WorldWideScience

Sample records for heat kernel estimates

  1. Hamilton's gradient estimate for the heat kernel on complete manifolds

    OpenAIRE

    Kotschwar, Brett

    2007-01-01

    In this paper we extend a gradient estimate of R. Hamilton for positive solutions to the heat equation on closed manifolds to bounded positive solutions on complete, non-compact manifolds with $Rc \\geq -Kg$. We accomplish this extension via a maximum principle of L. Karp and P. Li and a Bernstein-type estimate on the gradient of the solution. An application of our result, together with the bounds of P. Li and S.T. Yau, yields an estimate on the gradient of the heat kernel for complete manifol...

  2. Exact Heat Kernel on a Hypersphere and Its Applications in Kernel SVM

    Directory of Open Access Journals (Sweden)

    Chenchao Zhao

    2018-01-01

    Full Text Available Many contemporary statistical learning methods assume a Euclidean feature space. This paper presents a method for defining similarity based on hyperspherical geometry and shows that it often improves the performance of support vector machine compared to other competing similarity measures. Specifically, the idea of using heat diffusion on a hypersphere to measure similarity has been previously proposed and tested by Lafferty and Lebanon [1], demonstrating promising results based on a heuristic heat kernel obtained from the zeroth order parametrix expansion; however, how well this heuristic kernel agrees with the exact hyperspherical heat kernel remains unknown. This paper presents a higher order parametrix expansion of the heat kernel on a unit hypersphere and discusses several problems associated with this expansion method. We then compare the heuristic kernel with an exact form of the heat kernel expressed in terms of a uniformly and absolutely convergent series in high-dimensional angular momentum eigenmodes. Being a natural measure of similarity between sample points dwelling on a hypersphere, the exact kernel often shows superior performance in kernel SVM classifications applied to text mining, tumor somatic mutation imputation, and stock market analysis.

  3. Heat kernel analysis for Bessel operators on symmetric cones

    DEFF Research Database (Denmark)

    Möllers, Jan

    2014-01-01

    . The heat kernel is explicitly given in terms of a multivariable $I$-Bessel function on $Ω$. Its corresponding heat kernel transform defines a continuous linear operator between $L^p$-spaces. The unitary image of the $L^2$-space under the heat kernel transform is characterized as a weighted Bergmann space...

  4. Global Polynomial Kernel Hazard Estimation

    DEFF Research Database (Denmark)

    Hiabu, Munir; Miranda, Maria Dolores Martínez; Nielsen, Jens Perch

    2015-01-01

    This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically redu...

  5. Heat Kernel Asymptotics of Zaremba Boundary Value Problem

    Energy Technology Data Exchange (ETDEWEB)

    Avramidi, Ivan G. [Department of Mathematics, New Mexico Institute of Mining and Technology (United States)], E-mail: iavramid@nmt.edu

    2004-03-15

    The Zaremba boundary-value problem is a boundary value problem for Laplace-type second-order partial differential operators acting on smooth sections of a vector bundle over a smooth compact Riemannian manifold with smooth boundary but with discontinuous boundary conditions, which include Dirichlet boundary conditions on one part of the boundary and Neumann boundary conditions on another part of the boundary. We study the heat kernel asymptotics of Zaremba boundary value problem. The construction of the asymptotic solution of the heat equation is described in detail and the heat kernel is computed explicitly in the leading approximation. Some of the first nontrivial coefficients of the heat kernel asymptotic expansion are computed explicitly.

  6. A multi-resolution approach to heat kernels on discrete surfaces

    KAUST Repository

    Vaxman, Amir

    2010-07-26

    Studying the behavior of the heat diffusion process on a manifold is emerging as an important tool for analyzing the geometry of the manifold. Unfortunately, the high complexity of the computation of the heat kernel - the key to the diffusion process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel approximation method for the heat kernel at short times results in an efficient and robust algorithm for computing the heat kernels of detailed models. We show experimentally that our method can achieve good approximations in a fraction of the time required by traditional algorithms. Finally, we demonstrate how these heat kernels can be used to improve a diffusion-based feature extraction algorithm. © 2010 ACM.

  7. Lévy matters VI Lévy-type processes moments, construction and heat kernel estimates

    CERN Document Server

    Kühn, Franziska

    2017-01-01

    Presenting some recent results on the construction and the moments of Lévy-type processes, the focus of this volume is on a new existence theorem, which is proved using a parametrix construction. Applications range from heat kernel estimates for a class of Lévy-type processes to existence and uniqueness theorems for Lévy-driven stochastic differential equations with Hölder continuous coefficients. Moreover, necessary and sufficient conditions for the existence of moments of Lévy-type processes are studied and some estimates on moments are derived. Lévy-type processes behave locally like Lévy processes but, in contrast to Lévy processes, they are not homogeneous in space. Typical examples are processes with varying index of stability and solutions of Lévy-driven stochastic differential equations. This is the sixth volume in a subseries of the Lecture Notes in Mathematics called Lévy Matters. Each volume describes a number of important topics in the theory or applicati ons of Lévy processes and pays ...

  8. Modelling microwave heating of discrete samples of oil palm kernels

    International Nuclear Information System (INIS)

    Law, M.C.; Liew, E.L.; Chang, S.L.; Chan, Y.S.; Leo, C.P.

    2016-01-01

    Highlights: • Microwave (MW) drying of oil palm kernels is experimentally determined and modelled. • MW heating of discrete samples of oil palm kernels (OPKs) is simulated. • OPK heating is due to contact effect, MW interference and heat transfer mechanisms. • Electric field vectors circulate within OPKs sample. • Loosely-packed arrangement improves temperature uniformity of OPKs. - Abstract: Recently, microwave (MW) pre-treatment of fresh palm fruits has showed to be environmentally friendly compared to the existing oil palm milling process as it eliminates the condensate production of palm oil mill effluent (POME) in the sterilization process. Moreover, MW-treated oil palm fruits (OPF) also possess better oil quality. In this work, the MW drying kinetic of the oil palm kernels (OPK) was determined experimentally. Microwave heating/drying of oil palm kernels was modelled and validated. The simulation results show that temperature of an OPK is not the same over the entire surface due to constructive and destructive interferences of MW irradiance. The volume-averaged temperature of an OPK is higher than its surface temperature by 3–7 °C, depending on the MW input power. This implies that point measurement of temperature reading is inadequate to determine the temperature history of the OPK during the microwave heating process. The simulation results also show that arrangement of OPKs in a MW cavity affects the kernel temperature profile. The heating of OPKs were identified to be affected by factors such as local electric field intensity due to MW absorption, refraction, interference, the contact effect between kernels and also heat transfer mechanisms. The thermal gradient patterns of OPKs change as the heating continues. The cracking of OPKs is expected to occur first in the core of the kernel and then it propagates to the kernel surface. The model indicates that drying of OPKs is a much slower process compared to its MW heating. The model is useful

  9. Heat kernel estimates for pseudodifferential operators, fractional Laplacians and Dirichlet-to-Neumann operators

    DEFF Research Database (Denmark)

    Gimperlein, Heiko; Grubb, Gerd

    2014-01-01

    The purpose of this article is to establish upper and lower estimates for the integral kernel of the semigroup exp(−t P) associated to a classical, strongly elliptic pseudodifferential operator P of positive order on a closed manifold. The Poissonian bounds generalize those obtained for perturbat......The purpose of this article is to establish upper and lower estimates for the integral kernel of the semigroup exp(−t P) associated to a classical, strongly elliptic pseudodifferential operator P of positive order on a closed manifold. The Poissonian bounds generalize those obtained...... for perturbations of fractional powers of the Laplacian. In the selfadjoint case, extensions to t∈C+  are studied. In particular, our results apply to the Dirichlet-to-Neumann semigroup....

  10. Consistent Estimation of Pricing Kernels from Noisy Price Data

    OpenAIRE

    Vladislav Kargin

    2003-01-01

    If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $\\epsilon$-entropy, non-parametric estimation, pricing kernel, inverse problems.

  11. Discrete non-parametric kernel estimation for global sensitivity analysis

    International Nuclear Information System (INIS)

    Senga Kiessé, Tristan; Ventura, Anne

    2016-01-01

    This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.

  12. Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

    Science.gov (United States)

    Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

    2015-05-01

    We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Variable Kernel Density Estimation

    OpenAIRE

    Terrell, George R.; Scott, David W.

    1992-01-01

    We investigate some of the possibilities for improvement of univariate and multivariate kernel density estimates by varying the window over the domain of estimation, pointwise and globally. Two general approaches are to vary the window width by the point of estimation and by point of the sample observation. The first possibility is shown to be of little efficacy in one variable. In particular, nearest-neighbor estimators in all versions perform poorly in one and two dimensions, but begin to b...

  14. Variable kernel density estimation in high-dimensional feature spaces

    CSIR Research Space (South Africa)

    Van der Walt, Christiaan M

    2017-02-01

    Full Text Available Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high...

  15. The heating of UO_2 kernels in argon gas medium on the physical properties of sintered UO_2 kernels

    International Nuclear Information System (INIS)

    Damunir; Sri Rinanti Susilowati; Ariyani Kusuma Dewi

    2015-01-01

    The heating of UO_2 kernels in argon gas medium on the physical properties of sinter UO_2 kernels was conducted. The heated of the UO_2 kernels was conducted in a sinter reactor of a bed type. The sample used was the UO_2 kernels resulted from the reduction results at 800 °C temperature for 3 hours that had the density of 8.13 g/cm"3; porosity of 0.26; O/U ratio of 2.05; diameter of 1146 μm and sphericity of 1.05. The sample was put into a sinter reactor, then it was vacuumed by flowing the argon gas at 180 mmHg pressure to drain the air from the reactor. After that, the cooling water and argon gas were continuously flowed with the pressure of 5 mPa with 1.5 liter/minutes velocity. The reactor temperature was increased and variated at 1200-1500 °C temperature and for 1-4 hours. The sinters UO_2 kernels resulted from the study were analyzed in term of their physical properties including the density, porosity, diameter, sphericity, and specific surface area. The density was analyzed using pycnometer with CCl_4 solution. The porosity was determined using Haynes equation. The diameters and sphericity were showed using the Dino-lite microscope. The specific surface area was determined using surface area meter Nova-1000. The obtained products showed the the heating of UO_2 kernel in argon gas medium were influenced on the physical properties of sinters UO_2 kernel. The condition of best relatively at 1400 °C temperature and 2 hours time. The product resulted from the study was relatively at its best when heating was conducted at 1400 °C temperature and 2 hours time, produced sinters UO_2 kernel with density of 10.14 gr/ml; porosity of 7 %; diameters of 893 μm; sphericity of 1.07 and specific surface area of 4.68 m"2/g with solidify shrinkage of 22 %. (author)

  16. The heat kernel as the pagerank of a graph

    Science.gov (United States)

    Chung, Fan

    2007-01-01

    The concept of pagerank was first started as a way for determining the ranking of Web pages by Web search engines. Based on relations in interconnected networks, pagerank has become a major tool for addressing fundamental problems arising in general graphs, especially for large information networks with hundreds of thousands of nodes. A notable notion of pagerank, introduced by Brin and Page and denoted by PageRank, is based on random walks as a geometric sum. In this paper, we consider a notion of pagerank that is based on the (discrete) heat kernel and can be expressed as an exponential sum of random walks. The heat kernel satisfies the heat equation and can be used to analyze many useful properties of random walks in a graph. A local Cheeger inequality is established, which implies that, by focusing on cuts determined by linear orderings of vertices using the heat kernel pageranks, the resulting partition is within a quadratic factor of the optimum. This is true, even if we restrict the volume of the small part separated by the cut to be close to some specified target value. This leads to a graph partitioning algorithm for which the running time is proportional to the size of the targeted volume (instead of the size of the whole graph).

  17. Improved Variable Window Kernel Estimates of Probability Densities

    OpenAIRE

    Hall, Peter; Hu, Tien Chung; Marron, J. S.

    1995-01-01

    Variable window width kernel density estimators, with the width varying proportionally to the square root of the density, have been thought to have superior asymptotic properties. The rate of convergence has been claimed to be as good as those typical for higher-order kernels, which makes the variable width estimators more attractive because no adjustment is needed to handle the negativity usually entailed by the latter. However, in a recent paper, Terrell and Scott show that these results ca...

  18. Kernel bandwidth estimation for non-parametric density estimation: a comparative study

    CSIR Research Space (South Africa)

    Van der Walt, CM

    2013-12-01

    Full Text Available We investigate the performance of conventional bandwidth estimators for non-parametric kernel density estimation on a number of representative pattern-recognition tasks, to gain a better understanding of the behaviour of these estimators in high...

  19. The Kernel Estimation in Biosystems Engineering

    Directory of Open Access Journals (Sweden)

    Esperanza Ayuga Téllez

    2008-04-01

    Full Text Available In many fields of biosystems engineering, it is common to find works in which statistical information is analysed that violates the basic hypotheses necessary for the conventional forecasting methods. For those situations, it is necessary to find alternative methods that allow the statistical analysis considering those infringements. Non-parametric function estimation includes methods that fit a target function locally, using data from a small neighbourhood of the point. Weak assumptions, such as continuity and differentiability of the target function, are rather used than "a priori" assumption of the global target function shape (e.g., linear or quadratic. In this paper a few basic rules of decision are enunciated, for the application of the non-parametric estimation method. These statistical rules set up the first step to build an interface usermethod for the consistent application of kernel estimation for not expert users. To reach this aim, univariate and multivariate estimation methods and density function were analysed, as well as regression estimators. In some cases the models to be applied in different situations, based on simulations, were defined. Different biosystems engineering applications of the kernel estimation are also analysed in this review.

  20. On Improving Convergence Rates for Nonnegative Kernel Density Estimators

    OpenAIRE

    Terrell, George R.; Scott, David W.

    1980-01-01

    To improve the rate of decrease of integrated mean square error for nonparametric kernel density estimators beyond $0(n^{-\\frac{4}{5}}),$ we must relax the constraint that the density estimate be a bonafide density function, that is, be nonnegative and integrate to one. All current methods for kernel (and orthogonal series) estimators relax the nonnegativity constraint. In this paper we show how to achieve similar improvement by relaxing the integral constraint only. This is important in appl...

  1. Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation.

    Science.gov (United States)

    Chen, Rongda; Wang, Ze

    2013-01-01

    Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.

  2. Optimal Bandwidth Selection for Kernel Density Functionals Estimation

    Directory of Open Access Journals (Sweden)

    Su Chen

    2015-01-01

    Full Text Available The choice of bandwidth is crucial to the kernel density estimation (KDE and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals ∫γ(xf2(xdx with appropriate choice of γ(x and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. ∫γ(xf2(xdx can be estimated nonparametrically via kernel density functionals estimation (KDFE. However, the optimal bandwidth selection for KDFE of ∫γ(xf2(xdx has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE of the KDFE. Two main practical bandwidth selection techniques for the KDFE of ∫γ(xf2(xdx are provided: Normal scale bandwidth selection (namely, “Rule of Thumb” and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.

  3. Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation.

    Directory of Open Access Journals (Sweden)

    Rongda Chen

    Full Text Available Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody's. However, it has a fatal defect that it can't fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody's new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management.

  4. Curve Fitting of the Corporate Recovery Rates: The Comparison of Beta Distribution Estimation and Kernel Density Estimation

    Science.gov (United States)

    Chen, Rongda; Wang, Ze

    2013-01-01

    Recovery rate is essential to the estimation of the portfolio’s loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk. The study introduces two kinds of models of distribution, Beta distribution estimation and kernel density distribution estimation, to simulate the distribution of recovery rates of corporate loans and bonds. As is known, models based on Beta distribution are common in daily usage, such as CreditMetrics by J.P. Morgan, Portfolio Manager by KMV and Losscalc by Moody’s. However, it has a fatal defect that it can’t fit the bimodal or multimodal distributions such as recovery rates of corporate loans and bonds as Moody’s new data show. In order to overcome this flaw, the kernel density estimation is introduced and we compare the simulation results by histogram, Beta distribution estimation and kernel density estimation to reach the conclusion that the Gaussian kernel density distribution really better imitates the distribution of the bimodal or multimodal data samples of corporate loans and bonds. Finally, a Chi-square test of the Gaussian kernel density estimation proves that it can fit the curve of recovery rates of loans and bonds. So using the kernel density distribution to precisely delineate the bimodal recovery rates of bonds is optimal in credit risk management. PMID:23874558

  5. A survey of kernel-type estimators for copula and their applications

    Science.gov (United States)

    Sumarjaya, I. W.

    2017-10-01

    Copulas have been widely used to model nonlinear dependence structure. Main applications of copulas include areas such as finance, insurance, hydrology, rainfall to name but a few. The flexibility of copula allows researchers to model dependence structure beyond Gaussian distribution. Basically, a copula is a function that couples multivariate distribution functions to their one-dimensional marginal distribution functions. In general, there are three methods to estimate copula. These are parametric, nonparametric, and semiparametric method. In this article we survey kernel-type estimators for copula such as mirror reflection kernel, beta kernel, transformation method and local likelihood transformation method. Then, we apply these kernel methods to three stock indexes in Asia. The results of our analysis suggest that, albeit variation in information criterion values, the local likelihood transformation method performs better than the other kernel methods.

  6. A multi-resolution approach to heat kernels on discrete surfaces

    KAUST Repository

    Vaxman, Amir; Ben-Chen, Mirela; Gotsman, Craig

    2010-01-01

    process - limits this type of analysis to 3D models of modest resolution. We show how to use the unique properties of the heat kernel of a discrete two dimensional manifold to overcome these limitations. Combining a multi-resolution approach with a novel

  7. Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling

    Directory of Open Access Journals (Sweden)

    Hyojin Lee

    2015-01-01

    Full Text Available Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data through Kth nearest neighborhood (KNN regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the KNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.

  8. Heat kernels and zeta functions on fractals

    International Nuclear Information System (INIS)

    Dunne, Gerald V

    2012-01-01

    On fractals, spectral functions such as heat kernels and zeta functions exhibit novel features, very different from their behaviour on regular smooth manifolds, and these can have important physical consequences for both classical and quantum physics in systems having fractal properties. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical in honour of Stuart Dowker's 75th birthday devoted to ‘Applications of zeta functions and other spectral functions in mathematics and physics’. (paper)

  9. Kernel and wavelet density estimators on manifolds and more general metric spaces

    DEFF Research Database (Denmark)

    Cleanthous, G.; Georgiadis, Athanasios; Kerkyacharian, G.

    We consider the problem of estimating the density of observations taking values in classical or nonclassical spaces such as manifolds and more general metric spaces. Our setting is quite general but also sufficiently rich in allowing the development of smooth functional calculus with well localized...... spectral kernels, Besov regularity spaces, and wavelet type systems. Kernel and both linear and nonlinear wavelet density estimators are introduced and studied. Convergence rates for these estimators are established, which are analogous to the existing results in the classical setting of real...

  10. Corruption clubs: empirical evidence from kernel density estimates

    NARCIS (Netherlands)

    Herzfeld, T.; Weiss, Ch.

    2007-01-01

    A common finding of many analytical models is the existence of multiple equilibria of corruption. Countries characterized by the same economic, social and cultural background do not necessarily experience the same levels of corruption. In this article, we use Kernel Density Estimation techniques to

  11. The Visualization and Analysis of POI Features under Network Space Supported by Kernel Density Estimation

    Directory of Open Access Journals (Sweden)

    YU Wenhao

    2015-01-01

    Full Text Available The distribution pattern and the distribution density of urban facility POIs are of great significance in the fields of infrastructure planning and urban spatial analysis. The kernel density estimation, which has been usually utilized for expressing these spatial characteristics, is superior to other density estimation methods (such as Quadrat analysis, Voronoi-based method, for that the Kernel density estimation considers the regional impact based on the first law of geography. However, the traditional kernel density estimation is mainly based on the Euclidean space, ignoring the fact that the service function and interrelation of urban feasibilities is carried out on the network path distance, neither than conventional Euclidean distance. Hence, this research proposed a computational model of network kernel density estimation, and the extension type of model in the case of adding constraints. This work also discussed the impacts of distance attenuation threshold and height extreme to the representation of kernel density. The large-scale actual data experiment for analyzing the different POIs' distribution patterns (random type, sparse type, regional-intensive type, linear-intensive type discusses the POI infrastructure in the city on the spatial distribution of characteristics, influence factors, and service functions.

  12. Moderate deviations principles for the kernel estimator of ...

    African Journals Online (AJOL)

    Abstract. The aim of this paper is to provide pointwise and uniform moderate deviations principles for the kernel estimator of a nonrandom regression function. Moreover, we give an application of these moderate deviations principles to the construction of condence regions for the regression function. Resume. L'objectif de ...

  13. Considerations on absorbed dose estimates based on different β-dose point kernels in internal dosimetry

    International Nuclear Information System (INIS)

    Uchida, Isao; Yamada, Yasuhiko; Yamashita, Takashi; Okigaki, Shigeyasu; Oyamada, Hiyoshimaru; Ito, Akira.

    1995-01-01

    In radiotherapy with radiopharmaceuticals, more accurate estimates of the three-dimensional (3-D) distribution of absorbed dose is important in specifying the activity to be administered to patients to deliver a prescribed absorbed dose to target volumes without exceeding the toxicity limit of normal tissues in the body. A calculation algorithm for the purpose has already been developed by the authors. An accurate 3-D distribution of absorbed dose based on the algorithm is given by convolution of the 3-D dose matrix for a unit cubic voxel containing unit cumulated activity, which is obtained by transforming a dose point kernel into a 3-D cubic dose matrix, with the 3-D cumulated activity distribution given by the same voxel size. However, beta-dose point kernels affecting accurate estimates of the 3-D absorbed dose distribution have been different among the investigators. The purpose of this study is to elucidate how different beta-dose point kernels in water influence on the estimates of the absorbed dose distribution due to the dose point kernel convolution method by the authors. Computer simulations were performed using the MIRD thyroid and lung phantoms under assumption of uniform activity distribution of 32 P. Using beta-dose point kernels derived from Monte Carlo simulations (EGS-4 or ACCEPT computer code), the differences among their point kernels gave little differences for the mean and maximum absorbed dose estimates for the MIRD phantoms used. In the estimates of mean and maximum absorbed doses calculated using different cubic voxel sizes (4x4x4 mm and 8x8x8 mm) for the MIRD thyroid phantom, the maximum absorbed doses for the 4x4x4 mm-voxel were estimated approximately 7% greater than the cases of the 8x8x8 mm-voxel. They were found in every beta-dose point kernel used in this study. On the other hand, the percentage difference of the mean absorbed doses in the both voxel sizes for each beta-dose point kernel was less than approximately 0.6%. (author)

  14. A New Entropy Formula and Gradient Estimates for the Linear Heat Equation on Static Manifold

    Directory of Open Access Journals (Sweden)

    Abimbola Abolarinwa

    2014-08-01

    Full Text Available In this paper we prove a new monotonicity formula for the heat equation via a generalized family of entropy functionals. This family of entropy formulas generalizes both Perelman’s entropy for evolving metric and Ni’s entropy on static manifold. We show that this entropy satisfies a pointwise differential inequality for heat kernel. The consequences of which are various gradient and Harnack estimates for all positive solutions to the heat equation on compact manifold.

  15. A heat kernel proof of the index theorem for deformation quantization

    Science.gov (United States)

    Karabegov, Alexander

    2017-11-01

    We give a heat kernel proof of the algebraic index theorem for deformation quantization with separation of variables on a pseudo-Kähler manifold. We use normalizations of the canonical trace density of a star product and of the characteristic classes involved in the index formula for which this formula contains no extra constant factors.

  16. A heat kernel proof of the index theorem for deformation quantization

    OpenAIRE

    Karabegov, Alexander

    2017-01-01

    We give a heat kernel proof of the algebraic index theorem for deformation quantization with separation of variables on a pseudo-Kahler manifold. We use normalizations of the canonical trace density of a star product and of the characteristic classes involved in the index formula for which this formula contains no extra constant factors.

  17. Regularized Pre-image Estimation for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2011-01-01

    The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed...

  18. Partial Deconvolution with Inaccurate Blur Kernel.

    Science.gov (United States)

    Ren, Dongwei; Zuo, Wangmeng; Zhang, David; Xu, Jun; Zhang, Lei

    2017-10-17

    Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning-based models to suppress the adverse effect of kernel estimation error. Furthermore, an E-M algorithm is developed for estimating the partial map and recovering the latent sharp image alternatively. Experimental results show that our partial deconvolution model is effective in relieving artifacts caused by inaccurate blur kernel, and can achieve favorable deblurring quality on synthetic and real blurry images.Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution stage. In this paper, we tackle this issue by suggesting: (i) a partial map in the Fourier domain for modeling kernel estimation error, and (ii) a partial deconvolution model for robust deblurring with inaccurate blur kernel. The partial map is constructed by detecting the reliable Fourier entries of estimated blur kernel. And partial deconvolution is applied to wavelet-based and learning

  19. Calculation of heat-kernel coefficients and usage of computer algebra

    International Nuclear Information System (INIS)

    Bel'kov, A.A.; Lanev, A.V.; Schaale, A.

    1995-01-01

    The calculation of heat-kernel coefficients with the classical De Witt algorithm has been discussed. We present the explicit form of the coefficients up to h 5 in the general case and up to h 7 min for the minimal parts. The results are compared with the expressions in other papers. A method to optimize the usage of memory for working with large expressions on universal computer algebra systems has been proposed. 20 refs

  20. Observing integrals of heat kernels from a distance

    DEFF Research Database (Denmark)

    Heat kernels have integrals such as Brownian motion mean exit time, potential capacity, and torsional rigidity. We show how to obtain bounds on these values - essentially by observing their behaviour in terms of the distance function from a point and then comparing with corresponding values in ta...... and discussed as test cases. The talk is based on joint work with Vicente Palmer....... in tailor-made warped product spaces. The results will be illustrated by applications to the so-called 'type' problem: How to decide if a given manifold or surface is transient (hyperbolic) or recurrent (parabolic). Specific examples of minimal surfaces and constant pressure dry foams will be shown...

  1. A new fractional derivative without singular kernel: Application to the modelling of the steady heat flow

    Directory of Open Access Journals (Sweden)

    Yang Xiao-Jun

    2016-01-01

    Full Text Available In this article we propose a new fractional derivative without singular kernel. We consider the potential application for modeling the steady heat-conduction problem. The analytical solution of the fractional-order heat flow is also obtained by means of the Laplace transform.

  2. Some results from a Mellin transform expansion for the heat Kernel

    International Nuclear Information System (INIS)

    Malbouisson, A.P.C.; Simao, F.R.A.; Camargo Filho, A.F. de.

    1988-01-01

    The coefficients of a new Heat Kernel expansion, in the case of a differential operator containing a gauge field. The meromorphic structure of the generalized zeta-function obtained by that expansion is compared with the one obtained in a proceeding paper. The expansion is applied to anomalies, obtaining a general formula for arbitrary dimension D. The special cases D=2 and D=3 are investigated. (author) [pt

  3. Subsampling Realised Kernels

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

    2011-01-01

    In a recent paper we have introduced the class of realised kernel estimators of the increments of quadratic variation in the presence of noise. We showed that this estimator is consistent and derived its limit distribution under various assumptions on the kernel weights. In this paper we extend our...... that subsampling is impotent, in the sense that subsampling has no effect on the asymptotic distribution. Perhaps surprisingly, for the efficient smooth kernels, such as the Parzen kernel, we show that subsampling is harmful as it increases the asymptotic variance. We also study the performance of subsampled...

  4. Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

    DEFF Research Database (Denmark)

    Rasmussen, Peter Mondrup; Abrahamsen, Trine Julie; Madsen, Kristoffer Hougaard

    2012-01-01

    We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising...... procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We...

  5. Using kernel density estimates to investigate lymphatic filariasis in northeast Brazil

    Science.gov (United States)

    Medeiros, Zulma; Bonfim, Cristine; Brandão, Eduardo; Netto, Maria José Evangelista; Vasconcellos, Lucia; Ribeiro, Liany; Portugal, José Luiz

    2012-01-01

    After more than 10 years of the Global Program to Eliminate Lymphatic Filariasis (GPELF) in Brazil, advances have been seen, but the endemic disease persists as a public health problem. The aim of this study was to describe the spatial distribution of lymphatic filariasis in the municipality of Jaboatão dos Guararapes, Pernambuco, Brazil. An epidemiological survey was conducted in the municipality, and positive filariasis cases identified in this survey were georeferenced in point form, using the GPS. A kernel intensity estimator was applied to identify clusters with greater intensity of cases. We examined 23 673 individuals and 323 individuals with microfilaremia were identified, representing a mean prevalence rate of 1.4%. Around 88% of the districts surveyed presented cases of filarial infection, with prevalences of 0–5.6%. The male population was more affected by the infection, with 63.8% of the cases (P<0.005). Positive cases were found in all age groups examined. The kernel intensity estimator identified the areas of greatest intensity and least intensity of filarial infection cases. The case distribution was heterogeneous across the municipality. The kernel estimator identified spatial clusters of cases, thus indicating locations with greater intensity of transmission. The main advantage of this type of analysis lies in its ability to rapidly and easily show areas with the highest concentration of cases, thereby contributing towards planning, monitoring, and surveillance of filariasis elimination actions. Incorporation of geoprocessing and spatial analysis techniques constitutes an important tool for use within the GPELF. PMID:22943547

  6. Optimized Kernel Entropy Components.

    Science.gov (United States)

    Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

    2017-06-01

    This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

  7. Automated voxelization of 3D atom probe data through kernel density estimation

    International Nuclear Information System (INIS)

    Srinivasan, Srikant; Kaluskar, Kaustubh; Dumpala, Santoshrupa; Broderick, Scott; Rajan, Krishna

    2015-01-01

    Identifying nanoscale chemical features from atom probe tomography (APT) data routinely involves adjustment of voxel size as an input parameter, through visual supervision, making the final outcome user dependent, reliant on heuristic knowledge and potentially prone to error. This work utilizes Kernel density estimators to select an optimal voxel size in an unsupervised manner to perform feature selection, in particular targeting resolution of interfacial features and chemistries. The capability of this approach is demonstrated through analysis of the γ / γ’ interface in a Ni–Al–Cr superalloy. - Highlights: • Develop approach for standardizing aspects of atom probe reconstruction. • Use Kernel density estimators to select optimal voxel sizes in an unsupervised manner. • Perform interfacial analysis of Ni–Al–Cr superalloy, using new automated approach. • Optimize voxel size to preserve the feature of interest and minimizing loss / noise.

  8. Estimation of the applicability domain of kernel-based machine learning models for virtual screening

    Directory of Open Access Journals (Sweden)

    Fechner Nikolas

    2010-03-01

    Full Text Available Abstract Background The virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model. Results We evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening

  9. Estimation of the applicability domain of kernel-based machine learning models for virtual screening.

    Science.gov (United States)

    Fechner, Nikolas; Jahn, Andreas; Hinselmann, Georg; Zell, Andreas

    2010-03-11

    The virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model. We evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening. The proposed applicability domain formulations

  10. Heat damage and in vitro starch digestibility of puffed wheat kernels.

    Science.gov (United States)

    Cattaneo, Stefano; Hidalgo, Alyssa; Masotti, Fabio; Stuknytė, Milda; Brandolini, Andrea; De Noni, Ivano

    2015-12-01

    The effect of processing conditions on heat damage, starch digestibility, release of advanced glycation end products (AGEs) and antioxidant capacity of puffed cereals was studied. The determination of several markers arising from Maillard reaction proved pyrraline (PYR) and hydroxymethylfurfural (HMF) as the most reliable indices of heat load applied during puffing. The considerable heat load was evidenced by the high levels of both PYR (57.6-153.4 mg kg(-1) dry matter) and HMF (13-51.2 mg kg(-1) dry matter). For cost and simplicity, HMF looked like the most appropriate index in puffed cereals. Puffing influenced starch in vitro digestibility, being most of the starch (81-93%) hydrolyzed to maltotriose, maltose and glucose whereas only limited amounts of AGEs were released. The relevant antioxidant capacity revealed by digested puffed kernels can be ascribed to both the new formed Maillard reaction products and the conditions adopted during in vitro digestion. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Heat kernel expansion in the background field formalism

    CERN Document Server

    Barvinsky, Andrei

    2015-01-01

    Heat kernel expansion and background field formalism represent the combination of two calculational methods within the functional approach to quantum field theory. This approach implies construction of generating functionals for matrix elements and expectation values of physical observables. These are functionals of arbitrary external sources or the mean field of a generic configuration -- the background field. Exact calculation of quantum effects on a generic background is impossible. However, a special integral (proper time) representation for the Green's function of the wave operator -- the propagator of the theory -- and its expansion in the ultraviolet and infrared limits of respectively short and late proper time parameter allow one to construct approximations which are valid on generic background fields. Current progress of quantum field theory, its renormalization properties, model building in unification of fundamental physical interactions and QFT applications in high energy physics, gravitation and...

  12. Palm kernel cake extract exerts hepatoprotective activity in heat-induced oxidative stress in chicken hepatocytes.

    Science.gov (United States)

    Oskoueian, Ehsan; Abdullah, Norhani; Idrus, Zulkifli; Ebrahimi, Mahdi; Goh, Yong Meng; Shakeri, Majid; Oskoueian, Armin

    2014-10-02

    Palm kernel cake (PKC), the most abundant by-product of oil palm industry is believed to contain bioactive compounds with hepatoprotective potential. These compounds may serve as hepatoprotective agents which could help the poultry industry to alleviate adverse effects of heat stress on liver function in chickens. This study was performed to evaluate the hepatoprotective potential of PKC extract in heat-induced oxidative stress in chicken hepatocytes. The nature of the active metabolites and elucidation of the possible mechanism involved were also investigated. The PKC extract possessed free radical scavenging activity with values significantly (p < 0.05) lower than silymarin as the reference antioxidant. Heat-induced oxidative stress in chicken hepatocyte impaired the total protein, lipid peroxidation and antioxidant enzymes activity significantly (p < 0.05). Treatment of heat-induced hepatocytes with PKC extract (125 μg/ml) and silymarin as positive control increased these values significantly (p < 0.05). The real time PCR and western blot analyses revealed the significant (p < 0.05) up-regulation of oxidative stress biomarkers including TNF-like, IFN-γ and IL-1β genes; NF-κB, COX-2, iNOS and Hsp70 proteins expression upon heat stress in chicken hepatocytes. The PKC extract and silymarin were able to alleviate the expression of all of these biomarkers in heat-induced chicken hepatocytes. The gas chromatography-mass spectrometry analysis of PKC extract showed the presence of fatty acids, phenolic compounds, sugar derivatives and other organic compounds such as furfural which could be responsible for the observed hepatoprotective activity. Palm kernel cake extract could be a potential agent to protect hepatocytes function under heat induced oxidative stress.

  13. Kernel density estimation-based real-time prediction for respiratory motion

    International Nuclear Information System (INIS)

    Ruan, Dan

    2010-01-01

    Effective delivery of adaptive radiotherapy requires locating the target with high precision in real time. System latency caused by data acquisition, streaming, processing and delivery control necessitates prediction. Prediction is particularly challenging for highly mobile targets such as thoracic and abdominal tumors undergoing respiration-induced motion. The complexity of the respiratory motion makes it difficult to build and justify explicit models. In this study, we honor the intrinsic uncertainties in respiratory motion and propose a statistical treatment of the prediction problem. Instead of asking for a deterministic covariate-response map and a unique estimate value for future target position, we aim to obtain a distribution of the future target position (response variable) conditioned on the observed historical sample values (covariate variable). The key idea is to estimate the joint probability distribution (pdf) of the covariate and response variables using an efficient kernel density estimation method. Then, the problem of identifying the distribution of the future target position reduces to identifying the section in the joint pdf based on the observed covariate. Subsequently, estimators are derived based on this estimated conditional distribution. This probabilistic perspective has some distinctive advantages over existing deterministic schemes: (1) it is compatible with potentially inconsistent training samples, i.e., when close covariate variables correspond to dramatically different response values; (2) it is not restricted by any prior structural assumption on the map between the covariate and the response; (3) the two-stage setup allows much freedom in choosing statistical estimates and provides a full nonparametric description of the uncertainty for the resulting estimate. We evaluated the prediction performance on ten patient RPM traces, using the root mean squared difference between the prediction and the observed value normalized by the

  14. The Effect of Moisture Content and Temperature on the Specific Heat Capacity of Nut and Kernel of Two Iranian Pistachio Varieties

    Directory of Open Access Journals (Sweden)

    A.R Salari Kia

    2014-04-01

    Full Text Available Pistachio has a special ranking among Iranian agricultural products. Iran is known as the largest producer and exporter of pistachio in the world. Agricultural products are imposed under different thermal treatments during storage and processing. Designing all these processes requires thermal parameters of the products such as specific heat capacity. Regarding the importance of pistachio processing as an exportable product, in this study the specific heat capacity of nut and kernel of two varieties of Iranian pistachio (Kalle-Ghochi and Badami were investigated at four levels of moisture content (initial moisture content (5%, 15%, 25% and 40% w.b. and three levels of temperature (40, 50 and 60°C. In both varieties, the differences between the data were significant at the 1% of probability; however, the effect of moisture content was greater than that of temperature. The results indicated that the specific heat capacity of both nuts and kernels increase logarithmically with increase of moisture content and also increase linearly with increase of temperature. This parameter has altered for nut and kernel of Kalle-Ghochi and Badami varieties within the range of 1.039-2.936 kJ kg-1 K-1, 1.236-3.320 kJ kg-1 K-1, 0.887-2.773 kJ kg-1 K-1 and 0.811-2.914 kJ kg-1 K-1, respectively. Moreover, for any given level of temperature, the specific heat capacity of kernels was higher than that of nuts. Finally, regression models with high R2 values were developed to predict the specific heat capacity of pistachio varieties as a function of moisture content and temperature

  15. The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

    OpenAIRE

    Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric

    2017-01-01

    This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...

  16. Influence Function and Robust Variant of Kernel Canonical Correlation Analysis

    OpenAIRE

    Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

    2017-01-01

    Many unsupervised kernel methods rely on the estimation of the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both kernel CO and kernel CCO are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded robust kernel methods for statistical unsupervised learning. In addition, while the influence function (IF) of an estimator can characterize its robustness, asymptotic ...

  17. Free energy on a cycle graph and trigonometric deformation of heat kernel traces on odd spheres

    Science.gov (United States)

    Kan, Nahomi; Shiraishi, Kiyoshi

    2018-01-01

    We consider a possible ‘deformation’ of the trace of the heat kernel on odd dimensional spheres, motivated by the calculation of the free energy of a scalar field on a discretized circle. By using an expansion in terms of the modified Bessel functions, we obtain the values of the free energies after a suitable regularization.

  18. Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates

    International Nuclear Information System (INIS)

    Trapero, Juan R.

    2016-01-01

    In order to integrate solar energy into the grid it is important to predict the solar radiation accurately, where forecast errors can lead to significant costs. Recently, the increasing statistical approaches that cope with this problem is yielding a prolific literature. In general terms, the main research discussion is centred on selecting the “best” forecasting technique in accuracy terms. However, the need of the users of such forecasts require, apart from point forecasts, information about the variability of such forecast to compute prediction intervals. In this work, we will analyze kernel density estimation approaches, volatility forecasting models and combination of both of them in order to improve the prediction intervals performance. The results show that an optimal combination in terms of prediction interval statistical tests can achieve the desired confidence level with a lower average interval width. Data from a facility located in Spain are used to illustrate our methodology. - Highlights: • This work explores uncertainty forecasting models to build prediction intervals. • Kernel density estimators, exponential smoothing and GARCH models are compared. • An optimal combination of methods provides the best results. • A good compromise between coverage and average interval width is shown.

  19. The Influence of Reconstruction Kernel on Bone Mineral and Strength Estimates Using Quantitative Computed Tomography and Finite Element Analysis.

    Science.gov (United States)

    Michalski, Andrew S; Edwards, W Brent; Boyd, Steven K

    2017-10-17

    Quantitative computed tomography has been posed as an alternative imaging modality to investigate osteoporosis. We examined the influence of computed tomography convolution back-projection reconstruction kernels on the analysis of bone quantity and estimated mechanical properties in the proximal femur. Eighteen computed tomography scans of the proximal femur were reconstructed using both a standard smoothing reconstruction kernel and a bone-sharpening reconstruction kernel. Following phantom-based density calibration, we calculated typical bone quantity outcomes of integral volumetric bone mineral density, bone volume, and bone mineral content. Additionally, we performed finite element analysis in a standard sideways fall on the hip loading configuration. Significant differences for all outcome measures, except integral bone volume, were observed between the 2 reconstruction kernels. Volumetric bone mineral density measured using images reconstructed by the standard kernel was significantly lower (6.7%, p kernel. Furthermore, the whole-bone stiffness and the failure load measured in images reconstructed by the standard kernel were significantly lower (16.5%, p kernel. These data suggest that for future quantitative computed tomography studies, a standardized reconstruction kernel will maximize reproducibility, independent of the use of a quantitative calibration phantom. Copyright © 2017 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

  20. Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures.

    Science.gov (United States)

    Wang, Gang; Wang, Yalin

    2017-02-15

    In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Moisture Dependence of physical Properties and Specific Heat Capacity of Neem (Azadirachta Indica A. Juss Kernels

    Directory of Open Access Journals (Sweden)

    A. Dauda

    2017-02-01

    Full Text Available This study investigated the effect of moisture content on the physical properties and specific heat capacity of Neem (Azadirachta Indica A. Juss nut kernels. The major, intermediate and minor axial dimensions of the kernels increased from 1.04 to 1.23cm, 0.42 to 0.6cm, and 0.32 to 0.45cm respectively, as the moisture content increased from 5.2 to 44.9 % (db. The arithmetic and geometric mean diameters determined at the same moisture level were significantly different from each other, with the arithmetic mean diameter being higher. In the above moisture range, one thousand kernel weight, true density, porosity, sphericity, roundness and surface area all increased linearly from 0.0987 to 0.1755kg, 632 to 733kgm-3, 6.42 to 32.14%, 41.3 to 47.5%, 22 to 36% and 13 to 24cm2 respectively, while bulk density decreased from 591.4 to 497.4kgm-3 with increase in moisture content. Angle of repose increased from 21.22 to 29.8o with increase in moisture content. The Static coefficient of friction on ply wood with grains parallel to the direction of movement ranged from 0.41 to 0.61, it ranged from 0.19 to 0.24 on on fiber glass, 0.28 to .038 on hessian bag material and 0.25 to 0.33 on galvanized steel sheet. The specific heat of the seed varied from 2738.1- 4345.4J/kg/oC in the above moisture range.

  2. Flat-Top Realized Kernel Estimation of Quadratic Covariation with Non-Synchronous and Noisy Asset Prices

    DEFF Research Database (Denmark)

    Varneskov, Rasmus T.

    . Lastly, two small empirical applications to high frequency stock market data illustrate the bias reduction relative to competing estimators in estimating correlations, realized betas, and mean-variance frontiers, as well as the use of the new estimators in the dynamics of hedging....... problems. These transformations are all shown to inherit the desirable asymptotic properties of the generalized at-top realized kernels. A simulation study shows that the class of estimators has a superior finite sample tradeoff between bias and root mean squared error relative to competing estimators...

  3. On convergence of kernel learning estimators

    NARCIS (Netherlands)

    Norkin, V.I.; Keyzer, M.A.

    2009-01-01

    The paper studies convex stochastic optimization problems in a reproducing kernel Hilbert space (RKHS). The objective (risk) functional depends on functions from this RKHS and takes the form of a mathematical expectation (integral) of a nonnegative integrand (loss function) over a probability

  4. Predictive analysis and mapping of indoor radon concentrations in a complex environment using kernel estimation: An application to Switzerland

    Energy Technology Data Exchange (ETDEWEB)

    Kropat, Georg, E-mail: georg.kropat@chuv.ch [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Bochud, Francois [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Jaboyedoff, Michel [Faculty of Geosciences and Environment, University of Lausanne, GEOPOLIS — 3793, 1015 Lausanne (Switzerland); Laedermann, Jean-Pascal [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Murith, Christophe; Palacios, Martha [Swiss Federal Office of Public Health, Schwarzenburgstrasse 165, 3003 Berne (Switzerland); Baechler, Sébastien [Institute of Radiation Physics, Lausanne University Hospital, Rue du Grand-Pré 1, 1007 Lausanne (Switzerland); Swiss Federal Office of Public Health, Schwarzenburgstrasse 165, 3003 Berne (Switzerland)

    2015-02-01

    Purpose: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. Methods: We looked at about 240 000 IRC measurements carried out in about 150 000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m{sup 3}. Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. Results: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. Conclusions: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements

  5. Multivariate and semiparametric kernel regression

    OpenAIRE

    Härdle, Wolfgang; Müller, Marlene

    1997-01-01

    The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

  6. Multivariate realised kernels

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole; Hansen, Peter Reinhard; Lunde, Asger

    We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement noise of certain types and can also handle non-synchronous trading. It is the first estimator...

  7. One loop partition function of six dimensional conformal gravity using heat kernel on AdS

    Energy Technology Data Exchange (ETDEWEB)

    Lovreković, Iva [Institute for Theoretical Physics, Technische Universität Wien,Wiedner Hauptstrasse 8-10/136, A-1040 Vienna (Austria)

    2016-10-13

    We compute the heat kernel for the Laplacians of symmetric transverse traceless fields of arbitrary spin on the AdS background in even number of dimensions using the group theoretic approach introduced in http://dx.doi.org/10.1007/JHEP11(2011)010 and apply it on the partition function of six dimensional conformal gravity. The obtained partition function consists of the Einstein gravity, conformal ghost and two modes that contain mass.

  8. Scatter kernel estimation with an edge-spread function method for cone-beam computed tomography imaging

    International Nuclear Information System (INIS)

    Li Heng; Mohan, Radhe; Zhu, X Ronald

    2008-01-01

    The clinical applications of kilovoltage x-ray cone-beam computed tomography (CBCT) have been compromised by the limited quality of CBCT images, which typically is due to a substantial scatter component in the projection data. In this paper, we describe an experimental method of deriving the scatter kernel of a CBCT imaging system. The estimated scatter kernel can be used to remove the scatter component from the CBCT projection images, thus improving the quality of the reconstructed image. The scattered radiation was approximated as depth-dependent, pencil-beam kernels, which were derived using an edge-spread function (ESF) method. The ESF geometry was achieved with a half-beam block created by a 3 mm thick lead sheet placed on a stack of slab solid-water phantoms. Measurements for ten water-equivalent thicknesses (WET) ranging from 0 cm to 41 cm were taken with (half-blocked) and without (unblocked) the lead sheet, and corresponding pencil-beam scatter kernels or point-spread functions (PSFs) were then derived without assuming any empirical trial function. The derived scatter kernels were verified with phantom studies. Scatter correction was then incorporated into the reconstruction process to improve image quality. For a 32 cm diameter cylinder phantom, the flatness of the reconstructed image was improved from 22% to 5%. When the method was applied to CBCT images for patients undergoing image-guided therapy of the pelvis and lung, the variation in selected regions of interest (ROIs) was reduced from >300 HU to <100 HU. We conclude that the scatter reduction technique utilizing the scatter kernel effectively suppresses the artifact caused by scatter in CBCT.

  9. Panel data specifications in nonparametric kernel regression

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard; Henningsen, Arne

    parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...

  10. Heat kernel expansion for fermionic billiards in an external magnetic field

    International Nuclear Information System (INIS)

    Antoine, M.; Comtet, A.; Knecht, M.

    1989-05-01

    Using Seeley's heat kernel expansion, we compute the asymptotic density of states of the Dirac operator coupled to a magnetic field on a two dimensional manifold with boundary (fermionic billiard). Local boundary conditions compatible with vector current conservation depend on a free parameter α. It is shown that the perimeter correction identically vanishes for α = 0. In that case, the next order constant term is found to be proportional to the Euler characteristic of the manifold. These results are independent of the external magnetic field and of the shape of the billiard, provided the boundary is sufficiently smooth. For the flat circular billiard, the constant term is found to be - 1/12, in agreement with a numerical result by M.V. BERRY and R.J. MONDRAGON (1987)

  11. Adaptive Kernel in Meshsize Boosting Algorithm in KDE ...

    African Journals Online (AJOL)

    This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

  12. Kernel PLS Estimation of Single-trial Event-related Potentials

    Science.gov (United States)

    Rosipal, Roman; Trejo, Leonard J.

