Nonlinear Dimension Reduction and Visualization of Labeled Data
Bunte, K.; Hammer, B.; Biehl, M.; Jiang,; Petkov, N
2009-01-01
The amount of electronic information as well as the size and dimensionality of data sets have increased tremendously. Consequently, dimension reduction and visualization techniques have become increasingly popular in recent years. Dimension reduction is typically connected with loss of information.
Energy Technology Data Exchange (ETDEWEB)
Gell-Mann, M.; Zwiebach, B.
1985-10-28
We discuss general aspects of dimensional reduction induced by nonlinear scalar dynamics, including the small fluctuation expansion of the action. The case of compact positively curved scalar manifolds described by symmetric spaces G/H is shown to be free of tachyonic instabilities; the spectrum consists of a graviton, a massless scalar and towers of massive spin-two, spin-one, and spin-zero fields. These towers are worked out explicitly for the case of a two-sphere. The case of noncompact negatively curved scalar manifolds inducing a noncompact nonhomogeneous space for the extra dimensions is studied in the particular example of SU(1,1)/U(1). The massless spectrum consists of a graviton and a scalar and suitable boundary conditions are seen to give a discrete spectrum, actual conservation of formally conserved quantities, and no problems of interpretation. We discuss positive energy. (orig.).
Cannistraci, Carlo
2010-09-01
Motivation: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. Methods: \\'Minimum Curvilinearity\\' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. Results: Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. Conclusion: MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. © The Author(s) 2010. Published by Oxford University Press.
Nonlinear Filtering in High Dimension
2014-06-02
dimension cardV . Remark 4.9. In the language of statistical mechanics, we exploit the fact that the smoothing distribution Px(X0, . . . , Xn ∈ · |Y1...does the mixing property of the random field X imply the conditional mixing property of (X, Y )? It will be insightful to reformulate the problem in...edge observations in Example 7.17 is merely cosmetic: the same example can be reformulated in terms of vertex observations. Indeed, let us define the
Dimensional reduction of nonlinear time delay systems
Directory of Open Access Journals (Sweden)
M. S. Fofana
2005-01-01
infinite-dimensional problem without the assumption of small time delay. This dimensional reduction is illustrated in this paper with the delay versions of the Duffing and van der Pol equations. For both nonlinear delay equations, transcendental characteristic equations of linearized stability are examined through Hopf bifurcation. The infinite-dimensional nonlinear solutions of the delay equations are decomposed into stable and centre subspaces, whose respective dimensions are determined by the linearized stability of the transcendental equations. Linear semigroups, infinitesimal generators, and their adjoint forms with bilinear pairings are the additional candidates for the infinite-dimensional reduction.
On the dimension of complex responses in nonlinear structural vibrations
Wiebe, R.; Spottswood, S. M.
2016-07-01
The ability to accurately model engineering systems under extreme dynamic loads would prove a major breakthrough in many aspects of aerospace, mechanical, and civil engineering. Extreme loads frequently induce both nonlinearities and coupling which increase the complexity of the response and the computational cost of finite element models. Dimension reduction has recently gained traction and promises the ability to distill dynamic responses down to a minimal dimension without sacrificing accuracy. In this context, the dimensionality of a response is related to the number of modes needed in a reduced order model to accurately simulate the response. Thus, an important step is characterizing the dimensionality of complex nonlinear responses of structures. In this work, the dimensionality of the nonlinear response of a post-buckled beam is investigated. Significant detail is dedicated to carefully introducing the experiment, the verification of a finite element model, and the dimensionality estimation algorithm as it is hoped that this system may help serve as a benchmark test case. It is shown that with minor modifications, the method of false nearest neighbors can quantitatively distinguish between the response dimension of various snap-through, non-snap-through, random, and deterministic loads. The state-space dimension of the nonlinear system in question increased from 2-to-10 as the system response moved from simple, low-level harmonic to chaotic snap-through. Beyond the problem studied herein, the techniques developed will serve as a prescriptive guide in developing fast and accurate dimensionally reduced models of nonlinear systems, and eventually as a tool for adaptive dimension-reduction in numerical modeling. The results are especially relevant in the aerospace industry for the design of thin structures such as beams, panels, and shells, which are all capable of spatio-temporally complex dynamic responses that are difficult and computationally expensive to
Institute of Scientific and Technical Information of China (English)
康伟; 张家忠; 李凯伦
2011-01-01
Nonlinear Galerkin method using POD modes is presented for low-dimensional modeling of the fluid dynamical system. The method splits the complete space spanned by the POD modes satisfying the boundary condition of the flow field into two subspaces - a finite-dimensional one spanned by low-order modes and its complement spanned by high-order modes. Then the interaction nature between the low-order modes and high-order modes is considered via the introduction of approximate inertial manifold. Furthermore, nonlinear Galerkin method using POD modes is chosen to approximate the nonlinear partial differential equations for fluids and reduce the system from infinite dimension to lower dimension. The flow past NACA0012 airfoil at Re= 200 and the attack angle of 20° is simulated for low-dimensional modeling analysis to verify the efficiency of the method. The results show that because the effect of high-order modes is taken into account, the present method enables to give an accurate description of the system's dynamic behaviors and preserve the topological structure of the system with fewer modes, compared with conventional POD method.%提出了一种利用本征正交分解(POD)的非线性Galerkin方法,用于复杂流体动力系统的低维建模.该方法将满足流场边界条件的正交基(POD模态)张成的完备空间分解为有限维(低阶模态)子空间和无限维(高阶模态)子空间,并采用近似惯性流形逼近高阶模态和低阶模态的作用关系,用低阶分量来表示高阶分量,将无穷维流体动力系统降维成有限维动力系统.以雷诺数为200、攻角为20°时的NACA0012翼型绕流流动问题为例进行了低维建模分析,结果表明:由于考虑了高阶模态的影响,且不改变原系统的拓扑结构,因此该降维方法能够用较少的模态数来获得准确的动力学描述,弥补了传统POD降维方法由于忽略高阶模态影响而出现的不足,由此验证了该方法的有效性.
Dimensional reduction without continuous extra dimensions
Energy Technology Data Exchange (ETDEWEB)
Chamseddine, Ali H. [American University of Beirut, Physics Department, Beirut, Lebanon and I.H.E.S. F-91440 Bures-sur-Yvette (France); Froehlich, J.; Schubnel, B. [ETHZ, Mathematics and Physics Departments, Zuerich (Switzerland); Wyler, D. [Institute of Theoretical Physics, University of Zuerich (Switzerland)
2013-01-15
We describe a novel approach to dimensional reduction in classical field theory. Inspired by ideas from noncommutative geometry, we introduce extended algebras of differential forms over space-time, generalized exterior derivatives, and generalized connections associated with the 'geometry' of space-times with discrete extra dimensions. We apply our formalism to theories of gauge- and gravitational fields and find natural geometrical origins for an axion- and a dilaton field, as well as a Higgs field.
Nonlinear damped Schrodinger equation in two space dimensions
Directory of Open Access Journals (Sweden)
Tarek Saanouni
2015-04-01
Full Text Available In this article, we study the initial value problem for a semi-linear damped Schrodinger equation with exponential growth nonlinearity in two space dimensions. We show global well-posedness and exponential decay.
A Survey of Dimension Reduction Techniques
Energy Technology Data Exchange (ETDEWEB)
Fodor, I K
2002-05-09
Advances in data collection and storage capabilities during the past decades have led to an information overload in most sciences. Researchers working in domains as diverse as engineering, astronomy, biology, remote sensing, economics, and consumer transactions, face larger and larger observations and simulations on a daily basis. Such datasets, in contrast with smaller, more traditional datasets that have been studied extensively in the past, present new challenges in data analysis. Traditional statistical methods break down partly because of the increase in the number of observations, but mostly because of the increase in the number of variables associated with each observation. The dimension of the data, is the number of variables that are measured on each observation. High-dimensional datasets present many mathematical challenges as well as some opportunities, and are bound to give rise to new theoretical developments. One of the problems with high-dimensional datasets is that, in many cases, not all the measured variables are ''important'' for understanding the underlying phenomena of interest. While certain computationally expensive novel methods can construct predictive models with high accuracy from high-dimensional data, it is still of interest in many applications to reduce the dimension of the original data prior to any modeling of the data. In this paper, we described several dimension reduction methods.
Institute of Scientific and Technical Information of China (English)
方连娣; 胡凤霞
2012-01-01
In the article, we consider the nonlinear error-in-response models with the help of validation data. Using semiparametric dimension reduction to construct the estimated empirical likelihood and adjusted empirical likelihood of the unkown parameter, it is shown that the estimated empirical log-likelihood has the asymptotics weighted sum of chi-square variables distribution and adjusted empirical log-likelihood has the asymptotic standard chi-square distribution. The result can be used to construct the confidence regions of the unknown parameter.%本文研究了响应变量有误差的非线性模型.应用半参数降维技术构造未知参数的被估计经验似然及调整的经验似然,证明了所提出的被估计的经验对数似然与其调整的经验对数似然分别渐近于独立卡方变量加权和的分布与标准卡方分布,所得结果可用来构造未知参数的置信域.
Dimension reduction based on weighted variance estimate
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In this paper, we propose a new estimate for dimension reduction, called the weighted variance estimate (WVE), which includes Sliced Average Variance Estimate (SAVE) as a special case. Bootstrap method is used to select the best estimate from the WVE and to estimate the structure dimension. And this selected best estimate usually performs better than the existing methods such as Sliced Inverse Regression (SIR), SAVE, etc. Many methods such as SIR, SAVE, etc. usually put the same weight on each observation to estimate central subspace (CS). By introducing a weight function, WVE puts different weights on different observations according to distance of observations from CS. The weight function makes WVE have very good performance in general and complicated situations, for example, the distribution of regressor deviating severely from elliptical distribution which is the base of many methods, such as SIR, etc. And compared with many existing methods, WVE is insensitive to the distribution of the regressor. The consistency of the WVE is established. Simulations to compare the performances of WVE with other existing methods confirm the advantage of WVE.
Dimension reduction based on weighted variance estimate
Institute of Scientific and Technical Information of China (English)
ZHAO JunLong; XU XingZhong
2009-01-01
In this paper,we propose a new estimate for dimension reduction,called the weighted variance estimate (WVE),which includes Sliced Average Variance Estimate (SAVE) as a special case.Bootstrap method is used to select the best estimate from the WVE and to estimate the structure dimension.And this selected best estimate usually performs better than the existing methods such as Sliced Inverse Regression (SIR),SAVE,etc.Many methods such as SIR,SAVE,etc.usually put the same weight on each observation to estimate central subspace (CS).By introducing a weight function,WVE puts different weights on different observations according to distance of observations from CS.The weight function makes WVE have very good performance in general and complicated situations,for example,the distribution of regressor deviating severely from elliptical distribution which is the base of many methods,such as SIR,etc.And compared with many existing methods,WVE is insensitive to the distribution of the regressor.The consistency of the WVE is established.Simulations to compare the performances of WVE with other existing methods confirm the advantage of WVE.
Integrable nonlinear evolution partial differential equations in 4 + 2 and 3 + 1 dimensions.
Fokas, A S
2006-05-19
The derivation and solution of integrable nonlinear evolution partial differential equations in three spatial dimensions has been the holy grail in the field of integrability since the late 1970s. The celebrated Korteweg-de Vries and nonlinear Schrödinger equations, as well as the Kadomtsev-Petviashvili (KP) and Davey-Stewartson (DS) equations, are prototypical examples of integrable evolution equations in one and two spatial dimensions, respectively. Do there exist integrable analogs of these equations in three spatial dimensions? In what follows, I present a positive answer to this question. In particular, I first present integrable generalizations of the KP and DS equations, which are formulated in four spatial dimensions and which have the novelty that they involve complex time. I then impose the requirement of real time, which implies a reduction to three spatial dimensions. I also present a method of solution.
Sufficient dimension reduction for longitudinally measured predictors.
Pfeiffer, Ruth M; Forzani, Liliana; Bura, Efstathia
2012-09-28
We propose a method to combine several predictors (markers) that are measured repeatedly over time into a composite marker score without assuming a model and only requiring a mild condition on the predictor distribution. Assuming that the first and second moments of the predictors can be decomposed into a time and a marker component via a Kronecker product structure that accommodates the longitudinal nature of the predictors, we develop first-moment sufficient dimension reduction techniques to replace the original markers with linear transformations that contain sufficient information for the regression of the predictors on the outcome. These linear combinations can then be combined into a score that has better predictive performance than a score built under a general model that ignores the longitudinal structure of the data. Our methods can be applied to either continuous or categorical outcome measures. In simulations, we focus on binary outcomes and show that our method outperforms existing alternatives by using the AUC, the area under the receiver-operator characteristics (ROC) curve, as a summary measure of the discriminatory ability of a single continuous diagnostic marker for binary disease outcomes.
QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION.
Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy
We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method-named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)-for analyzing high-dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating non-polynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results.
Analysis of Semi-conductor Laser Diode with Two-dimension Nonlinearly Tapered Waveguide
Institute of Scientific and Technical Information of China (English)
LI Hong; HAUNG Dexiu
2001-01-01
A novel semiconductor laser diode with a two-dimension nonlinearly tapered waveguide is proposed and its property is studied by Fourier expanding method. It is shown that coupling loss between the semiconductor laser diode and a single mode fiber is reduced effectively, the reduction role of the nonlinearly tapered waveguide is more apparent than that of a linearly tapered waveguide , the minimum coupling loss is 0.36 dB, and the far field divergence is decreased. The reduction mechanism is discussed.
Universal and integrable nonlinear evolution systems of equations in 2+1 dimensions
Energy Technology Data Exchange (ETDEWEB)
Maccari, A. [Technical Institute G. Cardano, Piazza della Resistenza 1, 00015 Monterotondo, Rome (Italy)
1997-08-01
Integrable systems of nonlinear partial differential equations (PDEs) are obtained from integrable equations in 2+1 dimensions, by means of a reduction method of broad applicability based on Fourier expansion and spatio{endash}temporal rescalings, which is asymptotically exact in the limit of weak nonlinearity. The integrability by the spectral transform is explicitly demonstrated, because the corresponding Lax pairs have been derived, applying the same reduction method to the Lax pair of the initial equation. These systems of nonlinear PDEs are likely to be of applicative relevance and have a {open_quotes}universal{close_quotes} character, inasmuch as they may be derived from a very large class of nonlinear evolution equations with a linear dispersive part. {copyright} {ital 1997 American Institute of Physics.}
Dimension reduction for systems with slow relaxation
Venkataramani, Shankar C; Restrepo, Juan M
2016-01-01
We develop reduced, stochastic models for high dimensional, dissipative dynamical systems that relax very slowly to equilibrium and can encode long term memory. We present a variety of empirical and first principles approaches for model reduction, and build a mathematical framework for analyzing the reduced models. We introduce the notions of universal and asymptotic filters to characterize `optimal' model reductions. We discuss how our methods apply to the practically important problem of modeling oil spills.
Dimension and dimensional reduction in quantum gravity
Carlip, S.
2017-10-01
A number of very different approaches to quantum gravity contain a common thread, a hint that spacetime at very short distances becomes effectively two dimensional. I review this evidence, starting with a discussion of the physical meaning of ‘dimension’ and concluding with some speculative ideas of what dimensional reduction might mean for physics.
Dimension Reduction and Discretization in Stochastic Problems by Regression Method
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
1996-01-01
The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation, ......, Slepian models, Stochastic finite elements.......The chapter mainly deals with dimension reduction and field discretizations based directly on the concept of linear regression. Several examples of interesting applications in stochastic mechanics are also given.Keywords: Random fields discretization, Linear regression, Stochastic interpolation...
Propensity score modelling in observational studies using dimension reduction methods.
Ghosh, Debashis
2011-07-01
Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justification when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.
Multigrid Reduction in Time for Nonlinear Parabolic Problems
Energy Technology Data Exchange (ETDEWEB)
Falgout, R. D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Manteuffel, T. A. [Univ. of Colorado, Boulder, CO (United States); O' Neill, B. [Univ. of Colorado, Boulder, CO (United States); Schroder, J. B. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2016-01-04
The need for parallel-in-time is being driven by changes in computer architectures, where future speed-ups will be available through greater concurrency, but not faster clock speeds, which are stagnant.This leads to a bottleneck for sequential time marching schemes, because they lack parallelism in the time dimension. Multigrid Reduction in Time (MGRIT) is an iterative procedure that allows for temporal parallelism by utilizing multigrid reduction techniques and a multilevel hierarchy of coarse time grids. MGRIT has been shown to be effective for linear problems, with speedups of up to 50 times. The goal of this work is the efficient solution of nonlinear problems with MGRIT, where efficient is defined as achieving similar performance when compared to a corresponding linear problem. As our benchmark, we use the p-Laplacian, where p = 4 corresponds to a well-known nonlinear diffusion equation and p = 2 corresponds to our benchmark linear diffusion problem. When considering linear problems and implicit methods, the use of optimal spatial solvers such as spatial multigrid imply that the cost of one time step evaluation is fixed across temporal levels, which have a large variation in time step sizes. This is not the case for nonlinear problems, where the work required increases dramatically on coarser time grids, where relatively large time steps lead to worse conditioned nonlinear solves and increased nonlinear iteration counts per time step evaluation. This is the key difficulty explored by this paper. We show that by using a variety of strategies, most importantly, spatial coarsening and an alternate initial guess to the nonlinear time-step solver, we can reduce the work per time step evaluation over all temporal levels to a range similar with the corresponding linear problem. This allows for parallel scaling behavior comparable to the corresponding linear problem.
Phase reduction theory for hybrid nonlinear oscillators
Shirasaka, Sho; Kurebayashi, Wataru; Nakao, Hiroya
2017-01-01
Hybrid dynamical systems characterized by discrete switching of smooth dynamics have been used to model various rhythmic phenomena. However, the phase reduction theory, a fundamental framework for analyzing the synchronization of limit-cycle oscillations in rhythmic systems, has mostly been restricted to smooth dynamical systems. Here we develop a general phase reduction theory for weakly perturbed limit cycles in hybrid dynamical systems that facilitates analysis, control, and optimization of nonlinear oscillators whose smooth models are unavailable or intractable. On the basis of the generalized theory, we analyze injection locking of hybrid limit-cycle oscillators by periodic forcing and reveal their characteristic synchronization properties, such as ultrafast and robust entrainment to the periodic forcing and logarithmic scaling at the synchronization transition. We also illustrate the theory by analyzing the synchronization dynamics of a simple physical model of biped locomotion.
On the selection of dimension reduction techniques for scientific applications
Energy Technology Data Exchange (ETDEWEB)
Fan, Y J; Kamath, C
2012-02-17
Many dimension reduction methods have been proposed to discover the intrinsic, lower dimensional structure of a high-dimensional dataset. However, determining critical features in datasets that consist of a large number of features is still a challenge. In this paper, through a series of carefully designed experiments on real-world datasets, we investigate the performance of different dimension reduction techniques, ranging from feature subset selection to methods that transform the features into a lower dimensional space. We also discuss methods that calculate the intrinsic dimensionality of a dataset in order to understand the reduced dimension. Using several evaluation strategies, we show how these different methods can provide useful insights into the data. These comparisons enable us to provide guidance to a user on the selection of a technique for their dataset.
Dimension reduction methods for microarray data: a review
Directory of Open Access Journals (Sweden)
Rabia Aziz
2017-03-01
Full Text Available Dimension reduction has become inevitable for pre-processing of high dimensional data. “Gene expression microarray data” is an instance of such high dimensional data. Gene expression microarray data displays the maximum number of genes (features simultaneously at a molecular level with a very small number of samples. The copious numbers of genes are usually provided to a learning algorithm for producing a complete characterization of the classification task. However, most of the times the majority of the genes are irrelevant or redundant to the learning task. It will deteriorate the learning accuracy and training speed as well as lead to the problem of overfitting. Thus, dimension reduction of microarray data is a crucial preprocessing step for prediction and classification of disease. Various feature selection and feature extraction techniques have been proposed in the literature to identify the genes, that have direct impact on the various machine learning algorithms for classification and eliminate the remaining ones. This paper describes the taxonomy of dimension reduction methods with their characteristics, evaluation criteria, advantages and disadvantages. It also presents a review of numerous dimension reduction approaches for microarray data, mainly those methods that have been proposed over the past few years.
Determining the minimum embedding dimension of nonlinear time series based on prediction method
Institute of Scientific and Technical Information of China (English)
Bian Chun-Hua; Ning Xin-Bao
2004-01-01
Determining the embedding dimension of nonlinear time series plays an important role in the reconstruction of nonlinear dynamics. The paper first summarizes the current methods for determining the embedding dimension.Then, inspired by the fact that the optimum modelling dimension of nonlinear autoregressive (NAR) prediction model can characterize the embedding feature of the dynamics, the paper presents a new idea that the optimum modelling dimension of the NAR model can be taken as the minimum embedding dimension. Some validation examples and results are given and the present method shows its advantage for short data series.
Determining the input dimension of a neural network for nonlinear time series prediction
Institute of Scientific and Technical Information of China (English)
张胜; 刘红星; 高敦堂; 都思丹
2003-01-01
Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling.The paper first summarizes the current methods for determining the input dimension of the neural network.Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the mostimportant feature of it,the paper presents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension.Finally,some wlidation examples and results are given.
Nonlinear wave propagation through a ferromagnet with damping in (2+1) dimensions
Indian Academy of Sciences (India)
S G Bindu; V C Kuriakose
2000-02-01
We investigate how dissipation and nonlinearity can affect the electromagnetic wave propagating through a saturated ferromagnet in the presence of an external magnetic ﬁeld in (2+1) dimensions. The propagation of electromagnetic waves through a ferromagnet under an external magnetic ﬁeld in the presence of dissipative effect has been studied using reductive perturbation method. It is found that to the lowest order of perturbation the system of equations for the electromagnetic waves in a ferromagnet can be reduced to an integro-differential equation.
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
Institute of Scientific and Technical Information of China (English)
张振跃; 查宏远
2004-01-01
We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.
IMPROVING VOICE ACTIVITY DETECTION VIA WEIGHTING LIKELIHOOD AND DIMENSION REDUCTION
Institute of Scientific and Technical Information of China (English)
Wang Huanliang; Han Jiqing; Li Haifeng; Zheng Tieran
2008-01-01
The performance of the traditional Voice Activity Detection (VAD) algorithms declines sharply in lower Signal-to-Noise Ratio (SNR) environments. In this paper, a feature weighting likelihood method is proposed for noise-robust VAD. The contribution of dynamic features to likelihood score can be increased via the method, which improves consequently the noise robustness of VAD.Divergence based dimension reduction method is proposed for saving computation, which reduces these feature dimensions with smaller divergence value at the cost of degrading the performance a little.Experimental results on Aurora Ⅱ database show that the detection performance in noise environments can remarkably be improved by the proposed method when the model trained in clean data is used to detect speech endpoints. Using weighting likelihood on the dimension-reduced features obtains comparable, even better, performance compared to original full-dimensional feature.
Dimension reduction in stochastic analysis of coupled systems
Arnst, Maarten; Phipps, Eric; Red-Horse, John
2011-01-01
Coupled models with multiple physics, scales and/or domains arise in numerous areas of science and engineering. A key challenge in the formulation and implementation of coupled models is in facilitating the communication of information across physics, scale and/or domain interfaces. In a probabilistic context, any information that is communicated between model components is described in a statistical manner and requires an adapted probabilistic representation. While the number of sources of uncertainty can be expected to be large in many coupled problems, our contention is that exchanged statistical information often resides in a much lower dimensional space. In this work, we thus investigate the use of dimension-reduction techniques for the representation of exchanged information. We describe an adaptation of the Karhunen-Loeve decomposition to represent information as it is passed from component to component in a stochastic coupled model. The range of validity of the proposed dimension reduction is demonstr...
Incremental dimension reduction of tensors with random index
Sandin, Fredrik; Sahlgren, Magnus
2011-01-01
We present an incremental, scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding. Data is stored in a compactified representation with fixed size, which makes memory requirements low and predictable. Component encoding and decoding are performed on-line without computationally expensive re-analysis of the data set. The range of tensor indices can be extended dynamically without modifying the component representation. This idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing. We generalize the random-indexing algorithm to tensors and present signal-to-noise-ratio simulations for representations of vectors and matrices. We present also a mathematical analysis of the approximate orthogonality of high-dimensional ternary vectors, which is a property that underpins this and other similar random-coding approaches to dimension reduction. To further demonstrate the properties of random ...
Simultaneous dimension reduction and adjustment for confounding variation.
Lin, Zhixiang; Yang, Can; Zhu, Ying; Duchi, John; Fu, Yao; Wang, Yong; Jiang, Bai; Zamanighomi, Mahdi; Xu, Xuming; Li, Mingfeng; Sestan, Nenad; Zhao, Hongyu; Wong, Wing Hung
2016-12-20
Dimension reduction methods are commonly applied to high-throughput biological datasets. However, the results can be hindered by confounding factors, either biological or technical in origin. In this study, we extend principal component analysis (PCA) to propose AC-PCA for simultaneous dimension reduction and adjustment for confounding (AC) variation. We show that AC-PCA can adjust for (i) variations across individual donors present in a human brain exon array dataset and (ii) variations of different species in a model organism ENCODE RNA sequencing dataset. Our approach is able to recover the anatomical structure of neocortical regions and to capture the shared variation among species during embryonic development. For gene selection purposes, we extend AC-PCA with sparsity constraints and propose and implement an efficient algorithm. The methods developed in this paper can also be applied to more general settings. The R package and MATLAB source code are available at https://github.com/linzx06/AC-PCA.
Model Reduction for Nonlinear Systems by Incremental Balanced Truncation
Besselink, Bart; van de Wouw, Nathan; Scherpen, Jacquelien M. A.; Nijmeijer, Henk
2014-01-01
In this paper, the method of incremental balanced truncation is introduced as a tool for model reduction of nonlinear systems. Incremental balanced truncation provides an extension of balanced truncation for linear systems towards the nonlinear case and differs from existing nonlinear balancing tech
Model Reduction for Nonlinear Systems by Incremental Balanced Truncation
Besselink, Bart; van de Wouw, Nathan; Scherpen, Jacquelien M. A.; Nijmeijer, Henk
2014-01-01
In this paper, the method of incremental balanced truncation is introduced as a tool for model reduction of nonlinear systems. Incremental balanced truncation provides an extension of balanced truncation for linear systems towards the nonlinear case and differs from existing nonlinear balancing tech
Linear low-rank approximation and nonlinear dimensionality reduction
Institute of Scientific and Technical Information of China (English)
ZHANG Zhenyue; ZHA Hongyuan
2004-01-01
We present our recent work on both linear and nonlinear data reduction methods and algorithms: for the linear case we discuss results on structure analysis of SVD of column-partitioned matrices and sparse low-rank approximation; for the nonlinear case we investigate methods for nonlinear dimensionality reduction and manifold learning. The problems we address have attracted great deal of interest in data mining and machine learning.
A new method of determining the optimal embedding dimension based on nonlinear prediction
Institute of Scientific and Technical Information of China (English)
Meng Qing-Fang; Peng Yu-Hua; Xue Pei-Jun
2007-01-01
A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.
Mathematical Modeling and Dimension Reduction in Dynamical Systems
DEFF Research Database (Denmark)
Elmegård, Michael
the dimension of a certain class of dynamical systems by construction of k-dimensional submanifolds using the so-called graph transform. The method is suitable for a specific class of problems with spectral gaps, these are often observed. In particular the method is applied to a mechanical system. Furthermore......Processes that change in time are in mathematics typically described by differential equations. These may be applied to model everything from weather forecasting, brain patterns, reaction kinetics, water waves, finance, social dynamics, structural dynamics and electrodynamics to name only a few....... These systems are generically nonlinear and the studies of them often become enormously complex. The framework in which such systems are best understood is via the theory of dynamical systems, where the critical behavior is systematically analyzed by performing bifurcation theory. In that context the current...
Dimension Reduction of Hyperspectral Data on Beowulf Clusters
El-Ghazawi, Tarek
2000-01-01
Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operation. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold a great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, which is used widely in remote sensing, is the Principal Components Analysis (PCA). In light of the growing number of spectral channels of modern instruments, the paper reports on the development of a parallel PCA and its implementation on two Beowulf cluster configurations, on with fast Ethernet switch and the other is with a Myrinet interconnection.
A new method combining LDA and PLS for dimension reduction.
Tang, Liang; Peng, Silong; Bi, Yiming; Shan, Peng; Hu, Xiyuan
2014-01-01
Linear discriminant analysis (LDA) is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS) method with LDA algorithm, and then propose two improved methods, named LDA-PLS and ex-LDA-PLS, respectively. The LDA-PLS amends the projection direction of LDA by using the information of PLS, while ex-LDA-PLS is an extension of LDA-PLS by combining the result of LDA-PLS and LDA, making the result closer to the optimal direction by an adjusting parameter. Comparative studies are provided between the proposed methods and other traditional dimension reduction methods such as Principal component analysis (PCA), LDA and PLS-LDA on two data sets. Experimental results show that the proposed method can achieve better classification performance.
A new method combining LDA and PLS for dimension reduction.
Directory of Open Access Journals (Sweden)
Liang Tang
Full Text Available Linear discriminant analysis (LDA is a classical statistical approach for dimensionality reduction and classification. In many cases, the projection direction of the classical and extended LDA methods is not considered optimal for special applications. Herein we combine the Partial Least Squares (PLS method with LDA algorithm, and then propose two improved methods, named LDA-PLS and ex-LDA-PLS, respectively. The LDA-PLS amends the projection direction of LDA by using the information of PLS, while ex-LDA-PLS is an extension of LDA-PLS by combining the result of LDA-PLS and LDA, making the result closer to the optimal direction by an adjusting parameter. Comparative studies are provided between the proposed methods and other traditional dimension reduction methods such as Principal component analysis (PCA, LDA and PLS-LDA on two data sets. Experimental results show that the proposed method can achieve better classification performance.
Neural Network Machine Learning and Dimension Reduction for Data Visualization
Liles, Charles A.
2014-01-01
Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.
Model reduction of nonlinear systems subject to input disturbances
Ndoye, Ibrahima
2017-07-10
The method of convex optimization is used as a tool for model reduction of a class of nonlinear systems in the presence of disturbances. It is shown that under some conditions the nonlinear disturbed system can be approximated by a reduced order nonlinear system with similar disturbance-output properties to the original plant. The proposed model reduction strategy preserves the nonlinearity and the input disturbance nature of the model. It guarantees a sufficiently small error between the outputs of the original and the reduced-order systems, and also maintains the properties of input-to-state stability. The matrices of the reduced order system are given in terms of a set of linear matrix inequalities (LMIs). The paper concludes with a demonstration of the proposed approach on model reduction of a nonlinear electronic circuit with additive disturbances.
WIEDEMANN, A; MULLERKIRSTEN, HJW
1993-01-01
Considering the N = 1 supersymmetry transformations of supersymmetric nonlinear sigma models in 1 + 1 dimensions we construct explicitly conserved Noether currents associated with supersymmetry transformations and derive the associated conserved charges in terms of the basic fields. We compare this
Canonical reduction for dilatonic gravity in 3+1 dimensions
Scott, T C; Mann, R B; Fee, G J
2016-01-01
We generalize the 1+1-dimensional gravity formalism of Ohta and Mann to 3+1 dimensions by developing the canonical reduction of a proposed formalism applied to a system coupled with a set of point particles. This is done via the Arnowitt-Deser-Misner method and by eliminating the resulting constraints and imposing coordinate conditions. The reduced Hamiltonian is completely determined in terms of the particles' canonical variables (coordinates, dilaton field and momenta). It is found that the equation governing the dilaton field under suitable gauge and coordinate conditions, including the absence of transverse-traceless metric components, is a logarithmic Schroedinger equation. Thus, although different, the 3+1 formalism retains some essential features of the earlier 1+1 formalism, in particular the means of obtaining a quantum theory for dilatonic gravity.
Model-based reinforcement learning with dimension reduction.
Tangkaratt, Voot; Morimoto, Jun; Sugiyama, Masashi
2016-12-01
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction. We also further extend the proposed method to imitation learning scenarios. The experimental results show that policy search combined with LSCE performs well for high-dimensional control tasks including real humanoid robot control.
Sahadevan, R.; Prakash, P.
2017-01-01
We show how invariant subspace method can be extended to time fractional coupled nonlinear partial differential equations and construct their exact solutions. Effectiveness of the method has been illustrated through time fractional Hunter-Saxton equation, time fractional coupled nonlinear diffusion system, time fractional coupled Boussinesq equation and time fractional Whitman-Broer-Kaup system. Also we explain how maximal dimension of the time fractional coupled nonlinear partial differential equations can be estimated.
Spectral properties of a confined nonlinear quantum oscillator in one and three dimensions
Energy Technology Data Exchange (ETDEWEB)
Schulze-Halberg, Axel; Gordon, Christopher R. [Department of Mathematics and Actuarial Science, Indiana University Northwest, 3400 Broadway, Gary, Indiana 46408 (United States)
2013-04-15
We analyze the spectral behaviour of a nonlinear quantum oscillator model under confinement. The underlying potential is given by a harmonic oscillator interaction plus a nonlinear term that can be weakened or strengthened through a parameter. Numerical eigenvalues of the model in one and three dimensions are presented. The asymptotic behaviour of the eigenvalues for confinement relaxation and for vanishing nonlinear term in the potential is investigated. Our findings are compared with existing results.
Model reduction of systems with localized nonlinearities.
Energy Technology Data Exchange (ETDEWEB)
Segalman, Daniel Joseph
2006-03-01
An LDRD funded approach to development of reduced order models for systems with local nonlinearities is presented. This method is particularly useful for problems of structural dynamics, but has potential application in other fields. The key elements of this approach are (1) employment of eigen modes of a reference linear system, (2) incorporation of basis functions with an appropriate discontinuity at the location of the nonlinearity. Galerkin solution using the above combination of basis functions appears to capture the dynamics of the system with a small basis set. For problems involving small amplitude dynamics, the addition of discontinuous (joint) modes appears to capture the nonlinear mechanics correctly while preserving the modal form of the predictions. For problems involving large amplitude dynamics of realistic joint models (macro-slip), the use of appropriate joint modes along with sufficient basis eigen modes to capture the frequencies of the system greatly enhances convergence, though the modal nature the result is lost. Also observed is that when joint modes are used in conjunction with a small number of elastic eigen modes in problems of macro-slip of realistic joint models, the resulting predictions are very similar to those of the full solution when seen through a low pass filter. This has significance both in terms of greatly reducing the number of degrees of freedom of the problem and in terms of facilitating the use of much larger time steps.
Difference schemes for fully nonlinear pseudo-parabolic systems with two space dimensions
Institute of Scientific and Technical Information of China (English)
周毓麟; 袁光伟
1996-01-01
The first boundary value problem for the fully nonlinear pseudoparabolic systems of partial differential equations with two space dimensions by the finite difference method is studied. The existence and uniqueness of the discrete vector solutions for the difference systems are established by the fixed point technique. The stability and convergence of the discrete vector solutions of the difference schemes to the vector solutions of the original boundary problem of the fully nonlinear pseudo-parabolic system are obtained by way of a priori estimation. Here the unique smooth vector solution of the original problems for the fully nonlinear pseudo-parabolic system is assumed. Moreover, by the method used here, it can be proved that analogous results hold for fully nonlinear pseudo-parabolic system with three space dimensions, and improve the known results in the case of one space dimension.
Linear low-rank approximation and nonlinear dimensionality reduction
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
［1］Bishop, C. M., Svensen, M., Williams, C. K. I., GTM: the generative topographic mapping, Neural Computation,1998, 10: 215-234.［2］Freedman, D., Efficient simplicial reconstructions of manifolds from their samples, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24: 1349-1357.［3］Hinton, G., Roweis, S., Stochastic neighbor embedding, Neural Information Processing Systems, 2003, 15:833-840.［4］Kohonen, T., Self-organizing Maps, 3rd ed., Berlin: Springer-Verlag, 2000.［5］Ramsay, J. O., Silverman, B. W., Applied Functional Data Analysis, Berlin: Springer-Verlag, 2002.［6］Roweis, S., Saul, L., Nonlinear dimensionality reduction by locally linear embedding, Science, 2000, 290:2323-2326.［7］Tenenbaum, J., De Silva, V., Langford, J., A global geometric framework for nonlinear dimension reduction,Science, 2000, 290:2319-2323.［8］Xu, G., Kailath, T., Fast subspace decompsotion, IEEE Transactions on Signal Processing, 1994, 42: 539-551.［9］Xu, G., Zha, H., Golub, G. et al., Fast algorithms for updating signal subspaces, IEEE Transactions on Circuits and Systems, 1994, 41: 537-549.［10］Zha, H., Marques, O., Simon, H., Large-scale SVD and subspace-based methods for information retrieval, Proceedings of Irregular '98, Lecture Notes in Computer Science, 1998, 1457: 29-42.［11］Zhang, Z., Zha, H., Structure and perturbation analysis of truncated SVDs for column-partitioned matrices,SIAM Journal on Matrix Analysis and Applications, 2001, 22: 1245-1262.［12］Zhang, Z., Zha, H., Simon, H., Low-rank approximations with sparse factors I: basic algorithms and error analysis, SIAM Journal on Matrix Analysis and Applications, 2002, 23: 706-727.［13］Stewart, G. W., Four algorithms for the efficient computation of truncated pivoted QR approximation to a sparse matrix, Numerische Mathematik, 1999, 83:313-323.［14］Golub, G., Van Loan, C., Matrix Computations, 3nd ed., Baltimore, Maryland: Johns Hopkins University Press,1996.
Institute of Scientific and Technical Information of China (English)
WU; Shaoping(吴少平); YI; Fan(易帆)
2002-01-01
By using FICE scheme, a numerical simulation of nonlinear propagation of gravity wave packet in three-dimension compressible atmosphere is presented. The whole nonlinear propagation process of the gravity wave packet is shown; the basic characteristics of nonlinear propagation and the influence of the ambient winds on the propagation are analyzed. The results show that FICE scheme can be extended in three-dimension by which the calculation is steady and kept for a long time; the increase of wave amplitude is faster than the exponential increase according to the linear gravity theory; nonlinear propagation makes the horizontal perturbation velocity increase greatly which can lead to enhancement of the local ambient winds; the propagation path and the propagation velocity of energy are different from the results expected by the linear gravity waves theory, the nonlinearity causes the change in propagation characteristics of gravity wave; the ambient winds alter the propagation path and group velocity of gravity wave.
Transport maps and dimension reduction for Bayesian computation
Marzouk, Youssef
2015-01-07
problem used for map construction. In both settings, we will show links to recent ideas for dimension reduction in inverse problems.
Nonlinear Dimensionality Reduction Methods in Climate Data Analysis
Ross, Ian
2008-01-01
Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three such techniques to the study of El Nino/Southern Oscillation variability in tropical Pacific sea surface temperatures and thermocline depth, comparing observational data with simulations from coupled atmosphere-ocean general circulation models from the CMIP3 multi-model ensemble. The three methods used here are a nonlinear principal component analysis (NLPCA) approach based on neural networks, the Isomap isometric mappin...
Motion Planning for Robots with Topological Dimension Reduction Method
Institute of Scientific and Technical Information of China (English)
无
1990-01-01
This paper explores the realization of robotic motion planning,especially Findpath problem,which is a basic motion planning problem that arises in the development of robotics.Findpat means:Give the initial and desired final configurations of a robotic arm in 3-dimensional space,and give descriptions of the obstacles in the space,determine whether there is a continuous collision-free motion of the robotic arm from one configura tion to the other and find such a motion if it exists.There are several branches of approach in motion planning area,but in reality the important things are feasibility,efficiency and accuracy of the method.In this paper according to the concepts of Configuration Space(C-Space)and Rotation Mapping Graph(RMG) discussed in [1], a topological method named Dimension Reduction Method(DRM)for investigating the connectivity of the RMG(or the topologic structure of the RMG)is presented by using topologic technique.Based on this approach the Findpath problem is thus transformed to that of finding a connected way in a finite Characteristic Network(CN),The method has shown great potentiality in practice.Here a simulation system is designed to embody DRM[1-2] and it is in sight that DRM can be adopted in the first overall planning of real robot system in the near future.
Global dynamics for steep nonlinearities in two dimensions
Gedeon, Tomáš; Harker, Shaun; Kokubu, Hiroshi; Mischaikow, Konstantin; Oka, Hiroe
2017-01-01
This paper discusses a novel approach to obtaining mathematically rigorous results on the global dynamics of ordinary differential equations. We study switching models of regulatory networks. To each switching network we associate a Morse graph, a computable object that describes a Morse decomposition of the dynamics. In this paper we show that all smooth perturbations of the switching system share the same Morse graph and we compute explicit bounds on the size of the allowable perturbation. This shows that computationally tractable switching systems can be used to characterize dynamics of smooth systems with steep nonlinearities.
Institute of Scientific and Technical Information of China (English)
Wang Jun-Song; Yuan Jing; Li Qiang; Yuan Rui-Xi
2011-01-01
This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic,furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.
Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction
Institute of Scientific and Technical Information of China (English)
XIANG Jian; WENG Jian-guang; ZHUANG Yue-ting; WU Fei
2006-01-01
Along with the development of motion capture technique, more and more 3D motion databases become available. In this paper, a novel approach is presented for motion recognition and retrieval based on ensemble HMM (hidden Markov model)learning. Due to the high dimensionality of motion's features, Isomap nonlinear dimension reduction is used for training data of ensemble HMM learning. For handling new motion data, Isomap is generalized based on the estimation of underlying eigenfunctions. Then each action class is learned with one HMM. Since ensemble learning can effectively enhance supervised learning,ensembles of weak HMM learners are built. Experiment results showed that the approaches are effective for motion data recognition and retrieval.
Model Reduction of Nonlinear Aeroelastic Systems Experiencing Hopf Bifurcation
Abdelkefi, Abdessattar
2013-06-18
In this paper, we employ the normal form to derive a reduced - order model that reproduces nonlinear dynamical behavior of aeroelastic systems that undergo Hopf bifurcation. As an example, we consider a rigid two - dimensional airfoil that is supported by nonlinear springs in the pitch and plunge directions and subjected to nonlinear aerodynamic loads. We apply the center manifold theorem on the governing equations to derive its normal form that constitutes a simplified representation of the aeroelastic sys tem near flutter onset (manifestation of Hopf bifurcation). Then, we use the normal form to identify a self - excited oscillator governed by a time - delay ordinary differential equation that approximates the dynamical behavior while reducing the dimension of the original system. Results obtained from this oscillator show a great capability to predict properly limit cycle oscillations that take place beyond and above flutter as compared with the original aeroelastic system.
Attractors and Dimensions for Discretizations of a NLS Equation with a Non-local Nonlinear Term
Institute of Scientific and Technical Information of China (English)
Shu Qing MA; Qian Shun CHANG
2002-01-01
In this paper we consider a semi-dicretized nonlinear Schrodinger (NLS) equation withlocal integral nonlinearity. It is proved that for each mesh size, there exist attractors for the discretizedsystem. The bounds for the Hausdorff and fractal dimensions of the discrete attractors are obtained,and the various bounds are independent of the mesh sizes. Furthermore, numerical experiments aregiven and many interesting phenomena are observed such as limit cycles, chaotic attractors and aso-called crisis of the chaotic attractors.
Residual Minimizing Model Reduction for Parameterized Nonlinear Dynamical Systems
Constantine, Paul G
2010-01-01
We present a method for approximating the solution of a parameterized, nonlinear dynamical (or static) system using an affine combination of solutions computed at other points in the input parameter space. The coefficients of the affine combination are computed with a nonlinear least squares procedure that minimizes the residual of the dynamical system. The approximation properties of this residual minimizing scheme are comparable to existing reduced basis and POD-Galerkin model reduction methods, but its implementation requires only independent evaluations of the nonlinear forcing function. We prove some interesting characteristics of the scheme including uniqueness and an interpolatory property, and we present heuristics for mitigating the effects of the ill-conditioning and reducing the overall cost of the method. We apply the method to representative numerical examples from kinetics - a three state system with one parameter controlling the stiffness - and groundwater modeling - a nonlinear parabolic PDE w...
Energy Technology Data Exchange (ETDEWEB)
Alvarez-Estrada, R.F.
1979-08-01
A comprehensive review of the inverse scattering solution of certain non-linear evolution equations of physical interest in one space dimension is presented. We explain in some detail the interrelated techniques which allow to linearize exactly the following equations: (1) the Korteweg and de Vries equation; (2) the non-linear Schrodinger equation; (3) the modified Korteweg and de Vries equation; (4) the Sine-Gordon equation. We concentrate in discussing the pairs of linear operators which accomplish such an exact linearization and the solution of the associated initial value problem. The application of the method to other non-linear evolution equations is reviewed very briefly.
Modified wave operators for nonlinear Schrodinger equations in one and two dimensions
Directory of Open Access Journals (Sweden)
Nakao Hayashi
2004-04-01
Full Text Available We study the asymptotic behavior of solutions, in particular the scattering theory, for the nonlinear Schr"{o}dinger equations with cubic and quadratic nonlinearities in one or two space dimensions. The nonlinearities are summation of gauge invariant term and non-gauge invariant terms. The scattering problem of these equations belongs to the long range case. We prove the existence of the modified wave operators to those equations for small final data. Our result is an improvement of the previous work [13
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data
Bunte, Kerstin; Hammer, Barbara; Wismueller, Axel; Biehl, Michael
2010-01-01
Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensional data has become one of the key problems of data mining. Since embedding in lower dimensions necessarily includes a loss
Adaptive local dissimilarity measures for discriminative dimension reduction of labeled data
Bunte, Kerstin; Hammer, Barbara; Wismueller, Axel; Biehl, Michael
Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensional data has become one of the key problems of data mining. Since embedding in lower dimensions necessarily includes a loss
Feature Dimension Reduction of NaXi Pictographs Characters Recognition based on LDA
Directory of Open Access Journals (Sweden)
Hai Guo
2012-11-01
Full Text Available As a kind of pictographic character, Naxi pictographs character has received little academic attention. Proposing dimension reduction method of Naxi pictographs characters on the basis of LDA (Linear Discriminant Analysis, this paper thus makes an in-depth study of feature dimension reduction, an important issue in the recognition of Naxi pictographs characters. By constructing a recognition sample library involving four features of grid feature, permeability number feature, moment invariant feature, and directional element feature (DEF, 50% of data are selected from sample library as training set and testing set respectively. Two dimension reduction methods of LDA and FA (Factor Analysis are applied to dimension reduction experiment of features of Naxi pictographs characters. The experiment result proves LDA method to be significantly superior to FA method, as LDA method could still maintain a 99% recognition precision when the dimension is reduced to 10% of the original dimension.
Maximal Dimension of Invariant Subspaces to Systems of Nonlinear Evolution Equations
Institute of Scientific and Technical Information of China (English)
Shoufeng SHEN; ChangZheng QU; Yongyang JIN; Lina JI
2012-01-01
In this paper,the dimension of invariant subspaces admitted by nonlinear systems is estimated under certain conditions.It is shown that if the two-component nonlinear vector differential operator F =(F1,F2) with orders {k1,k2} (k1 ≥ k2) preserves the invariant subspace W1n1 × W2n2 (n1 ≥ n2),then n1 - n2 ≤ k2,n1 ≤ 2(k1 + k2) + 1,where Wqnq is the space generated by solutions of a linear ordinary differential equation of order nq (q =1,2).Several examples including the (1+1)-dimensional diffusion system and It(o)'s type,Drinfel'd-Sokolov-Wilson's type and Whitham-Broer-Kaup's type equations are presented to illustrate the result.Furthermore,the estimate of dimension for m-component nonlinear systems is also given.
Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping
2013-01-01
Nonlinear dimensionality reduction methods have demonstrated top-notch performance in many pattern recognition and image classification tasks. Despite their popularity, they suffer from highly expensive time and memory requirements, which render them inapplicable to large-scale datasets. To leverage such cases we propose a new method called "Path-Based Isomap". Similar to Isomap, we exploit geodesic paths to find the low-dimensional embedding. However, instead of preserving pairwise geodesic ...
Symmetries and Similarity Reductions of Nonlinear Diffusion Equation
Institute of Scientific and Technical Information of China (English)
LI Hui-Jun; RUAN Hang-Yu
2004-01-01
The inverse recursion operator, three new sets of symmetries, and infinite-dimensional Lie algebras for the nonlinear diffusion equation are given. Some nonlocal symmetries related to eigenvectors of the recursion operator Ф with the eigenvalue λi are also obtained with the help of the recursion operator Фi = Ф - λi. Using a part of these symmetries we get twelve types of nontrivial new similarity reduction.
Symmetries and Similarity Reductions of Nonlinear Diffusion Equation
Institute of Scientific and Technical Information of China (English)
LIHui-Jun; RUANHang-Yu
2004-01-01
The inverse recursion operator, three new sets of symmetries, and infinite-dimensional Lie algebras for the nonlinear diffusion equation are given. Some nonlocal symmetries related to eigenvectors of the recursion operator with the eigenvalue λi are also obtained with the help of the recursion operator φi=φ-λi. Using a part of these symmetries we get twelve types of nontrivial new similarity reduction.
Microscopic structures from reduction of continuum nonlinear problems
Lovison, Alberto
2011-01-01
We present an application of the Amann-Zehnder exact finite reduction to a class of nonlinear perturbations of elliptic elasto-static problems. We propose the existence of minmax solutions by applying Ljusternik-Schnirelmann theory to a finite dimensional variational formulation of the problem, based on a suitable spectral cut-off. As a by-product, with a choice of fit variables, we establish a variational equivalence between the above spectral finite description and a discrete mechanical model. By doing so, we decrypt the abstract information encoded in the AZ reduction and give rise to a concrete and finite description of the continuous problem.
Construction and exact solution of a nonlinear quantum field model in quasi-higher dimension
Energy Technology Data Exchange (ETDEWEB)
Kundu, Anjan, E-mail: anjan.kundu@saha.ac.in
2015-10-15
Nonperturbative exact solutions are allowed for quantum integrable models in one space-dimension. Going beyond this class we propose an alternative Lax matrix approach, exploiting the hidden multi-space–time concept in integrable systems and construct a novel nonlinear Schrödinger quantum field model in quasi-two dimensions. An intriguing field commutator is discovered, confirming the integrability of the model and yielding its exact Bethe ansatz solution with rich scattering and bound-state properties. The universality of the scheme is expected to cover diverse models, opening up a new direction in the field.
Maj, Omar
2008-01-01
This is the second part of a work aimed to study complex-phase oscillatory solutions of nonlinear symmetric hyperbolic systems. We consider, in particular, the case of one space dimension. That is a remarkable case, since one can always satisfy the \\emph{naive} coherence condition on the complex phases, which is required in the construction of the approximate solution. Formally the theory applies also in several space dimensions, but the \\emph{naive} coherence condition appears to be too restrictive; the identification of the optimal coherence condition is still an open problem.
Manifold Elastic Net: A Unified Framework for Sparse Dimension Reduction
Zhou, Tianyi; Wu, Xindong
2010-01-01
It is difficult to find the optimal sparse solution of a manifold learning based dimensionality reduction algorithm. The lasso or the elastic net penalized manifold learning based dimensionality reduction is not directly a lasso penalized least square problem and thus the least angle regression (LARS) (Efron et al. \\cite{LARS}), one of the most popular algorithms in sparse learning, cannot be applied. Therefore, most current approaches take indirect ways or have strict settings, which can be inconvenient for applications. In this paper, we proposed the manifold elastic net or MEN for short. MEN incorporates the merits of both the manifold learning based dimensionality reduction and the sparse learning based dimensionality reduction. By using a series of equivalent transformations, we show MEN is equivalent to the lasso penalized least square problem and thus LARS is adopted to obtain the optimal sparse solution of MEN. In particular, MEN has the following advantages for subsequent classification: 1) the local...
Simple noise-reduction method based on nonlinear forecasting
Tan, James P. L.
2017-03-01
Nonparametric detrending or noise reduction methods are often employed to separate trends from noisy time series when no satisfactory models exist to fit the data. However, conventional noise reduction methods depend on subjective choices of smoothing parameters. Here we present a simple multivariate noise reduction method based on available nonlinear forecasting techniques. These are in turn based on state-space reconstruction for which a strong theoretical justification exists for their use in nonparametric forecasting. The noise reduction method presented here is conceptually similar to Schreiber's noise reduction method using state-space reconstruction. However, we show that Schreiber's method has a minor flaw that can be overcome with forecasting. Furthermore, our method contains a simple but nontrivial extension to multivariate time series. We apply the method to multivariate time series generated from the Van der Pol oscillator, the Lorenz equations, the Hindmarsh-Rose model of neuronal spiking activity, and to two other univariate real-world data sets. It is demonstrated that noise reduction heuristics can be objectively optimized with in-sample forecasting errors that correlate well with actual noise reduction errors.
Nonlinear random resistor diode networks and fractal dimensions of directed percolation clusters.
Stenull, O; Janssen, H K
2001-07-01
We study nonlinear random resistor diode networks at the transition from the nonpercolating to the directed percolating phase. The resistor-like bonds and the diode-like bonds under forward bias voltage obey a generalized Ohm's law V approximately I(r). Based on general grounds such as symmetries and relevance we develop a field theoretic model. We focus on the average two-port resistance, which is governed at the transition by the resistance exponent straight phi(r). By employing renormalization group methods we calculate straight phi(r) for arbitrary r to one-loop order. Then we address the fractal dimensions characterizing directed percolation clusters. Via considering distinct values of the nonlinearity r, we determine the dimension of the red bonds, the chemical path, and the backbone to two-loop order.
Asymptotic reductions and solitons of nonlocal nonlinear Schr\\"{o}dinger equations
Horikis, Theodoros P
2016-01-01
Asymptotic reductions of a defocusing nonlocal nonlinear Schr\\"{o}dinger model in $(3+1)$-dimensions, in both Cartesian and cylindrical geometry, are presented. First, at an intermediate stage, a Boussinesq equation is derived, and then its far-field, in the form of a variety of Kadomtsev-Petviashvilli (KP) equations for right- and left-going waves, is found. KP models include versions of the KP-I and KP-II equations, in Cartesian and cylindrical geometry. Solitary waves solutions, planar or ring-shaped, and of dark or anti-dark type, are also predicted to occur. Their nature and stability is determined by a parameter defined by the physical parameters of the original nonlocal system. It is thus found that (dark) anti-dark solitary waves are only supported by a weak (strong) nonlocality, and are unstable (stable) in higher-dimensions. Our analytical predictions are corroborated by direct numerical simulations.
POD/DEIM Nonlinear model order reduction of an ADI implicit shallow water equations model
Stefanescu, Razvan
2012-01-01
In the present paper we consider a 2-D shallow-water equations (SWE) model on a $\\beta$-plane solved using an alternating direction fully implicit (ADI) finite-difference scheme on a rectangular domain. The scheme was shown to be unconditionally stable for the linearized equations. The discretization yields a number of nonlinear systems of algebraic equations. We then use a proper orthogonal decomposition (POD) to reduce the dimension of the SWE model. Due to the model nonlinearities, the computational complexity of the reduced model still depends on the number of variables of the full shallow - water equations model. By employing the discrete empirical interpolation method (DEIM) we reduce the computational complexity of the reduced order model due to its depending on the nonlinear full dimension model and regain the full model reduction expected from the POD model. To emphasize the CPU gain in performance due to use of POD/DEIM, we also propose testing an explicit Euler finite difference scheme (EE) as an a...
Adaptive sampling for nonlinear dimensionality reduction based on manifold learning
DEFF Research Database (Denmark)
Franz, Thomas; Zimmermann, Ralf; Goertz, Stefan
2017-01-01
We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space...... that is approximately isometric to the manifold that is assumed to be formed by the high-fidelity Navier-Stokes flow solutions under smooth variations of the inflow conditions. The focus of the work at hand is the adaptive construction and refinement of the Isomap emulator: We exploit the non-Euclidean Isomap metric...... to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime....
Phase reduction approach to synchronisation of nonlinear oscillators
Nakao, Hiroya
2016-04-01
Systems of dynamical elements exhibiting spontaneous rhythms are found in various fields of science and engineering, including physics, chemistry, biology, physiology, and mechanical and electrical engineering. Such dynamical elements are often modelled as nonlinear limit-cycle oscillators. In this article, we briefly review phase reduction theory, which is a simple and powerful method for analysing the synchronisation properties of limit-cycle oscillators exhibiting rhythmic dynamics. Through phase reduction theory, we can systematically simplify the nonlinear multi-dimensional differential equations describing a limit-cycle oscillator to a one-dimensional phase equation, which is much easier to analyse. Classical applications of this theory, i.e. the phase locking of an oscillator to a periodic external forcing and the mutual synchronisation of interacting oscillators, are explained. Further, more recent applications of this theory to the synchronisation of non-interacting oscillators induced by common noise and the dynamics of coupled oscillators on complex networks are discussed. We also comment on some recent advances in phase reduction theory for noise-driven oscillators and rhythmic spatiotemporal patterns.
Social resilience: the forgotten dimension of disaster risk reduction
Directory of Open Access Journals (Sweden)
Guy Sapirstein
2006-04-01
Full Text Available The current thinking in the Disaster Risk Reduction field emphasizes assessment and reduction of vulnerability and especially social vulnerability as an important factor in mitigating the effects of disasters. In the process of emphasizing vulnerability, the role and complexity of social resilience was somewhat lost and at times minimized. For example, Terry Cannon and his colleagues include resilience as a factor of social vulnerability in a report to United Kingdom Department for International Development (DFID (Cannon, Twigg and Rowell, 2002. The United Nations University, Institute for Environment and Human Security (UNU-EHS delineates “Social Vulnerability” and “Individual Vulnerability” as working areas, but does not mention Social or Individual Resilience (Bogardi, 2006.
Stanko, Z.; Boyce, S. E.; Yeh, W. W. G.
2015-12-01
Model reduction techniques using proper orthogonal decomposition (POD) have been very effective in applications to confined groundwater flow models. These techniques consist of performing a projection of the solution of the full model onto a reduced basis. POD combined with the snapshot approach has been successfully applied to highly discretized linear models. In many cases, the reduced model is orders of magnitude smaller than the full model and runs 1,000 times faster. For nonlinear models, such as the unconfined groundwater flow, direct application of POD requires additional calls to the full model to generate additional snapshots. This is time consuming and increases the dimension of the reduced model. The discrete empirical interpolation method (DEIM) is a technique that avoids the additional full model calls and captures the dynamics of the nonlinear term while reducing the dimensions. Here, POD and DEIM are combined to reduce both the nonlinear unconfined groundwater flow and solute transport equations. To prove the concept, simple one-dimensional models are created for MODFLOW and MT3DMS separately. The dual approach is then tested on a density-dependent flow and transport simulation using the LMT package developed for MODFLOW. For each iteration of the nonlinear flow solver and the transport solver, the respective reduced models are solved instead. Numerical experiments show that significant reduction is obtainable before errors become too large. This method is well suited for a coastal aquifer seawater intrusion scenario, where nonlinearities only exist in small subregions of the model domain. A fine discretization can be utilized and POD will effectively eliminate unnecessary parameterization by projecting the full model system matrix onto a subspace with fewer column dimensions. DEIM can then reduce the row dimension of the original system by using only those state variable nodes with the most influence. This combined approach allows for full
Directory of Open Access Journals (Sweden)
Shanshan Yang
Full Text Available Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson's disease (PD, and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS and kernel principal component analysis (KPCA methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher's linear discriminant analysis (FLDA was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP decision rule and support vector machine (SVM with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified.
Dimension reduction and multiscaling law through source extraction
Capobianco, Enrico
2003-04-01
Through the empirical analysis of financial return generating processes one may find features that are common to other research fields, such as internet data from network traffic, physiological studies about human heart beat, speech and sleep recorded time series, geophysics signals, just to mention well-known cases of study. In particular, long range dependence, intermittency, heteroscedasticity are clearly appearing, and consequently power laws and multi-scaling behavior result typical signatures of either the spectral or the time correlation diagnostics. We study these features and the dynamics underlying financial volatility, which can respectively be detected and inferred from high frequency realizations of stock index returns, and show that they vary according to the resolution levels used for both the analysis and the synthesis of the available information. Discovering whether the volatility dynamics are subject to changes in scaling regimes requires the consideration of a model embedding scale-dependent information packets, thus accounting for possible heterogeneous activity occurring in financial markets. Independent component analysis result to be an important tool for reducing the dimension of the problem and calibrating greedy approximation techniques aimed to learn the structure of the underlying volatility.
Unitarity cuts and Reduction to master integrals in d dimensions for one-loop amplitudes
Anastasiou, C; Feng, B; Kunszt, Z; Mastrolia, Pierpaolo; Anastasiou, Charalampos; Britto, Ruth; Feng, Bo; Kunszt, Zoltan; Mastrolia, Pierpaolo
2007-01-01
We present an alternative reduction to master integrals for one-loop amplitudes using a unitarity cut method in arbitrary dimensions. We carry out the reduction in two steps. The first step is a pure four-dimensional cut-integration of tree amplitudes with a mass parameter, and the second step is applying dimensional shift identities to master integrals. This reduction is performed at the integrand level, so that coefficients can be read out algebraically.
Directory of Open Access Journals (Sweden)
Da-Guang Zhang
2015-10-01
Full Text Available For magnetostrictive rods under combined axial pre-stress and magnetic field, a general one-dimension nonlinear magneto-elastic coupled constitutive model was built in this paper. First, the elastic Gibbs free energy was expanded into polynomial, and the relationship between stress and strain and the relationship between magnetization and magnetic field with the polynomial form were obtained with the help of thermodynamic relations. Then according to microscopic magneto-elastic coupling mechanism and some physical facts of magnetostrictive materials, a nonlinear magneto-elastic constitutive with concise form was obtained when the relations of nonlinear strain and magnetization in the polynomial constitutive were instead with transcendental functions. The comparisons between the prediction and the experimental data of different magnetostrictive materials, such as Terfenol-D, Metglas and Ni showed that the predicted magnetostrictive strain and magnetization curves were consistent with experimental results under different pre-stresses whether in the region of low and moderate field or high field. Moreover, the model can fully reflect the nonlinear magneto-mechanical coupling characteristics between magnetic, magnetostriction and elasticity, and it can effectively predict the changes of material parameters with pre-stress and bias field, which is useful in practical applications.
Energy Technology Data Exchange (ETDEWEB)
Zhang, Da-Guang; Li, Meng-Han; Zhou, Hao-Miao, E-mail: zhouhm@cjlu.edu.cn [College of Information Engineering, China Jiliang University, 310018, Hangzhou (China)
2015-10-15
For magnetostrictive rods under combined axial pre-stress and magnetic field, a general one-dimension nonlinear magneto-elastic coupled constitutive model was built in this paper. First, the elastic Gibbs free energy was expanded into polynomial, and the relationship between stress and strain and the relationship between magnetization and magnetic field with the polynomial form were obtained with the help of thermodynamic relations. Then according to microscopic magneto-elastic coupling mechanism and some physical facts of magnetostrictive materials, a nonlinear magneto-elastic constitutive with concise form was obtained when the relations of nonlinear strain and magnetization in the polynomial constitutive were instead with transcendental functions. The comparisons between the prediction and the experimental data of different magnetostrictive materials, such as Terfenol-D, Metglas and Ni showed that the predicted magnetostrictive strain and magnetization curves were consistent with experimental results under different pre-stresses whether in the region of low and moderate field or high field. Moreover, the model can fully reflect the nonlinear magneto-mechanical coupling characteristics between magnetic, magnetostriction and elasticity, and it can effectively predict the changes of material parameters with pre-stress and bias field, which is useful in practical applications.
Finite Temperature Induced Fermion Number In The Nonlinear sigma Model In (2+1) Dimensions
Dunne, G V; Rao, K; Dunne, Gerald V.; Lopez-Sarrion, Justo; Rao, Kumar
2002-01-01
We compute the finite temperature induced fermion number for fermions coupled to a static nonlinear sigma model background in (2+1) dimensions, in the derivative expansion limit. While the zero temperature induced fermion number is well known to be topological (it is the winding number of the background), at finite temperature there is a temperature dependent correction that is nontopological -- this finite T correction is sensitive to the detailed shape of the background. At low temperature we resum the derivative expansion to all orders, and we consider explicit forms of the background as a CP^1 instanton or as a baby skyrmion.
Isochronous Liénard-type nonlinear oscillators of arbitrary dimensions
Indian Academy of Sciences (India)
Ajey K Tiwari; A Durga Devi; R Gladwin Pradeep; V K Chandrasekar
2015-11-01
In this paper, we briefly present an overview of the recent developments made in identifying/generating systems of Liénard-type nonlinear oscillators exhibiting isochronous properties, including linear, quadratic and mixed cases and their higher-order generalizations. There exists several procedures/methods in the literature to identify/generate isochronous systems. The application of local as well as nonlocal transformations and -modified Hamiltonian method in identifying and generating systems exhibiting isochronous properties of arbitrary dimensions is also discussed in detail. The identified oscillators include singular and nonsingular Hamiltonian systems and PT-symmetric systems.
Nonlinear Dimensionality Reduction and Data Visualization: A Review
Institute of Scientific and Technical Information of China (English)
Hujun Yin
2007-01-01
Dimensionality reduction and data visualization are useful and important processes in pattern recognition. Many techniques have been developed in the recent years. The self-organizing map (SOM) can be an efficient method for this purpose. This paper reviews recent advances in this area and related approaches such as multidimensional scaling (MDS), nonlinear PC A, principal manifolds, as well as the connections of the SOM and its recent variant, the visualization induced SOM (ViSOM), with these approaches. The SOM is shown to produce a quantized, qualitative scaling and while the ViSOM a quantitative or metric scaling and approximates principal curve/surface. The SOM can also be regarded as a generalized MDS to relate two metric spaces by forming a topological mapping between them. The relationships among various recently proposed techniques such as ViSOM, Isomap, LLE, and eigenmap are discussed and compared.
Dimension reduction techniques for the integrative analysis of multi-omics data.
Meng, Chen; Zeleznik, Oana A; Thallinger, Gerhard G; Kuster, Bernhard; Gholami, Amin M; Culhane, Aedín C
2016-07-01
State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.
Non-linear dimensionality reduction of signaling networks
Directory of Open Access Journals (Sweden)
Ivakhno Sergii
2007-06-01
Full Text Available Abstract Background Systems wide modeling and analysis of signaling networks is essential for understanding complex cellular behaviors, such as the biphasic responses to different combinations of cytokines and growth factors. For example, tumor necrosis factor (TNF can act as a proapoptotic or prosurvival factor depending on its concentration, the current state of signaling network and the presence of other cytokines. To understand combinatorial regulation in such systems, new computational approaches are required that can take into account non-linear interactions in signaling networks and provide tools for clustering, visualization and predictive modeling. Results Here we extended and applied an unsupervised non-linear dimensionality reduction approach, Isomap, to find clusters of similar treatment conditions in two cell signaling networks: (I apoptosis signaling network in human epithelial cancer cells treated with different combinations of TNF, epidermal growth factor (EGF and insulin and (II combination of signal transduction pathways stimulated by 21 different ligands based on AfCS double ligand screen data. For the analysis of the apoptosis signaling network we used the Cytokine compendium dataset where activity and concentration of 19 intracellular signaling molecules were measured to characterise apoptotic response to TNF, EGF and insulin. By projecting the original 19-dimensional space of intracellular signals into a low-dimensional space, Isomap was able to reconstruct clusters corresponding to different cytokine treatments that were identified with graph-based clustering. In comparison, Principal Component Analysis (PCA and Partial Least Squares – Discriminant analysis (PLS-DA were unable to find biologically meaningful clusters. We also showed that by using Isomap components for supervised classification with k-nearest neighbor (k-NN and quadratic discriminant analysis (QDA, apoptosis intensity can be predicted for different
The Induced Dimension Reduction method applied to convection-diffusion-reaction problems
Astudillo, R.; Van Gijzen, M.B.
2016-01-01
Discretization of (linearized) convection-diffusion-reaction problems yields a large and sparse non symmetric linear system of equations, Ax = b. (1) In this work, we compare the computational behavior of the Induced Dimension Reduction method (IDR(s)) [10], with other short-recurrences Krylov met
Kernel Based Nonlinear Dimensionality Reduction and Classification for Genomic Microarray
Directory of Open Access Journals (Sweden)
Lan Shu
2008-07-01
Full Text Available Genomic microarrays are powerful research tools in bioinformatics and modern medicinal research because they enable massively-parallel assays and simultaneous monitoring of thousands of gene expression of biological samples. However, a simple microarray experiment often leads to very high-dimensional data and a huge amount of information, the vast amount of data challenges researchers into extracting the important features and reducing the high dimensionality. In this paper, a nonlinear dimensionality reduction kernel method based locally linear embedding(LLE is proposed, and fuzzy K-nearest neighbors algorithm which denoises datasets will be introduced as a replacement to the classical LLEÃ¢Â€Â™s KNN algorithm. In addition, kernel method based support vector machine (SVM will be used to classify genomic microarray data sets in this paper. We demonstrate the application of the techniques to two published DNA microarray data sets. The experimental results confirm the superiority and high success rates of the presented method.
New Reductions and Nonlinear Systems for 2D Schrodinger Operators
Mironov, A
2010-01-01
New Completely Integrable (2+1)-System is studied. It is based on the so-called L-A-B-triples $L_t=[H,L]-fL$ where L is a 2D Schrodinger Operator. This approach was invented by S.Manakov and B.Dubrovin, I.Krichever, S.Novikov(DKN) in the works published in 1976. A nonstandard reduction for the 2D Schrodinger Operator (completely different from the one found by S.Novikov and A.Veselov in 1984) compatible with time dynamics of the new Nonlinear System, is studied here. It can be naturally treated as a 2D extension of the famous Burgers System. The Algebro-Geometric (AG) Periodic Solutions here are very specific and unusual (for general and reduced cases). The reduced system is linearizable like Burgers. However, the general one (and probably the reduced one also) certainly lead in the stationary AG case to the nonstandard examples of algebraic curves $\\Gamma\\subset W$ in the full complex 2D manifold of Bloch-Floquet functions W for the periodic elliptic 2D operator H where $H\\psi(x,y,P)=\\lambda(P)\\psi(x,y,P),P\\...
Asymptotic behaviour for Schrodinger equations with a quadratic nonlinearity in one-space dimension
Directory of Open Access Journals (Sweden)
Nakao Hayashi
2001-07-01
Full Text Available We consider the Cauchy problem for the Schr"{o}dinger equation with a quadratic nonlinearity in one space dimension $$ iu_{t}+frac{1}{2}u_{xx}=t^{-alpha}| u_x| ^2,quad u(0,x = u_0(x, $$ where $alpha in (0,1$. From the heuristic point of view, solutions to this problem should have a quasilinear character when $alpha in (1/2,1$. We show in this paper that the solutions do not have a quasilinear character for all $alpha in (0,1$ due to the special structure of the nonlinear term. We also prove that for $alpha in [1/2,1$ if the initial data $u_0in H^{3,0}cap H^{2,2}$ are small, then the solution has a slow time decay such as $t^{-alpha /2}$. For $alpha in (0,1/2$, if we assume that the initial data $u_0$ are analytic and small, then the same time decay occurs.
Unified Gauge Theories and Reduction of Couplings: from Finiteness to Fuzzy Extra Dimensions
Directory of Open Access Journals (Sweden)
George Zoupanos
2008-02-01
Full Text Available Finite Unified Theories (FUTs are N = 1 supersymmetric Grand Unified Theories, which can be made all-loop finite, both in the dimensionless (gauge and Yukawa couplings and dimensionful (soft supersymmetry breaking terms sectors. This remarkable property, based on the reduction of couplings at the quantum level, provides a drastic reduction in the number of free parameters, which in turn leads to an accurate prediction of the top quark mass in the dimensionless sector, and predictions for the Higgs boson mass and the supersymmetric spectrum in the dimensionful sector. Here we examine the predictions of two such FUTs. Next we consider gauge theories defined in higher dimensions, where the extra dimensions form a fuzzy space (a finite matrix manifold. We reinterpret these gauge theories as four-dimensional theories with Kaluza-Klein modes. We then perform a generalized à la Forgacs-Manton dimensional reduction. We emphasize some striking features emerging such as (i the appearance of non-Abelian gauge theories in four dimensions starting from an Abelian gauge theory in higher dimensions, (ii the fact that the spontaneous symmetry breaking of the theory takes place entirely in the extra dimensions and (iii the renormalizability of the theory both in higher as well as in four dimensions. Then reversing the above approach we present a renormalizable four dimensional SU(N gauge theory with a suitable multiplet of scalar fields, which via spontaneous symmetry breaking dynamically develops extra dimensions in the form of a fuzzy sphere SN2. We explicitly find the tower of massive Kaluza-Klein modes consistent with an interpretation as gauge theory on M4 × S2, the scalars being interpreted as gauge fields on S2. Depending on the parameters of the model the low-energy gauge group can be SU(n, or broken further to SU(n1 × SU(n2 × U(1. Therefore the second picture justifies the first one in a renormalizable framework but in addition has the potential to
Age-related reduction of chromatin fractal dimension in toluidine blue - stained hepatocytes.
Pantic, Igor; Petrovic, Danica; Paunovic, Jovana; Vucevic, Danijela; Radosavljevic, Tatjana; Pantic, Senka
2016-07-01
In this study, we proposed a hypothesis that chromatin of mouse hepatocytes exhibits age-related reduction of fractal dimension. This hypothesis was based on previously published works demonstrating that complexity of biological systems such as tissues, decreases during the process of physiological aging. Liver tissue was obtained from 24 male mice divided into 3 age groups: 10-days-old (young, juvenile), 210-days-old (adult) and 390-days-old. The tissue was stained using a modification of toluidine blue (nucleic acid - specific) staining method. A total of 480 chromatin structures (20 for each animal) were analyzed. For each structure, the values of fractal dimension, lacunarity, textural angular second moment and inverse difference moment were calculated using ImageJ software and its plugins. The results indicated the age-related reduction in fractal dimension and increase in lacunarity (p<0.01). Fractal dimension is a potentially good indicator of age associated changes in chromatin structure. To our knowledge, this is the first study to show that fractal complexity of hepatocyte chromatin decreases during the process of physiological aging. Fractal analysis as a method could be useful in detection of small age-related changes in chromatin distribution not otherwise visible with naked eye on conventional tissue micrographs. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Domenech, Arnau Pons
2016-01-01
Many new nonlinear effects become relevant when the quantized nature of the quantum electrodynamics (QED) vacuum is taken into account. We derive an algorithm for simulating these effects in up to 3 spatial dimensions and verify its validity against known 1D analytic results. Finally we use the algorithm to explore systems in which analytic methods are inefficient.
Khoudeir, A.; Montemayor, R.; Urrutia, Luis F.
2008-09-01
Using the parent Lagrangian method together with a dimensional reduction from D to (D-1) dimensions, we construct dual theories for massive spin two fields in arbitrary dimensions in terms of a mixed symmetry tensor TA[A1A2…AD-2]. Our starting point is the well-studied massless parent action in dimension D. The resulting massive Stueckelberg-like parent actions in (D-1) dimensions inherit all the gauge symmetries of the original massless action and can be gauge fixed in two alternative ways, yielding the possibility of having a parent action with either a symmetric or a nonsymmetric Fierz-Pauli field eAB. Even though the dual sector in terms of the standard spin two field includes only the symmetrical part e{AB} in both cases, these two possibilities yield different results in terms of the alternative dual field TA[A1A2…AD-2]. In particular, the nonsymmetric case reproduces the Freund-Curtright action as the dual to the massive spin two field action in four dimensions.
Khoudeir, A; Urrutia, Luis F
2008-01-01
Using the parent Lagrangian method together with a dimensional reduction from D to (D-1) dimensions we constuct dual theories for massive spin two fields in arbitray dimensions in terms of a mixed symmetry tensor T_{A[A_1...A_{D-2}]}. Our starting point is the well studied massless parent action in dimension D. The resulting massive Stueckelberg-like parent actions in (D-1) dimensions inherits all the gauge symmetries of the original masless action and can be gauge fixed in two alternative ways, yielding the possibility of having either a parent action with a symmetric or a non-symmetric Fierz-Pauli field e_{AB}. Even though the dual sector in terms of the standard spin two field includes only the symmetrical part e_{{AB}} in both cases, these two alternatives yield different results in terms of the alternative dual field T_{A[A_1...A_{D-2}]}. In particular, the non-symmetric case reproduces the Freund-Curtright action as the dual to the massive spin two field action in four dimensions.
Yang, Jie; McArdle, Conor; Daniels, Stephen
2013-12-19
A new data dimension-reduction method, called Internal Information Redundancy Reduction (IIRR), is proposed for application to Optical Emission Spectroscopy (OES) datasets obtained from industrial plasma processes. For example in a semiconductor manufacturing environment, real-time spectral emission data is potentially very useful for inferring information about critical process parameters such as wafer etch rates, however, the relationship between the spectral sensor data gathered over the duration of an etching process step and the target process output parameters is complex. OES sensor data has high dimensionality (fine wavelength resolution is required in spectral emission measurements in order to capture data on all chemical species involved in plasma reactions) and full spectrum samples are taken at frequent time points, so that dynamic process changes can be captured. To maximise the utility of the gathered dataset, it is essential that information redundancy is minimised, but with the important requirement that the resulting reduced dataset remains in a form that is amenable to direct interpretation of the physical process. To meet this requirement and to achieve a high reduction in dimension with little information loss, the IIRR method proposed in this paper operates directly in the original variable space, identifying peak wavelength emissions and the correlative relationships between them. A new statistic, Mean Determination Ratio (MDR), is proposed to quantify the information loss after dimension reduction and the effectiveness of IIRR is demonstrated using an actual semiconductor manufacturing dataset. As an example of the application of IIRR in process monitoring/control, we also show how etch rates can be accurately predicted from IIRR dimension-reduced spectral data.
Prescott, Aaron M.; Abel, Steven M.
2016-12-01
The rational design of network behavior is a central goal of synthetic biology. Here, we combine in silico evolution with nonlinear dimensionality reduction to redesign the responses of fixed-topology signaling networks and to characterize sets of kinetic parameters that underlie various input-output relations. We first consider the earliest part of the T cell receptor (TCR) signaling network and demonstrate that it can produce a variety of input-output relations (quantified as the level of TCR phosphorylation as a function of the characteristic TCR binding time). We utilize an evolutionary algorithm (EA) to identify sets of kinetic parameters that give rise to: (i) sigmoidal responses with the activation threshold varied over 6 orders of magnitude, (ii) a graded response, and (iii) an inverted response in which short TCR binding times lead to activation. We also consider a network with both positive and negative feedback and use the EA to evolve oscillatory responses with different periods in response to a change in input. For each targeted input-output relation, we conduct many independent runs of the EA and use nonlinear dimensionality reduction to embed the resulting data for each network in two dimensions. We then partition the results into groups and characterize constraints placed on the parameters by the different targeted response curves. Our approach provides a way (i) to guide the design of kinetic parameters of fixed-topology networks to generate novel input-output relations and (ii) to constrain ranges of biological parameters using experimental data. In the cases considered, the network topologies exhibit significant flexibility in generating alternative responses, with distinct patterns of kinetic rates emerging for different targeted responses.
Institute of Scientific and Technical Information of China (English)
GUYanfeng; ZHANGYe; QUANTaifan
2003-01-01
A challenging problem in using hyper-spectral data is to eliminate redundancy and preserve useful spectral information for applications. In this pa-per, a kernel-based nonlinear subspace projection (KNSP)method is proposed for feature extraction and dimension-ality reduction in hyperspectral images. The proposed method includes three key steps: subspace partition of hyperspectral data, feature extraction using kernel-based principal component analysis (KPCA) and feature selec-tion based on class separability in the subspaces. Accord-ing to the strong correlation between neighboring bands,the whole data space is partitioned to requested subspaces.In each subspace, the KPCA method is used to effectively extract spectral feature and eliminate redundancies. A criterion function based on class discrimination and sepa-rability is used for the transformed feature selection. For the purpose of testifying its effectiveness, the proposed new method is compared with the classical principal component analysis (PCA) and segmented principal component trans-formation (SPCT). A hyperspectral image classification is performed on AVIRIS data. which have 224 svectral bands.Experimental results show that KNSP is very effective for feature extraction and dimensionality reduction of hyper-spectral data and provides significant improvement over classical PCA and current SPCT technique.
The First Integral Method to the Nonlinear Schrodinger Equations in Higher Dimensions
Directory of Open Access Journals (Sweden)
Shoukry Ibrahim Atia El-Ganaini
2013-01-01
Full Text Available The first integral method introduced by Feng is adopted for solving some important nonlinear partial differential equations, including the (2 + 1-dimensional hyperbolic nonlinear Schrodinger (HNLS equation, the generalized nonlinear Schrodinger (GNLS equation with a source, and the higher-order nonlinear Schrodinger equation in nonlinear optical fibers. This method provides polynomial first integrals for autonomous planar systems. Through the established first integrals, exact traveling wave solutions are formally derived in a concise manner.
Fujimoto, Kenji; Scherpen, Jacquelien M. A.
2010-01-01
This paper discusses balanced realization and model order reduction for both continuous-time and discrete-time general nonlinear systems based on singular value analysis of the corresponding Hankel operators. Singular value analysis clarifies the gain structure of a given nonlinear operator. Here it
Similarity Reduction and Integrability for the Nonlinear Wave Equations from EPM Model
Institute of Scientific and Technical Information of China (English)
YAN ZhenYa
2001-01-01
Four types of similarity reductions are obtained for the nonlinear wave equation arising in the elasto-plasticmicrostructure model by using both the direct method due to Clarkson and Kruskal and the improved direct method due to Lou. As a result, the nonlinear wave equation is not integrable.``
SIMILARITY REDUCTIONS FOR THE NONLINEAR EVOLUTION EQUATION ARISING IN THE FERMI-PASTA-ULAM PROBLEM
Institute of Scientific and Technical Information of China (English)
谢福鼎; 闫振亚; 张鸿庆
2002-01-01
Four families of similarity reductions are obtained for the nonlinear evolution equation arising in the Fermi-Pasta-Ulam problem via using both the direct method due to Clarkson and Kruskal and the improved direct method due to Lou.
An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets
Institute of Scientific and Technical Information of China (English)
Ying Li; Gang Wang; Huiling Chen; Lian Shi; Lei Qin
2013-01-01
In this paper,a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection (ACO-S) is proposed for high-dimensional datasets.Because microarray datasets comprise tens of thousands of features (genes),they are usually used to test the dimension reduction techniques.ACO-S consists of two stages in which two well-known ACO algorithms,namely ant system and ant colony system,are utilized to seek for genes,respectively.In the first stage,a modified ant system is used to filter the nonsignificant genes from high-dimensional space,and a number of promising genes are reserved in the next step.In the second stage,an improved ant colony system is applied to gene selection.In order to enhance the search ability of ACOs,we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system.Furthermore,we devise another fuzzy logic controller to tune the parameter (q0) in ant colony system.We evaluate the performance of ACO-S on five microarray datasets,which have dimensions varying from 7129 to 12000.We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms.The comparison results show that ACO-S has a notable ability to generate a gene subset with the smallest size and salient features while yielding high classification accuracy.The comparative results generated by ACO-S adopting different classifiers are also given.The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.
REDUCTION OF NONLINEAR PARTIAL DIFFERENTIAL EQUATION AND EXACT SOLUTIONS
Institute of Scientific and Technical Information of China (English)
YeCaier; PanZuliang
2003-01-01
Nonlinear partial differetial equation(NLPDE)is converted into ordinary differential equation(ODE)via a new ansatz.Using undetermined function method,the ODE obtained above is replaced by a set of algebraic equations which are solved out with the aid of Mathematica.The exact solutions and solitary solutions of NLPDE are obtained.
Nonpoint Symmetry and Reduction of Nonlinear Evolution and Wave Type Equations
Directory of Open Access Journals (Sweden)
Ivan Tsyfra
2015-01-01
Full Text Available We study the symmetry reduction of nonlinear partial differential equations with two independent variables. We propose new ansätze reducing nonlinear evolution equations to system of ordinary differential equations. The ansätze are constructed by using operators of nonpoint classical and conditional symmetry. Then we find solution to nonlinear heat equation which cannot be obtained in the framework of the classical Lie approach. By using operators of Lie-Bäcklund symmetries we construct the solutions of nonlinear hyperbolic equations depending on arbitrary smooth function of one variable too.
Loyer, A.; Sinou, J.-J.; Chiello, O.; Lorang, X.
2012-02-01
As noise reduction tends to be part of environmental directives, predicting squeal noise generated by disc brakes is an important industrial issue. It involves both the transient and stationary nonlinear dynamics of self-excited systems with frictional contact. Time simulation of the phenomenon is an attractive option for reducing experiment costs. However, since such computations using full finite element models of industrial disc brake systems is time-consuming, model reduction has to be performed. In this paper, both the transient and stationary nonlinear behaviors of the friction destabilized system and the effect of dynamical reduction on the nonlinear response of a simple friction destabilized system are carried out. The first part provides a description of the general modeling retained for friction destabilized systems. Then, discretization and solving processes for the stability analysis and the temporal evolution are presented. The third part presents an analysis of a sliding elastic layer for different operating conditions, in order to better understand the nonlinear behavior of such systems. Finally, spatial model reduction is performed with different kinds of reduction bases in order to analyze the different effects of modal reductions. This clearly shows the necessity of including static modes in the reduction basis and that nonlinear interactions between unstable modes are very difficult to represent with reduced bases. Finally, the proposed model and the associated studies are intended to be the benchmark cases for future comparison.
Institute of Scientific and Technical Information of China (English)
ZHANG Jia-zhong; LIU Yan; CHEN Dang-min
2005-01-01
From viewpoint of nonlinear dynamics, the model reduction and its influence on the long-term behaviours of a class of nonlinear dissipative autonomous dynamical system with higher dimension are investigated theoretically under some assumptions. The system is analyzed in the state space with an introduction of a distance definition which can be used to describe the distance between the full system and the reduced system, and the solution of the full system is then projected onto the complete space spanned by the eigenvectors of the linear operator of the governing equations. As a result, the influence of mode series tnncation on the long-term behaviours and the error estimate are derived, showing that the error is dependent on the first products of frequencies and damping ratios in the subspace spanned by the eigenvectors with higher modal damping. Furthermore, the fundamental understanding for the topological change of the solution due to the application of different model reduction is interpreted in a mathematically precise way, using the qualitative theory of nonlinear dynamics.
Effect of reduction time on third order optical nonlinearity of reduced graphene oxide
Sreeja, V. G.; Vinitha, G.; Reshmi, R.; Anila, E. I.; Jayaraj, M. K.
2017-04-01
We report the influence of reduction time on structural, linear and nonlinear optical properties of reduced graphene oxide (rGO) thin films synthesized by spin coating method. We observed that the structural, linear and nonlinear optical properties can be tuned with reduction time in GO is due to the increased structural ordering because of the restoration of sp2 carbon atoms with the time of reduction. The nonlinear absorption studies by open aperture Z-scan technique exhibited a saturable absorption. The nonlinear refraction studies showed the self de focusing nature of rGO by closed aperture Z scan technique. The nonlinear absorption coefficient and saturation intensity varies with the time for reduction of GO which is attributed to the depletion of valence band and the conduction band filling effect. Our results emphasize duration for reduction of GO dependent optical nonlinearity of rGO thin films to a great extent and explore its applications Q switched mode locking laser systems for generating ultra short laser pulses and in optical sensors. The rGO coated films were characterized by X-Ray diffraction method (XRD), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, UV-Vis absorption spectroscopy (UV-Vis), Photoluminescence (PL) and Scanning electron microscope (SEM) measurements.
Dimension reduction for p53 protein recognition by using incremental partial least squares.
Zeng, Xue-Qiang; Li, Guo-Zheng
2014-06-01
As an important tumor suppressor protein, reactivating mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In recent years, more and more data extracted from biophysical simulations, which makes the modelling of mutant p53 transcriptional activity suffering from the problems of huge amount of instances and high feature dimension. Incremental feature extraction is effective to facilitate analysis of large-scale data. However, most current incremental feature extraction methods are not suitable for processing big data with high feature dimension. Partial Least Squares (PLS) has been demonstrated to be an effective dimension reduction technique for classification. In this paper, we design a highly efficient and powerful algorithm named Incremental Partial Least Squares (IPLS), which conducts a two-stage extraction process. In the first stage, the PLS target function is adapted to be incremental with updating historical mean to extract the leading projection direction. In the last stage, the other projection directions are calculated through equivalence between the PLS vectors and the Krylov sequence. We compare IPLS with some state-of-the-arts incremental feature extraction methods like Incremental Principal Component Analysis, Incremental Maximum Margin Criterion and Incremental Inter-class Scatter on real p53 proteins data. Empirical results show IPLS performs better than other methods in terms of balanced classification accuracy.
Chu, J.; Zhang, C.; Fu, G.; Li, Y.; Zhou, H.
2015-08-01
This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.
Special Conditional Similarity Reduction Solutions for Two Nonlinear Partial Differential Equations
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
We present a method of special conditional similarity reduction solutions for nonlinear partial differential equations. As concrete examples of its application, we apply this method to the (2+1)-dimensional modified Broer-Kaup equations and the variable coefficient KdV-mKdV equation, which have extensive physics backgrounds, and obtain abundant exact solutions derived from some reduction equations.
Energy Technology Data Exchange (ETDEWEB)
Lim, Jong Min; Lee, Byung Chai; Lee, Ik Jin [KAIST, Daejeon (Korea, Republic of)
2015-04-15
This study develops an efficient and accurate methodology for reliability-based design optimization (RBDO) by combining the most probable point (MPP)-based dimension reduction method (DRM) to enhance accuracy and the sequential optimization and reliability assessment (SORA) to enhance efficiency. In many researches, first-order reliability method (FORM) has been utilized for RBDO methods due to its efficiency and simplicity. However, it might not be accurate enough for highly nonlinear performance functions. Therefore, the MPP-based DRM is introduced for the accurate reliability assessment in this study. Even though the MPP-based DRM significantly improves the accuracy, additional computations for the moment-based integration are required. It is desirable to reduce the number of reliability analyses in the RBDO process. Since decoupled approaches such as SORA reduce necessary reliability analyses considerably, DRM-based SORA is proposed in this study for accurate and efficient RBDO. Furthermore, convex linearization is introduced to approximate inactive probabilistic constraints to additionally improve the efficiency. The efficiency and accuracy of the proposed method are verified through numerical examples.
Seo, Y; Qin, Y; Vicente, C L; Choi, K S; Yoon, Jongsoo
2006-08-04
We have studied the effect of perpendicular magnetic fields and temperatures on nonlinear electronic transport in amorphous Ta superconducting thin films. The films exhibit a magnetic field-induced metallic behavior intervening the superconductor-insulator transition in the zero temperature limit. We show that the phase-identifying nonlinear transport in the superconducting and metallic phases arises from an intrinsic origin, not from an electron heating effect. The nonlinear transport is found to accompany an extraordinarily long voltage response time.
Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.
Yang, Jiaoyun; Wang, Haipeng; Ding, Huitong; An, Ning; Alterovitz, Gil
2017-01-19
Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps. By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks. By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases' quality, and make biobricks selection.
System Reduction in Nonlinear Multibody Dynamics of Wind Turbines
DEFF Research Database (Denmark)
Holm-Jørgensen, Kristian; Nielsen, Søren R.K.; Rubak, Rune
2007-01-01
. In the other case, the stiff body motion is defined as the chord line connecting the end points of the beam, and the elastic deformations are simply supported at the end points. The system reduction is performed by discretizing the spatial motion into a set of rigid body modes and linear elastic eigenmodes...
A new integrable discrete generalized nonlinear Schrodinger equation and its reductions
Li, Hongmin; Li, Yuqi; Chen, Yong
2013-01-01
A new integrable discrete system is constructed and studied, based on the algebraization of the difference operator. The model is named the discrete generalized nonlinear Schrodinger (GNLS) equation for which can be reduced to classical discrete nonlinear Schrodinger (NLS) equation. To show the complete integrability of the discrete GNLS equation, the recursion operator, symmetries and conservation quantities are obtained. Furthermore, all of reductions for the discrete GNLS equation are give...
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
［1］Fuller, W. A., Measurement Error Models, New York: John Wiley & Sons Inc., 1987.［2］Carroll, R. J., Ruppert, D., Stefanski, L. W., Measurement Error in Nonlinear Models, New York: Chapman and Hall, 1995.［3］Wittes, J., Lakatos, E., Probstfied, J., Surrogate endpoints in clinical trails: Cardiovascular diseases, Statist,Med., 1989, 8: 415-425.［4］Buonaccorsi, J. P., Measurement error in the response in the general linear model, J. Amer. Statist. Assoc., 1996,91(434): 633-642.［5］Carroll, R. J., Stefanski, L. A., Approximate quasi-likelihood estimation in models with surrogate predictors, J.Amer. Statist. Assoc., 1990, 85: 652-663.［6］Pepe, M. S., Inference using surrogate outcome data and a validation sample, Biometrika, 1992, 79: 355-365.［7］Duncan, G., Hill, D., An investigations of the extent and consequences of measurement error in labor-economics survey data, Journal of Labor Economics, 1985, 3: 508-532.［8］Stefanski, L. A., Carrol, R. J., Conditional scores and optimal scores for generalized linear measurement error models, Biometrika, 1987, 74:703-716.［9］Carroll, R. J., Wand, M. P., Semiparametric estimation in logistic measure error models, J. Roy. Statist. Soc.,Ser B, 1991, 53: 652-663.［10］Pepe, M. S., Fleming, T. R., A general nonparametric method for dealing with errors in missing or surrogate covariate data, J. Amer. Statist. Assoc. 1991, 86:108-113.［11］Pepe, M. S., Reilly, M., Fleming, T. R., Auxiliary outcome data and the mean score method, J. Statist. Plan.Inference, 1994, 42: 137-160.［12］Reilly, M., Pepe, M. S., A mean score method for missing and auxiliary covariate data in regression models,Biometrika, 1995, 82: 299-314.［13］Carroll, R. J., Knickerbocker, R. K., Wang, C. Y., Dimension reduction in a semiparametric regression model with errors in covariates, The Annals of Statistics, 1995, 23: 161-181.［14］Sepanski, J. H., Lee, L. F., Semiparametric estimation of nonlinear error-in-variables models
General moving objects recognition method based on graph embedding dimension reduction algorithm
Institute of Scientific and Technical Information of China (English)
Yi ZHANG; Jie YANG; Kun LIU
2009-01-01
Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.
A nonlinear Schroedinger equation with two symmetric point interactions in one dimension
Energy Technology Data Exchange (ETDEWEB)
Kovarik, Hynek [Dipartimento di Matematica, Politecnico di Torino, Torino (Italy); Sacchetti, Andrea [Facolta di Scienze, Universita di Modena e Reggio Emilia, Modena (Italy)], E-mail: Hynek.Kovarik@polito.it, E-mail: Andrea.Sacchetti@unimore.it
2010-04-16
We consider a time-dependent one-dimensional nonlinear Schroedinger equation with a symmetric double-well potential represented by two Dirac's {delta}. Among our results we give an explicit formula for the integral kernel of the unitary semigroup associated with the linear part of the Hamiltonian. Then we establish the corresponding Strichartz-type estimate and we prove local existence and uniqueness of the solution to the original nonlinear probl0008.
Model reduction of cavity nonlinear optics for photonic logic: a quasi-principal components approach
Shi, Zhan; Nurdin, Hendra I.
