Complexity of Gaussian-Radial-Basis Networks Approximating Smooth Functions
Czech Academy of Sciences Publication Activity Database
Kainen, P.C.; Kůrková, Věra; Sanguineti, M.
2009-01-01
Roč. 25, č. 1 (2009), s. 63-74 ISSN 0885-064X R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : Gaussian-radial-basis-function networks * rates of approximation * model complexity * variation norms * Bessel and Sobolev norms * tractability of approximation Subject RIV: IN - Informatics, Computer Science Impact factor: 1.227, year: 2009
The Gaussian radial basis function method for plasma kinetic theory
Energy Technology Data Exchange (ETDEWEB)
Hirvijoki, E., E-mail: eero.hirvijoki@chalmers.se [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden); Candy, J.; Belli, E. [General Atomics, PO Box 85608, San Diego, CA 92186-5608 (United States); Embréus, O. [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden)
2015-10-30
Description of a magnetized plasma involves the Vlasov equation supplemented with the non-linear Fokker–Planck collision operator. For non-Maxwellian distributions, the collision operator, however, is difficult to compute. In this Letter, we introduce Gaussian Radial Basis Functions (RBFs) to discretize the velocity space of the entire kinetic system, and give the corresponding analytical expressions for the Vlasov and collision operator. Outlining the general theory, we also highlight the connection to plasma fluid theories, and give 2D and 3D numerical solutions of the non-linear Fokker–Planck equation. Applications are anticipated in both astrophysical and laboratory plasmas. - Highlights: • A radically new method to address the velocity space discretization of the non-linear kinetic equation of plasmas. • Elegant and physically intuitive, flexible and mesh-free. • Demonstration of numerical solution of both 2-D and 3-D non-linear Fokker–Planck relaxation problem.
Unification of Plasma Fluid and Kinetic Theory via Gaussian Radial Basis Functions
Candy, J. M.
2015-11-01
A fundamental macroscopic description of a magnetized plasma is the Vlasov equation supplemented by the nonlinear inverse-square force Fokker-Planck collision operator [Rosenbluth et al., Phys. Rev. 107, 1957]. The Vlasov part describes advection in a six-dimensional phase space whereas the collision operator contains friction and diffusion coefficients that are weighted velocity-space integrals of the particle distribution function. The Fokker-Planck collision operator is an integro-differential, nonlinear (bilinear) operator. Numerical discretization of the operator, in particular for collisions of unlike species, is extremely challenging. In this work, we describe a new approach to discretize the entire kinetic system based on an expansion in Gaussian Radial Basis functions (RBFs). This approach is particularly well-suited to treat the collision operator because the friction and diffusion coefficients can be analytically calculated. Although the RBF method is known to be a powerful scheme for the interpolation of scattered multidimensional data, Gaussian RBFs also have a deep physical interpretation in statistical mechanics and plasma physics as local thermodynamic equilibria. We outline the general theory, highlight the connection to plasma fluid theories, and also give 2D and 3D numerical solutions of the nonlinear Fokker-Planck equation. A broad spectrum of applications for the new method is anticipated in both astrophysical and laboratory plasmas. In particular, we believe that the RBF method may provide a new bridge between fluid and kinetic descriptions of magnetized plasma. Work supported in part by US DOE under DE-FG02-08ER54963.
International Nuclear Information System (INIS)
Farivar, Faezeh; Aliyari Shoorehdeli, Mahdi; Nekoui, Mohammad Ali; Teshnehlab, Mohammad
2012-01-01
Highlights: ► A systematic procedure for GPS of unknown heavy chaotic gyroscope systems. ► Proposed methods are based on Lyapunov stability theory. ► Without calculating Lyapunov exponents and Eigen values of the Jacobian matrix. ► Capable to extend for a variety of chaotic systems. ► Useful for practical applications in the future. - Abstract: This paper proposes the chaos control and the generalized projective synchronization methods for heavy symmetric gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic motions of symmetric gyroscopes. In this paper, the switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. Using the neural variable structure control technique, control laws are established which guarantees the chaos control and the generalized projective synchronization of unknown gyroscope systems. In the neural variable structure control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimator are derived in the sense of Lyapunov function. Thus, the unknown gyro systems can be guaranteed to be asymptotically stable. Also, the proposed method can achieve the control objectives. Numerical simulations are presented to
Tien Bui, Dieu; Hoang, Nhat-Duc
2017-09-01
In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.
Learning Methods for Radial Basis Functions Networks
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Kudová, Petra
2005-01-01
Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005
Fast radial basis functions for engineering applications
Biancolini, Marco Evangelos
2017-01-01
This book presents the first “How To” guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF: multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive – and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers’ cultural background, but also due to the numerical complex...
Directory of Open Access Journals (Sweden)
N. Ahmadi
2017-02-01
Full Text Available Abstract In this paper, we present a collocation method based on Gaussian Radial Basis Functions (RBFs for approximating the solution of stochastic fractional differential equations (SFDEs. In this equation the fractional derivative is considered in the Caputo sense. Also we prove the existence and uniqueness of the presented method. Numerical examples confirm the proficiency of the method.
Positivity and monotonicity shape preserving using radial basis function
Ahmad, Afida; Ong, Wen Eng; Piah, Abd. Rahni Mt
2017-04-01
The objective of this paper is to investigate whether radial basis functions (RBF) can be used as an alternative to Bezier and Ball splines in preserving positivity and monotonicity of the data. For positivity shape preserving, multiquadric and Gaussian form of RBF are used in the analysis while for monotonicity, multiquadric quasi-interpolation is used. The analysis involved a free shape parameter, ɛ in preserving positivity and monotonicity for real data set. To preserve positivity, the selection of ɛ is based on the positivity constraint, s(x) > 0 and also a proposed upper bound value. The output from several real data sets are presented and the choice of ɛ varies depending on the data set. The interpolants are comparable with existing interpolation schemes using rational cubic Bezier and rational cubic Ball. For monotonicity shape preserving, the behaviour of the interpolants using different ɛ are investigated. From the examples, the resulted curves using multiquadric quasi-interpolation as the basis can only approximate the data.
Radial basis function neural networks applied to NASA SSME data
Wheeler, Kevin R.; Dhawan, Atam P.
1993-01-01
This paper presents a brief report on the application of Radial Basis Function Neural Networks (RBFNN) to the prediction of sensor values for fault detection and diagnosis of the Space Shuttle's Main Engines (SSME). The location of the Radial Basis Function (RBF) node centers was determined with a K-means clustering algorithm. A neighborhood operation about these center points was used to determine the variances of the individual processing notes.
Construction of global Lyapunov functions using radial basis functions
Giesl, Peter
2007-01-01
The basin of attraction of an equilibrium of an ordinary differential equation can be determined using a Lyapunov function. A new method to construct such a Lyapunov function using radial basis functions is presented in this volume intended for researchers and advanced students from both dynamical systems and radial basis functions. Besides an introduction to both areas and a detailed description of the method, it contains error estimates and many examples.
Generation of Radial Laguerre-Gaussian modes with a lower threshold using a digital laser
CSIR Research Space (South Africa)
Bell, Teboho
2015-07-01
Full Text Available Zulu-Natal, Westville, Private Bag X 54001, Durban 4000, South Africa. 3Centre de DÃ©veloppement des Techniques AvancÃ©es, Division Milieux IonisÃ©s et Lasers, P.O. Box 17 Baba Hassan, Algiers 16303, Algeria. 2Electrical field of Laguerre-Gaussian beams with radial...
Application of radial basis neural network for state estimation of ...
African Journals Online (AJOL)
user
An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this ... conventional Weighted Least Squares (WLS) State Estimator on basis of time, accuracy and robustness. ... redundant measurement data normally available in form of nodal injection and line flows.
Point Set Denoising Using Bootstrap-Based Radial Basis Function.
Liew, Khang Jie; Ramli, Ahmad; Abd Majid, Ahmad
2016-01-01
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Libcint: An efficient general integral library for Gaussian basis functions.
Sun, Qiming
2015-08-15
An efficient integral library Libcint was designed to automatically implement general integrals for Gaussian-type scalar and spinor basis functions. The library is able to evaluate arbitrary integral expressions on top of p, r and σ operators with one-electron overlap and nuclear attraction, two-electron Coulomb and Gaunt operators for segmented contracted and/or generated contracted basis in Cartesian, spherical or spinor form. Using a symbolic algebra tool, new integrals are derived and translated to C code programmatically. The generated integrals can be used in various types of molecular properties. To demonstrate the capability of the integral library, we computed the analytical gradients and NMR shielding constants at both nonrelativistic and 4-component relativistic Hartree-Fock level in this work. Due to the use of kinetically balanced basis and gauge including atomic orbitals, the relativistic analytical gradients and shielding constants requires the integral library to handle the fifth-order electron repulsion integral derivatives. The generality of the integral library is achieved without losing efficiency. On the modern multi-CPU platform, Libcint can easily reach the overall throughput being many times of the I/O bandwidth. On a 20-core node, we are able to achieve an average output 8.3 GB/s for C60 molecule with cc-pVTZ basis. © 2015 Wiley Periodicals, Inc.
Radial basis function neural network in fault detection of automotive ...
African Journals Online (AJOL)
Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection
Application of radial basis neural network for state estimation of ...
African Journals Online (AJOL)
An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...
Organisms modeling: The question of radial basis function networks
Directory of Open Access Journals (Sweden)
Muzy Alexandre
2014-01-01
Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
Directory of Open Access Journals (Sweden)
Agus Arif
2014-11-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.
Contracted auxiliary Gaussian basis integral and derivative evaluation
Giese, Timothy J.; York, Darrin M.
2008-02-01
The rapid evaluation of two-center Coulomb and overlap integrals between contracted auxiliary solid harmonic Gaussian functions is examined. Integral expressions are derived from the application of Hobson's theorem and Dunlap's product and differentiation rules of the spherical tensor gradient operator. It is shown that inclusion of the primitive normalization constants greatly simplifies the calculation of contracted functions corresponding to a Gaussian multipole expansion of a diffuse charge density. Derivative expressions are presented and it is shown that chain rules are avoided by expressing the derivatives as a linear combination of auxiliary integrals involving no more than five terms. Calculation of integrals and derivatives requires the contraction of a single vector corresponding to the monopolar result and its scalar derivatives. Implementation of the method is discussed and comparison is made with a Cartesian Gaussian-based method. The current method is superior for the evaluation of both integrals and derivatives using either primitive or contracted functions.
Control point selection for dimensionality reduction by radial basis function
Directory of Open Access Journals (Sweden)
Kotryna Paulauskienė
2016-02-01
Full Text Available This research deals with dimensionality reduction technique which is based on radial basis function (RBF theory. The technique uses RBF for mapping multidimensional data points into a low-dimensional space by interpolating the previously calculated position of so-called control points. This paper analyses various ways of selection of control points (regularized orthogonal least squares method, random and stratified selections. The experiments have been carried out with 8 real and artificial data sets. Positions of the control points in a low-dimensional space are found by principal component analysis. We demonstrate that random and stratified selections of control points are efficient and acceptable in terms of balance between projection error (stress and time-consumption.DOI: 10.15181/csat.v4i1.1095
Dynamics Model Abstraction Scheme Using Radial Basis Functions
Directory of Open Access Journals (Sweden)
Silvia Tolu
2012-01-01
Full Text Available This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF. Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme.
Efficient VLSI Architecture for Training Radial Basis Function Networks
Fan, Zhe-Cheng; Hwang, Wen-Jyi
2013-01-01
This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired. PMID:23519346
Neuronal spike sorting based on radial basis function neural networks
Directory of Open Access Journals (Sweden)
Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Directory of Open Access Journals (Sweden)
Dongliang Guo
2014-01-01
Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
Directory of Open Access Journals (Sweden)
Agus Arif
2011-08-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network
CSIR Research Space (South Africa)
Bell, Teboho
2017-01-01
Full Text Available In this paper, we use a digital laser to generate high-radial-order Laguerre-Gaussian, LGp,0 modes by loading digital holograms on a phase-only spatial light modulator that act as an end mirror of a diode-end-pumped laser resonator. The digital...
Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering.
Raitoharju, Jenni; Kiranyaz, Serkan; Gabbouj, Moncef
2016-12-01
In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.
Meshfree Local Radial Basis Function Collocation Method with Image Nodes
Energy Technology Data Exchange (ETDEWEB)
Baek, Seung Ki; Kim, Minjae [Pukyong National University, Busan (Korea, Republic of)
2017-07-15
We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem domain, similarly to the method of images in electrostatics, to perform collocation on the domain boundaries. We can thereby take into account the temperature profile as well as its gradients specified by boundary conditions at the same time, which holds true even for a node where two or more boundaries meet with different boundary conditions. We argue that the image method is computationally efficient when combined with the local RBF collocation method, whereas the addition of image nodes becomes very costly in case of the global collocation. We apply our modified method to a benchmark test of a boundary value problem, and find that this simple modification reduces the maximum error from the analytic solution significantly. The reduction is small for an initial value problem with simpler boundary conditions. We observe increased numerical instability, which has to be compensated for by a sufficient number of sample nodes and/or more careful parameter choices for time integration.
Design Optimization of Centrifugal Pump Using Radial Basis Function Metamodels
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Yu Zhang
2014-05-01
Full Text Available Optimization design of centrifugal pump is a typical multiobjective optimization (MOO problem. This paper presents an MOO design of centrifugal pump with five decision variables and three objective functions, and a set of centrifugal pumps with various impeller shroud shapes are studied by CFD numerical simulations. The important performance indexes for centrifugal pump such as head, efficiency, and required net positive suction head (NPSHr are investigated, and the results indicate that the geometry shape of impeller shroud has strong effect on the pump's performance indexes. Based on these, radial basis function (RBF metamodels are constructed to approximate the functional relationship between the shape parameters of impeller shroud and the performance indexes of pump. To achieve the objectives of maximizing head and efficiency and minimizing NPSHr simultaneously, multiobjective evolutionary algorithm based on decomposition (MOEA/D is applied to solve the triobjective optimization problem, and a final design point is selected from the Pareto solution set by means of robust design. Compared with the values of prototype test and CFD simulation, the solution of the final design point exhibits a good consistency.
Pseudospectral sampling of Gaussian basis sets as a new avenue to high-dimensional quantum dynamics
Heaps, Charles
This thesis presents a novel approach to modeling quantum molecular dynamics (QMD). Theoretical approaches to QMD are essential to understanding and predicting chemical reactivity and spectroscopy. We implement a method based on a trajectory-guided basis set. In this case, the nuclei are propagated in time using classical mechanics. Each nuclear configuration corresponds to a basis function in the quantum mechanical expansion. Using the time-dependent configurations as a basis set, we are able to evolve in time using relatively little information at each time step. We use a basis set of moving frozen (time-independent width) Gaussian functions that are well-known to provide a simple and efficient basis set for nuclear dynamics. We introduce a new perspective to trajectory-guided Gaussian basis sets based on existing numerical methods. The distinction is based on the Galerkin and collocation methods. In the former, the basis set is tested using basis functions, projecting the solution onto the functional space of the problem and requiring integration over all space. In the collocation method, the Dirac delta function tests the basis set, projecting the solution onto discrete points in space. This effectively reduces the integral evaluation to function evaluation, a fundamental characteristic of pseudospectral methods. We adopt this idea for independent trajectory-guided Gaussian basis functions. We investigate a series of anharmonic vibrational models describing dynamics in up to six dimensions. The pseudospectral sampling is found to be as accurate as full integral evaluation, while the former method is fully general and integration is only possible on very particular model potential energy surfaces. Nonadiabatic dynamics are also investigated in models of photodissociation and collinear triatomic vibronic coupling. Using Ehrenfest trajectories to guide the basis set on multiple surfaces, we observe convergence to exact results using hundreds of basis functions
CSIR Research Space (South Africa)
Bogaers, Alfred EJ
2016-10-01
Full Text Available In this paper we outline the use of radial basis function interpolation (RBF) to transfer information across non-matching and nonconforming interface meshes, with particular focus to partitioned fluid-structure interactions (FSI). In general...
ANALYTICAL SOLUTION OF BASIC SHIP HYDROSTATICS INTEGRALS USING POLYNOMIAL RADIAL BASIS FUNCTIONS
Dario Ban; Josip Bašić
2015-01-01
One of the main tasks of ship's computational geometry is calculation of basic integrals of ship's hydrostatics. In order to enable direct computation of those integrals it is necessary to describe geometry using analytical methods, like description using radial basis functions (RBF) with L1 norm. Moreover, using the composition of cubic and linear Polynomial radial basis functions, it is possible to give analytical solution of general global 2D description of ship geometry with discontinuiti...
Ruffato, G.; Carli, M.; Massari, M.; Romanato, F.
2015-03-01
The work of design, fabrication and characterization of spiral phase plates for the generation of Laguerre-Gaussian (LG) beams with non-null radial index is presented. Samples were fabricated by electron beam lithography on polymethylmethacrylate layers over glass substrates. The optical response of these phase optical elements was measured and the purity of the experimental beams was investigated in terms of Laguerre-Gaussian modes contributions. The farfield intensity pattern was compared with theoretical models and numerical simulations, while the expected phase features were confirmed by interferometric analyses. The high quality of the output beams confirms the applicability of these phase plates for the generation of high-order Laguerre-Gaussian beams. A novel application consisting in the design of computer-generated holograms encoding information for light beams carrying phase singularities is shown. A numerical code based on iterative Fourier transform algorithm has been developed for the computation of the phase pattern of phase-only diffractive optical element for illumination under LG beams. Numerical analysis and preliminary experimental results confirm the applicability of these devices as high-security optical elements.
Energy optimized Gaussian basis sets for the atoms T1 - Rn
International Nuclear Information System (INIS)
Faegri, K. Jr.
1987-01-01
Energy optimized Gaussian basis sets have been derived for the atoms Tl-Rn. Two sets are presented - a (20,16,10,6) set and a (22,17,13,8) set. The smallest sets yield atomic energies 107 to 123 mH above the numerical Hartree-Fock values, while the larger sets give energies 11 mH above the numerical results. Energy trends from the smaller sets indicate that reduced shielding by p-electrons may place a greater demand on the flexibility of d- and f-orbital description for the lighter elements of the series
Efficient evaluation of Coulomb integrals in a mixed Gaussian and plane-wave basis
Czech Academy of Sciences Publication Activity Database
Čársky, Petr
2007-01-01
Roč. 107, č. 1 (2007), s. 56-62 ISSN 0020-7608 R&D Projects: GA AV ČR IAA100400501; GA AV ČR 1ET400400413 Grant - others:European Science Foundation (EIPAM)(XE) PESC7-20; U.S. National Science Foundation(US) OISE-0532040 Institutional research plan: CEZ:AV0Z40400503 Keywords : two-electron integrals * mixed plane-wave and Gaussian basis sets * Coulomb integrals Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 1.368, year: 2007
International Nuclear Information System (INIS)
Nie, Zhongquan; Shi, Guang; Li, Dongyu; Zhang, Xueru; Wang, Yuxiao; Song, Yinglin
2015-01-01
The intensity distributions near the focus for radially polarized Laguerre–Bessel–Gaussian beams by a high numerical aperture objective in the immersion liquid are computed based on the vector diffraction theory. We compare the focusing properties of the radially polarized Laguerre–Bessel–Gaussian beams with those of Laguerre–Gaussian and Bessel–Gaussian modes. Furthermore, the effects of the optimally designed concentric three-zone phase filters on the intensity profiles in the focal region are examined. We further analyze the radiation forces on Rayleigh particles produced by the highly focused radially polarized Laguerre–Bessel–Gaussian beams using the specially engineered three-zone phase filters. - Highlights: • The tightly focusing of radially polarized LBG beams is examined. • The focusing performances of LBG beams are preferable over that of LG and BG modes. • A bright spot and an optical cage can be formed by special phase modulation. • These special focusing patterns can stably manipulate two types of particles
Satisfiability of logic programming based on radial basis function neural networks
International Nuclear Information System (INIS)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-01-01
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems
Satisfiability of logic programming based on radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Relativistic theory of nuclear magnetic resonance parameters in a Gaussian basis representation.
Kutzelnigg, Werner; Liu, Wenjian
2009-07-28
The calculation of NMR parameters from relativistic quantum theory in a Gaussian basis expansion requires some care. While in the absence of a magnetic field the expansion in a kinetically balanced basis converges for the wave function in the mean and for the energy with any desired accuracy, this is not necessarily the case for magnetic properties. The results for the magnetizability or the nuclear magnetic shielding are not even correct in the nonrelativistic limit (nrl) if one expands the original Dirac equation in a kinetically balanced Gaussian basis. This defect disappears if one starts from the unitary transformed Dirac equation as suggested by Kutzelnigg [Phys. Rev. A 67, 032109 (2003)]. However, a new difficulty can arise instead if one applies the transformation in the presence of the magnetic field of a point nucleus. If one decomposes certain contributions, the individual terms may diverge, although their sum is regular. A controlled cancellation may become difficult and numerical instabilities can arise. Various ways exist to avoid these singularities and at the same time get the correct nrl. There are essentially three approaches intermediate between the transformed and the untransformed formulation, namely, the bispinor decomposition, the decomposition of the lower component, and the hybrid unitary transformation partially at operator and partially at matrix level. All three possibilities were first considered by Xiao et al. [J. Chem. Phys. 126, 214101 (2007)] in a different context and in a different nomenclature. Their analysis and classification in a more general context are given here for the first time. Use of an extended balanced basis has no advantages and has other drawbacks and is not competitive, while the use of a restricted magnetic balance basis can be justified.
Gaussian-basis LDA and GGA calculations for alkali-metal equations of state
International Nuclear Information System (INIS)
Jaffe, J.E.; Lin, Z.; Hess, A.C.
1998-01-01
Recently there has been renewed interest in implementations of density-functional theory for solids using various types of localized basis sets, including atom-centered Gaussian-type functions. While such methods are clearly well adapted to most insulating and semiconducting systems, one might expect them to give a less-than-optimal description of metals relative to plane-wave-type methods. Nevertheless, several successful applications of local-basis methods to metals have recently been reported. Here, we report an application of our Gaussian linear combination of atomic orbitals (LCAO) code to some extremely free-electron-like metals, namely, the alkali metals Li, Na, and K. In agreement with other calculations (both local and plane wave) we find that the local-density approximation (LDA) lattice constants are relatively poor (∼-3% from experiment for the alkali metals versus ±1% for many other solids) and that the LDA bulk moduli are ∼30% too high. We find that the Perdew-Burke-Enzerhof (PBE) version of the generalized-gradient approximation (GGA) corrects most of this error, in agreement with earlier calculations using similar GGA functionals. The Becke-Lee-Yang-Parr GGA functional gives similar results for the alkali-metal equations of state but is found to overcorrect the errors of the LDA for the cohesive energies, for which the PBE functional is in better agreement with experiment. Our results indicate that the Gaussian-LCAO method should be able to give accurate results for nearly any crystalline solid, since it succeeds even where it would be expected to have the most difficulty. copyright 1998 The American Physical Society
Non-linear cancer classification using a modified radial basis function classification algorithm.
Wang, Hong-Qiang; Huang, De-Shuang
2005-10-01
This paper proposes a modified radial basis function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.
ANALYTICAL SOLUTION OF BASIC SHIP HYDROSTATICS INTEGRALS USING POLYNOMIAL RADIAL BASIS FUNCTIONS
Directory of Open Access Journals (Sweden)
Dario Ban
2015-09-01
Full Text Available One of the main tasks of ship's computational geometry is calculation of basic integrals of ship's hydrostatics. In order to enable direct computation of those integrals it is necessary to describe geometry using analytical methods, like description using radial basis functions (RBF with L1 norm. Moreover, using the composition of cubic and linear Polynomial radial basis functions, it is possible to give analytical solution of general global 2D description of ship geometry with discontinuities in the form of polynomials, thus enabling direct calculation of basic integrals of ship hydrostatics.
An editor for the maintenance and use of a bank of contracted Gaussian basis set functions
International Nuclear Information System (INIS)
Taurian, O.E.
1984-01-01
A bank of basis sets to be used in ab-initio calculations has been created. The bases are sets of contracted Gaussian type orbitals to be used as input to any molecular integral package. In this communication we shall describe the organization of the bank and a portable editor program which was designed for its maintenance and use. This program is operated by commands and it may be used to obtain any kind of information about the bases in the bank as well as to produce output to be directly used as input for different integral programs. The editor may also be used to format basis sets in the conventional way utilized in publications, as well as to generate a complete, or partial, manual of the contents of the bank if so desired. (orig.)
A data-driven approach to local gravity field modelling using spherical radial basis functions
Klees, R.; Tenzer, R.; Prutkin, I.; Wittwer, T.
2008-01-01
We propose a methodology for local gravity field modelling from gravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares
Radial Basis Function Network Assisted Space-Time Equalisation for Dispersive Fading Environments
Wolfgang, A.; Chen, S.; Hanzo, L.
2004-01-01
A novel radial basis function network assisted decision-feedback aided space-time equaliser designed for receivers employing multiple antennas is presented. The proposed receiver structure outperforms the linear minimum mean-squared error benchmarker and is less sensitive to both error propagation and channel estimation errors.
Zhu, Wuming; Trickey, S. B.
2017-12-01
In high magnetic field calculations, anisotropic Gaussian type orbital (AGTO) basis functions are capable of reconciling the competing demands of the spherically symmetric Coulombic interaction and cylindrical magnetic (B field) confinement. However, the best available a priori procedure for composing highly accurate AGTO sets for atoms in a strong B field [W. Zhu et al., Phys. Rev. A 90, 022504 (2014)] yields very large basis sets. Their size is problematical for use in any calculation with unfavorable computational cost scaling. Here we provide an alternative constructive procedure. It is based upon analysis of the underlying physics of atoms in B fields that allow identification of several principles for the construction of AGTO basis sets. Aided by numerical optimization and parameter fitting, followed by fine tuning of fitting parameters, we devise formulae for generating accurate AGTO basis sets in an arbitrary B field. For the hydrogen iso-electronic sequence, a set depends on B field strength, nuclear charge, and orbital quantum numbers. For multi-electron systems, the basis set formulae also include adjustment to account for orbital occupations. Tests of the new basis sets for atoms H through C (1 ≤ Z ≤ 6) and ions Li+, Be+, and B+, in a wide B field range (0 ≤ B ≤ 2000 a.u.), show an accuracy better than a few μhartree for single-electron systems and a few hundredths to a few mHs for multi-electron atoms. The relative errors are similar for different atoms and ions in a large B field range, from a few to a couple of tens of millionths, thereby confirming rather uniform accuracy across the nuclear charge Z and B field strength values. Residual basis set errors are two to three orders of magnitude smaller than the electronic correlation energies in multi-electron atoms, a signal of the usefulness of the new AGTO basis sets in correlated wavefunction or density functional calculations for atomic and molecular systems in an external strong B field.
Zhu, Wuming; Trickey, S B
2017-12-28
In high magnetic field calculations, anisotropic Gaussian type orbital (AGTO) basis functions are capable of reconciling the competing demands of the spherically symmetric Coulombic interaction and cylindrical magnetic (B field) confinement. However, the best available a priori procedure for composing highly accurate AGTO sets for atoms in a strong B field [W. Zhu et al., Phys. Rev. A 90, 022504 (2014)] yields very large basis sets. Their size is problematical for use in any calculation with unfavorable computational cost scaling. Here we provide an alternative constructive procedure. It is based upon analysis of the underlying physics of atoms in B fields that allow identification of several principles for the construction of AGTO basis sets. Aided by numerical optimization and parameter fitting, followed by fine tuning of fitting parameters, we devise formulae for generating accurate AGTO basis sets in an arbitrary B field. For the hydrogen iso-electronic sequence, a set depends on B field strength, nuclear charge, and orbital quantum numbers. For multi-electron systems, the basis set formulae also include adjustment to account for orbital occupations. Tests of the new basis sets for atoms H through C (1 ≤ Z ≤ 6) and ions Li + , Be + , and B + , in a wide B field range (0 ≤ B ≤ 2000 a.u.), show an accuracy better than a few μhartree for single-electron systems and a few hundredths to a few mHs for multi-electron atoms. The relative errors are similar for different atoms and ions in a large B field range, from a few to a couple of tens of millionths, thereby confirming rather uniform accuracy across the nuclear charge Z and B field strength values. Residual basis set errors are two to three orders of magnitude smaller than the electronic correlation energies in multi-electron atoms, a signal of the usefulness of the new AGTO basis sets in correlated wavefunction or density functional calculations for atomic and molecular systems in an external strong B
Peramalan Crude Palm Oil (CPO Menggunakan Support Vector Regression Kernel Radial Basis
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Rezzy Eko Caraka
2017-06-01
Full Text Available Recently, instead of selecting a kernel has been proposed which uses SVR, where the weight of each kernel is optimized during training. Along this line of research, many pioneering kernel learning algorithms have been proposed. The use of kernels provides a powerful and principled approach to modeling nonlinear patterns through linear patterns in a feature space. Another bene?t is that the design of kernels and linear methods can be decoupled, which greatly facilitates the modularity of machine learning methods. We perform experiments on real data sets crude palm oil prices for application and better illustration using kernel radial basis. We see that evaluation gives a good to fit prediction and actual also good values showing the validity and accuracy of the realized model based on MAPE and R2. Keywords: Crude Palm Oil; Forecasting; SVR; Radial Basis; Kernel
An enhanced radial basis function network for short-term electricity price forecasting
International Nuclear Information System (INIS)
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
2010-01-01
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)
Machine learning of radial basis function neural network based on Kalman filter: Implementation
Directory of Open Access Journals (Sweden)
Vuković Najdan L.
2014-01-01
Full Text Available In this paper we test three new sequential machine learning algorithms for radial basis function (RBF neural network based on Kalman filter theory. Three new algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. Having introduced and derived mathematical model of each algorithm in the previous part of the paper, in this part we test and assess their performance using standard test sets from machine learning community. RBF neural network and three developed algorithms are implemented in MATLAB® programming environment. Experimental results obtained on real data sets as well as on real engineering problem show that developed algorithms result in more accurate models of the problem being investigated based on radial basis function neural network.
Burken, John J.
2005-01-01
This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.
Misganaw Abebe; Jun-Seok Yoon; Beom-Soo Kang
2017-01-01
Springback in multi-point dieless forming (MDF) is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF) to replace the numerical simulation model by using statistical analyses that are based on a desi...
Radial Basis Neural Networks Based Fault Detection and Isolation Scheme for Pneumatic Actuator
Prabakaran, K; S, Kaushik; R, Mouleeshuwarapprabu
2014-01-01
Fault diagnosis is an ongoing significant research field due to the constantly increasing need for maintainability, reliability and safety of industrial plants. The pneumatic actuators are installed in harsh environment: high temperature, pressure, aggressive media and vibration, etc. This influenced the pneumatic actuator predicted life time. The failures in pneumatic actuator cause forces the installation shut down and may also determine the final quality of the product. A Radial Basis Neur...
Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network
Gómez-Mayorga, Margarita E.; Gallegos-Funes, Francisco J.; de-La-Rosa-Vázquez, José M.; Cruz-Santiago, Rene; Ponomaryov, Volodymyr
The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.
Directory of Open Access Journals (Sweden)
Wang Pidong
2016-01-01
Full Text Available Blind source separation is a hot topic in signal processing. Most existing works focus on dealing with linear combined signals, while in practice we always encounter with nonlinear mixed signals. To address the problem of nonlinear source separation, in this paper we propose a novel algorithm using radial basis function neutral network, optimized by multi-universe parallel quantum genetic algorithm. Experiments show the efficiency of the proposed method.
Machine learning of radial basis function neural network based on Kalman filter: Introduction
Directory of Open Access Journals (Sweden)
Vuković Najdan L.
2014-01-01
Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.
A prediction method for the wax deposition rate based on a radial basis function neural network
Directory of Open Access Journals (Sweden)
Ying Xie
2017-06-01
Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.
Chen, Guo-qing; Wei, Bai-lin; Wang, Jun; Wu, Ya-min; Gao, Shu-mei; Kong, Yan; Zhu, Tuo
2010-01-01
Based on the experimental study, it was found that melamine solution excited by UV light can generate a strong fluorescence. The fluorescence spectrum is within a range from 310 to 600 nm, the peak wavelength of the fluorescence is about 420 nm, and the relationship between fluorescence intensity and melamine solution concentration is nonlinear. A method for the determination of melamine solution concentration was presented, which was based on fluorescence spectroscopy and radial basis function neural networks. For each sample, 30 emission wavelength values were selected, the fluorescence intensity corresponding to the selected wavelength was used as the network data, and a radial basis function neural network was trained and constructed. The trained radial basis function neural network was employed to predict the melamine solution concentration in five kinds of samples, and the relative errors of the results were 0.93%, 0.09%, 0.31%, 1.55% and 4.61%, respectively. The results show that this method can determine the content of melamine quickly and accurately. The whole research outcomes will provide a new method for determining the content of melamine and food safety supervision.
International Nuclear Information System (INIS)
Holden, Zachary C.; Richard, Ryan M.; Herbert, John M.
2013-01-01
An implementation of Ewald summation for use in mixed quantum mechanics/molecular mechanics (QM/MM) calculations is presented, which builds upon previous work by others that was limited to semi-empirical electronic structure for the QM region. Unlike previous work, our implementation describes the wave function's periodic images using “ChElPG” atomic charges, which are determined by fitting to the QM electrostatic potential evaluated on a real-space grid. This implementation is stable even for large Gaussian basis sets with diffuse exponents, and is thus appropriate when the QM region is described by a correlated wave function. Derivatives of the ChElPG charges with respect to the QM density matrix are a potentially serious bottleneck in this approach, so we introduce a ChElPG algorithm based on atom-centered Lebedev grids. The ChElPG charges thus obtained exhibit good rotational invariance even for sparse grids, enabling significant cost savings. Detailed analysis of the optimal choice of user-selected Ewald parameters, as well as timing breakdowns, is presented
Wang, Pengbo
2017-11-01
In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.
Radial basis function neural networks with sequential learning MRAN and its applications
Sundararajan, N; Wei Lu Ying
1999-01-01
This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t
Ni, Shengqiao; Lv, Jiancheng; Cheng, Zhehao; Li, Mao
2015-01-01
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method. PMID:26161960
Directory of Open Access Journals (Sweden)
Shengqiao Ni
Full Text Available This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Ni, Shengqiao; Lv, Jiancheng; Cheng, Zhehao; Li, Mao
2015-01-01
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Zhineng Hu
2014-01-01
Full Text Available Regional logistics prediction is the key step in regional logistics planning and logistics resources rationalization. Since regional economy is the inherent and determinative factor of regional logistics demand, it is feasible to forecast regional logistics demand by investigating economic indicators which can accelerate the harmonious development of regional logistics industry and regional economy. In this paper, the PSO-RBFNN model, a radial basis function neural network (RBFNN combined with particle swarm optimization (PSO algorithm, is studied. The PSO-RBFNN model is trained by indicators data in a region to predict the regional logistics demand. And the corresponding results indicate the model’s applicability and potential advantages.
Energy Technology Data Exchange (ETDEWEB)
Guerra, Fabio A. [Institute of Technology for Development, LACTEC, Low Voltage Technology Unit, UTBT Centro Politecnico UFPR, Zip code 81531-980, Curitiba, PR (Brazil)], E-mail: guerra@lactec.org.br; Coelho, Leandro dos S. [Pontifical Catholic University of Parana, PUCPR, Production and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, PR (Brazil)], E-mail: leandro.coelho@pucpr.br
2008-03-15
An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting.
International Nuclear Information System (INIS)
Guerra, Fabio A.; Coelho, Leandro dos S.
2008-01-01
An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting
Patankar, S. J.; Jurs, P. C.
2003-02-01
HIV protease inhibitors are being used as frontline therapy in the treatment of HIV patients. Multi-drug-resistant HIV mutant strains are emerging with the initial aggressive multi-drug treatment of HIV patients. This necessitates continued search for novel inhibitors of viral replication. These protease inhibitors may further be useful as pharmacological agents for inhibition of other viral replication. Classification models of HIV Protease inhibitors are developed using a data set of 123 compounds containing several heterocycles. Their inhibitory concentrations expressed as log (IC50) ranged from -1.52 to 2.12 log units. The dataset was divided into active and inactive classes on the basis of their antiviral potency. Initially a two-class problem (active, inactive) is explored using k-nearest neighbor approach. In order to introduce non-linearity in the classifier different approaches were investigated. This led to the goal of a fast, simple, minimum user input, radial basis function neural network (RBFNN) classifier development. Then the same two-class problem was resolved using the (RBFNN) classifier. A genetic algorithm with RBFNN fitness evaluator was used to search for the optimum descriptor subsets. The application of majority rules was also tested for the RBFNN classification. The best six descriptor model found by the new cost function showed predictive ability in the high 80% range for an external prediction set.
Li, Yang; Wang, Xu-Dong; Luo, Mei-Lin; Li, Ke; Yang, Xiao-Feng; Guo, Qi
2018-03-01
The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time-frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
Computing single step operators of logic programming in radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
International Nuclear Information System (INIS)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-01-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
Energy Technology Data Exchange (ETDEWEB)
Rescigno, Thomas N.; Horner, Daniel A.; Yip, Frank L.; McCurdy,C. William
2005-08-29
Gaussian basis functions, routinely employed in molecular electronic structure calculations, can be combined with numerical grid-based functions in a discrete variable representation to provide an efficient method for computing molecular continuum wave functions. This approach, combined with exterior complex scaling, obviates the need for slowly convergent single-center expansions, and allows one to study a variety of electron-molecule collision problems. The method is illustrated by computation of various bound and continuum properties of H2+.
Yang, Fan; Kusche, Jürgen; Forootan, Ehsan; Rietbroek, Roelof
2017-08-01
We present a state-of-the-art approach of passive-ocean modified radial basis functions (MRBFs) that improves the recovery of time-variable gravity fields from Gravity Recovery and Climate Experiment (GRACE). As is well known, spherical harmonics (SHs), which are commonly used to recover gravity fields, are orthogonal basis functions with global coverage. However, the chosen SH truncation involves a global compromise between data coverage and obtainable resolution, and strong localized signals may not be fully captured. Radial basis functions (RBFs) provide another representation, which has been proposed in earlier works to be better suited to retrieve regional gravity signals. In this paper, we propose a MRBF approach by embedding the known coastal geometries in the RBF parameterization and imposing global mass conservation and equilibrium behavior of the oceans. Our hypothesis is that with this physically justified constraint, the GRACE-derived gravity signals can be more realistically partitioned into the land and ocean contributions along the coastlines. We test this new technique to invert monthly gravity fields from GRACE level-1b observations covering 2005-2010, for which the numerical results indicate that (1) MRBF-based solutions reduce the number of parameters by approximately 10% and allow for more flexible regularization when compared to ordinary RBF solutions and (2) the MRBF-derived mass flux is better confined along coastal areas. The latter is particularly tested in the southern Greenland, and our results indicate that the trend of mass loss from the MRBF solutions is approximately 11% larger than that from the SH solutions and approximately 4%-6% larger than that of RBF solutions.
International Nuclear Information System (INIS)
Smirnov, V.N.; Strokovskii, G.A.
1994-01-01
An analytical form of expansion coefficients of a diffracted field for an arbitrary Hermite-Gaussian beam in an alien Hermite-Gaussian basis is obtained. A possible physical interpretation of the well-known Young phenomenological diffraction principle and experiments on diffraction of Hermite-Gaussian beams of the lowest types (n = 0 - 5) from half-plane are discussed. The case of nearly homogenous expansion corresponding to misalignment and mismatch of optical systems is also analyzed. 7 refs., 2 figs
Florez, W. F.; Portapila, M.; Hill, A. F.; Power, H.; Orsini, P.; Bustamante, C. A.
2015-03-01
The aim of this paper is to present how to implement a control volume approach improved by Hermite radial basis functions (CV-RBF) for geochemical problems. A multi-step strategy based on Richardson extrapolation is proposed as an alternative to the conventional dual step sequential non-iterative approach (SNIA) for coupling the transport equations with the chemical model. Additionally, this paper illustrates how to use PHREEQC to add geochemical reaction capabilities to CV-RBF transport methods. Several problems with different degrees of complexity were solved including cases of cation exchange, dissolution, dissociation, equilibrium and kinetics at different rates for mineral species. The results show that the solution and strategies presented here are effective and in good agreement with other methods presented in the literature for the same cases.
An Incremental Radial Basis Function Network Based on Information Granules and Its Application
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Myung-Won Lee
2016-01-01
Full Text Available This paper is concerned with the design of an Incremental Radial Basis Function Network (IRBFN by combining Linear Regression (LR and local RBFN for the prediction of heating load and cooling load in residential buildings. Here the proposed IRBFN is designed by building a collection of information granules through Context-based Fuzzy C-Means (CFCM clustering algorithm that is guided by the distribution of error of the linear part of the LR model. After adopting a construct of a LR as global model, refine it through local RBFN that captures remaining and more localized nonlinearities of the system to be considered. The experiments are performed on the estimation of energy performance of 768 diverse residential buildings. The experimental results revealed that the proposed IRBFN showed good performance in comparison to LR, the standard RBFN, RBFN with information granules, and Linguistic Model (LM.
Directory of Open Access Journals (Sweden)
Yunfeng Wu
2014-01-01
Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.
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Huaiqing Zhang
2014-01-01
Full Text Available The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.
Selecting radial basis function network centers with recursive orthogonal least squares training.
Gomm, J B; Yu, D L
2000-01-01
Recursive orthogonal least squares (ROLS) is a numerically robust method for solving for the output layer weights of a radial basis function (RBF) network, and requires less computer memory than the batch alternative. In this paper, the use of ROLS is extended to selecting the centers of an RBF network. It is shown that the information available in an ROLS algorithm after network training can be used to sequentially select centers to minimize the network output error. This provides efficient methods for network reduction to achieve smaller architectures with acceptable accuracy and without retraining. Two selection methods are developed, forward and backward. The methods are illustrated in applications of RBF networks to modeling a nonlinear time series and a real multiinput-multioutput chemical process. The final network models obtained achieve acceptable accuracy with significant reductions in the number of required centers.
