01
People can learn to control electroencephalogram (EEG) features consisting of sensorimotor-rhythm amplitudes and use this control to move a cursor in one, two or three dimensions to a target on a video screen. This study evaluated several possible alternative models for translating these EEG features into two-dimensional cursor movement by building an offline simulation using data collected during online performance. In offline comparisons, support-vector regression (SVM) with a radial basis kernel produced somewhat better performance than simple multiple regression, the LASSO or a linear SVM. These results indicate that proper choice of a translation algorithm is an important factor in optimizing brain-computer interface (BCI) performance, and provide new insight into algorithm choice for multidimensional movement control.
Delrue, Steven; Tabatabaeipour, Morteza; Hettler, Jan; Van Den Abeele, Koen
2016-05-01
Friction stir welding (FSW) is a promising technology for the joining of aluminum alloys and other metallic admixtures that are hard to weld by conventional fusion welding. Although FSW generally provides better fatigue properties than traditional fusion welding methods, fatigue properties are still significantly lower than for the base material. Apart from voids, kissing bonds for instance, in the form of closed cracks propagating along the interface of the stirred and heat affected zone, are inherent features of the weld and can be considered as one of the main causes of a reduced fatigue life of FSW in comparison to the base material. The main problem with kissing bond defects in FSW, is that they currently are very difficult to detect using existing NDT methods. Besides, in most cases, the defects are not directly accessible from the exposed surface. Therefore, new techniques capable of detecting small kissing bond flaws need to be introduced. In the present paper, a novel and practical approach is introduced based on a nonlinear, single-sided, ultrasonic technique. The proposed inspection technique uses two single element transducers, with the first transducer transmitting an ultrasonic signal that focuses the ultrasonic waves at the bottom side of the sample where cracks are most likely to occur. The large amount of energy at the focus activates the kissing bond, resulting in the generation of nonlinear features in the wave propagation. These nonlinear features are then captured by the second transducer operating in pitch-catch mode, and are analyzed, using pulse inversion, to reveal the presence of a defect. The performance of the proposed nonlinear, pitch-catch technique, is first illustrated using a numerical study of an aluminum sample containing simple, vertically oriented, incipient cracks. Later, the proposed technique is also applied experimentally on a real-life friction stir welded butt joint containing a kissing bond flaw. Copyright © 2016
Non-linear imaging techniques visualize the lipid profile of C. elegans
Mari, Meropi; Petanidou, Barbara; Palikaras, Konstantinos; Fotakis, Costas; Tavernarakis, Nektarios; Filippidis, George
2015-07-01
The non-linear techniques Second and Third Harmonic Generation (SHG, THG) have been employed simultaneously to record three dimensional (3D) imaging and localize the lipid content of the muscular areas (ectopic fat) of Caenorhabditis elegans (C. elegans). Simultaneously, Two-Photon Fluorescence (TPEF) was used initially to localize the stained lipids with Nile Red, but also to confirm the THG potential to image lipids successfully. In addition, GFP labelling of the somatic muscles, proves the initial suggestion of the existence of ectopic fat on the muscles and provides complementary information to the SHG imaging of the pharynx. The ectopic fat may be related to a complex of pathological conditions including type-2 diabetes, hypertension and cardiovascular diseases. The elucidation of the molecular path leading to the development of metabolic syndrome is a vital issue with high biological significance and necessitates accurate methods competent of monitoring lipid storage distribution and dynamics in vivo. THG microscopy was employed as a quantitative tool to monitor the lipid accumulation in non-adipose tissues in the pharyngeal muscles of 12 unstained specimens while the SHG imaging revealed the anatomical structure of the muscles. The ectopic fat accumulation on the pharyngeal muscles increases in wild type (N2) C. elegans between 1 and 9 days of adulthood. This suggests a correlation of the ectopic fat accumulation with the aging. Our results can provide new evidence relating the deposition of ectopic fat with aging, but also validate SHG and THG microscopy modalities as new, non-invasive tools capable of localizing and quantifying selectively lipid accumulation and distribution.
Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad
2015-11-01
One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.
Nonlinear optical properties of natural laccaic acid dye studied using Z-scan technique
CSIR Research Space (South Africa)
Zongo, S
2015-08-01
Full Text Available We have investigated the nonlinear optical properties, including the optical limiting behaviour for five different concentrations of laccaic acid dye in solution and a thin film obtained through doping in poly (methyl methacrylate) (PMMA) polymer...
Hays, J. R.
1969-01-01
Lumped parametric system models are simplified and computationally advantageous in the frequency domain of linear systems. Nonlinear least squares computer program finds the least square best estimate for any number of parameters in an arbitrarily complicated model.
Stabilization of nonlinear systems with parametric uncertainty using variable structure techniques
Energy Technology Data Exchange (ETDEWEB)
Schoenwald, D.A. [Oak Ridge National Lab., TN (United States); Oezguener, Ue. [Ohio State Univ., Columbus, OH (United States). Dept. of Electrical Engineering
1995-07-01
The authors present a result on the robust stabilization of a class of nonlinear systems exhibiting parametric uncertainty. They consider feedback linearizable nonlinear systems with a vector of unknown constant parameters perturbed about a known value. A Taylor series of the system about the nominal parameter vector coupled with a feedback linearizing control law yields a linear system plus nonlinear perturbations. Via a structure matching condition, a variable structure control law is shown to exponentially stabilize the full system. The novelty of the result is that the linearizing coordinates are completely known since they are defined about the nominal parameter vector, and fewer restrictions are imposed on the nonlinear perturbations than elsewhere in the literature.
Directory of Open Access Journals (Sweden)
Kelly W Jones
Full Text Available Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1 matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2 fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case
Jones, Kelly W; Lewis, David J
2015-01-01
Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES)--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that
Institute of Scientific and Technical Information of China (English)
Min WANG; Xiuying WANG; Bing CHEN; Shaocheng TONG
2007-01-01
In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.
Energy Technology Data Exchange (ETDEWEB)
Weeratunga, S K; Kamath, C
2001-12-20
Removing noise from data is often the first step in data analysis. Denoising techniques should not only reduce the noise, but do so without blurring or changing the location of the edges. Many approaches have been proposed to accomplish this; in this paper, they focus on one such approach, namely the use of non-linear diffusion operators. This approach has been studied extensively from a theoretical viewpoint ever since the 1987 work of Perona and Malik showed that non-linear filters outperformed the more traditional linear Canny edge detector. They complement this theoretical work by investigating the performance of several isotropic diffusion operators on test images from scientific domains. They explore the effects of various parameters such as the choice of diffusivity function, explicit and implicit methods for the discretization of the PDE, and approaches for the spatial discretization of the non-linear operator etc. They also compare these schemes with simple spatial filters and the more complex wavelet-based shrinkage techniques. The empirical results show that, with an appropriate choice of parameters, diffusion-based schemes can be as effective as competitive techniques.
Numerical Studies of Nonlinear Schrodinger and Klein-Gordon Systems: Techniques and Applications
Choi, Dae-Il
ödinger-Poisson equations. In particular, I study the dynamics of stationary stars, stars with linear momentum, and binary star systems. The recent discovery of Bose-Einstein condensates (BEC) in dilute atomic gases has led to renewed experimental and theoretical interest in the study of quantum degenerate gases. These condensates provide a new testing ground for atomic and many-body physics, and there are many unanswered questions in this emerging field. I present methods for manipulating the condensates by an optical lattice generated by laser light and study the effect of atomic interaction on the quantum transport properties. I also study Bloch oscillations and Landau-Zener tunnelings in an accelerating optical lattice. The study of hydrogen atoms interacting with ultra-intense laser light in the non-perturbative regime has gained attention as a surprising new phenomena in nonlinear atomic physics. Particularly noteworthy are high harmonic generation (HHG) and stabilization, which provide interesting new physics. I use a 2D model to study stabilization behavior for an arbitrary polarization. As a first step, I studied circular and linear laser polarizations. In the circular case, I found spiral wave functions with strong stabilization. In the linear case, I found dichotomous wave functions with weaker stabilization. I have also observed the related HHG signatures. Finally, adaptive mesh refinement (AMR) techniques are crucial for the numerical solution of problems which have large dynamical range. I present results from the implementation and testing of some general algorithms for use in AMR work, including 2D and 3D clustering routines.
Directory of Open Access Journals (Sweden)
D. E. Panayotounakos
1996-01-01
Full Text Available We develop a new unique technique in constructing closed-form solutions for several nonlinear partial differential systems appearing in fluid mechanics and gas dynamics. The obtained solutions include fewer arbitrary functions than needed for general solutions, fact that permits us to specify them according to the initial state, or the geometry, of each specific problem under consideration. In order to apply the before mentioned technique we construct closed-form solutions concerning the gas-dynamic equations with constant pressure, the dynamic equations of an ideal gas in isentropic flow, and the two-dimensional incompressible boundary layer flow.
Directory of Open Access Journals (Sweden)
Panayotounakos D. E.
1996-01-01
Full Text Available We develop a new unique technique in constructing closed-form solutions for several nonlinear partial differential systems appearing in fluid mechanics and gas dynamics. The obtained solutions include fewer arbitrary functions than needed for general solutions, fact that permits us to specify them according to the initial state, or the geometry, of each specific problem under consideration. In order to apply the before mentioned technique we construct closed-form solutions concerning the gas-dynamic equations with constant pressure, the dynamic equations of an ideal gas in isentropic flow, and the two-dimensional incompressible boundary layer flow.
Guesmi, Latifa; Menif, Mourad
2016-04-01
The field of fiber optics nonlinearity is more discussed last years due to such remarkable enhancement in the nonlinear processes efficiency. In this paper, and for optical performance monitoring (OPM), a new achievement of nonlinear effects has been investigated. The use of cross-phase modulation (XPM) and four-wave mixing (FWM) effects between input optical signal and inserted continuous-wave probe has proposed for impairments monitoring. Indeed, transmitting a multi-channels phase modulated signal at high data rate (1 Tbps WDM Nyquist NRZ- DP-QPSK) improves the sensitivity and the dynamic range monitoring. It was observed by simulation results that various optical parameters including optical power, wavelength, chromatic dispersion (CD), polarization mode dispersion (PMD), optical signal-to-noise ratio (OSNR), Q-factor and so on, can be monitored. Also, the effect of increasing the channel spacing between WDM signals is studied and proved its use for FWM power monitoring.
Lijing Yu; Lingling Zhou; Li Tan; Hongbo Jiang; Ying Wang; Sheng Wei; Shaofa Nie
2014-01-01
BACKGROUND: Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. METHOD: In this paper, a hybrid model combining seasonal auto-regressive integrated moving average...
Non-linear swept frequency technique for CO2 measurements using a CW laser system
Campbell, Joel F
2013-01-01
A system using a non-linear multi-swept sine wave system is described which employs a multi-channel, multi-swept orthogonal waves, to separate channels and make multiple, simultaneous online/offline CO2 measurements. An analytic expression and systematic method for determining the orthogonal frequencies for the unswept, linear swept and non-linear swept cases is presented. It is shown that one may reduce sidelobes of the autocorrelation function while preserving cross channel orthogonality, for thin cloud rejection.
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.
CSIR Research Space (South Africa)
Ramoelo, Abel
2013-06-01
Full Text Available squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater...
Institute of Scientific and Technical Information of China (English)
Li Jiang(江丽); Shi'an Zhang(张诗按); Yufei Wang(王宇飞); Zhenrong Sun(孙真荣); Zugeng Wang(王祖赓); Jian Lin(林健); Wenhai Huang(黄文旵); Zhizhan Xu(徐至展); Ruxin Li(李儒新)
2004-01-01
We investigated nonlinear optical properties of ZnO-Nb2O5-TeO2 glass excited by a femtosecond laser with time-resolved four-wave mixing (FWM) technique. The unusual FWM signals were observed in samples with ZnO dopant. The mechanism for the optical nonlinearities was discussed.
Han, Ping
2017-01-01
A novel Giant Magnetostrictive Actuator (GMA) experimental system with Fiber Bragg Grating (FBG) sensing technique and its modeling method based on data driven principle are proposed. The FBG sensors are adopted to gather the multi-physics fields' status data of GMA considering the strong nonlinearity of the Giant Magnetostrictive Material and GMA micro-actuated structure. The feedback features are obtained from the raw dynamic status data, which are preprocessed by data fill and abnormal value detection algorithms. Correspondingly the Least Squares Support Vector Machine method is utilized to realize GMA online nonlinear modeling with data driven principle. The model performance and its relative algorithms are experimentally evaluated. The model can regularly run in the frequency range from 10 to 1000 Hz and temperature range from 20 to 100 °C with the minimum prediction error stable in the range from -1.2% to 1.1%.
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
Lasue, Jeremie; Wiens, Roger; Stepinski, Tom; Forni, Olivier; Clegg, Samuel; Maurice, Sylvestre; Chemcam Team
2011-02-01
ChemCam is a remote laser-induced breakdown spectroscopy (LIBS) instrument that will arrive on Mars in 2012, on-board the Mars Science Laboratory Rover. The LIBS technique is crucial to accurately identify samples and quantify elemental abundances at various distances from the rover. In this study, we compare different linear and nonlinear multivariate techniques to visualize and discriminate clusters in two dimensions (2D) from the data obtained with ChemCam. We have used principal components analysis (PCA) and independent components analysis (ICA) for the linear tools and compared them with the nonlinear Sammon's map projection technique. We demonstrate that the Sammon's map gives the best 2D representation of the data set, with optimization values from 2.8% to 4.3% (0% is a perfect representation), together with an entropy value of 0.81 for the purity of the clustering analysis. The linear 2D projections result in three (ICA) and five times (PCA) more stress, and their clustering purity is more than twice higher with entropy values about 1.8. We show that the Sammon's map algorithm is faster and gives a slightly better representation of the data set if the initial conditions are taken from the ICA projection rather than the PCA projection. We conclude that the nonlinear Sammon's map projection is the best technique for combining data visualization and clustering assessment of the ChemCam LIBS data in 2D. PCA and ICA projections on more dimensions would improve on these numbers at the cost of the intuitive interpretation of the 2D projection by a human operator.
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
Directory of Open Access Journals (Sweden)
B. Shank
2014-11-01
Full Text Available We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs connected to quasiparticle (qp traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.
Wittig, A; Di Lizia, P.; Armellin, R.; Zazzera, FB; Makino, K; Berzş, M
2014-01-01
Current approaches to uncertainty propagation in astrodynamics mainly refer to linearized models or Monte Carlo simulations. Naive linear methods fail in nonlinear dynamics, whereas Monte Carlo simulations tend to be computationally intensive. Differential algebra has already proven to be an efficient compromise by replacing thousands of pointwise integrations of Monte Carlo runs with the fast evaluation of the arbitrary order Taylor expansion of the flow of the dynamics. However, the current...
2012-12-01
or proof, rather as a review and reference for subsequent sections. Brau’s Modern Problems in Electrodynamics and Mill’s Nonlinear Optics are both... Modern Problems in Electrodynamics , follows from the Lorentz-Drude Model for the polarization of the atom[2]. In this model, the electron is harmonically...2] C. A. Brau, Modern Problems in Classical Electrodynamics , New York: Oxford University Press, 2004. [3] K. Than, “Scientists Create Cloak of
Shank, B; Cabrera, B; Kreikebaum, J M; Moffatt, R; Redl, P; Young, B A; Brink, P L; Cherry, M; Tomada, A
2014-01-01
We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs) connected to quasiparticle (qp) traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search) Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.
Efendiev, Y.
2009-11-01
The Markov chain Monte Carlo (MCMC) is a rigorous sampling method to quantify uncertainty in subsurface characterization. However, the MCMC usually requires many flow and transport simulations in evaluating the posterior distribution and can be computationally expensive for fine-scale geological models. We propose a methodology that combines coarse- and fine-scale information to improve the efficiency of MCMC methods. The proposed method employs off-line computations for modeling the relation between coarse- and fine-scale error responses. This relation is modeled using nonlinear functions with prescribed error precisions which are used in efficient sampling within the MCMC framework. We propose a two-stage MCMC where inexpensive coarse-scale simulations are performed to determine whether or not to run the fine-scale (resolved) simulations. The latter is determined on the basis of a statistical model developed off line. The proposed method is an extension of the approaches considered earlier where linear relations are used for modeling the response between coarse-scale and fine-scale models. The approach considered here does not rely on the proximity of approximate and resolved models and can employ much coarser and more inexpensive models to guide the fine-scale simulations. Numerical results for three-phase flow and transport demonstrate the advantages, efficiency, and utility of the method for uncertainty assessment in the history matching. Copyright 2009 by the American Geophysical Union.
