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.
Input saturation in nonlinear multivariable processes resolved by nonlinear decoupling
Directory of Open Access Journals (Sweden)
Jens G. Balchen
1995-04-01
Full Text Available A new method is presented for the resolution of the problem of input saturation in nonlinear multivariable process control by means of elementary nonlinear decoupling (END. Input saturation can have serious consequences particularly in multivariable control because it may lead to very undesirable system behaviour and quite often system instability. Many authors have searched for systematic techniques for designing multivariable control systems in which saturation may occur in any of the control variables (inputs, manipulated variables. No generally accepted method seems to have been presented so far which gives a solution in closed form. The method of elementary nonlinear decoupling (END can be applied directly to the case of saturation control variables by deriving as many control strategies as there are combinations of saturating control variables. The method is demonstrated by the multivariable control of a simulated Fluidized Catalytic Cracker (FCC with very convincing results.
Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market
Directory of Open Access Journals (Sweden)
Junhai Ma
2008-01-01
Full Text Available This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.
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.
Nonlinear and fault-tolerant flight control using multivariate splines
Tol, H.J.; De Visser, C.C.; Van Kampen, E.J.; Chu, Q.P.
2015-01-01
This paper presents a study on fault tolerant flight control of a high performance aircraft using multivariate splines. The controller is implemented by making use of spline model based adaptive nonlinear dynamic inversion (NDI). This method, indicated as SANDI, combines NDI control with nonlinear
Nonlinear and fault-tolerant flight control using multivariate splines
Tol, H.J.; De Visser, C.C.; Van Kampen, E.J.; Chu, Q.P.
2015-01-01
This paper presents a study on fault tolerant flight control of a high performance aircraft using multivariate splines. The controller is implemented by making use of spline model based adaptive nonlinear dynamic inversion (NDI). This method, indicated as SANDI, combines NDI control with nonlinear c
Tau identification using multivariate techniques in ATLAS
"O'Neil, D C; The ATLAS collaboration
2011-01-01
Tau leptons play an important role in the physics program of the LHC. They are being used in electroweak measurements, in detector related studies and in searches for new phenomena like the Higgs boson or Supersymmetry. Tau objects appear as collimated jets with low track multiplicity. Due to the background from QCD multijet processes, efficient tau identification techniques with large fake rejection are essential. Since single variable criteria are not enough to efficiently separate them from jets and electrons, modern multivariate techniques are used. In ATLAS, several advanced algorithms are applied to identify taus, including a projective likelihood estimator and boosted decision trees. All multivariate methods applied to the ATLAS simulated data perform better than the baseline cut analysis. Their performance is shown using high energy data collected at the ATLAS experiment. The strengths and weaknesses of each technique are also discussed.
Multivariable nonlinear identification of smart buildings
Kim, Yeesock; Kim, Young Hoon; Lee, Seongsoo
2015-10-01
This paper presents a new multi-input-multi-output (MIMO) fuzzy model for nonlinear system identification (SI) of smart structures under a variety of random forces. The fuzzy SI model is developed through the integration of wavelet transform (WT), multiple MIMO linear autoregressive exogenous (ARX) input models, Takagi-Sugeno (TS) fuzzy model, weighted linear least squares, and data clustering algorithms: MIMO WARX-TS fuzzy model. To demonstrate the effectiveness of the MIMO WARX-TS fuzzy model, a three-story building equipped with a magnetorheological (MR) damper under a variety of random signals is investigated. To train the proposed model, an artificial earthquake and control forces are used as input signals while displacement and acceleration responses are used as outputs. To validate the trained model, four real recorded earthquake signals are used. It is shown from the simulation that the proposed MIMO WARX-TS fuzzy identification algorithm is effective in estimating nonlinear behavior of a building-MR damper system under a variety of seismic excitations.
Tau identification using multivariate techniques in ATLAS
O'Neil, D; The ATLAS collaboration
2011-01-01
Tau leptons will play an important role in the physics program at the LHC. They will be used in electroweak measurements and in detector related studies like the determination of the missing transverse energy scale, but also in searches for new phenomena like the Higgs boson or Supersymmetry. Due to the huge background from QCD processes, efficient tau identification techniques with large fake rejection are essential. Tau object appear as collimated jets with low track multiplicity and single variable criteria are not enough to efficiently separate them from jets and electrons. This can be achieved using modern multivariate techniques which make optimal use of all the information available. They are particularly useful when the discriminating variables are not independent and no single variable provides good signal and background separation. In ATLAS several advanced algorithms are applied to identify taus, in particular a projective likelihood estimator and boosted decision trees. All multivariate methods ap...
Looking Back at the Gifi System of Nonlinear Multivariate Analysis
Directory of Open Access Journals (Sweden)
Peter G. M. van der Heijden
2016-09-01
Full Text Available Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper discusses the so-called Gifi system of nonlinear multivariate analysis, that entails homogeneity analysis (which is closely related to multiple correspondence analysis and generalizations. The history is discussed, giving attention to the scientific philosophy of this group, and links to machine learning are indicated.
Looking back at the gifi system of nonlinear multivariate analysis
van der Heijden, Peter G M; van Buuren, Stef
2016-01-01
Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper
Tau identification using multivariate techniques in ATLAS
O'Neil, D. C.; ATLAS Collaboration
2012-06-01
Tau leptons play an important role in the physics program of the LHC. They are being used in electroweak measurements, in detector related studies and in searches for new phenomena like the Higgs boson or Supersymmetry. In the detector, tau leptons are reconstructed as collimated jets with low track multiplicity. Due to the background from QCD multijet processes, efficient tau identification techniques with large fake rejection are essential. Since single variable criteria are not enough to efficiently separate them from jets and electrons, modern multivariate techniques are used. In ATLAS, several advanced algorithms are applied to identify taus, including a projective likelihood estimator and boosted decision trees. All multivariate methods applied to the ATLAS simulated data perform better than the baseline cut analysis. Their performance is shown using high energy data collected at the ATLAS experiment. The improvement ranges from a factor of 2 to 5 in rejection for the same efficiency, depending on the selected efficiency operating point and the number of prongs in the tau decay. The strengths and weaknesses of each technique are also discussed.
COSIMA data analysis using multivariate techniques
Directory of Open Access Journals (Sweden)
J. Silén
2014-08-01
Full Text Available We describe how to use multivariate analysis of complex TOF-SIMS spectra introducing the method of random projections. The technique allows us to do full clustering and classification of the measured mass spectra. In this paper we use the tool for classification purposes. The presentation describes calibration experiments of 19 minerals on Ag and Au substrates using positive mode ion spectra. The discrimination between individual minerals gives a crossvalidation Cohen κ for classification of typically about 80%. We intend to use the method as a fast tool to deduce a qualitative similarity of measurements.
Introduction to multivariate analysis linear and nonlinear modeling
Konishi, Sadanori
2014-01-01
""The presentation is always clear and several examples and figures facilitate an easy understanding of all the techniques. The book can be used as a textbook in advanced undergraduate courses in multivariate analysis, and can represent a valuable reference manual for biologists and engineers working with multivariate datasets.""-Fabio Rapallo, Zentralblatt MATH 1296
Directory of Open Access Journals (Sweden)
Veyis Turut
2013-01-01
Full Text Available Two tecHniques were implemented, the Adomian decomposition method (ADM and multivariate Padé approximation (MPA, for solving nonlinear partial differential equations of fractional order. The fractional derivatives are described in Caputo sense. First, the fractional differential equation has been solved and converted to power series by Adomian decomposition method (ADM, then power series solution of fractional differential equation was put into multivariate Padé series. Finally, numerical results were compared and presented in tables and figures.
Multivariate postprocessing techniques for probabilistic hydrological forecasting
Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian
2016-04-01
Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power
Analyzing the Dynamics of Nonlinear Multivariate Time Series Models
Institute of Scientific and Technical Information of China (English)
DenghuaZhong; ZhengfengZhang; DonghaiLiu; StefanMittnik
2004-01-01
This paper analyzes the dynamics of nonlinear multivariate time series models that is represented by generalized impulse response functions and asymmetric functions. We illustrate the measures of shock persistences and asymmetric effects of shocks derived from the generalized impulse response functions and asymmetric function in bivariate smooth transition regression models. The empirical work investigates a bivariate smooth transition model of US GDP and the unemployment rate.
LEARNING GRANGER CAUSALITY GRAPHS FOR MULTIVARIATE NONLINEAR TIME SERIES
Institute of Scientific and Technical Information of China (English)
Wei GAO; Zheng TIAN
2009-01-01
An information theory method is proposed to test the. Granger causality and contemporaneous conditional independence in Granger causality graph models. In the graphs, the vertex set denotes the component series of the multivariate time series, and the directed edges denote causal dependence, while the undirected edges reflect the instantaneous dependence. The presence of the edges is measured by a statistics based on conditional mutual information and tested by a permutation procedure. Furthermore, for the existed relations, a statistics based on the difference between general conditional mutual information and linear conditional mutual information is proposed to test the nonlinearity. The significance of the nonlinear test statistics is determined by a bootstrap method based on surrogate data. We investigate the finite sample behavior of the procedure through simulation time series with different dependence structures, including linear and nonlinear relations.
An Implementation Technique for Multivariate Robust Design
Institute of Scientific and Technical Information of China (English)
MA Yi-zhong; ZHAO Feng-yu
2005-01-01
This paper investigates systematically the problem of multivariate robust parameter design. First, a measurement criterion for the total variation of multivariate quality characteristics is introduced by the result of information theory. Then the implementation procedure in the robust design is presented. After that, a simulation example from a practical industrial process is provided. Finally, some comments and further work are discussed.
Abdolmaleki, Azizeh; Ghasemi, Jahan B; Shiri, Fereshteh; Pirhadi, Somayeh
2015-01-01
Data manipulation and maximum efficient extraction of useful information need a range of searching, modeling, mathematical, and statistical approaches. Hence, an adequate multivariate characterization is the first necessary step in investigation and the results are interpreted after multivariate analysis. Multivariate data analysis is capable of not only large dataset management but also interpret them surely and rapidly. Application of chemometrics and cheminformatics methods may be useful for design and discovery of new drug compounds. In this review, we present a variety of information sources on chemometrics, which we consider useful in different fields of drug design. This review describes exploratory analysis (PCA), classification and multivariate calibration (PCR, PLS) methods to data analysis. It summarizes the main facts of linear and nonlinear multivariate data analysis in drug discovery and provides an introduction to manipulation of data in this field. It handles the fundamental aspects of basic concepts of multivariate methods, principles of projections (PCA and PLS) and introduces the popular modeling and classification techniques. Enough theory behind these methods, more particularly concerning the chemometrics tools is included for those with little experience in multivariate data analysis techniques such as PCA, PLS, SIMCA, etc. We describe each method by avoiding unnecessary equations, and details of calculation algorithms. It provides a synopsis of the method followed by cases of applications in drug design (i.e., QSAR) and some of the features for each method.
Multivariate Permutation Polynomial Systems and Nonlinear Pseudorandom Number Generators
Ostafe, Alina
2009-01-01
In this paper we study a class of dynamical systems generated by iterations of multivariate permutation polynomial systems which lead to polynomial growth of the degrees of these iterations. Using these estimates and the same techniques studied previously for inversive generators, we bound exponential sums along the orbits of these dynamical systems and show that they admit much stronger estimates on average over all initial values than in the general case and thus can be of use for pseudorandom number generation.
Adaptive Fractional Fuzzy Sliding Mode Control for Multivariable Nonlinear Systems
Junhai Luo; Heng Liu
2014-01-01
This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. The fractional order calculus is employed in the parameter updating stage. The underlying stability analysis as well as parameter update law design is carried out by Lyapunov based technique. In the simulation, two examples including a comparison with the traditional integer order counterpart are given to show the effectiveness of the proposed method. The main contribution of th...
Kannan, Rohit; Tangirala, Arun K.
2014-06-01
Identification of directional influences in multivariate systems is of prime importance in several applications of engineering and sciences such as plant topology reconstruction, fault detection and diagnosis, and neurosciences. A spectrum of related directionality measures, ranging from linear measures such as partial directed coherence (PDC) to nonlinear measures such as transfer entropy, have emerged over the past two decades. The PDC-based technique is simple and effective, but being a linear directionality measure has limited applicability. On the other hand, transfer entropy, despite being a robust nonlinear measure, is computationally intensive and practically implementable only for bivariate processes. The objective of this work is to develop a nonlinear directionality measure, termed as KPDC, that possesses the simplicity of PDC but is still applicable to nonlinear processes. The technique is founded on a nonlinear measure called correntropy, a recently proposed generalized correlation measure. The proposed method is equivalent to constructing PDC in a kernel space where the PDC is estimated using a vector autoregressive model built on correntropy. A consistent estimator of the KPDC is developed and important theoretical results are established. A permutation scheme combined with the sequential Bonferroni procedure is proposed for testing hypothesis on absence of causality. It is demonstrated through several case studies that the proposed methodology effectively detects Granger causality in nonlinear processes.
Directory of Open Access Journals (Sweden)
Houda Salhi
2016-01-01
Full Text Available This paper deals with the parameter estimation problem for multivariable nonlinear systems described by MIMO state-space Wiener models. Recursive parameters and state estimation algorithms are presented using the least squares technique, the adjustable model, and the Kalman filter theory. The basic idea is to estimate jointly the parameters, the state vector, and the internal variables of MIMO Wiener models based on a specific decomposition technique to extract the internal vector and avoid problems related to invertibility assumption. The effectiveness of the proposed algorithms is shown by an illustrative simulation example.
Wang, Wan-Lun; Lin, Tsung-I
2014-07-30
The multivariate nonlinear mixed-effects model (MNLMM) has emerged as an effective tool for modeling multi-outcome longitudinal data following nonlinear growth patterns. In the framework of MNLMM, the random effects and within-subject errors are assumed to be normally distributed for mathematical tractability and computational simplicity. However, a serious departure from normality may cause lack of robustness and subsequently make invalid inference. This paper presents a robust extension of the MNLMM by considering a joint multivariate t distribution for the random effects and within-subject errors, called the multivariate t nonlinear mixed-effects model. Moreover, a damped exponential correlation structure is employed to capture the extra serial correlation among irregularly observed multiple repeated measures. An efficient expectation conditional maximization algorithm coupled with the first-order Taylor approximation is developed for maximizing the complete pseudo-data likelihood function. The techniques for the estimation of random effects, imputation of missing responses and identification of potential outliers are also investigated. The methodology is motivated by a real data example on 161 pregnant women coming from a study in a private fertilization obstetrics clinic in Santiago, Chile and used to analyze these data.
Adaptive Fractional Fuzzy Sliding Mode Control for Multivariable Nonlinear Systems
Directory of Open Access Journals (Sweden)
Junhai Luo
2014-01-01
Full Text Available This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. The fractional order calculus is employed in the parameter updating stage. The underlying stability analysis as well as parameter update law design is carried out by Lyapunov based technique. In the simulation, two examples including a comparison with the traditional integer order counterpart are given to show the effectiveness of the proposed method. The main contribution of this paper consists in the control performance is better for the fractional order updating law than that of traditional integer order.
Multivariate moment closure techniques for stochastic kinetic models
Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.
2015-09-01
Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.
Multivariate moment closure techniques for stochastic kinetic models
Energy Technology Data Exchange (ETDEWEB)
Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H., E-mail: m.stumpf@imperial.ac.uk [Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ (United Kingdom)
2015-09-07
Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.
Multivariate moment closure techniques for stochastic kinetic models.
Lakatos, Eszter; Ale, Angelique; Kirk, Paul D W; Stumpf, Michael P H
2015-09-07
Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.
Comparison of multivariate calibration techniques applied to experimental NIR data sets
Centner, V; Verdu-Andres, J; Walczak, B; Jouan-Rimbaud, D; Despagne, F; Pasti, L; Poppi, R; Massart, DL; de Noord, OE
2000-01-01
The present study compares the performance of different multivariate calibration techniques applied to four near-infrared data sets when test samples are well within the calibration domain. Three types of problems are discussed: the nonlinear calibration, the calibration using heterogeneous data sets, and the calibration in the presence of irrelevant information in the set of predictors. Recommendations are derived from the comparison, which should help to guide a nonchemometrician through th...
Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques
McDonald, G.; Storrie-Lombardi, M.; Nealson, K.
1999-01-01
The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.
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.
Multiple-model-and-neural-network-based nonlinear multivariable adaptive control
Institute of Scientific and Technical Information of China (English)
Yue FU; Tianyou CHAI
2007-01-01
A multivariable adaptive controller feasible for implementation on distributed computer systems (DCS) is presented for a class of uncertain nonlinear multivariable discrete time systems. The adaptive controller is composed of a linear adaptive controller, a neural network nonlinear adaptive controller and a switching mechanism. The linear controller can provide boundedness of the input and output signals, and the nonlinear controller can improve the performance of the system. The purpose of using the switching mechanism is to obtain the improved system performance and stability simultaneously. Theory analysis and simulation results are presented to show the effectiveness of the proposed method.
Application of multivariate statistical techniques in microbial ecology.
Paliy, O; Shankar, V
2016-03-01
Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure.
Design of a multivariable neural controller for control of a nonlinear MIMO plant
Directory of Open Access Journals (Sweden)
Bańka Stanisław
2014-06-01
Full Text Available The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship’s current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle. Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the speciﬁed yaw angle and the sea current velocity. The ﬁnal part of the paper includes simulation results of system operation with a neural controller along with conclusions and ﬁnal remarks.
Multivariate discrimination technique based on the Bayesian theory
Institute of Scientific and Technical Information of China (English)
JIN Ping; PAN Chang-zhou; XIAO Wei-guo
2007-01-01
A multivariate discrimination technique was established based on the Bayesian theory. Using this technique, P/S ratios of different types (e.g., Pn/Sn, Pn/Lg, Pg/Sn or Pg/Lg) measured within different frequency bands and from different stations were combined together to discriminate seismic events in Central Asia. Major advantages of the Bayesian approach are that the probability to be an explosion for any unknown event can be directly calculated given the measurements of a group of discriminants, and at the same time correlations among these discriminants can be fully taken into account. It was proved theoretically that the Bayesian technique would be optimal and its discriminating performance would be better than that of any individual discriminant as well as better than that yielded by the linear combination approach ignoring correlations among discriminants. This conclusion was also validated in this paper by applying the Bayesian approach to the above-mentioned observed data.
Multivariable nonlinear control of STATCOM for synchronous generator stabilization
Energy Technology Data Exchange (ETDEWEB)
Sahoo, N.C. [Multimedia Univ., Melaka (Malaysia). Faculty of Engineering and Technology; Panigrahi, B.K.; Panda, G. [Multimedia Univ., Selangor (Malaysia); Dash, P.K. [National Inst. of Technology, Rourkela (India)
2004-01-01
A static synchronous compensator (STATCOM) is a typical flexible ac transmission system device playing a vital role as a stability aid for small and large transient disturbances in an interconnected power system. This article deals with design and evaluation of a feedback linearizing nonlinear controller for STATCOM installed in a single-machine infinite-bus power system. In addition to the coordinated control of ac and dc bus voltages, the proposed controller also provides good damping to the electromechanical oscillation of the synchronous generator under transient disturbances. The efficiency of the control strategy is evaluated by computer simulation studies. The comparative study of these results with the conventional cascade control structure establishes the elegance of the proposed control scheme. (author)
Decoupling of Double Extraction Turbo-Unit by Nonlinear Multivariable Inverse System Method
Institute of Scientific and Technical Information of China (English)
黎浩荣; 李立勤; 李东海; 宋兆星; 王伟
2001-01-01
A multivariable inverse nonlinear control scheme is developed to decouple the strongly nonlinear double extraction steam turbo-unit, improving the transient stability of the power and heating system. Computer simulation tests show that not only does the control scheme achieve satisfactory decoupling of the high and low pressure turbines and the electric power, remarkably improving the transient stability, but also the design is very intuitive and concise.
Energy Technology Data Exchange (ETDEWEB)
Reichardt, Thomas A.; Timlin, Jerilyn Ann; Jones, Howland D. T.; Sickafoose, Shane M.; Schmitt, Randal L.
2010-09-01
Laser-induced fluorescence measurements of cuvette-contained laser dye mixtures are made for evaluation of multivariate analysis techniques to optically thick environments. Nine mixtures of Coumarin 500 and Rhodamine 610 are analyzed, as well as the pure dyes. For each sample, the cuvette is positioned on a two-axis translation stage to allow the interrogation at different spatial locations, allowing the examination of both primary (absorption of the laser light) and secondary (absorption of the fluorescence) inner filter effects. In addition to these expected inner filter effects, we find evidence that a portion of the absorbed fluorescence is re-emitted. A total of 688 spectra are acquired for the evaluation of multivariate analysis approaches to account for nonlinear effects.
Multivariate Analysis Techniques for Optimal Vision System Design
DEFF Research Database (Denmark)
Sharifzadeh, Sara
used in this thesis are described. The methodological strategies are outlined including sparse regression and pre-processing based on feature selection and extraction methods, supervised versus unsupervised analysis and linear versus non-linear approaches. One supervised feature selection algorithm......The present thesis considers optimization of the spectral vision systems used for quality inspection of food items. The relationship between food quality, vision based techniques and spectral signature are described. The vision instruments for food analysis as well as datasets of the food items...... (SSPCA) and DCT based characterization of the spectral diffused reflectance images for wavelength selection and discrimination. These methods together with some other state-of-the-art statistical and mathematical analysis techniques are applied on datasets of different food items; meat, diaries, fruits...
Multivariate nonlinear time series modeling of exposure and risk in road safety research
Bijleveld, F.; Commandeur, J.; Montfort, van K.; Koopman, S.J.
2010-01-01
A multivariate non-linear time series model for road safety data is presented. The model is applied in a case-study into the development of a yearly time series of numbers of fatal accidents (inside and outside urban areas) and numbers of kilometres driven by motor vehicles in the Netherlands betwee
Multivariable Adaptive Controller for the Nonlinear MIMO Model of a Container Ship
Directory of Open Access Journals (Sweden)
Michal Brasel
2014-03-01
Full Text Available The paper presents an adaptive multivariable control system for a Multi-Input, Multi-Output (MIMO nonlinear dynamic process. The problems under study are exemplified by a synthesis of a course angle and forward speed control system for the nonlinear four-Degrees-of-Freedom (4-DoF mathematical model of a single-screw, high-speed container ship. The paper presents the complexity of the assumed model to be analyzed and a synthesis method for the multivariable adaptive modal controller. Due to a strongly nonlinear nature of the ship movements equations a multivariable adaptive controller is tuned in relation to changeable hydrodynamic operating conditions of the ship. In accordance with the given operating conditions controller parameters are chosen on the basis of four measured auxiliary signals. The system synthesis is carried out by linearization of the nonlinear model of the ship at its nominal operating points in the steady-state and by means of a pole placement control method. The final part of the paper includes results of simulation tests of the proposed control system carried out in the MATLAB/Simulink environment along with conclusions and final remarks.