    2004-01-01

    Nonlinear kernel partial least squaes (KPLS) regressior, is a novel smoothing approach to nonparametric regression curve fitting. We have developed a KPLS approach to the estimation of single-trial event related potentials (ERPs). For improved accuracy of estimation, we also developed a local KPLS method for situations in which there exists prior knowledge about the approximate latency of individual ERP components. To assess the utility of the KPLS approach, we compared non-local KPLS and local KPLS smoothing with other nonparametric signal processing and smoothing methods. In particular, we examined wavelet denoising, smoothing splines, and localized smoothing splines. We applied these methods to the estimation of simulated mixtures of human ERPs and ongoing electroencephalogram (EEG) activity using a dipole simulator (BESA). In this scenario we considered ongoing EEG to represent spatially and temporally correlated noise added to the ERPs. This simulation provided a reasonable but simplified model of real-world ERP measurements. For estimation of the simulated single-trial ERPs, local KPLS provided a level of accuracy that was comparable with or better than the other methods. We also applied the local KPLS method to the estimation of human ERPs recorded in an experiment on co,onitive fatigue. For these data, the local KPLS method provided a clear improvement in visualization of single-trial ERPs as well as their averages. The local KPLS method may serve as a new alternative to the estimation of single-trial ERPs and improvement of ERP averages.

  13. Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

    Directory of Open Access Journals (Sweden)

    Samir Saoudi

    2008-07-01

    Full Text Available The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs. Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE depends directly upon J(f which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of J(f, the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

  14. Quantum Einstein gravity. Advancements of heat kernel-based renormalization group studies

    Energy Technology Data Exchange (ETDEWEB)

    Groh, Kai

    2012-10-15

    The asymptotic safety scenario allows to define a consistent theory of quantized gravity within the framework of quantum field theory. The central conjecture of this scenario is the existence of a non-Gaussian fixed point of the theory's renormalization group flow, that allows to formulate renormalization conditions that render the theory fully predictive. Investigations of this possibility use an exact functional renormalization group equation as a primary non-perturbative tool. This equation implements Wilsonian renormalization group transformations, and is demonstrated to represent a reformulation of the functional integral approach to quantum field theory. As its main result, this thesis develops an algebraic algorithm which allows to systematically construct the renormalization group flow of gauge theories as well as gravity in arbitrary expansion schemes. In particular, it uses off-diagonal heat kernel techniques to efficiently handle the non-minimal differential operators which appear due to gauge symmetries. The central virtue of the algorithm is that no additional simplifications need to be employed, opening the possibility for more systematic investigations of the emergence of non-perturbative phenomena. As a by-product several novel results on the heat kernel expansion of the Laplace operator acting on general gauge bundles are obtained. The constructed algorithm is used to re-derive the renormalization group flow of gravity in the Einstein-Hilbert truncation, showing the manifest background independence of the results. The well-studied Einstein-Hilbert case is further advanced by taking the effect of a running ghost field renormalization on the gravitational coupling constants into account. A detailed numerical analysis reveals a further stabilization of the found non-Gaussian fixed point. Finally, the proposed algorithm is applied to the case of higher derivative gravity including all curvature squared interactions. This establishes an improvement

  15. Quantum Einstein gravity. Advancements of heat kernel-based renormalization group studies

    International Nuclear Information System (INIS)

    Groh, Kai

    2012-10-01

    The asymptotic safety scenario allows to define a consistent theory of quantized gravity within the framework of quantum field theory. The central conjecture of this scenario is the existence of a non-Gaussian fixed point of the theory's renormalization group flow, that allows to formulate renormalization conditions that render the theory fully predictive. Investigations of this possibility use an exact functional renormalization group equation as a primary non-perturbative tool. This equation implements Wilsonian renormalization group transformations, and is demonstrated to represent a reformulation of the functional integral approach to quantum field theory. As its main result, this thesis develops an algebraic algorithm which allows to systematically construct the renormalization group flow of gauge theories as well as gravity in arbitrary expansion schemes. In particular, it uses off-diagonal heat kernel techniques to efficiently handle the non-minimal differential operators which appear due to gauge symmetries. The central virtue of the algorithm is that no additional simplifications need to be employed, opening the possibility for more systematic investigations of the emergence of non-perturbative phenomena. As a by-product several novel results on the heat kernel expansion of the Laplace operator acting on general gauge bundles are obtained. The constructed algorithm is used to re-derive the renormalization group flow of gravity in the Einstein-Hilbert truncation, showing the manifest background independence of the results. The well-studied Einstein-Hilbert case is further advanced by taking the effect of a running ghost field renormalization on the gravitational coupling constants into account. A detailed numerical analysis reveals a further stabilization of the found non-Gaussian fixed point. Finally, the proposed algorithm is applied to the case of higher derivative gravity including all curvature squared interactions. This establishes an improvement of

  16. Realized kernels in practice

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Hansen, P. Reinhard; Lunde, Asger

    2009-01-01

    and find a remarkable level of agreement. We identify some features of the high-frequency data, which are challenging for realized kernels. They are when there are local trends in the data, over periods of around 10 minutes, where the prices and quotes are driven up or down. These can be associated......Realized kernels use high-frequency data to estimate daily volatility of individual stock prices. They can be applied to either trade or quote data. Here we provide the details of how we suggest implementing them in practice. We compare the estimates based on trade and quote data for the same stock...

  17. Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods

    OpenAIRE

    Alam, Md. Ashad; Fukumizu, Kenji; Wang, Yu-Ping

    2016-01-01

    To the best of our knowledge, there are no general well-founded robust methods for statistical unsupervised learning. Most of the unsupervised methods explicitly or implicitly depend on the kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). They are sensitive to contaminated data, even when using bounded positive definite kernels. First, we propose robust kernel covariance operator (robust kernel CO) and robust kernel crosscovariance operator (robust kern...

  18. Higher-Order Hybrid Gaussian Kernel in Meshsize Boosting Algorithm

    African Journals Online (AJOL)

    In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in kernel density estimation. Bias reduction is guaranteed in this scheme like other existing schemes but uses the higher-order hybrid Gaussian kernel instead of the regular fixed kernels. A numerical verification of this scheme ...

  19. Adaptive Kernel In The Bootstrap Boosting Algorithm In KDE ...

    African Journals Online (AJOL)

    This paper proposes the use of adaptive kernel in a bootstrap boosting algorithm in kernel density estimation. The algorithm is a bias reduction scheme like other existing schemes but uses adaptive kernel instead of the regular fixed kernels. An empirical study for this scheme is conducted and the findings are comparatively ...

  20. Analytical Plug-In Method for Kernel Density Estimator Applied to Genetic Neutrality Study

    Science.gov (United States)

    Troudi, Molka; Alimi, Adel M.; Saoudi, Samir

    2008-12-01

    The plug-in method enables optimization of the bandwidth of the kernel density estimator in order to estimate probability density functions (pdfs). Here, a faster procedure than that of the common plug-in method is proposed. The mean integrated square error (MISE) depends directly upon [InlineEquation not available: see fulltext.] which is linked to the second-order derivative of the pdf. As we intend to introduce an analytical approximation of [InlineEquation not available: see fulltext.], the pdf is estimated only once, at the end of iterations. These two kinds of algorithm are tested on different random variables having distributions known for their difficult estimation. Finally, they are applied to genetic data in order to provide a better characterisation in the mean of neutrality of Tunisian Berber populations.

  1. Acceleration of the universe, vacuum metamorphosis, and the large-time asymptotic form of the heat kernel

    International Nuclear Information System (INIS)

    Parker, Leonard; Vanzella, Daniel A.T.

    2004-01-01

    We investigate the possibility that the late acceleration observed in the rate of expansion of the Universe is due to vacuum quantum effects arising in curved spacetime. The theoretical basis of the vacuum cold dark matter (VCDM), or vacuum metamorphosis, cosmological model of Parker and Raval is reexamined and improved. We show, by means of a manifestly nonperturbative approach, how the infrared behavior of the propagator (related to the large-time asymptotic form of the heat kernel) of a free scalar field in curved spacetime leads to nonperturbative terms in the effective action similar to those appearing in the earlier version of the VCDM model. The asymptotic form that we adopt for the propagator or heat kernel at large proper time s is motivated by, and consistent with, particular cases where the heat kernel has been calculated exactly, namely in de Sitter spacetime, in the Einstein static universe, and in the linearly expanding spatially flat Friedmann-Robertson-Walker (FRW) universe. This large-s asymptotic form generalizes somewhat the one suggested by the Gaussian approximation and the R-summed form of the propagator that earlier served as a theoretical basis for the VCDM model. The vacuum expectation value for the energy-momentum tensor of the free scalar field, obtained through variation of the effective action, exhibits a resonance effect when the scalar curvature R of the spacetime reaches a particular value related to the mass of the field. Modeling our Universe by an FRW spacetime filled with classical matter and radiation, we show that the back reaction caused by this resonance drives the Universe through a transition to an accelerating expansion phase, very much in the same way as originally proposed by Parker and Raval. Our analysis includes higher derivatives that were neglected in the earlier analysis, and takes into account the possible runaway solutions that can follow from these higher-derivative terms. We find that the runaway solutions do

  2. Soft Sensor of Vehicle State Estimation Based on the Kernel Principal Component and Improved Neural Network

    Directory of Open Access Journals (Sweden)

    Haorui Liu

    2016-01-01

    Full Text Available In the car control systems, it is hard to measure some key vehicle states directly and accurately when running on the road and the cost of the measurement is high as well. To address these problems, a vehicle state estimation method based on the kernel principal component analysis and the improved Elman neural network is proposed. Combining with nonlinear vehicle model of three degrees of freedom (3 DOF, longitudinal, lateral, and yaw motion, this paper applies the method to the soft sensor of the vehicle states. The simulation results of the double lane change tested by Matlab/SIMULINK cosimulation prove the KPCA-IENN algorithm (kernel principal component algorithm and improved Elman neural network to be quick and precise when tracking the vehicle states within the nonlinear area. This algorithm method can meet the software performance requirements of the vehicle states estimation in precision, tracking speed, noise suppression, and other aspects.

  3. Optimal kernel shape and bandwidth for atomistic support of continuum stress

    International Nuclear Information System (INIS)

    Ulz, Manfred H; Moran, Sean J

    2013-01-01

    The treatment of atomistic scale interactions via molecular dynamics simulations has recently found favour for multiscale modelling within engineering. The estimation of stress at a continuum point on the atomistic scale requires a pre-defined kernel function. This kernel function derives the stress at a continuum point by averaging the contribution from atoms within a region surrounding the continuum point. This averaging volume, and therefore the associated stress at a continuum point, is highly dependent on the bandwidth and shape of the kernel. In this paper we propose an effective and entirely data-driven strategy for simultaneously computing the optimal shape and bandwidth for the kernel. We thoroughly evaluate our proposed approach on copper using three classical elasticity problems. Our evaluation yields three key findings: firstly, our technique can provide a physically meaningful estimation of kernel bandwidth; secondly, we show that a uniform kernel is preferred, thereby justifying the default selection of this kernel shape in future work; and thirdly, we can reliably estimate both of these attributes in a data-driven manner, obtaining values that lead to an accurate estimation of the stress at a continuum point. (paper)

  4. Viscozyme L pretreatment on palm kernels improved the aroma of palm kernel oil after kernel roasting.

    Science.gov (United States)

    Zhang, Wencan; Leong, Siew Mun; Zhao, Feifei; Zhao, Fangju; Yang, Tiankui; Liu, Shaoquan

    2018-05-01

    With an interest to enhance the aroma of palm kernel oil (PKO), Viscozyme L, an enzyme complex containing a wide range of carbohydrases, was applied to alter the carbohydrates in palm kernels (PK) to modulate the formation of volatiles upon kernel roasting. After Viscozyme treatment, the content of simple sugars and free amino acids in PK increased by 4.4-fold and 4.5-fold, respectively. After kernel roasting and oil extraction, significantly more 2,5-dimethylfuran, 2-[(methylthio)methyl]-furan, 1-(2-furanyl)-ethanone, 1-(2-furyl)-2-propanone, 5-methyl-2-furancarboxaldehyde and 2-acetyl-5-methylfuran but less 2-furanmethanol and 2-furanmethanol acetate were found in treated PKO; the correlation between their formation and simple sugar profile was estimated by using partial least square regression (PLS1). Obvious differences in pyrroles and Strecker aldehydes were also found between the control and treated PKOs. Principal component analysis (PCA) clearly discriminated the treated PKOs from that of control PKOs on the basis of all volatile compounds. Such changes in volatiles translated into distinct sensory attributes, whereby treated PKO was more caramelic and burnt after aqueous extraction and more nutty, roasty, caramelic and smoky after solvent extraction. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator.

    Science.gov (United States)

    Fleming, C H; Fagan, W F; Mueller, T; Olson, K A; Leimgruber, P; Calabrese, J M

    2015-05-01

    Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.

  6. Combining Lactic Acid Spray with Near-Infrared Radiation Heating To Inactivate Salmonella enterica Serovar Enteritidis on Almond and Pine Nut Kernels.

    Science.gov (United States)

    Ha, Jae-Won; Kang, Dong-Hyun

    2015-07-01

    The aim of this study was to investigate the efficacy of near-infrared radiation (NIR) heating combined with lactic acid (LA) sprays for inactivating Salmonella enterica serovar Enteritidis on almond and pine nut kernels and to elucidate the mechanisms of the lethal effect of the NIR-LA combined treatment. Also, the effect of the combination treatment on product quality was determined. Separately prepared S. Enteritidis phage type (PT) 30 and non-PT 30 S. Enteritidis cocktails were inoculated onto almond and pine nut kernels, respectively, followed by treatments with NIR or 2% LA spray alone, NIR with distilled water spray (NIR-DW), and NIR with 2% LA spray (NIR-LA). Although surface temperatures of nuts treated with NIR were higher than those subjected to NIR-DW or NIR-LA treatment, more S. Enteritidis survived after NIR treatment alone. The effectiveness of NIR-DW and NIR-LA was similar, but significantly more sublethally injured cells were recovered from NIR-DW-treated samples. We confirmed that the enhanced bactericidal effect of the NIR-LA combination may not be attributable to cell membrane damage per se. NIR heat treatment might allow S. Enteritidis cells to become permeable to applied LA solution. The NIR-LA treatment (5 min) did not significantly (P > 0.05) cause changes in the lipid peroxidation parameters, total phenolic contents, color values, moisture contents, and sensory attributes of nut kernels. Given the results of the present study, NIR-LA treatment may be a potential intervention for controlling food-borne pathogens on nut kernel products. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  7. Calculation of the time resolution of the J-PET tomograph using kernel density estimation

    Science.gov (United States)

    Raczyński, L.; Wiślicki, W.; Krzemień, W.; Kowalski, P.; Alfs, D.; Bednarski, T.; Białas, P.; Curceanu, C.; Czerwiński, E.; Dulski, K.; Gajos, A.; Głowacz, B.; Gorgol, M.; Hiesmayr, B.; Jasińska, B.; Kamińska, D.; Korcyl, G.; Kozik, T.; Krawczyk, N.; Kubicz, E.; Mohammed, M.; Pawlik-Niedźwiecka, M.; Niedźwiecki, S.; Pałka, M.; Rudy, Z.; Rundel, O.; Sharma, N. G.; Silarski, M.; Smyrski, J.; Strzelecki, A.; Wieczorek, A.; Zgardzińska, B.; Zieliński, M.; Moskal, P.

    2017-06-01

    In this paper we estimate the time resolution of the J-PET scanner built from plastic scintillators. We incorporate the method of signal processing using the Tikhonov regularization framework and the kernel density estimation method. We obtain simple, closed-form analytical formulae for time resolution. The proposed method is validated using signals registered by means of the single detection unit of the J-PET tomograph built from a 30 cm long plastic scintillator strip. It is shown that the experimental and theoretical results obtained for the J-PET scanner equipped with vacuum tube photomultipliers are consistent.

  8. Pencil kernel correction and residual error estimation for quality-index-based dose calculations

    International Nuclear Information System (INIS)

    Nyholm, Tufve; Olofsson, Joergen; Ahnesjoe, Anders; Georg, Dietmar; Karlsson, Mikael

    2006-01-01

    Experimental data from 593 photon beams were used to quantify the errors in dose calculations using a previously published pencil kernel model. A correction of the kernel was derived in order to remove the observed systematic errors. The remaining residual error for individual beams was modelled through uncertainty associated with the kernel model. The methods were tested against an independent set of measurements. No significant systematic error was observed in the calculations using the derived correction of the kernel and the remaining random errors were found to be adequately predicted by the proposed method

  9. Predictive Model Equations for Palm Kernel (Elaeis guneensis J ...

    African Journals Online (AJOL)

    Estimated error of ± 0.18 and ± 0.2 are envisaged while applying the models for predicting palm kernel and sesame oil colours respectively. Keywords: Palm kernel, Sesame, Palm kernel, Oil Colour, Process Parameters, Model. Journal of Applied Science, Engineering and Technology Vol. 6 (1) 2006 pp. 34-38 ...

  10. Adaptive metric kernel regression

    DEFF Research Database (Denmark)

    Goutte, Cyril; Larsen, Jan

    2000-01-01

    Kernel smoothing is a widely used non-parametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this contribution, we propose an algorithm that adapts the input metric used in multivariate...... regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...

  11. Adaptive Metric Kernel Regression

    DEFF Research Database (Denmark)

    Goutte, Cyril; Larsen, Jan

    1998-01-01

    Kernel smoothing is a widely used nonparametric pattern recognition technique. By nature, it suffers from the curse of dimensionality and is usually difficult to apply to high input dimensions. In this paper, we propose an algorithm that adapts the input metric used in multivariate regression...... by minimising a cross-validation estimate of the generalisation error. This allows one to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms the standard...

  12. Efficient 3D movement-based kernel density estimator and application to wildlife ecology

    Science.gov (United States)

    Tracey-PR, Jeff; Sheppard, James K.; Lockwood, Glenn K.; Chourasia, Amit; Tatineni, Mahidhar; Fisher, Robert N.; Sinkovits, Robert S.

    2014-01-01

    We describe an efficient implementation of a 3D movement-based kernel density estimator for determining animal space use from discrete GPS measurements. This new method provides more accurate results, particularly for species that make large excursions in the vertical dimension. The downside of this approach is that it is much more computationally expensive than simpler, lower-dimensional models. Through a combination of code restructuring, parallelization and performance optimization, we were able to reduce the time to solution by up to a factor of 1000x, thereby greatly improving the applicability of the method.

  13. Probabilistic wind power forecasting based on logarithmic transformation and boundary kernel

    International Nuclear Information System (INIS)

    Zhang, Yao; Wang, Jianxue; Luo, Xu

    2015-01-01

    Highlights: • Quantitative information on the uncertainty of wind power generation. • Kernel density estimator provides non-Gaussian predictive distributions. • Logarithmic transformation reduces the skewness of wind power density. • Boundary kernel method eliminates the density leakage near the boundary. - Abstracts: Probabilistic wind power forecasting not only produces the expectation of wind power output, but also gives quantitative information on the associated uncertainty, which is essential for making better decisions about power system and market operations with the increasing penetration of wind power generation. This paper presents a novel kernel density estimator for probabilistic wind power forecasting, addressing two characteristics of wind power which have adverse impacts on the forecast accuracy, namely, the heavily skewed and double-bounded nature of wind power density. Logarithmic transformation is used to reduce the skewness of wind power density, which improves the effectiveness of the kernel density estimator in a transformed scale. Transformations partially relieve the boundary effect problem of the kernel density estimator caused by the double-bounded nature of wind power density. However, the case study shows that there are still some serious problems of density leakage after the transformation. In order to solve this problem in the transformed scale, a boundary kernel method is employed to eliminate the density leak at the bounds of wind power distribution. The improvement of the proposed method over the standard kernel density estimator is demonstrated by short-term probabilistic forecasting results based on the data from an actual wind farm. Then, a detailed comparison is carried out of the proposed method and some existing probabilistic forecasting methods

  14. Multivariate realised kernels

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

    2011-01-01

    We propose a multivariate realised kernel to estimate the ex-post covariation of log-prices. We show this new consistent estimator is guaranteed to be positive semi-definite and is robust to measurement error of certain types and can also handle non-synchronous trading. It is the first estimator...... which has these three properties which are all essential for empirical work in this area. We derive the large sample asymptotics of this estimator and assess its accuracy using a Monte Carlo study. We implement the estimator on some US equity data, comparing our results to previous work which has used...

  15. Migration of ThO2 kernels under the influence of a temperature gradient

    International Nuclear Information System (INIS)

    Smith, C.L.

    1976-11-01

    BISO coated ThO 2 fertile fuel kernels will migrate up the thermal gradients imposed across coated particles during HTGR operation. Thorium dioxide kernel migration has been studied as a function of temperature (1300 to 1700 0 C) and ThO 2 kernel burnup (0.9 to 5.8 percent FIMA) in out-of-pile, postirradiation thermal gradient heating experiments. The studies were conducted to obtain descriptions of migration rates that will be used in core design studies to evaluate the impact of ThO 2 migration on fertile fuel performance in an operating HTGR and to define characteristics needed by any comprehensive model describing ThO 2 kernel migration. The kinetics data generated in these postirradiation studies are consistent with in-pile data collected by investigators at Oak Ridge National Laboratory, which supports use of the more precise postirradiation heating results in HTGR core design studies. Observations of intergranular carbon deposits on the cool side of migrating kernels support the assumption that the kinetics of kernel migration are controlled by solid state diffusion within irradiated ThO 2 kernels. The migration is characterized by a period of no migration (incubation period) followed by migration at the equilibrium rate for ThO 2 . The incubation period decreases with increasing temperature and kernel burnup. The improved understanding of the kinetics of ThO 2 kernel migration provided by this work will contribute to an optimization of HTGR core design and an increased confidence in fuel performance predictions

  16. Input Space Regularization Stabilizes Pre-images for Kernel PCA De-noising

    DEFF Research Database (Denmark)

    Abrahamsen, Trine Julie; Hansen, Lars Kai

    2009-01-01

    Solution of the pre-image problem is key to efficient nonlinear de-noising using kernel Principal Component Analysis. Pre-image estimation is inherently ill-posed for typical kernels used in applications and consequently the most widely used estimation schemes lack stability. For de...

  17. Scientific opinion on the acute health risks related to the presence of cyanogenic glycosides in raw apricot kernels and products derived from raw apricot kernels

    DEFF Research Database (Denmark)

    Petersen, Annette

    of kernels promoted (10 and 60 kernels/day for the general population and cancer patients, respectively), exposures exceeded the ARfD 17–413 and 3–71 times in toddlers and adults, respectively. The estimated maximum quantity of apricot kernels (or raw apricot material) that can be consumed without exceeding...

  18. Explicit signal to noise ratio in reproducing kernel Hilbert spaces

    DEFF Research Database (Denmark)

    Gomez-Chova, Luis; Nielsen, Allan Aasbjerg; Camps-Valls, Gustavo

    2011-01-01

    This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose...... an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal features jointly. Results show that the proposed KMNF provides the most noise-free features when confronted...

  19. A generalized L1-approach for a kernel estimator of conditional quantile with functional regressors: Consistency and asymptotic normality

    OpenAIRE

    2009-01-01

    Abstract A kernel estimator of the conditional quantile is defined for a scalar response variable given a covariate taking values in a semi-metric space. The approach generalizes the median?s L1-norm estimator. The almost complete consistency and asymptotic normality are stated. correspondance: Corresponding author. Tel: +33 320 964 933; fax: +33 320 964 704. (Lemdani, Mohamed) (Laksaci, Ali) mohamed.lemdani@univ-lill...

  20. Examining Potential Boundary Bias Effects in Kernel Smoothing on Equating: An Introduction for the Adaptive and Epanechnikov Kernels.

    Science.gov (United States)

    Cid, Jaime A; von Davier, Alina A

    2015-05-01

    Test equating is a method of making the test scores from different test forms of the same assessment comparable. In the equating process, an important step involves continuizing the discrete score distributions. In traditional observed-score equating, this step is achieved using linear interpolation (or an unscaled uniform kernel). In the kernel equating (KE) process, this continuization process involves Gaussian kernel smoothing. It has been suggested that the choice of bandwidth in kernel smoothing controls the trade-off between variance and bias. In the literature on estimating density functions using kernels, it has also been suggested that the weight of the kernel depends on the sample size, and therefore, the resulting continuous distribution exhibits bias at the endpoints, where the samples are usually smaller. The purpose of this article is (a) to explore the potential effects of atypical scores (spikes) at the extreme ends (high and low) on the KE method in distributions with different degrees of asymmetry using the randomly equivalent groups equating design (Study I), and (b) to introduce the Epanechnikov and adaptive kernels as potential alternative approaches to reducing boundary bias in smoothing (Study II). The beta-binomial model is used to simulate observed scores reflecting a range of different skewed shapes.

  1. Effects of Defatting Combined or not to Heating of Jatropha curcas Kernel Meal on Feed Intake and Growth Performance in Broiler Chickens and Chicks in Senegal

    Directory of Open Access Journals (Sweden)

    Nesseim, TDT.

    2017-01-01

    Full Text Available Jatropha curcas is a tropical plant belonging to the Euphorbiaceae family whose cultivation has been largely promoted in recent years for the production of biofuels. The kernel of the seed contains approximately 55% lipid in dry matter and the meal obtained could be an exceptional source of proteins for family poultry farming, after treatments to remove toxic and anti-nutritional compounds. The ingestion and the growth performance of J. curcas kernel meal (JKM, obtained after partial physico-chemical de-oiling combined or not with heating was evaluated in broiler chickens and chicks. Sixty unsexed broiler chickens, 30 day-old, divided into three groups as well as twenty broiler chicks, 1 day-old, divided into two groups were used in two experiments. In experiment 1, jatropha kernel was de-oiled and incorporated into a control fattening feed at 40 and 80g/kg (diets 4JKM1 and 8JM1. In experiment 2, jatropha kernel meal obtained in experiment 1 was heat treated and incorporated into a growing diet at 80g/kg (diet 8JKM2. Daily dietary intakes as well as weight gain of the animals were affected by the incorporation of jatropha kernel meal in the ration. In experiment 1, average daily feed intake (ADFI1 of 139.2, 55.2 and 23.4g/day/animal and also average daily weight gain (ADWG1 of 61.9, 18.5 and -7.7g/animal were obtained respectively for the groups fed with diets 0JKM1, 4JKM1 and 8JKM1. In experiment 2, Average daily feed intake (ADFI2 of 18.7 and 3.1g/day/animal and also average daily weight gain (ADWG2 of 7.1 and 1.9g/animal were obtained respectively for the groups fed with diets 0JKM2 and 8JKM2. In both experiment, feed conversion ratio (FCR was also affected by the dietary treatments and the overall mortality rate showed an increase according to levels of jatropha kernel meal in diet.

  2. Heat and power demands in babassu palm oil extraction industry in Brazil

    International Nuclear Information System (INIS)

    Teixeira, Marcos A.

    2005-01-01

    The objective of this paper is to analyze the energy use profile of the babassu (Orbignya ssp-Palmae) oil extraction industry in Brazil in order to establish the basis for a cogeneration study of this important part of the Brazilian Northeast region economy, which is still ignored by energetic biomass studies. The work used information from new equipment suppliers that was analyzed against field information from operating units. The data was used to establish a basis for the thermal and mechanical energy consumption for the two main basic unit profiles for the sector: a simple one with just oil extraction and the other, more vertically integrated with other secondary by-products. For the energetic demand taken from the only oil extraction unit profile study, the minimum pressure for the steam process was estimated at 1.4MPa, electric demand at 5.79kW/ton of processed kernel and heat consumption at 2071MJ/ton of processed kernel (829kg steam/ton of processed kernel). For the vertically integrated unit profile, the following values were found: minimum pressure for the steam process 1.4MPa, electric demand 6.22kW/ton of processed kernel and heat consumption 21,503MJ/ton of processed kernel (7600kg steam/ton of processed kernel)

  3. Nonlinear Forecasting With Many Predictors Using Kernel Ridge Regression

    DEFF Research Database (Denmark)

    Exterkate, Peter; Groenen, Patrick J.F.; Heij, Christiaan

    This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related nonlinearly to the target variable. In kernel ridge regression, the observed predictor variables are mapped nonlinearly into a high-dimensional space, where estimation of the predi...

  4. Kernel-based noise filtering of neutron detector signals

    International Nuclear Information System (INIS)

    Park, Moon Ghu; Shin, Ho Cheol; Lee, Eun Ki

    2007-01-01

    This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests

  5. Locally linear approximation for Kernel methods : the Railway Kernel

    OpenAIRE

    Muñoz, Alberto; González, Javier

    2008-01-01

    In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capab...

  6. Dispersal kernel estimation: A comparison of empirical and modelled particle dispersion in a coastal marine system

    Science.gov (United States)

    Hrycik, Janelle M.; Chassé, Joël; Ruddick, Barry R.; Taggart, Christopher T.

    2013-11-01

    Early life-stage dispersal influences recruitment and is of significance in explaining the distribution and connectivity of marine species. Motivations for quantifying dispersal range from biodiversity conservation to the design of marine reserves and the mitigation of species invasions. Here we compare estimates of real particle dispersion in a coastal marine environment with similar estimates provided by hydrodynamic modelling. We do so by using a system of magnetically attractive particles (MAPs) and a magnetic-collector array that provides measures of Lagrangian dispersion based on the time-integration of MAPs dispersing through the array. MAPs released as a point source in a coastal marine location dispersed through the collector array over a 5-7 d period. A virtual release and observed (real-time) environmental conditions were used in a high-resolution three-dimensional hydrodynamic model to estimate the dispersal of virtual particles (VPs). The number of MAPs captured throughout the collector array and the number of VPs that passed through each corresponding model location were enumerated and compared. Although VP dispersal reflected several aspects of the observed MAP dispersal, the comparisons demonstrated model sensitivity to the small-scale (random-walk) particle diffusivity parameter (Kp). The one-dimensional dispersal kernel for the MAPs had an e-folding scale estimate in the range of 5.19-11.44 km, while those from the model simulations were comparable at 1.89-6.52 km, and also demonstrated sensitivity to Kp. Variations among comparisons are related to the value of Kp used in modelling and are postulated to be related to MAP losses from the water column and (or) shear dispersion acting on the MAPs; a process that is constrained in the model. Our demonstration indicates a promising new way of 1) quantitatively and empirically estimating the dispersal kernel in aquatic systems, and 2) quantitatively assessing and (or) improving regional hydrodynamic

  7. Migration of the ThO2 kernels under the influence of a temperature gradient

    International Nuclear Information System (INIS)

    Smith, C.L.

    1977-01-01

    Biso-coated ThO 2 fertile fuel kernels will migrate up the thermal gradients imposed across coated particles during high-temperature gas-cooled reactor (HTGR) operation. Thorium dioxide kernel migration has been studied as a function of temperature (1290 to 1705 0 C) (1563 to 1978 K) and ThO 2 kernel burnup (0.9 to 5.8 percent FIMA) in out-of-pile postirradiation thermal gradient heating experiments. The studies were conducted to obtain descriptions of migration rates that will be used in core design studies to evaluate the impact of ThO 2 migration on fertile fuel performance in an operating HTGR and to define characteristics needed by any comprehensive model describing ThO 2 kernel migration. The kinetics data generated in these postirradiation studies are consistent with in-pile data collected by investigators at Oak Ridge National Laboratory, which supports use of the more precise postirradiation heating results in HTGR core design studies. Observations of intergranular carbon deposits on the cool side of migrating kernels support the assumption that the kinetics of kernel migration are controlled by solid-state diffusion within irradiated ThO 2 kernels. The migration is characterized by a period of no migration (incubation period), followed by migration at the equilibrium rate for ThO 2 . The incubation period decreases with increasing temperature and kernel burnup. The improved understanding of the kinetics of ThO 2 kernel migration provided by this work will contribute to an optimization of HTGR core design and an increased confidence in fuel performance predictions

  8. An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

    Directory of Open Access Journals (Sweden)

    Senyue Zhang

    2016-01-01

    Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.

  9. Kernel density estimation and transition maps of Moldavian Neolithic and Eneolithic settlement

    Directory of Open Access Journals (Sweden)

    Robin Brigand

    2018-04-01

    Full Text Available The data presented in this article are related to the research article entitled “Neo-Eneolithic settlement pattern and salt exploitation in Romanian Moldavia” (Brigand and Weller, 2018 [1]. Kernel density estimation (KDE is used in order to move beyond the discrete distribution of sites and to enable us to work on a continuous surface that reflects the intensity of the occupation in the space. Maps of density per period – Neolithic I (Cris, Neolithic II (LBK, Eneolithic I (Precucuteni, Eneolithic II (Cucuteni A, Eneolithic III-IV (Cucuteni A-B and B – are used to create maps of density difference (Figs. 1–4 in order to analyse the dynamic (either non-existent, negative or positive between two chronological sequences.

  10. Network Kernel Density Estimation for the Analysis of Facility POI Hotspots

    Directory of Open Access Journals (Sweden)

    YU Wenhao

    2015-12-01

    Full Text Available The distribution pattern of urban facility POIs (points of interest usually forms clusters (i.e. "hotspots" in urban geographic space. To detect such type of hotspot, the methods mostly employ spatial density estimation based on Euclidean distance, ignoring the fact that the service function and interrelation of urban feasibilities is carried out on the network path distance, neither than conventional Euclidean distance. By using these methods, it is difficult to exactly and objectively delimitate the shape and the size of hotspot. Therefore, this research adopts the kernel density estimation based on the network distance to compute the density of hotspot and proposes a simple and efficient algorithm. The algorithm extends the 2D dilation operator to the 1D morphological operator, thus computing the density of network unit. Through evaluation experiment, it is suggested that the algorithm is more efficient and scalable than the existing algorithms. Based on the case study on real POI data, the range of hotspot can highlight the spatial characteristic of urban functions along traffic routes, in order to provide valuable spatial knowledge and information services for the applications of region planning, navigation and geographic information inquiring.

  11. Adaptive Nonparametric Variance Estimation for a Ratio Estimator ...

    African Journals Online (AJOL)

    Kernel estimators for smooth curves require modifications when estimating near end points of the support, both for practical and asymptotic reasons. The construction of such boundary kernels as solutions of variational problem is a difficult exercise. For estimating the error variance of a ratio estimator, we suggest an ...

  12. System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques

    DEFF Research Database (Denmark)

    Chen, Tianshi; Andersen, Martin Skovgaard; Ljung, Lennart

    2014-01-01

    Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels...

  13. Kernel Machine SNP-set Testing under Multiple Candidate Kernels

    Science.gov (United States)

    Wu, Michael C.; Maity, Arnab; Lee, Seunggeun; Simmons, Elizabeth M.; Harmon, Quaker E.; Lin, Xinyi; Engel, Stephanie M.; Molldrem, Jeffrey J.; Armistead, Paul M.

    2013-01-01

    Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel. PMID:23471868

  14. PERI - auto-tuning memory-intensive kernels for multicore

    International Nuclear Information System (INIS)

    Williams, S; Carter, J; Oliker, L; Shalf, J; Yelick, K; Bailey, D; Datta, K

    2008-01-01

    We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to sparse matrix vector multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the high-performance computing literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4x improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications

  15. PERI - Auto-tuning Memory Intensive Kernels for Multicore

    Energy Technology Data Exchange (ETDEWEB)

    Bailey, David H; Williams, Samuel; Datta, Kaushik; Carter, Jonathan; Oliker, Leonid; Shalf, John; Yelick, Katherine; Bailey, David H

    2008-06-24

    We present an auto-tuning approach to optimize application performance on emerging multicore architectures. The methodology extends the idea of search-based performance optimizations, popular in linear algebra and FFT libraries, to application-specific computational kernels. Our work applies this strategy to Sparse Matrix Vector Multiplication (SpMV), the explicit heat equation PDE on a regular grid (Stencil), and a lattice Boltzmann application (LBMHD). We explore one of the broadest sets of multicore architectures in the HPC literature, including the Intel Xeon Clovertown, AMD Opteron Barcelona, Sun Victoria Falls, and the Sony-Toshiba-IBM (STI) Cell. Rather than hand-tuning each kernel for each system, we develop a code generator for each kernel that allows us to identify a highly optimized version for each platform, while amortizing the human programming effort. Results show that our auto-tuned kernel applications often achieve a better than 4X improvement compared with the original code. Additionally, we analyze a Roofline performance model for each platform to reveal hardware bottlenecks and software challenges for future multicore systems and applications.

  16. On Convergence of Kernel Density Estimates in Particle Filtering

    Czech Academy of Sciences Publication Activity Database

    Coufal, David

    2016-01-01

    Roč. 52, č. 5 (2016), s. 735-756 ISSN 0023-5954 Grant - others:GA ČR(CZ) GA16-03708S; SVV(CZ) 260334/2016 Institutional support: RVO:67985807 Keywords : Fourier analysis * kernel methods * particle filter Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.379, year: 2016

  17. Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson's disease.

    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.

  18. Kernel Temporal Differences for Neural Decoding

    Science.gov (United States)

    Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2015-01-01

    We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

  19. RTOS kernel in portable electrocardiograph

    Science.gov (United States)

    Centeno, C. A.; Voos, J. A.; Riva, G. G.; Zerbini, C.; Gonzalez, E. A.

    2011-12-01

    This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

  20. RTOS kernel in portable electrocardiograph

    International Nuclear Information System (INIS)

    Centeno, C A; Voos, J A; Riva, G G; Zerbini, C; Gonzalez, E A

    2011-01-01

    This paper presents the use of a Real Time Operating System (RTOS) on a portable electrocardiograph based on a microcontroller platform. All medical device digital functions are performed by the microcontroller. The electrocardiograph CPU is based on the 18F4550 microcontroller, in which an uCOS-II RTOS can be embedded. The decision associated with the kernel use is based on its benefits, the license for educational use and its intrinsic time control and peripherals management. The feasibility of its use on the electrocardiograph is evaluated based on the minimum memory requirements due to the kernel structure. The kernel's own tools were used for time estimation and evaluation of resources used by each process. After this feasibility analysis, the migration from cyclic code to a structure based on separate processes or tasks able to synchronize events is used; resulting in an electrocardiograph running on one Central Processing Unit (CPU) based on RTOS.

  1. A kernel principal component analysis–based degradation model and remaining useful life estimation for the turbofan engine

    Directory of Open Access Journals (Sweden)

    Delong Feng

    2016-05-01

    Full Text Available Remaining useful life estimation of the prognostics and health management technique is a complicated and difficult research question for maintenance. In this article, we consider the problem of prognostics modeling and estimation of the turbofan engine under complicated circumstances and propose a kernel principal component analysis–based degradation model and remaining useful life estimation method for such aircraft engine. We first analyze the output data created by the turbofan engine thermodynamic simulation that is based on the kernel principal component analysis method and then distinguish the qualitative and quantitative relationships between the key factors. Next, we build a degradation model for the engine fault based on the following assumptions: the engine has only had constant failure (i.e. no sudden failure is included, and the engine has a Wiener process, which is a covariate stand for the engine system drift. To predict the remaining useful life of the turbofan engine, we built a health index based on the degradation model and used the method of maximum likelihood and the data from the thermodynamic simulation model to estimate the parameters of this degradation model. Through the data analysis, we obtained a trend model of the regression curve line that fits with the actual statistical data. Based on the predicted health index model and the data trend model, we estimate the remaining useful life of the aircraft engine as the index reaches zero. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this prediction method that we propose. At last, a case study involving engine simulation data demonstrates the precision and performance advantages of this proposed method, the precision of the method can reach to 98.9% and the average precision is 95.8%.

  2. Repeatedly heated palm kernel oil induces hyperlipidemia, atherogenic indices and hepatorenal toxicity in rats: Beneficial role of virgin coconut oil supplementation.

    Science.gov (United States)

    Famurewa, Ademola C; Nwankwo, Onyebuchi E; Folawiyo, Abiola M; Igwe, Emeka C; Epete, Michael A; Ufebe, Odomero G

    2017-01-01

    The literature reports that the health benefits of vegetable oil can be deteriorated by repeated heating, which leads to lipid oxidation and the formation of free radicals. Virgin coconut oil (VCO) is emerging as a functional food oil and its health benefits are attributed to its potent polyphenolic compounds. We investigated the beneficial effect of VCO supplementation on lipid profile, liver and kidney markers in rats fed repeatedly heated palm kernel oil (HPO). Rats were divided into four groups (n = 5). The control group rats were fed with   a normal diet; group 2 rats were fed a 10% VCO supplemented diet; group 3 administered 10 ml HPO/kg b.w. orally; group 4 were fed 10% VCO + 10 ml HPO/kg for 28 days. Subsequently, serum markers of liver damage (ALT, AST, ALP and albumin), kidney damage (urea, creatinine and uric acid), lipid profile and lipid ratios as cardiovascular risk indices were evaluated. HPO induced a significant increase in serum markers of liver and kidney damage as well as con- comitant lipid abnormalities and a marked reduction in serum HDL-C. The lipid ratios evaluated for atherogenic and coronary risk indices in rats administered HPO only were remarkably higher than control. It was observed that VCO supplementation attenuated the biochemical alterations, including the indices of cardiovascular risks. VCO supplementation demonstrates beneficial health effects against HPO-induced biochemical alterations in rats. VCO may serve to modulate the adverse effects associated with consumption of repeatedly heated palm kernel oil.

  3. Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

    Directory of Open Access Journals (Sweden)

    Hailun Wang

    2017-01-01

    Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

  4. Supersymmetry of noncompact MQCD-like membrane instantons and heat kernel asymptotics

    International Nuclear Information System (INIS)

    Belani, Kanishka; Kaura, Payal; Misra, Aalok

    2006-01-01

    We perform a heat kernel asymptotics analysis of the nonperturbative superpotential obtained from wrapping of an M2-brane around a supersymmetric noncompact three-fold embedded in a (noncompact) G 2 -manifold as obtained, the three-fold being the one relevant to domain walls in Witten's MQCD, in the limit of small 'ζ', a complex constant that appears in the Riemann surfaces relevant to defining the boundary conditions for the domain wall in MQCD. The MQCD-like configuration is interpretable, for small but non-zero ζ as a noncompact/'large open membrane instanton, and for vanishing ζ, as the type IIA D0-brane (for vanishing M-theory circle radius). We find that the eta-function Seeley de-Witt coefficients vanish, and we get a perfect match between the zeta-function Seeley de-Witt coefficients (up to terms quadratic in ζ) between the Dirac-type operator and one of the two Laplace-type operators figuring in the superpotential. Given the dissimilar forms of the bosonic and the square of the fermionic operators, this is an extremely nontrivial check, from a spectral analysis point of view, of the expected residual supersymmetry for the nonperturbative configurations in M-theory considered in this work

  5. Shielding calculations and collective dose estimations with the point-kernel-code VISIPLAN registered for the example of the project ZENT

    International Nuclear Information System (INIS)

    Boehlke, S.; Niegoth, H.

    2012-01-01

    In the nuclear power plant Leibstadt (KKL) during the next year large components will be dismantled and stored for final disposal within the interim storage facility ZENT at the NPP site. Before construction of ZENT appropriate estimations of the local dose rate inside and outside the building and the collective dose for the normal operation have to be performed. The shielding calculations are based on the properties of the stored components and radiation sources and on the concepts for working place requirements. The installation of control and monitoring areas will depend on these calculations. For the determination of the shielding potential of concrete walls and steel doors with the defined boundary conditions point-kernel codes like MICROSHIELd registered are used. Complex problems cannot be modeled with this code. Therefore the point-kernel code VISIPLAN registered was developed for the determination of the local dose distribution functions in 3D models. The possibility of motion sequence inputs allows an optimization of collective dose estimations for the operational phases of a nuclear facility.

  6. Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

    DEFF Research Database (Denmark)

    Arenas-Garcia, J.; Petersen, K.; Camps-Valls, G.

    2013-01-01

    correlation analysis (CCA), and orthonormalized PLS (OPLS), as well as their nonlinear extensions derived by means of the theory of reproducing kernel Hilbert spaces (RKHSs). We also review their connections to other methods for classification and statistical dependence estimation and introduce some recent...

  7. Data-variant kernel analysis

    CERN Document Server

    Motai, Yuichi

    2015-01-01

    Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include

  8. Kernel regression with functional response

    OpenAIRE

    Ferraty, Frédéric; Laksaci, Ali; Tadj, Amel; Vieu, Philippe

    2011-01-01

    We consider kernel regression estimate when both the response variable and the explanatory one are functional. The rates of uniform almost complete convergence are stated as function of the small ball probability of the predictor and as function of the entropy of the set on which uniformity is obtained.

  9. Dielectric properties of almond kernels associated with radio frequency and microwave pasteurization

    Science.gov (United States)

    Li, Rui; Zhang, Shuang; Kou, Xiaoxi; Ling, Bo; Wang, Shaojin

    2017-02-01

    To develop advanced pasteurization treatments based on radio frequency (RF) or microwave (MW) energy, dielectric properties of almond kernels were measured by using an open-ended coaxial-line probe and impedance analyzer at frequencies between 10 and 3000 MHz, moisture contents between 4.2% to 19.6% w.b. and temperatures between 20 and 90 °C. The results showed that both dielectric constant and loss factor of the almond kernels decreased sharply with increasing frequency over the RF range (10-300 MHz), but gradually over the measured MW range (300-3000 MHz). Both dielectric constant and loss factor of almond kernels increased with increasing temperature and moisture content, and largely enhanced at higher temperature and moisture levels. Quadratic polynomial equations were developed to best fit the relationship between dielectric constant or loss factor at 27, 40, 915 or 2450 MHz and sample temperature/moisture content with R2 greater than 0.967. Penetration depth of electromagnetic wave into samples decreased with increasing frequency (27-2450 MHz), moisture content (4.2-19.6% w.b.) and temperature (20-90 °C). The temperature profiles of RF heated almond kernels under three moisture levels were made using experiment and computer simulation based on measured dielectric properties. Based on the result of this study, RF treatment has potential to be practically used for pasteurization of almond kernels with acceptable heating uniformity.

  10. Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson’s Disease

    Science.gov (United States)

    Yang, Shanshan; Zheng, Fang; Luo, Xin; Cai, Suxian; Wu, Yunfeng; Liu, Kaizhi; Wu, Meihong; Chen, Jian; Krishnan, Sridhar

    2014-01-01

    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. PMID:24586406

  11. Kernel and divergence techniques in high energy physics separations

    Science.gov (United States)

    Bouř, Petr; Kůs, Václav; Franc, Jiří

    2017-10-01

    Binary decision trees under the Bayesian decision technique are used for supervised classification of high-dimensional data. We present a great potential of adaptive kernel density estimation as the nested separation method of the supervised binary divergence decision tree. Also, we provide a proof of alternative computing approach for kernel estimates utilizing Fourier transform. Further, we apply our method to Monte Carlo data set from the particle accelerator Tevatron at DØ experiment in Fermilab and provide final top-antitop signal separation results. We have achieved up to 82 % AUC while using the restricted feature selection entering the signal separation procedure.

  12. Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    . The obtained ensemble forecasts of wind power are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained...

  13. Estimating heat-to-heat variation from a statistician's point of view

    International Nuclear Information System (INIS)

    Hebble, T.L.

    1976-01-01

    Heat-to-heat variability is the change in results that occurs when the same tests under the same conditions are applied to samples from different heats of the same material. Heat-to-heat variability reflects, among other things, difference in chemistry and in processing history. Published Japanese tensile and creep tests on types 304 and 316 stainless steel tube are used to illustrate the analysis of variance technique as a tool for isolating heat-to-heat variation. The importance of the underlying model and the role of replication are indicated. Finally, confidence intervals and tolerance limits are computed from numerical estimates of heat-to-heat variation. 17 tables

  14. An iterative kernel based method for fourth order nonlinear equation with nonlinear boundary condition

    Science.gov (United States)

    Azarnavid, Babak; Parand, Kourosh; Abbasbandy, Saeid

    2018-06-01

    This article discusses an iterative reproducing kernel method with respect to its effectiveness and capability of solving a fourth-order boundary value problem with nonlinear boundary conditions modeling beams on elastic foundations. Since there is no method of obtaining reproducing kernel which satisfies nonlinear boundary conditions, the standard reproducing kernel methods cannot be used directly to solve boundary value problems with nonlinear boundary conditions as there is no knowledge about the existence and uniqueness of the solution. The aim of this paper is, therefore, to construct an iterative method by the use of a combination of reproducing kernel Hilbert space method and a shooting-like technique to solve the mentioned problems. Error estimation for reproducing kernel Hilbert space methods for nonlinear boundary value problems have yet to be discussed in the literature. In this paper, we present error estimation for the reproducing kernel method to solve nonlinear boundary value problems probably for the first time. Some numerical results are given out to demonstrate the applicability of the method.