2016-11-01
Kerr nonlinear cavities displaying optical thresholding have been proposed for the realization of ultra-low power photonic logic gates. In the ultra-low photon number regime, corresponding to energy levels in the attojoule scale, quantum input-output models become important to study the effect of unavoidable quantum fluctuations on the performance of such logic gates. However, being a quantum anharmonic oscillator, a Kerr-cavity has an infinite dimensional Hilbert space spanned by the Fock states of the oscillator. This poses a challenge to simulate and analyze photonic logic gates and circuits composed of multiple Kerr nonlinearities. For simulation, the Hilbert of the oscillator is typically truncated to the span of only a finite number of Fock states. This paper develops a quasi-principal components approach to identify important subspaces of a Kerr-cavity Hilbert space and exploits it to construct an approximate reduced model of the Kerr-cavity on a smaller Hilbert space. Using this approach, we find a reduced dimension model with a Hilbert space dimension of 15 that can closely match the magnitudes of the mean transmitted and reflected output fields of a conventional truncated Fock state model of dimension 75, when driven by an input coherent field that switches between two levels. For the same input, the reduced model also closely matches the magnitudes of the mean output fields of Kerr-cavity-based AND and NOT gates and a NAND latch obtained from simulation of the full 75 dimension model.
A DIMENSION REDUCTION-BASED METHOD FOR CLASSIFICATION OF HYPERSPECTRAL AND LIDAR DATA
Directory of Open Access Journals (Sweden)
B. Abbasi
2015-12-01
Full Text Available The existence of various natural objects such as grass, trees, and rivers along with artificial manmade features such as buildings and roads, make it difficult to classify ground objects. Consequently using single data or simple classification approach cannot improve classification results in object identification. Also, using of a variety of data from different sensors; increase the accuracy of spatial and spectral information. In this paper, we proposed a classification algorithm on joint use of hyperspectral and Lidar (Light Detection and Ranging data based on dimension reduction. First, some feature extraction techniques are applied to achieve more information from Lidar and hyperspectral data. Also Principal component analysis (PCA and Minimum Noise Fraction (MNF have been utilized to reduce the dimension of spectral features. The number of 30 features containing the most information of the hyperspectral images is considered for both PCA and MNF. In addition, Normalized Difference Vegetation Index (NDVI has been measured to highlight the vegetation. Furthermore, the extracted features from Lidar data calculated based on relation between every pixel of data and surrounding pixels in local neighbourhood windows. The extracted features are based on the Grey Level Co-occurrence Matrix (GLCM matrix. In second step, classification is operated in all features which obtained by MNF, PCA, NDVI and GLCM and trained by class samples. After this step, two classification maps are obtained by SVM classifier with MNF+NDVI+GLCM features and PCA+NDVI+GLCM features, respectively. Finally, the classified images are fused together to create final classification map by decision fusion based majority voting strategy.
Model reduction for nonlinear systems based on the differential eigenstructure of Hankel operators
Fujimoto, Kenji; Scherpen, Jacquelien M.A.
2001-01-01
This paper offers a new input-normal output-diagonal realization and model reduction procedure for nonlinear systems based on the differential eigenstructure of Hankel operators. Firstly, we refer to the preliminary results on input-normal realizations with original singular value functions and the
Ren, Zhuyin; Pope, Stephen B.; Vladimirsky, Alexander; Guckenheimer, John M.
2006-03-01
This work addresses the construction and use of low-dimensional invariant manifolds to simplify complex chemical kinetics. Typically, chemical kinetic systems have a wide range of time scales. As a consequence, reaction trajectories rapidly approach a hierarchy of attracting manifolds of decreasing dimension in the full composition space. In previous research, several different methods have been proposed to identify these low-dimensional attracting manifolds. Here we propose a new method based on an invariant constrained equilibrium edge (ICE) manifold. This manifold (of dimension nr) is generated by the reaction trajectories emanating from its (nr-1)-dimensional edge, on which the composition is in a constrained equilibrium state. A reasonable choice of the nr represented variables (e.g., nr "major" species) ensures that there exists a unique point on the ICE manifold corresponding to each realizable value of the represented variables. The process of identifying this point is referred to as species reconstruction. A second contribution of this work is a local method of species reconstruction, called ICE-PIC, which is based on the ICE manifold and uses preimage curves (PICs). The ICE-PIC method is local in the sense that species reconstruction can be performed without generating the whole of the manifold (or a significant portion thereof). The ICE-PIC method is the first approach that locally determines points on a low-dimensional invariant manifold, and its application to high-dimensional chemical systems is straightforward. The "inputs" to the method are the detailed kinetic mechanism and the chosen reduced representation (e.g., some major species). The ICE-PIC method is illustrated and demonstrated using an idealized H2/O system with six chemical species. It is then tested and compared to three other dimension-reduction methods for the test case of a one-dimensional premixed laminar flame of stoichiometric hydrogen/air, which is described by a detailed mechanism
Efficient control of ultrafast optical nonlinearity of reduced graphene oxide by infrared reduction
Energy Technology Data Exchange (ETDEWEB)
Bhattachraya, S.; Maiti, R.; Das, A. C.; Saha, S.; Mondal, S.; Ray, S. K.; Bhaktha, S. N. B.; Datta, P. K., E-mail: pkdatta.iitkgp@gmail.com [Department of Physics, Indian Institute of Technology Kharagpur, Kharagpur 721302 (India)
2016-07-07
Simultaneous occurrence of saturable absorption nonlinearity and two-photon absorption nonlinearity in the same medium is well sought for the devices like optical limiter and laser mode-locker. Pristine graphene sheet consisting entirely of sp{sup 2}-hybridized carbon atoms has already been identified having large optical nonlinearity. However, graphene oxide (GO), a precursor of graphene having both sp{sup 2} and sp{sup 3}-hybridized carbon atom, is increasingly attracting cross-discipline researchers for its controllable properties by reduction of oxygen containing groups. In this work, GO has been prepared by modified Hummers method, and it has been further reduced by infrared (IR) radiation. Characterization of reduced graphene oxide (RGO) by means of Raman spectroscopy, X-ray photoelectron spectroscopy, and UV-Visible absorption measurements confirms an efficient reduction with infrared radiation. Here, we report precise control of non-linear optical properties of RGO in femtosecond regime with increased degrees of IR reduction measured by open aperture z-scan technique. Depending on the intensity, both saturable absorption and two-photon absorption effects are found to contribute to the non-linearity of all the samples. Saturation dominates at low intensity (∼127 GW/cm{sup 2}) while two-photon absorption becomes prominent at higher intensities (from 217 GW/cm{sup 2} to 302 GW/cm{sup 2}). The values of two-photon absorption co-efficient (∼0.0022–0.0037 cm/GW for GO, and ∼0.0128–0.0143 cm/GW for RGO) and the saturation intensity (∼57 GW/cm{sup 2} for GO, and ∼194 GW/cm{sup 2} for RGO) increase with increasing reduction, indicating GO and RGO as novel tunable photonic devices. We have also explained the reason of tunable nonlinear optical properties by using amorphous carbon model.
Nonlinear Dynamics of Parity-Even Tricritical Gravity in Three and Four Dimensions
Apolo, Luis
2012-01-01
Recently proposed "multicritical" higher-derivative gravities in Anti de Sitter space carry logarithmic representations of the Anti de Sitter isometry group. While generically non-unitary already at the quadratic, free-theory level, in special cases these theories admit a unitary subspace. The simplest example of such behavior is "tricritical" gravity. In this paper, we extend the study of parity-even tricritical gravity in d = 3, 4 to the first nonlinear order. We show that the would-be unitary subspace suffers from a linearization instability and is absent in the full non-linear theory.
Dong, Zhizhong; Zuber, Christian; Pierce, Michael; Stanley, Pamela; Roth, Jürgen
2014-02-01
Various proteins are involved in the generation and maintenance of the membrane complex known as the Golgi apparatus. We have used mutant Chinese hamster ovary (CHO) cell lines Lec4 and Lec4A lacking N-acetylglucosaminyltransferase V (GlcNAcT-V, MGAT5) activity and protein in the Golgi apparatus to study the effects of the absence of a single glycosyltransferase on the Golgi apparatus dimension. Quantification of immunofluorescence in serial confocal sections for Golgi α-mannosidase II and electron microscopic morphometry revealed a reduction in Golgi volume density up to 49 % in CHO Lec4 and CHO Lec4A cells compared to parental CHO cells. This reduction in Golgi volume density could be reversed by stable transfection of Lec4 cells with a cDNA encoding Mgat5. Inhibition of the synthesis of β1,6-branched N-glycans by swainsonine had no effect on Golgi volume density. In addition, no effect on Golgi volume density was observed in CHO Lec1 cells that contain enzymatically active GlcNAcT-V, but cannot synthesize β1,6-branched glycans due to an inactive GlcNAcT-I in their Golgi apparatus. These results indicate that it may be the absence of the GlcNAcT-V protein that is the determining factor in reducing Golgi volume density. No dimensional differences existed in cross-sectioned cisternal stacks between Lec4 and control CHO cells, but significantly reduced Golgi stack hits were observed in cross-sectioned Lec4 cells. Therefore, the Golgi apparatus dimensional change in Lec4 and Lec4A cells may be due to a compaction of the organelle.
Directory of Open Access Journals (Sweden)
Wolfgang Witteveen
2014-01-01
Full Text Available The mechanical response of multilayer sheet structures, such as leaf springs or car bodies, is largely determined by the nonlinear contact and friction forces between the sheets involved. Conventional computational approaches based on classical reduction techniques or the direct finite element approach have an inefficient balance between computational time and accuracy. In the present contribution, the method of trial vector derivatives is applied and extended in order to obtain a-priori trial vectors for the model reduction which are suitable for determining the nonlinearities in the joints of the reduced system. Findings show that the result quality in terms of displacements and contact forces is comparable to the direct finite element method but the computational effort is extremely low due to the model order reduction. Two numerical studies are presented to underline the method’s accuracy and efficiency. In conclusion, this approach is discussed with respect to the existing body of literature.
A novel order reduction method for nonlinear dynamical system under external periodic excitations
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The concept of approximate inertial manifold (AIM) is extended to develop a kind of nonlinear order reduction technique for non-autonomous nonlinear systems in second-order form in this paper.Using the modal transformation,a large nonlinear dynamical system is split into a ’master’ subsystem,a ’slave’ subsystem,and a ’negligible’ subsystem.Accordingly,a novel order reduction method (Method I) is developed to construct a low order subsystem by neglecting the ’negligible’ subsystem and slaving the ’slave’ subsystem into the ’master’ subsystem using the extended AIM.As a comparison,Method II accounting for the effects of both ’slave’ subsystem and the ’negligible’ subsystem is also applied to obtain the reduced order subsystem.Then,a typical 5-degree-of-freedom nonlinear dynamical system is given to compare the accuracy and efficiency of the traditional Galerkin truncation (ignoring the contributions of the slave and negligible subsystems),Method I and Method II.It is shown that Method I gives a considerable increase in accuracy for little computational cost in comparison with the standard Galerkin method,and produces almost the same accuracy as Method II.Finally,a 3-degree-of-freedom nonlinear dynamical system is analyzed by using the analytic method for showing predominance and convenience of Method I to obtain the analytically reduced order system.
Institute of Scientific and Technical Information of China (English)
马军海; 陈予恕
2001-01-01
The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time series in the phase space adopting one kind of nonlinear chaotic model were reconstructed. At first, the model parameters were estimated by using the improved least square method. Then as the precision was satisfied,the optimization method was used to estimate these parameters. At the end by using the obtained chaotic model, the future data of the chaotic time series in the phase space was predicted. Some representative experimental examples were analyzed to testify the models and the algorithms developed in this paper. The results show that if the algorithms developed here are adopted, the parameters of the corresponding chaotic model will be easily calculated well and true. Predictions of chaotic series in phase space make the traditional methods change from outer iteration to interpolations. And if the optimal model rank is chosen, the prediction precision will increase notably. Long term superior predictability of nonlinear chaotic models is proved to be irrational and unreasonable.
Nonlinear model reduction for dynamical systems using sparse sensor locations from learned libraries
Sargsyan, Syuzanna; Brunton, Steven L.; Kutz, J. Nathan
2015-09-01
We demonstrate the synthesis of sparse sampling and dimensionality reduction to characterize and model nonlinear dynamical systems over a range of bifurcation parameters. First, we construct modal libraries using the classical proper orthogonal decomposition in order to expose the dominant low-rank coherent structures. Here, libraries of the nonlinear terms are also constructed in order to take advantage of the discrete empirical interpolation method and projection that allows for the approximation of nonlinear terms from a sparse number of grid points. The selected grid points are shown to be effective sensing and measurement locations for characterizing the underlying dynamics, stability, and bifurcations of nonlinear dynamical systems. The use of empirical interpolation points and sparse representation facilitates a family of local reduced-order models for each physical regime, rather than a higher-order global model, which has the benefit of physical interpretability of energy transfer between coherent structures. The method advocated also allows for orders-of-magnitude improvement in computational speed and memory requirements. To illustrate the method, the discrete interpolation points and nonlinear modal libraries are used for sparse representation in order to classify and reconstruct the dynamic bifurcation regimes in the complex Ginzburg-Landau equation. It is also shown that point measurements of the nonlinearity are more effective than linear measurements when sensor noise is present.
Dimensional Reduction for Filters of Nonlinear Systems with Time-Scale Separation
2013-03-01
Rapp, Edwin Kreuzer and N. Sri Namachchivaya, “Reduced Nor- mal Forms for Nonlinear Control of Underactuated Hoisting Systems ,” Archive of Applied Mechanics , Vol.82, 2012, pp. 297 - 315. 7 ... Mechanics , Vol. 78(6), 2011, pp. 61001-1 - 61001-10. 8. Lee DeVille, N. Sri Namachchivaya and Zoi Rapti, “Noisy Two Dimensional Non-Hamiltonian System ...AFRL-OSR-VA-TR-2013-0009 Dimensional Reduction for Filters of Nonlinear Systems with Time- Scale Separation Namachchivaya, N
Barker, Adrian J
2016-01-01
We perform global two-dimensional hydrodynamical simulations of Keplerian discs with free eccentricity over thousands of orbital periods. Our aim is to determine the validity of secular theory in describing the evolution of eccentric discs, and to explore their nonlinear evolution for moderate eccentricities. Linear secular theory is found to correctly predict the structure and precession rates of discs with small eccentricities. However, discs with larger eccentricities (and eccentricity gradients) are observed to precess faster (retrograde relative to the orbital motion), at a rate that depends on their eccentricities (and eccentricity gradients). We derive analytically a nonlinear secular theory for eccentric gas discs, which explains this result as a modification of the pressure forces whenever eccentric orbits in a disc nearly intersect. This effect could be particularly important for highly eccentric discs produced in tidal disruption events, or for narrow gaseous rings; it might also play a role in cau...
Choo, Jaegul; Lee, Hanseung; Liu, Zhicheng; Stasko, John; Park, Haesun
2013-01-01
Many of the modern data sets such as text and image data can be represented in high-dimensional vector spaces and have benefited from computational methods that utilize advanced computational methods. Visual analytics approaches have contributed greatly to data understanding and analysis due to their capability of leveraging humans' ability for quick visual perception. However, visual analytics targeting large-scale data such as text and image data has been challenging due to the limited screen space in terms of both the numbers of data points and features to represent. Among various computational methods supporting visual analytics, dimension reduction and clustering have played essential roles by reducing these numbers in an intelligent way to visually manageable sizes. Given numerous dimension reduction and clustering methods available, however, the decision on the choice of algorithms and their parameters becomes difficult. In this paper, we present an interactive visual testbed system for dimension reduction and clustering in a large-scale high-dimensional data analysis. The testbed system enables users to apply various dimension reduction and clustering methods with different settings, visually compare the results from different algorithmic methods to obtain rich knowledge for the data and tasks at hand, and eventually choose the most appropriate path for a collection of algorithms and parameters. Using various data sets such as documents, images, and others that are already encoded in vectors, we demonstrate how the testbed system can support these tasks.
The non-linear coupled spin 2-spin 3 Cotton equation in three dimensions
Linander, Hampus; Nilsson, Bengt E. W.
2016-07-01
In the context of three-dimensional conformal higher spin theory we derive, in the frame field formulation, the full non-linear spin 3 Cotton equation coupled to spin 2. This is done by solving the corresponding Chern-Simons gauge theory system of equations, that is, using F = 0 to eliminate all auxiliary fields and thus expressing the Cotton equation in terms of just the spin 3 frame field and spin 2 covariant derivatives and tensors (Schouten). In this derivation we neglect the spin 4 and higher spin sectors and approximate the star product commutator by a Poisson bracket. The resulting spin 3 Cotton equation is complicated but can be related to linearized versions in the metric formulation obtained previously by other authors. The expected symmetry (spin 3 "translation", "Lorentz" and "dilatation") properties are verified for Cotton and other relevant tensors but some perhaps unexpected features emerge in the process, in particular in relation to the non-linear equations. We discuss the structure of this non-linear spin 3 Cotton equation but its explicit form is only presented here, in an exact but not completely refined version, in appended files obtained by computer algebra methods. Both the frame field and metric formulations are provided.
Rapid decay of nonlinear whistler waves in two dimensions: Full particle simulation
Umeda, Takayuki; Saito, Shinji; Nariyuki, Yasuhiro
2017-05-01
The decay of a nonlinear, short-wavelength, and monochromatic electromagnetic whistler wave is investigated by utilizing a two-dimensional (2D) fully relativistic electromagnetic particle-in-cell code. The simulation is performed under a low-beta condition in which the plasma pressure is much lower than the magnetic pressure. It has been shown that the nonlinear (large-amplitude) parent whistler wave decays through the parametric instability in a one-dimensional (1D) system. The present study shows that there is another channel for the decay of the parent whistler wave in 2D, which is much faster than in the timescale of the parametric decay in 1D. The parent whistler wave decays into two sideband daughter whistlers propagating obliquely with respect to the ambient magnetic field with a frequency close to the parent wave and two quasi-perpendicular electromagnetic modes with a frequency close to zero via a 2D decay instability. The two sideband daughter oblique whistlers also enhance a nonlinear longitudinal electrostatic wave via a three-wave interaction as a secondary process.
Dynamics Near the Ground State for the Energy Critical Nonlinear Heat Equation in Large Dimensions
Collot, Charles; Merle, Frank; Raphaël, Pierre
2016-11-01
We consider the energy critical semilinear heat equation partial_tu = Δ u + |u|^{4/d-2}u, quad x in R^d and give a complete classification of the flow near the ground state solitary wave Q(x) = 1/(1+{|x|^2/{d(d-2)})^{d-2/2}} in dimension {d ≥ 7} , in the energy critical topology and without radial symmetry assumption. Given an initial data {Q + ɛ_0} with {|nabla ɛ_0|_{L^2} ≪ 1} , the solution either blows up in the ODE type I regime, or dissipates, and these two open sets are separated by a codimension one set of solutions asymptotically attracted by the solitary wave. In particular, non self similar type II blow up is ruled out in dimension {d ≥ 7} near the solitary wave even though it is known to occur in smaller dimensions (Schweyer, J Funct Anal 263(12):3922-3983, 2012). Our proof is based on sole energy estimates deeply connected to Martel et al. (Acta Math 212(1):59-140, 2014) and draws a route map for the classification of the flow near the solitary wave in the energy critical setting. A by-product of our method is the classification of minimal elements around Q belonging to the unstable manifold.
Nonlinear consensus protocols for multi-agent systems based on centre manifold reduction
Institute of Scientific and Technical Information of China (English)
Li Yu-Mei; Guan Xin-Ping
2009-01-01
Nonlinear consensus protocols for dynamic directed networks of multi-agent systems with fixed and switching topologies are investigated separately in this paper. Based on the centre manifold reduction technique,nonlinear consensus protocols are presented.We prove that a group of agents can reach a β-consensus,the value of which is the group decision value varying from the minimum and the maximum values of the initial states of the agents.Moreover,we derive the conditions to guarantee that all the agents reach a β-consensus on a desired group decision value.Finally,a simulation study concerning the vertical alignment manoeuvere of a team of unmanned air vehicles is performed.Simulation results show that the nonlinear consensus protocols proposed are more effective than the linear protocols for the formation control of the agents and they are an improvement over existing protocols.
Chen, Duan; Leon, Arturo S.; Gibson, Nathan L.; Hosseini, Parnian
2016-01-01
Optimizing the operation of a multireservoir system is challenging due to the high dimension of the decision variables that lead to a large and complex search space. A spectral optimization model (SOM), which transforms the decision variables from time domain to frequency domain, is proposed to reduce the dimensionality. The SOM couples a spectral dimensionality-reduction method called Karhunen-Loeve (KL) expansion within the routine of Nondominated Sorting Genetic Algorithm (NSGA-II). The KL expansion is used to represent the decision variables as a series of terms that are deterministic orthogonal functions with undetermined coefficients. The KL expansion can be truncated into fewer significant terms, and consequently, fewer coefficients by a predetermined number. During optimization, operators of the NSGA-II (e.g., crossover) are conducted only on the coefficients of the KL expansion rather than the large number of decision variables, significantly reducing the search space. The SOM is applied to the short-term operation of a 10-reservoir system in the Columbia River of the United States. Two scenarios are considered herein, the first with 140 decision variables and the second with 3360 decision variables. The hypervolume index is used to evaluate the optimization performance in terms of convergence and diversity. The evaluation of optimization performance is conducted for both conventional optimization model (i.e., NSGA-II without KL) and the SOM with different number of KL terms. The results show that the number of decision variables can be greatly reduced in the SOM to achieve a similar or better performance compared to the conventional optimization model. For the scenario with 140 decision variables, the optimal performance of the SOM model is found with six KL terms. For the scenario with 3360 decision variables, the optimal performance of the SOM model is obtained with 11 KL terms.
Off-shell superconformal nonlinear sigma-models in three dimensions
Kuzenko, Sergei M; Tartaglino-Mazzucchelli, Gabriele; von Unge, Rikard
2010-01-01
We develop superspace techniques to construct general off-shell N=1,2,3,4 superconformal sigma-models in three space-time dimensions. The most general N=3 and N=4 superconformal sigma-models are constructed in terms of N=2 chiral superfields. Several superspace proofs of the folklore statement that N=3 supersymmetry implies N=4 are presented both in the on-shell and off-shell settings. We also elaborate on (super)twistor realisations for (super)manifolds on which the three-dimensional N-extended superconformal groups act transitively and which include Minkowski space as a subspace.
The non-linear coupled spin 2 - spin 3 Cotton equation in three dimensions
Linander, Hampus
2016-01-01
In the context of three-dimensional conformal higher spin theory we derive, in the frame field formulation, the full non-linear spin 3 Cotton equation coupled to spin 2. This is done by solving the corresponding Chern-Simons gauge theory system of equations, that is, using $F=0$ to eliminate all auxiliary fields and thus expressing the Cotton equation in terms of just the spin 3 frame field and spin 2 covariant derivatives and tensors (Schouten). In this derivation we neglect the spin 4 and higher spin sectors and approximate the star product commutator by a Poisson bracket. The resulting spin 3 Cotton equation is complicated but can be related to linearized versions in the metric formulation obtained previously by other authors. The expected symmetry (spin 3 "translation", "Lorentz" and "dilatation") properties are verified for Cotton and other relevant tensors but some perhaps unexpected features emerge in the process, in particular in relation to the non-linear equations. We discuss the structure of this n...
Nonlinear sigma models with AdS supersymmetry in three dimensions
Butter, Daniel; Tartaglino-Mazzucchelli, Gabriele
2012-01-01
In three-dimensional anti-de Sitter (AdS) space, there exist several realizations of N-extended supersymmetry, which are traditionally labelled by two non-negative integers p>=q such that p+q=N. Different choices of p and q, with N fixed, prove to lead to different restrictions on the target space geometry of supersymmetric nonlinear sigma-models. We classify all possible types of hyperkahler target spaces for the cases N=3 and N=4 by making use of two different realizations for the most general (p,q) supersymmetric sigma-models: (i) off-shell formulations in terms of N=3 and N=4 projective supermultiplets; and (ii) on-shell formulations in terms of covariantly chiral scalar superfields in (2,0) AdS superspace. Depending on the type of N=3,4 AdS supersymmetry, nonlinear sigma-models can support one of the following target space geometries: (i) hyperkahler cones; (ii) non-compact hyperkahler manifolds with a U(1) isometry group which acts non-trivially on the two-sphere of complex structures; (iii) arbitrary h...
Shvarts, D.; Oron, D.; Kartoon, D.; Rikanati, A.; Sadot, O.; Srebro, Y.; Yedvab, Y.; Ofer, D.; Levin, A.; Sarid, E.; Ben-Dor, G.; Erez, L.; Erez, G.; Yosef-Hai, A.; Alon, U.; Arazi, L.
2016-10-01
The late-time nonlinear evolution of the Rayleigh-Taylor (RT) and Richtmyer-Meshkov (RM) instabilities for random initial perturbations is investigated using a statistical mechanics model based on single-mode and bubble-competition physics at all Atwood numbers (A) and full numerical simulations in two and three dimensions. It is shown that the RT mixing zone bubble and spike fronts evolve as h ~ α · A · gt2 with different values of a for the bubble and spike fronts. The RM mixing zone fronts evolve as h ~ tθ with different values of θ for bubbles and spikes. Similar analysis yields a linear growth with time of the Kelvin-Helmholtz mixing zone. The dependence of the RT and RM scaling parameters on A and the dimensionality will be discussed. The 3D predictions are found to be in good agreement with recent Linear Electric Motor (LEM) experiments.
Nonlinear Theory of Light Speed Reduction in a Three-Level A System
Institute of Scientific and Technical Information of China (English)
王德重; 李代军; 刘夏姬; 李师群; 王育竹
2001-01-01
We present a nonlinear theory of light velocity reduction in a three-level A system based on electromagneticllyinduced transparency. Analysis shows that the probe field propagates with a velocity that is quite strongly dependent on its intensity instead of being merely approximately dependent on the coupling intensity. Moreover,the minimum group velocity of the probe field is analytically given for a given input power.
Institute of Scientific and Technical Information of China (English)
Li WANG; Jixiu WANG
2014-01-01
Let B1 ⊂ RN be a unit ball centered at the origin. The main purpose of this paper is to discuss the critical dimension phenomenon for radial solutions of the following quasilinear elliptic problem involving critical Sobolev exponent and singular coefficients:-div(|∇u|p-2∇u)=|x|s|u|p*(s)-2u+λ|x|t|u|p-2u, x∈B1, u|∂B1 =0, where t, s>-p, 2≤pp(p-1)t+p(p2-p+1) andλ∈(0,λ1,t), whereλ1,t is the first eigenvalue of-∆p with the Dirichlet boundary condition. Meanwhile, the nonexistence of sign-changing radial solutions is proved if the space dimension N ≤ (ps+p) min{1, p+tp+s}+p2p-(p-1) min{1, p+tp+s} andλ>0 is small.
Institute of Scientific and Technical Information of China (English)
FAN En-Gui
2001-01-01
Two new applications of homogeneous balance (HB) method are presented.It is shown that HB methodcan be extended to search for the Backlund transformations and similarity reductions of nonlinear partial differentialequations.The close relations among the HB method,Weiss-Tabor-Carnevale method and Clarkson-Kruskal directreduction method are also found.KdV-MKdV equation is considered as an illustrative example,and its one kind of Backlund transformation,three kinds of similarity reductions and several kinds of travelling wave solutions are obtained by using extended HB method.
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.
Global existence for an L^2 critical Nonlinear Dirac equation in one dimension
Candy, Timothy
2011-01-01
We prove global existence from $L^2$ initial data for a nonlinear Dirac equation known as the Thirring model. Local existence in $H^s$ for $s>0$, and global existence for $s>1/2$, has recently been proven by Selberg and Tesfahun by using $X^{s, b}$ spaces together with a type of null form estimate. In contrast, motivated by the recent work of Machihara, Nakanishi, and Tsugawa, we first prove local existence in $L^2$ by using null coordinates, where the time of existence depends on the profile of the initial data. To extend this to a global existence result we need to rule out concentration of $L^2$ norm, or charge, at a point. This is done by decomposing the solution into an approximately linear component and a component with improved integrability. We then prove global existence for all $s>0$.
REMARKS ON THE LIFESPAN FOR THE SOLUTION TO NONLINEAR WAVE EQUATIONS IN THREE SPACE DIMENSIONS
Institute of Scientific and Technical Information of China (English)
杨晗; 刘法贵
2003-01-01
The authors consider the Cauchy problem for the following nonlinear waveequationsutt - △u = | vl2,vtt - △v = (△u)2,u(0, x) = εuo(x), ut(0, x) = ε(x),v(0, x) = εvo(x), vt(0, x) = εv1(x),where x ∈ R3, t ≥ 0, ε＞ 0 is a small parameter, and obtain the sharp bounds for thelifespan of solution to (0.1). Specially, it is proved that there exist two constants C1 andC2, which are independent of ε, then the lifespan T(ε) satisfies the folowing inequalitiesC1 ≤ limε→0ε In T(ε) ≤ C2.
FORTRAN programs for calculating nonlinear seismic ground response in two dimensions
Joyner, W.B.
1978-01-01
The programs described here were designed for calculating the nonlinear seismic response of a two-dimensional configuration of soil underlain by a semi-infinite elastic medium representing bedrock. There are two programs. One is for plane strain motions, that is, motions in the plane perpendicular to the long axis of the structure, and the other is for antiplane strain motions, that is motions parallel to the axis. The seismic input is provided by specifying what the motion of the rock-soil boundary would be if the soil were absent and the boundary were a free surface. This may be done by supplying a magnetic tape containing the values of particle velocity for every boundary point at every instant of time. Alternatively, a punch card deck may be supplied giving acceleration values at every instant of time. In the plane strain program it is assumed that the acceleration values apply simultaneously to every point on the boundary; in the antiplane strain program it is assumed that the acceleration values characterize a plane shear wave propagating upward in the underlying elastic medium at a specified angle with the vertical. The nonlinear hysteretic behavior of the soil is represented by a three-dimensional rheological model. A boundary condition is used which takes account of finite rigidity in the elastic substratum. The computations are performed by an explicit finite-difference scheme that proceeds step by step in space and time. Computations are done in terms of stress departures from an unspecified initial state. Source listings are provided here along with instructions for preparing the input. A more detailed discussion of the method is presented elsewhere.
Optimization of elstomeric micro-fluidic valve dimensions using non-linear finite element methods
Directory of Open Access Journals (Sweden)
H Khawaja
2016-04-01
Full Text Available We use a nonlinear finite element (FE method model to compare,optimize and determine the limits for useful geometries of microfluidicvalves in elastomer polydimethylsiloxane (PDMS. Simulations havebeen performed with the aim of finding the optimal shape, size andlocation of pressurization that minimizes the pressure required to operatethe valve. One important constraint governing the design parameters isthat the stresses should be within elastic limits, so that the componentremains safe from any type of structural failure. To obtain reliable results,non-linear stress analysis was performed using the Mooney-Rivlin 9parameter approximation which is based on the Hyper Elastic MaterialModel. A 20 noded brick element was used for the development of FEmodel. Mesh sensitivity analysis was also performed to assess the qualityof the results. The simulations were performed with commerciallyavailable FE modeling software, developed by ANSYS Inc. to determinethe effect of varying different geometric parameters on the performanceof micro-fluidic valves.The aim of this work is to determine the geometry of the channel crosssectionthat would result in the largest deflection for the least appliedpressure, i.e. to minimize the pressure needed to operate the valve.
A new Einstein-nonlinear electrodynamics solution in 2 + 1 dimensions
Energy Technology Data Exchange (ETDEWEB)
Mazharimousavi, S.H.; Halilsoy, M.; Gurtug, O. [Eastern Mediterranean University, Department of Physics, Mersin 10 (Turkey)
2014-01-15
We introduce a class of solutions in 2 + 1-dimensional Einstein-Power-Maxwell theory for a circularly symmetric electric field. The electromagnetic field is considered with an angular component given by F{sub μν} = E{sub 0}δ{sub ν}{sup θ} for E{sub 0} = constant. First, we show that the metric for zero cosmological constant and the Power-Maxwell Lagrangian of the form of √(vertical stroke F{sub μν}F{sup μν} vertical stroke) coincides with the solution given in 2 + 1-dimensional gravity coupled with a massless, self-interacting real scalar field. With the same Lagrangian and a non-zero cosmological constant we obtain a non-asymptotically flat wormhole solution in 2 + 1 dimensions. The confining motions of massive charged and chargeless particles are investigated too. Secondly, another interesting solution is given for zero cosmological constant together with the conformal invariant condition. The formation of a timelike naked singularity for this particular case is investigated within the framework of the quantum mechanics. Quantum fields obeying the Klein-Gordon and Dirac equations are used to probe the singularity and test the quantum mechanical status of the singularity. (orig.)
Global-local nonlinear model reduction for flows in heterogeneous porous media
AlOtaibi, Manal
2015-08-01
In this paper, we combine discrete empirical interpolation techniques, global mode decomposition methods, and local multiscale methods, such as the Generalized Multiscale Finite Element Method (GMsFEM), to reduce the computational complexity associated with nonlinear flows in highly-heterogeneous porous media. To solve the nonlinear governing equations, we employ the GMsFEM to represent the solution on a coarse grid with multiscale basis functions and apply proper orthogonal decomposition on a coarse grid. Computing the GMsFEM solution involves calculating the residual and the Jacobian on a fine grid. As such, we use local and global empirical interpolation concepts to circumvent performing these computations on the fine grid. The resulting reduced-order approach significantly reduces the flow problem size while accurately capturing the behavior of fully-resolved solutions. We consider several numerical examples of nonlinear multiscale partial differential equations that are numerically integrated using fully-implicit time marching schemes to demonstrate the capability of the proposed model reduction approach to speed up simulations of nonlinear flows in high-contrast porous media.
[Radiation dose reduction using a non-linear image filter in MDCT].
Nakashima, Junya; Takahashi, Toshiyuki; Takahashi, Yoshimasa; Imai, Yasuhiro; Ishihara, Yotaro; Kato, Kyoichi; Nakazawa, Yasuo
2010-05-20
The development of MDCT enabled various high-quality 3D imaging and optimized scan timing with contrast injection in a multi-phase dynamic study. Since radiation dose tends to increase to yield such high-quality images, we have to make an effort to reduce radiation dose. A non-linear image filter (Neuro 3D filter: N3D filter) has been developed in order to improve image noise. The purpose of this study was to evaluate the physical performance and effectiveness of this non-linear image filter using phantoms, and show how we can reduce radiation dose in clinical use of this filter. This N3D filter reduced radiation dose by about 50%, with minimum deterioration of spatial reduction in phantom and clinical studies. This filter shows great potential for clinical application.
Institute of Scientific and Technical Information of China (English)
Kasun Bandara,Atul Sewaiwar,; Yeon-Ho Chung
2015-01-01
Orthogonal frequency division multiplexing (OFDM) produces a high peak-to-average power ratio (PAPR) that ad-versely affects high-speed OFDM data transmission. In order to reduce the high PAPR, an efficient nonlinear companding trans-form (NCT) function is proposed. With the proposed NCT function, the compression and expansion weights can be applied indepen-dently with suitably chosen function parameter values. As a re-sult, the proposed function can easily maintain the average signal power approximately unchanged during the companding process. In this regard, the proposed function is superior to previously pro-posed schemes. Also, the simulations show the outstanding PAPR reduction performance of the proposed function. It is demonstrated that the proposed scheme performs wel with nonlinear transmitter amplifiers and delivers superior error performance, compared with error function and exponential function based schemes.
Kireeva, Natalia V; Ovchinnikova, Svetlana I; Tetko, Igor V; Asiri, Abdullah M; Balakin, Konstantin V; Tsivadze, Aslan Yu
2014-05-01
Over the years, a number of dimensionality reduction techniques have been proposed and used in chemoinformatics to perform nonlinear mappings. In this study, four representatives of nonlinear dimensionality reduction methods related to two different families were analyzed: distance-based approaches (Isomap and Diffusion Maps) and topology-based approaches (Generative Topographic Mapping (GTM) and Laplacian Eigenmaps). The considered methods were applied for the visualization of three toxicity datasets by using four sets of descriptors. Two methods, GTM and Diffusion Maps, were identified as the best approaches, which thus made it impossible to prioritize a single family of the considered dimensionality reduction methods. The intrinsic dimensionality assessment of data was performed by using the Maximum Likelihood Estimation. It was observed that descriptor sets with a higher intrinsic dimensionality contributed maps of lower quality. A new statistical coefficient, which combines two previously known ones, was proposed to automatically rank the maps. Instead of relying on one of the best methods, we propose to automatically generate maps with different parameter values for different descriptor sets. By following this procedure, the maps with the highest values of the introduced statistical coefficient can be automatically selected and used as a starting point for visual inspection by the user.
Model-order reduction of nonlinear models of electromagnetic phased-array hyperthermia.
Kowalski, Marc E; Jin, Jian-Ming
2003-11-01
A method based on the Karhunen-Loéve (KL) transform is proposed for the reduction of large-scale, nonlinear ordinary differential equations such as those arising from the finite difference modeling of biological heat transfer. The method of snapshots is used to expedite computation of the required quantities in the KL procedure. Guidelines are presented and validated for snapshot selection and resultant basis series truncation, emphasizing the special physical features of the electromagnetic phased-array heat transfer physics. Applications to fast temperature prediction are presented.
Balbuena Ortega, A; Arroyo Carrasco, M L; Méndez Otero, M M; Gayou, V L; Delgado Macuil, R; Martínez Gutiérrez, H; Iturbe Castillo, M D
2014-12-12
In this paper, the nonlinear refractive index of colloidal gold nanoparticles under continuous wave illumination is investigated with the z-scan technique. Gold nanoparticles were synthesized using ascorbic acid as reductant, phosphates as stabilizer and cetyltrimethylammonium chloride (CTAC) as surfactant agent. The nanoparticle size was controlled with the CTAC concentration. Experiments changing incident power and sample concentration were done. The experimental z-scan results were fitted with three models: thermal lens, aberrant thermal lens and the nonlocal model. It is shown that the nonlocal model reproduces with exceptionally good agreement; the obtained experimental behaviour.
Directory of Open Access Journals (Sweden)
Xiaoqiang Zhang
2010-01-01
Full Text Available The nonlinear-optical properties of metal Ag colloidal solutions, which were prepared by the reduction of silver nitrate, were investigated using Z-scan method. Under picosecond 532 nm excitation, the Ag colloidal solution exhibited negative nonlinear refractive index (n2=−5.17×10−4 cm2/W and reverse saturable absorption coefficient (β=4.32 cm/GW. The data fitting result of optical limiting (OL response of metal Ag colloidal solution indicated that the nonlinear absorption was attributed to two-photon absorption effect at 532 nm. Moreover, the fluorescence emission spectra of Ag colloidal solution were recorded under excitations at both 280 nm and 350 nm. Two fluorescence peaks, 336 nm and 543 nm for 280 nm excitation, while 544 nm and 694 nm for 350 nm excitation, were observed.
Energy Technology Data Exchange (ETDEWEB)
Webb-Robertson, Bobbie-Jo M.; Matzke, Melissa M.; Oehmen, Christopher S.
2009-02-26
Reducing the dimension of vectors used in training support vector machines (SVMs) results in a proportional speedup in training time. For large-scale problems this can make the difference between tractable and intractable training tasks. However, it is critical that classifiers trained on reduced datasets perform as reliably as their counterparts trained on high-dimensional data. We assessed principal component analysis (PCA) and sequential project pursuit (SPP) as dimension reduction strategies in the biology application of classifying proteins into well-defined functional ‘families’ (SVM-based protein family classification) by their impact on run-time, sensitivity and selectivity. Homology vectors of 4352 elements were reduced to approximately 2% of the original data size without significantly affecting accuracy using PCA and SPP, while leading to approximately a 28-fold speedup in run-time.
Directory of Open Access Journals (Sweden)
Ruilan Tian
2016-06-01
Full Text Available The coupled system of smooth and discontinuous absorber and beam bridge under moving loads is constructed in order to detect the effectiveness of smooth and discontinuous absorber. It is worth pointing out that the coupled system contains an irrational restoring force which is a barrier for conventional nonlinear techniques. Hence, the harmonic balance method and Fourier expansion are used to obtain the approximate solutions of the system. The first and the second kind of generalized complete elliptic integrals are introduced. Furthermore, using power flow approach, the performance of smooth and discontinuous absorber in vibration reduction is estimated through the input energy, the dissipated energy, and the damping efficiency. It is interesting that only depending on the value of the smoothness parameter, the efficiency parameter of vibration reduction is optimized. Therefore, smooth and discontinuous absorber can adapt itself to effectively reducing the amplitude of the vibration of the beam bridge, which provides an insight to the understanding of the applications of smooth and discontinuous oscillator in engineering and power flow characteristics in nonlinear system.
Liu, Yang; Chiaromonte, Francesca; Li, Bing
2017-06-01
In many scientific and engineering fields, advanced experimental and computing technologies are producing data that are not just high dimensional, but also internally structured. For instance, statistical units may have heterogeneous origins from distinct studies or subpopulations, and features may be naturally partitioned based on experimental platforms generating them, or on information available about their roles in a given phenomenon. In a regression analysis, exploiting this known structure in the predictor dimension reduction stage that precedes modeling can be an effective way to integrate diverse data. To pursue this, we propose a novel Sufficient Dimension Reduction (SDR) approach that we call structured Ordinary Least Squares (sOLS). This combines ideas from existing SDR literature to merge reductions performed within groups of samples and/or predictors. In particular, it leads to a version of OLS for grouped predictors that requires far less computation than recently proposed groupwise SDR procedures, and provides an informal yet effective variable selection tool in these settings. We demonstrate the performance of sOLS by simulation and present a first application to genomic data. The R package "sSDR," publicly available on CRAN, includes all procedures necessary to implement the sOLS approach. © 2016, The International Biometric Society.
Mass anomalous dimension of Adjoint QCD at large N from twisted volume reduction
Pérez, Margarita García; Keegan, Liam; Okawa, Masanori
2015-01-01
In this work we consider the $SU(N)$ gauge theory with two Dirac fermions in the adjoint representation, in the limit of large $N$. In this limit the infinite-volume physics of this model can be studied by means of the corresponding twisted reduced model defined on a single site lattice. Making use of this strategy we study the reduced model for various values of $N$ up to 289. By analyzing the eigenvalue distribution of the adjoint Dirac operator we test the conformality of the theory and extract the corresponding mass anomalous dimension.
Deepthi, Dasika Ratna; Eswaran, K
2007-01-01
In this paper, we present a Mirroring Neural Network architecture to perform non-linear dimensionality reduction and Object Recognition using a reduced lowdimensional characteristic vector. In addition to dimensionality reduction, the network also reconstructs (mirrors) the original high-dimensional input vector from the reduced low-dimensional data. The Mirroring Neural Network architecture has more number of processing elements (adalines) in the outer layers and the least number of elements in the central layer to form a converging-diverging shape in its configuration. Since this network is able to reconstruct the original image from the output of the innermost layer (which contains all the information about the input pattern), these outputs can be used as object signature to classify patterns. The network is trained to minimize the discrepancy between actual output and the input by back propagating the mean squared error from the output layer to the input layer. After successfully training the network, it ...
Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
Hughes, John
2010-01-01
Non-gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model (SGLMM) offers a very popular and flexible approach to modeling such data, but the SGLMM suffers from three major shortcomings: (1) uninterpretability of parameters due to spatial confounding, (2) variance inflation due to spatial confounding, and (3) high-dimensional spatial random effects that make fully Bayesian inference for such models computationally challenging. We propose a new parameterization of the SGLMM that alleviates spatial confounding and speeds computation by greatly reducing the dimension of the spatial random effects. We illustrate the application of our approach to simulated binary, count, and Gaussian spatial datasets, and to a large infant mortali...
Order reduction and efficient implementation of nonlinear nonlocal cochlear response models.
Filo, Maurice; Karameh, Fadi; Awad, Mariette
2016-12-01
The cochlea is an indispensable preliminary processing stage in auditory perception that employs mechanical frequency-tuning and electrical transduction of incoming sound waves. Cochlear mechanical responses are shown to exhibit active nonlinear spatiotemporal response dynamics (e.g., otoacoustic emission). To model such phenomena, it is often necessary to incorporate cochlear fluid-membrane interactions. This results in both excessively high-order model formulations and computationally intensive solutions that limit their practical use in simulating the model and analyzing its response even for simple single-tone inputs. In order to address these limitations, the current work employs a control-theoretic framework to reformulate a nonlinear two-dimensional cochlear model into discrete state space models that are of considerably lower order (factor of 8) and are computationally much simpler (factor of 25). It is shown that the reformulated models enjoy sparse matrix structures which permit efficient numerical manipulations. Furthermore, the spatially discretized models are linearized and simplified using balanced transformation techniques to result in lower-order (nonlinear) realizations derived from the dominant Hankel singular values of the system dynamics. Accuracy and efficiency of the reduced-order reformulations are demonstrated under the response to two fixed tones, sweeping tones and, more generally, a brief speech signal. The corresponding responses are compared to those produced by the original model in both frequency and spatiotemporal domains. Although carried out on a specific instance of cochlear models, the introduced framework of control-theoretic model reduction could be applied to a wide class of models that address the micro- and macro-mechanical properties of the cochlea.
Energy Technology Data Exchange (ETDEWEB)
Lehoucq, Richard B.; Segalman, Daniel Joseph; Hetmaniuk, Ulrich L. (University of Washington, Seattle, WA); Dohrmann, Clark R.
2009-10-01
Advanced computing hardware and software written to exploit massively parallel architectures greatly facilitate the computation of extremely large problems. On the other hand, these tools, though enabling higher fidelity models, have often resulted in much longer run-times and turn-around-times in providing answers to engineering problems. The impediments include smaller elements and consequently smaller time steps, much larger systems of equations to solve, and the inclusion of nonlinearities that had been ignored in days when lower fidelity models were the norm. The research effort reported focuses on the accelerating the analysis process for structural dynamics though combinations of model reduction and mitigation of some factors that lead to over-meshing.
Sparse nonlinear inverse imaging for shot count reduction in inverse lithography.
Wu, Xiaofei; Liu, Shiyuan; Lv, Wen; Lam, Edmund Y
2015-10-19
Inverse lithography technique (ILT) is significant to reduce the feature size of ArF optical lithography due to its strong ability to overcome the optical proximity effect. A critical issue for inverse lithography is the complex curvilinear patterns produced, which are very costly to write due to the large number of shots needed with the current variable shape beam (VSB) writers. In this paper, we devise an inverse lithography method to reduce the shot count by incorporating a model-based fracturing (MBF) in the optimization. The MBF is formulated as a sparse nonlinear inverse imaging problem based on representing the mask as a linear combination of shots followed by a threshold function. The problem is approached with a Gauss-Newton algorithm, which is adapted to promote sparsity of the solution, corresponding to the reduction of the shot count. Simulations of inverse lithography are performed on several test cases, and results demonstrate reduced shot count of the resulting mask.
A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation
Directory of Open Access Journals (Sweden)
Ying-Nong Chen
2015-10-01
Full Text Available In this paper, a general nearest feature line (NFL embedding (NFLE transformation called fuzzy-kernel NFLE (FKNFLE is proposed for hyperspectral image (HSI classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. According to the proposed scheme, samples were projected into a kernel space and assigned larger weights based on that of their neighbors. The within-class and between-class scatters were calculated using the fuzzy weights, and the best transformation was obtained by maximizing the Fisher criterion in the kernel space. In that way, the kernelized manifold learning preserved the local manifold structure in a Hilbert space as well as the locality of the manifold structure in the reduced low-dimensional space. The proposed method was compared with various state-of-the-art methods to evaluate the performance using three benchmark data sets. Based on the experimental results: the proposed FKNFLE outperformed the other, more conventional methods.