Synchronization Analysis: Are such Nonlinear Techniques Useful for Earth Sciences? (Invited)
Kurths, J.
2009-12-01
Synchronization phenomena are abundant in nature, science, engineering and social life, such as in organ pipes, fireflies and even in the mechanics of bridges. But synchronization was first recognized by Christiaan Huygens in 1665 for coupled pendulum clocks; this was the beginning of nonlinear sciences. In the last two decades, this concept has been successfully extended to more complex systems and the following basic phenomena have been found in coupled chaotic systems: complete synchronization (CS), generalized synchronization (GS), and phase synchronization (PS). Here we discuss two general concepts of nonlinear dynamics how to identify such complex synchronization phenomena in multivariate time series; the first is based on the Hilbert transform and the second one on recurrence. We show that the corresponding methods can be applied even to rather short and somewhat non-stationary data. Finally, applications are presented to study dynamic teleconnections, such as El Nino - Monsoon interactions. References Pikovsky, A., M. Rosenblum, and J. Kurths, Synchronization - A Universal Concept in Nonlinear Sciences, Cambridge University Press 2001. Boccaletti, S., J. Kurths, G. Osipov, and C. Zhou, Phys. Rep. 2002, 366, 1. Maraun, D., and J. Kurths, Geophys. Res. Lett. 2005, 32, L15709 Marwan, N., Thiel, M., Romano, M., and J. Kurths, Phys. Rep. 2007, 438, 237. Tokuda, I., Kurths, J., Kiss, I., and J. Hudson, Europhys. Lett. 2008 83, 50003.
The nonlinear evolution of rogue waves generated by means of wave focusing technique
Hu, HanHong; Ma, Ning
2011-01-01
Generating the rogue waves in offshore engineering is investigated, first of all, to forecast its occurrence to protect the offshore structure from being attacked, to study the mechanism and hydrodynamic properties of rouge wave experimentally as well as the rouge/structure interaction for the structure design. To achieve these purposes demands an accurate wave generation and calculation. In this paper, we establish a spatial domain model of fourth order nonlinear Schrödinger (NLS) equation for describing deep-water wave trains in the moving coordinate system. In order to generate rogue waves in the experimental tank efficiently, we take care that the transient water wave (TWW) determines precisely the concentration of time/place. First we simulate the three-dimensional wave using TWW in the numerical tank and modeling the deepwater basin with a double-side multi-segmented wave-maker in Shanghai Jiao Tong University (SJTU) under the linear superposing theory. To discuss its nonlinearity for guiding the experiment, we set the TWW as the initial condition of the NLS equation. The differences between the linear and nonlinear simulations are presented. Meanwhile, the characteristics of the transient water wave, including water particle velocity and wave slope, are investigated, which are important factors in safeguarding the offshore structures.
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
Institute of Scientific and Technical Information of China (English)
LIU Zheng-Feng; WANG Xiao-Hong
2008-01-01
Adopting Yoshizawa's two-scale expansion technique,the fluctuating field is expanded around the isotropic field.The renormalization group method is applied for calculating the covariance of the fluctuating field at the lower order expansion,A nonlinear Reynolds stress model is derived and the turbulent constants inside are evaluated analytically.Compared with the two-scale direct interaction approximation analysis for turbulent shear flows proposed by Yoshizawa,the calculation is much more simple.The analytical model presented here is close to the Speziale model,which is widely applied in the numerical simulations for the complex turbulent flows.
Institute of Scientific and Technical Information of China (English)
2013-01-01
In this paper, a numerical method based on a coupling between a mathematical model of nonlinear transient ship manoeu-vring motion in the horizontal plane and Mathematical Programming (MP) techniques is proposed. The aim of the proposed proce-dure is an efficient estimation of optimal ship hydrodynamic parameters in a dynamic model at the early design stage. The proposed procedure has been validated through turning circle and zigzag manoeuvres based on experimental data of sea trials of the 190 000-dwt oil tanker. Comparisons between experimental and computed data show a good agreement of overall tendency in manoeuvring trajectories.
Institute of Scientific and Technical Information of China (English)
Cao Wen-Jun; Xu Wen-Cheng; Luo Zhi-Chao; Wang Lu-Yan; Wang Hui-Yi; Dong Jiang-Li; Luo Ai-Ping
2011-01-01
We report on the generation of dual-wavelength dissipative solitons in a passively mode-locked fibre laser with a net normal dispersion using the nonlinear polarization rotation (NPR) technique.Taking the intrinsic advantage of the intracavity birefringence-induced spectral filtering effect in the NPR-based ring laser cavity,the dual-wavelength dissipative solitons are obtained.In addition,the wavelength separation and the lasing location of the dual-wavelength solitons can be flexibly tuned by changing the orientation of the polarization controller.
Directory of Open Access Journals (Sweden)
Edenio Detmann
2012-01-01
(n = 156 or F1 European × Nellore (n = 139 animals. Two approaches were presented, based on linear and non-linear regression models fitting; the average daily gain and dry matter intake were used as independent and dependent variables, respectively. The food utilization by the animal was stratified into maintenance demand and true efficiency of product conversion. The difference between groups was evaluated by using dummy variables. The Akaike information criterion was suggested to decide which model is more adequate. The comparative evaluation of feed into product conversion between two experimental groups of beef cattle by using regression techniques allows, if suitable, the stratification of groups according to efficiency of diet utilization for maintenance and production.
Indian Academy of Sciences (India)
K. Karami; R. Mohebi
2007-12-01
We use the method introduced by Karami & Mohebi (2007), and Karami & Teimoorinia (2007) which enable us to derive the orbital parameters of the spectroscopic binary stars by the nonlinear least squares of observed . curve fitting (o–c). Using the measured experimental data for radial velocities of the four double-lined spectroscopic binary systems PV Pup, HD 141929, EE Cet and V921 Her, we find both the orbital and the combined spectroscopic elements of these systems. Our numerical results are in good agreement with those obtained using the method of Lehmann-Filhés.
Haar basis and nonlinear modeling of complex systems
García, P.; Merlitti, A.
2007-04-01
In this work we introduce a technique to perform nonlinear modeling of chaotic time series using the kernel method. The basic idea behind this method is to map the data into a high dimensional space via nonlinear mapping and do a linear regression in this space. Here we use a Haar wavelet-like kernel to achieve the task. This strategy, in contrast to Support Vector Machines technique, shows the conceptual simplicity of least mean square algoritm for linear regression but allows local nonlinear aproximation of the system evolution, with low computational cost.
General solution to nonlinear optical quantum graphs using Dalgarno-Lewis summation techniques
Lytel, Rick; Kuzyk, Mark G
2016-01-01
We develop an algorithm to apply the Dalgarno-Lewis (DL) perturbation theory to quantum graphs with multiple, connected edges. We use it to calculate the nonlinear optical hyperpolarizability tensors for graphs and show that it replicates the sum over states computations, but executes ten to fifty times faster. DL requires only knowledge of the ground state of the graph, eliminating the requirement to determine all possible degeneracies of a complex network. The algorithm is general and may be applied to any quantum graph.
Rolling Regressions with Stata
Kit Baum
2004-01-01
This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...
Directory of Open Access Journals (Sweden)
Abd El-Naser A. Mohammed
2010-09-01
Full Text Available In the present paper, the problem amplification techniques of ultra dense wavelength division multiplexing (UDWDM in nonlinear optical networks are investigated through five transmission techniques. The impact of tailoring of chirped pulses of different temporal waveforms is investigated in a normal dispersion fiber. The set of multiplexed signals are tailored in a different a subset to assure approximately the same output level of power to hold the signal-to-noise ratio at the same level. Moreover, three different transmission techniques, namely, soliton propagation, maximum time division multiplexing (MTDM and ìShannonî capacity, are employed where successive section of alternating dispersion are used as a technique to manage the dispersion. Distributed ìRamanî amplifiers as well as Erbium doped fiber amplifier are engaged to maximize the repeater spacing. We have succeeded to multiplex 2400 (UDWDM channels in the optical range 1.45 1.65 µm with channel spacing ranging from 0.3 up to 0.6 nm where each channel has its own characteristic parameters of loss, dispersion, and amplification. The channels are divided into sub-groups ( each of 4, 5, 6, 7,Ö.,24 where the technique of space division multiplexing (SDM is applied. The multispan effects of ìKerrî nonlinearity and amplifier noise on ìShannonî channel capacity of dispersion-free nonlinear fiber is considered as a ceiling value for the sake of comparison. The case of soliton with modified Raman amplification via parametric gain also is investigated. Each link has special chemical structure, optical signals power, and optical Raman pumping. The cable contains {4, 5, 6, 7,Ö. , 24} links in SDM. It has been shown that the modified Raman gain yields higher effects on the variable under consideration if compared with the conventional Raman gain. The number of links is in positive correlations with the set of effects {Repeater spacing, Soliton product, MTDM product}. In general
Kostenko, Yuri T.; Shkvarko, Yuri V.
1994-06-01
The aim of this presentation is to address a new theoretic approach to the problem of the development of remote sensing imaging (RSI) nonlinear techniques that exploit the idea of fusion the experiment design and statistical regularization theory-based methods for inverse problems solution optimal/suboptimal in the mixed Bayesian-regularization setting. The basic purpose of such the information fusion-based methodology is twofold, namely, to design the appropriate system- oriented finite-dimensional model of the RSI experiment in the terms of projection schemes for wavefield inversion problems, and to derive the two-stage estimation techniques that provide the optimal/suboptimal restoration of the power distribution in the environment from the limited number of the wavefield measurements. We also discuss issues concerning the available control of some additional degrees of freedom while such an RSI experiment is conducted.
Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R
2012-01-01
The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Structure-selection techniques applied to continuous-time nonlinear models
Aguirre, Luis A.; Freitas, Ubiratan S.; Letellier, Christophe; Maquet, Jean
2001-10-01
This paper addresses the problem of choosing the multinomials that should compose a polynomial mathematical model starting from data. The mathematical representation used is a nonlinear differential equation of the polynomial type. Some approaches that have been used in the context of discrete-time models are adapted and applied to continuous-time models. Two examples are included to illustrate the main ideas. Models obtained with and without structure selection are compared using topological analysis. The main differences between structure-selected models and complete structure models are: (i) the former are more parsimonious than the latter, (ii) a predefined fixed-point configuration can be guaranteed for the former, and (iii) the former set of models produce attractors that are topologically closer to the original attractor than those produced by the complete structure models.
Directory of Open Access Journals (Sweden)
Samir Dey
2015-07-01
Full Text Available This paper proposes a new multi-objective intuitionistic fuzzy goal programming approach to solve a multi-objective nonlinear programming problem in context of a structural design. Here we describe some basic properties of intuitionistic fuzzy optimization. We have considered a multi-objective structural optimization problem with several mutually conflicting objectives. The design objective is to minimize weight of the structure and minimize the vertical deflection at loading point of a statistically loaded three-bar planar truss subjected to stress constraints on each of the truss members. This approach is used to solve the above structural optimization model based on arithmetic mean and compare with the solution by intuitionistic fuzzy goal programming approach. A numerical solution is given to illustrate our approach.
Rodrigo, M A; Seco, A; Ferrer, J; Penya-roja, J M; Valverde, J L
1999-01-01
In this paper, several tuning algorithms, specifically ITAE, IMC and Cohen and Coon, were applied in order to tune an activated sludge aeration PID controller. Performance results of these controllers were compared by simulation with those obtained by using a nonlinear fuzzy PID controller. In order to design this controller, a trial and error procedure was used to determine, as a function of error at current time and at a previous time, sets of parameters (including controller gain, integral time and derivative time) which achieve satisfactory response of a PID controller actuating over the aeration process. Once these sets of data were obtained, neural networks were used to obtain fuzzy membership functions and fuzzy rules of the fuzzy PID controller.
Feedback control linear, nonlinear and robust techniques and design with industrial applications
Dodds, Stephen J
2015-01-01
This book develops the understanding and skills needed to be able to tackle original control problems. The general approach to a given control problem is to try the simplest tentative solution first and, when this is insufficient, to explain why and use a more sophisticated alternative to remedy the deficiency and achieve satisfactory performance. This pattern of working gives readers a full understanding of different controllers and teaches them to make an informed choice between traditional controllers and more advanced modern alternatives in meeting the needs of a particular plant. Attention is focused on the time domain, covering model-based linear and nonlinear forms of control together with robust control based on sliding modes and the use of state observers such as disturbance estimation. Feedback Control is self-contained, paying much attention to explanations of underlying concepts, with detailed mathematical derivations being employed where necessary. Ample use is made of diagrams to aid these conce...
Introduction to regression graphics
Cook, R Dennis
2009-01-01
Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Abdeldayem, Hossin A.; Sheng, Wen; Venkateswarlu, P.; Witherow, William K.; Frazier, Don O.; Chandra Sekhar, P.; George, M. C.; Kispert, Lowell; Wasielewski, Michael R.
1993-01-01
Quantitative measurements of the nonlinear refractive index coefficient n(2) and the third-order nonlinear susceptibility chi(3) for a solution of 7-prime,7-prime-dicyano-7-prime-apo-beta-carotene (DCAC) in hexane have been measured at different concentrations. The measurements have been performed by both the self-trapping and self-phase modulation techniques using a CW Ar(+) laser. The results show that DCAC has a relatively large nonlinearity, attributed to a thermal mechanism, with n(2) of the order of 10 exp 9 times that of CS2.
Moen, Erick K.; Beier, Hope T.; Thompson, Gary L.; Roth, Caleb C.; Ibey, Bennett L.
2014-03-01
Nonlinear optical probes, especially those involving second harmonic generation (SHG), have proven useful as sensors for near-instantaneous detection of alterations to orientation or energetics within a substance. This has been exploited to some success for observing conformational changes in proteins. SHG probes, therefore, hold promise for reporting rapid and minute changes in lipid membranes. In this report, one of these probes is employed in this regard, using nanosecond electric pulses (nsEPs) as a vehicle for instigating subtle membrane perturbations. The result provides a useful tool and methodology for the observation of minute membrane perturbation, while also providing meaningful information on the phenomenon of electropermeabilization due to nsEP. The SHG probe Di- 4-ANEPPDHQ is used in conjunction with a tuned optical setup to demonstrate nanoporation preferential to one hemisphere, or pole, of the cell given a single square shaped pulse. The results also confirm a correlation of pulse width to the amount of poration. Furthermore, the polarity of this event and the membrane physics of both hemispheres, the poles facing either electrode, were tested using bipolar pulses consisting of two pulses of opposite polarity. The experiment corroborates findings by other researchers that these types of pulses are less effective in causing repairable damage to the lipid membrane of cells.
Owolabi, Kolade M.
2017-03-01
In this paper, some nonlinear space-fractional order reaction-diffusion equations (SFORDE) on a finite but large spatial domain x ∈ [0, L], x = x(x , y , z) and t ∈ [0, T] are considered. Also in this work, the standard reaction-diffusion system with boundary conditions is generalized by replacing the second-order spatial derivatives with Riemann-Liouville space-fractional derivatives of order α, for 0 < α < 2. Fourier spectral method is introduced as a better alternative to existing low order schemes for the integration of fractional in space reaction-diffusion problems in conjunction with an adaptive exponential time differencing method, and solve a range of one-, two- and three-components SFORDE numerically to obtain patterns in one- and two-dimensions with a straight forward extension to three spatial dimensions in a sub-diffusive (0 < α < 1) and super-diffusive (1 < α < 2) scenarios. It is observed that computer simulations of SFORDE give enough evidence that pattern formation in fractional medium at certain parameter value is practically the same as in the standard reaction-diffusion case. With application to models in biology and physics, different spatiotemporal dynamics are observed and displayed.