Radial basis functions in mathematical modelling of flow boiling in minichannels
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Hożejowska Sylwia
2017-01-01
Full Text Available The paper addresses heat transfer processes in flow boiling in a vertical minichannel of 1.7 mm depth with a smooth heated surface contacting fluid. The heated element for FC-72 flowing in a minichannel was a 0.45 mm thick plate made of Haynes-230 alloy. An infrared camera positioned opposite the central, axially symmetric part of the channel measured the plate temperature. K-type thermocouples and pressure converters were installed at the inlet and outlet of the minichannel. In the study radial basis functions were used to solve a problem concerning heat transfer in a heated plate supplied with the controlled direct current. According to the model assumptions, the problem is treated as twodimensional and governed by the Poisson equation. The aim of the study lies in determining the temperature field and the heat transfer coefficient. The results were verified by comparing them with those obtained by the Trefftz method.
International Nuclear Information System (INIS)
Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin
2006-01-01
High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)
Lam, Dao; Wunsch, Donald
2017-01-01
Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy. The features are learned by an unsupervised feature learning (UFL) algorithm. Then, those features are trained by a fast radial basis function (RBF) extreme learning machine (ELM). By exploiting the massive parallel computing attribute of modern graphics processing unit, a customized compute unified device architecture (CUDA) kernel is developed to further speed up the computing of the RBF kernel in the ELM. Results tested on Canadian Institute for Advanced Research and Mixed National Institute of Standards and Technology data sets confirm the UFL RBF ELM achieves high accuracy, and the CUDA implementation is up to 20 times faster than CPU and the naive parallel approach.
THE ALGORITHM OF MESHFREE METHOD OF RADIAL BASIS FUNCTIONS IN TASKS OF UNDERGROUND HYDROMECHANICS
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N. V. Medvid
2016-01-01
Full Text Available A Mathematical model of filtering consolidation in the body of soil dam with conduit andwashout zone in two-dimensional case is considered. The impact of such technogenic factors as temperature, salt concentration, subsidence of upper boundary and interior points of the dam with time is taken into account. The software to automate the calculation of numerical solution of the boundary problem by radial basis functions has been created, which enables to conduct numerical experiments by varying the input parameters and shape. The influence of the presence of conduit and washout zone on the pressure, temperature and concentration of salts in the dam body at different time intervals isinvestigated. A number of numerical experiments is conducted and the analysis of dam accidents is performed.
Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
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Wei Liu
2013-01-01
Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.
Multiquadric and Compactly Supported Radial Basis Functions for Shallow Water Equations
Alhuri, Y.; Taik, A.; Naji, A.
2009-04-01
Meshfree methods have gained much attention in recent years, not only in the mathematics but also in the engineering community. The computer and numerical methods are powerful tools of analysing wide rang of engineering and industrial application. For long time researchers recognised problems when using a mesh-based method. Developing the meshless methods overcome these problems. In the present paper, we present the application of both the global and the compactly supported radial basis functions (CSRBFs) for solving a system of shallow water hydrodynamic model for marine environments. As the technique is based on the collocation formulation and does not require the generation of a grid and any integral evaluation, the technique is considered as purely meshless method. The Computational efficiency and accuracy of both used functions are verified by comparing the analytic and observed solution.
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
Radial basis function neural network for power system load-flow
International Nuclear Information System (INIS)
Karami, A.; Mohammadi, M.S.
2008-01-01
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
Li, Bo; Rui, Xiaoting
2018-01-01
Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.
Gaussian basis sets for highly excited and resonance states of helium
Czech Academy of Sciences Publication Activity Database
Kaprálová-Žďánská, Petra Ruth; Šmydke, Jan
2013-01-01
Roč. 138, č. 2 (2013), 024105 ISSN 0021-9606 R&D Projects: GA AV ČR IAAX00100903; GA MŠk(CZ) ME10046; GA ČR GAP205/11/0571 Institutional support: RVO:68378271 Keywords : approximation theory * Gaussian processes * ground states * helium neutral atoms * optimisation * resonant states * Rydberg states Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 3.122, year: 2013
Directory of Open Access Journals (Sweden)
Jaime Alberto Echeverri Arias
2009-07-01
Full Text Available La eliminación del ruido impulsivo es un problema clásico del procesado no lineal para el mejoramiento de imágenes y las funciones de base radial de soporte global son útiles para enfrentarlo. Este trabajo presenta una técnica de interpolación que disminuye eficientemente el ruido impulsivo en imágenes, mediante el uso de interpolante obtenido por funciones de base radial en el marco de la investigación enfocada en el desarrollo de un Sistema de recuperación de imágenes de recursos acuáticos amazónicos. Esta técnica primero etiqueta los píxeles de la imagen que son ruidosos y, mediante la interpolación, genera un valor de reconstrucción de dicho píxel usando sus vecinos. Los resultados obtenidos son comparables y muchas veces mejores que otras técnicas ya publicadas y reconocidas. Según el análisis de resultados, se puede aplicar a imágenes con altas tasas de ruido, manteniendo un bajo error de reconstrucción de los píxeles "ruidosos", así como la calidad visual.Global support radial base functions are effective in eliminating impulsive noise in non-linear processing. This paper introduces an interpolation technique which efficiently reduces image impulsive noise by means of an interpolant obtained through radial base functions. These functions have been used in a research project designed to develop a system for the recovery of images of Amazonian aquatic resources. This technique starts with the tagging by interpolation of noisy image pixels. Thus, a value of reconstruction for the noisy pixels is generated using neighboring pixels. The results obtained with this technique have proved comparable and often better than those obtained with previously known techniques. According to results analysis, this technique can be successfully applied on images with high noise levels. The results are low error in noisy pixel reconstruction and better visual quality.
Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks
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Zhihong Liao
2017-11-01
Full Text Available A radial basis function network (RBFN method is proposed to reconstruct daily Sea surface temperatures (SSTs with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI v2 daily 0.25° SST (OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.
Stateczny, A.; Lubczonek, J.
2003-04-01
The basic problem in the construction of a numerical spatial sea chart is such transformation of the sounding data that it should be possible to determine the depth at any point of the bottom area. In recent years, much attention has been devoted to the problem of modelling the seabed shape in a numerical three-dimensional sea chart. Various methods for modelling the seabed shape are applied. These methods can be divided into analytical and neural. In the case of applying the model for navigational tasks, the selection of a suitable method should ensure high accuracy of surface projection. The model should be conformed to the surface shape, spatial distribution of the measurement points and their number. The application of universal methods like 'multiquadric' or 'kriging' does not ensure an optimal result either, as each of these methods can have a certain number of options and parameters, which frequently play a significant role during surface modelling. It is often difficult to optimise these factors and even experience does not guarantee a satisfactory result. This applies especially to modelling irregular surfaces, when it is difficult to select the method suitable for the surface shape that is sometimes unpredictable. It has been suggested that the method of selecting the shape parameter of the radial basis functions should be applied which makes it possible to minimise the mean square error of the approximated surface. The paper presents a new method of optimising the parameters of radial functions used for modelling the bottom surface. The accuracy of the surface projection obtained was the criterion for optimisation. The properties of self-organizing networks created the possibility of selecting testing points out of any set of measurement points and the determination of the minimum value of RMS error by means of the GRNN network. Optimisation of the shape parameter required building the proper polygon of the test points. For building such kind of polygon
Chapman, Craig T; Cheng, Xiaolu; Cina, Jeffrey A
2011-04-28
A recently framed quantum/semiclassical treatment for the internal nuclear dynamics of a small molecule and the induced small-amplitude coherent motion of a low-temperature host medium (Chapman, C. T.; Cina, J. A. J. Chem. Phys.2007,127, 114502) is further analyzed and subjected to initial tests of its numerical implementation. In the illustrative context of a 1D system interacting with a 1D medium, we rederive the fixed vibrational basis/gaussian bath (FVB/GB) equations of motion for the parameters defining the gaussian bath wave packet accompanying each of the energy eigenkets of the quantum mechanical system. The conditions of validity for the gaussian-bath approximation are shown to coincide with those supporting approximate population conservation. We perform initial numerical tests of the FVB/GB scheme and illustrate the semiclassical description it provides of coherent motion in the medium by comparing its predictions with the exact results for a high-frequency system harmonic oscillator bilinearly coupled to a lower-frequency bath oscillator. Linear vibronic absorption spectra or, equivalently, ultrafast wave packet interferometry signals are shown to be readily and accurately calculable within the FVB/GB framework.
Czech Academy of Sciences Publication Activity Database
Čársky, Petr
2009-01-01
Roč. 109, č. 620 (2009), s. 1237-1242 ISSN 0020-7608 R&D Projects: GA ČR GA203/07/0070; GA ČR GA202/08/0631; GA AV ČR 1ET400400413; GA AV ČR IAA100400501 Institutional research plan: CEZ:AV0Z40400503 Keywords : Derivatives of Coulomb integrals * mixed Gaussian and plane-wave basis sets * electron scattering * computer time saving Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 1.315, year: 2009
Directory of Open Access Journals (Sweden)
Eyad K Almaita
2017-03-01
Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application. International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17
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DT Wiyanti
2013-07-01
Full Text Available Salah satu metode peramalan yang paling dikembangkan saat ini adalah time series, yakni menggunakan pendekatan kuantitatif dengan data masa lampau yang dijadikan acuan untuk peramalan masa depan. Berbagai penelitian telah mengusulkan metode-metode untuk menyelesaikan time series, di antaranya statistik, jaringan syaraf, wavelet, dan sistem fuzzy. Metode-metode tersebut memiliki kekurangan dan keunggulan yang berbeda. Namun permasalahan yang ada dalam dunia nyata merupakan masalah yang kompleks. Satu metode saja mungkin tidak mampu mengatasi masalah tersebut dengan baik. Dalam artikel ini dibahas penggabungan dua buah metode yaitu Auto Regressive Integrated Moving Average (ARIMA dan Radial Basis Function (RBF. Alasan penggabungan kedua metode ini adalah karena adanya asumsi bahwa metode tunggal tidak dapat secara total mengidentifikasi semua karakteristik time series. Pada artikel ini dibahas peramalan terhadap data Indeks Harga Perdagangan Besar (IHPB dan data inflasi komoditi Indonesia; kedua data berada pada rentang tahun 2006 hingga beberapa bulan di tahun 2012. Kedua data tersebut masing-masing memiliki enam variabel. Hasil peramalan metode ARIMA-RBF dibandingkan dengan metode ARIMA dan metode RBF secara individual. Hasil analisa menunjukkan bahwa dengan metode penggabungan ARIMA dan RBF, model yang diberikan memiliki hasil yang lebih akurat dibandingkan dengan penggunaan salah satu metode saja. Hal ini terlihat dalam visual plot, MAPE, dan RMSE dari semua variabel pada dua data uji coba.Â The accuracy of time series forecasting is the subject of many decision-making processes. Time series use a quantitative approach to employ data from the past to make forecast for the future. Many researches have proposed several methods to solve time series, such as using statistics, neural networks, wavelets, and fuzzy systems. These methods have different advantages and disadvantages. But often the problem in the real world is just too complex that a
A radial basis classifier for the automatic detection of aspiration in children with dysphagia
Lee, Joon; Blain, Stefanie; Casas, Mike; Kenny, Dave; Berall, Glenn; Chau, Tom
2006-01-01
Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion The proposed aspiration
A radial basis classifier for the automatic detection of aspiration in children with dysphagia
Directory of Open Access Journals (Sweden)
Blain Stefanie
2006-07-01
Full Text Available Abstract Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion
Near Hartree-Fock quality Gaussian type orbital basis sets for the first- and third-row atoms
Partridge, Harry
1989-01-01
Energy-optimized, near Hartree-Fock (NHF) quality Gaussian type orbital (GTO) basis sets are reported for the second-row (Li to Ne) and fourth-row (K to Kr) atoms. The most accurate basis sets reported for the second row are (18s 13p) sets which are with 4 micro E(H) of the numerical Hartree-Fock (NHF) results. For B to Ne basis sets with more than 15s functions are quadruple zeta in the valence space. For the second-row transition metal atoms the (20s 12p 9d) basis sets are triple zeta in the valence space and are approximately equivalent to Clementi and Roetti's accurate Slater type orbital sets. Supplementing the (20s 12p 9d) basis sets optimized for the lowest state with the 4s(2)3d(n) occupation with a diffuse d function gives self-consistent-field energy separations to the 4s(1)3d(n+1) and 3d(n+2) states which are within 100 micro E(H) of the NHF results. The most accurate basis sets for the transition metal atoms are with 30 micro E(H) of the NHF results. In addition, energy optimized sets are reported for He(3P), Li(2P) and Be(3P).
Wang, Zhiheng
2014-12-10
A meshless local radial basis function method is developed for two-dimensional incompressible Navier-Stokes equations. The distributed nodes used to store the variables are obtained by the philosophy of an unstructured mesh, which results in two main advantages of the method. One is that the unstructured nodes generation in the computational domain is quite simple, without much concern about the mesh quality; the other is that the localization of the obtained collocations for the discretization of equations is performed conveniently with the supporting nodes. The algebraic system is solved by a semi-implicit pseudo-time method, in which the convective and source terms are explicitly marched by the Runge-Kutta method, and the diffusive terms are implicitly solved. The proposed method is validated by several benchmark problems, including natural convection in a square cavity, the lid-driven cavity flow, and the natural convection in a square cavity containing a circular cylinder, and very good agreement with the existing results are obtained.
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Misganaw Abebe
2017-11-01
Full Text Available Springback in multi-point dieless forming (MDF is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE. Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.
Radial basis function networks applied to DNBR calculation in digital core protection systems
International Nuclear Information System (INIS)
Lee, Gyu-Cheon; Heung Chang, Soon
2003-01-01
The nuclear power plant has to be operated with sufficient margin from the specified DNBR limit for assuring its safety. The digital core protection system calculates on-line real-time DNBR by using a complex subchannel analysis program, and triggers a reliable reactor shutdown if the calculated DNBR approaches the specified limit. However, it takes a relatively long calculation time even for a steady state condition, which may have an adverse effect on the operation flexibility. To overcome the drawback, a new method using a radial basis function network is presented in this paper. Nonparametric training approach is utilized, which shows dramatic reduction of the training time, no tedious heuristic process for optimizing parameters, and no local minima problem during the training. The test results show that the predicted DNBR is within about ±2% deviation from the target DNBR for the fixed axial flux shape case. For the variable axial flux case including severely skewed shapes that appeared during accidents, the deviation is within about ±10%. The suggested method could be the alternative that can calculate DNBR very quickly while guaranteeing the plant safety
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-09-18
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.
Solution of the quantum fluid dynamical equations with radial basis function interpolation
International Nuclear Information System (INIS)
Hu, Xu-Guang; Ho, Tak-San; Rabitz, Herschel; Askar, Attila
2000-01-01
The paper proposes a numerical technique within the Lagrangian description for propagating the quantum fluid dynamical (QFD) equations in terms of the Madelung field variables R and S, which are connected to the wave function via the transformation ψ=exp{(R+iS)/(ℎ/2π)}. The technique rests on the QFD equations depending only on the form, not the magnitude, of the probability density ρ=|ψ| 2 and on the structure of R=(ℎ/2π)/2 ln ρ generally being simpler and smoother than ρ. The spatially smooth functions R and S are especially suitable for multivariate radial basis function interpolation to enable the implementation of a robust numerical scheme. Examples of two-dimensional model systems show that the method rivals, in both efficiency and accuracy, the split-operator and Chebychev expansion methods. The results on a three-dimensional model system indicates that the present method is superior to the existing ones, especially, for its low storage requirement and its uniform accuracy. The advantage of the new algorithm is expected to increase for higher dimensional systems to provide a practical computational tool. (c) 2000 The American Physical Society
Solution of the quantum fluid dynamical equations with radial basis function interpolation
Hu, Xu-Guang; Ho, Tak-San; Rabitz, Herschel; Askar, Attila
2000-05-01
The paper proposes a numerical technique within the Lagrangian description for propagating the quantum fluid dynamical (QFD) equations in terms of the Madelung field variables R and S, which are connected to the wave function via the transformation ψ=exp\\{(R+iS)/ħ\\}. The technique rests on the QFD equations depending only on the form, not the magnitude, of the probability density ρ=\\|ψ\\|2 and on the structure of R=ħ/2 ln ρ generally being simpler and smoother than ρ. The spatially smooth functions R and S are especially suitable for multivariate radial basis function interpolation to enable the implementation of a robust numerical scheme. Examples of two-dimensional model systems show that the method rivals, in both efficiency and accuracy, the split-operator and Chebychev expansion methods. The results on a three-dimensional model system indicates that the present method is superior to the existing ones, especially, for its low storage requirement and its uniform accuracy. The advantage of the new algorithm is expected to increase for higher dimensional systems to provide a practical computational tool.
Prediction of reservoir brine properties using radial basis function (RBF neural network
Directory of Open Access Journals (Sweden)
Afshin Tatar
2015-12-01
Full Text Available Aquifers, which play a prominent role as an effective tool to recover hydrocarbon from reservoirs, assist the production of hydrocarbon in various ways. In so-called water flooding methods, the pressure of the reservoir is intensified by the injection of water into the formation, increasing the capacity of the reservoir to allow for more hydrocarbon extraction. Some studies have indicated that oil recovery can be increased by modifying the salinity of the injected brine in water flooding methods. Furthermore, various characteristics of brines are required for different calculations used within the petroleum industry. Consequently, it is of great significance to acquire the exact information about PVT properties of brine extracted from reservoirs. The properties of brine that are of great importance are density, enthalpy, and vapor pressure. In this study, radial basis function neural networks assisted with genetic algorithm were utilized to predict the mentioned properties. The root mean squared error of 0.270810, 0.455726, and 1.264687 were obtained for reservoir brine density, enthalpy, and vapor pressure, respectively. The predicted values obtained by the proposed models were in great agreement with experimental values. In addition, a comparison between the proposed model in this study and a previously proposed model revealed the superiority of the proposed GA-RBF model.
Online dimensionality reduction using competitive learning and Radial Basis Function network.
Tomenko, Vladimir
2011-06-01
The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. Copyright © 2011 Elsevier Ltd. All rights reserved.
Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs
International Nuclear Information System (INIS)
Bollig, Evan F.; Flyer, Natasha; Erlebacher, Gordon
2012-01-01
This paper presents parallelization strategies for the radial basis function-finite difference (RBF-FD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation and scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the first implementation of the RBF-FD method to leverage GPU accelerators for the solution of PDEs. Additionally, this implementation is the first to span both multiple CPUs and multiple GPUs. OpenCL kernels target the GPUs and inter-processor communication and synchronization is managed by the Message Passing Interface (MPI). We verify our implementation of the RBF-FD method with two hyperbolic PDEs on the sphere, and demonstrate up to 9x speedup on a commodity GPU with unoptimized kernel implementations. On a high performance cluster, the method achieves up to 7x speedup for the maximum problem size of 27,556 nodes.
Directory of Open Access Journals (Sweden)
M. Safish Mary
2012-04-01
Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.
Kumudha, P; Venkatesan, R
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.
Directory of Open Access Journals (Sweden)
Tatar Afshin
2016-03-01
Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction
Directory of Open Access Journals (Sweden)
P. Kumudha
2016-01-01
Full Text Available Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN and the novel adaptive dimensional biogeography based optimization (ADBBO model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.
High Rayleigh Number 3-D Spherical Mantle Convection with Radial Basis Functions
Flyer, N.; Yuen (3), G. Wright, D.
2009-04-01
In the last quarter of a century many numerical methods, such as finite-differences, finite-volume, their yin-yang variants, finite-elements and pseudo-spectral methods have been used to study the problem of 3-D spherical convection. All have their respective strengths but also serious weaknesses, such as low-order and can involve high algorithmic complexity, as in triangular elements. Spectrally accurate methods do not practically allow for local mesh refinement and often involve cumbersome algebra. We have recently introduced a new grid/mesh-free approach, using radial basis functions ( RBFs) . It has the advantage of being spectrally accurate for arbitrary node layouts in multi-dimensions with extreme algorithmic simplicity, and allows naturally node-refinement. One virtue of the RBF scheme is the ability to use a simple Cartesian geometry while implementing the required boundary conditions for the temperature, velocity and stresses on a spherical surface of both the outer( planetary surface ) and inner shell ( core-mantle boundary ). The velocity and stress components are expressed in terms of the scalar potential approach and the other remaining variable is the perturbed temperature field. We have studied the problem from the weakly nonlinear to a moderately nonlinear regime involving a Rayleigh number, about 1000 times super-critical. Both purely basal and partially internal -heating cases have been considered
Using radial basis functions in airborne gravimetry for local geoid improvement
Li, Xiaopeng
2017-10-01
Radial basis functions (RBFs) have been used extensively in satellite geodetic applications. However, to the author's knowledge, their role in processing and modeling airborne gravity data has not yet been fully advocated or extensively investigated in detail. Compared with satellite missions, the airborne data are more suitable for these kinds of localized basis functions especially considering the following facts: (1) Unlike the satellite missions that can provide global or near global data coverage, airborne gravity data are usually geographically limited. (2) It is also band limited in the frequency domain. (3) It is straightforward to formulate the RBF observation equations from an airborne gravimetric system. In this study, a set of band-limited RBF is developed to model and downward continue the airborne gravity data for local geoid improvement. First, EIGEN6c4 coefficients are used to simulate a harmonic field to test the performances of RBF on various sampling, noise, and flight height levels, in order to gain certain guidelines for processing the real data. Here, the RBF method not only successfully recovers the harmonic field but also presents filtering properties due to its particular design in the frequency domain. Next, the software was tested for the GSVS14 (Geoid Slope Validation Survey 2014) area in Iowa as well as for the area around Puerto Rico and the US Virgin Islands by use of the real airborne gravity data from the Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project. By fully utilizing the three-dimensional correlation information among the flight tracks, the RBF can also be used as a data cleaning tool for airborne gravity data adjustment and cleaning. This property is further extended to surface gravity data cleaning, where conventional approaches have various limitations. All the related numerical results clearly show the importance and contribution of the use of the RBF for high- resolution local gravity field
Energy Technology Data Exchange (ETDEWEB)
Bishop, R.F. (Manchester Univ. (United Kingdom). Inst. of Science and Technology); Buendia, E. (Granada Univ. (Spain). Facultad de Ciencias); Flynn, M.F. (Kent State Univ., OH (United States)); Guardiola, R. (Univ. de Valencia Estudi General (Spain). Dept. de Fisica Atomica y Nuclear)
1993-08-01
We develop and apply a series of extensible and flexible cluster expansions, tailored specifically to few-body systems. Our analysis begins by recalling previous work which examined the role of the center-of-mass (CM) motion in the few-body system. We show how the problems generated by the CM motion generally associated with correlated wavefunctions can be explicitly avoided altogether, and from the outset, by restricting the excitations induced to those which leave the CM undisturbed. This is accomplished by expressing all correlation functions solely in terms of relative coordinates. All such functions are expanded in bases of Gaussians, which are shown to be highly efficient computationally. Applications to the [sup 3]H and [sup 4]He nuclei are presented. Results show that the method produces both accurate energies and wavefunction properties. (author).
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Bae, C.H.; Blaabjerg, Frede
2005-01-01
A scheme to estimate the moment of inertia in a servo motor drive system at very low speed is proposed. The typical speed estimation scheme used in most servo systems operated at low speed is highly sensitive to variations in the moment of inertia. An observer that uses a radial basis function...
Kayri, Murat
2015-01-01
The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…
Czech Academy of Sciences Publication Activity Database
Bucha, B.; Bezděk, Aleš; Sebera, Josef; Janak, J.
2015-01-01
Roč. 36, č. 6 (2015), s. 773-801 ISSN 0169-3298 R&D Projects: GA ČR GA13-36843S Grant - others:SAV(SK) VEGA 1/0954/15 Institutional support: RVO:67985815 Keywords : spherical radial basis functions * spherical harmonics * geopotential Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 3.622, year: 2015
Image Super-Resolution Using Adaptive 2-D Gaussian Basis Function Interpolation
National Research Council Canada - National Science Library
Hunt, Terence
2004-01-01
... characteristics to be more effectively represented. The interpolation is constrained to reproduce the original image mean gray level, and the mean basis function variance is determined using the expected image smoothness for the increased resolution...
Radial basis function regression methods for predicting quantitative traits using SNP markers.
Long, Nanye; Gianola, Daniel; Rosa, Guilherme J M; Weigel, Kent A; Kranis, Andreas; González-Recio, Oscar
2010-06-01
A challenge when predicting total genetic values for complex quantitative traits is that an unknown number of quantitative trait loci may affect phenotypes via cryptic interactions. If markers are available, assuming that their effects on phenotypes are additive may lead to poor predictive ability. Non-parametric radial basis function (RBF) regression, which does not assume a particular form of the genotype-phenotype relationship, was investigated here by simulation and analysis of body weight and food conversion rate data in broilers. The simulation included a toy example in which an arbitrary non-linear genotype-phenotype relationship was assumed, and five different scenarios representing different broad sense heritability levels (0.1, 0.25, 0.5, 0.75 and 0.9) were created. In addition, a whole genome simulation was carried out, in which three different gene action modes (pure additive, additive+dominance and pure epistasis) were considered. In all analyses, a training set was used to fit the model and a testing set was used to evaluate predictive performance. The latter was measured by correlation and predictive mean-squared error (PMSE) on the testing data. For comparison, a linear additive model known as Bayes A was used as benchmark. Two RBF models with single nucleotide polymorphism (SNP)-specific (RBF I) and common (RBF II) weights were examined. Results indicated that, in the presence of complex genotype-phenotype relationships (i.e. non-linearity and non-additivity), RBF outperformed Bayes A in predicting total genetic values using SNP markers. Extension of Bayes A to include all additive, dominance and epistatic effects could improve its prediction accuracy. RBF I was generally better than RBF II, and was able to identify relevant SNPs in the toy example.
Sun, Jie; Yi, Hong-Liang; Tan, He-Ping
2016-02-20
A local radial basis function meshless scheme (LRBFM) is developed to solve polarized radiative transfer in participating media containing randomly oriented axisymmetric particles in which radial basis functions augmented with polynomial basis are employed to construct the trial functions, and the vector radiative-transfer equation based on the discrete-ordinates approach is discretized directly by collocation method. The LRBFM belongs to a class of truly meshless methods that do not need any mesh or any numerical integration scheme. Performances of the LRBFM are verified with analytical solutions and other numerical results reported earlier in the literature via five various test cases. The predicted angular distribution of brightness temperature and Stokes vector by the LRBFM agree very well with the benchmark. It is demonstrated that the LRBFM is accurate to solve vector radiative transfer in participating media with randomly oriented axisymmetric particles.
Bell, T.; Hasnaoui, A.; Ait-Ameur, K.; Ngcobo, S.
2017-10-01
In this paper we experimentally demonstrate selective excitation of high-radial-order Laguerre-Gaussian (LG p or LG{}p,0) modes with radial order p = 1-4 and azimuthal order l = 0 using a diode-pump solid-state laser (DPSSL) that is digitally controlled by a spatial light modulator (SLM). We encoded an amplitude mask containing p-absorbing rings, of various incompleteness (segmented) on grey-scale computer-generated digital holograms, and displayed them on an SLM which acted as an end mirror of the diode-pumped solid-state digital laser. The various incomplete (α) p-absorbing rings were digitally encoded to match the zero-intensity nulls of the desired LG p mode. We show that the creation of LG p , for p = 1 to p = 4, only requires an incomplete circular p-absorbing ring that has a completeness of ≈37.5%, giving the DPSSL resonator a lower pump threshold power while maintaining the same laser characteristics (such as beam propagation properties).
Krishnamurthy, Thiagarajan
2005-01-01
Response construction methods using Moving Least Squares (MLS), Kriging and Radial Basis Functions (RBF) are compared with the Global Least Squares (GLS) method in three numerical examples for derivative generation capability. Also, a new Interpolating Moving Least Squares (IMLS) method adopted from the meshless method is presented. It is found that the response surface construction methods using the Kriging and RBF interpolation yields more accurate results compared with MLS and GLS methods. Several computational aspects of the response surface construction methods also discussed.
Radial modal dependence of the azimuthal spectrum after parametric down-conversion
CSIR Research Space (South Africa)
Zhang, Y
2014-01-01
Full Text Available The radial degrees of freedom of the biphoton states that are produced in spontaneous parametric down-conversion (SPDC) in the Laguerre-Gaussian (LG) basis are investigated, theoretically and experimentally. We calculated the theoretical azimuthal...
International Nuclear Information System (INIS)
Vrankar, L.; Turk, G.; Runovc, F.; Kansa, E.J.
2006-01-01
Many heat-transfer problems involve a change of phase of material due to solidification or melting. Applications include: the safety studies of nuclear reactors (molten core concrete interaction), the drilling of high ice-content soil, the storage of thermal energy, etc. These problems are often called Stefan's or moving boundary value problems. Mathematically, the interface motion is expressed implicitly in an equation for the conservation of thermal energy at the interface (Stefan's conditions). This introduces a non-linear character to the system which treats each problem somewhat uniquely. The exact solution of phase change problems is limited exclusively to the cases in which e.g. the heat transfer regions are infinite or semi-infinite one dimensional-space. Therefore, solution is obtained either by approximate analytical solution or by numerical methods. Finite-difference methods and finite-element techniques have been used extensively for numerical solution of moving boundary problems. Recently, the numerical methods have focused on the idea of using a mesh-free methodology for the numerical solution of partial differential equations based on radial basis functions. In our case we will study solid-solid transformation. The numerical solutions will be compared with analytical solutions. Actually, in our work we will examine usefulness of radial basis functions (especially multiquadric-MQ) for one-dimensional Stefan's problems. The position of the moving boundary will be simulated by moving grid method. The resultant system of RBF-PDE will be solved by affine space decomposition. (author)
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...
Directory of Open Access Journals (Sweden)
Jaime A. Echeverri A.
2007-07-01
Full Text Available En este trabajo se muestra la utilización de funciones de base radial de soporte compacto para la reconstrucción tridimensional de rostros. En trabajos anteriores se habían explorado diferentes técnicas y diferentes funciones de base radial para reconstrucción de superficies; ahora presentamos los algoritmos y los resultados de la utilización de funciones de base radial de soporte compacto las cuales presentan ventajas comparativas en términos del tiempo de construcción de un interpolante para la reconstrucción. Se presentan comparaciones con técnicas ampliamente utilizadas en este campo y se detalla el proceso global de reconstrucción de superficies.In previous works, we have explored several radial basis techniques and functions for the reconstruction of surfaces. We now present the use of compact support radial basis functions for the tri-dimensional reconstruction of human faces. Therefore, we present algorithms and results coming from the application of compact support radial basis functions which have revealed comparative advantages in terms of the amount of time needed for the construction of an interpolant to be used in the reconstruction. We are also presenting some comparisons with techniques widely used in this field and we explain in detail the global process for the surfaces reconstruction.
Shankar, Varun; Wright, Grady B; Kirby, Robert M; Fogelson, Aaron L
2016-06-01
In this paper, we present a method based on Radial Basis Function (RBF)-generated Finite Differences (FD) for numerically solving diffusion and reaction-diffusion equations (PDEs) on closed surfaces embedded in ℝ d . Our method uses a method-of-lines formulation, in which surface derivatives that appear in the PDEs are approximated locally using RBF interpolation. The method requires only scattered nodes representing the surface and normal vectors at those scattered nodes. All computations use only extrinsic coordinates, thereby avoiding coordinate distortions and singularities. We also present an optimization procedure that allows for the stabilization of the discrete differential operators generated by our RBF-FD method by selecting shape parameters for each stencil that correspond to a global target condition number. We show the convergence of our method on two surfaces for different stencil sizes, and present applications to nonlinear PDEs simulated both on implicit/parametric surfaces and more general surfaces represented by point clouds.
Piret, Cécile
2012-05-01
Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper, we investigate methods to solve PDEs on arbitrary stationary surfaces embedded in . R3 using the RBF method. We present three RBF-based methods that easily discretize surface differential operators. We take advantage of the meshfree character of RBFs, which give us a high accuracy and the flexibility to represent the most complex geometries in any dimension. Two out of the three methods, which we call the orthogonal gradients (OGr) methods are the result of our work and are hereby presented for the first time. © 2012 Elsevier Inc.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2005-01-01
A new scheme to estimate the moment of inertia in the servo motor drive system in very low speed is proposed in this paper. The speed estimation scheme in most servo drive systems for low speed operation is sensitive to the variation of machine parameter, especially the moment of inertia....... To estimate the motor inertia value, the observer using the Radial Basis Function Network (RBFN) is applied. A control law for stabilizing the system and adaptive laws for updating both of the weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable...... in the sense of Lyapunov. The effectiveness of the proposed inertia estimation is verified by simulations and experiments. It is concluded that the speed control performance in low speed region is improved with the proposed disturbance observer using RBFN....
Directory of Open Access Journals (Sweden)
Meina Li
2016-09-01
Full Text Available Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR and movement index (MI monitoring. The embedded incremental network includes linear regression (LR and RBFNN based on context-based fuzzy c-means (CFCM clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.
Chen, Qian; Liu, Guohai; Xu, Dezhi; Xu, Liang; Xu, Gaohong; Aamir, Nazir
2018-05-01
This paper proposes a new decoupled control for a five-phase in-wheel fault-tolerant permanent magnet (IW-FTPM) motor drive, in which radial basis function neural network inverse (RBF-NNI) and internal model control (IMC) are combined. The RBF-NNI system is introduced into original system to construct a pseudo-linear system, and IMC is used as a robust controller. Hence, the newly proposed control system incorporates the merits of the IMC and RBF-NNI methods. In order to verify the proposed strategy, an IW-FTPM motor drive is designed based on dSPACE real-time control platform. Then, the experimental results are offered to verify that the d-axis current and the rotor speed are successfully decoupled. Besides, the proposed motor drive exhibits strong robustness even under load torque disturbance.
Directory of Open Access Journals (Sweden)
Ángel Gutiérrez
2015-04-01
Full Text Available The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space. Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network. The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique. A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.
Directory of Open Access Journals (Sweden)
Kevin Dalmasse
2016-07-01
Full Text Available The Coronal Multichannel Polarimeter (CoMP routinely performs coronal polarimetric measurements using the Fe XIII 10747 $AA$ and 10798 $AA$ lines, which are sensitive to the coronal magnetic field. However, inverting such polarimetric measurements into magnetic field data is a difficult task because the corona is optically thin at these wavelengths and the observed signal is therefore the integrated emission of all the plasma along the line of sight. To overcome this difficulty, we take on a new approach that combines a parameterized 3D magnetic field model with forward modeling of the polarization signal. For that purpose, we develop a new, fast and efficient, optimization method for model-data fitting: the Radial-basis-functions Optimization Approximation Method (ROAM. Model-data fitting is achieved by optimizing a user-specified log-likelihood function that quantifies the differences between the observed polarization signal and its synthetic/predicted analogue. Speed and efficiency are obtained by combining sparse evaluation of the magnetic model with radial-basis-function (RBF decomposition of the log-likelihood function. The RBF decomposition provides an analytical expression for the log-likelihood function that is used to inexpensively estimate the set of parameter values optimizing it. We test and validate ROAM on a synthetic test bed of a coronal magnetic flux rope and show that it performs well with a significantly sparse sample of the parameter space. We conclude that our optimization method is well-suited for fast and efficient model-data fitting and can be exploited for converting coronal polarimetric measurements, such as the ones provided by CoMP, into coronal magnetic field data.
Vavalle, Nicholas A; Schoell, Samantha L; Weaver, Ashley A; Stitzel, Joel D; Gayzik, F Scott
2014-11-01
Human body finite element models (FEMs) are a valuable tool in the study of injury biomechanics. However, the traditional model development process can be time-consuming. Scaling and morphing an existing FEM is an attractive alternative for generating morphologically distinct models for further study. The objective of this work is to use a radial basis function to morph the Global Human Body Models Consortium (GHBMC) average male model (M50) to the body habitus of a 95th percentile male (M95) and to perform validation tests on the resulting model. The GHBMC M50 model (v. 4.3) was created using anthropometric and imaging data from a living subject representing a 50th percentile male. A similar dataset was collected from a 95th percentile male (22,067 total images) and was used in the morphing process. Homologous landmarks on the reference (M50) and target (M95) geometries, with the existing FE node locations (M50 model), were inputs to the morphing algorithm. The radial basis function was applied to morph the FE model. The model represented a mass of 103.3 kg and contained 2.2 million elements with 1.3 million nodes. Simulations of the M95 in seven loading scenarios were presented ranging from a chest pendulum impact to a lateral sled test. The morphed model matched anthropometric data to within a rootmean square difference of 4.4% while maintaining element quality commensurate to the M50 model and matching other anatomical ranges and targets. The simulation validation data matched experimental data well in most cases.
Polynomials of Gaussians and vortex-Gaussian beams as complete, transversely confined bases.
Gutiérrez-Cuevas, Rodrigo; Alonso, Miguel A
2017-06-01
A novel type of discrete basis for paraxial beams is proposed, consisting of monomial vortices times polynomials of Gaussians in the radial variable. These bases have the distinctive property that the effective size of their elements is roughly independent of element order, meaning that the optimal scaling for expanding a localized field does not depend significantly on truncation order. This behavior contrasts with that of bases composed of polynomials times Gaussians, such as Hermite-Gauss and Laguerre-Gauss modes, where the scaling changes roughly as the inverse square root of the truncation order.
A hybrid radial basis function-pseudospectral method for thermal convection in a 3-D spherical shell
Wright, G. B.
2010-07-01
A novel hybrid spectral method that combines radial basis function (RBF) and Chebyshev pseudospectral methods in a "2 + 1" approach is presented for numerically simulating thermal convection in a 3-D spherical shell. This is the first study to apply RBFs to a full 3-D physical model in spherical geometry. In addition to being spectrally accurate, RBFs are not defined in terms of any surface-based coordinate system such as spherical coordinates. As a result, when used in the lateral directions, as in this study, they completely circumvent the pole issue with the further advantage that nodes can be "scattered" over the surface of a sphere. In the radial direction, Chebyshev polynomials are used, which are also spectrally accurate and provide the necessary clustering near the boundaries to resolve boundary layers. Applications of this new hybrid methodology are given to the problem of convection in the Earth\\'s mantle, which is modeled by a Boussinesq fluid at infinite Prandtl number. To see whether this numerical technique warrants further investigation, the study limits itself to an isoviscous mantle. Benchmark comparisons are presented with other currently used mantle convection codes for Rayleigh number (Ra) 7 × 10^{3} and 10^{5}. Results from a Ra = 10^{6} simulation are also given. The algorithmic simplicity of the code (mostly due to RBFs) allows it to be written in less than 400 lines of MATLAB and run on a single workstation. We find that our method is very competitive with those currently used in the literature. Copyright 2010 by the American Geophysical Union.
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Ali Mansourkhaki
2018-01-01
Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.
International Nuclear Information System (INIS)
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
In this paper, a novel method is proposed for predicting the density of liquid phase in stratified regime of liquid-gas two phase flows by utilizing dual modality densitometry technique and artificial neural network (ANN) model of radial basis function (RBF). The detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors for registering transmitted and scattered photons. At the first step, a Monte Carlo simulation model was utilized to obtain the optimum position for the scattering detector in dual modality densitometry configuration. At the next step, an experimental setup was designed based on obtained optimum position for detectors from simulation in order to generate the required data for training and testing the ANN. The results show that the proposed approach could be successfully applied for predicting the density of liquid phase in stratified regime of gas-liquid two phase flows with mean relative error (MRE) of less than 0.701. - Highlights: • Density of liquid phase in stratified regime of two phase flows was predicted. • Combination of dual modality densitometry technique and ANN was utilized. • Detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors. • MCNP simulation was done to obtain the optimum position for the scattering detector. • An experimental setup was designed to generate the required data for training the ANN.
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Deliang Yu
2017-01-01
Full Text Available This paper presents a new method to diagnose oil well pump faults using a modified radial basis function neural network. With the development of submersible linear motor technology, rodless pumping units have been widely used in oil exploration. However, the ground indicator diagram method cannot be used to diagnose the working conditions of rodless pumping units because it is based on the load change of the polished rod suspension point and its displacement. To solve this problem, this paper presents a new method that is applicable to rodless oil pumps. The advantage of this new method is its use of a simple feature extraction method and advanced genetic algorithm to optimize the threshold and weight of the RBF neural network. In this paper, we extract the characteristic value from the operation parameters of the submersible linear motor and oil wellhead as the input vector of the fault diagnosis model. Through experimental analysis, the proposed method is proven to have good convergence performance, high accuracy, and high reliability.
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Seng-Chi Chen
2014-01-01
Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.
Meng, Qinggang; Lee, M. H.
2007-03-01
Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.
International Nuclear Information System (INIS)
Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam
2007-01-01
Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported
Schmidt, J.; Piret, C.; Zhang, N.; Kadlec, B. J.; Liu, Y.; Yuen, D. A.; Wright, G. B.; Sevre, E. O.
2008-12-01
The faster growth curves in the speed of GPUs relative to CPUs in recent years and its rapidly gained popularity has spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations and producing the attendant visualization. We are concerned with modeling tsunami waves, where computational time is of extreme essence, for broadcasting warnings. In order to test the efficacy of the GPU on the set of shallow-water equations, we employed the NVIDIA board 8600M GT on a MacBook Pro. We have compared the relative speeds between the CPU and the GPU on a single processor for two types of spatial discretization based on second-order finite-differences and radial basis functions. RBFs are a more novel method based on a gridless and a multi- scale, adaptive framework. Using the NVIDIA 8600M GT, we received a speed up factor of 8 in favor of GPU for the finite-difference method and a factor of 7 for the RBF scheme. We have also studied the atmospheric dynamics problem of swirling flows over a spherical surface and found a speed-up of 5.3 using the GPU. The time steps employed for the RBF method are larger than those used in finite-differences, because of the much fewer number of nodal points needed by RBF. Thus, in modeling the same physical time, RBF acting in concert with GPU would be the fastest way to go.
Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli
2013-03-01
Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.
Belderrar, Ahmed; Hazzab, Abdeldjebar
2017-07-01
Controlling hospital high length of stay outliers can provide significant benefits to hospital management resources and lead to cost reduction. The strongest predictive factors influencing high length of stay outliers should be identified to build a high-performance prediction model for hospital outliers. We highlight the application of the hierarchical genetic algorithm to provide the main predictive factors and to define the optimal structure of the prediction model fuzzy radial basis function neural network. To establish the prediction model, we used a data set of 26,897 admissions from five different intensive care units with discharges between 2001 and 2012. We selected and analyzed the high length of stay outliers using the trimming method geometric mean plus two standard deviations. A total of 28 predictive factors were extracted from the collected data set and investigated. High length of stay outliers comprised 5.07% of the collected data set. The results indicate that the prediction model can provide effective forecasting. We found 10 common predictive factors within the studied intensive care units. The obtained main predictive factors include patient demographic characteristics, hospital characteristics, medical events, and comorbidities. The main initial predictive factors available at the time of admission are useful in evaluating high length of stay outliers. The proposed approach can provide a practical tool for healthcare providers, and its application can be extended to other hospital predictions, such as readmissions and cost.
Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.
2014-01-01
This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.
Directory of Open Access Journals (Sweden)
Jingwen Tian
2013-02-01
Full Text Available Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro-mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real-time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.
International Nuclear Information System (INIS)
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137 Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%. - Highlights: • Flow regime and void fraction were determined in two phase flows independent of the liquid phase density changes. • An experimental structure was set up and the required data was obtained. • 3 detectors and one gamma source were used in detection geometry. • RBF networks were utilized for flow regime and void fraction determination.
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A S Yogesh
2011-01-01
Full Text Available In the present case, we have reported a unilateral variation of the radial and musculocutaneous nerves on the left side in a 64-year-old male cadaver. The radial nerve supplied all the heads of the triceps brachii muscle and gave cutaneous branches such as lower lateral cutaneous nerve of the arm and posterior cutaneous nerve of forearm. The radial nerve ended without continuing further. The musculocutaneous nerve supplied the brachioradialis, extensor carpi radialis longus and extensor carpi radialis brevis muscles. The musculocutaneous nerve divided terminally into two branches, superficial and deep. The deep branch of musculocutaneous nerve corresponded to usual deep branch of the radial nerve while the superficial branch of musculocutaneous nerve corresponded to usual superficial branch of the radial nerve. The dissection was continued to expose the entire brachial plexus from its origin and it was found to be normal. The structures on the right upper limb were found to be normal. Surgeons should keep such variations in mind while performing the surgeries of the upper limb.
International Nuclear Information System (INIS)
Macedo, Luiz Guilherme M de; Borin, Antonio Carlos; Silva, Alberico B.F. da
2007-01-01
Prolapse-free basis sets suitable for four-component relativistic quantum chemical calculations are presented for the superheavy elements up to 118 Uuo ( 104 Rf, 105 Db, 106 Sg, 107 Bh, 108 Hs, 109 Mt, 110 Ds, 111 Rg, 112 Uub, 113 Uut, 114 Uuq, 115 Uup, 116 Uuh, 117 Uus, 118 Uuo) and 103 Lr. These basis sets were optimized by minimizing the absolute values of the energy difference between the Dirac-Fock-Roothaan total energy and the corresponding numerical value at a milli-Hartree order of magnitude, resulting in a good balance between cost and accuracy. Parameters for generating exponents and new numerical data for some superheavy elements are also presented
Quiney, HM; Glushkov, VN; Wilson, S
2004-01-01
Using large component basis sets of distributed s-type Gaussian functions with positions and exponents optimized so as to support Hartree-Fock total energies with an accuracy approaching the sub-muhartree level, Dirac-Hartree-Fock-Coulomb calculations are reported for the ground states of the
Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar
2017-12-01
The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction(v/v%) 0.05.
International Nuclear Information System (INIS)
Souza, T.J.; Medeiros, J.A.C.C.; Gonçalves, A.C.
2017-01-01
Highlights: • An alternative model capable of identifying the control rod that has accidentally dropped. • The identification model is based in readings of the thermocouples. • Radial basis function neural network is applied to predict the temperatures in control rod positions. - Abstract: The accidental dropping of a control rod may cause the reactor to operate unsafely. In this type of event, there is a distortion in the distribution of power and temperature in the core may exceed operating limits reactor safe. This work aims to develop an alternative model capable of identifying, at any time of the cycle, the control rod that has accidentally dropped at the core of a PWR reactor, using the readings of the thermocouples in order to minimize possible losses. The model assumes that in a possible drop of a control rod, the largest temperature change occurs in the position where the control rod is inserted. Considering the fact that there are no temperature gauges in all control rod positions, the proposed model uses radial basis function (RBF) neural networks to make a reconstruction of temperatures in these positions from the measurements of the thermocouples at the time of the accidental drop. The study found that the predictions of the temperatures made by the RBF neural networks showed good results, which enables the identification of the control rod dropped accidentally in the core, by simple inference of the fuel assembly of lowest temperature among temperatures reconstructed.
International Nuclear Information System (INIS)
Behloul, F.; Boudraa, A.; Janier, M.; Unterreiner, R.
1998-01-01
A self-organized Radial Basis Function Network (RBFN) is proposed for the problem of object extraction in Positron Emission Tomography Images of the heart. RBENs are supervised-learning networks. However, viewing the output of the networks as a fuzzy set, we have able to compute the error of the system using fuzziness measures. Thus, there is no need of target output for training the network. Besides the self-organizing feature of the network, our RBFN has a non linear output layer trained using the back-propagation algorithm. Two mathematical models of fuzzy measures have been considered: the index of fuzziness and fuzzy entropy. Preliminary results show that entropy measure produced a better extraction of healthy myocardium. (authors)
Czech Academy of Sciences Publication Activity Database
Kaprálová-Žďánská, Petra Ruth; Šmydke, Jan; Civiš, S.
2013-01-01
Roč. 139, č. 10 (2013), s. 104314 ISSN 0021-9606 R&D Projects: GA AV ČR IAAX00100903; GA MŠk(CZ) ME10046; GA ČR GAP205/11/0571 Institutional support: RVO:68378271 Keywords : Gaussian distribution * helium * oscillator strengths * quantum chemistry * rotational states * Rydberg states * two-photon processes Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 3.122, year: 2013
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.
Nourani, Vahid; Mousavi, Shahram; Dabrowska, Dominika; Sadikoglu, Fahreddin
2017-05-01
As an innovation, both black box and physical-based models were incorporated into simulating groundwater flow and contaminant transport. Time series of groundwater level (GL) and chloride concentration (CC) observed at different piezometers of study plain were firstly de-noised by the wavelet-based de-noising approach. The effect of de-noised data on the performance of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) was evaluated. Wavelet transform coherence was employed for spatial clustering of piezometers. Then for each cluster, ANN and ANFIS models were trained to predict GL and CC values. Finally, considering the predicted water heads of piezometers as interior conditions, the radial basis function as a meshless method which solves partial differential equations of GFCT, was used to estimate GL and CC values at any point within the plain where there is not any piezometer. Results indicated that efficiency of ANFIS based spatiotemporal model was more than ANN based model up to 13%.
D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel
2016-03-01
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
Directory of Open Access Journals (Sweden)
Shiva Kumar
2012-01-01
Full Text Available Radial basis function neural networks (RBFNNs, which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO. The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE, brake specific energy consumption (BSEC, exhaust gas temperature (, and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters.
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Nan YU
2014-09-01
Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.
International Nuclear Information System (INIS)
Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan
2011-01-01
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)
Mirbagheri, Seyed Ahmad; Bagheri, Majid; Boudaghpour, Siamak; Ehteshami, Majid; Bagheri, Zahra
2015-01-01
Treatment process models are efficient tools to assure proper operation and better control of wastewater treatment systems. The current research was an effort to evaluate performance of a submerged membrane bioreactor (SMBR) treating combined municipal and industrial wastewater and to simulate effluent quality parameters of the SMBR using a radial basis function artificial neural network (RBFANN). The results showed that the treatment efficiencies increase and hydraulic retention time (HRT) decreases for combined wastewater compared with municipal and industrial wastewaters. The BOD, COD, [Formula: see text] and total phosphorous (TP) removal efficiencies for combined wastewater at HRT of 7 hours were 96.9%, 96%, 96.7% and 92%, respectively. As desirable criteria for treating wastewater, the TBOD/TP ratio increased, the BOD and COD concentrations decreased to 700 and 1000 mg/L, respectively and the BOD/COD ratio was about 0.5 for combined wastewater. The training procedures of the RBFANN models were successful for all predicted components. The train and test models showed an almost perfect match between the experimental and predicted values of effluent BOD, COD, [Formula: see text] and TP. The coefficient of determination (R(2)) values were higher than 0.98 and root mean squared error (RMSE) values did not exceed 7% for train and test models.
International Nuclear Information System (INIS)
Ma, Denglong; Zhang, Zaoxiao
2016-01-01
Highlights: • The intelligent network models were built to predict contaminant gas concentrations. • The improved network models coupled with Gaussian dispersion model were presented. • New model has high efficiency and accuracy for concentration prediction. • New model were applied to indentify the leakage source with satisfied results. - Abstract: Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.
Laun, Joachim; Vilela Oliveira, Daniel; Bredow, Thomas
2018-02-22
Consistent basis sets of double- and triple-zeta valence with polarization quality for the fifth period have been derived for periodic quantum-chemical solid-state calculations with the crystalline-orbital program CRYSTAL. They are an extension of the pob-TZVP basis sets, and are based on the full-relativistic effective core potentials (ECPs) of the Stuttgart/Cologne group and on the def2-SVP and def2-TZVP valence basis of the Ahlrichs group. We optimized orbital exponents and contraction coefficients to supply robust and stable self-consistent field (SCF) convergence for a wide range of different compounds. The computed crystal structures are compared to those obtained with standard basis sets available from the CRYSTAL basis set database. For the applied hybrid density functional PW1PW, the average deviations of calculated lattice constants from experimental references are smaller with pob-DZVP and pob-TZVP than with standard basis sets. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.
Energy Technology Data Exchange (ETDEWEB)
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
Energy Technology Data Exchange (ETDEWEB)
Javaid, Zarrar; Unsworth, Charles P., E-mail: c.unsworth@auckland.ac.nz [Department of Engineering Science, The University of Auckland, Auckland 1010 (New Zealand); Boocock, Mark G.; McNair, Peter J. [Health and Rehabilitation Research Center, Auckland University of Technology, Auckland 1142 (New Zealand)
2016-03-15
Purpose: The aim of this work is to demonstrate a new image processing technique that can provide a “near real-time” 3D reconstruction of the articular cartilage of the human knee from MR images which is user friendly. This would serve as a point-of-care 3D visualization tool which would benefit a consultant radiologist in the visualization of the human articular cartilage. Methods: The authors introduce a novel fusion of an adaptation of the contour method known as “contour interpolation (CI)” with radial basis functions (RBFs) which they describe as “CI-RBFs.” The authors also present a spline boundary correction which further enhances volume estimation of the method. A subject cohort consisting of 17 right nonpathological knees (ten female and seven male) is assessed to validate the quality of the proposed method. The authors demonstrate how the CI-RBF method dramatically reduces the number of data points required for fitting an implicit surface to the entire cartilage, thus, significantly improving the speed of reconstruction over the comparable RBF reconstruction method of Carr. The authors compare the CI-RBF method volume estimation to a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Results: The authors demonstrate how the CI-RBF method significantly reduces the number of data points (p-value < 0.0001) required for fitting an implicit surface to the cartilage, by 48%, 31%, and 44% for the patellar, tibial, and femoral cartilages, respectively. Thus, significantly improving the speed of reconstruction (p-value < 0.0001) by 39%, 40%, and 44% for the patellar, tibial, and femoral cartilages over the comparable RBF model of Carr providing a near real-time reconstruction of 6.49, 8.88, and 9.43 min for the patellar, tibial, and femoral cartilages, respectively. In addition, it is demonstrated how the CI-RBF method matches the volume
International Nuclear Information System (INIS)
Javaid, Zarrar; Unsworth, Charles P.; Boocock, Mark G.; McNair, Peter J.
2016-01-01
Purpose: The aim of this work is to demonstrate a new image processing technique that can provide a “near real-time” 3D reconstruction of the articular cartilage of the human knee from MR images which is user friendly. This would serve as a point-of-care 3D visualization tool which would benefit a consultant radiologist in the visualization of the human articular cartilage. Methods: The authors introduce a novel fusion of an adaptation of the contour method known as “contour interpolation (CI)” with radial basis functions (RBFs) which they describe as “CI-RBFs.” The authors also present a spline boundary correction which further enhances volume estimation of the method. A subject cohort consisting of 17 right nonpathological knees (ten female and seven male) is assessed to validate the quality of the proposed method. The authors demonstrate how the CI-RBF method dramatically reduces the number of data points required for fitting an implicit surface to the entire cartilage, thus, significantly improving the speed of reconstruction over the comparable RBF reconstruction method of Carr. The authors compare the CI-RBF method volume estimation to a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Results: The authors demonstrate how the CI-RBF method significantly reduces the number of data points (p-value < 0.0001) required for fitting an implicit surface to the cartilage, by 48%, 31%, and 44% for the patellar, tibial, and femoral cartilages, respectively. Thus, significantly improving the speed of reconstruction (p-value < 0.0001) by 39%, 40%, and 44% for the patellar, tibial, and femoral cartilages over the comparable RBF model of Carr providing a near real-time reconstruction of 6.49, 8.88, and 9.43 min for the patellar, tibial, and femoral cartilages, respectively. In addition, it is demonstrated how the CI-RBF method matches the volume
Comparing Fixed and Variable-Width Gaussian Networks
Czech Academy of Sciences Publication Activity Database
Kůrková, Věra; Kainen, P.C.
2014-01-01
Roč. 57, September (2014), s. 23-28 ISSN 0893-6080 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : Gaussian radial and kernel networks * Functionally equivalent networks * Universal approximators * Stabilizers defined by Gaussian kernels * Argminima of error functionals Subject RIV: IN - Informatics, Computer Science Impact factor: 2.708, year: 2014
Hill, J. Grant; Peterson, Kirk A.
2017-12-01
New correlation consistent basis sets based on pseudopotential (PP) Hamiltonians have been developed from double- to quintuple-zeta quality for the late alkali (K-Fr) and alkaline earth (Ca-Ra) metals. These are accompanied by new all-electron basis sets of double- to quadruple-zeta quality that have been contracted for use with both Douglas-Kroll-Hess (DKH) and eXact 2-Component (X2C) scalar relativistic Hamiltonians. Sets for valence correlation (ms), cc-pVnZ-PP and cc-pVnZ-(DK,DK3/X2C), in addition to outer-core correlation [valence + (m-1)sp], cc-p(w)CVnZ-PP and cc-pwCVnZ-(DK,DK3/X2C), are reported. The -PP sets have been developed for use with small-core PPs [I. S. Lim et al., J. Chem. Phys. 122, 104103 (2005) and I. S. Lim et al., J. Chem. Phys. 124, 034107 (2006)], while the all-electron sets utilized second-order DKH Hamiltonians for 4s and 5s elements and third-order DKH for 6s and 7s. The accuracy of the basis sets is assessed through benchmark calculations at the coupled-cluster level of theory for both atomic and molecular properties. Not surprisingly, it is found that outer-core correlation is vital for accurate calculation of the thermodynamic and spectroscopic properties of diatomic molecules containing these elements.
Hill, J Grant; Peterson, Kirk A
2017-12-28
New correlation consistent basis sets based on pseudopotential (PP) Hamiltonians have been developed from double- to quintuple-zeta quality for the late alkali (K-Fr) and alkaline earth (Ca-Ra) metals. These are accompanied by new all-electron basis sets of double- to quadruple-zeta quality that have been contracted for use with both Douglas-Kroll-Hess (DKH) and eXact 2-Component (X2C) scalar relativistic Hamiltonians. Sets for valence correlation (ms), cc-pVnZ-PP and cc-pVnZ-(DK,DK3/X2C), in addition to outer-core correlation [valence + (m-1)sp], cc-p(w)CVnZ-PP and cc-pwCVnZ-(DK,DK3/X2C), are reported. The -PP sets have been developed for use with small-core PPs [I. S. Lim et al., J. Chem. Phys. 122, 104103 (2005) and I. S. Lim et al., J. Chem. Phys. 124, 034107 (2006)], while the all-electron sets utilized second-order DKH Hamiltonians for 4s and 5s elements and third-order DKH for 6s and 7s. The accuracy of the basis sets is assessed through benchmark calculations at the coupled-cluster level of theory for both atomic and molecular properties. Not surprisingly, it is found that outer-core correlation is vital for accurate calculation of the thermodynamic and spectroscopic properties of diatomic molecules containing these elements.
Gaussian process regression for tool wear prediction
Kong, Dongdong; Chen, Yongjie; Li, Ning
2018-05-01
To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.
Energy Technology Data Exchange (ETDEWEB)
Hoejstrup, J. [NEG Micon Project Development A/S, Randers (Denmark); Hansen, K.S. [Denmarks Technical Univ., Dept. of Energy Engineering, Lyngby (Denmark); Pedersen, B.J. [VESTAS Wind Systems A/S, Lem (Denmark); Nielsen, M. [Risoe National Lab., Wind Energy and Atmospheric Physics, Roskilde (Denmark)
1999-03-01
The pdf`s of atmospheric turbulence have somewhat wider tails than a Gaussian, especially regarding accelerations, whereas velocities are close to Gaussian. This behaviour is being investigated using data from a large WEB-database in order to quantify the amount of non-Gaussianity. Models for non-Gaussian turbulence have been developed, by which artificial turbulence can be generated with specified distributions, spectra and cross-correlations. The artificial time series will then be used in load models and the resulting loads in the Gaussian and the non-Gaussian cases will be compared. (au)
Polarization coupling of vector Bessel–Gaussian beams
International Nuclear Information System (INIS)
Takeuchi, Ryushi; Kozawa, Yuichi; Sato, Shunichi
2013-01-01
We report polarization coupling of radial and azimuthal electric field components of a vector light beam as predicted by the fact that the vector Helmholtz equation is expressed as coupled differential equations in cylindrical coordinates. To clearly observe the polarization variation of a beam as it propagates, higher order transverse modes of a vector Bessel–Gaussian beam were generated by a gain distribution modulation technique, which created a narrow ring-shaped gain region in a Nd:YVO 4 crystal. The polarization coupling was confirmed by the observation that the major polarization component of a vector Bessel–Gaussian beam alternates between radial and azimuthal components along with the propagation. (paper)
Gaussian and Non-Gaussian operations on non-Gaussian state: engineering non-Gaussianity
Directory of Open Access Journals (Sweden)
Olivares Stefano
2014-03-01
Full Text Available Multiple photon subtraction applied to a displaced phase-averaged coherent state, which is a non-Gaussian classical state, produces conditional states with a non trivial (positive Glauber-Sudarshan Prepresentation. We theoretically and experimentally demonstrate that, despite its simplicity, this class of conditional states cannot be fully characterized by direct detection of photon numbers. In particular, the non-Gaussianity of the state is a characteristics that must be assessed by phase-sensitive measurements. We also show that the non-Gaussianity of conditional states can be manipulated by choosing suitable conditioning values and composition of phase-averaged states.
Pseudospectral Gaussian quantum dynamics: Efficient sampling of potential energy surfaces.
Heaps, Charles W; Mazziotti, David A
2016-04-28
Trajectory-based Gaussian basis sets have been tremendously successful in describing high-dimensional quantum molecular dynamics. In this paper, we introduce a pseudospectral Gaussian-based method that achieves accurate quantum dynamics using efficient, real-space sampling of the time-dependent basis set. As in other Gaussian basis methods, we begin with a basis set expansion using time-dependent Gaussian basis functions guided by classical mechanics. Unlike other Gaussian methods but characteristic of the pseudospectral and collocation methods, the basis set is tested with N Dirac delta functions, where N is the number of basis functions, rather than using the basis function as test functions. As a result, the integration for matrix elements is reduced to function evaluation. Pseudospectral Gaussian dynamics only requires O(N) potential energy calculations, in contrast to O(N(2)) evaluations in a variational calculation. The classical trajectories allow small basis sets to sample high-dimensional potentials. Applications are made to diatomic oscillations in a Morse potential and a generalized version of the Henon-Heiles potential in two, four, and six dimensions. Comparisons are drawn to full analytical evaluation of potential energy integrals (variational) and the bra-ket averaged Taylor (BAT) expansion, an O(N) approximation used in Gaussian-based dynamics. In all cases, the pseudospectral Gaussian method is competitive with full variational calculations that require a global, analytical, and integrable potential energy surface. Additionally, the BAT breaks down when quantum mechanical coherence is particularly strong (i.e., barrier reflection in the Morse oscillator). The ability to obtain variational accuracy using only the potential energy at discrete points makes the pseudospectral Gaussian method a promising avenue for on-the-fly dynamics, where electronic structure calculations become computationally significant.
CSIR Research Space (South Africa)
Roux, FS
2009-01-01
Full Text Available , t0)} = P(du, dv) {FR{g(u, v, t0)}} Replacement: u→ du = t− t0 i2 ∂ ∂u′ v → dv = t− t0 i2 ∂ ∂v′ CSIR National Laser Centre – p.13/30 Differentiation i.s.o integration Evaluate the integral over the Gaussian beam (once and for all). Then, instead... . Gaussian beams with vortex dipoles CSIR National Laser Centre – p.2/30 Gaussian beam notation Gaussian beam in normalised coordinates: g(u, v, t) = exp ( −u 2 + v2 1− it ) u = xω0 v = yω0 t = zρ ρ = piω20 λ ω0 — 1/e2 beam waist radius; ρ— Rayleigh range ω ω...
Radially inhomogeneous bounded plasmas
Zakeri-Khatir, H.; Aghamir, F. M.
2016-07-01
On the basis of kinetic theory along with self-consistent field equations, the expressions for dielectric tensor of radially inhomogeneous magnetized plasma columns are obtained. The study of dielectric tensor characteristics allows the accurate analysis of the inhomogeneous properties, beyond limitations that exist in the conventional method. Through the Bessel-Fourier transformation, the localized form of material equations in a radially inhomogeneous medium are obtained. In order to verify the integrity of the model and reveal the effect of inhomogeneity, a special case of a cylindrical plasma waveguide completely filled with inhomogeneous magnetized cold plasma was considered. The dispersion relation curves for four families of electromagnetic (EH and HE) and electrostatic (SC and C) modes are obtained and compared with the findings of the conventional model. The numerical analysis indicates that the inhomogeneity effect leads to coupling of electromagnetic and electrostatic modes each having different radial eigen numbers. The study also reveals that the electrostatic modes are more sensitive to inhomogeneous effects than the electromagnetic modes.
Gaussian operations and privacy
International Nuclear Information System (INIS)
Navascues, Miguel; Acin, Antonio
2005-01-01
We consider the possibilities offered by Gaussian states and operations for two honest parties, Alice and Bob, to obtain privacy against a third eavesdropping party, Eve. We first extend the security analysis of the protocol proposed in [Navascues et al. Phys. Rev. Lett. 94, 010502 (2005)]. Then, we prove that a generalized version of this protocol does not allow one to distill a secret key out of bound entangled Gaussian states
Ahmadi Azqhandi, M H; Ghaedi, M; Yousefi, F; Jamshidi, M
2017-11-01
Two machine learning approach (i.e. Radial Basis Function Neural Network (RBF-NN) and Random Forest (RF) was developed and evaluated against a quadratic response surface model to predict the maximum removal efficiency of brilliant green (BG) from aqueous media in relation to BG concentration (4-20mgL -1 ), sonication time (2-6min) and ZnS-NP-AC mass (0.010-0.030g) by ultrasound-assisted. All three (i.e. RBF network, RF and polynomial) model were compared against the experimental data using four statistical indices namely, coefficient of determination (R 2 ), root mean square error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD). Graphical plots were also used for model comparison. The obtained results using RBF network and RF exhibit a better performance in comparison to classical statistical model for both dyes. The significant factors were optimized using desirability function approach (DFA) combined central composite design (CCD) and genetic algorithm (GA) approach. The obtained optimal point was located in the valid region and the experimental confirmation tests were conducted showing a good accordance between the predicted optimal points and the experimental data. The properties of ZnS-NPs-AC were identified by X-ray diffraction; field emission scanning electron microscopy, energy dispersive X-ray spectroscopy (EDS) and Fourier transformation infrared spectroscopy. Various isotherm models for fitting the experimental equilibrium data were studied and Langmuir model was chosen as an efficient model. Various kinetic models for analysis of experimental adsorption data were studied and pseudo second order model was chosen as an efficient model. Moreover, ZnS nanoparticles loaded on activated carbon efficiently were regenerated using methanol and after five cycles the removal percentage do not change significantly. Copyright © 2017 Elsevier Inc. All rights reserved.
Nonclassicality by Local Gaussian Unitary Operations for Gaussian States
Directory of Open Access Journals (Sweden)
Yangyang Wang
2018-04-01
Full Text Available A measure of nonclassicality N in terms of local Gaussian unitary operations for bipartite Gaussian states is introduced. N is a faithful quantum correlation measure for Gaussian states as product states have no such correlation and every non product Gaussian state contains it. For any bipartite Gaussian state ρ A B , we always have 0 ≤ N ( ρ A B < 1 , where the upper bound 1 is sharp. An explicit formula of N for ( 1 + 1 -mode Gaussian states and an estimate of N for ( n + m -mode Gaussian states are presented. A criterion of entanglement is established in terms of this correlation. The quantum correlation N is also compared with entanglement, Gaussian discord and Gaussian geometric discord.
Byrnes, Christian T; Tasinato, Gianmassimo; Wands, David
2012-01-01
We propose a method to probe higher-order correlators of the primordial density field through the inhomogeneity of local non-Gaussian parameters, such as f_NL, measured within smaller patches of the sky. Correlators between n-point functions measured in one patch of the sky and k-point functions measured in another patch depend upon the (n+k)-point functions over the entire sky. The inhomogeneity of non-Gaussian parameters may be a feasible way to detect or constrain higher-order correlators in local models of non-Gaussianity, as well as to distinguish between single and multiple-source scenarios for generating the primordial density perturbation, and more generally to probe the details of inflationary physics.
Extension of filament propagation in water with Bessel-Gaussian beams
International Nuclear Information System (INIS)
Kaya, G.; Sayrac, M.; Boran, Y.; Kolomenskii, A. A.; Kaya, N.; Schuessler, H. A.; Strohaber, J.; Amani, M.
2016-01-01
We experimentally studied intense femtosecond pulse filamentation and propagation in water for Bessel-Gaussian beams with different numbers of radial modal lobes. The transverse modes of the incident Bessel-Gaussian beam were created from a Gaussian beam of a Ti:sapphire laser system by using computer generated hologram techniques. We found that filament propagation length increased with increasing number of lobes under the conditions of the same peak intensity, pulse duration, and the size of the central peak of the incident beam, suggesting that the radial modal lobes may serve as an energy reservoir for the filaments formed by the central intensity peak.
Learning conditional Gaussian networks
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....
Symmetries of Trautman retarded radial coordinates
Kolanowski, Maciej; Lewandowski, Jerzy
2018-02-01
We consider spacetime described by an observer that uses a Trautman retarded radial coordinate system. Given a metric tensor, we find all the local symmetries of the coordinates. They set a 10D family that can be parametrized by Poincaré algebra. This result is similar to the symmetries of an observer using the Gaussian normal spacetime radial coordinates and experiencing algebra deformation induced by the spacetime Riemann tensor. A new, surprising property of the retarded coordinates is a generic lack of smoothness in the symmetries. We show that, in general, the symmetries are not twice differentiable. In other words, a family of smooth symmetries is smaller than in the Gaussian normal spacetime coordinate case. We demonstrate examples of that non-smoothness and find the necessary conditions for the differentiability to the second order. We also discuss the consequences and relevance of that result for the geometric relational observables program. One can interpret our result by the fact that Trautman coordinates provide gauge conditions stronger than the Gaussian spacetime radial gauge.
AUTONOMOUS GAUSSIAN DECOMPOSITION
International Nuclear Information System (INIS)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John
2015-01-01
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...
AUTONOMOUS GAUSSIAN DECOMPOSITION
Energy Technology Data Exchange (ETDEWEB)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian [Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States); Heiles, Carl [Radio Astronomy Lab, UC Berkeley, 601 Campbell Hall, Berkeley, CA 94720 (United States); Hennebelle, Patrick [Laboratoire AIM, Paris-Saclay, CEA/IRFU/SAp-CNRS-Université Paris Diderot, F-91191 Gif-sur Yvette Cedex (France); Goss, W. M. [National Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville, Socorro, NM 87801 (United States); Dickey, John, E-mail: rlindner@astro.wisc.edu [University of Tasmania, School of Maths and Physics, Private Bag 37, Hobart, TAS 7001 (Australia)
2015-04-15
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.
A closed form of a kurtosis parameter of a hypergeometric-Gaussian type-II beam
F, Khannous; A, A. A. Ebrahim; A, Belafhal
2016-04-01
Based on the irradiance moment definition and the analytical expression of waveform propagation for hypergeometric-Gaussian type-II beams passing through an ABCD system, the kurtosis parameter is derived analytically and illustrated numerically. The kurtosis parameters of the Gaussian beam, modified Bessel modulated Gaussian beam with quadrature radial and elegant Laguerre-Gaussian beams are obtained by treating them as special cases of the present treatment. The obtained results show that the kurtosis parameter depends on the change of the beam order m and the hollowness parameter p, such as its decrease with increasing m and increase with increasing p.
Interconversion of pure Gaussian states requiring non-Gaussian operations
Jabbour, Michael G.; García-Patrón, Raúl; Cerf, Nicolas J.
2015-01-01
We analyze the conditions under which local operations and classical communication enable entanglement transformations between bipartite pure Gaussian states. A set of necessary and sufficient conditions had been found [G. Giedke et al., Quant. Inf. Comput. 3, 211 (2003)] for the interconversion between such states that is restricted to Gaussian local operations and classical communication. Here, we exploit majorization theory in order to derive more general (sufficient) conditions for the interconversion between bipartite pure Gaussian states that goes beyond Gaussian local operations. While our technique is applicable to an arbitrary number of modes for each party, it allows us to exhibit surprisingly simple examples of 2 ×2 Gaussian states that necessarily require non-Gaussian local operations to be transformed into each other.
Yurinsky, Vadim Vladimirovich
1995-01-01
Surveys the methods currently applied to study sums of infinite-dimensional independent random vectors in situations where their distributions resemble Gaussian laws. Covers probabilities of large deviations, Chebyshev-type inequalities for seminorms of sums, a method of constructing Edgeworth-type expansions, estimates of characteristic functions for random vectors obtained by smooth mappings of infinite-dimensional sums to Euclidean spaces. A self-contained exposition of the modern research apparatus around CLT, the book is accessible to new graduate students, and can be a useful reference for researchers and teachers of the subject.
Rotating quantum Gaussian packets
International Nuclear Information System (INIS)
Dodonov, V V
2015-01-01
We study two-dimensional quantum Gaussian packets with a fixed value of mean angular momentum. This value is the sum of two independent parts: the ‘external’ momentum related to the motion of the packet center and the ‘internal’ momentum due to quantum fluctuations. The packets minimizing the mean energy of an isotropic oscillator with the fixed mean angular momentum are found. They exist for ‘co-rotating’ external and internal motions, and they have nonzero correlation coefficients between coordinates and momenta, together with some (moderate) amount of quadrature squeezing. Variances of angular momentum and energy are calculated, too. Differences in the behavior of ‘co-rotating’ and ‘anti-rotating’ packets are shown. The time evolution of rotating Gaussian packets is analyzed, including the cases of a charge in a homogeneous magnetic field and a free particle. In the latter case, the effect of initial shrinking of packets with big enough coordinate-momentum correlation coefficients (followed by the well known expansion) is discovered. This happens due to a competition of ‘focusing’ and ‘de-focusing’ in the orthogonal directions. (paper)
Radial fractional Laplace operators and Hessian inequalities
Ferrari, Fausto; Verbitsky, Igor E.
In this paper we deduce a formula for the fractional Laplace operator ( on radially symmetric functions useful for some applications. We give a criterion of subharmonicity associated with (, and apply it to a problem related to the Hessian inequality of Sobolev type: ∫Rn |(u| dx⩽C∫Rn -uFk[u] dx, where Fk is the k-Hessian operator on Rn, 1⩽kFerrari et al. [5] contains the extremal functions for the Hessian Sobolev inequality of X.-J. Wang (1994) [15]. This is proved using logarithmic convexity of the Gaussian ratio of hypergeometric functions which might be of independent interest.
Zhang, Lei; Dong, Zhen; Zhang, Chun-Lin; Gu, Yu-Dong
2016-11-01
Background C7 - T1 palsy results in complete loss of finger motion and poses a surgical challenge. This study investigated the anatomy of the radial nerve in the elbow and forearm and the feasibility of intraplexus nerve transfer to restore thumb and finger extension. Methods The radial nerves were dissected in 28 formalin-fixed upper extremities. Branching pattern, length, diameter, and number of myelinated fibers were recorded. Results Commonly, the branching pattern (from proximal to distal) was to the brachioradialis, extensor carpi radialis longus, superficial sensory proximal to the lateral epicondyle, extensor carpi radialis brevis, supinator, extensor digitorum communis, extensor digiti minimi, extensor carpi ulnaris, abductor pollicis longus, extensor pollicis brevis, extensor pollicis longus, and extensor indicis distal to the lateral epicondyle. Conclusions Branches to the brachioradialis, extensor carpi radialis longus, and supinator can be transferred to the posterior interosseous nerve to restore hand movement in patients with C7 - T1 brachial plexus palsies; the supinator branch is probably the best choice in this regard. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.
Axial acoustic radiation force on a sphere in Gaussian field
Energy Technology Data Exchange (ETDEWEB)
Wu, Rongrong; Liu, Xiaozhou, E-mail: xzliu@nju.edu.cn; Gong, Xiufen [Key Laboratory of Modern Acoustics, Institute of Acoustics and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093 (China)
2015-10-28
Based on the finite series method, the acoustical radiation force resulting from a Gaussian beam incident on a spherical object is investigated analytically. When the position of the particles deviating from the center of the beam, the Gaussian beam is expanded as a spherical function at the center of the particles and the expanded coefficients of the Gaussian beam is calculated. The analytical expression of the acoustic radiation force on spherical particles deviating from the Gaussian beam center is deduced. The acoustic radiation force affected by the acoustic frequency and the offset distance from the Gaussian beam center is investigated. Results have been presented for Gaussian beams with different wavelengths and it has been shown that the interaction of a Gaussian beam with a sphere can result in attractive axial force under specific operational conditions. Results indicate the capability of manipulating and separating spherical spheres based on their mechanical and acoustical properties, the results provided here may provide a theoretical basis for development of single-beam acoustical tweezers.
CONVEX BODIES AND GAUSSIAN PROCESSES
Directory of Open Access Journals (Sweden)
Richard A Vitale
2011-05-01
Full Text Available For several decades, the topics of the title have had a fruitful interaction. This survey will describe some of these connections, including the GB/GC classification of convex bodies, Ito-Nisio singularities from a geometric viewpoint, Gaussian representation of intrinsic volumes, theWills functional in a Gaussian context, and inequalities.
Spectral representation of Gaussian semimartingales
DEFF Research Database (Denmark)
Basse-O'Connor, Andreas
2009-01-01
The aim of the present paper is to characterize the spectral representation of Gaussian semimartingales. That is, we provide necessary and sufficient conditions on the kernel K for X t =∫ K t (s) dN s to be a semimartingale. Here, N denotes an independently scattered Gaussian random measure...
Closed form formula for Mie scattering of nonparaxial analogues of Gaussian beams.
Moore, Nicole J; Alonso, Miguel A
2008-04-14
A closed form formula is found for the Mie scattering coefficients of incident complex focus beams (which are a nonparaxial generalization of Gaussian beams) with any numerical aperture. This formula takes the compact form of multipoles evaluated at a single complex point. Included are the cases of incident scalar fields as well as electromagnetic fields with many polarizations, such as linear, circular, azimuthal and radial. Examples of incident radially and azimuthally polarized beams are presented.
Incremental Gaussian Processes
DEFF Research Database (Denmark)
Quiñonero-Candela, Joaquin; Winther, Ole
2002-01-01
In this paper, we consider Tipping's relevance vector machine (RVM) and formalize an incremental training strategy as a variant of the expectation-maximization (EM) algorithm that we call subspace EM. Working with a subset of active basis functions, the sparsity of the RVM solution will ensure...
Equi-Gaussian curvature folding
Indian Academy of Sciences (India)
curvature kf (p) i.e., kp = kf (p). In this case f will map curves to curves with equal equi-. Gaussian curvature at corresponding points. It will also map area with sectional curvature k(σ, p) into areas with the same sectional curvature, and so on. The set of all equi-Gaussian curvature foldings of M into N will be denoted by.
Spectral problem for the radial Schroedinger equation
International Nuclear Information System (INIS)
Vshivtsev, A.S.; Tatarintsev, A.V.; Prokopov, A.V.; Sorokin, V. N.
1998-01-01
For the first time, a procedure for determining spectra on the basis of generalized integral transformations is implemented for a wide class of radial Schroedinger equations. It is shown that this procedure works well for known types of potentials. Concurrently, this method makes it possible to obtain new analytic results for the Cornell potential. This may prove important for hadron physics
Triangular Numbers, Gaussian Integers, and KenKen
Watkins, John J.
2012-01-01
Latin squares form the basis for the recreational puzzles sudoku and KenKen. In this article we show how useful several ideas from number theory are in solving a KenKen puzzle. For example, the simple notion of triangular number is surprisingly effective. We also introduce a variation of KenKen that uses the Gaussian integers in order to…
A note on moving average models for Gaussian random fields
DEFF Research Database (Denmark)
Hansen, Linda Vadgård; Thorarinsdottir, Thordis L.
basis, a general modeling framework which includes several types of non-Gaussian models. We propose a new one-parameter spatial correlation model which arises from a power kernel and show that the associated Hausdorff dimension of the sample paths can take any value between 2 and 3. As a result...
International Nuclear Information System (INIS)
Lock, James A.
2013-01-01
The vector wave equation for electromagnetic waves, when subject to a number of constraints corresponding to propagation of a monochromatic beam, reduces to a pair of inhomogeneous differential equations describing the transverse electric and transverse magnetic polarized beam components. These differential equations are solved analytically to obtain the most general focused Gaussian beam to order s 4 , where s is the beam confinement parameter, and various properties of the most general Gaussian beam are then discussed. The radial fields of the most general Gaussian beam are integrated to obtain the on-axis beam shape coefficients of the generalized Lorenz–Mie theory formalism of light scattering. The beam shape coefficients are then compared with those of the localized Gaussian beam model and the Davis–Barton fifth-order symmetrized beam. -- Highlights: ► Derive the differential equation for the most general Gaussian beam. ► Solve the differential equation for the most general Gaussian beam. ► Determine the properties of the most general Gaussian beam. ► Determine the beam shape coefficients of the most general Gaussian beam
Intra-cavity generation of superpositions of Laguerre-Gaussian beams
CSIR Research Space (South Africa)
Naidoo, Darryl
2012-01-01
Full Text Available In this paper we demonstrate experimentally the intra-cavity generation of a coherent superposition of Laguerre–Gaussian modes of zero radial order but opposite azimuthal order. The superposition is created with a simple intra-cavity stop...
Palm distributions for log Gaussian Cox processes
DEFF Research Database (Denmark)
Coeurjolly, Jean-Francois; Møller, Jesper; Waagepetersen, Rasmus Plenge
2017-01-01
This paper establishes a remarkable result regarding Palm distributions for a log Gaussian Cox process: the reduced Palm distribution for a log Gaussian Cox process is itself a log Gaussian Cox process that only differs from the original log Gaussian Cox process in the intensity function. This new...
Bartoníček, J; Naňka, O; Tuček, M
2015-10-01
In the clinical practice, radial shaft may be exposed via two approaches, namely the posterolateral Thompson and volar (anterior) Henry approaches. A feared complication of both of them is the injury to the deep branch of the radial nerve. No consensus has been reached, yet, as to which of the two approaches is more beneficial for the proximal half of radius. According to our anatomical studies and clinical experience, Thompson approach is safe only in fractures of the middle and distal thirds of the radial shaft, but highly risky in fractures of its proximal third. Henry approach may be used in any fracture of the radial shaft and provides a safe exposure of the entire lateral and anterior surfaces of the radius.The Henry approach has three phases. In the first phase, incision is made along the line connecting the biceps brachii tendon and the styloid process of radius. Care must be taken not to damage the lateral cutaneous nerve of forearm.In the second phase, fascia is incised and the brachioradialis identified by the typical transition from the muscle belly to tendon and the shape of the tendon. On the lateral side, the brachioradialis lines the space with the radial artery and veins and the superficial branch of the radial nerve running at its bottom. On the medial side, the space is defined by the pronator teres in the proximal part and the flexor carpi radialis in the distal part. The superficial branch of the radial nerve is retracted together with the brachioradialis laterally, and the radial artery medially.In the third phase, the attachment of the pronator teres is identified by its typical tendon in the middle of convexity of the lateral surface of the radial shaft. The proximal half of the radius must be exposed very carefully in order not to damage the deep branch of the radial nerve. Dissection starts at the insertion of the pronator teres and proceeds proximally along its lateral border in interval between this muscle and insertion of the supinator
Smith, Karl H.
2002-01-01
A radial wedge flange clamp comprising a pair of flanges each comprising a plurality of peripheral flat wedge facets having flat wedge surfaces and opposed and mating flat surfaces attached to or otherwise engaged with two elements to be joined and including a series of generally U-shaped wedge clamps each having flat wedge interior surfaces and engaging one pair of said peripheral flat wedge facets. Each of said generally U-shaped wedge clamps has in its opposing extremities apertures for the tangential insertion of bolts to apply uniform radial force to said wedge clamps when assembled about said wedge segments.
Radially truncated galactic discs
Grijs, R. de; Kregel, M.; Wesson, K H
2000-01-01
Abstract: We present the first results of a systematic analysis of radially truncatedexponential discs for four galaxies of a sample of disc-dominated edge-onspiral galaxies. Edge-on galaxies are very useful for the study of truncatedgalactic discs, since we can follow their light distributions out
Are Gaussian spectra a viable perceptual assumption in color appearance?
Mizokami, Yoko; Webster, Michael A
2012-02-01
Natural illuminant and reflectance spectra can be roughly approximated by a linear model with as few as three basis functions, and this has suggested that the visual system might construct a linear representation of the spectra by estimating the weights of these functions. However, such models do not accommodate nonlinearities in color appearance, such as the Abney effect. Previously, we found that these nonlinearities are qualitatively consistent with a perceptual inference that stimulus spectra are instead roughly Gaussian, with the hue tied to the inferred centroid of the spectrum [J. Vision 6(9), 12 (2006)]. Here, we examined to what extent a Gaussian inference provides a sufficient approximation of natural color signals. Reflectance and illuminant spectra from a wide set of databases were analyzed to test how well the curves could be fit by either a simple Gaussian with three parameters (amplitude, peak wavelength, and standard deviation) versus the first three principal component analysis components of standard linear models. The resulting Gaussian fits were comparable to linear models with the same degrees of freedom, suggesting that the Gaussian model could provide a plausible perceptual assumption about stimulus spectra for a trichromatic visual system. © 2012 Optical Society of America
The Multivariate Gaussian Probability Distribution
DEFF Research Database (Denmark)
Ahrendt, Peter
2005-01-01
This technical report intends to gather information about the multivariate gaussian distribution, that was previously not (at least to my knowledge) to be found in one place and written as a reference manual. Additionally, some useful tips and tricks are collected that may be useful in practical...