Surrogate-based modeling and dimension reduction techniques for multi-scale mechanics problems
Institute of Scientific and Technical Information of China (English)
Wei Shyy; Young-Chang Cho; Wenbo Du; Amit Gupta; Chien-Chou Tseng; Ann Marie Sastry
2011-01-01
Successful modeling and/or design of engineering systems often requires one to address the impact of multiple “design variables” on the prescribed outcome.There are often multiple,competing objectives based on which we assess the outcome of optimization.Since accurate,high fidelity models are typically time consuming and computationally expensive,comprehensive evaluations can be conducted only if an efficient framework is available.Furthermore,informed decisions of the model/hardware's overall performance rely on an adequate understanding of the global,not local,sensitivity of the individual design variables on the objectives.The surrogate-based approach,which involves approximating the objectives as continuous functions of design variables from limited data,offers a rational framework to reduce the number of important input variables,i.e.,the dimension of a design or modeling space.In this paper,we review the fundamental issues that arise in surrogate-based analysis and optimization,highlighting concepts,methods,techniques,as well as modeling implications for mechanics problems.To aid the discussions of the issues involved,we summarize recent efforts in investigating cryogenic cavitating flows,active flow control based on dielectric barrier discharge concepts,and lithium (Li)-ion batteries.It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for “scale bridging.”
Fuss, Franz Konstantin
2013-01-01
Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.
Directory of Open Access Journals (Sweden)
Franz Konstantin Fuss
2013-01-01
Full Text Available Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal’s time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.
Objective Reduction Solutions to Higher-Order Boussinesq System in (2+1)-Dimensions
Institute of Scientific and Technical Information of China (English)
HU Ya-Hong; ZHENG Chun-Long
2009-01-01
With the help of an objective reduction approach (ORA), abundant exact solutions of (2+1)-dimensional higher-order Boussinesq system (including some hyperboloid function solutions, trigonometric function solutions, and a rational function solution) are obtained. It is shown that some novel soliton structures, like single linearity soliton structure, breath soliton structure, single linearity y-periodic solitary wave structure, libration dromion structure, and kink-like multisoliton structure with actual physical meaning exist in the (2+1)-dimensional higher-order Bonssinesq system.
CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss
Directory of Open Access Journals (Sweden)
Mark A. van de Wiel
2007-01-01
Full Text Available An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of clone regions is introduced. This reduction of the data is performed such that little information is lost, which is possible due to the high dependencies between neighboring clones. The algorithm is explained using a small example. The potential beneficial effects of the algorithm for downstream analysis are illustrated by re-analysis of previously published colorectal cancer data. Using multiple testing corrections suitable for these data, we provide statistical evidence for genomic differences on several clone regions between MSI+ and CIN+ tumors. The algorithm, named CGHregions, is available as an easy-to-use script in R.
A COMPARATIVE STUDY OF DIMENSION REDUCTION TECHNIQUES FOR CONTENT-BASED IMAGE RETRIEVAL
Directory of Open Access Journals (Sweden)
G. Sasikala
2010-08-01
Full Text Available Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content-based image retrieval is a promising approach because of its automatic indexing and retrieval based on their semantic features and visual appearance. This paper discusses the method for dimensionality reduction called Maximum Margin Projection (MMP. MMP aims at maximizing the margin between positive and negative sample at each neighborhood. It is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval systems, where nearest neighbor search is usually involved. The performance of these approaches is measured by a user evaluation. It is found that the MMP based technique provides more functionalities and capabilities to support the features of information seeking behavior and produces better performance in searching images.
FEATURE DIMENSION REDUCTION FOR EFFICIENT MEDICAL IMAGE RETRIEVAL SYSTEM USING UNIFIED FRAMEWORK
Directory of Open Access Journals (Sweden)
Yogapriya Jaganathan
2013-01-01
Full Text Available Feature dimensionality reduction problem is a major issue in Content Based Medical Image Retrieval (CBMIR for the effective management of medical images with the support of visual features for the purpose of diagnosis and educational research field. However, high dimensional features would be an origin for substantial challenges in retrieval. The proposed CBMIR is used a unified approach based on extraction of visual features, optimized feature selection, classification of optimized features and similarity measurements. However, high dimensional features would be an origin for substantial challenges in retrieval. The Texture features are selected using Gray Level Co-occurrence Matrix (GLCM, Tamura Features (TF and Gabor Filter (GF in which pull out of features are formed a feature vector database. Fuzzy based PSO (FPSO is applied for Feature selection to overcome the difficulty of feature vectors being surrounded in local optima of original PSO. This procedure also integrates a smart policymaking structure of ACO procedure into the novel FPSO where the global optimum position to be exclusive for every feature particle. The Fuzzy based Particle Swarm Optimization and Ant Colony Optimization (FPSO-ACO technique is used to trim down the feature vector dimensionality and classification is accomplished using an extensive Fuzzy based Relevance Vector Machine (FRVM to form collections of relevant image features that would provide an accepted way to classify dimensionally concentrated feature vectors of images. The Euclidean Distance (ED is recognized as finest for similarity measurement between the medical query image and the medical image database. This proposed approach can acquire the query from the user and had retrieved the desired images from the database. The retrieval performance would be assessed based on precision and recall. This proposed CBMIR is used to provide comfort to the physician to obtain more assurance in their decisions for
Directory of Open Access Journals (Sweden)
Ming-Wei Su
Full Text Available BACKGROUND: The importance of gene-gene and gene-environment interactions on asthma is well documented in literature, but a systematic analysis on the interaction between various genetic and environmental factors is still lacking. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a population-based, case-control study comprised of seventh-grade children from 14 Taiwanese communities. A total of 235 asthmatic cases and 1,310 non-asthmatic controls were selected for DNA collection and genotyping. We examined the gene-gene and gene-environment interactions between 17 single-nucleotide polymorphisms in antioxidative, inflammatory and obesity-related genes, and childhood asthma. Environmental exposures and disease status were obtained from parental questionnaires. The model-free and non-parametrical multifactor dimensionality reduction (MDR method was used for the analysis. A three-way gene-gene interaction was elucidated between the gene coding glutathione S-transferase P (GSTP1, the gene coding interleukin-4 receptor alpha chain (IL4Ra and the gene coding insulin induced gene 2 (INSIG2 on the risk of lifetime asthma. The testing-balanced accuracy on asthma was 57.83% with a cross-validation consistency of 10 out of 10. The interaction of preterm birth and indoor dampness had the highest training-balanced accuracy at 59.09%. Indoor dampness also interacted with many genes, including IL13, beta-2 adrenergic receptor (ADRB2, signal transducer and activator of transcription 6 (STAT6. We also used likelihood ratio tests for interaction and chi-square tests to validate our results and all tests showed statistical significance. CONCLUSIONS/SIGNIFICANCE: The results of this study suggest that GSTP1, INSIG2 and IL4Ra may influence the lifetime asthma susceptibility through gene-gene interactions in schoolchildren. Home dampness combined with each one of the genes STAT6, IL13 and ADRB2 could raise the asthma risk.
Cheng, Kung-Shan; Yuan, Yu; Li, Zhen; Stauffer, Paul R.; Joines, William T.; Dewhirst, Mark W.; Das, Shiva K.
2009-02-01
Purpose: Blood perfusion is a well-known factor that complicates accurate control of heating during hyperthermia treatments of cancer. Since blood perfusion varies as a function of time, temperature and location, determination of appropriate power deposition pattern from multiple antenna array Hyperthermia systems and heterogeneous tissues is a difficult control problem. Therefore, we investigate the applicability of a real-time eigenvalue model reduction (virtual source - VS) reduced-order controller for hyperthermic treatments of tissue with nonlinearly varying perfusion. Methods: We impose a piecewise linear approximation to a set of heat pulses, each consisting of a 1-min heat-up, followed by a 2-min cool-down. The controller is designed for feedback from magnetic resonance temperature images (MRTI) obtained after each iteration of heat pulses to adjust the projected optimal setting of antenna phase and magnitude for selective tumor heating. Simulated temperature patterns with additive Gaussian noise with a standard deviation of 1.0°C and zero mean were used as a surrogate for MRTI. Robustness tests were conducted numerically for a patient's right leg placed at the middle of a water bolus surrounded by a 10-antenna applicator driven at 150 MHz. Robustness tests included added discrepancies in perfusion, electrical and thermal properties, and patient model simplifications. Results: The controller improved selective tumor heating after an average of 4-9 iterative adjustments of power and phase, and fulfilled satisfactory therapeutic outcomes with approximately 75% of tumor volumes heated to temperatures >43°C while maintaining about 93% of healthy tissue volume time to only 4 to 9% of the original value. Conclusions: Using a piecewise linear approximation to a set of heat pulses in a VS reduced-order controller, the proposed algorithm greatly improves the efficiency of hyperthermic treatment of leg sarcomas while accommodating practical nonlinear variation of
Reduction of the curvature of a class of nonlinear regression models
Institute of Scientific and Technical Information of China (English)
吴翊; 易东云
2000-01-01
It is proved that the curvature of nonlinear model can be reduced to zero by increasing measured data for a class of nonlinear regression models. The result is important to actual problem and has obtained satisfying effect on data fusing.
Nonlinear manifold representations for functional data
Chen, Dong; Müller, Hans-Georg
2012-01-01
For functional data lying on an unknown nonlinear low-dimensional space, we study manifold learning and introduce the notions of manifold mean, manifold modes of functional variation and of functional manifold components. These constitute nonlinear representations of functional data that complement classical linear representations such as eigenfunctions and functional principal components. Our manifold learning procedures borrow ideas from existing nonlinear dimension reduction methods, which...
Carlberg, Kevin
2010-10-28
A Petrov-Galerkin projection method is proposed for reducing the dimension of a discrete non-linear static or dynamic computational model in view of enabling its processing in real time. The right reduced-order basis is chosen to be invariant and is constructed using the Proper Orthogonal Decomposition method. The left reduced-order basis is selected to minimize the two-norm of the residual arising at each Newton iteration. Thus, this basis is iteration-dependent, enables capturing of non-linearities, and leads to the globally convergent Gauss-Newton method. To avoid the significant computational cost of assembling the reduced-order operators, the residual and action of the Jacobian on the right reduced-order basis are each approximated by the product of an invariant, large-scale matrix, and an iteration-dependent, smaller one. The invariant matrix is computed using a data compression procedure that meets proposed consistency requirements. The iteration-dependent matrix is computed to enable the least-squares reconstruction of some entries of the approximated quantities. The results obtained for the solution of a turbulent flow problem and several non-linear structural dynamics problems highlight the merit of the proposed consistency requirements. They also demonstrate the potential of this method to significantly reduce the computational cost associated with high-dimensional non-linear models while retaining their accuracy. © 2010 John Wiley & Sons, Ltd.
Cong, Fengyu; Leppänen, Paavo H T; Astikainen, Piia; Hämäläinen, Jarmo; Hietanen, Jari K; Ristaniemi, Tapani
2011-09-30
The present study addresses benefits of a linear optimal filter (OF) for independent component analysis (ICA) in extracting brain event-related potentials (ERPs). A filter such as the digital filter is usually considered as a denoising tool. Actually, in filtering ERP recordings by an OF, the ERP' topography should not be changed by the filter, and the output should also be able to be modeled by the linear transformation. Moreover, an OF designed for a specific ERP source or component may remove noise, as well as reduce the overlap of sources and even reject some non-targeted sources in the ERP recordings. The OF can thus accomplish both the denoising and dimension reduction (reducing the number of sources) simultaneously. We demonstrated these effects using two datasets, one containing visual and the other auditory ERPs. The results showed that the method including OF and ICA extracted much more reliable components than the sole ICA without OF did, and that OF removed some non-targeted sources and made the underdetermined model of EEG recordings approach to the determined one. Thus, we suggest designing an OF based on the properties of an ERP to filter recordings before using ICA decomposition to extract the targeted ERP component. Copyright © 2011 Elsevier B.V. All rights reserved.
Wang, Xiuhong; Mao, Xingpeng; Wang, Yiming; Zhang, Naitong; Li, Bo
2016-09-15
Based on sparse representations, the problem of two-dimensional (2-D) direction of arrival (DOA) estimation is addressed in this paper. A novel sparse 2-D DOA estimation method, called Dimension Reduction Sparse Reconstruction (DRSR), is proposed with pairing by Spatial Spectrum Reconstruction of Sub-Dictionary (SSRSD). By utilizing the angle decoupling method, which transforms a 2-D estimation into two independent one-dimensional (1-D) estimations, the high computational complexity induced by a large 2-D redundant dictionary is greatly reduced. Furthermore, a new angle matching scheme, SSRSD, which is less sensitive to the sparse reconstruction error with higher pair-matching probability, is introduced. The proposed method can be applied to any type of orthogonal array without requirement of a large number of snapshots and a priori knowledge of the number of signals. The theoretical analyses and simulation results show that the DRSR-SSRSD method performs well for coherent signals, which performance approaches Cramer-Rao bound (CRB), even under a single snapshot and low signal-to-noise ratio (SNR) condition.
Directory of Open Access Journals (Sweden)
Xiuhong Wang
2016-09-01
Full Text Available Based on sparse representations, the problem of two-dimensional (2-D direction of arrival (DOA estimation is addressed in this paper. A novel sparse 2-D DOA estimation method, called Dimension Reduction Sparse Reconstruction (DRSR, is proposed with pairing by Spatial Spectrum Reconstruction of Sub-Dictionary (SSRSD. By utilizing the angle decoupling method, which transforms a 2-D estimation into two independent one-dimensional (1-D estimations, the high computational complexity induced by a large 2-D redundant dictionary is greatly reduced. Furthermore, a new angle matching scheme, SSRSD, which is less sensitive to the sparse reconstruction error with higher pair-matching probability, is introduced. The proposed method can be applied to any type of orthogonal array without requirement of a large number of snapshots and a priori knowledge of the number of signals. The theoretical analyses and simulation results show that the DRSR-SSRSD method performs well for coherent signals, which performance approaches Cramer–Rao bound (CRB, even under a single snapshot and low signal-to-noise ratio (SNR condition.
Parekh, Vishwa S.; Jacobs, Jeremy R.; Jacobs, Michael A.
2014-03-01
The evaluation and treatment of acute cerebral ischemia requires a technique that can determine the total area of tissue at risk for infarction using diagnostic magnetic resonance imaging (MRI) sequences. Typical MRI data sets consist of T1- and T2-weighted imaging (T1WI, T2WI) along with advanced MRI parameters of diffusion-weighted imaging (DWI) and perfusion weighted imaging (PWI) methods. Each of these parameters has distinct radiological-pathological meaning. For example, DWI interrogates the movement of water in the tissue and PWI gives an estimate of the blood flow, both are critical measures during the evolution of stroke. In order to integrate these data and give an estimate of the tissue at risk or damaged; we have developed advanced machine learning methods based on unsupervised non-linear dimensionality reduction (NLDR) techniques. NLDR methods are a class of algorithms that uses mathematically defined manifolds for statistical sampling of multidimensional classes to generate a discrimination rule of guaranteed statistical accuracy and they can generate a two- or three-dimensional map, which represents the prominent structures of the data and provides an embedded image of meaningful low-dimensional structures hidden in their high-dimensional observations. In this manuscript, we develop NLDR methods on high dimensional MRI data sets of preclinical animals and clinical patients with stroke. On analyzing the performance of these methods, we observed that there was a high of similarity between multiparametric embedded images from NLDR methods and the ADC map and perfusion map. It was also observed that embedded scattergram of abnormal (infarcted or at risk) tissue can be visualized and provides a mechanism for automatic methods to delineate potential stroke volumes and early tissue at risk.
Reduction of nonlinear patterning effects in SOA-based All-optical Switches using Optical filtering
DEFF Research Database (Denmark)
Nielsen, Mads Lønstrup; Mørk, Jesper; Skaguchi, J.
2005-01-01
We explain theoretically, and demonstrate and quantify experimentally, how appropriate filtering can reduce the dominant nonlinear patterning effect, which limits the performance of differential-mode SOA-based switches.......We explain theoretically, and demonstrate and quantify experimentally, how appropriate filtering can reduce the dominant nonlinear patterning effect, which limits the performance of differential-mode SOA-based switches....
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
We determine the asymptotic order of entropy number and optimal non - linear approximations of anisotropic periodic Besov class of Brpθ(Td) (1≤p≤∞, 1≤θ≤∞ ) by manifolds of finite pseudo-dimension in the metric Lq (Td), 1≤ q≤∞, where Td is the d-dimensional torus.
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
A new noise reduction method for nonlinear signal based on maximum variance unfolding(MVU)is proposed.The noisy sig- nal is firstly embedded into a high-dimensional phase space based on phase space reconstruction theory,and then the manifold learning algorithm MVU is used to perform nonlinear dimensionality reduction on the data of phase space in order to separate low-dimensional manifold representing the attractor from noise subspace.Finally,the noise-reduced signal is obtained through reconstructing the low-dimensional manifold.The simulation results of Lorenz system show that the proposed MVU-based noise reduction method outperforms the KPCA-based method and has the advantages of simple parameter estimation and low parameter sensitivity.The proposed method is applied to fault detection of a vibration signal from rotor-stator of aero engine with slight rubbing fault.The denoised results show that the slight rubbing features overwhelmed by noise can be effectively extracted by the proposed noise reduction method.
Energy Technology Data Exchange (ETDEWEB)
Carlberg, Kevin Thomas [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Drohmann, Martin [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Tuminaro, Raymond S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Computational Mathematics; Boggs, Paul T. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Ray, Jaideep [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; van Bloemen Waanders, Bart Gustaaf [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Optimization and Uncertainty Estimation
2014-10-01
Model reduction for dynamical systems is a promising approach for reducing the computational cost of large-scale physics-based simulations to enable high-fidelity models to be used in many- query (e.g., Bayesian inference) and near-real-time (e.g., fast-turnaround simulation) contexts. While model reduction works well for specialized problems such as linear time-invariant systems, it is much more difficult to obtain accurate, stable, and efficient reduced-order models (ROMs) for systems with general nonlinearities. This report describes several advances that enable nonlinear reduced-order models (ROMs) to be deployed in a variety of time-critical settings. First, we present an error bound for the Gauss-Newton with Approximated Tensors (GNAT) nonlinear model reduction technique. This bound allows the state-space error for the GNAT method to be quantified when applied with the backward Euler time-integration scheme. Second, we present a methodology for preserving classical Lagrangian structure in nonlinear model reduction. This technique guarantees that important properties--such as energy conservation and symplectic time-evolution maps--are preserved when performing model reduction for models described by a Lagrangian formalism (e.g., molecular dynamics, structural dynamics). Third, we present a novel technique for decreasing the temporal complexity --defined as the number of Newton-like iterations performed over the course of the simulation--by exploiting time-domain data. Fourth, we describe a novel method for refining projection-based reduced-order models a posteriori using a goal-oriented framework similar to mesh-adaptive h -refinement in finite elements. The technique allows the ROM to generate arbitrarily accurate solutions, thereby providing the ROM with a 'failsafe' mechanism in the event of insufficient training data. Finally, we present the reduced-order model error surrogate (ROMES) method for statistically quantifying reduced- order
Reduction of the equation for lower hybrid waves in a plasma to a nonlinear Schroedinger equation
Karney, C. F. F.
1977-01-01
Equations describing the nonlinear propagation of waves in an anisotropic plasma are rarely exactly soluble. However it is often possible to make approximations that reduce the exact equations into a simpler equation. The use of MACSYMA to make such approximations, and so reduce the equation describing lower hybrid waves into the nonlinear Schrodinger equation which is soluble by the inverse scattering method is demonstrated. MACSYMA is used at several stages in the calculation only because there is a natural division between calculations that are easiest done by hand, and those that are easiest done by machine.
Zhang, Peng; Hou, Xiuli; Mi, Jianli; He, Yanqiong; Lin, Lin; Jiang, Qing; Dong, Mingdong
2014-09-07
For the goal of practical industrial development of fuel cells, inexpensive, sustainable, and highly efficient electrocatalysts for oxygen reduction reactions (ORR) are highly desirable alternatives to platinum (Pt) and other rare metals. In this work, based on density functional theory, silicon (Si)-doped carbon nanotubes (CNTs) and graphene as metal-free, low cost, and high-performance electrocatalysts for ORR are studied systematically. It is found that the curvature effect plays an important role in the adsorption and reduction of oxygen. The adsorption of O2 becomes weaker as the curvature varies from positive values (outside CNTs) to negative values (inside CNTs). The free energy change of the rate-determining step of ORR on the concave inner surface of Si-doped CNTs is smaller than that on the counterpart of Si-doped graphene, while that on the convex outer surface of Si-doped CNTs is larger than that on Si-doped graphene. Uncovering this new ORR mechanism on silicon-doped carbon electrodes is significant as the same principle could be applied to the development of various other metal-free efficient ORR catalysts for fuel cell applications.
De Filippis, G.; Noël, J. P.; Kerschen, G.; Soria, L.; Stephan, C.
2017-09-01
The introduction of the frequency-domain nonlinear subspace identification (FNSI) method in 2013 constitutes one in a series of recent attempts toward developing a realistic, first-generation framework applicable to complex structures. If this method showed promising capabilities when applied to academic structures, it is still confronted with a number of limitations which needs to be addressed. In particular, the removal of nonphysical poles in the identified nonlinear models is a distinct challenge. In the present paper, it is proposed as a first contribution to operate directly on the identified state-space matrices to carry out spurious pole removal. A modal-space decomposition of the state and output matrices is examined to discriminate genuine from numerical poles, prior to estimating the extended input and feedthrough matrices. The final state-space model thus contains physical information only and naturally leads to nonlinear coefficients free of spurious variations. Besides spurious variations due to nonphysical poles, vibration modes lying outside the frequency band of interest may also produce drifts of the nonlinear coefficients. The second contribution of the paper is to include residual terms, accounting for the existence of these modes. The proposed improved FNSI methodology is validated numerically and experimentally using a full-scale structure, the Morane-Saulnier Paris aircraft.
Günther, U; Bezerra, V; Romero, C; Guenther, Uwe; Zhuk, Alexander; Bezerra, Valdir; Romero, Carlos
2004-01-01
We study multidimensional gravitational models with scalar curvature nonlinearities of the type 1/R and R^4. It is assumed that the corresponding higher dimensional spacetime manifolds undergo a spontaneous compactification to manifolds with warped product structure. Special attention is paid to the stability of the extra-dimensional factor spaces. It is shown that for certain parameter regions the systems allow for a freezing stabilization of these spaces. In particular, we find for the 1/R model that configurations with stabilized extra dimensions do not provide a late-time acceleration (they are AdS), whereas the solution branch which allows for accelerated expansion (the dS branch) is incompatible with stabilized factor spaces. In the case of the R^4 model, we obtain that the stability region in parameter space depends on the total dimension D=dim(M) of the higher dimensional spacetime M. For D>8 the stability region consists of a single (absolutely stable) sector which is shielded from a conformal singul...
2008-09-22
optimization ( RBDO ) problems with correlated input variables, a joint cumulative distribution function (CDF) needs to be obtained to transform, using the...based dimension reduction method (DRM) for more accurate inverse reliability analysis and RBDO . As an example of the proposed method, an RBDO study...of an M1A1 Abrams tank roadarm is carried out. 1 ∈ 1. INTRODUCTION In many RBDO problems of automotive engineering, input random variables
Gaonkar, A. K.; Kulkarni, S. S.
2015-01-01
In the present paper, a method to reduce the computational cost associated with solving a nonlinear transient heat conduction problem is presented. The proposed method combines the ideas of two level discretization and the multilevel time integration schemes with the proper orthogonal decomposition model order reduction technique. The accuracy and the computational efficiency of the proposed methods is discussed. Several numerical examples are presented for validation of the approach. Compared to the full finite element model, the proposed method significantly reduces the computational time while maintaining an acceptable level of accuracy.
Xu, Tian; Chen, Guo Chong; Zhai, Lin; Ke, Kai Fu
2015-07-01
Observational studies between magnesium int- ake and risk of type 2 diabetes yielded inconsistent results. We conducted a system literature search of PubMed database through March 2015 for prospective cohort studies of magnesium intake and type 2 diabetes risk. Study-specific results were pooled in a random-effects model. Subgroup and sensitivity analysis were performed to assess the potential sources of heterogeneity and the robustness of the pooled estimation. Generalized least squares trend estimation was used to investigate the dose-response relationship. A total of 15 papers with 19 analyses were identified with 539,735 participants and 25,252 incident diabetes cases. Magnesium intake was associated with a significant lower risk of type 2 diabetes (RR: 0.77; 95% CI: 0.71-0.82) for the highest compared with lowest category. This association was not significantly modified by the pre-specified study characteristics. In the dose-response analysis, a magnesium intake increment of 100 mg/day was associated with a 16% reduction in type 2 diabetes risk (RR: 0.84; 95% CI: 0.80-0.88). A nonlinear relationship existed between magnesium intake and type 2 diabetes (P-nonlinearity=0.003). This meta-analysis further verified a protective effect of magnesium intake on type 2 diabetes in a nonlinear dose-response manner.
Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
Sun, Xiaodian; Medvedovic, Mario
2016-02-01
Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.
Integral Invariance and Non-linearity Reduction for Proliferating Vorticity Scales in Fluid Dynamics
Lam, F
2013-01-01
A vorticity theory for incompressible fluid flows in the absence of solid boundaries is proposed. Some apriori bounds are established. They are used in an interpolation theory to show the well-posedness of the vorticity Cauchy problem. A non-linear integral equation for vorticity is derived and its solution is expressed in an expansion. Interpretations of flow evolutions starting from given initial data are given and elaborated. The kinetic theory for Maxwellian molecules with cut-off is revisited in order to link microscopic properties to flow characters on the continuum.
Out-of-band and adjacent-channel interference reduction by analog nonlinear filters
Nikitin, Alexei V.; Davidchack, Ruslan L.; Smith, Jeffrey E.
2015-12-01
In a perfect world, we would have `brick wall' filters, no-distortion amplifiers and mixers, and well-coordinated spectrum operations. The real world, however, is prone to various types of unintentional and intentional interference of technogenic (man-made) origin that can disrupt critical communication systems. In this paper, we introduce a methodology for mitigating technogenic interference in communication channels by analog nonlinear filters, with an emphasis on the mitigation of out-of-band and adjacent-channel interference. Interference induced in a communications receiver by external transmitters can be viewed as wide-band non-Gaussian noise affecting a narrower-band signal of interest. This noise may contain a strong component within the receiver passband, which may dominate over the thermal noise. While the total wide-band interference seen by the receiver may or may not be impulsive, we demonstrate that the interfering component due to power emitted by the transmitter into the receiver channel is likely to appear impulsive under a wide range of conditions. We give an example of mechanisms of impulsive interference in digital communication systems resulting from the nonsmooth nature of any physically realizable modulation scheme for transmission of a digital (discontinuous) message. We show that impulsive interference can be effectively mitigated by nonlinear differential limiters (NDLs). An NDL can be configured to behave linearly when the input signal does not contain outliers. When outliers are encountered, the nonlinear response of the NDL limits the magnitude of the respective outliers in the output signal. The signal quality is improved in excess of that achievable by the respective linear filter, increasing the capacity of a communications channel. The behavior of an NDL, and its degree of nonlinearity, is controlled by a single parameter in a manner that enables significantly better overall suppression of the noise-containing impulsive components
Institute of Scientific and Technical Information of China (English)
钱进; 邓喀中; 范洪冬; 刘冬
2012-01-01
Considering the intrinsic nonlinear structure of hyperspectral remote sensing data and the characteristic of unsupervision of traditional manifold learning, during the process of dimension reduction of classification-oriented hyperspectral remote sensing data, we propose a new method of supervised isometric mapping (S-Isonmap). The method is based on the idea that the between-class distance is greater than the within-class distance. First it obtains initial category labels of the samples by using KMEANS algorithm on primary data for clustering; then it searches the K-Nearest neighbour of the data points with new distances, and further executes the dimension reduction by Isomap. Experiments demonstrate that the presented method outperforms the traditional Isomap.%在面向分类的高光谱遥感数据降维过程中,考虑到高光谱遥感数据内在的非线性结构和传统流形学习非监督的特点,提出一种新的监督等距映射方法(S-Isomap).方法基于类间距离大于类内距离的思想,首先利用KMEANS算法对原始数据进行聚类得到样本的初始类别标签,采用新距离搜寻数据点的K近邻,进而实施等距映射降维.实验证明了该方法优于传统Isomap.
Directory of Open Access Journals (Sweden)
Esra Pamukçu
2015-01-01
Full Text Available Gene expression data typically are large, complex, and highly noisy. Their dimension is high with several thousand genes (i.e., features but with only a limited number of observations (i.e., samples. Although the classical principal component analysis (PCA method is widely used as a first standard step in dimension reduction and in supervised and unsupervised classification, it suffers from several shortcomings in the case of data sets involving undersized samples, since the sample covariance matrix degenerates and becomes singular. In this paper we address these limitations within the context of probabilistic PCA (PPCA by introducing and developing a new and novel approach using maximum entropy covariance matrix and its hybridized smoothed covariance estimators. To reduce the dimensionality of the data and to choose the number of probabilistic PCs (PPCs to be retained, we further introduce and develop celebrated Akaike’s information criterion (AIC, consistent Akaike’s information criterion (CAIC, and the information theoretic measure of complexity (ICOMP criterion of Bozdogan. Six publicly available undersized benchmark data sets were analyzed to show the utility, flexibility, and versatility of our approach with hybridized smoothed covariance matrix estimators, which do not degenerate to perform the PPCA to reduce the dimension and to carry out supervised classification of cancer groups in high dimensions.
对人脸识别特征数据降维算法的优化%Optimization of Dimension Reduction Algorithm for Face Recognition Character Data
Institute of Scientific and Technical Information of China (English)
杨玉平; 向华
2012-01-01
在模式识别领域,人脸特征数据相对庞大,为了提取人脸主要的特征数据,提高识别系统的运行效率,对特征数据的降维是必须的操作。针对现有降维算法对识别率有较大影响的问题,本文总结了各类降维算法,提出了一种优化的降维算法。%In the field of pattern recognition,facial character data is relatively large,and therefore it is necessary to reduce the dimension of the character data in order to extract the primary facial main data and improve the efficiency of the recognition system.For the existing dimension reduction algorithm has some negative effect on the recognition rate,this article sums up various kinds of dimension reduction algorithms and brings forward a better algorithm.
Bell, Iris R; Howerter, Amy; Jackson, Nicholas; Aickin, Mikel; Bootzin, Richard R; Brooks, Audrey J
2012-07-01
Investigators of homeopathy have proposed that nonlinear dynamical systems (NDS) and complex systems science offer conceptual and analytic tools for evaluating homeopathic remedy effects. Previous animal studies demonstrate that homeopathic medicines alter delta electroencephalographic (EEG) slow wave sleep. The present study extended findings of remedy-related sleep stage alterations in human subjects by testing the feasibility of using two different NDS analytic approaches to assess remedy effects on human slow wave sleep EEG. Subjects (N=54) were young adult male and female college students with a history of coffee-related insomnia who participated in a larger 4-week study of the polysomnographic effects of homeopathic medicines on home-based all-night sleep recordings. Subjects took one bedtime dose of a homeopathic remedy (Coffea cruda or Nux vomica 30c). We computed multiscale entropy (MSE) and the correlation dimension (Mekler-D2) for stages 3 and 4 slow wave sleep EEG sampled in artifact-free 2-min segments during the first two rapid-eye-movement (REM) cycles for remedy and post-remedy nights, controlling for placebo and post-placebo night effects. MSE results indicate significant, remedy-specific directional effects, especially later in the night (REM cycle 2) (CC: remedy night increases and post-remedy night decreases in MSE at multiple sites for both stages 3 and 4 in both REM cycles; NV: remedy night decreases and post-remedy night increases, mainly in stage 3 REM cycle 2 MSE). D2 analyses yielded more sporadic and inconsistent findings. Homeopathic medicines Coffea cruda and Nux vomica in 30c potencies alter short-term nonlinear dynamic parameters of slow wave sleep EEG in healthy young adults. MSE may provide a more sensitive NDS analytic method than D2 for evaluating homeopathic remedy effects on human sleep EEG patterns. Copyright © 2012 The Faculty of Homeopathy. Published by Elsevier Ltd. All rights reserved.
分维自适应稀疏网格积分非线性滤波器%Dimension-wise Adaptive Spare Grid Quadrature Nonlinear Filter
Institute of Scientific and Technical Information of China (English)
徐嵩; 孙秀霞; 刘树光; 刘希; 蔡鸣
2014-01-01
For nonlinear discrete systems with addictive Gaus-sian noises, a new quadrature filter is proposed, which can fix sample points according to each dimension0s nonlinear function, respectively. In order to match higher-order terms of the nonlin-ear function0s Taylor expanding with reusing the sample points matching lower-order ones, an adaptive sampled multi variable quadrature rule is designed based on the embedded Gaussian sampled quadrature and the spare grid quadrature (SGQ) for-mula. A group of effective data structures and traversal algo-rithms are proposed for the sampled quadrature rule to be used for calculating the predict expectations of the states and mea-surements with their covariances. This filter could not only fix sampled points for different dimensions separately, but also reuse these points and their weights completely, thus enhancing the ef-ficiency of the filter. This filter achieves a higher accuracy than the unscented Kalman filter (UKF) , more effciency than the fixed SGQ filter, as well as generalized form of these two filters. The calculating cost of adaptive steps is much less than comput-ing the function sampled values. Simulations also validates the accuracy and effciency of this filter.%针对含加性高斯噪声的非线性离散系统，提出了可分别根据各维状态及量测方程的非线性函数特性来确定采样点及其权重的积分滤波器。设计了基于嵌入式高斯采样积分和稀疏网格法则的自适应多变量采样积分方法，可在匹配函数高阶泰勒展开项时，利用低阶采样点，提出了高效的数据结构和遍历算法，便于采用该积分方法分别估计系统状态/量测的预测均值和协方差矩阵。该滤波器既能根据各维非线性函数的特性确定采样点，又实现了对采样值和权重的完全复用，保证了算法效率。理论分析和仿真表明，该滤波算法中自适应调整的运算量小于计算非线性函数采样值。该滤
Romanov, Dmitri; Smith, Stanley; Brady, John; Levis, Robert J.
2008-02-01
We have studied the application of the diffusion mapping technique to dimensionality reduction and clustering in multidimensional optical datasets. The combinational (input-output) data were obtained by sampling search spaces related to optimization of a nonlinear physical process, short-pulse second harmonic generation. The diffusion mapping technique hierarchically reduces the dimensionality of the data set and unifies the statistics of input (the pulse shape) and output (the integral output intensity) parameters. The information content of the emerging clustered pattern can be optimized by modifying the parameters of the mapping procedure. The low-dimensional pattern captures essential features of the nonlinear process, based on a finite sampling set. In particular, the apparently parabolic two-dimensional projection of this pattern exhibits regular evolution with the increase of higher-intensity data in the sampling set. The basic shape of the pattern and the evolution are relatively insensitive to the size of the sampling set, as well as to the details of the mapping procedure. Moreover, the experimental data sets and the sets produced numerically on the basis of a theoretical model are mapped into patterns of remarkable similarity (as quantified by the similarity of the related quadratic-form coefficients). The diffusion mapping method is robust and capable of predicting higher-intensity points from a set of low-intensity points. With these attractive features, diffusion mapping stands poised to become a helpful statistical tool for preprocessing analysis of vast and multidimensional combinational optical datasets.
Noncommutative Nonlinear Supersymmetry
Nishino, H; Nishino, Hitoshi; Rajpoot, Subhash
2002-01-01
We present noncommutative nonlinear supersymmetric theories. The first example is a non-polynomial Akulov-Volkov-type lagrangian with noncommutative nonlinear global supersymmetry in arbitrary space-time dimensions. The second example is the generalization of this lagrangian to Dirac-Born-Infeld lagrangian with nonlinear supersymmetry realized in dimensions D=2,3,4 and 6 (mod 8).
Directory of Open Access Journals (Sweden)
Nam Lyong Kang
2013-07-01
Full Text Available The projection-reduction method introduced by the present authors is known to give a validated theory for optical transitions in the systems of electrons interacting with phonons. In this work, using this method, we derive the linear and first order nonlinear optical conductivites for an electron-impurity system and examine whether the expressions faithfully satisfy the quantum mechanical philosophy, in the same way as for the electron-phonon systems. The result shows that the Fermi distribution function for electrons, energy denominators, and electron-impurity coupling factors are contained properly in organized manners along with absorption of photons for each electron transition process in the final expressions. Furthermore, the result is shown to be represented properly by schematic diagrams, as in the formulation of electron-phonon interaction. Therefore, in conclusion, we claim that this method can be applied in modeling optical transitions of electrons interacting with both impurities and phonons.
Pesenson, Meyer; Pesenson, I. Z.; McCollum, B.
2009-05-01
The complexity of multitemporal/multispectral astronomical data sets together with the approaching petascale of such datasets and large astronomical surveys require automated or semi-automated methods for knowledge discovery. Traditional statistical methods of analysis may break down not only because of the amount of data, but mostly because of the increase of the dimensionality of data. Image fusion (combining information from multiple sensors in order to create a composite enhanced image) and dimension reduction (finding lower-dimensional representation of high-dimensional data) are effective approaches to "the curse of dimensionality,” thus facilitating automated feature selection, classification and data segmentation. Dimension reduction methods greatly increase computational efficiency of machine learning algorithms, improve statistical inference and together with image fusion enable effective scientific visualization (as opposed to mere illustrative visualization). The main approach of this work utilizes recent advances in multidimensional image processing, as well as representation of essential structure of a data set in terms of its fundamental eigenfunctions, which are used as an orthonormal basis for the data visualization and analysis. We consider multidimensional data sets and images as manifolds or combinatorial graphs and construct variational splines that minimize certain Sobolev norms. These splines allow us to reconstruct the eigenfunctions of the combinatorial Laplace operator by using only a small portion of the graph. We use the first two or three eigenfunctions for embedding large data sets into two- or three-dimensional Euclidean space. Such reduced data sets allow efficient data organization, retrieval, analysis and visualization. We demonstrate applications of the algorithms to test cases from the Spitzer Space Telescope. This work was carried out with funding from the National Geospatial-Intelligence Agency University Research Initiative
Time Hierarchies and Model Reduction in Canonical Non-linear Models
Löwe, Hannes; Kremling, Andreas; Marin-Sanguino, Alberto
2016-01-01
The time-scale hierarchies of a very general class of models in differential equations is analyzed. Classical methods for model reduction and time-scale analysis have been adapted to this formalism and a complementary method is proposed. A unified theoretical treatment shows how the structure of the system can be much better understood by inspection of two sets of singular values: one related to the stoichiometric structure of the system and another to its kinetics. The methods are exemplified first through a toy model, then a large synthetic network and finally with numeric simulations of three classical benchmark models of real biological systems. PMID:27708665
Kerfriden, P.; Goury, O.; Rabczuk, T.; Bordas, S.P.A.
2013-01-01
We propose in this paper a reduced order modelling technique based on domain partitioning for parametric problems of fracture. We show that coupling domain decomposition and projection-based model order reduction permits to focus the numerical effort where it is most needed: around the zones where damage propagates. No a priori knowledge of the damage pattern is required, the extraction of the corresponding spatial regions being based solely on algebra. The efficiency of the proposed approach is demonstrated numerically with an example relevant to engineering fracture. PMID:23750055
Spectral Methods for Linear and Non-Linear Semi-Supervised Dimensionality Reduction
Chatpatanasiri, Ratthachat
2008-01-01
We present a general framework of spectral methods for semi-supervised dimensionality reduction. Applying an approach called manifold regularization, our framework naturally generalizes existent supervised frameworks. Furthermore, by our two semi-supervised versions of the representer theorem, our framework can be kernelized as well. Using our framework, we give three examples of semi-supervised algorithms which are extended from three recent supervised algorithms, namely, ``discriminant neighborhood embedding'', ``marginal Fisher analysis'' and ``local Fisher discriminant analysis''. We also give three more semi-supervised examples of the kernel versions of these algorithms. Numerical results of the six semi-supervised algorithms compared to their supervised versions are presented.
Reduction of nonlinear dynamic systems with an application to signal transduction pathways.
Petrov, V; Nikolova, E; Wolkenhauer, O
2007-01-01
Mathematical modelling of kinetic processes with different time scales allows a reduction of the governing equations using quasi-steady-state approximations (QSSA). A QSSA theorem is applied to a mathematical model of the influence that Raf kinase inhibitor protein (RKIP) has on the ERK signalling pathway. On the basis of previously published parameter values, the system of 11 ordinary differential equations is rewritten in a form suitable for model reduction. In accordance with the terminology of the QSSA theorem, it is established that four of the protein and protein-complex concentrations are 'fast varying', such that the corresponding kinetic equations form an attached system. Another concentration is 'medium varying' such that the corresponding equation is reduced with respect to the four fast ones. The other six concentrations are 'slow varying', which means the corresponding kinetic equations also present a reduced system with respect to the others. Analytical solutions, relating the steady-state values of the fast varying protein concentrations and the slow varying ones, are derived and interpreted as restrictions on the regulatory role of RKIP on ERK-pathway.
文本分类中的特征降维方法研究%Research on feature dimension reduction in text classification
Institute of Scientific and Technical Information of China (English)
张玉芳; 万斌候; 熊忠阳
2012-01-01
特征降维是文本分类过程中的一个重要环节,为了提高特征降维的准确率,选出能有效区分文本类别的特征词,提高文本分类的效果,提出了结合文本类间集中度、文本类内分散度和词频类间集中度的特征降维方法.当获取特征词在文本集上的整体评价时,提出了一种新的全局评估函数,用最大值与次大值之差作为最终的评价函数值.实验比较了该方法与传统的特征降维方法,结果表明该方法在中文文本分类中具有较好的降维效果.%Feature dimension reduction is an important part of the procedure of text categorization, in order to improve the ac-curacy of feature dimension reduction, select the words that can distinguish categories effectively, and ultimately improve the effect of text classification, this paper proposed a new approach for feature selection by comprehensively taking account of text concentration among classes, dispersion within the text classes and word frequency concentration among classes. While getting overall assessment of the word in text set,it proposed new function of overall assessment by using the final assessment value, which was the difference of the maximum and the second largest value. The test compared this method with the traditional fea-ture dimension method, results indicate better effect in Chinese text categorization.
Institute of Scientific and Technical Information of China (English)
李德启; 刘传领
2011-01-01
The structure of high dimensional images with high recognition rate of Viti Levu down the key link, and some of the traditional algorithm to reduce the dimensions of the image processing has yielded some results, but exposed the shortcomings of their own. In order to achieve the high ideals of nonlinear effects of image recognition, dimension reduction in the traditional algorithm analysis and refinement of their advantages, put forward an improved algorithm for nonlinear dimensionality reduction to solve the shortcomings of the traditional algorithm. Respectively, in the ORL and CMU PIE database, the simulation experiment on the image to show that the algorithm for high-dimensional image pattern recognition viability.%对高维非线性结构的图像进行降维是提高识别率的关键环节,而一些传统的算法在对图像进行降维处理过程中虽然取得了一定的成效,但也暴露了它们各自的缺陷.为了达到对高维非线性图像识别的理想效果,在对传统的降维算法分析并对其优势进行提炼的基础上,提出了一种改进的非线性降维算法,解决了传统算法的缺陷,并分别在ORL和CMU PIE数据库图像上进行了仿真实验,从而验证了该算法对高维非线性图像在模式识别上的可行性.
DEFF Research Database (Denmark)
Clemmensen, Line Katrine Harder; Hansen, M. E.; Ersbøll, Bjarne Kjær
2010-01-01
sand types were examined with 20-60 images for each type. To reduce the amount of data, features were extracted from the multi-spectral images; the features were summary statistics on single bands and pairs of bands as well as morphological summaries. The number of features (2,016) is high in relation...... reduction (forward selection and principal components) combined with ordinary least squares and one sophisticated chemometrics algorithm (genetic algorithm-partial least squares) are compared to the recently proposed least angle regression-elastic net (LARS-EN) model selection method....
Bao, Bin; Guyomar, Daniel; Lallart, Mickaël
2017-01-01
Smart periodic structures covered by periodically distributed piezoelectric patches have drawn more and more attention in recent years for wave propagation attenuation and corresponding structural vibration suppression. Since piezoelectric materials are special type of energy conversion materials that link mechanical characteristics with electrical characteristics, shunt circuits coupled with such materials play a key role in the wave propagation and/or vibration control performance in smart periodic structures. Conventional shunt circuit designs utilize resistive shunt (R-shunt) and resonant shunt (RL-shunt). More recently, semi-passive nonlinear approaches have also been developed for efficiently controlling the vibrations of such structures. In this paper, an innovative smart periodic beam structure with nonlinear interleaved-switched electric networks based on synchronized switching damping on inductor (SSDI) is proposed and investigated for vibration reduction and wave propagation attenuation. Different from locally resonant band gap mechanism forming narrow band gaps around the desired resonant frequencies, the proposed interleaved electrical networks can induce new broadly low-frequency stop bands and broaden primitive Bragg stop bands by virtue of unique interleaved electrical configurations and the SSDI technique which has the unique feature of realizing automatic impedance adaptation with a small inductance. Finite element modeling of a Timoshenko electromechanical beam structure is also presented for validating dispersion properties of the structure. Both theoretical and experimental results demonstrate that the proposed beam structure not only shows better vibration and wave propagation attenuation than the smart beam structure with independent switched networks, but also has technical simplicity of requiring only half of the number of switches than the independent switched network needs.
Xia, Shaoyan; Huang, Yong; Tan, Xiaodi
2016-03-01
Partial differential equation (PDE)-based nonlinear diffusion processes have been widely used for image denoising. In the traditional nonlinear anisotropic diffusion denoising techniques, behavior of the diffusion depends highly on the gradient of image. However, it is difficult to get a good effect if we use these methods to reduce noise in optical coherence tomography images. Because background has the gradient that is very similar to regions of interest, so background noise will be mistaken for edge information and cannot be reduced. Therefore, nonlinear complex diffusion approaches using texture feature(NCDTF) for noise reduction in phase-resolved optical coherence tomography is proposed here, which uses texture feature in OCT images and structural OCT images to remove noise in phase-resolved OCT. Taking into account the fact that texture between background and signal region is different, which can be linked with diffusion coefficient of nonlinear complex diffusion model, we use NCDTF method to reduce noises of structure and phase images first. Then, we utilize OCT structure images to filter phase image in OCT. Finally, to validate our method, parameters such as image SNR, contrast-to-noise ratio (CNR), equivalent number of looks (ENL), and edge preservation were compared between our approach and median filter, Gaussian filter, wavelet filter, nonlinear complex diffusion filter (NCDF). Preliminary results demonstrate that NCDTF method is more effective than others in keeping edges and denoising for phase-resolved OCT.
Franz Konstantin Fuss
2013-01-01
Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that co...
基于LLTSA的轴承故障数据降维方法研究%Dimension Reduction of Bearing Fault Data Based on LLTSA
Institute of Scientific and Technical Information of China (English)
陈保家; 吴志平; 严文超; 朱晨希
2016-01-01
针对工作中轴承内外圈及滚动体难以分别监测以及轴承故障数据特征指标多、维数高的特点，考虑对轴承状态数据进行降维。考虑到常用降维方法自身存在的各种缺陷，采用线性局部切空间排列算法（LLTSA）对轴承故障数据进行降维并投影到三维空间，并与PCA以及KPCA方法进行比较。结果表明LLTSA算法对于滚动轴承内外圈以及滚动体不同故障具有较好的分类性能。%Aiming at the difficulty in separate monitor of bearing inner race , outter race and ball,as well as the high-dimensional property of fault data, dimension reduction of fault data is considered. To avoid the defects of normal ways, LLTSA method is adoptted to extract the eigenvectors of high dimensional matrixes and eigenvectors are projected to visual space. LLTSA method shows a better performance in bearing fault pattern recognition in comparison to the PCA and KPCA methods.