  15. Modeling reactive transport with particle tracking and kernel estimators

    Science.gov (United States)

    Rahbaralam, Maryam; Fernandez-Garcia, Daniel; Sanchez-Vila, Xavier

    2015-04-01

    Groundwater reactive transport models are useful to assess and quantify the fate and transport of contaminants in subsurface media and are an essential tool for the analysis of coupled physical, chemical, and biological processes in Earth Systems. Particle Tracking Method (PTM) provides a computationally efficient and adaptable approach to solve the solute transport partial differential equation. On a molecular level, chemical reactions are the result of collisions, combinations, and/or decay of different species. For a well-mixed system, the chem- ical reactions are controlled by the classical thermodynamic rate coefficient. Each of these actions occurs with some probability that is a function of solute concentrations. PTM is based on considering that each particle actually represents a group of molecules. To properly simulate this system, an infinite number of particles is required, which is computationally unfeasible. On the other hand, a finite number of particles lead to a poor-mixed system which is limited by diffusion. Recent works have used this effect to actually model incomplete mix- ing in naturally occurring porous media. In this work, we demonstrate that this effect in most cases should be attributed to a defficient estimation of the concentrations and not to the occurrence of true incomplete mixing processes in porous media. To illustrate this, we show that a Kernel Density Estimation (KDE) of the concentrations can approach the well-mixed solution with a limited number of particles. KDEs provide weighting functions of each particle mass that expands its region of influence, hence providing a wider region for chemical reactions with time. Simulation results show that KDEs are powerful tools to improve state-of-the-art simulations of chemical reactions and indicates that incomplete mixing in diluted systems should be modeled based on alternative conceptual models and not on a limited number of particles.

  16. Approximate kernel competitive learning.

    Science.gov (United States)

    Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

    2015-03-01

    Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Estimating Nitrogen Availability of Heat-Dried Bio solids

    International Nuclear Information System (INIS)

    Cogger, C.G.; Bary, A.I.; Myhre, E.A.

    2011-01-01

    As heat-dried bio solids become more widely produced and marketed, it is important to improve estimates of N availability from these materials. Objectives were to compare plant-available N among three different heat-dried bio solids and determine if current guidelines were adequate for estimating application rates. Heat-dried bio solids were surface applied to tall fescue (Festuca arundinacea Schreb.) in Washington State, USA, and forage yield and N uptake measured for two growing seasons following application. Three rates of urea and a zero-N control were used to calculate N fertilizer efficiency regressions. Application year plant-available N (estimated as urea N equivalent) for two bio solids exceeded 60% of total N applied, while urea N equivalent for the third bio solids was 45%. Residual (second-year) urea N equivalent ranged from 5 to 10%. Guidelines for the Pacific Northwest USA recommend mineralization estimates of 35 to 40% for heat-dried bio solids, but this research shows that some heat-dried materials fall well above that range.

  18. Selection and properties of alternative forming fluids for TRISO fuel kernel production

    Energy Technology Data Exchange (ETDEWEB)

    Baker, M.P. [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); King, J.C., E-mail: kingjc@mines.edu [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); Gorman, B.P. [Colorado School of Mines, 1500 Illinois St., Golden, CO 80401 (United States); Marshall, D.W. [Idaho National Laboratory, 2525 N. Fremont Avenue, P.O. Box 1625, Idaho Falls, ID 83415 (United States)

    2013-01-15

    Highlights: Black-Right-Pointing-Pointer Forming fluid selection criteria developed for TRISO kernel production. Black-Right-Pointing-Pointer Ten candidates selected for further study. Black-Right-Pointing-Pointer Density, viscosity, and surface tension measured for first time. Black-Right-Pointing-Pointer Settling velocity and heat transfer rates calculated. Black-Right-Pointing-Pointer Three fluids recommended for kernel production testing. - Abstract: Current Very High Temperature Reactor (VHTR) designs incorporate TRi-structural ISOtropic (TRISO) fuel, which consists of a spherical fissile fuel kernel surrounded by layers of pyrolytic carbon and silicon carbide. An internal sol-gel process forms the fuel kernel using wet chemistry to produce uranium oxyhydroxide gel spheres by dropping a cold precursor solution into a hot column of trichloroethylene (TCE). Over time, gelation byproducts inhibit complete gelation, and the TCE must be purified or discarded. The resulting TCE waste stream contains both radioactive and hazardous materials and is thus considered a mixed hazardous waste. Changing the forming fluid to a non-hazardous alternative could greatly improve the economics of TRISO fuel kernel production. Selection criteria for a replacement forming fluid narrowed a list of {approx}10,800 chemicals to yield ten potential replacement forming fluids: 1-bromododecane, 1-bromotetradecane, 1-bromoundecane, 1-chlorooctadecane, 1-chlorotetradecane, 1-iododecane, 1-iodododecane, 1-iodohexadecane, 1-iodooctadecane, and squalane. The density, viscosity, and surface tension for each potential replacement forming fluid were measured as a function of temperature between 25 Degree-Sign C and 80 Degree-Sign C. Calculated settling velocities and heat transfer rates give an overall column height approximation. 1-bromotetradecane, 1-chlorooctadecane, and 1-iodododecane show the greatest promise as replacements, and future tests will verify their ability to form satisfactory

  19. Omnibus risk assessment via accelerated failure time kernel machine modeling.

    Science.gov (United States)

    Sinnott, Jennifer A; Cai, Tianxi

    2013-12-01

    Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. © 2013, The International Biometric Society.

  20. The relative effects of fuel concentration, residual-gas fraction, gas motion, spark energy and heat losses to the electrodes on flame-kernel development in a lean-burn spark ignition engine

    Energy Technology Data Exchange (ETDEWEB)

    Aleiferis, P.G.; Taylor, A.M.K.P. [Imperial College of Science, Technology and Medicine, London (United Kingdom). Dept. of Mechanical Engineering; Ishii, K. [Honda International Technical School, Saitama (Japan); Urata, Y. [Honda R and D Co., Ltd., Tochigi (Japan). Tochigi R and D Centre

    2004-04-01

    The potential of lean combustion for the reduction in exhaust emissions and fuel consumption in spark ignition engines has long been established. However, the operating range of lean-burn spark ignition engines is limited by the level of cyclic variability in the early-flame development stage that typically corresponds to the 0-5 per cent mass fraction burned duration. In the current study, the cyclic variations in early flame development were investigated in an optical stratified-charge spark ignition engine at conditions close to stoichiometry [air-to-fuel ratio (A/F) = 15] and to the lean limit of stable operation (A/F = 22). Flame images were acquired through either a pentroof window ('tumble plane' of view) or the piston crown ('swirl plane' of view) and these were processed to calculate the intra-cycle flame-kernel radius evolution. In order to quantify the relative effects of local fuel concentration, gas motion, spark-energy release and heat losses to the electrodes on the flame-kernel growth rate, a zero-dimensional flame-kernel growth model, in conjunction with a one-dimensional spark ignition model, was employed. Comparison of the calculated flame-radius evolutions with the experimental data suggested that a variation in A/F around the spark plug of {delta}(A/F) {approx} 4 or, in terms of equivalence ratio {phi}, a variation in {delta}{phi} {approx} 0.15 at most was large enough to account for 100 per cent of the observed cyclic variability in flame-kernel radius. A variation in the residual-gas fraction of about 20 per cent around the mean was found to account for up to 30 per cent of the variability in flame-kernel radius at the timing of 5 per cent mass fraction burned. The individual effect of 20 per cent variations in the 'mean' in-cylinder velocity at the spark plug at ignition timing was found to account for no more than 20 per cent of the measured cyclic variability in flame kernel radius. An individual effect of

  1. Classification With Truncated Distance Kernel.

    Science.gov (United States)

    Huang, Xiaolin; Suykens, Johan A K; Wang, Shuning; Hornegger, Joachim; Maier, Andreas

    2018-05-01

    This brief proposes a truncated distance (TL1) kernel, which results in a classifier that is nonlinear in the global region but is linear in each subregion. With this kernel, the subregion structure can be trained using all the training data and local linear classifiers can be established simultaneously. The TL1 kernel has good adaptiveness to nonlinearity and is suitable for problems which require different nonlinearities in different areas. Though the TL1 kernel is not positive semidefinite, some classical kernel learning methods are still applicable which means that the TL1 kernel can be directly used in standard toolboxes by replacing the kernel evaluation. In numerical experiments, the TL1 kernel with a pregiven parameter achieves similar or better performance than the radial basis function kernel with the parameter tuned by cross validation, implying the TL1 kernel a promising nonlinear kernel for classification tasks.

  2. Approximation of the breast height diameter distribution of two-cohort stands by mixture models III Kernel density estimators vs mixture models

    Science.gov (United States)

    Rafal Podlaski; Francis A. Roesch

    2014-01-01

    Two-component mixtures of either the Weibull distribution or the gamma distribution and the kernel density estimator were used for describing the diameter at breast height (dbh) empirical distributions of two-cohort stands. The data consisted of study plots from the Å wietokrzyski National Park (central Poland) and areas close to and including the North Carolina section...

  3. A Comparison of Kernel Equating and Traditional Equipercentile Equating Methods and the Parametric Bootstrap Methods for Estimating Standard Errors in Equipercentile Equating

    Science.gov (United States)

    Choi, Sae Il

    2009-01-01

    This study used simulation (a) to compare the kernel equating method to traditional equipercentile equating methods under the equivalent-groups (EG) design and the nonequivalent-groups with anchor test (NEAT) design and (b) to apply the parametric bootstrap method for estimating standard errors of equating. A two-parameter logistic item response…

  4. Results from ORNL Characterization of Nominal 350 (micro)m NUCO Kernels from the BWXT 59344 batch

    International Nuclear Information System (INIS)

    Hunn, John D.; Kercher, Andrew K.; Menchhofer, Paul A.; Price, Jeffery R.

    2005-01-01

    This document is a compilation of characterization data obtained on nominal 350 (micro)m natural enrichment uranium oxide/uranium carbide kernels (NUCO) produced by BWXT for the Advanced Gas Reactor Fuel Development and Qualification Program. These kernels were produced as part of a development effort at BWXT to address issues involving forming and heat treatment and were shipped to ORNL for additional characterization and for coating tests. The kernels were identified as G73N-NU-59344. 250 grams were shipped to ORNL. Size, shape, and microstructural analysis was performed. These kernels were preceded by G73B-NU-69300 and G73B-NU-69301, which were kernels produced and delivered to ORNL earlier in the development phase. Characterization of the kernels from G73B-NU-69300 was summarized in ORNL/CF-04/07 'Results from ORNL Characterization of Nominal 350 (micro)m NUCO Kernels from the BWXT 69300 composite'.

  5. A new discrete dipole kernel for quantitative susceptibility mapping.

    Science.gov (United States)

    Milovic, Carlos; Acosta-Cabronero, Julio; Pinto, José Miguel; Mattern, Hendrik; Andia, Marcelo; Uribe, Sergio; Tejos, Cristian

    2018-09-01

    Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. A shortest-path graph kernel for estimating gene product semantic similarity

    Directory of Open Access Journals (Sweden)

    Alvarez Marco A

    2011-07-01

    Full Text Available Abstract Background Existing methods for calculating semantic similarity between gene products using the Gene Ontology (GO often rely on external resources, which are not part of the ontology. Consequently, changes in these external resources like biased term distribution caused by shifting of hot research topics, will affect the calculation of semantic similarity. One way to avoid this problem is to use semantic methods that are "intrinsic" to the ontology, i.e. independent of external knowledge. Results We present a shortest-path graph kernel (spgk method that relies exclusively on the GO and its structure. In spgk, a gene product is represented by an induced subgraph of the GO, which consists of all the GO terms annotating it. Then a shortest-path graph kernel is used to compute the similarity between two graphs. In a comprehensive evaluation using a benchmark dataset, spgk compares favorably with other methods that depend on external resources. Compared with simUI, a method that is also intrinsic to GO, spgk achieves slightly better results on the benchmark dataset. Statistical tests show that the improvement is significant when the resolution and EC similarity correlation coefficient are used to measure the performance, but is insignificant when the Pfam similarity correlation coefficient is used. Conclusions Spgk uses a graph kernel method in polynomial time to exploit the structure of the GO to calculate semantic similarity between gene products. It provides an alternative to both methods that use external resources and "intrinsic" methods with comparable performance.

  7. Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

    Science.gov (United States)

    Ma, Yuntao; Chen, Youjia; Zhu, Jinyu; Meng, Lei; Guo, Yan; Li, Baoguo; Hoogenboom, Gerrit

    2018-02-13

    Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source- and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels. © The Author(s) 2018. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  8. Coupling heat and chemical tracer experiments for estimating heat transfer parameters in shallow alluvial aquifers.

    Science.gov (United States)

    Wildemeersch, S; Jamin, P; Orban, P; Hermans, T; Klepikova, M; Nguyen, F; Brouyère, S; Dassargues, A

    2014-11-15

    Geothermal energy systems, closed or open, are increasingly considered for heating and/or cooling buildings. The efficiency of such systems depends on the thermal properties of the subsurface. Therefore, feasibility and impact studies performed prior to their installation should include a field characterization of thermal properties and a heat transfer model using parameter values measured in situ. However, there is a lack of in situ experiments and methodology for performing such a field characterization, especially for open systems. This study presents an in situ experiment designed for estimating heat transfer parameters in shallow alluvial aquifers with focus on the specific heat capacity. This experiment consists in simultaneously injecting hot water and a chemical tracer into the aquifer and monitoring the evolution of groundwater temperature and concentration in the recovery well (and possibly in other piezometers located down gradient). Temperature and concentrations are then used for estimating the specific heat capacity. The first method for estimating this parameter is based on a modeling in series of the chemical tracer and temperature breakthrough curves at the recovery well. The second method is based on an energy balance. The values of specific heat capacity estimated for both methods (2.30 and 2.54MJ/m(3)/K) for the experimental site in the alluvial aquifer of the Meuse River (Belgium) are almost identical and consistent with values found in the literature. Temperature breakthrough curves in other piezometers are not required for estimating the specific heat capacity. However, they highlight that heat transfer in the alluvial aquifer of the Meuse River is complex and contrasted with different dominant process depending on the depth leading to significant vertical heat exchange between upper and lower part of the aquifer. Furthermore, these temperature breakthrough curves could be included in the calibration of a complex heat transfer model for

  9. Estimation of heat transfer and heat source in a molten pool

    Energy Technology Data Exchange (ETDEWEB)

    Yun, J.I.; Suh, K.Y.; Kang, C.S. [Seoul National Univ., Dept. of Nuclear Engineering (Korea, Republic of)

    2001-07-01

    Heat transfer and fluid flow in a molten pool are influenced by internal volumetric heat generated from the radioactive decay of fission product species retained in the pool. The pool superheat is determined based on the overall energy balance that equates the heat production rate to the heat loss rate. Decay heat of fission products in the pool was estimated by product of the mass concentration and energy conversion factor of each fission product. For the calculation of heat generation rate in the pool, twenty-nine (29) elements were chosen and classified by their chemical properties. The mass concentration of a fission product is obtained from released fraction and the tabular output of the ORIGEN 2 code. The initial core and pool inventories at each time can also be estimated using ORIGEN 2. The released fraction of each fission product is calculated based on the bubble dynamics and mass transport. Numerical analysis was performed for the TMI-2 accident. The pool is assumed to be a partially filled hemispherical geometry, 1.45 m in radius and 32,700 kg in mass. The change of pool geometry during the numerical calculation was neglected. The peak temperature sizably decreased by about 60 K as the fission products were released from the pool. (author)

  10. Estimation of heat transfer and heat source in a molten pool

    International Nuclear Information System (INIS)

    Yun, J.I.; Suh, K.Y.; Kang, C.S.

    2001-01-01

    Heat transfer and fluid flow in a molten pool are influenced by internal volumetric heat generated from the radioactive decay of fission product species retained in the pool. The pool superheat is determined based on the overall energy balance that equates the heat production rate to the heat loss rate. Decay heat of fission products in the pool was estimated by product of the mass concentration and energy conversion factor of each fission product. For the calculation of heat generation rate in the pool, twenty-nine (29) elements were chosen and classified by their chemical properties. The mass concentration of a fission product is obtained from released fraction and the tabular output of the ORIGEN 2 code. The initial core and pool inventories at each time can also be estimated using ORIGEN 2. The released fraction of each fission product is calculated based on the bubble dynamics and mass transport. Numerical analysis was performed for the TMI-2 accident. The pool is assumed to be a partially filled hemispherical geometry, 1.45 m in radius and 32,700 kg in mass. The change of pool geometry during the numerical calculation was neglected. The peak temperature sizably decreased by about 60 K as the fission products were released from the pool. (author)

  11. Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.

    Science.gov (United States)

    Poon, Art F Y

    2015-09-01

    The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  12. Kernel abortion in maize. II. Distribution of 14C among kernel carboydrates

    International Nuclear Information System (INIS)

    Hanft, J.M.; Jones, R.J.

    1986-01-01

    This study was designed to compare the uptake and distribution of 14 C among fructose, glucose, sucrose, and starch in the cob, pedicel, and endosperm tissues of maize (Zea mays L.) kernels induced to abort by high temperature with those that develop normally. Kernels cultured in vitro at 309 and 35 0 C were transferred to [ 14 C]sucrose media 10 days after pollination. Kernels cultured at 35 0 C aborted prior to the onset of linear dry matter accumulation. Significant uptake into the cob, pedicel, and endosperm of radioactivity associated with the soluble and starch fractions of the tissues was detected after 24 hours in culture on atlageled media. After 8 days in culture on [ 14 C]sucrose media, 48 and 40% of the radioactivity associated with the cob carbohydrates was found in the reducing sugars at 30 and 35 0 C, respectively. Of the total carbohydrates, a higher percentage of label was associated with sucrose and lower percentage with fructose and glucose in pedicel tissue of kernels cultured at 35 0 C compared to kernels cultured at 30 0 C. These results indicate that sucrose was not cleaved to fructose and glucose as rapidly during the unloading process in the pedicel of kernels induced to abort by high temperature. Kernels cultured at 35 0 C had a much lower proportion of label associated with endosperm starch (29%) than did kernels cultured at 30 0 C (89%). Kernels cultured at 35 0 C had a correspondingly higher proportion of 14 C in endosperm fructose, glucose, and sucrose

  13. Preparation and characterization of active carbon using palm kernel ...

    African Journals Online (AJOL)

    Activated carbons were prepared from Palm kernel shells. Carbonization temperature was 6000C, at a residence time of 5 min for each process. Chemical activation was done by heating a mixture of carbonized material and the activating agents at a temperature of 700C to form a paste, followed by subsequent cooling and ...

  14. Mixed kernel function support vector regression for global sensitivity analysis

    Science.gov (United States)

    Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

    2017-11-01

    Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

  15. Feature Selection and Kernel Learning for Local Learning-Based Clustering.

    Science.gov (United States)

    Zeng, Hong; Cheung, Yiu-ming

    2011-08-01

    The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

  16. Nonparametric evaluation of dynamic disease risk: a spatio-temporal kernel approach.

    Directory of Open Access Journals (Sweden)

    Zhijie Zhang

    Full Text Available Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the "average" spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled "spatio-temporal kernel density estimation (stKDE" that employs hybrid kernel (i.e., weight functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also "borrows" information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based kernel density estimation (trKDE which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method.

  17. Kolkhoung (Pistacia khinjuk) Hull Oil and Kernel Oil as Antioxidative Vegetable Oils with High Oxidative Stability 
and Nutritional Value.

    Science.gov (United States)

    Asnaashari, Maryam; Hashemi, Seyed Mohammad Bagher; Mehr, Hamed Mahdavian; Yousefabad, Seyed Hossein Asadi

    2015-03-01

    In this study, in order to introduce natural antioxidative vegetable oil in food industry, the kolkhoung hull oil and kernel oil were extracted. To evaluate their antioxidant efficiency, gas chromatography analysis of the composition of kolkhoung hull and kernel oil fatty acids and high-performance liquid chromatography analysis of tocopherols were done. Also, the oxidative stability of the oil was considered based on the peroxide value and anisidine value during heating at 100, 110 and 120 °C. Gas chromatography analysis showed that oleic acid was the major fatty acid of both types of oil (hull and kernel) and based on a low content of saturated fatty acids, high content of monounsaturated fatty acids, and the ratio of ω-6 and ω-3 polyunsaturated fatty acids, they were nutritionally well--balanced. Moreover, both hull and kernel oil showed high oxidative stability during heating, which can be attributed to high content of tocotrienols. Based on the results, kolkhoung hull oil acted slightly better than its kernel oil. However, both of them can be added to oxidation-sensitive oils to improve their shelf life.

  18. The development of the production process for the thorium/uranium dicarbide fuel kernels for the first charge of the Dragon Reactor

    International Nuclear Information System (INIS)

    Burnett, R.C.; Hankart, L.J.; Horsley, G.W.

    1965-05-01

    The development of methods of producing spheroidal sintered porous kernels of hyperstoichiometric thorium/uranium dicarbide solid solution from thorium/uranium monocarbide/carbon and thoria/urania/carbon powder mixes is described. The work has involved study of (i) Methods of preparing green kernels from UC/Th/C powder mixes using the rotary sieve technique. (ii) Methods of producing green kernels from UO2/Th02/C powder mixes using the planetary mill technique. (iii) The conversion by appropriate heat treatment of green kernels produced by both routes to sintered porous kernels of thorium/uranium carbide. (iv) The efficiency of the processes. (author)

  19. Estimating heat-to-heat variation in mechanical properties from a statistician's point of view

    International Nuclear Information System (INIS)

    Hebble, T.L.

    1976-01-01

    A statistical technique known as analysis of variance (ANOVA) is used to estimate the variance and standard deviation of differences among heats. The total variation of a collection of observations and how an ANOVA can be used to partition the total variation into its sources are discussed. Then, the ANOVA is adapted to published Japanese data indicating how to estimate heat-to-heat variation. Finally, numerical results are computed for several tensile and creep properties of Types 304 and 316 SS

  20. Output-Only Modal Parameter Recursive Estimation of Time-Varying Structures via a Kernel Ridge Regression FS-TARMA Approach

    Directory of Open Access Journals (Sweden)

    Zhi-Sai Ma

    2017-01-01

    Full Text Available Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA. A kernel ridge regression functional series TARMA (FS-TARMA recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.

  1. Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5

    Science.gov (United States)

    Pendergrass, Angeline G.; Conley, Andrew; Vitt, Francis M.

    2018-02-01

    Radiative kernels at the top of the atmosphere are useful for decomposing changes in atmospheric radiative fluxes due to feedbacks from atmosphere and surface temperature, water vapor, and surface albedo. Here we describe and validate radiative kernels calculated with the large-ensemble version of CAM5, CESM1.1.2, at the top of the atmosphere and the surface. Estimates of the radiative forcing from greenhouse gases and aerosols in RCP8.5 in the CESM large-ensemble simulations are also diagnosed. As an application, feedbacks are calculated for the CESM large ensemble. The kernels are freely available at https://doi.org/10.5065/D6F47MT6" target="_blank">https://doi.org/10.5065/D6F47MT6, and accompanying software can be downloaded from https://github.com/apendergrass/cam5-kernels" target="_blank">https://github.com/apendergrass/cam5-kernels.

  2. Kolkhoung (Pistacia khinjuk Hull Oil and Kernel Oil as Antioxidative Vegetable Oils with High Oxidative Stability and Nutritional Value

    Directory of Open Access Journals (Sweden)

    Maryam Asnaashari

    2015-01-01

    Full Text Available In this study, in order to introduce natural antioxidative vegetable oil in food industry, the kolkhoung hull oil and kernel oil were extracted. To evaluate their antioxidant efficiency, gas chromatography analysis of the composition of kolkhoung hull and kernel oil fatty acids and high–performance liquid chromatography analysis of tocopherols were done. Also, the oxidative stability of the oil was considered based on the peroxide value and anisidine value during heating at 100, 110 and 120 °C. Gas chromatography analysis showed that oleic acid was the major fatty acid of both types of oil (hull and kernel and based on a low content of saturated fatty acids, high content of monounsaturated fatty acids, and the ratio of ω-6 and ω-3 polyunsaturated fatty acids, they were nutritionally well-balanced. Moreover, both hull and kernel oil showed high oxidative stability during heating, which can be attributed to high content of tocotrienols. Based on the results, kolkhoung hull oil acted slightly better than its kernel oil. However, both of them can be added to oxidation–sensitive oils to improve their shelf life.

  3. A novel adaptive kernel method with kernel centers determined by a support vector regression approach

    NARCIS (Netherlands)

    Sun, L.G.; De Visser, C.C.; Chu, Q.P.; Mulder, J.A.

    2012-01-01

    The optimality of the kernel number and kernel centers plays a significant role in determining the approximation power of nearly all kernel methods. However, the process of choosing optimal kernels is always formulated as a global optimization task, which is hard to accomplish. Recently, an

  4. Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

    Science.gov (United States)

    Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

    2017-12-15

    Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

  5. Estimating the Heat of Formation of Foodstuffs and Biomass

    Energy Technology Data Exchange (ETDEWEB)

    Burnham, Alan K. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2010-11-23

    Calorie estimates for expressing the energy content of food are common, however they are inadequate for the purpose of estimating the chemically defined heat of formation of foodstuffs for two reasons. First, they assume utilization factors by the body.1,2,3 Second, they are usually based on average values for their components. The best way to solve this problem would be to measure the heat of combustion of each material of interest. The heat of formation can then be calculated from the elemental composition and the heats of formation of CO2, H2O, and SO2. However, heats of combustion are not always available. Sometimes elemental analysis only is available, or in other cases, a breakdown into protein, carbohydrates, and lipids. A simple way is needed to calculate the heat of formation from various sorts of data commonly available. This report presents improved correlations for relating the heats of combustion and formation to the elemental composition, moisture content, and ash content. The correlations are also able to calculate heats of combustion of carbohydrates, proteins, and lipids individually, including how they depend on elemental composition. The starting point for these correlations are relationships commonly used to estimate the heat of combustion of fossil fuels, and they have been modified slightly to agree better with the ranges of chemical structures found in foodstuffs and biomass.

  6. Auto-associative Kernel Regression Model with Weighted Distance Metric for Instrument Drift Monitoring

    International Nuclear Information System (INIS)

    Shin, Ho Cheol; Park, Moon Ghu; You, Skin

    2006-01-01

    Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto associative kernel regression (AAKR) by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression

  7. Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

    DEFF Research Database (Denmark)

    Quinonero, Joaquin; Girard, Agathe; Larsen, Jan

    2003-01-01

    The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaus......The object of Bayesian modelling is predictive distribution, which, in a forecasting scenario, enables evaluation of forecasted values and their uncertainties. We focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models...... such as the Gaussian process and the relevance vector machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting...

  8. Gradient estimates on the weighted p-Laplace heat equation

    Science.gov (United States)

    Wang, Lin Feng

    2018-01-01

    In this paper, by a regularization process we derive new gradient estimates for positive solutions to the weighted p-Laplace heat equation when the m-Bakry-Émery curvature is bounded from below by -K for some constant K ≥ 0. When the potential function is constant, which reduce to the gradient estimate established by Ni and Kotschwar for positive solutions to the p-Laplace heat equation on closed manifolds with nonnegative Ricci curvature if K ↘ 0, and reduce to the Davies, Hamilton and Li-Xu's gradient estimates for positive solutions to the heat equation on closed manifolds with Ricci curvature bounded from below if p = 2.

  9. DBKGrad: An R Package for Mortality Rates Graduation by Discrete Beta Kernel Techniques

    Directory of Open Access Journals (Sweden)

    Angelo Mazza

    2014-04-01

    Full Text Available We introduce the R package DBKGrad, conceived to facilitate the use of kernel smoothing in graduating mortality rates. The package implements univariate and bivariate adaptive discrete beta kernel estimators. Discrete kernels have been preferred because, in this context, variables such as age, calendar year and duration, are pragmatically considered as discrete and the use of beta kernels is motivated since it reduces boundary bias. Furthermore, when data on exposures to the risk of death are available, the use of adaptive bandwidth, that may be selected by cross-validation, can provide additional benefits. To exemplify the use of the package, an application to Italian mortality rates, for different ages and calendar years, is presented.

  10. 7 CFR 981.7 - Edible kernel.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Edible kernel. 981.7 Section 981.7 Agriculture... Regulating Handling Definitions § 981.7 Edible kernel. Edible kernel means a kernel, piece, or particle of almond kernel that is not inedible. [41 FR 26852, June 30, 1976] ...

  11. Bit Error-Rate Minimizing Detector for Amplify-and-Forward Relaying Systems Using Generalized Gaussian Kernel

    KAUST Repository

    Ahmed, Qasim Zeeshan

    2013-01-01

    In this letter, a new detector is proposed for amplifyand- forward (AF) relaying system when communicating with the assistance of relays. The major goal of this detector is to improve the bit error rate (BER) performance of the receiver. The probability density function is estimated with the help of kernel density technique. A generalized Gaussian kernel is proposed. This new kernel provides more flexibility and encompasses Gaussian and uniform kernels as special cases. The optimal window width of the kernel is calculated. Simulations results show that a gain of more than 1 dB can be achieved in terms of BER performance as compared to the minimum mean square error (MMSE) receiver when communicating over Rayleigh fading channels.

  12. Kernel versions of some orthogonal transformations

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    Kernel versions of orthogonal transformations such as principal components are based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced...... by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution also known as the kernel trick these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel...... function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component analysis (PCA) and kernel minimum noise fraction (MNF) analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function...

  13. Model Selection in Kernel Ridge Regression

    DEFF Research Database (Denmark)

    Exterkate, Peter

    Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...

  14. Penetuan Bilangan Iodin pada Hydrogenated Palm Kernel Oil (HPKO) dan Refined Bleached Deodorized Palm Kernel Oil (RBDPKO)

    OpenAIRE

    Sitompul, Monica Angelina

    2015-01-01

    Have been conducted Determination of Iodin Value by method titration to some Hydrogenated Palm Kernel Oil (HPKO) and Refined Bleached Deodorized Palm Kernel Oil (RBDPKO). The result of analysis obtained the Iodin Value in Hydrogenated Palm Kernel Oil (A) = 0,16 gr I2/100gr, Hydrogenated Palm Kernel Oil (B) = 0,20 gr I2/100gr, Hydrogenated Palm Kernel Oil (C) = 0,24 gr I2/100gr. And in Refined Bleached Deodorized Palm Kernel Oil (A) = 17,51 gr I2/100gr, Refined Bleached Deodorized Palm Kernel ...

  15. 7 CFR 981.8 - Inedible kernel.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.8 Section 981.8 Agriculture... Regulating Handling Definitions § 981.8 Inedible kernel. Inedible kernel means a kernel, piece, or particle of almond kernel with any defect scored as serious damage, or damage due to mold, gum, shrivel, or...

  16. 7 CFR 981.408 - Inedible kernel.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Inedible kernel. 981.408 Section 981.408 Agriculture... Administrative Rules and Regulations § 981.408 Inedible kernel. Pursuant to § 981.8, the definition of inedible kernel is modified to mean a kernel, piece, or particle of almond kernel with any defect scored as...

  17. Digestibility of solvent-treated Jatropha curcas kernel by broiler chickens in Senegal.

    Science.gov (United States)

    Nesseim, Thierry Daniel Tamsir; Dieng, Abdoulaye; Mergeai, Guy; Ndiaye, Saliou; Hornick, Jean-Luc

    2015-12-01

    Jatropha curcas is a drought-resistant shrub belonging to the Euphorbiaceae family. The kernel contains approximately 60 % lipid in dry matter, and the meal obtained after oil extraction could be an exceptional source of protein for family poultry farming, in the absence of curcin and, especially, some diterpene derivatives phorbol esters that are partially lipophilic. The nutrient digestibility of J. curcas kernel meal (JKM), obtained after partial physicochemical deoiling was thus evaluated in broiler chickens. Twenty broiler chickens, 6 weeks old, were maintained in individual metabolic cages and divided into four groups of five animals, according to a 4 × 4 Latin square design where deoiled JKM was incorporated into grinded corn at 0, 4, 8, and 12 % levels (diets 0, 4, 8, and 12 J), allowing measurement of nutrient digestibility by the differential method. The dry matter (DM) and organic matter (OM) digestibility of diets was affected to a low extent by JKM (85 and 86 % in 0 J and 81 % in 12 J, respectively) in such a way that DM and OM digestibility of JKM was estimated to be close to 50 %. The ether extract (EE) digestibility of JKM remained high, at about 90 %, while crude protein (CP) and crude fiber (CF) digestibility were largely impacted by JKM, with values closed to 40 % at the highest levels of incorporation. J. curcas kernel presents various nutrient digestibilities but has adverse effects on CP and CF digestibility of the diet. The effects of an additional heat or biological treatment on JKM remain to be assessed.

  18. Model selection in kernel ridge regression

    DEFF Research Database (Denmark)

    Exterkate, Peter

    2013-01-01

    Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...

  19. LZW-Kernel: fast kernel utilizing variable length code blocks from LZW compressors for protein sequence classification.

    Science.gov (United States)

    Filatov, Gleb; Bauwens, Bruno; Kertész-Farkas, Attila

    2018-05-07

    Bioinformatics studies often rely on similarity measures between sequence pairs, which often pose a bottleneck in large-scale sequence analysis. Here, we present a new convolutional kernel function for protein sequences called the LZW-Kernel. It is based on code words identified with the Lempel-Ziv-Welch (LZW) universal text compressor. The LZW-Kernel is an alignment-free method, it is always symmetric, is positive, always provides 1.0 for self-similarity and it can directly be used with Support Vector Machines (SVMs) in classification problems, contrary to normalized compression distance (NCD), which often violates the distance metric properties in practice and requires further techniques to be used with SVMs. The LZW-Kernel is a one-pass algorithm, which makes it particularly plausible for big data applications. Our experimental studies on remote protein homology detection and protein classification tasks reveal that the LZW-Kernel closely approaches the performance of the Local Alignment Kernel (LAK) and the SVM-pairwise method combined with Smith-Waterman (SW) scoring at a fraction of the time. Moreover, the LZW-Kernel outperforms the SVM-pairwise method when combined with BLAST scores, which indicates that the LZW code words might be a better basis for similarity measures than local alignment approximations found with BLAST. In addition, the LZW-Kernel outperforms n-gram based mismatch kernels, hidden Markov model based SAM and Fisher kernel, and protein family based PSI-BLAST, among others. Further advantages include the LZW-Kernel's reliance on a simple idea, its ease of implementation, and its high speed, three times faster than BLAST and several magnitudes faster than SW or LAK in our tests. LZW-Kernel is implemented as a standalone C code and is a free open-source program distributed under GPLv3 license and can be downloaded from https://github.com/kfattila/LZW-Kernel. akerteszfarkas@hse.ru. Supplementary data are available at Bioinformatics Online.

  20. Viscosity kernel of molecular fluids

    DEFF Research Database (Denmark)

    Puscasu, Ruslan; Todd, Billy; Daivis, Peter

    2010-01-01

    , temperature, and chain length dependencies of the reciprocal and real-space viscosity kernels are presented. We find that the density has a major effect on the shape of the kernel. The temperature range and chain lengths considered here have by contrast less impact on the overall normalized shape. Functional...... forms that fit the wave-vector-dependent kernel data over a large density and wave-vector range have also been tested. Finally, a structural normalization of the kernels in physical space is considered. Overall, the real-space viscosity kernel has a width of roughly 3–6 atomic diameters, which means...

  1. Kernel learning algorithms for face recognition

    CERN Document Server

    Li, Jun-Bao; Pan, Jeng-Shyang

    2013-01-01

    Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

  2. Revisiting the Fundamental Analytical Solutions of Heat and Mass Transfer: The Kernel of Multirate and Multidimensional Diffusion

    Science.gov (United States)

    Zhou, Quanlin; Oldenburg, Curtis M.; Rutqvist, Jonny; Birkholzer, Jens T.

    2017-11-01

    There are two types of analytical solutions of temperature/concentration in and heat/mass transfer through boundaries of regularly shaped 1-D, 2-D, and 3-D blocks. These infinite-series solutions with either error functions or exponentials exhibit highly irregular but complementary convergence at different dimensionless times, td. In this paper, approximate solutions were developed by combining the error-function-series solutions for early times and the exponential-series solutions for late times and by using time partitioning at the switchover time, td0. The combined solutions contain either the leading term of both series for normal-accuracy approximations (with less than 0.003 relative error) or the first two terms for high-accuracy approximations (with less than 10-7 relative error) for 1-D isotropic (spheres, cylinders, slabs) and 2-D/3-D rectangular blocks (squares, cubes, rectangles, and rectangular parallelepipeds). This rapid and uniform convergence for rectangular blocks was achieved by employing the same time partitioning with individual dimensionless times for different directions and the product of their combined 1-D slab solutions. The switchover dimensionless time was determined to minimize the maximum approximation errors. Furthermore, the analytical solutions of first-order heat/mass flux for 2-D/3-D rectangular blocks were derived for normal-accuracy approximations. These flux equations contain the early-time solution with a three-term polynomial in √td and the late-time solution with the limited-term exponentials for rectangular blocks. The heat/mass flux equations and the combined temperature/concentration solutions form the ultimate kernel for fast simulations of multirate and multidimensional heat/mass transfer in porous/fractured media with millions of low-permeability blocks of varying shapes and sizes.

  3. Clustering via Kernel Decomposition

    DEFF Research Database (Denmark)

    Have, Anna Szynkowiak; Girolami, Mark A.; Larsen, Jan

    2006-01-01

    Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain...... posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets....

  4. UO{sub 2} Kernel Preparation by M-EG Process and Its Irradiation Test

    Energy Technology Data Exchange (ETDEWEB)

    Jeong, K. C.; Eom, S. H.; Kim, Y. K.; Yeo, S. H.; Kim, Y. M.; Kim, B. G.; Cho, M. S. [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    Kernels of KAERI TRISO fuels are prepared in the following steps: (1) preparation of a raw material solution(UN solution) by UO{sub 3} (or U{sub 3}O{sub 8}) powder dissolution in the concentrated HNO{sub 3}; (2) broth preparation and physical property control by mixing UN, THFA, PVA, and H{sub 2}O; (3) preparation of spherical liquid gel droplets and dried-ADU gels in sequence through a reaction between uranyl ions and ammonia ions in a gelation column; (4) ageing, washing, and drying processes of ADU gel using AWD equipment; (5) UO{sub 3} calcination by thermal decomposition of driedADU gel in the air; (6) fabrication of UO{sub 2} kernel by reducing the UO{sub 3} and sintering in the H{sub 2}. In this study, improved KAERI processes for UO{sub 2} kernel preparation were presented. ADU gel washing procedure in AWD processes and the heating mode in sintering process were modified and the internal structures of UO{sub 2} kernels are presented as a result.

  5. Kernel methods for deep learning

    OpenAIRE

    Cho, Youngmin

    2012-01-01

    We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector mach...

  6. Dose calculation methods in photon beam therapy using energy deposition kernels

    International Nuclear Information System (INIS)

    Ahnesjoe, A.

    1991-01-01

    The problem of calculating accurate dose distributions in treatment planning of megavoltage photon radiation therapy has been studied. New dose calculation algorithms using energy deposition kernels have been developed. The kernels describe the transfer of energy by secondary particles from a primary photon interaction site to its surroundings. Monte Carlo simulations of particle transport have been used for derivation of kernels for primary photon energies form 0.1 MeV to 50 MeV. The trade off between accuracy and calculational speed has been addressed by the development of two algorithms; one point oriented with low computional overhead for interactive use and one for fast and accurate calculation of dose distributions in a 3-dimensional lattice. The latter algorithm models secondary particle transport in heterogeneous tissue by scaling energy deposition kernels with the electron density of the tissue. The accuracy of the methods has been tested using full Monte Carlo simulations for different geometries, and found to be superior to conventional algorithms based on scaling of broad beam dose distributions. Methods have also been developed for characterization of clinical photon beams in entities appropriate for kernel based calculation models. By approximating the spectrum as laterally invariant, an effective spectrum and dose distribution for contaminating charge particles are derived form depth dose distributions measured in water, using analytical constraints. The spectrum is used to calculate kernels by superposition of monoenergetic kernels. The lateral energy fluence distribution is determined by deconvolving measured lateral dose distributions by a corresponding pencil beam kernel. Dose distributions for contaminating photons are described using two different methods, one for estimation of the dose outside of the collimated beam, and the other for calibration of output factors derived from kernel based dose calculations. (au)

  7. Considering a non-polynomial basis for local kernel regression problem

    Science.gov (United States)

    Silalahi, Divo Dharma; Midi, Habshah

    2017-01-01

    A common used as solution for local kernel nonparametric regression problem is given using polynomial regression. In this study, we demonstrated the estimator and properties using maximum likelihood estimator for a non-polynomial basis such B-spline to replacing the polynomial basis. This estimator allows for flexibility in the selection of a bandwidth and a knot. The best estimator was selected by finding an optimal bandwidth and knot through minimizing the famous generalized validation function.

  8. Estimation of transient heat flux density during the heat supply of a catalytic wall steam methane reformer

    Science.gov (United States)

    Settar, Abdelhakim; Abboudi, Saïd; Madani, Brahim; Nebbali, Rachid

    2018-02-01

    Due to the endothermic nature of the steam methane reforming reaction, the process is often limited by the heat transfer behavior in the reactors. Poor thermal behavior sometimes leads to slow reaction kinetics, which is characterized by the presence of cold spots in the catalytic zones. Within this framework, the present work consists on a numerical investigation, in conjunction with an experimental one, on the one-dimensional heat transfer phenomenon during the heat supply of a catalytic-wall reactor, which is designed for hydrogen production. The studied reactor is inserted in an electric furnace where the heat requirement of the endothermic reaction is supplied by electric heating system. During the heat supply, an unknown heat flux density, received by the reactive flow, is estimated using inverse methods. In the basis of the catalytic-wall reactor model, an experimental setup is engineered in situ to measure the temperature distribution. Then after, the measurements are injected in the numerical heat flux estimation procedure, which is based on the Function Specification Method (FSM). The measured and estimated temperatures are confronted and the heat flux density which crosses the reactor wall is determined.

  9. 7 CFR 981.9 - Kernel weight.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Kernel weight. 981.9 Section 981.9 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Regulating Handling Definitions § 981.9 Kernel weight. Kernel weight means the weight of kernels, including...

  10. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

    KAUST Repository

    Liu, Bo

    2015-11-11

    We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

  11. Veto-Consensus Multiple Kernel Learning

    NARCIS (Netherlands)

    Zhou, Y.; Hu, N.; Spanos, C.J.

    2016-01-01

    We propose Veto-Consensus Multiple Kernel Learning (VCMKL), a novel way of combining multiple kernels such that one class of samples is described by the logical intersection (consensus) of base kernelized decision rules, whereas the other classes by the union (veto) of their complements. The

  12. 7 CFR 51.2295 - Half kernel.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Half kernel. 51.2295 Section 51.2295 Agriculture... Standards for Shelled English Walnuts (Juglans Regia) Definitions § 51.2295 Half kernel. Half kernel means the separated half of a kernel with not more than one-eighth broken off. ...

  13. Kernel-based tests for joint independence

    DEFF Research Database (Denmark)

    Pfister, Niklas; Bühlmann, Peter; Schölkopf, Bernhard

    2018-01-01

    if the $d$ variables are jointly independent, as long as the kernel is characteristic. Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation. We prove that the permutation test......We investigate the problem of testing whether $d$ random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method builds on ideas of the two variable Hilbert-Schmidt independence criterion (HSIC) but allows for an arbitrary number of variables. We embed...... the $d$-dimensional joint distribution and the product of the marginals into a reproducing kernel Hilbert space and define the $d$-variable Hilbert-Schmidt independence criterion (dHSIC) as the squared distance between the embeddings. In the population case, the value of dHSIC is zero if and only...

  14. An Approximate Approach to Automatic Kernel Selection.

    Science.gov (United States)

    Ding, Lizhong; Liao, Shizhong

    2016-02-02

    Kernel selection is a fundamental problem of kernel-based learning algorithms. In this paper, we propose an approximate approach to automatic kernel selection for regression from the perspective of kernel matrix approximation. We first introduce multilevel circulant matrices into automatic kernel selection, and develop two approximate kernel selection algorithms by exploiting the computational virtues of multilevel circulant matrices. The complexity of the proposed algorithms is quasi-linear in the number of data points. Then, we prove an approximation error bound to measure the effect of the approximation in kernel matrices by multilevel circulant matrices on the hypothesis and further show that the approximate hypothesis produced with multilevel circulant matrices converges to the accurate hypothesis produced with kernel matrices. Experimental evaluations on benchmark datasets demonstrate the effectiveness of approximate kernel selection.

  15. Iterative software kernels

    Energy Technology Data Exchange (ETDEWEB)

    Duff, I.

    1994-12-31

    This workshop focuses on kernels for iterative software packages. Specifically, the three speakers discuss various aspects of sparse BLAS kernels. Their topics are: `Current status of user lever sparse BLAS`; Current status of the sparse BLAS toolkit`; and `Adding matrix-matrix and matrix-matrix-matrix multiply to the sparse BLAS toolkit`.

  16. Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification.

    Science.gov (United States)

    Lu, Zhao; Sun, Jing; Butts, Kenneth

    2016-02-03

    A giant leap has been made in the past couple of decades with the introduction of kernel-based learning as a mainstay for designing effective nonlinear computational learning algorithms. In view of the geometric interpretation of conditional expectation and the ubiquity of multiscale characteristics in highly complex nonlinear dynamic systems [1]-[3], this paper presents a new orthogonal projection operator wavelet kernel, aiming at developing an efficient computational learning approach for nonlinear dynamical system identification. In the framework of multiresolution analysis, the proposed projection operator wavelet kernel can fulfill the multiscale, multidimensional learning to estimate complex dependencies. The special advantage of the projection operator wavelet kernel developed in this paper lies in the fact that it has a closed-form expression, which greatly facilitates its application in kernel learning. To the best of our knowledge, it is the first closed-form orthogonal projection wavelet kernel reported in the literature. It provides a link between grid-based wavelets and mesh-free kernel-based methods. Simulation studies for identifying the parallel models of two benchmark nonlinear dynamical systems confirm its superiority in model accuracy and sparsity.

  17. A kernel version of spatial factor analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2009-01-01

    . Schölkopf et al. introduce kernel PCA. Shawe-Taylor and Cristianini is an excellent reference for kernel methods in general. Bishop and Press et al. describe kernel methods among many other subjects. Nielsen and Canty use kernel PCA to detect change in univariate airborne digital camera images. The kernel...... version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply kernel versions of PCA, maximum autocorrelation factor (MAF) analysis...

  18. Detoxification of Jatropha curcas kernel cake by a novel Streptomyces fimicarius strain.

    Science.gov (United States)

    Wang, Xing-Hong; Ou, Lingcheng; Fu, Liang-Liang; Zheng, Shui; Lou, Ji-Dong; Gomes-Laranjo, José; Li, Jiao; Zhang, Changhe

    2013-09-15

    A huge amount of kernel cake, which contains a variety of toxins including phorbol esters (tumor promoters), is projected to be generated yearly in the near future by the Jatropha biodiesel industry. We showed that the kernel cake strongly inhibited plant seed germination and root growth and was highly toxic to carp fingerlings, even though phorbol esters were undetectable by HPLC. Therefore it must be detoxified before disposal to the environment. A mathematic model was established to estimate the general toxicity of the kernel cake by determining the survival time of carp fingerling. A new strain (Streptomyces fimicarius YUCM 310038) capable of degrading the total toxicity by more than 97% in a 9-day solid state fermentation was screened out from 578 strains including 198 known strains and 380 strains isolated from air and soil. The kernel cake fermented by YUCM 310038 was nontoxic to plants and carp fingerlings and significantly promoted tobacco plant growth, indicating its potential to transform the toxic kernel cake to bio-safe animal feed or organic fertilizer to remove the environmental concern and to reduce the cost of the Jatropha biodiesel industry. Microbial strain profile essential for the kernel cake detoxification was discussed. Copyright © 2013 Elsevier B.V. All rights reserved.

