Point Set Denoising Using Bootstrap-Based Radial Basis Function.

Directory of Open Access Journals (Sweden)

Khang Jie Liew

Full Text Available 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.

A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions

KAUST Repository

Fowkes, Jaroslav M.

2012-06-21

We present a branch and bound algorithm for the global optimization of a twice differentiable nonconvex objective function with a Lipschitz continuous Hessian over a compact, convex set. The algorithm is based on applying cubic regularisation techniques to the objective function within an overlapping branch and bound algorithm for convex constrained global optimization. Unlike other branch and bound algorithms, lower bounds are obtained via nonconvex underestimators of the function. For a numerical example, we apply the proposed branch and bound algorithm to radial basis function approximations. © 2012 Springer Science+Business Media, LLC.

Problem-Matched Basis Functions for Microstrip Coupled Slot Antennas based on Transmission Line Greens Functions

NARCIS (Netherlands)

Bruni, S.; Llombart Juan, N.; Neto, A.; Gerini, G.; Maci, S.

2004-01-01

A general algorithm for the analysis of microstrip coupled leaky wave slot antennas was discussed. The method was based on the construction of physically appealing entire domain Methods of Moments (MoM) basis function that allowed a consistent reduction of the number of unknowns and of total

Radial basis function neural networks with sequential learning MRAN and its applications

CERN Document Server

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

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

Science.gov (United States)

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

New Method for Mesh Moving Based on Radial Basis Function Interpolation

NARCIS (Netherlands)

De Boer, A.; Van der Schoot, M.S.; Bijl, H.

2006-01-01

A new point-by-point mesh movement algorithm is developed for the deformation of unstructured grids. The method is based on using radial basis function, RBFs, to interpolate the displacements of the boundary nodes to the whole flow mesh. A small system of equations has to be solved, only involving

Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

Science.gov (United States)

Seghouane, Abd-Krim; Iqbal, Asif

2017-09-01

Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

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.

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.

Exponential Convergence for Numerical Solution of Integral Equations Using Radial Basis Functions

Directory of Open Access Journals (Sweden)

Zakieh Avazzadeh

2014-01-01

Full Text Available We solve some different type of Urysohn integral equations by using the radial basis functions. These types include the linear and nonlinear Fredholm, Volterra, and mixed Volterra-Fredholm integral equations. Our main aim is to investigate the rate of convergence to solve these equations using the radial basis functions which have normic structure that utilize approximation in higher dimensions. Of course, the use of this method often leads to ill-posed systems. Thus we propose an algorithm to improve the results. Numerical results show that this method leads to the exponential convergence for solving integral equations as it was already confirmed for partial and ordinary differential equations.

Incorporating functional inter-relationships into protein function prediction algorithms

Directory of Open Access Journals (Sweden)

Kumar Vipin

2009-05-01

Full Text Available Abstract Background Functional classification schemes (e.g. the Gene Ontology that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches. Results We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the k-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of

Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks

Directory of Open Access Journals (Sweden)

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.

A CDMA multiuser detection algorithm on the basis of belief propagation

International Nuclear Information System (INIS)

Kabashima, Yoshiyuki

2003-01-01

An iterative algorithm for the multiuser detection problem that arises in code division multiple access (CDMA) systems is developed on the basis of Pearl's belief propagation (BP). We show that the BP-based algorithm exhibits nearly optimal performance in a practical time scale by utilizing the central limit theorem and self-averaging property appropriately, whereas direct application of BP to the detection problem is computationally difficult and far from practical. We further present close relationships of the proposed algorithm to the Thouless-Anderson-Palmer approach and replica analysis known in spin-glass research

New algorithm for tensor contractions on multi-core CPUs, GPUs, and accelerators enables CCSD and EOM-CCSD calculations with over 1000 basis functions on a single compute node.

Science.gov (United States)

Kaliman, Ilya A; Krylov, Anna I

2017-04-30

A new hardware-agnostic contraction algorithm for tensors of arbitrary symmetry and sparsity is presented. The algorithm is implemented as a stand-alone open-source code libxm. This code is also integrated with general tensor library libtensor and with the Q-Chem quantum-chemistry package. An overview of the algorithm, its implementation, and benchmarks are presented. Similarly to other tensor software, the algorithm exploits efficient matrix multiplication libraries and assumes that tensors are stored in a block-tensor form. The distinguishing features of the algorithm are: (i) efficient repackaging of the individual blocks into large matrices and back, which affords efficient graphics processing unit (GPU)-enabled calculations without modifications of higher-level codes; (ii) fully asynchronous data transfer between disk storage and fast memory. The algorithm enables canonical all-electron coupled-cluster and equation-of-motion coupled-cluster calculations with single and double substitutions (CCSD and EOM-CCSD) with over 1000 basis functions on a single quad-GPU machine. We show that the algorithm exhibits predicted theoretical scaling for canonical CCSD calculations, O(N 6 ), irrespective of the data size on disk. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Algorithmic transformation of multi-loop master integrals to a canonical basis with CANONICA

Science.gov (United States)

Meyer, Christoph

2018-01-01

The integration of differential equations of Feynman integrals can be greatly facilitated by using a canonical basis. This paper presents the Mathematica package CANONICA, which implements a recently developed algorithm to automatize the transformation to a canonical basis. This represents the first publicly available implementation suitable for differential equations depending on multiple scales. In addition to the presentation of the package, this paper extends the description of some aspects of the algorithm, including a proof of the uniqueness of canonical forms up to constant transformations.

Elementary functions algorithms and implementation

CERN Document Server

Muller, Jean-Michel

2016-01-01

This textbook presents the concepts and tools necessary to understand, build, and implement algorithms for computing elementary functions (e.g., logarithms, exponentials, and the trigonometric functions). Both hardware- and software-oriented algorithms are included, along with issues related to accurate floating-point implementation. This third edition has been updated and expanded to incorporate the most recent advances in the field, new elementary function algorithms, and function software. After a preliminary chapter that briefly introduces some fundamental concepts of computer arithmetic, such as floating-point arithmetic and redundant number systems, the text is divided into three main parts. Part I considers the computation of elementary functions using algorithms based on polynomial or rational approximations and using table-based methods; the final chapter in this section deals with basic principles of multiple-precision arithmetic. Part II is devoted to a presentation of “shift-and-add” algorithm...

Quantum algorithms for testing Boolean functions

Directory of Open Access Journals (Sweden)

Erika Andersson

2010-06-01

Full Text Available We discuss quantum algorithms, based on the Bernstein-Vazirani algorithm, for finding which variables a Boolean function depends on. There are 2^n possible linear Boolean functions of n variables; given a linear Boolean function, the Bernstein-Vazirani quantum algorithm can deterministically identify which one of these Boolean functions we are given using just one single function query. The same quantum algorithm can also be used to learn which input variables other types of Boolean functions depend on, with a success probability that depends on the form of the Boolean function that is tested, but does not depend on the total number of input variables. We also outline a procedure to futher amplify the success probability, based on another quantum algorithm, the Grover search.

An algorithm to compute the canonical basis of an irreducible Uq(g)-module

OpenAIRE

de Graaf, W. A.

2002-01-01

An algorithm is described to compute the canonical basis of an irreducible module over a quantized enveloping algebra of a finite-dimensional semisimple Lie algebra. The algorithm works for modules that are constructed as a submodule of a tensor product of modules with known canonical bases.

Calculating vibrational spectra with sum of product basis functions without storing full-dimensional vectors or matrices.

Science.gov (United States)

Leclerc, Arnaud; Carrington, Tucker

2014-05-07

We propose an iterative method for computing vibrational spectra that significantly reduces the memory cost of calculations. It uses a direct product primitive basis, but does not require storing vectors with as many components as there are product basis functions. Wavefunctions are represented in a basis each of whose functions is a sum of products (SOP) and the factorizable structure of the Hamiltonian is exploited. If the factors of the SOP basis functions are properly chosen, wavefunctions are linear combinations of a small number of SOP basis functions. The SOP basis functions are generated using a shifted block power method. The factors are refined with a rank reduction algorithm to cap the number of terms in a SOP basis function. The ideas are tested on a 20-D model Hamiltonian and a realistic CH3CN (12 dimensional) potential. For the 20-D problem, to use a standard direct product iterative approach one would need to store vectors with about 10(20) components and would hence require about 8 × 10(11) GB. With the approach of this paper only 1 GB of memory is necessary. Results for CH3CN agree well with those of a previous calculation on the same potential.

STUDY ON ALGORITHM OF SENSOR MANAGEMENT BASED ON FUNCTIONS OF EFFICIENCY AND WASTE

Institute of Scientific and Technical Information of China (English)

无

2000-01-01

Sensor management plays an important role in data fusion system, and this paper presents an algorithm of sensor management that can be used in target detection, identification and tracking. First, the basic concept, rule, range and function of sensor management are introduced. Then, the quantifying problems of target priority and sensor (or combination)-target pairing in multisensor management are discussed and the efficiency and waste functions are established based on the functions of target priority and sensor-target pairing. On this basis, a distribution algorithm of multisensor resources is given, which is optimized by the principle of maximum synthesis efficiency in the multisensor system and constrained by sensor maximum tracking power and what target must be scanned. In addition, the waste measure of sensor resources is introduced to improve the algorithm. Finally, a tactical task that includes three sensors and ten targets is set, and the simulation results show that the algorithm is feasible and effective.