Discriminant Manifold Learning Approach for Hyperspectral Image Dimension Reduction%高光谱图像降维的判别流形学习方法
Institute of Scientific and Technical Information of China (English)
杜博; 张乐飞; 张良培; 胡文斌
2013-01-01
A discriminant manifold learning approach for hyperspectral image dimension reduction was proposed. In order to overcome the high dimensional and high redundancy of remotely sensed earth observation images, a modified manifold learning algorithm was suggested for dataset linear dimensional reduction to improve the performance of image classification. The proposed method addressed the discriminative information of given training samples into the current manifold learning framework to learn an optimal subspace for subsequent classification, in particular, the linearization of discriminant manifold learning is introduced to deal with the out of sample problem. Experiments on hyperspectral image demonstrated that the proposed method could achieve higher classification rate than the conventional image classification technologies.%本文提出了一种高光谱图像降维的判别流形学习方法.针对获取的大量遥感对地观测数据存在大量冗余信息的特点,引入改进的流形学习方法对高光谱遥感数据进行降维处理,以提高遥感图像自动分类的总体准确度.该方法充分利用遥感图像自动分类中训练样本的判别信息,将输入样本的类别信息加入到常规流形学习方法的框架中,从本质上提高输出的特征在低维空间中的判别力.同时,引入线性化模型以解决流形学习方法中常见的小样本问题.对高光谱遥感图像自动分类的实验表明,基于判别流形学习的高光谱遥感图像自动分类方法能够显著地提高图像分类准确度.
Hartl, M; Mikhailov, R.; Passi, I. B. S.
2008-01-01
We present two approaches, one homological and the other simplicial, for the investigation of dimension quotients of groups. The theory is illustrated, in particular, with a conceptual discussion of the fourth and fifth dimension quotients.
Kundu, Anjan
2016-12-01
Integrable quantum field models are known to exist mostly in one space-dimension. Exploiting the concept of multi-time in integrable systems and a Lax matrix of higher scaling order, we construct a novel quantum field model in quasi-two dimensions involving interacting fields. The Yang-Baxter integrability is proved for the model by finding a new kind of commutation rule for its basic fields, representing nonstandard scalar fields along the transverse direction. In spite of a close link with the quantum Landau-Lifshitz equation, the present model differs widely from it, in its content and the result obtained. Using further the algebraic Bethe ansatz we solve exactly the eigenvalue problem of this quantum field model for all its higher conserved operators. The idea presented here should instigate the construction of a novel class of integrable field and lattice models and exploration of a new type of underlying algebras.
Energy Technology Data Exchange (ETDEWEB)
Kundu, Anjan, E-mail: anjan.kundu@saha.ac.in
2016-12-15
Integrable quantum field models are known to exist mostly in one space-dimension. Exploiting the concept of multi-time in integrable systems and a Lax matrix of higher scaling order, we construct a novel quantum field model in quasi-two dimensions involving interacting fields. The Yang–Baxter integrability is proved for the model by finding a new kind of commutation rule for its basic fields, representing nonstandard scalar fields along the transverse direction. In spite of a close link with the quantum Landau–Lifshitz equation, the present model differs widely from it, in its content and the result obtained. Using further the algebraic Bethe ansatz we solve exactly the eigenvalue problem of this quantum field model for all its higher conserved operators. The idea presented here should instigate the construction of a novel class of integrable field and lattice models and exploration of a new type of underlying algebras.
Institute of Scientific and Technical Information of China (English)
黎超敏; 崔荣乐
2013-01-01
This paper researched on energy dissipation - seismic reduction structure (with nonlinear viscous liquid damper) with the nonlinear time history analysis. Authors mainly discussed the research background, current research at home and aboard, the relevant provisions of the current regulation and standards, several problems and difficulties of the theory and its practical applications, social significance and economic benefits of the energy dissipation - seismic reduction. And take a brief introduction to the nonlinear time history analysis. At last, authors made an example of engineering to make a contrast between the original structure and the energy dissipation - seismic reduction focusing mainly on the axial force of the column, top displacement, displacement angle between layers, and get some useful conclusion which can be used in the practical engineering.%本文对减震结构(附设阻尼器的消能减震结构)进行了非线性时程分析的研究.探讨了减震结构的研究背景、国内外的研究现状、现行规范对减震结构的相关规定、减震结构在理论、实际应用上的问题和难点、减震结构的社会意义和经济效益以及对非线性时程分析进行了简要介绍.最后通过一个工程案例,对减震结构和无控结构进行了比较(柱轴力、顶点位移、层间位移角),得出了一些可以用于工程实际的结论.
Winebrake, James J; Corbett, James J; Wang, Chengfeng; Farrell, Alexander E; Woods, Pippa
2005-04-01
Emissions from passenger ferries operating in urban harbors may contribute significantly to emissions inventories and commuter exposure to air pollution. In particular, ferries are problematic because of high emissions of oxides of nitrogen (NOx) and particulate matter (PM) from primarily unregulated diesel engines. This paper explores technical solutions to reduce pollution from passenger ferries operating in the New York-New Jersey Harbor. The paper discusses and demonstrates a mixed-integer, non-linear programming model used to identify optimal control strategies for meeting NOx and PM reduction targets for 45 privately owned commuter ferries in the harbor. Results from the model can be used by policy-makers to craft programs aimed at achieving least-cost reduction targets.
Liu, Y.; Zheng, L.; Pau, G. S. H.
2016-12-01
A careful assessment of the risk associated with geologic CO2 storage is critical to the deployment of large-scale storage projects. While numerical modeling is an indispensable tool for risk assessment, there has been increasing need in considering and addressing uncertainties in the numerical models. However, uncertainty analyses have been significantly hindered by the computational complexity of the model. As a remedy, reduced-order models (ROM), which serve as computationally efficient surrogates for high-fidelity models (HFM), have been employed. The ROM is constructed at the expense of an initial set of HFM simulations, and afterwards can be relied upon to predict the model output values at minimal cost. The ROM presented here is part of National Risk Assessment Program (NRAP) and intends to predict the water quality change in groundwater in response to hypothetical CO2 and brine leakage. The HFM based on which the ROM is derived is a multiphase flow and reactive transport model, with 3-D heterogeneous flow field and complex chemical reactions including aqueous complexation, mineral dissolution/precipitation, adsorption/desorption via surface complexation and cation exchange. Reduced-order modeling techniques based on polynomial basis expansion, such as polynomial chaos expansion (PCE), are widely used in the literature. However, the accuracy of such ROMs can be affected by the sparse structure of the coefficients of the expansion. Failing to identify vanishing polynomial coefficients introduces unnecessary sampling errors, the accumulation of which deteriorates the accuracy of the ROMs. To address this issue, we treat the PCE as a sparse Bayesian learning (SBL) problem, and the sparsity is obtained by detecting and including only the non-zero PCE coefficients one at a time by iteratively selecting the most contributing coefficients. The computational complexity due to predicting the entire 3-D concentration fields is further mitigated by a dimension
Exterior dimension of fat fractals
Grebogi, C.; Mcdonald, S. W.; Ott, E.; Yorke, J. A.
1985-01-01
Geometric scaling properties of fat fractal sets (fractals with finite volume) are discussed and characterized via the introduction of a new dimension-like quantity which is called the exterior dimension. In addition, it is shown that the exterior dimension is related to the 'uncertainty exponent' previously used in studies of fractal basin boundaries, and it is shown how this connection can be exploited to determine the exterior dimension. Three illustrative applications are described, two in nonlinear dynamics and one dealing with blood flow in the body. Possible relevance to porous materials and ballistic driven aggregation is also noted.
Institute of Scientific and Technical Information of China (English)
马婧华; 汤宝平; 宋涛
2015-01-01
Aiming at the problem that the actual engineering vibration signals are interfered by strong noise with strong nonlinear characteristic,a phase space reconstruction method based on adaptive intrinsic dimension estimation manifold learning was proposed.Firstly,one-dimensional time series containing noise were reconstructed into a high dimensional phase space with the phase space reconstruction method.Secondly,the intrinsic dimension of each sample point in the phase space was estimated based on the maximum likelihood estimate (MLE),the adaptive weighted average method was used to calculate the global intrinsic dimension.At last,the manifold learning algorithm and the local tangent space alignment (LTSA)were employed to project the signal containing noise from the high-dimensional phase space into the intrinsic dimensional space of useful signals.After eliminating the noise distributing in the high-dimensional space, the signals were reconstructed back into one-dimensional time series.Lorenz simulation and an example of vibration signals'noise reduction for a wind power generator unit showed that the proposed method has a good performance of nonlinear noise reduction.%针对实际工程领域振动信号噪声干扰大、具有强烈非线性等问题，提出了基于自适应本征维数估计流形学习的相空间重构降噪方法。利用相空间重构将一维含噪时间序列重构到高维相空间；基于极大似然估计法（maximum likelihood estimate，MLE）估计相空间中每个样本点的本征维数并使用自适应加权平均法计算全局本征维数；采用局部切空间排列（Local tangent space Alignment，LTSA）流形学习方法将含噪信号从高维相空间投影到有用信号的本征维空间中，剔除分布在高维空间中的噪声后，重构回一维时间序列。通过 Lorenz 仿真实验和风电机组振动信号降噪实例，证实了该方法具有良好的非线性降噪性能。
Thermal dimension of quantum spacetime
Amelino-Camelia, Giovanni; Gubitosi, Giulia; Santos, Grasiele
2016-01-01
Recent results suggest that a crucial crossroad for quantum gravity is the characterization of the effective dimension of spacetime at short distances, where quantum properties of spacetime become significant. This is relevant in particular for various scenarios of "dynamical dimensional reduction" which have been discussed in the literature. We are here concerned with the fact that the related research effort has been based exclusively on analyses of the "spectral dimension", which involves an unphysical Euclideanization of spacetime and is highly sensitive to the off-shell properties of a theory. As here shown, different formulations of the same physical theory can have wildly different spectral dimension. We propose that dynamical dimensional reduction should be described in terms of the "thermal dimension" which we here introduce, a notion that only depends on the physical content of the theory. We analyze a few models with dynamical reduction both of the spectral dimension and of our thermal dimension, f...
Nonlinear manifold representations for functional data
Chen, Dong; 10.1214/11-AOS936
2012-01-01
For functional data lying on an unknown nonlinear low-dimensional space, we study manifold learning and introduce the notions of manifold mean, manifold modes of functional variation and of functional manifold components. These constitute nonlinear representations of functional data that complement classical linear representations such as eigenfunctions and functional principal components. Our manifold learning procedures borrow ideas from existing nonlinear dimension reduction methods, which we modify to address functional data settings. In simulations and applications, we study examples of functional data which lie on a manifold and validate the superior behavior of manifold mean and functional manifold components over traditional cross-sectional mean and functional principal components. We also include consistency proofs for our estimators under certain assumptions.
Institute of Scientific and Technical Information of China (English)
YANZhen－Ya
2002-01-01
We have found two types of important exact solutions,compacton solutions,which are solitary waves with the property that after colliding with their own kind,they re-emerge with the same coherent shape very much as the solitons do during a completely elastic interaction,in the (1+1)D,(1+2)D and even (1+3)D models,and dromion solutions (exponentially decaying solutions in all direction) in many (1+2)D and (1+3)D models.In this paper,symmetry reductions in (1+2)D are considered for the break soliton-type equation with fully nonlinear dispersion (called BS(m,n) equation)ut+b(um)xxy+4b(un δx-1uy)x=0,which is a generalized model of (1+2)D break soliton equation ut+buxxy+4buuy+4buxδx-1uy=0,by using the extended direct reduction method.As a result,six types of symmetry reductions are obtained.Starting from the reduction equations and some simple transformations,we obtain the solitary wavke solutions of BS(1,n) equations,compacton solutions of BS(m,m-1) equations and the compacton-like solution of the potential form (called PBS(3,2)) ωxt+b(uxm)xxy+4b(ωxnωy)x=0.In addition,we show that the variable ∫x uy dx admits dromion solutions rather than the field u itself in BS(1,n) equation.
Institute of Scientific and Technical Information of China (English)
YAN ZhenYa
2002-01-01
We have found two types of important exact solutions, compacton sohuttions, which are solitary waveswith the property that after colliding with their own kind, they re-emerge with the same coherent shape very much asthe solitons do during a completely elastic interaction, in the (1+1)D, (1+2)D and even (1+3)D models, and dromionsolutions (exponentially decaying solutions in all direction) in many (1+2)D and (1+3)D models. In this paper, symmetryreductions in (1+-2)D are considered for the break soliton-type equation with fully nonlinear dispersion (called BS(m, n)equation) ut + b(um)xxy+ 4b(un uy)x = 0, which is a generalized model of (1+2)D break soliton equation ut +buxxy + 4buuy + 4bux-1uy = 0, by using the extended direct reduction method. As a result, six types of symmetryreductions are obtained. Starting from the reduction equations and some simple transformations, we obtain the solitarywave solutions ofBS(l, n) equations, compacton solutions ofBS(m, m - 1) equations and the compacton-like solution ofthe potential form (called PBS(3, 2)) wxt + b(umx )xxy + 4b(wnxwy)x = 0. In addition, we show that the variable fx uy dxadmits dromion solutions rather than the field u itself in BS(1, n) equation.
Christensen, Lars Winther
2000-01-01
This book is intended as a reference for mathematicians working with homological dimensions in commutative algebra and as an introduction to Gorenstein dimensions for graduate students with an interest in the same. Any admirer of classics like the Auslander-Buchsbaum-Serre characterization of regular rings, and the Bass and Auslander-Buchsbaum formulas for injective and projective dimension of f.g. modules will be intrigued by this book's content. Readers should be well-versed in commutative algebra and standard applications of homological methods. The framework is that of complexes, but all major results are restated for modules in traditional notation, and an appendix makes the proofs accessible for even the casual user of hyperhomological methods.
Improved nonlinear prediction method
Adenan, Nur Hamiza; Md Noorani, Mohd Salmi
2014-06-01
The analysis and prediction of time series data have been addressed by researchers. Many techniques have been developed to be applied in various areas, such as weather forecasting, financial markets and hydrological phenomena involving data that are contaminated by noise. Therefore, various techniques to improve the method have been introduced to analyze and predict time series data. In respect of the importance of analysis and the accuracy of the prediction result, a study was undertaken to test the effectiveness of the improved nonlinear prediction method for data that contain noise. The improved nonlinear prediction method involves the formation of composite serial data based on the successive differences of the time series. Then, the phase space reconstruction was performed on the composite data (one-dimensional) to reconstruct a number of space dimensions. Finally the local linear approximation method was employed to make a prediction based on the phase space. This improved method was tested with data series Logistics that contain 0%, 5%, 10%, 20% and 30% of noise. The results show that by using the improved method, the predictions were found to be in close agreement with the observed ones. The correlation coefficient was close to one when the improved method was applied on data with up to 10% noise. Thus, an improvement to analyze data with noise without involving any noise reduction method was introduced to predict the time series data.
Institute of Scientific and Technical Information of China (English)
李鑫
2015-01-01
对一维非线性 Schrdinger 方程构造参数形式的差分格式进行研究。运用能量方法证明了方程的离散守恒律，并通过先验估计验证了格式的收敛性和稳定性。数值实验结果表明，差分格式的收敛阶为 O（h 2＋τ2），格式的效果明显优于先前格式。%A parameter finite difference scheme is proposed for nonlinear Schrdinger equation in one dimension. Then the discrete conservation law is proved by the energy method.The stability and convergence are demonstrated by the prior estimation.The numerical results have been carried out to confirm the convergence order is O(h 2 +τ2 ). Moreover,the new scheme shows the superiority through comparing with the scheme before.
Wang, Sijia; Liu, Bowen; Song, Youjian; Hu, Minglie
2016-04-01
We report on a simple passive scheme to reduce the intensity noise of high-power nonlinear fiber amplifiers by use of the spectral-breathing parabolic evolution of the pulse amplification with an optimized negative initial chirp. In this way, the influences of amplified spontaneous emission (ASE) on the amplifier intensity noise can be efficiently suppressed, owing to the lower overall pulse chirp, shorter spectral broadening distance, as well as the asymptotic attractive nature of self-similar pulse amplification. Systematic characterizations of the relative intensity noise (RIN) of a free-running nonlinear Yb-doped fiber amplifier are performed over a series of initial pulse parameters. Experiments show that the measured amplifier RIN increases respect to the decreased input pulse energy, due to the increased amount of ASE noise. For pulse amplification with a proper negative initial chirp, the increase of RIN is found to be smaller than with a positive initial chirp, confirming the ASE noise tolerance of the proposed spectral-breathing parabolic amplification scheme. At the maximum output average power of 27W (25-dB amplification gain), the incorporation of an optimum negative initial chirp (-0.84 chirp parameter) leads to a considerable amplifier root-mean-square (rms) RIN reduction of ~20.5% (integrated from 10 Hz to 10 MHz Fourier frequency). The minimum amplifier rms RIN of 0.025% (integrated from 1 kHz to 5 MHz Fourier frequency) is obtained along with the transform-limited compressed pulse duration of 55fs. To our knowledge, the demonstrated intensity noise performance is the lowest RIN level measured from highpower free-running femtosecond fiber amplifiers.
Parsons, Todd L.; Rogers, Tim
2017-10-01
Systems composed of large numbers of interacting agents often admit an effective coarse-grained description in terms of a multidimensional stochastic dynamical system, driven by small-amplitude intrinsic noise. In applications to biological, ecological, chemical and social dynamics it is common for these models to posses quantities that are approximately conserved on short timescales, in which case system trajectories are observed to remain close to some lower-dimensional subspace. Here, we derive explicit and general formulae for a reduced-dimension description of such processes that is exact in the limit of small noise and well-separated slow and fast dynamics. The Michaelis–Menten law of enzyme-catalysed reactions, and the link between the Lotka–Volterra and Wright–Fisher processes are explored as a simple worked examples. Extensions of the method are presented for infinite dimensional systems and processes coupled to non-Gaussian noise sources.
Reduced-Dimension Multiuser Detection
Xie, Yao; Goldsmith, Andrea
2011-01-01
We explore several reduced-dimension multiuser detection (RD-MUD) structures that significantly decrease the number of required correlation branches at the receiver front-end, while still achieving performance similar to that of the conventional matched-filter (MF) bank. RD-MUD exploits the fact that the number of active users is typically small relative to the total number of users in the system and relies on ideas of analog compressed sensing to reduce the number of correlators. We first develop a general framework for both linear and nonlinear RD-MUD detectors. We then present theoretical performance analysis for two specific detectors: the linear reduced-dimension decorrelating (RDD) detector, which combines subspace projection and thresholding to determine active users and sign detection for data recovery, and the nonlinear reduced-dimension decision-feedback (RDDF) detector, which combines decision-feedback orthogonal matching pursuit for active user detection and sign detection for data recovery. The t...
面向文本分类的混合特征降维策略%Mixed feature dimension reduction strategy for text categorization
Institute of Scientific and Technical Information of China (English)
王东
2012-01-01
Feature dimensionality reduction has been an important research on text classification. An effective way to achieve feature dimensionality reduction is to design efficient feature selection methods. Based on the existing feature selection methods, in which the phenomenon of removing the strong features of distinction between the catego- ries ability and keeping the weak ones exists, the paper presents an efficient feature reduction algorithm, which firstly defines and quantifies features to establish the unisource feature retained set and forcibly removes the common features in all classes, and then adjusts the weights of the multi - source feature so as to achieve the target of feature reduction and improve the classification performance. Finally, a comparative analysis experiment is conducted in the Reuters - -21 578, NewsGroups corpus. The experimental result indicates that the algorithm is effective and feasible.%特征降维一直是文本分类的重要研究内容，针对现有特征选择方法中普遍存在误删除强区分类别能力特征而保留弱区分类别能力特征的现象，提出了一种有效的特征降维策略，该方法首先对特征进行了定义和量化，通过建立单源特征保留集，删除所有类中的公共特征，再对多源特征权值进行调整，从而迭到特征削减和提高分类性能的目的。在Reuters-21578，NewsGmup语料集上进行的实验对比中表明，新的降维策略是有效可行的。
Curling up two spatial dimensions with SU(1,1)/U(1)
Energy Technology Data Exchange (ETDEWEB)
Gell-Mann, M.; Zwiebach, B.
1984-11-01
It is seen that a nonlinear sigma model based on the noncompact coset space SU(1,1)/U(1) can curl up two spatial dimensions into a topologically noncompact surface of finite area with a compact U(1) isometry group. This mechanism can be used for several higher-dimensional supergravity theories. In particular, chiral N = 2, D = 10 supergravity would reduce to an N = 1, D = 8 theory in which the masslessness of fermions does not depend only on supersymmetry. Further reduction to four dimensions is possible.
Efficient Estimation of first Passage Probability of high-Dimensional Nonlinear Systems
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Nielsen, Søren R.K.; Bucher, Christian
2011-01-01
on the system memory. Consequently, high-dimensional problems can be handled, and nonlinearities in the model neither bring any difficulty in applying it nor lead to considerable reduction of its efficiency. These characteristics suggest that the method is a powerful candidate for complicated problems. First......, the failure probabilities of three well-known nonlinear systems are estimated. Next, a reduced degree-of-freedom model of a wind turbine is developed and is exposed to a turbulent wind field. The model incorporates very high dimensions and strong nonlinearities simultaneously. The failure probability...
Directory of Open Access Journals (Sweden)
Braga Ion Cristian
2017-01-01
Full Text Available As of the definition presented by Harashima, Tomizuka, and Fukada in 1996, the mechatronics is the synergistic combination of precision mechanical engineering, electronic control and systems thinking in the design of products and manufacturing processes. The most of the mechatronic devices need the precise dimensions of the plastic parts, as long as the combination of those parts leads to a final haptic characteristic defined within specific limits or when the certain travel way is linked with an electrical contact. The increasing of the risks to produce bad mechatronic devices are directly related to the combination of the plastic injectionmolded parts out of different cavities. The paper’s aim is to present reducing of the risks to have bad final parts assembled with the components out of plastic injection-molded parts by using optical 3D measuring techniques at first validation of the parts out of the tool and setting parameters in the injection machines. The shrinkage and the warpage are more easily detected in that way and this will support first article inspection, but also during the entire production process. A case study presents the analysis of the data coming from the measurements of the plastic parts from each cavity and the combination of those parts, by using the ATOS inspection software. The CAD data are compared with the measured ones and the differences will be visible in the colored plotted areas, also the differences of the parts out of distinct cavities will be also displayed by overlaying of the measurements.
Spectral Dimension from Causal Set Nonlocal Dynamics
Belenchia, Alessio; Marciano, Antonino; Modesto, Leonardo
2015-01-01
We investigate the spectral dimension obtained from non-local continuum d'Alembertians derived from causal sets. We find a universal dimensional reduction to 2 dimensions, in all dimensions. We conclude by discussing the validity and relevance of our results within the broader context of quantum field theories based on these nonlocal dynamics.
维数约简在电信运营商绩效评价中的应用%Application of Dimension Reduction in Telecom-operator’s Performance Evaluation
Institute of Scientific and Technical Information of China (English)
丁伟; 张亚非
2015-01-01
目前在绩效评价过程中，常使用的包络数据分析方法的评价结果对所选用的财务指标非常敏感，为避免维度灾难，必须对众多特征所构成的高维财务数据进行维度约简。以电信运营商省分公司的绩效评价为例，为找寻最适财务指标组合，运用支持向量机方法建模分析，实现了从158种特征集合到5种特征组合的维数约简。%Currently in the process of performance evaluation, the results with data envelopment analysis method which is commonly used are very sensitive to the chosen financial ratios. In order to avoid the curse of dimensionality, it is necessary to reduce the dimensions of the financial data sets including multiple attributes and features. Taking telecom operator provincial branch as ex-ample, to find the optimal combination of financial indicators, the support vector machine approach is introduced to model and analyze performance evaluation. It achieves the effect of dimension reduction from 158 to five.
改进的非线性数据降维方法及其应用%Improved non-linear data dimensionality reduction method and its application
Institute of Scientific and Technical Information of China (English)
吴晓婷; 闫德勤
2011-01-01
Locally Linear Embedding(LLE) algorithm is one of the non-linear dimensionality reduction methods which are based on manifold learning. In LLE, each sample point is reconstructed from a linear combination of its nearest neighbors.However, different number of neighbors will produce different reconstruction errors, which will make the result different directly. This paper structures the approximate reconstruction coefficient making use of their category information which is obtained by clustering, and proposes an improved algorithm.The proposed algorithm can reduce the influence of the number of neighbors efficiently and the probability of the database is retained. This is confirmed by experiments on both synthetic and real-world data.%局部线性嵌入算法(Locally Linear Embedding,LLE)是基于流形学习的非线性降维方法之一.LLE利用样本点的近邻点的线性组合对每个样本点进行局部重构,而不同近邻个数的选取会产生不同的重构误差,从而影响整体算法的实施.提出了一种LLE的改进算法,算法有效地降低了近邻点个数对算法的影响,并很好地学习了高维数据的流形结构.所提方法的有效性在人造和真实数据的对比实验中得到了证实.
基于流形学习的非线性维数约简方法%Nonlinear Dimensionality Reduction Method Based on Manifold Learning
Institute of Scientific and Technical Information of China (English)
段志臣; 芮小平; 张立媛
2012-01-01
流形学习是一种新的非线性维数约简方法,近年来正引起可视化等领域研究者的高度重视.为加深对流形学习的理解,介绍了流形学习的基本原理,总结了其研究进展和分类方法,最后阐述了几种常用的流形学习方法的基本思想、算法步骤和各自的优缺点.通过在人工数据集Swiss-Roll上进行实验,将各类方法在近邻值选取和噪声影响等方面进行了对比分析,结果表明:与传统的线性维数约简方法相比,流形学习方法能够有效地发现观测样本的低维结构.最后对流形学习未来的研究方向作出展望,以期在这一领域取得更大进展.%As a new kind of nonlinear dimensionality reduction method, manifold learning is capturing increasing interests of researchers. To understand manifold learning better, the principle is firstly introduced, and then its development history and different representations are summarized, finally several major method are introduced, whose basic thoughts, steps and advantages are pointed out respectively. By the experiments on Swiss-Roll, the selection of neighbors and noise effect are analyzed, the results shows: compared with traditional linear method, manifold learning can discover the intrinsic structure of the samples better. Finally the prospect of manifold learning was discussed for more developments.
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
With the aid of a nonlinear transformation, a class of nonlinear convectiondiffusion PDE in one space dimension is converted into a linear one, the unique solution of a nonlinear boundary-initial value problem for the nonlinear PDE can be exactly expressed by the nonlinear transformation, and several illustrative examples are given
Energy Technology Data Exchange (ETDEWEB)
Santos, Douglas A.; Lucena, Sergio [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil)
2004-07-01
At the present time, the operation of combustion systems and the design of combustors continue being important problems in the Engineering, and don't involve just the size increase of combustors, but also changes of characteristics in the details of projects. The combustors applications are directly related to the needs, like: material transformation for heating, drying or incineration; and all have the inconvenience of emanating of pollutant gaseous (such like NOx). In combustion systems of gases, NOx is basically created in the reaction between nitrogen and oxygen to high temperatures ({approx} 1200 deg C). Below such conditions, the contribution of thermal NOx is recognisably small. The efficient reduction, safe control and economical elimination of pollutant emissions in the systems of burning are the main focuses of environmental legislation and concern to several industrialized countries, besides Brazil. Furthermore, in appeal at the Environmental Laws and at the rising consumption of combustible gases (Natural Gas), new technologies more attractive and economically viable have been studied, for example the combustion systems in fluidized bed. In this kind of system is possible to obtain high combustion efficiency at low temperatures ({approx} 900 deg C) with NOx reduction. In this work is intended of characterizing and dimensioning an industrial fluidized bed combustor that uses Natural Gas like feedstock in the combustion system, with smaller amounts of emitted NOx. (author)
2013-01-01
A few weeks ago, I had a vague notion of what TED was, and how it worked, but now I’m a confirmed fan. It was my privilege to host CERN’s first TEDx event last Friday, and I can honestly say that I can’t remember a time when I was exposed to so much brilliance in such a short time. TEDxCERN was designed to give a platform to science. That’s why we called it Multiplying Dimensions – a nod towards the work we do here, while pointing to the broader importance of science in society. We had talks ranging from the most subtle pondering on the nature of consciousness to an eighteen year old researcher urging us to be patient, and to learn from our mistakes. We had musical interludes that included encounters between the choirs of local schools and will.i.am, between an Israeli pianist and an Iranian percussionist, and between Grand Opera and high humour. And although I opened the event by announcing it as a day off from physics, we had a quite brill...
Dimension Reduction Quantization of LSP Parameters Based on Compressed Sensing%基于压缩感知的线谱对参数降维量化算法
Institute of Scientific and Technical Information of China (English)
肖强; 陈亮; 朱涛; 黄建军
2011-01-01
To achieve good reconstruction speech quality in very low bit rate speech codecs, an efficient dimension reduction quantization acheme for linear spectrum pair (LSP) parameters was proposed based on compressed sensing. In the encoder, the LSP parameters extracted from consecutive speech frames are shaped into a high dimensional vector, and then the dimension of the vector is reduced by CS to produce a measurement vector, the measurements are quantized using the split vector quantizer. In the decoder, according to the quantized measurements, the original LSP vector is reconstructed by the orthogonal matching pursuit method. Experimental results show that the scheme is more efficient than that of conventional matrix quantization scheme and DCT based dimension reduction quantization scheme, the average spectral distortion reduction of up to 0.23dB and 0.13dB is achieved respectively. Informal subjective listening test shows that the reconstructed speech has moderate intelligibility and naturalness, it is observed that the degradation in speech quality is tolerable and with low codebook storage requirements.%为实现高质量的极低速语音编码,提出一种基于压缩感知理论的线谱对(LSP)参数降维量化算法.编码端利用压缩感知理论对超帧LSP高维矢量进行降维处理,将原始LSP参数投影到低维空间,得到低维测量值,然后采用分裂矢量量化算法对测量值进行量化;解码端以量化后的测量值为已知条件,利用正交匹配追踪算法重构出原始LSP高维矢量.实验结果表明,本算法相对低速语音编码中的矩阵量化方案,平均谱失真降低了0.23dB,相对基于DCT变换的降维量化方案,平均谱失真降低了0.13dB.这种先降维再量化的思想可以大幅减少编码所需的比特数及码本存储复杂度,有效降低语音编码速率,并且合成语音可懂度、自然度较高,音质虽有所失真,但基本上感觉不到明显的听觉质量下降.
Institute of Scientific and Technical Information of China (English)
荀鹏程; 钱国华; 赵杨; 于浩; 陈峰
2012-01-01
目的 探讨高维生物学数据的多阶段组合降维策略.方法 以微阵列数据的判别分析为例,采用实际数据和模拟数据相结合的方法,提出“初步选维→进一步降维”的两阶段组合降维策略,并与后续的“判别→验证”相结合,形成了“选维→降维→判别→验证”的判别分析思路.以后续判别分析的预测效果、预测结果的稳定性与敏感性等为指标,对2种单一降维( PCA,PLS)方法和4种组合降维方法(PCA+ SIR、PCA+SAVE、PLS+ SIR和PLS+ SAVE)进行了考察.结果 从判别模型的预测效果、预测结果的稳定性及敏感性来看,PLS优于PCA,PLS+ SIR/SAVE的组合降维效果更佳.结论 用t计分法选维,以“PLS+SIR/SAVE”法进行降维的两阶段组合降维策略,对于微阵列数据判别分析,是实用的、可行的.%Objective To explore multi-stage combinational dimension reduction strategy for analyzing high-dimensional data in biology field. Methods Two-stage combinational strategy incorporated in a four-step procedure,i.e. "variable pre-selection→ further dimensionality reduction→discrimination→validation" ,was put forward and applied to publicly available microarray data as well as simulated ones. In this process,the relative performances of six dimension reduction methods, including PCA, PLS、 PCA + SIR、PCA + SAVE 、PLS + SIR and PLS + SAVE, were evaluated. Results Considering the prediction quality,the stability of the prediction results as well as the sensitivity to the number of genes: (1) PLS performed was superior to PCA; (2) PLS + SIR or PLS + SAVE performed much better than other methods. Conclusion The results indicate that two stage combinational strategy proposed,i. e. variable pre-selection based on t-scores followed by PLS + SIR or PLS + SAVE, is feasible and practical in the discriminate analysis for microarray data.
Minimal Krylov Subspaces for Dimension Reduction
2013-01-01
stiffness matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 7.4.2 Enron email corpus experiment...the Enron email corpus and eigenvalue gaps. . . . 65 4.6 Low-rank approximation errors of Enron email corpus. . . . . . . . . . . . . . . . . . . . . 66...4.7 Maximum loss of orthogonality in projection basis for Enron email corpus. . . . . . . . . 67 4.8 FLOP counts for producing low-rank
Passive RFID Rotation Dimension Reduction via Aggregation
Matthews, Eric
Radio Frequency IDentification (RFID) has applications in object identification, position, and orientation tracking. RFID technology can be applied in hospitals for patient and equipment tracking, stores and warehouses for product tracking, robots for self-localisation, tracking hazardous materials, or locating any other desired object. Efficient and accurate algorithms that perform localisation are required to extract meaningful data beyond simple identification. A Received Signal Strength Indicator (RSSI) is the strength of a received radio frequency signal used to localise passive and active RFID tags. Many factors affect RSSI such as reflections, tag rotation in 3D space, and obstacles blocking line-of-sight. LANDMARC is a statistical method for estimating tag location based on a target tag's similarity to surrounding reference tags. LANDMARC does not take into account the rotation of the target tag. By either aggregating multiple reference tag positions at various rotations, or by determining a rotation value for a newly read tag, we can perform an expected value calculation based on a comparison to the k-most similar training samples via an algorithm called K-Nearest Neighbours (KNN) more accurately. By choosing the average as the aggregation function, we improve the relative accuracy of single-rotation LANDMARC localisation by 10%, and any-rotation localisation by 20%.
Spatiotemporal accessible solitons in fractional dimensions
Zhong, Wei-Ping; Belić, Milivoj R.; Malomed, Boris A.; Zhang, Yiqi; Huang, Tingwen
2016-07-01
We report solutions for solitons of the "accessible" type in globally nonlocal nonlinear media of fractional dimension (FD), viz., for self-trapped modes in the space of effective dimension 2 functions that include Gegenbauer polynomials, associated Laguerre polynomials, and associated Legendre functions. The validity of these solutions is verified by direct simulations. The model can be realized in various physical settings emulated by FD spaces; in particular, it applies to excitons trapped in quantum wells.
Higuchi dimension of digital images.
Directory of Open Access Journals (Sweden)
Helmut Ahammer
Full Text Available There exist several methods for calculating the fractal dimension of objects represented as 2D digital images. For example, Box counting, Minkowski dilation or Fourier analysis can be employed. However, there appear to be some limitations. It is not possible to calculate only the fractal dimension of an irregular region of interest in an image or to perform the calculations in a particular direction along a line on an arbitrary angle through the image. The calculations must be made for the whole image. In this paper, a new method to overcome these limitations is proposed. 2D images are appropriately prepared in order to apply 1D signal analyses, originally developed to investigate nonlinear time series. The Higuchi dimension of these 1D signals is calculated using Higuchi's algorithm, and it is shown that both regions of interests and directional dependencies can be evaluated independently of the whole picture. A thorough validation of the proposed technique and a comparison of the new method to the Fourier dimension, a common two dimensional method for digital images, are given. The main result is that Higuchi's algorithm allows a direction dependent as well as direction independent analysis. Actual values for the fractal dimensions are reliable and an effective treatment of regions of interests is possible. Moreover, the proposed method is not restricted to Higuchi's algorithm, as any 1D method of analysis, can be applied.
Solitons in nonlinear lattices
Kartashov, Yaroslav V; Torner, Lluis
2010-01-01
This article offers a comprehensive survey of results obtained for solitons and complex nonlinear wave patterns supported by purely nonlinear lattices (NLs), which represent a spatially periodic modulation of the local strength and sign of the nonlinearity, and their combinations with linear lattices. A majority of the results obtained, thus far, in this field and reviewed in this article are theoretical. Nevertheless, relevant experimental settings are surveyed too, with emphasis on perspectives for implementation of the theoretical predictions in the experiment. Physical systems discussed in the review belong to the realms of nonlinear optics (including artificial optical media, such as photonic crystals, and plasmonics) and Bose-Einstein condensation (BEC). The solitons are considered in one, two, and three dimensions (1D, 2D, and 3D). Basic properties of the solitons presented in the review are their existence, stability, and mobility. Although the field is still far from completion, general conclusions c...
Nonlinear Stokes Mueller Polarimetry
Samim, Masood; Barzda, Virginijus
2015-01-01
The Stokes Mueller polarimetry is generalized to include nonlinear optical processes such as second- and third-harmonic generation, sum- and difference-frequency generations. The overall algebraic form of the polarimetry is preserved, where the incoming and outgoing radiations are represented by column vectors and the intervening medium is represented by a matrix. Expressions for the generalized nonlinear Stokes vector and the Mueller matrix are provided in terms of coherency and correlation matrices, expanded by higher-dimensional analogues of Pauli matrices. In all cases, the outgoing radiation is represented by the conventional $4\\times 1$ Stokes vector, while dimensions of the incoming radiation Stokes vector and Mueller matrix depend on the order of the process being examined. In addition, relation between nonlinear susceptibilities and the measured Mueller matrices are explicitly provided. Finally, the approach of combining linear and nonlinear optical elements is discussed within the context of polarim...
非线性偏微分方程的约化和精确解%REDUCTION OF NONLINEAR PARTIAL DIFFERENTIAL EQUATION AND EXACT SOLUTIONS
Institute of Scientific and Technical Information of China (English)
叶彩儿; 潘祖梁
2003-01-01
Nonlinear partial differetial equation(NLPDE) is converted into ordinary differentialequation (ODE) via a new ansatzUsing undetermined function method ,the ODE obtained aboveis replaced by a set of algebraic equations which are solved out with the aid of MathematicaTheexact solutions and solitary solutions of NLPDE are obtained.
Institute of Scientific and Technical Information of China (English)
刘颖; 王瑞
2014-01-01
In the light of that in the model of rigid bodies with Cardan angular coordinates for plain-weave fabrics (CMRBF) the dimensionality is high and poor of the efficiency, the discrete null space method is applied to reduce the dimensionality of the equations of motion for multibody systems. Applying the implicit Runge-Kutta method(IRK) to directly discretize the equations and combining with discrete null space equivalent transformation , the Lagrange multipliers are eliminated, and the nodal reparametrisation is carryied out, thus twice dimensionality reduction of system is realized. Hence, the dimension reduction multibody model of rigid bodies for woven fabrics based on discrete null space method (DR-CMRBF) is proposed. Through the Kawabata Evaluation System for Fabrics(KES) uniaxial tensile experimens and simulations, the correctness and validity of the proposed model are tested and verified. Furthermore, by comparing the index of computational complexity and computational efficiency of two models, the superiority of dimensionality reduction and high efficiency is reflected. The proposed model is especially suitable for large-scale fabric simulation.%针对机织物卡尔丹角多体模型维数较高、计算效率较低的问题，采用离散零空间法降低多体动力学方程维数。通过隐式龙格库塔法对方程直接离散，结合离散零空间等效变换，消去拉格朗日乘子项和结点参数化实现两次系统降维，提出基于离散零空间法的机织物降维多体模型；通过KES单轴拉伸试验与仿真，验证了模型的正确性和有效性；并通过两种模型计算复杂度及计算效率的特征指标对比，证实了本文模型在降维和计算高效方面的优势，尤适于大型织物仿真。
Institute of Scientific and Technical Information of China (English)
翁秀奇; 陈加国
2016-01-01
In order to overcome the optimum sampling problem in reliability analysis of reliability-based design optimum (RBDO), a new enhanced dimension reduction (eDR) method based on variable sampling points is proposed. It employs different axial sampling points for each random design variable. The concept of importing extra sampling points can increase the efficiency compared with conventional eDR methods without losing accuracy. In addition, the performance of proposed method is evaluated by applying in specific mathematic and engineering RBDO problems. The results demonstrated that when compared with conventional eDR methods with fixed sampling points and performance measure approach, the proposed methods perform superiorly in both aspects of accuracy and efficiency.%针对目前可靠性优化设计中可靠性分析存在的难以得到优化抽样点问题，本文提出了一种基于变量抽样点的增强降维（enhanced dimension reduction，eDR）方法。该方法针对每个随机设计变量采用不同的轴向抽样点。通过距离准则确定是否增加额外抽样点。这种引入额外抽样点的概念可以在保证传统eDR精度的情况下还可以进一步提高分析效率。通过具体的数学和工程案例对所提出的抽样法进行分析性能研究。对比于传统的基于固定抽样点的eDR方法和功能度量法，所提出的改进抽样法在精度和效率方面都要更优。
Detecting the Nonlinearity of Fish Acoustic Signals
Institute of Scientific and Technical Information of China (English)
REN Xinmin; YIN Li
2006-01-01
This paper discusses the nonlinearity of fish acoustic signals by using the surrogate data method.We compare the difference of three test statistics - time-irreversibility Trey, correlation dimension D2 and auto mutual information function Ⅰbetween the original data and the surrogate data.We come to the conclusion that there exists nonlinearity in the fish acoustic signals and there exist deterministic nonlinear components; therefore nonlinear dynamic theory can be used to analyze fish acoustic signals.
Spectral dimension flow on continuum random multigraph
Giasemidis, Georgios; Zohren, Stefan
2012-01-01
We review a recently introduced effective graph approximation of causal dynamical triangulations (CDT), the multigraph ensemble. We argue that it is well suited for analytical computations and that it captures the physical degrees of freedom which are important for the reduction of the spectral dimension as observed in numerical simulations of CDT. In addition multigraph models allow us to study the relationship between the spectral dimension and the Hausdorff dimension, thus establishing a link to other approaches to quantum gravity
Accessible solitons of fractional dimension
Energy Technology Data Exchange (ETDEWEB)
Zhong, Wei-Ping, E-mail: zhongwp6@126.com [Department of Electronic and Information Engineering, Shunde Polytechnic, Guangdong Province, Shunde 528300 (China); Texas A& M University at Qatar, P.O. Box 23874, Doha (Qatar); Belić, Milivoj [Texas A& M University at Qatar, P.O. Box 23874, Doha (Qatar); Zhang, Yiqi [Key Laboratory for Physical Electronics and Devices of the Ministry of Education & Shaanxi Key Lab of Information Photonic Technique, Xi’an Jiaotong University, Xi’an 710049 (China)
2016-05-15
We demonstrate that accessible solitons described by an extended Schrödinger equation with the Laplacian of fractional dimension can exist in strongly nonlocal nonlinear media. The soliton solutions of the model are constructed by two special functions, the associated Legendre polynomials and the Laguerre polynomials in the fraction-dimensional space. Our results show that these fractional accessible solitons form a soliton family which includes crescent solitons, and asymmetric single-layer and multi-layer necklace solitons. -- Highlights: •Analytic solutions of a fractional Schrödinger equation are obtained. •The solutions are produced by means of self-similar method applied to the fractional Schrödinger equation with parabolic potential. •The fractional accessible solitons form crescent, asymmetric single-layer and multilayer necklace profiles. •The model applies to the propagation of optical pulses in strongly nonlocal nonlinear media.
Monte Carlo and nonlinearities
Dauchet, Jérémi; Blanco, Stéphane; Caliot, Cyril; Charon, Julien; Coustet, Christophe; Hafi, Mouna El; Eymet, Vincent; Farges, Olivier; Forest, Vincent; Fournier, Richard; Galtier, Mathieu; Gautrais, Jacques; Khuong, Anaïs; Pelissier, Lionel; Piaud, Benjamin; Roger, Maxime; Terrée, Guillaume; Weitz, Sebastian
2016-01-01
The Monte Carlo method is widely used to numerically predict systems behaviour. However, its powerful incremental design assumes a strong premise which has severely limited application so far: the estimation process must combine linearly over dimensions. Here we show that this premise can be alleviated by projecting nonlinearities on a polynomial basis and increasing the configuration-space dimension. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles and concentrated-solar-power-plant productions, we prove the real world usability of this advance on four test-cases that were so far regarded as impracticable by Monte Carlo approaches. We also illustrate an outstanding feature of our method when applied to sharp problems with interacting particles: handling rare events is now straightforward. Overall, our extension preserves the features that made the method popular: addressing nonlinearities does not compromise o...
Highlighting nonlinear patterns in population genetics datasets
Alanis Lobato, Gregorio
2015-01-30
Detecting structure in population genetics and case-control studies is important, as it exposes phenomena such as ecoclines, admixture and stratification. Principal Component Analysis (PCA) is a linear dimension-reduction technique commonly used for this purpose, but it struggles to reveal complex, nonlinear data patterns. In this paper we introduce non-centred Minimum Curvilinear Embedding (ncMCE), a nonlinear method to overcome this problem. Our analyses show that ncMCE can separate individuals into ethnic groups in cases in which PCA fails to reveal any clear structure. This increased discrimination power arises from ncMCE\\'s ability to better capture the phylogenetic signal in the samples, whereas PCA better reflects their geographic relation. We also demonstrate how ncMCE can discover interesting patterns, even when the data has been poorly pre-processed. The juxtaposition of PCA and ncMCE visualisations provides a new standard of analysis with utility for discovering and validating significant linear/nonlinear complementary patterns in genetic data.
Analytical exact solution of the non-linear Schroedinger equation
Energy Technology Data Exchange (ETDEWEB)
Martins, Alisson Xavier; Rocha Filho, Tarcisio Marciano da [Universidade de Brasilia (UnB), DF (Brazil). Inst. de Fisica. Grupo de Fisica e Matematica
2011-07-01
Full text: In this work we present how to classify and obtain analytical solutions of the Schroedinger equation with a generic non-linearity in 1+1 dimensions. Our approach is based on the determination of Lie symmetry transformation mapping solutions into solutions, and non-classical symmetry transformations, mapping a given solution into itself. From these symmetries it is then possible to reduce the equation to a system of ordinary differential equations which can then be solved using standard methods. The generic non-linearity is handled by considering it as an additional unknown in the determining equations for the symmetry transformations. This results in an over-determined system of non-linear partial differential equations. Its solution can then be determined in some cases by reducing it to the so called involutive (triangular) form, and then solved. This reduction is very tedious and can only performed using a computer algebra system. Once the determining system is solved, we obtain the explicit form for the non-linearity admitting a Lie or non-classical symmetry. The analytical solutions are then derived by solving the reduced ordinary differential equations. The non-linear determining system for the non-classical symmetry transformations and Lie symmetry generators are obtaining using the computer algebra package SADE (symmetry analysis of differential equations), developed at our group. (author)
Rashidian Vaziri, Mohammad Reza
2013-07-10
In this paper, the Z-scan theory for nonlocal nonlinear media has been further developed when nonlinear absorption and nonlinear refraction appear simultaneously. To this end, the nonlinear photoinduced phase shift between the impinging and outgoing Gaussian beams from a nonlocal nonlinear sample has been generalized. It is shown that this kind of phase shift will reduce correctly to its known counterpart for the case of pure refractive nonlinearity. Using this generalized form of phase shift, the basic formulas for closed- and open-aperture beam transmittances in the far field have been provided, and a simple procedure for interpreting the Z-scan results has been proposed. In this procedure, by separately performing open- and closed-aperture Z-scan experiments and using the represented relations for the far-field transmittances, one can measure the nonlinear absorption coefficient and nonlinear index of refraction as well as the order of nonlocality. Theoretically, it is shown that when the absorptive nonlinearity is present in addition to the refractive nonlinearity, the sample nonlocal response can noticeably suppress the peak and enhance the valley of the Z-scan closed-aperture transmittance curves, which is due to the nonlocal action's ability to change the beam transverse dimensions.