  19. 7 CFR 51.1441 - Half-kernel.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Half-kernel. 51.1441 Section 51.1441 Agriculture... Standards for Grades of Shelled Pecans Definitions § 51.1441 Half-kernel. Half-kernel means one of the separated halves of an entire pecan kernel with not more than one-eighth of its original volume missing...

  20. Local Observed-Score Kernel Equating

    Science.gov (United States)

    Wiberg, Marie; van der Linden, Wim J.; von Davier, Alina A.

    2014-01-01

    Three local observed-score kernel equating methods that integrate methods from the local equating and kernel equating frameworks are proposed. The new methods were compared with their earlier counterparts with respect to such measures as bias--as defined by Lord's criterion of equity--and percent relative error. The local kernel item response…

  1. Credit scoring analysis using kernel discriminant

    Science.gov (United States)

    Widiharih, T.; Mukid, M. A.; Mustafid

    2018-05-01

    Credit scoring model is an important tool for reducing the risk of wrong decisions when granting credit facilities to applicants. This paper investigate the performance of kernel discriminant model in assessing customer credit risk. Kernel discriminant analysis is a non- parametric method which means that it does not require any assumptions about the probability distribution of the input. The main ingredient is a kernel that allows an efficient computation of Fisher discriminant. We use several kernel such as normal, epanechnikov, biweight, and triweight. The models accuracy was compared each other using data from a financial institution in Indonesia. The results show that kernel discriminant can be an alternative method that can be used to determine who is eligible for a credit loan. In the data we use, it shows that a normal kernel is relevant to be selected for credit scoring using kernel discriminant model. Sensitivity and specificity reach to 0.5556 and 0.5488 respectively.

  2. On the Operator ⨁Bk Related to Bessel Heat Equation

    Directory of Open Access Journals (Sweden)

    Wanchak Satsanit

    2010-01-01

    Full Text Available We study the equation (∂/∂tu(x,t=c2⊕Bku(x,t with the initial condition u(x,0=f(x for x∈Rn+. The operator ⊕Bk is the operator iterated k-times and is defined by ⊕Bk=((∑i=1pBxi4-(∑j=p+1p+qBxi4k, where p+q=n is the dimension of the Rn+, Bxi=∂2/∂xi2+(2vi/xi(∂/∂xi, 2vi=2αi+1, αi>-1/2, i=1,2,3,…,n, and k is a nonnegative integer, u(x,t is an unknown function for (x,t=(x1,x2,…,xn,t∈Rn+×(0,∞, f(x is a given generalized function, and c is a positive constant. We obtain the solution of such equation, which is related to the spectrum and the kernel, which is so called Bessel heat kernel. Moreover, such Bessel heat kernel has interesting properties and also related to the kernel of an extension of the heat equation.

  3. An inverse hyperbolic heat conduction problem in estimating surface heat flux by the conjugate gradient method

    International Nuclear Information System (INIS)

    Huang, C.-H.; Wu, H.-H.

    2006-01-01

    In the present study an inverse hyperbolic heat conduction problem is solved by the conjugate gradient method (CGM) in estimating the unknown boundary heat flux based on the boundary temperature measurements. Results obtained in this inverse problem will be justified based on the numerical experiments where three different heat flux distributions are to be determined. Results show that the inverse solutions can always be obtained with any arbitrary initial guesses of the boundary heat flux. Moreover, the drawbacks of the previous study for this similar inverse problem, such as (1) the inverse solution has phase error and (2) the inverse solution is sensitive to measurement error, can be avoided in the present algorithm. Finally, it is concluded that accurate boundary heat flux can be estimated in this study

  4. Quantized kernel least mean square algorithm.

    Science.gov (United States)

    Chen, Badong; Zhao, Songlin; Zhu, Pingping; Príncipe, José C

    2012-01-01

    In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering. The basic idea behind this method is to quantize and hence compress the input (or feature) space. Different from sparsification, the new approach uses the "redundant" data to update the coefficient of the closest center. In particular, a quantized kernel least mean square (QKLMS) algorithm is developed, which is based on a simple online vector quantization method. The analytical study of the mean square convergence has been carried out. The energy conservation relation for QKLMS is established, and on this basis we arrive at a sufficient condition for mean square convergence, and a lower and upper bound on the theoretical value of the steady-state excess mean square error. Static function estimation and short-term chaotic time-series prediction examples are presented to demonstrate the excellent performance.

  5. Bayesian Frequency Domain Identification of LTI Systems with OBFs kernels

    NARCIS (Netherlands)

    Darwish, M.A.H.; Lataire, J.P.G.; Tóth, R.

    2017-01-01

    Regularised Frequency Response Function (FRF) estimation based on Gaussian process regression formulated directly in the frequency-domain has been introduced recently The underlying approach largely depends on the utilised kernel function, which encodes the relevant prior knowledge on the system

  6. Kernel parameter dependence in spatial factor analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2010-01-01

    kernel PCA. Shawe-Taylor and Cristianini [4] is an excellent reference for kernel methods in general. Bishop [5] and Press et al. [6] describe kernel methods among many other subjects. The kernel version of PCA handles nonlinearities by implicitly transforming data into high (even infinite) dimensional...... feature space via the kernel function and then performing a linear analysis in that space. In this paper we shall apply a kernel version of maximum autocorrelation factor (MAF) [7, 8] analysis to irregularly sampled stream sediment geochemistry data from South Greenland and illustrate the dependence...... of the kernel width. The 2,097 samples each covering on average 5 km2 are analyzed chemically for the content of 41 elements....

  7. Spatiotemporal characteristics of elderly population's traffic accidents in Seoul using space-time cube and space-time kernel density estimation.

    Science.gov (United States)

    Kang, Youngok; Cho, Nahye; Son, Serin

    2018-01-01

    The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people's traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people's traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people's traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers' traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims' hotspots are more sporadic than elderly drivers' hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future.

  8. Characterisation and final disposal behaviour of theoria-based fuel kernels in aqueous phases

    International Nuclear Information System (INIS)

    Titov, M.

    2005-08-01

    Two high-temperature reactors (AVR and THTR) operated in Germany have produced about 1 million spent fuel elements. The nuclear fuel in these reactors consists mainly of thorium-uranium mixed oxides, but also pure uranium dioxide and carbide fuels were tested. One of the possible solutions of utilising spent HTR fuel is the direct disposal in deep geological formations. Under such circumstances, the properties of fuel kernels, and especially their leaching behaviour in aqueous phases, have to be investigated for safety assessments of the final repository. In the present work, unirradiated ThO 2 , (Th 0.906 ,U 0.094 )O 2 , (Th 0.834 ,U 0.166 )O 2 and UO 2 fuel kernels were investigated. The composition, crystal structure and surface of the kernels were investigated by traditional methods. Furthermore, a new method was developed for testing the mechanical properties of ceramic kernels. The method was successfully used for the examination of mechanical properties of oxide kernels and for monitoring their evolution during contact with aqueous phases. The leaching behaviour of thoria-based oxide kernels and powders was investigated in repository-relevant salt solutions, as well as in artificial leachates. The influence of different experimental parameters on the kernel leaching stability was investigated. It was shown that thoria-based fuel kernels possess high chemical stability and are indifferent to presence of oxidative and radiolytic species in solution. The dissolution rate of thoria-based materials is typically several orders of magnitude lower than of conventional UO 2 fuel kernels. The life time of a single intact (Th,U)O 2 kernel under aggressive conditions of salt repository was estimated as about hundred thousand years. The importance of grain boundary quality on the leaching stability was demonstrated. Numerical Monte Carlo simulations were performed in order to explain the results of leaching experiments. (orig.)

  9. Design analysis and performance test of reduction furnace of kernel U3O8

    International Nuclear Information System (INIS)

    Moch Setyadji; Triyono; Dedy Husnurrofiq

    2015-01-01

    High Temperature Reactor (HTR) with coated particle fuel is a future reactor (generation IV) because it is not only having high efficiency but also release no fission product into the environment. It has a passive safety principles and negative reactivity. Coated particle fuel for high temperature reactor is made through Sol-Gel process followed by aging, drying, calcination, reduction, sintering and coating process. Research of design analysis of heating system, electrical system, and insulation systems continued construction and performance test of reduction furnace has been done. The aim of this research was to obtain a reduction furnace with adequate performance that can be used to reduce the kernel of U 3 O 8 into UO 2 . The results of the performance test of the reduction furnace showed that heating zones 1-3 can generate heat to a temperature of 900°C with operation time 144 minutes and heat rate of 5.979°C/min. The coefficient of convection in the outer wall of fireproof stone having 7 cm thick was around 30 W/m 2 C. UO 2 kernel resulting from the reduction process has a diameter of between 0.850 to 0.992 mm and qualify as feed sintering process. (author)

  10. A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction.

    Science.gov (United States)

    Marceau, Rachel; Lu, Wenbin; Holloway, Shannon; Sale, Michèle M; Worrall, Bradford B; Williams, Stephen R; Hsu, Fang-Chi; Tzeng, Jung-Ying

    2015-09-01

    Kernel machine (KM) models are a powerful tool for exploring associations between sets of genetic variants and complex traits. Although most KM methods use a single kernel function to assess the marginal effect of a variable set, KM analyses involving multiple kernels have become increasingly popular. Multikernel analysis allows researchers to study more complex problems, such as assessing gene-gene or gene-environment interactions, incorporating variance-component based methods for population substructure into rare-variant association testing, and assessing the conditional effects of a variable set adjusting for other variable sets. The KM framework is robust, powerful, and provides efficient dimension reduction for multifactor analyses, but requires the estimation of high dimensional nuisance parameters. Traditional estimation techniques, including regularization and the "expectation-maximization (EM)" algorithm, have a large computational cost and are not scalable to large sample sizes needed for rare variant analysis. Therefore, under the context of gene-environment interaction, we propose a computationally efficient and statistically rigorous "fastKM" algorithm for multikernel analysis that is based on a low-rank approximation to the nuisance effect kernel matrices. Our algorithm is applicable to various trait types (e.g., continuous, binary, and survival traits) and can be implemented using any existing single-kernel analysis software. Through extensive simulation studies, we show that our algorithm has similar performance to an EM-based KM approach for quantitative traits while running much faster. We also apply our method to the Vitamin Intervention for Stroke Prevention (VISP) clinical trial, examining gene-by-vitamin effects on recurrent stroke risk and gene-by-age effects on change in homocysteine level. © 2015 WILEY PERIODICALS, INC.

  11. Two-Phase Iteration for Value Function Approximation and Hyperparameter Optimization in Gaussian-Kernel-Based Adaptive Critic Design

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2015-01-01

    Full Text Available Adaptive Dynamic Programming (ADP with critic-actor architecture is an effective way to perform online learning control. To avoid the subjectivity in the design of a neural network that serves as a critic network, kernel-based adaptive critic design (ACD was developed recently. There are two essential issues for a static kernel-based model: how to determine proper hyperparameters in advance and how to select right samples to describe the value function. They all rely on the assessment of sample values. Based on the theoretical analysis, this paper presents a two-phase simultaneous learning method for a Gaussian-kernel-based critic network. It is able to estimate the values of samples without infinitively revisiting them. And the hyperparameters of the kernel model are optimized simultaneously. Based on the estimated sample values, the sample set can be refined by adding alternatives or deleting redundances. Combining this critic design with actor network, we present a Gaussian-kernel-based Adaptive Dynamic Programming (GK-ADP approach. Simulations are used to verify its feasibility, particularly the necessity of two-phase learning, the convergence characteristics, and the improvement of the system performance by using a varying sample set.

  12. Multiple Kernel Learning with Data Augmentation

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:49–64, 2016 ACML 2016 Multiple Kernel Learning with Data Augmentation Khanh Nguyen nkhanh@deakin.edu.au...University, Australia Editors: Robert J. Durrant and Kee-Eung Kim Abstract The motivations of multiple kernel learning (MKL) approach are to increase... kernel expres- siveness capacity and to avoid the expensive grid search over a wide spectrum of kernels . A large amount of work has been proposed to

  13. OS X and iOS Kernel Programming

    CERN Document Server

    Halvorsen, Ole Henry

    2011-01-01

    OS X and iOS Kernel Programming combines essential operating system and kernel architecture knowledge with a highly practical approach that will help you write effective kernel-level code. You'll learn fundamental concepts such as memory management and thread synchronization, as well as the I/O Kit framework. You'll also learn how to write your own kernel-level extensions, such as device drivers for USB and Thunderbolt devices, including networking, storage and audio drivers. OS X and iOS Kernel Programming provides an incisive and complete introduction to the XNU kernel, which runs iPhones, i

  14. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  15. Analysis of genetic diversity among the maize inbred lines (Zea mays L. under heat stress condition

    Directory of Open Access Journals (Sweden)

    Manoj Kandel

    2017-12-01

    Full Text Available High temperature adversely affects the plant physiological processes: limits plant growth and reduction in grain yield. Heat stress is often encountered to spring sowing of maize in spring season. Twenty maize inbred lines were studied for days to 50 % anthesis and silking, anthesis–silking interval, leaf firing, tassel blast, SPAD reading and leaf senescence, plant and ear height, leaf area index, ear per plant, cob length and diameter, number of kernel/ear, number of kernel row/ear, number of kernel row, silk receptivity, shelling percentage, thousand kernel weight and grain yield in alpha lattice design at National Maize Research Program at Rampur, Chitwan,Nepal with the objective to identify superior heat stress tolerant lines. Analysis of variance showed significant difference for all the traits. Result of multivariable analysis revealed that twenty inbred lines formed four clusters. The resistance inbred lines and susceptible inbred lines formed different clusters. The members of cluster 4 were found to be tolerant to heat stress due to they had lowest value of tassel blast, leaf firing, and leaf area index with highest value of cob diameter and length, ear per plant, number of kernel row/ear, number of kernel/ear, number of kernel row, shelling percentage, silk receptivity and grain yield whereas as members of cluster 1were found most susceptible due to they had longer anthesis silking interval, with maximum tassel blast and leaf firing along with no grain yield under heat stress condition. From this study inbred lines RL-140, RML-76, RML-91 and RML-40 were found most tolerant to heat stress. These inbred lines belonging to superior cluster could be considered very useful in developing heat tolerant variety and other breeding activities.

  16. Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

    Directory of Open Access Journals (Sweden)

    Yang Zhang

    2015-11-01

    Full Text Available Prognostics is necessary to ensure the reliability and safety of lithium-ion batteries for hybrid electric vehicles or satellites. This process can be achieved by capacity estimation, which is a direct fading indicator for assessing the state of health of a battery. However, the capacity of a lithium-ion battery onboard is difficult to monitor. This paper presents a data-driven approach for online capacity estimation. First, six novel features are extracted from cyclic charge/discharge cycles and used as indirect health indicators. An adaptive multi-kernel relevance machine (MKRVM based on accelerated particle swarm optimization algorithm is used to determine the optimal parameters of MKRVM and characterize the relationship between extracted features and battery capacity. The overall estimation process comprises offline and online stages. A supervised learning step in the offline stage is established for model verification to ensure the generalizability of MKRVM for online application. Cross-validation is further conducted to validate the performance of the proposed model. Experiment and comparison results show the effectiveness, accuracy, efficiency, and robustness of the proposed approach for online capacity estimation of lithium-ion batteries.

  17. Paramecium: An Extensible Object-Based Kernel

    NARCIS (Netherlands)

    van Doorn, L.; Homburg, P.; Tanenbaum, A.S.

    1995-01-01

    In this paper we describe the design of an extensible kernel, called Paramecium. This kernel uses an object-based software architecture which together with instance naming, late binding and explicit overrides enables easy reconfiguration. Determining which components reside in the kernel protection

  18. Theory of reproducing kernels and applications

    CERN Document Server

    Saitoh, Saburou

    2016-01-01

    This book provides a large extension of the general theory of reproducing kernels published by N. Aronszajn in 1950, with many concrete applications. In Chapter 1, many concrete reproducing kernels are first introduced with detailed information. Chapter 2 presents a general and global theory of reproducing kernels with basic applications in a self-contained way. Many fundamental operations among reproducing kernel Hilbert spaces are dealt with. Chapter 2 is the heart of this book. Chapter 3 is devoted to the Tikhonov regularization using the theory of reproducing kernels with applications to numerical and practical solutions of bounded linear operator equations. In Chapter 4, the numerical real inversion formulas of the Laplace transform are presented by applying the Tikhonov regularization, where the reproducing kernels play a key role in the results. Chapter 5 deals with ordinary differential equations; Chapter 6 includes many concrete results for various fundamental partial differential equations. In Chapt...

  19. Kernels for structured data

    CERN Document Server

    Gärtner, Thomas

    2009-01-01

    This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by

  20. A Experimental Study of the Growth of Laser Spark and Electric Spark Ignited Flame Kernels.

    Science.gov (United States)

    Ho, Chi Ming

    1995-01-01

    Better ignition sources are constantly in demand for enhancing the spark ignition in practical applications such as automotive and liquid rocket engines. In response to this practical challenge, the present experimental study was conducted with the major objective to obtain a better understanding on how spark formation and hence spark characteristics affect the flame kernel growth. Two laser sparks and one electric spark were studied in air, propane-air, propane -air-nitrogen, methane-air, and methane-oxygen mixtures that were initially at ambient pressure and temperature. The growth of the kernels was monitored by imaging the kernels with shadowgraph systems, and by imaging the planar laser -induced fluorescence of the hydroxyl radicals inside the kernels. Characteristic dimensions and kernel structures were obtained from these images. Since different energy transfer mechanisms are involved in the formation of a laser spark as compared to that of an electric spark; a laser spark is insensitive to changes in mixture ratio and mixture type, while an electric spark is sensitive to changes in both. The detailed structures of the kernels in air and propane-air mixtures primarily depend on the spark characteristics. But the combustion heat released rapidly in methane-oxygen mixtures significantly modifies the kernel structure. Uneven spark energy distribution causes remarkably asymmetric kernel structure. The breakdown energy of a spark creates a blast wave that shows good agreement with the numerical point blast solution, and a succeeding complex spark-induced flow that agrees reasonably well with a simple puff model. The transient growth rates of the propane-air, propane-air -nitrogen, and methane-air flame kernels can be interpreted in terms of spark effects, flame stretch, and preferential diffusion. For a given mixture, a spark with higher breakdown energy produces a greater and longer-lasting enhancing effect on the kernel growth rate. By comparing the growth

  1. 7 CFR 981.401 - Adjusted kernel weight.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Adjusted kernel weight. 981.401 Section 981.401... Administrative Rules and Regulations § 981.401 Adjusted kernel weight. (a) Definition. Adjusted kernel weight... kernels in excess of five percent; less shells, if applicable; less processing loss of one percent for...

  2. Non-separable pairing interaction kernels applied to superconducting cuprates

    International Nuclear Information System (INIS)

    Haley, Stephen B.; Fink, Herman J.

    2014-01-01

    Highlights: • Non-separable interaction kernels with weak interactions produces HTS. • A probabilistic approach is used in filling the electronic states in the unit cell. • A set of coupled equations is derived which describes the energy gap. • SC properties of separable with non-separable interactions are compared. • There is agreement with measured properties of the SC and normal states. - Abstract: A pairing Hamiltonian H(Γ) with a non-separable interaction kernel Γ produces HTS for relatively weak interactions. The doping and temperature dependence of Γ(x,T) and the chemical potential μ(x) is determined by a probabilistic filling of the electronic states in the cuprate unit cell. A diverse set of HTS and normal state properties is examined, including the SC phase transition boundary T C (x), SC gap Δ(x,T), entropy S(x,T), specific heat C(x,T), and spin susceptibility χ s (x,T). Detailed x,T agreement with cuprate experiment is obtained for all properties

  3. Testing Infrastructure for Operating System Kernel Development

    DEFF Research Database (Denmark)

    Walter, Maxwell; Karlsson, Sven

    2014-01-01

    Testing is an important part of system development, and to test effectively we require knowledge of the internal state of the system under test. Testing an operating system kernel is a challenge as it is the operating system that typically provides access to this internal state information. Multi......-core kernels pose an even greater challenge due to concurrency and their shared kernel state. In this paper, we present a testing framework that addresses these challenges by running the operating system in a virtual machine, and using virtual machine introspection to both communicate with the kernel...... and obtain information about the system. We have also developed an in-kernel testing API that we can use to develop a suite of unit tests in the kernel. We are using our framework for for the development of our own multi-core research kernel....

  4. 7 CFR 51.1403 - Kernel color classification.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Kernel color classification. 51.1403 Section 51.1403... STANDARDS) United States Standards for Grades of Pecans in the Shell 1 Kernel Color Classification § 51.1403 Kernel color classification. (a) The skin color of pecan kernels may be described in terms of the color...

  5. Analysis of Advanced Fuel Kernel Technology

    International Nuclear Information System (INIS)

    Oh, Seung Chul; Jeong, Kyung Chai; Kim, Yeon Ku; Kim, Young Min; Kim, Woong Ki; Lee, Young Woo; Cho, Moon Sung

    2010-03-01

    The reference fuel for prismatic reactor concepts is based on use of an LEU UCO TRISO fissile particle. This fuel form was selected in the early 1980s for large high-temperature gas-cooled reactor (HTGR) concepts using LEU, and the selection was reconfirmed for modular designs in the mid-1980s. Limited existing irradiation data on LEU UCO TRISO fuel indicate the need for a substantial improvement in performance with regard to in-pile gaseous fission product release. Existing accident testing data on LEU UCO TRISO fuel are extremely limited, but it is generally expected that performance would be similar to that of LEU UO 2 TRISO fuel if performance under irradiation were successfully improved. Initial HTGR fuel technology was based on carbide fuel forms. In the early 1980s, as HTGR technology was transitioning from high-enriched uranium (HEU) fuel to LEU fuel. An initial effort focused on LEU prismatic design for large HTGRs resulted in the selection of UCO kernels for the fissile particles and thorium oxide (ThO 2 ) for the fertile particles. The primary reason for selection of the UCO kernel over UO 2 was reduced CO pressure, allowing higher burnup for equivalent coating thicknesses and reduced potential for kernel migration, an important failure mechanism in earlier fuels. A subsequent assessment in the mid-1980s considering modular HTGR concepts again reached agreement on UCO for the fissile particle for a prismatic design. In the early 1990s, plant cost-reduction studies led to a decision to change the fertile material from thorium to natural uranium, primarily because of a lower long-term decay heat level for the natural uranium fissile particles. Ongoing economic optimization in combination with anticipated capabilities of the UCO particles resulted in peak fissile particle burnup projection of 26% FIMA in steam cycle and gas turbine concepts

  6. The definition of kernel Oz

    OpenAIRE

    Smolka, Gert

    1994-01-01

    Oz is a concurrent language providing for functional, object-oriented, and constraint programming. This paper defines Kernel Oz, a semantically complete sublanguage of Oz. It was an important design requirement that Oz be definable by reduction to a lean kernel language. The definition of Kernel Oz introduces three essential abstractions: the Oz universe, the Oz calculus, and the actor model. The Oz universe is a first-order structure defining the values and constraints Oz computes with. The ...

  7. Fabrication of Uranium Oxycarbide Kernels for HTR Fuel

    International Nuclear Information System (INIS)

    Barnes, Charles; Richardson, Clay; Nagley, Scott; Hunn, John; Shaber, Eric

    2010-01-01

    Babcock and Wilcox (B and W) has been producing high quality uranium oxycarbide (UCO) kernels for Advanced Gas Reactor (AGR) fuel tests at the Idaho National Laboratory. In 2005, 350-(micro)m, 19.7% 235U-enriched UCO kernels were produced for the AGR-1 test fuel. Following coating of these kernels and forming the coated-particles into compacts, this fuel was irradiated in the Advanced Test Reactor (ATR) from December 2006 until November 2009. B and W produced 425-(micro)m, 14% enriched UCO kernels in 2008, and these kernels were used to produce fuel for the AGR-2 experiment that was inserted in ATR in 2010. B and W also produced 500-(micro)m, 9.6% enriched UO2 kernels for the AGR-2 experiments. Kernels of the same size and enrichment as AGR-1 were also produced for the AGR-3/4 experiment. In addition to fabricating enriched UCO and UO2 kernels, B and W has produced more than 100 kg of natural uranium UCO kernels which are being used in coating development tests. Successive lots of kernels have demonstrated consistent high quality and also allowed for fabrication process improvements. Improvements in kernel forming were made subsequent to AGR-1 kernel production. Following fabrication of AGR-2 kernels, incremental increases in sintering furnace charge size have been demonstrated. Recently small scale sintering tests using a small development furnace equipped with a residual gas analyzer (RGA) has increased understanding of how kernel sintering parameters affect sintered kernel properties. The steps taken to increase throughput and process knowledge have reduced kernel production costs. Studies have been performed of additional modifications toward the goal of increasing capacity of the current fabrication line to use for production of first core fuel for the Next Generation Nuclear Plant (NGNP) and providing a basis for the design of a full scale fuel fabrication facility.

  8. Spatiotemporal characteristics of elderly population’s traffic accidents in Seoul using space-time cube and space-time kernel density estimation

    Science.gov (United States)

    Cho, Nahye; Son, Serin

    2018-01-01

    The purpose of this study is to analyze how the spatiotemporal characteristics of traffic accidents involving the elderly population in Seoul are changing by time period. We applied kernel density estimation and hotspot analyses to analyze the spatial characteristics of elderly people’s traffic accidents, and the space-time cube, emerging hotspot, and space-time kernel density estimation analyses to analyze the spatiotemporal characteristics. In addition, we analyzed elderly people’s traffic accidents by dividing cases into those in which the drivers were elderly people and those in which elderly people were victims of traffic accidents, and used the traffic accidents data in Seoul for 2013 for analysis. The main findings were as follows: (1) the hotspots for elderly people’s traffic accidents differed according to whether they were drivers or victims. (2) The hourly analysis showed that the hotspots for elderly drivers’ traffic accidents are in specific areas north of the Han River during the period from morning to afternoon, whereas the hotspots for elderly victims are distributed over a wide area from daytime to evening. (3) Monthly analysis showed that the hotspots are weak during winter and summer, whereas they are strong in the hiking and climbing areas in Seoul during spring and fall. Further, elderly victims’ hotspots are more sporadic than elderly drivers’ hotspots. (4) The analysis for the entire period of 2013 indicates that traffic accidents involving elderly people are increasing in specific areas on the north side of the Han River. We expect the results of this study to aid in reducing the number of traffic accidents involving elderly people in the future. PMID:29768453

  9. Residual stresses estimation in tubes after rapid heating of surface

    International Nuclear Information System (INIS)

    Serikov, S.V.

    1992-01-01

    Results are presented on estimation of residual stresses in tubes of steel types ShKh15, EhP836 and 12KIMF after heating by burning pyrotechnic substance inside tubes. External tube surface was heated up to 400-450 deg C under such treatment. Axial stresses distribution over tube wall thickness was determined for initial state, after routine heat treatment and after heating with the use of fireworks. Inner surface heating was shown to essentially decrease axial stresses in tubes

  10. Anisotropic hydrodynamics with a scalar collisional kernel

    Science.gov (United States)

    Almaalol, Dekrayat; Strickland, Michael

    2018-04-01

    Prior studies of nonequilibrium dynamics using anisotropic hydrodynamics have used the relativistic Anderson-Witting scattering kernel or some variant thereof. In this paper, we make the first study of the impact of using a more realistic scattering kernel. For this purpose, we consider a conformal system undergoing transversally homogenous and boost-invariant Bjorken expansion and take the collisional kernel to be given by the leading order 2 ↔2 scattering kernel in scalar λ ϕ4 . We consider both classical and quantum statistics to assess the impact of Bose enhancement on the dynamics. We also determine the anisotropic nonequilibrium attractor of a system subject to this collisional kernel. We find that, when the near-equilibrium relaxation-times in the Anderson-Witting and scalar collisional kernels are matched, the scalar kernel results in a higher degree of momentum-space anisotropy during the system's evolution, given the same initial conditions. Additionally, we find that taking into account Bose enhancement further increases the dynamically generated momentum-space anisotropy.

  11. Object classification and detection with context kernel descriptors

    DEFF Research Database (Denmark)

    Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

    2014-01-01

    Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial...... consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component...

  12. Recov'Heat: An estimation tool of urban waste heat recovery potential in sustainable cities

    Science.gov (United States)

    Goumba, Alain; Chiche, Samuel; Guo, Xiaofeng; Colombert, Morgane; Bonneau, Patricia

    2017-02-01

    Waste heat recovery is considered as an efficient way to increase carbon-free green energy utilization and to reduce greenhouse gas emission. Especially in urban area, several sources such as sewage water, industrial process, waste incinerator plants, etc., are still rarely explored. Their integration into a district heating system providing heating and/or domestic hot water could be beneficial for both energy companies and local governments. EFFICACITY, a French research institute focused on urban energy transition, has developed an estimation tool for different waste heat sources potentially explored in a sustainable city. This article presents the development method of such a decision making tool which, by giving both energetic and economic analysis, helps local communities and energy service companies to make preliminary studies in heat recovery projects.

  13. Ranking Support Vector Machine with Kernel Approximation.

    Science.gov (United States)

    Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

    2017-01-01

    Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  14. Ranking Support Vector Machine with Kernel Approximation

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2017-01-01

    Full Text Available Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels can give higher accuracy than linear RankSVM (RankSVM with a linear kernel for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  15. Dose point kernels for beta-emitting radioisotopes

    International Nuclear Information System (INIS)

    Prestwich, W.V.; Chan, L.B.; Kwok, C.S.; Wilson, B.

    1986-01-01

    Knowledge of the dose point kernel corresponding to a specific radionuclide is required to calculate the spatial dose distribution produced in a homogeneous medium by a distributed source. Dose point kernels for commonly used radionuclides have been calculated previously using as a basis monoenergetic dose point kernels derived by numerical integration of a model transport equation. The treatment neglects fluctuations in energy deposition, an effect which has been later incorporated in dose point kernels calculated using Monte Carlo methods. This work describes new calculations of dose point kernels using the Monte Carlo results as a basis. An analytic representation of the monoenergetic dose point kernels has been developed. This provides a convenient method both for calculating the dose point kernel associated with a given beta spectrum and for incorporating the effect of internal conversion. An algebraic expression for allowed beta spectra has been accomplished through an extension of the Bethe-Bacher approximation, and tested against the exact expression. Simplified expression for first-forbidden shape factors have also been developed. A comparison of the calculated dose point kernel for 32 P with experimental data indicates good agreement with a significant improvement over the earlier results in this respect. An analytic representation of the dose point kernel associated with the spectrum of a single beta group has been formulated. 9 references, 16 figures, 3 tables

  16. Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

    Science.gov (United States)

    Urrutia, Eugene; Lee, Seunggeun; Maity, Arnab; Zhao, Ni; Shen, Judong; Li, Yun; Wu, Michael C

    Analysis of rare genetic variants has focused on region-based analysis wherein a subset of the variants within a genomic region is tested for association with a complex trait. Two important practical challenges have emerged. First, it is difficult to choose which test to use. Second, it is unclear which group of variants within a region should be tested. Both depend on the unknown true state of nature. Therefore, we develop the Multi-Kernel SKAT (MK-SKAT) which tests across a range of rare variant tests and groupings. Specifically, we demonstrate that several popular rare variant tests are special cases of the sequence kernel association test which compares pair-wise similarity in trait value to similarity in the rare variant genotypes between subjects as measured through a kernel function. Choosing a particular test is equivalent to choosing a kernel. Similarly, choosing which group of variants to test also reduces to choosing a kernel. Thus, MK-SKAT uses perturbation to test across a range of kernels. Simulations and real data analyses show that our framework controls type I error while maintaining high power across settings: MK-SKAT loses power when compared to the kernel for a particular scenario but has much greater power than poor choices.

  17. Wigner functions defined with Laplace transform kernels.

    Science.gov (United States)

    Oh, Se Baek; Petruccelli, Jonathan C; Tian, Lei; Barbastathis, George

    2011-10-24

    We propose a new Wigner-type phase-space function using Laplace transform kernels--Laplace kernel Wigner function. Whereas momentum variables are real in the traditional Wigner function, the Laplace kernel Wigner function may have complex momentum variables. Due to the property of the Laplace transform, a broader range of signals can be represented in complex phase-space. We show that the Laplace kernel Wigner function exhibits similar properties in the marginals as the traditional Wigner function. As an example, we use the Laplace kernel Wigner function to analyze evanescent waves supported by surface plasmon polariton. © 2011 Optical Society of America

  18. Metabolic network prediction through pairwise rational kernels.

    Science.gov (United States)

    Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

    2014-09-26

    Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

  19. Moisture Adsorption Isotherm and Storability of Hazelnut Inshells and Kernels Produced in Oregon, USA.

    Science.gov (United States)

    Jung, Jooyeoun; Wang, Wenjie; McGorrin, Robert J; Zhao, Yanyun

    2018-02-01

    Moisture adsorption isotherms and storability of dried hazelnut inshells and kernels produced in Oregon were evaluated and compared among cultivars, including Barcelona, Yamhill, and Jefferson. Experimental moisture adsorption data fitted to Guggenheim-Anderson-de Boer (GAB) model, showing less hygroscopic properties in Yamhill than other cultivars of inshells and kernels due to lower content of carbohydrate and protein, but higher content of fat. The safe levels of moisture content (MC, dry basis) of dried inshells and kernels for reaching kernel water activity (a w ) ≤0.65 were estimated using the GAB model as 11.3% and 5.0% for Barcelona, 9.4% and 4.2% for Yamhill, and 10.7% and 4.9% for Jefferson, respectively. Storage conditions (2 °C at 85% to 95% relative humidity [RH], 10 °C at 65% to 75% RH, and 27 °C at 35% to 45% RH), times (0, 4, 8, or 12 mo), and packaging methods (atmosphere vs. vacuum) affected MC, a w , bioactive compounds, lipid oxidation, and enzyme activity of dried hazelnut inshells or kernels. For inshells packaged at woven polypropylene bag, MC and a w of inshells and kernels (inside shells) increased at 2 and 10 °C, but decreased at 27 °C during storage. For kernels, lipid oxidation and polyphenol oxidase activity also increased with extended storage time (P adsorption and physicochemical and enzymatic stability during storage. Moisture adsorption isotherm of hazelnut inshells and kernels is useful for predicting the storability of nuts. This study found that water adsorption and storability varied among the different cultivars of nuts, in which Yamhill was less hygroscopic than Barcelona and Jefferson, thus more stable during storage. For ensuring food safety and quality of nuts during storage, each cultivar of kernels should be dried to a certain level of MC. Lipid oxidation and enzyme activity of kernel could be increased with extended storage time. Vacuum packaging was recommended to kernels for reducing moisture adsorption

  20. Fine tuning of process parameters for improving briquette production from palm kernel shell gasification waste.

    Science.gov (United States)

    Bazargan, Alireza; Rough, Sarah L; McKay, Gordon

    2018-04-01

    Palm kernel shell biochars (PKSB) ejected as residues from a gasifier have been used for solid fuel briquette production. With this approach, palm kernel shells can be used for energy production twice: first, by producing rich syngas during gasification; second, by compacting the leftover residues from gasification into high calorific value briquettes. Herein, the process parameters for the manufacture of PKSB biomass briquettes via compaction are optimized. Two possible optimum process scenarios are considered. In the first, the compaction speed is increased from 0.5 to 10 mm/s, the compaction pressure is decreased from 80 Pa to 40 MPa, the retention time is reduced from 10 s to zero, and the starch binder content of the briquette is halved from 0.1 to 0.05 kg/kg. With these adjustments, the briquette production rate increases by more than 20-fold; hence capital and operational costs can be reduced and the service life of compaction equipment can be increased. The resulting product satisfactorily passes tensile (compressive) crushing strength and impact resistance tests. The second scenario involves reducing the starch weight content to 0.03 kg/kg, while reducing the compaction pressure to a value no lower than 60 MPa. Overall, in both cases, the PKSB biomass briquettes show excellent potential as a solid fuel with calorific values on par with good-quality coal. CHNS: carbon, hydrogen, nitrogen, sulfur; FFB: fresh fruit bunch(es); HHV: higher heating value [J/kg]; LHV: lower heating value [J/kg]; PKS: palm kernel shell(s); PKSB: palm kernel shell biochar(s); POME: palm oil mill effluent; RDF: refuse-derived fuel; TGA: thermogravimetric analysis.

  1. The Linux kernel as flexible product-line architecture

    NARCIS (Netherlands)

    M. de Jonge (Merijn)

    2002-01-01

    textabstractThe Linux kernel source tree is huge ($>$ 125 MB) and inflexible (because it is difficult to add new kernel components). We propose to make this architecture more flexible by assembling kernel source trees dynamically from individual kernel components. Users then, can select what

  2. Exploiting graph kernels for high performance biomedical relation extraction.

    Science.gov (United States)

    Panyam, Nagesh C; Verspoor, Karin; Cohn, Trevor; Ramamohanarao, Kotagiri

    2018-01-30

    Relation extraction from biomedical publications is an important task in the area of semantic mining of text. Kernel methods for supervised relation extraction are often preferred over manual feature engineering methods, when classifying highly ordered structures such as trees and graphs obtained from syntactic parsing of a sentence. Tree kernels such as the Subset Tree Kernel and Partial Tree Kernel have been shown to be effective for classifying constituency parse trees and basic dependency parse graphs of a sentence. Graph kernels such as the All Path Graph kernel (APG) and Approximate Subgraph Matching (ASM) kernel have been shown to be suitable for classifying general graphs with cycles, such as the enhanced dependency parse graph of a sentence. In this work, we present a high performance Chemical-Induced Disease (CID) relation extraction system. We present a comparative study of kernel methods for the CID task and also extend our study to the Protein-Protein Interaction (PPI) extraction task, an important biomedical relation extraction task. We discuss novel modifications to the ASM kernel to boost its performance and a method to apply graph kernels for extracting relations expressed in multiple sentences. Our system for CID relation extraction attains an F-score of 60%, without using external knowledge sources or task specific heuristic or rules. In comparison, the state of the art Chemical-Disease Relation Extraction system achieves an F-score of 56% using an ensemble of multiple machine learning methods, which is then boosted to 61% with a rule based system employing task specific post processing rules. For the CID task, graph kernels outperform tree kernels substantially, and the best performance is obtained with APG kernel that attains an F-score of 60%, followed by the ASM kernel at 57%. The performance difference between the ASM and APG kernels for CID sentence level relation extraction is not significant. In our evaluation of ASM for the PPI task, ASM

  3. GRIM : Leveraging GPUs for Kernel integrity monitoring

    NARCIS (Netherlands)

    Koromilas, Lazaros; Vasiliadis, Giorgos; Athanasopoulos, Ilias; Ioannidis, Sotiris

    2016-01-01

    Kernel rootkits can exploit an operating system and enable future accessibility and control, despite all recent advances in software protection. A promising defense mechanism against rootkits is Kernel Integrity Monitor (KIM) systems, which inspect the kernel text and data to discover any malicious

  4. Inverse heat transfer analysis of a functionally graded fin to estimate time-dependent base heat flux and temperature distributions

    International Nuclear Information System (INIS)

    Lee, Haw-Long; Chang, Win-Jin; Chen, Wen-Lih; Yang, Yu-Ching

    2012-01-01

    Highlights: ► Time-dependent base heat flux of a functionally graded fin is inversely estimated. ► An inverse algorithm based on the conjugate gradient method and the discrepancy principle is applied. ► The distributions of temperature in the fin are determined as well. ► The influence of measurement error and measurement location upon the precision of the estimated results is also investigated. - Abstract: In this study, an inverse algorithm based on the conjugate gradient method and the discrepancy principle is applied to estimate the unknown time-dependent base heat flux of a functionally graded fin from the knowledge of temperature measurements taken within the fin. Subsequently, the distributions of temperature in the fin can be determined as well. It is assumed that no prior information is available on the functional form of the unknown base heat flux; hence the procedure is classified as the function estimation in inverse calculation. The temperature data obtained from the direct problem are used to simulate the temperature measurements. The influence of measurement errors and measurement location upon the precision of the estimated results is also investigated. Results show that an excellent estimation on the time-dependent base heat flux and temperature distributions can be obtained for the test case considered in this study.

  5. 7 CFR 51.2296 - Three-fourths half kernel.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 2 2010-01-01 2010-01-01 false Three-fourths half kernel. 51.2296 Section 51.2296 Agriculture Regulations of the Department of Agriculture AGRICULTURAL MARKETING SERVICE (Standards...-fourths half kernel. Three-fourths half kernel means a portion of a half of a kernel which has more than...

  6. Music recommendation according to human motion based on kernel CCA-based relationship

    Science.gov (United States)

    Ohkushi, Hiroyuki; Ogawa, Takahiro; Haseyama, Miki

    2011-12-01

    In this article, a method for recommendation of music pieces according to human motions based on their kernel canonical correlation analysis (CCA)-based relationship is proposed. In order to perform the recommendation between different types of multimedia data, i.e., recommendation of music pieces from human motions, the proposed method tries to estimate their relationship. Specifically, the correlation based on kernel CCA is calculated as the relationship in our method. Since human motions and music pieces have various time lengths, it is necessary to calculate the correlation between time series having different lengths. Therefore, new kernel functions for human motions and music pieces, which can provide similarities between data that have different time lengths, are introduced into the calculation of the kernel CCA-based correlation. This approach effectively provides a solution to the conventional problem of not being able to calculate the correlation from multimedia data that have various time lengths. Therefore, the proposed method can perform accurate recommendation of best matched music pieces according to a target human motion from the obtained correlation. Experimental results are shown to verify the performance of the proposed method.

  7. Simple future weather files for estimating heating and cooling demand

    DEFF Research Database (Denmark)

    Cox, Rimante Andrasiunaite; Drews, Martin; Rode, Carsten

    2015-01-01

    useful estimates of future energy demand of a building. Experimental results based on both the degree-day method and dynamic simulations suggest that this is indeed the case. Specifically, heating demand estimates were found to be within a few per cent of one another, while estimates of cooling demand...... were slightly more varied. This variation was primarily due to the very few hours of cooling that were required in the region examined. Errors were found to be most likely when the air temperatures were close to the heating or cooling balance points, where the energy demand was modest and even...... relatively large errors might thus result in only modest absolute errors in energy demand....

  8. A kernel version of multivariate alteration detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

    2013-01-01

    Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations.......Based on the established methods kernel canonical correlation analysis and multivariate alteration detection we introduce a kernel version of multivariate alteration detection. A case study with SPOT HRV data shows that the kMAD variates focus on extreme change observations....

  9. Estimating Antarctic Geothermal Heat Flux using Gravity Inversion

    Science.gov (United States)

    Vaughan, Alan P. M.; Kusznir, Nick J.; Ferraccioli, Fausto; Leat, Phil T.; Jordan, Tom A. R. M.; Purucker, Michael E.; Golynsky, A. V.; Sasha Rogozhina, Irina

    2013-04-01

    Geothermal heat flux (GHF) in Antarctica is very poorly known. We have determined (Vaughan et al. 2012) top basement heat-flow for Antarctica and adjacent rifted continental margins using gravity inversion mapping of crustal thickness and continental lithosphere thinning (Chappell & Kusznir 2008). Continental lithosphere thinning and post-breakup residual thicknesses of continental crust determined from gravity inversion have been used to predict the preservation of continental crustal radiogenic heat productivity and the transient lithosphere heat-flow contribution within thermally equilibrating rifted continental and oceanic lithosphere. The sensitivity of present-day Antarctic top basement heat-flow to initial continental radiogenic heat productivity, continental rift and margin breakup age has been examined. Knowing GHF distribution for East Antarctica and the Gamburtsev Subglacial Mountains (GSM) region in particular is critical because: 1) The GSM likely acted as key nucleation point for the East Antarctic Ice Sheet (EAIS); 2) the region may contain the oldest ice of the EAIS - a prime target for future ice core drilling; 3) GHF is important to understand proposed ice accretion at the base of the EAIS in the GSM and its links to sub-ice hydrology (Bell et al. 2011). An integrated multi-dataset-based GHF model for East Antarctica is planned that will resolve the wide range of estimates previously published using single datasets. The new map and existing GHF distribution estimates available for Antarctica will be evaluated using direct ice temperature measurements obtained from deep ice cores, estimates of GHF derived from subglacial lakes, and a thermodynamic ice-sheet model of the Antarctic Ice Sheet driven by past climate reconstructions and each of analysed heat flow maps, as has recently been done for the Greenland region (Rogozhina et al. 2012). References Bell, R.E., Ferraccioli, F., Creyts, T.T., Braaten, D., Corr, H., Das, I., Damaske, D., Frearson, N

  10. Implementing Kernel Methods Incrementally by Incremental Nonlinear Projection Trick.

    Science.gov (United States)

    Kwak, Nojun

    2016-05-20

    Recently, the nonlinear projection trick (NPT) was introduced enabling direct computation of coordinates of samples in a reproducing kernel Hilbert space. With NPT, any machine learning algorithm can be extended to a kernel version without relying on the so called kernel trick. However, NPT is inherently difficult to be implemented incrementally because an ever increasing kernel matrix should be treated as additional training samples are introduced. In this paper, an incremental version of the NPT (INPT) is proposed based on the observation that the centerization step in NPT is unnecessary. Because the proposed INPT does not change the coordinates of the old data, the coordinates obtained by INPT can directly be used in any incremental methods to implement a kernel version of the incremental methods. The effectiveness of the INPT is shown by applying it to implement incremental versions of kernel methods such as, kernel singular value decomposition, kernel principal component analysis, and kernel discriminant analysis which are utilized for problems of kernel matrix reconstruction, letter classification, and face image retrieval, respectively.

  11. a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

    Science.gov (United States)

    Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

    2014-10-01

    In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

  12. Robust anti-synchronization of uncertain chaotic systems based on multiple-kernel least squares support vector machine modeling

    International Nuclear Information System (INIS)

    Chen Qiang; Ren Xuemei; Na Jing

    2011-01-01

    Highlights: Model uncertainty of the system is approximated by multiple-kernel LSSVM. Approximation errors and disturbances are compensated in the controller design. Asymptotical anti-synchronization is achieved with model uncertainty and disturbances. Abstract: In this paper, we propose a robust anti-synchronization scheme based on multiple-kernel least squares support vector machine (MK-LSSVM) modeling for two uncertain chaotic systems. The multiple-kernel regression, which is a linear combination of basic kernels, is designed to approximate system uncertainties by constructing a multiple-kernel Lagrangian function and computing the corresponding regression parameters. Then, a robust feedback control based on MK-LSSVM modeling is presented and an improved update law is employed to estimate the unknown bound of the approximation error. The proposed control scheme can guarantee the asymptotic convergence of the anti-synchronization errors in the presence of system uncertainties and external disturbances. Numerical examples are provided to show the effectiveness of the proposed method.

  13. Uranium kernel formation via internal gelation

    International Nuclear Information System (INIS)

    Hunt, R.D.; Collins, J.L.

    2004-01-01

    In the 1970s and 1980s, U.S. Department of Energy (DOE) conducted numerous studies on the fabrication of nuclear fuel particles using the internal gelation process. These amorphous kernels were prone to flaking or breaking when gases tried to escape from the kernels during calcination and sintering. These earlier kernels would not meet today's proposed specifications for reactor fuel. In the interim, the internal gelation process has been used to create hydrous metal oxide microspheres for the treatment of nuclear waste. With the renewed interest in advanced nuclear fuel by the DOE, the lessons learned from the nuclear waste studies were recently applied to the fabrication of uranium kernels, which will become tri-isotropic (TRISO) fuel particles. These process improvements included equipment modifications, small changes to the feed formulations, and a new temperature profile for the calcination and sintering. The modifications to the laboratory-scale equipment and its operation as well as small changes to the feed composition increased the product yield from 60% to 80%-99%. The new kernels were substantially less glassy, and no evidence of flaking was found. Finally, key process parameters were identified, and their effects on the uranium microspheres and kernels are discussed. (orig.)