Characterization of Lavandula spp. Honey Using Multivariate Techniques
2016-01-01
Traditionally, melissopalynological and physicochemical analyses have been the most used to determine the botanical origin of honey. However, when performed individually, these analyses may provide less unambiguous results, making it difficult to discriminate between mono and multifloral honeys. In this context, with the aim of better characterizing this beehive product, a selection of 112 Lavandula spp. monofloral honey samples from several regions were evaluated by association of multivariate statistical techniques with physicochemical, melissopalynological and phenolic compounds analysis. All honey samples fulfilled the quality standards recommended by international legislation, except regarding sucrose content and diastase activity. The content of sucrose and the percentage of Lavandula spp. pollen have a strong positive association. In fact, it was found that higher amounts of sucrose in honey are related with highest percentage of pollen of Lavandula spp.. The samples were very similar for most of the physicochemical parameters, except for proline, flavonoids and phenols (bioactive factors). Concerning the pollen spectrum, the variation of Lavandula spp. pollen percentage in honey had little contribution to the formation of samples groups. The formation of two groups regarding the physicochemical parameters suggests that the presence of other pollen types in small percentages influences the factor termed as “bioactive”, which has been linked to diverse beneficial health effects. PMID:27588420
Directory of Open Access Journals (Sweden)
Shahrukh Adnan Khan M. D.
2017-01-01
Full Text Available This paper presents a Graphical User Interface (GUI software utility for the input/output characterization of single variable and multivariable nonlinear systems by obtaining the sinusoidal input describing function (SIDF of the plant. The software utility is developed on MATLAB R2011a environment. The developed GUI holds no restriction on the nonlinearity type, arrangement and system order; provided that output(s of the system is obtainable either though simulation or experiments. An insight to the GUI and its features are presented in this paper and example problems from both single variable and multivariable cases are demonstrated. The formulation of input/output behavior of the system is discussed and the nucleus of the MATLAB command underlying the user interface has been outlined. Some of the industries that would benefit from this software utility includes but not limited to aerospace, defense technology, robotics and automotive.
M. N. Evans; Smerdon, Jason E.; Kaplan, A.; S. E. Tolwinski-Ward; González-Rouco, J. F.
2014-01-01
©2014. American Geophysical Union. All Rights Reserved. Climate field reconstructions (CFRs) of the global annual surface air temperature (SAT) field and associated global area-weighted mean annual temperature (GMAT) are derived in a collection of pseudoproxy experiments for the past millennium. Pseudoproxies are modeled from temperature (T), precipitation (P), T+P, and VS-Lite (VSL), a nonlinear and multivariate proxy system model for tree ring widths. Spatial patterns of reconstruction skil...
Evans, M.N.; J. E. Smerdon; Kaplan, A.; Tolwinski-Ward, S.E.; J. F. González-Rouco
2014-01-01
©2014. American Geophysical Union. All Rights Reserved. Climate field reconstructions (CFRs) of the global annual surface air temperature (SAT) field and associated global area-weighted mean annual temperature (GMAT) are derived in a collection of pseudoproxy experiments for the past millennium. Pseudoproxies are modeled from temperature (T), precipitation (P), T+P, and VS-Lite (VSL), a nonlinear and multivariate proxy system model for tree ring widths. Spatial patterns of reconstruction skil...
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.
Uniform approach to linear and nonlinear interrelation patterns in multivariate time series
Rummel, Christian; Abela, Eugenio; Müller, Markus; Hauf, Martinus; Scheidegger, Olivier; Wiest, Roland; Schindler, Kaspar
2011-06-01
Currently, a variety of linear and nonlinear measures is in use to investigate spatiotemporal interrelation patterns of multivariate time series. Whereas the former are by definition insensitive to nonlinear effects, the latter detect both nonlinear and linear interrelation. In the present contribution we employ a uniform surrogate-based approach, which is capable of disentangling interrelations that significantly exceed random effects and interrelations that significantly exceed linear correlation. The bivariate version of the proposed framework is explored using a simple model allowing for separate tuning of coupling and nonlinearity of interrelation. To demonstrate applicability of the approach to multivariate real-world time series we investigate resting state functional magnetic resonance imaging (rsfMRI) data of two healthy subjects as well as intracranial electroencephalograms (iEEG) of two epilepsy patients with focal onset seizures. The main findings are that for our rsfMRI data interrelations can be described by linear cross-correlation. Rejection of the null hypothesis of linear iEEG interrelation occurs predominantly for epileptogenic tissue as well as during epileptic seizures.
Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes☆
Institute of Scientific and Technical Information of China (English)
Yuan Xu; Ying Liu; Qunxiong Zhu
2016-01-01
Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in-cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal com-ponent analysis. In this approach, mutual information estimation and Bayesian information criterion (BIC) are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation (BP) network is applied whose input is multivar-iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis (LKPCA) model for incipient fault prognosis. The new method has been exemplified in a sim-ple nonlinear process and the complicated Tennessee Eastman (TE) benchmark process. The results indicate that the new method has superiority in the fault prognosis sensitivity over other traditional fault prognosis methods. © 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. Al rights reserved.
Multivariate meta-analysis for non-linear and other multi-parameter associations
Gasparrini, A; Armstrong, B; Kenward, M G
2012-01-01
In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043
Institute of Scientific and Technical Information of China (English)
Yong-ping Liu; Gui-qiao Xu
2002-01-01
The classes of the multivariate functions with bounded moduli on Rd and Td are given and their average a-widths and non-linear n-widths are discussed. The weak asymptotic behaviors are established for the corresponding quantities.
MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques
Directory of Open Access Journals (Sweden)
Cerqueira Fabio R
2012-10-01
Full Text Available Abstract Background The shotgun strategy (liquid chromatography coupled with tandem mass spectrometry is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity. Results Here, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches. Conclusion Our approach not only enhances the computational performance, and
Wang, Zhe; Li, Lizhi; Ni, Weidou; Li, Zheng
2011-01-01
A multivariate dominant factor based non-linearized PLS model is proposed. The intensities of different lines were taken to construct a multivariate dominant factor model, which describes the dominant concentration information of the measured species. In constructing such a multivariate model, non-linear transformation of multi characteristic line intensities according to the physical mechanisms of lased induced plasma spectrum were made, combined with linear-correlation-based PLS method, to model the nonlinear self-absorption and inter-element interference effects. This enables the linear PLS method to describe non-linear relationship more accurately and provides the statistics-based PLS method with physical backgrounds. Moreover, a secondary PLS is applied utilizing the whole spectra information to further correct the model results. Experiments were conducted using standard brass samples. Taylor expansion was applied to make the nonlinear transformation to describe the self-absorption effect of Cu. Then, li...
DEFF Research Database (Denmark)
Hilger, Klaus Baggesen
2002-01-01
This work describes different methods that are useful in the analysis of multivariate single and multiset data. The thesis covers selected aspects of relevant data analysis techniques in this context. Methods dedicated to handling data of a spatial nature are of primary interest with focus on dat...
Multivariable adaptive control and estimation of a nonlinear wastewater treatment process
Energy Technology Data Exchange (ETDEWEB)
Ben Youssef, C.; Dahhou, B. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France)]|[Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1995-12-31
In this paper, an approach for estimating biological state and parameter variables and for controlling a non linear wastewater treatment process is developed. Combination of a nonlinear estimation procedure and a multivariable reference model control law provides favourable performances for tracking a given model-based reference model despite disturbances and system parameter uncertainties. Convergence of both estimation and control scheme are demonstrated via Lyapunov`s method. Simulation study with additive measurements noises and parameter jumps shows the efficiency and significant robustness of the control methodology developed for this non linear process. (author) 13 refs.
[Neuroimaging in psychiatry: multivariate analysis techniques for diagnosis and prognosis].
Kambeitz, J; Koutsouleris, N
2014-06-01
Multiple studies successfully applied multivariate analysis to neuroimaging data demonstrating the potential utility of neuroimaging for clinical diagnostic and prognostic purposes. Summary of the current state of research regarding the application of neuroimaging in the field of psychiatry. Literature review of current studies. Results of current studies indicate the potential application of neuroimaging data across various diagnoses, such as depression, schizophrenia, bipolar disorder and dementia. Potential applications include disease classification, differential diagnosis and prediction of disease course. The results of the studies are heterogeneous although some studies report promising findings. Further multicentre studies are needed with clearly specified patient populations to systematically investigate the potential utility of neuroimaging for the clinical routine.
Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.
Chai, Tianyou; Zhang, Yajun; Wang, Hong; Su, Chun-Yi; Sun, Jing
2011-12-01
For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.
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.
Analysis techniques for multivariate root loci. [a tool in linear control systems
Thompson, P. M.; Stein, G.; Laub, A. J.
1980-01-01
Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.
A multivariate nonlinear mixed effects method for analyzing energy partitioning in growing pigs
DEFF Research Database (Denmark)
Strathe, Anders Bjerring; Danfær, Allan Christian; Chwalibog, André
2010-01-01
Simultaneous equations have become increasingly popular for describing the effects of nutrition on the utilization of ME for protein (PD) and lipid deposition (LD) in animals. The study developed a multivariate nonlinear mixed effects (MNLME) framework and compared it with an alternative method...... for estimating parameters in simultaneous equations that described energy metabolism in growing pigs, and then proposed new PD and LD equations. The general statistical framework was implemented in the NLMIXED procedure in SAS. Alternative PD and LD equations were also developed, which assumed...... that the instantaneous response curve of an animal to varying energy supply followed the law of diminishing returns behavior. The Michaelis-Menten function was adopted to represent a biological relationship in which the affinity constant (k) represented the sensitivity of PD to ME above maintenance. The approach...
Ecological prediction with nonlinear multivariate time-frequency functional data models
Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.
2013-01-01
Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.
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.
Deng, Linhua
2015-07-01
Three nonlinear analysis techniques, including cross-recurrence plot, line of synchronization, and cross-wavelet transform, are proposed to estimate the coherent phase vibrations of nonlinear and non-stationary time series. The case study utilizes the monthly averages of sunspot areas during the time interval from May 1874 to August 2014. The following prominent results are found: (1) the phase-leading hemisphere of long-term sunspot areas has changed twice in the past 140 years, indicating that the hemispheric imbalances and apparent phase differences on both hemispheres are a prevalent behavior and are not anomalous; (2) the alternating regularity of hemispheric asynchronism exhibits a cyclical pattern of 4.5+3.5 cycles, and the magnetic flux excess in a certain hemisphere during the ascending branch of a cycle can be taken as an indication of the phase-leading hemisphere in this cycle. We firmly believe that powerful nonlinear approaches are more advanced than classical linear methods when they are combined to determine the dynamic complexity of nonlinear physical systems.
Publishing nutrition research: a review of multivariate techniques--part 2: analysis of variance.
Harris, Jeffrey E; Sheean, Patricia M; Gleason, Philip M; Bruemmer, Barbara; Boushey, Carol
2012-01-01
This article is the eighth in a series exploring the importance of research design, statistical analysis, and epidemiology in nutrition and dietetics research, and the second in a series focused on multivariate statistical analytical techniques. The purpose of this review is to examine the statistical technique, analysis of variance (ANOVA), from its simplest to multivariate applications. Many dietetics practitioners are familiar with basic ANOVA, but less informed of the multivariate applications such as multiway ANOVA, repeated-measures ANOVA, analysis of covariance, multiple ANOVA, and multiple analysis of covariance. The article addresses all these applications and includes hypothetical and real examples from the field of dietetics.
Stalked protozoa identification by image analysis and multivariable statistical techniques.
Amaral, A L; Ginoris, Y P; Nicolau, A; Coelho, M A Z; Ferreira, E C
2008-06-01
Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.
Adaptive sensor-fault tolerant control for a class of multivariable uncertain nonlinear systems.
Khebbache, Hicham; Tadjine, Mohamed; Labiod, Salim; Boulkroune, Abdesselem
2015-03-01
This paper deals with the active fault tolerant control (AFTC) problem for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems subject to sensor faults and external disturbances. The proposed AFTC method can tolerate three additive (bias, drift and loss of accuracy) and one multiplicative (loss of effectiveness) sensor faults. By employing backstepping technique, a novel adaptive backstepping-based AFTC scheme is developed using the fact that sensor faults and system uncertainties (including external disturbances and unexpected nonlinear functions caused by sensor faults) can be on-line estimated and compensated via robust adaptive schemes. The stability analysis of the closed-loop system is rigorously proven using a Lyapunov approach. The effectiveness of the proposed controller is illustrated by two simulation examples.
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.
Directory of Open Access Journals (Sweden)
Laila Khalilzadeh Ganjali-khani
2013-01-01
Full Text Available One of the most effective strategies for steam generator efficiency enhancement is to improve the control system. For such an improvement, it is essential to have an accurate model for the steam generator of power plant. In this paper, an industrial steam generator is considered as a nonlinear multivariable system for identification. An important step in nonlinear system identification is the development of a nonlinear model. In recent years, artificial neural networks have been successfully used for identification of nonlinear systems in many researches. Wavelet neural networks (WNNs also are used as a powerful tool for nonlinear system identification. In this paper we present a time delay neural network model and a WNN model in order to identify an industrial steam generator. Simulation results show the effectiveness of the proposed models in the system identification and demonstrate that the WNN model is more precise to estimate the plant outputs.
Directory of Open Access Journals (Sweden)
Luiz Augusto da Cruz Meleiro
2005-06-01
Full Text Available In this work a MIMO non-linear predictive controller was developed for an extractive alcoholic fermentation process. The internal model of the controller was represented by two MISO Functional Link Networks (FLNs, identified using simulated data generated from a deterministic mathematical model whose kinetic parameters were determined experimentally. The FLN structure presents as advantages fast training and guaranteed convergence, since the estimation of the weights is a linear optimization problem. Besides, the elimination of non-significant weights generates parsimonious models, which allows for fast execution in an MPC-based algorithm. The proposed algorithm showed good potential in identification and control of non-linear processes.Neste trabalho um controlador preditivo não linear multivariável foi desenvolvido para um processo de fermentação alcoólica extrativa. O modelo interno do controlador foi representado por duas redes do tipo Functional Link (FLN, identificadas usando dados de simulação gerados a partir de um modelo validado experimentalmente. A estrutura FLN apresenta como vantagem o treinamento rápido e convergência garantida, já que a estimação dos seus pesos é um problema de otimização linear. Além disso, a eliminação de pesos não significativos gera modelos parsimoniosos, o que permite a rápida execução em algoritmos de controle preditivo baseado em modelo. Os resultados mostram que o algoritmo proposto tem grande potencial para identificação e controle de processos não lineares.
Multivariate techniques of analysis for ToF-E recoil spectrometry data
Energy Technology Data Exchange (ETDEWEB)
Whitlow, H.J.; Bouanani, M.E.; Persson, L.; Hult, M.; Jonsson, P.; Johnston, P.N. [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M. [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M.; Zaring, C. [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P.N.; Bubb, I.F.; Walker, B.R.; Stannard, W.B. [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)
1996-12-31
Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.
Evans, M. N.; Smerdon, J. E.; Kaplan, A.; Tolwinski-Ward, S. E.; González-Rouco, J. F.
2014-12-01
Climate field reconstructions (CFRs) of the global annual surface air temperature (SAT) field and associated global area-weighted mean annual temperature (GMAT) are derived in a collection of pseudoproxy experiments for the past millennium. Pseudoproxies are modeled from temperature (T), precipitation (P), T+P, and VS-Lite (VSL), a nonlinear and multivariate proxy system model for tree ring widths. Spatial patterns of reconstruction skill and spectral bias for the T+P and VSL-derived CFRs are similar to those previously shown using temperature-only pseudoproxies but demonstrate overall degraded skill and spectral bias for SAT reconstruction. Analysis of GMAT spectra nevertheless suggests that the true GMAT frequency spectrum is resolved by those pseudoproxies (T, T+P, and VSL) that contain some temperature information. The results suggest that mixed temperature and moisture-responding paleoclimate data may produce actual GMAT reconstructions with skill, error, and spectral characteristics like those expected from univariate and linear temperature responders, but spatially resolved CFR results should be analyzed cautiously.
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.
Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)
2001-01-01
A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.
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
Directory of Open Access Journals (Sweden)
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.
Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis
DEFF Research Database (Denmark)
Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.
2007-01-01
The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...
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.
Horton, Rebecca B; McConico, Morgan; Landry, Currie; Tran, Tho; Vogt, Frank
2012-10-09
Innovations in chemometrics are required for studies of chemical systems which are governed by nonlinear responses to chemical parameters and/or interdependencies (coupling) among these parameters. Conventional and linear multivariate models have limited use for quantitative and qualitative investigations of such systems because they are based on the assumption that the measured data are simple superpositions of several input parameters. 'Predictor Surfaces' were developed for studies of more chemically complex systems such as biological materials in order to ensure accurate quantitative analyses and proper chemical modeling for in-depth studies of such systems. Predictor Surfaces are based on approximating nonlinear multivariate model functions by multivariate Taylor expansions which inherently introduce the required coupled and higher-order predictor variables. As proof-of-principle for the Predictor Surfaces' capabilities, an application from environmental analytical chemistry was chosen. Microalgae cells are known to sensitively adapt to changes in environmental parameters such as pollution and/or nutrient availability and thus have potential as novel in situ sensors for environmental monitoring. These adaptations of the microalgae cells are reflected in their chemical signatures which were then acquired by means of FT-IR spectroscopy. In this study, the concentrations of three nutrients, namely inorganic carbon and two nitrogen containing ions, were chosen. Biological considerations predict that changes in nutrient availability produce a nonlinear response in the cells' biomass composition; it is also known that microalgae need certain nutrient mixes to thrive. The nonlinear Predictor Surfaces were demonstrated to be more accurate in predicting the values of these nutrients' concentrations than principal component regression. For qualitative chemical studies of biological systems, the Predictor Surfaces themselves are a novel tool as they visualize
Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam
2014-07-01
This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies.
Bilgel, Murat; Prince, Jerry L; Wong, Dean F; Resnick, Susan M; Jedynak, Bruno M
2016-07-01
It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. These are especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain at the level of voxels, whose volumes are on the order of mm(3). These voxelwise measurements provide a rich collection of disease indicators. Longitudinal neuroimaging studies enable the analysis of changes in these voxelwise measures. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different rates, and generally consider each voxelwise measure independently. We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals. The method involves the prediction of a progression score for each visit based on a collective analysis of voxelwise biomarker data within an expectation-maximization framework that efficiently handles large amounts of measurements and variable number of visits per individual, and accounts for spatial correlations among voxels. This score allows individuals with similar progressions to be aligned and analyzed together, which enables the construction of a trajectory of brain changes as a function of an underlying progression or disease stage. We apply our method to studying cortical β-amyloid deposition, a hallmark of preclinical Alzheimer's disease, as measured using positron emission tomography. Results on 104 individuals with a total of 300 visits suggest that precuneus is the earliest cortical region to
Chen, Peiying; Zhang, Weidong
2007-04-01
This paper improves an inverted decoupling technique for a class of stable linear multivariable processes with multiple time delays and nonminimum-phase zeros. Two decoupling schemes are proposed based on the inverted decoupling technique. One is a developed inverted decoupling scheme. In this scheme, the decoupler is designed such that the inverted decoupling technique accommodates a wider field than the one introduced in the published literature. However, due to the stability issue, some multivariable processes still cannot be decoupled by the inverted decoupling structure. To solve this problem, another modified decoupling scheme with unity feedback structure is suggested for implementation. The Internal Model Control (IMC) theory is applied here to design PI/PID controllers for the decoupled processes. Furthermore, in the presence of multiplicative input uncertainty, low bounds of the control parameters are derived quantitatively for guaranteeing robust stability of the system. Simulations are illustrated for demonstrating the validity of the proposed control schemes.
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.
Long, C. L.
1991-02-01
Multivariate calibration techniques can reduce the time required for routine testing and can provide new methods of analysis. Multivariate calibration is commonly used with near infrared reflectance analysis (NIRA) and Fourier transform infrared (FTIR) spectroscopy. Two feasibility studies were performed to determine the capability of NIRA, using multivariate calibration techniques, to perform analyses on the types of samples that are routinely analyzed at this laboratory. The first study performed included a variety of samples and indicated that NIRA would be well-suited to perform analyses on selected materials properties such as water content and hydroxyl number on polyol samples, epoxy content on epoxy resins, water content of desiccants, and the amine values of various amine cure agents. A second study was performed to assess the capability of NIRA to perform quantitative analysis of hydroxyl numbers and water contents of hydroxyl-containing materials. Hydroxyl number and water content were selected for determination because these tests are frequently run on polyol materials and the hydroxyl number determination is time consuming. This study pointed out the necessity of obtaining calibration standards identical to the samples being analyzed for each type of polyol or other material being analyzed. Multivariate calibration techniques are frequently used with FTIR data to determine the composition of a large variety of complex mixtures. A literature search indicated many applications of multivariate calibration to FTIR data. Areas identified where quantitation by FTIR would provide a new capability are quantitation of components in epoxy and silicone resins, polychlorinated biphenyls (PCBs) in oils, and additives to polymers.
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...
Nonlinear Multivariate Spline-Based Control Allocation for High-Performance Aircraft
Tol, H.J.; De Visser, C.C.; Van Kampen, E.; Chu, Q.P.