Ma, Yongjie; Dong, Wenbin; Bao, Hongliang; Fang, Yue; Fan, Cheng
2017-04-15
This paper proposed a nonlinear chemical fingerprint method for simultaneous determination of urea and melamine in milk powder using "H(+)+Ce(4+)+BrO3(-)+malonic acid" as reaction system. A multiple linear relationship was obtained between the adulterants content in milk powder and inductive time of corresponding mixed milk powder. System analysis model established with classical least squares (CLS) method was then used to calculate the content of urea and melamine in milk powder. The method was successfully applied to milk powder samples and had good recoveries in the range of 99.17-100.25%, with the relative standard deviation (RSD) in the range of 0.60-4.12%. The limits of detection for urea and melamine were 0.33μg·g(-1) and 0.05μg·g(-1), respectively. The limits of quantification were 1.11μg·g(-1) and 0.18μg·g(-1), respectively. The results indicated that the new method was feasible and had the advantages of low cost, simple operation and without pretreatment of samples.
Estimating monotonic rates from biological data using local linear regression.
Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R
2017-03-01
Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.
Advances in the Techniques and Technology of the Application of Nonlinear Filters and Kalman Filters
1982-03-01
June 1981. 61 Madan , Y., and bar-Xtahack, I. Y., "Error and Sensitivity Analysis Scheme of a Now Data Compression Technique in Estimation," TAE...other radio transmicsawis cloe Ai. fre- .quency to the radar’s.I A illitary. tartwt, Pa.s, nxample. might transmit signals ta cofuse the ra44r. These
Bhowmick, Arup; Mohapatra, Ashok K
2016-01-01
We demonstrate the phenomenon of blockade in two-photon excitations to the Rydberg state in thermal vapor. A technique based on optical heterodyne is used to measure the dispersion of a probe beam far off resonant to the D2 line of rubidium in the presence of a strong laser beam that couples to the Rydberg state via two-photon resonance. Density dependent suppression of the dispersion peak is observed while coupling to the Rydberg state with principal quantum number, n = 60. The experimental observation is explained using the phenomenon of Rydberg blockade. The blockade radius is measured to be about 2.2 {\\mu}m which is consistent with the scaling due to the Doppler width of 2-photon resonance in thermal vapor. Our result promises the realization of single photon source and strong single photon non-linearity based on Rydberg blockade in thermal vapor.
Mani, S.; Jang, J. I.; Ketterson, J. B.
2010-09-01
Employing a modified Z-scan technique at 2 K, we monitor not only the fundamental (ω) but also the frequency-doubled (2ω) and tripled (3ω) Z-scan responses in Cu2O when the input laser frequency ω is tuned to the two-photon quadrupole polariton resonance. The Z-scan response at ω allows us to accurately estimate the absolute number of polaritons generated via two-photon absorption. A striking dip is observed near the 2ω Z-scan focus which basically arises from Auger-type recombination of polaritons. Under high excitation levels, the 3ω Z-scan shows strong third harmonic generation. Based on the nonlinear optical parameters determined, we estimate the experimental polariton density achievable and propose a direction for polariton-based Bose-Einstein condensation in Cu2O .
Directory of Open Access Journals (Sweden)
Ali Volkan Bilgili
2013-07-01
Full Text Available In the Harran Plain, southeastern Turkey, soil salinisation causes land degradation threatening the sustainability of agricultural production. According to a recent survey, approximately 18000 ha area has been affected by soil salinity and sodicity at various levels. Determining the distribution of saline and sodic soils in the study area is the first step for effective management of these soils. Over 200 soil samples have been randomly selected across the plain and analyzed for selected soil salinity and sodicity variables in soil salinity laboratory. Indicator kriging (IK, a non-linear interpolation technique, was used to map the probability levels of occurrence of saline and sodic soils across the plain. The results of IK showed the probability distributions of risky areas under different types of soil salinity classes; nonsaline, saline, saline – sodic and sodic.
Edwards, Jack R.; Mcrae, D. S.
1993-01-01
An efficient implicit method for the computation of steady, three-dimensional, compressible Navier-Stokes flowfields is presented. A nonlinear iteration strategy based on planar Gauss-Seidel sweeps is used to drive the solution toward a steady state, with approximate factorization errors within a crossflow plane reduced by the application of a quasi-Newton technique. A hybrid discretization approach is employed, with flux-vector splitting utilized in the streamwise direction and central differences with artificial dissipation used for the transverse fluxes. Convergence histories and comparisons with experimental data are presented for several 3-D shock-boundary layer interactions. Both laminar and turbulent cases are considered, with turbulent closure provided by a modification of the Baldwin-Barth one-equation model. For the problems considered (175,000-325,000 mesh points), the algorithm provides steady-state convergence in 900-2000 CPU seconds on a single processor of a Cray Y-MP.
The Application of Nonlinear Regression in the Copper Price Prediction%非线性规划在金属铜价格预测中的应用
Institute of Scientific and Technical Information of China (English)
赵雪松; 张宏哲
2013-01-01
金属铜作为一种十分重要的资源在世界经济发展的过程中发挥着越来越重要的作用。金属铜被广泛地运用在建筑、装饰、电线电缆制造等方面。期铜价格不仅对企业投资决策很重要，而且对矿业权评估来说也是一个很重要的参数。文章以期铜每年的中间价格作为历史数据，根据散点图，运用对多项式拟合方法对其价格进行预测。分别用excel的添加趋势线法和变量替换法这两种方法，基于同一数据，进行了非线性拟合，综合评判后，认为第一种方法较优。%Copper as an important resource in the process of development of the world economy is playing an more and more important role. Copper is widely used in construction, decoration, electric wire and cable manufacturing etc. Copper price is important not only for enterprise decisions, but also for mining right evaluation. This paper takes the annual medium prices of the copper as historical data, uses the polynomial fitting method to estimate the price based on the scatter plot diagram. Based on the same data, it uses respectively the excel add a trend line method and the variable substitution method for a nonlinear fitting, the first approach is preferred after an integrated appraisal.
Directory of Open Access Journals (Sweden)
V.M. Deshmukh
2015-05-01
Full Text Available This paper proposed closed loop control of nonlinear system connected inverter based on the optimal neural controller (ONC. The novelty of the proposed method rests on the hybrid technique which is the combined performance of both, particle swarm optimization (PSO technique and Radial basis function neural network (RBFNN. It effectively optimizes the feasible solutions by updating the generations, by taking lesser time with greater reliability. In the proposed method, the PSO generates the dataset according to different loading conditions. The RBFNN is trained by using the target control signals along with the corresponding input load voltage error and change in error. Depending on the load variations, the RBFNN predicts the exact control signals of the inverter during the testing time. Since experimentation and comparison of such inverter models on hardware being relatively expensive, the proposed method is implemented in the MATLAB/Simulink platform and the performance has been validated through the comparison analysis with the conventional techniques. The comparison results have proved the superiority of the proposed method.
A New Non-linear Technique for Measurement of Splitting Functions of Normal Modes of the Earth
Pachhai, S.; Masters, G.; Tkalcic, H.
2014-12-01
Normal modes are the vibrating patterns of the Earth in response to the large earthquakes. Normal mode spectra are split due to Earth's rotation, ellipticity, and heterogeneity. The normal mode splitting is visualized through splitting functions, which represent the local radial average of Earth's structure seen by a mode of vibration. The analysis of the splitting of normal modes can provide unique information about the lateral variation of the Earth's elastic properties that cannot be directly imaged in body wave tomographic images. The non-linear iterative spectral fitting of the observed complex spectra and autoregressive linear inversion have been widely utilized to compute the Earth's 3-D structure. However, the non-linear inversion requires a model of the earthquake source and the retrieved 3-D structure is sensitive to the initial constraints. In contrast, the autoregressive linear inversion does not require the source model. However, this method requires many events to achieve full convergence. In addition, significant disagreement exists between different studies because of the non-uniqueness of the problem and limitations of different methods. We thus apply the neighbourhood algorithm (NA) to measure splitting functions. The NA is an efficient model space search technique and works in two steps: In the first step, the algorithm finds all the models compatible with given data while the posterior probability density of the model parameters are obtained in the second step. The NA can address the problem of non-uniqueness by taking advantage of random sampling of the full model space. The parameter trade-offs are conveniently visualized using joint marginal distributions. In addition, structure coefficients uncertainties can be extracted from the posterior probability distribution. After demonstrating the feasibility of NA with synthetic examples, we compute the splitting functions for the mode 13S2 (sensitive to the inner core) from several large
Nayfeh, Ali Hasan
1995-01-01
Nonlinear Oscillations is a self-contained and thorough treatment of the vigorous research that has occurred in nonlinear mechanics since 1970. The book begins with fundamental concepts and techniques of analysis and progresses through recent developments and provides an overview that abstracts and introduces main nonlinear phenomena. It treats systems having a single degree of freedom, introducing basic concepts and analytical methods, and extends concepts and methods to systems having degrees of freedom. Most of this material cannot be found in any other text. Nonlinear Oscillations uses sim
Kassavou, Aikaterini; Sutton, Stephen
2017-09-12
Automated telecommunication interventions, including short message service and interactive voice response, are increasingly being used to promote adherence to medications prescribed for cardio-metabolic conditions. This systematic review aimed to comprehensively assess the effectiveness of such interventions to support medication adherence, and to identify the behaviour change techniques (BCTs) and other intervention characteristics that are positively associated with greater intervention effectiveness. Meta-analysis of 17 randomised controlled trials showed a small but statistically significant effect on medication adherence, OR = 1.89, 95% CI [1.51, 2.36], I(2) = 89%, N = 25,101. Multivariable meta-regression analysis including eight BCTs explained 88% of the observed variance in effect size (ES). The BCTs 'tailored' and 'information about health consequences' were positively and significantly associated with ES. Future studies could explore whether the inclusion of these and/or additional techniques (e.g., 'implementation intentions') would increase the effect of automated telecommunication interventions, using rigorous designs and objective outcome measures.
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Nick, Todd G; Campbell, Kathleen M
2007-01-01
The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.
Nonlinear inversion for arbitrarily-oriented anisotropic models II: Inversion techniques
Bremner, P. M.; Panning, M. P.
2011-12-01
We present output models from inversion of a synthetic surface wave dataset. We implement new 3-D finite-frequency kernels, based on the Born approximation, to invert for upper mantle structure beneath western North America. The kernels are formulated based on a hexagonal symmetry with an arbitrary orientation. Numerical tests were performed to achieve a robust inversion scheme. Four synthetic input models were created, to include: isotropic, constant strength anisotropic, variable strength anisotropic, and both anisotropic and isotropic together. The reference model was a simplified version of PREM (dubbed PREM LIGHT) in which the crust and 220 km discontinuity have been removed. Output models from inversions of calculated synthetic data are compared against these input models to test for accurate reproduction of input model features, and the resolution of those features. The object of this phase of the study was to determine appropriate nonlinear inversion schemes that adequately recover the input models. The synthetic dataset consists of collected seismic waveforms of 126 earthquake mechanisms, of magnitude 6-7 from Dec 2006 to Feb 2009, from the IRIS database. Events were selected to correlate with USArray deployments, and to have as complete an azimuthal coverage as possible. The events occurred within a circular region of radius 150o centered about 44o lat, -110o lon (an arbitrary location within USArray coverage). Synthetic data were calculated utilizing a spectral element code (SEM) coupled to a normal mode solution. The mesh consists of a 3-D heterogeneous outer shell, representing the upper mantle above 450 km depth, coupled to a spherically symmetric inner sphere. From the synthetic dataset, multi-taper fundamental mode surface wave phase delay measurements are taken. The orthogonal 2.5π -prolate spheroidal wave function eigentapers (Slepian tapers) reduce noise biasing, and can provide error estimates in phase delay measurements. This study is a
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Energy Technology Data Exchange (ETDEWEB)
Moody, Neville Reid; Bahr, David F.
2005-11-01
This work covers three distinct aspects of deformation and fracture during indentations. In particular, we develop an approach to verification of nanoindentation induced film fracture in hard film/soft substrate systems; we examine the ability to perform these experiments in harsh environments; we investigate the methods by which the resulting deformation from indentation can be quantified and correlated to computational simulations, and we examine the onset of plasticity during indentation testing. First, nanoindentation was utilized to induce fracture of brittle thin oxide films on compliant substrates. During the indentation, a load is applied and the penetration depth is continuously measured. A sudden discontinuity, indicative of film fracture, was observed upon the loading portion of the load-depth curve. The mechanical properties of thermally grown oxide films on various substrates were calculated using two different numerical methods. The first method utilized a plate bending approach by modeling the thin film as an axisymmetric circular plate on a compliant foundation. The second method measured the applied energy for fracture. The crack extension force and applied stress intensity at fracture was then determined from the energy measurements. Secondly, slip steps form on the free surface around indentations in most crystalline materials when dislocations reach the free surface. Analysis of these slip steps provides information about the deformation taking place in the material. Techniques have now been developed to allow for accurate and consistent measurement of slip steps and the effects of crystal orientation and tip geometry are characterized. These techniques will be described and compared to results from dislocation dynamics simulations.
Zayed, Elsayed M. E.; Al-Nowehy, Abdul-Ghani; Elshater, Mona E. M.
2017-06-01
The (G^'/G)-expansion method, the improved Sub-ODE method, the extended auxiliary equation method, the new mapping method and the Jacobi elliptic function method are applied in this paper for finding many new exact solutions including Jacobi elliptic solutions, solitary solutions, singular solitary solutions, trigonometric function solutions and other solutions to the nonlinear Schrödinger equation with fourth-order dispersion and dual power law nonlinearity whose balance number is not positive integer. The used methods present a wider applicability for handling the nonlinear partial differential equations. A comparison of our new results with the well-known results is made. Also, we compare our results with each other yielding from these five integration tools.
Weisberg, Sanford
2013-01-01
Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus
Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea
2016-12-01
The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.
Lim, Hyung Jin; Kim, Yongtak; Koo, Gunhee; Yang, Suyoung; Sohn, Hoon; Bae, In-hwan; Jang, Jeong-Hwan
2016-09-01
In this study, a fatigue crack detection technique, which detects a fatigue crack without relying on any reference data obtained from the intact condition of a target structure, is developed using nonlinear ultrasonic modulation and applied to a real bridge structure. Using two wafer-type lead zirconate titanate (PZT) transducers, ultrasonic excitations at two distinctive frequencies are applied to a target inspection spot and the corresponding ultrasonic response is measured by another PZT transducer. Then, the nonlinear modulation components produced by a breathing-crack are extracted from the measured ultrasonic response, and a statistical classifier, which can determine if the nonlinear modulation components are statistically significant in comparison with the background noise level, is proposed. The effectiveness of the proposed fatigue crack detection technique is experimentally validated using the data obtained from aluminum plates and aircraft fitting-lug specimens under varying temperature and loading conditions, and through a field testing of Yeongjong Grand Bridge in South Korea. The uniqueness of this study lies in that (1) detection of a micro fatigue crack with less than 1 μm width and fatigue cracks in the range of 10-20 μm in width using nonlinear ultrasonic modulation, (2) automated detection of fatigue crack formation without using reference data obtained from an intact condition, (3) reliable and robust diagnosis under varying temperature and loading conditions, (4) application of a local fatigue crack detection technique to online monitoring of a real bridge.
Scarselli, G.; Ciampa, F.; Ginzburg, D.; Meo, M.
2015-04-01
Nonlinear ultrasonic non-destructive evaluation (NDE) methods can be used for the identification of defects within adhesive bonds as they rely on the detection of nonlinear elastic features for the evaluation of the bond strength. In this paper the nonlinear content of the structural response of a single lap joint subjected to ultrasonic harmonic excitation is both numerically and experimentally evaluated to identify and characterize the defects within the bonded region. Different metallic samples with the same geometry were experimentally tested in order to characterize the debonding between two plates by using two surface bonded piezoelectric transducers in pitch-catch mode. The dynamic response of the damaged samples acquired by the single receiver sensor showed the presence of higher harmonics (2nd and 3rd) and subharmonics of the fundamental frequencies. These nonlinear elastic phenomena are clearly due to nonlinear effects induced by the poor adhesion between the two plates. A new constitutive model aimed at representing the nonlinear material response generated by the interaction of the ultrasonic waves with the adhesive joint is also presented. Such a model is implemented in an explicit FE software and uses a nonlinear user defined traction-displacement relationship implemented by means of a cohesive material user model interface. The developed model is verified for the different geometrical and material configurations. Good agreement between the experimental and numerical nonlinear response showed that this model can be used as a simple and useful tool for understanding the quality of the adhesive joint.