Quartic scaling MP2 for solids: A highly parallelized algorithm in the plane wave basis

Science.gov (United States)

Schäfer, Tobias; Ramberger, Benjamin; Kresse, Georg

2017-03-01

We present a low-complexity algorithm to calculate the correlation energy of periodic systems in second-order Møller-Plesset (MP2) perturbation theory. In contrast to previous approximation-free MP2 codes, our implementation possesses a quartic scaling, O ( N 4 ) , with respect to the system size N and offers an almost ideal parallelization efficiency. The general issue that the correlation energy converges slowly with the number of basis functions is eased by an internal basis set extrapolation. The key concept to reduce the scaling is to eliminate all summations over virtual orbitals which can be elegantly achieved in the Laplace transformed MP2 formulation using plane wave basis sets and fast Fourier transforms. Analogously, this approach could allow us to calculate second order screened exchange as well as particle-hole ladder diagrams with a similar low complexity. Hence, the presented method can be considered as a step towards systematically improved correlation energies.

On grouping individual wire segments into equivalent wires or chains, and introduction of multiple domain basis functions

CSIR Research Space (South Africa)

Lysko, AA

2009-06-01

Full Text Available The paper introduces a method to cover several wire segments with a single basis function, describes related practical algorithms, and gives some results. The process involves three steps: identifying chains of wire segments, splitting the chains...

Algorithm for programming function generators

International Nuclear Information System (INIS)

Bozoki, E.

1981-01-01

The present paper deals with a mathematical problem, encountered when driving a fully programmable μ-processor controlled function generator. An algorithm is presented to approximate a desired function by a set of straight segments in such a way that additional restrictions (hardware imposed) are also satisfied. A computer program which incorporates this algorithm and automatically generates the necessary input for the function generator for a broad class of desired functions is also described

An algorithm for the basis of the finite Fourier transform

Science.gov (United States)

Santhanam, Thalanayar S.

1995-01-01

The Finite Fourier Transformation matrix (F.F.T.) plays a central role in the formulation of quantum mechanics in a finite dimensional space studied by the author over the past couple of decades. An outstanding problem which still remains open is to find a complete basis for F.F.T. In this paper we suggest a simple algorithm to find the eigenvectors of F.T.T.

Conjugate schema and basis representation of crossover and mutation operators.

Science.gov (United States)

Kazadi, S T

1998-01-01

In genetic search algorithms and optimization routines, the representation of the mutation and crossover operators are typically defaulted to the canonical basis. We show that this can be influential in the usefulness of the search algorithm. We then pose the question of how to find a basis for which the search algorithm is most useful. The conjugate schema is introduced as a general mathematical construct and is shown to separate a function into smaller dimensional functions whose sum is the original function. It is shown that conjugate schema, when used on a test suite of functions, improves the performance of the search algorithm on 10 out of 12 of these functions. Finally, a rigorous but abbreviated mathematical derivation is given in the appendices.

Algorithm for locating the extremum of a multi-dimensional constrained function and its application to the PPPL Hybrid Study

International Nuclear Information System (INIS)

Bathke, C.

1978-03-01

A description is presented of a general algorithm for locating the extremum of a multi-dimensional constrained function. The algorithm employs a series of techniques dominated by random shrinkage, steepest descent, and adaptive creeping. A discussion follows of the algorithm's application to a ''real world'' problem, namely the optimization of the price of electricity, P/sub eh/, from a hybrid fusion-fission reactor. Upon the basis of comparisons with other optimization schemes of a survey nature, the algorithm is concluded to yield a good approximation to the location of a function's optimum

Computing single step operators of logic programming in radial basis function neural networks

Science.gov (United States)

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

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

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

Critical function monitoring system algorithm development

International Nuclear Information System (INIS)

Harmon, D.L.

1984-01-01

Accurate critical function status information is a key to operator decision-making during events threatening nuclear power plant safety. The Critical Function Monitoring System provides continuous critical function status monitoring by use of algorithms which mathematically represent the processes by which an operating staff would determine critical function status. This paper discusses in detail the systematic design methodology employed to develop adequate Critical Function Monitoring System algorithms

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)

Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA

Directory of Open Access Journals (Sweden)

Alisson C. D. de Souza

2014-09-01

Full Text Available This paper proposes a parallel fixed point radial basis function (RBF artificial neural network (ANN, implemented in a field programmable gate array (FPGA trained online with a least mean square (LMS algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx, with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.

Reconfiguration of face expressions based on the discrete capture data of radial basis function interpolation

Institute of Scientific and Technical Information of China (English)

ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang

2012-01-01

Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function （Multiquadric, MQ for short） to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.

Lyapunov Function Synthesis - Algorithm and Software

DEFF Research Database (Denmark)

Leth, Tobias; Sloth, Christoffer; Wisniewski, Rafal

2016-01-01

In this paper we introduce an algorithm for the synthesis of polynomial Lyapunov functions for polynomial vector fields. The Lyapunov function is a continuous piecewisepolynomial defined on simplices, which compose a collection of simplices. The algorithm is elaborated and crucial features are ex...

Wavelets as basis functions in electronic structure calculations

International Nuclear Information System (INIS)

Chauvin, C.

2005-11-01

This thesis is devoted to the definition and the implementation of a multi-resolution method to determine the fundamental state of a system composed of nuclei and electrons. In this work, we are interested in the Density Functional Theory (DFT), which allows to express the Hamiltonian operator with the electronic density only, by a Coulomb potential and a non-linear potential. This operator acts on orbitals, which are solutions of the so-called Kohn-Sham equations. Their resolution needs to express orbitals and density on a set of functions owing both physical and numerical properties, as explained in the second chapter. One can hardly satisfy these two properties simultaneously, that is why we are interested in orthogonal and bi-orthogonal wavelets basis, whose properties of interpolation are presented in the third chapter. We present in the fourth chapter three dimensional solvers for the Coulomb's potential, using not only the preconditioning property of wavelets, but also a multigrid algorithm. Determining this potential allows us to solve the self-consistent Kohn-Sham equations, by an algorithm presented in chapter five. The originality of our method consists in the construction of the stiffness matrix, combining a Galerkin formulation and a collocation scheme. We analyse the approximation properties of this method in case of linear Hamiltonian, such as harmonic oscillator and hydrogen, and present convergence results of the DFT for small electrons. Finally we show how orbital compression reduces considerably the number of coefficients to keep, while preserving a good accuracy of the fundamental energy. (author)

Integration by cell algorithm for Slater integrals in a spline basis

International Nuclear Information System (INIS)

Qiu, Y.; Fischer, C.F.

1999-01-01

An algorithm for evaluating Slater integrals in a B-spline basis is introduced. Based on the piecewise property of the B-splines, the algorithm divides the two-dimensional (r 1 , r 2 ) region into a number of rectangular cells according to the chosen grid and implements the two-dimensional integration over each individual cell using Gaussian quadrature. Over the off-diagonal cells, the integrands are separable so that each two-dimensional cell-integral is reduced to a product of two one-dimensional integrals. Furthermore, the scaling invariance of the B-splines in the logarithmic region of the chosen grid is fully exploited such that only some of the cell integrations need to be implemented. The values of given Slater integrals are obtained by assembling the cell integrals. This algorithm significantly improves the efficiency and accuracy of the traditional method that relies on the solution of differential equations and renders the B-spline method more effective when applied to multi-electron atomic systems

Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functions

International Nuclear Information System (INIS)

Havu, V.; Blum, V.; Havu, P.; Scheffler, M.

2009-01-01

We consider the problem of developing O(N) scaling grid-based operations needed in many central operations when performing electronic structure calculations with numeric atom-centered orbitals as basis functions. We outline the overall formulation of localized algorithms, and specifically the creation of localized grid batches. The choice of the grid partitioning scheme plays an important role in the performance and memory consumption of the grid-based operations. Three different top-down partitioning methods are investigated, and compared with formally more rigorous yet much more expensive bottom-up algorithms. We show that a conceptually simple top-down grid partitioning scheme achieves essentially the same efficiency as the more rigorous bottom-up approaches.

The mathematics of some tomography algorithms used at JET

Energy Technology Data Exchange (ETDEWEB)

Ingesson, L

2000-03-01

Mathematical details are given of various tomographic reconstruction algorithms that are in use at JET. These algorithms include constrained optimization (CO) with local basis functions, the Cormack method, methods with natural basis functions and the iterative projection-space reconstruction method. Topics discussed include: derivation of the matrix equation for constrained optimization, variable grid size, basis functions, line integrals, derivative matrices, smoothness matrices, analytical expression of the CO solution, sparse matrix storage, projection-space coordinates, the Cormack method in elliptical coordinates, interpolative generalized natural basis functions and some details of the implementation of the filtered backprojection method. (author)

Recent advances in radial basis function collocation methods

CERN Document Server

Chen, Wen; Chen, C S

2014-01-01

This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...

Linear scaling of density functional algorithms

International Nuclear Information System (INIS)

Stechel, E.B.; Feibelman, P.J.; Williams, A.R.

1993-01-01

An efficient density functional algorithm (DFA) that scales linearly with system size will revolutionize electronic structure calculations. Density functional calculations are reliable and accurate in determining many condensed matter and molecular ground-state properties. However, because current DFA's, including methods related to that of Car and Parrinello, scale with the cube of the system size, density functional studies are not routinely applied to large systems. Linear scaling is achieved by constructing functions that are both localized and fully occupied, thereby eliminating the need to calculate global eigenfunctions. It is, however, widely believed that exponential localization requires the existence of an energy gap between the occupied and unoccupied states. Despite this, the authors demonstrate that linear scaling can still be achieved for metals. Using a linear scaling algorithm, they have explicitly constructed localized, almost fully occupied orbitals for the quintessential metallic system, jellium. The algorithm is readily generalizable to any system geometry and Hamiltonian. They will discuss the conceptual issues involved, convergence properties and scaling for their new algorithm

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

Science.gov (United States)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

Doubly stochastic radial basis function methods

Science.gov (United States)

Yang, Fenglian; Yan, Liang; Ling, Leevan

2018-06-01

We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation. A careful operation count is provided in order to determine the ranges of all the parameters in our methods. The overhead cost for setting up the proposed DSRBF method is O (n2) for function recovery problems with n basis. Numerical experiments confirm that the proposed method not only outperforms constant shape parameter formulation (in terms of accuracy with comparable computational cost) but also the optimal LOOCV formulation (in terms of both accuracy and computational cost).