2014-01-01
High performance flight control systems based on the nonlinear dynamic inversion (NDI) principle require highly accurate models of aircraft aerodynamics. In general, the accuracy of the internal model determines to what degree the system nonlinearities can be canceled; the more accurate the model,
Directory of Open Access Journals (Sweden)
S. I. Samsudin
2014-01-01
Full Text Available The wastewater treatment plant (WWTP is highly known with the nonlinearity of the control parameters, thus it is difficult to be controlled. In this paper, the enhancement of nonlinear PI controller (ENon-PI to compensate the nonlinearity of the activated sludge WWTP is proposed. The ENon-PI controller is designed by cascading a sector-bounded nonlinear gain to linear PI controller. The rate variation of the nonlinear gain kn is automatically updated based on adaptive interaction algorithm. Initiative to simplify the ENon-PI control structure by adapting kn has been proved by significant improvement under various dynamic influents. More than 30% of integral square error and 14% of integral absolute error are reduced compared to benchmark PI for DO control and nitrate in nitrogen removal control. Better average effluent qualities, less number of effluent violations, and lower aeration energy consumption resulted.
Alvarez, Odalys Quevedo; Tagle, Margarita Edelia Villanueva; Pascual, Jorge L Gómez; Marín, Ma Teresa Larrea; Clemente, Ana Catalina Nuñez; Medina, Miriam Odette Cora; Palau, Raiza Rey; Alfonso, Mario Simeón Pomares
2014-10-01
Spatial and temporal variations of sediment quality in Matanzas Bay (Cuba) were studied by determining a total of 12 variables (Zn, Cu, Pb, As, Ni, Co, Al, Fe, Mn, V, CO₃²⁻, and total hydrocarbons (THC). Surface sediments were collected, annually, at eight stations during 2005-2008. Multivariate statistical techniques, such as principal component (PCA), cluster (CA), and lineal discriminant (LDA) analyses were applied for identification of the most significant variables influencing the environmental quality of sediments. Heavy metals (Zn, Cu, Pb, V, and As) and THC were the most significant species contributing to sediment quality variations during the sampling period. Concentrations of V and As were determined in sediments of this ecosystem for the first time. The variation of sediment environmental quality with the sampling period and the differentiation of samples in three groups along the bay were obtained. The usefulness of the multivariate statistical techniques employed for the environmental interpretation of a limited dataset was confirmed.
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.
DEFF Research Database (Denmark)
Kallevik, H.; Hansen, Susanne Brunsgaard; Sæther, Ø.
2000-01-01
Water-in-oil emulsions are investigated by means of multivariate analysis of near infrared (NIR) spectroscopic profiles in the range 1100 - 2250 nm. The oil phase is a paraffin-diluted crude oil from the Norwegian Continental Shelf. The influence of water absorption and light scattering...... of the water droplets are shown to be strong. Despite the strong influence of the water phase, the NIR technique is still capable of predicting the composition of the investigated oil phase....
Directory of Open Access Journals (Sweden)
Yang Yu
2013-01-01
Full Text Available Based on a brief review on current harmonics generation mechanism for grid-connected inverter under distorted grid voltage, the harmonic disturbances and uncertain items are immersed into the original state-space differential equation of grid-connected inverter. A new algorithm of global current harmonic rejection based on nonlinear backstepping control with multivariable internal model principle is proposed for grid-connected inverter with exogenous disturbances and uncertainties. A type of multivariable internal model for a class of nonlinear harmonic disturbances is constructed. Based on application of backstepping control law of the nominal system, a multivariable adaptive state feedback controller combined with multivariable internal model and adaptive control law is designed to guarantee the closed-loop system globally uniformly bounded, which is proved by a constructed Lyapunov function. The presented algorithm extends rejection of nonlinear single-input systems to multivariable globally defined normal form, the correctness and effectiveness of which are verified by the simulation results.
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
Directory of Open Access Journals (Sweden)
Dimitrios Kourkoutas
2009-04-01
Full Text Available Dimitrios Kourkoutas1,2, Gerasimos Georgopoulos1, Antonios Maragos1, et al1Department of Ophthalmology, Medical School, Athens University, Athens, Greece; 2Department of Ophthalmology, 417 Hellenic Army Shared Fund Hospital, Athens, GreecePurpose: In this paper a new nonlinear multivariable regression method is presented in order to investigate the relationship between the central corneal thickness (CCT and the Heidelberg Retina Tomograph (HRTII optic nerve head (ONH topographic measurements, in patients with established glaucoma.Methods: Forty nine eyes of 49 patients with glaucoma were included in this study. Inclusion criteria were patients with (a HRT II ONH imaging of good quality (SD 30 < μm, (b reliable Humphrey visual field tests (30-2 program, and (c bilateral CCT measurements with ultrasonic contact pachymetry. Patients were classified as glaucomatous based on visual field and/or ONH damage. The relationship between CCT and topographic parameters was analyzed by using the new nonlinear multivariable regression model.Results: In the entire group, CCT was 549.78 ± 33.08 μm (range: 484–636 μm; intraocular pressure (IOP was 16.4 ± 2.67 mmHg (range: 11–23 mmHg; MD was −3.80 ± 4.97 dB (range: 4.04 – [−20.4] dB; refraction was −0.78 ± 2.46 D (range: −6.0 D to +3.0 D. The new nonlinear multivariable regression model we used indicated that CCT was significantly related (R2 = 0.227, p < 0.01 with rim volume nasally and type of diagnosis.Conclusions: By using the new nonlinear multivariable regression model, in patients with established glaucoma, our data showed that there is a statistically significant correlation between CCT and HRTII ONH structural measurements, in glaucoma patients.Keywords: central corneal thickness, glaucoma, optic nerve head, HRT
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.
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.
Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.
Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei
2013-12-03
We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in
Hwang, Chih-Lyang; Jan, Chau
2016-02-01
At the beginning, an approximate nonlinear autoregressive moving average (NARMA) model is employed to represent a class of multivariable nonlinear dynamic systems with time-varying delay. It is known that the disadvantages of robust control for the NARMA model are as follows: 1) suitable control parameters for larger time delay are more sensitive to achieving desirable performance; 2) it only deals with bounded uncertainty; and 3) the nominal NARMA model must be learned in advance. Due to the dynamic feature of the NARMA model, a recurrent neural network (RNN) is online applied to learn it. However, the system performance becomes deteriorated due to the poor learning of the larger variation of system vector functions. In this situation, a simple network is employed to compensate the upper bound of the residue caused by the linear parameterization of the approximation error of RNN. An e -modification learning law with a projection for weight matrix is applied to guarantee its boundedness without persistent excitation. Under suitable conditions, the semiglobally ultimately bounded tracking with the boundedness of estimated weight matrix is obtained by the proposed RNN-based multivariable adaptive control. Finally, simulations are presented to verify the effectiveness and robustness of the proposed control.
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.
Directory of Open Access Journals (Sweden)
Michel J. Anzanello
2014-09-01
Full Text Available A typical application of multivariate techniques in forensic analysis consists of discriminating between authentic and unauthentic samples of seized drugs, in addition to finding similar properties in the unauthentic samples. In this paper, the performance of several methods belonging to two different classes of multivariate techniques–supervised and unsupervised techniques–were compared. The supervised techniques (ST are the k-Nearest Neighbor (KNN, Support Vector Machine (SVM, Probabilistic Neural Networks (PNN and Linear Discriminant Analysis (LDA; the unsupervised techniques are the k-Means CA and the Fuzzy C-Means (FCM. The methods are applied to Infrared Spectroscopy by Fourier Transform (FTIR from authentic and unauthentic Cialis and Viagra. The FTIR data are also transformed by Principal Components Analysis (PCA and kernel functions aimed at improving the grouping performance. ST proved to be a more reasonable choice when the analysis is conducted on the original data, while the UT led to better results when applied to transformed data.
Ajorlo, Majid; Abdullah, Ramdzani B; Yusoff, Mohd Kamil; Halim, Ridzwan Abd; Hanif, Ahmad Husni Mohd; Willms, Walter D; Ebrahimian, Mahboubeh
2013-10-01
This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.
Energy Technology Data Exchange (ETDEWEB)
Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE
2008-01-01
Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.
Directory of Open Access Journals (Sweden)
Arayne M
2009-01-01
Full Text Available A sensitive and accurate UV spectrophotometric method with multivariate calibration technique for the determination of metformin hydrochloride in bulk drug and different pharmaceutical formulations has been described. This technique is based on the use of the linear regression equations by using relationship between concentration and absorbance at five different wavelength. The results were treated statistically and were found highly accurate, precise and reproducible. The method is accurate, precise (% recovery 102.500.063, CV≤0.56, r =0.997 and linear within the range 1-10 mg/ml. There was no interference from the excipients i.e Povidone K 30, magnesium stearate, lactose and hydroxypropylmethylcellulose. This statistical approach gives optimum results for the eliminating fluctuations coming from instrumental or experimental conditions.
Recursive identification and tracking of parameters for linear and nonlinear multivariable systems
Sidar, M.
1975-01-01
The problem of identifying constant and variable parameters in multi-input, multi-output, linear and nonlinear systems is considered, using the maximum likelihood approach. An iterative algorithm, leading to recursive identification and tracking of the unknown parameters and the noise covariance matrix, is developed. Agile tracking, and accurate and unbiased identified parameters are obtained. Necessary conditions for a globally, asymptotically stable identification process are provided; the conditions proved to be useful and efficient. Among different cases studied, the stability derivatives of an aircraft were identified and some of the results are shown as examples.
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
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.
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.
Output feedback adaptive control of multivariable nonlinear systems using Nussbaum gain method
Institute of Scientific and Technical Information of China (English)
Zhou Ying; Wu Yuqiang
2006-01-01
A new output feedback adaptive control scheme for multi-input and multi-output nonlinear systems with parametric uncertainty is presented based on the Nussbaum gain method and the backstepping approach. The high frequency gain matrix of the linear part of the system is not necessarily positive definite, but can be transformed into a lower or upper triangular matrix whose signs of diagonal elements are unknown. The new required condition for the high frequency gain matrix can be easily checked for certain plants so that the proposed method is widely applicable. The global stability of the closed loop systems is guaranteed through this control scheme, at the same time the tracking error converges to zero.
Thriumani, Reena; Zakaria, Ammar; Hashim, Yumi Zuhanis Has-Yun; Helmy, Khaled Mohamed; Omar, Mohammad Iqbal; Jeffree, Amanina; Adom, Abdul Hamid; Shakaff, Ali Yeon Md; Kamarudin, Latifah Munirah
2017-03-01
In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extracted by taking into consideration of both steady state and the transient information. The extracted data are then being processed by multivariate analysis, Linear Discriminant Analysis (LDA) to provide visualization of the clustering vector information in multi-sensor space. The Probabilistic Neural Network (PNN) classifier was used to test the performance of the E-Nose on determining the volatile organic compounds (VOCs) of lung cancer cell line. The LDA data projection was able to differentiate between the lung cancer cell samples and other samples (breast cancer, normal cell and blank medium) effectively. The features extracted from the steady state response reached 100% of classification rate while the transient response with the aid of LDA dimension reduction methods produced 100% classification performance using PNN classifier with a spread value of 0.1. The results also show that E-Nose application is a promising technique to be applied to real patients in further work and the aid of Multivariate Analysis; it is able to be the alternative to the current lung cancer diagnostic methods.
Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques
Nagaraju, A.; Sreedhar, Y.; Thejaswi, A.; Sayadi, Mohammad Hossein
2016-11-01
The groundwater samples from Rapur area were collected from different sites to evaluate the major ion chemistry. The large number of data can lead to difficulties in the integration, interpretation, and representation of the results. Two multivariate statistical methods, hierarchical cluster analysis (HCA) and factor analysis (FA), were applied to evaluate their usefulness to classify and identify geochemical processes controlling groundwater geochemistry. Four statistically significant clusters were obtained from 30 sampling stations. This has resulted two important clusters viz., cluster 1 (pH, Si, CO3, Mg, SO4, Ca, K, HCO3, alkalinity, Na, Na + K, Cl, and hardness) and cluster 2 (EC and TDS) which are released to the study area from different sources. The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area. From PCA, it is clear that the first factor (factor 1), accounted for 36.2% of the total variance, was high positive loading in EC, Mg, Cl, TDS, and hardness. Based on the PCA scores, four significant cluster groups of sampling locations were detected on the basis of similarity of their water quality.
Energy Technology Data Exchange (ETDEWEB)
Park, Jinyong [Univ. of Arizona, Tucson, AZ (United States); Balasingham, P [Univ. of Arizona, Tucson, AZ (United States); McKenna, Sean Andrew [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kulatilake, Pinnaduwa H.S.W. [Univ. of Arizona, Tucson, AZ (United States)
2004-09-01
Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater
On the potential of multivariate techniques for the determination of multidimensional efficiencies
Viaud, Benoit
2016-01-01
Differential measurements of particle collisions or decays can provide stringent constraints on physics beyond the Standard Model of particle physics. In particular, the distributions of the kinematical and angular variables that characterise heavy me- son multibody decays are non trivial and can sign the underlying interaction physics. In the era of high luminosity opened by the advent of the Large Hadron Collider and of Flavor Factories, differential measurements are less and less dominated by statistical precision and require a precise determination of efficiencies that depend simultaneously on several variables and do not factorise in these variables. This docu- ment is a reflection on the potential of multivariate techniques for the determination of such multidimensional efficiencies. We carried out two case studies that show that multilayer perceptron neural networks can determine and correct for the distortions introduced by reconstruction and selection criteria in the multidimensional phase space of t...
Nnane, Daniel Ekane
2011-11-15
Contamination of surface waters is a pervasive threat to human health, hence, the need to better understand the sources and spatio-temporal variations of contaminants within river catchments. River catchment managers are required to sustainably monitor and manage the quality of surface waters. Catchment managers therefore need cost-effective low-cost long-term sustainable water quality monitoring and management designs to proactively protect public health and aquatic ecosystems. Multivariate and phage-lysis techniques were used to investigate spatio-temporal variations of water quality, main polluting chemophysical and microbial parameters, faecal micro-organisms sources, and to establish 'sentry' sampling sites in the Ouse River catchment, southeast England, UK. 350 river water samples were analysed for fourteen chemophysical and microbial water quality parameters in conjunction with the novel human-specific phages of Bacteroides GB-124 (Bacteroides GB-124). Annual, autumn, spring, summer, and winter principal components (PCs) explained approximately 54%, 75%, 62%, 48%, and 60%, respectively, of the total variance present in the datasets. Significant loadings of Escherichia coli, intestinal enterococci, turbidity, and human-specific Bacteroides GB-124 were observed in all datasets. Cluster analysis successfully grouped sampling sites into five clusters. Importantly, multivariate and phage-lysis techniques were useful in determining the sources and spatial extent of water contamination in the catchment. Though human faecal contamination was significant during dry periods, the main source of contamination was non-human. Bacteroides GB-124 could potentially be used for catchment routine microbial water quality monitoring. For a cost-effective low-cost long-term sustainable water quality monitoring design, E. coli or intestinal enterococci, turbidity, and Bacteroides GB-124 should be monitored all-year round in this river catchment. Copyright © 2011 Elsevier B.V. All
Robust Nonlinear Causality Analysis of Non-Stationary Multivariate Physiological Time Series.
Schaeck, Tim; Muma, Michael; Feng, Mengling; Guan, Cuntai; Zoubir, Abdelhak
2017-05-26
An important research area in biomedical signal processing is that of quantifying the relationship between simultaneously observed time series and to reveal interactions between the signals. Since biomedical signals are potentially non-stationary and the measurements may contain outliers and artifacts, we introduce a robust time-varying generalized partial directed coherence (rTV-gPDC) function. The proposed method, which is based on a robust estimator of the timevarying autoregressive (TVAR) parameters, is capable of revealing directed interactions between signals. By definition, the rTV-gPDC only displays the linear relationships between the signals. We therefore suggest to approximate the residuals of the TVAR process, which potentially carry information about the nonlinear causality by a piece-wise linear time-varying moving-average (TVMA) model. The performance of the proposed method is assessed via extensive simulations. To illustrate the method's applicability to real-world problems, it is applied to a neurophysiological study that involves intracranial pressure (ICP), arterial blood pressure (ABP), and brain tissue oxygenation level (PtiO2) measurements. The rTV-gPDC reveals causal patterns that are in accordance with expected cardiosudoral meachanisms and potentially provides new insights regarding traumatic brain injuries (TBI). The rTV-gPDC is not restricted to the above problem but can be useful in revealing interactions in a broad range of applications.
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.
Abbas Alkarkhi, F M; Ismail, Norli; Easa, Azhar Mat
2008-02-11
Cockles (Anadara granosa) sample obtained from two rivers in the Penang State of Malaysia were analyzed for the content of arsenic (As) and heavy metals (Cr, Cd, Zn, Cu, Pb, and Hg) using a graphite flame atomic absorption spectrometer (GF-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometer (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data. MANOVA showed a strong significant difference between the two rivers in term of As and heavy metals contents in cockles. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used only two parameters (Zn and Cd) affording more than 72% correct assignations. Results indicated that the two rivers were different in terms of As and heavy metal contents in cockle, and the major difference was due to the contribution of Zn and Cd. A positive correlation was found between discriminate functions (DF) and Zn, Cd and Cr, whereas negative correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metals and arsenic content. Taking into account of these results, it can be suggested that a continuous monitoring of As and heavy metals in cockles be performed in these two rivers.
Directory of Open Access Journals (Sweden)
Gledsneli Maria Lima Lins
2010-12-01
Full Text Available Water has a decisive influence on populations’ life quality – specifically in areas like urban supply, drainage, and effluents treatment – due to its sound impact over public health. Water rational use constitutes the greatest challenge faced by water demand management, mainly with regard to urban household water consumption. This makes it important to develop researches to assist water managers and public policy-makers in planning and formulating water demand measures which may allow urban water rational use to be met. This work utilized the multivariate techniques Factor Analysis and Multiple Linear Regression Analysis – in order to determine the participation level of socioeconomic and climatic variables in monthly urban household consumption changes – applying them to two districts of Campina Grande city (State of Paraíba, Brazil. The districts were chosen based on socioeconomic criterion (income level so as to evaluate their water consumer’s behavior. A 9-year monthly data series (from year 2000 up to 2008 was utilized, comprising family income, water tariff, and quantity of household connections (economies – as socioeconomic variables – and average temperature and precipitation, as climatic variables. For both the selected districts of Campina Grande city, the obtained results point out the variables “water tariff” and “family income” as indicators of these district’s household consumption.
Hussein, Mohammed Tahir
Hydrochemical evaluation of groundwater systems can be carried out using conventional and multivariate techniques, namely cluster, factor analyses and others such as correspondence analysis. The main objective of this study is to investigate the groundwater quality in the Blue Nile basin of eastern Sudan, and to workout a hydrochemical evaluation for the aquifer system. Conventional methods and multivariate techniques were applied to achieve these goals. Two water-bearing layers exist in the study area: the Nubian Sandstone Formation and the Al-Atshan Formation. The Nubian aquifer is recharged mainly from the Blue Nile and Dinder Rivers through lateral subsurface flow and through direct rainfall in outcrop areas. The Al-Atshan aquifer receives water through underground flow from River Rahad and from rainfall infiltration. The prevailing hydrochemical processes are simple dissolution, mixing, partial ion exchange and ion exchange. Limited reverse ion exchange has been witnessed in the Nubian aquifer. Three factors control the overall mineralization and water quality of the Blue Nile Basin. The first factor includes high values of total dissolved solids, electrical conductivity, sodium, potassium, chloride, bicarbonate, sulphate and magnesium. The second factor includes calcium and pH. The third factor is due to fluoride concentration in the groundwater. The study highlights the descriptive capabilities of conventional and multivariate techniques as effective tools in groundwater quality evaluation. Une étude hydrochimique de systèmes aquifères a pu être réalisée au moyen des techniques conventionnelles et multidimensionnelles, telles que les analyses de cluster et factorielles, ainsi que d'autres comme l'analyse des correspondances. Le principal objectif de ce travail est d'étudier la qualité des eaux souterraines du bassin du Nil bleu au Soudan oriental, et de réaliser une évaluation hydrochimique du système aquifère. Des méthodes conventionnelles et
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
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.
Directory of Open Access Journals (Sweden)
Vujović Svetlana R.
2013-01-01
Full Text Available This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA/principal component analysis (PCA and cluster analysis (CA, were applied for the evaluation of variations and for the interpretation of a water quality data set of the natural water bodies obtained during 2010 year of monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physico-chemical parameters of natural water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78 % of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounting for 28 % of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounting for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounting 17 % of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounting 13 % of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA is an
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.
Tay, C. K.; Hayford, E. K.; Hodgson, I. O. A.
2017-02-01
Multivariate statistical technique and hydrogeochemical approach were employed for groundwater assessment within the Lower Pra Basin. The main objective was to delineate the main processes that are responsible for the water chemistry and pollution of groundwater within the basin. Fifty-four (54) (No) boreholes were sampled in January 2012 for quality assessment. PCA using Varimax with Kaiser Normalization method of extraction for both rotated space and component matrix have been applied to the data. Results show that Spearman's correlation matrix of major ions revealed expected process-based relationships derived mainly from the geochemical processes, such as ion-exchange and silicate/aluminosilicate weathering within the aquifer. Three main principal components influence the water chemistry and pollution of groundwater within the basin. The three principal components have accounted for approximately 79% of the total variance in the hydrochemical data. Component 1 delineates the main natural processes (water-soil-rock interactions) through which groundwater within the basin acquires its chemical characteristics, Component 2 delineates the incongruent dissolution of silicate/aluminosilicates, while Component 3 delineates the prevalence of pollution principally from agricultural input as well as trace metal mobilization in groundwater within the basin. The loadings and score plots of the first two PCs show grouping pattern which indicates the strength of the mutual relation among the hydrochemical variables. In terms of proper management and development of groundwater within the basin, communities, where intense agriculture is taking place, should be monitored and protected from agricultural activities. especially where inorganic fertilizers are used by creating buffer zones. Monitoring of the water quality especially the water pH is recommended to ensure the acid neutralizing potential of groundwater within the basin thereby, curtailing further trace metal
Tay, C. K.; Hayford, E. K.; Hodgson, I. O. A.