On Gaussian conditional independence structures
Czech Academy of Sciences Publication Activity Database
Lněnička, Radim; Matúš, František
2007-01-01
Roč. 43, č. 3 (2007), s. 327-342 ISSN 0023-5954 R&D Projects: GA AV ČR IAA100750603 Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate Gaussian distribution * positive definite matrices * determinants * gaussoids * covariance selection models * Markov perfectness Subject RIV: BA - General Mathematics Impact factor: 0.552, year: 2007
Gaussian processes for machine learning.
Seeger, Matthias
2004-04-01
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.
Xiang, Yu; Xu, Buqing; Mišta, Ladislav; Tufarelli, Tommaso; He, Qiongyi; Adesso, Gerardo
2017-10-01
Einstein-Podolsky-Rosen (EPR) steering is an asymmetric form of correlations which is intermediate between quantum entanglement and Bell nonlocality, and can be exploited as a resource for quantum communication with one untrusted party. In particular, steering of continuous-variable Gaussian states has been extensively studied theoretically and experimentally, as a fundamental manifestation of the EPR paradox. While most of these studies focused on quadrature measurements for steering detection, two recent works revealed that there exist Gaussian states which are only steerable by suitable non-Gaussian measurements. In this paper we perform a systematic investigation of EPR steering of bipartite Gaussian states by pseudospin measurements, complementing and extending previous findings. We first derive the density-matrix elements of two-mode squeezed thermal Gaussian states in the Fock basis, which may be of independent interest. We then use such a representation to investigate steering of these states as detected by a simple nonlinear criterion, based on second moments of the correlation matrix constructed from pseudospin operators. This analysis reveals previously unexplored regimes where non-Gaussian measurements are shown to be more effective than Gaussian ones to witness steering of Gaussian states in the presence of local noise. We further consider an alternative set of pseudospin observables, whose expectation value can be expressed more compactly in terms of Wigner functions for all two-mode Gaussian states. However, according to the adopted criterion, these observables are found to be always less sensitive than conventional Gaussian observables for steering detection. Finally, we investigate continuous-variable Werner states, which are non-Gaussian mixtures of Gaussian states, and find that pseudospin measurements are always more effective than Gaussian ones to reveal their steerability. Our results provide useful insights on the role of non-Gaussian
Radial Halbach Magnetic Bearings
Eichenberg, Dennis J.; Gallo, Christopher A.; Thompson, William K.
2009-01-01
Radial Halbach magnetic bearings have been investigated as part of an effort to develop increasingly reliable noncontact bearings for future high-speed rotary machines that may be used in such applications as aircraft, industrial, and land-vehicle power systems and in some medical and scientific instrumentation systems. Radial Halbach magnetic bearings are based on the same principle as that of axial Halbach magnetic bearings, differing in geometry as the names of these two types of bearings suggest. Both radial and axial Halbach magnetic bearings are passive in the sense that unlike most other magnetic bearings that have been developed in recent years, they effect stable magnetic levitation without need for complex active control. Axial Halbach magnetic bearings were described in Axial Halbach Magnetic Bearings (LEW-18066-1), NASA Tech Briefs, Vol. 32, No. 7 (July 2008), page 85. In the remainder of this article, the description of the principle of operation from the cited prior article is recapitulated and updated to incorporate the present radial geometry. In simplest terms, the basic principle of levitation in an axial or radial Halbach magnetic bearing is that of the repulsive electromagnetic force between (1) a moving permanent magnet and (2) an electric current induced in a stationary electrical conductor by the motion of the magnetic field. An axial or radial Halbach bearing includes multiple permanent magnets arranged in a Halbach array ("Halbach array" is defined below) in a rotor and multiple conductors in the form of wire coils in a stator, all arranged so the rotary motion produces an axial or radial repulsion that is sufficient to levitate the rotor. A basic Halbach array (see Figure 1) consists of a row of permanent magnets, each oriented so that its magnetic field is at a right angle to that of the adjacent magnet, and the right-angle turns are sequenced so as to maximize the magnitude of the magnetic flux density on one side of the row while
Laguerre Gaussian beam multiplexing through turbulence
CSIR Research Space (South Africa)
Trichili, A
2014-08-17
Full Text Available We analyze the effect of atmospheric turbulence on the propagation of multiplexed Laguerre Gaussian modes. We present a method to multiplex Laguerre Gaussian modes using digital holograms and decompose the resulting field after encountering a...
Stable and Efficient Gaussian Process Calculations
National Aeronautics and Space Administration — The use of Gaussian processes can be an effective approach to prediction in a supervised learning environment. For large data sets, the standard Gaussian process...
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Analytic matrix elements with shifted correlated Gaussians
DEFF Research Database (Denmark)
Fedorov, D. V.
2017-01-01
Matrix elements between shifted correlated Gaussians of various potentials with several form-factors are calculated analytically. Analytic matrix elements are of importance for the correlated Gaussian method in quantum few-body physics.......Matrix elements between shifted correlated Gaussians of various potentials with several form-factors are calculated analytically. Analytic matrix elements are of importance for the correlated Gaussian method in quantum few-body physics....
Czech Academy of Sciences Publication Activity Database
Coufal, David
2017-01-01
Roč. 319, 15 July (2017), s. 1-27 ISSN 0165-0114 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : fuzzy systems * radial functions * coherence Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.718, year: 2016
Perceived radial translation during centrifugation.
Bos, J.E.; Correia Gracio, B.J.
2015-01-01
BACKGROUND: Linear acceleration generally gives rise to translation perception. Centripetal acceleration during centrifugation, however, has never been reported giving rise to a radial, inward translation perception. OBJECTIVE: To study whether centrifugation can induce a radial translation
Perceived radial translation during centrifugation
Bos, J.E.; Correia Grácio, B.J.
2015-01-01
BACKGROUND: Linear acceleration generally gives rise to translation perception. Centripetal acceleration during centrifugation, however, has never been reported giving rise to a radial, inward translation perception. OBJECTIVE: To study whether centrifugation can induce a radial translation
Azimuthal spectrum after parametric down-convertion with radial degrees of freedom
CSIR Research Space (South Africa)
Zhang, Y
2014-08-01
Full Text Available Considering the quantum state produced in type I spontaneous parametric down-conversion with collinear, degenerate signal and idler beams, and a Gaussian pump, we show that the azimuthal Schmidt number in the Laguerre-Gaussian (LG) basis increases...
Statistical properties of the neoclassical radial diffusion in a tokamak equilibrium
Energy Technology Data Exchange (ETDEWEB)
Maluckov, A. [Department of Fusion Science, Graduate Univ. for Advanced Studies, Toki, Gifu (Japan); Nakajima, N.; Okamoto, M.; Murakami, S.; Kanno, R. [National Inst. for Fusion Science, Toki, Gifu (Japan)
2001-04-01
The statistical properties of the neoclassical radial diffusion are confirmed through direct comparison with a Wiener process by the numerical evaluations of the cumulant, diffusion and autocorrelation coefficients. Within the neoclassical framework the origin of stochasticity exists only in velocity space. It is characterized by the stationary, subdiffusive, uniform and Markov process. Through the drift motion of particle guiding centers, the stochasticity in velocity space leads to that in configuration space, i.e., the radial diffusion. It is shown that such a radial diffusion develops as an approximately Wiener process, i.e. the statistically non-stationary, normal diffusive, Gaussian, and Markov process in the asymptotic time region. (author)
General Galilei Covariant Gaussian Maps
Gasbarri, Giulio; Toroš, Marko; Bassi, Angelo
2017-09-01
We characterize general non-Markovian Gaussian maps which are covariant under Galilean transformations. In particular, we consider translational and Galilean covariant maps and show that they reduce to the known Holevo result in the Markovian limit. We apply the results to discuss measures of macroscopicity based on classicalization maps, specifically addressing dissipation, Galilean covariance and non-Markovianity. We further suggest a possible generalization of the macroscopicity measure defined by Nimmrichter and Hornberger [Phys. Rev. Lett. 110, 16 (2013)].
Equi-Gaussian curvature folding
Indian Academy of Sciences (India)
have the same equi-Gaussian curvature 1/a2, where a is the radius of the sphere. Now let f : S2 → Pn be a cellular folding. Then we have the following possibilities: Firstly, there are no cellular foldings f : S2 → Pn, for any n > 3 [2]. Secondly, any cellular folding f : S2 → P3 for which Gf forms a regular graph is equivalent to ...
Gaussian Embeddings for Collaborative Filtering
Dos Santos , Ludovic; Piwowarski , Benjamin; Gallinari , Patrick
2017-01-01
International audience; Most collaborative ltering systems, such as matrix factorization, use vector representations for items and users. Those representations are deterministic, and do not allow modeling the uncertainty of the learned representation, which can be useful when a user has a small number of rated items (cold start), or when there is connict-ing information about the behavior of a user or the ratings of an item. In this paper, we leverage recent works in learning Gaussian embeddi...
Liu, Peng; Wang, Yanfei
2018-04-01
We study problems associated with seismic data decomposition and migration imaging. We first represent the seismic data utilizing Gaussian beam basis functions, which have nonzero curvature, and then consider the sparse decomposition technique. The sparse decomposition problem is an l0-norm constrained minimization problem. In solving the l0-norm minimization, a polynomial Radon transform is performed to achieve sparsity, and a fast gradient descent method is used to calculate the waveform functions. The waveform functions can subsequently be used for sparse Gaussian beam migration. Compared with traditional sparse Gaussian beam methods, the seismic data can be properly reconstructed employing fewer Gaussian beams with nonzero initial curvature. The migration approach described in this paper is more efficient than the traditional sparse Gaussian beam migration.
Integrals for fully correlated Gaussians in relative coordinates
Harris, Frank E.; Monkhorst, Hendrik J.
This article considers the integrals needed in energy computations on Coulombic systems using many-particle basis functions that are products of Gaussians in all the relative coordinates rij. The formulas presented are applicable to systems with arbitrary numbers of particles and with wave functions containing arbitrary nonnegative powers of all the r 2ij. Recursive procedures for evaluating the integrals are also given, and graph-theoretic representations are provided for multinomials entering the formulation.
Predicting the occurrence of rainfall using improved radial basis ...
African Journals Online (AJOL)
Journal of Computer Science and Its Application. Journal Home · ABOUT THIS JOURNAL · Advanced Search · Current Issue · Archives · Journal Home > Vol 22, No 2 (2015) >. Log in or Register to get access to full text downloads.
Directory of Open Access Journals (Sweden)
Stone M.B.
2015-01-01
Full Text Available We have designed, installed, and commissioned a scattered beam radial collimator for use at the ARCS Wide Angular Range Chopper Spectrometer at the Spallation Neutron Source. The collimator has been designed to work effectively for thermal and epithermal neutrons and with a range of sample environments. Other design considerations include the accommodation of working within a high vacuum environment and having the ability to quickly install and remove the collimator from the scattered beam. We present here characterization of the collimator's performance and methodologies for its effective use.
Energy Technology Data Exchange (ETDEWEB)
Robertson, M.C.
1997-01-08
The project`s aim is to complete development of the Radial Cutting Torch, a pyrotechnic cutter, for use in all downhole tubular cutting operations in the petroleum industry. Project objectives are to redesign and pressure test nozzle seals to increase product quality, reliability, and manufacturability; improve the mechanical anchor to increase its temperature tolerance and its ability to function in a wider variety of wellbore fluids; and redesign and pressure test the RCT nozzle for operation at pressures from 10 to 20 ksi. The proposal work statement is included in the statement of work for the grant via this reference.
Blitz, Arie; Osterday, Robert M; Brodman, Richard F
2013-07-01
The radial artery (RA) has emerged as an important arterial graft for coronary bypass surgery. With improving five-year patency rates and increasing uptake, great attention has been focused on the optimal conduit harvesting technique. We herein present our approach to RA harvesting. Prerequisites of a successful harvest include adherence to important anatomical landmarks, protection of the sensory innervation to the volar forearm, and meticulous handling of the RA branches. Regardless of the harvesting methodology chosen, adherence to a "no-touch" technique will optimize the patency and durability of the RA conduit.
Scheuer, Jacob; Sun, Xiankai
Circular resonators are promising candidates for a wide range of applications, ranging from optical communication systems through basic research involving highly confined fields and strong photon-atom interactions to biochemical and rotation sensing. The main characteristics of circular resonators are the Q factor, the free spectral range (FSR), and the modal volume, where the last two are primarily determined by the resonator radius. The total internal reflection (TIR) mechanism used for guidance in "conventional" resonators couples these attributes and limits the ability to realize compact devices exhibiting large FSR, small modal volume, and high Q. Recently, a new class of annular resonator, based on a single defect surrounded by radial Bragg reflectors, has been proposed and analyzed. The radial Bragg confinement decouples the modal volume from the Q and paves the way for the realization of compact, low-loss resonators. These properties as well as the unique mode profile of these circular Bragg nanoresonators (CBNRs) and nanolasers (CBNLs) make the devices within this class an excellent tool to realize nanometer scale semiconductor lasers and ultrasensitive detectors, as well as to study nonlinear optics.
International Nuclear Information System (INIS)
Schlegel, H.B.; Binkley, J.S.; Pople, J.A.
1984-01-01
Formulas are developed for the first and second derivatives of two electron integrals over Cartesian Gaussians. Integrals and integral derivatives are evaluated by the Rys polynomial method. Higher angular momentum functions are not used to calculate the integral derivatives; instead the integral formulas are differentiated directly to produce compact and efficient expressions for the integral derivatives. The use of this algorithm in the ab initio molecular orbital programs gaussIan 80 and gaussIan 82 is discussed. Representative timings for some small molecules with several basis sets are presented. This method is compared with previously published algorithms and its computational merits are discussed
1983-01-01
There were 37 (normal) + 3 (special) Radial Field magnets in the ISR to adjust vertically the closed orbit. Gap heights and strengths were 200 mm and .12 Tm in the normal magnets, 220 mm and .18 Tm in the special ones. The core length was 430 mm in both types. Due to their small length as compared to the gap heights the end fringe field errors were very important and had to be compensated by suitably shaping the poles. In order to save on cables, as these magnets were located very far from their power supplies, the coils of the normal type magnets were formed by many turns of solid cpper conductor with some interleaved layers of hollow conductor directly cooled by circulating water
Detecting periodicities with Gaussian processes
Directory of Open Access Journals (Sweden)
Nicolas Durrande
2016-04-01
Full Text Available We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples. Our approach is based on Gaussian process regression, which provides a flexible non-parametric framework for modelling periodic data. We introduce a novel decomposition of the covariance function as the sum of periodic and aperiodic kernels. This decomposition allows for the creation of sub-models which capture the periodic nature of the signal and its complement. To quantify the periodicity of the signal, we derive a periodicity ratio which reflects the uncertainty in the fitted sub-models. Although the method can be applied to many kernels, we give a special emphasis to the Matérn family, from the expression of the reproducing kernel Hilbert space inner product to the implementation of the associated periodic kernels in a Gaussian process toolkit. The proposed method is illustrated by considering the detection of periodically expressed genes in the arabidopsis genome.
Breaking Gaussian incompatibility on continuous variable quantum systems
Energy Technology Data Exchange (ETDEWEB)
Heinosaari, Teiko, E-mail: teiko.heinosaari@utu.fi [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Kiukas, Jukka, E-mail: jukka.kiukas@aber.ac.uk [Department of Mathematics, Aberystwyth University, Penglais, Aberystwyth, SY23 3BZ (United Kingdom); Schultz, Jussi, E-mail: jussi.schultz@gmail.com [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy)
2015-08-15
We characterise Gaussian quantum channels that are Gaussian incompatibility breaking, that is, transform every set of Gaussian measurements into a set obtainable from a joint Gaussian observable via Gaussian postprocessing. Such channels represent local noise which renders measurements useless for Gaussian EPR-steering, providing the appropriate generalisation of entanglement breaking channels for this scenario. Understanding the structure of Gaussian incompatibility breaking channels contributes to the resource theory of noisy continuous variable quantum information protocols.
Coulomb Sturmians as a basis for molecular calculations
DEFF Research Database (Denmark)
Avery, John Scales; Avery, James Emil
2012-01-01
Almost all modern quantum chemistry programs use Gaussian basis sets even though Gaussians cannot accurately represent the cusp at atomic nuclei, nor can they represent the slow decay of the wave function at large distances. The reason that Gaussians dominate quantum chemistry today is the great...... mathematical difficulty of evaluating interelectron repulsion integrals when exponential-type orbitals (ETOs) are used. In this paper we show that when many-centre Coulomb Sturmian ETOs are used as a basis, the most important integrals can be evaluated rapidly and accurately by means of the theory...
Energy Technology Data Exchange (ETDEWEB)
Krausche, S.; Ohlsson, Johan
1998-04-01
The objective of this work was to develop a program dealing with design point calculations of radial turbine machinery, including both compressor and turbine, with as few input data as possible. Some simple stress calculations and turbine metal blade temperatures were also included. This program was then implanted in a German thermodynamics program, Gasturb, a program calculating design and off-design performance of gas turbines. The calculations proceed with a lot of assumptions, necessary to finish the task, concerning pressure losses, velocity distribution, blockage, etc., and have been correlated with empirical data from VAT. Most of these values could have been input data, but to prevent the user of the program from drowning in input values, they are set as default values in the program code. The output data consist of geometry, Mach numbers, predicted component efficiency etc., and a number of graphical plots of geometry and velocity triangles. For the cases examined, the error in predicted efficiency level was within {+-} 1-2% points, and quite satisfactory errors in geometrical and thermodynamic conditions were obtained Examination paper. 18 refs, 36 figs
Valenzuela, Javier
2001-01-01
A radial flow heat exchanger (20) having a plurality of first passages (24) for transporting a first fluid (25) and a plurality of second passages (26) for transporting a second fluid (27). The first and second passages are arranged in stacked, alternating relationship, are separated from one another by relatively thin plates (30) and (32), and surround a central axis (22). The thickness of the first and second passages are selected so that the first and second fluids, respectively, are transported with laminar flow through the passages. To enhance thermal energy transfer between first and second passages, the latter are arranged so each first passage is in thermal communication with an associated second passage along substantially its entire length, and vice versa with respect to the second passages. The heat exchangers may be stacked to achieve a modular heat exchange assembly (300). Certain heat exchangers in the assembly may be designed slightly differently than other heat exchangers to address changes in fluid properties during transport through the heat exchanger, so as to enhance overall thermal effectiveness of the assembly.
Gaussian queues in light and heavy traffic
Dębicki, K.; Kosiński, K.M.; Mandjes, M.
2012-01-01
In this paper we investigate Gaussian queues in the light-traffic and in the heavy-traffic regime. Let $Q^{(c)}_{X}\\equiv\\{Q^{(c)}_{X}(t):t\\ge0\\}$ denote a stationary buffer content process for a fluid queue fed by the centered Gaussian process X≡{X(t):t∈ℝ} with stationary increments, X(0)=0,
How Gaussian can our Universe be?
Cabass, Giovanni; Pajer, Enrico; Schmidt, Fabian
Gravity is a non-linear theory, and hence, barring cancellations, the initial super-horizon perturbations produced by inflation must contain some minimum amount of mode coupling, or primordial non-Gaussianity. In single-field slow-roll models, where this lower bound is saturated, non-Gaussianity is
Palm distributions for log Gaussian Cox processes
DEFF Research Database (Denmark)
Coeurjolly, Jean-Francois; Møller, Jesper; Waagepetersen, Rasmus
This paper reviews useful results related to Palm distributions of spatial point processes and provides a new result regarding the characterization of Palm distributions for the class of log Gaussian Cox processes. This result is used to study functional summary statistics for a log Gaussian Cox...
Conditional and unconditional Gaussian quantum dynamics
Genoni, Marco G.; Lami, Ludovico; Serafini, Alessio
2016-07-01
This article focuses on the general theory of open quantum systems in the Gaussian regime and explores a number of diverse ramifications and consequences of the theory. We shall first introduce the Gaussian framework in its full generality, including a classification of Gaussian (also known as 'general-dyne') quantum measurements. In doing so, we will give a compact proof for the parametrisation of the most general Gaussian completely positive map, which we believe to be missing in the existing literature. We will then move on to consider the linear coupling with a white noise bath, and derive the diffusion equations that describe the evolution of Gaussian states under such circumstances. Starting from these equations, we outline a constructive method to derive general master equations that apply outside the Gaussian regime. Next, we include the general-dyne monitoring of the environmental degrees of freedom and recover the Riccati equation for the conditional evolution of Gaussian states. Our derivation relies exclusively on the standard quantum mechanical update of the system state, through the evaluation of Gaussian overlaps. The parametrisation of the conditional dynamics we obtain is novel and, at variance with existing alternatives, directly ties in to physical detection schemes. We conclude our study with two examples of conditional dynamics that can be dealt with conveniently through our formalism, demonstrating how monitoring can suppress the noise in optical parametric processes as well as stabilise systems subject to diffusive scattering.
Gaussian vs non-Gaussian turbulence: impact on wind turbine loads
DEFF Research Database (Denmark)
Berg, Jacob; Natarajan, Anand; Mann, Jakob
2016-01-01
taking into account the safety factor for extreme moments. Other extreme load moments as well as the fatigue loads are not affected because of the use of non-Gaussian turbulent inflow. It is suggested that the turbine thus acts like a low-pass filter that averages out the non-Gaussian behaviour, which......From large-eddy simulations of atmospheric turbulence, a representation of Gaussian turbulence is constructed by randomizing the phases of the individual modes of variability. Time series of Gaussian turbulence are constructed and compared with its non-Gaussian counterpart. Time series from the two...
Detecting exoplanets: jointly modeling radial velocity and stellar activity time series
Jones, David Edward; Stenning, David; Ford, Eric B.; Wolpert, Robert L.; Loredo, Thomas J.
2017-06-01
The radial velocity method is one of the most successful techniques for detecting exoplanets, but stellar activity often corrupts the radial velocity signal. This corruption can make it difficult to detect low mass planets and planets orbiting more active stars. A principled approach to recovering planet radial velocity signals in the presence of stellar activity was proposed by Rajpaul et al. (2015) and involves the use of dependent Gaussian processes to jointly model the corrupted radial velocity signal and multiple proxies for stellar activity. We build on this work in two ways: (i) we propose using dimension reduction techniques to construct more informative stellar activity proxies; (ii) we extend the Rajpaul et al. (2015) model to a larger class of models and use a model comparison procedure to select the best model for the particular stellar activity proxies at hand. Our framework enables us to compare the performance of various proxies in terms of the resulting statistical power for planet detection.
Representation of Gaussian semimartingales with applications to the covariance function
DEFF Research Database (Denmark)
Basse-O'Connor, Andreas
2010-01-01
stationary Gaussian semimartingales and their canonical decomposition. Thirdly, we give a new characterization of the covariance function of Gaussian semimartingales, which enable us to characterize the class of martingales and the processes of bounded variation among the Gaussian semimartingales. We...
Generating higher-order radial Laguerre-Gaussian modes using a digital laser
CSIR Research Space (South Africa)
Bell, Teboho
2015-07-01
Full Text Available was conducted to test the purity. Mode purity for 0â€pâ€2 is over 90 %, dropping to 75% for p=3, one of the factors that contribute to this drop is the pump beam size overlapping...
Dedicated radial ventriculography pigtail catheter
Energy Technology Data Exchange (ETDEWEB)
Vidovich, Mladen I., E-mail: miv@uic.edu
2013-05-15
A new dedicated cardiac ventriculography catheter was specifically designed for radial and upper arm arterial access approach. Two catheter configurations have been developed to facilitate retrograde crossing of the aortic valve and to conform to various subclavian, ascending aortic and left ventricular anatomies. The “short” dedicated radial ventriculography catheter is suited for horizontal ascending aortas, obese body habitus, short stature and small ventricular cavities. The “long” dedicated radial ventriculography catheter is suited for vertical ascending aortas, thin body habitus, tall stature and larger ventricular cavities. This new design allows for improved performance, faster and simpler insertion in the left ventricle which can reduce procedure time, radiation exposure and propensity for radial artery spasm due to excessive catheter manipulation. Two different catheter configurations allow for optimal catheter selection in a broad range of patient anatomies. The catheter is exceptionally stable during contrast power injection and provides equivalent cavity opacification to traditional femoral ventriculography catheter designs.
Using Gaussian Processes to Construct Flexible Models of Stellar Spectra
Czekala, Ian
2018-01-01
The use of spectra is fundamental to astrophysical fields ranging from exoplanets to stars to galaxies. In spite of this ubiquity, or perhaps because of it, there are a plethora of use cases that do not yet have physics-based forward models that can fit high signal-to-noise data to within the observational noise. These inadequacies result in subtle but systematic residuals not captured by any model, which complicates and biases parameter inference. Fortunately, the now-prevalent collection and archiving of large spectral datasets also provides an opening for empirical, data-driven approaches. We introduce one example of a time-series dataset of high-resolution stellar spectra, as is commonly delivered by planet-search radial velocity instruments like TRES, HIRES, and HARPS. Measurements of radial velocity variations of stars and their companions are essential for stellar and exoplanetary study; these measurements provide access to the fundamental physical properties that dictate all phases of stellar evolution and facilitate the quantitative study of planetary systems. In observations of a (spatially unresolved) spectroscopic binary star, one only ever records the composite sum of the spectra from the primary and secondary stars, complicating photospheric analysis of each individual star. Our technique “disentangles” the composite spectra by treating each underlying stellar spectrum as a Gaussian process, whose posterior predictive distribution is inferred simultaneously with the orbital parameters. To demonstrate the potential of this technique, we deploy it on red-optical time-series spectra of the mid-M-dwarf eclipsing binary LP661-13, which was recently discovered by the MEarth project. We successfully reconstruct the primary and secondary stellar spectra and report orbital parameters with improved precision compared to traditional radial velocity analysis techniques.
Non-Gaussian signatures of tachyacoustic cosmology
Energy Technology Data Exchange (ETDEWEB)
Bessada, Dennis, E-mail: dennis.bessada@unifesp.br [UNIFESP — Universidade Federal de São Paulo, Laboratório de Física Teórica e Computação Científica, Rua São Nicolau, 210, 09913-030, Diadema, SP (Brazil)
2012-09-01
I investigate non-Gaussian signatures in the context of tachyacoustic cosmology, that is, a noninflationary model with superluminal speed of sound. I calculate the full non-Gaussian amplitude A, its size f{sub NL}, and corresponding shapes for a red-tilted spectrum of primordial scalar perturbations. Specifically, for cuscuton-like models I show that f{sub NL} ∼ O(1), and the shape of its non-Gaussian amplitude peaks for both equilateral and local configurations, the latter being dominant. These results, albeit similar, are quantitatively distinct from the corresponding ones obtained by Magueijo et al. in the context of superluminal bimetric models.
Gaussian mixture model of heart rate variability.
Directory of Open Access Journals (Sweden)
Tommaso Costa
Full Text Available Heart rate variability (HRV is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters.
Loop corrections to primordial non-Gaussianity
Boran, Sibel; Kahya, E. O.
2018-02-01
We discuss quantum gravitational loop effects to observable quantities such as curvature power spectrum and primordial non-Gaussianity of cosmic microwave background (CMB) radiation. We first review the previously shown case where one gets a time dependence for zeta-zeta correlator due to loop corrections. Then we investigate the effect of loop corrections to primordial non-Gaussianity of CMB. We conclude that, even with a single scalar inflaton, one might get a huge value for non-Gaussianity which would exceed the observed value by at least 30 orders of magnitude. Finally we discuss the consequences of this result for scalar driven inflationary models.
Some continual integrals from gaussian forms
International Nuclear Information System (INIS)
Mazmanishvili, A.S.
1985-01-01
The result summary of continual integration of gaussian functional type is given. The summary contains 124 continual integrals which are the mathematical expectation of the corresponding gaussian form by the continuum of random trajectories of four types: real-valued Ornstein-Uhlenbeck process, Wiener process, complex-valued Ornstein-Uhlenbeck process and the stochastic harmonic one. The summary includes both the known continual integrals and the unpublished before integrals. Mathematical results of the continual integration carried in the work may be applied in the problem of the theory of stochastic process, approaching to the finding of mean from gaussian forms by measures generated by the pointed stochastic processes
VizieR Online Data Catalog: l Car radial velocity curves (Anderson, 2016)
Anderson, R. I.
2018-02-01
Line-of-sight (radial) velocities of the long-period classical Cepheid l Carinae were measured from 925 high-quality optical spectra recorded using the fiber-fed high-resolution (R~60,000) Coralie spectrograph located at the Euler telescope at La Silla Observatory, Chile. The data were taken between 2014 and 2016. This is the full version of Tab. 2 presented partially in the paper. Line shape parameters (depth, width, asymmetry) are listed for the computed cross-correlation profiles (CCFs). Radial velocities were determined using different techniques (Gaussian, bi-Gaussian) and measured on CCFs computed using three different numerical masks (G2, weak lines, strong lines). (1 data file).
Integration of non-Gaussian fields
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Mohr, Gunnar; Hoffmeyer, Pernille
1996-01-01
The limitations of the validity of the central limit theorem argument as applied to definite integrals of non-Gaussian random fields are empirically explored by way of examples. The purpose is to investigate in specific cases whether the asymptotic convergence to the Gaussian distribution is fast...... enough to justify that it is sufficiently accurate for the applications to shortcut the problem and just assume that the distribution of the relevant stochastic integral is Gaussian. An earlier published example exhibiting this problem concerns silo pressure fields. [Ditlevsen, O., Christensen, C......, 1994](1) The numerical technique applied to obtain approximate information about the distribution of the integral is based on a recursive application of Winterstein approximations (moment fitted linear combinations of Hermite polynomials of standard Gaussian variables). The method uses the very long...
Gravitational Lensing Mass Mapping with Gaussian Processes
Schneider, Michael; Ng, Karen; Dawson, William; Marshall, Phil; Meyers, Joshua; Bard, Deborah
2018-01-01
We infer gravitational lensing shear and convergence fields from galaxy ellipticity catalogs under a Gaussian Process prior for the lensing potential. We demonstrate the performance of our algorithm with simulated Gaussian-distributed cosmological lensing shear maps and a reconstruction of the mass distribution of the merging galaxy cluster Abell 781 using galaxy ellipticities measured with the Deep Lens Survey. Given interim posterior samples of lensing shear or convergence fields on the sky, we describe an algorithm to infer cosmological parameters via lens field marginalization. In the most general formulation of our algorithm we make no assumptions about weak shear orGaussian-distributed shape noise or shears. Because we require solutions and matrix determinants of a linear system of dimension that scales with the number of galaxies, we present computational performance metrics with approximate algorithms that introduce sparsity in the Gaussian Process kernel.
A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families
Dutta, Subhajit
2014-07-28
Several fascinating examples of non-Gaussian bivariate distributions which have marginal distribution functions to be Gaussian have been proposed in the literature. These examples often clarify several properties associated with the normal distribution. In this paper, we generalize this result in the sense that we construct a pp-dimensional distribution for which any proper subset of its components has the Gaussian distribution. However, the jointpp-dimensional distribution is inconsistent with the distribution of these subsets because it is not Gaussian. We study the probabilistic properties of this non-Gaussian multivariate distribution in detail. Interestingly, several popular tests of multivariate normality fail to identify this pp-dimensional distribution as non-Gaussian. We further extend our construction to a class of elliptically contoured distributions as well as skewed distributions arising from selections, for instance the multivariate skew-normal distribution.
Bipartite and Multipartite Entanglement of Gaussian States
Adesso, Gerardo; Illuminati, Fabrizio
In this chapter we review the characterization of entanglement in Gaussian states of continuous variable systems. For two-mode Gaussian states, we discuss how their bipartite entanglement can be accurately quantified in terms of the global and local amounts of mixedness, and efficiently estimated by direct measurements of the associated purities. For multimode Gaussian states endowed with local symmetry with respect to a given bipartition, we show how the multimode block entanglement can be completely and reversibly localized onto a single pair of modes by local, unitary operations. We then analyze the distribution of entanglement among multiple parties in multimode Gaussian states. We introduce the continuous-variable tangle to quantify entanglement sharing in Gaussian states and we prove that it satisfies the Coffman-Kundu-Wootters monogamy inequality. Nevertheless, we show that pure, symmetric three-mode Gaussian states, at variance with their discrete-variable counterparts, allow a promiscuous sharing of quantum correlations, exhibiting both maximum tripartite residual entanglement and maximum couplewise entanglement between any pair of modes. Finally, we investigate the connection between multipartite entanglement and the optimal fidelity in a continuous-variable quantum teleportation network. We show how the fidelity can be maximized in terms of the best preparation of the shared entangled resources and, viceversa, that this optimal fidelity provides a clearcut operational interpretation of several measures of bipartite and multipartite entanglement, including the entanglement of formation, the localizable entanglement, and the continuous-variable tangle.
Non-Gaussian halo assembly bias
International Nuclear Information System (INIS)
Reid, Beth A.; Verde, Licia; Dolag, Klaus; Matarrese, Sabino; Moscardini, Lauro
2010-01-01
The strong dependence of the large-scale dark matter halo bias on the (local) non-Gaussianity parameter, f NL , offers a promising avenue towards constraining primordial non-Gaussianity with large-scale structure surveys. In this paper, we present the first detection of the dependence of the non-Gaussian halo bias on halo formation history using N-body simulations. We also present an analytic derivation of the expected signal based on the extended Press-Schechter formalism. In excellent agreement with our analytic prediction, we find that the halo formation history-dependent contribution to the non-Gaussian halo bias (which we call non-Gaussian halo assembly bias) can be factorized in a form approximately independent of redshift and halo mass. The correction to the non-Gaussian halo bias due to the halo formation history can be as large as 100%, with a suppression of the signal for recently formed halos and enhancement for old halos. This could in principle be a problem for realistic galaxy surveys if observational selection effects were to pick galaxies occupying only recently formed halos. Current semi-analytic galaxy formation models, for example, imply an enhancement in the expected signal of ∼ 23% and ∼ 48% for galaxies at z = 1 selected by stellar mass and star formation rate, respectively
Honda, Hiroaki; Yamaki, Takayoshi; Obara, Shigeru
2002-07-01
General recurrence formulas for evaluating molecular integrals over contracted Cartesian Gaussian functions are derived by introducing auxiliary contracted hyper-Gaussian (ACH) functions. By using a contracted Gaussian function, this ACH represents an extension of the Gaussian function named derivative of Fourier-kernel multiplied Gaussian [J. Chem. Phys. 94, 3790 (1991)]. The ACH is reducible to contracted Cartesian Gaussian functions, contracted modified Hermite Gaussian functions, and to contracted Gaussian functions multiplied by phase factors, or the so-called GIAO, and is also reducible to various spatial operators necessary for ab initio molecular orbital calculations. In our formulation, all molecular integrals are expressed in terms of ACH. Therefore, the formulations have wide applicability for calculating various kinds of molecular integrals in ab initio calculations. Recursive calculations based on our formulation do not depend on the number of contraction terms, because the contraction step is completed at the evaluation of the initial integrals. Therefore, we expect that more efficient recursive calculations will be accomplished by using our formulas for evaluating molecular integrals over contracted Gaussian functions.
Adaptive Laguerre-Gaussian variant of the Gaussian beam expansion method.
Cagniot, Emmanuel; Fromager, Michael; Ait-Ameur, Kamel
2009-11-01
A variant of the Gaussian beam expansion method consists in expanding the Bessel function J0 appearing in the Fresnel-Kirchhoff integral into a finite sum of complex Gaussian functions to derive an analytical expression for a Laguerre-Gaussian beam diffracted through a hard-edge aperture. However, the validity range of the approximation depends on the number of expansion coefficients that are obtained by optimization-computation directly. We propose another solution consisting in expanding J0 onto a set of collimated Laguerre-Gaussian functions whose waist depends on their number and then, depending on its argument, predicting the suitable number of expansion functions to calculate the integral recursively.
Czech Academy of Sciences Publication Activity Database
Polášek, M.; Čársky, Petr
2002-01-01
Roč. 181, č. 1 (2002), s. 1-8 ISSN 0021-9991 R&D Projects: GA ČR GA203/99/0839 Institutional research plan: CEZ:AV0Z4040901 Keywords : two-electron integrals * mixed plane wave Gaussian basis sets Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 1.553, year: 2002
Baysian estimation of P(X > x) from a small sample of Gaussian data
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager
2017-01-01
The classical statistical uncertainty problem of estimation of upper tail probabilities on the basis of a small sample of observations of a Gaussian random variable is considered. Predictive posterior estimation is discussed, adopting the standard statistical model with diffuse priors of the two...
Radial lean direct injection burner
Khan, Abdul Rafey; Kraemer, Gilbert Otto; Stevenson, Christian Xavier
2012-09-04
A burner for use in a gas turbine engine includes a burner tube having an inlet end and an outlet end; a plurality of air passages extending axially in the burner tube configured to convey air flows from the inlet end to the outlet end; a plurality of fuel passages extending axially along the burner tube and spaced around the plurality of air passage configured to convey fuel from the inlet end to the outlet end; and a radial air swirler provided at the outlet end configured to direct the air flows radially toward the outlet end and impart swirl to the air flows. The radial air swirler includes a plurality of vanes to direct and swirl the air flows and an end plate. The end plate includes a plurality of fuel injection holes to inject the fuel radially into the swirling air flows. A method of mixing air and fuel in a burner of a gas turbine is also provided. The burner includes a burner tube including an inlet end, an outlet end, a plurality of axial air passages, and a plurality of axial fuel passages. The method includes introducing an air flow into the air passages at the inlet end; introducing a fuel into fuel passages; swirling the air flow at the outlet end; and radially injecting the fuel into the swirling air flow.
Operator-sum representation for bosonic Gaussian channels
International Nuclear Information System (INIS)
Ivan, J. Solomon; Sabapathy, Krishna Kumar; Simon, R.
2011-01-01
Operator-sum or Kraus representations for single-mode bosonic Gaussian channels are developed, and several of their consequences explored. The fact that the two-mode metaplectic operators acting as unitary purification of these channels do not, in their canonical form, mix the position and momentum variables is exploited to present a procedure which applies uniformly to all families in the Holevo classification. In this procedure the Kraus operators of every quantum-limited Gaussian channel can be simply read off from the matrix elements of a corresponding metaplectic operator. Kraus operators are employed to bring out, in the Fock basis, the manner in which the antilinear, unphysical matrix transposition map when accompanied by injection of a threshold classical noise becomes a physical channel, denoted D(κ) in the Holevo classification. The matrix transposition channels D(κ), D(κ -1 ) turn out to be a dual pair in the sense that their Kraus operators are related by the adjoint operation. The amplifier channel with amplification factor κ and the beam-splitter channel with attenuation factor κ -1 turn out to be mutually dual in the same sense. The action of the quantum-limited attenuator and amplifier channels as simply scaling maps on suitable quasiprobabilities in phase space is examined in the Kraus picture. Consideration of cumulants is used to examine the issue of fixed points. The semigroup property of the amplifier and attenuator families leads in both cases to a Zeno-like effect arising as a consequence of interrupted evolution. In the cases of entanglement-breaking channels a description in terms of rank 1 Kraus operators is shown to emerge quite simply. In contradistinction, it is shown that there is not even one finite rank operator in the entire linear span of Kraus operators of the quantum-limited amplifier or attenuator families, an assertion far stronger than the statement that these are not entanglement breaking channels. A characterization of
International Nuclear Information System (INIS)
Hazeltine, R.D.
1988-12-01
The boundary layer arising in the radial vicinity of a tokamak limiter is examined, with special reference to the TEXT tokamak. It is shown that sheath structure depends upon the self-consistent effects of ion guiding-center orbit modification, as well as the radial variation of E /times/ B-induced toroidal rotation. Reasonable agreement with experiment is obtained from an idealized model which, however simplified, preserves such self-consistent effects. It is argued that the radial sheath, which occurs whenever confining magnetic field-lines lie in the plasma boundary surface, is an object of some intrinsic interest. It differs from the more familiar axial sheath because magnetized charges respond very differently to parallel and perpendicular electric fields. 11 refs., 1 fig
Acanthamoeba infection after radial keratotomy.
Friedman, R F; Wolf, T C; Chodosh, J
1997-03-01
To describe a case of Acanthamoeba infection of the cornea after radial and astigmatic keratotomy. A 29-year-old man developed ulcerative keratitis in the right eye 6 weeks after uncomplicated radial and astigmatic keratotomy. Three sets of corneal cultures for bacteria and fungi were negative. Culture on non-nutrient agar grew Acanthamoeba organisms. Clinical improvement occurred after topical antiamebic therapy was instituted. Incisional keratotomy may predispose the cornea to delayed-onset infectious keratitis. Acanthamoeba should be considered as a possible cause of infection and should be cultured for in refractory cases.
Detonation in supersonic radial outflow
Kasimov, Aslan R.
2014-11-07
We report on the structure and dynamics of gaseous detonation stabilized in a supersonic flow emanating radially from a central source. The steady-state solutions are computed and their range of existence is investigated. Two-dimensional simulations are carried out in order to explore the stability of the steady-state solutions. It is found that both collapsing and expanding two-dimensional cellular detonations exist. The latter can be stabilized by putting several rigid obstacles in the flow downstream of the steady-state sonic locus. The problem of initiation of standing detonation stabilized in the radial flow is also investigated numerically. © 2014 Cambridge University Press.
Spatial solitons in a three-level atomic medium supported by a Laguerre-Gaussian control beam
International Nuclear Information System (INIS)
Hang Chao; Konotop, V. V.
2011-01-01
We investigate the existence and stability of various types of spatial solitons in a three-level atomic medium with Laguerre-Gaussian control beam. Radial and azimuthal modulations of the medium properties, introduced by the control beam, provide possibilities for existence of diverse soliton patterns and dynamics. Beam diffraction provides additional soliton controllability. All types of solitons can be generated at very low input energy at a few-photon level.
DEFF Research Database (Denmark)
Bennedsen, Mikkel
Using theory on (conditionally) Gaussian processes with stationary increments developed in Barndorff-Nielsen et al. (2009, 2011), this paper presents a general semiparametric approach to conducting inference on the fractal index, α, of a time series. Our setup encompasses a large class of Gaussian...