Inference for a Nonlinear Semimartingale Regression Model.
1987-11-01
increases. If K is left continuous and of bounded variation , then by integration by parts (see Dellacherie and Meyer (1982), Chapter VIII, (19.4)) for n...dt uniformly in z as n oo, where x- = .K 2(u) du. (c) Suppose A1-A3, B1, B2 hold, K is left continuous, of bounded variation and nwn - 00, nw 3 -3 0
Novel algorithm for constructing support vector machine regression ensemble
Institute of Scientific and Technical Information of China (English)
Li Bo; Li Xinjun; Zhao Zhiyan
2006-01-01
A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.
KRIGING-HDMR METAMODELING TECHNIQUE FOR NONLINEAR PROBLEMS%Kriging-HDMR非线性近似模型方法
Institute of Scientific and Technical Information of China (English)
汤龙; 李光耀; 王琥
2011-01-01
Some large-scale structural engineering problems need to be solved by metamodels.With the increasing of complexity and dimensionality,metamodeling techniques confront two major challenges.First, the size of sample points should be increase exponentially as the number of design variables increases.Second, it is difficult to give the explicit correlation relationships amongst design variables by popular metamodeling techniques.Therefore,a new high-dimension model representation（HDMR） based on the Kriging interpolation, Kriging-HDMR,is suggested in this paper.The most remarkable advantage of this method is its capacity to exploit relationships among variables of the underlying function.Furthermore,Kriging-HDMR can reduce the corresponding computational cost from exponential growth to polynomial level.Thus,the essence of the assigned problem could be presented efficiently.To prove the feasibility of this method,several high dimensional and nonlinear functions are tested.The algorithm is also applied to a simple engineering problem.Compared with the classical metamodeling techniques,the efficiency and accuracy are improved.%提出基于克里金（Kriging）插值的高维模型表示（high dimensional model representation,HDMR）方法,即Kriging-HDMR方法.Kriging-HDMR方法的最大优势在于：能够明确输入参数的耦合特性,将构造模型复杂度由指数级增长降阶为多项式级增长,进而用有限样本确定待求问题的物理实质.为了验证算法的建模性能,采用高维非线性函数成功地验证了该算法的可行性,并将该算法初步应用于简单的非线性工程问题,同传统算法相比,其精度和效率都得到了明显提升.
Directory of Open Access Journals (Sweden)
Fernanda Gomes da Silveira
2011-04-01
Full Text Available Este estudo teve como objetivo utilizar a análise de agrupamento para classificar modelos de regressão não-lineares usados para descrever a curva de crescimento de ovinos cruzados, tendo em vista os resultados de diferentes avaliadores de qualidade de ajuste. Para tanto, utilizaram-se dados de peso-idade dos seguintes cruzamentos entre raças de ovinos de corte: Dorper x Morada Nova, Dorper x Rabo Largo e Dorper x Santa Inês. Após a indicação do melhor modelo, objetivou-se ainda aplicar a técnica de identidade de modelos a fim de identificar o cruzamento mais produtivo. Foram ajustados doze modelos não-lineares, cuja qualidade de ajuste foi medida pelo coeficiente de determinação ajustado, critérios de informação de Akaike e Bayesiano, erro quadrático médio de predição e coeficiente de determinação de predição. A análise de agrupamento indicou o modelo Richards como o mais adequado para descrever as curvas de crescimento dos três grupos genéticos considerados, e os testes de identidade de modelos indicaram o cruzamento Dorper x Santa Inês como sendo o mais indicado para a pecuária local.This study had the objectives to use the cluster analysis in order to classify nonlinear regression models used to describe the growth curve in relation to different quality fit evaluators. Were utilized weight-age data from the following crossbred beef lambs Dorper x Morada Nova, Dorper x Rabo Largo e Dorper x Santa Inês. After the choice of the best model, we aimed also to apply the model identity in order to identify the most efficient crossbred group. Eleven nonlinear models were used, whose fit quality was measured by determination coefficient, Akaike information criterion, Bayesian information criterion, mean quadratic error of prediction and predicted determination coefficient. The cluster analysis indicated the Richards as the best model for the three data sets, and the model identity tests revealed that the Dorper x Santa In
Rasouli, Saifollah; Ghasemi, H; Tavassoly, M T; Khalesifard, H R
2011-06-01
In this paper, the application of "parallel" moiré deflectometry in measuring the nonlinear refractive index of materials is reported. In "parallel" moiré deflectometry the grating vectors are parallel, and the resulting moiré fringes are also parallel to the grating lines. Compared to "rotational" moiré deflectometry and the Z-scan technique, which cannot easily determine the moiré fringe's angle of rotation and is sensitive to power fluctuations, respectively, "parallel" moiré deflectometry is more reliable, which allows one to measure the radius of curvature of the light beam by measuring the moiré fringe spacing. The nonlinear refractive index of the sample, including the sense of the change, is obtained from the moiré fringe spacing curve. The method is applied for measuring the nonlinear refractive index of ferrofluids.
Institute of Scientific and Technical Information of China (English)
贾润达; 毛志忠; 常玉清
2009-01-01
提出了一种径向基函数网络(Radial basis function networks,RBFNs)与偏鲁棒M-回归(Partial robust M-regression,PRM)相结合的非线性PRM (Nonlinear PRM,NLPRM) 建模方法,用以解决鲁棒非线性系统建模问题.该方法首先通过RBF变换获得扩展的输入数据矩阵;接下来PRM算法通过反复迭代计算,自适应地为变换后的数据分配不同的连续权值,用以克服离群点对模型的影响.本文通过仿真实验,验证了方法的有效性;并将其应用于湿法冶金萃取过程萃余液pH值软测量建模问题,获得了相比于偏最小二乘法(Partial least squares,PLS)、PRM以及RBF-PLS方法更高的预测精度.
Institute of Scientific and Technical Information of China (English)
刘建平; 郑崇勋; 张崇
2009-01-01
Computing the Nonlinear regressive (NLR) coefficients of electroencephalogram (EEG) rhythms at different brain cortical areas for the mental fatigue caused by long term cognitive task, the variations of NLR coefficients of EEG rhythms under different mental fatigue level are sought out.The experimental results show that the NLR coefficients of EEG rhythms can effectively characterize the changes of amplitude coupling at different brain cortical areas under different mental fatigue level.The NLR coefficient provides a powerful tool for the EEG functional coupling analysis of mental fatigue.%本文通过对连续长时间脑力劳动前后状态下的脑电节律进行幅度耦合分析,提取了非线性回归系数,研究它们在不同中枢疲劳状态下的变化规律.实验结果表明,非线性回归系数能有效地反映出导联间幅度耦合同步程度随中枢疲劳程度的变化情况.为中枢疲劳脑电幅度耦合分析提供了有力工具.
Material Parameter Inverse Technique Based on Support Vector Regression%基于支持向量机回归的材料参数反求方法
Institute of Scientific and Technical Information of China (English)
李恩颖; 王琥; 李光耀
2012-01-01
The study on strain rate effect of advanced high stiffness steel (AHSS) for vehicle crashworthiness became the hot spot in this research field recent year. Different strain rate should lead to different influence. The accurate material parameter is the key important issue for reliable simulation. The direct material parameter identification methods commonly don't consider the crash effect. It might introduce the large errors. Therefore, a parameter inverse method by considering the crash effectis is proposed. Moreover, there are a lot of uncertain factors during inverse identification procedure. In order to enhance the efficiency and reliability of the proposed inverse method, the least square support vector regression (LSSVR)-based metamodeling is implemented for the inverse method. The LSSVR is a modeling algorithm based on structural risk minimal, therefore the reliability of the proposed inverse method can be promise. The metamodeling technique is used to improve the efficiency. According to the comparison between the data from experiments and inverse method, the suggested inverse method is proved to be a feasible technique for AHSS.%高强度钢的应变率效应对汽车件碰撞性能的影响研究是国际上的研究热点,不同类型的高强度钢将呈现出不同的应变率效应.因此,如何获取精确的材料参数是保证汽车碰撞计算机仿真结果可靠性的前提.如果直接通过标准拉伸试验获取相关材料参数,并没有考虑材料在碰撞过程中的特性,会引入较大的误差.为此,采用直接碰撞过程反求材料参数的方法,将参数反求的问题转换为测最值和仿真值最小二乘最小的优化问题.此外,由于参数反求中存在大量不确定性因素,为同时保证反求结果的稳健性和精度,采用基于最小二乘支持向量机回归技术的近似模型算法.近似模型技术保证了反求的效率、最小二乘支持向量机最大限度地保证了反求结果的精度
Bizon, Nicu; Mahdavi Tabatabaei, Naser
2014-01-01
This book explains and analyzes the dynamic performance of linear and nonlinear systems, particularly for Power Systems including Hybrid Power Sources. Offers a detailed description of system stability using state space energy conservation principle, and more.
Directory of Open Access Journals (Sweden)
Chi-Chang Wang
2013-09-01
Full Text Available This paper seeks to use the proposed residual correction method in coordination with the monotone iterative technique to obtain upper and lower approximate solutions of singularly perturbed non-linear boundary value problems. First, the monotonicity of a non-linear differential equation is reinforced using the monotone iterative technique, then the cubic-spline method is applied to discretize and convert the differential equation into the mathematical programming problems of an inequation, and finally based on the residual correction concept, complex constraint solution problems are transformed into simpler questions of equational iteration. As verified by the four examples given in this paper, the method proposed hereof can be utilized to fast obtain the upper and lower solutions of questions of this kind, and to easily identify the error range between mean approximate solutions and exact solutions.
Chen, Jianxin; Zhuo, Shuangmu; Luo, Tianshu; Liu, Dingzhong; Zhao, Jingjun
2008-08-01
Collagen and elastin are the most important proteins of the connective tissues in higher vertebrates. In this paper, we present a combined nonlinear optical imaging technique of second-harmonic generation and two-photon excited fluorescence to simultaneously observe the collagen and elastic fiber of dermis in a freshly excised human skin and rabbit aorta using a two-channel synchronized detection method. The obtained two-channel overlay image in the backward direction can clearly distinguish the morphological structure and distribution of collagen and elastic fibers. Tissue spectrum further confirms the obtained structural information. These results suggest that the combined nonlinear optical imaging technique coupled with two-channel synchronized detection method can be an effective tool for detecting collage and elastic fibers without any invasive tissue procedure of slicing, embedding, fixation and staining when two structural proteins are simultaneously present in the biological tissue.
Common pitfalls in statistical analysis: Logistic regression.
Ranganathan, Priya; Pramesh, C S; Aggarwal, Rakesh
2017-01-01
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
Meta-Regression: A Framework for Robust Reactive Optimization
DEFF Research Database (Denmark)
McClary, Dan; Syrotiuk, Violet R.; Kulahci, Murat
2007-01-01
Maintaining optimal performance as the conditions of a system change is a challenging problem. To solve this problem, we present meta-regression, a general methodology for alleviating traditional difficulties in nonlinear regression modelling. Meta-regression allows for reactive optimization, in ...... of a nonlinear system....
Institute of Scientific and Technical Information of China (English)
李清; 任朝阳
2016-01-01
Chaos theory suggested that most of human behavior appeared to be non‐linear .Accounting fraud belonged to the field of behavior accounting . Traditionally ,fraud identification model based on statistical theory limited to linear constraint assumptions mostly . T here may be such defects as model specification errors and information extraction insufficiently . It chose Shanghai and Shenzhen A‐share listed companies subject to regulatory sanctions and matching companies as samples . Based on the nonlinear ideology of Taylor expansion , and the principal component analysis to eliminate variables multicollinearity ,it constructed a nonlinear‐principal component Logistic regression of accounting fraud recognition model . The model has a higher recognition accuracy ratio , more reliability on parameter estimation and has higher goodness of fit than the linear regression model .The model is helpful to extract fraud identification information more fully ,and improves the efficiency of fraud identification .%混沌理论认为，人类行为大多具有非线性特征。会计舞弊属于行为会计的研究范畴，而传统上基于统计理论构建的舞弊识别模型大多受限于线性约束假设，可能存在模型设定偏误和信息提取不充分的缺陷。以沪深A股受到监管处罚的上市公司及其配对公司为样本，借鉴Taylor展开式的非线性思想，并使用主成分分析消除变量多重共线性，构建了非线性－主成分Logistic回归的会计舞弊识别模型。与线性回归模型对比发现，前者具有更高的舞弊识别正确率，模型拟合度更优。应用这一模型有助于更加充分提取舞弊识别信息，提高舞弊识别效率。
Institute of Scientific and Technical Information of China (English)
朱慧明; 周峰; 曾昭法; 李荣; 游万海
2015-01-01
In the method of testing smooth transition cointegration, estimating parameters are uncertain and the problem of cointegration test is complex.This paper proposes a smooth transition regression model and conducts a Bayesian nonlinear cointegration analysis.Based on the selection of parameters prior of the model and the charac-teristics of the posterior conditional distributions of the parameters, Metropolis-Hasting within Gibbs sampling algorithm is designed to estimate the parameters and bayesian unit root test is utilized to test the stationarity of regression residual, addressing the uncertainty of parameters estimation and the complexity of cointegration test. At the same time, the research applies exchange rate of RMB against U.S.dollar and interest rate differential between China and U.S.to conduct an empirical analysis.The research outcome indicates that MH-Gibbs can effectively a estimate the parameters of the smooth transition model, and we find there is smooth transition cointe-gration relationship between exchange rate fluctuation and interest rate differential.%针对平滑转移模型参数估计不确定性导致的协整检验方法相对复杂问题，提出基于平滑转移模型的贝叶斯非线性协整分析。通过模型的统计结构分析，选择参数先验分布，结合参数的后验条件分布特征设计Me-tropolis-Hasting-Gibbs混合抽样方案，据此估计平滑转移模型的参数，并对回归残差进行贝叶斯单位根检验，解决参数估计过程中遇到的参数估计不确定性及协整检验复杂的问题；利用人民币对美元汇率与中美两国的利率数据进行实证分析。研究结果表明：MH-Gibbs抽样方案能够有效估计平滑转移模型的参数，中美汇率波动和利差之间存在平滑转移协整关系。
Institute of Scientific and Technical Information of China (English)
侯文; 宋立新; 黄玉洁
2012-01-01
考察了响应变量在随机删失情形下的非线性半参数回归模型,构造了未知参数的经验对数似然比统计量和调整经验对数似然比统计量,证明在一定条件下,所构造的经验似然比统计量渐近于x2分布,并由此构造出未知参数的置信域.此外,又构造了未知参数的最小二乘估计量,证明了它的渐近性质.通过模拟研究表明,经验似然方法在置信域的覆盖概率以及精度方面要优于最小二乘法.%In this paper, a censored nonlinear semiparametric regression model is investigated. Empirical log-likelihood ratio statistics and adjust empirical log-likelihood ratio statistics for the unknown parameters in the model are suggested. It is shown that the proposed statistics have asymptotically chi-squared distribution under some mild conditions, and hence it can be used to construct the confidence region of the unknown parameter. In addition the least squares estimator of unknown parameter is constructed, and its asymptotic behavior is proved. A simulation study is carried out to show the empirical likelihood methods appears to be better than the least-squares method in terms of the confidence regions and its coverage probabilities.