Bloembergen, Nicolaas
1996-01-01
Nicolaas Bloembergen, recipient of the Nobel Prize for Physics (1981), wrote Nonlinear Optics in 1964, when the field of nonlinear optics was only three years old. The available literature has since grown by at least three orders of magnitude.The vitality of Nonlinear Optics is evident from the still-growing number of scientists and engineers engaged in the study of new nonlinear phenomena and in the development of new nonlinear devices in the field of opto-electronics. This monograph should be helpful in providing a historical introduction and a general background of basic ideas both for expe
On Universal Quantum Dimensions
Mkrtchyan, R L
2016-01-01
We derive universal expressions for quantum dimensions (universal characters) of some series of irreps of simple Lie algebras. This allows us to check Deligne's hypothesis on universal quantum dimensions for symmetric cube of adjoint representation.
Kawata, Y.; Niki, N.; Ohmatsu, H.; Aokage, K.; Kusumoto, M.; Tsuchida, T.; Eguchi, K.; Kaneko, M.
2015-03-01
Advantages of CT scanners with high resolution have allowed the improved detection of lung cancers. In the recent release of positive results from the National Lung Screening Trial (NLST) in the US showing that CT screening does in fact have a positive impact on the reduction of lung cancer related mortality. While this study does show the efficacy of CT based screening, physicians often face the problems of deciding appropriate management strategies for maximizing patient survival and for preserving lung function. Several key manifold-learning approaches efficiently reveal intrinsic low-dimensional structures latent in high-dimensional data spaces. This study was performed to investigate whether the dimensionality reduction can identify embedded structures from the CT histogram feature of non-small-cell lung cancer (NSCLC) space to improve the performance in predicting the likelihood of RFS for patients with NSCLC.
Energy Technology Data Exchange (ETDEWEB)
Geniet, F; Leon, J [Physique Mathematique et Theorique, CNRS-UMR 5825, 34095 Montpellier (France)
2003-05-07
A nonlinear system possessing a natural forbidden band gap can transmit energy of a signal with a frequency in the gap, as recently shown for a nonlinear chain of coupled pendulums (Geniet and Leon 2002 Phys. Rev. Lett. 89 134102). This process of nonlinear supratransmission, occurring at a threshold that is exactly predictable in many cases, is shown to have a simple experimental realization with a mechanical chain of pendulums coupled by a coil spring. It is then analysed in more detail. First we go to different (nonintegrable) systems which do sustain nonlinear supratransmission. Then a Josephson transmission line (a one-dimensional array of short Josephson junctions coupled through superconducting wires) is shown to also sustain nonlinear supratransmission, though being related to a different class of boundary conditions, and despite the presence of damping, finiteness, and discreteness. Finally, the mechanism at the origin of nonlinear supratransmission is found to be a nonlinear instability, and this is briefly discussed here.
Nonlinear hyperbolic waves in multidimensions
Prasad, Phoolan
2001-01-01
The propagation of curved, nonlinear wavefronts and shock fronts are very complex phenomena. Since the 1993 publication of his work Propagation of a Curved Shock and Nonlinear Ray Theory, author Phoolan Prasad and his research group have made significant advances in the underlying theory of these phenomena. This volume presents their results and provides a self-contained account and gradual development of mathematical methods for studying successive positions of these fronts.Nonlinear Hyperbolic Waves in Multidimensions includes all introductory material on nonlinear hyperbolic waves and the theory of shock waves. The author derives the ray theory for a nonlinear wavefront, discusses kink phenomena, and develops a new theory for plane and curved shock propagation. He also derives a full set of conservation laws for a front propagating in two space dimensions, and uses these laws to obtain successive positions of a front with kinks. The treatment includes examples of the theory applied to converging wavefronts...
Goparaju Purna SUDHAKAR
2014-01-01
Popularity of teams is growing in 21st Century. Organizations are getting their work done through different types of teams. Teams have proved that the collective performance is more than the sum of the individual performances. Thus, the teams have got different dimensions such as quantitative dimensions and qualitative dimensions. The Quantitative dimensions of teams such as team performance, team productivity, team innovation, team effectiveness, team efficiency, team decision making and tea...
Sudhakar, Goparaju Purna
2013-01-01
Popularity of teams is growing in 21st Century. Organizations are getting their work done through different types of teams. Teams have proved that the collective performance is more than the sum of the individual performances. Thus, the teams have got different dimensions such as quantitative dimensions and qualitative dimensions. The Quantitative dimensions of teams such as team performance, team productivity, team innovation, team effectiveness, team efficiency, team decision making and tea...
Strongly Gorenstein Flat Dimensions
Institute of Scientific and Technical Information of China (English)
Chun Xia ZHANG; Li Min WANG
2011-01-01
This article is concerned with the strongly Gorenstein flat dimensions of modules and rings.We show this dimension has nice properties when the ring is coherent,and extend the well-known Hilbert's syzygy theorem to the strongly Gorenstein flat dimensions of rings.Also,we investigate the strongly Gorenstein flat dimensions of direct products of rings and (almost)excellent extensions of rings.
Institute of Scientific and Technical Information of China (English)
张振跃; 查宏远
2004-01-01
We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized data points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approximation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data points with respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can be quite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimensional Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.
Spectral dimension from nonlocal dynamics on causal sets
Belenchia, Alessio; Benincasa, Dionigi M. T.; Marcianò, Antonino; Modesto, Leonardo
2016-02-01
We investigate the spectral dimension obtained from nonlocal continuum d'Alembertians derived from causal sets. We find a universal dimensional reduction to two dimensions, in all dimensions. We conclude by discussing the validity and relevance of our results within the broader context of quantum field theories based on these nonlocal dynamics.
Dimensions of Creative Evaluation
DEFF Research Database (Denmark)
Christensen, Bo; Ball, Linden J.
2016-01-01
We examined evaluative reasoning taking place during expert ‘design critiques’. We focused on key dimensions of creative evaluation (originality, functionality and aesthetics) and ways in which these dimensions impact reasoning strategies and suggestions offered by experts for how the student could...... continue. Each dimension was associated with a specific underpinning ‘logic’ determining how these dimensions were evaluated in practice. Our analysis clarified how these dimensions triggered reasoning strategies such as running mental simulations or making design suggestions, ranging from ‘go...
On balanced truncation for symmetric nonlinear systems
Fujimoto, K.; Scherpen, Jacqueline M.A.
2014-01-01
This paper is concerned with model order reduction based on balanced realization for symmetric nonlinear systems. A new notion of symmetry for nonlinear systems was characterized recently. It plays an important role in linear systems theory and is expected to provide new insights to nonlinear system
2016-07-01
Advanced Research Projects Agency (DARPA) Dynamics-Enabled Frequency Sources (DEFYS) program is focused on the convergence of nonlinear dynamics and...Early work in this program has shown that nonlinear dynamics can provide performance advantages. However, the pathway from initial results to...dependent nonlinear stiffness observed in these devices. This work is ongoing, and will continue through the final period of this program . Reference 9
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
Yoshida, Zensho
2010-01-01
This book gives a general, basic understanding of the mathematical structure "nonlinearity" that lies in the depths of complex systems. Analyzing the heterogeneity that the prefix "non" represents with respect to notions such as the linear space, integrability and scale hierarchy, "nonlinear science" is explained as a challenge of deconstruction of the modern sciences. This book is not a technical guide to teach mathematical tools of nonlinear analysis, nor a zoology of so-called nonlinear phenomena. By critically analyzing the structure of linear theories, and cl
Nanda, Sudarsan
2013-01-01
"Nonlinear analysis" presents recent developments in calculus in Banach space, convex sets, convex functions, best approximation, fixed point theorems, nonlinear operators, variational inequality, complementary problem and semi-inner-product spaces. Nonlinear Analysis has become important and useful in the present days because many real world problems are nonlinear, nonconvex and nonsmooth in nature. Although basic concepts have been presented here but many results presented have not appeared in any book till now. The book could be used as a text for graduate students and also it will be useful for researchers working in this field.
Agrawal, Govind
2012-01-01
Since the 4e appeared, a fast evolution of the field has occurred. The 5e of this classic work provides an up-to-date account of the nonlinear phenomena occurring inside optical fibers, the basis of all our telecommunications infastructure as well as being used in the medical field. Reflecting the big developments in research, this new edition includes major new content: slow light effects, which offers a reduction in noise and power consumption and more ordered network traffic-stimulated Brillouin scattering; vectorial treatment of highly nonlinear fibers; and a brand new chapter o
Nonlinear dynamics analysis of a new autonomous chaotic system
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper, a new nonlinear autonomous system introduced by Chlouverakis and Sprott is studied further, to present very rich and complex nonlinear dynamical behaviors. Some basic dynamical properties are studied either analytically or nuchaotic system with very high Lyapunov dimensions is constructed and investigated. Two new nonlinear autonomous systems can be changed into one another by adding or omitting some constant coefficients.
Supergravity Fluxbranes in Various Dimensions
Chen, C M; Saffin, P M; Chen, Chiang-Mei; Gal'tsov, Dmitri V.; Saffin, Paul M.
2002-01-01
We investigate fluxbrane solutions to the Einstein-antisymmetric form-dilaton theory in arbitrary space-time dimensions for a transverse space of cylindrical topology $S^k\\times R^n$, corresponding to smeared and unsmeared solutions. A master equation for a single metric function is derived. This is a non-linear second-order ordinary differential equation admitting an analytic solution, singular at the origin, which serves as an attractor for globally regular solutions, whose existence is demonstrated numerically. For all fluxbranes of different levels of smearing the metric function diverges at infinity as the same power of the radial coordinate except for the maximally smeared case, where a global solution is known in closed form and can be obtained algebraically using U-duality. The particular cases of F6 and F3 fluxbranes in D=11 supergravity and fluxbranes in IIA, IIB supergravities are discussed.
DEFF Research Database (Denmark)
Lykke, Marianne; Jantzen, Christian
2016-01-01
The present study develops a set of 10 dimensions based on a systematic understanding of the concept of experience as a holistic psychological. Seven of these are derived from a psychological conception of what experiencing and experiences are. Three supplementary dimensions spring from...... the observation that experiences apparently have become especially valuable phenomena in Western societies. The 10 dimensions are tried out in a field study at the Center for Art and Media (ZKM) in Germany with the purpose to study their applicability in the evaluation of interactive sound archives. 29 walk......-alongs were carried out with 58 museums visitors. Our analysis showed that it was possible to identify the 10 experience dimensions in the study material. Some dimensions were expressed more frequently than others. The distribution of expressed dimensions and the content of the user comments provided a clear...
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
In this paper, we are interested in the following general question: Given a module Mwhich has finite hollow dimension and which has a finite collection of submodules Ki (1≤i≤n) such that M=K1+... +Kn, can we find an expression for the hollow dimension of Min terms of hollow dimensions of modules built up in some way from K1 Kn? We prove the following theorem:Let Mbe an amply supplemented module having finite hollow dimension and let Ki (1≤i≤n) be a finite collection of submodules of Msuch that M=K1+...+Kn. Then the hollow dimension h(M) of Mis the sum of the hollow dimensions of Ki (1≤i≤n) ifand only if Ki is a supplement of K1+...+Ki-1+Ki+1+...+Kn in Mfor each 1≤i≤n.
DEFF Research Database (Denmark)
Høskuldsson, Agnar
1996-01-01
Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four...... the basic problems in determining the dimension of linear models. Then each of the eight measures are treated. The results are illustrated by examples....
Van Leeuwen, Peter Jan; Reich, Sebastian
2015-01-01
This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
Multiple dimensions of performance
Torenvlied, René
2013-01-01
This presentation considers the multiple dimensions of performance in performance studies, and potentially contradicting effects of different management strategies on separate indicators of performance
Directory of Open Access Journals (Sweden)
Quznetsov G.
2014-10-01
Full Text Available Each vector of state has its own corresponing element of the CayleyDickson algebra. Properties of a state vector require that this algebra was a normalized division algebra. By the Hurwitz and Frobenius theorems maximal dimension of s uch algebra is 8. Con- sequently, a dimension of corresponding complex state vectors is 4, and a dimension of the Clifford set elements is 4 × 4. Such set contains 5 matrices — among them — 3-diagonal. Hence, a dimension of the dot events space is equal to 3 + 1.
Perspectives on Nonlinear Filtering
Law, Kody
2015-01-07
The solution to the problem of nonlinear filtering may be given either as an estimate of the signal (and ideally some measure of concentration), or as a full posterior distribution. Similarly, one may evaluate the fidelity of the filter either by its ability to track the signal or its proximity to the posterior filtering distribution. Hence, the field enjoys a lively symbiosis between probability and control theory, and there are plenty of applications which benefit from algorithmic advances, from signal processing, to econometrics, to large-scale ocean, atmosphere, and climate modeling. This talk will survey some recent theoretical results involving accurate signal tracking with noise-free (degenerate) dynamics in high-dimensions (infinite, in principle, but say d between 103 and 108 , depending on the size of your application and your computer), and high-fidelity approximations of the filtering distribution in low dimensions (say d between 1 and several 10s).
Institute of Scientific and Technical Information of China (English)
熊春宇; 吴春梅; 王艳芹; 李欣欣
2012-01-01
For the nonlinear characteristics of switched reluctancemotorwith two protruding pole, this paper analyzed the entire filed of the switched reluctance motor with three dimensional finite element analyses by using three-dimensional modeling method. In three-dmi ension finite elementmethod, wholemodelingwas adopted and racetrack coilwas introduced while adding loads. The result indicates thatthree-dmi ension finite elementanalysis can reflectthemagnetic field ditribution ofmotorand getstate character curve to be ready fordeveloping control study ofswitched reluctancemotor. Magnetization curves, inductance and torque charactercan be connected with switched reluctance drive to validate the steady and dynamic performance ofmotor.%针对开关磁阻电机双凸极结构导致的磁场严重非线性,利用三维有限元分析法对开关磁阻电机的电磁场进行研究.在三维分析中,考虑了端部效应对电机电磁场分布的影响.采用整体建模的方法,并在施加载荷时引入跑道线圈这一概念.通过仿真表明,三维有限元分析能更好地反映电机实际的磁场分布,得出电机静态曲线,为电机的控制研究奠定了基础.将有限元分析所得磁化曲线及电感、转矩数据用于改进的开关磁阻电机调速系统的模型中,来验证电机的静态性能和动态性能.
THE DISTRIBUTIONAL DIMENSION OF FRACTALS
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In the book [1] H.Triebel introduces the distributional dimension of fractals in and distributional dimension, respectively. Thus we might say that the distributional dimension is an analytical definition for Hausdorff dimension. Therefore we can study Hausdorff dimension through the distributional dimension analytically.By discussing the distributional dimension, this paper intends to set up a criterion for estimating the upper and lower bounds of Hausdorff dimension analytically. Examples illustrating the criterion are included in the end.
DEFF Research Database (Denmark)
Lykke, Marianne; Jantzen, Christian
2016-01-01
The present study develops a set of 10 dimensions based on a systematic understanding of the concept of experience as a holistic psychological. Seven of these are derived from a psychological conception of what experiencing and experiences are. Three supplementary dimensions spring from the obser...
Gorenstein homological dimensions
DEFF Research Database (Denmark)
Holm, Henrik Granau
2004-01-01
In basic homological algebra, the projective, injective and 2at dimensions of modules play an important and fundamental role. In this paper, the closely related Gorenstein projective, Gorenstein injective and Gorenstein 2at dimensions are studied. There is a variety of nice results about Gorenstein...
Gorenstein homological dimensions
DEFF Research Database (Denmark)
Holm, Henrik Granau
2004-01-01
In basic homological algebra, the projective, injective and 2at dimensions of modules play an important and fundamental role. In this paper, the closely related Gorenstein projective, Gorenstein injective and Gorenstein 2at dimensions are studied. There is a variety of nice results about Gorenstein...
THE USE OF GENETIC ALGORITHM IN DIMENSIONING HYBRID AUTONOMOUS SYSTEMS
Directory of Open Access Journals (Sweden)
RUS T.
2016-03-01
Full Text Available In this paper is presented the working principle of genetic algorithms used to dimension autonomous hybrid systems. It is presented a study case in which is dimensioned and optimized an autonomous hybrid system for a residential house located in Cluj-Napoca. After the autonomous hybrid system optimization is performed, it is achieved a reduction of the total cost of system investment, a reduction of energy produced in excess and a reduction of CO2 emissions.
Institute of Scientific and Technical Information of China (English)
高红民; 李臣明; 周惠; 张振; 陈玲慧; 何振宇
2016-01-01
The high dimensions of hyperspectral remote sensing images will cause the redundancy of information and complexity of data processing, which also brings tremendous computing workload and damages application accuracy. Therefore, before the analysis of hyperspectral image processing, it is necessary to reduce the high dimensions of hyperspectral data. The Sensitivity Analysis (SA) of artificial neural network can be used in dimension reduction of the model. Now the Sensitivity Analysis of artificial neural network is applied to dimension reduction for hyperspectral remote sensing images in the paper. First of all, all bands are divided into several groups as long as a lower correlation exists between adjacent bands. Furthermore, Differential Evolution (DE) algorithm is used for optimizing neural network structure. Moreover, the bands which make small contribution will be given up based on Ruck sensitivity analysis method. Finally, experiments are conducted with AVIRIS images. The results show that the proposed method can get high classification accuracy of 85.83%at small training samples, 0.31%higher than the best one among other similar methods of dimension reduction and classification.%高光谱遥感影像由于其巨大的波段数直接导致信息的高冗余和数据处理的复杂,这不仅带来庞大的计算量,而且会损害分类精度.因此,在对高光谱影像进行处理、分析之前进行降维变得非常必要.神经网络敏感性分析可以用于对模型的简化降维,该文将该方法运用于高光谱遥感影像降维中,通过子空间划分弱化波段之间的相关性,利用差分进化算法(DE)优化神经网络结构,采用Ruck敏感性分析方法剔除掉对分类贡献较小的波段,从而实现降维.最后,采用AVIRIS影像进行实验,所提算法相比其他相近的降维与分类方法能获得更高的分类精度,达到85.83%,比其他相近方法中最优方法高出0.31%.
Zhu, Hong-Ming; Pen, Ue-Li; Chen, Xuelei; Yu, Hao-Ran
2016-01-01
We present a direct approach to non-parametrically reconstruct the linear density field from an observed non-linear map. We solve for the unique displacement potential consistent with the non-linear density and positive definite coordinate transformation using a multigrid algorithm. We show that we recover the linear initial conditions up to $k\\sim 1\\ h/\\mathrm{Mpc}$ with minimal computational cost. This reconstruction approach generalizes the linear displacement theory to fully non-linear fields, potentially substantially expanding the BAO and RSD information content of dense large scale structure surveys, including for example SDSS main sample and 21cm intensity mapping.
Boyd, Robert W
2013-01-01
Nonlinear Optics is an advanced textbook for courses dealing with nonlinear optics, quantum electronics, laser physics, contemporary and quantum optics, and electrooptics. Its pedagogical emphasis is on fundamentals rather than particular, transitory applications. As a result, this textbook will have lasting appeal to a wide audience of electrical engineering, physics, and optics students, as well as those in related fields such as materials science and chemistry.Key Features* The origin of optical nonlinearities, including dependence on the polarization of light* A detailed treatment of the q
Fractal Dimension in Epileptic EEG Signal Analysis
Uthayakumar, R.
Fractal Analysis is the well developed theory in the data analysis of non-linear time series. Especially Fractal Dimension is a powerful mathematical tool for modeling many physical and biological time signals with high complexity and irregularity. Fractal dimension is a suitable tool for analyzing the nonlinear behaviour and state of the many chaotic systems. Particularly in analysis of chaotic time series such as electroencephalograms (EEG), this feature has been used to identify and distinguish specific states of physiological function.Epilepsy is the main fatal neurological disorder in our brain, which is analyzed by the biomedical signal called Electroencephalogram (EEG). The detection of Epileptic seizures in the EEG Signals is an important tool in the diagnosis of epilepsy. So we made an attempt to analyze the EEG in depth for knowing the mystery of human consciousness. EEG has more fluctuations recorded from the human brain due to the spontaneous electrical activity. Hence EEG Signals are represented as Fractal Time Series.The algorithms of fractal dimension methods have weak ability to the estimation of complexity in the irregular graphs. Divider method is widely used to obtain the fractal dimension of curves embedded into a 2-dimensional space. The major problem is choosing initial and final step length of dividers. We propose a new algorithm based on the size measure relationship (SMR) method, quantifying the dimensional behaviour of irregular rectifiable graphs with minimum time complexity. The evidence for the suitability (equality with the nature of dimension) of the algorithm is illustrated graphically.We would like to demonstrate the criterion for the selection of dividers (minimum and maximum value) in the calculation of fractal dimension of the irregular curves with minimum time complexity. For that we design a new method of computing fractal dimension (FD) of biomedical waveforms. Compared to Higuchi's algorithm, advantages of this method include
Ruszczynski, Andrzej
2011-01-01
Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates t
DEFF Research Database (Denmark)
Dalsgaard, Christian; Thestrup, Klaus
2015-01-01
as a matter of engaging educational activities in sociocultural practices of a surrounding society. Openness is not only a matter of opening up the existing, but of developing new educational practices that interact with society. The paper outlines three pedagogical dimensions of openness: transparency...... practices. Openness as joint engagement in the world aims at establishing interdependent collaborative relationships between educational institutions and external practices. To achieve these dimensions of openness, educational activities need to change and move beyond the course as the main format...... for openness. With examples from a university case, the paper discusses how alternative pedagogical formats and educational technologies can support the three dimensions of openness....
Rucker, Rudy
2014-01-01
""This is an invigorating book, a short but spirited slalom for the mind."" - Timothy Ferris, The New York Times Book Review ""Highly readable. One is reminded of the breadth and depth of Hofstadter's Gödel, Escher, Bach."" - Science""Anyone with even a minimal interest in mathematics and fantasy will find The Fourth Dimension informative and mind-dazzling... [Rucker] plunges into spaces above three with a zest and energy that is breathtaking."" - Martin Gardner ""Those who think the fourth dimension is nothing but time should be encouraged to read The Fourth Dimension, along with anyone else
Directory of Open Access Journals (Sweden)
Goparaju Purna SUDHAKAR
2014-01-01
Full Text Available Popularity ofteams is growing in 21st Century. Organizations are getting theirwork done through different types of teams. Teams have proved that thecollective performance is more than the sum of the individual performances.Thus, the teams have got different dimensions such as quantitative dimensionsand qualitative dimensions. The Quantitative dimensions of teams such as teamperformance, team productivity, team innovation, team effectiveness, teamefficiency, team decision making and team conflicts and Qualitative dimensionsof teams such as team communication, team coordination, team cooperation, teamcohesion, team climate, team creativity, team leadership and team conflictshave been discussed in this article.
Dimension control of Superradiance
Hill, Tyler; Hui Deng Collaboration; Barry C. Sanders Collaboration
2016-05-01
We develop a theory for quantum dipole-dipole coupling when the electromagnetic fields are confined to an open line, open plane, or open space, commensurate with experimental capability for collective atomic effects subject to dimensional confinement. Our mathematical model naturally interpolates for all real dimension between one dimension for the line to three dimensions for open space. We show how superradiant emission can be controlled by dimensional confinement, including near-field and dipole-orientation effects, and we propose a two-dimensional confinement experiment to test our theory's efficacy. University of Michigan.
Dynamics and Fractal Dimension of Steffensen-Type Methods
Directory of Open Access Journals (Sweden)
Francisco I. Chicharro
2015-06-01
Full Text Available In this paper, the dynamical behavior of different optimal iterative schemes for solving nonlinear equations with increasing order, is studied. The tendency of the complexity of the Julia set is analyzed and referred to the fractal dimension. In fact, this fractal dimension can be shown to be a powerful tool to compare iterative schemes that estimate the solution of a nonlinear equation. Based on the box-counting algorithm, several iterative derivative-free methods of different convergence orders are compared.
Spectral Asymmetry and Higuchi's Fractal Dimension Measures of Depression Electroencephalogram
Maie Bachmann; Jaanus Lass; Anna Suhhova; Hiie Hinrikus
2013-01-01
This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi's fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control...
DEFF Research Database (Denmark)
Due, Jesper Jørgen; Madsen, Jørgen Steen; Jensen, Carsten Strøby
En analyse af EU's institutioner og udviklingen af den sociale dimension i forbindelse med etbaleringen af det indre marked med særlig henblik på effekterne på det danske aftalesystem.......En analyse af EU's institutioner og udviklingen af den sociale dimension i forbindelse med etbaleringen af det indre marked med særlig henblik på effekterne på det danske aftalesystem....
Hirshberg, Ilan; Szabó, Gábor; Winter, Wilhelm; Wu, Jianchao
2017-07-01
We introduce a notion of Rokhlin dimension for one parameter automorphism groups of {C^*}-algebras. This generalizes Kishimoto's Rokhlin property for flows, and is analogous to the notion of Rokhlin dimension for actions of the integers and other discrete groups introduced by the authors and Zacharias in previous papers. We show that finite nuclear dimension and absorption of a strongly self-absorbing {C^*}-algebra are preserved under forming crossed products by flows with finite Rokhlin dimension, and that these crossed products are stable. Furthermore, we show that a flow on a commutative {C^*}-algebra arising from a free topological flow has finite Rokhlin dimension, whenever the spectrum is a locally compact metrizable space with finite covering dimension. For flows that are both free and minimal, this has strong consequences for the associated crossed product {C^{*}}-algebras: Those containing a non-zero projection are classified by the Elliott invariant (for compact manifolds this consists of topological {K}-theory together with the space of invariant probability measures and a natural pairing given by the Ruelle-Sullivan map).
A variational principle for the Hausdorff dimension of fractal sets
DEFF Research Database (Denmark)
Olsen, Lars; Cutler, Colleen D.
1994-01-01
Matematik, fraktal (fractal), Hausdorff dimension, Renyi dimension, pakke dimension (packing dimension)......Matematik, fraktal (fractal), Hausdorff dimension, Renyi dimension, pakke dimension (packing dimension)...
Scaling effects in a non-linear electromagnetic energy harvester for wearable sensors
Geisler, M.; Boisseau, S.; Perez, M.; Ait-Ali, I.; Perraud, S.
2016-11-01
In the field of inertial energy harvesters targeting human mechanical energy, the ergonomics of the solutions impose to find the best compromise between dimensions reduction and electrical performance. In this paper, we study the properties of a non-linear electromagnetic generator at different scales, by performing simulations based on an experimentally validated model and real human acceleration recordings. The results display that the output power of the structure is roughly proportional to its scaling factor raised to the power of five, which indicates that this system is more relevant at lengths over a few centimetres.
In, Visarath; Longhini, Patrick; Kho, Andy; Neff, Joseph D.; Leung, Daniel; Liu, Norman; Meadows, Brian K.; Gordon, Frank; Bulsara, Adi R.; Palacios, Antonio
2012-12-01
The nonlinear channelizer is an integrated circuit made up of large parallel arrays of analog nonlinear oscillators, which, collectively, serve as a broad-spectrum analyzer with the ability to receive complex signals containing multiple frequencies and instantaneously lock-on or respond to a received signal in a few oscillation cycles. The concept is based on the generation of internal oscillations in coupled nonlinear systems that do not normally oscillate in the absence of coupling. In particular, the system consists of unidirectionally coupled bistable nonlinear elements, where the frequency and other dynamical characteristics of the emergent oscillations depend on the system's internal parameters and the received signal. These properties and characteristics are being employed to develop a system capable of locking onto any arbitrary input radio frequency signal. The system is efficient by eliminating the need for high-speed, high-accuracy analog-to-digital converters, and compact by making use of nonlinear coupled systems to act as a channelizer (frequency binning and channeling), a low noise amplifier, and a frequency down-converter in a single step which, in turn, will reduce the size, weight, power, and cost of the entire communication system. This paper covers the theory, numerical simulations, and some engineering details that validate the concept at the frequency band of 1-4 GHz.
Model Reduction of Nonlinear Fire Dynamics Models
Lattimer, Alan Martin
2016-01-01
Due to the complexity, multi-scale, and multi-physics nature of the mathematical models for fires, current numerical models require too much computational effort to be useful in design and real-time decision making, especially when dealing with fires over large domains. To reduce the computational time while retaining the complexity of the domain and physics, our research has focused on several reduced-order modeling techniques. Our contributions are improving wildland fire reduced-order mod...
Reduction of Photodiode Nonlinearities by Adaptive Biasing
2016-10-14
2016 Approved for public release; distribution is unlimited. Meredith N. hutchiNsoN Nicholas J. Frigo Photonics Technology Branch Optical Sciences...behavior assumes that its behavior can be modeled as a memoryless transfer function relating the output photocurrent to the input light intensity [1...enhanced tremendously. That is, rather than accepting the “passive” estimation3 of a system’s SFDR, one could use a detailed knowledge of the photodiode
Fermion masses from dimensional reduction
Energy Technology Data Exchange (ETDEWEB)
Kapetanakis, D. (National Research Centre for the Physical Sciences Democritos, Athens (Greece)); Zoupanos, G. (European Organization for Nuclear Research, Geneva (Switzerland))
1990-10-11
We consider the fermion masses in gauge theories obtained from ten dimensions through dimensional reduction on coset spaces. We calculate the general fermion mass matrix and we apply the mass formula in illustrative examples. (orig.).
Energy Technology Data Exchange (ETDEWEB)
Turchetti, G. (Bologna Univ. (Italy). Dipt. di Fisica)
1989-01-01
Research in nonlinear dynamics is rapidly expanding and its range of applications is extending beyond the traditional areas of science where it was first developed. Indeed while linear analysis and modelling, which has been very successful in mathematical physics and engineering, has become a mature science, many elementary phenomena of intrinsic nonlinear nature were recently experimentally detected and investigated, suggesting new theoretical work. Complex systems, as turbulent fluids, were known to be governed by intrinsically nonlinear laws since a long time ago, but received purely phenomenological descriptions. The pioneering works of Boltzmann and Poincare, probably because of their intrinsic difficulty, did not have a revolutionary impact at their time; it is only very recently that their message is reaching a significant number of mathematicians and physicists. Certainly the development of computers and computer graphics played an important role in developing geometric intuition of complex phenomena through simple numerical experiments, while a new mathematical framework to understand them was being developed.
Broadband Nonlinear Signal Processing in Silicon Nanowires
DEFF Research Database (Denmark)
Yvind, Kresten; Pu, Minhao; Hvam, Jørn Märcher;
The fast non-linearity of silicon allows Tbit/s optical signal processing. By choosing suitable dimensions of silicon nanowires their dispersion can be tailored to ensure a high nonlinearity at power levels low enough to avoid significant two-photon abso We have fabricated low insertion and propa......The fast non-linearity of silicon allows Tbit/s optical signal processing. By choosing suitable dimensions of silicon nanowires their dispersion can be tailored to ensure a high nonlinearity at power levels low enough to avoid significant two-photon abso We have fabricated low insertion...... and propagation loss silicon nanowires and use them to demonstrate the broadband capabilities of silicon....
Nonlinear dynamics of resistive electrostatic drift waves
DEFF Research Database (Denmark)
Korsholm, Søren Bang; Michelsen, Poul; Pécseli, H.L.
1999-01-01
The evolution of weakly nonlinear electrostatic drift waves in an externally imposed strong homogeneous magnetic field is investigated numerically in three spatial dimensions. The analysis is based on a set of coupled, nonlinear equations, which are solved for an initial condition which is pertur......The evolution of weakly nonlinear electrostatic drift waves in an externally imposed strong homogeneous magnetic field is investigated numerically in three spatial dimensions. The analysis is based on a set of coupled, nonlinear equations, which are solved for an initial condition which...... is perturbed by a small amplitude incoherent wave-field. The initial evolution is exponential, following the growth of perturbations predicted by linear stability theory. The fluctuations saturate at relatively high amplitudes, by forming a pair of magnetic field aligned vortex-like structures of opposite...
Nonlinear evolution of whistler wave modulational instability
DEFF Research Database (Denmark)
Karpman, V.I.; Lynov, Jens-Peter; Michelsen, Poul;
1995-01-01
The nonlinear evolution of the modulational instability of whistler waves coupled to fast magnetosonic waves (FMS) and to slow magnetosonic waves (SMS) is investigated. Results from direct numerical solutions in two spatial dimensions agree with simplified results from a set of ordinary different......The nonlinear evolution of the modulational instability of whistler waves coupled to fast magnetosonic waves (FMS) and to slow magnetosonic waves (SMS) is investigated. Results from direct numerical solutions in two spatial dimensions agree with simplified results from a set of ordinary...
Seider, Warren D.; Ungar, Lyle H.
1987-01-01
Describes a course in nonlinear mathematics courses offered at the University of Pennsylvania which provides an opportunity for students to examine the complex solution spaces that chemical engineers encounter. Topics include modeling many chemical processes, especially those involving reaction and diffusion, auto catalytic reactions, phase…
GLOBAL ATTRACTOR FOR THE NONLINEAR STRAIN WAVES IN ELASTIC WAVEGUIDES
Institute of Scientific and Technical Information of China (English)
戴正德; 杜先云
2001-01-01
In this paper the authors consider the initial boundary value problems of the generalized nonlinear strain waves in elastic waveguides and prove the existence of global attractors and thefiniteness of the Hausdorff and the fractal dimensions of the attractors.
Selective Attention to Perceptual Dimensions and Switching between Dimensions
Meiran, Nachshon; Dimov, Eduard; Ganel, Tzvi
2013-01-01
In the present experiments, the question being addressed was whether switching attention between perceptual dimensions and selective attention to dimensions are processes that compete over a common resource? Attention to perceptual dimensions is usually studied by requiring participants to ignore a never-relevant dimension. Selection failure…
Cultural dimensions and innovation
Directory of Open Access Journals (Sweden)
Anna Strychalska-Rudzewicz
2015-11-01
Full Text Available This paper examines the effect of culture’s dimensions on national innovation index. The results of Pearson correlation coefficient between culture dimensions and the Global Innovation Index (GII are very similar to the results obtained in the case of Summary Innovation Index (SII in European countries. The strong negative correlation was observed in the case of power distance and uncertainty avoidance whereas individualism has a positive effect on innovation index. The results suggest that low power distance and uncertainty-accepting countries may be more innovative than high power distance and uncertainty-avoiding societies.
Multichannel transfer function with dimensionality reduction
Kim, Han Suk
2010-01-17
The design of transfer functions for volume rendering is a difficult task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for transfer function design. Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. In this publication we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal microscopy data.
Bifurcation of Fredholm Maps II; The Dimension of the Set of Bifurcation Points
Pejsachowicz, Jacobo
2010-01-01
We obtain an estimate for the covering dimension of the set of bifurcation points for solutions of nonlinear elliptic boundary value problems from the principal symbol of the linearization of the problem along the trivial branch of solutions.
Spatiotemporal accessible solitons in fractional dimensions
Zhong, Wei-Ping; Malomed, Boris A; Zhang, Yiqi; Huang, Tingwen
2016-01-01
We report solutions for solitons of the "accessible" type in globally nonlocal nonlinear media of fractional dimension (FD), viz., for self-trapped modes in the space of effective dimension $2
Capturing Ridge Functions in High Dimensions from Point Queries
Cohen, Albert
2011-12-21
Constructing a good approximation to a function of many variables suffers from the "curse of dimensionality". Namely, functions on ℝ N with smoothness of order s can in general be captured with accuracy at most O(n -s/N) using linear spaces or nonlinear manifolds of dimension n. If N is large and s is not, then n has to be chosen inordinately large for good accuracy. The large value of N often precludes reasonable numerical procedures. On the other hand, there is the common belief that real world problems in high dimensions have as their solution, functions which are more amenable to numerical recovery. This has led to the introduction of models for these functions that do not depend on smoothness alone but also involve some form of variable reduction. In these models it is assumed that, although the function depends on N variables, only a small number of them are significant. Another variant of this principle is that the function lives on a low dimensional manifold. Since the dominant variables (respectively the manifold) are unknown, this leads to new problems of how to organize point queries to capture such functions. The present paper studies where to query the values of a ridge function f(x)=g(a · x) when both a∈ℝ N and g ∈ C[0,1] are unknown. We establish estimates on how well f can be approximated using these point queries under the assumptions that g ∈ C s[0,1]. We also study the role of sparsity or compressibility of a in such query problems. © 2011 Springer Science+Business Media, LLC.
Nonlinear Schroedinger excitations scattering on local barrier in one dimension
Kovrizhin, D L
2001-01-01
The task on the excitations scattering of the Bose condensate under consideration on the unidimensional barrier is nontrivial one even in the case of a low barrier because the barrier itself and change in the condensate density in its vicinity play the similar important role. It is shown that if any repulsive barrier for a bare particle within the range of the waves high lengths is impermeable, than the coefficient of the delta-functional transmission for the phonons within this range strives to the unity and the barrier becomes transparent
Dimension-Independent Likelihood-Informed MCMC
Cui, Tiangang
2015-01-07
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters, which in principle can be described as functions. By exploiting low-dimensional structure in the change from prior to posterior [distributions], we introduce a suite of MCMC samplers that can adapt to the complex structure of the posterior distribution, yet are well-defined on function space. Posterior sampling in nonlinear inverse problems arising from various partial di erential equations and also a stochastic differential equation are used to demonstrate the e ciency of these dimension-independent likelihood-informed samplers.
Directory of Open Access Journals (Sweden)
Ross S Williamson
2015-04-01
Full Text Available Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID, uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex.
Williamson, Ross S; Sahani, Maneesh; Pillow, Jonathan W
2015-04-01
Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex.
Dimensionality of InGaAs nonlinear optical response
Energy Technology Data Exchange (ETDEWEB)
Bolton, S.R. [Univ. of California, Berkeley, CA (United States). Dept. of Physics]|[Lawrence Berkeley National Lab., CA (United States). Materials Sciences Div.
1995-07-01
In this thesis the ultrafast optical properties of a series of InGaAs samples ranging from the two to the three dimensional limit are discussed. An optical system producing 150 fs continuum centered at 1.5 microns was built. Using this system, ultrafast pump-probe and four wave mixing experiments were performed. Carrier thermalization measurements reveal that screening of the Coulomb interaction is relatively unaffected by confinement, while Pauli blocking nonlinearities at the band edge are approximately twice as strong in two dimensions as in three. Carrier cooling via phonon emission is influenced by confinement due both to the change in electron distribution function and the reduction in electron phonon coupling. Purely coherent band edge effects, as measured by the AC Stark effect and four wave mixing, are found to be dominated by the changes in excitonic structure which take place with confinement.
Improved fiber nonlinearity mitigation in dispersion managed optical OFDM links
Tamilarasan, Ilavarasan; Saminathan, Brindha; Murugappan, Meenakshi
2017-02-01
Fiber nonlinearity is seen as a capacity limiting factor in OFDM based dispersion managed links since the Four Wave Mixing effects become enhanced due to the high PAPR. In this paper, the authors have compared the linear and nonlinear PAPR reduction techniques for fiber nonlinearity mitigation in OFDM based dispersion managed links. In the existing optical systems, linear transform techniques such as SLM and PTS have been implemented to reduce nonlinear effects. In the proposed study, superior performance of the L2-by-3 nonlinear transform technique is demonstrated for PAPR reduction to mitigate fiber nonlinearities. The performance evaluation is carried out by interfacing multiple simulators. The results of both linear and nonlinear transform techniques have been compared and the results show that nonlinear transform technique outperforms the linear transform in terms of nonlinearity mitigation and improved BER performance.
DEFF Research Database (Denmark)
Wölfel, Christiane; Merritt, T.
2013-01-01
. The card-based tools are explained in terms of five design dimensions including the intended purpose and scope of use, duration of use, methodology, customization, and formal/material qualities. Our analysis suggests three design patterns or archetypes for existing card-based design method tools...
DEFF Research Database (Denmark)
Frederiksen, Morten
2012-01-01
in scope and mode influenced by the intersecting dimensions of relations, objects and situations. Furthermore, trust exists between an outer threshold of expected deceit and an inner threshold of confident reliance. The findings from the qualitative study contribute new knowledge on the diversity of trust...
Reduced nonlinear prognostic model construction from high-dimensional data
Gavrilov, Andrey; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander
2017-04-01
Construction of a data-driven model of evolution operator using universal approximating functions can only be statistically justified when the dimension of its phase space is small enough, especially in the case of short time series. At the same time in many applications real-measured data is high-dimensional, e.g. it is space-distributed and multivariate in climate science. Therefore it is necessary to use efficient dimensionality reduction methods which are also able to capture key dynamical properties of the system from observed data. To address this problem we present a Bayesian approach to an evolution operator construction which incorporates two key reduction steps. First, the data is decomposed into a set of certain empirical modes, such as standard empirical orthogonal functions or recently suggested nonlinear dynamical modes (NDMs) [1], and the reduced space of corresponding principal components (PCs) is obtained. Then, the model of evolution operator for PCs is constructed which maps a number of states in the past to the current state. The second step is to reduce this time-extended space in the past using appropriate decomposition methods. Such a reduction allows us to capture only the most significant spatio-temporal couplings. The functional form of the evolution operator includes separately linear, nonlinear (based on artificial neural networks) and stochastic terms. Explicit separation of the linear term from the nonlinear one allows us to more easily interpret degree of nonlinearity as well as to deal better with smooth PCs which can naturally occur in the decompositions like NDM, as they provide a time scale separation. Results of application of the proposed method to climate data are demonstrated and discussed. The study is supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS). 1. Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical
Energy Technology Data Exchange (ETDEWEB)
Lallart, Mickael; Guyomar, Daniel, E-mail: mickael.lallart@insa-lyon.fr [LGEF, INSA-Lyon, Universite de Lyon, 8 rue de la Physique, F-69621 (France)
2011-10-29
The proliferation of wearable and left-behind devices has raised the issue of powering such systems. While primary batteries have been widely used in order to address this issue, recent trends have focused on energy harvesting products that feature high reliability and low maintenance issues. Among all the ambient sources available for energy harvesting, vibrations and heat have been of significant interest among the research community for small-scale devices. However, the conversion abilities of materials are still limited when dealing with systems featuring small dimensions. The purpose of this paper is to presents an up-to-date view of nonlinear approaches for increasing the efficiency of electromechanical and electrocaloric conversion mechanisms. From the modeling of the operation principles of the different architectures, a comparative analysis will be exposed, emphasizing the advantages and drawbacks of the presented concepts, in terms of maximal output power (under constant vibration magnitude or taking into account the damping effect), load independence, and implementation easiness.
Reduced-dimension multiuser detection: detectors and performance guarantees
Xie, Yao; Goldsmith, Andrea
2011-01-01
We explore several reduced-dimension multiuser detection (RD-MUD) structures that significantly decrease the number of required correlation branches at the receiver front-end, while still achieving performance similar to that of the conventional matched-filter (MF) bank. RD-MUD exploits the fact that the number of active users is typically small relative to the total number of users in the system and relies on ideas of analog compressed sensing to reduce the number of correlators. We first develop a general framework for both linear and nonlinear RD-MUD detectors. We then present theoretical performance analysis for two specific detectors: the linear reduced-dimension decorrelating (RDD) detector, which combines subspace projection and thresholding to determine active users and sign detection for data recovery, and the nonlinear reduced-dimension decision-feedback (RDDF) detector, which combines decision-feedback orthogonal matching pursuit for active user detection and sign detection for data recovery. The t...
Design of a nonlinear torsional vibration absorber
Tahir, Ammaar Bin
Tuned mass dampers (TMD) utilizing linear spring mechanisms to mitigate destructive vibrations are commonly used in practice. A TMD is usually tuned for a specific resonant frequency or an operating frequency of a system. Recently, nonlinear vibration absorbers attracted attention of researchers due to some potential advantages they possess over the TMDs. The nonlinear vibration absorber, or the nonlinear energy sink (NES), has an advantage of being effective over a broad range of excitation frequencies, which makes it more suitable for systems with several resonant frequencies, or for a system with varying excitation frequency. Vibration dissipation mechanism in an NES is passive and ensures that there is no energy backflow to the primary system. In this study, an experimental setup of a rotational system has been designed for validation of the concept of nonlinear torsional vibration absorber with geometrically induced cubic stiffness nonlinearity. Dimensions of the primary system have been optimized so as to get the first natural frequency of the system to be fairly low. This was done in order to excite the dynamic system for torsional vibration response by the available motor. Experiments have been performed to obtain the modal parameters of the system. Based on the obtained modal parameters, the design optimization of the nonlinear torsional vibration absorber was carried out using an equivalent 2-DOF modal model. The optimality criterion was chosen to be maximization of energy dissipation in the nonlinear absorber attached to the equivalent 2-DOF system. The optimized design parameters of the nonlinear absorber were tested on the original 5-DOF system numerically. A comparison was made between the performance of linear and nonlinear absorbers using the numerical models. The comparison showed the superiority of the nonlinear absorber over its linear counterpart for the given set of primary system parameters as the vibration energy dissipation in the former is
2015-01-01
From the Back Cover: The emphasis throughout the present volume is on the practical application of theoretical mathematical models helping to unravel the underlying mechanisms involved in processes from mathematical physics and biosciences. It has been conceived as a unique collection of abstract methods dealing especially with nonlinear partial differential equations (either stationary or evolutionary) that are applied to understand concrete processes involving some important applications re...
Institute of Scientific and Technical Information of China (English)
张斌; 庄池杰; 胡军; 陈水明; 张明明; 王科; 曾嵘
2015-01-01
large datasets clustering. Various techniques for reducing the dimension of the input datasets were studied and the results were compared from perspectives of computing time and information losses. The results indicate that the combination of principal component analysis and ensemble clustering algorithm performs better both in efficiency and accuracy for clustering large-scale load profiles.
Linear and Nonlinear Thinking: A Multidimensional Model and Measure
Groves, Kevin S.; Vance, Charles M.
2015-01-01
Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…
Linear and Nonlinear Thinking: A Multidimensional Model and Measure
Groves, Kevin S.; Vance, Charles M.
2015-01-01
Building upon previously developed and more general dual-process models, this paper provides empirical support for a multidimensional thinking style construct comprised of linear thinking and multiple dimensions of nonlinear thinking. A self-report assessment instrument (Linear/Nonlinear Thinking Style Profile; LNTSP) is presented and…
Defocusing regimes of nonlinear waves in media with negative dispersion
DEFF Research Database (Denmark)
Bergé, L.; Kuznetsov, E.A.; Juul Rasmussen, J.
1996-01-01
Defocusing regimes of quasimonochromatic waves governed by a nonlinear Schrodinger equation with mixed-sign dispersion are investigated. For a power-law nonlinearity, we show that localized solutions to this equation defined at the so-called critical dimension cannot collapse in finite time...
Global Well-Posedness for Cubic NLS with Nonlinear Damping
Antonelli, Paolo
2010-11-04
We study the Cauchy problem for the cubic nonlinear Schrödinger equation, perturbed by (higher order) dissipative nonlinearities. We prove global in-time existence of solutions for general initial data in the energy space. In particular we treat the energy-critical case of a quintic dissipation in three space dimensions. © Taylor & Francis Group, LLC.
Cosmology With Extra Dimensions
Martín, J
2005-01-01
We review several properties of models that include extra dimensions, focusing on aspects related to cosmology and particle physics phenomenology. The properties of effective four dimensional inflationary geometry are studied in two distinct frameworks: (i) in Kaluza- Klein (KK) compactifications and (ii) in braneworld scenarios. From numerical simulations we find that inflationary braneworlds are unstable if the scale of inflation is too large in comparison with the stabilization scale of the interbrane distance. The analysis of perturbations confirms the existence of a tachyon associated with the volume modulus of the extra dimensions both in braneworlds and KK compactifications. With the numerical program BRANECODE non- perturbative properties of braneworlds are studied. We fully understand the non-perturbative consequences of this instability. Generic attractors are (i) an increase of the interbrane distance and the formation of a naked singularity, (ii) the brane colli...