THE ALGORITHM OF MESHFREE METHOD OF RADIAL BASIS FUNCTIONS IN TASKS OF UNDERGROUND HYDROMECHANICS

Directory of Open Access Journals (Sweden)

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.

Optimal Piecewise Linear Basis Functions in Two Dimensions

Energy Technology Data Exchange (ETDEWEB)

Brooks III, E D; Szoke, A

2009-01-26

We use a variational approach to optimize the center point coefficients associated with the piecewise linear basis functions introduced by Stone and Adams [1], for polygonal zones in two Cartesian dimensions. Our strategy provides optimal center point coefficients, as a function of the location of the center point, by minimizing the error induced when the basis function interpolation is used for the solution of the time independent diffusion equation within the polygonal zone. By using optimal center point coefficients, one expects to minimize the errors that occur when these basis functions are used to discretize diffusion equations, or transport equations in optically thick zones (where they approach the solution of the diffusion equation). Our optimal center point coefficients satisfy the requirements placed upon the basis functions for any location of the center point. We also find that the location of the center point can be optimized, but this requires numerical calculations. Curiously, the optimum center point location is independent of the values of the dependent variable on the corners only for quadrilaterals.

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

Science.gov (United States)

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.

Objective Function and Learning Algorithm for the General Node Fault Situation.

Science.gov (United States)

Xiao, Yi; Feng, Rui-Bin; Leung, Chi-Sing; Sum, John

2016-04-01

Fault tolerance is one interesting property of artificial neural networks. However, the existing fault models are able to describe limited node fault situations only, such as stuck-at-zero and stuck-at-one. There is no general model that is able to describe a large class of node fault situations. This paper studies the performance of faulty radial basis function (RBF) networks for the general node fault situation. We first propose a general node fault model that is able to describe a large class of node fault situations, such as stuck-at-zero, stuck-at-one, and the stuck-at level being with arbitrary distribution. Afterward, we derive an expression to describe the performance of faulty RBF networks. An objective function is then identified from the formula. With the objective function, a training algorithm for the general node situation is developed. Finally, a mean prediction error (MPE) formula that is able to estimate the test set error of faulty networks is derived. The application of the MPE formula in the selection of basis width is elucidated. Simulation experiments are then performed to demonstrate the effectiveness of the proposed method.

A Cooperative Harmony Search Algorithm for Function Optimization

Directory of Open Access Journals (Sweden)

Gang Li

2014-01-01

Full Text Available Harmony search algorithm (HS is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs and particle swarm optimizers (PSOs. It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.

Mixtures of truncated basis functions

DEFF Research Database (Denmark)

Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

2012-01-01

In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for representing general hybrid Bayesian networks. The proposed framework generalizes both the mixture of truncated exponentials (MTEs) framework and the mixture of polynomials (MoPs) framework. Similar t...

An algorithm for constructing Lyapunov functions

Directory of Open Access Journals (Sweden)

Sigurdur Freyr Hafstein

2007-08-01

Full Text Available In this monograph we develop an algorithm for constructing Lyapunov functions for arbitrary switched dynamical systems $dot{mathbf{x}} = mathbf{f}_sigma(t,mathbf{x}$, possessing a uniformly asymptotically stable equilibrium. Let $dot{mathbf{x}}=mathbf{f}_p(t,mathbf{x}$, $pinmathcal{P}$, be the collection of the ODEs, to which the switched system corresponds. The number of the vector fields $mathbf{f}_p$ on the right-hand side of the differential equation is assumed to be finite and we assume that their components $f_{p,i}$ are $mathcal{C}^2$ functions and that we can give some bounds, not necessarily close, on their second-order partial derivatives. The inputs of the algorithm are solely a finite number of the function values of the vector fields $mathbf{f}_p$ and these bounds. The domain of the Lyapunov function constructed by the algorithm is only limited by the size of the equilibrium's region of attraction. Note, that the concept of a Lyapunov function for the arbitrary switched system $dot{mathbf{x}} = mathbf{f}_sigma(t,mathbf{x}$ is equivalent to the concept of a common Lyapunov function for the systems $dot{mathbf{x}}=mathbf{f}_p(t,mathbf{x}$, $pinmathcal{P}$, and that if $mathcal{P}$ contains exactly one element, then the switched system is just a usual ODE $dot{mathbf{x}}=mathbf{f}(t,mathbf{x}$. We give numerous examples of Lyapunov functions constructed by our method at the end of this monograph.

Assessing the utility of phase-space-localized basis functions: Exploiting direct product structure and a new basis function selection procedure.

Science.gov (United States)

Brown, James; Carrington, Tucker

2016-06-28

In this paper we show that it is possible to use an iterative eigensolver in conjunction with Halverson and Poirier's symmetrized Gaussian (SG) basis [T. Halverson and B. Poirier, J. Chem. Phys. 137, 224101 (2012)] to compute accurate vibrational energy levels of molecules with as many as five atoms. This is done, without storing and manipulating large matrices, by solving a regular eigenvalue problem that makes it possible to exploit direct-product structure. These ideas are combined with a new procedure for selecting which basis functions to use. The SG basis we work with is orders of magnitude smaller than the basis made by using a classical energy criterion. We find significant convergence errors in previous calculations with SG bases. For sum-of-product Hamiltonians, SG bases large enough to compute accurate levels are orders of magnitude larger than even simple pruned bases composed of products of harmonic oscillator functions.

Assessing the utility of phase-space-localized basis functions: Exploiting direct product structure and a new basis function selection procedure

International Nuclear Information System (INIS)

Brown, James; Carrington, Tucker

2016-01-01

In this paper we show that it is possible to use an iterative eigensolver in conjunction with Halverson and Poirier’s symmetrized Gaussian (SG) basis [T. Halverson and B. Poirier, J. Chem. Phys. 137, 224101 (2012)] to compute accurate vibrational energy levels of molecules with as many as five atoms. This is done, without storing and manipulating large matrices, by solving a regular eigenvalue problem that makes it possible to exploit direct-product structure. These ideas are combined with a new procedure for selecting which basis functions to use. The SG basis we work with is orders of magnitude smaller than the basis made by using a classical energy criterion. We find significant convergence errors in previous calculations with SG bases. For sum-of-product Hamiltonians, SG bases large enough to compute accurate levels are orders of magnitude larger than even simple pruned bases composed of products of harmonic oscillator functions.

FIREWORKS ALGORITHM FOR UNCONSTRAINED FUNCTION OPTIMIZATION PROBLEMS

Directory of Open Access Journals (Sweden)

Evans BAIDOO

2017-03-01

Full Text Available Modern real world science and engineering problems can be classified as multi-objective optimisation problems which demand for expedient and efficient stochastic algorithms to respond to the optimization needs. This paper presents an object-oriented software application that implements a firework optimization algorithm for function optimization problems. The algorithm, a kind of parallel diffuse optimization algorithm is based on the explosive phenomenon of fireworks. The algorithm presented promising results when compared to other population or iterative based meta-heuristic algorithm after it was experimented on five standard benchmark problems. The software application was implemented in Java with interactive interface which allow for easy modification and extended experimentation. Additionally, this paper validates the effect of runtime on the algorithm performance.

Functional Basis for Efficient Physical Layer Classical Control in Quantum Processors

Science.gov (United States)

Ball, Harrison; Nguyen, Trung; Leong, Philip H. W.; Biercuk, Michael J.

2016-12-01

The rapid progress seen in the development of quantum-coherent devices for information processing has motivated serious consideration of quantum computer architecture and organization. One topic which remains open for investigation and optimization relates to the design of the classical-quantum interface, where control operations on individual qubits are applied according to higher-level algorithms; accommodating competing demands on performance and scalability remains a major outstanding challenge. In this work, we present a resource-efficient, scalable framework for the implementation of embedded physical layer classical controllers for quantum-information systems. Design drivers and key functionalities are introduced, leading to the selection of Walsh functions as an effective functional basis for both programing and controller hardware implementation. This approach leverages the simplicity of real-time Walsh-function generation in classical digital hardware, and the fact that a wide variety of physical layer controls, such as dynamic error suppression, are known to fall within the Walsh family. We experimentally implement a real-time field-programmable-gate-array-based Walsh controller producing Walsh timing signals and Walsh-synthesized analog waveforms appropriate for critical tasks in error-resistant quantum control and noise characterization. These demonstrations represent the first step towards a unified framework for the realization of physical layer controls compatible with large-scale quantum-information processing.

A new BP Fourier algorithm and its application in English teaching evaluation

Science.gov (United States)

Pei, Xuehui; Pei, Guixin

2017-08-01

BP neural network algorithm has wide adaptability and accuracy when used in complicated system evaluation, but its calculation defects such as slow convergence have limited its practical application. The paper tries to speed up the calculation convergence of BP neural network algorithm with Fourier basis functions and presents a new BP Fourier algorithm for complicated system evaluation. First, shortages and working principle of BP algorithm are analyzed for subsequent targeted improvement; Second, the presented BP Fourier algorithm adopts Fourier basis functions to simplify calculation structure, designs new calculation transfer function between input and output layers, and conducts theoretical analysis to prove the efficiency of the presented algorithm; Finally, the presented algorithm is used in evaluating university English teaching and the application results shows that the presented BP Fourier algorithm has better performance in calculation efficiency and evaluation accuracy and can be used in evaluating complicated system practically.