Exploring super-gaussianity towards robust information-theoretical time delay estimation
DEFF Research Database (Denmark)
Petsatodis, Theodoros; Talantzis, Fotios; Boukis, Christos
2013-01-01
Time delay estimation (TDE) is a fundamental component of speaker localization and tracking algorithms. Most of the existing systems are based on the generalized cross-correlation method assuming gaussianity of the source. It has been shown that the distribution of speech, captured with far...... the effect upon TDE when modeling the source signal with different speech-based distributions. An information theoretical TDE method indirectly encapsulating higher order statistics (HOS) formed the basis of this work. The underlying assumption of Gaussian distributed source has been replaced...
Approximate reversal of quantum Gaussian dynamics
Lami, Ludovico; Das, Siddhartha; Wilde, Mark M.
2018-03-01
Recently, there has been focus on determining the conditions under which the data processing inequality for quantum relative entropy is satisfied with approximate equality. The solution of the exact equality case is due to Petz, who showed that the quantum relative entropy between two quantum states stays the same after the action of a quantum channel if and only if there is a reversal channel that recovers the original states after the channel acts. Furthermore, this reversal channel can be constructed explicitly and is now called the Petz recovery map. Recent developments have shown that a variation of the Petz recovery map works well for recovery in the case of approximate equality of the data processing inequality. Our main contribution here is a proof that bosonic Gaussian states and channels possess a particular closure property, namely, that the Petz recovery map associated to a bosonic Gaussian state σ and a bosonic Gaussian channel N is itself a bosonic Gaussian channel. We furthermore give an explicit construction of the Petz recovery map in this case, in terms of the mean vector and covariance matrix of the state σ and the Gaussian specification of the channel N .
Perceived radial translation during centrifugation.
Bos, Jelte E; Correia Grácio, Bruno J
2015-01-01
Linear acceleration generally gives rise to translation perception. Centripetal acceleration during centrifugation, however, has never been reported giving rise to a radial, inward translation perception. To study whether centrifugation can induce a radial translation perception in the absence of visual cues. To that end, we exposed 12 subjects to a centripetal acceleration with eyes closed. To avoid confounding with angular motion perception, subjects were fist rotated on-axis, and were shifted out fast and slow only after rotation sensation had vanished. They were asked for translation direction and velocity right after the shift-out, as well as after about 60 seconds of constant centrifugation. Independent of fast or slow shift-out, the vast statistically significant majority of trials yielded an inward radial translation perception, which velocity was constant after 60 seconds of constant centrifugation. We therefore conclude that during centrifugation, an inward radial translation perception does exist in humans, which perception reaches a constant, non-zero value during constant rotation, lasting for at least one minute. These results can be understood by high-pass filtering of otolith afferents to make a distinction between inertial and gravitational acceleration, followed by a mere integration over time to reach a constant velocity perception.
Vortex Whistle in Radial Intake
National Research Council Canada - National Science Library
Tse, Man-Chun
2004-01-01
In a radial-to-axial intake with inlet guide vanes (IGV) at the entry, a strong flow circulation Gamma can be generated from the tangential flow components created by the IGVs when their setting exceed about halfclosing (approx. 45 deg...
Radial head prosthesis: results overview.
Carità, E; Donadelli, A; Cugola, L; Perazzini, P
2017-12-01
Radial head replacement is frequently used in treatment of radial head fractures or sequela. Impossibility to restore a correct anatomy, acute elbow traumatic instability and failure of osteosynthesis hardware are the most common indications. The authors describe their case studies and results on the implantation of various radial head prostheses. Between June 2005 and June 2016, 28 radial head prostheses were implanted in the same number of patients with an average follow-up of 49 months (6-104). Indications for implantation were: Mason type III and IV radial head fractures and post-traumatic arthritis due to failure of previous treatments. Monopolar prostheses were used and were press-fit implanted via Kaplan's lateral access and Kocher's anconeus approach to the humeroradial joint. At the follow-up, assessments were made of the pain, according to the visual analogic scale, range of motion (ROM), stability and functionality according to the Mayo Elbow Performance Score, presence of osteolysis and mobilization during radiography tests, personal satisfaction of the patients, Disabilities of the Arm, Shoulder and Hand and Patient-Rated Wrist Evaluation outcomes measurements. At the follow-up, we recorded an average level of pain of 1.8 in patients under acute treatments for radial head fractures and a marked reduction in the remaining cases from 6.7 to 2.1. ROM was found on average to be 107° of flexion-extension and 159° of pronosupination. Personal satisfaction was good-excellent in 23 cases. There was no case of infection; removal of the implant was necessary in three cases due to mobilization of the stem and oversized implants. In six cases, bone resorption was seen at the level of the prosthetic collar and it was in all cases asymptomatic. The results of this study suggest that the use of prostheses, if well positioned, is a valid solution in the treatment of secondary arthritis and fractures of the radial head with poor prognosis, with good results in the
An Analytical Method for the Abel Inversion of Asymmetrical Gaussian Profiles
International Nuclear Information System (INIS)
Xu Guosheng; Wan Baonian
2007-01-01
An analytical algorithm for fast calculation of the Abel inversion for density profile measurement in tokamak is developed. Based upon the assumptions that the particle source is negligibly small in the plasma core region, density profiles can be approximated by an asymmetrical Gaussian distribution controlled only by one parameter V 0 /D and V 0 /D is constant along the radial direction, the analytical algorithm is presented and examined against a testing profile. The validity is confirmed by benchmark with the standard Abel inversion method and the theoretical profile. The scope of application as well as the error analysis is also discussed in detail
Polar Functions for Anisotropic Gaussian Random Fields
Directory of Open Access Journals (Sweden)
Zhenlong Chen
2014-01-01
Full Text Available Let X be an (N, d-anisotropic Gaussian random field. Under some general conditions on X, we establish a relationship between a class of continuous functions satisfying the Lipschitz condition and a class of polar functions of X. We prove upper and lower bounds for the intersection probability for a nonpolar function and X in terms of Hausdorff measure and capacity, respectively. We also determine the Hausdorff and packing dimensions of the times set for a nonpolar function intersecting X. The class of Gaussian random fields that satisfy our conditions includes not only fractional Brownian motion and the Brownian sheet, but also such anisotropic fields as fractional Brownian sheets, solutions to stochastic heat equation driven by space-time white noise, and the operator-scaling Gaussian random field with stationary increments.
HE11 radiation patterns and gaussian approximations
International Nuclear Information System (INIS)
Rebuffi, L.; Crenn, J.P.
1986-12-01
The possibility of approximating the HE11 radiation pattern with a Gaussian distribution is presented. A numerical comparison between HE11 far-field theoretical patterns and Abrams and Crenn approximations permits an evaluation of the validity of these two approximations. A new numerically optimized HE11 Gaussian approximation for the far-field, extended to great part of the near field, has been found. In particular, the value given for the beam radius at the waist, has been demonstrated to give the best HE11 Gaussian approximation in the far-field. The Crenn approximation is found to be very close to this optimal approximation, while the Abrams approximation is shown to be less precise. Universal curves for intensity, amplitude and power distribution are given for the HE11 radiated mode. These results are of interest for laser waveguide applications and for plasma ECRH transmission systems
Semisupervised Gaussian Process for Automated Enzyme Search.
Mellor, Joseph; Grigoras, Ioana; Carbonell, Pablo; Faulon, Jean-Loup
2016-06-17
Synthetic biology is today harnessing the design of novel and greener biosynthesis routes for the production of added-value chemicals and natural products. The design of novel pathways often requires a detailed selection of enzyme sequences to import into the chassis at each of the reaction steps. To address such design requirements in an automated way, we present here a tool for exploring the space of enzymatic reactions. Given a reaction and an enzyme the tool provides a probability estimate that the enzyme catalyzes the reaction. Our tool first considers the similarity of a reaction to known biochemical reactions with respect to signatures around their reaction centers. Signatures are defined based on chemical transformation rules by using extended connectivity fingerprint descriptors. A semisupervised Gaussian process model associated with the similar known reactions then provides the probability estimate. The Gaussian process model uses information about both the reaction and the enzyme in providing the estimate. These estimates were validated experimentally by the application of the Gaussian process model to a newly identified metabolite in Escherichia coli in order to search for the enzymes catalyzing its associated reactions. Furthermore, we show with several pathway design examples how such ability to assign probability estimates to enzymatic reactions provides the potential to assist in bioengineering applications, providing experimental validation to our proposed approach. To the best of our knowledge, the proposed approach is the first application of Gaussian processes dealing with biological sequences and chemicals, the use of a semisupervised Gaussian process framework is also novel in the context of machine learning applied to bioinformatics. However, the ability of an enzyme to catalyze a reaction depends on the affinity between the substrates of the reaction and the enzyme. This affinity is generally quantified by the Michaelis constant KM
Radial head dislocation during proximal radial shaft osteotomy.
Hazel, Antony; Bindra, Randy R
2014-03-01
The following case report describes a 48-year-old female patient with a longstanding both-bone forearm malunion, who underwent osteotomies of both the radius and ulna to improve symptoms of pain and lack of rotation at the wrist. The osteotomies were templated preoperatively. During surgery, after performing the planned radial shaft osteotomy, the authors recognized that the radial head was subluxated. The osteotomy was then revised from an opening wedge to a closing wedge with improvement of alignment and rotation. The case report discusses the details of the operation, as well as ways in which to avoid similar shortcomings in the future. Copyright © 2014 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.
Construction of Capacity Achieving Lattice Gaussian Codes
Alghamdi, Wael
2016-04-01
We propose a new approach to proving results regarding channel coding schemes based on construction-A lattices for the Additive White Gaussian Noise (AWGN) channel that yields new characterizations of the code construction parameters, i.e., the primes and dimensions of the codes, as functions of the block-length. The approach we take introduces an averaging argument that explicitly involves the considered parameters. This averaging argument is applied to a generalized Loeliger ensemble [1] to provide a more practical proof of the existence of AWGN-good lattices, and to characterize suitable parameters for the lattice Gaussian coding scheme proposed by Ling and Belfiore [3].
Gaussian processes and constructive scalar field theory
International Nuclear Information System (INIS)
Benfatto, G.; Nicolo, F.
1981-01-01
The last years have seen a very deep progress of constructive euclidean field theory, with many implications in the area of the random fields theory. The authors discuss an approach to super-renormalizable scalar field theories, which puts in particular evidence the connections with the theory of the Gaussian processes associated to the elliptic operators. The paper consists of two parts. Part I treats some problems in the theory of Gaussian processes which arise in the approach to the PHI 3 4 theory. Part II is devoted to the discussion of the ultraviolet stability in the PHI 3 4 theory. (Auth.)
Adesso, Gerardo; Serafini, Alessio; Illuminati, Fabrizio
2007-03-01
We present a novel, detailed study on the usefulness of three-mode Gaussian states for realistic processing of continuous variable (CV) quantum information, with a particular emphasis on the possibilities opened up by their genuine tripartite entanglement. We describe practical schemes to engineer several classes of pure and mixed three-mode states that stand out for their informational and/or entanglement properties. In particular, we introduce a simple procedure—based on passive optical elements—to produce pure three-mode Gaussian states with arbitrary entanglement structure (upon availability of an initial two-mode squeezed state). We analyse in depth the properties of distributed entanglement and the origin of its sharing structure, showing that the promiscuity of entanglement sharing is a feature peculiar to symmetric Gaussian states that survives even in the presence of significant degrees of mixedness and decoherence. Next, we discuss the suitability of the considered tripartite entangled states to the implementation of quantum information and communication protocols with CVs. This will lead to a feasible experimental proposal to test the promiscuous sharing of CV tripartite entanglement, in terms of the optimal fidelity of teleportation networks with Gaussian resources. We finally focus on the application of three-mode states to symmetric and asymmetric telecloning, and single out the structural properties of the optimal Gaussian resources for the latter protocol in different settings. Our analysis aims to lay the basis for a practical quantum communication with CVs beyond the bipartite scenario.
A Gaussian graphical model approach to climate networks
Energy Technology Data Exchange (ETDEWEB)
Zerenner, Tanja, E-mail: tanjaz@uni-bonn.de [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Friederichs, Petra; Hense, Andreas [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany); Lehnertz, Klaus [Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn (Germany); Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany)
2014-06-15
Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.
A Gaussian graphical model approach to climate networks
International Nuclear Information System (INIS)
Zerenner, Tanja; Friederichs, Petra; Hense, Andreas; Lehnertz, Klaus
2014-01-01
Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately
Radial Coordinates for Conformal Blocks
Hogervorst, Matthijs
2013-01-01
We develop the theory of conformal blocks in CFT_d expressing them as power series with Gegenbauer polynomial coefficients. Such series have a clear physical meaning when the conformal block is analyzed in radial quantization: individual terms describe contributions of descendants of a given spin. Convergence of these series can be optimized by a judicious choice of the radial quantization origin. We argue that the best choice is to insert the operators symmetrically. We analyze in detail the resulting "rho-series" and show that it converges much more rapidly than for the commonly used variable z. We discuss how these conformal block representations can be used in the conformal bootstrap. In particular, we use them to derive analytically some bootstrap bounds whose existence was previously found numerically.
Superficial radial neuropathy following venepuncture.
Sheu, J J; Yuan, R Y
2001-01-01
A 42-year-old female suffered excruciating pain and paraesthesia on venepuncture of the cephalic vein in her left wrist. The left superficial radial nerve was injured. A flexed wrist during venepuncture renders the superficial radial nerve immobile and vulnerable to being punctured by the needle. To reduce the risk of nerve injury during venepuncture, the phlebotomist should choose a large and visible vein and insert the needle at a 5-15 degrees angle with the skin. The wrist should be selected only if the veins in the antecubital area are deemed unsuitable. The feeling of an electric shock along the distribution of the nerve, or rupture of the vein during venepuncture, should alert the phlebotomist to the possibility of nerve injury and the procedure should be stopped immediately.
Gaussian-Charge Polarizable and Nonpolarizable Models for CO2.
Jiang, Hao; Moultos, Othonas A; Economou, Ioannis G; Panagiotopoulos, Athanassios Z
2016-02-11
A polarizable intermolecular potential model using three classical Drude oscillators on the atomic sites has been developed for CO2. The model is rigid with bond lengths and molecular geometries set to their experimental values. Electrostatic interactions are represented by three Gaussian charges connected to the molecular frame by harmonic springs. Nonelectrostatic interactions are represented by the Buckingham exponential-6 potential, with potential parameters optimized to vapor-liquid equilibria (VLE) data. A nonpolarizable CO2 model that shares the other ingredients of the polarizable model was also developed and optimized to VLE data. Gibbs ensemble Monte Carlo and molecular dynamics simulations were used to evaluate the two models with respect to a variety of thermodynamic and transport properties, including the enthalpy of vaporization, second virial coefficient, density in the one-phase fluid region, isobaric and isochoric heat capacities, radial distribution functions, self-diffusion coefficient, and shear viscosity. Excellent agreement between model predictions and experimental data was found for all properties studied. The polarizable and nonpolarizable models provide a similar representation of CO2 properties, which indicates that the properties of pure CO2 fluid are not strongly affected by polarization. The polarizable model, which has an order of magnitude higher computational cost than the nonpolarizable model, will likely be useful for the study of a mixture of CO2 and polar components for which polarization is important.
[Primary humero-radial arthrodesis].
Sándor, T
1975-01-01
The surgical treatment of a severe injury in the cubital region of a bus-driver, aged 47, is reported. Because of the extended contamination and the splintered fracture radical wound excision - involving also the chondral surfaces - has been performed and hereupon humero-radial arthrodesis was carried out. The skin defect has been successfully treated secondarily by insert of a flap. After uneventful recovery the patient could resume his work 6 months after the injury again.
Statistically tuned Gaussian background subtraction technique for ...
Indian Academy of Sciences (India)
ground, small objects, moving background and multiple objects are considered for evaluation. The technique is statistically compared with frame differencing technique, temporal median method and mixture of Gaussian model and performance evaluation is done to check the effectiveness of the proposed technique after ...
Log Gaussian Cox processes on the sphere
DEFF Research Database (Denmark)
Pacheco, Francisco Andrés Cuevas; Møller, Jesper
We define and study the existence of log Gaussian Cox processes (LGCPs) for the description of inhomogeneous and aggregated/clustered point patterns on the d-dimensional sphere, with d = 2 of primary interest. Useful theoretical properties of LGCPs are studied and applied for the description of sky...
Open problems in Gaussian fluid queueing theory
Dȩbicki, K.; Mandjes, M.
2011-01-01
We present three challenging open problems that originate from the analysis of the asymptotic behavior of Gaussian fluid queueing models. In particular, we address the problem of characterizing the correlation structure of the stationary buffer content process, the speed of convergence to
Fourth Power Diophantine Equations in Gaussian Integers
Indian Academy of Sciences (India)
25
Fourth Power Diophantine Equations in Gaussian Integers. 7. 7. U. Schneiders and H.G. Zimmer, The rank of elliptic curves upon quadratic extensions,. Computational Number Theory (A. Petho, H.C. Williams,H.G. Zimmer, eds.), de Gruyter,. 239-260, Berlin, (1991). 8. Y. Suzuki, On the Diophantine Equation 2aX4 + 2bY 4 ...
Statistically tuned Gaussian background subtraction technique for ...
Indian Academy of Sciences (India)
The non-parametric background modelling approach proposed by Martin Hofmann et al (2012) involves modelling of foreground by the history of recently ... background subtraction system with mixture of Gaussians, deviation scaling factor and max– min background model for outdoor environment. Selection of detection ...
Gaussian vector fields on triangulated surfaces
DEFF Research Database (Denmark)
Ipsen, John H
2016-01-01
proven to be very useful to resolve the complex interplay between in-plane ordering of membranes and membrane conformations. In the present work we have developed a procedure for realistic representations of Gaussian models with in-plane vector degrees of freedoms on a triangulated surface. The method...
The Wehrl entropy has Gaussian optimizers
DEFF Research Database (Denmark)
De Palma, Giacomo
2018-01-01
We determine the minimum Wehrl entropy among the quantum states with a given von Neumann entropy and prove that it is achieved by thermal Gaussian states. This result determines the relation between the von Neumann and the Wehrl entropies. The key idea is proving that the quantum-classical channel...
Gaussian curvature on hyperelliptic Riemann surfaces
Indian Academy of Sciences (India)
Indian Acad. Sci. (Math. Sci.) Vol. 124, No. 2, May 2014, pp. 155–167. c Indian Academy of Sciences. Gaussian curvature on hyperelliptic Riemann surfaces. ABEL CASTORENA. Centro de Ciencias Matemáticas (Universidad Nacional Autónoma de México,. Campus Morelia) Apdo. Postal 61-3 Xangari, C.P. 58089 Morelia,.
Bregman Cost for Non-Gaussian Noise
DEFF Research Database (Denmark)
Burger, Martin; Dong, Yiqiu; Sciacchitano, Federica
estimator for the Bregman cost if the image is corrupted by Gaussian noise. In this work we extend this result to other noise models with log-concave likelihood density, by introducing two related Bregman cost functions for which the CM and the MAP estimates are proper Bayes estima-tors. Moreover, we also...
Non-Gaussianity effects in petrophysical quantities
Koohi Lai, Z.; Jafari, G. R.
2013-10-01
It has been proved that there are many indicators (petrophysical quantities) for the estimation of petroleum reservoirs. The value of information contained in each indicator is yet to be addressed. In this work, the most famous and applicable petrophysical quantities for a reservoir, which are the gamma emission (GR), sonic transient time (DT), neutron porosity (NPHI), bulk density (RHOB), and deep induced resistivity (ILD), have been analyzed in order to characterize a reservoir. The implemented technique is the well-logging method. Based on the log-normal model defined in random multiplicative processes, the probability distribution function (PDF) for the data sets is described. The shape of the PDF depends on the parameter λ2 which determines the efficiency of non-Gaussianity. When non-Gaussianity appears, it is a sign of uncertainty and phase transition in the critical regime. The large value and scale-invariant behavior of the non-Gaussian parameter λ2 is an indication of a new phase which proves adequate for the existence of petroleum reservoirs. Our results show that one of the indicators (GR) is more non-Gaussian than the other indicators, scale wise. This means that GR is a continuously critical indicator. But by moving windows with various scales, the estimated λ2 shows that the most appropriate indicator for distinguishing the critical regime is ILD, which shows an increase at the end of the measured region of the well.
The Gaussian entropy of fermionic systems
Energy Technology Data Exchange (ETDEWEB)
Prokopec, Tomislav, E-mail: T.Prokopec@uu.nl [Institute for Theoretical Physics (ITP) and Spinoza Institute, Utrecht University, Postbus 80195, 3508 TD Utrecht (Netherlands); Schmidt, Michael G., E-mail: M.G.Schmidt@thphys.uni-heidelberg.de [Institut fuer Theoretische Physik, Heidelberg University, Philosophenweg 16, D-69120 Heidelberg (Germany); Weenink, Jan, E-mail: J.G.Weenink@uu.nl [Institute for Theoretical Physics (ITP) and Spinoza Institute, Utrecht University, Postbus 80195, 3508 TD Utrecht (Netherlands)
2012-12-15
We consider the entropy and decoherence in fermionic quantum systems. By making a Gaussian Ansatz for the density operator of a collection of fermions we study statistical 2-point correlators and express the entropy of a system fermion in terms of these correlators. In a simple case when a set of N thermalised environmental fermionic oscillators interacts bi-linearly with the system fermion we can study its time dependent entropy, which also represents a quantitative measure for decoherence and classicalization. We then consider a relativistic fermionic quantum field theory and take a mass mixing term as a simple model for the Yukawa interaction. It turns out that even in this Gaussian approximation, the fermionic system decoheres quite effectively, such that in a large coupling and high temperature regime the system field approaches the temperature of the environmental fields. - Highlights: Black-Right-Pointing-Pointer We construct the Gaussian density operator for relativistic fermionic systems. Black-Right-Pointing-Pointer The Gaussian entropy of relativistic fermionic systems is described in terms of 2-point correlators. Black-Right-Pointing-Pointer We explicitly show the growth of entropy for fermionic fields mixing with a thermal fermionic environment.
Fourth Power Diophantine Equations in Gaussian Integers
Indian Academy of Sciences (India)
25
Fourth Power Diophantine Equations in Gaussian. Integers. Farzali Izadi · Rasool Naghdali. Forooshani · Amaneh Amiryousefi. Varnousfaderani . Received: date / Accepted: date. Abstract In this paper we examine a class of fourth power Diophantine equa- tions of the form x4 + kx2y2 + y4 = z2 and ax4 + by4 = cz2, in the ...
Gaussian shaping filter for nuclear spectrometry
International Nuclear Information System (INIS)
Menezes, A.S.C. de.
1980-01-01
A theorical study of a gaussian shaping filter, using Pade approximation, for using in gamma spectroscopy is presented. This approximation has proved superior to the classical cascade RC integrators approximation in therms of signal-to-noise ratio and pulse simmetry. An experimental filter was designed, simulated in computer, constructed, and tested in the laboratory. (author) [pt
Gaussian processes for prediction in intensive care
Guiza Grandas, Fabian; Ramon, Jan; Blockeel, Hendrik
2006-01-01
In this paper we present the use of Gaussian Processes for regression in the application of prediction in Intensive Care. We propose a preliminary solution to predicting the evolution of a patient's state during his stay in intensive care by means of defined patient specific characteristics.
Survival Exponents for Some Gaussian Processes
Directory of Open Access Journals (Sweden)
G. Molchan
2012-01-01
Full Text Available The problem is a power-law asymptotics of the probability that a self-similar process does not exceed a fixed level during long time. The exponent in such asymptotics is estimated for some Gaussian processes, including the fractional Brownian motion (FBM in , and the integrated FBM in , .
Radial magnetic bearings: An overview
Directory of Open Access Journals (Sweden)
Weiyu Zhang
Full Text Available Radial magnetic bearings (RMBs are one of the most commonly used magnetic bearings. They are used widely in the field of ultra-high speed and ultra-precise numerical control machine tools, bearingless motors, high speed flywheels, artificial heart pumps, and molecular pumps, and they are being strengthened and extended in various important areas. In this paper, a comprehensive overview is given of different bearing topologies of RMBs with different stator poles that differ in their construction, the driving mode of electromagnets, power consumption, cost, magnetic circuits, and symmetry. RMBs with different poles and couplings between the two bearing axes in the radial direction responsible for cross-coupling generation are compared. In addition, different shaped rotors are compared, as the performances of magnetic bearing-rotor systems are of great concern to rotor constructions. Furthermore, the parameter design methods, the mathematical models and control strategies of the RMBs are described in detail. From the comparison of topologies, models and control methods for RMBs, the advantages, disadvantages and utilizable perspectives are also analyzed. Moreover, several possible development trends of the RMBs are discussed. Keywords: Radial magnetic bearings (RMBs, Topologies, Mathematical mode, Control strategies, Development trends
Velocidades radiales en Collinder 121
Arnal, M.; Morrell, N.
Se han llevado a cabo observaciones espectroscópicas de unas treinta estrellas que son posibles miembros del cúmulo abierto Collinder 121. Las mismas fueron realizadas con el telescopio de 2.15m del Complejo Astronómico El Leoncito (CASLEO). El análisis de las velocidades radiales derivadas del material obtenido, confirma la realidad de Collinder 121, al menos desde el punto de vista cinemático. La velocidad radial baricentral (LSR) del cúmulo es de +17 ± 3 km.s-1. Esta velocidad coincide, dentro de los errores, con la velocidad radial (LSR) de la nebulosa anillo S308, la cual es de ~20 ± 10 km.s-1. Como S308 se encuentra físicamente asociada a la estrella Wolf-Rayet HD~50896, es muy probable que esta última sea un miembro de Collinder 121. Desde un punto de vista cinemático, la supergigante roja HD~50877 (K3Iab) también pertenecería a Collinder 121. Basándonos en la pertenencia de HD~50896 a Collinder 121, y en la interacción encontrada entre el viento de esta estrella y el medio interestelar circundante a la misma, se estima para este cúmulo una distancia del orden de 1 kpc.
Estimators for local non-Gaussianities
International Nuclear Information System (INIS)
Creminelli, P.; Senatore, L.; Zaldarriaga, M.
2006-05-01
We study the Likelihood function of data given f NL for the so-called local type of non-Gaussianity. In this case the curvature perturbation is a non-linear function, local in real space, of a Gaussian random field. We compute the Cramer-Rao bound for f NL and show that for small values of f NL the 3- point function estimator saturates the bound and is equivalent to calculating the full Likelihood of the data. However, for sufficiently large f NL , the naive 3-point function estimator has a much larger variance than previously thought. In the limit in which the departure from Gaussianity is detected with high confidence, error bars on f NL only decrease as 1/ln N pix rather than N pix -1/2 as the size of the data set increases. We identify the physical origin of this behavior and explain why it only affects the local type of non- Gaussianity, where the contribution of the first multipoles is always relevant. We find a simple improvement to the 3-point function estimator that makes the square root of its variance decrease as N pix -1/2 even for large f NL , asymptotically approaching the Cramer-Rao bound. We show that using the modified estimator is practically equivalent to computing the full Likelihood of f NL given the data. Thus other statistics of the data, such as the 4-point function and Minkowski functionals, contain no additional information on f NL . In particular, we explicitly show that the recent claims about the relevance of the 4-point function are not correct. By direct inspection of the Likelihood, we show that the data do not contain enough information for any statistic to be able to constrain higher order terms in the relation between the Gaussian field and the curvature perturbation, unless these are orders of magnitude larger than the size suggested by the current limits on f NL . (author)
Detection of random signals in dependent Gaussian noise
Gualtierotti, Antonio F
2015-01-01
The book presents the necessary mathematical basis to obtain and rigorously use likelihoods for detection problems with Gaussian noise. To facilitate comprehension the text is divided into three broad areas – reproducing kernel Hilbert spaces, Cramér-Hida representations and stochastic calculus – for which a somewhat different approach was used than in their usual stand-alone context. One main applicable result of the book involves arriving at a general solution to the canonical detection problem for active sonar in a reverberation-limited environment. Nonetheless, the general problems dealt with in the text also provide a useful framework for discussing other current research areas, such as wavelet decompositions, neural networks, and higher order spectral analysis. The structure of the book, with the exposition presenting as many details as necessary, was chosen to serve both those readers who are chiefly interested in the results and those who want to learn the material from scratch. Hence, the text...
Gaussian-input Gaussian mixture model for representing density maps and atomic models.
Kawabata, Takeshi
2018-03-06
A new Gaussian mixture model (GMM) has been developed for better representations of both atomic models and electron microscopy 3D density maps. The standard GMM algorithm employs an EM algorithm to determine the parameters. It accepted a set of 3D points with weights, corresponding to voxel or atomic centers. Although the standard algorithm worked reasonably well; however, it had three problems. First, it ignored the size (voxel width or atomic radius) of the input, and thus it could lead to a GMM with a smaller spread than the input. Second, the algorithm had a singularity problem, as it sometimes stopped the iterative procedure due to a Gaussian function with almost zero variance. Third, a map with a large number of voxels required a long computation time for conversion to a GMM. To solve these problems, we have introduced a Gaussian-input GMM algorithm, which considers the input atoms or voxels as a set of Gaussian functions. The standard EM algorithm of GMM was extended to optimize the new GMM. The new GMM has identical radius of gyration to the input, and does not suddenly stop due to the singularity problem. For fast computation, we have introduced a down-sampled Gaussian functions (DSG) by merging neighboring voxels into an anisotropic Gaussian function. It provides a GMM with thousands of Gaussian functions in a short computation time. We also have introduced a DSG-input GMM: the Gaussian-input GMM with the DSG as the input. This new algorithm is much faster than the standard algorithm. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
MCEM algorithm for the log-Gaussian Cox process
Delmas, Celine; Dubois-Peyrard, Nathalie; Sabbadin, Regis
2014-01-01
Log-Gaussian Cox processes are an important class of models for aggregated point patterns. They have been largely used in spatial epidemiology (Diggle et al., 2005), in agronomy (Bourgeois et al., 2012), in forestry (Moller et al.), in ecology (sightings of wild animals) or in environmental sciences (radioactivity counts). A log-Gaussian Cox process is a Poisson process with a stochastic intensity depending on a Gaussian random eld. We consider the case where this Gaussian random eld is ...
Deriving High-Precision Radial Velocities
Figueira, Pedro
This chapter describes briefly the key aspects behind the derivation of precise radial velocities. I start by defining radial velocity precision in the context of astrophysics in general and exoplanet searches in particular. Next I discuss the different basic elements that constitute a spectrograph, and how these elements and overall technical choices impact on the derived radial velocity precision. Then I go on to discuss the different wavelength calibration and radial velocity calculation techniques, and how these are intimately related to the spectrograph's properties. I conclude by presenting some interesting examples of planets detected through radial velocity, and some of the new-generation instruments that will push the precision limit further.
Gaussian Process Noise Modeling with RadVel: a Case Study of HD 3167
Blunt, Sarah; Fulton, Benjamin; Petigura, Erik; Howard, Andrew; Sinukoff, Evan
2018-01-01
Gaussian process regression is a promising technique to account for the presence of correlated noise in radial velocity (RV) time series. We present version 2 of RadVel, an open-source RV fitting toolkit that can model the effects of stellar variability using Gaussian process regression. To illustrate the features of our code and the power of Gaussian process regression, we present a re-analysis of the HD 3167 system (Vanderberg et al. 2016, Christiansen et al. 2017, Gandolfi et al. 2017), using a quasi-periodic kernel to model the stellar activity. We combine RV datasets from HARPS, HARPS-N, FIES, APF, and HIRES in our analysis, yielding a total of 366 RV measurements. Our fit indicates that the magnitude of the RV variation due to stellar activity has an amplitude comparable to those of the planetary signals, confirming that a detailed activity model is needed for this system. We obtain a planet b mass consistent with that of Christiansen et al, but a significantly higher planet c mass and a lower mass for planet d.
Linking network usage patterns to traffic Gaussianity fit
de Oliveira Schmidt, R.; Sadre, R.; Melnikov, Nikolay; Schönwälder, Jürgen; Pras, Aiko
Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001,
Axial and Radial Oxylipin Transport.
Gasperini, Debora; Chauvin, Adeline; Acosta, Ivan F; Kurenda, Andrzej; Stolz, Stéphanie; Chételat, Aurore; Wolfender, Jean-Luc; Farmer, Edward E
2015-11-01
Jasmonates are oxygenated lipids (oxylipins) that control defense gene expression in response to cell damage in plants. How mobile are these potent mediators within tissues? Exploiting a series of 13-lipoxygenase (13-lox) mutants in Arabidopsis (Arabidopsis thaliana) that displays impaired jasmonic acid (JA) synthesis in specific cell types and using JA-inducible reporters, we mapped the extent of the transport of endogenous jasmonates across the plant vegetative growth phase. In seedlings, we found that jasmonate (or JA precursors) could translocate axially from wounded shoots to unwounded roots in a LOX2-dependent manner. Grafting experiments with the wild type and JA-deficient mutants confirmed shoot-to-root oxylipin transport. Next, we used rosettes to investigate radial cell-to-cell transport of jasmonates. After finding that the LOX6 protein localized to xylem contact cells was not wound inducible, we used the lox234 triple mutant to genetically isolate LOX6 as the only JA precursor-producing LOX in the plant. When a leaf of this mutant was wounded, the JA reporter gene was expressed in distal leaves. Leaf sectioning showed that JA reporter expression extended from contact cells throughout the vascular bundle and into extravascular cells, revealing a radial movement of jasmonates. Our results add a crucial element to a growing picture of how the distal wound response is regulated in rosettes, showing that both axial (shoot-to-root) and radial (cell-to-cell) transport of oxylipins plays a major role in the wound response. The strategies developed herein provide unique tools with which to identify intercellular jasmonate transport routes. © 2015 American Society of Plant Biologists. All Rights Reserved.
Exceptional circles of radial potentials
International Nuclear Information System (INIS)
Music, M; Perry, P; Siltanen, S
2013-01-01
A nonlinear scattering transform is studied for the two-dimensional Schrödinger equation at zero energy with a radial potential. Explicit examples are presented, both theoretically and computationally, of potentials with nontrivial singularities in the scattering transform. The singularities arise from non-uniqueness of the complex geometric optics solutions that define the scattering transform. The values of the complex spectral parameter at which the singularities appear are called exceptional points. The singularity formation is closely related to the fact that potentials of conductivity type are ‘critical’ in the sense of Murata. (paper)
Radial to axillary nerve transfer.
Vanaclocha, Vicente; Herrera, Juan Manuel; Rivera-Paz, Marlon; Martínez-Gómez, Deborah; Vanaclocha, Leyre
2018-01-01
Axillary nerve injury is common after brachial plexus injuries, particularly with shoulder luxation. Nerve grafting is the traditional procedure for postganglionic injuries. Nerve transfer is emerging as a viable option particularly in late referrals. At the proximal arm the radial and axillary nerves lie close by. Sacrificing one of the triceps muscle nerve branches induces little negative consequences. Transferring the long head of the triceps nerve branch is a good option to recover axillary nerve function. The surgical technique is presented in a video, stressing the steps to achieve a successful result. The video can be found here: https://youtu.be/WbVbpMuPxIE .
On Alternate Relaying with Improper Gaussian Signaling
Gaafar, Mohamed
2016-06-06
In this letter, we investigate the potential benefits of adopting improper Gaussian signaling (IGS) in a two-hop alternate relaying (AR) system. Given the known benefits of using IGS in interference-limited networks, we propose to use IGS to relieve the inter-relay interference (IRI) impact on the AR system assuming no channel state information is available at the source. In this regard, we assume that the two relays use IGS and the source uses proper Gaussian signaling (PGS). Then, we optimize the degree of impropriety of the relays signal, measured by the circularity coefficient, to maximize the total achievable rate. Simulation results show that using IGS yields a significant performance improvement over PGS, especially when the first hop is a bottleneck due to weak source-relay channel gains and/or strong IRI.
Interweave Cognitive Radio with Improper Gaussian Signaling
Hedhly, Wafa
2018-01-15
Improper Gaussian signaling (IGS) has proven its ability in improving the performance of underlay and overlay cognitive radio paradigms. In this paper, the interweave cognitive radio paradigm is studied when the cognitive user employs IGS. The instantaneous achievable rate performance of both the primary and secondary users are analyzed for specific secondary user sensing and detection capabilities. Next, the IGS scheme is optimized to maximize the achievable rate secondary user while satisfying a target minimum rate requirement for the primary user. Proper Gaussian signaling (PGS) scheme design is also derived to be used as benchmark of the IGS scheme design. Finally, different numerical results are introduced to show the gain reaped from adopting IGS over PGS under different system parameters. The main advantage of employing IGS is observed at low sensing and detection capabilities of the SU, lower PU direct link and higher SU interference on the PU side.
Geometrical approach to gaussian beam propagation.
Laures, P
1967-04-01
The curvature of the wavefront and the spot size of a propagating Gaussian beam may be determined from simple geometrical transformations of the lateral foci. The analysis starts from the construction of the lateral foci in the case of a spherical Fabry-Perot. Then the cases of Gaussian beam propagation through media with different refractive indices, lenses, and simple optical systems are treated. Constructions show how propagation in the image space is readily determined in each case. This analysis is the generalization of the technique outlined by Deschamps and Mast. The geometrical constructions developed for simple cases are applied to the design of some special cases of interest in laser optics: cavities by a lens, laser zoom telescope, and ring cavity.
Extended Linear Models with Gaussian Priors
DEFF Research Database (Denmark)
Quinonero, Joaquin
2002-01-01
In extended linear models the input space is projected onto a feature space by means of an arbitrary non-linear transformation. A linear model is then applied to the feature space to construct the model output. The dimension of the feature space can be very large, or even infinite, giving the model...... a very big flexibility. Support Vector Machines (SVM's) and Gaussian processes are two examples of such models. In this technical report I present a model in which the dimension of the feature space remains finite, and where a Bayesian approach is used to train the model with Gaussian priors...... on the parameters. The Relevance Vector Machine, introduced by Tipping, is a particular case of such a model. I give the detailed derivations of the expectation-maximisation (EM) algorithm used in the training. These derivations are not found in the literature, and might be helpful for newcomers....
Environmental Modeling Framework using Stacked Gaussian Processes
Abdelfatah, Kareem; Bao, Junshu; Terejanu, Gabriel
2016-01-01
A network of independently trained Gaussian processes (StackedGP) is introduced to obtain predictions of quantities of interest with quantified uncertainties. The main applications of the StackedGP framework are to integrate different datasets through model composition, enhance predictions of quantities of interest through a cascade of intermediate predictions, and to propagate uncertainties through emulated dynamical systems driven by uncertain forcing variables. By using analytical first an...
Adaptive multiple importance sampling for Gaussian processes
Czech Academy of Sciences Publication Activity Database
Xiong, X.; Šmídl, Václav; Filippone, M.
2017-01-01
Roč. 87, č. 8 (2017), s. 1644-1665 ISSN 0094-9655 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Gaussian Process * Bayesian estimation * Adaptive importance sampling Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 0.757, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/smidl-0469804.pdf
Dimensionality Reduction by Local Discriminative Gaussians
Parrish, Nathan; Gupta, Maya
2012-01-01
We present local discriminative Gaussian (LDG) dimensionality reduction, a supervised dimensionality reduction technique for classification. The LDG objective function is an approximation to the leave-one-out training error of a local quadratic discriminant analysis classifier, and thus acts locally to each training point in order to find a mapping where similar data can be discriminated from dissimilar data. While other state-of-the-art linear dimensionality reduction methods require gradien...
Recognition of Images Degraded by Gaussian Blur
Czech Academy of Sciences Publication Activity Database
Flusser, Jan; Farokhi, Sajad; Höschl, Cyril; Suk, Tomáš; Zitová, Barbara; Pedone, M.
2016-01-01
Roč. 25, č. 2 (2016), s. 790-806 ISSN 1057-7149 R&D Projects: GA ČR(CZ) GA15-16928S Institutional support: RVO:67985556 Keywords : Blurred image * object recognition * blur invariant comparison * Gaussian blur * projection operators * image moments * moment invariants Subject RIV: JD - Computer Applications, Robotics Impact factor: 4.828, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/flusser-0454335.pdf
Stochastic Energetics for Non-Gaussian Processes
Kanazawa, Kiyoshi; Sagawa, Takahiro; Hayakawa, Hisao
2012-05-01
By introducing a new stochastic integral, we investigate the energetics of classical stochastic systems driven by non-Gaussian white noises. In particular, we introduce a decomposition of the total energy difference into the work and the heat for each trajectory, and derive a formula to calculate the heat from experimental data on the dynamics. We apply our formulation and results to a Langevin system driven by a Poisson noise.
A Gaussian IV estimator of cointegrating relations
DEFF Research Database (Denmark)
Bårdsen, Gunnar; Haldrup, Niels
2006-01-01
-nonparametricestimators. Theoretically ideal instruments can be defined to ensure a limitingGaussian distribution of IV estimators, but unfortunately such instruments areunlikely to be found in real data. In the present paper we suggest an IV estimatorwhere the Hodrick-Prescott filtered trends are used as instruments forthe regressors...... in cointegrating regressions. These instruments are almost idealand simulations show that the IV estimator using such instruments alleviatethe endogeneity problem extremely well in both finite and large samples....
Modeling text with generalizable Gaussian mixtures
DEFF Research Database (Denmark)
Hansen, Lars Kai; Sigurdsson, Sigurdur; Kolenda, Thomas
2000-01-01
We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss ...... the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model....
Development and Testing of a Radial Halbach Magnetic Bearing
Eichenberg, Dennis J.; Gallo, Christopher A.; Thompson, William K.
2006-01-01
The NASA John H. Glenn Research Center has developed and tested a revolutionary Radial Halbach Magnetic Bearing. The objective of this work is to develop a viable non-contact magnetic bearing utilizing Halbach arrays for all-electric flight, and many other applications. This concept will help reduce harmful emissions, reduce the Nation s dependence on fossil fuels and mitigate many of the concerns and limitations encountered in conventional axial bearings such as bearing wear, leaks, seals and friction loss. The Radial Halbach Magnetic Bearing is inherently stable and requires no active feedback control system or superconductivity as required in many magnetic bearing designs. The Radial Halbach Magnetic Bearing is useful for very high speed applications including turbines, instrumentation, medical applications, manufacturing equipment, and space power systems such as flywheels. Magnetic fields suspend and support a rotor assembly within a stator. Advanced technologies developed for particle accelerators, and currently under development for maglev trains and rocket launchers, served as the basis for this application. Experimental hardware was successfully designed and developed to validate the basic principles and analyses. The report concludes that the implementation of Radial Halbach Magnetic Bearings can provide significant improvements in rotational system performance and reliability.