  14. Kernel learning at the first level of inference.

    Science.gov (United States)

    Cawley, Gavin C; Talbot, Nicola L C

    2014-05-01

    Kernel learning methods, whether Bayesian or frequentist, typically involve multiple levels of inference, with the coefficients of the kernel expansion being determined at the first level and the kernel and regularisation parameters carefully tuned at the second level, a process known as model selection. Model selection for kernel machines is commonly performed via optimisation of a suitable model selection criterion, often based on cross-validation or theoretical performance bounds. However, if there are a large number of kernel parameters, as for instance in the case of automatic relevance determination (ARD), there is a substantial risk of over-fitting the model selection criterion, resulting in poor generalisation performance. In this paper we investigate the possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation. The kernel parameters and the coefficients of the kernel expansion are jointly optimised at the first level of inference, minimising a training criterion with an additional regularisation term acting on the kernel parameters. The key advantage of this approach is that the values of only two regularisation parameters need be determined in model selection, substantially alleviating the problem of over-fitting the model selection criterion. The benefits of this approach are demonstrated using a suite of synthetic and real-world binary classification benchmark problems, where kernel learning at the first level of inference is shown to be statistically superior to the conventional approach, improves on our previous work (Cawley and Talbot, 2007) and is competitive with Multiple Kernel Learning approaches, but with reduced computational expense. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Anthropogenic Heat Flux Estimation from Space: Results of the second phase of the URBANFLUXES Project

    Science.gov (United States)

    Chrysoulakis, Nektarios; Marconcini, Mattia; Gastellu-Etchegorry, Jean-Philippe; Grimmond, Sue; Feigenwinter, Christian; Lindberg, Fredrik; Del Frate, Fabio; Klostermann, Judith; Mitraka, Zina; Esch, Thomas; Landier, Lucas; Gabey, Andy; Parlow, Eberhard; Olofson, Frans

    2017-04-01

    The H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the Urban Energy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban heat island and consequently on energy consumption in cities. In URBANFLUXES, the anthropogenic heat flux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the net change in heat storage and the turbulent sensible and latent heat fluxes are independently estimated from Earth Observation (EO), whereas the advection term is included in the error of the anthropogenic heat flux estimation from the UEB closure. The Discrete Anisotropic Radiative Transfer (DART) model is employed to improve the estimation of the net all-wave radiation balance, whereas the Element Surface Temperature Method (ESTM), adjusted to satellite observations is used to improve the estimation the estimation of the net change in heat storage. Furthermore the estimation of the turbulent sensible and latent heat fluxes is based on the Aerodynamic Resistance Method (ARM). Based on these outcomes, QF is estimated by regressing the sum of the turbulent heat fluxes versus the available energy. In-situ flux measurements are used to evaluate URBANFLUXES outcomes, whereas uncertainties are specified and analyzed. URBANFLUXES is expected to prepare the ground for further innovative exploitation of EO in scientific activities (climate variability studies at local and regional scales) and future and emerging applications (sustainable urban planning, mitigation technologies) to benefit climate change mitigation/adaptation. This study presents the results of the second phase of the project and detailed information on URBANFLUXES is available at: http://urbanfluxes.eu

  16. Quantum tomography, phase-space observables and generalized Markov kernels

    International Nuclear Information System (INIS)

    Pellonpaeae, Juha-Pekka

    2009-01-01

    We construct a generalized Markov kernel which transforms the observable associated with the homodyne tomography into a covariant phase-space observable with a regular kernel state. Illustrative examples are given in the cases of a 'Schroedinger cat' kernel state and the Cahill-Glauber s-parametrized distributions. Also we consider an example of a kernel state when the generalized Markov kernel cannot be constructed.

  17. Single pass kernel k-means clustering method

    Indian Academy of Sciences (India)

    paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.

  18. Moisture Sorption Isotherms and Properties of Sorbed Water of Neem ( Azadirichta indica A. Juss) Kernels

    Science.gov (United States)

    Ngono Mbarga, M. C.; Bup Nde, D.; Mohagir, A.; Kapseu, C.; Elambo Nkeng, G.

    2017-01-01

    A neem tree growing abundantly in India as well as in some regions of Asia and Africa gives fruits whose kernels have about 40-50% oil. This oil has high therapeutic and cosmetic qualities and is recently projected to be an important raw material for the production of biodiesel. Its seed is harvested at high moisture contents, which leads tohigh post-harvest losses. In the paper, the sorption isotherms are determined by the static gravimetric method at 40, 50, and 60°C to establish a database useful in defining drying and storage conditions of neem kernels. Five different equations are validated for modeling the sorption isotherms of neem kernels. The properties of sorbed water, such as the monolayer moisture content, surface area of adsorbent, number of adsorbed monolayers, and the percent of bound water are also defined. The critical moisture content necessary for the safe storage of dried neem kernels is shown to range from 5 to 10% dry basis, which can be obtained at a relative humidity less than 65%. The isosteric heats of sorption at 5% moisture content are 7.40 and 22.5 kJ/kg for the adsorption and desorption processes, respectively. This work is the first, to the best of our knowledge, to give the important parameters necessary for drying and storage of neem kernels, a potential raw material for the production of oil to be used in pharmaceutics, cosmetics, and biodiesel manufacturing.

  19. Relationship between attenuation coefficients and dose-spread kernels

    International Nuclear Information System (INIS)

    Boyer, A.L.

    1988-01-01

    Dose-spread kernels can be used to calculate the dose distribution in a photon beam by convolving the kernel with the primary fluence distribution. The theoretical relationships between various types and components of dose-spread kernels relative to photon attenuation coefficients are explored. These relations can be valuable as checks on the conservation of energy by dose-spread kernels calculated by analytic or Monte Carlo methods

  20. Mixture Density Mercer Kernels: A Method to Learn Kernels

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian...

  1. A combined ANN-GA and experimental based technique for the estimation of the unknown heat flux for a conjugate heat transfer problem

    Science.gov (United States)

    M K, Harsha Kumar; P S, Vishweshwara; N, Gnanasekaran; C, Balaji

    2018-05-01

    The major objectives in the design of thermal systems are obtaining the information about thermophysical, transport and boundary properties. The main purpose of this paper is to estimate the unknown heat flux at the surface of a solid body. A constant area mild steel fin is considered and the base is subjected to constant heat flux. During heating, natural convection heat transfer occurs from the fin to ambient. The direct solution, which is the forward problem, is developed as a conjugate heat transfer problem from the fin and the steady state temperature distribution is recorded for any assumed heat flux. In order to model the natural convection heat transfer from the fin, an extended domain is created near the fin geometry and air is specified as a fluid medium and Navier Stokes equation is solved by incorporating the Boussinesq approximation. The computational time involved in executing the forward model is then reduced by developing a neural network (NN) between heat flux values and temperatures based on back propagation algorithm. The conjugate heat transfer NN model is now coupled with Genetic algorithm (GA) for the solution of the inverse problem. Initially, GA is applied to the pure surrogate data, the results are then used as input to the Levenberg- Marquardt method and such hybridization is proven to result in accurate estimation of the unknown heat flux. The hybrid method is then applied for the experimental temperature to estimate the unknown heat flux. A satisfactory agreement between the estimated and actual heat flux is achieved by incorporating the hybrid method.

  2. A comprehensive benchmark of kernel methods to extract protein-protein interactions from literature.

    Directory of Open Access Journals (Sweden)

    Domonkos Tikk

    Full Text Available The most important way of conveying new findings in biomedical research is scientific publication. Extraction of protein-protein interactions (PPIs reported in scientific publications is one of the core topics of text mining in the life sciences. Recently, a new class of such methods has been proposed - convolution kernels that identify PPIs using deep parses of sentences. However, comparing published results of different PPI extraction methods is impossible due to the use of different evaluation corpora, different evaluation metrics, different tuning procedures, etc. In this paper, we study whether the reported performance metrics are robust across different corpora and learning settings and whether the use of deep parsing actually leads to an increase in extraction quality. Our ultimate goal is to identify the one method that performs best in real-life scenarios, where information extraction is performed on unseen text and not on specifically prepared evaluation data. We performed a comprehensive benchmarking of nine different methods for PPI extraction that use convolution kernels on rich linguistic information. Methods were evaluated on five different public corpora using cross-validation, cross-learning, and cross-corpus evaluation. Our study confirms that kernels using dependency trees generally outperform kernels based on syntax trees. However, our study also shows that only the best kernel methods can compete with a simple rule-based approach when the evaluation prevents information leakage between training and test corpora. Our results further reveal that the F-score of many approaches drops significantly if no corpus-specific parameter optimization is applied and that methods reaching a good AUC score often perform much worse in terms of F-score. We conclude that for most kernels no sensible estimation of PPI extraction performance on new text is possible, given the current heterogeneity in evaluation data. Nevertheless, our study

  3. Inverse Estimation of Heat Flux and Temperature Distribution in 3D Finite Domain

    International Nuclear Information System (INIS)

    Muhammad, Nauman Malik

    2009-02-01

    Inverse heat conduction problems occur in many theoretical and practical applications where it is difficult or practically impossible to measure the input heat flux and the temperature of the layer conducting the heat flux to the body. Thus it becomes imperative to devise some means to cater for such a problem and estimate the heat flux inversely. Adaptive State Estimator is one such technique which works by incorporating the semi-Markovian concept into a Bayesian estimation technique thereby developing an inverse input and state estimator consisting of a bank of parallel adaptively weighted Kalman filters. The problem presented in this study deals with a three dimensional system of a cube with one end conducting heat flux and all the other sides are insulated while the temperatures are measured on the accessible faces of the cube. The measurements taken on these accessible faces are fed into the estimation algorithm and the input heat flux and the temperature distribution at each point in the system is calculated. A variety of input heat flux scenarios have been examined to underwrite the robustness of the estimation algorithm and hence insure its usability in practical applications. These include sinusoidal input flux, a combination of rectangular, linearly changing and sinusoidal input flux and finally a step changing input flux. The estimator's performance limitations have been examined in these input set-ups and error associated with each set-up is compared to conclude the realistic application of the estimation algorithm in such scenarios. Different sensor arrangements, that is different sensor numbers and their locations are also examined to impress upon the importance of number of measurements and their location i.e. close or farther from the input area. Since practically it is both economically and physically tedious to install more number of measurement sensors, hence optimized number and location is very important to determine for making the study more

  4. Methodology for estimation of potential for solar water heating in a target area

    International Nuclear Information System (INIS)

    Pillai, Indu R.; Banerjee, Rangan

    2007-01-01

    Proper estimation of potential of any renewable energy technology is essential for planning and promotion of the technology. The methods reported in literature for estimation of potential of solar water heating in a target area are aggregate in nature. A methodology for potential estimation (technical, economic and market potential) of solar water heating in a target area is proposed in this paper. This methodology links the micro-level factors and macro-level market effects affecting the diffusion or adoption of solar water heating systems. Different sectors with end uses of low temperature hot water are considered for potential estimation. Potential is estimated at each end use point by simulation using TRNSYS taking micro-level factors. The methodology is illustrated for a synthetic area in India with an area of 2 sq. km and population of 10,000. The end use sectors considered are residential, hospitals, nursing homes and hotels. The estimated technical potential and market potential are 1700 m 2 and 350 m 2 of collector area, respectively. The annual energy savings for the technical potential in the area is estimated as 110 kW h/capita and 0.55 million-kW h/sq. km. area, with an annual average peak saving of 1 MW. The annual savings is 650-kW h per m 2 of collector area and accounts for approximately 3% of the total electricity consumption of the target area. Some of the salient features of the model are the factors considered for potential estimation; estimation of electrical usage pattern for typical day, amount of electricity savings and savings during the peak load. The framework is general and enables accurate estimation of potential of solar water heating for a city, block. Energy planners and policy makers can use this framework for tracking and promotion of diffusion of solar water heating systems. (author)

  5. Integral equations with contrasting kernels

    Directory of Open Access Journals (Sweden)

    Theodore Burton

    2008-01-01

    Full Text Available In this paper we study integral equations of the form $x(t=a(t-\\int^t_0 C(t,sx(sds$ with sharply contrasting kernels typified by $C^*(t,s=\\ln (e+(t-s$ and $D^*(t,s=[1+(t-s]^{-1}$. The kernel assigns a weight to $x(s$ and these kernels have exactly opposite effects of weighting. Each type is well represented in the literature. Our first project is to show that for $a\\in L^2[0,\\infty$, then solutions are largely indistinguishable regardless of which kernel is used. This is a surprise and it leads us to study the essential differences. In fact, those differences become large as the magnitude of $a(t$ increases. The form of the kernel alone projects necessary conditions concerning the magnitude of $a(t$ which could result in bounded solutions. Thus, the next project is to determine how close we can come to proving that the necessary conditions are also sufficient. The third project is to show that solutions will be bounded for given conditions on $C$ regardless of whether $a$ is chosen large or small; this is important in real-world problems since we would like to have $a(t$ as the sum of a bounded, but badly behaved function, and a large well behaved function.

  6. Kernel methods in orthogonalization of multi- and hypervariate data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2009-01-01

    A kernel version of maximum autocorrelation factor (MAF) analysis is described very briefly and applied to change detection in remotely sensed hyperspectral image (HyMap) data. The kernel version is based on a dual formulation also termed Q-mode analysis in which the data enter into the analysis...... via inner products in the Gram matrix only. In the kernel version the inner products are replaced by inner products between nonlinear mappings into higher dimensional feature space of the original data. Via kernel substitution also known as the kernel trick these inner products between the mappings...... are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MAF analysis handle nonlinearities by implicitly transforming data into high (even infinite...

  7. Kernel based subspace projection of near infrared hyperspectral images of maize kernels

    DEFF Research Database (Denmark)

    Larsen, Rasmus; Arngren, Morten; Hansen, Per Waaben

    2009-01-01

    In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods ......- tor transform outperform the linear methods as well as kernel principal components in producing interesting projections of the data.......In this paper we present an exploratory analysis of hyper- spectral 900-1700 nm images of maize kernels. The imaging device is a line scanning hyper spectral camera using a broadband NIR illumi- nation. In order to explore the hyperspectral data we compare a series of subspace projection methods...... including principal component analysis and maximum autocorrelation factor analysis. The latter utilizes the fact that interesting phenomena in images exhibit spatial autocorrelation. However, linear projections often fail to grasp the underlying variability on the data. Therefore we propose to use so...

  8. Kernel-density estimation and approximate Bayesian computation for flexible epidemiological model fitting in Python.

    Science.gov (United States)

    Irvine, Michael A; Hollingsworth, T Déirdre

    2018-05-26

    Fitting complex models to epidemiological data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, the complex models may take a long time to run, and it can be difficult to fully capture the heterogeneity in the data. We develop an adaptive approximate Bayesian computation scheme to fit a variety of epidemiologically relevant data with minimal hyper-parameter tuning by using an adaptive tolerance scheme. We implement a novel kernel density estimation scheme to capture both dispersed and multi-dimensional data, and directly compare this technique to standard Bayesian approaches. We then apply the procedure to a complex individual-based simulation of lymphatic filariasis, a human parasitic disease. The procedure and examples are released alongside this article as an open access library, with examples to aid researchers to rapidly fit models to data. This demonstrates that an adaptive ABC scheme with a general summary and distance metric is capable of performing model fitting for a variety of epidemiological data. It also does not require significant theoretical background to use and can be made accessible to the diverse epidemiological research community. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

  9. Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

    Science.gov (United States)

    2016-01-05

    SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

  10. The Classification of Diabetes Mellitus Using Kernel k-means

    Science.gov (United States)

    Alamsyah, M.; Nafisah, Z.; Prayitno, E.; Afida, A. M.; Imah, E. M.

    2018-01-01

    Diabetes Mellitus is a metabolic disorder which is characterized by chronicle hypertensive glucose. Automatics detection of diabetes mellitus is still challenging. This study detected diabetes mellitus by using kernel k-Means algorithm. Kernel k-means is an algorithm which was developed from k-means algorithm. Kernel k-means used kernel learning that is able to handle non linear separable data; where it differs with a common k-means. The performance of kernel k-means in detecting diabetes mellitus is also compared with SOM algorithms. The experiment result shows that kernel k-means has good performance and a way much better than SOM.

  11. Evaluating the Application of Tissue-Specific Dose Kernels Instead of Water Dose Kernels in Internal Dosimetry : A Monte Carlo Study

    NARCIS (Netherlands)

    Moghadam, Maryam Khazaee; Asl, Alireza Kamali; Geramifar, Parham; Zaidi, Habib

    2016-01-01

    Purpose: The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration. Materials and Methods: Tissue-specific dose point kernels (DPKs) and

  12. Deep-Burn MHR Neutronic Analysis with a SiC-Gettered TRU Kernel

    International Nuclear Information System (INIS)

    Jo, Chang Keun; Noh, Jae Man; Kim, Yong Hee; Venneric, F.

    2010-01-01

    This paper is focused on the nuclear core design of a DB-MHR (Deep Burn-Modular Helium Reactor) core loaded with a SiC-gettered TRU fuel. The SiC oxygen getter is added to reduce the CO pressure in the buffer zone of TRISO. In the paper, the cycle length, reactivity swing, discharged burnup, and the burning rate of plutonium were calculated for the DB-MHR. Also, impacts of uranium addition to the TRU kernel were investigated. Recently, the decay heat of TRU fueled DB core was found to be highly dependent on the TRU loading: the higher the loading, the higher the decay heat. The high decay heat of TRU fuel may lead to unacceptably high peak fuel temperature during an LPCC (Low Pressure Conduction Cooling) accident. Thus, we tried to minimize the decay heat of the core for a minimal peak fuel temperature during LPCC

  13. Parsimonious Wavelet Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wang Qin

    2015-11-01

    Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

  14. Estimating Mutual Information by Local Gaussian Approximation

    Science.gov (United States)

    2015-07-13

    proposed a variety of methods to overcome the bias, such as the reflection method (Schuster, 1985), ( Silverman , 1986); the boundary kernel method...Stephen Marron and David Ruppert. Transformations to reduce boundary bias in kernel density estimation. Journal of the Royal Statistical Society. Series B...estimation with applications to machine learning on distributions. In Proceedings of Uncertainty in Artificial In- telligence (UAI), 2011. David N Reshef

  15. Designing realised kernels to measure the ex-post variation of equity prices in the presence of noise

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Hansen, Peter Reinhard; Lunde, Asger

    This paper shows how to use realised kernels to carry out efficient feasible inference on the ex-post variation of underlying equity prices in the presence of simple models of market frictions. The issue is subtle with only estimators which have symmetric weights delivering consistent estimators...... with mixed Gaussian limit theorems. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which is close to that of the maximum likelihood estimator in the parametric version of this problem. Realised kernels can also be selected to (i) be analysed...... using endogenously spaced data such as that in databases on transactions, (ii) allow for market frictions which are endogenous, (iii) allow for temporally dependent noise. The finite sample performance of our estimators is studied using simulation, while empirical work illustrates their use in practice....

  16. Condition monitoring of steam generator by estimating the overall heat transfer coefficient

    International Nuclear Information System (INIS)

    Furusawa, Hiroaki; Gofuku, Akio

    2013-01-01

    This study develops a technique for monitoring in on-line the state of the steam generator of the fast-breeder reactor (FBR) “Monju”. Because the FBR uses liquid sodium as coolant, it is necessary to handle liquid sodium with caution due to its chemical characteristics. The steam generator generates steam by the heat of secondary sodium coolant. The sodium-water reaction may happen if a pinhole or crack occurs at the thin metal tube wall that separates the secondary sodium coolant and water/steam. Therefore, it is very important to detect an anomaly of the wall of heat transfer tubes at an early stage. This study aims at developing an on-line condition monitoring technique of the steam generator by estimating overall heat transfer coefficient from process signals. This paper describes simplified mathematical models of superheater and evaporator to estimate the overall heat transfer coefficient and a technique to diagnose the state of the steam generator. The applicability of the technique is confirmed by several estimations using simulated process signals with artificial noises. The results of the estimations show that the developed technique can detect the occurrence of an anomaly. (author)

  17. Difference between standard and quasi-conformal BFKL kernels

    International Nuclear Information System (INIS)

    Fadin, V.S.; Fiore, R.; Papa, A.

    2012-01-01

    As it was recently shown, the colour singlet BFKL kernel, taken in Möbius representation in the space of impact parameters, can be written in quasi-conformal shape, which is unbelievably simple compared with the conventional form of the BFKL kernel in momentum space. It was also proved that the total kernel is completely defined by its Möbius representation. In this paper we calculated the difference between standard and quasi-conformal BFKL kernels in momentum space and discovered that it is rather simple. Therefore we come to the conclusion that the simplicity of the quasi-conformal kernel is caused mainly by using the impact parameter space.

  18. A priori estimates of global solutions of superlinear parabolic systems

    Directory of Open Access Journals (Sweden)

    Julius Pacuta

    2016-04-01

    Full Text Available We consider the parabolic system $ u_{t}-\\Delta u = u^{r}v^{p}$, $v_{t}-\\Delta v = u^{q}v^{s}$ in $\\Omega\\times(0,\\infty$, complemented by the homogeneous Dirichlet boundary conditions and the initial conditions $(u,v(\\cdot,0 = (u_{0},v_{0}$ in $\\Omega$, where $\\Omega $ is a smooth bounded domain in $ \\mathbb{R}^{N} $ and $ u_{0},v_{0}\\in L^{\\infty}(\\Omega$ are nonnegative functions. We find conditions on $ p,q,r,s $ guaranteeing a priori estimates of nonnegative classical global solutions. More precisely every such solution is bounded by a constant depending on suitable norm of the initial data. Our proofs are based on bootstrap in weighted Lebesgue spaces, universal estimates of auxiliary functions and estimates of the Dirichlet heat kernel.

  19. Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt Seed Viability with Multivariate Data Analysis

    Directory of Open Access Journals (Sweden)

    Guangjun Qiu

    2018-03-01

    Full Text Available The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.

  20. Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis.

    Science.gov (United States)

    Qiu, Guangjun; Lü, Enli; Lu, Huazhong; Xu, Sai; Zeng, Fanguo; Shui, Qin

    2018-03-28

    The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.

  1. A laser optical method for detecting corn kernel defects

    Energy Technology Data Exchange (ETDEWEB)

    Gunasekaran, S.; Paulsen, M. R.; Shove, G. C.

    1984-01-01

    An opto-electronic instrument was developed to examine individual corn kernels and detect various kernel defects according to reflectance differences. A low power helium-neon (He-Ne) laser (632.8 nm, red light) was used as the light source in the instrument. Reflectance from good and defective parts of corn kernel surfaces differed by approximately 40%. Broken, chipped, and starch-cracked kernels were detected with nearly 100% accuracy; while surface-split kernels were detected with about 80% accuracy. (author)

  2. Kernel maximum autocorrelation factor and minimum noise fraction transformations

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2010-01-01

    in hyperspectral HyMap scanner data covering a small agricultural area, and 3) maize kernel inspection. In the cases shown, the kernel MAF/MNF transformation performs better than its linear counterpart as well as linear and kernel PCA. The leading kernel MAF/MNF variates seem to possess the ability to adapt...

  3. Identification of Fusarium damaged wheat kernels using image analysis

    Directory of Open Access Journals (Sweden)

    Ondřej Jirsa

    2011-01-01

    Full Text Available Visual evaluation of kernels damaged by Fusarium spp. pathogens is labour intensive and due to a subjective approach, it can lead to inconsistencies. Digital imaging technology combined with appropriate statistical methods can provide much faster and more accurate evaluation of the visually scabby kernels proportion. The aim of the present study was to develop a discrimination model to identify wheat kernels infected by Fusarium spp. using digital image analysis and statistical methods. Winter wheat kernels from field experiments were evaluated visually as healthy or damaged. Deoxynivalenol (DON content was determined in individual kernels using an ELISA method. Images of individual kernels were produced using a digital camera on dark background. Colour and shape descriptors were obtained by image analysis from the area representing the kernel. Healthy and damaged kernels differed significantly in DON content and kernel weight. Various combinations of individual shape and colour descriptors were examined during the development of the model using linear discriminant analysis. In addition to basic descriptors of the RGB colour model (red, green, blue, very good classification was also obtained using hue from the HSL colour model (hue, saturation, luminance. The accuracy of classification using the developed discrimination model based on RGBH descriptors was 85 %. The shape descriptors themselves were not specific enough to distinguish individual kernels.

  4. Digital signal processing with kernel methods

    CERN Document Server

    Rojo-Alvarez, José Luis; Muñoz-Marí, Jordi; Camps-Valls, Gustavo

    2018-01-01

    A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necess...

  5. Windows Vista Kernel-Mode: Functions, Security Enhancements and Flaws

    Directory of Open Access Journals (Sweden)

    Mohammed D. ABDULMALIK

    2008-06-01

    Full Text Available Microsoft has made substantial enhancements to the kernel of the Microsoft Windows Vista operating system. Kernel improvements are significant because the kernel provides low-level operating system functions, including thread scheduling, interrupt and exception dispatching, multiprocessor synchronization, and a set of routines and basic objects.This paper describes some of the kernel security enhancements for 64-bit edition of Windows Vista. We also point out some weakness areas (flaws that can be attacked by malicious leading to compromising the kernel.

  6. Methodology for estimation of time-dependent surface heat flux due to cryogen spray cooling.

    Science.gov (United States)

    Tunnell, James W; Torres, Jorge H; Anvari, Bahman

    2002-01-01

    Cryogen spray cooling (CSC) is an effective technique to protect the epidermis during cutaneous laser therapies. Spraying a cryogen onto the skin surface creates a time-varying heat flux, effectively cooling the skin during and following the cryogen spurt. In previous studies mathematical models were developed to predict the human skin temperature profiles during the cryogen spraying time. However, no studies have accounted for the additional cooling due to residual cryogen left on the skin surface following the spurt termination. We formulate and solve an inverse heat conduction (IHC) problem to predict the time-varying surface heat flux both during and following a cryogen spurt. The IHC formulation uses measured temperature profiles from within a medium to estimate the surface heat flux. We implement a one-dimensional sequential function specification method (SFSM) to estimate the surface heat flux from internal temperatures measured within an in vitro model in response to a cryogen spurt. Solution accuracy and experimental errors are examined using simulated temperature data. Heat flux following spurt termination appears substantial; however, it is less than that during the spraying time. The estimated time-varying heat flux can subsequently be used in forward heat conduction models to estimate temperature profiles in skin during and following a cryogen spurt and predict appropriate timing for onset of the laser pulse.

  7. Generalization Performance of Regularized Ranking With Multiscale Kernels.

    Science.gov (United States)

    Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin

    2016-05-01

    The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.

  8. Multineuron spike train analysis with R-convolution linear combination kernel.

    Science.gov (United States)

    Tezuka, Taro

    2018-06-01

    A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. An analysis of 1-D smoothed particle hydrodynamics kernels

    International Nuclear Information System (INIS)

    Fulk, D.A.; Quinn, D.W.

    1996-01-01

    In this paper, the smoothed particle hydrodynamics (SPH) kernel is analyzed, resulting in measures of merit for one-dimensional SPH. Various methods of obtaining an objective measure of the quality and accuracy of the SPH kernel are addressed. Since the kernel is the key element in the SPH methodology, this should be of primary concern to any user of SPH. The results of this work are two measures of merit, one for smooth data and one near shocks. The measure of merit for smooth data is shown to be quite accurate and a useful delineator of better and poorer kernels. The measure of merit for non-smooth data is not quite as accurate, but results indicate the kernel is much less important for these types of problems. In addition to the theory, 20 kernels are analyzed using the measure of merit demonstrating the general usefulness of the measure of merit and the individual kernels. In general, it was decided that bell-shaped kernels perform better than other shapes. 12 refs., 16 figs., 7 tabs

  10. Putting Priors in Mixture Density Mercer Kernels

    Science.gov (United States)

    Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

    2004-01-01

    This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

  11. Total decay heat estimates in a proto-type fast reactor

    International Nuclear Information System (INIS)

    Sridharan, M.S.

    2003-01-01

    Full text: In this paper, total decay heat values generated in a proto-type fast reactor are estimated. These values are compared with those of certain fast reactors. Simple analytical fits are also obtained for these values which can serve as a handy and convenient tool in engineering design studies. These decay heat values taken as their ratio to the nominal operating power are, in general, applicable to any typical plutonium based fast reactor and are useful inputs to the design of decay-heat removal systems

  12. NLO corrections to the Kernel of the BKP-equations

    Energy Technology Data Exchange (ETDEWEB)

    Bartels, J. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Fadin, V.S. [Budker Institute of Nuclear Physics, Novosibirsk (Russian Federation); Novosibirskij Gosudarstvennyj Univ., Novosibirsk (Russian Federation); Lipatov, L.N. [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Petersburg Nuclear Physics Institute, Gatchina, St. Petersburg (Russian Federation); Vacca, G.P. [INFN, Sezione di Bologna (Italy)

    2012-10-02

    We present results for the NLO kernel of the BKP equations for composite states of three reggeized gluons in the Odderon channel, both in QCD and in N=4 SYM. The NLO kernel consists of the NLO BFKL kernel in the color octet representation and the connected 3{yields}3 kernel, computed in the tree approximation.

  13. A Fast and Simple Graph Kernel for RDF

    NARCIS (Netherlands)

    de Vries, G.K.D.; de Rooij, S.

    2013-01-01

    In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster

  14. An SVM model with hybrid kernels for hydrological time series

    Science.gov (United States)

    Wang, C.; Wang, H.; Zhao, X.; Xie, Q.

    2017-12-01

    Support Vector Machine (SVM) models have been widely applied to the forecast of climate/weather and its impact on other environmental variables such as hydrologic response to climate/weather. When using SVM, the choice of the kernel function plays the key role. Conventional SVM models mostly use one single type of kernel function, e.g., radial basis kernel function. Provided that there are several featured kernel functions available, each having its own advantages and drawbacks, a combination of these kernel functions may give more flexibility and robustness to SVM approach, making it suitable for a wide range of application scenarios. This paper presents such a linear combination of radial basis kernel and polynomial kernel for the forecast of monthly flowrate in two gaging stations using SVM approach. The results indicate significant improvement in the accuracy of predicted series compared to the approach with either individual kernel function, thus demonstrating the feasibility and advantages of such hybrid kernel approach for SVM applications.

  15. Kernel based eigenvalue-decomposition methods for analysing ham

    DEFF Research Database (Denmark)

    Christiansen, Asger Nyman; Nielsen, Allan Aasbjerg; Møller, Flemming

    2010-01-01

    methods, such as PCA, MAF or MNF. We therefore investigated the applicability of kernel based versions of these transformation. This meant implementing the kernel based methods and developing new theory, since kernel based MAF and MNF is not described in the literature yet. The traditional methods only...... have two factors that are useful for segmentation and none of them can be used to segment the two types of meat. The kernel based methods have a lot of useful factors and they are able to capture the subtle differences in the images. This is illustrated in Figure 1. You can see a comparison of the most...... useful factor of PCA and kernel based PCA respectively in Figure 2. The factor of the kernel based PCA turned out to be able to segment the two types of meat and in general that factor is much more distinct, compared to the traditional factor. After the orthogonal transformation a simple thresholding...

  16. Novel applications of the temporal kernel method: Historical and future radiative forcing

    Science.gov (United States)

    Portmann, R. W.; Larson, E.; Solomon, S.; Murphy, D. M.

    2017-12-01

    We present a new estimate of the historical radiative forcing derived from the observed global mean surface temperature and a model derived kernel function. Current estimates of historical radiative forcing are usually derived from climate models. Despite large variability in these models, the multi-model mean tends to do a reasonable job of representing the Earth system and climate. One method of diagnosing the transient radiative forcing in these models requires model output of top of the atmosphere radiative imbalance and global mean temperature anomaly. It is difficult to apply this method to historical observations due to the lack of TOA radiative measurements before CERES. We apply the temporal kernel method (TKM) of calculating radiative forcing to the historical global mean temperature anomaly. This novel approach is compared against the current regression based methods using model outputs and shown to produce consistent forcing estimates giving confidence in the forcing derived from the historical temperature record. The derived TKM radiative forcing provides an estimate of the forcing time series that the average climate model needs to produce the observed temperature record. This forcing time series is found to be in good overall agreement with previous estimates but includes significant differences that will be discussed. The historical anthropogenic aerosol forcing is estimated as a residual from the TKM and found to be consistent with earlier moderate forcing estimates. In addition, this method is applied to future temperature projections to estimate the radiative forcing required to achieve those temperature goals, such as those set in the Paris agreement.

  17. Reduced multiple empirical kernel learning machine.

    Science.gov (United States)

    Wang, Zhe; Lu, MingZhe; Gao, Daqi

    2015-02-01

    Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3

  18. Kernel principal component analysis for change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Morton, J.C.

    2008-01-01

    region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

  19. Enhanced gluten properties in soft kernel durum wheat

    Science.gov (United States)

    Soft kernel durum wheat is a relatively recent development (Morris et al. 2011 Crop Sci. 51:114). The soft kernel trait exerts profound effects on kernel texture, flour milling including break flour yield, milling energy, and starch damage, and dough water absorption (DWA). With the caveat of reduce...

  20. 7 CFR 981.61 - Redetermination of kernel weight.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Redetermination of kernel weight. 981.61 Section 981... GROWN IN CALIFORNIA Order Regulating Handling Volume Regulation § 981.61 Redetermination of kernel weight. The Board, on the basis of reports by handlers, shall redetermine the kernel weight of almonds...

  1. Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan

    Directory of Open Access Journals (Sweden)

    Muhammad Aslam

    2007-07-01

    Full Text Available For the problem of estimation of Money demand model of Pakistan, money supply (M1 shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons are made on the basis standard errors of the estimated coefficients, standard error of regression, Akaike Information Criteria (AIC value, and the Durban-Watson statistic for autocorrelation. We further show that nearest neighbour regression estimator performs better when comparing with the other nonparametric kernel estimator.

  2. Estimation of low-potential heat recuperation efficiency of smoke fumes in a condensation heat utilizer under various operation conditions of a boiler and a heating system

    Science.gov (United States)

    Ionkin, I. L.; Ragutkin, A. V.; Luning, B.; Zaichenko, M. N.

    2016-06-01

    For enhancement of the natural gas utilization efficiency in boilers, condensation heat utilizers of low-potential heat, which are constructed based on a contact heat exchanger, can be applied. A schematic of the contact heat exchanger with a humidifier for preheating and humidifying of air supplied in the boiler for combustion is given. Additional low-potential heat in this scheme is utilized for heating of the return delivery water supplied from a heating system. Preheating and humidifying of air supplied for combustion make it possible to use the condensation utilizer for heating of a heat-transfer agent to temperature exceeding the dewpoint temperature of water vapors contained in combustion products. The decision to mount the condensation heat utilizer on the boiler was taken based on the preliminary estimation of the additionally obtained heat. The operation efficiency of the condensation heat utilizer is determined by its structure and operation conditions of the boiler and the heating system. The software was developed for the thermal design of the condensation heat utilizer equipped by the humidifier. Computation investigations of its operation are carried out as a function of various operation parameters of the boiler and the heating system (temperature of the return delivery water and smoke fumes, air excess, air temperature at the inlet and outlet of the condensation heat utilizer, heating and humidifying of air in the humidifier, and portion of the circulating water). The heat recuperation efficiency is estimated for various operation conditions of the boiler and the condensation heat utilizer. Recommendations on the most effective application of the condensation heat utilizer are developed.

  3. Spectral estimates of net radiation and soil heat flux

    International Nuclear Information System (INIS)

    Daughtry, C.S.T.; Kustas, W.P.; Moran, M.S.; Pinter, P.J. Jr.; Jackson, R.D.; Brown, P.W.; Nichols, W.D.; Gay, L.W.

    1990-01-01

    Conventional methods of measuring surface energy balance are point measurements and represent only a small area. Remote sensing offers a potential means of measuring outgoing fluxes over large areas at the spatial resolution of the sensor. The objective of this study was to estimate net radiation (Rn) and soil heat flux (G) using remotely sensed multispectral data acquired from an aircraft over large agricultural fields. Ground-based instruments measured Rn and G at nine locations along the flight lines. Incoming fluxes were also measured by ground-based instruments. Outgoing fluxes were estimated using remotely sensed data. Remote Rn, estimated as the algebraic sum of incoming and outgoing fluxes, slightly underestimated Rn measured by the ground-based net radiometers. The mean absolute errors for remote Rn minus measured Rn were less than 7%. Remote G, estimated as a function of a spectral vegetation index and remote Rn, slightly overestimated measured G; however, the mean absolute error for remote G was 13%. Some of the differences between measured and remote values of Rn and G are associated with differences in instrument designs and measurement techniques. The root mean square error for available energy (Rn - G) was 12%. Thus, methods using both ground-based and remotely sensed data can provide reliable estimates of the available energy which can be partitioned into sensible and latent heat under non advective conditions

  4. A Bayesian approach to estimate sensible and latent heat over vegetated land surface

    Directory of Open Access Journals (Sweden)

    C. van der Tol

    2009-06-01

    Full Text Available Sensible and latent heat fluxes are often calculated from bulk transfer equations combined with the energy balance. For spatial estimates of these fluxes, a combination of remotely sensed and standard meteorological data from weather stations is used. The success of this approach depends on the accuracy of the input data and on the accuracy of two variables in particular: aerodynamic and surface conductance. This paper presents a Bayesian approach to improve estimates of sensible and latent heat fluxes by using a priori estimates of aerodynamic and surface conductance alongside remote measurements of surface temperature. The method is validated for time series of half-hourly measurements in a fully grown maize field, a vineyard and a forest. It is shown that the Bayesian approach yields more accurate estimates of sensible and latent heat flux than traditional methods.

  5. Stable Kernel Representations as Nonlinear Left Coprime Factorizations

    NARCIS (Netherlands)

    Paice, A.D.B.; Schaft, A.J. van der

    1994-01-01

    A representation of nonlinear systems based on the idea of representing the input-output pairs of the system as elements of the kernel of a stable operator has been recently introduced. This has been denoted the kernel representation of the system. In this paper it is demonstrated that the kernel

  6. 7 CFR 981.60 - Determination of kernel weight.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Determination of kernel weight. 981.60 Section 981.60... Regulating Handling Volume Regulation § 981.60 Determination of kernel weight. (a) Almonds for which settlement is made on kernel weight. All lots of almonds, whether shelled or unshelled, for which settlement...

  7. End-use quality of soft kernel durum wheat

    Science.gov (United States)

    Kernel texture is a major determinant of end-use quality of wheat. Durum wheat has very hard kernels. We developed soft kernel durum wheat via Ph1b-mediated homoeologous recombination. The Hardness locus was transferred from Chinese Spring to Svevo durum wheat via back-crossing. ‘Soft Svevo’ had SKC...

  8. Long-distance wind-dispersal of spores in a fungal plant pathogen: estimation of anisotropic dispersal kernels from an extensive field experiment.

    Directory of Open Access Journals (Sweden)

    Adrien Rieux

    Full Text Available Given its biological significance, determining the dispersal kernel (i.e., the distribution of dispersal distances of spore-producing pathogens is essential. Here, we report two field experiments designed to measure disease gradients caused by sexually- and asexually-produced spores of the wind-dispersed banana plant fungus Mycosphaerella fijiensis. Gradients were measured during a single generation and over 272 traps installed up to 1000 m along eight directions radiating from a traceable source of inoculum composed of fungicide-resistant strains. We adjusted several kernels differing in the shape of their tail and tested for two types of anisotropy. Contrasting dispersal kernels were observed between the two types of spores. For sexual spores (ascospores, we characterized both a steep gradient in the first few metres in all directions and rare long-distance dispersal (LDD events up to 1000 m from the source in two directions. A heavy-tailed kernel best fitted the disease gradient. Although ascospores distributed evenly in all directions, average dispersal distance was greater in two different directions without obvious correlation with wind patterns. For asexual spores (conidia, few dispersal events occurred outside of the source plot. A gradient up to 12.5 m from the source was observed in one direction only. Accordingly, a thin-tailed kernel best fitted the disease gradient, and anisotropy in both density and distance was correlated with averaged daily wind gust. We discuss the validity of our results as well as their implications in terms of disease diffusion and management strategy.

  9. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Ling-Yu Duan

    2010-01-01

    Full Text Available Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  10. Per-Sample Multiple Kernel Approach for Visual Concept Learning

    Directory of Open Access Journals (Sweden)

    Tian Yonghong

    2010-01-01

    Full Text Available Abstract Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.

  11. Estimation of shutdown heat generation rates in GHARR-1 due to ...

    African Journals Online (AJOL)

    Fission products decay power and residual fission power generated after shutdown of Ghana Research Reactor-1 (GHARR-1) by reactivity insertion accident were estimated by solution of the decay and residual heat equations. A Matlab program code was developed to simulate the heat generation rates by fission product ...

  12. Geothermal Heat Flux Underneath Ice Sheets Estimated From Magnetic Satellite Data

    DEFF Research Database (Denmark)

    Fox Maule, Cathrine; Purucker, M.E.; Olsen, Nils

    The geothermal heat flux is an important factor in the dynamics of ice sheets, and it is one of the important parameters in the thermal budgets of subglacial lakes. We have used satellite magnetic data to estimate the geothermal heat flux underneath the ice sheets in Antarctica and Greenland...

  13. Segmentation of 3D microPET images of the rat brain via the hybrid gaussian mixture method with kernel density estimation.

    Science.gov (United States)

    Chen, Tai-Been; Chen, Jyh-Cheng; Lu, Henry Horng-Shing

    2012-01-01

    Segmentation of positron emission tomography (PET) is typically achieved using the K-Means method or other approaches. In preclinical and clinical applications, the K-Means method needs a prior estimation of parameters such as the number of clusters and appropriate initialized values. This work segments microPET images using a hybrid method combining the Gaussian mixture model (GMM) with kernel density estimation. Segmentation is crucial to registration of disordered 2-deoxy-2-fluoro-D-glucose (FDG) accumulation locations with functional diagnosis and to estimate standardized uptake values (SUVs) of region of interests (ROIs) in PET images. Therefore, simulation studies are conducted to apply spherical targets to evaluate segmentation accuracy based on Tanimoto's definition of similarity. The proposed method generates a higher degree of similarity than the K-Means method. The PET images of a rat brain are used to compare the segmented shape and area of the cerebral cortex by the K-Means method and the proposed method by volume rendering. The proposed method provides clearer and more detailed activity structures of an FDG accumulation location in the cerebral cortex than those by the K-Means method.

  14. Delimiting areas of endemism through kernel interpolation.

    Science.gov (United States)

    Oliveira, Ubirajara; Brescovit, Antonio D; Santos, Adalberto J

    2015-01-01

    We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.

  15. Delimiting areas of endemism through kernel interpolation.

    Directory of Open Access Journals (Sweden)

    Ubirajara Oliveira

    Full Text Available We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE, based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units.

  16. Deep Restricted Kernel Machines Using Conjugate Feature Duality.

    Science.gov (United States)

    Suykens, Johan A K

    2017-08-01

    The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

  17. A kernel plus method for quantifying wind turbine performance upgrades

    KAUST Repository

    Lee, Giwhyun

    2014-04-21

    Power curves are commonly estimated using the binning method recommended by the International Electrotechnical Commission, which primarily incorporates wind speed information. When such power curves are used to quantify a turbine\\'s upgrade, the results may not be accurate because many other environmental factors in addition to wind speed, such as temperature, air pressure, turbulence intensity, wind shear and humidity, all potentially affect the turbine\\'s power output. Wind industry practitioners are aware of the need to filter out effects from environmental conditions. Toward that objective, we developed a kernel plus method that allows incorporation of multivariate environmental factors in a power curve model, thereby controlling the effects from environmental factors while comparing power outputs. We demonstrate that the kernel plus method can serve as a useful tool for quantifying a turbine\\'s upgrade because it is sensitive to small and moderate changes caused by certain turbine upgrades. Although we demonstrate the utility of the kernel plus method in this specific application, the resulting method is a general, multivariate model that can connect other physical factors, as long as their measurements are available, with a turbine\\'s power output, which may allow us to explore new physical properties associated with wind turbine performance. © 2014 John Wiley & Sons, Ltd.

  18. Improved modeling of clinical data with kernel methods.

    Science.gov (United States)

    Daemen, Anneleen; Timmerman, Dirk; Van den Bosch, Thierry; Bottomley, Cecilia; Kirk, Emma; Van Holsbeke, Caroline; Valentin, Lil; Bourne, Tom; De Moor, Bart

    2012-02-01

    Despite the rise of high-throughput technologies, clinical data such as age, gender and medical history guide clinical management for most diseases and examinations. To improve clinical management, available patient information should be fully exploited. This requires appropriate modeling of relevant parameters. When kernel methods are used, traditional kernel functions such as the linear kernel are often applied to the set of clinical parameters. These kernel functions, however, have their disadvantages due to the specific characteristics of clinical data, being a mix of variable types with each variable its own range. We propose a new kernel function specifically adapted to the characteristics of clinical data. The clinical kernel function provides a better representation of patients' similarity by equalizing the influence of all variables and taking into account the range r of the variables. Moreover, it is robust with respect to changes in r. Incorporated in a least squares support vector machine, the new kernel function results in significantly improved diagnosis, prognosis and prediction of therapy response. This is illustrated on four clinical data sets within gynecology, with an average increase in test area under the ROC curve (AUC) of 0.023, 0.021, 0.122 and 0.019, respectively. Moreover, when combining clinical parameters and expression data in three case studies on breast cancer, results improved overall with use of the new kernel function and when considering both data types in a weighted fashion, with a larger weight assigned to the clinical parameters. The increase in AUC with respect to a standard kernel function and/or unweighted data combination was maximum 0.127, 0.042 and 0.118 for the three case studies. For clinical data consisting of variables of different types, the proposed kernel function--which takes into account the type and range of each variable--has shown to be a better alternative for linear and non-linear classification problems

  19. Linear and kernel methods for multi- and hypervariate change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Canty, Morton J.

    2010-01-01

    . Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual...... formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution......, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component...

  20. Kernel based orthogonalization for change detection in hyperspectral images

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...

  1. Mitigation of artifacts in rtm with migration kernel decomposition

    KAUST Repository

    Zhan, Ge; Schuster, Gerard T.

    2012-01-01

    The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently

  2. Semi-Supervised Kernel PCA

    DEFF Research Database (Denmark)

    Walder, Christian; Henao, Ricardo; Mørup, Morten

    We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA, penalises within class variances similar to Fisher discriminant analysis. The second, LSKPCA is a hybrid of least...... squares regression and kernel PCA. The final LR-KPCA is an iteratively reweighted version of the previous which achieves a sigmoid loss function on the labeled points. We provide a theoretical risk bound as well as illustrative experiments on real and toy data sets....

  3. 21 CFR 176.350 - Tamarind seed kernel powder.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 3 2010-04-01 2009-04-01 true Tamarind seed kernel powder. 176.350 Section 176... Substances for Use Only as Components of Paper and Paperboard § 176.350 Tamarind seed kernel powder. Tamarind seed kernel powder may be safely used as a component of articles intended for use in producing...

  4. Designing realized kernels to measure the ex post variation of equity prices in the presence of noise

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Hansen, P.R.; Lunde, Asger

    2008-01-01

    This paper shows how to use realized kernels to carry out efficient feasible inference on the ex post variation of underlying equity prices in the presence of simple models of market frictions. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic va......) allow for temporally dependent noise. The finite sample performance of our estimators is studied using simulation, while empirical work illustrates their use in practice.......This paper shows how to use realized kernels to carry out efficient feasible inference on the ex post variation of underlying equity prices in the presence of simple models of market frictions. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic...... variance which equals that of the maximum likelihood estimator in the parametric version of this problem. Realized kernels can also be selected to (i) be analyzed using endogenously spaced data such as that in data bases on transactions, (ii) allow for market frictions which are endogenous, and (iii...

  5. Probabilistic Representations of Solutions to the Heat Equation

    Indian Academy of Sciences (India)

    In this paper we provide a new (probabilistic) proof of a classical result in partial differential equations, viz. if is a tempered distribution, then the solution of the heat equation for the Laplacian, with initial condition , is given by the convolution of with the heat kernel (Gaussian density). Our results also extend the ...