A Hybrid Algorithm for Optimizing Multi- Modal Functions

Institute of Scientific and Technical Information of China (English)

Li Qinghua; Yang Shida; Ruan Youlin

2006-01-01

A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.

An Algorithm Computing the Local $b$ Function by an Approximate Division Algorithm in $\\hat{\\mathcal{D}}$

OpenAIRE

Nakayama, Hiromasa

2006-01-01

We give an algorithm to compute the local $b$ function. In this algorithm, we use the Mora division algorithm in the ring of differential operators and an approximate division algorithm in the ring of differential operators with power series coefficient.

Dynamic Fault Diagnosis for Semi-Batch Reactor under Closed-Loop Control via Independent Radial Basis Function Neural Network

OpenAIRE

Abdelkarim M. Ertiame; D. W. Yu; D. L. Yu; J. B. Gomm

2015-01-01

In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is emplo...

Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

Directory of Open Access Journals (Sweden)

Emer Bernal

2017-01-01

Full Text Available In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.

A new algorithm for the integration of exponential and logarithmic functions

Science.gov (United States)

Rothstein, M.

1977-01-01

An algorithm for symbolic integration of functions built up from the rational functions by repeatedly applying either the exponential or logarithm functions is discussed. This algorithm does not require polynomial factorization nor partial fraction decomposition and requires solutions of linear systems with only a small number of unknowns. It is proven that if this algorithm is applied to rational functions over the integers, a computing time bound for the algorithm can be obtained which is a polynomial in a bound on the integer length of the coefficients, and in the degrees of the numerator and denominator of the rational function involved.

Large-Eddy Simulation Using Projection onto Local Basis Functions

Science.gov (United States)

Pope, S. B.

In the traditional approach to LES for inhomogeneous flows, the resolved fields are obtained by a filtering operation (with filter width Delta). The equations governing the resolved fields are then partial differential equations, which are solved numerically (on a grid of spacing h). For an LES computation of a given magnitude (i.e., given h), there are conflicting considerations in the choice of Delta: to resolve a large range of turbulent motions, Delta should be small; to solve the equations with numerical accuracy, Delta should be large. In the alternative approach advanced here, this conflict is avoided. The resolved fields are defined by projection onto local basis functions, so that the governing equations are ordinary differential equations for the evolution of the basis-function coefficients. There is no issue of numerical spatial discretization errors. A general methodology for modelling the effects of the residual motions is developed. The model is based directly on the basis-function coefficients, and its effect is to smooth the fields where their rates of change are not well resolved by the basis functions. Demonstration calculations are performed for Burgers' equation.

Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Directory of Open Access Journals (Sweden)

Sharma Rakesh

2004-05-01

Full Text Available Abstract Functional magnetic resonance imaging (fMRI is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities.

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

Science.gov (United States)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Using piecewise sinusoidal basis functions to blanket multiple wire segments

CSIR Research Space (South Africa)

Lysko, AA

2009-06-01

Full Text Available This paper discusses application of the piecewise sinusoidal (PWS) basis function (BF) over a chain of several wire segments, for example as a multiple domain basis function. The usage of PWS BF is compared to results based on the piecewise linear...

An algorithm of improving speech emotional perception for hearing aid

Science.gov (United States)

Xi, Ji; Liang, Ruiyu; Fei, Xianju

2017-07-01

In this paper, a speech emotion recognition (SER) algorithm was proposed to improve the emotional perception of hearing-impaired people. The algorithm utilizes multiple kernel technology to overcome the drawback of SVM: slow training speed. Firstly, in order to improve the adaptive performance of Gaussian Radial Basis Function (RBF), the parameter determining the nonlinear mapping was optimized on the basis of Kernel target alignment. Then, the obtained Kernel Function was used as the basis kernel of Multiple Kernel Learning (MKL) with slack variable that could solve the over-fitting problem. However, the slack variable also brings the error into the result. Therefore, a soft-margin MKL was proposed to balance the margin against the error. Moreover, the relatively iterative algorithm was used to solve the combination coefficients and hyper-plane equations. Experimental results show that the proposed algorithm can acquire an accuracy of 90% for five kinds of emotions including happiness, sadness, anger, fear and neutral. Compared with KPCA+CCA and PIM-FSVM, the proposed algorithm has the highest accuracy.

Experimental evaluation and basis function optimization of the spatially variant image-space PSF on the Ingenuity PET/MR scanner

International Nuclear Information System (INIS)

Kotasidis, Fotis A.; Zaidi, Habib

2014-01-01

Purpose: The Ingenuity time-of-flight (TF) PET/MR is a recently developed hybrid scanner combining the molecular imaging capabilities of PET with the excellent soft tissue contrast of MRI. It is becoming common practice to characterize the system's point spread function (PSF) and understand its variation under spatial transformations to guide clinical studies and potentially use it within resolution recovery image reconstruction algorithms. Furthermore, due to the system's utilization of overlapping and spherical symmetric Kaiser-Bessel basis functions during image reconstruction, its image space PSF and reconstructed spatial resolution could be affected by the selection of the basis function parameters. Hence, a detailed investigation into the multidimensional basis function parameter space is needed to evaluate the impact of these parameters on spatial resolution. Methods: Using an array of 12 × 7 printed point sources, along with a custom made phantom, and with the MR magnet on, the system's spatially variant image-based PSF was characterized in detail. Moreover, basis function parameters were systematically varied during reconstruction (list-mode TF OSEM) to evaluate their impact on the reconstructed resolution and the image space PSF. Following the spatial resolution optimization, phantom, and clinical studies were subsequently reconstructed using representative basis function parameters. Results: Based on the analysis and under standard basis function parameters, the axial and tangential components of the PSF were found to be almost invariant under spatial transformations (∼4 mm) while the radial component varied modestly from 4 to 6.7 mm. Using a systematic investigation into the basis function parameter space, the spatial resolution was found to degrade for basis functions with a large radius and small shape parameter. However, it was found that optimizing the spatial resolution in the reconstructed PET images, while having a good basis function

Experimental evaluation and basis function optimization of the spatially variant image-space PSF on the Ingenuity PET/MR scanner

Energy Technology Data Exchange (ETDEWEB)

Kotasidis, Fotis A., E-mail: Fotis.Kotasidis@unige.ch [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland and Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, Manchester M20 3LJ (United Kingdom); Zaidi, Habib [Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva (Switzerland); Geneva Neuroscience Centre, Geneva University, CH-1205 Geneva (Switzerland); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB (Netherlands)

2014-06-15

Purpose: The Ingenuity time-of-flight (TF) PET/MR is a recently developed hybrid scanner combining the molecular imaging capabilities of PET with the excellent soft tissue contrast of MRI. It is becoming common practice to characterize the system's point spread function (PSF) and understand its variation under spatial transformations to guide clinical studies and potentially use it within resolution recovery image reconstruction algorithms. Furthermore, due to the system's utilization of overlapping and spherical symmetric Kaiser-Bessel basis functions during image reconstruction, its image space PSF and reconstructed spatial resolution could be affected by the selection of the basis function parameters. Hence, a detailed investigation into the multidimensional basis function parameter space is needed to evaluate the impact of these parameters on spatial resolution. Methods: Using an array of 12 × 7 printed point sources, along with a custom made phantom, and with the MR magnet on, the system's spatially variant image-based PSF was characterized in detail. Moreover, basis function parameters were systematically varied during reconstruction (list-mode TF OSEM) to evaluate their impact on the reconstructed resolution and the image space PSF. Following the spatial resolution optimization, phantom, and clinical studies were subsequently reconstructed using representative basis function parameters. Results: Based on the analysis and under standard basis function parameters, the axial and tangential components of the PSF were found to be almost invariant under spatial transformations (∼4 mm) while the radial component varied modestly from 4 to 6.7 mm. Using a systematic investigation into the basis function parameter space, the spatial resolution was found to degrade for basis functions with a large radius and small shape parameter. However, it was found that optimizing the spatial resolution in the reconstructed PET images, while having a good basis

The Algorithm Theoretical Basis Document for Tidal Corrections

Science.gov (United States)

Fricker, Helen A.; Ridgway, Jeff R.; Minster, Jean-Bernard; Yi, Donghui; Bentley, Charles R.`

2012-01-01

This Algorithm Theoretical Basis Document deals with the tidal corrections that need to be applied to range measurements made by the Geoscience Laser Altimeter System (GLAS). These corrections result from the action of ocean tides and Earth tides which lead to deviations from an equilibrium surface. Since the effect of tides is dependent of the time of measurement, it is necessary to remove the instantaneous tide components when processing altimeter data, so that all measurements are made to the equilibrium surface. The three main tide components to consider are the ocean tide, the solid-earth tide and the ocean loading tide. There are also long period ocean tides and the pole tide. The approximate magnitudes of these components are illustrated in Table 1, together with estimates of their uncertainties (i.e. the residual error after correction). All of these components are important for GLAS measurements over the ice sheets since centimeter-level accuracy for surface elevation change detection is required. The effect of each tidal component is to be removed by approximating their magnitude using tidal prediction models. Conversely, assimilation of GLAS measurements into tidal models will help to improve them, especially at high latitudes.

DESIGNING ALGORITHMS FOR SERVICE ROBOTS ON THE BASIS OF MIVAR APPROACH

Directory of Open Access Journals (Sweden)

Alexey Andreevich Panferov

2017-05-01

Full Text Available Opportunities of mivar-based approach for robots have been analyzed. Mivar-based method of rapid logical inference for calculating random algorithms of service robot functioning has been tested successfully. The logical model of office robot-guide functioning with the application of mivar-based method of rapid logical inference in the software environment “KESMI” (Wi!Mi 1.1 has been developed. Formalized map of the office for service robot has been described in mivar matrix, 63 objects for 100 rules. Simulation of robot functioning in the software environment V-REP has been performed.