2017-06-01
Multivariate statistical technique and hydrogeochemical approach were employed for groundwater assessment within the Lower Pra Basin. The main objective was to delineate the main processes that are responsible for the water chemistry and pollution of groundwater within the basin. Fifty-four (54) (No) boreholes were sampled in January 2012 for quality assessment. PCA using Varimax with Kaiser Normalization method of extraction for both rotated space and component matrix have been applied to the data. Results show that Spearman's correlation matrix of major ions revealed expected process-based relationships derived mainly from the geochemical processes, such as ion-exchange and silicate/aluminosilicate weathering within the aquifer. Three main principal components influence the water chemistry and pollution of groundwater within the basin. The three principal components have accounted for approximately 79% of the total variance in the hydrochemical data. Component 1 delineates the main natural processes (water-soil-rock interactions) through which groundwater within the basin acquires its chemical characteristics, Component 2 delineates the incongruent dissolution of silicate/aluminosilicates, while Component 3 delineates the prevalence of pollution principally from agricultural input as well as trace metal mobilization in groundwater within the basin. The loadings and score plots of the first two PCs show grouping pattern which indicates the strength of the mutual relation among the hydrochemical variables. In terms of proper management and development of groundwater within the basin, communities, where intense agriculture is taking place, should be monitored and protected from agricultural activities. especially where inorganic fertilizers are used by creating buffer zones. Monitoring of the water quality especially the water pH is recommended to ensure the acid neutralizing potential of groundwater within the basin thereby, curtailing further trace metal
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.
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.
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.
Bonetti, Jennifer; Quarino, Lawrence
2014-05-01
This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.
Directory of Open Access Journals (Sweden)
M.A. Delavar
2016-02-01
Full Text Available Introduction: The accumulation of heavy metals (HMs in the soil is of increasing concern due to food safety issues, potential health risks, and the detrimental effects on soil ecosystems. HMs may be considered as the most important soil pollutants, because they are not biodegradable and their physical movement through the soil profile is relatively limited. Therefore, root uptake process may provide a big chance for these pollutants to transfer from the surface soil to natural and cultivated plants, which may eventually steer them to human bodies. The general behavior of HMs in the environment, especially their bioavailability in the soil, is influenced by their origin. Hence, source apportionment of HMs may provide some essential information for better management of polluted soils to restrict the HMs entrance to the human food chain. This paper explores the applicability of multivariate statistical techniques in the identification of probable sources that can control the concentration and distribution of selected HMs in the soils surrounding the Zanjan Zinc Specialized Industrial Town (briefly Zinc Town. Materials and Methods: The area under investigation has a size of approximately 4000 ha.It is located around the Zinc Town, Zanjan province. A regular grid sampling pattern with an interval of 500 meters was applied to identify the sample location, and 184 topsoil samples (0-10 cm were collected. The soil samples were air-dried and sieved through a 2 mm polyethylene sieve and then, were digested using HNO3. The total concentrations of zinc (Zn, lead (Pb, cadmium (Cd, Nickel (Ni and copper (Cu in the soil solutions were determined via Atomic Absorption Spectroscopy (AAS. Data were statistically analyzed using the SPSS software version 17.0 for Windows. Correlation Matrix (CM, Principal Component Analyses (PCA and Factor Analyses (FA techniques were performed in order to identify the probable sources of HMs in the studied soils. Results and
Marinović Ruždjak, Andrea; Ruždjak, Domagoj
2015-04-01
For the evaluation of seasonal and spatial variations and the interpretation of a large and complex water quality dataset obtained during a 7-year monitoring program of the Sava River in Croatia, different multivariate statistical techniques were applied in this study. Basic statistical properties and correlations of 18 water quality parameters (variables) measured at 18 sampling sites (a total of 56,952 values) were examined. Correlations between air temperature and some water quality parameters were found in agreement with the previous studies of relationship between climatic and hydrological parameters. Principal component analysis (PCA) was used to explore the most important factors determining the spatiotemporal dynamics of the Sava River. PCA has determined a reduced number of seven principal components that explain over 75 % of the data set variance. The results revealed that parameters related to temperature and organic pollutants (CODMn and TSS) were the most important parameters contributing to water quality variation. PCA analysis of seasonal subsets confirmed this result and showed that the importance of parameters is changing from season to season. PCA of the four seasonal data subsets yielded six PCs with eigenvalues greater than one explaining 73.6 % (spring), 71.4 % (summer), 70.3 % (autumn), and 71.3 % (winter) of the total variance. To check the influence of the outliers in the data set whose distribution strongly deviates from the normal one, in addition to standard principal component analysis algorithm, two robust estimates of covariance matrix were calculated and subjected to PCA. PCA in both cases yielded seven principal components explaining 75 % of the total variance, and the results do not differ significantly from the results obtained by the standard PCA algorithm. With the implementation of robust PCA algorithm, it is demonstrated that the usage of standard algorithm is justified for data sets with small numbers of missing data
DEFF Research Database (Denmark)
Kallevik, H.; Hansen, Susanne Brunsgaard; Sæther, Ø.
2000-01-01
Water-in-oil emulsions are investigated by means of multivariate analysis of near infrared (NIR) spectroscopic profiles in the range 1100 - 2250 nm. The oil phase is a paraffin-diluted crude oil from the Norwegian Continental Shelf. The influence of water absorption and light scattering of the wa......Water-in-oil emulsions are investigated by means of multivariate analysis of near infrared (NIR) spectroscopic profiles in the range 1100 - 2250 nm. The oil phase is a paraffin-diluted crude oil from the Norwegian Continental Shelf. The influence of water absorption and light scattering...
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.
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.
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.
Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques.
Teutonico, D; Musuamba, F; Maas, H J; Facius, A; Yang, S; Danhof, M; Della Pasqua, O
2015-10-01
Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates. COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods. Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols. Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.
Flach, Milan; Gans, Fabian; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus; Rodner, Erik; Bathiany, Sebastian; Bodesheim, Paul; Guanche, Yanira; Sippel, Sebastian; Mahecha, Miguel D.
2017-08-01
Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies
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
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...
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.
Hague, D. S.; Vanderberg, J. D.; Woodbury, N. W.
1974-01-01
A method for rapidly examining the probable applicability of weight estimating formulae to a specific aerospace vehicle design is presented. The Multivariate Analysis Retrieval and Storage System (MARS) is comprised of three computer programs which sequentially operate on the weight and geometry characteristics of past aerospace vehicles designs. Weight and geometric characteristics are stored in a set of data bases which are fully computerized. Additional data bases are readily added to the MARS system and/or the existing data bases may be easily expanded to include additional vehicles or vehicle characteristics.
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...
Genetic divergence of rubber tree estimated by multivariate techniques and microsatellite markers
Directory of Open Access Journals (Sweden)
Lígia Regina Lima Gouvêa
2010-01-01
Full Text Available Genetic diversity of 60 Hevea genotypes, consisting of Asiatic, Amazonian, African and IAC clones, and pertaining to the genetic breeding program of the Agronomic Institute (IAC, Brazil, was estimated. Analyses were based on phenotypic multivariate parameters and microsatellites. Five agronomic descriptors were employed in multivariate procedures, such as Standard Euclidian Distance, Tocher clustering and principal component analysis. Genetic variability among the genotypes was estimated with 68 selected polymorphic SSRs, by way of Modified Rogers Genetic Distance and UPGMA clustering. Structure software in a Bayesian approach was used in discriminating among groups. Genetic diversity was estimated through Nei's statistics. The genotypes were clustered into 12 groups according to the Tocher method, while the molecular analysis identified six groups. In the phenotypic and microsatellite analyses, the Amazonian and IAC genotypes were distributed in several groups, whereas the Asiatic were in only a few. Observed heterozygosity ranged from 0.05 to 0.96. Both high total diversity (H T' = 0.58 and high gene differentiation (Gst' = 0.61 were observed, and indicated high genetic variation among the 60 genotypes, which may be useful for breeding programs. The analyzed agronomic parameters and SSRs markers were effective in assessing genetic diversity among Hevea genotypes, besides proving to be useful for characterizing genetic variability.
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.
Energy Technology Data Exchange (ETDEWEB)
Palit, Mousumi [Department of Electronics and Telecommunication Engineering, Central Calcutta Polytechnic, Kolkata 700014 (India); Tudu, Bipan, E-mail: bt@iee.jusl.ac.in [Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700098 (India); Bhattacharyya, Nabarun [Centre for Development of Advanced Computing, Kolkata 700091 (India); Dutta, Ankur; Dutta, Pallab Kumar [Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700098 (India); Jana, Arun [Centre for Development of Advanced Computing, Kolkata 700091 (India); Bandyopadhyay, Rajib [Department of Instrumentation and Electronics Engineering, Jadavpur University, Kolkata 700098 (India); Chatterjee, Anutosh [Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata 700107 (India)
2010-08-18
In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.
Palit, Mousumi; Tudu, Bipan; Bhattacharyya, Nabarun; Dutta, Ankur; Dutta, Pallab Kumar; Jana, Arun; Bandyopadhyay, Rajib; Chatterjee, Anutosh
2010-08-18
In an electronic tongue, preprocessing on raw data precedes pattern analysis and choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier. While attempting to classify different grades of black tea using a voltammetric electronic tongue, different preprocessing techniques have been explored and a comparison of their performances is presented in this paper. The preprocessing techniques are compared first by a quantitative measurement of separability followed by principle component analysis; and then two different supervised pattern recognition models based on neural networks are used to evaluate the performance of the preprocessing techniques.
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.
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.
Benson, Nsikak U; Asuquo, Francis E; Williams, Akan B; Essien, Joseph P; Ekong, Cyril I; Akpabio, Otobong; Olajire, Abaas A
2016-01-01
Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources.
Publishing nutrition research: a review of multivariate techniques--part 3: data reduction methods.
Gleason, Philip M; Boushey, Carol J; Harris, Jeffrey E; Zoellner, Jamie
2015-07-01
This is the ninth in a series of monographs on research design and analysis, and the third in a set of these monographs devoted to multivariate methods. The purpose of this article is to provide an overview of data reduction methods, including principal components analysis, factor analysis, reduced rank regression, and cluster analysis. In the field of nutrition, data reduction methods can be used for three general purposes: for descriptive analysis in which large sets of variables are efficiently summarized, to create variables to be used in subsequent analysis and hypothesis testing, and in questionnaire development. The article describes the situations in which these data reduction methods can be most useful, briefly describes how the underlying statistical analyses are performed, and summarizes how the results of these data reduction methods should be interpreted.
Corvucci, Francesca; Nobili, Lara; Melucci, Dora; Grillenzoni, Francesca-Vittoria
2015-02-15
Honey traceability to food quality is required by consumers and food control institutions. Melissopalynologists traditionally use percentages of nectariferous pollens to discriminate the botanical origin and the entire pollen spectrum (presence/absence, type and quantities and association of some pollen types) to determinate the geographical origin of honeys. To improve melissopalynological routine analysis, principal components analysis (PCA) was used. A remarkable and innovative result was that the most significant pollens for the traditional discrimination of the botanical and geographical origin of honeys were the same as those individuated with the chemometric model. The reliability of assignments of samples to honey classes was estimated through explained variance (85%). This confirms that the chemometric model properly describes the melissopalynological data. With the aim to improve honey discrimination, FT-microRaman spectrography and multivariate analysis were also applied. Well performing PCA models and good agreement with known classes were achieved. Encouraging results were obtained for botanical discrimination.
Directory of Open Access Journals (Sweden)
Nsikak U Benson
Full Text Available Trace metals (Cd, Cr, Cu, Ni and Pb concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria. The degree of contamination was assessed using the individual contamination factors (ICF and global contamination factor (GCF. Multivariate statistical approaches including principal component analysis (PCA, cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources.
Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.
2016-01-01
Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934
Khoshayand, Mohammad Reza; Abdollahi, Hamid; Moeini, Ali; Shamsaie, Ali; Ghaffari, Alireza; Abbasian, Sepideh
2010-09-01
Three multivariate modelling approaches including partial least squares regression (PLS), genetic algorithm-partial least squares regression (GA-PLS), and principal components-artificial neural network (PC-ANN) analysis were investigated for their application to the simultaneous determination of chlordiazepoxide and clidinium levels in pharmaceuticals. A set of synthetic mixtures of drugs in ethanol and 0.1 M HCL was made, and the prediction abilities of the aforementioned methods were examined using RSE% (relative standard error of the prediction). The PLS and PC-ANN methods were found to be comparable, and GA-PLS produced slightly better results. The predictive models that we built were successfully applied to simultaneously determine the levels of chlordiazepoxide and clidinium in coated tablets.
Enlow, Elizabeth M; Kennedy, Jennifer L; Nieuwland, Alexander A; Hendrix, James E; Morgan, Stephen L
2005-08-01
Nylons are an important class of synthetic polymers, from an industrial, as well as forensic, perspective. A spectroscopic method, such as Fourier transform infrared (FT-IR) spectroscopy, is necessary to determine the nylon subclasses (e. g., nylon 6 or nylon 6,6). Library searching using absolute difference and absolute derivative difference algorithms gives inconsistent results for identifying nylon subclasses. The objective of this study was to evaluate the usefulness of peak ratio analysis and multivariate statistics for the identification of nylon subclasses using attenuated total reflection (ATR) spectral data. Many nylon subclasses could not be distinguished by the peak ratio of the N-H vibrational stretch to the sp(3) C-H(2) vibrational stretch intensities. Linear discriminant analysis, however, provided a graphical visualization of differences between nylon subclasses and was able to correctly classify a set of 270 spectra from eight different subclasses with 98.5% cross-validated accuracy.
Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Qing, Sihan; Susilo, Willy; Wang, Guilin; Liu, Dongmei
2011-01-01
The quality of feature has significant impact on the performance of detection techniques used for Denial-of-Service (DoS) attack. The features that fail to provide accurate characterization for network traffic records make the techniques suffer from low accuracy in detection. Although researches hav
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.
Feng, Jie; Wang, Zhe; Li, Lizhi; Li, Zheng; Ni, Weidou
2013-03-01
A nonlinearized multivariate dominant factor-based partial least-squares (PLS) model was applied to coal elemental concentration measurement. For C concentration determination in bituminous coal, the intensities of multiple characteristic lines of the main elements in coal were applied to construct a comprehensive dominant factor that would provide main concentration results. A secondary PLS thereafter applied would further correct the model results by using the entire spectral information. In the dominant factor extraction, nonlinear transformation of line intensities (based on physical mechanisms) was embedded in the linear PLS to describe nonlinear self-absorption and inter-element interference more effectively and accurately. According to the empirical expression of self-absorption and Taylor expansion, nonlinear transformations of atomic and ionic line intensities of C were utilized to model self-absorption. Then, the line intensities of other elements, O and N, were taken into account for inter-element interference, considering the possible recombination of C with O and N particles. The specialty of coal analysis by using laser-induced breakdown spectroscopy (LIBS) was also discussed and considered in the multivariate dominant factor construction. The proposed model achieved a much better prediction performance than conventional PLS. Compared with our previous, already improved dominant factor-based PLS model, the present PLS model obtained the same calibration quality while decreasing the root mean square error of prediction (RMSEP) from 4.47 to 3.77%. Furthermore, with the leave-one-out cross-validation and L-curve methods, which avoid the overfitting issue in determining the number of principal components instead of minimum RMSEP criteria, the present PLS model also showed better performance for different splits of calibration and prediction samples, proving the robustness of the present PLS model.
Directory of Open Access Journals (Sweden)
Evans Corey J
2006-10-01
Full Text Available Abstract Background Three-dimensional (3D multivariate Fourier Transform Infrared (FTIR image maps of tissue sections are presented. A villoglandular adenocarcinoma from a cervical biopsy with a number of interesting anatomical features was used as a model system to demonstrate the efficacy of the technique. Methods Four FTIR images recorded using a focal plane array detector of adjacent tissue sections were stitched together using a MATLAB® routine and placed in a single data matrix for multivariate analysis using Cytospec™. Unsupervised Hierarchical Cluster Analysis (UHCA was performed simultaneously on all 4 sections and 4 clusters plotted. The four UHCA maps were then stacked together and interpolated with a box function using SCIRun software. Results The resultant 3D-images can be rotated in three-dimensions, sliced and made semi-transparent to view the internal structure of the tissue block. A number of anatomical and histopathological features including connective tissue, red blood cells, inflammatory exudate and glandular cells could be identified in the cluster maps and correlated with Hematoxylin & Eosin stained sections. The mean extracted spectra from individual clusters provide macromolecular information on tissue components. Conclusion 3D-multivariate imaging provides a new avenue to study the shape and penetration of important anatomical and histopathological features based on the underlying macromolecular chemistry and therefore has clear potential in biology and medicine.
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.
Directory of Open Access Journals (Sweden)
Jonas eKaplan
2015-03-01
Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.
Directory of Open Access Journals (Sweden)
Qing Gu
2016-03-01
Full Text Available Qiandao Lake (Xin’an Jiang reservoir plays a significant role in drinking water supply for eastern China, and it is an attractive tourist destination. Three multivariate statistical methods were comprehensively applied to assess the spatial and temporal variations in water quality as well as potential pollution sources in Qiandao Lake. Data sets of nine parameters from 12 monitoring sites during 2010–2013 were obtained for analysis. Cluster analysis (CA was applied to classify the 12 sampling sites into three groups (Groups A, B and C and the 12 monitoring months into two clusters (April-July, and the remaining months. Discriminant analysis (DA identified Secchi disc depth, dissolved oxygen, permanganate index and total phosphorus as the significant variables for distinguishing variations of different years, with 79.9% correct assignments. Dissolved oxygen, pH and chlorophyll-a were determined to discriminate between the two sampling periods classified by CA, with 87.8% correct assignments. For spatial variation, DA identified Secchi disc depth and ammonia nitrogen as the significant discriminating parameters, with 81.6% correct assignments. Principal component analysis (PCA identified organic pollution, nutrient pollution, domestic sewage, and agricultural and surface runoff as the primary pollution sources, explaining 84.58%, 81.61% and 78.68% of the total variance in Groups A, B and C, respectively. These results demonstrate the effectiveness of integrated use of CA, DA and PCA for reservoir water quality evaluation and could assist managers in improving water resources management.
Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; Ferrão, Marco Flores; da Silva, Fabiana Ernestina Barcellos; Müller, Edson Irineu; Flores, Erico Marlon de Moraes
2012-06-01
A method for simultaneous determination of clavulanic acid (CA) and amoxicillin (AMO) in commercial tablets was developed using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and multivariate calibration. Twenty-five samples (10 commercial and 15 synthetic) were used as a calibration set and 15 samples (10 commercial and 5 synthetic) were used for a prediction set. Calibration models were developed using partial least squares (PLS), interval PLS (iPLS), and synergy interval PLS (siPLS) algorithms. The best algorithm for CA determination was siPLS model with spectra divided in 30 intervals and combinations of 2 intervals. This model showed a root mean square error of prediction (RMSEP) of 5.1 mg g(-1). For AMO determination, the best siPLS model was obtained with spectra divided in 10 intervals and combinations of 4 intervals. This model showed a RMSEP of 22.3 mg g(-1). The proposed method was considered as a suitable for the simultaneous determination of CA and AMO in commercial pharmaceuticals products.
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.
Institute of Scientific and Technical Information of China (English)
张燕; 陈增强; 杨鹏; 袁著祉
2004-01-01
A nonlinear proportional-integral-derivative (PID) controller is constructed based on recurrent neural networks. In the control process of nonlinear multivariable systems, several nonlinear PID controllers have been adopted in parallel. Under the decoupling cost function, a decoupling control strategy is proposed. Then the stability condition of the controller is presented based on the Lyapunov theory. Simulation examples are given to show effectiveness of the proposed decoupling control.
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.
Ham, L.V. van der; Bakker, D.E.; Geers, L.F.G.; Goetheer, E.L.V.
2014-01-01
The solvent and the dissolved CO2 concentrations are two essential properties of CO2 absorption processes. Currently, they are typically monitored using time-consuming offline analytical techniques. Initial development efforts aiming at a cost-effective and reliable inline monitoring system are desc
New techniques for the scientific visualization of three-dimensional multi-variate and vector fields
Energy Technology Data Exchange (ETDEWEB)
Crawfis, Roger A. [Univ. of California, Davis, CA (United States)
1995-10-01
Volume rendering allows us to represent a density cloud with ideal properties (single scattering, no self-shadowing, etc.). Scientific visualization utilizes this technique by mapping an abstract variable or property in a computer simulation to a synthetic density cloud. This thesis extends volume rendering from its limitation of isotropic density clouds to anisotropic and/or noisy density clouds. Design aspects of these techniques are discussed that aid in the comprehension of scientific information. Anisotropic volume rendering is used to represent vector based quantities in scientific visualization. Velocity and vorticity in a fluid flow, electric and magnetic waves in an electromagnetic simulation, and blood flow within the body are examples of vector based information within a computer simulation or gathered from instrumentation. Understand these fields can be crucial to understanding the overall physics or physiology. Three techniques for representing three-dimensional vector fields are presented: Line Bundles, Textured Splats and Hair Splats. These techniques are aimed at providing a high-level (qualitative) overview of the flows, offering the user a substantial amount of information with a single image or animation. Non-homogenous volume rendering is used to represent multiple variables. Computer simulations can typically have over thirty variables, which describe properties whose understanding are useful to the scientist. Trying to understand each of these separately can be time consuming. Trying to understand any cause and effect relationships between different variables can be impossible. NoiseSplats is introduced to represent two or more properties in a single volume rendering of the data. This technique is also aimed at providing a qualitative overview of the flows.
DEFF Research Database (Denmark)
Kjems, Ulrik; Storther, Stephen C.; Anderson, Jon
1999-01-01
This paper addresses the problem of neuro-anatomical registration across individuals for functional [15O]water PET activation studies. A new algorithm for 3D non-linear structural registration (warping) of MR scans is presented. The method performs a hierarchically scaled search for a displacement...
DEFF Research Database (Denmark)
Kjems, Ulrik; Storther, Stephen C.; Anderson, Jon
1999-01-01
This paper addresses the problem of neuro-anatomical registration across individuals for functional [15O]water PET activation studies. A new algorithm for 3D non-linear structural registration (warping) of MR scans is presented. The method performs a hierarchically scaled search for a displacemen...