  6. Dense Medium Machine Processing Method for Palm Kernel/ Shell ...

    African Journals Online (AJOL)

    ADOWIE PERE

    Cracked palm kernel is a mixture of kernels, broken shells, dusts and other impurities. In ... machine processing method using dense medium, a separator, a shell collector and a kernel .... efficiency, ease of maintenance and uniformity of.

  7. Notes on the gamma kernel

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole E.

    The density function of the gamma distribution is used as shift kernel in Brownian semistationary processes modelling the timewise behaviour of the velocity in turbulent regimes. This report presents exact and asymptotic properties of the second order structure function under such a model......, and relates these to results of von Karmann and Horwath. But first it is shown that the gamma kernel is interpretable as a Green’s function....

  8. Estimating end-use emissions factors for policy analysis: the case of space cooling and heating.

    Science.gov (United States)

    Jacobsen, Grant D

    2014-06-17

    This paper provides the first estimates of end-use specific emissions factors, which are estimates of the amount of a pollutant that is emitted when a unit of electricity is generated to meet demand from a specific end-use. In particular, this paper provides estimates of emissions factors for space cooling and heating, which are two of the most significant end-uses. The analysis is based on a novel two-stage regression framework that estimates emissions factors that are specific to cooling or heating by exploiting variation in cooling and heating demand induced by weather variation. Heating is associated with similar or greater CO2 emissions factor than cooling in all regions. The difference is greatest in the Midwest and Northeast, where the estimated CO2 emissions factor for heating is more than 20% larger than the emissions factor for cooling. The minor differences in emissions factors in other regions, combined with the substantial difference in the demand pattern for cooling and heating, suggests that the use of overall regional emissions factors is reasonable for policy evaluations in certain locations. Accurately quantifying the emissions factors associated with different end-uses across regions will aid in designing improved energy and environmental policies.

  9. Calculation of the thermal neutron scattering kernel using the synthetic model. Pt. 2. Zero-order energy transfer kernel

    International Nuclear Information System (INIS)

    Drozdowicz, K.

    1995-01-01

    A comprehensive unified description of the application of Granada's Synthetic Model to the slow-neutron scattering by the molecular systems is continued. Detailed formulae for the zero-order energy transfer kernel are presented basing on the general formalism of the model. An explicit analytical formula for the total scattering cross section as a function of the incident neutron energy is also obtained. Expressions of the free gas model for the zero-order scattering kernel and for total scattering kernel are considered as a sub-case of the Synthetic Model. (author). 10 refs

  10. Convergence of barycentric coordinates to barycentric kernels

    KAUST Repository

    Kosinka, Jiří

    2016-02-12

    We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

  11. Convergence of barycentric coordinates to barycentric kernels

    KAUST Repository

    Kosinka, Jiří ; Barton, Michael

    2016-01-01

    We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

  12. Hadamard Kernel SVM with applications for breast cancer outcome predictions.

    Science.gov (United States)

    Jiang, Hao; Ching, Wai-Ki; Cheung, Wai-Shun; Hou, Wenpin; Yin, Hong

    2017-12-21

    Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation. Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods. Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.

  13. Analysis of total hydrogen content in palm oil and palm kernel oil using thermal neutron moderation method

    International Nuclear Information System (INIS)

    Akaho, E.H.K.; Dagadu, C.P.K.; Maaku, B.T.; Anim-Sampong, S.; Kyere, A.W.K.; Jonah, S.A.

    2001-01-01

    A fast and non-destructive technique based on thermal neutron moderation has been used for determining the total hydrogen content in two types of red palm oil (dzomi and amidze) and palm kernel oil produced by traditional methods in Ghana. An equipment consisting of an 241 Am-Be neutron source and 3 He neutron detector was used in the investigation. The equipment was originally designed for detection of liquid levels in petrochemical and other process industries. Standards in the form of liquid hydrocarbons were used to obtain calibration lines for thermal neutron reflection parameter as a function of hydrogen content. Measured reflection parameters with respective hydrogen content with or without heat treatment of the three edible palm oils available on the market were compared with a brand cooking oil (frytol). The average total hydrogen content in the local oil samples prior to heating was measured to be 11.62 w% which compared well with acceptable value of 12 w% for palm oils in the sub-region. After heat treatment, the frytol oil (produced through bleaching process) had the least loss of hydrogen content of 0.26% in comparison with palm kernel oil of 0.44% followed by dzomi of 1.96% and by amidze of 3.22%. (author)

  14. Aflatoxin contamination of developing corn kernels.

    Science.gov (United States)

    Amer, M A

    2005-01-01

    Preharvest of corn and its contamination with aflatoxin is a serious problem. Some environmental and cultural factors responsible for infection and subsequent aflatoxin production were investigated in this study. Stage of growth and location of kernels on corn ears were found to be one of the important factors in the process of kernel infection with A. flavus & A. parasiticus. The results showed positive correlation between the stage of growth and kernel infection. Treatment of corn with aflatoxin reduced germination, protein and total nitrogen contents. Total and reducing soluble sugar was increase in corn kernels as response to infection. Sucrose and protein content were reduced in case of both pathogens. Shoot system length, seeding fresh weigh and seedling dry weigh was also affected. Both pathogens induced reduction of starch content. Healthy corn seedlings treated with aflatoxin solution were badly affected. Their leaves became yellow then, turned brown with further incubation. Moreover, their total chlorophyll and protein contents showed pronounced decrease. On the other hand, total phenolic compounds were increased. Histopathological studies indicated that A. flavus & A. parasiticus could colonize corn silks and invade developing kernels. Germination of A. flavus spores was occurred and hyphae spread rapidly across the silk, producing extensive growth and lateral branching. Conidiophores and conidia had formed in and on the corn silk. Temperature and relative humidity greatly influenced the growth of A. flavus & A. parasiticus and aflatoxin production.

  15. Influent of Carbonization of Sol Solution at the External Gelation Process on the Quality of Uranium Oxide Kernel

    International Nuclear Information System (INIS)

    Damunir; Sukarsono

    2007-01-01

    The influent of carbonization of sol solution at the external gelation process on the quality of uranium oxide kernel was done. Variables observed are the influent of carbon, temperature and time of reduction process of U 3 O 8 kernel resulted from carbonization of sol solution. First of all, uranyl nitrate was reacted with 1 M NH 4 OH solution, producing the colloid of UO 3 . Then by mixing and heating up to the temperature of 60-80 °C, the colloid solution was reacted with PVA, mono sorbitol oleate and paraffin producing of uranium-PVA sol. Then sol solution was carbonized with carbon black of mol ratio of carbon to uranium =2.32-6.62, produce of carbide gel. Gel then washed, dried and calcined at 800 °C for 4 hours to produce of U 3 O 8 kernel containing carbon. Then the kernel was reduced by H 2 gas in the medium of N 2 gas at 500-800 °C, 50 mmHg pressure for 3 hours. The process was repeated at 700 °C, 50 mmHg pressure for 1-4 hours. The characterization of chemical properties of the gel grains and uranium oxide kernel using FTIR covering the analysis of absorption band of infra red spectrum of UO 3 , C-OH, NH 3 , C-C, C-H and OH functional group. The physical properties of uranium oxide covering specific surface area, void volume, mean diameter using surface area meter Nova-1000 and as N 2 gas an absorbent. And O/U ratio of uranium dioxide kernel by gravimetry method. The result of experiment showed that carbonization of sol solution at the external gelation process give influencing the quality of uranium oxide kernel. (author)

  16. Best estimate radiation heat transfer model developed for TRAC-BD1

    International Nuclear Information System (INIS)

    Spore, J.W.; Giles, M.M.; Shumway, R.W.

    1981-01-01

    A best estimate radiation heat transfer model for analysis of BWR fuel bundles has been developed and compared with 8 x 8 fuel bundle data. The model includes surface-to-surface and surface-to-two-phase fluid radiation heat transfer. A simple method of correcting for anisotropic reflection effects has been included in the model

  17. Kernel Korner : The Linux keyboard driver

    NARCIS (Netherlands)

    Brouwer, A.E.

    1995-01-01

    Our Kernel Korner series continues with an article describing the Linux keyboard driver. This article is not for "Kernel Hackers" only--in fact, it will be most useful to those who wish to use their own keyboard to its fullest potential, and those who want to write programs to take advantage of the

  18. Palm kernel cake obtained from biodiesel production in diets for goats: feeding behavior and physiological parameters.

    Science.gov (United States)

    de Oliveira, R L; de Carvalho, G G P; Oliveira, R L; Tosto, M S L; Santos, E M; Ribeiro, R D X; Silva, T M; Correia, B R; de Rufino, L M A

    2017-10-01

    The objective of this study was to evaluate the effects of the inclusion of palm kernel (Elaeis guineensis) cake in diets for goats on feeding behaviors, rectal temperature, and cardiac and respiratory frequencies. Forty crossbred Boer male, non-castrated goats (ten animals per treatment), with an average age of 90 days and an initial body weight of 15.01 ± 1.76 kg, were used. The goats were fed Tifton 85 (Cynodon spp.) hay and palm kernel supplemented at the rates of 0, 7, 14, and 21% of dry matter (DM). The feeding behaviors (rumination, feeding, and idling times) were observed for three 24-h periods. DM and neutral detergent fiber (NDF) intake values were estimated as the difference between the total DM and NDF contents of the feed offered and the total DM and NDF contents of the orts. There was no effect of palm kernel cake inclusion in goat diets on DM intake (P > 0.05). However, palm kernel cake promoted a linear increase (P kernel cakes had no effects (P > 0.05) on the chewing, feeding, and rumination efficiency (DM and NDF) or on physiological variables. The use up to 21% palm kernel cake in the diet of crossbred Boer goats maintained the feeding behaviors and did not change the physiological parameters of goats; therefore, its use is recommended in the diet of these animals.

  19. Inverse estimation of heat flux and temperature on nozzle throat-insert inner contour

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Tsung-Chien [Department of Power Vehicle and Systems Engineering, Chung Cheng Institute of Technology, National Defense University, Ta-Hsi, Tao-Yuan 33509 (China); Liu, Chiun-Chien [Chung Shan Institute of Science and Technology, Lung-Tan, Tao-Yuan 32526 (China)

    2008-07-01

    During the missile flight, the jet flow with high temperature comes from the heat flux of propellant burning. An enormous heat flux from the nozzle throat-insert inner contour conducted into the nozzle shell will degrade the material strength of nozzle shell and reduce the nozzle thrust efficiency. In this paper, an on-line inverse method based on the input estimation method combined with the finite-element scheme is proposed to inversely estimate the unknown heat flux on the nozzle throat-insert inner contour and the inner wall temperature by applying the temperature measurements of the nozzle throat-insert. The finite-element scheme can easily define the irregularly shaped boundary. The superior capability of the proposed method is demonstrated in two major time-varying estimation cases. The computational results show that the proposed method has good estimation performance and highly facilitates the practical implementation. An effective analytical method can be offered to increase the operation reliability and thermal-resistance layer design in the solid rocket motor. (author)

  20. Mitigation of artifacts in rtm with migration kernel decomposition

    KAUST Repository

    Zhan, Ge

    2012-01-01

    The migration kernel for reverse-time migration (RTM) can be decomposed into four component kernels using Born scattering and migration theory. Each component kernel has a unique physical interpretation and can be interpreted differently. In this paper, we present a generalized diffraction-stack migration approach for reducing RTM artifacts via decomposition of migration kernel. The decomposition leads to an improved understanding of migration artifacts and, therefore, presents us with opportunities for improving the quality of RTM images.

  1. Anatomically-aided PET reconstruction using the kernel method.

    Science.gov (United States)

    Hutchcroft, Will; Wang, Guobao; Chen, Kevin T; Catana, Ciprian; Qi, Jinyi

    2016-09-21

    This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.

  2. On the Asymptotic Behavior of the Kernel Function in the Generalized Langevin Equation: A One-Dimensional Lattice Model

    Science.gov (United States)

    Chu, Weiqi; Li, Xiantao

    2018-01-01

    We present some estimates for the memory kernel function in the generalized Langevin equation, derived using the Mori-Zwanzig formalism from a one-dimensional lattice model, in which the particles interactions are through nearest and second nearest neighbors. The kernel function can be explicitly expressed in a matrix form. The analysis focuses on the decay properties, both spatially and temporally, revealing a power-law behavior in both cases. The dependence on the level of coarse-graining is also studied.

  3. Embedded real-time operating system micro kernel design

    Science.gov (United States)

    Cheng, Xiao-hui; Li, Ming-qiang; Wang, Xin-zheng

    2005-12-01

    Embedded systems usually require a real-time character. Base on an 8051 microcontroller, an embedded real-time operating system micro kernel is proposed consisting of six parts, including a critical section process, task scheduling, interruption handle, semaphore and message mailbox communication, clock managent and memory managent. Distributed CPU and other resources are among tasks rationally according to the importance and urgency. The design proposed here provides the position, definition, function and principle of micro kernel. The kernel runs on the platform of an ATMEL AT89C51 microcontroller. Simulation results prove that the designed micro kernel is stable and reliable and has quick response while operating in an application system.

  4. Characterization of microstructure and local deformation in 316NG weld heat-affected zone and stress corrosion cracking in high temperature water

    International Nuclear Information System (INIS)

    Lu Zhanpeng; Shoji, Tetsuo; Meng Fanjiang; Xue He; Qiu Yubing; Takeda, Yoichi; Negishi, Koji

    2011-01-01

    Research highlights: → Away from the fusion line, kernel average misorientation and hardness decrease. → Away from the fusion line, the fraction of Σ3 boundaries increases. → Crack growth in high temperature water correlates to kernel average misorientation and hardness. → SCC along random boundaries as well as extensive intergranular branching near the fusion line. - Abstract: Microstructure and local deformation in 316NG weld heat-affected zones were measured by electron-back scattering diffraction and hardness measurements. With increasing the distance from the fusion line, kernel average misorientation decreases and the fraction of Σ3 boundaries increases. Stress corrosion cracking growth rates in high temperature water were measured at different locations in the heat-affected zones that correspond to different levels of strain-hardening represented by kernel average misorientation and hardness distribution. Intergranular cracking along random boundaries as well as extensive intergranular crack branching is observed in the heat-affected zone near the weld fusion line.

  5. Collision kernels in the eikonal approximation for Lennard-Jones interaction potential

    International Nuclear Information System (INIS)

    Zielinska, S.

    1985-03-01

    The velocity changing collisions are conveniently described by collisional kernels. These kernels depend on an interaction potential and there is a necessity for evaluating them for realistic interatomic potentials. Using the collision kernels, we are able to investigate the redistribution of atomic population's caused by the laser light and velocity changing collisions. In this paper we present the method of evaluating the collision kernels in the eikonal approximation. We discuss the influence of the potential parameters Rsub(o)sup(i), epsilonsub(o)sup(i) on kernel width for a given atomic state. It turns out that unlike the collision kernel for the hard sphere model of scattering the Lennard-Jones kernel is not so sensitive to changes of Rsub(o)sup(i) as the previous one. Contrary to the general tendency of approximating collisional kernels by the Gaussian curve, kernels for the Lennard-Jones potential do not exhibit such a behaviour. (author)

  6. Classification of maize kernels using NIR hyperspectral imaging

    DEFF Research Database (Denmark)

    Williams, Paul; Kucheryavskiy, Sergey V.

    2016-01-01

    NIR hyperspectral imaging was evaluated to classify maize kernels of three hardness categories: hard, medium and soft. Two approaches, pixel-wise and object-wise, were investigated to group kernels according to hardness. The pixel-wise classification assigned a class to every pixel from individual...... and specificity of 0.95 and 0.93). Both feature extraction methods can be recommended for classification of maize kernels on production scale....

  7. Influence of wheat kernel physical properties on the pulverizing process.

    Science.gov (United States)

    Dziki, Dariusz; Cacak-Pietrzak, Grażyna; Miś, Antoni; Jończyk, Krzysztof; Gawlik-Dziki, Urszula

    2014-10-01

    The physical properties of wheat kernel were determined and related to pulverizing performance by correlation analysis. Nineteen samples of wheat cultivars about similar level of protein content (11.2-12.8 % w.b.) and obtained from organic farming system were used for analysis. The kernel (moisture content 10 % w.b.) was pulverized by using the laboratory hammer mill equipped with round holes 1.0 mm screen. The specific grinding energy ranged from 120 kJkg(-1) to 159 kJkg(-1). On the basis of data obtained many of significant correlations (p kernel physical properties and pulverizing process of wheat kernel, especially wheat kernel hardness index (obtained on the basis of Single Kernel Characterization System) and vitreousness significantly and positively correlated with the grinding energy indices and the mass fraction of coarse particles (> 0.5 mm). Among the kernel mechanical properties determined on the basis of uniaxial compression test only the rapture force was correlated with the impact grinding results. The results showed also positive and significant relationships between kernel ash content and grinding energy requirements. On the basis of wheat physical properties the multiple linear regression was proposed for predicting the average particle size of pulverized kernel.

  8. Evolution kernel for the Dirac field

    International Nuclear Information System (INIS)

    Baaquie, B.E.

    1982-06-01

    The evolution kernel for the free Dirac field is calculated using the Wilson lattice fermions. We discuss the difficulties due to which this calculation has not been previously performed in the continuum theory. The continuum limit is taken, and the complete energy eigenfunctions as well as the propagator are then evaluated in a new manner using the kernel. (author)

  9. Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

    Science.gov (United States)

    Zhang, Sen; Zhang, Tao; Yin, Yixin; Xiao, Wendong

    2017-09-01

    The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

  10. Gradient-based adaptation of general gaussian kernels.

    Science.gov (United States)

    Glasmachers, Tobias; Igel, Christian

    2005-10-01

    Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.

  11. Crustal heat production and estimate of terrestrial heat flow in central East Antarctica, with implications for thermal input to the East Antarctic ice sheet

    Science.gov (United States)

    Goodge, John W.

    2018-02-01

    Terrestrial heat flow is a critical first-order factor governing the thermal condition and, therefore, mechanical stability of Antarctic ice sheets, yet heat flow across Antarctica is poorly known. Previous estimates of terrestrial heat flow in East Antarctica come from inversion of seismic and magnetic geophysical data, by modeling temperature profiles in ice boreholes, and by calculation from heat production values reported for exposed bedrock. Although accurate estimates of surface heat flow are important as an input parameter for ice-sheet growth and stability models, there are no direct measurements of terrestrial heat flow in East Antarctica coupled to either subglacial sediment or bedrock. As has been done with bedrock exposed along coastal margins and in rare inland outcrops, valuable estimates of heat flow in central East Antarctica can be extrapolated from heat production determined by the geochemical composition of glacial rock clasts eroded from the continental interior. In this study, U, Th, and K concentrations in a suite of Proterozoic (1.2-2.0 Ga) granitoids sourced within the Byrd and Nimrod glacial drainages of central East Antarctica indicate average upper crustal heat production (Ho) of about 2.6 ± 1.9 µW m-3. Assuming typical mantle and lower crustal heat flux for stable continental shields, and a length scale for the distribution of heat production in the upper crust, the heat production values determined for individual samples yield estimates of surface heat flow (qo) ranging from 33 to 84 mW m-2 and an average of 48.0 ± 13.6 mW m-2. Estimates of heat production obtained for this suite of glacially sourced granitoids therefore indicate that the interior of the East Antarctic ice sheet is underlain in part by Proterozoic continental lithosphere with an average surface heat flow, providing constraints on both geodynamic history and ice-sheet stability. The ages and geothermal characteristics of the granites indicate that crust in central

  12. Determining the minimum required uranium carbide content for HTGR UCO fuel kernels

    International Nuclear Information System (INIS)

    McMurray, Jacob W.; Lindemer, Terrence B.; Brown, Nicholas R.; Reif, Tyler J.; Morris, Robert N.; Hunn, John D.

    2017-01-01

    Highlights: • The minimum required uranium carbide content for HTGR UCO fuel kernels is calculated. • More nuclear and chemical factors have been included for more useful predictions. • The effect of transmutation products, like Pu and Np, on the oxygen distribution is included for the first time. - Abstract: Three important failure mechanisms that must be controlled in high-temperature gas-cooled reactor (HTGR) fuel for certain higher burnup applications are SiC layer rupture, SiC corrosion by CO, and coating compromise from kernel migration. All are related to high CO pressures stemming from O release when uranium present as UO 2 fissions and the O is not subsequently bound by other elements. In the HTGR kernel design, CO buildup from excess O is controlled by the inclusion of additional uranium apart from UO 2 in the form of a carbide, UC x and this fuel form is designated UCO. Here general oxygen balance formulas were developed for calculating the minimum UC x content to ensure negligible CO formation for 15.5% enriched UCO taken to 16.1% actinide burnup. Required input data were obtained from CALPHAD (CALculation of PHAse Diagrams) chemical thermodynamic models and the Serpent 2 reactor physics and depletion analysis tool. The results are intended to be more accurate than previous estimates by including more nuclear and chemical factors, in particular the effect of transmuted Pu and Np oxides on the oxygen distribution as the fuel kernel composition evolves with burnup.

  13. Analog forecasting with dynamics-adapted kernels

    Science.gov (United States)

    Zhao, Zhizhen; Giannakis, Dimitrios

    2016-09-01

    Analog forecasting is a nonparametric technique introduced by Lorenz in 1969 which predicts the evolution of states of a dynamical system (or observables defined on the states) by following the evolution of the sample in a historical record of observations which most closely resembles the current initial data. Here, we introduce a suite of forecasting methods which improve traditional analog forecasting by combining ideas from kernel methods developed in harmonic analysis and machine learning and state-space reconstruction for dynamical systems. A key ingredient of our approach is to replace single-analog forecasting with weighted ensembles of analogs constructed using local similarity kernels. The kernels used here employ a number of dynamics-dependent features designed to improve forecast skill, including Takens’ delay-coordinate maps (to recover information in the initial data lost through partial observations) and a directional dependence on the dynamical vector field generating the data. Mathematically, our approach is closely related to kernel methods for out-of-sample extension of functions, and we discuss alternative strategies based on the Nyström method and the multiscale Laplacian pyramids technique. We illustrate these techniques in applications to forecasting in a low-order deterministic model for atmospheric dynamics with chaotic metastability, and interannual-scale forecasting in the North Pacific sector of a comprehensive climate model. We find that forecasts based on kernel-weighted ensembles have significantly higher skill than the conventional approach following a single analog.

  14. Open Problem: Kernel methods on manifolds and metric spaces

    DEFF Research Database (Denmark)

    Feragen, Aasa; Hauberg, Søren

    2016-01-01

    Radial kernels are well-suited for machine learning over general geodesic metric spaces, where pairwise distances are often the only computable quantity available. We have recently shown that geodesic exponential kernels are only positive definite for all bandwidths when the input space has strong...... linear properties. This negative result hints that radial kernel are perhaps not suitable over geodesic metric spaces after all. Here, however, we present evidence that large intervals of bandwidths exist where geodesic exponential kernels have high probability of being positive definite over finite...... datasets, while still having significant predictive power. From this we formulate conjectures on the probability of a positive definite kernel matrix for a finite random sample, depending on the geometry of the data space and the spread of the sample....

  15. Production and detailed characterization of bio-oil from fast pyrolysis of palm kernel shell

    International Nuclear Information System (INIS)

    Asadullah, Mohammad; Ab Rasid, Nurul Suhada; Kadir, Sharifah Aishah Syed A.; Azdarpour, Amin

    2013-01-01

    Bio-oil has been produced from palm kernel shell in a fluidized bed reactor. The process conditions were optimized and the detailed characteristics of bio-oil were carried out. The higher feeding rate and higher gas flow rate attributed to higher bio-oil yield. The maximum mass fraction of biomass (57%) converted to bio-oil at 550 °C when 2 L min −1 of gas and 10 g min −1 of biomass were fed. The bio-oil produced up to 500 °C existed in two distinct phases, while it formed one homogeneous phase when it was produced above 500 °C. The higher heating value of bio-oil produced at 550 °C was found to be 23.48 MJ kg −1 . As GC–MS data shows, the area ratio of phenol is the maximum among the area ratio of identified compounds in 550 °C bio-oil. The UV–Fluorescence absorption, which is the indication of aromatic content, is also the highest in 550 °C bio-oil. -- Highlights: • Maximum 56 wt% yield of bio-oil was obtained at 550 °C from palm kernel shell. • Two layer of bio-oil was observed up to 500 °C, while it was one layer above 500 °C. • Bio-oil from palm kernel shell provides more than 40% area ratio of phenol in GC–MS analysis. • The calorific value of palm kernel shell bio-oil is higher than other bio-oil

  16. Analyzing kernel matrices for the identification of differentially expressed genes.

    Directory of Open Access Journals (Sweden)

    Xiao-Lei Xia

    Full Text Available One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs, followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing [Formula: see text]-like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate.

  17. Genetic dissection of the maize kernel development process via conditional QTL mapping for three developing kernel-related traits in an immortalized F2 population.

    Science.gov (United States)

    Zhang, Zhanhui; Wu, Xiangyuan; Shi, Chaonan; Wang, Rongna; Li, Shengfei; Wang, Zhaohui; Liu, Zonghua; Xue, Yadong; Tang, Guiliang; Tang, Jihua

    2016-02-01

    Kernel development is an important dynamic trait that determines the final grain yield in maize. To dissect the genetic basis of maize kernel development process, a conditional quantitative trait locus (QTL) analysis was conducted using an immortalized F2 (IF2) population comprising 243 single crosses at two locations over 2 years. Volume (KV) and density (KD) of dried developing kernels, together with kernel weight (KW) at different developmental stages, were used to describe dynamic changes during kernel development. Phenotypic analysis revealed that final KW and KD were determined at DAP22 and KV at DAP29. Unconditional QTL mapping for KW, KV and KD uncovered 97 QTLs at different kernel development stages, of which qKW6b, qKW7a, qKW7b, qKW10b, qKW10c, qKV10a, qKV10b and qKV7 were identified under multiple kernel developmental stages and environments. Among the 26 QTLs detected by conditional QTL mapping, conqKW7a, conqKV7a, conqKV10a, conqKD2, conqKD7 and conqKD8a were conserved between the two mapping methodologies. Furthermore, most of these QTLs were consistent with QTLs and genes for kernel development/grain filling reported in previous studies. These QTLs probably contain major genes associated with the kernel development process, and can be used to improve grain yield and quality through marker-assisted selection.

  18. Adaptive Learning in Cartesian Product of Reproducing Kernel Hilbert Spaces

    OpenAIRE

    Yukawa, Masahiro

    2014-01-01

    We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The task is estimating/tracking nonlinear functions which are supposed to contain multiple components such as (i) linear and nonlinear components, (ii) high- and low- frequency components etc. In this case, the use of multiple RKHSs permits a compact representation of multicomponent functions. The proposed algorithm is where t...

  19. A trace ratio maximization approach to multiple kernel-based dimensionality reduction.

    Science.gov (United States)

    Jiang, Wenhao; Chung, Fu-lai

    2014-01-01

    Most dimensionality reduction techniques are based on one metric or one kernel, hence it is necessary to select an appropriate kernel for kernel-based dimensionality reduction. Multiple kernel learning for dimensionality reduction (MKL-DR) has been recently proposed to learn a kernel from a set of base kernels which are seen as different descriptions of data. As MKL-DR does not involve regularization, it might be ill-posed under some conditions and consequently its applications are hindered. This paper proposes a multiple kernel learning framework for dimensionality reduction based on regularized trace ratio, termed as MKL-TR. Our method aims at learning a transformation into a space of lower dimension and a corresponding kernel from the given base kernels among which some may not be suitable for the given data. The solutions for the proposed framework can be found based on trace ratio maximization. The experimental results demonstrate its effectiveness in benchmark datasets, which include text, image and sound datasets, for supervised, unsupervised as well as semi-supervised settings. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. A multi-scale kernel bundle for LDDMM

    DEFF Research Database (Denmark)

    Sommer, Stefan Horst; Nielsen, Mads; Lauze, Francois Bernard

    2011-01-01

    The Large Deformation Diffeomorphic Metric Mapping framework constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations...

  1. An alternative covariance estimator to investigate genetic heterogeneity in populations.

    Science.gov (United States)

    Heslot, Nicolas; Jannink, Jean-Luc

    2015-11-26

    For genomic prediction and genome-wide association studies (GWAS) using mixed models, covariance between individuals is estimated using molecular markers. Based on the properties of mixed models, using available molecular data for prediction is optimal if this covariance is known. Under this assumption, adding individuals to the analysis should never be detrimental. However, some empirical studies showed that increasing training population size decreased prediction accuracy. Recently, results from theoretical models indicated that even if marker density is high and the genetic architecture of traits is controlled by many loci with small additive effects, the covariance between individuals, which depends on relationships at causal loci, is not always well estimated by the whole-genome kinship. We propose an alternative covariance estimator named K-kernel, to account for potential genetic heterogeneity between populations that is characterized by a lack of genetic correlation, and to limit the information flow between a priori unknown populations in a trait-specific manner. This is similar to a multi-trait model and parameters are estimated by REML and, in extreme cases, it can allow for an independent genetic architecture between populations. As such, K-kernel is useful to study the problem of the design of training populations. K-kernel was compared to other covariance estimators or kernels to examine its fit to the data, cross-validated accuracy and suitability for GWAS on several datasets. It provides a significantly better fit to the data than the genomic best linear unbiased prediction model and, in some cases it performs better than other kernels such as the Gaussian kernel, as shown by an empirical null distribution. In GWAS simulations, alternative kernels control type I errors as well as or better than the classical whole-genome kinship and increase statistical power. No or small gains were observed in cross-validated prediction accuracy. This alternative

  2. Modeling electro-magneto-hydrodynamic thermo-fluidic transport of biofluids with new trend of fractional derivative without singular kernel

    Science.gov (United States)

    Abdulhameed, M.; Vieru, D.; Roslan, R.

    2017-10-01

    This paper investigates the electro-magneto-hydrodynamic flow of the non-Newtonian behavior of biofluids, with heat transfer, through a cylindrical microchannel. The fluid is acted by an arbitrary time-dependent pressure gradient, an external electric field and an external magnetic field. The governing equations are considered as fractional partial differential equations based on the Caputo-Fabrizio time-fractional derivatives without singular kernel. The usefulness of fractional calculus to study fluid flows or heat and mass transfer phenomena was proven. Several experimental measurements led to conclusion that, in such problems, the models described by fractional differential equations are more suitable. The most common time-fractional derivative used in Continuum Mechanics is Caputo derivative. However, two disadvantages appear when this derivative is used. First, the definition kernel is a singular function and, secondly, the analytical expressions of the problem solutions are expressed by generalized functions (Mittag-Leffler, Lorenzo-Hartley, Robotnov, etc.) which, generally, are not adequate to numerical calculations. The new time-fractional derivative Caputo-Fabrizio, without singular kernel, is more suitable to solve various theoretical and practical problems which involve fractional differential equations. Using the Caputo-Fabrizio derivative, calculations are simpler and, the obtained solutions are expressed by elementary functions. Analytical solutions of the biofluid velocity and thermal transport are obtained by means of the Laplace and finite Hankel transforms. The influence of the fractional parameter, Eckert number and Joule heating parameter on the biofluid velocity and thermal transport are numerically analyzed and graphic presented. This fact can be an important in Biochip technology, thus making it possible to use this analysis technique extremely effective to control bioliquid samples of nanovolumes in microfluidic devices used for biological

  3. The Impacts of Heating Strategy on Soil Moisture Estimation Using Actively Heated Fiber Optics.

    Science.gov (United States)

    Dong, Jianzhi; Agliata, Rosa; Steele-Dunne, Susan; Hoes, Olivier; Bogaard, Thom; Greco, Roberto; van de Giesen, Nick

    2017-09-13

    Several recent studies have highlighted the potential of Actively Heated Fiber Optics (AHFO) for high resolution soil moisture mapping. In AHFO, the soil moisture can be calculated from the cumulative temperature ( T cum ), the maximum temperature ( T max ), or the soil thermal conductivity determined from the cooling phase after heating ( λ ). This study investigates the performance of the T cum , T max and λ methods for different heating strategies, i.e., differences in the duration and input power of the applied heat pulse. The aim is to compare the three approaches and to determine which is best suited to field applications where the power supply is limited. Results show that increasing the input power of the heat pulses makes it easier to differentiate between dry and wet soil conditions, which leads to an improved accuracy. Results suggest that if the power supply is limited, the heating strength is insufficient for the λ method to yield accurate estimates. Generally, the T cum and T max methods have similar accuracy. If the input power is limited, increasing the heat pulse duration can improve the accuracy of the AHFO method for both of these techniques. In particular, extending the heating duration can significantly increase the sensitivity of T cum to soil moisture. Hence, the T cum method is recommended when the input power is limited. Finally, results also show that up to 50% of the cable temperature change during the heat pulse can be attributed to soil background temperature, i.e., soil temperature changed by the net solar radiation. A method is proposed to correct this background temperature change. Without correction, soil moisture information can be completely masked by the background temperature error.

  4. Training Lp norm multiple kernel learning in the primal.

    Science.gov (United States)

    Liang, Zhizheng; Xia, Shixiong; Zhou, Yong; Zhang, Lei

    2013-10-01

    Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method. Copyright © 2013 Elsevier Ltd. All rights reserved.

  5. Background field removal technique using regularization enabled sophisticated harmonic artifact reduction for phase data with varying kernel sizes.

    Science.gov (United States)

    Kan, Hirohito; Kasai, Harumasa; Arai, Nobuyuki; Kunitomo, Hiroshi; Hirose, Yasujiro; Shibamoto, Yuta

    2016-09-01

    An effective background field removal technique is desired for more accurate quantitative susceptibility mapping (QSM) prior to dipole inversion. The aim of this study was to evaluate the accuracy of regularization enabled sophisticated harmonic artifact reduction for phase data with varying spherical kernel sizes (REV-SHARP) method using a three-dimensional head phantom and human brain data. The proposed REV-SHARP method used the spherical mean value operation and Tikhonov regularization in the deconvolution process, with varying 2-14mm kernel sizes. The kernel sizes were gradually reduced, similar to the SHARP with varying spherical kernel (VSHARP) method. We determined the relative errors and relationships between the true local field and estimated local field in REV-SHARP, VSHARP, projection onto dipole fields (PDF), and regularization enabled SHARP (RESHARP). Human experiment was also conducted using REV-SHARP, VSHARP, PDF, and RESHARP. The relative errors in the numerical phantom study were 0.386, 0.448, 0.838, and 0.452 for REV-SHARP, VSHARP, PDF, and RESHARP. REV-SHARP result exhibited the highest correlation between the true local field and estimated local field. The linear regression slopes were 1.005, 1.124, 0.988, and 0.536 for REV-SHARP, VSHARP, PDF, and RESHARP in regions of interest on the three-dimensional head phantom. In human experiments, no obvious errors due to artifacts were present in REV-SHARP. The proposed REV-SHARP is a new method combined with variable spherical kernel size and Tikhonov regularization. This technique might make it possible to be more accurate backgroud field removal and help to achive better accuracy of QSM. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Stochastic subset selection for learning with kernel machines.

    Science.gov (United States)

    Rhinelander, Jason; Liu, Xiaoping P

    2012-06-01

    Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

  7. RKRD: Runtime Kernel Rootkit Detection

    Science.gov (United States)

    Grover, Satyajit; Khosravi, Hormuzd; Kolar, Divya; Moffat, Samuel; Kounavis, Michael E.

    In this paper we address the problem of protecting computer systems against stealth malware. The problem is important because the number of known types of stealth malware increases exponentially. Existing approaches have some advantages for ensuring system integrity but sophisticated techniques utilized by stealthy malware can thwart them. We propose Runtime Kernel Rootkit Detection (RKRD), a hardware-based, event-driven, secure and inclusionary approach to kernel integrity that addresses some of the limitations of the state of the art. Our solution is based on the principles of using virtualization hardware for isolation, verifying signatures coming from trusted code as opposed to malware for scalability and performing system checks driven by events. Our RKRD implementation is guided by our goals of strong isolation, no modifications to target guest OS kernels, easy deployment, minimal infra-structure impact, and minimal performance overhead. We developed a system prototype and conducted a number of experiments which show that the per-formance impact of our solution is negligible.

  8. Denoising by semi-supervised kernel PCA preimaging

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Abrahamsen, Trine Julie; Hansen, Lars Kai

    2014-01-01

    Kernel Principal Component Analysis (PCA) has proven a powerful tool for nonlinear feature extraction, and is often applied as a pre-processing step for classification algorithms. In denoising applications Kernel PCA provides the basis for dimensionality reduction, prior to the so-called pre-imag...

  9. Sentiment classification with interpolated information diffusion kernels

    NARCIS (Netherlands)

    Raaijmakers, S.

    2007-01-01

    Information diffusion kernels - similarity metrics in non-Euclidean information spaces - have been found to produce state of the art results for document classification. In this paper, we present a novel approach to global sentiment classification using these kernels. We carry out a large array of

  10. Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

    Science.gov (United States)

    Zhou, Yuyu; Weng, Qihao; Gurney, Kevin R.; Shuai, Yanmin; Hu, Xuefei

    2012-01-01

    This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

  11. Cost estimation of hydrogen and DME produced by nuclear heat utilization system. Joint research

    International Nuclear Information System (INIS)

    Shiina, Yasuaki; Nishihara, Tetsuo

    2003-09-01

    Research of hydrogen energy has been performed in order to spread use of the hydrogen energy in 2020 or 2030. It will take, however, many years for the hydrogen energy to be used very easily like gasoline, diesel oil and city gas in all of countries. During the periods, low CO 2 release liquid fuels would be used together with hydrogen. Recently, di-methyl-either (DME) has been noticed as one of the substitute liquid fuels of petroleum. Such liquid fuels can be produced from the mixed gas such as hydrogen and carbon oxide which are produced by steam reforming hydrogen generation system by the use of nuclear heat. Therefore, the system would be one of the candidates of future system of nuclear heat utilization. In the present study, we focused on the production of hydrogen and DME. Economic evaluation was estimated for hydrogen and DME production in commercial and nuclear heat utilization plant. At first, heat and mass balance of each process in commercial plant of hydrogen production was estimated and commercial prices of each process were derived. Then, price was estimated when nuclear heat was used instead of required heat of commercial plant. Results showed that the production prices produced by nuclear heat were cheaper by 10% for hydrogen and 3% for DME. With the consideration of reduction effect of CO 2 release, utilization of nuclear heat would be more effective. (author)

  12. CHARACTERISTIC SIZE OF FLARE KERNELS IN THE VISIBLE AND NEAR-INFRARED CONTINUA

    International Nuclear Information System (INIS)

    Xu, Yan; Jing, Ju; Wang, Haimin; Cao, Wenda

    2012-01-01

    In this Letter, we present a new approach to estimate the formation height of visible and near-infrared emission of an X10 flare. The sizes of flare emission cores in three wavelengths are accurately measured during the peak of the flare. The source size is the largest in the G band at 4308 Å and shrinks toward longer wavelengths, namely the green continuum at 5200 Å and NIR at 15600 Å, where the emission is believed to originate from the deeper atmosphere. This size-wavelength variation is likely explained by the direct heating model as electrons need to move along converging field lines from the corona to the photosphere. Therefore, one can observe the smallest source, which in our case is 0.''65 ± 0.''02 in the bottom layer (represented by NIR), and observe relatively larger kernels in upper layers of 1.''03 ± 0.''14 and 1.''96 ± 0.''27, using the green continuum and G band, respectively. We then compare the source sizes with a simple magnetic geometry to derive the formation height of the white-light sources and magnetic pressure in different layers inside the flare loop.

  13. Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2017-09-01

    Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

  14. Linear and kernel methods for multivariate change detection

    DEFF Research Database (Denmark)

    Canty, Morton J.; Nielsen, Allan Aasbjerg

    2012-01-01

    ), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...... that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed...

  15. Scuba: scalable kernel-based gene prioritization.

    Science.gov (United States)

    Zampieri, Guido; Tran, Dinh Van; Donini, Michele; Navarin, Nicolò; Aiolli, Fabio; Sperduti, Alessandro; Valle, Giorgio

    2018-01-25

    The uncovering of genes linked to human diseases is a pressing challenge in molecular biology and precision medicine. This task is often hindered by the large number of candidate genes and by the heterogeneity of the available information. Computational methods for the prioritization of candidate genes can help to cope with these problems. In particular, kernel-based methods are a powerful resource for the integration of heterogeneous biological knowledge, however, their practical implementation is often precluded by their limited scalability. We propose Scuba, a scalable kernel-based method for gene prioritization. It implements a novel multiple kernel learning approach, based on a semi-supervised perspective and on the optimization of the margin distribution. Scuba is optimized to cope with strongly unbalanced settings where known disease genes are few and large scale predictions are required. Importantly, it is able to efficiently deal both with a large amount of candidate genes and with an arbitrary number of data sources. As a direct consequence of scalability, Scuba integrates also a new efficient strategy to select optimal kernel parameters for each data source. We performed cross-validation experiments and simulated a realistic usage setting, showing that Scuba outperforms a wide range of state-of-the-art methods. Scuba achieves state-of-the-art performance and has enhanced scalability compared to existing kernel-based approaches for genomic data. This method can be useful to prioritize candidate genes, particularly when their number is large or when input data is highly heterogeneous. The code is freely available at https://github.com/gzampieri/Scuba .

  16. Empirically Estimated Heats of Combustion of Oxygenated Hydrocarbon Bio-type Oils

    Directory of Open Access Journals (Sweden)

    Dmitry A. Ponomarev

    2015-04-01

    Full Text Available An empirical method is proposed by which the heats of combustion of oxygenated hydrocarbon oils, typically found from wood pyrolysis, may be calculated additively from empirically predicted heats of combustion of individual compounds. The predicted values are in turn based on four types of energetically inequivalent carbon and four types of energetically inequivalent hydrogen atomic energy values. A method is also given to estimate the condensation heats of oil mixtures based on the presence of four types of intermolecular forces. Agreement between predicted and experimental values of combustion heats for a typical mixture of known compounds was ± 2% and < 1% for a freshly prepared mixture of known compounds.

  17. MULTITASKER, Multitasking Kernel for C and FORTRAN Under UNIX

    International Nuclear Information System (INIS)

    Brooks, E.D. III

    1988-01-01

    1 - Description of program or function: MULTITASKER implements a multitasking kernel for the C and FORTRAN programming languages that runs under UNIX. The kernel provides a multitasking environment which serves two purposes. The first is to provide an efficient portable environment for the development, debugging, and execution of production multiprocessor programs. The second is to provide a means of evaluating the performance of a multitasking program on model multiprocessor hardware. The performance evaluation features require no changes in the application program source and are implemented as a set of compile- and run-time options in the kernel. 2 - Method of solution: The FORTRAN interface to the kernel is identical in function to the CRI multitasking package provided for the Cray XMP. This provides a migration path to high speed (but small N) multiprocessors once the application has been coded and debugged. With use of the UNIX m4 macro preprocessor, source compatibility can be achieved between the UNIX code development system and the target Cray multiprocessor. The kernel also provides a means of evaluating a program's performance on model multiprocessors. Execution traces may be obtained which allow the user to determine kernel overhead, memory conflicts between various tasks, and the average concurrency being exploited. The kernel may also be made to switch tasks every cpu instruction with a random execution ordering. This allows the user to look for unprotected critical regions in the program. These features, implemented as a set of compile- and run-time options, cause extra execution overhead which is not present in the standard production version of the kernel

  18. Multiple kernel boosting framework based on information measure for classification

    International Nuclear Information System (INIS)

    Qi, Chengming; Wang, Yuping; Tian, Wenjie; Wang, Qun

    2016-01-01

    The performance of kernel-based method, such as support vector machine (SVM), is greatly affected by the choice of kernel function. Multiple kernel learning (MKL) is a promising family of machine learning algorithms and has attracted many attentions in recent years. MKL combines multiple sub-kernels to seek better results compared to single kernel learning. In order to improve the efficiency of SVM and MKL, in this paper, the Kullback–Leibler kernel function is derived to develop SVM. The proposed method employs an improved ensemble learning framework, named KLMKB, which applies Adaboost to learning multiple kernel-based classifier. In the experiment for hyperspectral remote sensing image classification, we employ feature selected through Optional Index Factor (OIF) to classify the satellite image. We extensively examine the performance of our approach in comparison to some relevant and state-of-the-art algorithms on a number of benchmark classification data sets and hyperspectral remote sensing image data set. Experimental results show that our method has a stable behavior and a noticeable accuracy for different data set.

  19. Kernel Methods for Mining Instance Data in Ontologies

    Science.gov (United States)

    Bloehdorn, Stephan; Sure, York

    The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

  20. KERNELHR: A program for estimating animal home ranges

    Science.gov (United States)

    Seaman, D.E.; Griffith, B.; Powell, R.A.

    1998-01-01

    Kernel methods are state of the art for estimating animal home-range area and utilization distribution (UD). The KERNELHR program was developed to provide researchers and managers a tool to implement this extremely flexible set of methods with many variants. KERNELHR runs interactively or from the command line on any personal computer (PC) running DOS. KERNELHR provides output of fixed and adaptive kernel home-range estimates, as well as density values in a format suitable for in-depth statistical and spatial analyses. An additional package of programs creates contour files for plotting in geographic information systems (GIS) and estimates core areas of ranges.

  1. Inverse problem of estimating transient heat transfer rate on external wall of forced convection pipe

    International Nuclear Information System (INIS)

    Chen, W.-L.; Yang, Y.-C.; Chang, W.-J.; Lee, H.-L.

    2008-01-01

    In this study, a conjugate gradient method based inverse algorithm is applied to estimate the unknown space and time dependent heat transfer rate on the external wall of a pipe system using temperature measurements. It is assumed that no prior information is available on the functional form of the unknown heat transfer rate; hence, the procedure is classified as function estimation in the inverse calculation. The accuracy of the inverse analysis is examined by using simulated exact and inexact temperature measurements. Results show that an excellent estimation of the space and time dependent heat transfer rate can be obtained for the test case considered in this study

  2. A Fast Soft Bit Error Rate Estimation Method

    Directory of Open Access Journals (Sweden)

    Ait-Idir Tarik

    2010-01-01

    Full Text Available We have suggested in a previous publication a method to estimate the Bit Error Rate (BER of a digital communications system instead of using the famous Monte Carlo (MC simulation. This method was based on the estimation of the probability density function (pdf of soft observed samples. The kernel method was used for the pdf estimation. In this paper, we suggest to use a Gaussian Mixture (GM model. The Expectation Maximisation algorithm is used to estimate the parameters of this mixture. The optimal number of Gaussians is computed by using Mutual Information Theory. The analytical expression of the BER is therefore simply given by using the different estimated parameters of the Gaussian Mixture. Simulation results are presented to compare the three mentioned methods: Monte Carlo, Kernel and Gaussian Mixture. We analyze the performance of the proposed BER estimator in the framework of a multiuser code division multiple access system and show that attractive performance is achieved compared with conventional MC or Kernel aided techniques. The results show that the GM method can drastically reduce the needed number of samples to estimate the BER in order to reduce the required simulation run-time, even at very low BER.

  3. Biasing anisotropic scattering kernels for deep-penetration Monte Carlo calculations

    International Nuclear Information System (INIS)

    Carter, L.L.; Hendricks, J.S.

    1983-01-01

    The exponential transform is often used to improve the efficiency of deep-penetration Monte Carlo calculations. This technique is usually implemented by biasing the distance-to-collision kernel of the transport equation, but leaving the scattering kernel unchanged. Dwivedi obtained significant improvements in efficiency by biasing an isotropic scattering kernel as well as the distance-to-collision kernel. This idea is extended to anisotropic scattering, particularly the highly forward Klein-Nishina scattering of gamma rays

  4. Determining the number of clusters for kernelized fuzzy C-means algorithms for automatic medical image segmentation

    Directory of Open Access Journals (Sweden)

    E.A. Zanaty

    2012-03-01

    Full Text Available In this paper, we determine the suitable validity criterion of kernelized fuzzy C-means and kernelized fuzzy C-means with spatial constraints for automatic segmentation of magnetic resonance imaging (MRI. For that; the original Euclidean distance in the FCM is replaced by a Gaussian radial basis function classifier (GRBF and the corresponding algorithms of FCM methods are derived. The derived algorithms are called as the kernelized fuzzy C-means (KFCM and kernelized fuzzy C-means with spatial constraints (SKFCM. These methods are implemented on eighteen indexes as validation to determine whether indexes are capable to acquire the optimal clusters number. The performance of segmentation is estimated by applying these methods independently on several datasets to prove which method can give good results and with which indexes. Our test spans various indexes covering the classical and the rather more recent indexes that have enjoyed noticeable success in that field. These indexes are evaluated and compared by applying them on various test images, including synthetic images corrupted with noise of varying levels, and simulated volumetric MRI datasets. Comparative analysis is also presented to show whether the validity index indicates the optimal clustering for our datasets.