Excited state nuclear forces from the Tamm-Dancoff approximation to time-dependent density functional theory within the plane wave basis set framework

Science.gov (United States)

Hutter, Jürg

2003-03-01

An efficient formulation of time-dependent linear response density functional theory for the use within the plane wave basis set framework is presented. The method avoids the transformation of the Kohn-Sham matrix into the canonical basis and references virtual orbitals only through a projection operator. Using a Lagrangian formulation nuclear derivatives of excited state energies within the Tamm-Dancoff approximation are derived. The algorithms were implemented into a pseudo potential/plane wave code and applied to the calculation of adiabatic excitation energies, optimized geometries and vibrational frequencies of three low lying states of formaldehyde. An overall good agreement with other time-dependent density functional calculations, multireference configuration interaction calculations and experimental data was found.

Fast radial basis functions for engineering applications

CERN Document Server

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...

Discrete Wigner Function Derivation of the Aaronson–Gottesman Tableau Algorithm

Directory of Open Access Journals (Sweden)

Lucas Kocia

2017-07-01

Full Text Available The Gottesman–Knill theorem established that stabilizer states and Clifford operations can be efficiently simulated classically. For qudits with odd dimension three and greater, stabilizer states and Clifford operations have been found to correspond to positive discrete Wigner functions and dynamics. We present a discrete Wigner function-based simulation algorithm for odd-d qudits that has the same time and space complexity as the Aaronson–Gottesman algorithm for qubits. We show that the efficiency of both algorithms is due to harmonic evolution in the symplectic structure of discrete phase space. The differences between the Wigner function algorithm for odd-d and the Aaronson–Gottesman algorithm for qubits are likely due only to the fact that the Weyl–Heisenberg group is not in S U ( d for d = 2 and that qubits exhibit state-independent contextuality. This may provide a guide for extending the discrete Wigner function approach to qubits.

Explicit appropriate basis function method for numerical solution of stiff systems

International Nuclear Information System (INIS)

Chen, Wenzhen; Xiao, Hongguang; Li, Haofeng; Chen, Ling

2015-01-01

Highlights: • An explicit numerical method called the appropriate basis function method is presented. • The method differs from the power series method for obtaining approximate numerical solutions. • Two cases show the method is fit for linear and nonlinear stiff systems. • The method is very simple and effective for most of differential equation systems. - Abstract: In this paper, an explicit numerical method, called the appropriate basis function method, is presented. The explicit appropriate basis function method differs from the power series method because it employs an appropriate basis function such as the exponential function, or periodic function, other than a polynomial, to obtain approximate numerical solutions. The method is successful and effective for the numerical solution of the first order ordinary differential equations. Two examples are presented to show the ability of the method for dealing with linear and nonlinear systems of differential equations

An ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir models

KAUST Repository

Fsheikh, Ahmed H.

2013-01-01

A nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.

Generalization of Risch's algorithm to special functions

International Nuclear Information System (INIS)

Raab, Clemens G.

2013-05-01

Symbolic integration deals with the evaluation of integrals in closed form. We present an overview of Risch's algorithm including recent developments. The algorithms discussed are suited for both indefinite and definite integration. They can also be used to compute linear relations among integrals and to find identities for special functions given by parameter integrals. The aim of this presentation is twofold: to introduce the reader to some basic ideas of differential algebra in the context of integration and to raise awareness in the physics community of computer algebra algorithms for indefinite and definite integration.

Adaptive radial basis function mesh deformation using data reduction

Science.gov (United States)

Gillebaart, T.; Blom, D. S.; van Zuijlen, A. H.; Bijl, H.

2016-09-01

Radial Basis Function (RBF) mesh deformation is one of the most robust mesh deformation methods available. Using the greedy (data reduction) method in combination with an explicit boundary correction, results in an efficient method as shown in literature. However, to ensure the method remains robust, two issues are addressed: 1) how to ensure that the set of control points remains an accurate representation of the geometry in time and 2) how to use/automate the explicit boundary correction, while ensuring a high mesh quality. In this paper, we propose an adaptive RBF mesh deformation method, which ensures the set of control points always represents the geometry/displacement up to a certain (user-specified) criteria, by keeping track of the boundary error throughout the simulation and re-selecting when needed. Opposed to the unit displacement and prescribed displacement selection methods, the adaptive method is more robust, user-independent and efficient, for the cases considered. Secondly, the analysis of a single high aspect ratio cell is used to formulate an equation for the correction radius needed, depending on the characteristics of the correction function used, maximum aspect ratio, minimum first cell height and boundary error. Based on the analysis two new radial basis correction functions are derived and proposed. This proposed automated procedure is verified while varying the correction function, Reynolds number (and thus first cell height and aspect ratio) and boundary error. Finally, the parallel efficiency is studied for the two adaptive methods, unit displacement and prescribed displacement for both the CPU as well as the memory formulation with a 2D oscillating and translating airfoil with oscillating flap, a 3D flexible locally deforming tube and deforming wind turbine blade. Generally, the memory formulation requires less work (due to the large amount of work required for evaluating RBF's), but the parallel efficiency reduces due to the limited

Algorithm of Functional Musculoskeletal Disorders Diagnostics

OpenAIRE

Alexandra P. Eroshenko

2012-01-01

The article scientifically justifies the algorithm of complex diagnostics of functional musculoskeletal disorders during resort treatment, aimed at the optimal application of modern methods of physical rehabilitation (correction programs formation), based on diagnostic methodologies findings

Surface interpolation with radial basis functions for medical imaging

International Nuclear Information System (INIS)

Carr, J.C.; Beatson, R.K.; Fright, W.R.

1997-01-01

Radial basis functions are presented as a practical solution to the problem of interpolating incomplete surfaces derived from three-dimensional (3-D) medical graphics. The specific application considered is the design of cranial implants for the repair of defects, usually holes, in the skull. Radial basis functions impose few restrictions on the geometry of the interpolation centers and are suited to problems where interpolation centers do not form a regular grid. However, their high computational requirements have previously limited their use to problems where the number of interpolation centers is small (<300). Recently developed fast evaluation techniques have overcome these limitations and made radial basis interpolation a practical approach for larger data sets. In this paper radial basis functions are fitted to depth-maps of the skull's surface, obtained from X-ray computed tomography (CT) data using ray-tracing techniques. They are used to smoothly interpolate the surface of the skull across defect regions. The resulting mathematical description of the skull's surface can be evaluated at any desired resolution to be rendered on a graphics workstation or to generate instructions for operating a computer numerically controlled (CNC) mill

Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

Science.gov (United States)

DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

2016-03-29

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.

Gaussian basis functions for highly oscillatory scattering wavefunctions

Science.gov (United States)

Mant, B. P.; Law, M. M.

2018-04-01

We have applied a basis set of distributed Gaussian functions within the S-matrix version of the Kohn variational method to scattering problems involving deep potential energy wells. The Gaussian positions and widths are tailored to the potential using the procedure of Bačić and Light (1986 J. Chem. Phys. 85 4594) which has previously been applied to bound-state problems. The placement procedure is shown to be very efficient and gives scattering wavefunctions and observables in agreement with direct numerical solutions. We demonstrate the basis function placement method with applications to hydrogen atom–hydrogen atom scattering and antihydrogen atom–hydrogen atom scattering.

A Data Forward Stepwise Fitting Algorithm Based on Orthogonal Function System

Directory of Open Access Journals (Sweden)

Li Han-Ju

2017-01-01

Full Text Available Data fitting is the main method of functional data analysis, and it is widely used in the fields of economy, social science, engineering technology and so on. Least square method is the main method of data fitting, but the least square method is not convergent, no memory property, big fitting error and it is easy to over fitting. Based on the orthogonal trigonometric function system, this paper presents a data forward stepwise fitting algorithm. This algorithm takes forward stepwise fitting strategy, each time using the nearest base function to fit the residual error generated by the previous base function fitting, which makes the residual mean square error minimum. In this paper, we theoretically prove the convergence, the memory property and the fitting error diminishing character for the algorithm. Experimental results show that the proposed algorithm is effective, and the fitting performance is better than that of the least square method and the forward stepwise fitting algorithm based on the non-orthogonal function system.

DETERMINATION ALGORITHM OF OPTIMAL GEOMETRICAL PARAMETERS FOR COMPONENTS OF FREIGHT CARS ON THE BASIS OF GENERALIZED MATHEMATICAL MODELS

Directory of Open Access Journals (Sweden)

O. V. Fomin

2013-10-01

Full Text Available Purpose. Presentation of features and example of the use of the offered determination algorithm of optimum geometrical parameters for the components of freight cars on the basis of the generalized mathematical models, which is realized using computer. Methodology. The developed approach to search for optimal geometrical parameters can be described as the determination of optimal decision of the selected set of possible variants. Findings. The presented application example of the offered algorithm proved its operation capacity and efficiency of use. Originality. The determination procedure of optimal geometrical parameters for freight car components on the basis of the generalized mathematical models was formalized in the paper. Practical value. Practical introduction of the research results for universal open cars allows one to reduce container of their design and accordingly to increase the carrying capacity almost by100 kg with the improvement of strength characteristics. Taking into account the mass of their park this will provide a considerable economic effect when producing and operating. The offered approach is oriented to the distribution of the software packages (for example Microsoft Excel, which are used by technical services of the most enterprises, and does not require additional capital investments (acquisitions of the specialized programs and proper technical staff training. This proves the correctness of the research direction. The offered algorithm can be used for the solution of other optimization tasks on the basis of the generalized mathematical models.