Directory of Open Access Journals (Sweden)
Farooq Ahmad
2011-12-01
Full Text Available Multivariate statistical techniques such as factor analysis (FA, cluster analysis (CA and discriminant analysis (DA, were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of three cities (Lahore, Gujranwala and Sialkot in Punjab, Pakistan. 16 parameters of water samples collected from nine different sampling stations of each city were determined. Factor analysis indicates five factors, which explained 74% of the total variance in water quality data set. Five factors are salinization, alkalinity, temperature, domestic waste and chloride, which explained 31.1%, 14.3%, 10.6%, 10.0% and 8.0% of the total variance respectively. Hierarchical cluster analysis grouped nine sampling stations of each city into three clusters, i.e., relatively less polluted (LP, and moderately polluted (MP and highly polluted (HP sites, based on the similarity of water quality characteristics. Discriminant analysis (DA identified ten significant parameters (Calcium (Ca, Ammonia, Sulphate, Sodium (Na, electrical conductivity (EC, chloride, temperature (Temp, total hardness(TH, Turbidity, which discriminate the groundwater quality of three cities, with close to 100.0% correct assignment for spatial variations. This study illustrates the benefit of multivariate statistical techniques for interpreting complex data sets in the analysis of spatial variations in water quality, and to plan for future studies.
Indian Academy of Sciences (India)
Işık Yilmaz; Marian Marschalko; Martin Bednarik
2013-04-01
The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.
Directory of Open Access Journals (Sweden)
Thomas Lefèvre
Full Text Available Cost containment policies and the need to satisfy patients' health needs and care expectations provide major challenges to healthcare systems. Identification of homogeneous groups in terms of healthcare utilisation could lead to a better understanding of how to adjust healthcare provision to society and patient needs.This study used data from the third wave of the SIRS cohort study, a representative, population-based, socio-epidemiological study set up in 2005 in the Paris metropolitan area, France. The data were analysed using a cross-sectional design. In 2010, 3000 individuals were interviewed in their homes. Non-conventional multivariate clustering techniques were used to determine homogeneous user groups in data. Multinomial models assessed a wide range of potential associations between user characteristics and their pattern of healthcare utilisation.We identified four distinct patterns of healthcare use. Patterns of consumption and the socio-demographic characteristics of users differed qualitatively and quantitatively between these four profiles. Extensive and intensive use by older, wealthier and unhealthier people contrasted with narrow and parsimonious use by younger, socially deprived people and immigrants. Rare, intermittent use by young healthy men contrasted with regular targeted use by healthy and wealthy women.The use of an original technique of massive multivariate analysis allowed us to characterise different types of healthcare users, both in terms of resource utilisation and socio-demographic variables. This method would merit replication in different populations and healthcare systems.
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
Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques
Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi
2017-08-01
The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.
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.
Sharma, Sandeep; Goodarzi, Mohammad; Ramon, Herman; Saeys, Wouter
2014-04-01
Partial Least Squares (PLS) regression is one of the most used methods for extracting chemical information from Near Infrared (NIR) spectroscopic measurements. The success of a PLS calibration relies largely on the representativeness of the calibration data set. This is not trivial, because not only the expected variation in the analyte of interest, but also the variation of other contributing factors (interferents) should be included in the calibration data. This also implies that changes in interferent concentrations not covered in the calibration step can deteriorate the prediction ability of the calibration model. Several researchers have suggested that PLS models can be robustified against changes in the interferent structure by incorporating expert knowledge in the preprocessing step with the aim to efficiently filter out the spectral influence of the spectral interferents. However, these methods have not yet been compared against each other. Therefore, in the present study, various preprocessing techniques exploiting expert knowledge were compared on two experimental data sets. In both data sets, the calibration and test set were designed to have a different interferent concentration range. The performance of these techniques was compared to that of preprocessing techniques which do not use any expert knowledge. Using expert knowledge was found to improve the prediction performance for both data sets. For data set-1, the prediction error improved nearly 32% when pure component spectra of the analyte and the interferents were used in the Extended Multiplicative Signal Correction framework. Similarly, for data set-2, nearly 63% improvement in the prediction error was observed when the interferent information was utilized in Spectral Interferent Subtraction preprocessing.
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.
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.
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
A Space-Filling Visualization Technique for Multivariate Small World Graphs
Energy Technology Data Exchange (ETDEWEB)
Wong, Pak C.; Foote, Harlan P.; Mackey, Patrick S.; Chin, George; Huang, Zhenyu; Thomas, James J.
2012-03-15
We introduce an information visualization technique, known as GreenCurve, for large sparse graphs that exhibit small world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a sophisticated algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. We show in the paper that the GreenCurve technology is (1) theoretically sustainable by introducing an error estimation metric to measure the fidelity of the new graph representation, (2) empirically rigorous by conducting a usability study to investigate its strengths and weaknesses against the traditional graph layout, and (3) pragmatically feasible by applying it to analyze stressed conditions of the large scale electric power grid on the west coast.
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.
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.
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
Energy Technology Data Exchange (ETDEWEB)
Mecozzi, M.; Amici, M. [Istituto Centrale per la Ricerca Scientifica e Technological Applicata al Mare, Rome (Italy); Acquistucci, R. [Istituto Nazionale per la Nutrizione e gli Alimenti, Rome (Italy)
2003-07-01
We report a procedure for describing the gas chromatographic retention time of polychlorinated biphenyls (PCBs) as a function of simple mono-dimensional molecular descriptors such as the number and position of chlorine atoms on the aromatic rings. The mathematical relationships between relative retention time (RRT) of all 209 possible congeners of PCBs and the mono-dimensional molecular descriptors (MDDs) were obtained by the multivariate techniques principal component regression (PCR) and partial least squares (PLS) used as modelling tools. The good agreement found between experimental and predicted retention times of PCBs shows that a well established mathematical model relating retention time to specific mono-dimensional molecular descriptors can be a useful tool to enhance identification of these pollutants in real samples. (orig.)
Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad
2014-01-24
In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then
Institute of Scientific and Technical Information of China (English)
郑力燕; 于宏兵; 王启山
2016-01-01
Multivariate statistical techniques, such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River (SSHR) basin in China, obtained during two years (2012−2013) of monitoring of 10 physicochemical parameters at 15 different sites. The results showed that most of physicochemical parameters varied significantly among the sampling sites. Three significant groups, highly polluted (HP), moderately polluted (MP) and less polluted (LP), of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics. DA identified pH, F, DO, NH3-N, COD and VPhs were the most important parameters contributing to spatial variations of surface water quality. However, DA did not give a considerable data reduction (40%reduction). PCA/FA resulted in three, three and four latent factors explaining 70%, 62%and 71%of the total variance in water quality data sets of HP, MP and LP regions, respectively. FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities (point sources: industrial effluents and wastewater treatment plants; non-point sources:domestic sewage, livestock operations and agricultural activities) and natural processes (seasonal effect, and natural inputs). PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters (about 80%reduction) as the most important parameters to explain 72%of the data variation. Thus, this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and, in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.
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.
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.
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.
Directory of Open Access Journals (Sweden)
Z. Pasandidehfard
2014-03-01
Full Text Available Nonpoint source (NPS pollution is a major surface water contaminant commonly caused by agricultural runoff. The purpose of this study was to assess seasonal variation in water quality parameters in Gorganrood watershed (Golestan Province, Iran. It also tried to clarify the effects of agricultural practices and NPS pollution on them. Water quality parameters including potassium, sodium, pH, water flow rate, total dissolved solids (TDS, electrical conductivity (EC, hardness, sulfate, bicarbonate, chlorine, magnesium, and calcium ions during 1966-2010 were evaluated using multivariate statistical techniques. Multivariate analysis of variance (MANOVA was implemented to determine the significance of differences between mean seasonal values. Discriminant analysis (DA was also carried out to identify correlations between seasons and the water quality parameters. Parameters of water quality index were measured through principal component analysis (PCA and factor analysis (FA. Based on the results of statistical tests, climate (freezing, weathering and rainfall and human activities such as agriculture had crucial effects on water quality. The most important parameters in differentiation between seasons in descending order were potassium, pH, carbonic acid, calcium, and magnesium. According to load factor analysis, chlorine, calcium, and potassium were the most important parameters in spring and summer, indicating the application of fertilizers (especially potassium chloride fertilizer and existence of NPS pollution during these seasons. In the next stage, the months during which crops had excessive water requirements were detected using CROPWAT software. Almost all water requirements of the area’s major crops, i.e. cotton, rice, soya, wheat, and oat, happen in the late spring until mid/late summer. According to our findings, agricultural practices had a great impact on water pollution. Results of analysis with CROPWAT software also confirmed this
Directory of Open Access Journals (Sweden)
Yeon-Jee Yoo
2013-08-01
Full Text Available Objectives The aim of this study was to compare the cleaning efficacy of different final irrigation regimens in canal and isthmus of mandibular molars, and to evaluate the influence of related variables on cleaning efficacy of the irrigation systems. Materials and Methods Mesial root canals from 60 mandibular molars were prepared and divided into 4 experimental groups according to the final irrigation technique: Group C, syringe irrigation; Group U, ultrasonics activation; Group SC, VPro StreamClean irrigation; Group EV, EndoVac irrigation. Cross-sections at 1, 3 and 5 mm levels from the apex were examined to calculate remaining debris area in the canal and isthmus spaces. Statistical analysis was completed by using Kruskal-Wallis test and Mann-Whitney U test for comparison among groups, and multivariate linear analysis to identify the significant variables (regular replenishment of irrigant, vapor lock management, and ultrasonic activation of irrigant affecting the cleaning efficacy of the experimental groups. Results Group SC and EV showed significantly higher canal cleanliness values than group C and U at 1 mm level (p < 0.05, and higher isthmus cleanliness values than group U at 3 mm and all levels of group C (p < 0.05. Multivariate linear regression analysis demonstrated that all variables had independent positive correlation at 1 mm level of canal and at all levels of isthmus with statistical significances. Conclusions Both VPro StreamClean and EndoVac system showed favorable result as final irrigation regimens for cleaning debris in the complicated root canal system having curved canal and/or isthmus. The debridement of the isthmi significantly depends on the variables rather than the canals.
Marengo, Emilio; Manfredi, Marcello; Zerbinati, Orfeo; Robotti, Elisa; Mazzucco, Eleonora; Gosetti, Fabio; Bearman, Greg; France, Fenella; Shor, Pnina
2011-09-01
The aim of this project is the development of a noninvasive technique based on LED multispectral imaging (MSI) for monitoring the conservation state of the Dead Sea Scrolls (DSS) collection. It is well-known that changes in the parchment reflectance drive the transition of the scrolls from legible to illegible. Capitalizing on this fact, we will use spectral imaging to detect changes in the reflectance before they become visible to the human eye. The technique uses multivariate analysis and statistical process control theory. The present study was carried out on a "sample" parchment of calfskin. The monitoring of the surface of a commercial modern parchment aged consecutively for 2 h and 6 h at 80 °C and 50% relative humidity (ASTM) was performed at the Imaging Lab of the Library of Congress (Washington, DC, U.S.A.). MSI is here carried out in the vis-NIR range limited to 1 μm, with a number of bands of 13 and bandwidths that range from about 10 nm in UV to 40 nm in IR. Results showed that we could detect and locate changing pixels, on the basis of reflectance changes, after only a few "hours" of aging.
Reynolds, Richard J; Dudash, Michele R; Fenster, Charles B
2010-02-01
Pollination syndromes suggest that convergent evolution of floral traits and trait combinations reflects similar selection pressures. Accordingly, a pattern of selection on floral traits is expected to be consistent with increasing the attraction and pollen transfer of the important pollinator. We measured individual variation in six floral traits and yearly and lifetime total plant seed and fruit production of 758 plants across nine years of study in natural populations of Ruby-Throated Hummingbird-pollinated Silene virginica. The type, strength, and direction of selection gradients were observed by year, and for two cohorts selection was estimated through lifetime maternal fitness. Positive directional selection was detected on floral display height in all years of study and stigma exsertion in all years but one. Significant quadratic and correlational selection gradients were rare. However, a canonical analysis of the gamma matrix indicated nonlinear selection was common; if significant curvature was detected it was convex with one exception. Our analyses demonstrated selection favored trait combinations and the integration of floral features of attraction and pollen transfer efficiency that were consistent with the hummingbird pollination syndrome.
Institute of Scientific and Technical Information of China (English)
Katsuaki Koike
2011-01-01
Sample data in the Earth and environmental sciences are limited in quantity and sampling location and therefore, sophisticated spatial modeling techniques are indispensable for accurate imaging of complicated structures and properties of geomaterials. This paper presents several effective methods that are grouped into two categories depending on the nature of regionalized data used. Type I data originate from plural populations and type II data satisfy the prerequisite of stationarity and have distinct spatial correlations. For the type I data, three methods are shown to be effective and demonstrated to produce plausible results: (1) a spline-based method, (2) a combination of a spline-based method with a stochastic simulation, and (3) a neural network method. Geostatistics proves to be a powerful tool for type II data. Three new approaches of geostatistics are presented with case studies: an application to directional data such as fracture, multi-scale modeling that incorporates a scaling law,and space-time joint analysis for multivariate data. Methods for improving the contribution of such spatial modeling to Earth and environmental sciences are also discussed and future important problems to be solved are summarized.
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.
Energy Technology Data Exchange (ETDEWEB)
Baig, Jameel A., E-mail: jab_mughal@yahoo.com [National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Sindh (Pakistan); Kazi, Tasneem G., E-mail: tgkazi@yahoo.com [National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Sindh (Pakistan); Shah, Abdul Q., E-mail: aqshah07@yahoo.com [National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Sindh (Pakistan); Arain, Mohammad B. [National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Sindh (Pakistan); Afridi, Hassan I., E-mail: hassanimranafridi@yahoo.com [National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Sindh (Pakistan); Kandhro, Ghulam A., E-mail: gakandhro@yahoo.com [National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Sindh (Pakistan); Khan, Sumaira, E-mail: skhanzai@gmail.com [National Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Sindh (Pakistan)
2009-09-28
The simple and rapid pre-concentration techniques viz. cloud point extraction (CPE) and solid phase extraction (SPE) were applied for the determination of As{sup 3+} and total inorganic arsenic (iAs) in surface and ground water samples. The As{sup 3+} was formed complex with ammonium pyrrolidinedithiocarbamate (APDC) and extracted by surfactant-rich phases in the non-ionic surfactant Triton X-114, after centrifugation the surfactant-rich phase was diluted with 0.1 mol L{sup -1} HNO{sub 3} in methanol. While total iAs in water samples was adsorbed on titanium dioxide (TiO{sub 2}); after centrifugation, the solid phase was prepared to be slurry for determination. The extracted As species were determined by electrothermal atomic absorption spectrometry. The multivariate strategy was applied to estimate the optimum values of experimental factors for the recovery of As{sup 3+} and total iAs by CPE and SPE. The standard addition method was used to validate the optimized methods. The obtained result showed sufficient recoveries for As{sup 3+} and iAs (>98.0%). The concentration factor in both cases was found to be 40.
Muhammad, Said; Tahir Shah, M; Khan, Sardar
2010-10-01
The present study was conducted in Kohistan region, where mafic and ultramafic rocks (Kohistan island arc and Indus suture zone) and metasedimentary rocks (Indian plate) are exposed. Water samples were collected from the springs, streams and Indus river and analyzed for physical parameters, anions, cations and arsenic (As(3+), As(5+) and arsenic total). The water quality in Kohistan region was evaluated by comparing the physio-chemical parameters with permissible limits set by Pakistan environmental protection agency and world health organization. Most of the studied parameters were found within their respective permissible limits. However in some samples, the iron and arsenic concentrations exceeded their permissible limits. For health risk assessment of arsenic, the average daily dose, hazards quotient (HQ) and cancer risk were calculated by using statistical formulas. The values of HQ were found >1 in the samples collected from Jabba, Dubair, while HQ values were samples. This level of contamination should have low chronic risk and medium cancer risk when compared with US EPA guidelines. Furthermore, the inter-dependence of physio-chemical parameters and pollution load was also calculated by using multivariate statistical techniques like one-way ANOVA, correlation analysis, regression analysis, cluster analysis and principle component analysis.
Khan, Firdos; Pilz, Jürgen
2016-04-01
South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological
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.
Herojeet, Rajkumar; Rishi, Madhuri S.; Lata, Renu; Dolma, Konchok
2017-09-01
multivariate techniques for reliable quality characterization of surface water quality to develop effective pollution reduction strategies and maintain a fine balance between the industrialization and ecological integrity.
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.
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.
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.
Multivariable Intelligent Decoupling Control System and its Application
Institute of Scientific and Technical Information of China (English)
TianYou CHAI; Heng YUE
2005-01-01
Many industrial processes have compositive complexities including multivariable, strong coupling, nonlinearity, time-variant and operating condition variations. Combining multivariable adaptive decoupling control with neural networks, this paper presents a multivariable neural networkbased decoupling control algorithm. This control algorithm is integrated with distributed control technique and intelligent control technique, and a three-leveled intelligent decoupling control system consisting of basic control level, coordinating control level, and management and decision level is developed. The configuration and function of the control system are discussed in detail. This system has been successfully applied in ball mill pulverizing systems of 200MW power units, and remarkable benefits have been obtained.
Saraiva, Cristina; Oliveira, I; Silva, J A; Martins, C; Ventanas, J; García, C
2015-06-01
This study was performed in order to select volatile compounds to predict the off-odour and overall assessment of raw beef's freshness Maronesa breed, using multivariate analysis. M. longissimus dorsi packed in vacuum and MAP (70 % O2/20 % CO2/10 % N2) stored at 4 ºC were examined for off-odour perception as well as the overall assessment of freshness at 10 and 21 days post mortem. The results achieved in this study demonstrated that the selected volatile compounds could be considered as volatile indicators of beef spoilage, enclosing information for discrimination of Maronesa beef samples in sensory classes of odour corresponding to unspoiled and spoiled levels. Fifty-four volatile compounds were detected. A significant increase of aldehydes, ketones and alcohols were observed during storage in MAP. 2 and 3-methylbutanal, 2 and 3-methylbutanol, 1-pentanol, 1-hexanol, 2,3-octanedione, 3,5-octanedione, octanal and nonanal were suggested as indicators of beef spoilage. 3-methylpentane was considered as a marker in the first stages of spoilage in beef, decreasing during storage. Data were examined using PCR and PLSR models for different optimal subsets of volatile compounds. The simplicity and usefulness of the technique in using 0/1 data in preserving high levels of accuracy was also prevalent. The powerful analytical methodologies for reducing variables and the choice of optimal subsets could be advantageous in both basic research and the routine quality control of chilled beef.
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.
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.
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.
Rasouli, Zolaikha; Ghavami, Raouf
2016-08-01
Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD = 0.12], 0.67-23.19 [LOD = 0.13] and 0.73-25.12 [LOD = 0.15] μg mL- 1 for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples.
Rasouli, Zolaikha; Ghavami, Raouf
2016-08-05
Vanillin (VA), vanillic acid (VAI) and syringaldehyde (SIA) are important food additives as flavor enhancers. The current study for the first time is devote to the application of partial least square (PLS-1), partial robust M-regression (PRM) and feed forward neural networks (FFNNs) as linear and nonlinear chemometric methods for the simultaneous detection of binary and ternary mixtures of VA, VAI and SIA using data extracted directly from UV-spectra with overlapped peaks of individual analytes. Under the optimum experimental conditions, for each compound a linear calibration was obtained in the concentration range of 0.61-20.99 [LOD=0.12], 0.67-23.19 [LOD=0.13] and 0.73-25.12 [LOD=0.15] μgmL(-1) for VA, VAI and SIA, respectively. Four calibration sets of standard samples were designed by combination of a full and fractional factorial designs with the use of the seven and three levels for each factor for binary and ternary mixtures, respectively. The results of this study reveal that both the methods of PLS-1 and PRM are similar in terms of predict ability each binary mixtures. The resolution of ternary mixture has been accomplished by FFNNs. Multivariate curve resolution-alternating least squares (MCR-ALS) was applied for the description of spectra from the acid-base titration systems each individual compound, i.e. the resolution of the complex overlapping spectra as well as to interpret the extracted spectral and concentration profiles of any pure chemical species identified. Evolving factor analysis (EFA) and singular value decomposition (SVD) were used to distinguish the number of chemical species. Subsequently, their corresponding dissociation constants were derived. Finally, FFNNs has been used to detection active compounds in real and spiked water samples. Copyright © 2016 Elsevier B.V. All rights reserved.
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.
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.
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.
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.
Energy Technology Data Exchange (ETDEWEB)
Aguado Garcia, D.; Ferrer Riquelme, A. J.; Seco Torrecillas, A.; Ferrer Polo, J.
2006-07-01
Due to the increasingly stringent effluents quality requirements imposed by the regulations, monitoring wastewater treatment plants (WWTP) becomes extremely important in order to achieve efficient process operations. Nowadays, at modern WWTP large number of online process variables are collected and these variable are usually highly correlated. Therefore, appropriate techniques are required to extract the information from the huge amount of collected data. In this work, the application of multivariate statistical projection techniques is presented as an effective strategy for monitoring a sequencing batch reactor (SBR) operated for enhanced biological phosphorus removal. (Author)
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
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.
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.
Directory of Open Access Journals (Sweden)
Cláudio Roberto Rosário
2012-07-01
Full Text Available The purpose of this research is to improve the practice on customer satisfaction analysis The article presents an analysis model to analyze the answers of a customer satisfaction evaluation in a systematic way with the aid of multivariate statistical techniques, specifically, exploratory analysis with PCA – Partial Components Analysis with HCA - Hierarchical Cluster Analysis. It was tried to evaluate the applicability of the model to be used by the issue company as a tool to assist itself on identifying the value chain perceived by the customer when applied the questionnaire of customer satisfaction. It was found with the assistance of multivariate statistical analysis that it was observed similar behavior among customers. It also allowed the company to conduct reviews on questions of the questionnaires, using analysis of the degree of correlation between the questions that was not a company’s practice before this research.
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.
Xiao, Li; Wei, Hui; Himmel, Michael E; Jameel, Hasan; Kelley, Stephen S
2014-01-01
Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review
Directory of Open Access Journals (Sweden)
Li eXiao
2014-08-01
Full Text Available Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR and pyrolysis-molecular beam mass spectrometry (Py-mbms are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis and for building regression models (partial least square regression between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated
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)
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.