  5. A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes

    Science.gov (United States)

    Chow, Winston T. L.; Salamanca, Francisco; Georgescu, Matei; Mahalov, Alex; Milne, Jeffrey M.; Ruddell, Benjamin L.

    2014-12-01

    A multi-method approach estimating summer waste heat emissions from anthropogenic activities (QF) was applied for a major subtropical city (Phoenix, AZ). These included detailed, quality-controlled inventories of city-wide population density and traffic counts to estimate waste heat emissions from population and vehicular sources respectively, and also included waste heat simulations derived from urban electrical consumption generated by a coupled building energy - regional climate model (WRF-BEM + BEP). These component QF data were subsequently summed and mapped through Geographic Information Systems techniques to enable analysis over local (i.e. census-tract) and regional (i.e. metropolitan area) scales. Through this approach, local mean daily QF estimates compared reasonably versus (1.) observed daily surface energy balance residuals from an eddy covariance tower sited within a residential area and (2.) estimates from inventory methods employed in a prior study, with improved sensitivity to temperature and precipitation variations. Regional analysis indicates substantial variations in both mean and maximum daily QF, which varied with urban land use type. Average regional daily QF was ∼13 W m-2 for the summer period. Temporal analyses also indicated notable differences using this approach with previous estimates of QF in Phoenix over different land uses, with much larger peak fluxes averaging ∼50 W m-2 occurring in commercial or industrial areas during late summer afternoons. The spatio-temporal analysis of QF also suggests that it may influence the form and intensity of the Phoenix urban heat island, specifically through additional early evening heat input, and by modifying the urban boundary layer structure through increased turbulence.

  6. The integral first collision kernel method for gamma-ray skyshine analysis[Skyshine; Gamma-ray; First collision kernel; Monte Carlo calculation

    Energy Technology Data Exchange (ETDEWEB)

    Sheu, R.-D.; Chui, C.-S.; Jiang, S.-H. E-mail: shjiang@mx.nthu.edu.tw

    2003-12-01

    A simplified method, based on the integral of the first collision kernel, is presented for performing gamma-ray skyshine calculations for the collimated sources. The first collision kernels were calculated in air for a reference air density by use of the EGS4 Monte Carlo code. These kernels can be applied to other air densities by applying density corrections. The integral first collision kernel (IFCK) method has been used to calculate two of the ANSI/ANS skyshine benchmark problems and the results were compared with a number of other commonly used codes. Our results were generally in good agreement with others but only spend a small fraction of the computation time required by the Monte Carlo calculations. The scheme of the IFCK method for dealing with lots of source collimation geometry is also presented in this study.

  7. Suitability of point kernel dose calculation techniques in brachytherapy treatment planning

    Directory of Open Access Journals (Sweden)

    Lakshminarayanan Thilagam

    2010-01-01

    Full Text Available Brachytherapy treatment planning system (TPS is necessary to estimate the dose to target volume and organ at risk (OAR. TPS is always recommended to account for the effect of tissue, applicator and shielding material heterogeneities exist in applicators. However, most brachytherapy TPS software packages estimate the absorbed dose at a point, taking care of only the contributions of individual sources and the source distribution, neglecting the dose perturbations arising from the applicator design and construction. There are some degrees of uncertainties in dose rate estimations under realistic clinical conditions. In this regard, an attempt is made to explore the suitability of point kernels for brachytherapy dose rate calculations and develop new interactive brachytherapy package, named as BrachyTPS, to suit the clinical conditions. BrachyTPS is an interactive point kernel code package developed to perform independent dose rate calculations by taking into account the effect of these heterogeneities, using two regions build up factors, proposed by Kalos. The primary aim of this study is to validate the developed point kernel code package integrated with treatment planning computational systems against the Monte Carlo (MC results. In the present work, three brachytherapy applicators commonly used in the treatment of uterine cervical carcinoma, namely (i Board of Radiation Isotope and Technology (BRIT low dose rate (LDR applicator and (ii Fletcher Green type LDR applicator (iii Fletcher Williamson high dose rate (HDR applicator, are studied to test the accuracy of the software. Dose rates computed using the developed code are compared with the relevant results of the MC simulations. Further, attempts are also made to study the dose rate distribution around the commercially available shielded vaginal applicator set (Nucletron. The percentage deviations of BrachyTPS computed dose rate values from the MC results are observed to be within plus/minus 5

  8. A kernel adaptive algorithm for quaternion-valued inputs.

    Science.gov (United States)

    Paul, Thomas K; Ogunfunmi, Tokunbo

    2015-10-01

    The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.

  9. Improving the Bandwidth Selection in Kernel Equating

    Science.gov (United States)

    Andersson, Björn; von Davier, Alina A.

    2014-01-01

    We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners…

  10. Point kernels and superposition methods for scatter dose calculations in brachytherapy

    International Nuclear Information System (INIS)

    Carlsson, A.K.

    2000-01-01

    Point kernels have been generated and applied for calculation of scatter dose distributions around monoenergetic point sources for photon energies ranging from 28 to 662 keV. Three different approaches for dose calculations have been compared: a single-kernel superposition method, a single-kernel superposition method where the point kernels are approximated as isotropic and a novel 'successive-scattering' superposition method for improved modelling of the dose from multiply scattered photons. An extended version of the EGS4 Monte Carlo code was used for generating the kernels and for benchmarking the absorbed dose distributions calculated with the superposition methods. It is shown that dose calculation by superposition at and below 100 keV can be simplified by using isotropic point kernels. Compared to the assumption of full in-scattering made by algorithms currently in clinical use, the single-kernel superposition method improves dose calculations in a half-phantom consisting of air and water. Further improvements are obtained using the successive-scattering superposition method, which reduces the overestimates of dose close to the phantom surface usually associated with kernel superposition methods at brachytherapy photon energies. It is also shown that scatter dose point kernels can be parametrized to biexponential functions, making them suitable for use with an effective implementation of the collapsed cone superposition algorithm. (author)

  11. Online learning control using adaptive critic designs with sparse kernel machines.

    Science.gov (United States)

    Xu, Xin; Hou, Zhongsheng; Lian, Chuanqiang; He, Haibo

    2013-05-01

    In the past decade, adaptive critic designs (ACDs), including heuristic dynamic programming (HDP), dual heuristic programming (DHP), and their action-dependent ones, have been widely studied to realize online learning control of dynamical systems. However, because neural networks with manually designed features are commonly used to deal with continuous state and action spaces, the generalization capability and learning efficiency of previous ACDs still need to be improved. In this paper, a novel framework of ACDs with sparse kernel machines is presented by integrating kernel methods into the critic of ACDs. To improve the generalization capability as well as the computational efficiency of kernel machines, a sparsification method based on the approximately linear dependence analysis is used. Using the sparse kernel machines, two kernel-based ACD algorithms, that is, kernel HDP (KHDP) and kernel DHP (KDHP), are proposed and their performance is analyzed both theoretically and empirically. Because of the representation learning and generalization capability of sparse kernel machines, KHDP and KDHP can obtain much better performance than previous HDP and DHP with manually designed neural networks. Simulation and experimental results of two nonlinear control problems, that is, a continuous-action inverted pendulum problem and a ball and plate control problem, demonstrate the effectiveness of the proposed kernel ACD methods.

  12. Estimation of respiratory heat flows in prediction of heat strain among Taiwanese steel workers.

    Science.gov (United States)

    Chen, Wang-Yi; Juang, Yow-Jer; Hsieh, Jung-Yu; Tsai, Perng-Jy; Chen, Chen-Peng

    2017-01-01

    International Organization for Standardization 7933 standard provides evaluation of required sweat rate (RSR) and predicted heat strain (PHS). This study examined and validated the approximations in these models estimating respiratory heat flows (RHFs) via convection (C res ) and evaporation (E res ) for application to Taiwanese foundry workers. The influence of change in RHF approximation to the validity of heat strain prediction in these models was also evaluated. The metabolic energy consumption and physiological quantities of these workers performing at different workloads under elevated wet-bulb globe temperature (30.3 ± 2.5 °C) were measured on-site and used in the calculation of RHFs and indices of heat strain. As the results show, the RSR model overestimated the C res for Taiwanese workers by approximately 3 % and underestimated the E res by 8 %. The C res approximation in the PHS model closely predicted the convective RHF, while the E res approximation over-predicted by 11 %. Linear regressions provided better fit in C res approximation (R 2  = 0.96) than in E res approximation (R 2  ≤ 0.85) in both models. The predicted C res deviated increasingly from the observed value when the WBGT reached 35 °C. The deviations of RHFs observed for the workers from those predicted using the RSR or PHS models did not significantly alter the heat loss via the skin, as the RHFs were in general of a level less than 5 % of the metabolic heat consumption. Validation of these approximations considering thermo-physiological responses of local workers is necessary for application in scenarios of significant heat exposure.

  13. Wheat kernel dimensions: how do they contribute to kernel weight at ...

    Indian Academy of Sciences (India)

    2011-12-02

    Dec 2, 2011 ... yield components, is greatly influenced by kernel dimensions. (KD), such as ..... six linkage gaps, and it covered 3010.70 cM of the whole genome with an ...... Ersoz E. et al. 2009 The Genetic architecture of maize flowering.

  14. Microwave heat treatment as a substitute for conventional treatment of palm oil fruits

    International Nuclear Information System (INIS)

    Mujahid H Al-Fayadh; Nor Azura Masabbir Ali

    1996-01-01

    Microwave energy has become a sound method of heat treatment because of its high penetration power, cleanliness and possible economic significance. In this research, microwave heat was used as a substitute for conventional blanching method of palm oil fruits. Microwave treatment at 2450 MHz and 800 watts gave very close color and frn,frying characteristics to oil of blanched fruits after one minute exposure time. However, five minutes of microwave heat gave severe husk oil discoloration after 49 hours of deep frying, compared to all oils extracted from fruits treated by either low, microwave exposure time or conventional steam treatment. Kernel oil, after five minutes of microwave treatment, was less discolored than both steam or microwave-treated fruits for one minute. More carotenes and discoloration compounds may be contributed to discoloration during microwave treatments. Oil chemical constants of both husk and kernel oils treated by microwave heat were close to those treated by conventional heat. Further research is needed to investigate detailed oil characteristics and evaluate the feasibility study for using microwave energy, as a substitute for conventional heat in palm oil industry

  15. Estimation of surface absorptivity in laser surface heating process with experimental data

    International Nuclear Information System (INIS)

    Chen, H-T; Wu, X-Y

    2006-01-01

    This study applies a hybrid technique of the Laplace transform and finite-difference methods in conjunction with the least-squares method and experimental temperature data inside the test material to predict the unknown surface temperature, heat flux and absorptivity for various surface conditions in the laser surface heating process. In this study, the functional form of the surface temperature is unknown a priori and is assumed to be a function of time before performing the inverse calculation. In addition, the whole time domain is divided into several analysis sub-time intervals and then these unknown estimates on each analysis interval can be predicted. In order to show the accuracy of the present inverse method, comparisons are made among the present estimates, direct results and previous results, showing that the present estimates agree with the direct results for the simulated problem. However, the present estimates of the surface absorptivity deviate slightly from previous estimated results under the assumption of constant thermal properties. The effect of the surface conditions on the surface absorptivity and temperature is not negligible

  16. A multi-label learning based kernel automatic recommendation method for support vector machine.

    Science.gov (United States)

    Zhang, Xueying; Song, Qinbao

    2015-01-01

    Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

  17. Estimation of the temperature, heat gain and heat loss by solar parabolic trough collector under Algerian climate using different thermal oils

    International Nuclear Information System (INIS)

    Ouagued, Malika; Khellaf, Abdallah; Loukarfi, Larbi

    2013-01-01

    Highlights: • Estimation of direct solar radiations for different tracking systems at six typical locations in Algeria. • PTC thermal model uses energy balances from the HTF to the atmosphere. • The model depends on the collector type, nature of HTF, optical properties, and ambient conditions. • Estimation of temperature, heat gain and energy cost of thermal oils used in the model. • Comparison between monthly mean heat gain of the various thermal oils for six Algerian locations. - Abstract: Algeria is blessed with a very important renewable, and more particularly solar, energy potential. This potential opens for Algeria reel opportunities to cope with the increasing energy demand and the growing environmental problems link to the use of fossil fuel. In order to develop and to promote concrete actions in the areas of renewable energy and energy efficiency, Algeria has introduced a national daring program for the period 2011–2030. In this program, solar energy, and more particularly solar thermal energy plays an important role. In this paper, the potential of direct solar irradiance in Algeria and the performance of solar parabolic trough collector (PTC) are estimated under the climate conditions of the country. These two factors are treated as they play an important role in the design of solar thermal plant. In order to determine the most promising solar sites in Algeria, monthly mean daily direct solar radiation have been estimated and compared for different locations corresponding to different climatic region. Different tilted and tracking collectors are considered so as to determine the most efficient system for the PTC. In order to evaluate the performance of a tracking solar parabolic trough collector, a heat transfer model is developed. The receiver, heat collector element (HCE), is divided into several segments and heat balance is applied in each segment over a section of the solar receiver. Different oils are considered to determine the thermal

  18. Heat experiment design to estimate temperature dependent thermal properties

    International Nuclear Information System (INIS)

    Romanovski, M

    2008-01-01

    Experimental conditions are studied to optimize transient experiments for estimating temperature dependent thermal conductivity and volumetric heat capacity. A mathematical model of a specimen is the one-dimensional heat equation with boundary conditions of the second kind. Thermal properties are assumed to vary nonlinearly with temperature. Experimental conditions refer to the thermal loading scheme, sampling times and sensor location. A numerical model of experimental configurations is studied to elicit the optimal conditions. The numerical solution of the design problem is formulated on a regularization scheme with a stabilizer minimization without a regularization parameter. An explicit design criterion is used to reveal the optimal sensor location, heating duration and flux magnitude. Results obtained indicate that even the strongly nonlinear experimental design problem admits the aggregation of its solution and has a strictly defined optimal measurement scheme. Additional region of temperature measurements with allowable identification error is revealed.

  19. Using the Intel Math Kernel Library on Peregrine | High-Performance

    Science.gov (United States)

    Computing | NREL the Intel Math Kernel Library on Peregrine Using the Intel Math Kernel Library on Peregrine Learn how to use the Intel Math Kernel Library (MKL) with Peregrine system software. MKL architectures. Core math functions in MKL include BLAS, LAPACK, ScaLAPACK, sparse solvers, fast Fourier

  20. Estimating heats of detonation and detonation velocities of aromatic energetic compounds

    Energy Technology Data Exchange (ETDEWEB)

    Keshavarz, Mohammad Hossein [Department of Chemistry, Malek-ashtar University of Technology, Shahin-shahr, P. O. Box 83145/115 (Iran)

    2008-12-15

    A new method is introduced to predict reliable estimation of heats of detonation of aromatic energetic compounds. At first step, this procedure assumes that the heat of detonation of an explosive compound of composition C{sub a}H{sub b}N{sub c}O{sub d} can be approximated as the difference between the heat of formation of all H{sub 2}O-CO{sub 2} arbitrary (H{sub 2}O, CO{sub 2}, N{sub 2}) detonation products and that of the explosive, divided by the formula weight of the explosive. Overestimated results based on (H{sub 2}O-CO{sub 2} arbitrary) can be corrected in the next step. Predicted heats of detonation of pure energetic compounds with the product H{sub 2}O in the liquid state for 31 aromatic energetic compounds have a root mean square (rms) deviation of 2.08 and 0.34 kJ g{sup -1} from experiment for (H{sub 2}O-CO{sub 2} arbitrary) and new method, respectively. The new method also gives good results as compared to the second sets of decomposition products, which consider H{sub 2},N{sub 2}, H{sub 2}O,CO, and CO{sub 2} as major gaseous products. It is shown here how the predicted heats of detonation by the new method can be used to obtain reliable estimation of detonation velocity over a wide range of loading densities. (Abstract Copyright [2008], Wiley Periodicals, Inc.)

  1. Protein fold recognition using geometric kernel data fusion.

    Science.gov (United States)

    Zakeri, Pooya; Jeuris, Ben; Vandebril, Raf; Moreau, Yves

    2014-07-01

    Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information. We design several techniques to combine kernel matrices by taking more involved, geometry inspired means of these matrices instead of convex linear combinations. We consider various sequence-based protein features including information extracted directly from position-specific scoring matrices and local sequence alignment. We evaluate our methods for classification on the SCOP PDB-40D benchmark dataset for protein fold recognition. The best overall accuracy on the protein fold recognition test set obtained by our methods is ∼ 86.7%. This is an improvement over the results of the best existing approach. Moreover, our computational model has been developed by incorporating the functional domain composition of proteins through a hybridization model. It is observed that by using our proposed hybridization model, the protein fold recognition accuracy is further improved to 89.30%. Furthermore, we investigate the performance of our approach on the protein remote homology detection problem by fusing multiple string kernels. The MATLAB code used for our proposed geometric kernel fusion frameworks are publicly available at http://people.cs.kuleuven.be/∼raf.vandebril/homepage/software/geomean.php?menu=5/. © The Author 2014. Published by Oxford University Press.

  2. Unsupervised multiple kernel learning for heterogeneous data integration.

    Science.gov (United States)

    Mariette, Jérôme; Villa-Vialaneix, Nathalie

    2018-03-15

    Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single kernel PCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/. jerome.mariette@inra.fr or nathalie.villa-vialaneix@inra.fr. Supplementary data are available at Bioinformatics online.

  3. Kernel bundle EPDiff

    DEFF Research Database (Denmark)

    Sommer, Stefan Horst; Lauze, Francois Bernard; Nielsen, Mads

    2011-01-01

    In the LDDMM framework, optimal warps for image registration are found as end-points of critical paths for an energy functional, and the EPDiff equations describe the evolution along such paths. The Large Deformation Diffeomorphic Kernel Bundle Mapping (LDDKBM) extension of LDDMM allows scale space...

  4. Estimate of the global-scale joule heating rates in the thermosphere due to time mean currents

    International Nuclear Information System (INIS)

    Roble, R.G.; Matsushita, S.

    1975-01-01

    An estimate of the global-scale joule heating rates in the thermosphere is made based on derived global equivalent overhead electric current systems in the dynamo region during geomagnetically quiet and disturbed periods. The equivalent total electric field distribution is calculated from Ohm's law. The global-scale joule heating rates are calculated for various monthly average periods in 1965. The calculated joule heating rates maximize at high latitudes in the early evening and postmidnight sectors. During geomagnetically quiet times the daytime joule heating rates are considerably lower than heating by solar EUV radiation. However, during geomagnetically disturbed periods the estimated joule heating rates increase by an order of magnitude and can locally exceed the solar EUV heating rates. The results show that joule heating is an important and at times the dominant energy source at high latitudes. However, the global mean joule heating rates calculated near solar minimum are generally small compared to the global mean solar EUV heating rates. (auth)

  5. Proteome analysis of the almond kernel (Prunus dulcis).

    Science.gov (United States)

    Li, Shugang; Geng, Fang; Wang, Ping; Lu, Jiankang; Ma, Meihu

    2016-08-01

    Almond (Prunus dulcis) is a popular tree nut worldwide and offers many benefits to human health. However, the importance of almond kernel proteins in the nutrition and function in human health requires further evaluation. The present study presents a systematic evaluation of the proteins in the almond kernel using proteomic analysis. The nutrient and amino acid content in almond kernels from Xinjiang is similar to that of American varieties; however, Xinjiang varieties have a higher protein content. Two-dimensional electrophoresis analysis demonstrated a wide distribution of molecular weights and isoelectric points of almond kernel proteins. A total of 434 proteins were identified by LC-MS/MS, and most were proteins that were experimentally confirmed for the first time. Gene ontology (GO) analysis of the 434 proteins indicated that proteins involved in primary biological processes including metabolic processes (67.5%), cellular processes (54.1%), and single-organism processes (43.4%), the main molecular function of almond kernel proteins are in catalytic activity (48.0%), binding (45.4%) and structural molecule activity (11.9%), and proteins are primarily distributed in cell (59.9%), organelle (44.9%), and membrane (22.8%). Almond kernel is a source of a wide variety of proteins. This study provides important information contributing to the screening and identification of almond proteins, the understanding of almond protein function, and the development of almond protein products. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

  6. Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

    Science.gov (United States)

    Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

    2014-01-01

    Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

  7. Control Transfer in Operating System Kernels

    Science.gov (United States)

    1994-05-13

    microkernel system that runs less code in the kernel address space. To realize the performance benefit of allocating stacks in unmapped kseg0 memory, the...review how I modified the Mach 3.0 kernel to use continuations. Because of Mach’s message-passing microkernel structure, interprocess communication was...critical control transfer paths, deeply- nested call chains are undesirable in any case because of the function call overhead. 4.1.3 Microkernel Operating

  8. Bivariate discrete beta Kernel graduation of mortality data.

    Science.gov (United States)

    Mazza, Angelo; Punzo, Antonio

    2015-07-01

    Various parametric/nonparametric techniques have been proposed in literature to graduate mortality data as a function of age. Nonparametric approaches, as for example kernel smoothing regression, are often preferred because they do not assume any particular mortality law. Among the existing kernel smoothing approaches, the recently proposed (univariate) discrete beta kernel smoother has been shown to provide some benefits. Bivariate graduation, over age and calendar years or durations, is common practice in demography and actuarial sciences. In this paper, we generalize the discrete beta kernel smoother to the bivariate case, and we introduce an adaptive bandwidth variant that may provide additional benefits when data on exposures to the risk of death are available; furthermore, we outline a cross-validation procedure for bandwidths selection. Using simulations studies, we compare the bivariate approach proposed here with its corresponding univariate formulation and with two popular nonparametric bivariate graduation techniques, based on Epanechnikov kernels and on P-splines. To make simulations realistic, a bivariate dataset, based on probabilities of dying recorded for the US males, is used. Simulations have confirmed the gain in performance of the new bivariate approach with respect to both the univariate and the bivariate competitors.

  9. Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

    Science.gov (United States)

    Echinaka, Yuki; Ozeki, Yukiyasu

    2016-10-01

    The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

  10. Evaluation of palm kernel fibers (PKFs for production of asbestos-free automotive brake pads

    Directory of Open Access Journals (Sweden)

    K.K. Ikpambese

    2016-01-01

    Full Text Available In this study, asbestos-free automotive brake pads produced from palm kernel fibers with epoxy-resin binder was evaluated. Resins varied in formulations and properties such as friction coefficient, wear rate, hardness test, porosity, noise level, temperature, specific gravity, stopping time, moisture effects, surface roughness, oil and water absorptions rates, and microstructure examination were investigated. Other basic engineering properties of mechanical overload, thermal deformation fading behaviour shear strength, cracking resistance, over-heat recovery, and effect on rotor disc, caliper pressure, pad grip effect and pad dusting effect were also investigated. The results obtained indicated that the wear rate, coefficient of friction, noise level, temperature, and stopping time of the produced brake pads increased as the speed increases. The results also show that porosity, hardness, moisture content, specific gravity, surface roughness, and oil and water absorption rates remained constant with increase in speed. The result of microstructure examination revealed that worm surfaces were characterized by abrasion wear where the asperities were ploughed thereby exposing the white region of palm kernel fibers, thus increasing the smoothness of the friction materials. Sample S6 with composition of 40% epoxy-resin, 10% palm wastes, 6% Al2O3, 29% graphite, and 15% calcium carbonate gave better properties. The result indicated that palm kernel fibers can be effectively used as a replacement for asbestos in brake pad production.

  11. A framework for optimal kernel-based manifold embedding of medical image data.

    Science.gov (United States)

    Zimmer, Veronika A; Lekadir, Karim; Hoogendoorn, Corné; Frangi, Alejandro F; Piella, Gemma

    2015-04-01

    Kernel-based dimensionality reduction is a widely used technique in medical image analysis. To fully unravel the underlying nonlinear manifold the selection of an adequate kernel function and of its free parameters is critical. In practice, however, the kernel function is generally chosen as Gaussian or polynomial and such standard kernels might not always be optimal for a given image dataset or application. In this paper, we present a study on the effect of the kernel functions in nonlinear manifold embedding of medical image data. To this end, we first carry out a literature review on existing advanced kernels developed in the statistics, machine learning, and signal processing communities. In addition, we implement kernel-based formulations of well-known nonlinear dimensional reduction techniques such as Isomap and Locally Linear Embedding, thus obtaining a unified framework for manifold embedding using kernels. Subsequently, we present a method to automatically choose a kernel function and its associated parameters from a pool of kernel candidates, with the aim to generate the most optimal manifold embeddings. Furthermore, we show how the calculated selection measures can be extended to take into account the spatial relationships in images, or used to combine several kernels to further improve the embedding results. Experiments are then carried out on various synthetic and phantom datasets for numerical assessment of the methods. Furthermore, the workflow is applied to real data that include brain manifolds and multispectral images to demonstrate the importance of the kernel selection in the analysis of high-dimensional medical images. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Measurement of Weight of Kernels in a Simulated Cylindrical Fuel Compact for HTGR

    International Nuclear Information System (INIS)

    Kim, Woong Ki; Lee, Young Woo; Kim, Young Min; Kim, Yeon Ku; Eom, Sung Ho; Jeong, Kyung Chai; Cho, Moon Sung; Cho, Hyo Jin; Kim, Joo Hee

    2011-01-01

    The TRISO-coated fuel particle for the high temperature gas-cooled reactor (HTGR) is composed of a nuclear fuel kernel and outer coating layers. The coated particles are mixed with graphite matrix to make HTGR fuel element. The weight of fuel kernels in an element is generally measured by the chemical analysis or a gamma-ray spectrometer. Although it is accurate to measure the weight of kernels by the chemical analysis, the samples used in the analysis cannot be put again in the fabrication process. Furthermore, radioactive wastes are generated during the inspection procedure. The gamma-ray spectrometer requires an elaborate reference sample to reduce measurement errors induced from the different geometric shape of test sample from that of reference sample. X-ray computed tomography (CT) is an alternative to measure the weight of kernels in a compact nondestructively. In this study, X-ray CT is applied to measure the weight of kernels in a cylindrical compact containing simulated TRISO-coated particles with ZrO 2 kernels. The volume of kernels as well as the number of kernels in the simulated compact is measured from the 3-D density information. The weight of kernels was calculated from the volume of kernels or the number of kernels. Also, the weight of kernels was measured by extracting the kernels from a compact to review the result of the X-ray CT application

  13. 3-D waveform tomography sensitivity kernels for anisotropic media

    KAUST Repository

    Djebbi, Ramzi

    2014-01-01

    The complications in anisotropic multi-parameter inversion lie in the trade-off between the different anisotropy parameters. We compute the tomographic waveform sensitivity kernels for a VTI acoustic medium perturbation as a tool to investigate this ambiguity between the different parameters. We use dynamic ray tracing to efficiently handle the expensive computational cost for 3-D anisotropic models. Ray tracing provides also the ray direction information necessary for conditioning the sensitivity kernels to handle anisotropy. The NMO velocity and η parameter kernels showed a maximum sensitivity for diving waves which results in a relevant choice of those parameters in wave equation tomography. The δ parameter kernel showed zero sensitivity; therefore it can serve as a secondary parameter to fit the amplitude in the acoustic anisotropic inversion. Considering the limited penetration depth of diving waves, migration velocity analysis based kernels are introduced to fix the depth ambiguity with reflections and compute sensitivity maps in the deeper parts of the model.

  14. A Fourier-series-based kernel-independent fast multipole method

    International Nuclear Information System (INIS)

    Zhang Bo; Huang Jingfang; Pitsianis, Nikos P.; Sun Xiaobai

    2011-01-01

    We present in this paper a new kernel-independent fast multipole method (FMM), named as FKI-FMM, for pairwise particle interactions with translation-invariant kernel functions. FKI-FMM creates, using numerical techniques, sufficiently accurate and compressive representations of a given kernel function over multi-scale interaction regions in the form of a truncated Fourier series. It provides also economic operators for the multipole-to-multipole, multipole-to-local, and local-to-local translations that are typical and essential in the FMM algorithms. The multipole-to-local translation operator, in particular, is readily diagonal and does not dominate in arithmetic operations. FKI-FMM provides an alternative and competitive option, among other kernel-independent FMM algorithms, for an efficient application of the FMM, especially for applications where the kernel function consists of multi-physics and multi-scale components as those arising in recent studies of biological systems. We present the complexity analysis and demonstrate with experimental results the FKI-FMM performance in accuracy and efficiency.

  15. Heat flux estimation for neutral beam line components using inverse heat conduction procedures

    International Nuclear Information System (INIS)

    Bharathi, P.; Prahlad, V.; Quereshi, K.; Bansal, L.K.; Rambabu, S.; Sharma, S.K.; Parmar, S.; Patel, P.J.; Baruah, U.K.; Patel, Ravi

    2015-01-01

    In this work, we describe and compare the analytical IHCP methods such-as semi-infinite method, finite slab method and a numerical method called Stolz method for estimating the incident heat flux from the experimentally measured temperature data. In case of analytical methods, the finite time response of the sensor is needed to be accounted for an accurate power density estimations. The modified models corrected for the response time of the sensors are also discussed in this paper. Application of these methods using example temperature waveforms obtained on the SST1-NBI test stand is presented and discussed. For choosing the suitable method for the calorimetry on beam line components, the estimated results are also validated using the ANSYS analysis done on these beam Iine components. As a conclusion, the finite slab method corrected for the influence of the sensor response time found out to be the most suitable method for the inversion of temperature data in case of neutral beam line components

  16. Resummed memory kernels in generalized system-bath master equations

    International Nuclear Information System (INIS)

    Mavros, Michael G.; Van Voorhis, Troy

    2014-01-01

    Generalized master equations provide a concise formalism for studying reduced population dynamics. Usually, these master equations require a perturbative expansion of the memory kernels governing the dynamics; in order to prevent divergences, these expansions must be resummed. Resummation techniques of perturbation series are ubiquitous in physics, but they have not been readily studied for the time-dependent memory kernels used in generalized master equations. In this paper, we present a comparison of different resummation techniques for such memory kernels up to fourth order. We study specifically the spin-boson Hamiltonian as a model system bath Hamiltonian, treating the diabatic coupling between the two states as a perturbation. A novel derivation of the fourth-order memory kernel for the spin-boson problem is presented; then, the second- and fourth-order kernels are evaluated numerically for a variety of spin-boson parameter regimes. We find that resumming the kernels through fourth order using a Padé approximant results in divergent populations in the strong electronic coupling regime due to a singularity introduced by the nature of the resummation, and thus recommend a non-divergent exponential resummation (the “Landau-Zener resummation” of previous work). The inclusion of fourth-order effects in a Landau-Zener-resummed kernel is shown to improve both the dephasing rate and the obedience of detailed balance over simpler prescriptions like the non-interacting blip approximation, showing a relatively quick convergence on the exact answer. The results suggest that including higher-order contributions to the memory kernel of a generalized master equation and performing an appropriate resummation can provide a numerically-exact solution to system-bath dynamics for a general spectral density, opening the way to a new class of methods for treating system-bath dynamics

  17. The dipole form of the gluon part of the BFKL kernel

    International Nuclear Information System (INIS)

    Fadin, V.S.; Fiore, R.; Grabovsky, A.V.; Papa, A.

    2007-01-01

    The dipole form of the gluon part of the color singlet BFKL kernel in the next-to-leading order (NLO) is obtained in the coordinate representation by direct transfer from the momentum representation, where the kernel was calculated before. With this paper the transformation of the NLO BFKL kernel to the dipole form, started a few months ago with the quark part of the kernel, is completed

  18. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

    Science.gov (United States)

    Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

    2018-02-19

    Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

  19. The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis

    DEFF Research Database (Denmark)

    Czekaj, Tomasz Gerard

    and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...... within a nonparametric panel data regression framework. The fourth paper analyses the technical efficiency of dairy farms with environmental output using nonparametric kernel regression in a semiparametric stochastic frontier analysis. The results provided in this PhD thesis show that nonparametric......This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...

  20. Efficient estimation of dynamic density functions with an application to outlier detection

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali; Zhang, Xiangliang; Wang, Suojin

    2012-01-01

    In this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data. © 2012 ACM.

  1. Genetic Analysis of Kernel Traits in Maize-Teosinte Introgression Populations

    Directory of Open Access Journals (Sweden)

    Zhengbin Liu

    2016-08-01

    Full Text Available Seed traits have been targeted by human selection during the domestication of crop species as a way to increase the caloric and nutritional content of food during the transition from hunter-gather to early farming societies. The primary seed trait under selection was likely seed size/weight as it is most directly related to overall grain yield. Additional seed traits involved in seed shape may have also contributed to larger grain. Maize (Zea mays ssp. mays kernel weight has increased more than 10-fold in the 9000 years since domestication from its wild ancestor, teosinte (Z. mays ssp. parviglumis. In order to study how size and shape affect kernel weight, we analyzed kernel morphometric traits in a set of 10 maize-teosinte introgression populations using digital imaging software. We identified quantitative trait loci (QTL for kernel area and length with moderate allelic effects that colocalize with kernel weight QTL. Several genomic regions with strong effects during maize domestication were detected, and a genetic framework for kernel traits was characterized by complex pleiotropic interactions. Our results both confirm prior reports of kernel domestication loci and identify previously uncharacterized QTL with a range of allelic effects, enabling future research into the genetic basis of these traits.

  2. SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

    Energy Technology Data Exchange (ETDEWEB)

    Khazaee, M [shahid beheshti university, Tehran, Tehran (Iran, Islamic Republic of); Asl, A Kamali [Shahid Beheshti University, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of); Geramifar, P [Shariati Hospital, Tehran, Iran., Tehran, Tehran (Iran, Islamic Republic of)

    2015-06-15

    Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine.

  3. SU-E-T-154: Calculation of Tissue Dose Point Kernels Using GATE Monte Carlo Simulation Toolkit to Compare with Water Dose Point Kernel

    International Nuclear Information System (INIS)

    Khazaee, M; Asl, A Kamali; Geramifar, P

    2015-01-01

    Purpose: the objective of this study was to assess utilizing water dose point kernel (DPK)instead of tissue dose point kernels in convolution algorithms.to the best of our knowledge, in providing 3D distribution of absorbed dose from a 3D distribution of the activity, the human body is considered equivalent to water. as a Result tissue variations are not considered in patient specific dosimetry. Methods: In this study Gate v7.0 was used to calculate tissue dose point kernel. the beta emitter radionuclides which have taken into consideration in this simulation include Y-90, Lu-177 and P-32 which are commonly used in nuclear medicine. the comparison has been performed for dose point kernels of adipose, bone, breast, heart, intestine, kidney, liver, lung and spleen versus water dose point kernel. Results: In order to validate the simulation the Result of 90Y DPK in water were compared with published results of Papadimitroulas et al (Med. Phys., 2012). The results represented that the mean differences between water DPK and other soft tissues DPKs range between 0.6 % and 1.96% for 90Y, except for lung and bone, where the observed discrepancies are 6.3% and 12.19% respectively. The range of DPK difference for 32P is between 1.74% for breast and 18.85% for bone. For 177Lu, the highest difference belongs to bone which is equal to 16.91%. For other soft tissues the least discrepancy is observed in kidney with 1.68%. Conclusion: In all tissues except for lung and bone, the results of GATE for dose point kernel were comparable to water dose point kernel which demonstrates the appropriateness of applying water dose point kernel instead of soft tissues in the field of nuclear medicine

  4. Pipeline heating method based on optimal control and state estimation

    Energy Technology Data Exchange (ETDEWEB)

    Vianna, F.L.V. [Dept. of Subsea Technology. Petrobras Research and Development Center - CENPES, Rio de Janeiro, RJ (Brazil)], e-mail: fvianna@petrobras.com.br; Orlande, H.R.B. [Dept. of Mechanical Engineering. POLI/COPPE, Federal University of Rio de Janeiro - UFRJ, Rio de Janeiro, RJ (Brazil)], e-mail: helcio@mecanica.ufrj.br; Dulikravich, G.S. [Dept. of Mechanical and Materials Engineering. Florida International University - FIU, Miami, FL (United States)], e-mail: dulikrav@fiu.edu

    2010-07-01

    In production of oil and gas wells in deep waters the flowing of hydrocarbon through pipeline is a challenging problem. This environment presents high hydrostatic pressures and low sea bed temperatures, which can favor the formation of solid deposits that in critical operating conditions, as unplanned shutdown conditions, may result in a pipeline blockage and consequently incur in large financial losses. There are different methods to protect the system, but nowadays thermal insulation and chemical injection are the standard solutions normally used. An alternative method of flow assurance is to heat the pipeline. This concept, which is known as active heating system, aims at heating the produced fluid temperature above a safe reference level in order to avoid the formation of solid deposits. The objective of this paper is to introduce a Bayesian statistical approach for the state estimation problem, in which the state variables are considered as the transient temperatures within a pipeline cross-section, and to use the optimal control theory as a design tool for a typical heating system during a simulated shutdown condition. An application example is presented to illustrate how Bayesian filters can be used to reconstruct the temperature field from temperature measurements supposedly available on the external surface of the pipeline. The temperatures predicted with the Bayesian filter are then utilized in a control approach for a heating system used to maintain the temperature within the pipeline above the critical temperature of formation of solid deposits. The physical problem consists of a pipeline cross section represented by a circular domain with four points over the pipe wall representing heating cables. The fluid is considered stagnant, homogeneous, isotropic and with constant thermo-physical properties. The mathematical formulation governing the direct problem was solved with the finite volume method and for the solution of the state estimation problem

  5. Kernel Function Tuning for Single-Layer Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Vidnerová, Petra; Neruda, Roman

    -, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

  6. Broken rice kernels and the kinetics of rice hydration and texture during cooking.

    Science.gov (United States)

    Saleh, Mohammed; Meullenet, Jean-Francois

    2013-05-01

    During rice milling and processing, broken kernels are inevitably present, although to date it has been unclear as to how the presence of broken kernels affects rice hydration and cooked rice texture. Therefore, this work intended to study the effect of broken kernels in a rice sample on rice hydration and texture during cooking. Two medium-grain and two long-grain rice cultivars were harvested, dried and milled, and the broken kernels were separated from unbroken kernels. Broken rice kernels were subsequently combined with unbroken rice kernels forming treatments of 0, 40, 150, 350 or 1000 g kg(-1) broken kernels ratio. Rice samples were then cooked and the moisture content of the cooked rice, the moisture uptake rate, and rice hardness and stickiness were measured. As the amount of broken rice kernels increased, rice sample texture became increasingly softer (P hardness was negatively correlated to the percentage of broken kernels in rice samples. Differences in the proportions of broken rice in a milled rice sample play a major role in determining the texture properties of cooked rice. Variations in the moisture migration kinetics between broken and unbroken kernels caused faster hydration of the cores of broken rice kernels, with greater starch leach-out during cooking affecting the texture of the cooked rice. The texture of cooked rice can be controlled, to some extent, by varying the proportion of broken kernels in milled rice. © 2012 Society of Chemical Industry.

  7. Estimation of bulk transfer coefficient for latent heat flux (Ce)

    Digital Repository Service at National Institute of Oceanography (India)

    Sadhuram, Y.

    The bulk transfer coefficient for latent heat flux (Ce) has been estimated over the Arabian Sea from the moisture budget during the pre-monsoon season of 1988. The computations have been made over two regions (A: 0-8 degrees N: 60-68 degrees E: B: 0...

  8. Local coding based matching kernel method for image classification.

    Directory of Open Access Journals (Sweden)

    Yan Song

    Full Text Available This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.

  9. Process for producing metal oxide kernels and kernels so obtained

    International Nuclear Information System (INIS)

    Lelievre, Bernard; Feugier, Andre.

    1974-01-01

    The process desbribed is for producing fissile or fertile metal oxide kernels used in the fabrication of fuels for high temperature nuclear reactors. This process consists in adding to an aqueous solution of at least one metallic salt, particularly actinide nitrates, at least one chemical compound capable of releasing ammonia, in dispersing drop by drop the solution thus obtained into a hot organic phase to gel the drops and transform them into solid particles. These particles are then washed, dried and treated to turn them into oxide kernels. The organic phase used for the gel reaction is formed of a mixture composed of two organic liquids, one acting as solvent and the other being a product capable of extracting the anions from the metallic salt of the drop at the time of gelling. Preferably an amine is used as product capable of extracting the anions. Additionally, an alcohol that causes a part dehydration of the drops can be employed as solvent, thus helping to increase the resistance of the particles [fr

  10. Exogenous Cytokinins Increase Grain Yield of Winter Wheat Cultivars by Improving Stay-Green Characteristics under Heat Stress.

    Directory of Open Access Journals (Sweden)

    Dongqing Yang

    Full Text Available Stay-green, a key trait of wheat, can not only increase the yield of wheat but also its resistance to heat stress during active photosynthesis. Cytokinins are the most potent general coordinator between the stay-green trait and senescence. The objectives of the present study were to identify and assess the effects of cytokinins on the photosynthetic organ and heat resistance in wheat. Two winter wheat cultivars, Wennong 6 (a stay-green cultivar and Jimai 20 (a control cultivar, were subjected to heat stress treatment from 1 to 5 days after anthesis (DAA. The two cultivars were sprayed daily with 10 mg L-1 of 6-benzylaminopurine (6-BA between 1 and 3 DAA under ambient and elevated temperature conditions. We found that the heat stress significantly decreased the number of kernels per spike and the grain yield (P < 0.05. Heat stress also decreased the zeatin riboside (ZR content, but increased the gibberellin (GA3, indole-3-acetic acid (IAA, and abscisic acid (ABA contents at 3 to 15 DAA. Application of 6-BA significantly (P < 0.05 increased the grain-filling rate, endosperm cell division rate, endosperm cell number, and 1,000-grain weight under heated condition. 6-BA application increased ZR and IAA contents at 3 to 28 DAA, but decreased GA3 and ABA contents. The contents of ZR, ABA, and IAA in kernels were positively and significantly correlated with the grain-filling rate (P < 0.05, whereas GA3 was counter-productive at 3 to 15 DAA. These results suggest that the decrease in grain yield under heat stress was due to a lower ZR content and a higher GA3 content compared to that at elevated temperature during the early development of the kernels, which resulted in less kernel number and lower grain-filling rate. The results also provide essential information for further utilization of the cytokinin substances in the cultivation of heat-resistant wheat.

  11. Ideal Gas Resonance Scattering Kernel Routine for the NJOY Code

    International Nuclear Information System (INIS)

    Rothenstein, W.

    1999-01-01

    In a recent publication an expression for the temperature-dependent double-differential ideal gas scattering kernel is derived for the case of scattering cross sections that are energy dependent. Some tabulations and graphical representations of the characteristics of these kernels are presented in Ref. 2. They demonstrate the increased probability that neutron scattering by a heavy nuclide near one of its pronounced resonances will bring the neutron energy nearer to the resonance peak. This enhances upscattering, when a neutron with energy just below that of the resonance peak collides with such a nuclide. A routine for using the new kernel has now been introduced into the NJOY code. Here, its principal features are described, followed by comparisons between scattering data obtained by the new kernel, and the standard ideal gas kernel, when such comparisons are meaningful (i.e., for constant values of the scattering cross section a 0 K). The new ideal gas kernel for variable σ s 0 (E) at 0 K leads to the correct Doppler-broadened σ s T (E) at temperature T

  12. Estimating thermal diffusivity and specific heat from needle probe thermal conductivity data

    Science.gov (United States)

    Waite, W.F.; Gilbert, L.Y.; Winters, W.J.; Mason, D.H.

    2006-01-01

    Thermal diffusivity and specific heat can be estimated from thermal conductivity measurements made using a standard needle probe and a suitably high data acquisition rate. Thermal properties are calculated from the measured temperature change in a sample subjected to heating by a needle probe. Accurate thermal conductivity measurements are obtained from a linear fit to many tens or hundreds of temperature change data points. In contrast, thermal diffusivity calculations require a nonlinear fit to the measured temperature change occurring in the first few tenths of a second of the measurement, resulting in a lower accuracy than that obtained for thermal conductivity. Specific heat is calculated from the ratio of thermal conductivity to diffusivity, and thus can have an uncertainty no better than that of the diffusivity estimate. Our thermal conductivity measurements of ice Ih and of tetrahydrofuran (THF) hydrate, made using a 1.6 mm outer diameter needle probe and a data acquisition rate of 18.2 pointss, agree with published results. Our thermal diffusivity and specific heat results reproduce published results within 25% for ice Ih and 3% for THF hydrate. ?? 2006 American Institute of Physics.

  13. Local heat transfer estimation in microchannels during convective boiling under microgravity conditions: 3D inverse heat conduction problem using BEM techniques

    Science.gov (United States)

    Luciani, S.; LeNiliot, C.

    2008-11-01

    Two-phase and boiling flow instabilities are complex, due to phase change and the existence of several interfaces. To fully understand the high heat transfer potential of boiling flows in microscale's geometry, it is vital to quantify these transfers. To perform this task, an experimental device has been designed to observe flow patterns. Analysis is made up by using an inverse method which allows us to estimate the local heat transfers while boiling occurs inside a microchannel. In our configuration, the direct measurement would impair the accuracy of the searched heat transfer coefficient because thermocouples implanted on the surface minichannels would disturb the established flow. In this communication, we are solving a 3D IHCP which consists in estimating using experimental data measurements the surface temperature and the surface heat flux in a minichannel during convective boiling under several gravity levels (g, 1g, 1.8g). The considered IHCP is formulated as a mathematical optimization problem and solved using the boundary element method (BEM).

  14. Geodesic exponential kernels: When Curvature and Linearity Conflict

    DEFF Research Database (Denmark)

    Feragen, Aase; Lauze, François; Hauberg, Søren

    2015-01-01

    manifold, the geodesic Gaussian kernel is only positive definite if the Riemannian manifold is Euclidean. This implies that any attempt to design geodesic Gaussian kernels on curved Riemannian manifolds is futile. However, we show that for spaces with conditionally negative definite distances the geodesic...

  15. Real time kernel performance monitoring with SystemTap

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    SystemTap is a dynamic method of monitoring and tracing the operation of a running Linux kernel. In this talk I will present a few practical use cases where SystemTap allowed me to turn otherwise complex userland monitoring tasks in simple kernel probes.

  16. A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature

    Directory of Open Access Journals (Sweden)

    Yongmin Yang

    2017-01-01

    Full Text Available The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H to analytically estimate the radiometric-convective resistance (rae via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD of H and latent heat flux (LE from OSML are 34.5 W/m2 and 46.5 W/m2 at SMACEX site and 50.1 W/m2 and 67.0 W/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System. Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation

  17. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2011-01-01

    In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator

  18. Comparative Analysis of Kernel Methods for Statistical Shape Learning

    National Research Council Canada - National Science Library

    Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen

    2006-01-01

    .... In this work, we perform a comparative analysis of shape learning techniques such as linear PCA, kernel PCA, locally linear embedding and propose a new method, kernelized locally linear embedding...

  19. Estimation of Surface Temperature and Heat Flux by Inverse Heat Transfer Methods Using Internal Temperatures Measured While Radiantly Heating a Carbon/Carbon Specimen up to 1920 F

    Science.gov (United States)

    Pizzo, Michelle; Daryabeigi, Kamran; Glass, David

    2015-01-01

    The ability to solve the heat conduction equation is needed when designing materials to be used on vehicles exposed to extremely high temperatures; e.g. vehicles used for atmospheric entry or hypersonic flight. When using test and flight data, computational methods such as finite difference schemes may be used to solve for both the direct heat conduction problem, i.e., solving between internal temperature measurements, and the inverse heat conduction problem, i.e., using the direct solution to march forward in space to the surface of the material to estimate both surface temperature and heat flux. The completed research first discusses the methods used in developing a computational code to solve both the direct and inverse heat transfer problems using one dimensional, centered, implicit finite volume schemes and one dimensional, centered, explicit space marching techniques. The developed code assumed the boundary conditions to be specified time varying temperatures and also considered temperature dependent thermal properties. The completed research then discusses the results of analyzing temperature data measured while radiantly heating a carbon/carbon specimen up to 1920 F. The temperature was measured using thermocouple (TC) plugs (small carbon/carbon material specimens) with four embedded TC plugs inserted into the larger carbon/carbon specimen. The purpose of analyzing the test data was to estimate the surface heat flux and temperature values from the internal temperature measurements using direct and inverse heat transfer methods, thus aiding in the thermal and structural design and analysis of high temperature vehicles.