Algorithm of Functional Musculoskeletal Disorders Diagnostics

Directory of Open Access Journals (Sweden)

Alexandra P. Eroshenko

2012-04-01

Full Text Available The article scientifically justifies the algorithm of complex diagnostics of functional musculoskeletal disorders during resort treatment, aimed at the optimal application of modern methods of physical rehabilitation (correction programs formation, based on diagnostic methodologies findings

Explicit symplectic algorithms based on generating functions for charged particle dynamics

Science.gov (United States)

Zhang, Ruili; Qin, Hong; Tang, Yifa; Liu, Jian; He, Yang; Xiao, Jianyuan

2016-07-01

Dynamics of a charged particle in the canonical coordinates is a Hamiltonian system, and the well-known symplectic algorithm has been regarded as the de facto method for numerical integration of Hamiltonian systems due to its long-term accuracy and fidelity. For long-term simulations with high efficiency, explicit symplectic algorithms are desirable. However, it is generally believed that explicit symplectic algorithms are only available for sum-separable Hamiltonians, and this restriction limits the application of explicit symplectic algorithms to charged particle dynamics. To overcome this difficulty, we combine the familiar sum-split method and a generating function method to construct second- and third-order explicit symplectic algorithms for dynamics of charged particle. The generating function method is designed to generate explicit symplectic algorithms for product-separable Hamiltonian with form of H (x ,p ) =pif (x ) or H (x ,p ) =xig (p ) . Applied to the simulations of charged particle dynamics, the explicit symplectic algorithms based on generating functions demonstrate superiorities in conservation and efficiency.

Comparison of optimization of loading patterns on the basis of SA and PMA algorithms

International Nuclear Information System (INIS)

Beliczai, Botond

2007-01-01

Optimization of loading patterns is a very important task from economical point of view in a nuclear power plant. The optimization algorithms used for this purpose can be categorized basically into two categories: deterministic ones and stochastic ones. In the Paks nuclear power plant a deterministic optimization procedure is used to optimize the loading pattern at BOC, so that the core would have maximal reactivity reserve. To the group of stochastic optimization procedures belong mainly simulated annealing (SA) procedures and genetic algorithms (GA). There are new procedures as well, which try to combine the advantages of SAs and GAs. One of them is called population mutation annealing algorithm (PMA). In the Paks NPP we would like to introduce fuel assemblies including burnable poison (Gd) in the near future. In order to be able to find the optimal loading pattern (or near-optimal loading patterns) in that case, we have to optimize our core not only for objective functions defined at BOC, but at EOC as well. For this purpose I used stochastic algorithms (SA and PMA) to investigate loading pattern optimization results for different objective functions at BOC. (author)

New deconvolution method for microscopic images based on the continuous Gaussian radial basis function interpolation model.

Science.gov (United States)

Chen, Zhaoxue; Chen, Hao

2014-01-01

A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.

Development of new auxiliary basis functions of the Karlsruhe segmented contracted basis sets including diffuse basis functions (def2-SVPD, def2-TZVPPD, and def2-QVPPD) for RI-MP2 and RI-CC calculations.

Science.gov (United States)

Hellweg, Arnim; Rappoport, Dmitrij

2015-01-14

We report optimized auxiliary basis sets for use with the Karlsruhe segmented contracted basis sets including moderately diffuse basis functions (Rappoport and Furche, J. Chem. Phys., 2010, 133, 134105) in resolution-of-the-identity (RI) post-self-consistent field (post-SCF) computations for the elements H-Rn (except lanthanides). The errors of the RI approximation using optimized auxiliary basis sets are analyzed on a comprehensive test set of molecules containing the most common oxidation states of each element and do not exceed those of the corresponding unaugmented basis sets. During these studies an unsatisfying performance of the def2-SVP and def2-QZVPP auxiliary basis sets for Barium was found and improved sets are provided. We establish the versatility of the def2-SVPD, def2-TZVPPD, and def2-QZVPPD basis sets for RI-MP2 and RI-CC (coupled-cluster) energy and property calculations. The influence of diffuse basis functions on correlation energy, basis set superposition error, atomic electron affinity, dipole moments, and computational timings is evaluated at different levels of theory using benchmark sets and showcase examples.

When do evolutionary algorithms optimize separable functions in parallel?

DEFF Research Database (Denmark)

Doerr, Benjamin; Sudholt, Dirk; Witt, Carsten

2013-01-01

is that evolutionary algorithms make progress on all subfunctions in parallel, so that optimizing a separable function does not take not much longer than optimizing the hardest subfunction-subfunctions are optimized "in parallel." We show that this is only partially true, already for the simple (1+1) evolutionary...... algorithm ((1+1) EA). For separable functions composed of k Boolean functions indeed the optimization time is the maximum optimization time of these functions times a small O(log k) overhead. More generally, for sums of weighted subfunctions that each attain non-negative integer values less than r = o(log1...

Quantum algorithms on Walsh transform and Hamming distance for Boolean functions

Science.gov (United States)

Xie, Zhengwei; Qiu, Daowen; Cai, Guangya

2018-06-01

Walsh spectrum or Walsh transform is an alternative description of Boolean functions. In this paper, we explore quantum algorithms to approximate the absolute value of Walsh transform W_f at a single point z0 (i.e., |W_f(z0)|) for n-variable Boolean functions with probability at least 8/π 2 using the number of O(1/|W_f(z_{0)|ɛ }) queries, promised that the accuracy is ɛ , while the best known classical algorithm requires O(2n) queries. The Hamming distance between Boolean functions is used to study the linearity testing and other important problems. We take advantage of Walsh transform to calculate the Hamming distance between two n-variable Boolean functions f and g using O(1) queries in some cases. Then, we exploit another quantum algorithm which converts computing Hamming distance between two Boolean functions to quantum amplitude estimation (i.e., approximate counting). If Ham(f,g)=t≠0, we can approximately compute Ham( f, g) with probability at least 2/3 by combining our algorithm and {Approx-Count(f,ɛ ) algorithm} using the expected number of Θ( √{N/(\\lfloor ɛ t\\rfloor +1)}+√{t(N-t)}/\\lfloor ɛ t\\rfloor +1) queries, promised that the accuracy is ɛ . Moreover, our algorithm is optimal, while the exact query complexity for the above problem is Θ(N) and the query complexity with the accuracy ɛ is O(1/ɛ 2N/(t+1)) in classical algorithm, where N=2n. Finally, we present three exact quantum query algorithms for two promise problems on Hamming distance using O(1) queries, while any classical deterministic algorithm solving the problem uses Ω(2n) queries.

Implementation of trigonometric function using CORDIC algorithms

Science.gov (United States)

Mokhtar, A. S. N.; Ayub, M. I.; Ismail, N.; Daud, N. G. Nik

2018-02-01

In 1959, Jack E. Volder presents a brand new formula to the real-time solution of the equation raised in navigation system. This new algorithm was the most beneficial replacement of analog navigation system by the digital. The CORDIC (Coordinate Rotation Digital Computer) algorithm are used for the rapid calculation associated with elementary operates like trigonometric function, multiplication, division and logarithm function, and also various conversions such as conversion of rectangular to polar coordinate including the conversion between binary coded information. In this current time CORDIC formula have many applications in the field of communication, signal processing, 3-D graphics, and others. This paper would be presents the trigonometric function implementation by using CORDIC algorithm in rotation mode for circular coordinate system. The CORDIC technique is used in order to generating the output angle between range 0o to 90o and error analysis is concern. The result showed that the average percentage error is about 0.042% at angles between ranges 00 to 900. But the average percentage error rose up to 45% at angle 90o and above. So, this method is very accurate at the 1st quadrant. The mirror properties method is used to find out an angle at 2nd, 3rd and 4th quadrant.

Closed fringe demodulation using phase decomposition by Fourier basis functions.

Science.gov (United States)

Kulkarni, Rishikesh; Rastogi, Pramod

2016-06-01

We report a new technique for the demodulation of a closed fringe pattern by representing the phase as a weighted linear combination of a certain number of linearly independent Fourier basis functions in a given row/column at a time. A state space model is developed with the weights of the basis functions as the elements of the state vector. The iterative extended Kalman filter is effectively utilized for the robust estimation of the weights. A coarse estimate of the fringe density based on the fringe frequency map is used to determine the initial row/column to start with and subsequently the optimal number of basis functions. The performance of the proposed method is evaluated with several noisy fringe patterns. Experimental results are also reported to support the practical applicability of the proposed method.

DESIGNING ALGORITHMS FOR SOLVING PHYSICS PROBLEMS ON THE BASIS OF MIVAR APPROACH

Directory of Open Access Journals (Sweden)

Dmitry Alekseevich Chuvikov

2017-05-01

Full Text Available The paper considers the process of designing algorithms for solving physics problems on the basis of mivar approach. The work also describes general principles of mivar theory. The concepts of parameter, relation and class in mivar space are considered. There are descriptions of properties which every object in Wi!Mi model should have. An experiment in testing capabilities of the Wi!Mi software has been carried out, thus the model has been designed which solves physics problems from year 8 school course in Russia. To conduct the experiment a new version of Wi!Mi 2.1 software has been used. The physics model deals with the following areas: thermal phenomena, electric and electromagnetic phenomena, optical phenomena.

Spherical radial basis functions, theory and applications

CERN Document Server

Hubbert, Simon; Morton, Tanya M

2015-01-01

This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging as one of the most promising techniques for solving problems where approximations are needed on the surface of a sphere. The aim of the book is to provide enough theoretical and practical details for the reader to be able to implement the SBF methods to solve real world problems. The authors stress the close connection between the theory of SBFs and that of the more well-known family of radial basis functions (RBFs), which are well-established tools for solving approximation theory problems on more general domains. The unique solvability of the SBF interpolation method for data fitting problems is established and an in-depth investigation of its accuracy is provided. Two chapters are devoted to partial differential equations (PDEs). One deals with the practical implementation of an SBF-based solution to an elliptic PDE and another which describes an SBF approach for solvi...