Institute of Scientific and Technical Information of China (English)
牛翔宇; 冯予
2016-01-01
研究了数据缺失情况下广义非线性回归模型的统计诊断问题；在响应变量随机缺失的情况下，先利用经验似然方法进行参数估计，得到其渐近置信区间，并通过随机模拟比较出经验似然方法比一般方法求置信区间的优越性；对模型进行影响分析，提出经验似然距离、经验Cook距离以及标准化残差等诊断统计量，最后通过实例验证统计诊断方法的有效性和可行性。%This paper studies the diagnosis problems of generalized nonlinear regression model under data missing, under random missing of response variables, firstly uses empirical likelihood method to conduct parameter estimation, obtains its asymptotic confidence interval, then through random simulation and comparison, gets that empirical likelihood method is more superior than general methods in solving the asymptotic confidence interval, based on the analysis of the impact of the model, proposes the diagnosis statistical data such as empirical likelihood distance, empirical Cook distance, and standardized pseudo⁃residuals and finally uses examples to verify the effectiveness and feasibility of the statistical diagnosis method.
Nonlinear optical properties of cobalt and iron doped CdSe nanoparticles using Z-scan technique
Energy Technology Data Exchange (ETDEWEB)
Gaur, Poonam, E-mail: poonam.gaur612@gmail.com [Department of Physics, Deenbandhu Chhotu Ram University of Science and Technology, Sonipat 131001, Haryana (India); Malik, B.P. [Department of Physics, Deenbandhu Chhotu Ram University of Science and Technology, Sonipat 131001, Haryana (India); Gaur, Arun [Department of Physics, Hindu College, Sonipat 131001, Haryana (India)
2015-01-15
The present work aims at the synthesis of pure, Cobalt (Co) and Iron (Fe) doped CdSe nanoparticles by the wet chemical method. The optical properties of synthesized nanoparticles have been characterized by X-ray diffraction (XRD), UV–vis spectroscopy to find the optical direct band gap and estimation of particle size by using Debye–Scherrer formula and HRTEM. The nonlinear optical properties such as nonlinear absorption co-efficient, nonlinear refraction co-efficient and third order nonlinear susceptibility χ{sup (3)} are investigated. The calculations have been performed with the help of Z-scan experimental set-up using Nd: YAG laser emitting 532 nm, 5 ns laser pulses with intensity maintained at 2.296 TW/cm{sup 2}. The nanoparticles clearly exhibit a negative value of nonlinear refraction, which is attributed to the two photon absorption and free carrier absorption. Further the optical limiting behavior is determined (figure of merit (FOM)). The presence of RSA in these nanoparticles makes them a potential material for the development of optical limiter.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Energy Technology Data Exchange (ETDEWEB)
Baik, I.C.; Kim, K.H.; Cho, K.Y.; Youn, M.J. [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)
1998-04-01
A DSP-based robust nonlinear speed control of a permanent magnet synchronous motor (PMSM) which is robust to unknown parameter variations and speed measurement error is presented. The model reference adaptive system (MRAS) based adaptation mechanisms for the estimation of slowly varying parameters are derived using the Lyapunov stability theory. For the disturbances or quickly varying parameters, a quasi-linearized and decoupled model including the influence of parameter variations and speed measurement error on the nonlinear speed control of a PMSM is derived. Based on this model, a boundary layer integral sliding mode controller to improve the robustness and performance of the nonlinear speed control of a PMSM is designed and compared with the conventional controller. To show the validity of the proposed control scheme, simulations and experimental works are carried out and compared with the conventional control scheme. (author). 19 refs., 14 figs., 6 tabs.
Zidan, M. D.; Al-Ktaifani, M. M.; Allahham, A.
2017-05-01
Z-scan measurements were performed with a CW diode laser at 635 nm to investigate the nonlinear optical properties of Tris(2‧,2-bipyridyl)iron(II) tetrafluoroborate in ethanol at two concentrations. Theoretical fit was carried out to evaluate the nonlinear absorption coefficient (β) and the negative nonlinear refractive index (n2) for the studied complex. Furthermore, the ground-state absorption cross sections (σg), the excited-state absorption cross sections (σex) and thermo-optic coefficient were also estimated. The investigations show large NLO response, which is predominantly associated with substantial conjugation between the aromatic ring π-electron system and d-electron set metal center. The obtained results give a strong indication that Tris(2‧,2-bipyridyl)iron(II) tetrafluoroborate have a potential application in optical domain.
Rodríguez-Rosales, A. A.; Ortega-Martínez, R.; Morales-Saavedra, O. G.
2011-01-01
The study of the nonlinear refractive index response γ of several organic dyes and their impact on the nonlinear optical (NLO) properties of nematic liquid crystals (LC) was performed via Z-scan measurements. For his purpose, a low power CW He-Ne laser system (λ approx 633 nm) was implemented. Studies were carried out at the low absorption spectroscopic region of the implemented samples (dyes, liquid crystals and mixtures at different ratios of these materials). Samples were prepared at 1% weight of the used solvent (THF) and were sandwiched in glass cells with a gap thickness of ~100 μm. The implemented dyes have shown the largest optical nonlinearities and represent the main contributors to the cubic NLO-properties of the LC:Dye mixtures. In our particular studies, 5CB liquid crystal doped with DR1 azo-dye, resulted in the simultaneous positive and negative exhibition of nonlinear refractive indexes γ, depending on the polarization state of the excitation laser beam. Experimental conditions and results are described in detail.
Directory of Open Access Journals (Sweden)
Carolina Monari
2009-05-01
Full Text Available This paper is part of a comprehensive study to assess the suitability of the use of multivariate statistical techniques in theses and dissertations of some higher education institutions in the marketing area on the topic of consumer behavior between 1997 and 2006. In this article the logistic regression analysis is focused, a technique with great potential for use in marketing studies. This study aimed at analyzing the suitability of this technique to the needs of research problems presented in the theses and dissertations and measuring the level of success in meeting their premises. Overall, the results suggest the need for an increased commitment of researchers to verify all theoretical rules of application of the logistic regression analysis technique.Este artigo faz parte de um amplo estudo de avaliação da adequação no uso de técnicas estatísticas multivariadas em teses e dissertações de algumas instituições de ensino superior na área de marketing na temática do comportamento do consumidor entre 1997 e 2006. Neste artigo é focalizada a análise de regressão logística, uma técnica com grande potencial de uso em estudos de marketing. Foi objetivo deste trabalho a análise da adequação do emprego desta técnica às necessidades dos problemas de pesquisa apresentados nas teses e dissertações e a aferição do nível de acerto no atendimento de suas premissas. De forma geral, os resultados sugerem a necessidade de um aumento do comprometimento dos pesquisadores na verificação de todos os preceitos teóricos de aplicação da técnica de análise de regressão logística.
Tunusluoglu, M. C.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.
2008-03-01
Debris flow is one of the most destructive mass movements. Sometimes regional debris flow susceptibility or hazard assessments can be more difficult than the other mass movements. Determination of debris accumulation zones and debris source areas, which is one of the most crucial stages in debris flow investigations, can be too difficult because of morphological restrictions. The main goal of the present study is to extract debris source areas by logistic regression analyses based on the data from the slopes of the Barla, Besparmak and Kapi Mountains in the SW part of the Taurids Mountain belt of Turkey, where formation of debris material are clearly evident and common. In this study, in order to achieve this goal, extensive field observations to identify the areal extent of debris source areas and debris material, air-photo studies to determine the debris source areas and also desk studies including Geographical Information System (GIS) applications and statistical assessments were performed. To justify the training data used in logistic regression analyses as representative, a random sampling procedure was applied. By using the results of the logistic regression analysis, the debris source area probability map of the region is produced. However, according to the field experiences of the authors, the produced map yielded over-predicted results. The main source of the over-prediction is structural relation between the bedding planes and slope aspects on the basis of the field observations, for the generation of debris, the dip of the bedding planes must be taken into consideration regarding the slope face. In order to eliminate this problem, in this study, an approach has been developed using probability distribution of the aspect values. With the application of structural adjustment, the final adjusted debris source area probability map is obtained for the study area. The field observations revealed that the actual debris source areas in the field coincide with
Ciotoli, G; Voltaggio, M; Tuccimei, P; Soligo, M; Pasculli, A; Beaubien, S E; Bigi, S
2017-01-01
In many countries, assessment programmes are carried out to identify areas where people may be exposed to high radon levels. These programmes often involve detailed mapping, followed by spatial interpolation and extrapolation of the results based on the correlation of indoor radon values with other parameters (e.g., lithology, permeability and airborne total gamma radiation) to optimise the radon hazard maps at the municipal and/or regional scale. In the present work, Geographical Weighted Regression and geostatistics are used to estimate the Geogenic Radon Potential (GRP) of the Lazio Region, assuming that the radon risk only depends on the geological and environmental characteristics of the study area. A wide geodatabase has been organised including about 8000 samples of soil-gas radon, as well as other proxy variables, such as radium and uranium content of homogeneous geological units, rock permeability, and faults and topography often associated with radon production/migration in the shallow environment. All these data have been processed in a Geographic Information System (GIS) using geospatial analysis and geostatistics to produce base thematic maps in a 1000 m × 1000 m grid format. Global Ordinary Least Squared (OLS) regression and local Geographical Weighted Regression (GWR) have been applied and compared assuming that the relationships between radon activities and the environmental variables are not spatially stationary, but vary locally according to the GRP. The spatial regression model has been elaborated considering soil-gas radon concentrations as the response variable and developing proxy variables as predictors through the use of a training dataset. Then a validation procedure was used to predict soil-gas radon values using a test dataset. Finally, the predicted values were interpolated using the kriging algorithm to obtain the GRP map of the Lazio region. The map shows some high GRP areas corresponding to the volcanic terrains (central
1984-09-01
scores of 0.26 ( unad - justed, 0.32). This peak score was achieved with the inclu- sion of the first predictor. In this case, the first selected predictor...models. The results of these models are very similar with the EVAR model yielding an independent adjusted VISCAT I threat score of 0.17 ( unad - justed...68.91%) for MAXPROB I and -16.87% ( unad - justed, 61.78%) for natural regression. Fig. 38 shows the relationship of equally populous grouping size to
Directory of Open Access Journals (Sweden)
Mehmet Arslan
2010-06-01
Full Text Available The objective of the study is to evaluate risk- reward relationship and relative performances of the 4 different groups of mutual funds. To this end, daily return data of these 12 mutual funds (3 type variable fund; 3 B type variable fund; 3 A type stock fund and 3 A type Exchange traded fund together with daily market index (imkb100 return and daily return of riskless rate for the period from January 2006 to Feb 2010. The 180-day maturity T-Bill has been selected to represent riskless rate. To determine performances of mutual funds; Sharpe ratio, M2 measure, Treynor index, Jensen index, Sortino ratio, T2 ratio, Valuation ratio has been applied and these indicators produced conflicting results in ranking mutual funds. Then timingand selection capability of the fund manager has been determined by applying simple regression and Quadratic regression. Interestingly all funds found to have positive coefficient, indicating positive election capability of managers; but in terms of timing capability only one fund managers showed success. Finally, to determine extent to which mean returns are differs between mutual funds, market index (imkb100 and riskless rate (180 day TBill results of the analysis revealed that mean returns of individual security returns differs at P≤0,01 level. That shows instability in returns and poor ex-ante forecast modeling capability.
Hu, Qinghua; Zhang, Shiguang; Xie, Zongxia; Mi, Jusheng; Wan, Jie
2014-09-01
Support vector regression (SVR) techniques are aimed at discovering a linear or nonlinear structure hidden in sample data. Most existing regression techniques take the assumption that the error distribution is Gaussian. However, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy Gaussian distribution, but a beta distribution, Laplacian distribution, or other models. In these cases the current regression techniques are not optimal. According to the Bayesian approach, we derive a general loss function and develop a technique of the uniform model of ν-support vector regression for the general noise model (N-SVR). The Augmented Lagrange Multiplier method is introduced to solve N-SVR. Numerical experiments on artificial data sets, UCI data and short-term wind speed prediction are conducted. The results show the effectiveness of the proposed technique.
Institute of Scientific and Technical Information of China (English)
张庆丰; 张浩; 陆彪
2016-01-01
以钢渣作为研究对象，采用水玻璃、氢氧化钠与氢氧化钙三元复合活化剂，制备碱钢渣胶凝材料。基于均匀设计和多元非线性回归法研究了各因素对碱钢渣胶凝材料力学性能的影响。结果表明，各因素对性能影响的主次顺序为：3 d时钢渣用量＞氢氧化钠用量＞水玻璃用量＞氢氧化钙用量，7 d时钢渣用量＞水玻璃用量＞氢氧化钠用量＞氢氧化钙用量，28 d时钢渣用量＞水玻璃用量＞氢氧化钙用量＞氢氧化钠用量；28 d碱钢渣胶凝材料的优化制备方案为：钢渣用量为225 g，水玻璃用量为22.5 g，氢氧化钠用量为9.0 g，氢氧化钙用量为13.2 g；优化制备模型选择正确，其相对误差仅为2.19%。%Alkaline steel slag cement materials were prepared with steel slag as the research object, sodium silicate, sodium hydroxide and calcium hydroxide as the ternary compound activator. The effect of every factor on mechanical property of alkaline steel slag cement materials was studied by orthogonal design and multivariate nonlinear regression. The results show that primary and secondary sequence of factors is steel slag dosage>sodium hydroxide dosage>sodium silicate dosage>calcium hydroxide dosage in 3 d, steel slag dosage>sodium silicate dosage>sodium hydroxide dosage>calcium hydroxide dosage in 7 d, steel slag dosage>sodium silicate dosage>calcium hydroxide dosage>sodium hydroxide dosage in 28 d. The optimization program of alkaline steel slag cement materials in 28 d is steel slag dosage 225 g, sodium silicate dosage 22.5 g, sodium hydroxide dosage 9.0 g and calcium hydroxide dosage 13.2 g. Optimized preparation model is correct, its relative error is only 2.19%.
Institute of Scientific and Technical Information of China (English)
王玉华; 惠晓峰; 李敦亮
2014-01-01
本文应用非线性平滑转换回归模型研究了2002年1月份到2011年12月份我国与美国、欧元区、日本、韩国等有效汇率指数、综合利差之间的关系。实证分析表明，汇率对利率的影响具有明显的非对称性，具有较强的非线性转移动态特征。分国别看，四个国家或地区之间的上期利差均是影响本期利差的重要因素；在短期内汇率对利率影响较大。因此，短期内人民币汇率弹性的扩大应该主动、逐步、稳定进行，防止人民币汇率弹性的急剧扩大导致利率的过度波动。其次，逐步有序加快利率市场化进程并加强与汇率市场化的配合，构建高效的汇率-利率联动机制。%In order to analyze the relationship of interest rate and effective exchange rate , this paper uses the Smooth Transition Regression Model and then chooses the monthly data of these two variables of China 、USA、Eu-rozone、Japan and Korea from Jan .2002 to Dec.2011.The results show that exchange rate has a nonlinear influ-ence on interest rate and obvious asymmetry .The last month interest rate and exchange rate have an important influence on interest rate in these four countries .Based on this , in order to prevent the flexibility of exchange rate surge caused by excessive fluctuations of the interest rate , we should progressively and stablely take the initi-ative to expand the flexibility of exchange rate in the short term .Secondly , we should gradually speed up the process of interest rate and exchange rate co-ordination liberalization ,and build an efficient linkage system .
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Energy Technology Data Exchange (ETDEWEB)
Skokos, Ch., E-mail: haris.skokos@uct.ac.za [Physics Department, Aristotle University of Thessaloniki, GR-54124 Thessaloniki (Greece); Department of Mathematics and Applied Mathematics, University of Cape Town, Rondebosch 7701 (South Africa); Gerlach, E. [Lohrmann Observatory, Technical University Dresden, D-01062 Dresden (Germany); Bodyfelt, J.D., E-mail: J.Bodyfelt@massey.ac.nz [Centre for Theoretical Chemistry and Physics, The New Zealand Institute for Advanced Study, Massey University, Albany, Private Bag 102904, North Shore City, Auckland 0745 (New Zealand); Papamikos, G. [School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7NF (United Kingdom); Eggl, S. [IMCCE, Observatoire de Paris, 77 Avenue Denfert-Rochereau, F-75014 Paris (France)
2014-05-01
While symplectic integration methods based on operator splitting are well established in many branches of science, high order methods for Hamiltonian systems that split in more than two parts have not been studied in great detail. Here, we present several high order symplectic integrators for Hamiltonian systems that can be split in exactly three integrable parts. We apply these techniques, as a practical case, for the integration of the disordered, discrete nonlinear Schrödinger equation (DDNLS) and compare their efficiencies. Three part split algorithms provide effective means to numerically study the asymptotic behavior of wave packet spreading in the DDNLS – a hotly debated subject in current scientific literature.