  20. KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali; Wang, Suojin; Zhang, Xiangliang

    2016-01-01

    Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

  1. KDE-Track: An Efficient Dynamic Density Estimator for Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali

    2016-11-08

    Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.

  2. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. A class of kernel based real-time elastography algorithms.

    Science.gov (United States)

    Kibria, Md Golam; Hasan, Md Kamrul

    2015-08-01

    In this paper, a novel real-time kernel-based and gradient-based Phase Root Seeking (PRS) algorithm for ultrasound elastography is proposed. The signal-to-noise ratio of the strain image resulting from this method is improved by minimizing the cross-correlation discrepancy between the pre- and post-compression radio frequency signals with an adaptive temporal stretching method and employing built-in smoothing through an exponentially weighted neighborhood kernel in the displacement calculation. Unlike conventional PRS algorithms, displacement due to tissue compression is estimated from the root of the weighted average of the zero-lag cross-correlation phases of the pair of corresponding analytic pre- and post-compression windows in the neighborhood kernel. In addition to the proposed one, the other time- and frequency-domain elastography algorithms (Ara et al., 2013; Hussain et al., 2012; Hasan et al., 2012) proposed by our group are also implemented in real-time using Java where the computations are serially executed or parallely executed in multiple processors with efficient memory management. Simulation results using finite element modeling simulation phantom show that the proposed method significantly improves the strain image quality in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe) and mean structural similarity (MSSIM) for strains as high as 4% as compared to other reported techniques in the literature. Strain images obtained for the experimental phantom as well as in vivo breast data of malignant or benign masses also show the efficacy of our proposed method over the other reported techniques in the literature. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. Ideal gas scattering kernel for energy dependent cross-sections

    International Nuclear Information System (INIS)

    Rothenstein, W.; Dagan, R.

    1998-01-01

    A third, and final, paper on the calculation of the joint kernel for neutron scattering by an ideal gas in thermal agitation is presented, when the scattering cross-section is energy dependent. The kernel is a function of the neutron energy after scattering, and of the cosine of the scattering angle, as in the case of the ideal gas kernel for a constant bound atom scattering cross-section. The final expression is suitable for numerical calculations

  5. Parameter optimization in the regularized kernel minimum noise fraction transformation

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Vestergaard, Jacob Schack

    2012-01-01

    Based on the original, linear minimum noise fraction (MNF) transformation and kernel principal component analysis, a kernel version of the MNF transformation was recently introduced. Inspired by we here give a simple method for finding optimal parameters in a regularized version of kernel MNF...... analysis. We consider the model signal-to-noise ratio (SNR) as a function of the kernel parameters and the regularization parameter. In 2-4 steps of increasingly refined grid searches we find the parameters that maximize the model SNR. An example based on data from the DLR 3K camera system is given....

  6. Estimating Conditional Distributions by Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1998-01-01

    Neural Networks for estimating conditionaldistributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications...

  7. Surface layer scintillometry for estimating the sensible heat flux component of the surface energy balance

    Directory of Open Access Journals (Sweden)

    M. J. Savage

    2010-01-01

    Full Text Available The relatively recently developed scintillometry method, with a focus on the dual-beam surface layer scintillometer (SLS, allows boundary layer atmospheric turbulence, surface sensible heat and momentum flux to be estimated in real-time. Much of the previous research using the scintillometer method has involved the large aperture scintillometer method, with only a few studies using the SLS method. The SLS method has been mainly used by agrometeorologists, hydrologists and micrometeorologists for atmospheric stability and surface energy balance studies to obtain estimates of sensible heat from which evaporation estimates representing areas of one hectare or larger are possible. Other applications include the use of the SLS method in obtaining crucial input parameters for atmospheric dispersion and turbulence models. The SLS method relies upon optical scintillation of a horizontal laser beam between transmitter and receiver for a separation distance typically between 50 and 250 m caused by refractive index inhomogeneities in the atmosphere that arise from turbulence fluctuations in air temperature and to a much lesser extent the fluctuations in water vapour pressure. Measurements of SLS beam transmission allow turbulence of the atmosphere to be determined, from which sub-hourly, real-time and in situ path-weighted fluxes of sensible heat and momentum may be calculated by application of the Monin-Obukhov similarity theory. Unlike the eddy covariance (EC method for which corrections for flow distortion and coordinate rotation are applied, no corrections to the SLS measurements, apart from a correction for water vapour pressure, are applied. Also, path-weighted SLS estimates over the propagation path are obtained. The SLS method also offers high temporal measurement resolution and usually greater spatial coverage compared to EC, Bowen ratio energy balance, surface renewal and other sensible heat measurement methods. Applying the shortened surface

  8. On flame kernel formation and propagation in premixed gases

    Energy Technology Data Exchange (ETDEWEB)

    Eisazadeh-Far, Kian; Metghalchi, Hameed [Northeastern University, Mechanical and Industrial Engineering Department, Boston, MA 02115 (United States); Parsinejad, Farzan [Chevron Oronite Company LLC, Richmond, CA 94801 (United States); Keck, James C. [Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)

    2010-12-15

    Flame kernel formation and propagation in premixed gases have been studied experimentally and theoretically. The experiments have been carried out at constant pressure and temperature in a constant volume vessel located in a high speed shadowgraph system. The formation and propagation of the hot plasma kernel has been simulated for inert gas mixtures using a thermodynamic model. The effects of various parameters including the discharge energy, radiation losses, initial temperature and initial volume of the plasma have been studied in detail. The experiments have been extended to flame kernel formation and propagation of methane/air mixtures. The effect of energy terms including spark energy, chemical energy and energy losses on flame kernel formation and propagation have been investigated. The inputs for this model are the initial conditions of the mixture and experimental data for flame radii. It is concluded that these are the most important parameters effecting plasma kernel growth. The results of laminar burning speeds have been compared with previously published results and are in good agreement. (author)

  9. Insights from Classifying Visual Concepts with Multiple Kernel Learning

    Science.gov (United States)

    Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; Brefeld, Ulf; Müller, Klaus-Robert; Kawanabe, Motoaki

    2012-01-01

    Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25). PMID:22936970

  10. On estimation of reliability for pipe lines of heat power plants under cyclic loading

    International Nuclear Information System (INIS)

    Verezemskij, V.G.

    1986-01-01

    One of the possible methods to obtain a quantitative estimate of the reliability for pipe lines of the welded heat power plants under cyclic loading due to heating-cooling and due to vibration is considered. Reliability estimate is carried out for a common case of loading by simultaneous cycles with different amplitudes and loading asymmetry. It is shown that scattering of the breaking number of cycles for the metal of welds may perceptibly decrease reliability of the welded pipe line

  11. BrachyTPS -Interactive point kernel code package for brachytherapy treatment planning of gynaecological cancers

    International Nuclear Information System (INIS)

    Thilagam, L.; Subbaiah, K.V.

    2008-01-01

    Brachytherapy treatment planning systems (TPS) are always recommended to account for the effect of tissue, applicator and shielding material heterogeneities exist in Intracavitary brachytherapy (ICBT) applicators. Most of the commercially available brachytherapy TPS softwares estimate the absorbed dose at a point, only taking care of the contributions of individual sources and the source distribution, neglecting the dose perturbations arising from the applicator design and construction. So the doses estimated by them are not much accurate under realistic clinical conditions. In this regard, interactive point kernel rode (BrachyTPS) has been developed to perform independent dose calculations by taking into account the effect of these heterogeneities, using two regions build up factors, proposed by Kalos. As primary input data, the code takes patients' planning data including the source specifications, dwell positions, dwell times and it computes the doses at reference points by dose point kernel formalisms, with multi-layer shield build-up factors accounting for the contributions from scattered radiation. In addition to performing dose distribution calculations, this code package is capable of displaying an isodose distribution curve into the patient anatomy images. The primary aim of this study is to validate the developed point kernel code integrated with treatment planning systems against the other tools which are available in the market. In the present work, three brachytherapy applicators commonly used in the treatment of uterine cervical carcinoma, Board of Radiation Isotope and Technology (BRIT) made low dose rate (LDR) applicator, Fletcher Green type LDR applicator and Fletcher Williamson high dose rate (HDR) applicator were studied to test the accuracy of the software

  12. Assessing opportunities for physical activity in the built environment of children: interrelation between kernel density and neighborhood scale.

    Science.gov (United States)

    Buck, Christoph; Kneib, Thomas; Tkaczick, Tobias; Konstabel, Kenn; Pigeot, Iris

    2015-12-22

    Built environment studies provide broad evidence that urban characteristics influence physical activity (PA). However, findings are still difficult to compare, due to inconsistent measures assessing urban point characteristics and varying definitions of spatial scale. Both were found to influence the strength of the association between the built environment and PA. We simultaneously evaluated the effect of kernel approaches and network-distances to investigate the association between urban characteristics and physical activity depending on spatial scale and intensity measure. We assessed urban measures of point characteristics such as intersections, public transit stations, and public open spaces in ego-centered network-dependent neighborhoods based on geographical data of one German study region of the IDEFICS study. We calculated point intensities using the simple intensity and kernel approaches based on fixed bandwidths, cross-validated bandwidths including isotropic and anisotropic kernel functions and considering adaptive bandwidths that adjust for residential density. We distinguished six network-distances from 500 m up to 2 km to calculate each intensity measure. A log-gamma regression model was used to investigate the effect of each urban measure on moderate-to-vigorous physical activity (MVPA) of 400 2- to 9.9-year old children who participated in the IDEFICS study. Models were stratified by sex and age groups, i.e. pre-school children (2 to kernel approaches. Smallest variation in effect estimates over network-distances was found for kernel intensity measures based on isotropic and anisotropic cross-validated bandwidth selection. We found a strong variation in the association between the built environment and PA of children based on the choice of intensity measure and network-distance. Kernel intensity measures provided stable results over various scales and improved the assessment compared to the simple intensity measure. Considering different spatial

  13. A method for manufacturing kernels of metallic oxides and the thus obtained kernels

    International Nuclear Information System (INIS)

    Lelievre Bernard; Feugier, Andre.

    1973-01-01

    A method is described for manufacturing fissile or fertile metal oxide kernels, consisting in adding at least a chemical compound capable of releasing ammonia to an aqueous solution of actinide nitrates dispersing the thus obtained solution dropwise in a hot organic phase so as to gelify the drops and transform them into solid particles, washing drying and treating said particles so as to transform them into oxide kernels. Such a method is characterized in that the organic phase used in the gel-forming reactions comprises a mixture of two organic liquids, one of which acts as a solvent, whereas the other is a product capable of extracting the metal-salt anions from the drops while the gel forming reaction is taking place. This can be applied to the so-called high temperature nuclear reactors [fr

  14. Regularization and error estimates for nonhomogeneous backward heat problems

    Directory of Open Access Journals (Sweden)

    Duc Trong Dang

    2006-01-01

    Full Text Available In this article, we study the inverse time problem for the non-homogeneous heat equation which is a severely ill-posed problem. We regularize this problem using the quasi-reversibility method and then obtain error estimates on the approximate solutions. Solutions are calculated by the contraction principle and shown in numerical experiments. We obtain also rates of convergence to the exact solution.

  15. New Fukui, dual and hyper-dual kernels as bond reactivity descriptors.

    Science.gov (United States)

    Franco-Pérez, Marco; Polanco-Ramírez, Carlos-A; Ayers, Paul W; Gázquez, José L; Vela, Alberto

    2017-06-21

    We define three new linear response indices with promising applications for bond reactivity using the mathematical framework of τ-CRT (finite temperature chemical reactivity theory). The τ-Fukui kernel is defined as the ratio between the fluctuations of the average electron density at two different points in the space and the fluctuations in the average electron number and is designed to integrate to the finite-temperature definition of the electronic Fukui function. When this kernel is condensed, it can be interpreted as a site-reactivity descriptor of the boundary region between two atoms. The τ-dual kernel corresponds to the first order response of the Fukui kernel and is designed to integrate to the finite temperature definition of the dual descriptor; it indicates the ambiphilic reactivity of a specific bond and enriches the traditional dual descriptor by allowing one to distinguish between the electron-accepting and electron-donating processes. Finally, the τ-hyper dual kernel is defined as the second-order derivative of the Fukui kernel and is proposed as a measure of the strength of ambiphilic bonding interactions. Although these quantities have never been proposed, our results for the τ-Fukui kernel and for τ-dual kernel can be derived in zero-temperature formulation of the chemical reactivity theory with, among other things, the widely-used parabolic interpolation model.

  16. Quasi-Dual-Packed-Kerneled Au49 (2,4-DMBT)27 Nanoclusters and the Influence of Kernel Packing on the Electrochemical Gap.

    Science.gov (United States)

    Liao, Lingwen; Zhuang, Shengli; Wang, Pu; Xu, Yanan; Yan, Nan; Dong, Hongwei; Wang, Chengming; Zhao, Yan; Xia, Nan; Li, Jin; Deng, Haiteng; Pei, Yong; Tian, Shi-Kai; Wu, Zhikun

    2017-10-02

    Although face-centered cubic (fcc), body-centered cubic (bcc), hexagonal close-packed (hcp), and other structured gold nanoclusters have been reported, it was unclear whether gold nanoclusters with mix-packed (fcc and non-fcc) kernels exist, and the correlation between kernel packing and the properties of gold nanoclusters is unknown. A Au 49 (2,4-DMBT) 27 nanocluster with a shell electron count of 22 has now been been synthesized and structurally resolved by single-crystal X-ray crystallography, which revealed that Au 49 (2,4-DMBT) 27 contains a unique Au 34 kernel consisting of one quasi-fcc-structured Au 21 and one non-fcc-structured Au 13 unit (where 2,4-DMBTH=2,4-dimethylbenzenethiol). Further experiments revealed that the kernel packing greatly influences the electrochemical gap (EG) and the fcc structure has a larger EG than the investigated non-fcc structure. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Chemical Characteristics of Mango (Mangifera Indica L.) Kernel Oil and Palm Oil Blends for Probable use as Vanaspati

    International Nuclear Information System (INIS)

    Atta Muhammad Arif; Irman Javed; Muhammad Abdullah; Muhammad Irman; Athar Mahmud; Muhammad Nadeem; Muhammad Ayaz

    2016-01-01

    Chemical characteristics of blends of palm oil and mango kernel oil for their probable use as vanaspati was studied. Crude mango kernel oil was blended with refined, bleached and deodorised palm oil from 10 %, 20 %, 30 %, 40 % and 50 % (T 1 , T 2 , T 3 , T 4 and T 5 ) market vanaspati was used as control. Concentration of trans fatty acids in control was 22.7 %, whereas, all the vanaspati samples were virtually trans-free. Slip melting points (degree Celsius) of control, T 1 , T 2 , T 3 , T 4 and T 5 were 37.5, 37.3, 36.4, 35.6, 34.8 and 34. Free fatty acids of control and T5 were respectively 0.11, 0.12 %. Polymer contents of control, T 1 , T 2 , T 3 , T 4 and T 5 , after three heating cycles (18 degree Celsius, for 8 hr) were 21.55 %, 20.97 %, 18.66 %, 17.61 % and 10.22 %, respectively with lower solid fat index (p<0.05). Blends of mango kernel oil and palm oil can be used for the formulation of trans-free vanaspati. (author)

  18. CHARACTERISTIC SIZE OF FLARE KERNELS IN THE VISIBLE AND NEAR-INFRARED CONTINUA

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Yan; Jing, Ju; Wang, Haimin [Space Weather Research Lab, Center for Solar-Terrestrial Research, New Jersey Institute of Technology, 323 Martin Luther King Blvd, Newark, NJ 07102-1982 (United States); Cao, Wenda, E-mail: yx2@njit.edu [Big Bear Solar Observatory, Center for Solar-Terrestrial Research, New Jersey Institute of Technology, 323 Martin Luther King Blvd, Newark, NJ 07102-1982 (United States)

    2012-05-01

    In this Letter, we present a new approach to estimate the formation height of visible and near-infrared emission of an X10 flare. The sizes of flare emission cores in three wavelengths are accurately measured during the peak of the flare. The source size is the largest in the G band at 4308 A and shrinks toward longer wavelengths, namely the green continuum at 5200 A and NIR at 15600 A, where the emission is believed to originate from the deeper atmosphere. This size-wavelength variation is likely explained by the direct heating model as electrons need to move along converging field lines from the corona to the photosphere. Therefore, one can observe the smallest source, which in our case is 0.''65 {+-} 0.''02 in the bottom layer (represented by NIR), and observe relatively larger kernels in upper layers of 1.''03 {+-} 0.''14 and 1.''96 {+-} 0.''27, using the green continuum and G band, respectively. We then compare the source sizes with a simple magnetic geometry to derive the formation height of the white-light sources and magnetic pressure in different layers inside the flare loop.

  19. Multivariable Christoffel-Darboux Kernels and Characteristic Polynomials of Random Hermitian Matrices

    Directory of Open Access Journals (Sweden)

    Hjalmar Rosengren

    2006-12-01

    Full Text Available We study multivariable Christoffel-Darboux kernels, which may be viewed as reproducing kernels for antisymmetric orthogonal polynomials, and also as correlation functions for products of characteristic polynomials of random Hermitian matrices. Using their interpretation as reproducing kernels, we obtain simple proofs of Pfaffian and determinant formulas, as well as Schur polynomial expansions, for such kernels. In subsequent work, these results are applied in combinatorics (enumeration of marked shifted tableaux and number theory (representation of integers as sums of squares.

  20. Compactly Supported Basis Functions as Support Vector Kernels for Classification.

    Science.gov (United States)

    Wittek, Peter; Tan, Chew Lim

    2011-10-01

    Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

  1. Genome-wide Association Analysis of Kernel Weight in Hard Winter Wheat

    Science.gov (United States)

    Wheat kernel weight is an important and heritable component of wheat grain yield and a key predictor of flour extraction. Genome-wide association analysis was conducted to identify genomic regions associated with kernel weight and kernel weight environmental response in 8 trials of 299 hard winter ...

  2. Shielding calculations and collective dose estimations with the point-kernel-code VISIPLAN {sup registered} for the example of the project ZENT; Abschirmberechnungen und Kollektivdosisabschaetzungen mit dem Punkt-Kern-Code VISIPLAN {sup registered} am Beispiel des Projektes ZENT

    Energy Technology Data Exchange (ETDEWEB)

    Boehlke, S.; Niegoth, H. [STEAG Energy Services GmbH, Essen (Germany). Nuclear Technologies; Stalder, I. [Kernkraftwerk Leibstadt AG, Leibstadt (Switzerland)

    2012-11-01

    In the nuclear power plant Leibstadt (KKL) during the next year large components will be dismantled and stored for final disposal within the interim storage facility ZENT at the NPP site. Before construction of ZENT appropriate estimations of the local dose rate inside and outside the building and the collective dose for the normal operation have to be performed. The shielding calculations are based on the properties of the stored components and radiation sources and on the concepts for working place requirements. The installation of control and monitoring areas will depend on these calculations. For the determination of the shielding potential of concrete walls and steel doors with the defined boundary conditions point-kernel codes like MICROSHIELd {sup registered} are used. Complex problems cannot be modeled with this code. Therefore the point-kernel code VISIPLAN {sup registered} was developed for the determination of the local dose distribution functions in 3D models. The possibility of motion sequence inputs allows an optimization of collective dose estimations for the operational phases of a nuclear facility.

  3. A Heterogeneous Multi-core Architecture with a Hardware Kernel for Control Systems

    DEFF Research Database (Denmark)

    Li, Gang; Guan, Wei; Sierszecki, Krzysztof

    2012-01-01

    Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints. This pa......Rapid industrialisation has resulted in a demand for improved embedded control systems with features such as predictability, high processing performance and low power consumption. Software kernel implementation on a single processor is becoming more difficult to satisfy those constraints......). Second, a heterogeneous multi-core architecture is investigated, focusing on its performance in relation to hard real-time constraints and predictable behavior. Third, the hardware implementation of HARTEX is designated to support the heterogeneous multi-core architecture. This hardware kernel has...... several advantages over a similar kernel implemented in software: higher-speed processing capability, parallel computation, and separation between the kernel itself and the applications being run. A microbenchmark has been used to compare the hardware kernel with the software kernel, and compare...

  4. Satellite data based approach for the estimation of anthropogenic heat flux over urban areas

    Science.gov (United States)

    Nitis, Theodoros; Tsegas, George; Moussiopoulos, Nicolas; Gounaridis, Dimitrios; Bliziotis, Dimitrios

    2017-09-01

    Anthropogenic effects in urban areas influence the thermal conditions in the environment and cause an increase of the atmospheric temperature. The cities are sources of heat and pollution, affecting the thermal structure of the atmosphere above them which results to the urban heat island effect. In order to analyze the urban heat island mechanism, it is important to estimate the anthropogenic heat flux which has a considerable impact on the urban energy budget. The anthropogenic heat flux is the result of man-made activities (i.e. traffic, industrial processes, heating/cooling) and thermal releases from the human body. Many studies have underlined the importance of the Anthropogenic Heat Flux to the calculation of the urban energy budget and subsequently, the estimation of mesoscale meteorological fields over urban areas. Therefore, spatially disaggregated anthropogenic heat flux data, at local and city scales, are of major importance for mesoscale meteorological models. The main objectives of the present work are to improve the quality of such data used as input for mesoscale meteorological models simulations and to enhance the application potential of GIS and remote sensing in the fields of climatology and meteorology. For this reason, the Urban Energy Budget concept is proposed as the foundation for an accurate determination of the anthropogenic heat discharge as a residual term in the surface energy balance. The methodology is applied to the cities of Athens and Paris using the Landsat ETM+ remote sensing data. The results will help to improve our knowledge on Anthropogenic Heat Flux, while the potential for further improvement of the methodology is also discussed.

  5. Generalized synthetic kernel approximation for elastic moderation of fast neutrons

    International Nuclear Information System (INIS)

    Yamamoto, Koji; Sekiya, Tamotsu; Yamamura, Yasunori.

    1975-01-01

    A method of synthetic kernel approximation is examined in some detail with a view to simplifying the treatment of the elastic moderation of fast neutrons. A sequence of unified kernel (fsub(N)) is introduced, which is then divided into two subsequences (Wsub(n)) and (Gsub(n)) according to whether N is odd (Wsub(n)=fsub(2n-1), n=1,2, ...) or even (Gsub(n)=fsub(2n), n=0,1, ...). The W 1 and G 1 kernels correspond to the usual Wigner and GG kernels, respectively, and the Wsub(n) and Gsub(n) kernels for n>=2 represent generalizations thereof. It is shown that the Wsub(n) kernel solution with a relatively small n (>=2) is superior on the whole to the Gsub(n) kernel solution for the same index n, while both converge to the exact values with increasing n. To evaluate the collision density numerically and rapidly, a simple recurrence formula is derived. In the asymptotic region (except near resonances), this recurrence formula allows calculation with a relatively coarse mesh width whenever hsub(a)<=0.05 at least. For calculations in the transient lethargy region, a mesh width of order epsilon/10 is small enough to evaluate the approximate collision density psisub(N) with an accuracy comparable to that obtained analytically. It is shown that, with the present method, an order of approximation of about n=7 should yield a practically correct solution diviating not more than 1% in collision density. (auth.)

  6. Heat flux exchange estimation by using ATSR SST data in TOGA area

    Science.gov (United States)

    Xue, Yong; Lawrence, Sean P.; Llewellyn-Jones, David T.

    1995-12-01

    The study of phenomena such as ENSO requires consideration of the dynamics and thermodynamics of the coupled ocean-atmosphere system. The dynamic and thermal properties of the atmosphere and ocean are directly affected by air-sea transfers of fluxes of momentum, heat and moisture. In this paper, we present results of turbulent heat fluxes calculated by using two years (1992 and 1993) monthly average TOGA data and ATSR SST data in TOGA area. A comparison with published results indicates good qualitative agreement. Also, we compared the results of heat flux exchange by using ATSR SST data and by using the TOGA bucket SST data. The ATSR SST data set has been shown to be useful in helping to estimate the large space scale heat flux exchange.

  7. Validation of Born Traveltime Kernels

    Science.gov (United States)

    Baig, A. M.; Dahlen, F. A.; Hung, S.

    2001-12-01

    Most inversions for Earth structure using seismic traveltimes rely on linear ray theory to translate observed traveltime anomalies into seismic velocity anomalies distributed throughout the mantle. However, ray theory is not an appropriate tool to use when velocity anomalies have scale lengths less than the width of the Fresnel zone. In the presence of these structures, we need to turn to a scattering theory in order to adequately describe all of the features observed in the waveform. By coupling the Born approximation to ray theory, the first order dependence of heterogeneity on the cross-correlated traveltimes (described by the Fréchet derivative or, more colourfully, the banana-doughnut kernel) may be determined. To determine for what range of parameters these banana-doughnut kernels outperform linear ray theory, we generate several random media specified by their statistical properties, namely the RMS slowness perturbation and the scale length of the heterogeneity. Acoustic waves are numerically generated from a point source using a 3-D pseudo-spectral wave propagation code. These waves are then recorded at a variety of propagation distances from the source introducing a third parameter to the problem: the number of wavelengths traversed by the wave. When all of the heterogeneity has scale lengths larger than the width of the Fresnel zone, ray theory does as good a job at predicting the cross-correlated traveltime as the banana-doughnut kernels do. Below this limit, wavefront healing becomes a significant effect and ray theory ceases to be effective even though the kernels remain relatively accurate provided the heterogeneity is weak. The study of wave propagation in random media is of a more general interest and we will also show our measurements of the velocity shift and the variance of traveltime compare to various theoretical predictions in a given regime.

  8. Effect of Palm Kernel Cake Replacement and Enzyme ...

    African Journals Online (AJOL)

    A feeding trial which lasted for twelve weeks was conducted to study the performance of finisher pigs fed five different levels of palm kernel cake replacement for maize (0%, 40%, 40%, 60%, 60%) in a maize-palm kernel cake based ration with or without enzyme supplementation. It was a completely randomized design ...

  9. Solutions of Cattaneo-Hristov model of elastic heat diffusion with Caputo-Fabrizio and Atangana-Baleanu fractional derivatives

    Directory of Open Access Journals (Sweden)

    Koca Ilknur

    2017-01-01

    Full Text Available Recently Hristov using the concept of a relaxation kernel with no singularity developed a new model of elastic heat diffusion equation based on the Caputo-Fabrizio fractional derivative as an extended version of Cattaneo model of heat diffusion equation. In the present article, we solve exactly the Cattaneo-Hristov model and extend it by the concept of a derivative with non-local and non-singular kernel by using the new Atangana-Baleanu derivative. The Cattaneo-Hristov model with the extended derivative is solved analytically with the Laplace transform, and numerically using the Crank-Nicholson scheme.

  10. Generalized Jackknife Estimators of Weighted Average Derivatives

    DEFF Research Database (Denmark)

    Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael

    With the aim of improving the quality of asymptotic distributional approximations for nonlinear functionals of nonparametric estimators, this paper revisits the large-sample properties of an important member of that class, namely a kernel-based weighted average derivative estimator. Asymptotic...

  11. SU-F-T-450: The Investigation of Radiotherapy Quality Assurance and Automatic Treatment Planning Based On the Kernel Density Estimation Method

    Energy Technology Data Exchange (ETDEWEB)

    Fan, J; Fan, J; Hu, W; Wang, J [Fudan University Shanghai Cancer Center, Shanghai, Shanghai (China)

    2016-06-15

    Purpose: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. Methods: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features. The joint distribution provides an estimation of the conditional probability of the dose given the values of the predictive features. For the new patient, the prediction consists of estimating the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimation of the DVH. The 2D KDE is implemented to predict the joint probability distribution of the training set and the distribution of the predictive features for the new patient. Two variables, including the signed minimal distance from each OAR (organs at risk) voxel to the target boundary and its opening angle with respect to the origin of voxel coordinate, are considered as the predictive features to represent the OAR-target spatial relationship. The feasibility of our method has been demonstrated with the rectum, breast and head-and-neck cancer cases by comparing the predicted DVHs with the planned ones. Results: The consistent result has been found between these two DVHs for each cancer and the average of relative point-wise differences is about 5% within the clinical acceptable extent. Conclusion: According to the result of this study, our method can be used to predict the clinical acceptable DVH and has ability to evaluate the quality and consistency of the treatment planning.

  12. SU-F-T-450: The Investigation of Radiotherapy Quality Assurance and Automatic Treatment Planning Based On the Kernel Density Estimation Method

    International Nuclear Information System (INIS)

    Fan, J; Fan, J; Hu, W; Wang, J

    2016-01-01

    Purpose: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. Methods: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features. The joint distribution provides an estimation of the conditional probability of the dose given the values of the predictive features. For the new patient, the prediction consists of estimating the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimation of the DVH. The 2D KDE is implemented to predict the joint probability distribution of the training set and the distribution of the predictive features for the new patient. Two variables, including the signed minimal distance from each OAR (organs at risk) voxel to the target boundary and its opening angle with respect to the origin of voxel coordinate, are considered as the predictive features to represent the OAR-target spatial relationship. The feasibility of our method has been demonstrated with the rectum, breast and head-and-neck cancer cases by comparing the predicted DVHs with the planned ones. Results: The consistent result has been found between these two DVHs for each cancer and the average of relative point-wise differences is about 5% within the clinical acceptable extent. Conclusion: According to the result of this study, our method can be used to predict the clinical acceptable DVH and has ability to evaluate the quality and consistency of the treatment planning.

  13. Integrating K-means Clustering with Kernel Density Estimation for the Development of a Conditional Weather Generation Downscaling Model

    Science.gov (United States)

    Chen, Y.; Ho, C.; Chang, L.

    2011-12-01

    In previous decades, the climate change caused by global warming increases the occurrence frequency of extreme hydrological events. Water supply shortages caused by extreme events create great challenges for water resource management. To evaluate future climate variations, general circulation models (GCMs) are the most wildly known tools which shows possible weather conditions under pre-defined CO2 emission scenarios announced by IPCC. Because the study area of GCMs is the entire earth, the grid sizes of GCMs are much larger than the basin scale. To overcome the gap, a statistic downscaling technique can transform the regional scale weather factors into basin scale precipitations. The statistic downscaling technique can be divided into three categories include transfer function, weather generator and weather type. The first two categories describe the relationships between the weather factors and precipitations respectively based on deterministic algorithms, such as linear or nonlinear regression and ANN, and stochastic approaches, such as Markov chain theory and statistical distributions. In the weather type, the method has ability to cluster weather factors, which are high dimensional and continuous variables, into weather types, which are limited number of discrete states. In this study, the proposed downscaling model integrates the weather type, using the K-means clustering algorithm, and the weather generator, using the kernel density estimation. The study area is Shihmen basin in northern of Taiwan. In this study, the research process contains two steps, a calibration step and a synthesis step. Three sub-steps were used in the calibration step. First, weather factors, such as pressures, humidities and wind speeds, obtained from NCEP and the precipitations observed from rainfall stations were collected for downscaling. Second, the K-means clustering grouped the weather factors into four weather types. Third, the Markov chain transition matrixes and the

  14. Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition

    NARCIS (Netherlands)

    Liwicki, Stephan; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Pantic, Maja

    2012-01-01

    We propose an exact framework for online learning with a family of indefinite (not positive) kernels. As we study the case of nonpositive kernels, we first show how to extend kernel principal component analysis (KPCA) from a reproducing kernel Hilbert space to Krein space. We then formulate an

  15. Estimate of thermoelastic heat production from superconducting composites in pulsed poloidal coil systems

    International Nuclear Information System (INIS)

    Ballou, J.K.; Gray, W.H.

    1976-01-01

    In the design of the cryogenic system and superconducting magnets for the poloidal field system in a tokamak, it is important to have an accurate estimate of the heat produced in superconducting magnets as a result of rapidly changing magnetic fields. A computer code, PLASS (Pulsed Losses in Axisymmetric Superconducting Solenoids), was written to estimate the contributions to the heat production from superconductor hysteresis losses, superconductor coupling losses, stabilizing material eddy current losses, and structural material eddy current losses. Recently, it has been shown that thermoelastic dissipation in superconducting composites can contribute as much to heat production as the other loss mechanisms mentioned above. A modification of PLASS which takes into consideration thermoelastic dissipation in superconducting composites is discussed. A comparison between superconductor thermoelastic dissipation and the other superconductor loss mechanisms is presented in terms of the poloidal coil system of the ORNL Experimental Power Reactor design

  16. Flour quality and kernel hardness connection in winter wheat

    Directory of Open Access Journals (Sweden)

    Szabó B. P.

    2016-12-01

    Full Text Available Kernel hardness is controlled by friabilin protein and it depends on the relation between protein matrix and starch granules. Friabilin is present in high concentration in soft grain varieties and in low concentration in hard grain varieties. The high gluten, hard wheat our generally contains about 12.0–13.0% crude protein under Mid-European conditions. The relationship between wheat protein content and kernel texture is usually positive and kernel texture influences the power consumption during milling. Hard-textured wheat grains require more grinding energy than soft-textured grains.

  17. Deep kernel learning method for SAR image target recognition

    Science.gov (United States)

    Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

    2017-10-01

    With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

  18. Influence of differently processed mango seed kernel meal on ...

    African Journals Online (AJOL)

    Influence of differently processed mango seed kernel meal on performance response of west African ... and TD( consisted spear grass and parboiled mango seed kernel meal with concentrate diet in a ratio of 35:30:35). ... HOW TO USE AJOL.

  19. Mapping Fire Severity Using Imaging Spectroscopy and Kernel Based Image Analysis

    Science.gov (United States)

    Prasad, S.; Cui, M.; Zhang, Y.; Veraverbeke, S.

    2014-12-01

    Improved spatial representation of within-burn heterogeneity after wildfires is paramount to effective land management decisions and more accurate fire emissions estimates. In this work, we demonstrate feasibility and efficacy of airborne imaging spectroscopy (hyperspectral imagery) for quantifying wildfire burn severity, using kernel based image analysis techniques. Two different airborne hyperspectral datasets, acquired over the 2011 Canyon and 2013 Rim fire in California using the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) sensor, were used in this study. The Rim Fire, covering parts of the Yosemite National Park started on August 17, 2013, and was the third largest fire in California's history. Canyon Fire occurred in the Tehachapi mountains, and started on September 4, 2011. In addition to post-fire data for both fires, half of the Rim fire was also covered with pre-fire images. Fire severity was measured in the field using Geo Composite Burn Index (GeoCBI). The field data was utilized to train and validate our models, wherein the trained models, in conjunction with imaging spectroscopy data were used for GeoCBI estimation wide geographical regions. This work presents an approach for using remotely sensed imagery combined with GeoCBI field data to map fire scars based on a non-linear (kernel based) epsilon-Support Vector Regression (e-SVR), which was used to learn the relationship between spectra and GeoCBI in a kernel-induced feature space. Classification of healthy vegetation versus fire-affected areas based on morphological multi-attribute profiles was also studied. The availability of pre- and post-fire imaging spectroscopy data over the Rim Fire provided a unique opportunity to evaluate the performance of bi-temporal imaging spectroscopy for assessing post-fire effects. This type of data is currently constrained because of limited airborne acquisitions before a fire, but will become widespread with future spaceborne sensors such as those on

  20. Structural observability analysis and EKF based parameter estimation of building heating models

    Directory of Open Access Journals (Sweden)

    D.W.U. Perera

    2016-07-01

    Full Text Available Research for enhanced energy-efficient buildings has been given much recognition in the recent years owing to their high energy consumptions. Increasing energy needs can be precisely controlled by practicing advanced controllers for building Heating, Ventilation, and Air-Conditioning (HVAC systems. Advanced controllers require a mathematical building heating model to operate, and these models need to be accurate and computationally efficient. One main concern associated with such models is the accurate estimation of the unknown model parameters. This paper presents the feasibility of implementing a simplified building heating model and the computation of physical parameters using an off-line approach. Structural observability analysis is conducted using graph-theoretic techniques to analyze the observability of the developed system model. Then Extended Kalman Filter (EKF algorithm is utilized for parameter estimates using the real measurements of a single-zone building. The simulation-based results confirm that even with a simple model, the EKF follows the state variables accurately. The predicted parameters vary depending on the inputs and disturbances.

  1. A heat conduction simulator to estimate lung temperature distribution during percutaneous transthoracic cryoablation for lung cancer

    International Nuclear Information System (INIS)

    Futami, Hikaru; Arai, Tsunenori; Yashiro, Hideki; Nakatsuka, Seishi; Kuribayashi, Sachio; Izumi, Youtaro; Tsukada, Norimasa; Kawamura, Masafumi

    2006-01-01

    To develop an evaluation method for the curative field when using X-ray CT imaging during percutaneous transthoracic cryoablation for lung cancer, we constructed a finite-element heat conduction simulator to estimate temperature distribution in the lung during cryo-treatment. We calculated temperature distribution using a simple two-dimensional finite element model, although the actual temperature distribution spreads in three dimensions. Temperature time-histories were measured within 10 minutes using experimental ex vivo and in vivo lung cryoablation conditions. We adjusted specific heat and thermal conductivity in the heat conduction calculation and compared them with measured temperature time-histories ex vivo. Adjusted lung specific heat was 3.7 J/ (g·deg C) for unfrozen lung and 1.8 J/ (g·deg C) for frozen lung. Adjusted lung thermal conductivity in our finite element model fitted proportionally to the exponential function of lung density. We considered the heat input by blood flow circulation and metabolic heat when we calculated the temperature time-histories during in vivo cryoablation of the lung. We assumed that the blood flow varies in inverse proportion to the change in blood viscosity up to the maximum blood flow predicted from cardiac output. Metabolic heat was set as heat generation in the calculation. The measured temperature time-histories of in vivo cryoablation were then estimated with an accuracy of ±3 deg C when calculated based on this assumption. Therefore, we successfully constructed a two-dimensional heat conduction simulator that is capable of estimating temperature distribution in the lung at the time of first freezing during cryoablation. (author)

  2. Solution of axisymmetric transient inverse heat conduction problems using parameter estimation and multi block methods

    International Nuclear Information System (INIS)

    Azimi, A.; Hannani, S.K.; Farhanieh, B.

    2005-01-01

    In this article, a comparison between two iterative inverse techniques to solve simultaneously two unknown functions of axisymmetric transient inverse heat conduction problems in semi complex geometries is presented. The multi-block structured grid together with blocked-interface nodes is implemented for geometric decomposition of physical domain. Numerical scheme for solution of transient heat conduction equation is the finite element method with frontal technique to solve algebraic system of discrete equations. The inverse heat conduction problem involves simultaneous unknown time varying heat generation and time-space varying boundary condition estimation. Two parameter-estimation techniques are considered, Levenberg-Marquardt scheme and conjugate gradient method with adjoint problem. Numerically computed exact and noisy data are used for the measured transient temperature data needed in the inverse solution. The results of the present study for a configuration including two joined disks with different heights are compared to those of exact heat source and temperature boundary condition, and show good agreement. (author)

  3. A framework for dense triangular matrix kernels on various manycore architectures

    KAUST Repository

    Charara, Ali

    2017-06-06

    We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar\\'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.

  4. A framework for dense triangular matrix kernels on various manycore architectures

    KAUST Repository

    Charara, Ali; Keyes, David E.; Ltaief, Hatem

    2017-01-01

    We present a new high-performance framework for dense triangular Basic Linear Algebra Subroutines (BLAS) kernels, ie, triangular matrix-matrix multiplication (TRMM) and triangular solve (TRSM), on various manycore architectures. This is an extension of a previous work on a single GPU by the same authors, presented at the EuroPar'16 conference, in which we demonstrated the effectiveness of recursive formulations in enhancing the performance of these kernels. In this paper, the performance of triangular BLAS kernels on a single GPU is further enhanced by implementing customized in-place CUDA kernels for TRMM and TRSM, which are called at the bottom of the recursion. In addition, a multi-GPU implementation of TRMM and TRSM is proposed and we show an almost linear performance scaling, as the number of GPUs increases. Finally, the algorithmic recursive formulation of these triangular BLAS kernels is in fact oblivious to the targeted hardware architecture. We, therefore, port these recursive kernels to homogeneous x86 hardware architectures by relying on the vendor optimized BLAS implementations. Results reported on various hardware architectures highlight a significant performance improvement against state-of-the-art implementations. These new kernels are freely available in the KAUST BLAS (KBLAS) open-source library at https://github.com/ecrc/kblas.

  5. Kernel Bayesian ART and ARTMAP.

    Science.gov (United States)

    Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan

    2018-02-01

    Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-01-01

    Full Text Available In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL algorithm and support vector machine (SVM. We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.

  7. Design and construction of palm kernel cracking and separation ...

    African Journals Online (AJOL)

    Design and construction of palm kernel cracking and separation machines. ... Username, Password, Remember me, or Register. DOWNLOAD FULL TEXT Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Design and construction of palm kernel cracking and separation machines. JO Nordiana, K ...

  8. Graphical analyses of connected-kernel scattering equations

    International Nuclear Information System (INIS)

    Picklesimer, A.

    1982-10-01

    Simple graphical techniques are employed to obtain a new (simultaneous) derivation of a large class of connected-kernel scattering equations. This class includes the Rosenberg, Bencze-Redish-Sloan, and connected-kernel multiple scattering equations as well as a host of generalizations of these and other equations. The graphical method also leads to a new, simplified form for some members of the class and elucidates the general structural features of the entire class

  9. An Ensemble Approach to Building Mercer Kernels with Prior Information

    Science.gov (United States)

    Srivastava, Ashok N.; Schumann, Johann; Fischer, Bernd

    2005-01-01

    This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

  10. Exploration of Shorea robusta (Sal seeds, kernels and its oil

    Directory of Open Access Journals (Sweden)

    Shashi Kumar C.

    2016-12-01

    Full Text Available Physical, mechanical, and chemical properties of Shorea robusta seed with wing, seed without wing, and kernel were investigated in the present work. The physico-chemical composition of sal oil was also analyzed. The physico-mechanical properties and proximate composition of seed with wing, seed without wing, and kernel at three moisture contents of 9.50% (w.b, 9.54% (w.b, and 12.14% (w.b, respectively, were studied. The results show that the moisture content of the kernel was highest as compared to seed with wing and seed without wing. The sphericity of the kernel was closer to that of a sphere as compared to seed with wing and seed without wing. The hardness of the seed with wing (32.32, N/mm and seed without wing (42.49, N/mm was lower than the kernels (72.14, N/mm. The proximate composition such as moisture, protein, carbohydrates, oil, crude fiber, and ash content were also determined. The kernel (30.20%, w/w contains higher oil percentage as compared to seed with wing and seed without wing. The scientific data from this work are important for designing of equipment and processes for post-harvest value addition of sal seeds.

  11. Irradiation performance of coated fuel particles with fission product retaining kernel additives

    International Nuclear Information System (INIS)

    Foerthmann, R.

    1979-10-01

    The four irradiation experiments FRJ2-P17, FRJ2-P18, FRJ2-P19, and FRJ2-P20 for testing the efficiency of fission product-retaining kernel additives in coated fuel particles are described. The evaluation of the obtained experimental data led to the following results: - zirconia and alumina kernel additives are not suitable for an effective fission product retention in oxide fuel kernels, - alumina-silica kernel additives reduce the in-pile release of Sr 90 and Ba 140 from BISO-coated particles at temperatures of about 1200 0 C by two orders of magnitude, and the Cs release from kernels by one order of magnitude, - effective transport coefficients including all parameters which contribute to kernel release are given for (Th,U)O 2 mixed oxide kernels and low enriched UO 2 kernels containing 5 wt.% alumina-silica additives: 10g sub(K)/cm 2 s -1 = - 36 028/T + 6,261 (Sr 90), 10g Dsub(K)/cm 2 c -2 = - 29 646/T + 5,826 (Cs 134/137), alumina-silica kernel additives are ineffective for retaining Ag 110 m in coated particles. However, also an intact SiC-interlayer was found not to be effective at temperatures above 1200 0 C, - the penetration of the buffer layer by fission product containing eutectic additive melt during irradiation can be avoided by using additives which consist of alumina and mullite without an excess of silica, - annealing of LASER-failed irradiated particles and the irradiation test FRJ12-P20 indicate that the efficiency of alumina-silica kernel additives is not altered if the coating becomes defect. (orig.) [de

  12. Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies

    Science.gov (United States)

    Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike

    2017-01-01

    The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility. PMID:28785300

  13. Pathway-Based Kernel Boosting for the Analysis of Genome-Wide Association Studies.

    Science.gov (United States)

    Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin

    2017-01-01

    The analysis of genome-wide association studies (GWAS) benefits from the investigation of biologically meaningful gene sets, such as gene-interaction networks (pathways). We propose an extension to a successful kernel-based pathway analysis approach by integrating kernel functions into a powerful algorithmic framework for variable selection, to enable investigation of multiple pathways simultaneously. We employ genetic similarity kernels from the logistic kernel machine test (LKMT) as base-learners in a boosting algorithm. A model to explain case-control status is created iteratively by selecting pathways that improve its prediction ability. We evaluated our method in simulation studies adopting 50 pathways for different sample sizes and genetic effect strengths. Additionally, we included an exemplary application of kernel boosting to a rheumatoid arthritis and a lung cancer dataset. Simulations indicate that kernel boosting outperforms the LKMT in certain genetic scenarios. Applications to GWAS data on rheumatoid arthritis and lung cancer resulted in sparse models which were based on pathways interpretable in a clinical sense. Kernel boosting is highly flexible in terms of considered variables and overcomes the problem of multiple testing. Additionally, it enables the prediction of clinical outcomes. Thus, kernel boosting constitutes a new, powerful tool in the analysis of GWAS data and towards the understanding of biological processes involved in disease susceptibility.

  14. Boundary singularity of Poisson and harmonic Bergman kernels

    Czech Academy of Sciences Publication Activity Database

    Engliš, Miroslav

    2015-01-01

    Roč. 429, č. 1 (2015), s. 233-272 ISSN 0022-247X R&D Projects: GA AV ČR IAA100190802 Institutional support: RVO:67985840 Keywords : harmonic Bergman kernel * Poisson kernel * pseudodifferential boundary operators Subject RIV: BA - General Mathematics Impact factor: 1.014, year: 2015 http://www.sciencedirect.com/science/article/pii/S0022247X15003170

  15. Optimal Bandwidth Selection in Observed-Score Kernel Equating

    Science.gov (United States)

    Häggström, Jenny; Wiberg, Marie

    2014-01-01

    The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…

  16. Kernel structures for Clouds

    Science.gov (United States)

    Spafford, Eugene H.; Mckendry, Martin S.

    1986-01-01

    An overview of the internal structure of the Clouds kernel was presented. An indication of how these structures will interact in the prototype Clouds implementation is given. Many specific details have yet to be determined and await experimentation with an actual working system.

  17. Commutators of Integral Operators with Variable Kernels on Hardy ...

    Indian Academy of Sciences (India)

    Home; Journals; Proceedings – Mathematical Sciences; Volume 115; Issue 4. Commutators of Integral Operators with Variable Kernels on Hardy Spaces. Pu Zhang Kai Zhao. Volume 115 Issue 4 November 2005 pp 399-410 ... Keywords. Singular and fractional integrals; variable kernel; commutator; Hardy space.

  18. Oven-drying reduces ruminal starch degradation in maize kernels

    NARCIS (Netherlands)

    Ali, M.; Cone, J.W.; Hendriks, W.H.; Struik, P.C.

    2014-01-01

    The degradation of starch largely determines the feeding value of maize (Zea mays L.) for dairy cows. Normally, maize kernels are dried and ground before chemical analysis and determining degradation characteristics, whereas cows eat and digest fresh material. Drying the moist maize kernels

  19. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2009-01-01

    In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

  20. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2010-01-01

    In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By