Density functional theory and evolution algorithm calculations of elastic properties of AlON

Energy Technology Data Exchange (ETDEWEB)

Batyrev, I. G.; Taylor, D. E.; Gazonas, G. A.; McCauley, J. W. [U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005 (United States)

2014-01-14

Different models for aluminum oxynitride (AlON) were calculated using density functional theory and optimized using an evolutionary algorithm. Evolutionary algorithm and density functional theory (DFT) calculations starting from several models of AlON with different Al or O vacancy locations and different positions for the N atoms relative to the vacancy were carried out. The results show that the constant anion model [McCauley et al., J. Eur. Ceram. Soc. 29(2), 223 (2009)] with a random distribution of N atoms not adjacent to the Al vacancy has the lowest energy configuration. The lowest energy structure is in a reasonable agreement with experimental X-ray diffraction spectra. The optimized structure of a 55 atom unit cell was used to construct 220 and 440 atom models for simulation cells using DFT with a Gaussian basis set. Cubic elastic constant predictions were found to approach the experimentally determined AlON single crystal elastic constants as the model size increased from 55 to 440 atoms. The pressure dependence of the elastic constants found from simulated stress-strain relations were in overall agreement with experimental measurements of polycrystalline and single crystal AlON. Calculated IR intensity and Raman spectra are compared with available experimental data.

Factorization of products of discontinuous functions applied to Fourier-Bessel basis.

Science.gov (United States)

Popov, Evgeny; Nevière, Michel; Bonod, Nicolas

2004-01-01

The factorization rules of Li [J. Opt. Soc. Am. A 13, 1870 (1996)] are generalized to a cylindrical geometry requiring the use of a Bessel function basis. A theoretical study confirms the validity of the Laurent rule when a product of two continuous functions or of one continuous and one discontinuous function is factorized. The necessity of applying the so-called inverse rule in factorizing a continuous product of two discontinuous functions in a truncated basis is demonstrated theoretically and numerically.

Large-scale sequential quadratic programming algorithms

Energy Technology Data Exchange (ETDEWEB)

Eldersveld, S.K.

1992-09-01

The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.

Quantum Computations: Fundamentals and Algorithms

International Nuclear Information System (INIS)

Duplij, S.A.; Shapoval, I.I.

2007-01-01

Basic concepts of quantum information theory, principles of quantum calculations and the possibility of creation on this basis unique on calculation power and functioning principle device, named quantum computer, are concerned. The main blocks of quantum logic, schemes of quantum calculations implementation, as well as some known today effective quantum algorithms, called to realize advantages of quantum calculations upon classical, are presented here. Among them special place is taken by Shor's algorithm of number factorization and Grover's algorithm of unsorted database search. Phenomena of decoherence, its influence on quantum computer stability and methods of quantum errors correction are described

Cheap contouring of costly functions: the Pilot Approximation Trajectory algorithm

International Nuclear Information System (INIS)

Huttunen, Janne M J; Stark, Philip B

2012-01-01

The Pilot Approximation Trajectory (PAT) contour algorithm can find the contour of a function accurately when it is not practical to evaluate the function on a grid dense enough to use a standard contour algorithm, for instance, when evaluating the function involves conducting a physical experiment or a computationally intensive simulation. PAT relies on an inexpensive pilot approximation to the function, such as interpolating from a sparse grid of inexact values, or solving a partial differential equation (PDE) numerically using a coarse discretization. For each level of interest, the location and ‘trajectory’ of an approximate contour of this pilot function are used to decide where to evaluate the original function to find points on its contour. Those points are joined by line segments to form the PAT approximation of the contour of the original function. Approximating a contour numerically amounts to estimating a lower level set of the function, the set of points on which the function does not exceed the contour level. The area of the symmetric difference between the true lower level set and the estimated lower level set measures the accuracy of the contour. PAT measures its own accuracy by finding an upper confidence bound for this area. In examples, PAT can estimate a contour more accurately than standard algorithms, using far fewer function evaluations than standard algorithms require. We illustrate PAT by constructing a confidence set for viscosity and thermal conductivity of a flowing gas from simulated noisy temperature measurements, a problem in which each evaluation of the function to be contoured requires solving a different set of coupled nonlinear PDEs. (paper)

An inductive algorithm for smooth approximation of functions

International Nuclear Information System (INIS)

Kupenova, T.N.

2011-01-01

An inductive algorithm is presented for smooth approximation of functions, based on the Tikhonov regularization method and applied to a specific kind of the Tikhonov parametric functional. The discrepancy principle is used for estimation of the regularization parameter. The principle of heuristic self-organization is applied for assessment of some parameters of the approximating function

A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.

Science.gov (United States)

Huertas, Ismael; Oldehinkel, Marianne; van Oort, Erik S B; Garcia-Solis, David; Mir, Pablo; Beckmann, Christian F; Marquand, Andre F

2017-11-01

The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach

Chaos control of ferroresonance system based on RBF-maximum entropy clustering algorithm

International Nuclear Information System (INIS)

Liu Fan; Sun Caixin; Sima Wenxia; Liao Ruijin; Guo Fei

2006-01-01

With regards to the ferroresonance overvoltage of neutral grounded power system, a maximum-entropy learning algorithm based on radial basis function neural networks is used to control the chaotic system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers. It improves the regression and learning ability of neural networks. The numerical experiment of ferroresonance system testifies the effectiveness and feasibility of using the algorithm to control chaos in neutral grounded system

Multiscale finite element methods for high-contrast problems using local spectral basis functions

KAUST Repository

Efendiev, Yalchin

2011-02-01

In this paper we study multiscale finite element methods (MsFEMs) using spectral multiscale basis functions that are designed for high-contrast problems. Multiscale basis functions are constructed using eigenvectors of a carefully selected local spectral problem. This local spectral problem strongly depends on the choice of initial partition of unity functions. The resulting space enriches the initial multiscale space using eigenvectors of local spectral problem. The eigenvectors corresponding to small, asymptotically vanishing, eigenvalues detect important features of the solutions that are not captured by initial multiscale basis functions. Multiscale basis functions are constructed such that they span these eigenfunctions that correspond to small, asymptotically vanishing, eigenvalues. We present a convergence study that shows that the convergence rate (in energy norm) is proportional to (H/Λ*)1/2, where Λ* is proportional to the minimum of the eigenvalues that the corresponding eigenvectors are not included in the coarse space. Thus, we would like to reach to a larger eigenvalue with a smaller coarse space. This is accomplished with a careful choice of initial multiscale basis functions and the setup of the eigenvalue problems. Numerical results are presented to back-up our theoretical results and to show higher accuracy of MsFEMs with spectral multiscale basis functions. We also present a hierarchical construction of the eigenvectors that provides CPU savings. © 2010.

Implementation of digital image encryption algorithm using logistic function and DNA encoding

Science.gov (United States)

Suryadi, MT; Satria, Yudi; Fauzi, Muhammad

2018-03-01

Cryptography is a method to secure information that might be in form of digital image. Based on past research, in order to increase security level of chaos based encryption algorithm and DNA based encryption algorithm, encryption algorithm using logistic function and DNA encoding was proposed. Digital image encryption algorithm using logistic function and DNA encoding use DNA encoding to scramble the pixel values into DNA base and scramble it in DNA addition, DNA complement, and XOR operation. The logistic function in this algorithm used as random number generator needed in DNA complement and XOR operation. The result of the test show that the PSNR values of cipher images are 7.98-7.99 bits, the entropy values are close to 8, the histogram of cipher images are uniformly distributed and the correlation coefficient of cipher images are near 0. Thus, the cipher image can be decrypted perfectly and the encryption algorithm has good resistance to entropy attack and statistical attack.

DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING

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.

Using neural networks to speed up optimization algorithms

CERN Document Server

Bazan, M

2000-01-01

The paper presents the application of radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables. (13 refs).

Higher-Order Hierarchical Legendre Basis Functions in Applications

DEFF Research Database (Denmark)

Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter

2007-01-01

The higher-order hierarchical Legendre basis functions have been developed for effective solution of integral equations with the method of moments. They are derived from orthogonal Legendre polynomials modified to enforce normal continuity between neighboring mesh elements, while preserving a high...

A verified LLL algorithm

NARCIS (Netherlands)

Divasón, Jose; Joosten, Sebastiaan; Thiemann, René; Yamada, Akihisa

2018-01-01

The Lenstra-Lenstra-Lovász basis reduction algorithm, also known as LLL algorithm, is an algorithm to find a basis with short, nearly orthogonal vectors of an integer lattice. Thereby, it can also be seen as an approximation to solve the shortest vector problem (SVP), which is an NP-hard problem,

Accurate correlation energies in one-dimensional systems from small system-adapted basis functions

Science.gov (United States)

Baker, Thomas E.; Burke, Kieron; White, Steven R.

2018-02-01

We propose a general method for constructing system-dependent basis functions for correlated quantum calculations. Our construction combines features from several traditional approaches: plane waves, localized basis functions, and wavelets. In a one-dimensional mimic of Coulomb systems, it requires only 2-3 basis functions per electron to achieve high accuracy, and reproduces the natural orbitals. We illustrate its effectiveness for molecular energy curves and chains of many one-dimensional atoms. We discuss the promise and challenges for realistic quantum chemical calculations.