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Pedrini, D. T.; Pedrini, Bonnie C.
Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…
Directory of Open Access Journals (Sweden)
Edenio Detmann
2011-12-01
mathematical and statistical procedures were developed using a database composed of measurements of dry matter intake and average daily gain of 380 pure or crossbred zebu animals in 15 different experiments in the database from the BR-CORTE nutritional system. Two approaches were presented, based on linear and non-linear regression, with average daily gain and dry matter intake used as independent and dependent variables, respectively. The feed utilization by the animal was stratified into maintenance demand and real efficiency of conversion into product. The maintenance demand presents similar interpretation in both approaches. When linear solution is considered, the real efficiency of product conversion is assumed to be steady and independent on average daily gain. On the other hand, when the non-linear solution is adopted, the real efficiency of product conversion is assumed to be variable and dependent on gain. Alaike information criterion was proposed to decide which model suits best. The evaluation of the efficiency of transformation of feed into product, in beef cattle, by regression techniques allows a broader understanding of system efficiency for it enables stratification of maintenance and production demands. Several inconvenient aspects concerning ratio indexes, such as food conversion ratio, are avoided and the nutritional inferences become more accurate and precise.
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.
Sharqawy, Mostafa H.
2016-12-01
Pore network models (PNM) of Berea and Fontainebleau sandstones were constructed using nonlinear programming (NLP) and optimization methods. The constructed PNMs are considered as a digital representation of the rock samples which were based on matching the macroscopic properties of the porous media and used to conduct fluid transport simulations including single and two-phase flow. The PNMs consisted of cubic networks of randomly distributed pores and throats sizes and with various connectivity levels. The networks were optimized such that the upper and lower bounds of the pore sizes are determined using the capillary tube bundle model and the Nelder-Mead method instead of guessing them, which reduces the optimization computational time significantly. An open-source PNM framework was employed to conduct transport and percolation simulations such as invasion percolation and Darcian flow. The PNM model was subsequently used to compute the macroscopic properties; porosity, absolute permeability, specific surface area, breakthrough capillary pressure, and primary drainage curve. The pore networks were optimized to allow for the simulation results of the macroscopic properties to be in excellent agreement with the experimental measurements. This study demonstrates that non-linear programming and optimization methods provide a promising method for pore network modeling when computed tomography imaging may not be readily available.
Smirnov, A. M.; Golinskaya, A. D.; Ezhova, K.; Kozlova, M.; Stebakova, J. V.; Valchuk, Y. V.
2017-05-01
One-dimensional dynamic photonic crystal was formed by a periodic spatial modulation of dielectric permittivity induced by the two ultrashort laser pulses interference in semiconductor quantum dots CdSe/ZnS (QDs) colloidal solution intersecting at angle θ. The fundamental differences of dynamic photonic crystals from static ones which determine the properties of these transient structures are the following. I. Dynamic photonic crystals lifetimes are determined by the nature of nonlinear changes of dielectric permittivity. II. The refractive index changing is determined by the intensity of the induced standing wave maxima and nonlinear susceptibility of the sample. We use the pump and probe method to create the dynamic one-dimensional photonic crystal and to analyze its features. Two focused laser beams are the pump beams, that form in the colloidal solution of quantum dots dynamic one-dimensional photonic crystal. The picosecond continuum, generated by the first harmonic of laser (1064 nm) passing through a heavy water is used as the probe beam. The self-diffraction of pumping beams on self induced dynamic one-dimensional photonic crystal provides information about spatial combining of laser beams.
Prediction accuracy and stability of regression with optimal scaling transformations
Kooij, van der Anita J.
2007-01-01
The central topic of this thesis is the CATREG approach to nonlinear regression. This approach finds optimal quantifications for categorical variables and/or nonlinear transformations for numerical variables in regression analysis. (CATREG is implemented in SPSS Categories by the author of the thesi
Arunkumar, K.; Kalainathan, S.
2017-03-01
An organic nonlinear optical (NLO) material 1,3-bis(4-methoxyphenyl)prop-2-en-1-one (BMP) single crystal has been successfully grown by vertical Bridgman stockbarger technique (VBT) using single wall ampoule. The grown crystal was subjected to single-crystal X-ray diffraction analysis (SXRD) to confirm the cell parameters and powder X-ray diffraction analysis (PXRD) to confirm the crystallinity. FTIR analyses were carried to identify the functional groups. The UV-Vis spectrum of BMP showed the lower optical cut off at 435 nm and is transparent in the visible region. The mechanical property of the titled crystal is analyzed by using microhardness measurements. Laser damage threshold energy was determined using Nd: YAG laser (1064 nm). The photoconductivity study of BMP reveals the positive photoconducting nature. The NLO property of the grown crystal confirmed by Kurtz and Perry powder technique and the SHG efficiency of the grown crystal was obtained to be 1.04 times greater than Urea. Z-scan studies calculated the third order nonlinear optical parameters like refractive index (n2), the absorption coefficient (β) and third order susceptibility (χ3).
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.
Logistic Regression for Evolving Data Streams Classification
Institute of Scientific and Technical Information of China (English)
YIN Zhi-wu; HUANG Shang-teng; XUE Gui-rong
2007-01-01
Logistic regression is a fast classifier and can achieve higher accuracy on small training data. Moreover,it can work on both discrete and continuous attributes with nonlinear patterns. Based on these properties of logistic regression, this paper proposed an algorithm, called evolutionary logistical regression classifier (ELRClass), to solve the classification of evolving data streams. This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier, to keep this classifier if its performance is deteriorated by the reason of bursting noise, or to construct a new classifier if a major concept drift is detected. The intensive experimental results demonstrate the effectiveness of this algorithm.
Wrong Signs in Regression Coefficients
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
Regression analysis by example
National Research Council Canada - National Science Library
Chatterjee, Samprit; Hadi, Ali S
2012-01-01
.... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...
Energy Technology Data Exchange (ETDEWEB)
Cassano, T [Department of Physics and CNR-INFM, University of Bari, Via Amendola 173, 70126 Bari (Italy); Tommasi, R [Department of Medical Biochemistry, Biology, and Physics, University of Bari, Piazza G. Cesare 11, 70124 Bari (Italy); Arca, M [Dipartimento di Chimica Inorganica ed Analitica, S.S. 554 Bivio per Sestu, 09042 Monserrato-Cagliari (Italy); Devillanova, F A [Dipartimento di Chimica Inorganica ed Analitica, S.S. 554 Bivio per Sestu, 09042 Monserrato-Cagliari (Italy)
2006-06-14
The nonlinear optical absorption of two neutral metal dithiolenes [M(Et{sub 2}timdt){sub 2}] (M = Pd and Pt) has been investigated at {lambda} = 1064 nm by the open-aperture Z-scan technique using nanosecond and picosecond pulses. For picosecond photoexcitation, both dithiolenes mainly exhibit saturable absorption. Conversely, using nanosecond pulses, a switch from saturable absorption to reverse saturable absorption has been observed depending on the central metal. Based on experimental results, energy-level structures are suggested to explain the nonlinear absorption for both temporal regimes and, in particular, the sign-reversal of nonlinear absorption.
Directory of Open Access Journals (Sweden)
A. Zakery
2005-03-01
Full Text Available Chalcogenide glasses such as arseic sulfide(As2 S3 have attracted attention for applications such as all-optical switching in high speed communication. This is due to their high non-linear refractive-index. Z-scan and the Degenerate four wave mixing (DFWM techniques can be used to measure the non-linear refractive index n 2 and the two photon absorption coefficient β . A simaltanous closed-aperture and open-aperture Z-scan experimental set up was used to obtain the experimental results. The results were then fitted into a theoretical formula. Values of n2=3×10-17m2/W and β= 0.29 cm/GW have been obtained. DFWM measurements were made on arsenic sulfide films. A Box-cars forward geometry was used in these measurements. Experimental results based on non-phase matched signals were again fitted into a theoretical formula and a value of n2 =3.9×10-17 m2 /W was obtained .
Jiang, Yi; Li, Guo-Yang; Qian, Lin-Xue; Hu, Xiang-Dong; Liu, Dong; Liang, Si; Cao, Yanping
2015-02-01
Dynamic elastography has become a new clinical tool in recent years to characterize the elastic properties of soft tissues in vivo, which are important for the disease diagnosis, e.g., the detection of breast and thyroid cancer and liver fibrosis. This paper investigates the supersonic shear imaging (SSI) method commercialized in recent years with the purpose to determine the nonlinear elastic properties based on this promising technique. Particularly, we explore the propagation of the shear wave induced by the acoustic radiation force in a stressed hyperelastic soft tissue described via the Demiray-Fung model. Based on the elastodynamics theory, an analytical solution correlating the wave speed with the hyperelastic parameters of soft tissues is first derived. Then an inverse approach is established to determine the hyperelastic parameters of biological soft tissues based on the measured wave speeds at different stretch ratios. The property of the inverse method, e.g., the existence, uniqueness and stability of the solution, has been investigated. Numerical experiments based on finite element simulations and the experiments conducted on the phantom and pig livers have been employed to validate the new method. Experiments performed on the human breast tissue and human heel fat pads have demonstrated the capability of the proposed method for measuring the in vivo nonlinear elastic properties of soft tissues. Generalization of the inverse analysis to other material models and the implication of the results reported here for clinical diagnosis have been discussed. Copyright © 2014 Elsevier B.V. All rights reserved.
Yamagishi, Hideki; Fukuhara, Mikio
2015-11-01
Three acoustic probe configurations were used to assess cyclic-tension fatigue in SS400 steel at room temperature via a diffracted horizontally polarized shear wave (SH) transmission method. Linear analysis of the propagation time and amplitude of shear and longitudinal waves with fatigue progression revealed that the linear behavior was governed by residual stress, attributed to the acoustoelastic effect. Specifically, the propagation time of the shear waves increased and the wave amplitude decreased with fatigue progression. Our results also revealed that the propagation paths of the waves became deeper with progressive fatigue. Additionally, when the probe angle was optimized for diffraction, the estimated change in the length prior to fatigue breakage was 0.61 pct. Nonlinear analysis results revealed that second harmonic β-parameters increased as fatigue progressed, up to ~800 pct for the optimal frequency configuration; this was attributed to an increase in the number of dislocation-associated viscoelastic effects. The proposed approach shows great potential for nondestructive evaluation of metal fatigue via parameter analysis of residual stress and dislocation variations.
Rajamannan, B; Mugundan, S; Viruthagiri, G; Praveen, P; Shanmugam, N
2014-01-24
In general, the nanoparticles of TiO2 may exist in the phases of anatase, rutile and brookite. In the present work, we used titanium terta iso propoxide and 2-propanol as a common starting material to prepare the precursors of bare and copper doped nanosized TiO2. Then the synthesized products were calcinated at 500°C and after calcination the pure TiO2 nanoparticles in anatase phase were harvested. The crystallite sizes of bare and copper doped TiO2 nanoparticles were calculated from X-ray diffraction analysis. The existence of functional groups of the samples was identified by Fourier transform infrared spectroscopy. The optical properties of bare and doped samples were carried out using UV-DRS and photoluminescence measurements. The surface morphology and the element constitution of the copper doped TiO2 nanoparticles were studied by scanning electron microscope fitted with energy dispersive X-ray spectrometer arrangement. The nonlinear optical properties of the products were confirmed by Kurtz second harmonic generation (SHG) test and the output power generated by the nanoparticle was compared with that of potassium di hydrogen phosphate (KDP). Copyright © 2013 Elsevier B.V. All rights reserved.
Magga, Zoi; Tzovolou, Dimitra N.; Theodoropoulou, Maria A.; Tsakiroglou, Christos D.
2012-03-01
The risk assessment of groundwater pollution by pesticides may be based on pesticide sorption and biodegradation kinetic parameters estimated with inverse modeling of datasets from either batch or continuous flow soil column experiments. In the present work, a chemical non-equilibrium and non-linear 2-site sorption model is incorporated into solute transport models to invert the datasets of batch and soil column experiments, and estimate the kinetic sorption parameters for two pesticides: N-phosphonomethyl glycine (glyphosate) and 2,4-dichlorophenoxy-acetic acid (2,4-D). When coupling the 2-site sorption model with the 2-region transport model, except of the kinetic sorption parameters, the soil column datasets enable us to estimate the mass-transfer coefficients associated with solute diffusion between mobile and immobile regions. In order to improve the reliability of models and kinetic parameter values, a stepwise strategy that combines batch and continuous flow tests with adequate true-to-the mechanism analytical of numerical models, and decouples the kinetics of purely reactive steps of sorption from physical mass-transfer processes is required.
Phase retrieval using nonlinear diversity.
Lu, Chien-Hung; Barsi, Christopher; Williams, Matthew O; Kutz, J Nathan; Fleischer, Jason W
2013-04-01
We extend the Gerchberg-Saxton algorithm to phase retrieval in a nonlinear system. Using a tunable photorefractive crystal, we experimentally demonstrate the noninterferometric technique by reconstructing an unknown phase object from optical intensity measurements taken at different nonlinear strengths.
Energy Technology Data Exchange (ETDEWEB)
YATES,GEORGE J.; MCDONALD,THOMAS E. JR.; BLISS,DAVID E.; CAMERON,STEWART M.; GREIVES,KENNETH H.; ZUTAVERN,FRED J.
2000-12-20
Laboratory experiments utilizing different near-infrared (NIR) sensitive imaging techniques for LADAR range gated imaging at eye-safe wavelengths are presented. An OPO/OPA configuration incorporating a nonlinear crystal for wavelength conversion of 1.56 micron probe or broadcast laser light to 807 nm light by utilizing a second pump laser at 532 nm for gating and gain, was evaluated for sensitivity, resolution, and general image quality. These data are presented with similar test results obtained from an image intensifier based upon a transferred electron (TE) photocathode with high quantum efficiency (QE) in the 1-2 micron range, with a P-20 phosphor output screen. Data presented include range-gated imaging performance in a cloud chamber with varying optical attenuation of laser reflectance images.
Ju, Taeho; Achenbach, Jan D.; Jacobs, Laurence J.; Qu, Jianmin
2017-02-01
In this study, we developed a non-collinear mixing technique to measure the Acoustic Nonlinearity Parameter (ANLP) of adhesive bonds. One of the most significant features of the new method is that it requires only one-side access to the adhesive bond being measured, which significantly increases its utility in field measurements. To demonstrate the effectiveness of the newly developed technique, an adhesively jointed aluminum sample was measured with different thermal aging times, using the non-collinear mixing technique with a longitudinal and a shear wave as incident waves to obtain the ANLP of the adhesive bond. The measured results clearly show that the ANLP varies with aging time. To verify that the signals received from the shear wave receiver are indeed the mixing wave, the finite element method was used to simulate the wave motion in the test sample. The simulation results clearly show that the signals recorded by the shear wave receiver are the desired mixing wave, whose amplitude is proportional to the ANLP of the adhesive bond.
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...
Beardsell, Alec; Collier, William; Han, Tao
2016-09-01
There is a trend in the wind industry towards ever larger and more flexible turbine blades. Blade tip deflections in modern blades now commonly exceed 10% of blade length. Historically, the dynamic response of wind turbine blades has been analysed using linear models of blade deflection which include the assumption of small deflections. For modern flexible blades, this assumption is becoming less valid. In order to continue to simulate dynamic turbine performance accurately, routine use of non-linear models of blade deflection may be required. This can be achieved by representing the blade as a connected series of individual flexible linear bodies - referred to in this paper as the multi-part approach. In this paper, Bladed is used to compare load predictions using single-part and multi-part blade models for several turbines. The study examines the impact on fatigue and extreme loads and blade deflection through reduced sets of load calculations based on IEC 61400-1 ed. 3. Damage equivalent load changes of up to 16% and extreme load changes of up to 29% are observed at some turbine load locations. It is found that there is no general pattern in the loading differences observed between single-part and multi-part blade models. Rather, changes in fatigue and extreme loads with a multi-part blade model depend on the characteristics of the individual turbine and blade. Key underlying causes of damage equivalent load change are identified as differences in edgewise- torsional coupling between the multi-part and single-part models, and increased edgewise rotor mode damping in the multi-part model. Similarly, a causal link is identified between torsional blade dynamics and changes in ultimate load results.