Gaussian Process-Mixture Conditional Heteroscedasticity.
Platanios, Emmanouil A; Chatzis, Sotirios P
2014-05-01
Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning. Specifically, we propose a novel nonparametric Bayesian mixture of Gaussian process regression models, each component of which models the noise variance process that contaminates the observed data as a separate latent Gaussian process driven by the observed data. This way, we essentially obtain a Gaussian process-mixture conditional heteroscedasticity (GPMCH) model for volatility modeling in financial return series. We impose a nonparametric prior with power-law nature over the distribution of the model mixture components, namely the Pitman-Yor process prior, to allow for better capturing modeled data distributions with heavy tails and skewness. Finally, we provide a copula-based approach for obtaining a predictive posterior for the covariances over the asset returns modeled by means of a postulated GPMCH model. We evaluate the efficacy of our approach in a number of benchmark scenarios, and compare its performance to state-of-the-art methodologies.
Resonant non-Gaussianity with equilateral properties
International Nuclear Information System (INIS)
Gwyn, Rhiannon; Rummel, Markus
2012-11-01
We discuss the effect of superimposing multiple sources of resonant non-Gaussianity, which arise for instance in models of axion inflation. The resulting sum of oscillating shape contributions can be used to ''Fourier synthesize'' different non-oscillating shapes in the bispectrum. As an example we reproduce an approximately equilateral shape from the superposition of O(10) oscillatory contributions with resonant shape. This implies a possible degeneracy between the equilateral-type non-Gaussianity typical of models with non-canonical kinetic terms, such as DBI inflation, and an equilateral-type shape arising from a superposition of resonant-type contributions in theories with canonical kinetic terms. The absence of oscillations in the 2-point function together with the structure of the resonant N-point functions, imply that detection of equilateral non-Gaussianity at a level greater than the PLANCK sensitivity of f NL ∝O(5) will rule out a resonant origin. We comment on the questions arising from possible embeddings of this idea in a string theory setting.
Neutron inverse kinetics via Gaussian Processes
International Nuclear Information System (INIS)
Picca, Paolo; Furfaro, Roberto
2012-01-01
Highlights: ► A novel technique for the interpretation of experiments in ADS is presented. ► The technique is based on Bayesian regression, implemented via Gaussian Processes. ► GPs overcome the limits of classical methods, based on PK approximation. ► Results compares GPs and ANN performance, underlining similarities and differences. - Abstract: The paper introduces the application of Gaussian Processes (GPs) to determine the subcriticality level in accelerator-driven systems (ADSs) through the interpretation of pulsed experiment data. ADSs have peculiar kinetic properties due to their special core design. For this reason, classical – inversion techniques based on point kinetic (PK) generally fail to generate an accurate estimate of reactor subcriticality. Similarly to Artificial Neural Networks (ANNs), Gaussian Processes can be successfully trained to learn the underlying inverse neutron kinetic model and, as such, they are not limited to the model choice. Importantly, GPs are strongly rooted into the Bayes’ theorem which makes them a powerful tool for statistical inference. Here, GPs have been designed and trained on a set of kinetics models (e.g. point kinetics and multi-point kinetics) for homogeneous and heterogeneous settings. The results presented in the paper show that GPs are very efficient and accurate in predicting the reactivity for ADS-like systems. The variance computed via GPs may provide an indication on how to generate additional data as function of the desired accuracy.
Single field inflation and non-Gaussianity
International Nuclear Information System (INIS)
Gangui, Alejandro; Martin, Jerome; Sakellariadou, Mairi
2002-01-01
We study non-Gaussian signatures on the cosmic microwave background (CMB) radiation predicted within inflationary models with non-vacuum initial states for cosmological perturbations. The model incorporates a privileged scale, which implies the existence of a feature in the primordial power spectrum. This broken-scale-invariant model predicts a vanishing three-point correlation function for the CMB temperature anisotropies (or any other odd-numbered-point correlation function) whilst an intrinsic non-Gaussian signature arises for any even-numbered-point correlation function. We thus focus on the first non-vanishing moment, the CMB four-point function at zero lag, namely the kurtosis, and compute its expected value for different locations of the primordial feature in the spectrum, as suggested in the literature to conform with observations of large scale structure. The excess kurtosis is found to be negative and the signal to noise ratio for the dimensionless excess kurtosis parameter is equal to |S/N|≅4x10 -4 , almost independently of the free parameters of the model. This signature turns out to be undetectable. We conclude that, subject to current tests, Gaussianity is a generic property of single field inflationary models. The only uncertainty concerning this prediction is that the effect of back reaction has not yet been properly incorporated. The implications for the trans-Planckian problem of inflation are also briefly discussed
Resonant non-Gaussianity with equilateral properties
Energy Technology Data Exchange (ETDEWEB)
Gwyn, Rhiannon [Max-Planck-Institut fuer Gravitationsphysik (Albert-Einstein-Institut), Potsdam (Germany); Rummel, Markus [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Westphal, Alexander [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2012-11-15
We discuss the effect of superimposing multiple sources of resonant non-Gaussianity, which arise for instance in models of axion inflation. The resulting sum of oscillating shape contributions can be used to ''Fourier synthesize'' different non-oscillating shapes in the bispectrum. As an example we reproduce an approximately equilateral shape from the superposition of O(10) oscillatory contributions with resonant shape. This implies a possible degeneracy between the equilateral-type non-Gaussianity typical of models with non-canonical kinetic terms, such as DBI inflation, and an equilateral-type shape arising from a superposition of resonant-type contributions in theories with canonical kinetic terms. The absence of oscillations in the 2-point function together with the structure of the resonant N-point functions, imply that detection of equilateral non-Gaussianity at a level greater than the PLANCK sensitivity of f{sub NL} {proportional_to}O(5) will rule out a resonant origin. We comment on the questions arising from possible embeddings of this idea in a string theory setting.
Gaussian Hypothesis Testing and Quantum Illumination.
Wilde, Mark M; Tomamichel, Marco; Lloyd, Seth; Berta, Mario
2017-09-22
Quantum hypothesis testing is one of the most basic tasks in quantum information theory and has fundamental links with quantum communication and estimation theory. In this paper, we establish a formula that characterizes the decay rate of the minimal type-II error probability in a quantum hypothesis test of two Gaussian states given a fixed constraint on the type-I error probability. This formula is a direct function of the mean vectors and covariance matrices of the quantum Gaussian states in question. We give an application to quantum illumination, which is the task of determining whether there is a low-reflectivity object embedded in a target region with a bright thermal-noise bath. For the asymmetric-error setting, we find that a quantum illumination transmitter can achieve an error probability exponent stronger than a coherent-state transmitter of the same mean photon number, and furthermore, that it requires far fewer trials to do so. This occurs when the background thermal noise is either low or bright, which means that a quantum advantage is even easier to witness than in the symmetric-error setting because it occurs for a larger range of parameters. Going forward from here, we expect our formula to have applications in settings well beyond those considered in this paper, especially to quantum communication tasks involving quantum Gaussian channels.
Perturbative Gaussianizing transforms for cosmological fields
Hall, Alex; Mead, Alexander
2018-01-01
Constraints on cosmological parameters from large-scale structure have traditionally been obtained from two-point statistics. However, non-linear structure formation renders these statistics insufficient in capturing the full information content available, necessitating the measurement of higher order moments to recover information which would otherwise be lost. We construct quantities based on non-linear and non-local transformations of weakly non-Gaussian fields that Gaussianize the full multivariate distribution at a given order in perturbation theory. Our approach does not require a model of the fields themselves and takes as input only the first few polyspectra, which could be modelled or measured from simulations or data, making our method particularly suited to observables lacking a robust perturbative description such as the weak-lensing shear. We apply our method to simulated density fields, finding a significantly reduced bispectrum and an enhanced correlation with the initial field. We demonstrate that our method reconstructs a large proportion of the linear baryon acoustic oscillations, improving the information content over the raw field by 35 per cent. We apply the transform to toy 21 cm intensity maps, showing that our method still performs well in the presence of complications such as redshift-space distortions, beam smoothing, pixel noise and foreground subtraction. We discuss how this method might provide a route to constructing a perturbative model of the fully non-Gaussian multivariate likelihood function.
Persistent homology and non-Gaussianity
Cole, Alex; Shiu, Gary
2018-03-01
In this paper, we introduce the topological persistence diagram as a statistic for Cosmic Microwave Background (CMB) temperature anisotropy maps. A central concept in 'Topological Data Analysis' (TDA), the idea of persistence is to represent a data set by a family of topological spaces. One then examines how long topological features 'persist' as the family of spaces is traversed. We compute persistence diagrams for simulated CMB temperature anisotropy maps featuring various levels of primordial non-Gaussianity of local type. Postponing the analysis of observational effects, we show that persistence diagrams are more sensitive to local non-Gaussianity than previous topological statistics including the genus and Betti number curves, and can constrain Δ fNLloc= 35.8 at the 68% confidence level on the simulation set, compared to Δ fNLloc= 60.6 for the Betti number curves. Given the resolution of our simulations, we expect applying persistence diagrams to observational data will give constraints competitive with those of the Minkowski Functionals. This is the first in a series of papers where we plan to apply TDA to different shapes of non-Gaussianity in the CMB and Large Scale Structure.
Fixing convergence of Gaussian belief propagation
Energy Technology Data Exchange (ETDEWEB)
Johnson, Jason K [Los Alamos National Laboratory; Bickson, Danny [IBM RESEARCH LAB; Dolev, Danny [HEBREW UNIV
2009-01-01
Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimate of the Gaussian random vector and simple sufficient conditions for its convergence have been established. In this paper we develop a double-loop algorithm for forcing convergence of GaBP. Our method computes the correct MAP estimate even in cases where standard GaBP would not have converged. We further extend this construction to compute least-squares solutions of over-constrained linear systems. We believe that our construction has numerous applications, since the GaBP algorithm is linked to solution of linear systems of equations, which is a fundamental problem in computer science and engineering. As a case study, we discuss the linear detection problem. We show that using our new construction, we are able to force convergence of Montanari's linear detection algorithm, in cases where it would originally fail. As a consequence, we are able to increase significantly the number of users that can transmit concurrently.
Production and propagation of Hermite-sinusoidal-Gaussian laser beams.
Tovar, A A; Casperson, L W
1998-09-01
Hermite-sinusoidal-Gaussian solutions to the wave equation have recently been obtained. In the limit of large Hermite-Gaussian beam size, the sinusoidal factors are dominant and reduce to the conventional modes of a rectangular waveguide. In the opposite limit the beams reduce to the familiar Hermite-Gaussian form. The propagation of these beams is examined in detail, and resonators are designed that will produce them. As an example, a special resonator is designed to produce hyperbolic-sine-Gaussian beams. This ring resonator contains a hyperbolic-cosine-Gaussian apodized aperture. The beam mode has finite energy and is perturbation stable.
Formulas for Radial Transport in Protoplanetary Disks
Desch, Steven J.; Estrada, Paul R.; Kalyaan, Anusha; Cuzzi, Jeffrey N.
2017-05-01
The quantification of the radial transport of gaseous species and solid particles is important to many applications in protoplanetary disk evolution. An especially important example is determining the location of the water snow lines in a disk, which requires computing the rates of outward radial diffusion of water vapor and the inward radial drift of icy particles; however, the application is generalized to evaporation fronts of all volatiles. We review the relevant formulas using a uniform formalism. This uniform treatment is necessary because the literature currently contains at least six mutually exclusive treatments of radial diffusion of gas, only one of which is correct. We derive the radial diffusion equations from first principles using Fick's law. For completeness, we also present the equations for radial transport of particles. These equations may be applied to studies of diffusion of gases and particles in protoplanetary and other accretion disks.
Ulnar nerve entrapment complicating radial head excision
Directory of Open Access Journals (Sweden)
Kevin Parfait Bienvenu Bouhelo-Pam
Full Text Available Introduction: Several mechanisms are involved in ischemia or mechanical compression of ulnar nerve at the elbow. Presentation of case: We hereby present the case of a road accident victim, who received a radial head excision for an isolated fracture of the radial head and complicated by onset of cubital tunnel syndrome. This outcome could be the consequence of an iatrogenic valgus of the elbow due to excision of the radial head. Hitherto the surgical treatment of choice it is gradually been abandoned due to development of radial head implant arthroplasty. However, this management option is still being performed in some rural centers with low resources. Discussion: The radial head plays an important role in the stability of the elbow and his iatrogenic deformity can be complicated by cubital tunnel syndrome. Conclusion: An ulnar nerve release was performed with favorable outcome. Keywords: Cubital tunnel syndrome, Peripheral nerve palsy, Radial head excision, Elbow valgus
Smolin, John A; Gambetta, Jay M; Smith, Graeme
2012-02-17
We provide an efficient method for computing the maximum-likelihood mixed quantum state (with density matrix ρ) given a set of measurement outcomes in a complete orthonormal operator basis subject to Gaussian noise. Our method works by first changing basis yielding a candidate density matrix μ which may have nonphysical (negative) eigenvalues, and then finding the nearest physical state under the 2-norm. Our algorithm takes at worst O(d(4)) for the basis change plus O(d(3)) for finding ρ where d is the dimension of the quantum state. In the special case where the measurement basis is strings of Pauli operators, the basis change takes only O(d(3)) as well. The workhorse of the algorithm is a new linear-time method for finding the closest probability distribution (in Euclidean distance) to a set of real numbers summing to one.
Radial smoothing and closed orbit
International Nuclear Information System (INIS)
Burnod, L.; Cornacchia, M.; Wilson, E.
1983-11-01
A complete simulation leading to a description of one of the error curves must involve four phases: (1) random drawing of the six set-up points within a normal population having a standard deviation of 1.3 mm; (b) random drawing of the six vertices of the curve in the sextant mode within a normal population having a standard deviation of 1.2 mm. These vertices are to be set with respect to the axis of the error lunes, while this axis has as its origins the positions defined by the preceding drawing; (c) mathematical definition of six parabolic curves and their junctions. These latter may be curves with very slight curvatures, or segments of a straight line passing through the set-up point and having lengths no longer than one LSS. Thus one gets a mean curve for the absolute errors; (d) plotting of the actually observed radial positions with respect to the mean curve (results of smoothing)
VanOsdol, John G.
2013-06-25
The disclosure provides a pulse jet mixing vessel for mixing a plurality of solid particles. The pulse jet mixing vessel is comprised of a sludge basin, a flow surface surrounding the sludge basin, and a downcoming flow annulus between the flow surface and an inner shroud. The pulse jet mixing vessel is additionally comprised of an upper vessel pressurization volume in fluid communication with the downcoming flow annulus, and an inner shroud surge volume separated from the downcoming flow annulus by the inner shroud. When the solid particles are resting on the sludge basin and a fluid such as water is atop the particles and extending into the downcoming flow annulus and the inner shroud surge volume, mixing occurs by pressurization of the upper vessel pressurization volume, generating an inward radial flow over the flow surface and an upwash jet at the center of the sludge basin.
High-Order Local Pooling and Encoding Gaussians Over a Dictionary of Gaussians.
Li, Peihua; Zeng, Hui; Wang, Qilong; Shiu, Simon C K; Zhang, Lei
2017-07-01
Local pooling (LP) in configuration (feature) space proposed by Boureau et al. explicitly restricts similar features to be aggregated, which can preserve as much discriminative information as possible. At the time it appeared, this method combined with sparse coding achieved competitive classification results with only a small dictionary. However, its performance lags far behind the state-of-the-art results as only the zero-order information is exploited. Inspired by the success of high-order statistical information in existing advanced feature coding or pooling methods, we make an attempt to address the limitation of LP. To this end, we present a novel method called high-order LP (HO-LP) to leverage the information higher than the zero-order one. Our idea is intuitively simple: we compute the first- and second-order statistics per configuration bin and model them as a Gaussian. Accordingly, we employ a collection of Gaussians as visual words to represent the universal probability distribution of features from all classes. Our problem is naturally formulated as encoding Gaussians over a dictionary of Gaussians as visual words. This problem, however, is challenging since the space of Gaussians is not a Euclidean space but forms a Riemannian manifold. We address this challenge by mapping Gaussians into the Euclidean space, which enables us to perform coding with common Euclidean operations rather than complex and often expensive Riemannian operations. Our HO-LP preserves the advantages of the original LP: pooling only similar features and using a small dictionary. Meanwhile, it achieves very promising performance on standard benchmarks, with either conventional, hand-engineered features or deep learning-based features.
Exploiting cellophane birefringence to generate radially and azimuthally polarised vector beams
International Nuclear Information System (INIS)
Kalwe, Johnston; Ominde, Calvine; Rurimo, Geoffrey; Neugebauer, Martin; Leuchs, Gerd; Banzer, Peter
2015-01-01
We exploit the birefringence of cellophane to convert a linearly polarised Gaussian beam into either a radially or azimuthally polarised beam. For that, we fabricated a low-cost polarisation mask consisting of four segments of cellophane. The fast axis of each segment is oriented appropriately in order to rotate the polarisation of the incident linearly polarised beam as desired. To ensure the correct operation of the polarisation mask, we tested the polarisation state of the generated beam by measuring the spatial distribution of the Stokes parameters. Such a device is very cost efficient and allows for the generation of cylindrical vector beams of high quality. (paper)
International Nuclear Information System (INIS)
Tan, Cheng-Yang; Fermilab
2006-01-01
One common way for measuring the emittance of an electron beam is with the slits method. The usual approach for analyzing the data is to calculate an emittance that is a subset of the parent emittance. This paper shows an alternative way by using the method of correlations which ties the parameters derived from the beamlets to the actual parameters of the parent emittance. For parent distributions that are Gaussian, this method yields exact results. For non-Gaussian beam distributions, this method yields an effective emittance that can serve as a yardstick for emittance comparisons
Stochastic differential calculus for Gaussian and non-Gaussian noises: A critical review
Falsone, G.
2018-03-01
In this paper a review of the literature works devoted to the study of stochastic differential equations (SDEs) subjected to Gaussian and non-Gaussian white noises and to fractional Brownian noises is given. In these cases, particular attention must be paid in treating the SDEs because the classical rules of the differential calculus, as the Newton-Leibnitz one, cannot be applied or are applicable with many difficulties. Here all the principal approaches solving the SDEs are reported for any kind of noise, highlighting the negative and positive properties of each one and making the comparisons, where it is possible.
Recursive formula to compute Zernike radial polynomials.
Honarvar Shakibaei, Barmak; Paramesran, Raveendran
2013-07-15
In optics, Zernike polynomials are widely used in testing, wavefront sensing, and aberration theory. This unique set of radial polynomials is orthogonal over the unit circle and finite on its boundary. This Letter presents a recursive formula to compute Zernike radial polynomials using a relationship between radial polynomials and Chebyshev polynomials of the second kind. Unlike the previous algorithms, the derived recurrence relation depends neither on the degree nor on the azimuthal order of the radial polynomials. This leads to a reduction in the computational complexity.
Humanoid environmental perception with Gaussian process regression
Directory of Open Access Journals (Sweden)
Dingsheng Luo
2016-11-01
Full Text Available Nowadays, humanoids are increasingly expected acting in the real world to complete some high-level tasks humanly and intelligently. However, this is a hard issue due to that the real world is always extremely complicated and full of miscellaneous variations. As a consequence, for a real-world-acting robot, precisely perceiving the environmental changes might be an essential premise. Unlike human being, humanoid robot usually turns out to be with much less sensors to get enough information from the real world, which further leads the environmental perception problem to be more challenging. Although it can be tackled by establishing direct sensory mappings or adopting probabilistic filtering methods, the nonlinearity and uncertainty caused by both the complexity of the environment and the high degree of freedom of the robots will result in tough modeling difficulties. In our study, with the Gaussian process regression framework, an alternative learning approach to address such a modeling problem is proposed and discussed. Meanwhile, to debase the influence derived from limited sensors, the idea of fusing multiple sensory information is also involved. To evaluate the effectiveness, with two representative environment changing tasks, that is, suffering unknown external pushing and suddenly encountering sloped terrains, the proposed approach is applied to a humanoid, which is only equipped with a three-axis gyroscope and a three-axis accelerometer. Experimental results reveal that the proposed Gaussian process regression-based approach is effective in coping with the nonlinearity and uncertainty of the humanoid environmental perception problem. Further, a humanoid balancing controller is developed, which takes the output of the Gaussian process regression-based environmental perception as the seed to activate the corresponding balancing strategy. Both simulated and hardware experiments consistently show that our approach is valuable and leads to a
Morse basis expansion applied to diatomic molecules
Energy Technology Data Exchange (ETDEWEB)
Lima, Emanuel F. de, E-mail: eflima@rc.unesp.br [Departamento de Estatística, Matemática Aplicada e Computação, Instituto de Geociências e Ciências Exatas, Universidade Estadual Paulista – UNESP, Rio Claro, São Paulo 13506-900 (Brazil)
2012-02-20
This work explores the use of the eigenfunctions of the Morse potential with a infinite barrier at long range to solve the radial Schrödinger equation for diatomic molecules. Analytical formulas are obtained for the kinetic energy operator matrix elements in the Morse basis. The Morse basis expansion is applied to find the vibrational–rotational levels of the sodium molecule in the electronic ground state. -- Highlights: ► The Morse potential basis is invoked to find the rovibrational levels of diatomic molecules. ► Analytical formulas for the kinetic energy operator in the Morse basis are obtained. ► The results of the Morse basis expansion show good agreement with the Fourier Grid technique.
Entanglement rate for Gaussian continuous variable beams
Jiao Deng, Zhi; Habraken, Steven J. M.; Marquardt, Florian
2016-06-01
We derive a general expression that quantifies the total entanglement production rate in continuous variable systems, where a source emits two entangled Gaussian beams with arbitrary correlators. This expression is especially useful for situations where the source emits an arbitrary frequency spectrum, e.g. when cavities are involved. To exemplify its meaning and potential, we apply it to a four-mode optomechanical setup that enables the simultaneous up- and down-conversion of photons from a drive laser into entangled photon pairs. This setup is efficient in that both the drive and the optomechanical up- and down-conversion can be fully resonant.
Non-Gaussianity from Broken Symmetries
Kolb, Edward W; Vallinotto, A; Kolb, Edward W.; Riotto, Antonio; Vallinotto, Alberto
2006-01-01
Recently we studied inflation models in which the inflaton potential is characterized by an underlying approximate global symmetry. In the first work we pointed out that in such a model curvature perturbations are generated after the end of the slow-roll phase of inflation. In this work we develop further the observational implications of the model and compute the degree of non-Gaussianity predicted in the scenario. We find that the corresponding nonlinearity parameter, $f_{NL}$, can be as large as 10^2.
Ripple Tank Demonstration of Gaussian Beam Propagation
Jude, Kathy J.; Wilson, Thomas E.
1997-10-01
We have reproduced a ripple tank experimentfootnote 'Ripple Tank Studies of Wave Motion' by W. Llowarch (Oxford University Press, 1961) in order to illustrate the effects of focusing and diffraction of a capillary planewave by a bi-convex carbon tetrachloride 'lens'. We compare the experimental results, captured by a video camera, to the theoretical results obtained using the ABCD matrix method for the gaussian beam propagation through this paraxial 'optical' systemfootnote 'Lasers' by Anthony Siegman (University Science Book, Mill Valley, CA, 1986). The diffraction-limited focusing of the propagating wave is clearly visible; this is a feature not easily demonstrable in optics.
Return probability: Exponential versus Gaussian decay
Energy Technology Data Exchange (ETDEWEB)
Izrailev, F.M. [Instituto de Fisica, BUAP, Apdo. Postal J-48, 72570 Puebla (Mexico)]. E-mail: izrailev@sirio.ifuap.buap.mx; Castaneda-Mendoza, A. [Instituto de Fisica, BUAP, Apdo. Postal J-48, 72570 Puebla (Mexico)
2006-02-13
We analyze, both analytically and numerically, the time-dependence of the return probability in closed systems of interacting particles. Main attention is paid to the interplay between two regimes, one of which is characterized by the Gaussian decay of the return probability, and another one is the well-known regime of the exponential decay. Our analytical estimates are confirmed by the numerical data obtained for two models with random interaction. In view of these results, we also briefly discuss the dynamical model which was recently proposed for the implementation of a quantum computation.
CMB constraints on running non-Gaussianity
Oppizzi, Filippo; Liguori, Michele; Renzi, Alessandro; Arroja, Frederico; Bartolo, Nicola
2017-01-01
We develop a complete set of tools for CMB forecasting, simulation and estimation of primordial running bispectra, arising from a variety of curvaton and single-field (DBI) models of Inflation. We validate our pipeline using mock CMB running non-Gaussianity realizations and test it on real data by obtaining experimental constraints on the $f_{\\rm NL}$ running spectral index, $n_{\\rm NG}$, using WMAP 9-year data. Our final bounds (68\\% C.L.) read $-0.3< n_{\\rm NG}
Optical trapping with Super-Gaussian beams
CSIR Research Space (South Africa)
Mc
2013-04-01
Full Text Available stream_source_info McLaren1_2013.pdf.txt stream_content_type text/plain stream_size 2236 Content-Encoding UTF-8 stream_name McLaren1_2013.pdf.txt Content-Type text/plain; charset=UTF-8 JT2A.34.pdf Optics in the Life... Sciences Congress Technical Digest © 2013 The Optical Society (OSA) Optical trapping with Super-Gaussian beams Melanie McLaren, Thulile Khanyile, Patience Mthunzi and Andrew Forbes* National Laser Centre, Council for Scientific and Industrial Research...
The role of Gouy phase on the mechanical effects of Laguerre-Gaussian light interacting with atoms
International Nuclear Information System (INIS)
Lembessis, V. E.; Babiker, M.; Ellinas, D.
2016-01-01
We consider the case of Laguerre-Gaussian (LG) light with high values of radial index, p, and/or winding number l, focussing on the effects of the Gouy phase together with other phase contributions due to the curvature in a Laguerre Gaussian beam when it interacts with atoms at near resonance. We show here that these phase anomalies amount to a significant reduction of the axial wavevector and thus lead to additional contributions to the phase gradient in the vicinity of the focus plane. In consequence, the axial recoil effects due to the stimulated emission and absorption of light by the atom become smaller. This has important effects on the dissipative axial forces acting on the atom, on the momentum fluctuations associated with the photon absorption and stimulated emission and on diffraction of atoms through light masks created by LG beams.
Radial head button holing: a cause of irreducible anterior radial head dislocation
Energy Technology Data Exchange (ETDEWEB)
Shin, Su-Mi; Chai, Jee Won; You, Ja Yeon; Park, Jina [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Radiology, Seoul (Korea, Republic of); Bae, Kee Jeong [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Orthopedic Surgery, Seoul (Korea, Republic of)
2016-10-15
''Buttonholing'' of the radial head through the anterior joint capsule is a known cause of irreducible anterior radial head dislocation associated with Monteggia injuries in pediatric patients. To the best of our knowledge, no report has described an injury consisting of buttonholing of the radial head through the annular ligament and a simultaneous radial head fracture in an adolescent. In the present case, the radiographic findings were a radial head fracture with anterior dislocation and lack of the anterior fat pad sign. Magnetic resonance imaging (MRI) clearly demonstrated anterior dislocation of the fractured radial head through the torn annular ligament. The anterior joint capsule and proximal portion of the annular ligament were interposed between the radial head and capitellum, preventing closed reduction of the radial head. Familiarity with this condition and imaging findings will aid clinicians to make a proper diagnosis and fast decision to perform an open reduction. (orig.)
Approximation problems with the divergence criterion for Gaussian variablesand Gaussian processes
A.A. Stoorvogel; J.H. van Schuppen (Jan)
1996-01-01
textabstractSystem identification for stationary Gaussian processes includes an approximation problem. Currently the subspace algorithm for this problem enjoys much attention. This algorithm is based on a transformation of a finite time series to canonical variable form followed by a truncation.
DEFF Research Database (Denmark)
Møller, Jesper; Jacobsen, Robert Dahl
We introduce a promising alternative to the usual hidden Markov tree model for Gaussian wavelet coefficients, where their variances are specified by the hidden states and take values in a finite set. In our new model, the hidden states have a similar dependence structure but they are jointly Gaus...... detection problems in two-dimensional images....
DEFF Research Database (Denmark)
Jacobsen, Christian Robert Dahl; Møller, Jesper
2017-01-01
We introduce new estimation methods for a subclass of the Gaussian scale mixture models for wavelet trees by Wainwright, Simoncelli and Willsky that rely on modern results for composite likelihoods and approximate Bayesian inference. Our methodology is illustrated for denoising and edge detection...... problems in two-dimensional images....
On the structure of Gaussian pricing models and Gaussian Markov functional models
C.D.D. Neumann
2002-01-01
textabstractThis article investigates the structure of Gaussian pricing models (that is, models in which future returns are normally distributed). Although much is already known about such models, this article differs in that it is based on a formulation of the theory of derivative pricing in which
Multimodal Similarity Gaussian Process Latent Variable Model.
Song, Guoli; Wang, Shuhui; Huang, Qingming; Tian, Qi
2017-09-01
Data from real applications involve multiple modalities representing content with the same semantics from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized modality specific mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy in multimodal data. In this paper, we build our work based on the Gaussian process latent variable model (GPLVM) to learn the non-parametric mapping functions and transform heterogeneous modalities into a shared latent space. We propose multimodal Similarity Gaussian Process latent variable model (m-SimGP), which learns the mapping functions between the intra-modal similarities and latent representation. We further propose multimodal distance-preserved similarity GPLVM (m-DSimGP) to preserve the intra-modal global similarity structure, and multimodal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the latent space. We propose m-DRSimGP, which combines the distance preservation in m-DSimGP and semantic preservation in m-RSimGP to learn the latent representation. The overall objective functions of the four models are solved by simple and scalable gradient decent techniques. They can be applied to various tasks to discover the nonlinear correlations and to obtain the comparable low-dimensional representation for heterogeneous modalities. On five widely used real-world data sets, our approaches outperform existing models on cross-modal content retrieval and multimodal classification.
Overlay Spectrum Sharing using Improper Gaussian Signaling
Amin, Osama
2016-11-30
Improper Gaussian signaling (IGS) scheme has been recently shown to provide performance improvements in interference limited networks as opposed to the conventional proper Gaussian signaling (PGS) scheme. In this paper, we implement the IGS scheme in overlay cognitive radio system, where the secondary transmitter broadcasts a mixture of two different signals. The first signal is selected from the PGS scheme to match the primary message transmission. On the other hand, the second signal is chosen to be from the IGS scheme in order to reduce the interference effect on the primary receiver. We then optimally design the overlay cognitive radio to maximize the secondary link achievable rate while satisfying the primary network quality of service requirements. In particular, we consider full and partial channel knowledge scenarios and derive the feasibility conditions of operating the overlay cognitive radio systems. Moreover, we derive the superiority conditions of the IGS schemes over the PGS schemes supported with closed form expressions for the corresponding power distribution and the circularity coefficient and parameters. Simulation results are provided to support our theoretical derivations.
Photoelectric Radial Velocities, Paper XIX Additional Spectroscopic ...
Indian Academy of Sciences (India)
ian velocity curve that does justice to the measurements, but it cannot be expected to have much predictive power. Key words. Stars: late-type—stars: radial velocities—spectroscopic binaries—orbits. 0. Preamble. The 'Redman K stars' are a lot of seventh-magnitude K stars whose radial velocities were first observed by ...
Concepts of radial and angular kinetic energies
DEFF Research Database (Denmark)
Dahl, Jens Peder; Schleich, W.P.
2002-01-01
We consider a general central-field system in D dimensions and show that the division of the kinetic energy into radial and angular parts proceeds differently in the wave-function picture and the Weyl-Wigner phase-space picture, Thus, the radial and angular kinetic energies are different quantities...
An innovation approach to non-Gaussian time series analysis
Ozaki, Tohru; Iino, Mitsunori
2001-01-01
The paper shows that the use of both types of random noise, white noise and Poisson noise, can be justified when using an innovations approach. The historical background for this is sketched, and then several methods of whitening dependent time series are outlined, including a mixture of Gaussian white noise and a compound Poisson process: this appears as a natural extension of the Gaussian white noise model for the prediction errors of a non-Gaussian time series. A stati...
Combination radial and thrust magnetic bearing
Blumenstock, Kenneth A. (Inventor)
2002-01-01
A combination radial and thrust magnetic bearing is disclosed that allows for both radial and thrust axes control of an associated shaft. The combination radial and thrust magnetic bearing comprises a rotor and a stator. The rotor comprises a shaft, and first and second rotor pairs each having respective rotor elements. The stator comprises first and second stator elements and a magnet-sensor disk. In one embodiment, each stator element has a plurality of split-poles and a corresponding plurality of radial force coils and, in another embodiment, each stator element does not require thrust force coils, and radial force coils are replaced by double the plurality of coils serving as an outer member of each split-pole half.
Prediction and retrodiction with continuously monitored Gaussian states
DEFF Research Database (Denmark)
Zhang, Jinglei; Mølmer, Klaus
2017-01-01
Gaussian states of quantum oscillators are fully characterized by the mean values and the covariance matrix of their quadrature observables. We consider the dynamics of a system of oscillators subject to interactions, damping, and continuous probing which maintain their Gaussian state property......(t)$ to Gaussian states implies that the matrix $E(t)$ is also fully characterized by a vector of mean values and a covariance matrix. We derive the dynamical equations for these quantities and we illustrate their use in the retrodiction of measurements on Gaussian systems....
Gaussian polynomials and content ideal in trivial extensions
International Nuclear Information System (INIS)
Bakkari, C.; Mahdou, N.
2006-12-01
The goal of this paper is to exhibit a class of Gaussian non-coherent rings R (with zero-divisors) such that wdim(R) = ∞ and fPdim(R) is always at most one and also exhibits a new class of rings (with zerodivisors) which are neither locally Noetherian nor locally domain where Gaussian polynomials have a locally principal content. For this purpose, we study the possible transfer of the 'Gaussian' property and the property 'the content ideal of a Gaussian polynomial is locally principal' to various trivial extension contexts. This article includes a brief discussion of the scopes and limits of our result. (author)
BEAM-BEAM SIMULATIONS FOR DOUBLE-GAUSSIAN BEAMS.
Energy Technology Data Exchange (ETDEWEB)
MONTAG, C.; MALITSKY, N.; BEN-ZVI, I.; LITVINENKO, V.
2005-05-16
Electron cooling together with intra-beam scattering results in a transverse distribution that can best be described by a sum of two gaussians, one for the high-density core and one for the tails of the distribution. Simulation studies are being performed to understand the beam-beam interaction of these double-gaussian beams. Here we report the effect of low-frequency random tune modulations on diffusion in double-gaussian beams and compare the effects to those in beam-beam interactions with regular gaussian beams and identical tune shift parameters.
BEAM-BEAM SIMULATIONS FOR DOUBLE-GAUSSIAN BEAMS
International Nuclear Information System (INIS)
MONTAG, C.; MALITSKY, N.; BEN-ZVI, I.; LITVINENKO, V.
2005-01-01
Electron cooling together with intra-beam scattering results in a transverse distribution that can best be described by a sum of two gaussians, one for the high-density core and one for the tails of the distribution. Simulation studies are being performed to understand the beam-beam interaction of these double-gaussian beams. Here we report the effect of loW--frequency random tune modulations on diffusion in double-gaussian beams and compare the effects to those in beam-beam interactions with regular gaussian beams and identical tune shift parameters
Beam-Beam Simulations for Double-Gaussian Beams
Montag, Christoph; Litvinenko, Vladimir N; Malitsky, Nikolay
2005-01-01
Electron cooling together with intra-beam scattering results in a transverse distribution that can best be described by a sum of two Gaussians, one for the high-density core and one for the tails of the distribution. Simulation studies are being performed to understand the beam-beam interaction of these double-Gaussian beams. Here we report the effect of low-frequency random tune modulations on diffusion in double-Gaussian beams and compare the effects to those in beam-beam interactions with regular Gaussian beams and identical tuneshift parameters.
Gaussian basis sets for highly excited and resonance states of helium
Czech Academy of Sciences Publication Activity Database
Kaprálová-Žďánská, Petra Ruth; Šmydke, Jan
2013-01-01
Roč. 138, č. 2 (2013), 024105 ISSN 0021-9606 R&D Projects: GA ČR GAP205/11/0571; GA AV ČR IAAX00100903 Institutional support: RVO:61388955 Keywords : RYDBERG STATES * PHASE-SPACE * He Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.122, year: 2013
Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles
Directory of Open Access Journals (Sweden)
Yu-shan Sun
2016-05-01
Full Text Available Autonomous Underwater Vehicles (AUVs generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.
Yan, Yuan
2017-07-13
Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.
Energy Technology Data Exchange (ETDEWEB)
Zou, Zhichao [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Wang, Fujun, E-mail: wangfj@cau.edu.cn [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Yao, Zhifeng [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Tao, Ran [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Xiao, Ruofu [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Li, Huaicheng [Shanghai Liancheng (Group) Co., Ltd., Shanghai 201812 (China)
2016-12-15
Highlights: • Conclude the characteristics of transient radial force in the startup process for a large double-suction centrifugal pump. • The overall direction of the radial force during startup process is also confirmed. • A formula used to calculate the transient radial force during startup process is proposed. • A relationship between radial force variation and axial vortex development in blade channel during the startup process is established. The mechanism of the radial force evolution is revealed. - Abstract: Double-suction centrifugal pumps play an important role in the main feedwater systems of nuclear power plant. The impeller radial force in a centrifugal pump varies dramatically during startup at the shut-off condition. In this study, the startup process of a large double-suction centrifugal pump is investigated using CFD. During testing, the impeller speed is accelerated from zero to its rated speed in 1.0 s (marked as t{sub 0}) and is then maintained at the rated speed. The results show that the radial force increase lags behind the impeller speed increase. At 0–0.4t{sub 0}, the radial force is small (approaching zero). At 0.4–1.4t{sub 0}, the radial force increases rapidly. After 1.4t{sub 0}, the average radial force stabilizes and reaches its maximum value of 55,619 N. The observed maximum radial force value during startup is approximately nine times as high as the radial force under rated condition. During startup, the overall radial force direction is proximate to the radial line located 25° from the volute tongue along circumferential direction. A transient radial force formula is proposed to evaluate the changes in radial force during startup. The streamline distribution in impeller passages and the impeller outlet pressure profile varying over time are produced. The relationship between radial force evolution and the varying axial-to-spiral vortex structure is analyzed. The radial force change mechanism is revealed. This research
Gaussian process regression for geometry optimization
Denzel, Alexander; Kästner, Johannes
2018-03-01
We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.
IBS for non-gaussian distributions
International Nuclear Information System (INIS)
Fedotov, A.; Sidorin, A.O.; Smirnov, A.V.
2010-01-01
In many situations distribution can significantly deviate from Gaussian which requires accurate treatment of IBS. Our original interest in this problem was motivated by the need to have an accurate description of beam evolution due to IBS while distribution is strongly affected by the external electron cooling force. A variety of models with various degrees of approximation were developed and implemented in BETACOOL in the past to address this topic. A more complete treatment based on the friction coefficient and full 3-D diffusion tensor was introduced in BETACOOL at the end of 2007 under the name 'local IBS model'. Such a model allowed us calculation of IBS for an arbitrary beam distribution. The numerical benchmarking of this local IBS algorithm and its comparison with other models was reported before. In this paper, after briefly describing the model and its limitations, they present its comparison with available experimental data.
Image reconstruction under non-Gaussian noise
DEFF Research Database (Denmark)
Sciacchitano, Federica
During acquisition and transmission, images are often blurred and corrupted by noise. One of the fundamental tasks of image processing is to reconstruct the clean image from a degraded version. The process of recovering the original image from the data is an example of inverse problem. Due...... that the CM estimate outperforms the MAP estimate, when the error depends on Bregman distances. This PhD project can have many applications in the modern society, in fact the reconstruction of high quality images with less noise and more details enhances the image processing operations, such as edge detection......D thesis intends to solve some of the many open questions for image restoration under non-Gaussian noise. The two main kinds of noise studied in this PhD project are the impulse noise and the Cauchy noise. Impulse noise is due to for instance the malfunctioning pixel elements in the camera sensors, errors...
Bayesian multitask classification with Gaussian process priors.
Skolidis, Grigorios; Sanguinetti, Guido
2011-12-01
We present a novel approach to multitask learning in classification problems based on Gaussian process (GP) classification. The method extends previous work on multitask GP regression, constraining the overall covariance (across tasks and data points) to factorize as a Kronecker product. Fully Bayesian inference is possible but time consuming using sampling techniques. We propose approximations based on the popular variational Bayes and expectation propagation frameworks, showing that they both achieve excellent accuracy when compared to Gibbs sampling, in a fraction of time. We present results on a toy dataset and two real datasets, showing improved performance against the baseline results obtained by learning each task independently. We also compare with a recently proposed state-of-the-art approach based on support vector machines, obtaining comparable or better results.
Large Deviations for Gaussian Diffusions with Delay
Azencott, Robert; Geiger, Brett; Ott, William
2018-01-01
Dynamical systems driven by nonlinear delay SDEs with small noise can exhibit important rare events on long timescales. When there is no delay, classical large deviations theory quantifies rare events such as escapes from metastable fixed points. Near such fixed points, one can approximate nonlinear delay SDEs by linear delay SDEs. Here, we develop a fully explicit large deviations framework for (necessarily Gaussian) processes X_t driven by linear delay SDEs with small diffusion coefficients. Our approach enables fast numerical computation of the action functional controlling rare events for X_t and of the most likely paths transiting from X_0 = p to X_T=q. Via linear noise local approximations, we can then compute most likely routes of escape from metastable states for nonlinear delay SDEs. We apply our methodology to the detailed dynamics of a genetic regulatory circuit, namely the co-repressive toggle switch, which may be described by a nonlinear chemical Langevin SDE with delay.