Caneca, Arnobio Roberto; Pimentel, M Fernanda; Galvão, Roberto Kawakami Harrop; da Matta, Cláudia Eliane; de Carvalho, Florival Rodrigues; Raimundo, Ivo M; Pasquini, Celio; Rohwedder, Jarbas J R
2006-09-15
This paper presents two methodologies for monitoring the service condition of diesel-engine lubricating oils on the basis of infrared spectra. In the first approach, oils samples are discriminated into three groups, each one associated to a given wear stage. An algorithm is proposed to select spectral variables with good discriminant power and small collinearity for the purpose of discriminant analysis classification. As a result, a classification accuracy of 93% was obtained both in the middle (MIR) and near-infrared (NIR) ranges. The second approach employs multivariate calibration methods to predict the viscosity of the lubricant. In this case, the use of absorbance measurements in the NIR spectral range was not successful, because of experimental difficulties associated to the presence of particulate matter. Such a problem was circumvented by the use of attenuated total reflectance (ATR) measurements in the MIR spectral range, in which an RMSEP of 3.8cSt and a relative average error of 3.2% were attained.
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.
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.
Borràs, Eva; Ferré, Joan; Boqué, Ricard; Mestres, Montserrat; Aceña, Laura; Calvo, Angels; Busto, Olga
2016-07-15
Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils.
Mispelaar, V.G. van; Smilde, A.K.; Noord, O.E. de; Blomberg, J.; Schoenmakers, P.J.
2005-01-01
Comprehensive two-dimensional gas chromatography (GC × GC) has proven to be an extremely powerful separation technique for the analysis of complex volatile mixtures. This separation power can be used to discriminate between highly similar samples. In this article we will describe the use of GC × GC
Truu, Jaak; Heinaru, Eeva; Talpsep, Ene; Heinaru, Ain
2002-01-01
The oil-shale industry has created serious pollution problems in northeastern Estonia. Untreated, phenol-rich leachate from semi-coke mounds formed as a by-product of oil-shale processing is discharged into the Baltic Sea via channels and rivers. An exploratory analysis of water chemical and microbiological data sets from the low-flow period was carried out using different multivariate analysis techniques. Principal component analysis allowed us to distinguish different locations in the river system. The riverine microbial community response to water chemical parameters was assessed by co-inertia analysis. Water pH, COD and total nitrogen were negatively related to the number of biodegradative bacteria, while oxygen concentration promoted the abundance of these bacteria. The results demonstrate the utility of multivariate statistical techniques as tools for estimating the magnitude and extent of pollution based on river water chemical and microbiological parameters. An evaluation of river chemical and microbiological data suggests that the ambient natural attenuation mechanisms only partly eliminate pollutants from river water, and that a sufficient reduction of more recalcitrant compounds could be achieved through the reduction of wastewater discharge from the oil-shale chemical industry into the rivers.
Moni, Chrysanthi
2014-01-01
The main purpose of this study is the correction for the energy losses of the e± in the tran- sition region between the barrel and the end-caps of the Electromagnetic Calorimeter (EMCal) of ATLAS, by using Multivariate techniques. The crack region is the one with the largest amount of material upstream the EMCal and this is the reason for which e± lose a great part of their energy as they pass through it. In this project, the contribution of the Multivariate Analysis in the correction of the E/Etrue distribution as well as in the derivation of the Gaussian peak versus |η| and ET , is examined. η is the pseudorapidity used as a spatial coordinate for the description of the angle of a particle relative to the beam axis and ET= Etrue /cosh(|η|), where Etrue is the true energy of the particles. Finally, the improvement of the resolution by using MVA techniques with and without scintillator is also explored.
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...
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.
Multivariate strategies in functional magnetic resonance imaging
DEFF Research Database (Denmark)
Hansen, Lars Kai
2007-01-01
We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a `mind reading' predictive multivariate fMRI model....
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.
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.
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.
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.
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.
Xiao, Li
Despite the great passion and endless efforts on development of renewable energy from biomass, the commercialization and scale up of biofuel production is still under pressure and facing challenges. New ideas and facilities are being tested around the world targeting at reducing cost and improving product value. Cutting edge technologies involving analytical chemistry, statistics analysis, industrial engineering, computer simulation, and mathematics modeling, etc. keep integrating modern elements into this classic research. One of those challenges of commercializing biofuel production is the complexity from chemical composition of biomass feedstock and the products. Because of this, feedstock selection and process optimization cannot be conducted efficiently. This dissertation attempts to further evaluate biomass thermal decomposition process using both traditional methods and advanced technique (Pyrolysis Molecular Beam Mass Spectrometry). Focus has been made on data base generation of thermal decomposition products from biomass at different temperatures, finding out the relationship between traditional methods and advanced techniques, evaluating process efficiency and optimizing reaction conditions, comparison of typically utilized biomass feedstock and new search on innovative species for economical viable feedstock preparation concepts, etc. Lab scale quartz tube reactors and 80il stainless steel sample cups coupled with auto-sampling system were utilized to simulate the complicated reactions happened in real fluidized or entrained flow reactors. Two main high throughput analytical techniques used are Near Infrared Spectroscopy (NIR) and Pyrolysis Molecular Beam Mass Spectrometry (Py-MBMS). Mass balance, carbon balance, and product distribution are presented in detail. Variations of thermal decomposition temperature range from 200°C to 950°C. Feedstocks used in the study involve typical hardwood and softwood (red oak, white oak, yellow poplar, loblolly pine
Energy Technology Data Exchange (ETDEWEB)
Pilot, Justin R. [Ohio State U.
2011-01-01
We present a search for the Standard Model Higgs Boson using the process $ZH\\to\\mu^+\\mu^- b\\bar{b}$. We use a dataset corresponding to 9.2 fb$^{-1}$ of integrated luminosity from proton-antiproton collisions with center-of-mass energy 1.96 TeV at the Fermilab Tevatron, collected with the CDF II detector. This analysis benefits from several new multivariate techniques that have not been used in previous analyses at CDF. We use a multivariate function to select muon candidates, increasing signal acceptance while simultaneously keeping fake rates small. We employ an inclusive trigger selection to further increase acceptance. To enhance signal discrimination, we utilize a multi-layer approach consisting of expert discriminants. This multi-layer discriminant method helps isolate the two main classes of background events, $t\\bar{t}$ and $Z$+jets production. It also includes a flavor separator, to distinguish light flavor jets from jets consistent with the decay of a $B$-hadron. Wit h this novel multi-layer approach, we proceed to set limits on the $ZH$ production cross section times branching ratio. For a Higgs boson with mass 115 GeV/$c^2$, we observe (expect) a limit of 8.0 (4.9) times the Standard Model prediction.
Samadi-Maybodi, Abdolraouf; Darzi, S. K. Hassani Nejad
2008-10-01
Resolution of binary mixtures of vitamin B12, methylcobalamin and B12 coenzyme with minimum sample pre-treatment and without analyte separation has been successfully achieved by methods of partial least squares algorithm with one dependent variable (PLS1), orthogonal signal correction/partial least squares (OSC/PLS), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV-vis spectra. The UV-vis spectra of the vitamin B12, methylcobalamin and B12 coenzyme were recorded in the same spectral conditions. The method of central composite design was used in the ranges of 10-80 mg L -1 for vitamin B12 and methylcobalamin and 20-130 mg L -1 for B12 coenzyme. The models refinement procedure and validation were performed by cross-validation. The minimum root mean square error of prediction (RMSEP) was 2.26 mg L -1 for vitamin B12 with PLS1, 1.33 mg L -1 for methylcobalamin with OSC/PLS and 3.24 mg L -1 for B12 coenzyme with HLA techniques. Figures of merit such as selectivity, sensitivity, analytical sensitivity and LOD were determined for three compounds. The procedure was successfully applied to simultaneous determination of three compounds in synthetic mixtures and in a pharmaceutical formulation.
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.
Rahman, Inayat Ur; Khan, Nasrullah; Ali, Kishwar
2017-04-01
An understory vegetation survey of the Pinus wallichiana-dominated temperate forests of Swat District was carried out to inspect the structure, composition and ecological associations of the forest vegetation. A quadrat method of sampling was used to record the floristic and phytosociological data necessary for the analysis using 300 quadrats of 10 × 10 m each. Some vegetation parameters viz. frequency and density for trees (overstory vegetation) as well as for the understory vegetation were recorded. The results revealed that in total, 92 species belonging to 77 different genera and 45 families existed in the area. The largest families were Asteraceae, Rosaceae and Lamiaceae with 12, ten and nine species, respectively. Ward's agglomerative cluster analysis for tree species resulted in three floristically and ecologically distinct community types along different topographic and soil variables. Importance value indices (IVI) were also calculated for understory vegetation and were subjected to ordination techniques, i.e. canonical correspondence analysis (CCA) and detrended correspondence analysis (DCA). DCA bi-plots for stands show that most of the stands were scattered around the centre of the DCA bi-plot, identified by two slightly scattered clusters. DCA for species bi-plot clearly identified three clusters of species revealing three types of understory communities in the study area. Results of the CCA were somewhat different from the DCA showing the impact of environmental variables on the understory species. CCA results reveal that three environmental variables, i.e. altitude, slope and P (mg/kg), have a strong influence on distribution of stands and species. Impact of tree species on the understory vegetation was also tested by CCA which showed that four tree species, i.e. P. wallichiana A.B. Jackson, Juglans regia Linn., Quercus dilatata Lindl. ex Royle and Cedrus deodara (Roxb. ex Lamb.) G. Don, have strong influences on associated understory vegetation. It
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.
Di Anibal, Carolina V; Odena, Marta; Ruisánchez, Itziar; Callao, M Pilar
2009-08-15
We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L(-1). Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV. Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.
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
DEFF Research Database (Denmark)
Hansen, Michael Adsetts Edberg
Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...
DEFF Research Database (Denmark)
Hansen, Michael Adsetts Edberg
Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...
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.
Techniques for multivariate sample design
Energy Technology Data Exchange (ETDEWEB)
Williamson, M.A.
1990-04-01
In this report we consider sampling methods applicable to the multi-product Annual Fuel Oil and Kerosene Sales Report (Form EIA-821) Survey. For years prior to 1989, the purpose of the survey was to produce state-level estimates of total sales volumes for each of five target variables: residential No. 2 distillate, other retail No. 2 distillate, wholesale No. 2 distillate, retail residual, and wholesale residual. For the year 1989, the other retail No. 2 distillate and wholesale No. 2 distillate variables were replaced by a new variable defined to be the maximum of the two. The strata for this variable were crossed with the strata for the residential No. 2 distillate variable, resulting in a single stratified No. 2 distillate variable. Estimation for 1989 focused on the single No. 2 distillate variable and the two residual variables. Sampling accuracy requirements for each product were specified in terms of the coefficients of variation (CVs) for the various estimates based on data taken from recent surveys. The target population for the Form EIA-821 survey includes companies that deliver or sell fuel oil or kerosene to end-users. The Petroleum Product Sales Identification Survey (Form EIA-863) data base and numerous state and commercial lists provide the basis of the sampling frame, which is updated as new data become available. In addition, company/state-level volumes for distillates fuel oil, residual fuel oil, and motor gasoline are added to aid the design and selection process. 30 refs., 50 figs., 10 tabs.
Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik
2016-04-01
Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.
Hamchevici, Carmen; Udrea, Ion
2013-11-01
The concept of basin-wide Joint Danube Survey (JDS) was launched by the International Commission for the Protection of the Danube River (ICPDR) as a tool for investigative monitoring under the Water Framework Directive (WFD), with a frequency of 6 years. The first JDS was carried out in 2001 and its success in providing key information for characterisation of the Danube River Basin District as required by WFD lead to the organisation of the second JDS in 2007, which was the world's biggest river research expedition in that year. The present paper presents an approach for improving the survey strategy for the next planned survey JDS3 (2013) by means of several multivariate statistical techniques. In order to design the optimum structure in terms of parameters and sampling sites, principal component analysis (PCA), factor analysis (FA) and cluster analysis were applied on JDS2 data for 13 selected physico-chemical and one biological element measured in 78 sampling sites located on the main course of the Danube. Results from PCA/FA showed that most of the dataset variance (above 75%) was explained by five varifactors loaded with 8 out of 14 variables: physical (transparency and total suspended solids), relevant nutrients (N-nitrates and P-orthophosphates), feedback effects of primary production (pH, alkalinity and dissolved oxygen) and algal biomass. Taking into account the representation of the factor scores given by FA versus sampling sites and the major groups generated by the clustering procedure, the spatial network of the next survey could be carefully tailored, leading to a decreasing of sampling sites by more than 30%. The approach of target oriented sampling strategy based on the selected multivariate statistics can provide a strong reduction in dimensionality of the original data and corresponding costs as well, without any loss of information.
Schenone, Agustina V; Culzoni, María J; Marsili, Nilda R; Goicoechea, Héctor C
2013-06-01
The performance of MCR-ALS was studied in the modeling of non-linear kinetic-spectrophotometric data acquired by a stopped-flow system for the quantitation of tartrazine in the presence of brilliant blue and sunset yellow FCF as possible interferents. In the present work, MCR-ALS and U-PCA/RBL were firstly applied to remove the contribution of unexpected components not included in the calibration set. Secondly, a polynomial function was used to model the non-linear data obtained by the implementation of the algorithms. MCR-ALS was the only strategy that allowed the determination of tartrazine in test samples accurately. Therefore, it was applied for the analysis of tartrazine in beverage samples with minimum sample preparation and short analysis time. The proposed method was validated by comparison with a chromatographic procedure published in the literature. Mean recovery values between 98% and 100% and relative errors of prediction values between 4% and 9% were indicative of the good performance of the method.
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.
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.
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.
Multivariate Birkhoff interpolation
Lorentz, Rudolph A
1992-01-01
The subject of this book is Lagrange, Hermite and Birkhoff (lacunary Hermite) interpolation by multivariate algebraic polynomials. It unifies and extends a new algorithmic approach to this subject which was introduced and developed by G.G. Lorentz and the author. One particularly interesting feature of this algorithmic approach is that it obviates the necessity of finding a formula for the Vandermonde determinant of a multivariate interpolation in order to determine its regularity (which formulas are practically unknown anyways) by determining the regularity through simple geometric manipulations in the Euclidean space. Although interpolation is a classical problem, it is surprising how little is known about its basic properties in the multivariate case. The book therefore starts by exploring its fundamental properties and its limitations. The main part of the book is devoted to a complete and detailed elaboration of the new technique. A chapter with an extensive selection of finite elements follows as well a...
Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi
2015-03-15
Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate
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.
Multivariate Bioclimatic Ecosystem Change Approaches
2015-02-06
conclude that an analogous patch did not exist. It must exist somewhere, but some of the other MVA techniques were restricted by the mathematical ...found that the Primarily Analogous Multivariate approach developed during this research clearly distinguished itself from the other five approaches in...Principally Analogous Multivariate (PAM) approach ............................................... 29 4.6.1 Introduction to the PAM approach
Luo, Kun; Hu, Xuebin; He, Qiang; Wu, Zhengsong; Cheng, Hao; Hu, Zhenlong; Mazumder, Asit
2017-04-01
Rapid urbanization in China has been causing dramatic deterioration in the water quality of rivers and threatening aquatic ecosystem health. In this paper, multivariate techniques, such as factor analysis (FA) and cluster analysis (CA), were applied to analyze the water quality datasets for 19 rivers in Liangjiang New Area (LJNA), China, collected in April (dry season) and September (wet season) of 2014 and 2015. In most sampling rivers, total phosphorus, total nitrogen, and fecal coliform exceeded the Class V guideline (GB3838-2002), which could thereby threaten the water quality in Yangtze and Jialing Rivers. FA clearly identified the five groups of water quality variables, which explain majority of the experimental data. Nutritious pollution, seasonal changes, and construction activities were three key factors influencing rivers' water quality in LJNA. CA grouped 19 sampling sites into two clusters, which located at sub-catchments with high- and low-level urbanization, respectively. One-way ANOVA showed the nutrients (total phosphorus, soluble reactive phosphorus, total nitrogen, ammonium nitrogen, and nitrite), fecal coliform, and conductivity in cluster 1 were significantly greater than in cluster 2. Thus, catchment urbanization degraded rivers' water quality in Liangjiang New Area. Identifying effective buffer zones at riparian scale to weaken the negative impacts of catchment urbanization was recommended.
Velasco-Tapia, Fernando
2014-01-01
Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures).
Raimondi, Valentina; Palombi, Lorenzo; Cecchi, Giovanna; Lognoli, David; Trambusti, Massimo; Gomoiu, Ioana
2007-05-01
Remotely sensed laser-induced autofluorescence spectra of pure cultures of fungal strains ( Aureobasidium pullulans, Verticillium sp.) and of bacterial strains ( Bacillus sp., Pseudomonas sp.) are presented. The strains were isolated from samples collected in a Roman archaeological site ( Tropaeum Traiani) near Constanta, Romania. The fluorescence spectra were detected in vivo from a distance of 25 m in the outdoor, using a high spectral resolution fluorescence LIDAR featuring a UV laser (XeCl@308 nm) as an excitation source. All the examined strains, except for the A. pullulans, showed fluorescence features such to allow their characterisation by processing data with multivariate techniques. Both Principal Component Analysis and Cluster Analysis were applied to the data set and compared to discriminate between the examined strains. Results demonstrate the feasibility of fluorescence-based detection and characterisation of fungi and bacteria in the outdoor with a high spectral resolution fluorescence LIDAR. In addition, they show that the proposed processing methods offer a means to discriminate between the fluorescence features due to the investigated samples and that of a fluorescence background of a known spectral shape, as that of the culture medium. This can be exploited for the remote fluorescence mapping of heterotrophic organisms on stone surfaces when the latter show a typical broad fluorescence band.
Ma, Xiaoling; Zuo, Hang; Tian, Mengjing; Zhang, Liyang; Meng, Jia; Zhou, Xuening; Min, Na; Chang, Xinyuan; Liu, Ying
2016-02-01
Metal chemical fractions obtained by optimized BCR three-stage extraction procedure and multivariate analysis techniques were exploited for assessing 7 heavy metals (Cr, Pb, Cd, Co, Cu, Zn and Ni) in sediments from Gansu province, Ningxia and Inner Mongolia Autonomous Regions of the Yellow River in Northern China. The results indicated that higher susceptibility and bioavailability of Cr and Cd with a strong anthropogenic source were due to their higher availability in the exchangeable fraction. A portion of Pb, Cd, Co, Zn, and Ni in reducible fraction may be due to the fact that they can form stable complexes with Fe and Mn oxides. Substantial amount of Pb, Co, Ni and Cu was observed as oxidizable fraction because of their strong affinity to the organic matters so that they can complex with humic substances in sediments. The high geo-accumulation indexes (I(geo)) for Cr and Cd showed their higher environmental risk to the aquatic biota. Principal component analysis (PCA) revealed that high toxic Cr and Cd in polluted sites (Cd in S10, S11 and Cr in S13) may be contributed to anthropogenic sources, it was consistent with the results of dual hierarchical clustering analysis (DHCA), which could give more details about contributing sources.
Directory of Open Access Journals (Sweden)
Fernando Velasco-Tapia
2014-01-01
Full Text Available Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC volcanic range (Mexican Volcanic Belt. In this locality, the volcanic activity (3.7 to 0.5 Ma was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward’s linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas in the comingled lavas (binary mixtures.
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
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.
Huo, R.; Wehrens, H.R.M.J.; Buydens, L.M.C.
2004-01-01
The quality of DOSY NMR data can be improved by careful pre-processing techniques. Baseline drift, peak shift, and phase shift commonly exist in real-world DOSY NMR data. These phenomena seriously hinder the data analysis and should be removed as much as possible. In this paper, a series of preproce
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.
Multivariate statistics exercises and solutions
Härdle, Wolfgang Karl
2015-01-01
The authors present tools and concepts of multivariate data analysis by means of exercises and their solutions. The first part is devoted to graphical techniques. The second part deals with multivariate random variables and presents the derivation of estimators and tests for various practical situations. The last part introduces a wide variety of exercises in applied multivariate data analysis. The book demonstrates the application of simple calculus and basic multivariate methods in real life situations. It contains altogether more than 250 solved exercises which can assist a university teacher in setting up a modern multivariate analysis course. All computer-based exercises are available in the R language. All R codes and data sets may be downloaded via the quantlet download center www.quantlet.org or via the Springer webpage. For interactive display of low-dimensional projections of a multivariate data set, we recommend GGobi.
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.
Multivariate Quantitative Chemical Analysis
Kinchen, David G.; Capezza, Mary
1995-01-01
Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.
Multivariate Quantitative Chemical Analysis
Kinchen, David G.; Capezza, Mary
1995-01-01
Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.
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%.
A primer of multivariate statistics
Harris, Richard J
2014-01-01
Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why
Hong, Haoyuan; Pourghasemi, Hamid Reza; Pourtaghi, Zohre Sadat
2016-04-01
Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua
Zhang, Haijiang; Maceira, Monica; Benson, Thomas; Nafi Toksoz, M.
2010-05-01
We present an advanced multivariate inversion technique to generate a realistic, comprehensive, and high-resolution 3D model of the seismic structure of the crust and upper mantle. The model satisfies several independent geophysical datasets including seismic surface wave dispersion measurements, gravity, and seismic arrival time. The joint inversion method takes advantage of strengths of individual data sets and is able to better constrain the seismic velocity models from shallower to greater depths. To combine different geophysical datasets into a common system, we design an optimal weighting scheme that is based on relative uncertainties of individual observations, their sensitivities to model parameters, and the trade-off of different data fitting. We apply this joint inversion method to determine the 3D Vp and Vs models of the Utah area. The seismic body wave arrival times are assembled from waveform data recorded by the University of Utah Seismograph Stations (UUSS) regional network and the EarthScope/USArray network. The surface wave dispersion measurements are obtained from the ambient noise tomography study by the University of Colorado group using EarthScope/USArray stations. The gravity data for the Utah area is extracted from the North American Gravity Database managed by the University of Texas at El Paso. The joint inversions using two individual data sets such as seismic arrival time and gravity data, as well as seismic surface wave and gravity data indicate strong low velocity anomalies in middle crust beneath some known geothermal sites in Utah. The joint inversion of all three data sets will be presented and is expected to produce a reasonably well-constrained velocity structure of the Utah area, which is helpful for characterizing and exploring existing and potential geothermal reservoirs.