Prakash, M; Geetha, D; Lydia Caroline, M
2013-04-15
Single crystals of L-phenylalanine-benzoic acid (LPBA) were successfully grown from aqueous solution by solvent evaporation technique. Purity of the crystals was increased by the method of recrystallization. The XRD analysis confirms that the crystal belongs to the monoclinic system with noncentrosymmetric space group P21. The chemical structure of compound was established by FT-NMR technique. The presence of functional groups was estimated qualitatively by Fourier transform infrared analysis (FT-IR). Ultraviolet-visible spectral analyses showed that the crystal has low UV cut-off at 254 nm combined with very good transparency of 90% in a wide range. The optical band gap was estimated to be 6.91 eV. Thermal behavior has been studied with TGA/DTA analyses. The existence of second harmonic generation (SHG) efficiency was found to be 0.56 times the value of KDP. The dielectric behavior of the sample was also studied for the first time.
Directory of Open Access Journals (Sweden)
W. L. Fouché
1983-03-01
Full Text Available In this article we discuss some aspects of nonlinear functional analysis. It included reviews of Banach’s contraction theorem, Schauder’s fixed point theorem, globalising techniques and applications of homotopy theory to nonlinear functional analysis. The author emphasises that fundamentally new ideas are required in order to achieve a better understanding of phenomena which contain both nonlinear and definite infinite dimensional features.
Regression Testing Cost Reduction Suite
Directory of Open Access Journals (Sweden)
Mohamed Alaa El-Din
2014-08-01
Full Text Available The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
Energy Technology Data Exchange (ETDEWEB)
Radu, I.E.
2006-03-15
This thesis presents the femtosecond laser-induced electron, lattice and spin dynamics on two representative rare-earth systems: The ferromagnetic gadolinium Gd(0001) and the paramagnetic yttrium Y(0001) metals. The employed investigation tools are the time-resolved linear reflectivity and second-harmonic generation, which provide complementary information about the bulk and surface/interface dynamics, respectively. The femtosecond laser excitation of the exchange-split surface state of Gd(0001) triggers simultaneously the coherent vibrational dynamics of the lattice and spin subsystems in the surface region at a frequency of 3 THz. The coherent optical phonon corresponds to the vibration of the topmost atomic layer against the underlying bulk along the normal direction to the surface. The coupling mechanism between phonons and magnons is attributed to the modulation of the exchange interaction J between neighbour atoms due to the coherent lattice vibration. This leads to an oscillatory motion of the magnetic moments having the same frequency as the lattice vibration. Thus these results reveal a new type of phonon-magnon coupling mediated by the modulation of the exchange interaction and not by the conventional spin-orbit interaction. Moreover, we show that coherent spin dynamics in the THz frequency domain is achievable, which is at least one order of magnitude faster than previously reported. The laser-induced (de)magnetization dynamics of the ferromagnetic Gd(0001) thin films have been studied. Upon photo-excitation, the nonlinear magneto-optics measurements performed in this work show a sudden drop in the spin polarization of the surface state by more than 50% in a <100 fs time interval. Under comparable experimental conditions, the time-resolved photoemission studies reveal a constant exchange splitting of the surface state. The ultrafast decrease of spin polarization can be explained by the quasi-elastic spin-flip scattering of the hot electrons among spin
Plotting partial correlation and regression in ecological studies
Directory of Open Access Journals (Sweden)
J. Moya-Laraño
2008-06-01
Full Text Available Multiple regression, the General linear model (GLM and the Generalized linear model (GLZ are widely used in ecology. The widespread use of graphs that include fitted regression lines to document patterns in simple linear regression can be easily extended to these multivariate techniques in plots that show the partial relationship of the dependent variable with each independent variable. However, the latter procedure is not nearly as widely used in ecological studies. In fact, a brief review of the recent ecological literature showed that in ca. 20% of the papers the results of multiple regression are displayed by plotting the dependent variable against the raw values of the independent variable. This latter procedure may be misleading because the value of the partial slope may change in magnitude and even in sign relative to the slope obtained in simple least-squares regression. Plots of partial relationships should be used in these situations. Using numerical simulations and real data we show how displaying plots of partial relationships may also be useful for: 1 visualizing the true scatter of points around the partial regression line, and 2 identifying influential observations and non-linear patterns more efficiently than using plots of residuals vs. fitted values. With the aim to help in the assessment of data quality, we show how partial residual plots (residuals from overall model + predicted values from the explanatory variable vs. the explanatory variable should only be used in restricted situations, and how partial regression plots (residuals of Y on the remaining explanatory variables vs. residuals of the target explanatory variable on the remaining explanatory variables should be the ones displayed in publications because they accurately reflect the scatter of partial correlations. Similarly, these partial plots can be applied to visualize the effect of continuous variables in GLM and GLZ for normal distributions and identity link
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
DEFF Research Database (Denmark)
Clausen, Carl A. Balslev; Christiansen, Peter Leth; Torner, L.
1999-01-01
We show that with the quasi-phase-matching technique it is possible to fabricate stripes of nonlinearity that trap and guide light like waveguides. We investigate an array of such stripes and find that when the stripes are sufficiently narrow, the beam dynamics is governed by a quadratic nonlinear...
Ahn, Jae Joon; Kim, Young Min; Yoo, Keunje; Park, Joonhong; Oh, Kyong Joo
2012-11-01
For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.
Regression Verification Using Impact Summaries
Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana
2013-01-01
Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program
Lespinats, Sylvain; Pinker-Domenig, Katja; Wengert, Georg; Houben, Ivo; Lobbes, Marc; Stadlbauer, Andreas; Meyer-Bäse, Anke
2016-05-01
Glioma-derived cancer stem cells (GSCs) are tumor-initiating cells and may be refractory to radiation and chemotherapy and thus have important implications for tumor biology and therapeutics. The analysis and interpretation of large proteomic data sets requires the development of new data mining and visualization approaches. Traditional techniques are insufficient to interpret and visualize these resulting experimental data. The emphasis of this paper lies in the application of novel approaches for the visualization, clustering and projection representation to unveil hidden data structures relevant for the accurate interpretation of biological experiments. These qualitative and quantitative methods are applied to the proteomic analysis of data sets derived from the GSCs. The achieved clustering and visualization results provide a more detailed insight into the protein-level fold changes and putative upstream regulators for the GSCs. However the extracted molecular information is insufficient in classifying GSCs and paving the pathway to an improved therapeutics of the heterogeneous glioma.
Directory of Open Access Journals (Sweden)
M. Zagrouba
2014-01-01
Full Text Available The present work deals with functionally graded materials (FGM isotropic plates in the neighborhood of the first-order symmetric zero group velocity (S1-ZGV point. The mechanical properties of functionally graded material (FGM are assumed to vary continuously through the thickness of the plate and obey a power law of the volume fraction of the constituents. Governing equations for the problem are derived, and the power series technique (PST is employed to solve the recursive equations. The impact of the FGM basic materials properties on S1-ZGV frequency of FGM plate is investigated. Numerical results show that S1-ZGV frequency is comparatively more sensitive to the shear modulus. The gradient coefficient p does not affect the linear dependence of ZGV frequency fo as function of cut-off frequency fc; only the slope is slightly varied.
Institute of Scientific and Technical Information of China (English)
Zolfaghar Mehdizadeh; Hamid Reza Lotfizadeh; S. S. Mortazavi; Hadi Noorizadeh
2012-01-01
Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communities. Genetic algorithm and kernel partial least square (GA-KPLS) and Levenberg- Marquardt artificial neural network (L-M ANN) techniques were used to investigate the correlation between retention time (tR) and descriptors for 150 organic contaminants in natural water and wastewater, which are obtained by gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC-TOF MS). The L-M ANN model gave a significantly better performance than the GA-KPLS model. This indicates that L-M ANN can be used as an alternative modeling toot for quantitative structure-retention relationship (QSRR) studies.
Directory of Open Access Journals (Sweden)
Giovanni Salini Calderón
2010-12-01
Full Text Available Se indica cómo manejar una gran base de datos consistente de series temporales no lineales, aplicando distintas técnicas de modelamiento no lineal a estas series. Aunque no existen guías explícitas de manipulación de series temporales no lineales en la profusa bibliografía actual, existen diferentes enfoques que pueden ser tomados en cuenta. Para ello se estudió una base de datos mensual correspondiente a datos del Fenómeno del Niño (ENSO, entre los años 1866 y 2006. Se explica cómo debe manipularse esta base de datos que poseen características de no linealidad, la cual será usada para hacer pronósticos varios pasos en adelante. Se aplicaron dos test estándar: Información Mutua Promedio (AMI y Falsos Vecinos más Cercanos (FNN. Se obtuvo el espaciamiento óptimo de los datos, así como el número de datos hacia atrás necesarios para pronosticar valores hacia el futuro. Luego, se diseñaron varios modelos de redes neuronales artificiales (RNA, con diferentes reglas de aprendizajes, funciones de transferencia, elementos de procesamiento (o neuronas en la capa escondida, etc., que permitieron hacer pronóstico de hasta 20 pasos en adelante. Las mejores redes correspondieron a aquellas que poseían como regla de aprendizaje la Regla Delta y la Regla Extendida, con función de transferencia sigmoide y tangente hiperbólica. El tipo de RNA usada fue una de multicapas alimentada hacia adelante y entrenada mediante la técnica de propagación hacia atrás. Se probaron redes con una, dos capas ocultas y sin ninguna capa. El mejor modelo que se obtuvo resultó ser uno consistente de una capa oculta.We indicate how to handle a large database consisting of nonlinear time series, applying different nonlinear modelling techniques to this kind of times series. Nowadays in the current references there is no explicit guide of how to manipulate data from nonlinear time series; however, there are approaches that can be taken account. To this end
Institute of Scientific and Technical Information of China (English)
武大勇; 李锋
2015-01-01
The linear semiparametric regression models with missing data were considered.The maximum empirical es-timations of the regression coefficients,and the smoothing function were obtained by the maximum empirical method. The asymptotic normality and consistency of the proposed estimations were proved under some appropriate conditions.%考虑了随机缺失数据下非线性回归模型的估计问题，利用最大经验似然估计的方法给出了回归系数、光滑函数的最大经验似然估计，并在一定条件下证明了所得估计量的渐近正态性和强相合性。
Kono, Mitsuo
2010-01-01
A nonlinearity is one of the most important notions in modern physics. A plasma is rich in nonlinearities and provides a variety of behaviors inherent to instabilities, coherent wave structures and turbulence. The book covers the basic concepts and mathematical methods, necessary to comprehend nonlinear problems widely encountered in contemporary plasmas, but also in other fields of physics and current research on self-organized structures and magnetized plasma turbulence. The analyses make use of strongly nonlinear models solved by analytical techniques backed by extensive simulations and available experiments. The text is written for senior undergraduates, graduate students, lecturers and researchers in laboratory, space and fusion plasmas.
Huang, Ting; Javaherian, Hossein; Liu, Derong
2011-06-01
This paper presents a new approach for the calibration and control of spark ignition engines using a combination of neural networks and sliding mode control technique. Two parallel neural networks are utilized to realize a neuro-sliding mode control (NSLMC) for self-learning control of automotive engines. The equivalent control and the corrective control terms are the outputs of the neural networks. Instead of using error backpropagation algorithm, the network weights of equivalent control are updated using the Levenberg-Marquardt algorithm. Moreover, a new approach is utilized to update the gain of corrective control. Both modifications of the NSLMC are aimed at improving the transient performance and speed of convergence. Using the data from a test vehicle with a V8 engine, we built neural network models for the engine torque (TRQ) and the air-to-fuel ratio (AFR) dynamics and developed NSLMC controllers to achieve tracking control. The goal of TRQ control and AFR control is to track the commanded values under various operating conditions. From simulation studies, the feasibility and efficiency of the approach are illustrated. For both control problems, excellent tracking performance has been achieved.
Institute of Scientific and Technical Information of China (English)
H.SAGHI; M.J.KETABDARI; S.BOOSHI
2012-01-01
A two-dimensional (2D) numerical model is developed for the wave simulation and propagation in a wave flume.The fluid flow is assumed to be viscous and incompressible,and the Navier-Stokes and continuity equations are used as the governing equations.The standard κ-ε model is used to model the turbulent flow.The NavierStokes equations are discretized using the staggered grid finite difference method and solved by the simplified marker and cell (SMAC) method. Waves are generated and propagated using a piston type wave maker. An open boundary condition is used at the end of the numerical flume.Some standard tests,such as the lid-driven cavity,the constant unidirectional velocity field,the shearing flow,and the dam-break on the dry bed,are performed to valid the model.To demonstrate the capability and accuracy of the present method,the results of generated waves are compared with available wave theories.Finally,the clustering technique (CT) is used for the mesh generation,and the best condition is suggested.
Regression analysis by example
Chatterjee, Samprit
2012-01-01
Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded
A Multi-objective Procedure for Efficient Regression Modeling
Sinha, Ankur; Kuosmanen, Timo
2012-01-01
Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by over-abundance of explanatory variables, non-linearities and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance of the variables. To provide a robust method for model selection, this paper introduces a technique called the Multi-objective Genetic Algorithm for Variable Selection (MOGA-VS) which provides the user with an efficient set of regression models for a given data-set. The algorithm considers the regression problem as a two objective task, where the purpose is to choose those models over the other which have less number of regression coefficients and better goodness of fit. In MOGA-VS, the model selection procedure is implemented in two steps. First, we generate the frontier of all efficient or non-dominated regression m...
Unitary Response Regression Models
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Flexible survival regression modelling
DEFF Research Database (Denmark)
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights by m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
Lopes, Marta B; Calado, Cecília R C; Figueiredo, Mário A T; Bioucas-Dias, José M
2016-11-16
The monitoring of biopharmaceutical products using Fourier transform infrared (FT-IR) spectroscopy relies on calibration techniques involving the acquisition of spectra of bioprocess samples along the process. The most commonly used method for that purpose is partial least squares (PLS) regression, under the assumption that a linear model is valid. Despite being successful in the presence of small nonlinearities, linear methods may fail in the presence of strong nonlinearities. This paper studies the potential usefulness of nonlinear regression methods for predicting, from in situ near-infrared (NIR) and mid-infrared (MIR) spectra acquired in high-throughput mode, biomass and plasmid concentrations in Escherichia coli DH5-α cultures producing the plasmid model pVAX-LacZ. The linear methods PLS and ridge regression (RR) are compared with their kernel (nonlinear) versions, kPLS and kRR, as well as with the (also nonlinear) relevance vector machine (RVM) and Gaussian process regression (GPR). For the systems studied, RR provided better predictive performances compared to the remaining methods. Moreover, the results point to further investigation based on larger data sets whenever differences in predictive accuracy between a linear method and its kernelized version could not be found. The use of nonlinear methods, however, shall be judged regarding the additional computational cost required to tune their additional parameters, especially when the less computationally demanding linear methods herein studied are able to successfully monitor the variables under study.
Nonlinear optics and photonics
He, Guang S
2015-01-01
This book provides a comprehensive presentation on most of the major topics in nonlinear optics and photonics, with equal emphasis on principles, experiments, techniques, and applications. It covers many major new topics including optical solitons, multi-photon effects, nonlinear photoelectric effects, fast and slow light , and Terahertz photonics. Chapters 1-10 present the fundamentals of modern nonlinear optics, and could be used as a textbook with problems provided at the end of each chapter. Chapters 11-17 cover the more advanced topics of techniques and applications of nonlinear optics and photonics, serving as a highly informative reference for researchers and experts working in related areas. There are also 16 pages of color photographs to illustrate the visual appearances of some typical nonlinear optical effects and phenomena. The book could be adopted as a textbook for both undergraduates and graduate students, and serve as a useful reference work for researchers and experts in the fields of physics...