Reduced Wiener Chaos representation of random fields via basis adaptation and projection
Energy Technology Data Exchange (ETDEWEB)
Tsilifis, Panagiotis, E-mail: tsilifis@usc.edu [Department of Mathematics, University of Southern California, Los Angeles, CA 90089 (United States); Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089 (United States); Ghanem, Roger G., E-mail: ghanem@usc.edu [Department of Civil Engineering, University of Southern California, Los Angeles, CA 90089 (United States)
2017-07-15
A new characterization of random fields appearing in physical models is presented that is based on their well-known Homogeneous Chaos expansions. We take advantage of the adaptation capabilities of these expansions where the core idea is to rotate the basis of the underlying Gaussian Hilbert space, in order to achieve reduced functional representations that concentrate the induced probability measure in a lower dimensional subspace. For a smooth family of rotations along the domain of interest, the uncorrelated Gaussian inputs are transformed into a Gaussian process, thus introducing a mesoscale that captures intermediate characteristics of the quantity of interest.
Radial electric fields for improved tokamak performance
International Nuclear Information System (INIS)
Downum, W.B.
1981-01-01
The influence of externally-imposed radial electric fields on the fusion energy output, energy multiplication, and alpha-particle ash build-up in a TFTR-sized, fusing tokamak plasma is explored. In an idealized tokamak plasma, an externally-imposed radial electric field leads to plasma rotation, but no charge current flows across the magnetic fields. However, a realistically-low neutral density profile generates a non-zero cross-field conductivity and the species dependence of this conductivity allows the electric field to selectively alter radial particle transport
Generation of very-high order Laguerre-Gaussian modes in Yb:YAG ceramic laser
International Nuclear Information System (INIS)
Thirugnanasambandam, M P; Ueda, K; Senatsky, Yu
2010-01-01
The use of a simple short-focus plano-convex glass lens with strong spherical aberration for Laguerre-Gaussian mode selection in a continuous wave (CW) LD-end-pumped Yb:YAG ceramic laser is demonstrated. Mode selection was obtained in a nearly meter long plane-parallel cavity by shifting an intra-cavity lens of 2.5 cm focal length along the resonator axis. Sequence of Laguerre-Gaussian (LG p,l ) modes with different combinations of radial (p) and azimuthal (l) indices from low to high orders (p = 0 – 12, l = 0 – 28) with output beam diameters 2 – 13 mm and power up to 30 mW was produced. Along with many low order modes, the whole lineage of very high order pure ''hollow'' LG p,l modes (p = 5 – 10, l = 7 – 28) was produced by this method for the first time. The region of stability of the resonator with an intra-cavity aberrating lens was found to be enclosed between ''focusing'' and ''imaging'' configurations in the cavity, which the aberrating lens could provide simultaneously. The mechanism of LG p,l mode selection in such a cavity and possible applications of the proposed laser scheme are considered
Application Of Shared Gamma And Inverse-Gaussian Frailty Models ...
African Journals Online (AJOL)
Shared Gamma and Inverse-Gaussian Frailty models are used to analyze the survival times of patients who are clustered according to cancer/tumor types under Parametric Proportional Hazard framework. The result of the ... However, no evidence is strong enough for preference of either Gamma or Inverse Gaussian Frailty.
Understanding the spreading of a Gaussian wave packet using the ...
Indian Academy of Sciences (India)
Ex- ploiting the machinery of the Bohmian model of quantum mechanics, the way the wave packet spreads is re-examined. Keywords. Bohmian model; Gaussian wave packet; turning point. PACS No. 03.65.Ta. 1. Introduction and motivation. The time evolution of a Gaussian wave packet is an undergraduate textbook issue.
Non-negative matrix factorization with Gaussian process priors
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Laurberg, Hans
2008-01-01
We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors. We assume that the nonnegative factors in the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance...
Using Mixture of Gaussians to Compare Approaches to Signal Separation
DEFF Research Database (Denmark)
Petersen, Kaare Brandt
2004-01-01
is an example of how such different approaches to separation can be compared using Mixtures of Gaussians as a prior distribution. This not only illuminates some interesting properties of Maximum Likelihood and Energy Based Models, but is also an example of how Mixtures of Gaussians can serve as a both flexible...
Higher-order Gaussian kernel in bootstrap boosting algorithm ...
African Journals Online (AJOL)
The bootstrap boosting algorithm is a bias reduction scheme. The adoption of higher-order Gaussian kernel in a bootstrap boosting algorithm in kernel density estimation was investigated. The algorithm used the higher-order. Gaussian kernel instead of the regular fixed kernels. A comparison of the scheme with existing ...
Optimality of Gaussian attacks in continuous-variable quantum cryptography.
Navascués, Miguel; Grosshans, Frédéric; Acín, Antonio
2006-11-10
We analyze the asymptotic security of the family of Gaussian modulated quantum key distribution protocols for continuous-variables systems. We prove that the Gaussian unitary attack is optimal for all the considered bounds on the key rate when the first and second momenta of the canonical variables involved are known by the honest parties.
New gaussian points for the solution of first order ordinary ...
African Journals Online (AJOL)
New gaussian points for the solution of first order ordinary differential equations. ... Science World Journal ... In this paper, a new set of Gaussian points has been proposed and used as collocation points for the construction of block numerical methods for the solution of first order IVP through transformation within the step ...
Convergence of posteriors for discretized log Gaussian Cox processes
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge
2004-01-01
In Markov chain Monte Carlo posterior computation for log Gaussian Cox processes (LGCPs) a discretization of the continuously indexed Gaussian field is required. It is demonstrated that approximate posterior expectations computed from discretized LGCPs converge to the exact posterior expectations...... when the cell sizes of the discretization tends to zero. The effect of discretization is studied in a data example....
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 ...
A complete conformal metric of preassigned negative Gaussian ...
Indian Academy of Sciences (India)
Let ℎ be a complete metric of Gaussian curvature 0 on a punctured Riemann surface of genus ≥ 1 (or the sphere with at least three punctures). Given a smooth negative function with =0 in neighbourhoods of the punctures we prove that there exists a metric conformal to ℎ which attains this function as its Gaussian ...
Angle-domain common-image gathers from anisotropic Gaussian ...
Indian Academy of Sciences (India)
An approach for extracting angle-domain common-image gathers (ADCIGs) from anisotropic Gaussian beam prestack depth migration (GB-PSDM) is presented in this paper. The propagation angle is calcu- lated in the process of migration using the real-value traveltime information of Gaussian beam. Based on the above ...
Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution
Ahmed, Qasim Zeeshan
2015-04-01
Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.
Gaussian cloning of coherent states with known phases
International Nuclear Information System (INIS)
Alexanian, Moorad
2006-01-01
The fidelity for cloning coherent states is improved over that provided by optimal Gaussian and non-Gaussian cloners for the subset of coherent states that are prepared with known phases. Gaussian quantum cloning duplicates all coherent states with an optimal fidelity of 2/3. Non-Gaussian cloners give optimal single-clone fidelity for a symmetric 1-to-2 cloner of 0.6826. Coherent states that have known phases can be cloned with a fidelity of 4/5. The latter is realized by a combination of two beam splitters and a four-wave mixer operated in the nonlinear regime, all of which are realized by interaction Hamiltonians that are quadratic in the photon operators. Therefore, the known Gaussian devices for cloning coherent states are extended when cloning coherent states with known phases by considering a nonbalanced beam splitter at the input side of the amplifier
The many radial access learning curves.
Hillegass, William B
2017-04-01
The radial approach to endovascular procedures has a series of learning curves: diagnostic heart catheterization, low-risk settings and "straightforward" percutaneous coronary intervention, high-risk settings, and complex coronary intervention, and peripheral vascular angiography and intervention. For diagnostic and low-risk interventional procedures, incremental improvements in technical success and safety are observed in the initial 200 procedures for most operators compared to highly experienced operators. Formal didactic training and ongoing support/review from an experienced radial operator(s) may expedite surmounting the series of radial learning curves while maintaining optimal procedural success and safety. Advances in technology and understanding will require the most experienced radial operators to continually embrace their next learning curve. © 2017 Wiley Periodicals, Inc.
Radial Velocity Fluctuations of RZ Psc
Potravnov, I. S.; Gorynya, N. A.; Grinin, V. P.; Minikulov, N. Kh.
2014-12-01
The behavior of the radial velocity of the UX Ori type star RZ Psc is studied. The existence of an inner cavity with a radius of about 0.7 a.u. in the circumstellar disk of this star allows to suggest the presence of a companion. A study of the radial velocity of RZ Psc based on our own measurements and published data yields no periodic component in its variability. The two most accurate measurements of V r , based on high resolution spectra obtained over a period of three months, show that the radial velocity is constant over this time interval to within 0.5 km/s. This imposes a limit of M p ≤10 M Jup on the mass of the hypothetical companion. Possible reasons for the observed strong fluctuations in the radial velocity of this star are discussed.
Radial pulsations in DB white dwarfs?
Kawaler, Steven D.
1993-01-01
Theoretical models of DB white dwarfs are unstable against radial pulsation at effective temperatures near 20,000-30,000 K. Many high-overtone modes are unstable, with periods ranging from 12 s down to the acoustic cutoff period of approximately 0.1 s. The blue edge for radial instability lies at slightly higher effective temperatures than for nonradial pulsations, with the temperature of the blue edge dependent on the assumed efficiency of convection. Models with increased convective efficiency have radial blue edges that are increasingly closer to the nonradial blue edge; in all models the instability persists into the nonradial instability strip. Radial pulsations therefore may exist in the hottest DB stars that lie below the DB gap; the greatest chance for detection would be observations in the ultraviolet. These models also explain why searches for radial pulsations in DA white dwarfs have failed: the efficient convection needed to explain the blue edge for nonradial DA pulsation means that the radial instability strip is 1000 K cooler than found in previous investigations. The multiperiodic nature of the expected pulsations can be used to advantage to identify very low amplitude modes using the uniform spacing of the modes in frequency. This frequency spacing is a direct indicator of the mass of the star.
International Nuclear Information System (INIS)
Zhi, Dong; Chen, Yizhu; Tao, Rumao; Ma, Yanxing; Zhou, Pu; Si, Lei
2015-01-01
The propagation properties of a radial Gaussian beam array through oceanic turbulence are studied analytically. The analytical expressions for the average intensity and the beam quality (power-in-the-bucket (PIB) and M 2 -factor) of a radial beam array in a turbulent ocean are derived based on an account of statistical optics methods, the extended Huygens-Fresnel principle, and the second order moments of the Wigner distribution function. The influences of w, ε, and χ T on the average intensity are investigated. The array divergence increases and the laser beam spreads as the salinity-induced dominant, ε decreased, and χ T increased. Further, the analytical expression of PIB and the M 2 -factor in the target plane is obtained. The changes of PIB and the M 2 -factor with three oceanic turbulence parameters indicate that the stronger turbulence with a larger w, smaller ε, and larger χ T results in the value of PIB decreasing, the value of the M 2 -factor increasing, and the beam quality degrading. (letter)
Acoustic radiation force on a multilayered sphere in a Gaussian standing field
Wang, Haibin; Liu, Xiaozhou; Gao, Sha; Cui, Jun; Liu, Jiehui; He, Aijun; Zhang, Gutian
2018-03-01
We develop a model for calculating the radiation force on spherically symmetric multilayered particles based on the acoustic scattering approach. An expression is derived for the radiation force on a multilayered sphere centered on the axis of a Gaussian standing wave propagating in an ideal fluid. The effects of the sound absorption of the materials and sound wave on acoustic radiation force of a multilayered sphere immersed in water are analyzed, with particular emphasis on the shell thickness of every layer, and the width of the Gaussian beam. The results reveal that the existence of particle trapping behavior depends on the choice of the non-dimensional frequency ka, as well as the shell thickness of each layer. This study provides a theoretical basis for the development of acoustical tweezers in a Gaussian standing wave, which may benefit the improvement and development of acoustic control technology, such as trapping, sorting, and assembling a cell, and drug delivery applications. Project supported by National Key R&D Program (Grant No. 2016YFF0203000), the National Natural Science Foundation of China (Grant Nos. 11774167 and 61571222), the Fundamental Research Funds for the Central Universities of China (Grant No. 020414380001), the Key Laboratory of Underwater Acoustic Environment, Institute of Acoustics, Chinese Academy of Sciences (Grant No. SSHJ-KFKT-1701), and the AQSIQ Technology R&D Program of China (Grant No. 2017QK125).
Recovering dark-matter clustering from galaxies with Gaussianization
McCullagh, Nuala; Neyrinck, Mark; Norberg, Peder; Cole, Shaun
2016-04-01
The Gaussianization transform has been proposed as a method to remove the issues of scale-dependent galaxy bias and non-linearity from galaxy clustering statistics, but these benefits have yet to be thoroughly tested for realistic galaxy samples. In this paper, we test the effectiveness of the Gaussianization transform for different galaxy types by applying it to realistic simulated blue and red galaxy samples. We show that in real space, the shapes of the Gaussianized power spectra of both red and blue galaxies agree with that of the underlying dark matter, with the initial power spectrum, and with each other to smaller scales than do the statistics of the usual (untransformed) density field. However, we find that the agreement in the Gaussianized statistics breaks down in redshift space. We attribute this to the fact that red and blue galaxies exhibit very different fingers of god in redshift space. After applying a finger-of-god compression, the agreement on small scales between the Gaussianized power spectra is restored. We also compare the Gaussianization transform to the clipped galaxy density field and find that while both methods are effective in real space, they have more complicated behaviour in redshift space. Overall, we find that Gaussianization can be useful in recovering the shape of the underlying dark-matter power spectrum to k ˜ 0.5 h Mpc-1 and of the initial power spectrum to k ˜ 0.4 h Mpc-1 in certain cases at z = 0.
Asymptotic expansions for the Gaussian unitary ensemble
DEFF Research Database (Denmark)
Haagerup, Uffe; Thorbjørnsen, Steen
2012-01-01
Let g : R ¿ C be a C8-function with all derivatives bounded and let trn denote the normalized trace on the n × n matrices. In Ref. 3 Ercolani and McLaughlin established asymptotic expansions of the mean value ¿{trn(g(Xn))} for a rather general class of random matrices Xn, including the Gaussian U...... and covariance considered above correspond to, respectively, the one- and two-dimensional Cauchy (or Stieltjes) transform of the ....... Unitary Ensemble (GUE). Using an analytical approach, we provide in the present paper an alternative proof of this asymptotic expansion in the GUE case. Specifically we derive for a random matrix Xn that where k is an arbitrary positive integer. Considered as mappings of g, we determine the coefficients...... aj(g), j ¿ N, as distributions (in the sense of L. Schwarts). We derive a similar asymptotic expansion for the covariance Cov{Trn[f(Xn)], Trn[g(Xn)]}, where f is a function of the same kind as g, and Trn = n trn. Special focus is drawn to the case where and for ¿, µ in C\\R. In this case the mean...
Interpolation of intermolecular potentials using Gaussian processes
Uteva, Elena; Graham, Richard S.; Wilkinson, Richard D.; Wheatley, Richard J.
2017-10-01
A procedure is proposed to produce intermolecular potential energy surfaces from limited data. The procedure involves generation of geometrical configurations using a Latin hypercube design, with a maximin criterion, based on inverse internuclear distances. Gaussian processes are used to interpolate the data, using over-specified inverse molecular distances as covariates, greatly improving the interpolation. Symmetric covariance functions are specified so that the interpolation surface obeys all relevant symmetries, reducing prediction errors. The interpolation scheme can be applied to many important molecular interactions with trivial modifications. Results are presented for three systems involving CO2, a system with a deep energy minimum (HF-HF), and a system with 48 symmetries (CH4-N2). In each case, the procedure accurately predicts an independent test set. Training this method with high-precision ab initio evaluations of the CO2-CO interaction enables a parameter-free, first-principles prediction of the CO2-CO cross virial coefficient that agrees very well with experiments.
Primordial non-Gaussianity from LAMOST surveys
International Nuclear Information System (INIS)
Gong Yan; Wang Xin; Chen Xuelei; Zheng Zheng
2010-01-01
The primordial non-Gaussianity (PNG) in the matter density perturbation is a very powerful probe of the physics of the very early Universe. The local PNG can induce a distinct scale-dependent bias on the large scale structure distribution of galaxies and quasars, which could be used for constraining it. We study the detection limits of PNG from the surveys of the LAMOST telescope. The cases of the main galaxy survey, the luminous red galaxy (LRG) survey, and the quasar survey of different magnitude limits are considered. We find that the Main1 sample (i.e. the main galaxy survey which is one magnitude deeper than the SDSS main galaxy survey, or r NL are |f NL | NL | NL | is between 50 and 103, depending on the magnitude limit of the survey. With Planck-like priors on cosmological parameters, the quasar survey with g NL | < 43 (2σ). We also discuss the possibility of further tightening the constraint by using the relative bias method proposed by Seljak.
A Decentralized Receiver in Gaussian Interference
Directory of Open Access Journals (Sweden)
Christian D. Chapman
2018-04-01
Full Text Available Bounds are developed on the maximum communications rate between a transmitter and a fusion node aided by a cluster of distributed receivers with limited resources for cooperation, all in the presence of an additive Gaussian interferer. The receivers cannot communicate with one another and can only convey processed versions of their observations to the fusion center through a Local Array Network (LAN with limited total throughput. The effectiveness of each bound’s approach for mitigating a strong interferer is assessed over a wide range of channels. It is seen that, if resources are shared effectively, even a simple quantize-and-forward strategy can mitigate an interferer 20 dB stronger than the signal in a diverse range of spatially Ricean channels. Monte-Carlo experiments for the bounds reveal that, while achievable rates are stable when varying the receiver’s observed scattered-path to line-of-sight signal power, the receivers must adapt how they share resources in response to this change. The bounds analyzed are proven to be achievable and are seen to be tight with capacity when LAN resources are either ample or limited.
Link Prediction via Sparse Gaussian Graphical Model
Directory of Open Access Journals (Sweden)
Liangliang Zhang
2016-01-01
Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.
A Gaussian Mixture Model for Nulling Pulsars
Kaplan, D. L.; Swiggum, J. K.; Fichtenbauer, T. D. J.; Vallisneri, M.
2018-03-01
The phenomenon of pulsar nulling—where pulsars occasionally turn off for one or more pulses—provides insight into pulsar-emission mechanisms and the processes by which pulsars turn off when they cross the “death line.” However, while ever more pulsars are found that exhibit nulling behavior, the statistical techniques used to measure nulling are biased, with limited utility and precision. In this paper, we introduce an improved algorithm, based on Gaussian mixture models, for measuring pulsar nulling behavior. We demonstrate this algorithm on a number of pulsars observed as part of a larger sample of nulling pulsars, and show that it performs considerably better than existing techniques, yielding better precision and no bias. We further validate our algorithm on simulated data. Our algorithm is widely applicable to a large number of pulsars even if they do not show obvious nulls. Moreover, it can be used to derive nulling probabilities of nulling for individual pulses, which can be used for in-depth studies.
Anomalous dimensions and non-gaussianity
Energy Technology Data Exchange (ETDEWEB)
Green, Daniel; Lewandowski, Matthew; Senatore, Leonardo; Silverstein, Eva; Zaldarriaga, Matias
2013-10-01
We analyze the signatures of inflationary models that are coupled to interacting field theories, a basic class of multifield models also motivated by their role in providing dynamically small scales. Near the squeezed limit of the bispectrum, we find a simple scaling behavior determined by operator dimensions, which are constrained by the appropriate unitarity bounds. Specifically, we analyze two simple and calculable classes of examples: conformal field theories (CFTs), and large-N CFTs deformed by relevant time-dependent double-trace operators. Together these two classes of examples exhibit a wide range of scalings and shapes of the bispectrum, including nearly equilateral, orthogonal and local non-Gaussianity in different regimes. Along the way, we compare and contrast the shape and amplitude with previous results on weakly coupled fields coupled to inflation. This signature provides a precision test for strongly coupled sectors coupled to inflation via irrelevant operators suppressed by a high mass scale up to ~ 103 times the inflationary Hubble scale.
Radial velocities for the HIPPARCOS-Gaia Hundred-Thousand-Proper-Motion project
de Bruijne, J. H. J.; Eilers, A.-C.
2012-10-01
Context. The Hundred-Thousand-Proper-Motion (HTPM) project will determine the proper motions of ~113 500 stars using a ~23-year baseline. The proper motions will be based on space-based measurements exclusively, with the Hipparcos data, with epoch 1991.25, as first epoch and with the first intermediate-release Gaia astrometry, with epoch ~2014.5, as second epoch. The expected HTPM proper-motion standard errors are 30-190 μas yr-1, depending on stellar magnitude. Aims: Depending on the astrometric characteristics of an object, in particular its distance and velocity, its radial velocity can have a significant impact on the determination of its proper motion. The impact of this perspective acceleration is largest for fast-moving, nearby stars. Our goal is to determine, for each star in the Hipparcos catalogue, the radial-velocity standard error that is required to guarantee a negligible contribution of perspective acceleration to the HTPM proper-motion precision. Methods: We employ two evaluation criteria, both based on Monte-Carlo simulations, with which we determine which stars need to be spectroscopically (re-)measured. Both criteria take the Hipparcos measurement errors into account. The first criterion, the Gaussian criterion, is applicable to nearby stars. For distant stars, this criterion works but returns overly pessimistic results. We therefore use a second criterion, the robust criterion, which is equivalent to the Gaussian criterion for nearby stars but avoids biases for distant stars and/or objects without literature radial velocity. The robust criterion is hence our prefered choice for all stars, regardless of distance. Results: For each star in the Hipparcos catalogue, we determine the confidence level with which the available radial velocity and its standard error, taken from the XHIP compilation catalogue, are acceptable. We find that for 97 stars, the radial velocities available in the literature are insufficiently precise for a 68.27% confidence
A linearly approximated iterative Gaussian decomposition method for waveform LiDAR processing
Mountrakis, Giorgos; Li, Yuguang
2017-07-01
Full-waveform LiDAR (FWL) decomposition results often act as the basis for key LiDAR-derived products, for example canopy height, biomass and carbon pool estimation, leaf area index calculation and under canopy detection. To date, the prevailing method for FWL product creation is the Gaussian Decomposition (GD) based on a non-linear Levenberg-Marquardt (LM) optimization for Gaussian node parameter estimation. GD follows a "greedy" approach that may leave weak nodes undetected, merge multiple nodes into one or separate a noisy single node into multiple ones. In this manuscript, we propose an alternative decomposition method called Linearly Approximated Iterative Gaussian Decomposition (LAIGD method). The novelty of the LAIGD method is that it follows a multi-step "slow-and-steady" iterative structure, where new Gaussian nodes are quickly discovered and adjusted using a linear fitting technique before they are forwarded for a non-linear optimization. Two experiments were conducted, one using real full-waveform data from NASA's land, vegetation, and ice sensor (LVIS) and another using synthetic data containing different number of nodes and degrees of overlap to assess performance in variable signal complexity. LVIS data revealed considerable improvements in RMSE (44.8% lower), RSE (56.3% lower) and rRMSE (74.3% lower) values compared to the benchmark GD method. These results were further confirmed with the synthetic data. Furthermore, the proposed multi-step method reduces execution times in half, an important consideration as there are plans for global coverage with the upcoming Global Ecosystem Dynamics Investigation LiDAR sensor on the International Space Station.
Option pricing for non-Gaussian price fluctuations
Kleinert, Hagen
2004-07-01
From the path integral description of price fluctuations with non-Gaussian distributions we derive a stochastic calculus which replaces Itô's calculus for harmonic fluctuations. We set up a natural martingale for option pricing from the wealth balance of options, stocks, and bonds, and evaluate the resulting formula for truncated Lévy distributions. After this, an alternative formula is derived for a model of multivariant Gaussian price fluctuations which leads to non-Gaussian return distributions fitting Dow Jones data excellently from long to short time scales with a tail behavior e - x/ x3/2.
Higher moments of weighted integrals of non-Gaussian fields
DEFF Research Database (Denmark)
Mohr, Gunnar
1999-01-01
In general, the exact probability distribution of a definite integral of a given non-Gaussian random field is not known. Some information about this unknown distribution can be obtained from the 3rd and 4th moment of the integral. Approximations to these moments can be calculated by discretizing...... the integral and replacing the integrand by third-degree polynomials of correlated Gaussian Variables which reproduce the first four moments and the correlation function of the field correctly. The method described (see Ditlevsen O, Mohr G, Hoffmeyer P. Integration of non-Gaussian fields. Probabilistic...
Generalised Hermite–Gaussian beams and mode transformations
International Nuclear Information System (INIS)
Wang, Yi; Chen, Yujie; Zhang, Yanfeng; Chen, Hui; Yu, Siyuan
2016-01-01
Generalised Hermite–Gaussian modes (gHG modes), an extended notion of Hermite–Gaussian modes (HG modes), are formed by the summation of normal HG modes with a characteristic function α, which can be used to unite conventional HG modes and Laguerre–Gaussian modes (LG modes). An infinite number of normalised orthogonal modes can thus be obtained by modulation of the function α. The gHG mode notion provides a useful tool in analysis of the deformation and transformation phenomena occurring in propagation of HG and LG modes with astigmatic perturbation. (paper)
Anomalies of radial and ulnar arteries
Directory of Open Access Journals (Sweden)
Rajani Singh
Full Text Available Abstract During dissection conducted in an anatomy department of the right upper limb of the cadaver of a 70-year-old male, both origin and course of the radial and ulnar arteries were found to be anomalous. After descending 5.5 cm from the lower border of the teres major, the brachial artery anomalously bifurcated into a radial artery medially and an ulnar artery laterally. In the arm, the ulnar artery lay lateral to the median nerve. It followed a normal course in the forearm. The radial artery was medial to the median nerve in the arm and then, at the level of the medial epicondyle, it crossed from the medial to the lateral side of the forearm, superficial to the flexor muscles. The course of the radial artery was superficial and tortuous throughout the arm and forearm. The variations of radial and ulnar arteries described above were associated with anomalous formation and course of the median nerve in the arm. Knowledge of neurovascular anomalies are important for vascular surgeons and radiologists.
International Nuclear Information System (INIS)
Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.
2013-01-01
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability
International Nuclear Information System (INIS)
Ida, Katsumi; Miura, Yukitoshi; Itoh, Sanae
1994-10-01
Radial structures of plasma rotation and radial electric field are experimentally studied in tokamak, heliotron/torsatron and stellarator devices. The perpendicular and parallel viscosities are measured. The parallel viscosity, which is dominant in determining the toroidal velocity in heliotron/torsatron and stellarator devices, is found to be neoclassical. On the other hand, the perpendicular viscosity, which is dominant in dictating the toroidal rotation in tokamaks, is anomalous. Even without external momentum input, both a plasma rotation and a radial electric field exist in tokamaks and heliotrons/torsatrons. The observed profiles of the radial electric field do not agree with the theoretical prediction based on neoclassical transport. This is mainly due to the existence of anomalous perpendicular viscosity. The shear of the radial electric field improves particle and heat transport both in bulk and edge plasma regimes of tokamaks. (author) 95 refs
Statistical analysis of radial interface growth
International Nuclear Information System (INIS)
Masoudi, A A; Hosseinabadi, S; Khorrami, M; Davoudi, J; Kohandel, M
2012-01-01
Recent studies have questioned the application of standard scaling analysis to study radially growing interfaces (Escudero 2008 Phys. Rev. Lett. 100 116101; 2009 Ann. Phys. 324 1796). We show that the radial Edwards–Wilkinson (EW) equation belongs to the same universality as that obtained in the planar geometry. In addition, we use numerical simulations to calculate the interface width for both random deposition with surface relaxation (RDSR) and restricted solid on solid (RSOS) models, assuming that the system size increases linearly with time (due to radial geometry). By applying appropriate rules for each model, we show that the interface width increases with time as t β , where the exponent β is the same as those obtained from the corresponding planar geometries. (letter)
Radial anisotropy ambient noise tomography of volcanoes
Mordret, Aurélien; Rivet, Diane; Shapiro, Nikolai; Jaxybulatov, Kairly; Landès, Matthieu; Koulakov, Ivan; Sens-Schönfelder, Christoph
2016-04-01
The use of ambient seismic noise allows us to perform surface-wave tomography of targets which could hardly be imaged by other means. The frequencies involved (~ 0.5 - 20 s), somewhere in between active seismic and regular teleseismic frequency band, make possible the high resolution imaging of intermediate-size targets like volcanic edifices. Moreover, the joint inversion of Rayleigh and Love waves dispersion curves extracted from noise correlations allows us to invert for crustal radial anisotropy. We present here the two first studies of radial anisotropy on volcanoes by showing results from Lake Toba Caldera, a super-volcano in Indonesia, and from Piton de la Fournaise volcano, a hot-spot effusive volcano on the Réunion Island (Indian Ocean). We will see how radial anisotropy can be used to infer the main fabric within a magmatic system and, consequently, its dominant type of intrusion.
Making tensor factorizations robust to non-gaussian noise.
Energy Technology Data Exchange (ETDEWEB)
Chi, Eric C. (Rice University, Houston, TX); Kolda, Tamara Gibson
2011-03-01
Tensors are multi-way arrays, and the CANDECOMP/PARAFAC (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective function, which is a maximum likelihood estimate under the assumption of independent and identically distributed (i.i.d.) Gaussian noise. We demonstrate that this loss function can be highly sensitive to non-Gaussian noise. Therefore, we propose a loss function based on the 1-norm because it can accommodate both Gaussian and grossly non-Gaussian perturbations. We also present an alternating majorization-minimization (MM) algorithm for fitting a CP model using our proposed loss function (CPAL1) and compare its performance to the workhorse algorithm for fitting CP models, CP alternating least squares (CPALS).
Optimal multicopy asymmetric Gaussian cloning of coherent states
Fiurášek, Jaromír; Cerf, Nicolas J.
2007-05-01
We investigate the asymmetric Gaussian cloning of coherent states which produces M copies from N input replicas in such a way that the fidelity of each copy may be different. We show that the optimal asymmetric Gaussian cloning can be performed with a single phase-insensitive amplifier and an array of beam splitters. We obtain a simple analytical expression characterizing the set of optimal asymmetric Gaussian cloning machines and prove the optimality of these cloners using the formalism of Gaussian completely positive maps and semidefinite programming techniques. We also present an alternative implementation of the asymmetric cloning machine where the phase-insensitive amplifier is replaced with a beam splitter, heterodyne detector, and feedforward.
Optimal multicopy asymmetric Gaussian cloning of coherent states
International Nuclear Information System (INIS)
Fiurasek, Jaromir; Cerf, Nicolas J.
2007-01-01
We investigate the asymmetric Gaussian cloning of coherent states which produces M copies from N input replicas in such a way that the fidelity of each copy may be different. We show that the optimal asymmetric Gaussian cloning can be performed with a single phase-insensitive amplifier and an array of beam splitters. We obtain a simple analytical expression characterizing the set of optimal asymmetric Gaussian cloning machines and prove the optimality of these cloners using the formalism of Gaussian completely positive maps and semidefinite programming techniques. We also present an alternative implementation of the asymmetric cloning machine where the phase-insensitive amplifier is replaced with a beam splitter, heterodyne detector, and feedforward
Quantifying entanglement in two-mode Gaussian states
Tserkis, Spyros; Ralph, Timothy C.
2017-12-01
Entangled two-mode Gaussian states are a key resource for quantum information technologies such as teleportation, quantum cryptography, and quantum computation, so quantification of Gaussian entanglement is an important problem. Entanglement of formation is unanimously considered a proper measure of quantum correlations, but for arbitrary two-mode Gaussian states no analytical form is currently known. In contrast, logarithmic negativity is a measure that is straightforward to calculate and so has been adopted by most researchers, even though it is a less faithful quantifier. In this work, we derive an analytical lower bound for entanglement of formation of generic two-mode Gaussian states, which becomes tight for symmetric states and for states with balanced correlations. We define simple expressions for entanglement of formation in physically relevant situations and use these to illustrate the problematic behavior of logarithmic negativity, which can lead to spurious conclusions.
Scalable Gaussian Processes and the search for exoplanets
CERN. Geneva
2015-01-01
Gaussian Processes are a class of non-parametric models that are often used to model stochastic behavior in time series or spatial data. A major limitation for the application of these models to large datasets is the computational cost. The cost of a single evaluation of the model likelihood scales as the third power of the number of data points. In the search for transiting exoplanets, the datasets of interest have tens of thousands to millions of measurements with uneven sampling, rendering naive application of a Gaussian Process model impractical. To attack this problem, we have developed robust approximate methods for Gaussian Process regression that can be applied at this scale. I will describe the general problem of Gaussian Process regression and offer several applicable use cases. Finally, I will present our work on scaling this model to the exciting field of exoplanet discovery and introduce a well-tested open source implementation of these new methods.
ARC Code TI: Block-GP: Scalable Gaussian Process Regression
National Aeronautics and Space Administration — Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear...
Schweiner, Frank; Laturner, Jeanine; Main, Jörg; Wunner, Günter
2017-11-01
Until now only for specific crossovers between Poissonian statistics (P), the statistics of a Gaussian orthogonal ensemble (GOE), or the statistics of a Gaussian unitary ensemble (GUE) have analytical formulas for the level spacing distribution function been derived within random matrix theory. We investigate arbitrary crossovers in the triangle between all three statistics. To this aim we propose an according formula for the level spacing distribution function depending on two parameters. Comparing the behavior of our formula for the special cases of P→GUE, P→GOE, and GOE→GUE with the results from random matrix theory, we prove that these crossovers are described reasonably. Recent investigations by F. Schweiner et al. [Phys. Rev. E 95, 062205 (2017)2470-004510.1103/PhysRevE.95.062205] have shown that the Hamiltonian of magnetoexcitons in cubic semiconductors can exhibit all three statistics in dependence on the system parameters. Evaluating the numerical results for magnetoexcitons in dependence on the excitation energy and on a parameter connected with the cubic valence band structure and comparing the results with the formula proposed allows us to distinguish between regular and chaotic behavior as well as between existent or broken antiunitary symmetries. Increasing one of the two parameters, transitions between different crossovers, e.g., from the P→GOE to the P→GUE crossover, are observed and discussed.
International Nuclear Information System (INIS)
Kenfack, Lionel Tenemeza; Tchoffo, Martin; Fai, Lukong Cornelius; Fouokeng, Georges Collince
2017-01-01
We address the entanglement dynamics of a three-qubit system interacting with a classical fluctuating environment described either by a Gaussian or non-Gaussian noise in three different configurations namely: common, independent and mixed environments. Specifically, we focus on the Ornstein-Uhlenbeck (OU) noise and the random telegraph noise (RTN). The qubits are prepared in a state composed of a Greenberger-Horne-Zeilinger (GHZ) and a W state. With the help of the tripartite negativity, we show that the entanglement evolution is not only affected by the type of system-environment coupling but also by the kind and the memory properties of the considered noise. We also compared the dynamics induced by the two kinds of noise and we find that even if both noises have a Lorentzian spectrum, the effects of the OU noise cannot be in a simple way deduced from those of the RTN and vice-versa. In addition, we show that the entanglement can be indefinitely preserved when the qubits are coupled to the environmental noise in a common environment (CE). Finally, the presence or absence of peculiar phenomena such as entanglement revivals (ER) and entanglement sudden death (ESD) is observed.
Energy Technology Data Exchange (ETDEWEB)
Kenfack, Lionel Tenemeza, E-mail: kenfacklionel300@gmail.com [Mesoscopic and Multilayer Structure Laboratory, Department of Physics, Faculty of Science, University of Dschang, PO Box: 67 Dschang (Cameroon); Tchoffo, Martin; Fai, Lukong Cornelius [Mesoscopic and Multilayer Structure Laboratory, Department of Physics, Faculty of Science, University of Dschang, PO Box: 67 Dschang (Cameroon); Fouokeng, Georges Collince [Mesoscopic and Multilayer Structure Laboratory, Department of Physics, Faculty of Science, University of Dschang, PO Box: 67 Dschang (Cameroon); Laboratoire de Génie des Matériaux, Pôle Recherche-Innovation-Entrepreneuriat (PRIE), Institut Universitaire de la Côte, BP 3001 Douala (Cameroon)
2017-04-15
We address the entanglement dynamics of a three-qubit system interacting with a classical fluctuating environment described either by a Gaussian or non-Gaussian noise in three different configurations namely: common, independent and mixed environments. Specifically, we focus on the Ornstein-Uhlenbeck (OU) noise and the random telegraph noise (RTN). The qubits are prepared in a state composed of a Greenberger-Horne-Zeilinger (GHZ) and a W state. With the help of the tripartite negativity, we show that the entanglement evolution is not only affected by the type of system-environment coupling but also by the kind and the memory properties of the considered noise. We also compared the dynamics induced by the two kinds of noise and we find that even if both noises have a Lorentzian spectrum, the effects of the OU noise cannot be in a simple way deduced from those of the RTN and vice-versa. In addition, we show that the entanglement can be indefinitely preserved when the qubits are coupled to the environmental noise in a common environment (CE). Finally, the presence or absence of peculiar phenomena such as entanglement revivals (ER) and entanglement sudden death (ESD) is observed.
Carpal alignment in distal radial fractures
Directory of Open Access Journals (Sweden)
Jain Pankaj
2002-05-01
Full Text Available Abstract Background Carpal malalignment following the malunited distal radial fracture is described to develop as an adaptation to realign the hand to the malunion. It worsens gradually after healing of the fracture due to continued loading of the wrist. It is also reported to develop during the immobilization itself rather than after fracture healing. The present work was aimed to study the natural course and the quantitative assessment of such adaptive carpal realignment following distal radial fracture. Methods In a prospective study, 118 distal radial fractures treated with different modalities were followed-up with serial radiographs for a year for assessment of various radiological parameters. Results Two patterns of carpal malalignment were identified depending upon the effective radio-lunate flexion (ERLF measured on pre-reduction radiographs. The midcarpal malalignment was seen in 98 radial fractures (83% with the lunate following the dorsiflexed fracture fragment and a measured ERLF of less than 25°. The second pattern of radio-carpal malalignment showed the fracture fragment to dorsiflex without taking the lunate with a measured ERLF of more than 25°. The scaphoid did not follow the fracture fragment in both the patterns of malalignment. Conclusion It is better to assess distal radial fractures for any wrist ligamentous injury on the post-reduction film with the restored radial anatomy than on the pre-reduction film since most carpal malalignments get corrected with the reduction of the fracture. Similar carpal malalignment reappear with the redisplacement of the fracture as seen in pre-reduction radiographs and develops during the immobilization rather than as a later compensatory mechanism for the malunion.
Mashhadi, L.
2017-12-01
Optical vortices are currently one of the most intensively studied topics in light–matter interaction. In this work, a three-step axial Doppler- and recoil-free Gaussian–Gaussian-Laguerre–Gaussian (GGLG) excitation of a localized atom to the highly excited Rydberg state is presented. By assuming a large detuning for intermediate states, an effective quadrupole excitation related to the Laguerre–Gaussian (LG) excitation to the highly excited Rydberg state is obtained. This special excitation system radially confines the single highly excited Rydberg atom independently of the trapping system into a sharp potential landscape into the so-called ‘far-off-resonance optical dipole-quadrupole trap’ (FORDQT). The key parameters of the Rydberg excitation to the highly excited state, namely the effective Rabi frequency and the effective detuning including a position-dependent AC Stark shift, are calculated in terms of the basic parameters of the LG beam and of the polarization of the excitation lasers. It is shown that the obtained parameters can be tuned to have a precise excitation of a single atom to the desired Rydberg state as well. The features of transferring the optical orbital and spin angular momentum of the polarized LG beam to the atom via quadrupole Rydberg excitation offer a long-lived and controllable qudit quantum memory. In addition, in contrast to the Gaussian laser beam, the doughnut-shaped LG beam makes it possible to use a high intensity laser beam to increase the signal-to-noise ratio in quadrupole excitation with minimized perturbations coming from stray light broadening in the last Rydberg excitation process.
Identification and estimation of non-Gaussian structural vector autoregressions
DEFF Research Database (Denmark)
Lanne, Markku; Meitz, Mika; Saikkonen, Pentti
Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are typically imposed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non......, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions....
Properties of Orthogonal Gaussian-Hermite Moments and Their Applications
Directory of Open Access Journals (Sweden)
Jun Shen
2005-03-01
Full Text Available Moments are widely used in pattern recognition, image processing, and computer vision and multiresolution analysis. In this paper, we first point out some properties of the orthogonal Gaussian-Hermite moments, and propose a new method to detect the moving objects by using the orthogonal Gaussian-Hermite moments. The experiment results are reported, which show the good performance of our method.
Gaussian Filtering with Tapered Liquid Crystal Photonic Bandgap Fibers
DEFF Research Database (Denmark)
Scolari, Lara; Alkeskjold, Thomas Tanggaard; Bjarklev, Anders Overgaard
2006-01-01
We present a device based on a tapered Liquid Crystal Photonic Bandgap Fiber that allows active all-in-fiber filtering. The resulting Photonic Bandgap Fiber device provides a Gaussian filter covering the wavelength range 1200-1600 nm......We present a device based on a tapered Liquid Crystal Photonic Bandgap Fiber that allows active all-in-fiber filtering. The resulting Photonic Bandgap Fiber device provides a Gaussian filter covering the wavelength range 1200-1600 nm...
Non-Gaussianity and Excursion Set Theory: Halo Bias
Energy Technology Data Exchange (ETDEWEB)
Adshead, Peter [Enrico Fermi Institute, Univ. of Chicago, IL (United States); Baxter, Eric J. [Univ. of Chicago, Chicago, IL (United States); Dodelson, Scott [Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lidz, Adam [Univ. of Pennsylvania, Philadelphia, PA (United States)
2012-09-01
We study the impact of primordial non-Gaussianity generated during inflation on the bias of halos using excursion set theory. We recapture the familiar result that the bias scales as $k^{-2}$ on large scales for local type non-Gaussianity but explicitly identify the approximations that go into this conclusion and the corrections to it. We solve the more complicated problem of non-spherical halos, for which the collapse threshold is scale dependent.
Ultrasonic scanner for radial and flat panels
Spencer, R. L.; Hill, E. K. (Inventor)
1973-01-01
An ultrasonic scanning mechanism is described that scans panels of honeycomb construction or with welded seams. It incorporates a device which by simple adjustment is adapted to scan either a flat panel or a radial panel. The supporting structure takes the form of a pair of spaced rails. An immersion tank is positioned between the rails and below their level. A work holder is mounted in the tank and is adapted to hold the flat or radial panel. A traveling bridge is movable along the rails and a carriage is mounted on the bridge.
Reble, a radially converging electron beam accelerator
International Nuclear Information System (INIS)
Ramirez, J.J.; Prestwich, K.R.