Institute of Scientific and Technical Information of China (English)
汪萍; 李队员
2016-01-01
锰元素是植物所需的微量元素之一。采用激光诱导击穿光谱（laser‐induced breakdown spectrosco‐py ，LIBS）技术对土壤中锰元素进行定量分析。以46个土壤样品为研究对象，获取土壤激光诱导击穿光谱数据，选取锰元素403.1 nm的特征谱线为分析线。根据谱线强度与元素浓度建立定标曲线，相关系数仅为0.78，定标结果说明，由于土壤样品成分的复杂性，锰元素浓度受土壤基体效应影响严重，应根据锰元素在土壤中的存在形式，选取相关元素，建立多元非线性回归定量分析方法，消除基体效应，从而提高LIBS测量的准确性。在多元非线性回归方法中分别考虑碳和铁元素对锰元素浓度的影响。与定标曲线相比，在考虑碳和铁元素对锰元素影响时，L IBS预测浓度与参考浓度的相关系数为0.97，相对误差为3.2％～10.3％，测量的准确度得到提高。实验结果表明，将多元非线性回归方法和激光诱导击穿光谱技术结合可以对土壤中微量锰元素进行定量分析。%Manganese (Mn) is one of the indispensable micronutrients for plants .The concentration of Mn in soil was deter‐mined with laser‐induced breakdown spectroscopy (LIBS) in this paper .Forty‐six soil samples were used to obtain the spectrum data of LIBS .The characteristic line of 403.1 nm was selected as the analytical line of Mn .The calibration curve of the net in‐tensity of Mn line at 403.1 nm versus the corresponding concentration was constructed withthe correlation coefficient 0.78 .The results showed that ,due to the complex chemical compositions of soil samples ,the calibration method influenced by the matrix effect seriously .It should utilize more information of LIBS spectra to construct the multivariate nonlinear calibration method , which can reduce the matrix effect and improve the measurement accuracy of LIBS .With multivariate nonlinear calibration meth
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.
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.
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.
Institute of Scientific and Technical Information of China (English)
陈伟华; 彭继慎; 赵忠建
2013-01-01
In the electric hydraulic servo control system during injection process of die casting machine,this paper presents a nonlinear-predictive-control method of die casting machine for multivariable injection process based on adaptive differential chaotic evolutional algorithm.The tent map is embedded into the adaptive differential evolution algorithm (DE),which has improved the generating process of DE algorithm for the crossover factor and compiling factor.The results of simulation experiment show that by using the intelligent algorithm to optimize injection parameters of die-casting PID controller,we can achieve fast optimization of die-casting machine injection speed and make the dynamic characteristics of nonlinear predictive control better,and the control precision more higher,so as to achieve the optimal-control purpose for the system.%在压铸机压射过程的电液压伺服控制系统中,提出了一种基于混沌自适应差分进化算法(CADE)的压铸机多变量压射过程非线性预测控制方法.该方法将帐篷映射嵌入到自适应差分进化算法(DE)中,改进了DE算法中交叉因子及编译因子的产生过程.通过仿真实验结果表明,利用该智能算法对压射控制PID控制器进行参数优化,能实现压铸机压射速度的快速寻优,使其非线性预测控制动态特性更好,控制精度更高,从而达到系统的最优控制目的.
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.
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.
Stability analysis for nonlinear multi-variable delay perturbation problems%非线性多变延迟奇异摄动问题的稳定性分析
Institute of Scientific and Technical Information of China (English)
王洪山; 张诚坚
2003-01-01
This paper discusses the stability of theoretical solutions for nonlinear multi-variable delay perturbation problems (MVDPP) of the form x′(t)=f(x(t),x(t-τ1(t)),...,x(t-τm(t)),y(t),y(t-τ1(t)),...,y(t-τm(t))), and εy′(t)=g(x(t),x(t-τ1(t)),...,x(t-τm(t)),y(t),y(t-τ1(t)),...,y(t-τm(t))), where 0<ε<1. A sufficient condition of stability for the systems is obtained. Additionally we prove the numerical solutions of the implicit Euler method are stable under this condition.%讨论了形如x′(t)=f(x(t),x(t-τ1(t)),...,x(t-τm(t)),y(t),y(t-τ1(t)),...,y(t-τm(t)))和εy′(t)=g(x(t),x(t-τ1(t)),...,x(t-τm(t)),y(t),y(t-τ1(t)),...,y(t-τm(t)))(0<ε<1)的非线性多变延迟奇异摄动系统的理论解的稳定性,得到了系统稳定的一个充分条件.在此条件下还证明了隐式Euler方法的数值解是稳定的.
Zghibi, Adel; Merzougui, Amira; Zouhri, Lahcen; Tarhouni, Jamila
2014-01-01
the dissolution of gypsum, dolomite and halite, as well as contamination by nitrate caused mainly by extensive irrigation activity. The application of Multivariate Statistics Techniques based on Principal component Analysis and Hierarchical Cluster Analysis has lead to the corroboration of the hypotheses developed from the previous hydrochemical study. Two factors were found that explained major hydrochemical processes in the aquifer. These factors reveal the existence of an intensive intrusion of seawater and mechanisms of nitrate contamination of groundwater.
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
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.
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.
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...
Control Configuration Selection for Multivariable Nonlinear Systems
DEFF Research Database (Denmark)
Shaker, Hamid Reza; Komareji, Mohammad
2012-01-01
Control configuration selection is the procedure of choosing the appropriate input and output pairs for the design of SISO (or block) controllers. This step is an important prerequisite for a successful industrial control strategy. In industrial practices, it is often the case that systems, which...
Robust Adaptive Control of Multivariable Nonlinear Systems
2011-03-28
IEEE Transactions on Automatic Control , 42(9): 1200-1221, 1997. 6. D. Li, N. Hovakimyan...limitations of performance,” IEEE Transactions on Automatic Control , vol. 52, no. 7, pp. 1604–1615, 2008. 8. X. Wang, N. Hovakimyan, 1L Adaptive...550-564, 2010. 5. C. Cao, N. Hovakimyan, Stability Margins of 1L Adaptive Control Architecture, IEEE Transactions on Automatic Control , vol. 55,
Observability of multivariate differential embeddings
Energy Technology Data Exchange (ETDEWEB)
Aguirre, Luis Antonio [Laboratorio de Modelagem, Analise e Controle de Sistemas Nao Lineares, Departamento de Engenharia Eletronica, Universidade Federeal de Minas Gerais, Av. Antonio Carlos 6627, 31270-901 Belo Horizonte, MG (Brazil); Letellier, Christophe [Universite de Rouen-CORIA UMR 6614, Av. de l' Universite, BP 12, F-76801 Saint-Etienne du Rouvray Cedex (France)
2005-07-15
The present paper extends some results recently developed for the analysis of observability in nonlinear dynamical systems. The aim of the paper is to address the problem of embedding an attractor using more than one observable. A multivariate nonlinear observability matrix is proposed which includes the monovariable nonlinear and linear observability matrices as particular cases. Using the developed framework and a number of worked examples, it is shown that the choice of embedding coordinates is critical. Moreover, in some cases, to reconstruct the dynamics using more than one observable could be worse than to reconstruct using a scalar measurement. Finally, using the developed framework it is shown that increasing the embedding dimension, observability problems diminish and can even be eliminated. This seems to be a physically meaningful interpretation of the Takens embedding theorem.
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.
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.
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
Augmented Classical Least Squares Multivariate Spectral Analysis
Energy Technology Data Exchange (ETDEWEB)
Haaland, David M. (Albuquerque, NM); Melgaard, David K. (Albuquerque, NM)
2005-01-11
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
Augmented Classical Least Squares Multivariate Spectral Analysis
Energy Technology Data Exchange (ETDEWEB)
Haaland, David M. (Albuquerque, NM); Melgaard, David K. (Albuquerque, NM)
2005-07-26
A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.
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
Energy Technology Data Exchange (ETDEWEB)
Garcia A, J.M.; Torres de la Cruz, E. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)]. e-mail: jmga@nuclear.inin.mx
2004-07-01
Its were evaluated 20 lines of Chenopodium quinoa respect characters of agronomical interest finding that nine lines overcame the witness highlighting the lines: 20R1-41, 20R1-10, 20R2-27 that presented near yield to 1.5 ton/ha. The multivariate analysis of main components generated a dendrogram in that is appreciated that at an Euclidean distance of 0.75 its were formed seven groups according to its morphological characteristics and of yield, it highlights the formation of two big groups at a distance of 1.125, that they separate according to the radiation dose (200 and 250 Gy). (Author)
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.
Multivariate stochastic simulation with subjective multivariate normal distributions
P. J. Ince; J. Buongiorno
1991-01-01
In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...
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.
Multivariate analysis in thoracic research.
Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego
2015-03-01
Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.
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%.
Kumar, Manoj; Ramanathan, A L; Tripathi, Ritu; Farswan, Sandhya; Kumar, Devendra; Bhattacharya, Prosun
2017-01-01
This study is an investigation on spatio-chemical, contamination sources (using multivariate statistics), and health risk assessment arising from the consumption of groundwater contaminated with trace and toxic elements in the Chhaprola Industrial Area, Gautam Buddha Nagar, Uttar Pradesh, India. In this study 33 tubewell water samples were analyzed for 28 elements using ICP-OES. Concentration of some trace and toxic elements such as Al, As, B, Cd, Cr, Mn, Pb and U exceeded their corresponding WHO (2011) guidelines and BIS (2012) standards while the other analyzed elements remain below than those values. Background γ and β radiation levels were observed and found to be within their acceptable limits. Multivariate statistics PCA (explains 82.07 cumulative percent for total 6 of factors) and CA indicated (mixed origin) that natural and anthropogenic activities like industrial effluent and agricultural runoff are responsible for the degrading of groundwater quality in the research area. In this study area, an adult consumes 3.0 L (median value) of water therefore consuming 39, 1.94, 1461, 0.14, 11.1, 292.6, 13.6, 23.5 μg of Al, As, B, Cd, Cr, Mn, Pb and U from drinking water per day respectively. The hazard quotient (HQ) value exceeded the safe limit of 1 which for As, B, Al, Cr, Mn, Cd, Pb and U at few locations while hazard index (HI) > 5 was observed in about 30% of the samples which indicated potential health risk from these tubewells for the local population if the groundwater is consumed. Copyright © 2016 Elsevier Ltd. All rights reserved.
Evaluation of multivariate surveillance
Frisén,Marianne; Andersson, Eva; Schiöler, Linus
2009-01-01
Multivariate surveillance is of interest in many areas such as industrial production, bioterrorism detection, spatial surveillance, and financial transaction strategies. Some of the suggested approaches to multivariate surveillance have been multivariate counterparts to the univariate Shewhart, EWMA, and CUSUM methods. Our emphasis is on the special challenges of evaluating multivariate surveillance methods. Some new measures are suggested and the properties of several measures are demonstrat...
Common large innovations across nonlinear time series
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
2002-01-01
textabstractWe propose a multivariate nonlinear econometric time series model, which can be used to examine if there is common nonlinearity across economic variables. The model is a multivariate censored latent effects autoregression. The key feature of this model is that nonlinearity appears as sep
Basics of Multivariate Analysis in Neuroimaging Data
Habeck, Christian Georg
2010-01-01
Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic
Directory of Open Access Journals (Sweden)
Terezinha Ferreira de Oliveira
2006-12-01
Full Text Available The present work uses multivariate statistical analysis as a form of establishing the main sources of error in the Quantitative Phase Analysis (QPA using the Rietveld method. The quantitative determination of crystalline phases using x ray powder diffraction is a complex measurement process whose results are influenced by several factors. Ternary mixtures of Al2O3, MgO and NiO were prepared under controlled conditions and the diffractions were obtained using the Bragg-Brentano geometric arrangement. It was possible to establish four sources of critical variations: the experimental absorption and the scale factor of NiO, which is the phase with the greatest linear absorption coefficient of the ternary mixture; the instrumental characteristics represented by mechanical errors of the goniometer and sample displacement; the other two phases (Al2O3 and MgO; and the temperature and relative humidity of the air in the laboratory. The error sources excessively impair the QPA with the Rietveld method. Therefore it becomes necessary to control them during the measurement procedure.
Institute of Scientific and Technical Information of China (English)
KARIM-NEZHAD Ghasem; SAGHATFOROUSH Lotfali; ERSHAD Sohrab; BAHRAMI Kokab
2008-01-01
Multivariate calibration models(PCR and PLS)were developed for simultaneous determination of Fe(Ⅲand Cu(Ⅱ)with 1-(2-pyridylazo)-2-naphthol and AOT as chromogenic reagent and micellizing agent,respectively.In the presence of AOT the spectrum of Fe(Ⅲ)-PAN complex was shifted to higher wavelength and the overlapping with Cu-PAN spectrum decreased.It seems that this anionic surfactant enters the structure of the Fe-PAN complex to cause a shift in the absorption spectrum of it.The parameters controlling behavior of the systems were investigated and optimum conditions were selected.Sixteen ternary mixtures were selected as the calibration set.To select the number of factors in PCR and PLS algorithms,a cross validation method,leaving out one sample at a time,was employed.The calibration models were validated with 8 synthetic mixtures containing the metal ions in different proportions that were randomly designed.The best calibration model was obtained by using PLS regression.The method was successfully applied to simultaneous determination of copper and iron in biological samples.
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.
Multivariate Modelling via Matrix Subordination
DEFF Research Database (Denmark)
Nicolato, Elisa
Extending the vast library of univariate models to price multi-asset derivatives is still a challenge in the field of Quantitative Finance. Within the literature on multivariate modelling, a dichotomy may be noticed. On one hand, the focus has been on the construction of models displaying...... stochastic correlation within the framework of discussion processes (see e.g. Pigorsh and Stelzer (2008), Hubalek and Nicolato (2008) and Zhu (2000)). On the other hand a number of authors have proposed multivariate Levy models, which allow for flexible modelling of returns, but at the expenses of a constant...... correlation structure (see e.g. Leoni and Schoutens (2007) and Leoni and Schoutens (2007) among others). Tractable multivariate models displaying flexible and stochastic correlation structures combined with jumps is proving to be rather problematic. In particular, the classical technique of introducing...
Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring
Harrou, Fouzi
2017-02-16
This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.
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.
Medland, Sarah E; Loehlin, John C
2008-06-01
The ratio of the lengths of the second to fourth digits of the hand (2D:4D) is a sexually dimorphic trait that has been proposed as a measure of prenatal testosterone exposure and a putative correlate of a variety of later behavioral and physiological outcomes including personality, fitness and sexual orientation. We present analyses of 2D:4D ratios collected from twins (1413 individuals) and their nontwin siblings (328 individuals) from 757 families. In this sample 2D:4D was measured from photocopies using digital calipers, and for a subset of participants, computer-aided measurement. Multivariate modeling of the left- and right-hand measurements revealed significant genetic and environmental covariation between hands. The two methods yielded very similar results, and the majority of variance was explained by factors shared by both measurement methods. Neither common environmental nor dominant genetic effects were found, and the covariation between siblings could be accounted for by additive genetic effects accounting for 80% and 71% of the variance for the left and right hands, respectively. There was no evidence of sex differences in the total variance, nor in the magnitude or source of genetic and environmental influences, suggesting that X-linked effects (such as the previously identified association with the Androgen receptor) are likely to be small. However, there were also nonshared environmental effects specific to each hand, which, in addition to measurement error, may in part explain why some studies within in the literature find effects for the 2D:4D ratio of one hand but not the other.
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.
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.
Exploratory and multivariate data analysis
Jambu, Michel
1991-01-01
With a useful index of notations at the beginning, this book explains and illustrates the theory and application of data analysis methods from univariate to multidimensional and how to learn and use them efficiently. This book is well illustrated and is a useful and well-documented review of the most important data analysis techniques.Key Features* Describes, in detail, exploratory data analysis techniques from the univariate to the multivariate ones* Features a complete description of correspondence analysis and factor analysis techniques as multidimensional statistical data a
Plant, Emma L; Smernik, Ronald J; van Leeuwen, John; Greenwood, Paul; Macdonald, Lynne M
2014-03-01
The paper-making process can produce large amounts of wastewater (WW) with high particulate and dissolved organic loads. Generally, in developed countries, stringent international regulations for environmental protection require pulp and paper mill WW to be treated to reduce the organic load prior to discharge into the receiving environment. This can be achieved by primary and secondary treatments involving both chemical and biological processes. These processes result in complex changes in the nature of the organic material, as some components are mineralised and others are transformed. In this study, changes in the nature of organics through different stages of secondary treatment of pulp and paper mill WW were followed using three advanced characterisation techniques: solid-state (13)C nuclear magnetic resonance (NMR) spectroscopy, pyrolysis-gas chromatography mass spectrometry (py-GCMS) and high-performance size-exclusion chromatography (HPSEC). Each technique provided a different perspective on the changes that occurred. To compare the different chemical perspectives in terms of the degree of similarity/difference between samples, we employed non-metric multidimensional scaling. Results indicate that NMR and HPSEC provided strongly correlated perspectives, with 86 % of the discrimination between the organic samples common to both techniques. Conversely, py-GCMS was found to provide a unique, and thus complementary, perspective.
Affum, Andrews Obeng; Osae, Shiloh Dede; Nyarko, Benjamin Jabez Botwe; Afful, Samuel; Fianko, Joseph Richmond; Akiti, Tetteh Thomas; Adomako, Dickson; Acquaah, Samuel Osafo; Dorleku, Micheal; Antoh, Emmanuel; Barnes, Felix; Affum, Enoch Acheampong
2015-02-01
In recent times, surface water resource in the Western Region of Ghana has been found to be inadequate in supply and polluted by various anthropogenic activities. As a result of these problems, the demand for groundwater by the human populations in the peri-urban communities for domestic, municipal and irrigation purposes has increased without prior knowledge of its water quality. Water samples were collected from 14 public hand-dug wells during the rainy season in 2013 and investigated for total coliforms, Escherichia coli, mercury (Hg), arsenic (As), cadmium (Cd) and physicochemical parameters. Multivariate statistical analysis of the dataset and a linear stoichiometric plot of major ions were applied to group the water samples and to identify the main factors and sources of contamination. Hierarchal cluster analysis revealed four clusters from the hydrochemical variables (R-mode) and three clusters in the case of water samples (Q-mode) after z score standardization. Principal component analysis after a varimax rotation of the dataset indicated that the four factors extracted explained 93.3 % of the total variance, which highlighted salinity, toxic elements and hardness pollution as the dominant factors affecting groundwater quality. Cation exchange, mineral dissolution and silicate weathering influenced groundwater quality. The ranking order of major ions was Na(+) > Ca(2+) > K(+) > Mg(2+) and Cl(-) > SO4 (2-) > HCO3 (-). Based on piper plot and the hydrogeology of the study area, sodium chloride (86 %), sodium hydrogen carbonate and sodium carbonate (14 %) water types were identified. Although E. coli were absent in the water samples, 36 % of the wells contained total coliforms (Enterobacter species) which exceeded the WHO guidelines limit of zero colony-forming unit (CFU)/100 mL of drinking water. With the exception of Hg, the concentration of As and Cd in 79 and 43 % of the water samples exceeded the WHO guideline limits of 10 and 3
Multivariate analysis with LISREL
Jöreskog, Karl G; Y Wallentin, Fan
2016-01-01
This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.
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.
Switching Between Multivariable Controllers
DEFF Research Database (Denmark)
Niemann, H.; Stoustrup, Jakob; Abrahamsen, R.B.
2004-01-01
A concept for implementation of multivariable controllers is presented in this paper. The concept is based on the Youla-Jabr-Bongiorno-Kucera (YJBK) parameterization of all stabilizing controllers. By using this architecture for implementation of multivariable controllers, it is shown how...
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...... in which several multivariate GARCH models are fitted to the same data set and the results compared....
Multivariate irregular sampling theorem
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In this paper,we prove a Marcinkiewicz-Zygmund type inequality for multivariate entire functions of exponential type with non-equidistant spaced sampling points. And from this result,we establish a multivariate irregular Whittaker-Kotelnikov-Shannon type sampling theorem.
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...... in which several multivariate GARCH models are fitted to the same data set and the results compared....
Multivariate irregular sampling theorem
Institute of Scientific and Technical Information of China (English)
CHEN GuangGui; FANG GenSun
2009-01-01
In this paper, we prove a Marcinkiewicz-Zygmund type inequality for multivariate entire functions of exponential type with non-equidistant spaced sampling points. And from this result, we establish a multivariate irregular Whittaker-Kotelnikov-Shannon type sampling theorem.
Switching Between Multivariable Controllers
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Stoustrup, Jakob; Abrahamsen, Rune
2004-01-01
it is possible to smoothly switch between multivariable controllers with guaranteed closed-loop stability. This includes also the case where one or more controllers are unstable. The concept for smooth online changes of multivariable controllers based on the YJBK architecture can also handle the start up...
Lectures in feedback design for multivariable systems
Isidori, Alberto
2017-01-01
This book focuses on methods that relate, in one form or another, to the “small-gain theorem”. It is aimed at readers who are interested in learning methods for the design of feedback laws for linear and nonlinear multivariable systems in the presence of model uncertainties. With worked examples throughout, it includes both introductory material and more advanced topics. Divided into two parts, the first covers relevant aspects of linear-systems theory, the second, nonlinear theory. In order to deepen readers’ understanding, simpler single-input–single-output systems generally precede treatment of more complex multi-input–multi-output (MIMO) systems and linear systems precede nonlinear systems. This approach is used throughout, including in the final chapters, which explain the latest advanced ideas governing the stabilization, regulation, and tracking of nonlinear MIMO systems. Two major design problems are considered, both in the presence of model uncertainties: asymptotic stabilization with a “...
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.
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.
Multivariate meta-analysis: potential and promise.
Jackson, Dan; Riley, Richard; White, Ian R
2011-09-10
The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.
Methods of Multivariate Analysis
Rencher, Alvin C
2012-01-01
Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit
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.
Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data
Brandon, Jay M.; Morelli, Eugene A.
2014-01-01
Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring and analysis, and near-real-time global nonlinear aerodynamic modeling and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed.
Multivariate Time Series Search
National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...
Flotation control -- A multivariable stabilizer
Energy Technology Data Exchange (ETDEWEB)
Schubert, J.H.; Henning, R.G.D.; Hulbert, D.G.; Craig, I.K. [Mintek, Randburg (South Africa)
1995-12-31
This paper presents a stabilizing controller for flotation plants which uses a quasi-multivariable technique. The controller monitors all the levels in the plant, and by anticipating interactions between various parts of the plant, is able to stabilize the plant far more successfully than the normal plant control. Once stabilizing control has been achieved, optimization of the process becomes easier and more sustainable. An estimate of the improvement in metallurgical performance is made and a singular value analysis was conducted to verify that the multivariable algorithm will theoretically control better than a collection of individual PID loops. Metallurgical results are presented to show that the improvements are attainable in practice. Control by the Mintek algorithm was alternated with normal plant control, to show that the improvements are statistically significant.