Directory of Open Access Journals (Sweden)
Larissa Ribeiro de Andrade
2014-01-01
Full Text Available The bootstrap method is generally performed by presupposing that each sample unit would show the same probability of being re-sampled. However, when a sample with outliers is taken into account, the empirical distribution generated by this method may be influenced, or rather, it may not accurately represent the original sample. Current study proposes a bootstrap algorithm that allows the use of measures of influence in the calculation of re-sampling probabilities. The method was reproduced in simulation scenarios taking into account the logistic growth curve model and the CovRatio measurement to evaluate the impact of an influential observation in the determinacy of the matrix of the co-variance of parameter estimates. In most cases, bias estimates were reduced. Consequently, the method is suitable to be used in non-linear models and allows the researcher to apply other measures for better bias reductions.
Regression Cloud Models and Their Applications in Energy Consumption of Data Center
Directory of Open Access Journals (Sweden)
Yanshuang Zhou
2015-01-01
Full Text Available As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.
Biswas, S.; Kumbhakar, P.
2017-02-01
We have reported here, for the first time, to the best of our knowledge, a high nonlinear refractive index (n2e) of a natural pigment extracted from Hibiscus rosa-sinensis leaves by using spatial self-phase modulation technique (SSPM) with a low power CW He-Ne laser radiation at 632.8 nm. It is found by UV-Vis absorption spectroscopic analysis that chlrophyll-a, chlrophyll-b and carotenoid are present in the pigment extract with 56%, 25% and 19%, respectively. The photoluminescence (PL) emission characteristics of the extracted samples have also been measured at room temperature as well as in the temperature range of 283-333 K to investigate the effect of temperature on luminescent properties of the sample. By analyzing the SSPM experimental data, the nonlinear refractive index value of pigment extract has been determined to be 3.5 × 10- 5 cm2/W. The large nonlinear refractive index has been assigned due to asymmetrical structure, molecular reorientation and thermally induced nonlinearity in the sample. The presented results might open new avenues for the green and economical technique of syntheses of organic dyes with such a large nonlinear optical property.
ARC Code TI: Block-GP: Scalable Gaussian Process Regression
National Aeronautics and Space Administration — Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear...
Maximum likelihood polynomial regression for robust speech recognition
Institute of Scientific and Technical Information of China (English)
LU Yong; WU Zhenyang
2011-01-01
The linear hypothesis is the main disadvantage of maximum likelihood linear re- gression （MLLR）. This paper applies the polynomial regression method to model adaptation and establishes a nonlinear model adaptation algorithm using maximum likelihood polyno
Experimental Data Mining Techniques(Using Multiple Statistical Methods
Directory of Open Access Journals (Sweden)
Mustafa Zaidi
2012-05-01
Full Text Available This paper discusses the possible solutions of non-linear multivariable by experimental Data mining techniques using on orthogonal array. Taguchi method is a very useful technique to reduce the time and cost of the experiment but the ignoring all kind of interaction effects. The results are not much encouraging and motivate to study Laser cutting process of non-linear multivariable is modeled by one and two way analysis of variance also linear and non linear regression analysis. These techniques are used to explore better analysis techniques and improve the laser cutting quality by reducing process variations caused by controllable process parameters. The size of data set causes difficulties in modeling and simulation of the problem such as decision tree is useful technique but it is not able to predict better results. The results of analysis of variance are encouraging. Taguchi and regression normally optimizes input process parameters for single characteristics.
Autistic epileptiform regression.
Canitano, Roberto; Zappella, Michele
2006-01-01
Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.
Scaled Sparse Linear Regression
Sun, Tingni
2011-01-01
Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...
Institute of Scientific and Technical Information of China (English)
Guijun YANG; Lu LIN; Runchu ZHANG
2007-01-01
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
Directory of Open Access Journals (Sweden)
José Claudio Mura
2016-05-01
Full Text Available This work presents an investigation to determine ground deformation based on an integration of DInSAR Time-Series (DTS and Persistent Scatterer Interferometry (PSI techniques aiming at detecting high rates of linear and non-linear ground movement. The combined techniques were applied in an open pit iron mine located in Carajás Mineral Province (Brazilian Amazon region, using a set of 33 TerraSAR-X-1 images acquired from March 2012 to April 2013 when, due to a different deformation behavior during the dry and wet seasons in the Amazon region, a non-linear deformation was detected. The DTS analysis was performed on a stack of multi-look unwrapped interferograms using an extension of the SVD (Singular Value Decomposition, where a set of additional weighted constraints on the acceleration of the displacement was incorporated to control the smoothness of the time-series solutions, whose objective was to correct the atmospheric phase artifacts. The height errors and the deformation history provided by the DTS technique were used as previous information to perform the PSI analysis. This procedure improved the capability of the PSI technique to detect non-linear movement as well as to increase the numbers of point density of the final results. The results of the combined techniques are presented and compared with total station/prisms and ground-based radar (GBR measurements.
DEFF Research Database (Denmark)
Stroescu, Ionut Emanuel; Sørensen, Lasse; Frigaard, Peter Bak
2016-01-01
A non-linear stretching method was implemented for stream function theory to solve wave kinematics for physical conditions close to breaking waves in shallow waters, with wave heights limited by the water depth. The non-linear stretching method proves itself robust, efficient and fast, showing good...
Nonlinear Filtering and Approximation Techniques
1988-10-01
e par des iquations de dimension finie, les 6quations du filtre de Kalman : X +h~pklk Xk=(1 + bAt).kk..I + e2+h2 pl_(k - h( + b~t)Xk-... (6 -kIj (1...Equation. 3. Piecewise Linear Filtering with Small Observation Noise. 4. Filtres Approches pour un Probleme de Fitrage Nonlineaire Discretise avec Petit...finite dimensional solution, namely the Kalman filter (which is the extended Kalman filter for (0.1) ). The above considerations tend to indicate that
Bulcock, J. W.
The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…
Energy Technology Data Exchange (ETDEWEB)
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...
Rajasekar, Shanmuganathan
2016-01-01
This introductory text presents the basic aspects and most important features of various types of resonances and anti-resonances in dynamical systems. In particular, for each resonance, it covers the theoretical concepts, illustrates them with case studies, and reviews the available information on mechanisms, characterization, numerical simulations, experimental realizations, possible quantum analogues, applications and significant advances made over the years. Resonances are one of the most fundamental phenomena exhibited by nonlinear systems and refer to specific realizations of maximum response of a system due to the ability of that system to store and transfer energy received from an external forcing source. Resonances are of particular importance in physical, engineering and biological systems - they can prove to be advantageous in many applications, while leading to instability and even disasters in others. The book is self-contained, providing the details of mathematical derivations and techniques invo...
Fatigue design of a cellular phone folder using regression model-based multi-objective optimization
Kim, Young Gyun; Lee, Jongsoo
2016-08-01
In a folding cellular phone, the folding device is repeatedly opened and closed by the user, which eventually results in fatigue damage, particularly to the front of the folder. Hence, it is important to improve the safety and endurance of the folder while also reducing its weight. This article presents an optimal design for the folder front that maximizes its fatigue endurance while minimizing its thickness. Design data for analysis and optimization were obtained experimentally using a test jig. Multi-objective optimization was carried out using a nonlinear regression model. Three regression methods were employed: back-propagation neural networks, logistic regression and support vector machines. The AdaBoost ensemble technique was also used to improve the approximation. Two-objective Pareto-optimal solutions were identified using the non-dominated sorting genetic algorithm (NSGA-II). Finally, a numerically optimized solution was validated against experimental product data, in terms of both fatigue endurance and thickness index.
[Is regression of atherosclerosis possible?].
Thomas, D; Richard, J L; Emmerich, J; Bruckert, E; Delahaye, F
1992-10-01
Experimental studies have shown the regression of atherosclerosis in animals given a cholesterol-rich diet and then given a normal diet or hypolipidemic therapy. Despite favourable results of clinical trials of primary prevention modifying the lipid profile, the concept of atherosclerosis regression in man remains very controversial. The methodological approach is difficult: this is based on angiographic data and requires strict standardisation of angiographic views and reliable quantitative techniques of analysis which are available with image processing. Several methodologically acceptable clinical coronary studies have shown not only stabilisation but also regression of atherosclerotic lesions with reductions of about 25% in total cholesterol levels and of about 40% in LDL cholesterol levels. These reductions were obtained either by drugs as in CLAS (Cholesterol Lowering Atherosclerosis Study), FATS (Familial Atherosclerosis Treatment Study) and SCOR (Specialized Center of Research Intervention Trial), by profound modifications in dietary habits as in the Lifestyle Heart Trial, or by surgery (ileo-caecal bypass) as in POSCH (Program On the Surgical Control of the Hyperlipidemias). On the other hand, trials with non-lipid lowering drugs such as the calcium antagonists (INTACT, MHIS) have not shown significant regression of existing atherosclerotic lesions but only a decrease on the number of new lesions. The clinical benefits of these regression studies are difficult to demonstrate given the limited period of observation, relatively small population numbers and the fact that in some cases the subjects were asymptomatic. The decrease in the number of cardiovascular events therefore seems relatively modest and concerns essentially subjects who were symptomatic initially. The clinical repercussion of studies of prevention involving a single lipid factor is probably partially due to the reduction in progression and anatomical regression of the atherosclerotic plaque
Khan, Iftekhar; Morris, Stephen
2014-11-12
The performance of the Beta Binomial (BB) model is compared with several existing models for mapping the EORTC QLQ-C30 (QLQ-C30) on to the EQ-5D-3L using data from lung cancer trials. Data from 2 separate non small cell lung cancer clinical trials (TOPICAL and SOCCAR) are used to develop and validate the BB model. Comparisons with Linear, TOBIT, Quantile, Quadratic and CLAD models are carried out. The mean prediction error, R(2), proportion predicted outside the valid range, clinical interpretation of coefficients, model fit and estimation of Quality Adjusted Life Years (QALY) are reported and compared. Monte-Carlo simulation is also used. The Beta-Binomial regression model performed 'best' among all models. For TOPICAL and SOCCAR trials, respectively, residual mean square error (RMSE) was 0.09 and 0.11; R(2) was 0.75 and 0.71; observed vs. predicted means were 0.612 vs. 0.608 and 0.750 vs. 0.749. Mean difference in QALY's (observed vs. predicted) were 0.051 vs. 0.053 and 0.164 vs. 0.162 for TOPICAL and SOCCAR respectively. Models tested on independent data show simulated 95% confidence from the BB model containing the observed mean more often (77% and 59% for TOPICAL and SOCCAR respectively) compared to the other models. All algorithms over-predict at poorer health states but the BB model was relatively better, particularly for the SOCCAR data. The BB model may offer superior predictive properties amongst mapping algorithms considered and may be more useful when predicting EQ-5D-3L at poorer health states. We recommend the algorithm derived from the TOPICAL data due to better predictive properties and less uncertainty.
Cortazar, E; Usobiaga, A; Fernández, L A; de, Diego A; Madariaga, J M
2002-02-01
A MATHEMATICA package, 'CONDU.M', has been developed to find the polynomial in concentration and temperature which best fits conductimetric data of the type (kappa, c, T) or (kappa, c1, c2, T) of electrolyte solutions (kappa: specific conductivity; ci: concentration of component i; T: temperature). In addition, an interface, 'TKONDU', has been written in the TCL/Tk language to facilitate the use of CONDU.M by an operator not familiarised with MATHEMATICA. All this software is available on line (UPV/EHU, 2001). 'CONDU.M' has been programmed to: (i) select the optimum grade in c1 and/or c2; (ii) compare models with linear or quadratic terms in temperature; (iii) calculate the set of adjustable parameters which best fits data; (iv) simplify the model by elimination of 'a priori' included adjustable parameters which after the regression analysis result in low statistical significance; (v) facilitate the location of outlier data by graphical analysis of the residuals; and (vi) provide quantitative statistical information on the quality of the fit, allowing a critical comparison among different models. Due to the multiple options offered the software allows testing different conductivity models in a short time, even if a large set of conductivity data is being considered simultaneously. Then, the user can choose the best model making use of the graphical and statistical information provided in the output file. Although the program has been initially designed to treat conductimetric data, it can be also applied for processing data with similar structure, e.g. (P, c, T) or (P, c1, c2, T), being P any appropriate transport, physical or thermodynamic property.
Constraint or choice? Disentangling fertility determinants by switching regressions
Directory of Open Access Journals (Sweden)
Christoph Sax
2011-11-01
Full Text Available In 1953, many poor countries had not yet approached the demographic transition. Accordingly, income generally had a positive impact on fertility in poor countries, while it has a negative impact today. Easterlin's supply-demand framework offers an explanation for this nonlinearity by attributing the positive relationship to Malthusian (or "supply" factors and the negative relationship to "demand" factors. This paper estimates Easterlin's supply-demand framework by switching regressions in a panel data set of 152 countries from 1953 to 1998. The technique allows identifying several factors affecting the Malthusian constraint and the demand for children, such as income, the source of income, urbanization, religion and the medical environment. It is found that a combination of higher GDP per capita, a decrease in the infant death rate and an increase in education explain a substantial part of the reversal of the relationship between income and net fertility over the sample period.
Hosmer, David W; Sturdivant, Rodney X
2013-01-01
A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
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.
Regression of lumbar disk herniation
Directory of Open Access Journals (Sweden)
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Directory of Open Access Journals (Sweden)
Robson Marcelo Rossi
2010-02-01
Full Text Available This paper shows the Bayesian approach as an alternative to the classical analysis of nonlinear models for ruminal degradation data. The data set was obtained from a Latin square experimental design, established for testing the ruminal degradation of dry matter, crude protein and fiber in neutral detergent of three silages: elephant grass (Pennisetum purpureum Schum with bacterial inoculant or enzyme-bacterial inoculant and corn silage (Zea mays L.. The incubation times were 0, 2, 6, 12, 24, 48, 72 and 96 hours. The parameter estimates of the equations fitted by both methods showed small differences, but by the Bayesian approach it was possible to compare the estimates correctly, that does not happen with the frequentist methodology because it is much more restricted in the applications due to the demand for a larger number of presuppositions.Neste trabalho a abordagem Bayesiana é apresentada como alternativa à abordagem clássica na modelagem não-linear de dados de degradação ruminal. Foram utilizados dados provenientes de um experimento em delineamento quadrado latino para avaliar a degradabilidade da matéria seca, da proteína bruta e da fibra em detergente neutro de três silagens: silagem de capim-elefante (Pennisetum purpureum Schum com inoculante bacteriano, com inoculante enzimo-bacteriano e silagem de milho (Zea mays L., nos tempos de incubação: 0, 2, 6, 12, 24, 48, 72 e 96 horas. Obtidas as estimativas dos parâmetros do modelo ajustado, pelos dois métodos, observou-se que não há diferenças marcantes entre as mesmas para nenhuma das variáveis estudadas. No entanto, por meio da metodologia Bayesiana, foi possível comparar as estimativas dos parâmetros para cada tratamento, o que não ocorre com a metodologia frequentista, por ser muito mais restrita nas aplicações devido à exigência de maior número de pressuposições.
Transductive Ordinal Regression
Seah, Chun-Wei; Ong, Yew-Soon
2011-01-01
Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Optothermal nonlinearity of silica aerogel
Braidotti, Maria Chiara; Fleming, Adam; Samuels, Michiel C; Di Falco, Andrea; Conti, Claudio
2016-01-01
We report on the characterization of silica aerogel thermal optical nonlinearity, obtained by z-scan technique. The results show that typical silica aerogels have nonlinear optical coefficient similar to that of glass $(\\simeq 10^{-12} $m$^2/$W), with negligible optical nonlinear absorption. The non\\-li\\-near coefficient can be increased to values in the range of $10^{-10} $m$^2/$W by embedding an absorbing dye in the aerogel. This value is one order of magnitude higher than that observed in the pure dye and in typical highly nonlinear materials like liquid crystals.