1976-01-01
The Reble accelerator at Sandia Laboratories is described. This accelerator was developed to provide an experimental source for studying the relevant diode physics, beam propagation, beam energy deposition in a gas using a radially converging e-beam. The nominal parameters for Reble are 1 MV, 200 kA, 20 ns e-beam pulse. The anode and cathode are concentric cylinders with the anode as the inner cylinder. The radial beam can be propagated through the thin foil anode into the laser gas volume. The design and performance of the various components of the accelerator are presented
Radial Plasma Flow Switch ^=A7
Terry, R. E.; Thornhill, J. W.
1996-11-01
A radial plasma flow switch configuration for use with longer quarter cycle time Marx bank drivers is characterized by 2D MHD calculations (MACH 2). A primary plasma armature implodes radially into a trap to establish the conduction phase. A secondary armature born from this plasma then commutes current to the load region at an Alfven speed characteristic of the mass splitting between the trap and the output port. The efficiency of current and energy transfer to simple inductive loads and plasma radiation source (PRS) loads is examined with respect to different models of anomalous resistivity and several geometries for controlling the motion of the primary plasma armature.
Radial velocity observations of VB10
Deshpande, R.; Martin, E.; Zapatero Osorio, M. R.; Del Burgo, C.; Rodler, F.; Montgomery, M. M.
2011-07-01
VB 10 is the smallest star known to harbor a planet according to the recent astrometric study of Pravdo & Shaklan [1]. Here we present near-infrared (J-band) radial velocity of VB 10 performed from high resolution (R~20,000) spectroscopy (NIRSPEC/KECK II). Our results [2] suggest radial velocity variability with amplitude of ~1 km/s, a result that is consistent with the presence of a massive planet companion around VB10 as found via long-term astrometric monitoring of the star by Pravdo & Shaklan. Employing an entirely different technique we verify the results of Pravdo & Shaklan.
Non-Gaussianity from inflation: theory and observations
Bartolo, N.; Komatsu, E.; Matarrese, S.; Riotto, A.
2004-11-01
This is a review of models of inflation and of their predictions for the primordial non-Gaussianity in the density perturbations which are thought to be at the origin of structures in the Universe. Non-Gaussianity emerges as a key observable to discriminate among competing scenarios for the generation of cosmological perturbations and is one of the primary targets of present and future Cosmic Microwave Background satellite missions. We give a detailed presentation of the state-of-the-art of the subject of non-Gaussianity, both from the theoretical and the observational point of view, and provide all the tools necessary to compute at second order in perturbation theory the level of non-Gaussianity in any model of cosmological perturbations. We discuss the new wave of models of inflation, which are firmly rooted in modern particle physics theory and predict a significant amount of non-Gaussianity. The review is addressed to both astrophysicists and particle physicists and contains useful tables which summarize the theoretical and observational results regarding non-Gaussianity.
Superstatistical generalised Langevin equation: non-Gaussian viscoelastic anomalous diffusion
Ślęzak, Jakub; Metzler, Ralf; Magdziarz, Marcin
2018-02-01
Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations.
Automatic image equalization and contrast enhancement using Gaussian mixture modeling.
Celik, Turgay; Tjahjadi, Tardi
2012-01-01
In this paper, we propose an adaptive image equalization algorithm that automatically enhances the contrast in an input image. The algorithm uses the Gaussian mixture model to model the image gray-level distribution, and the intersection points of the Gaussian components in the model are used to partition the dynamic range of the image into input gray-level intervals. The contrast equalized image is generated by transforming the pixels' gray levels in each input interval to the appropriate output gray-level interval according to the dominant Gaussian component and the cumulative distribution function of the input interval. To take account of the hypothesis that homogeneous regions in the image represent homogeneous silences (or set of Gaussian components) in the image histogram, the Gaussian components with small variances are weighted with smaller values than the Gaussian components with larger variances, and the gray-level distribution is also used to weight the components in the mapping of the input interval to the output interval. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several state-of-the-art algorithms. Unlike the other algorithms, the proposed algorithm is free of parameter setting for a given dynamic range of the enhanced image and can be applied to a wide range of image types.
Current inversion induced by colored non-Gaussian noise
International Nuclear Information System (INIS)
Bag, Bidhan Chandra; Hu, Chin-Kung
2009-01-01
We study a stochastic process driven by colored non-Gaussian noises. For the flashing ratchet model we find that there is a current inversion in the variation of the current with the half-cycle period which accounts for the potential on–off operation. The current inversion almost disappears if one switches from non-Gaussian (NG) to Gaussian (G) noise. We also find that at low value of the asymmetry parameter of the potential the mobility controlled current is more negative for NG noise as compared to G noise. But at large magnitude of the parameter the diffusion controlled positive current is higher for the former than for the latter. On increasing the noise correlation time (τ), keeping the noise strength fixed, the mean velocity of a particle first increases and then decreases after passing through a maximum if the noise is non-Gaussian. For Gaussian noise, the current monotonically decreases. The current increases with the noise parameter p, 0< p<5/3, which is 1 for Gaussian noise
Pye, Cory C.; Mercer, Colin J.
2012-01-01
The symbolic algebra program Maple and the spreadsheet Microsoft Excel were used in an attempt to reproduce the Gaussian fits to a Slater-type orbital, required to construct the popular STO-NG basis sets. The successes and pitfalls encountered in such an approach are chronicled. (Contains 1 table and 3 figures.)
Energy Technology Data Exchange (ETDEWEB)
Firouzjaei, Ali Shekari; Shokri, Babak [Department of Physics, Shahid Beheshti University, G.C., Evin, Tehran 19839-63113 (Iran, Islamic Republic of)
2016-06-15
In the present paper, we study the wakes known as the donut wake which is generated by Laguerre-Gauss (LG) laser pulses. Effects of the special spatial profile of a LG pulse on the radial and longitudinal wakefields are presented via an analytical model in a weakly non-linear regime in two dimensions. Different aspects of the donut-shaped wakefields have been analyzed and compared with Gaussian-driven wakes. There is also some discussion about the accelerating-focusing phase of the donut wake. Variations of longitudinal and radial wakes with laser amplitude, pulse length, and pulse spot size have been presented and discussed. Finally, we present the optimum pulse duration for such wakes.
One-year results of cemented bipolar radial head prostheses for comminuted radial head fractures
Directory of Open Access Journals (Sweden)
Laun, Reinhold
2015-12-01
Full Text Available Introduction: Comminuted radial head fractures (Mason type III continue to pose a challenge to orthopedic surgeons. When internal fixation is not possible, radial head arthroplasty has been advocated as the treatment of choice. The purpose of this retrospective study was to evaluate clinical and radiological short-term results of patients with Mason type III radial head fractures treated with a cemented bipolar radial prosthesis. Methods: Twelve patients received cemented bipolar radial head hemiarthroplasty for comminuted radial head fractures. In all patients a CT scan was obtained prior to surgical treatment to assess all associated injuries. Postoperatively an early motion protocol was applied. All patients were evaluated clinically and radiologically at an average of 12.7 months.Results: According to the Mayo Modified Wrist Score, the Mayo Elbow Performance Score, the functional rating index of Broberg and Morrey, and the DASH Score good to excellent results were obtained. Grip strength and range of motion were almost at the level of the unaffected contralateral side. Patient satisfaction was high, no instability or signs of loosening of the implant, and only mild signs of osteoarthritis were seen.Conclusion: Overall good to excellent short-term results for primary arthroplasty for comminuted radial head fractures were observed. These encouraging results warrant the conduction of further studies with long-term follow-up and more cases to see if these short-term results can be maintained over time.
Scaled unscented transform Gaussian sum filter: Theory and application
Luo, Xiaodong
2010-05-01
In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. We introduce a framework, called the scaled unscented transform Gaussian sum filter (SUT-GSF), which combines two ideas: the scaled unscented Kalman filter (SUKF) based on the concept of scaled unscented transform (SUT) (Julier and Uhlmann (2004) [16]), and the Gaussian mixture model (GMM). The SUT is used to approximate the mean and covariance of a Gaussian random variable which is transformed by a nonlinear function, while the GMM is adopted to approximate the probability density function (pdf) of a random variable through a set of Gaussian distributions. With these two tools, a framework can be set up to assimilate nonlinear systems in a recursive way. Within this framework, one can treat a nonlinear stochastic system as a mixture model of a set of sub-systems, each of which takes the form of a nonlinear system driven by a known Gaussian random process. Then, for each sub-system, one applies the SUKF to estimate the mean and covariance of the underlying Gaussian random variable transformed by the nonlinear governing equations of the sub-system. Incorporating the estimations of the sub-systems into the GMM gives an explicit (approximate) form of the pdf, which can be regarded as a "complete" solution to the state estimation problem, as all of the statistical information of interest can be obtained from the explicit form of the pdf (Arulampalam et al. (2002) [7]). In applications, a potential problem of a Gaussian sum filter is that the number of Gaussian distributions may increase very rapidly. To this end, we also propose an auxiliary algorithm to conduct pdf re-approximation so that the number of Gaussian distributions can be reduced. With the auxiliary algorithm, in principle the SUT-GSF can achieve almost the same computational speed as the SUKF if the SUT-GSF is implemented in parallel. As an example, we will use the SUT-GSF to assimilate a 40-dimensional system due to
The extraordinary spectral properties of radially periodic ...
Indian Academy of Sciences (India)
R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22
coupling constant, such that a rather precise picture of the spectrum of radially periodic. Schrödinger operators has now been obtained. Keywords. Schr ödinger operator; self-adjointness; embedded eigenvalue; exponential decay; dense point spectrum. 0. Introduction and preliminaries. The Schrödinger equation i. ∂. ∂t.
Revealing the radial modes in vortex beams
CSIR Research Space (South Africa)
Sephton, Bereneice C
2016-10-01
Full Text Available is neglected in this generation approach. Here, we show that a consequence of this is that vortex beams carry very little energy in the desired zeroth radial order, as little as only a few percent of the incident power. We demonstrate this experimentally...
Radial interchange motions of plasma filaments
DEFF Research Database (Denmark)
Garcia, O.E.; Bian, N.H.; Fundamenski, W.
2006-01-01
on a biperiodic domain perpendicular to the magnetic field. It is demonstrated that a blob-like plasma structure develops dipolar vorticity and electrostatic potential fields, resulting in rapid radial acceleration and formation of a steep front and a trailing wake. While the dynamical evolution strongly depends...
Three versus four radial keratotomy incisions.
Melles, G R; Go, A T; Beekhuis, W H; van Rij, G; Binder, P S
1992-01-01
Radial keratotomy (RK) is currently performed with four or eight semi-radial incisions. To evaluate the effect of a theoretically more stable three-incision RK pattern, centripetal incisions were made in 16 human donor eyes (eight pairs), using a double-edged diamond blade set to 90% of central pachymetry and a 3.5 mm optical clear zone. Intraocular pressure was maintained at 15 mm Hg during surgery and while keratometry readings were made. One randomly selected eye of each pair had three radial incisions made at 12, 4 and 8 o'clock; the other eye had four radial incisions at 12, 3, 6, and 9 o'clock. Corneal flattening was 6.08 diopters (D) with four incisions and 4.84 D with three incisions (P less than .05). Astigmatism increased 0.44 D and 0.69 D, respectively (P greater than .1). Histologically measured mean incision depth (77.4%) did not differ significantly between the groups (P greater than .1). This study shows that 80% of the effect of a four-incision RK pattern can be obtained with a theoretically more stable three-incision pattern.
Computing modal dispersion characteristics of radially Asymmetric ...
African Journals Online (AJOL)
We developed a matrix theory that applies to with non-circular/circular but concentric layers fibers. And we compute the dispersion characteristics of radially unconventional fiber, known as Asymmetric Bragg fiber. An attempt has been made to determine how the modal characteristics change as circular Bragg fiber is ...
Statistical-mechanics analysis of Gaussian labeled-unlabeled classification problems
International Nuclear Information System (INIS)
Tanaka, Toshiyuki
2013-01-01
The labeled-unlabeled classification problem in semi-supervised learning is studied via statistical-mechanics approach. We analytically investigate performance of a learner with an equal-weight mixture of two symmetrically-located Gaussians, performing posterior mean estimation of the parameter vector on the basis of a dataset consisting of labeled and unlabeled data generated from the same probability model as that assumed by the learner. Under the assumption of replica symmetry, we have analytically obtained a set of saddle-point equations, which allows us to numerically evaluate performance of the learner. On the basis of the analytical result we have observed interesting phenomena, in particular the coexistence of good and bad solutions, which may happen when the number of unlabeled data is relatively large compared with that of labeled data
Microinstabilities in a radially contracting inhomogeneous cylindrical plasma slab
International Nuclear Information System (INIS)
Deutsch, R.; Kaeppeler, H.J.
1980-07-01
In order to study the development of microinstabilities in a collapsing cylindrical plasma sheath, corresponding to the situations in a z-pinch or a plasma focus, the dispersion relation for electromagnetic perturbations is derived with the aid of a newly established slab-model for an inhomogeneous, radially contracting plasma. In contrast to previously used slab-models, the orientation of the electric field is in direction of the cylinder axis and the azimuthal magnetic field is induced by the current flowing through the cylindrical plasma slab. The Vlasov equation is used together with the Krook collision term in order to include the influence of collisions. The results of this theory presented in this report will be used to calculate the growth of drift instabilities in the compression phase of a plasma focus, and shall serve as a basis for further development of a more general dispersion relation including runaway-effects. (orig.)
LENUS (Irish Health Repository)
Pate, G
2011-10-01
A survey was conducted of medication administered during radial artery cannulation for coronary angiography in 2009 in Ireland; responses were obtained for 15 of 20 centres, in 5 of which no radial access procedures were undertaken. All 10 (100%) centres which provided data used heparin and one or more anti-spasmodics; verapamil in 9 (90%), nitrate in 1 (10%), both in 2 (20%). There were significant variations in the doses used. Further work needs to be done to determine the optimum cocktail to prevent radial artery injury following coronary angiography.
Pirmoradi, Zhila; Haji Hajikolaei, Kambiz; Wang, G. Gary
2015-10-01
Product family design is cost-efficient for achieving the best trade-off between commonalization and diversification. However, for computationally intensive design functions which are viewed as black boxes, the family design would be challenging. A two-stage platform configuration method with generalized commonality is proposed for a scale-based family with unknown platform configuration. Unconventional sensitivity analysis and information on variation in the individual variants' optimal design are used for platform configuration design. Metamodelling is employed to provide the sensitivity and variable correlation information, leading to significant savings in function calls. A family of universal electric motors is designed for product performance and the efficiency of this method is studied. The impact of the employed parameters is also analysed. Then, the proposed method is modified for obtaining higher commonality. The proposed method is shown to yield design solutions with better objective function values, allowable performance loss and higher commonality than the previously developed methods in the literature.
Hybrid model decomposition of speech and noise in a radial basis function neural model framework
DEFF Research Database (Denmark)
Sørensen, Helge Bjarup Dissing; Hartmann, Uwe
1994-01-01
The aim of the paper is to focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the presence of additive car noise. The technique...
Falat, Lukas; Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia
Directory of Open Access Journals (Sweden)
Pragya Bagwari
2017-08-01
Full Text Available Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker’s capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input.
Lukas Falat; Dusan Marcek; Maria Durisova
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the sug...
Havinga, Gosse Tjipke; van den Boogaard, Antonius H.; Klaseboer, G.
The performance of the sequential metamodel based optimization procedure depends strongly on the chosen building blocks for the algorithm, such as the used metamodeling method and sequential improvement criterion. In this study, the effect of these choices on the efficiency of the robust
Directory of Open Access Journals (Sweden)
Lukas Falat
2016-01-01
Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Bos, F.M.
2010-01-01
Both biological and engineering scientist have always been intrigued by the flight of insects and birds. For a long time, the aerodynamic mechanism behind flapping insect flight was a complete mystery. Recently, several experimental and numerical flow visualisations were performed to investigate the
Learning Errors by Radial Basis Function Neural Networks and Regularization Networks
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Vidnerová, Petra
2009-01-01
Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf
Maneuvering Target Detection Based on JRC System in Gaussian and Non-Gaussian Clutter
Directory of Open Access Journals (Sweden)
Yu Yao
2015-01-01
Full Text Available Aimed at the problem of detecting maneuvering targets in the Gaussian and sea clutter environments and based on the established motion state model, this paper proposed a new scheme that uses a joint radar-communication (JRC system with Kalman filter to accurately detect the target with the generalized likelihood ratio test (GLRT theory and a constant false alarm rate (CFAR based threshold. Also, the theoretical threshold and probability function of GLRT target detection based on CFAR were given. Moreover, target detection probability of the new JRC system in Weibull and K distribution clutter is deduced. In addition to theoretical considerations, simulations and measurement results of the new JRC systems demonstrate excellent detection performance for maneuvering targets in the Weibull and K distribution channel.
Dentz, Marco; Kang, Peter K.; Le Borgne, Tanguy
2015-04-01
Solute transport in heterogeneous porous media is characterized by features that do not conform to advection-dispersion models characterized by equivalent transport parameters. This has been observed in tracer experiments under forced and natural flow conditions. Key questions are (i) how non-Fickian solute transport can be quantified under radial flow conditions, and (ii) how different heterogeneity sources of non-Fickian behavior manifest in non-Fickian radial transport models. In order to approach these questions, we develop a radial continuous time random walk (CTRW) formulation for the quantification and interpretation of non-Fickian solute transport under forced flow conditions and different heterogeneity scenarios. The derived radial CTRW approaches model anomalous behavior induced by heterogeneous flow distributions and mobile-immobile mass transfer processes (matrix diffusion). We start by establishing a general CTRW framework in radial coordinates on the basis of the random walk equations for radial particle positions and times. The evolution of solute concentration is governed by a non-local radial advection-dispersion equation. Unlike in CTRWs for uniform flow scenarios, particle transition times here depend on the radial particle position, which renders the CTRW non-stationary. We then derive radial CTRW implementations that (i) emulate non-local radial transport due to heterogeneous advection, (ii) model multirate mass transfer (MRMT) between mobile and immobile continua, and (iii) quantify both heterogeneous advection in a mobile and mass transfer between mobile and immobile regions. We analyze the transport signatures for the distinct CTRW models in terms of solute breakthrough curves and their dependence on the heterogeneity scenarios.
Directory of Open Access Journals (Sweden)
Diaa M. Uliyan
2016-07-01
Full Text Available Region duplication forgery where a part of the image itself is copied and pasted onto a different part of the same image grid is becoming more popular in image manipulation. The forgers often apply geometric transformations such as rotation and scaling operations to make the forgery imperceptible. In this study, an image region duplication forgery detection algorithm is proposed based on the angular radial partitioning and Harris key-points. Two standard databases have been used: image data manipulation and MICC-F220 (Media Integration and Communication Center– of the University of Florence for experimentation. Experiment results demonstrate that the proposed technique can detect rotated regions in multiples of 30 degrees and can detect region duplication with different scaling factors from 0.8, to 1.2. More experimental results are presented to confirm the effectiveness of detecting region duplication that has undergone other changes, such as Gaussian noise, and JPEG compression.
Ford, Chelcy R; Goranson, Carol E; Mitchell, Robert J; Will, Rodney E; Teskey, Robert O
2004-09-01
We monitored the radial distribution of sap flux density (v; g H2O m(-2) s(-1)) in the sapwood of six plantation-grown Pinus taeda L. trees during wet and dry soil periods. Mean basal diameter of the 32-year-old trees was 33.3 cm. For all trees, the radial distribution of sap flow in the base of the stem (i.e., radial profile) was Gaussian in shape. Sap flow occurred maximally in the outer 4 cm of sapwood, comprising 50-60% of total stem flow (F), and decreased toward the center, with the innermost 4 cm of sapwood (11-15 cm) comprising less than 10% of F. The percent of flow occurring in the outer 4 cm of sapwood was stable with time (average CV 40%). Diurnally, the radial profile changed predictably with time and with total stem flow. Seasonally, the radial profile became less steep as the soil water content (theta) declined from 0.38 to 0.21. Throughout the season, daytime sap flow also decreased as theta decreased; however, nighttime sap flow (an estimate of stored water use) remained relatively constant. As a result, the percentage of stored water use increased as theta declined. Time series analysis of 15-min values of F, theta, photosynthetically active radiation (PAR) and vapor pressure deficit (D) showed that F lagged behind D by 0-15 min and behind PAR by 15-30 min. Diurnally, the relationship between F and D was much stronger than the relationship between F and PAR, whereas no relationship was found between F and theta. An autoregressive moving average (ARIMA) model estimated that 97% of the variability in F could be predicted by D alone. Although total sap flow in all trees responded similarly to D, we show that the radial distribution of sap flow comprising total flow could change temporally, both on daily and seasonal scales.
Gaussian curvature on hyperelliptic Riemann surfaces
Indian Academy of Sciences (India)
basis of holomorphic 1-forms on C and let H = (hij ) g i,j=1 be a positive definite Her- mitian matrix. It is well ..... Denote by F (z) the complex vector of derivatives of the entries of F (z). Let H = (hij ) g i,j=1 ..... is easy to see that in polar coordinates the partial derivatives for K2 are. 1 α2 ·. ∂K2(r, θ ). ∂r. = 10r9 − 10r4 cos(5θ).
Bayesian electron density inference from JET lithium beam emission spectra using Gaussian processes
Kwak, Sehyun; Svensson, J.; Brix, M.; Ghim, Y.-C.; Contributors, JET
2017-03-01
A Bayesian model to infer edge electron density profiles is developed for the JET lithium beam emission spectroscopy (Li-BES) system, measuring Li I (2p-2s) line radiation using 26 channels with ∼1 cm spatial resolution and 10∼ 20 ms temporal resolution. The density profile is modelled using a Gaussian process prior, and the uncertainty of the density profile is calculated by a Markov Chain Monte Carlo (MCMC) scheme. From the spectra measured by the transmission grating spectrometer, the Li I line intensities are extracted, and modelled as a function of the plasma density by a multi-state model which describes the relevant processes between neutral lithium beam atoms and plasma particles. The spectral model fully takes into account interference filter and instrument effects, that are separately estimated, again using Gaussian processes. The line intensities are inferred based on a spectral model consistent with the measured spectra within their uncertainties, which includes photon statistics and electronic noise. Our newly developed method to infer JET edge electron density profiles has the following advantages in comparison to the conventional method: (i) providing full posterior distributions of edge density profiles, including their associated uncertainties, (ii) the available radial range for density profiles is increased to the full observation range (∼26 cm), (iii) an assumption of monotonic electron density profile is not necessary, (iv) the absolute calibration factor of the diagnostic system is automatically estimated overcoming the limitation of the conventional technique and allowing us to infer the electron density profiles for all pulses without preprocessing the data or an additional boundary condition, and (v) since the full spectrum is modelled, the procedure of modulating the beam to measure the background signal is only necessary for the case of overlapping of the Li I line with impurity lines.
Compensation of Gaussian curvature in developable cones is local
Wang, Jin W.; Witten, Thomas A.
2009-10-01
We use the angular deficit scheme [V. Borrelli, F. Cazals, and J.-M. Morvan, Comput. Aided Geom. Des. 20, 319 (2003)] to determine the distribution of Gaussian curvature in developable cones (d-cones) [E. Cerda, S. Chaieb, F. Melo, and L. Mahadevan, Nature (London) 401, 46 (1999)] numerically. These d-cones are formed by pushing a thin elastic sheet into a circular container. Negative Gaussian curvatures are identified at the rim where the sheet touches the container. Around the rim there are two narrow bands with positive Gaussian curvatures. The integral of the (negative) Gaussian curvature near the rim is almost completely compensated by that of the two adjacent bands. This suggests that the Gauss-Bonnet theorem which constrains the integral of Gaussian curvature globally does not explain the spontaneous curvature cancellation phenomenon [T. Liang and T. A. Witten, Phys. Rev. E 73, 046604 (2006)]. The locality of the compensation seems to increase for decreasing d-cone thickness. The angular deficit scheme also provides a way to confirm the curvature cancellation phenomenon.
Gaussianization for fast and accurate inference from cosmological data
Schuhmann, Robert L.; Joachimi, Benjamin; Peiris, Hiranya V.
2016-06-01
We present a method to transform multivariate unimodal non-Gaussian posterior probability densities into approximately Gaussian ones via non-linear mappings, such as Box-Cox transformations and generalizations thereof. This permits an analytical reconstruction of the posterior from a point sample, like a Markov chain, and simplifies the subsequent joint analysis with other experiments. This way, a multivariate posterior density can be reported efficiently, by compressing the information contained in Markov Chain Monte Carlo samples. Further, the model evidence integral (I.e. the marginal likelihood) can be computed analytically. This method is analogous to the search for normal parameters in the cosmic microwave background, but is more general. The search for the optimally Gaussianizing transformation is performed computationally through a maximum-likelihood formalism; its quality can be judged by how well the credible regions of the posterior are reproduced. We demonstrate that our method outperforms kernel density estimates in this objective. Further, we select marginal posterior samples from Planck data with several distinct strongly non-Gaussian features, and verify the reproduction of the marginal contours. To demonstrate evidence computation, we Gaussianize the joint distribution of data from weak lensing and baryon acoustic oscillations, for different cosmological models, and find a preference for flat Λcold dark matter. Comparing to values computed with the Savage-Dickey density ratio, and Population Monte Carlo, we find good agreement of our method within the spread of the other two.
Generation of Quasi-Gaussian Pulses Based on Correlation Techniques
Directory of Open Access Journals (Sweden)
POHOATA, S.
2012-02-01
Full Text Available The Gaussian pulses have been mostly used within communications, where some applications can be emphasized: mobile telephony (GSM, where GMSK signals are used, as well as the UWB communications, where short-period pulses based on Gaussian waveform are generated. Since the Gaussian function signifies a theoretical concept, which cannot be accomplished from the physical point of view, this should be expressed by using various functions, able to determine physical implementations. New techniques of generating the Gaussian pulse responses of good precision are approached, proposed and researched in this paper. The second and third order derivatives with regard to the Gaussian pulse response are accurately generated. The third order derivates is composed of four individual rectangular pulses of fixed amplitudes, being easily to be generated by standard techniques. In order to generate pulses able to satisfy the spectral mask requirements, an adequate filter is necessary to be applied. This paper emphasizes a comparative analysis based on the relative error and the energy spectra of the proposed pulses.
Maximal trace distance between isoenergetic bosonic Gaussian states
Volkoff, T. J.
2017-12-01
We locate the set of pairs (ρ1, ρ2) of Gaussian states of a single mode electromagnetic field that exhibit maximal trace distance subject to the energy constraint ⟨a†a⟩ ρ1=⟨a†a⟩ ρ2=E . Any such pair allows to achieve the minimum possible error in the task of binary distinguishability of two single mode, isoenergetic Gaussian quantum signals. In particular, we show that the logarithm of the minimal error probability for distinguishing two maximally trace distant, isoenergetic Gaussian states scales as -E2, less than the achievable scaling of the minimal error probability for distinguishing, e.g., a pair of isoenergetic Heisenberg-Weyl coherent states with energy E or a pair of isoenergetic quadrature squeezed states with energy E. For the case of a field consisting of M > 1 modes, we locate the set of pairs of maximally trace distant isoenergetic, isocovariant Gaussian states. These results have basic applications in the theory of continuous variable quantum communications with Gaussian states of light.
Consistency relations for sharp inflationary non-Gaussian features
International Nuclear Information System (INIS)
Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris; Soto, Alex
2016-01-01
If cosmic inflation suffered tiny time-dependent deviations from the slow-roll regime, these would induce the existence of small scale-dependent features imprinted in the primordial spectra, with their shapes and sizes revealing information about the physics that produced them. Small sharp features could be suppressed at the level of the two-point correlation function, making them undetectable in the power spectrum, but could be amplified at the level of the three-point correlation function, offering us a window of opportunity to uncover them in the non-Gaussian bispectrum. In this article, we show that sharp features may be analyzed using only data coming from the three point correlation function parametrizing primordial non-Gaussianity. More precisely, we show that if features appear in a particular non-Gaussian triangle configuration (e.g. equilateral, folded, squeezed), these must reappear in every other configuration according to a specific relation allowing us to correlate features across the non-Gaussian bispectrum. As a result, we offer a method to study scale-dependent features generated during inflation that depends only on data coming from measurements of non-Gaussianity, allowing us to omit data from the power spectrum.
Research on Radial Motion Characteristic of the Cropping Hammer in Radial-Forging Cropping Method
Directory of Open Access Journals (Sweden)
Lijun Zhang
2015-01-01
Full Text Available The radial loading form applied to the bar is very important for reducing or avoiding the impact and vibration of the radial-forging cropping system and obtaining the high-quality cross section. A new radial stroke loading curve of the cropping hammer based on the cycloid form is proposed and the dynamic model of radial stroke loading mechanism is built. With the aim of obtaining the equivalent stiffness of the bar with V-shaped notch, which is a key parameter affecting the dynamic characteristic of radial stroke loading mechanism, the analytic model of the bar is built and the simulation experiments are designed by means of the orthogonal test method. The analytical results show that the diameter of the bar has the significant influence on the equivalent stiffness of the bar. Furthermore, the equivalent stiffness of the bar with V-shaped notch can be directly calculated according to the equivalent stiffness of smooth bar when h/d0.15. By using the cycloid stroke curve, the cropping experimental results for 45 steel bars and 20 steel bars show that the radial impact and vibration of the cropping system are decreased and the bar cross-section qualities have been significantly improved.
Precise Near-Infrared Radial Velocities
Plavchan, Peter; Gao, Peter; Gagne, Jonathan; Furlan, Elise; Brinkworth, Carolyn; Bottom, Michael; Tanner, Angelle; Anglada-Escude, Guillem; White, Russel; Davison, Cassy; Mills, Sean; Beichman, Chas; Johnson, John Asher; Ciardi, David; Wallace, Kent; Mennesson, Bertrand; Vasisht, Gautam; Prato, Lisa; Kane, Stephen; Crawford, Sam; Crawford, Tim; Sung, Keeyoon; Drouin, Brian; Lin, Sean; Leifer, Stephanie; Catanzarite, Joe; Henry, Todd; von Braun, Kaspar; Walp, Bernie; Geneser, Claire; Ogden, Nick; Stufflebeam, Andrew; Pohl, Garrett; Regan, Joe
2016-01-01
We present the results of two 2.3 μm near-infrared (NIR) radial velocity (RV) surveys to detect exoplanets around 36 nearby and young M dwarfs. We use the CSHELL spectrograph (R ~ 46,000) at the NASA InfraRed Telescope Facility (IRTF), combined with an isotopic methane absorption gas cell for common optical path relative wavelength calibration. We have developed a sophisticated RV forward modeling code that accounts for fringing and other instrumental artifacts present in the spectra. With a spectral grasp of only 5 nm, we are able to reach long-term radial velocity dispersions of ~20-30 m s-1 on our survey targets.
WWER radial reflector modeling by diffusion codes
International Nuclear Information System (INIS)
Petkov, P. T.; Mittag, S.
2005-01-01
The two commonly used approaches to describe the WWER radial reflectors in diffusion codes, by albedo on the core-reflector boundary and by a ring of diffusive assembly size nodes, are discussed. The advantages and disadvantages of the first approach are presented first, then the Koebke's equivalence theory is outlined and its implementation for the WWER radial reflectors is discussed. Results for the WWER-1000 reactor are presented. Then the boundary conditions on the outer reflector boundary are discussed. The possibility to divide the library into fuel assembly and reflector parts and to generate each library by a separate code package is discussed. Finally, the homogenization errors for rodded assemblies are presented and discussed (Author)
Douma, M.; Ligierko, G.; Angelov, I.
2008-10-01
The need for information has increased exponentially over the past decades. The current systems for constructing, exploring, classifying, organizing, and searching information face the growing challenge of enabling their users to operate efficiently and intuitively in knowledge-heavy environments. This paper presents SpicyNodes, an advanced user interface for difficult interaction contexts. It is based on an underlying structure known as a radial map, which allows users to manipulate and interact in a natural manner with entities called nodes. This technology overcomes certain limitations of existing solutions and solves the problem of browsing complex sets of linked information. SpicyNodes is also an organic system that projects users into a living space, stimulating exploratory behavior and fostering creative thought. Our interactive radial layout is used for educational purposes and has the potential for numerous other applications.
Gaussian geometric discord in terms of Hellinger distance
Suciu, Serban; Isar, Aurelian
2015-12-01
In the framework of the theory of open systems based on completely positive quantum dynamical semigroups, we address the quantification of general non-classical correlations in Gaussian states of continuous variable systems from a geometric perspective. We give a description of the Gaussian geometric discord by using the Hellinger distance as a measure for quantum correlations between two non-interacting non-resonant bosonic modes embedded in a thermal environment. We evaluate the Gaussian geometric discord by taking two-mode squeezed thermal states as initial states of the system and show that it has finite values between 0 and 1 and that it decays asymptotically to zero in time under the effect of the thermal bath.
Continuous-variable quantum teleportation with non-Gaussian resources
International Nuclear Information System (INIS)
Dell'Anno, F.; De Siena, S.; Albano, L.; Illuminati, F.
2007-01-01
We investigate continuous variable quantum teleportation using non-Gaussian states of the radiation field as entangled resources. We compare the performance of different classes of degaussified resources, including two-mode photon-added and two-mode photon-subtracted squeezed states. We then introduce a class of two-mode squeezed Bell-like states with one-parameter dependence for optimization. These states interpolate between and include as subcases different classes of degaussified resources. We show that optimized squeezed Bell-like resources yield a remarkable improvement in the fidelity of teleportation both for coherent and nonclassical input states. The investigation reveals that the optimal non-Gaussian resources for continuous variable teleportation are those that most closely realize the simultaneous maximization of the content of entanglement, the degree of affinity with the two-mode squeezed vacuum, and the, suitably measured, amount of non-Gaussianity
Learning with Uncertainty - Gaussian Processes and Relevance Vector Machines
DEFF Research Database (Denmark)
Candela, Joaquin Quinonero
2004-01-01
This thesis is concerned with Gaussian Processes (GPs) and Relevance Vector Machines (RVMs), both of which are particular instances of probabilistic linear models. We look at both models from a Bayesian perspective, and are forced to adopt an approximate Bayesian treatment to learning for two....... Computational efficiency is obtained through sparseness: sparse linear models have a significant number of their weights set to zero. For the RVM, which we treat in Chap. 2, we show that it is precisely the particular choice of Bayesian approximation that enforces sparseness. Probabilistic models have...... family of approximations to Gaussian Processes, Reduced Rank Gaussian Processes (RRGPs), which take the form of nite extended linear models; we show that GPs are in general equivalent to in nite extended linear models. We also show that RRGPs result in degenerate GPs, which suffer, like RVMs...
Outage performance of cognitive radio systems with Improper Gaussian signaling
Amin, Osama
2015-06-14
Improper Gaussian signaling has proved its ability to improve the achievable rate of the systems that suffer from interference compared with proper Gaussian signaling. In this paper, we first study impact of improper Gaussian signaling on the performance of the cognitive radio system by analyzing the outage probability of both the primary user (PU) and the secondary user (SU). We derive exact expression of the SU outage probability and upper and lower bounds for the PU outage probability. Then, we design the SU signal by adjusting its transmitted power and the circularity coefficient to minimize the SU outage probability while maintaining a certain PU quality-of-service. Finally, we evaluate the proposed bounds and adaptive algorithms by numerical results.
Can an ensemble give anything more than Gaussian probabilities?
Directory of Open Access Journals (Sweden)
J. C. W. Denholm-Price
2003-01-01
Full Text Available Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Verification using Brier skill scores suggests that there can be statistically-significant skill in the ensemble forecast PDF compared with a Gaussian fit to the ensemble. The most significant increases in skill were achieved from bias-corrected, calibrated forecasts and for probability forecasts of thresholds that are located well inside the climatological limits at the examined sites. Forecast probabilities for more climatologically-extreme thresholds, where the verification more often lies within the tails or outside of the PDF, showed little difference in skill between the forecast PDF and the Gaussian forecast.
Standard sirens and dark sector with Gaussian process
Cai, Rong-Gen; Yang, Tao
2018-01-01
The gravitational waves from compact binary systems are viewed as a standard siren to probe the evolution of the universe. This paper summarizes the potential and ability to use the gravitational waves to constrain the cosmological parameters and the dark sector interaction in the Gaussian process methodology. After briefly introducing the method to reconstruct the dark sector interaction by the Gaussian process, the concept of standard sirens and the analysis of reconstructing the dark sector interaction with LISA are outlined. Furthermore, we estimate the constraint ability of the gravitational waves on cosmological parameters with ET. The numerical methods we use are Gaussian process and the Markov-Chain Monte-Carlo. Finally, we also forecast the improvements of the abilities to constrain the cosmological parameters with ET and LISA combined with the Planck.
How Gaussian competition leads to lumpy or uniform species distributions
DEFF Research Database (Denmark)
Pigolotti, Simone; Lopez, Cristóbal; Hernandez-Garcia, Emilio
2010-01-01
A central model in theoretical ecology considers the competition of a range of species for a broad spectrum of resources. Recent studies have shown that essentially two different outcomes are possible. Either the species surviving competition are more or less uniformly distributed over the resource...... spectrum, or their distribution is “lumped” (or “clumped”), consisting of clusters of species with similar resource use that are separated by gaps in resource space. Which of these outcomes will occur crucially depends on the competition kernel, which reflects the shape of the resource utilization pattern...... of the competing species. Most models considered in the literature assume a Gaussian competition kernel. This is unfortunate, since predictions based on such a Gaussian assumption are not robust. In fact, Gaussian kernels are a border case scenario, and slight deviations from this function can lead to either...
Folded resonant non-Gaussianity in general single field inflation
International Nuclear Information System (INIS)
Chen, Xingang
2010-01-01
We compute a novel type of large non-Gaussianity due to small periodic features in general single field inflationary models. We show that the non-Bunch-Davies vacuum component generated by features, although has a very small amplitude, can have significant impact on the non-Gaussianity. Three mechanisms are turned on simultaneously in such models, namely the resonant effect, non-Bunch-Davies vacuum and higher derivative kinetic terms, resulting in a bispectrum with distinctive shapes and running. The size can be equal to or larger than that previously found in each single mechanism. Our full results, including the resonant and folded resonant non-Gaussianities, give the leading order bispectra due to general periodic features in general single field inflation
Differential detection of Gaussian MSK in a mobile radio environment
Simon, M. K.; Wang, C. C.
1984-01-01
Minimum shift keying with Gaussian shaped transmit pulses is a strong candidate for a modulation technique that satisfies the stringent out-of-band radiated power requirements of the mobil radio application. Numerous studies and field experiments have been conducted by the Japanese on urban and suburban mobile radio channels with systems employing Gaussian minimum-shift keying (GMSK) transmission and differentially coherent reception. A comprehensive analytical treatment is presented of the performance of such systems emphasizing the important trade-offs among the various system design parameters such as transmit and receiver filter bandwidths and detection threshold level. It is shown that two-bit differential detection of GMSK is capable of offering far superior performance to the more conventional one-bit detection method both in the presence of an additive Gaussian noise background and Rician fading.
Gaussian white noise as a resource for work extraction.
Dechant, Andreas; Baule, Adrian; Sasa, Shin-Ichi
2017-03-01
We show that uncorrelated Gaussian noise can drive a system out of equilibrium and can serve as a resource from which work can be extracted. We consider an overdamped particle in a periodic potential with an internal degree of freedom and a state-dependent friction, coupled to an equilibrium bath. Applying additional Gaussian white noise drives the system into a nonequilibrium steady state and causes a finite current if the potential is spatially asymmetric. The model thus operates as a Brownian ratchet, whose current we calculate explicitly in three complementary limits. Since the particle current is driven solely by additive Gaussian white noise, this shows that the latter can potentially perform work against an external load. By comparing the extracted power to the energy injection due to the noise, we discuss the efficiency of such a ratchet.
Generation of correlated finite alphabet waveforms using gaussian random variables
Ahmed, Sajid
2016-01-13
Various examples of methods and systems are provided for generation of correlated finite alphabet waveforms using Gaussian random variables in, e.g., radar and communication applications. In one example, a method includes mapping an input signal comprising Gaussian random variables (RVs) onto finite-alphabet non-constant-envelope (FANCE) symbols using a predetermined mapping function, and transmitting FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The FANCE waveforms can be based upon the mapping of the Gaussian RVs onto the FANCE symbols. In another example, a system includes a memory unit that can store a plurality of digital bit streams corresponding to FANCE symbols and a front end unit that can transmit FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The system can include a processing unit that can encode the input signal and/or determine the mapping function.
Primordial black holes from inflation and non-Gaussianity
Franciolini, G.; Kehagias, A.; Matarrese, S.; Riotto, A.
2018-03-01
Primordial black holes may owe their origin to the small-scale enhancement of the comoving curvature perturbation generated during inflation. Their mass fraction at formation is markedly sensitive to possible non-Gaussianities in such large, but rare fluctuations. We discuss a path-integral formulation which provides the exact mass fraction of primordial black holes at formation in the presence of non-Gaussianity. Through a couple of classes of models, one based on single-field inflation and the other on spectator fields, we show that restricting to a Gaussian statistics may lead to severe inaccuracies in the estimate of the mass fraction as well as on the clustering properties of the primordial black holes.
Standard sirens and dark sector with Gaussian process*
Directory of Open Access Journals (Sweden)
Cai Rong-Gen
2018-01-01
Full Text Available The gravitational waves from compact binary systems are viewed as a standard siren to probe the evolution of the universe. This paper summarizes the potential and ability to use the gravitational waves to constrain the cosmological parameters and the dark sector interaction in the Gaussian process methodology. After briefly introducing the method to reconstruct the dark sector interaction by the Gaussian process, the concept of standard sirens and the analysis of reconstructing the dark sector interaction with LISA are outlined. Furthermore, we estimate the constraint ability of the gravitational waves on cosmological parameters with ET. The numerical methods we use are Gaussian process and the Markov-Chain Monte-Carlo. Finally, we also forecast the improvements of the abilities to constrain the cosmological parameters with ET and LISA combined with the Planck.
Self-similar Gaussian processes for modeling anomalous diffusion
Lim, S. C.; Muniandy, S. V.
2002-08-01
We study some Gaussian models for anomalous diffusion, which include the time-rescaled Brownian motion, two types of fractional Brownian motion, and models associated with fractional Brownian motion based on the generalized Langevin equation. Gaussian processes associated with these models satisfy the anomalous diffusion relation which requires the mean-square displacement to vary with tα, 0Brownian motion and time-rescaled Brownian motion all have the same probability distribution function, the Slepian theorem can be used to compare their first passage time distributions, which are different. Finally, in order to model anomalous diffusion with a variable exponent α(t) it is necessary to consider the multifractional extensions of these Gaussian processes.