Cultural dimensions of learning
Eyford, Glen A.
1990-06-01
How, what, when and where we learn is frequently discussed, as are content versus process, or right brain versus left brain learning. What is usually missing is the cultural dimension. This is not an easy concept to define, but various aspects can be identified. The World Decade for Cultural Development emphasizes the need for a counterbalance to a quantitative, economic approach. In the last century poets also warned against brutalizing materialism, and Sorokin and others have described culture more recently in terms of cohesive basic values expressed through aesthetics and institutions. Bloom's taxonomy incorporates the category of affective learning, which internalizes values. If cultural learning goes beyond knowledge acquisition, perhaps the surest way of understanding the cultural dimension of learning is to examine the aesthetic experience. This can use myths, metaphors and symbols, and to teach and learn by using these can help to unlock the human potential for vision and creativity.
Introduction to Extra Dimensions
Energy Technology Data Exchange (ETDEWEB)
Rizzo, Thomas G.; /SLAC
2010-04-29
Extra dimensions provide a very useful tool in addressing a number of the fundamental problems faced by the Standard Model. The following provides a very basic introduction to this very broad subject area as given at the VIII School of the Gravitational and Mathematical Physics Division of the Mexican Physical Society in December 2009. Some prospects for extra dimensional searches at the 7 TeV LHC with {approx}1 fb{sup -1} of integrated luminosity are provided.
DEFF Research Database (Denmark)
Eskjær, Mikkel Fugl
2013-01-01
is largely dependent on regional media systems, yet the role this regional dimension plays has been largely overlooked. This article presents a comparative study of climate-change coverage in three geo-cultural regions, The Middle East, Scandinavia, and North America, and explores the link between global...... climate-change communication and regional media systems. It finds that regional variations in climate-change communication carry important communicative implications concerning perceptions of climate change's relevance and urgency...
Average Transient Lifetime and Lyapunov Dimension for Transient Chaos in a High-Dimensional System
Institute of Scientific and Technical Information of China (English)
陈洪; 汤建新; 唐少炎; 向红; 陈新
2001-01-01
The average transient lifetime of a chaotic transient versus the Lyapunov dimension of a chaotic saddle is studied for high-dimensional nonlinear dynamical systems. Typically the average lifetime depends upon not only the system parameter but also the Lyapunov dimension of the chaotic saddle. The numerical example uses the delayed feedback differential equation.
The necessity for a time local dimension in systems with time-varying attractors
DEFF Research Database (Denmark)
Særmark, Knud H; Ashkenazy, Y; Levitan, J;
1997-01-01
We show that a simple non-linear system for ordinary differential equations may possess a time-varying attractor dimension. This indicates that it is infeasible to characterize EEG and MEG time series with a single time global dimension. We suggest another measure for the description of non...
Quantum well nonlinear microcavities
Oudar, J. L.; Kuszelewicz, R.; Sfez, B.; Pellat, D.; Azoulay, R.
We report on recent progress in reducing the power threshold of all-optical bistable quantum well vertical microcavities. Significant improvements are achieved through an increase of the cavity finesse, together with a reduction of the device active layer thickness. A critical intensity of 5 μW/μm 2 has been observed on a microcavity of finesse 250, with a nonlinear medium of only 18 GaAs quantum wells of 10 nm thickness. Further improvements of the Bragg mirror quality resulted in a finesse of 700 and a power-lifetime product of 15 fJ/μm 2. Microresonator pixellation allows to obtain 2-dimensional arrays. A thermally-induced alloy-mixing technique is described, which produced a 110 meV carrier confinement energy, together with a refractive index change of -.012, averaged over the 2.6 μm nonlinear medium thickness. The resulting electrical and optical confinement is shown to improve the nonlinear characteristics, by limiting lateral carrier diffusion and light diffraction.
Physical mechanisms of nonlinear conductivity: A model analysis
Heuer, Andreas; Lühning, Lars
2014-03-01
Nonlinear effects are omnipresent in thin films of ion conducting materials showing up as a significant increase of the conductivity. For a disordered hopping model general physical mechanisms are identified giving rise to the occurrence of positive or negative nonlinear effects, respectively. Analytical results are obtained in the limit of high but finite dimensions. They are compared with the numerical results for 3D up to 6D systems. A very good agreement can be found, in particular for higher dimensions. The results can also be used to rationalize previous numerical simulations. The implications for the interpretation of nonlinear conductivity experiments on inorganic ion conductors are discussed.
Linear and nonlinear approach for DEM smoothening
Directory of Open Access Journals (Sweden)
2006-01-01
Full Text Available One of the biggest problems faced while analyzing digital elevation models (DEMs, particularly DEMs that are produced using photogrammetry, is to avoid pits and peaks in DEMs. Peaks and pits, which are errors, are generated during the surface generation process. DEM smoothening is an important preprocessing step meant for removing these errors. This paper discusses two linear DEM smoothening methods, Gaussian blurring and mean smoothening, and two nonlinear DEM smoothening methods, morphological smoothening and morphological smoothening by reconstruction. The four methods are implemented on a photogrammetrically generated DEM. The drainage network of the resultant DEM is obtained using skeletonization by morphological thinning, and the fractal dimension of the extracted network is computed using the box dimension method. The fractal dimensions are then compared to study the effects of the four smoothening methods. The advantages of nonlinear DEM smoothening over linear DEM smoothening are discussed. This study is useful in landscape descriptions.
C T for non-unitary CFTs in higher dimensions
Osborn, Hugh; Stergiou, Andreas
2016-06-01
The coefficient C T of the conformal energy-momentum tensor two-point function is determined for the non-unitary scalar CFTs with four- and six-derivative kinetic terms. The results match those expected from large- N calculations for the CFTs arising from the O( N) non-linear sigma and Gross-Neveu models in specific even dimensions. C T is also calculated for the CFT arising from ( n - 1)-form gauge fields with derivatives in 2 n + 2 dimensions. Results for ( n - 1)-form theory extended to general dimensions as a non-gauge-invariant CFT are also obtained; the resulting C T differs from that for the gauge-invariant theory. The construction of conformal primaries by subtracting descendants of lower-dimension primaries is also discussed. For free theories this also leads to an alternative construction of the energy-momentum tensor, which can be quite involved for higher-derivative theories.
Classification of surface EMG signal with fractal dimension
Institute of Scientific and Technical Information of China (English)
HU Xiao; WANG Zhi-zhong; REN Xiao-mei
2005-01-01
Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm supination (FS)or forearm pronation (FP). After the high frequency noise is filtered from surface EMG signal by a low-pass filter, fractal dimension is calculated from the filtered surface EMG signal. The results showed that the fractal dimensions of filtered FS surface EMG signals and those of filtered FP surface EMG signals distribute in two different regions, so the fractal dimensions can represent different patterns of surface EMG signals.
Robust methods for data reduction
Farcomeni, Alessio
2015-01-01
Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications. The main areas covered include principal components analysis, sparse principal component analysis, canonical correlation analysis, factor analysis, clustering, double clustering, and discriminant analysis.The first part of the book illustrates how dimension reduction techniques synthesize available information by reducing the dimensionality of the data. The second part focuses on cluster and discriminant analy
DEFF Research Database (Denmark)
Andersen, lotte bøgh; Beck Jørgensen, Torben; Kjeldsen, Anne-Mette
2012-01-01
Further integration of the public value literature with other strands of literature within Public Administration necessitates a more specific classification of public values. This paper applies a typology linked to organizational design principles, because this is useful for empirical public...... administration studies. Based on an existing typology of modes of governance, we develop a classification and test it empirically, using survey data from a study of the values of 501 public managers. We distinguish between seven value dimensions (the public at large, rule abidance, societal interests, budget...... the integration between the public value literature and other parts of the Public Administration discipline....
Nonlinear Dynamic Analysis of MPEG-4 Video Traffic
Institute of Scientific and Technical Information of China (English)
GE Fei; CAO Yang; WANG Yuan-ni
2005-01-01
The main research motive is to analysis and to verify the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/fβ and periodic characteristics. The principal components analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts. So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear behavior and can be characterized by its power spectral density, principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.
Adjoint Functors and Representation Dimensions
Institute of Scientific and Technical Information of China (English)
Chang Chang XI
2006-01-01
We study the global dimensions of the coherent functors over two categories that are linked by a pair of adjoint functors. This idea is then exploited to compare the representation dimensions of two algebras. In particular, we show that if an Artin algebra is switched from the other, then they have the same representation dimension.
Rajasekar, Shanmuganathan
2016-01-01
This introductory text presents the basic aspects and most important features of various types of resonances and anti-resonances in dynamical systems. In particular, for each resonance, it covers the theoretical concepts, illustrates them with case studies, and reviews the available information on mechanisms, characterization, numerical simulations, experimental realizations, possible quantum analogues, applications and significant advances made over the years. Resonances are one of the most fundamental phenomena exhibited by nonlinear systems and refer to specific realizations of maximum response of a system due to the ability of that system to store and transfer energy received from an external forcing source. Resonances are of particular importance in physical, engineering and biological systems - they can prove to be advantageous in many applications, while leading to instability and even disasters in others. The book is self-contained, providing the details of mathematical derivations and techniques invo...
Institute of Scientific and Technical Information of China (English)
1996-01-01
3.1 A Unified Nonlinear Feedback Functional Method for Study Both Control and Synchronization of Spatiotemporal Chaos Fang Jinqing Ali M. K. (Department of Physics, The University of Lethbridge,Lethbridge, Alberta T1K 3M4,Canada) Two fundamental questions dominate future chaos control theories.The first is the problem of controlling hyperchaos in higher dimensional systems.The second question has yet to be addressed:the problem of controlling spatiotemporal chaos in a spatiotemporal system.In recent years, control and synchronization of spatiotemporal chaos and hyperchaos have became a much more important and challenging subject. The reason for this is the control and synchronism of such behaviours have extensive and great potential of interdisciplinary applications, such as security communication, information processing, medicine and so on. However, this subject is not much known and remains an outstanding open.
PREFACE Integrability and nonlinear phenomena Integrability and nonlinear phenomena
Gómez-Ullate, David; Lombardo, Sara; Mañas, Manuel; Mazzocco, Marta; Nijhoff, Frank; Sommacal, Matteo
2010-10-01
sleigh Fedorov Y N and Garcia-Naranjo L C [21] A normal form for beam and non-local nonlinear Schroedinger equations Procesi M [22] Nonlocal transformations and linearization of second-order ordinary differential equations Muriel and Romero J L [23] Reductions of integrable equations on A.III-type symmetric spaces Gerdjikov V S, Mikhailov A V and Valchev T I [24] On Darboux-integrable semi-discrete chains Habibullin I, Zheltukhina N and Sakieva A [25] Loop coproducts, Gaudin models and Poisson coalgebras Musso F [26] Classification of integrable hydrodynamic chains Odesskii A V and Sokolov V V [27] Noncommutative Schur polynomials and the crystal limit of the Uq sl(2)-vertex model Korff C [28] Axially symmetric soliton solutions in a Skyrme-Faddeev-type model with Gies's extension Ferreira L A, Sawado N and Toda K [29] Vortices on hyperbolic surfaces Manton N S and Rink N A [30] Multivariate hypergeometric cascades, isomonodromy problems and Ward ansatze Shah M R and Woodhouse N J M [31] Coherently coupled bright optical solitons and their collisions Kanna T, Vijayajayanthi M and Lakshmanan M [32] Isochronous rate equations describing chemical reactions Calogero F, Leyvraz F and Sommacal M [33] Asymptotic expansions for solitary gravity-capillary waves in two and three dimensions Ablowitz M J and Haut T S
Modulational instability of plasma waves in two dimensions
DEFF Research Database (Denmark)
Karpman, V.I.; Lynov, Jens-Peter; Michelsen, Poul
1996-01-01
The nonlinear behavior of whistler waves coupled to either fast magnetosonic waves (FMS) or slow magnetosonic waves (SMS) is investigated in two spatial dimensions. For each branch our investigation is based on a numerical solution of a reduced set of equations consisting of two partial...... differential equations, of which one, describing the evolution of the whistler wave envelope, is complex of first order in time and the other, describing the slow response of the medium in which the whistler wave is propagating, is real and of second order in time. These equations were solved in a two...... of nonlinear waves in dispersive media....
Nonlinear analysis of EAS clusters
Zotov, M Yu; Fomin, Y A; Fomin, Yu. A.
2002-01-01
We apply certain methods of nonlinear time series analysis to the extensive air shower clusters found earlier in the data set obtained with the EAS-1000 Prototype array. In particular, we use the Grassberger-Procaccia algorithm to compute the correlation dimension of samples in the vicinity of the clusters. The validity of the results is checked by surrogate data tests and some additional quantities. We compare our conclusions with the results of similar investigations performed by the EAS-TOP and LAAS groups.
Nonlinear Multigrid for Reservoir Simulation
DEFF Research Database (Denmark)
Christensen, Max la Cour; Eskildsen, Klaus Langgren; Engsig-Karup, Allan Peter
2016-01-01
A feasibility study is presented on the effectiveness of applying nonlinear multigrid methods for efficient reservoir simulation of subsurface flow in porous media. A conventional strategy modeled after global linearization by means of Newton’s method is compared with an alternative strategy...... modeled after local linearization, leading to a nonlinear multigrid method in the form of the full-approximation scheme (FAS). It is demonstrated through numerical experiments that, without loss of robustness, the FAS method can outperform the conventional techniques in terms of algorithmic and numerical...... efficiency for a black-oil model. Furthermore, the use of the FAS method enables a significant reduction in memory usage compared with conventional techniques, which suggests new possibilities for improved large-scale reservoir simulation and numerical efficiency. Last, nonlinear multilevel preconditioning...
Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex
Zhuo, Ran; Xue, Hongbo; Chambers, Anna R.; Kolaczyk, Eric; Polley, Daniel B.
2016-01-01
Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices. PMID:27622211
Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex.
Sloas, David C; Zhuo, Ran; Xue, Hongbo; Chambers, Anna R; Kolaczyk, Eric; Polley, Daniel B; Sen, Kamal
2016-01-01
Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices.
Urban Screen and Spatial Dimension
Directory of Open Access Journals (Sweden)
Litta Primasari
2013-11-01
Full Text Available This paper is discussing about the urban screen phenomena and its influence to spatial dimension in urban space. The visual characteristics which are forming a spatial dimension will be an emphasis to be presented. Urban screen as a visual intervention has an impact to spatial configuration in urban space. The space dimension was not dominated with materiality limitation, but also images. We have to consider that people senses can measure a spatial dimension, knowing as a perception. That is a human visual and mind relation. The spatial dimension has no longer tangible boundary, but also has intangible ones. Spatial dimension in urban screens phenomena is not merely mathematics, nor spatial dimension in physics which are based on a three-dimensional Cartesian coordinate system. Movement can be expressed in other terms, by how far we can move depends on our eyes to catch that space limitation, and how fast we can move is depends on our mind to perceive some visual phenomenon, that is a spatial dimension. So, the dimension will depend on a visual quality that we perceived. The movement of the body and people’s thought will be an important term to generate the space dimension in urban screen phenomenon. The activity of body’s movement and thought will influence the depth of space dimension.
Correlation dimension of financial market
Nie, Chun-Xiao
2017-05-01
In this paper, correlation dimension is applied to financial data analysis. We calculate the correlation dimensions of some real market data and find that the dimensions are significantly smaller than those of the simulation data based on geometric Brownian motion. Based on the analysis of the Chinese and US stock market data, the main results are as follows. First, by calculating three data sets for the Chinese and US market, we find that large market volatility leads to a significant decrease in the dimensions. Second, based on 5-min stock price data, we find that the Chinese market dimension is significantly larger than the US market; this shows a significant difference between the two markets for high frequency data. Third, we randomly extract stocks from a stock set and calculate the correlation dimensions, and find that the average value of these dimensions is close to the dimension of the original set. In addition, we analyse the intuitional meaning of the relevant dimensions used in this paper, which are directly related to the average degree of the financial threshold network. The dimension measures the speed of the average degree that varies with the threshold value. A smaller dimension means that the rate of change is slower.
Local dimension and finite time prediction in coupled map lattices
Indian Academy of Sciences (India)
P Muruganandam; G Francisco
2005-03-01
Forecasting, for obvious reasons, often become the most important goal to be achieved. For spatially extended systems (e.g. atmospheric system) where the local nonlinearities lead to the most unpredictable chaotic evolution, it is highly desirable to have a simple diagnostic tool to identify regions of predictable behaviour. In this paper, we discuss the use of the bred vector (BV) dimension, a recently introduced statistics, to identify the regimes where a finite time forecast is feasible. Using the tools from dynamical systems theory and Bayesian modelling, we show the finite time predictability in two-dimensional coupled map lattices in the regions of low BV dimension.
Dimension Reduction Near Periodic Orbits of Hybrid Systems: Appendix
2011-09-07
Workshop on Lagrangian and Hamiltonian Meth for Nonlin Ctrl, 2006. [28] J.E. Marsden and T.S. Ratiu. Introduction to mechanics and symmetry. Springer-Verlag...suggest a mechanism by which a many-legged locomotor may formally collapse a large number of degrees- of-freedom to produce a low-dimensional coordinated...S.V. Gusev. Transverse lineariza- tion for controlled mechanical systems with several passive degrees of freedom. IEEE TAC, 55(4):893–906, 2010. [26] I
Dimension Reduction Near Periodic Orbits of Hybrid Systems
Burden, Samuel; Sastry, S Shankar
2011-01-01
When the Poincar\\'{e} map associated with a periodic orbit of a hybrid dynamical system has constant-rank iterates, we demonstrate the existence of a constant-dimensional invariant subsystem near the orbit which attracts all nearby trajectories in finite time. This result shows that the long-term behavior of a hybrid model with a large number of degrees-of-freedom may be governed by a low-dimensional smooth dynamical system. The appearance of such simplified models enables the translation of analytical tools from smooth systems-such as Floquet theory-to the hybrid setting and provides a bridge between the efforts of biologists and engineers studying legged locomotion.
Limited Rank Matrix Learning, discriminative dimension reduction and visualization
Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael
2012-01-01
We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-di
Segmentation and Dimension Reduction: Exploratory and Model-Based Approaches
J.M. van Rosmalen (Joost)
2009-01-01
textabstractRepresenting the information in a data set in a concise way is an important part of data analysis. A variety of multivariate statistical techniques have been developed for this purpose, such as k-means clustering and principal components analysis. These techniques are often based on the
Bifurcation analysis to the Lugiato-Lefever equation in one space dimension
Miyaji, T.; Ohnishi, I.; Tsutsumi, Y.
2010-11-01
We study the stability and bifurcation of steady states for a certain kind of damped driven nonlinear Schrödinger equation with cubic nonlinearity and a detuning term in one space dimension, mathematically in a rigorous sense. It is known by numerical simulations that the system shows lots of coexisting spatially localized structures as a result of subcritical bifurcation. Since the equation does not have a variational structure, unlike the conservative case, we cannot apply a variational method for capturing the ground state. Hence, we analyze the equation from a viewpoint of bifurcation theory. In the case of a finite interval, we prove the fold bifurcation of nontrivial stationary solutions around the codimension two bifurcation point of the trivial equilibrium by exact computation of a fifth-order expansion on a center manifold reduction. In addition, we analyze the steady-state mode interaction and prove the bifurcation of mixed-mode solutions, which will be a germ of localized structures on a finite interval. Finally, we study the corresponding problem on the entire real line by use of spatial dynamics. We obtain a small dissipative soliton bifurcated adequately from the trivial equilibrium.
Jackson, David J
2016-01-01
A physical theory of the world is presented under the unifying principle that all of nature is laid out before us and experienced through the passage of time. The one-dimensional progression in time is opened out into a multi-dimensional mathematically consistent flow, with the simplicity of the former giving rise to symmetries of the latter. The act of perception identifies an extended spacetime arena of intermediate dimension, incorporating the symmetry of geometric spatial rotations, against which physical objects are formed and observed. The spacetime symmetry is contained as a subgroup of, and provides a natural breaking mechanism for, the higher general symmetry of time. It will be described how the world of gravitation and cosmology, as well as quantum theory and particle physics, arises from these considerations.
DEFF Research Database (Denmark)
Wölfel, Christiane; Merritt, T.
2013-01-01
. The card-based tools are explained in terms of five design dimensions including the intended purpose and scope of use, duration of use, methodology, customization, and formal/material qualities. Our analysis suggests three design patterns or archetypes for existing card-based design method tools...... and highlights unexplored areas in the design space. The paper concludes with recommendations for the future development of card-based methods for the field of interaction design.......There are many examples of cards used to assist or provide structure to the design process, yet there has not been a thorough articulation of the strengths and weaknesses of the various examples. We review eighteen card-based design tools in order to understand how they might benefit designers...
Schweitzer, Eugen
2009-01-01
In different passages of his dialogues, Plato showed deep mathematically-based physical insights. Regrettably most readers overlooked the respective statements, or they utterly did not understand those hints since they were full of philological fallacious terms. Respectable translators misinterpreted such statements and therefore Plato's respective remarks were not recognized as substantial knowledge. Furthermore, Plato often supplemented such basic remarks by diffusely veiled and varied allusions that were often ironically hidden somewhere in his dialogues by inconspicuous double meanings. However, this mode of intentionally coded discrete communication was generally not understood because such irony is not to everyone's taste. However, the attempts to reconstruct Plato's system on the basis of admittedly individually interpreted double meanings lead to a conclusive mathematical-physical cyclical system of dimensions. Additionally it was possible to assign Plato's system of philosophical ideas analogously to...
Phenomenology of Extra Dimensions
Energy Technology Data Exchange (ETDEWEB)
Hewett, J.L.; /SLAC
2006-11-07
If the structure of spacetime is different than that readily observed, gravitational physics, particle physics and cosmology are all immediately affected. The physics of extra dimensions offers new insights and solutions to fundamental questions arising in these fields. Novel ideas and frameworks are continuously born and evolved. They make use of string theoretical features and tools and they may reveal if and how the 11-dimensional string theory is relevant to our four-dimensional world. We have outlined some of the experimental observations in particle and gravitational physics as well as astrophysical and cosmological considerations that can constrain or confirm these scenarios. These developing ideas and the wide interdisciplinary experimental program that is charted out to investigate them mark a renewed effort to describe the dynamics behind spacetime. We look forward to the discovery of a higher dimensional spacetime.
Lévay, Péter
2011-01-01
We link the recently discovered black hole-qubit correspondence to the structure of extra dimensions. In particular we show that for toroidal compactifications of type IIB string theory simple qubit systems arise naturally from the geometrical data of the tori parametrized by the moduli. We also generalize the recently suggested idea of the attractor mechanism as a distillation procedure of GHZ-like entangled states on the event horizon, to moduli stabilization for flux attractors in F-theory compactifications on elliptically fibered Calabi-Yau four-folds. Finally using a simple example we show that the natural arena for qubits to show up is an embedded one within the realm of fermionic entanglement of quantum systems with indistinguishable constituents.
Dudas, E; Rubakov, V A
2006-01-01
We analyze the properties of a model with four-dimensional brane-localized Higgs type potential of a six dimensional scalar field satisfying the Dirichlet boundary condition on the boundary of a transverse two-dimensional compact space. The regularization of the localized couplings generates classical renormalization group running. A tachyonic mass parameter grows in the infrared, in analogy with the QCD gauge coupling in four dimensions. We find a phase transition at a critical value of the bare mass parameter such that the running mass parameter becomes large in the infrared precisely at the compactification scale. Below the critical coupling, the theory is in symmetric phase, whereas above it spontaneous symmetry breaking occurs. Close to the phase transition point there is a very light mode in the spectrum. The massive Kaluza-Klein spectrum at the critical coupling becomes independent of the UV cutoff.
Nonlinear Materials Characterization Facility
Federal Laboratory Consortium — The Nonlinear Materials Characterization Facility conducts photophysical research and development of nonlinear materials operating in the visible spectrum to protect...
Large nonlinear w$_{\\infty}$ algebras from nonlinear integrable deformations of self dual gravity
Castro, C
1994-01-01
A proposal for constructing a universal nonlinear {\\hat W}_{\\infty} algebra is made as the symmetry algebra of a rotational Killing-symmetry reduction of the nonlinear perturbations of Moyal-Integrable deformations of D=4 Self Dual Gravity (IDSDG). This is attained upon the construction of a nonlinear bracket based on nonlinear gauge theories associated with infinite dimensional Lie algebras. A Quantization and supersymmetrization program can also be carried out. The relevance to the Kadomtsev-Petviashvili hierarchy, 2D dilaton gravity, quantum gravity and black hole physics is discussed in the concluding remarks.
Nonlinear analysis and prediction of time series in multiphase reactors
Liu, Mingyan
2014-01-01
This book reports on important nonlinear aspects or deterministic chaos issues in the systems of multi-phase reactors. The reactors treated in the book include gas-liquid bubble columns, gas-liquid-solid fluidized beds and gas-liquid-solid magnetized fluidized beds. The authors take pressure fluctuations in the bubble columns as time series for nonlinear analysis, modeling and forecasting. They present qualitative and quantitative non-linear analysis tools which include attractor phase plane plot, correlation dimension, Kolmogorov entropy and largest Lyapunov exponent calculations and local non-linear short-term prediction.
Interactive Dimensioning of Parametric Models
Kelly, T.
2015-05-01
We propose a solution for the dimensioning of parametric and procedural models. Dimensioning has long been a staple of technical drawings, and we present the first solution for interactive dimensioning: A dimension line positioning system that adapts to the view direction, given behavioral properties. After proposing a set of design principles for interactive dimensioning, we describe our solution consisting of the following major components. First, we describe how an author can specify the desired interactive behavior of a dimension line. Second, we propose a novel algorithm to place dimension lines at interactive speeds. Third, we introduce multiple extensions, including chained dimension lines, controls for different parameter types (e.g. discrete choices, angles), and the use of dimension lines for interactive editing. Our results show the use of dimension lines in an interactive parametric modeling environment for architectural, botanical, and mechanical models. © 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
Dimensions and Gravitational Waves
van Haasteren, Rutger
2014-10-01
High-precision timing of Galactic millisecond pulsars with radio telescopes holds great promise for the detection of astrophysical gravitational-waves in frequency range 10--100 nHz. Modern Bayesian data analysis methods rely mostly on Markov Chain Monte Carlo (MCMC) to explore the model parameter space when searching for signals in the pulsar timing data. Current challenges involve parameter spaces with large dimensionality, and linear algebra of high-dimensional systems. I will present sampling methods (taken from the Planck analysis team), and rank-reduction methods for large linear systems, that have enabled us to decrease the dimensionality of such problems. These methods are now being used to search for gravitational-waves in pulsar timing array projects. Especially our rank-reduction techniques are useful for any data analysis problem that involve large linear least-squares systems.
Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram.
Bachmann, Maie; Lass, Jaanus; Suhhova, Anna; Hinrikus, Hiie
2013-01-01
This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi's fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control subjects were used as a database for calculations. SASI values were positive for depressive and negative for control group (P 0.05). The results indicated that the linear EEG analysis method SASI and the nonlinear HFD method both demonstrated a good sensitivity for detection of characteristic features of depression in a single-channel EEG.
Gauged/Massive Supergravities in Diverse Dimensions
Alonso-Alberca, N; Alonso-Alberca, Natxo; Ortin, Tomas
2003-01-01
We show how massive/gauged maximal supergravities in 11-n dimensions with SO(n-l,l) gauge groups (and other non-semisimple subgroups of Sl(n,R)) can be systematically obtained by dimensional reduction of ``massive 11-dimensional supergravity''. This series of massive/gauged supergravities includes, for instance, Romans' massive N=2A,d=10 supergravity for n=1, N=2,d=9 SO(2) and SO(1,1) gauged supergravities for n=2, and N=8,d=5 SO(6-l,l) gauged supergravity. In all cases, higher p-form fields get masses through the Stuckelberg mechanism which is an alternative to self-duality in odd dimensions.
Scattering in Three Dimensions from Rational Maps
Cachazo, Freddy; Yuan, Ellis Ye
2013-01-01
The complete tree-level S-matrix of four dimensional ${\\cal N}=4$ super Yang-Mills and ${\\cal N} = 8$ supergravity has compact forms as integrals over the moduli space of certain rational maps. In this note we derive formulas for amplitudes in three dimensions by using the fact that when amplitudes are dressed with proper wave functions dimensional reduction becomes straightforward. This procedure leads to formulas in terms of rational maps for three dimensional maximally supersymmetric Yang-Mills and gravity theories. The integrand of the new formulas contains three basic structures: Parke-Taylor-like factors, Vandermonde determinants and resultants. Integrating out some of the Grassmann directions produces formulas for theories with less than maximal supersymmetry, which exposes yet a fourth kind of structure. Combining all four basic structures we start a search for consistent S-matrices in three dimensions. Very nicely, the most natural ones are those corresponding to ABJM and BLG theories. We also make a...
LINEAR AND NONLINEAR SEMIDEFINITE PROGRAMMING
Directory of Open Access Journals (Sweden)
Walter Gómez Bofill
2014-12-01
Full Text Available This paper provides a short introduction to optimization problems with semidefinite constraints. Basic duality and optimality conditions are presented. For linear semidefinite programming some advances by dealing with degeneracy and the semidefinite facial reduction are discussed. Two relatively recent areas of application are presented. Finally a short overview of relevant literature on algorithmic approaches for efficiently solving linear and nonlinear semidefinite programming is provided.
Nonlinear singular vectors and nonlinear singular values
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A novel concept of nonlinear singular vector and nonlinear singular value is introduced, which is a natural generalization of the classical linear singular vector and linear singular value to the nonlinear category. The optimization problem related to the determination of nonlinear singular vectors and singular values is formulated. The general idea of this approach is demonstrated by a simple two-dimensional quasigeostrophic model in the atmospheric and oceanic sciences. The advantage and its applications of the new method to the predictability, ensemble forecast and finite-time nonlinear instability are discussed. This paper makes a necessary preparation for further theoretical and numerical investigations.
Fractal dimension of critical clusters in the Φ44 model
Jansen, K.; Lang, C. B.
1991-06-01
We study the d=4 O(4) symmetric nonlinear sigma model at the pseudocritical points for 84-284 lattices. The Fortuin-Kasteleyn-Coniglio-Klein clusters are shown to have fractal dimension df~=3-in accordance with the conjectured scaling relation involving the odd critical exponent δ. For the one cluster algorithm introduced recently by Wolff the dynamical critical exponent z comes out to be compatible with zero in this model.
Active vibration control of nonlinear benchmark buildings
Institute of Scientific and Technical Information of China (English)
ZHOU Xing-de; CHEN Dao-zheng
2007-01-01
The present nonlinear model reduction methods unfit the nonlinear benchmark buildings as their vibration equations belong to a non-affine system. Meanwhile,the controllers designed directly by the nonlinear control strategy have a high order, and they are difficult to be applied actually. Therefore, a new active vibration control way which fits the nonlinear buildings is proposed. The idea of the proposed way is based on the model identification and structural model linearization, and exerting the control force to the built model according to the force action principle. This proposed way has a better practicability as the built model can be reduced by the balance reduction method based on the empirical Grammian matrix. A three-story benchmark structure is presented and the simulation results illustrate that the proposed method is viable for the civil engineering structures.
Black holes, cosmology and extra dimensions
Bronnikov, Kirill A
2013-01-01
Assuming foundational knowledge of special and general relativity, this book guides the reader on issues surrounding black holes, wormholes, cosmology, and extra dimensions. Its first part is devoted to local strong field configurations (black holes and wormholes) in general relativity and the most relevant of alternative theories: scalar-tensor, f(R) and multidimensional theories. The second part is on cosmology, including inflation and a unified description of the whole evolution of the universe. The third part concerns multidimensional theories of gravity and contains a number of original results obtained by the authors. Expository work is conducted for a mechanism of symmetries and fundamental constants formation, while the original approach to nonlinear multidimensional gravity that is able to construct a unique perspective describing different phenomena is highlighted. Much of the content is new in book publications, because it was previously found only in journal publications, e.g. regarding regular bl...
Anomalous Dimensions of Conformal Baryons
DEFF Research Database (Denmark)
Pica, Claudio; Sannino, Francesco
2016-01-01
We determine the anomalous dimensions of baryon operators for the three color theory as function of the number of massless flavours within the conformal window to the maximum known order in perturbation theory. We show that the anomalous dimension of the baryon is controllably small, within...... the $\\delta$-expansion, for a wide range of number of flavours. We also find that this is always smaller than the anomalous dimension of the fermion mass operator. These findings challenge the partial compositeness paradigm....
Beta Function and Anomalous Dimensions
Pica, Claudio
2010-01-01
We demonstrate that it is possible to determine the coefficients of an all-order beta function linear in the anomalous dimensions using as data the two-loop coefficients together with the first one of the anomalous dimensions which are universal. The beta function allows to determine the anomalous dimension of the fermion masses at the infrared fixed point, and the resulting values compare well with the lattice determinations.
Anomalous Dimensions of Conformal Baryons
Pica, Claudio
2016-01-01
We determine the anomalous dimensions of baryon operators for the three color theory as function of the number of massless flavours within the conformal window to the maximum known order in perturbation theory. We show that the anomalous dimension of the baryon is controllably small for a wide range of number of flavours. We also find that this is always smaller than the anomalous dimension of the fermion mass operator. These findings challenge the partial compositeness paradigm.
Prakash, Manu
2011-01-01
Diversity and specialization of behavior in insects is unmatched. Insects hop, walk, run, jump, row, swim, glide and fly to propel themselves in a variety of environments. We have uncovered an unusual mode of propulsion of aerodynamic flight in two dimensions in Waterlilly Beetles \\emph{(Galerucella)}. The adult beetles, often found in water lilly ponds, propel themselves strictly in a two-dimensional plane on the surface of water via flapping wing flight. Here we analyze the aerodynamics of this peculiar flight mode with respect to forces exerted on the organism during flight. The complexity of 2-D flight is captured by accounting for additional forces beyond gravitational, thrust, lift and drag, exerted on the insect body in 3D flight. Understanding this constrained propulsion mode requires accounting for viscous drag, surface tension, buoyancy force, and capillary-wave drag. Moreover, dramatic differences exist in the magnitude of the resultant forces in 2D vs. 3D flight. Here, in this fluid dynamics video...
van Houselt, A.; Schäfer, J.; Zandvliet, H. J. W.; Claessen, R.
2013-01-01
With modern microelectronics moving towards smaller and smaller length scales on the (sub-) nm scale, quantum effects (apart from band structure and band gaps) have begun to play an increasingly important role. This especially concerns dimensional confinement to 2D (high electron mobility transistors and integer/fractional quantum Hall effect physics, graphene and topological insulators) and 1D (with electrical connections eventually reaching the quantum limit). Recent developments in the above-mentioned areas have revealed that the properties of electron systems become increasingly exotic as one progresses from the 3D case into lower dimensions. As compared to 2D electron systems, much less experimental progress has been achieved in the field of 1D electron systems. The main reason for the lack of experimental results in this field is related to the difficulty of realizing 1D electron systems. Atom chains created in quantum mechanical break junction set-ups are too short to exhibit the typically 1D signatures. As an alternative, atomic chains can be produced on crystal surfaces, either via assembling them one-by-one using a scanning tunnelling microscope or via self-assembly. The drawback of the latter systems is that the atomic chains are not truly 1D since they are coupled to the underlying crystal and sometimes even to the neighbouring chains. In retrospect, this coupling turns out to be an absolute necessity in the experiment since true 1D systems are disordered at any non-zero temperature [1]. The coupling to the crystal and/or neighbouring chains shifts the phase transition, for example, a Peierls instability, to a non-zero temperature and thus allows experiments to be performed in the ordered state. Here, we want to emphasize that the electronic properties of the 1D electron system are fundamentally different from its 2D and 3D counterparts. The Fermi liquid theory, which is applicable to 2D and 3D electron systems, breaks down spectacularly in the 1D case
Naturally stable Sagnac-Michelson nonlinear interferometer
Lukens, Joseph M.; Peters, Nicholas A.; Pooser, Raphael C.
2016-12-01
Interferometers measure a wide variety of dynamic processes by converting a phase change into an intensity change. Nonlinear interferometers, making use of nonlinear media in lieu of beamsplitters, promise substantial improvement in the quest to reach the ultimate sensitivity limits. Here we demonstrate a new nonlinear interferometer utilizing a single parametric amplifier for mode mixing---conceptually, a nonlinear version of the conventional Michelson interferometer with its arms collapsed together. We observe up to 99.9\\% interference visibility and find evidence for noise reduction based on phase-sensitive gain. Our configuration utilizes fewer components than previous demonstrations and requires no active stabilization, offering new capabilities for practical nonlinear interferometric-based sensors.
Localized modes in nonlinear photonic kagome nanoribbons
Energy Technology Data Exchange (ETDEWEB)
Molina, Mario I., E-mail: mmolina@uchile.cl [Departamento de Física, MSI – Nucleus for Advanced Optics, and Center for Optics and Photonics (CEFOP), Facultad de Ciencias, Universidad de Chile, Santiago (Chile)
2012-10-01
We examine localization of light in nonlinear (Kerr) kagome lattices in the shape of narrow strips of varying width. For the narrowest ribbon, the band structure features a flat band leading to linear dynamical trapping of an initially localized excitation. We also find a geometry-induced bistability of the nonlinear modes as the width of the strip is changed. A crossover from one to two dimensions localization behavior is observed as the width is increased, attaining two-dimensional behavior for relatively narrow ribbons.
DIMENSION STONE DEPOSITS IN CROATIA
Directory of Open Access Journals (Sweden)
Branko Crnković
1993-12-01
Full Text Available The geology, petrographycal composition and properties of dimension stone deposits in Croatia are described. Dimension stone deposits in the conception of mobilistic view of the genesis and structure of Dinarides, as well as after stratigraphic units, are considered. Valuation of the dimension stones of the active quarries is exposed. The marketable categories of dimension stone in Croatia are different varietes of limestones and calcareous clastites, primarly of Cretaceous age, and to lesser degree of Jurassic and Paleogene. The greatest part of deposits is concentrated in the Adriatic carbonate platform or Adriaticum.
The cyclic reduction algorithm
Bini, Dario; Meini, Beatrice
2009-05-01
Cyclic reduction is an algorithm invented by G.H. Golub and R. W. Hockney in the mid 1960s for solving linear systems related to the finite differences discretization of the Poisson equation over a rectangle. Among the algorithms of Gene Golub, it is one of the most versatile and powerful ever created. Recently, it has been applied to solve different problems from different applicative areas. In this paper we survey the main features of cyclic reduction, relate it to properties of analytic functions, recall its extension to solving more general finite and infinite linear systems, and different kinds of nonlinear matrix equations, including algebraic Riccati equations, with applications to Markov chains, queueing models and transport theory. Some new results concerning the convergence properties of cyclic reduction and its applicability are proved under very weak assumptions. New formulae for overcoming breakdown are provided.
Barbosa-Cendejas, Nandinii; Kanakoglou, Konstantinos; Paschalis, Joannis E
2011-01-01
In this paper we recall a simple formulation of the stationary electrovacuum theory in terms of the famous complex Ernst potentials, a pair of functions which allows one to generate new exact solutions from known ones by means of the so-called nonlinear hidden symmetries of Lie-Backlund type. This formalism turned out to be very useful to perform a complete classification of all 4D solutions which present two spacetime symmetries or possess two Killing vectors. Curiously enough, the Ernst formalism can be extended and applied to stationary General Relativity as well as the effective heterotic string theory reduced down to three spatial dimensions by means of a (real) matrix generalization of the Ernst potentials. Thus, in this theory one can also make use of nonlinear matrix hidden symmetries in order to generate new exact solutions from seed ones. Due to the explicit independence of the matrix Ernst potential formalism of the original theory (prior to dimensional reduction) on the dimension D, in the case wh...
Warped Geometry in Higher Dimensions with an Orbifold Extra Dimension
Ito, M
2001-01-01
We solve the Einstein equations in higher dimensions with warped geometry where an extra dimension is assumed to have orbifold symmetry, $S^{1}/Z_{2}$. The setup we consider here is an extension to (5+D)-dimensions of the 5-dimensional Randall-Sundrum model, and two hidden brane and observable brane are fixed on orbifold. Anisotropic cosmological constant on each brane with (4+D)-dimensional spacetime is assumed, and the warped metric of 4-dimensions is generally different from one of extra D-dimensions. It is pointed out that the form of metric depends on both the sign of bulk cosmological constant and initial condition of brane world. Furthermore, anisotropic cosmological constant on each brane can be realized due to the presence of brane.
Deimling, Klaus
1985-01-01
topics. However, only a modest preliminary knowledge is needed. In the first chapter, where we introduce an important topological concept, the so-called topological degree for continuous maps from subsets ofRn into Rn, you need not know anything about functional analysis. Starting with Chapter 2, where infinite dimensions first appear, one should be familiar with the essential step of consider ing a sequence or a function of some sort as a point in the corresponding vector space of all such sequences or functions, whenever this abstraction is worthwhile. One should also work out the things which are proved in § 7 and accept certain basic principles of linear functional analysis quoted there for easier references, until they are applied in later chapters. In other words, even the 'completely linear' sections which we have included for your convenience serve only as a vehicle for progress in nonlinearity. Another point that makes the text introductory is the use of an essentially uniform mathematical languag...
Anomalous dimensions in CFT with weakly broken higher spin symmetry
Giombi, Simone; Kirilin, Vladimir
2016-11-01
In a conformal field theory with weakly broken higher spin symmetry, the leading order anomalous dimensions of the broken currents can be efficiently determined from the structure of the classical non-conservation equations. We apply this method to the explicit example of O( N) invariant scalar field theories in various dimensions, including the large N critical O( N) model in general d, the Wilson-Fisher fixed point in d = 4 - ɛ, cubic scalar models in d = 6 - ɛ and the nonlinear sigma model in d = 2 + ɛ. Using information from the d = 4 - ɛ and d = 2 + ɛ expansions, we obtain some estimates for the dimensions of the higher spin operators in the critical 3d O( N) models for a few low values of N and spin.
Saliency of social comparison dimensions
Kuyper, H.
2007-01-01
The present article discusses a theory of the saliency of social comparison dimensions and presents the results of an experiment about the effects of two different experimental situations on the saliency of exterior, task-related and socio-emotional dimensions. Saliency was operationalized with a
Dimensioning, Tolerancing, and Machine Finishes.
Adams, George C.
Intended for use with the vocational education student interested in technical drawing, this guide provides answers to questions relating to dimensioning and tolerancing machine drawings. It also gives examples of standard dimensioning practices, tolerancing applications, and finish applications. The problems and examples presented are based on…
Anomalous Dimensions of Conformal Baryons
DEFF Research Database (Denmark)
Pica, Claudio; Sannino, Francesco
2016-01-01
We determine the anomalous dimensions of baryon operators for the three color theory as function of the number of massless flavours within the conformal window to the maximum known order in perturbation theory. We show that the anomalous dimension of the baryon is controllably small, within...
Mathematics Teachers' Criteria of Dimension
Ural, Alattin
2014-01-01
The aim of the study is to determine mathematics teachers' decisions about dimensions of the geometric figures, criteria of dimension and consistency of decision-criteria. The research is a qualitative research and the model applied in the study is descriptive method on the basis of general scanning model. 15 mathematics teachers attended the…
The Hausdorff Dimension of Sections
Institute of Scientific and Technical Information of China (English)
Min NIU; Lifeng XI
2007-01-01
The notion of finite-type open set condition is defined to calculate the Hausdorff dimensions of the sections of some self-similar sets, such as the dimension of intersection of the Koch curve and the line x = a with a ∈(Q).
Anomalous Dimensions of Conformal Baryons
DEFF Research Database (Denmark)
Pica, Claudio; Sannino, Francesco
2016-01-01
We determine the anomalous dimensions of baryon operators for the three color theory as function of the number of massless flavours within the conformal window to the maximum known order in perturbation theory. We show that the anomalous dimension of the baryon is controllably small, within the $...
Beta Function and Anomalous Dimensions
DEFF Research Database (Denmark)
Pica, Claudio; Sannino, Francesco
2011-01-01
We demonstrate that it is possible to determine the coefficients of an all-order beta function linear in the anomalous dimensions using as data the two-loop coefficients together with the first one of the anomalous dimensions which are universal. The beta function allows to determine the anomalou...
Mass generation and related issues from exotic higher dimensions
Energy Technology Data Exchange (ETDEWEB)
Colatto, Luiz Paulo [Centro Federal de Educacao Tecnologica Celso Suckow da Fonseca (CEFET), Petropolis, RJ (Brazil); Andrade, Marco Antonio de [Universidade do Estado do Rio de Janeiro (UERJ), Resende, RJ (Brazil); Assis, Leonardo Paulo Guimaraes de; Helayel-Neto, Jose Abdalla [Centro Brasileiro de Pesquisas Fisicas(LAFEX/CBPF), Rio de Janeiro, RJ (Brazil). Coordenacao de Fisica Experimental de Altas Energias; Matheus-Valle, Jose Luiz [Universidade Federal de Juiz de Fora (UFJF), MG (Brazil); Rojas, Moises [Universidade Federal de Lavras, MG (Brazil)
2011-07-01
Full text: he main purpose of this work is to show that massless Dirac equation formulated for non-interacting Majorana-Weyl spinors in higher dimensions, particularly in D = 1 + 9 and D = 5 + 5, may yield to an interpretation of massive Majorana and Dirac spinors in D = 1 + 3 dimensions. The particular case of a dimensional reduction from D = 4 + 4 to D = 1 + 3 has already been fairly-well discussed in the literature. By adopting suitable representations of the Dirac matrices in higher dimensions, we pursue the investigation of which higher dimensional space-times and which metric signatures concerning massless Dirac equations in highermay induce massive spinors in D = 1+3 dimensions. The mixing of the chiral fermions in higher dimensions may induce a mechanism such that four massive Majorana fermions may show up and, at an appropriate limit an almost zero and a huge mass show up with corresponding left-handed and right-handed eigenstates. This mechanism could reassess a peculiar connection with the See-Saw scheme associated to neutrino with Majorana-type masses. The masses of the particle are fixed by the dimensional reduction scheme, which the decoupled dimensions contribute coordinates and depend on the mass invariants in lower dimensions. This proposal should allow us to understand the generation of hierarchies for the fermionic masses in D = 1 + 3, or in lower dimensions in general, starting from the constraints between the energy and the momentum in (n; n) dimensions. For the initial D = 5 + 5 Majorana-Weyl spinors framework using the Weyl representation to the Dirac matrices we observe an intriguing decomposition of space-time that result in two equivalent D = 1 + 4 massive spinors which mass term, in D = 1 + 3 included, is originated from the remained component and that could induce a Brane-World mechanism. (author)
NONLINEAR EXPECTATIONS AND NONLINEAR MARKOV CHAINS
Institute of Scientific and Technical Information of China (English)
PENG SHIGE
2005-01-01
This paper deals with nonlinear expectations. The author obtains a nonlinear generalization of the well-known Kolmogorov's consistent theorem and then use it to construct filtration-consistent nonlinear expectations via nonlinear Markov chains. Compared to the author's previous results, i.e., the theory of g-expectations introduced via BSDE on a probability space, the present framework is not based on a given probability measure. Many fully nonlinear and singular situations are covered. The induced topology is a natural generalization of Lp-norms and L∞-norm in linear situations.The author also obtains the existence and uniqueness result of BSDE under this new framework and develops a nonlinear type of von Neumann-Morgenstern representation theorem to utilities and present dynamic risk measures.