Okou, Francis A.; Akhrif, Ouassima; Dessaint, Louis A.; Bouchard, Derrick
2013-05-01
This papter introduces a decentralized multivariable robust adaptive voltage and frequency regulator to ensure the stability of large-scale interconnnected generators. Interconnection parameters (i.e. load, line and transormer parameters) are assumed to be unknown. The proposed design approach requires the reformulation of conventiaonal power system models into a multivariable model with generator terminal voltages as state variables, and excitation and turbine valve inputs as control signals. This model, while suitable for the application of modern control methods, introduces problems with regards to current design techniques for large-scale systems. Interconnection terms, which are treated as perturbations, do not meet the common matching condition assumption. A new adaptive method for a certain class of large-scale systems is therefore introduces that does not require the matching condition. The proposed controller consists of nonlinear inputs that cancel some nonlinearities of the model. Auxiliary controls with linear and nonlinear components are used to stabilize the system. They compensate unknown parametes of the model by updating both the nonlinear component gains and excitation parameters. The adaptation algorithms involve the sigma-modification approach for auxiliary control gains, and the projection approach for excitation parameters to prevent estimation drift. The computation of the matrix-gain of the controller linear component requires the resolution of an algebraic Riccati equation and helps to solve the perturbation-mismatching problem. A realistic power system is used to assess the proposed controller performance. The results show that both stability and transient performance are considerably improved following a severe contingency.
Classification Techniques for Multivariate Data Analysis.
1980-03-28
analysis among biologists, botanists, and ecologists, while some social scientists may refer "typology". Other frequently encountered terms are pattern...the determinantal equation: lB -XW 0 (42) 49 The solutions X. are the eigenvalues of the matrix W-1 B 1 as in discriminant analysis. There are t non...Statistical Package for Social Sciences (SPSS) (14) subprogram FACTOR was used for the principal components analysis. It is designed both for the factor
Arslan, Hakan; Ayyildiz Turan, Nazlı
2015-08-01
Monitoring of heavy metal concentrations in groundwater potentially used for drinking and irrigation is very important. This study collected groundwater samples from 78 wells in July 2012 and analyzed them for 17 heavy metals (Pb, Zn, Cr, Mn, Fe, Cu, Cd, Co, Ni, Al, As, Mo, Se, B, Ti, V, Ba). Spatial distributions of these elements were identified using three different interpolation methods [inverse distance weighing (IDW), radial basis function (RBF), and ordinary kriging (OK)]. Root mean squared error (RMSE) and mean absolute error (MAE) for cross validation were used to select the best interpolation methods for each parameter. Multivariate statistical analysis [cluster analysis (CA) and factor analysis (FA)] were used to identify similarities among sampling sites and the contribution of variables to groundwater pollution. Fe and Mn levels exceeded World Health Organization (WHO) recommended limits for drinking water in almost all of the study area, and some locations had Fe and Mn levels that exceeded Food and Agriculture Organization (FAO) guidelines for drip irrigation systems. Al, As, and Cd levels also exceeded WHO guidelines for drinking water. Cluster analysis classified groundwater in the study area into three groups, and factor analysis identified five factors that explained 73.39% of the total variation in groundwater, which are as follows: factor 1: Se, Ti, Cr, Mo; factor 2: Ni, Mn, Co, Ba; factor 3: Pb, Cd; factor 4: B, V, Fe, Cu; and factor 5: AS, Zn. As a result of this study, it could be said that interpolation methods and multivariate statistical techniques gave very useful results for the determination of the source.
Temperature uniformity control in RTP using multivariable adaptive control
Energy Technology Data Exchange (ETDEWEB)
Morales, S.; Dahhou, B.; Dilhac, J.M. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Morales, S.
1995-12-31
In Rapid Thermal Processing (RTP) control of the wafer temperature during all processing to get good trajectory following, together with spatial temperature uniformity, is essential. It is well know as RTP process is nonlinear, classical control laws are not very efficient. In this work, the authors aim at studying the applicability of MIMO (Multiple Inputs Multiple Outputs) adaptive techniques to solve the temperature control problems in RTP. A multivariable linear discrete time CARIMA (Controlled Auto Regressive Integrating Moving Average) model of the highly non-linear process is identified on-line using a robust identification technique. The identified model is used to compute an infinite time LQ (Linear Quadratic) based control law, with a partial state reference model. This reference model smooths the original setpoint sequence, and at the same time gives a tracking capability to the LQ control law. After an experimental open-loop investigation, the results of the application of the adaptive control law are presented. Finally, some comments on the future difficulties and developments of the application of adaptive control in RTP are given. (author) 13 refs.
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.
Multivariable Feedback Control of Nuclear Reactors
Directory of Open Access Journals (Sweden)
Rune Moen
1982-07-01
Full Text Available Multivariable feedback control has been adapted for optimal control of the spatial power distribution in nuclear reactor cores. Two design techniques, based on the theory of automatic control, were developed: the State Variable Feedback (SVF is an application of the linear optimal control theory, and the Multivariable Frequency Response (MFR is based on a generalization of the traditional frequency response approach to control system design.
Multiscale and Multivariate Visualizations of Software Evolution
Voinea, Lucian; Telea, Alexandru
2006-01-01
Software evolution visualization is a promising technique for assessing the software development process. We study how complex correlations of software evolution attributes can be made using multivariate visualization techniques. We use a combination of color and textures to depict up to four artifa
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.
Multivariate residues and maximal unitarity
Søgaard, Mads; Zhang, Yang
2013-12-01
We extend the maximal unitarity method to amplitude contributions whose cuts define multidimensional algebraic varieties. The technique is valid to all orders and is explicitly demonstrated at three loops in gauge theories with any number of fermions and scalars in the adjoint representation. Deca-cuts realized by replacement of real slice integration contours by higher-dimensional tori encircling the global poles are used to factorize the planar triple box onto a product of trees. We apply computational algebraic geometry and multivariate complex analysis to derive unique projectors for all master integral coefficients and obtain compact analytic formulae in terms of tree-level data.
Essentials of multivariate data analysis
Spencer, Neil H
2013-01-01
""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015
Applied multivariate statistical analysis
Härdle, Wolfgang Karl
2015-01-01
Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners. It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added. All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior. All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...
Method for statistical data analysis of multivariate observations
Gnanadesikan, R
1997-01-01
A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte
Multivariate bubbles and antibubbles
Fry, John
2014-08-01
In this paper we develop models for multivariate financial bubbles and antibubbles based on statistical physics. In particular, we extend a rich set of univariate models to higher dimensions. Changes in market regime can be explicitly shown to represent a phase transition from random to deterministic behaviour in prices. Moreover, our multivariate models are able to capture some of the contagious effects that occur during such episodes. We are able to show that declining lending quality helped fuel a bubble in the US stock market prior to 2008. Further, our approach offers interesting insights into the spatial development of UK house prices.
DEFF Research Database (Denmark)
Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger
2011-01-01
We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...
A MULTIVARIATE WEIBULL DISTRIBUTION
Directory of Open Access Journals (Sweden)
Cheng Lee
2010-07-01
Full Text Available A multivariate survival function of Weibull Distribution is developed by expanding the theorem by Lu and Bhattacharyya. From the survival function, the probability density function, the cumulative probability function, the determinant of the Jacobian Matrix, and the general moment are derived.
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.
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).
Multivariate image analysis in biomedicine.
Nattkemper, Tim W
2004-10-01
In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.
Optimal model-free prediction from multivariate time series.
Runge, Jakob; Donner, Reik V; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
Skopina, Maria; Protasov, Vladimir
2016-01-01
This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...
Multivariate calculus and geometry
Dineen, Seán
2014-01-01
Multivariate calculus can be understood best by combining geometric insight, intuitive arguments, detailed explanations and mathematical reasoning. This textbook has successfully followed this programme. It additionally provides a solid description of the basic concepts, via familiar examples, which are then tested in technically demanding situations. In this new edition the introductory chapter and two of the chapters on the geometry of surfaces have been revised. Some exercises have been replaced and others provided with expanded solutions. Familiarity with partial derivatives and a course in linear algebra are essential prerequisites for readers of this book. Multivariate Calculus and Geometry is aimed primarily at higher level undergraduates in the mathematical sciences. The inclusion of many practical examples involving problems of several variables will appeal to mathematics, science and engineering students.
Multivariate $\\alpha$-molecules
Flinth, Axel; Schäfer, Martin
2015-01-01
The suboptimal performance of wavelets with regard to the approximation of multivariate data gave rise to new representation systems, specifically designed for data with anisotropic features. Some prominent examples of these are given by ridgelets, curvelets, and shearlets, to name a few. The great variety of such so-called directional systems motivated the search for a common framework, which unites many under one roof and enables a simultaneous analysis, for example with respect to approxim...
Transient multivariable sensor evaluation
Energy Technology Data Exchange (ETDEWEB)
Vilim, Richard B.; Heifetz, Alexander
2017-02-21
A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.
Transient multivariable sensor evaluation
Vilim, Richard B.; Heifetz, Alexander
2017-02-21
A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.
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.
Multivariate respiratory motion prediction
Dürichen, R.; Wissel, T.; Ernst, F.; Schlaefer, A.; Schweikard, A.
2014-10-01
In extracranial robotic radiotherapy, tumour motion is compensated by tracking external and internal surrogates. To compensate system specific time delays, time series prediction of the external optical surrogates is used. We investigate whether the prediction accuracy can be increased by expanding the current clinical setup by an accelerometer, a strain belt and a flow sensor. Four previously published prediction algorithms are adapted to multivariate inputs—normalized least mean squares (nLMS), wavelet-based least mean squares (wLMS), support vector regression (SVR) and relevance vector machines (RVM)—and evaluated for three different prediction horizons. The measurement involves 18 subjects and consists of two phases, focusing on long term trends (M1) and breathing artefacts (M2). To select the most relevant and least redundant sensors, a sequential forward selection (SFS) method is proposed. Using a multivariate setting, the results show that the clinically used nLMS algorithm is susceptible to large outliers. In the case of irregular breathing (M2), the mean root mean square error (RMSE) of a univariate nLMS algorithm is 0.66 mm and can be decreased to 0.46 mm by a multivariate RVM model (best algorithm on average). To investigate the full potential of this approach, the optimal sensor combination was also estimated on the complete test set. The results indicate that a further decrease in RMSE is possible for RVM (to 0.42 mm). This motivates further research about sensor selection methods. Besides the optical surrogates, the sensors most frequently selected by the algorithms are the accelerometer and the strain belt. These sensors could be easily integrated in the current clinical setup and would allow a more precise motion compensation.
Multivariate Statistical Process Control
DEFF Research Database (Denmark)
Kulahci, Murat
2013-01-01
As sensor and computer technology continues to improve, it becomes a normal occurrence that we confront with high dimensional data sets. As in many areas of industrial statistics, this brings forth various challenges in statistical process control (SPC) and monitoring for which the aim...... is to identify “out-of-control” state of a process using control charts in order to reduce the excessive variation caused by so-called assignable causes. In practice, the most common method of monitoring multivariate data is through a statistic akin to the Hotelling’s T2. For high dimensional data with excessive...
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.
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.
Multivariate Analysis of Industrial Scale Fermentation Data
DEFF Research Database (Denmark)
Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.
2015-01-01
Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...
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.
Acoustic multivariate condition monitoring - AMCM
Energy Technology Data Exchange (ETDEWEB)
Rosenhave, P.E. [Vestfold College, Maritime Dept., Toensberg (Norway)
1997-12-31
In Norway, Vestfold College, Maritime Department presents new opportunities for non-invasive, on- or off-line acoustic monitoring of rotating machinery such as off-shore pumps and diesel engines. New developments within acoustic sensor technology coupled with chemometric data analysis of complex signals now allow condition monitoring of hitherto unavailable flexibility and diagnostic specificity. Chemometrics paired with existing knowledge yields a new and powerful tool for condition monitoring. By the use of multivariate techniques and acoustics it is possible to quantify wear and tear as well as predict the performance of working components in complex machinery. This presentation describes the AMCM method and one result of a feasibility study conducted onboard the LPG/C `Norgas Mariner` owned by Norwegian Gas Carriers as (NGC), Oslo. (orig.) 6 refs.
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.
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
Approximation by Multivariate Singular Integrals
Anastassiou, George A
2011-01-01
Approximation by Multivariate Singular Integrals is the first monograph to illustrate the approximation of multivariate singular integrals to the identity-unit operator. The basic approximation properties of the general multivariate singular integral operators is presented quantitatively, particularly special cases such as the multivariate Picard, Gauss-Weierstrass, Poisson-Cauchy and trigonometric singular integral operators are examined thoroughly. This book studies the rate of convergence of these operators to the unit operator as well as the related simultaneous approximation. The last cha
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...
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Analog multivariate counting analyzers
Nikitin, A V; Armstrong, T P
2003-01-01
Characterizing rates of occurrence of various features of a signal is of great importance in numerous types of physical measurements. Such signal features can be defined as certain discrete coincidence events, e.g. crossings of a signal with a given threshold, or occurrence of extrema of a certain amplitude. We describe measuring rates of such events by means of analog multivariate counting analyzers. Given a continuous scalar or multicomponent (vector) input signal, an analog counting analyzer outputs a continuous signal with the instantaneous magnitude equal to the rate of occurrence of certain coincidence events. The analog nature of the proposed analyzers allows us to reformulate many problems of the traditional counting measurements, and cast them in a form which is readily addressed by methods of differential calculus rather than by algebraic or logical means of digital signal processing. Analog counting analyzers can be easily implemented in discrete or integrated electronic circuits, do not suffer fro...
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.
Differential constraints for bounded recursive identification with multivariate splines
De Visser, C.C.; Chu, Q.P.; Mulder, J.A.
2011-01-01
The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able
Multivariate statistical methods a primer
Manly, Bryan FJ
2004-01-01
THE MATERIAL OF MULTIVARIATE ANALYSISExamples of Multivariate DataPreview of Multivariate MethodsThe Multivariate Normal DistributionComputer ProgramsGraphical MethodsChapter SummaryReferencesMATRIX ALGEBRAThe Need for Matrix AlgebraMatrices and VectorsOperations on MatricesMatrix InversionQuadratic FormsEigenvalues and EigenvectorsVectors of Means and Covariance MatricesFurther Reading Chapter SummaryReferencesDISPLAYING MULTIVARIATE DATAThe Problem of Displaying Many Variables in Two DimensionsPlotting index VariablesThe Draftsman's PlotThe Representation of Individual Data P:ointsProfiles o
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
A globally convergent matricial algorithm for multivariate spectral estimation
Ramponi, Federico; Pavon, Michele
2008-01-01
In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as MATLAB's PEM and MATLAB's N4SID.
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.
Hammer, C.; Maduro, J. H.; Bantema-Joppe, E. J.; van der Schaaf, A.; van der Laan, H. P.; Langendijk, J. A.; Crijns, A. P. G.
2017-01-01
Background and purpose: To develop a multivariable prediction model for the risk of grade >= 2 fibrosis in the boost area after breast conserving surgery (BCS) followed by three-dimensional conformal radiotherapy (RT) with a simultaneous integrated photon boost (3D-CRT-SIB), five years after RT. Mat
Soeder, J. F.
1983-01-01
As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.
Recurrent neural networks-based multivariable system PID predictive control
Institute of Scientific and Technical Information of China (English)
ZHANG Yan; WANG Fanzhen; SONG Ying; CHEN Zengqiang; YUAN Zhuzhi
2007-01-01
A nonlinear proportion integration differentiation (PID) controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multistep predictive cost energy to train the weights of the decoupling controller.Simulation studies have shown the efficiency of these strategies.
The toolkit for multivariate data analysis TMVA 4
Speckmayer, P; Stelzer, J; Voss, H
2010-01-01
The toolkit for multivariate analysis, TMVA, provides a large set of advanced multivariate analysis techniques for signal/background classification. In addition, TMVA now also contains regression analysis, all embedded in a framework capable of handling the preprocessing of the data and the evaluation of the output, thus allowing a simple and convenient use of multivariate techniques. The analysis techniques implemented in TMVA can be invoked easily and the direct comparison of their performance allows the user to choose the most appropriate for a particular data analysis. This article gives an overview of the TMVA package and presents recently developed features.
Energy Technology Data Exchange (ETDEWEB)
Morales Mago, S.J.
1995-12-20
In this work the problem of temperature uniformity control in rapid thermal processing is addressed by means of multivariable adaptive control. Rapid Thermal Processing (RTP) is a set of techniques proposed for semiconductor fabrication processes such as annealing, oxidation, chemical vapour deposition and others. The product quality depends on two mains issues: precise trajectory following and spatial temperature uniformity. RTP is a fabrication technique that requires a sophisticated real-time multivariable control system to achieve acceptable results. Modelling of the thermal behaviour of the process leads to very complex mathematical models. These are the reasons why adaptive control techniques are chosen. A multivariable linear discrete time model of the highly non-linear process is identified on-line, using an identification scheme which includes supervisory actions. This identified model, combined with a multivariable predictive control law allows to prevent the controller from systems variations. The control laws are obtained by minimization of a quadratic cost function or by pole placement. In some of these control laws, a partial state reference model was included. This reference model allows to incorporate an appropriate tracking capability into the control law. Experimental results of the application of the involved multivariable adaptive control laws on a RTP system are presented. (author) refs
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...
Relationship between Multiple Regression and Selected Multivariable Methods.
Schumacker, Randall E.
The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…
Directory of Open Access Journals (Sweden)
João de Andrade Dutra Filho
2011-03-01
Full Text Available Estudos sobre divergência genética são importantes na identificação de genitores potenciais para a obtenção de novos indivíduos com maior efeito heterótico. Em cana-de-açúcar esses estudos assumem fundamental importância, pois com o passar do tempo as variedades comerciais devem ser substituídas de suas áreas de cultivo, devido a sérios declínios agronômicos e industriais ocasionados pela degenerescência varietal. Sendo assim, o objetivo deste trabalho foi avaliar a divergência genética em progênies de cana-de-açúcar, através de técnicas multivariadas, com base em oito caracteres agroindustriais. O trabalho foi conduzido na área agrícola da Usina Santa Tereza, município de Goiana (PE, durante o ano agrícola 2007/2008. Foi utilizado o delineamento experimental casualizado em blocos completos com cinco repetições. As variáveis analisadas foram: toneladas de pol por hectare, toneladas de cana por hectare, fibra, pol % corrigida, pureza, teor de sólidos solúveis, açúcares redutores e açúcar total recuperável. Após análise de variância e estimação de parâmetros genéticos, a distância generalizada de Mahalanobis foi calculada para quantificar a dissimilaridade. Foram utilizados o método hierárquico de ligações médias (UPGMA e o método de otimização de Tocher. O coeficiente de herdabilidade média foi de alta magnitude para as variáveis TPH e TCH, indicando possibilidade de sucesso na seleção com base nesses caracteres. A metodologia aplicada permitiu a identificação de progênies de maior divergência genética proporcionando ao fitomelhoramento canavieiro da RIDESA sugestão de cruzamentos a serem realizados futuramente.This study aimed to evaluate the genetic diversity in progenies of sugar cane by means of multivariate techniques based on eight agroindustrial traits. The work was carried out in the agricultural area of Usina Santa Teresa, Goiana (PE, during the agricultural year 2007
Marques, R.; Amaral, P.; Zêzere, J. L.; Queiroz, G.; Goulart, C.
2009-04-01
Slope instability research and susceptibility mapping is a fundamental component of hazard assessment and is of extreme importance for risk mitigation, land-use management and emergency planning. Landslide susceptibility zonation has been actively pursued during the last two decades and several methodologies are still being improved. Among all the methods presented in the literature, indirect quantitative probabilistic methods have been extensively used. In this work different linear probabilistic methods, both bi-variate and multi-variate (Informative Value, Fuzzy Logic, Weights of Evidence and Logistic Regression), were used for the computation of the spatial probability of landslide occurrence, using the pixel as mapping unit. The methods used are based on linear relationships between landslides and 9 considered conditioning factors (altimetry, slope angle, exposition, curvature, distance to streams, wetness index, contribution area, lithology and land-use). It was assumed that future landslides will be conditioned by the same factors as past landslides in the study area. The presented work was developed for Ribeira Quente Valley (S. Miguel Island, Azores), a study area of 9,5 km2, mainly composed of volcanic deposits (ash and pumice lapilli) produced by explosive eruptions in Furnas Volcano. This materials associated to the steepness of the slopes (38,9% of the area has slope angles higher than 35°, reaching a maximum of 87,5°), make the area very prone to landslide activity. A total of 1.495 shallow landslides were mapped (at 1:5.000 scale) and included in a GIS database. The total affected area is 401.744 m2 (4,5% of the study area). Most slope movements are translational slides frequently evolving into debris-flows. The landslides are elongated, with maximum length generally equivalent to the slope extent, and their width normally does not exceed 25 m. The failure depth rarely exceeds 1,5 m and the volume is usually smaller than 700 m3. For modelling
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.
Multivariate Bonferroni-type inequalities theory and applications
Chen, John
2014-01-01
Multivariate Bonferroni-Type Inequalities: Theory and Applications presents a systematic account of research discoveries on multivariate Bonferroni-type inequalities published in the past decade. The emergence of new bounding approaches pushes the conventional definitions of optimal inequalities and demands new insights into linear and Fréchet optimality. The book explores these advances in bounding techniques with corresponding innovative applications. It presents the method of linear programming for multivariate bounds, multivariate hybrid bounds, sub-Markovian bounds, and bounds using Hamil
Nonlinear Latent Curve Models for Multivariate Longitudinal Data
Blozis, Shelley A.; Conger, Katherine J.; Harring, Jeffrey R.
2007-01-01
Latent curve models have become a useful approach to analyzing longitudinal data, due in part to their allowance of and emphasis on individual differences in features that describe change. Common applications of latent curve models in developmental studies rely on polynomial functions, such as linear or quadratic functions. Although useful for…
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.
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...