Nonlinear adhesion dynamics of confined lipid membranes
To, Tung; Le Goff, Thomas; Pierre-Louis, Olivier
Lipid membranes, which are ubiquitous objects in biological environments are often confined. For example, they can be sandwiched between a substrate and the cytoskeleton between cell adhesion, or between other membranes in stacks, or in the Golgi apparatus. We present a study of the nonlinear dynamics of membranes in a model system, where the membrane is confined between two flat walls. The dynamics derived from the lubrication approximation is highly nonlinear and nonlocal. The solution of this model in one dimension exhibits frozen states due to oscillatory interactions between membranes caused by the bending rigidity. We develope a kink model for these phenomena based on the historical work of Kawasaki and Otha. In two dimensions, the dynamics is more complex, and depends strongly on the amount of excess area in the system. We discuss the relevance of our findings for experiments on model membranes, and for biological systems. Supported by the grand ANR Biolub.
Dimensional reduction over fuzzy coset spaces
Energy Technology Data Exchange (ETDEWEB)
Aschieri, P. E-mail: aschieri@theorie.physik.uni-muenchen.de; Madore, J.; Manousselis, P.; Zoupanos, G
2004-04-01
We examine gauge theories on Minkowski space-time times fuzzy coset spaces. This means that the extra space dimensions instead of being a continuous coset space S/R are a corresponding finite matrix approximation. The gauge theory defined on this non-commutative setup is reduced to four dimensions and the rules of the corresponding dimensional reduction are established. We investigate in particular the case of the fuzzy sphere including the dimensional reduction of fermion fields. (author)
1:2 INTERNAL RESONANCE OF COUPLED DYNAMIC SYSTEM WITH QUADRATIC AND CUBIC NONLINEARITIES
Institute of Scientific and Technical Information of China (English)
陈予恕; 杨彩霞; 吴志强; 陈芳启
2001-01-01
The 1:2 internal resonance of coupled dynamic system with quadratic and cubic nonlinearities is studied. The normal forms of this system in 1: 2 internal resonance were derived by using the direct method of normal form. In the normal forms, quadratic and cubic nonlinearities were remained. Based on a new convenient transformation technique, the 4-dimension bifurcation equations were reduced to 3-dimension. A bifurcation equation with one-dimension was obtained. Then the bifurcation behaviors of a universal unfolding were studied by using the singularity theory. The method of this paper can be applied to analyze the bifurcation behavior in strong internal resonance on 4-dimension center manifolds.
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.
Ortiz, Isabel
2007-01-01
The paper reviews poverty trends and measurements, poverty reduction in historical perspective, the poverty-inequality-growth debate, national poverty reduction strategies, criticisms of the agenda and the need for redistribution, international policies for poverty reduction, and ultimately understanding poverty at a global scale. It belongs to a series of backgrounders developed at Joseph Stiglitz's Initiative for Policy Dialogue.
Nonlinear optics with stationary pulses of light
Andre, A.; Bajcsy, M.; Zibrov, A. S.; Lukin, M. D.
2004-01-01
We show that the recently demonstrated technique for generating stationary pulses of light [Nature {\\bf 426}, 638 (2003)] can be extended to localize optical pulses in all three spatial dimensions in a resonant atomic medium. This method can be used to dramatically enhance the nonlinear interaction between weak optical pulses. In particular, we show that an efficient Kerr-like interaction between two pulses can be implemented as a sequence of several purely linear optical processes. The resul...
Geometrodynamics: The Nonlinear Dynamics of Curved Spacetime
Scheel, Mark A.; Thorne, Kip S.
2017-01-01
We review discoveries in the nonlinear dynamics of curved spacetime, largely made possible by numerical solutions of Einstein's equations. We discuss critical phenomena and self-similarity in gravitational collapse, the behavior of spacetime curvature near singularities, the instability of black strings in 5 spacetime dimensions, and the collision of four-dimensional black holes. We also discuss the prospects for further discoveries in geometrodynamics via observation of gravitational waves.
Energy Technology Data Exchange (ETDEWEB)
Watts, Christopher A. [Univ. of Wisconsin, Madison, WI (United States)
1993-09-01
In this dissertation the possibility that chaos and simple determinism are governing the dynamics of reversed field pinch (RFP) plasmas is investigated. To properly assess this possibility, data from both numerical simulations and experiment are analyzed. A large repertoire of nonlinear analysis techniques is used to identify low dimensional chaos in the data. These tools include phase portraits and Poincare sections, correlation dimension, the spectrum of Lyapunov exponents and short term predictability. In addition, nonlinear noise reduction techniques are applied to the experimental data in an attempt to extract any underlying deterministic dynamics. Two model systems are used to simulate the plasma dynamics. These are the DEBS code, which models global RFP dynamics, and the dissipative trapped electron mode (DTEM) model, which models drift wave turbulence. Data from both simulations show strong indications of low dimensional chaos and simple determinism. Experimental date were obtained from the Madison Symmetric Torus RFP and consist of a wide array of both global and local diagnostic signals. None of the signals shows any indication of low dimensional chaos or low simple determinism. Moreover, most of the analysis tools indicate the experimental system is very high dimensional with properties similar to noise. Nonlinear noise reduction is unsuccessful at extracting an underlying deterministic system.
Multi-Channel Transfer Function with Dimensionality Reduction
Kim, Han Suk; Schulze, Jürgen P.; Cone, Angela C.; Sosinsky, Gina E.; Martone, Maryann E.
2010-01-01
The design of transfer functions for volume rendering is a difficult task. This is particularly true for multi-channel data sets, where multiple data values exist for each voxel. In this paper, we propose a new method for transfer function design. Our new method provides a framework to combine multiple approaches and pushes the boundary of gradient-based transfer functions to multiple channels, while still keeping the dimensionality of transfer functions to a manageable level, i.e., a maximum of three dimensions, which can be displayed visually in a straightforward way. Our approach utilizes channel intensity, gradient, curvature and texture properties of each voxel. The high-dimensional data of the domain is reduced by applying recently developed nonlinear dimensionality reduction algorithms. In this paper, we used Isomap as well as a traditional algorithm, Principle Component Analysis (PCA). Our results show that these dimensionality reduction algorithms significantly improve the transfer function design process without compromising visualization accuracy. In this publication we report on the impact of the dimensionality reduction algorithms on transfer function design for confocal microscopy data. PMID:20582228
Epileptic EEG: a comprehensive study of nonlinear behavior.
Daneshyari, Moayed; Kamkar, L Lily; Daneshyari, Matin
2010-01-01
In this study, the nonlinear properties of the electroencephalograph (EEG) signals are investigated by comparing two sets of EEG, one set for epileptic and another set for healthy brain activities. Adopting measures of nonlinear theory such as Lyapunov exponent, correlation dimension, Hurst exponent, fractal dimension, and Kolmogorov entropy, the chaotic behavior of these two sets is quantitatively computed. The statistics for the two groups of all measures demonstrate the differences between the normal healthy group and epileptic one. The statistical results along with phase-space diagram verify that brain under epileptic seizures possess limited trajectory in the state space than in healthy normal state, consequently behaves less chaotically compared to normal condition.
Preimage entropy dimension of topological dynamical systems
Lei LIU; Zhou, Xiaomin; Zhou, Xiaoyao
2014-01-01
We propose a new definition of preimage entropy dimension for continuous maps on compact metric spaces, investigate fundamental properties of the preimage entropy dimension, and compare the preimage entropy dimension with the topological entropy dimension. The defined preimage entropy dimension holds various basic properties of topological entropy dimension, for example, the preimage entropy dimension of a subsystem is bounded by that of the original system and topologically conjugated system...
Nonlinear self-flipping of polarization states in asymmetric waveguides
Zhang, Wen Qi; Monro, Tanya M; Afshar, V Shahraam
2012-01-01
Waveguides of subwavelength dimensions with asymmetric geometries, such as rib waveguides, can display nonlinear polarization effects in which the nonlinear phase difference dominates the linear contribution, provided the birefringence is sufficiently small. We demonstrate that self-flipping polarization states can appear in such rib waveguides at low (mW) power levels. We describe an optical power limiting device with optimized rib waveguide parameters that can operate at low powers with switching properties.
A TRUST-REGION ALGORITHM FOR NONLINEAR INEQUALITY CONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Xiaojiao Tong; Shuzi Zhou
2003-01-01
This paper presents a new trust-region algorithm for n-dimension nonlinear optimization subject to m nonlinear inequality constraints. Equivalent KKT conditions are derived,which is the basis for constructing the new algorithm. Global convergence of the algorithm to a first-order KKT point is established under mild conditions on the trial steps, local quadratic convergence theorem is proved for nondegenerate minimizer point. Numerical experiment is presented to show the effectiveness of our approach.
First premolar extraction effects on upper airway dimension in bimaxillary proclination patients.
Al Maaitah, Emad; El Said, Nizar; Abu Alhaija, E S
2012-09-01
To determine how orthodontic treatment with first premolar teeth extracted and the associated arch dimensional changes in bimaxillary proclination patients affect the upper airway dimensions. Pre- and postorthodontic treatment cephalograms and dental casts of 40 bimaxillary proclination patients (ages ranged between 18 and 23 years) were used for this study. Patients were all treated with extraction of the four first premolars. Cephalometric radiographs were used to measure airway dimensions, and dental casts were used to measure the changes in the arch dimensions. A paired t-test was used to detect differences at P bimaxillary proclination does not affect upper airway dimensions despite the significant reduction in tongue length and arch dimensions.
Supersymmetry breaking with extra dimensions
Indian Academy of Sciences (India)
Fabio Zwirner
2004-02-01
This talk reviews some aspects of supersymmetry breaking in the presence of extra dimensions. The first part is a general introduction, recalling the motivations for supersymmetry and extra dimensions, as well as some unsolved problems of four-dimensional models of supersymmetry breaking. The central part is a more focused introduction to a mechanism for (super)symmetry breaking, proposed first by Scherk and Schwarz, where extra dimensions play a crucial role. The last part is devoted to the description of some recent results and of some open problems.
The Ethical Dimension of Innovation
DEFF Research Database (Denmark)
Nogueira, Leticia Antunes; Nogueira, Tadeu Fernando
2014-01-01
The view of innovation as a positive concept has been deeply rooted in business and academic cultures ever since Schumpeter coined the concept of creative destruction. Even though there is a large body of literature on innovation studies, limited attention has been given to its ethical dimension....... In this chapter, the ethical implications of innovations are illustrated with a case study of “destructive creation” in the food industry, and upon which an argumentative analysis is conducted. The main message of this chapter is that innovations have inherent ethical dimensions and that quality innovations...... depend on systematic consideration of these dimensions in the innovation process....
Critical dimension for chaotic cosmology
Energy Technology Data Exchange (ETDEWEB)
Hosoya, Akio; Jensen, L.G.; Stein-Schabes, J.A.
1987-03-16
Using the ADM formalism for general relativity the approach to a space-time singularity of a general inhomogeneous universe, in an arbitrary number of dimensions, is studied. The question of whether chaotic behaviour is a generic feature of Einstein's equations, in an arbitrary number of dimensions, is explored. We find that models that contain ten or more spatial dimensions are non-chaotic and their approach toward the initial singularity is monotonic, whereas for those with dimensionality between four and nine their approach is chaotic. A clear geometrical picture is constructed whereby this result can be understood.
Signatures of Large Extra Dimensions
Hossenfelder, S; Stöcker, H
2004-01-01
String theory suggests modifications of our spacetime such as extra dimensions and the existence of a mininal length scale. In models with addidional dimensions, the Planck scale can be lowered to values accessible by future colliders. Effective theories which extend beyond the standart-model by including extra dimensions and a minimal length allow computation of observables and can be used to make testable predictions. Expected effects that arise within these models are the production of gravitons and black holes. Furthermore, the Planck-length is a lower bound to the possible resolution of spacetime which might be reached soon.
Mesoscale Engineering of Nanocomposite Nonlinear Optical Materials
Energy Technology Data Exchange (ETDEWEB)
Afonso, C.N.; Feldman, L.C.; Gonella, F.; Haglund, R.F.; Luepke, G.; Magruder, R.H.; Mazzoldi, P.; Osborne, D.H.; Solis, J.; Zuhr, R.A.
1999-11-01
Complex nonlinear optical materials comprising elemental, compound or alloy quantum dots embedded in appropriate dielectric or semiconducting hosts may be suitable for deployment in photonic devices. Ion implantation, ion exchange followed by ion implantation, and pulsed laser deposition have ail been used to synthesize these materials. However, the correlation between the parameters of energetic-beam synthesis and the nonlinear optical properties is still very rudimentary when one starts to ask what is happening at nanoscale dimensions. Systems integration of complex nonlinear optical materials requires that the mesoscale materials science be well understood within the context of device structures. We discuss the effects of beam energy and energy density on quantum-dot size and spatial distribution, thermal conductivity, quantum-dot composition, crystallinity and defects - and, in turn, on the third-order optical susceptibility of the composite material. Examples from recent work in our laboratories are used to illustrate these effects.
Synthetic dimensions for cold atoms from shaking a harmonic trap
Price, Hannah M.; Ozawa, Tomoki; Goldman, Nathan
2017-02-01
We introduce a simple scheme to implement synthetic dimensions in ultracold atomic gases, which only requires two basic and ubiquitous ingredients: the harmonic trap, which confines the atoms, combined with a periodic shaking. In our approach, standard harmonic oscillator eigenstates are reinterpreted as lattice sites along a synthetic dimension, while the coupling between these lattice sites is controlled by the applied time modulation. The phase of this modulation enters as a complex hopping phase, leading straightforwardly to an artificial magnetic field upon adding a second dimension. We show that this artificial gauge field has important consequences, such as the counterintuitive reduction of average energy under resonant driving, or the realization of quantum Hall physics. Our approach offers significant advantages over previous implementations of synthetic dimensions, providing an intriguing route towards higher-dimensional topological physics and strongly-correlated states.
Employment of CB models for non-linear dynamic analysis
Klein, M. R. M.; Deloo, P.; Fournier-Sicre, A.
1990-01-01
The non-linear dynamic analysis of large structures is always very time, effort and CPU consuming. Whenever possible the reduction of the size of the mathematical model involved is of main importance to speed up the computational procedures. Such reduction can be performed for the part of the structure which perform linearly. Most of the time, the classical Guyan reduction process is used. For non-linear dynamic process where the non-linearity is present at interfaces between different structures, Craig-Bampton models can provide a very rich information, and allow easy selection of the relevant modes with respect to the phenomenon driving the non-linearity. The paper presents the employment of Craig-Bampton models combined with Newmark direct integration for solving non-linear friction problems appearing at the interface between the Hubble Space Telescope and its solar arrays during in-orbit maneuvers. Theory, implementation in the FEM code ASKA, and practical results are shown.
The Wilson exact renormalization group equation and the anomalous dimension parameter
Bervillier, C
2013-01-01
The non-linear way the anomalous dimension parameter has been introduced in the historic first version of the exact renormalization group equation is compared to current practice. A simple expression for the exactly marginal redundant operator proceeds from this non-linearity, whereas in the linear case, first order differential equations must be solved to get it. The role of this operator in the construction of the flow equation is highlighted.
Metastability Thresholds for Anisotropic Bootstrap Percolation in Three Dimensions
Van Enter, A.C.D.; Fey, A.
2012-01-01
In this paper we analyze several anisotropic bootstrap percolation models in three dimensions. We present the order of magnitude for the metastability thresholds for a fairly general class of models. In our proofs, we use an adaptation of the technique of dimensional reduction. We find that the orde
Metastability thresholds for anisotropic bootstrap percolation in three dimensions
Van Enter, A.C.D.; Fey, A.
2012-01-01
In this paper we analyze several anisotropic bootstrap percolation models in three dimensions. We present the order of magnitude for the metastability thresholds for a fairly general class of models. In our proofs, we use an adaptation of the technique of dimensional reduction. We find that the orde
Global existence for semilinear wave equations with the critical blow-up term in high dimensions
Takamura, Hiroyuki; Wakasa, Kyouhei
2016-07-01
We are interested in almost global existence cases in the general theory for nonlinear wave equations, which are caused by critical exponents of nonlinear terms. Such situations can be found in only three cases in the theory, cubic terms in two space dimensions, quadratic terms in three space dimensions and quadratic terms including a square of unknown functions itself in four space dimensions. Except for the last case, criteria to classify nonlinear terms into the almost global, or global existence case, are well-studied and known to be so-called null condition and non-positive condition. Our motivation of this work is to find such a kind of the criterion in four space dimensions. In our previous paper, an example of the non-single term for the almost global existence case is introduced. In this paper, we show an example of the global existence case. These two examples have nonlinear integral terms which are closely related to derivative loss due to high dimensions. But it may help us to describe the final form of the criterion.
Logarithmic singularities of solutions to nonlinear partial differential equations
Tahara, Hidetoshi
2007-01-01
We construct a family of singular solutions to some nonlinear partial differential equations which have resonances in the sense of a paper due to T. Kobayashi. The leading term of a solution in our family contains a logarithm, possibly multiplied by a monomial. As an application, we study nonlinear wave equations with quadratic nonlinearities. The proof is by the reduction to a Fuchsian equation with singular coefficients.
An Audio Data Encryption with Single and Double Dimension Discrete-Time Chaotic Systems
AKGÜL, Akif; KAÇAR, Sezgin; Pehlivan, İhsan
2015-01-01
— In this article, a study on increasing security of audio data encryption with single and double dimension discrete-time chaotic systems was carried out and application and security analyses were executed. Audio data samples of both mono and stereo types were encrypted. In the application here, single and double dimension discrete-time chaotic systems were used. In order to enhance security during encryption, a different method was applied by also using a non-linear function. In the chaos ba...
Radiation reaction in various dimensions
Galtsov, D V
2002-01-01
We discuss the radiation reaction problem for an electric charge moving in flat space-time of arbitrary dimensions. It is shown that four is the unique dimension where a local differential equation exists accounting for the radiation reaction and admitting a consistent mass-renormalization (the Dirac-Lorentz equation). In odd dimensions the Huygens principle does not hold; as a result, the radiation reaction force depends on the whole past history of a charge (radiative tail). We show that the divergence in the tail integral can be removed by the mass renormalization only in the 2+1 theory. In even dimensions higher than four, divergences can not be removed by a renormalization.
Dimensions of Intercultural Communication Competence
Institute of Scientific and Technical Information of China (English)
郭飞燕
2016-01-01
Intercultural communication competence can help us adapt better to the host culture and deal with culture shock suc-cessfully. This paper mainly discusses the dimensions of intercultural communication competence.
Radiation reaction in various dimensions
Gal'Tsov, Dmitri V.
2002-07-01
We discuss the radiation reaction problem for an electric charge moving in flat space-time of arbitrary dimensions. It is shown that four is the unique dimension where a local differential equation exists accounting for the radiation reaction and admitting a consistent mass renormalization (the Lorentz-Dirac equation). In odd dimensions Huygens's principle does not hold, and, as a result, the radiation reaction force depends on the whole past history of a charge (radiative tail). We show that the divergence in the tail integral can be removed by the mass renormalization only in the 2+1 theory. In even dimensions higher than four, divergences cannot be removed by the mass renormalization.
The social dimension of entrepreneurship
DEFF Research Database (Denmark)
Ulhøi, John Parm
2005-01-01
This paper proposes an integrative framework to conceptualize important social dimensions of entrepreneurship. The paper reviews and evaluates the current status of research dealing with entrepreneurship, social capital and trust. The proposed framework rests on the recognition that entrepreneurial...
Inflation from periodic extra dimensions
Higaki, Tetsutaro
2016-01-01
We discuss a realization of a small field inflation based on string inspired supergravities. In theories accompanying extra dimensions, compactification of them with small radii is required for realistic situations. Since the extra dimension can have a periodicity, there will appear (quasi-)periodic functions under transformations of moduli of the extra dimensions in low energy scales. Such a periodic property can lead to a UV completion of so-called multi-natural inflation model where inflaton potential consists of a sum of multiple sinusoidal functions with a decay constant smaller than the Planck scale. As an illustration, we construct a SUSY breaking model, and then show that such an inflaton potential can be generated by a sum of world sheet instantons in intersecting brane models on extra dimensions containing $T^2/{\\mathbb Z}_2$ orbifold. We show also predictions of cosmic observables by numerical analyzes.
Short- and long-term variations in non-linear dynamics of heart rate variability
DEFF Research Database (Denmark)
Kanters, J K; Højgaard, M V; Agner, E;
1996-01-01
OBJECTIVES: The purpose of the study was to investigate the short- and long-term variations in the non-linear dynamics of heart rate variability, and to determine the relationships between conventional time and frequency domain methods and the newer non-linear methods of characterizing heart rate...... variability. METHODS: Twelve healthy subjects were investigated by 3-h ambulatory ECG recordings repeated on 3 separate days. Correlation dimension, non-linear predictability, mean heart rate, and heart rate variability in the time and frequency domains were measured and compared with the results from...... corresponding surrogate time series. RESULTS: A small significant amount of non-linear dynamics exists in heart rate variability. Correlation dimensions and non-linear predictability are relatively specific parameters for each individual examined. The correlation dimension is inversely correlated to the heart...
Keynote speech: Dimensions of Change
DEFF Research Database (Denmark)
Jørgensen, Kenneth Mølbjerg
2004-01-01
The presentation seeks to construct a framework for understanding knowledge and knowledge work. I argue that knowledge may be understood as a social construction of reality. I argue that people construct their reality by integrating four dimensions of reality: Facts, logic, values and communication...... introduce a basic framework for understanding knowledge. This is done by means of Wittgenstein's concept of language games. Second, I introduce the four dimensions of reality. Third I relate the model to the disciplines organizational learning and knowledge management...
Phenomenology of universal extra dimensions
Energy Technology Data Exchange (ETDEWEB)
Kong, Kyoungchul; Matchev, Konstantin T.; /Florida U.
2006-10-01
In this proceeding, the phenomenology of Universal Extra Dimensions (UED), in which all the Standard Model fields propagate, is explored. We focus on models with one universal extra dimension, compactified on an S{sub 1}/Z{sub 2} orbifold. We revisit calculations of Kaluza-Klein (KK) dark matter without an assumption of the KK mass degeneracy including all possible coannihilations. We then contrast the experimental signatures of low energy supersymmetry and UED.
Timbre Dimensions for Musical Control
Giese, Gregory Roy
This dissertation addresses the folowing question: Given the technologies to develop and implement any kind of sound generating and controlling device, what will the instrument designer, the composer, and the performer need to know in order to more fully utilize the dimensions of timbre in music and musical performance? This question is approached from the standpoint of music theory. Definitions of timbre and a few examples of related physical and perceptual research are reviewed. Included is a discussion of the essential elements of musical control and of intelligent organization of sound in music. This discussion raises more questions than can be answered simply. It is an attempt to unravel the nature of sound clues and sound qualities as they convey sound identities and musical gesture. A theoretical simplification of sound dimensions for musical use is proposed. Sounds which can be sustained indefinitely consist of steady-state acoustical dimensions. These dimensions rely upon the perceptual phenomenon of simultaneous fusion (synance). Sounds which can not be sustained indefinitely consist of transitions. Transitions may cause successive fusion (sonance). The discussion of steady-state and transition dimensions includes a review of a few informal experiments. This work reveals problems that will influence the musical use of timbre dimensions. It also leads to a theory for the organization and control of timbre dimensions in music. Among the timbre dimensions discussed are: spectral envelope, harmonic content, brightness, phase, inharmonicity, aperiodicity, and temporal transitions. Questions are raised regarding the perception of harmonic content. The effect of register on perception of tones consisting of from two to nine partials is explored and discussed. The size of interval between partials determines a unique quality. This is most apparent with tones consisting of only two or three partials (dions or trions).
Chen, Xianfeng; Zeng, Heping; Guo, Qi; She, Weilong
2015-01-01
This book presents an overview of the state of the art of nonlinear optics from weak light nonlinear optics, ultrafast nonlinear optics to electro-optical theory and applications. Topics range from the fundamental studies of the interaction between matter and radiation to the development of devices, components, and systems of tremendous commercial interest for widespread applications in optical telecommunications, medicine, and biotechnology.
Distributed nonlinear optical response
DEFF Research Database (Denmark)
Nikolov, Nikola Ivanov
2005-01-01
The purpose of the research presented here is to investigate basic physical properties in nonlinear optical materials with delayed or nonlocal nonlinearity. Soliton propagation, spectral broadening and the influence of the nonlocality or delay of the nonlinearity are the main focusses in the work...
Fiber Nonlinearities: A Tutorial
Institute of Scientific and Technical Information of China (English)
Govind P. Agrawal
2003-01-01
Fiber nonlinearities have long been regarded as being mostly harmful for fiber-optic communication systems. Over the last few years, however, the nonlinear effects are increasingly being used for practical telecommunications applications,the Raman amplification being only one of the recent examples. In this tutorial I review the vario us nonlinear effects occurring in optical fibers from both standpoints..
Fiber Nonlinearities: A Tutorial
Institute of Scientific and Technical Information of China (English)
Govind; P.; Agrawal
2003-01-01
Fiber nonlinearities have long been regarded as being mostly harmful for fiber-optic communication systems. Over the last few years, however, the nonlinear effects are increasingly being used for practical telecommunications applications, the Raman amplification being only one of the recent examples. In this tutorial I review the various nonlinear effects occurring in optical fibers from both standpoints..
Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition.
Bouchaffra, Djamel
2015-07-01
We introduce a novel formalism that performs dimensionality reduction and captures topological features (such as the shape of the observed data) to conduct pattern classification. This mission is achieved by: 1) reducing the dimension of the observed variables through a kernelized radial basis function technique and expressing the latent variables probability distribution in terms of the observed variables; 2) disclosing the data manifold as a 3-D polyhedron via the α -shape constructor and extracting topological features; and 3) classifying a data set using a mixture of multinomial distributions. We have applied our methodology to the problem of age-invariant face recognition. Experimental results obtained demonstrate the efficiency of the proposed methodology named nonlinear topological component analysis when compared with some state-of-the-art approaches.
FULLY NONLINEAR WAVE COMPUTATIONS FOR ARBITRARY FLOATING BODIES USING THE DELTA METHOD
Institute of Scientific and Technical Information of China (English)
Lee Tzung-hang
2003-01-01
Fully nonlinear water wave problems are solved using Eulerian-Lagrangian time stepping methods in conjunction with a desingularized approach to solve the mixed boundary value problem that arises at each time step. In the desingularized approach, the singularities generating the flow field are outside the fluid domain. This allows the singularity distribution to be replaced by isolated Rankine sources with the corresponding reduction in computational complexity and computer time. A moving boundary technique is applied to eliminate the reflection waves from limited computational boundaries. Examples of the use of the method in three-dimensions are given for the exciting forces acting on a modified wigley hull and Series 60 are presented. The numerical results show good agreements with those of experiments.
Nonlinear Super Integrable Couplings of Super Classical-Boussinesq Hierarchy
Directory of Open Access Journals (Sweden)
Xiuzhi Xing
2014-01-01
Full Text Available Nonlinear integrable couplings of super classical-Boussinesq hierarchy based upon an enlarged matrix Lie super algebra were constructed. Then, its super Hamiltonian structures were established by using super trace identity. As its reduction, nonlinear integrable couplings of the classical integrable hierarchy were obtained.
Interaction nonlinearity in asphalt binders
Motamed, Arash; Bhasin, Amit; Liechti, Kenneth M.
2012-05-01
Asphalt mixtures are complex composites that comprise aggregate, asphalt binder, and air. Several research studies have shown that the mechanical behavior of the asphalt mixture is strongly influenced by the matrix, i.e. the asphalt binder. Characterization and a thorough understanding of the binder behavior is the first and crucial step towards developing an accurate constitutive model for the composite. Accurate constitutive models for the constituent materials are critical to ensure accurate performance predictions at a material and structural level using micromechanics. This paper presents the findings from a systematic investigation into the nature of the linear and nonlinear response of asphalt binders subjected to different types of loading using the Dynamic Shear Rheometer (DSR). Laboratory test data show that a compressive normal force is generated in an axially constrained specimen subjected to torsional shear. This paper investigates the source of this normal force and demonstrates that the asphalt binder can dilate when subjected to shear loads. This paper also presents the findings from a study conducted to investigate the source of the nonlinearity in the asphalt binder. Test results demonstrate that the application of cyclic shear loads results in the development of a normal force and a concomitant reduction in the dynamic shear modulus. This form of nonlinear response is referred to as an "interaction nonlinearity". A combination of experimental and analytical tools is used to demonstrate and verify the presence of this interaction nonlinearity in asphalt binders. The findings from this study highlight the importance of modeling the mechanical behavior of asphalt binders based on the overall stress state of the material.
Fried, Jasper P.; Fangohr, Hans; Kostylev, Mikhail; Metaxas, Peter J.
2016-12-01
We have performed micromagnetic simulations of low-amplitude gyrotropic dynamics of magnetic vortices in the presence of spatially uniform out-of-plane magnetic fields. For disks having small lateral dimensions, we observe a frequency drop-off when approaching the disk's out-of-plane saturation field. This nonlinear frequency response is shown to be associated with a vortex core deformation driven by nonuniform demagnetizing fields that act on the shifted core. The deformation results in an increase in the average out-of-plane magnetization of the displaced vortex state (contrasting the effect of gyrofield-driven deformation at low field), which causes the exchange contribution to the vortex stiffness to switch from positive to negative. This generates an enhanced reduction of the core stiffness at high field, leading to a nonlinear field dependence of the gyrotropic mode frequency.
PBH tests for nonlinear systems
Kawano, Yu; Ohtsuka, Toshiyuki
2017-01-01
Recently, concepts of nonlinear eigenvalues and eigenvectors are introduced. In this paper, we establish connections between the nonlinear eigenvalues and nonlinear accessibility/observability. In particular, we provide a generalization of Popov- Belevitch-Hautus (PBH) test to nonlinear accessibilit
Testing of Nonlinear Filters For Coloured Noise
Macek, Wieslaw M.; Redaelli, Stefano; Plewczynski, Dariusz
We focus on nonlinearity and deterministic behaviour of classical model systems cor- rupted by white or coloured noise. Therefore, we use nonlinear filters to give a faith- ful representation of nonlinear behaviour of the systems. We also analyse time series of a real system, namely, we study velocities of of the solar wind plasma including Alfvénic fluctuations measured in situ by the Helios spacecraft in the inner helio- sphere. We demonstrate that the influence of white and coloured noise in the data records can be efficiently reduced by a nonlinear filter. We show that due to this non- linear noise reduction we get with much reliability estimates of the largest Lyapunov exponent and the Kolmogorov entropy.
Some nonlinear parameters of PP intervals of pulse main peaks
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The PP intervals of pulse main peaks from healthy and unhealthy people (arrhythmia) have different nonlinear characteristics. In this paper, the extraction of PP intervals of pulse main peaks is achieved by picking up P peaks of pulse wave with wavelet transform. Furthermore, several nonlinear parameters (correlative dimensions, maximum Lyapunov exponents, complexity and approximate entropy) of the PP intervals of pulse main peaks extracted from normal and unhealthy pulse signals are calculated, with the results showing that these nonlinear parameters calculated from the main wave interval signals are helpful for analyzing human's health state and diagnosing heart diseases.
Solutions to nonlinear Schrodinger equations for special initial data
Directory of Open Access Journals (Sweden)
Takeshi Wada
2015-11-01
Full Text Available This article concerns the solvability of the nonlinear Schrodinger equation with gauge invariant power nonlinear term in one space dimension. The well-posedness of this equation is known only for $H^s$ with $s\\ge 0$. Under some assumptions on the nonlinearity, this paper shows that this equation is uniquely solvable for special but typical initial data, namely the linear combinations of $\\delta(x$ and p.v. (1/x, which belong to $H^{-1/2-0}$. The proof in this article allows $L^2$-perturbations on the initial data.
Boundary induced nonlinearities at small Reynolds numbers
Sbragaglia, M.; Sugiyama, K.
2007-01-01
We investigate the importance of boundary slip at finite Reynolds numbers for mixed boundary conditions. Nonlinear effects are induced by the non-homogeneity of the boundary condition and change the symmetry properties of the flow with an overall mean flow reduction. To explain the observed drag
On the Nonuniqueness of Balanced Nonlinear Realizations
Gray, W. Steven; Scherpen, Jacquelien M.A.
1999-01-01
The notion of balanced realizations for nonlinear state space model reduction problems was first introduced by Scherpen in 1993. Analogous to'the linear case, the so called singular value functions of a system describe the relative importance of each state component from an input-output point of vie
Soil-structure interaction including nonlinear soil
Gicev, Vlado
2008-01-01
There are two types of models of soil-structure system depending upon the rigidity of foundation: models with rigid and models with flexible foundation. Main features of the soil-structure interaction phenomenon: -wave scattering, -radiation damping, -reduction of the system frequencies. In this presentation, the influence of interaction on the development of nonlinear zones in the soil is studied.
Nonlinear dynamics and complexity
Luo, Albert; Fu, Xilin
2014-01-01
This important collection presents recent advances in nonlinear dynamics including analytical solutions, chaos in Hamiltonian systems, time-delay, uncertainty, and bio-network dynamics. Nonlinear Dynamics and Complexity equips readers to appreciate this increasingly main-stream approach to understanding complex phenomena in nonlinear systems as they are examined in a broad array of disciplines. The book facilitates a better understanding of the mechanisms and phenomena in nonlinear dynamics and develops the corresponding mathematical theory to apply nonlinear design to practical engineering.
Fast Multiscale Reservoir Simulations using POD-DEIM Model Reduction
Ghasemi, Mohammadreza
2015-02-23
In this paper, we present a global-local model reduction for fast multiscale reservoir simulations in highly heterogeneous porous media with applications to optimization and history matching. Our proposed approach identifies a low dimensional structure of the solution space. We introduce an auxiliary variable (the velocity field) in our model reduction that allows achieving a high degree of model reduction. The latter is due to the fact that the velocity field is conservative for any low-order reduced model in our framework. Because a typical global model reduction based on POD is a Galerkin finite element method, and thus it can not guarantee local mass conservation. This can be observed in numerical simulations that use finite volume based approaches. Discrete Empirical Interpolation Method (DEIM) is used to approximate the nonlinear functions of fine-grid functions in Newton iterations. This approach allows achieving the computational cost that is independent of the fine grid dimension. POD snapshots are inexpensively computed using local model reduction techniques based on Generalized Multiscale Finite Element Method (GMsFEM) which provides (1) a hierarchical approximation of snapshot vectors (2) adaptive computations by using coarse grids (3) inexpensive global POD operations in a small dimensional spaces on a coarse grid. By balancing the errors of the global and local reduced-order models, our new methodology can provide an error bound in simulations. Our numerical results, utilizing a two-phase immiscible flow, show a substantial speed-up and we compare our results to the standard POD-DEIM in finite volume setup.
The Analysis of Dimensionality Reduction Techniques in Cryptographic Object Code Classification
Energy Technology Data Exchange (ETDEWEB)
Jason L. Wright; Milos Manic
2010-05-01
This paper compares the application of three different dimension reduction techniques to the problem of locating cryptography in compiled object code. A simple classi?er is used to compare dimension reduction via sorted covariance, principal component analysis, and correlation-based feature subset selection. The analysis concentrates on the classi?cation accuracy as the number of dimensions is increased.
On transfinite extension of asymptotic dimension
Radul, Taras
2006-01-01
We prove that a transfinite extension of asymptotic dimension asind is trivial. We introduce a transfinite extension of asymptotic dimension asdim and give an example of metric proper space which has transfinite infinite dimension.
Extra dimensions in space and time
Bars, Itzhak
2010-01-01
Covers topics such as Einstein and the Fourth Dimension; Waves in a Fifth Dimension; and String Theory and Branes Experimental Tests of Extra Dimensions. This book offers a discussion on Two-Time Physics
On Gorenstein projective, injective and flat dimensions
DEFF Research Database (Denmark)
Christensen, Lars Winther; Frankild, Anders Juel; Holm, Henrik Granau
2006-01-01
Gorenstein homological dimensions are refinements of the classical homological dimensions, and finiteness singles out modules with amenable properties reflecting those of modules over Gorenstein rings. As opposed to their classical counterparts, these dimensions do not immediately come with pract...
On Gorenstein projective, injective and flat dimensions
DEFF Research Database (Denmark)
Christensen, Lars Winther; Frankild, Anders Juel; Holm, Henrik Granau
2006-01-01
Gorenstein homological dimensions are refinements of the classical homological dimensions, and finiteness singles out modules with amenable properties reflecting those of modules over Gorenstein rings. As opposed to their classical counterparts, these dimensions do not immediately come with pract...
On the Estimation of Pointwise Dimension
Hidaka, Shohei
2013-01-01
Our goal in this paper is to develop an effective estimator of fractal dimension. We survey existing ideas in dimension estimation, with a focus on the currently popular method of Grassberger and Procaccia for the estimation of correlation dimension. There are two major difficulties in estimation based on this method. The first is the insensitivity of correlation dimension itself to differences in dimensionality over data, which we term {\\em dimension blindness}. The second comes from the reliance of the method on the inference of limiting behavior from finite data. We propose pointwise dimension as an object for estimation in response to the dimension blindness of correlation dimension. Pointwise dimension is a local quantity, and the distribution of pointwise dimensions over the data contains the information to which correlation dimension is blind. We use a "limit-free" description of pointwise dimension to develop a new estimator. We conclude by discussing potential applications of our estimator as well as...
Nonlinear Chemical Dynamics and Synchronization
Li, Ning
Alan Turing's work on morphogenesis, more than half a century ago, continues to motivate and inspire theoretical and experimental biologists even today. That said, there are very few experimental systems for which Turing's theory is applicable. In this thesis we present an experimental reaction-diffusion system ideally suited for testing Turing's ideas in synthetic "cells" consisting of microfluidically produced surfactant-stabilized emulsions in which droplets containing the Belousov-Zhabotinsky (BZ) oscillatory chemical reactants are dispersed in oil. The BZ reaction has become the prototype of nonlinear dynamics in chemistry and a preferred system for exploring the behavior of coupled nonlinear oscillators. Our system consists of a surfactant stabilized monodisperse emulsion of drops of aqueous BZ solution dispersed in a continuous phase of oil. In contrast to biology, here the chemistry is understood, rate constants are measured and interdrop coupling is purely diffusive. We explore a large set of parameters through control of rate constants, drop size, spacing, and spatial arrangement of the drops in lines and rings in one-dimension (1D) and hexagonal arrays in two-dimensions (2D). The Turing model is regarded as a metaphor for morphogenesis in biology but not for prediction. Here, we develop a quantitative and falsifiable reaction-diffusion model that we experimentally test with synthetic cells. We quantitatively establish the extent to which the Turing model in 1D describes both stationary pattern formation and temporal synchronization of chemical oscillators via reaction-diffusion and in 2D demonstrate that chemical morphogenesis drives physical differentiation in synthetic cells.
Dimensions of attractors in pinched skew products
Gröger, M.; Jäger, T.
2011-01-01
We study dimensions of strange non-chaotic attractors and their associated physical measures in so-called pinched skew products, introduced by Grebogi and his coworkers in 1984. Our main results are that the Hausdorff dimension, the pointwise dimension and the information dimension are all equal to one, although the box-counting dimension is known to be two. The assertion concerning the pointwise dimension is deduced from the stronger result that the physical measure is rectifiable. Our findi...
A Master Equation for Multi-Dimensional Non-Linear Field Theories
Park, Q H
1992-01-01
A master equation ( $n$ dimensional non--Abelian current conservation law with mutually commuting current components ) is introduced for multi-dimensional non-linear field theories. It is shown that the master equation provides a systematic way to understand 2-d integrable non-linear equations as well as 4-d self-dual equations and, more importantly, their generalizations to higher dimensions.
Nonlinear Elliptic Differential Equations with Multivalued Nonlinearities
Indian Academy of Sciences (India)
Antonella Fiacca; Nikolaos Matzakos; Nikolaos S Papageorgiou; Raffaella Servadei
2001-11-01
In this paper we study nonlinear elliptic boundary value problems with monotone and nonmonotone multivalued nonlinearities. First we consider the case of monotone nonlinearities. In the first result we assume that the multivalued nonlinearity is defined on all $\\mathbb{R}$. Assuming the existence of an upper and of a lower solution, we prove the existence of a solution between them. Also for a special version of the problem, we prove the existence of extremal solutions in the order interval formed by the upper and lower solutions. Then we drop the requirement that the monotone nonlinearity is defined on all of $\\mathbb{R}$. This case is important because it covers variational inequalities. Using the theory of operators of monotone type we show that the problem has a solution. Finally in the last part we consider an eigenvalue problem with a nonmonotone multivalued nonlinearity. Using the critical point theory for nonsmooth locally Lipschitz functionals we prove the existence of at least two nontrivial solutions (multiplicity theorem).
Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform
Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah
2017-02-01
Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.
Personality dimensions of opiate addicts.
Vukov, M; Baba-Milkic, N; Lecic, D; Mijalkovic, S; Marinkovic, J
1995-02-01
A survey of 80 opiate addicts included in a detoxification program was conducted at the Institute on Addictions in Belgrade. In addition to a dependence diagnosis and mental disorders based on DSM-III-R, we applied a Tridimensional Personality Questionnaire (TPQ) that measures the 3 major personality dimensions: novelty-seeking (NS), harm avoidance (HA) and reward dependence (RD). When compared with a control group (a sample of Yugoslav undergraduate students), the opiate addicts demonstrate significantly high NS dimension as well as significant divergences of HA and RD subscales. The surveyed opiate addicts demonstrate a high percentage of personality disorders specifically in cluster B. The personality dimensions of opiate addicts showed certain temperament traits, such as: impulsiveness, shyness with strangers, fear of uncertainty and dependence. NS, HA and RD determined by temperament specifics may be an etiological factor in forming of a personality disorder, an affective disorder as well as of a drug choice.
The Geographical Dimension of Terrorism
Hawkins, Houston T.
The events of September 11 ushered us all into a world in which our security and sense of invulnerability were savagely replaced by vulnerability and irrational fear. To the delight of our adversaries who planned these attacks, we often responded in ways that furthered their agenda by weakening the cultural colossus that we call home. Normally terrorism is viewed as intense but localized violence. Seldom is terrorism viewed in its more expansive dimensions. It is burned into our collective memories as a collapsed building, a shattered bus, an incinerated nightclub, or facilities closed by a few anthrax-laced letters. However, terrorism must be studied in dimensions larger than the view from a news camera. This conclusion forms the intellectual basis for The Geographical Dimension of Terrorism.
Correlated Electrons in Reduced Dimensions
Energy Technology Data Exchange (ETDEWEB)
Bonesteel, Nicholas E [Florida State Univ., Tallahassee, FL (United States)
2015-01-31
This report summarizes the work accomplished under the support of US DOE grant # DE-FG02-97ER45639, "Correlated Electrons in Reduced Dimensions." The underlying hypothesis of the research supported by this grant has been that studying the unique behavior of correlated electrons in reduced dimensions can lead to new ways of understanding how matter can order and how it can potentially be used. The systems under study have included i) fractional quantum Hall matter, which is realized when electrons are confined to two-dimensions and placed in a strong magnetic field at low temperature, ii) one-dimensional chains of spins and exotic quasiparticle excitations of topologically ordered matter, and iii) electrons confined in effectively ``zero-dimensional" semiconductor quantum dots.
The Creative Dimension of Visuality
DEFF Research Database (Denmark)
Michelsen, Anders Ib
2013-01-01
analysis relying on language/linguistics as a model for explaining culture? More specifically, how can the – creative – novelty of visual culture be addressed by a notion of discourse? This essay will argue that the debate on visual culture is lacking with regard to discerning the creative dimension of its...... own appearance. It will indicate an alternative conceptual framework based on Johann P. Arnason’s draft of tripartite culturalization which focuses on a shift from essences to dimensions of culture. This will be further developed by relating Maurice Merleau-Ponty’s idea of ‘chiasm’ of ‘the visible...... and the invisible’ to the notion of collective creativity and ‘the imaginary institution of society’ of Cornelius Castoriadis. In the theoretical relationship between Merleau-Ponty and Castoriadis it is possible to indicate a notion of visuality as a creative dimension....
Collider searches for extra dimensions
Energy Technology Data Exchange (ETDEWEB)
Landsberg, Greg; /Brown U.
2004-12-01
Searches for extra spatial dimensions remain among the most popular new directions in our quest for physics beyond the Standard Model. High-energy collider experiments of the current decade should be able to find an ultimate answer to the question of their existence in a variety of models. Until the start of the LHC in a few years, the Tevatron will remain the key player in this quest. In this paper, we review the most recent results from the Tevatron on searches for large, TeV{sup -1}-size, and Randall-Sundrum extra spatial dimensions, which have reached a new level of sensitivity and currently probe the parameter space beyond the existing constraints. While no evidence for the existence of extra dimensions has been found so far, an exciting discovery might be just steps away.
Correlation dimension estimates of human postural sway.
Gurses, Senih; Celik, Huseyin
2013-02-01
Human postural sway during quiet standing demonstrates a complex structured dynamics, which has been studied by applying numerous methods, such as linear system identification methods, stochastic analysis, and nonlinear system dynamics tools. Although each of the methods applied revealed some particular features of the sway data none of them have succeeded to present a global picture of the quiet stance dynamics, which probably has both stochastic and deterministic properties. In this study we have started applying ergodic theory of dynamical systems to explore statistical characteristic of the sway dynamics observed in successive trials of a subject, different subjects in an age group, and finally different age groups constituted by children, adults, and elderly subjects. Five successive 180-s long trials were performed by each of 28 subjects in four age groups at quiet stance with eyes open. Stationary and ergodic signal characteristics of five successive center of pressure time series collected from a subject in antero-posterior direction (CoPx) were examined. 97% of the trials were found to be stationary by applying Run Test while children and elderly groups demonstrated significant nonstationary behavior. On the other hand 13 out of 24 subjects were found to be nonergodic. We expected to observe differences in complexity of CoPx dynamics due to aging (Farmer, Ott, & Yorke, 1983). However linear metrics such as standard deviation and Fourier spectra of CoPx signals did not show differences due to the age groups. Correlation dimension (Dk) estimates of stationary CoPx signals being an invariant measure of nonlinear system dynamics were computed by using the average displacement method (Eckmann & Ruelle, 1985). Postural dynamics was expanded in m-dimensional space through CoPx signal by introducing optimum time delays, τcritical. 112 out of 136 stationary CoPx signals for 24 stationary subjects converged to Dk estimates. Average of Dk estimates for children and
On the UV Dimensions of Loop Quantum Gravity
Directory of Open Access Journals (Sweden)
Michele Ronco
2016-01-01
Full Text Available Planck-scale dynamical dimensional reduction is attracting more and more interest in the quantum-gravity literature since it seems to be a model independent effect. However, different studies base their results on different concepts of space-time dimensionality. Most of them rely on the spectral dimension; others refer to the Hausdorff dimension; and, very recently, the thermal dimension has also been introduced. We here show that all these distinct definitions of dimension give the same outcome in the case of the effective regime of Loop Quantum Gravity (LQG. This is achieved by deriving a modified dispersion relation from the hypersurface-deformation algebra with quantum corrections. Moreover, we also observe that the number of UV dimensions can be used to constrain the ambiguities in the choice of these LQG-based modifications of the Dirac space-time algebra. In this regard, introducing the polymerization of connections, that is, K→sinδK/δ, we find that the leading quantum correction gives dUV=2.5. This result may indicate that the running to the expected value of two dimensions is ongoing, but it has not been completed yet. Finding dUV at ultrashort distances would require going beyond the effective approach we here present.
Variational approach to various nonlinear problems in geometry and physics
Institute of Scientific and Technical Information of China (English)
2008-01-01
In this survey, we will summarize the existence results of nonlinear partial differential equations which arises from geometry or physics by using variational method. We use the method to study Kazdan-Warner problem, Chern-Simons-Higgs model, Toda systems, and the prescribed Q-curvature problem in 4-dimension.
Similarities Derived from 3-D Nonlinear Psychophysics: Variance Distributions.
Gregson, Robert A. M.
1994-01-01
The derivation of the variance of similarity judgments is made from the 3-D process in nonlinear psychophysics. The idea of separability of dimensions in metric space theories of similarity is replaced by one parameter that represents the degree of a form of interdimensional cross-sampling. (SLD)