Dynamic Sensor Management Algorithm Based on Improved Efficacy Function

Directory of Open Access Journals (Sweden)

TANG Shujuan

2016-01-01

Full Text Available A dynamic sensor management algorithm based on improved efficacy function is proposed to solve the multi-target and multi-sensory management problem. The tracking task precision requirements (TPR, target priority and sensor use cost were considered to establish the efficacy function by weighted sum the normalized value of the three factors. The dynamic sensor management algorithm was accomplished through control the diversities of the desired covariance matrix (DCM and the filtering covariance matrix (FCM. The DCM was preassigned in terms of TPR and the FCM was obtained by the centralized sequential Kalman filtering algorithm. The simulation results prove that the proposed method could meet the requirements of desired tracking precision and adjust sensor selection according to target priority and cost of sensor source usage. This makes sensor management scheme more reasonable and effective.

Determination of many-electron basis functions for a quantum Hall ground state using Schur polynomials

Science.gov (United States)

Mandal, Sudhansu S.; Mukherjee, Sutirtha; Ray, Koushik

2018-03-01

A method for determining the ground state of a planar interacting many-electron system in a magnetic field perpendicular to the plane is described. The ground state wave-function is expressed as a linear combination of a set of basis functions. Given only the flux and the number of electrons describing an incompressible state, we use the combinatorics of partitioning the flux among the electrons to derive the basis wave-functions as linear combinations of Schur polynomials. The procedure ensures that the basis wave-functions form representations of the angular momentum algebra. We exemplify the method by deriving the basis functions for the 5/2 quantum Hall state with a few particles. We find that one of the basis functions is precisely the Moore-Read Pfaffian wave function.

Quick fuzzy backpropagation algorithm.

Science.gov (United States)

Nikov, A; Stoeva, S

2001-03-01

A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

Learning Mixtures of Truncated Basis Functions from Data

DEFF Research Database (Denmark)

Langseth, Helge; Nielsen, Thomas Dyhre; Pérez-Bernabé, Inmaculada

2014-01-01

In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing a ke...... propose an alternative learning method that relies on the cumulative distribution function of the data. Empirical results demonstrate the usefulness of the approaches: Even though the methods produce estimators that are slightly poorer than the state of the art (in terms of log......In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing......-likelihood), they are significantly faster, and therefore indicate that the MoTBF framework can be used for inference and learning in reasonably sized domains. Furthermore, we show how a particular sub- class of MoTBF potentials (learnable by the proposed methods) can be exploited to significantly reduce complexity during inference....

Radial Basis Function Based Quadrature over Smooth Surfaces

Science.gov (United States)

2016-03-24

Radial Basis Functions φ(r) Piecewise Smooth (Conditionally Positive Definite) MN Monomial |r|2m+1 TPS thin plate spline |r|2mln|r| Infinitely Smooth...smooth surfaces using polynomial interpolants, while [27] couples Thin - Plate Spline interpolation (see table 1) with Green’s integral formula [29

Efficient computation of smoothing splines via adaptive basis sampling

KAUST Repository

Ma, Ping

2015-06-24

© 2015 Biometrika Trust. Smoothing splines provide flexible nonparametric regression estimators. However, the high computational cost of smoothing splines for large datasets has hindered their wide application. In this article, we develop a new method, named adaptive basis sampling, for efficient computation of smoothing splines in super-large samples. Except for the univariate case where the Reinsch algorithm is applicable, a smoothing spline for a regression problem with sample size n can be expressed as a linear combination of n basis functions and its computational complexity is generally O(n³). We achieve a more scalable computation in the multivariate case by evaluating the smoothing spline using a smaller set of basis functions, obtained by an adaptive sampling scheme that uses values of the response variable. Our asymptotic analysis shows that smoothing splines computed via adaptive basis sampling converge to the true function at the same rate as full basis smoothing splines. Using simulation studies and a large-scale deep earth core-mantle boundary imaging study, we show that the proposed method outperforms a sampling method that does not use the values of response variables.

Efficient computation of smoothing splines via adaptive basis sampling

KAUST Repository

Ma, Ping; Huang, Jianhua Z.; Zhang, Nan

2015-01-01

© 2015 Biometrika Trust. Smoothing splines provide flexible nonparametric regression estimators. However, the high computational cost of smoothing splines for large datasets has hindered their wide application. In this article, we develop a new method, named adaptive basis sampling, for efficient computation of smoothing splines in super-large samples. Except for the univariate case where the Reinsch algorithm is applicable, a smoothing spline for a regression problem with sample size n can be expressed as a linear combination of n basis functions and its computational complexity is generally O(n³). We achieve a more scalable computation in the multivariate case by evaluating the smoothing spline using a smaller set of basis functions, obtained by an adaptive sampling scheme that uses values of the response variable. Our asymptotic analysis shows that smoothing splines computed via adaptive basis sampling converge to the true function at the same rate as full basis smoothing splines. Using simulation studies and a large-scale deep earth core-mantle boundary imaging study, we show that the proposed method outperforms a sampling method that does not use the values of response variables.

Algorithms in Singular

Directory of Open Access Journals (Sweden)

Hans Schonemann

1996-12-01

Full Text Available Some algorithms for singularity theory and algebraic geometry The use of Grobner basis computations for treating systems of polynomial equations has become an important tool in many areas. This paper introduces of the concept of standard bases (a generalization of Grobner bases and the application to some problems from algebraic geometry. The examples are presented as SINGULAR commands. A general introduction to Grobner bases can be found in the textbook [CLO], an introduction to syzygies in [E] and [St1]. SINGULAR is a computer algebra system for computing information about singularities, for use in algebraic geometry. The basic algorithms in SINGULAR are several variants of a general standard basis algorithm for general monomial orderings (see [GG]. This includes wellorderings (Buchberger algorithm ([B1], [B2] and tangent cone orderings (Mora algorithm ([M1], [MPT] as special cases: It is able to work with non-homogeneous and homogeneous input and also to compute in the localization of the polynomial ring in 0. Recent versions include algorithms to factorize polynomials and a factorizing Grobner basis algorithm. For a complete description of SINGULAR see [Si].

The performance of the backpropagation algorithm with varying slope of the activation function

International Nuclear Information System (INIS)

Bai Yanping; Zhang Haixia; Hao Yilong

2009-01-01

Some adaptations are proposed to the basic BP algorithm in order to provide an efficient method to non-linear data learning and prediction. In this paper, an adopted BP algorithm with varying slope of activation function and different learning rates is put forward. The results of experiment indicated that this algorithm can get very good performance of training. We also test the prediction performance of our adopted BP algorithm on 16 instances. We compared the test results to the ones of the BP algorithm with gradient descent momentum and an adaptive learning rate. The results indicate this adopted BP algorithm gives best performance (100%) for test example, which conclude this adopted BP algorithm produces a smoothed reconstruction that learns better to new prediction function values than the BP algorithm improved with momentum.

Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian-Wishart processes.

Science.gov (United States)

Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon

2017-12-01

Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.

An Evaluation of the Sniffer Global Optimization Algorithm Using Standard Test Functions

Science.gov (United States)

Butler, Roger A. R.; Slaminka, Edward E.

1992-03-01

The performance of Sniffer—a new global optimization algorithm—is compared with that of Simulated Annealing. Using the number of function evaluations as a measure of efficiency, the new algorithm is shown to be significantly better at finding the global minimum of seven standard test functions. Several of the test functions used have many local minima and very steep walls surrounding the global minimum. Such functions are intended to thwart global minimization algorithms.

featsel: A framework for benchmarking of feature selection algorithms and cost functions

OpenAIRE

Marcelo S. Reis; Gustavo Estrela; Carlos Eduardo Ferreira; Junior Barrera

2017-01-01

In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions. This framework allows the user to deal with the search space as a Boolean lattice and has its core coded in C++ for computational efficiency purposes. Moreover, featsel includes Perl scripts to add new algorithms and/or cost functions, generate random instances, plot graphs and organize results into tables. Besides, this framework already comes with dozens of algorithms and co...

Improving the quantum cost of reversible Boolean functions using reorder algorithm

Science.gov (United States)

Ahmed, Taghreed; Younes, Ahmed; Elsayed, Ashraf

2018-05-01

This paper introduces a novel algorithm to synthesize a low-cost reversible circuits for any Boolean function with n inputs represented as a Positive Polarity Reed-Muller expansion. The proposed algorithm applies a predefined rules to reorder the terms in the function to minimize the multi-calculation of common parts of the Boolean function to decrease the quantum cost of the reversible circuit. The paper achieves a decrease in the quantum cost and/or the circuit length, on average, when compared with relevant work in the literature.

A hybrid artificial bee colony algorithm for numerical function optimization

Science.gov (United States)

Alqattan, Zakaria N.; Abdullah, Rosni

2015-02-01

Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

A new algorithm for 3D reconstruction from support functions

DEFF Research Database (Denmark)

Gardner, Richard; Kiderlen, Markus

2009-01-01

We introduce a new algorithm for reconstructing an unknown shape from a finite number of noisy measurements of its support function. The algorithm, based on a least squares procedure, is very easy to program in standard software such as Matlab and allows, for the first time, good 3D reconstructio...

Motion planning for autonomous vehicle based on radial basis function neural network in unstructured environment.

Science.gov (United States)

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.

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.

Quantum phase space with a basis of Wannier functions

Science.gov (United States)

Fang, Yuan; Wu, Fan; Wu, Biao

2018-02-01

A quantum phase space with Wannier basis is constructed: (i) classical phase space is divided into Planck cells; (ii) a complete set of Wannier functions are constructed with the combination of Kohn’s method and Löwdin method such that each Wannier function is localized at a Planck cell. With these Wannier functions one can map a wave function unitarily onto phase space. Various examples are used to illustrate our method and compare it to Wigner function. The advantage of our method is that it can smooth out the oscillations in wave functions without losing any information and is potentially a better tool in studying quantum-classical correspondence. In addition, we point out that our method can be used for time-frequency analysis of signals.

IMPLEMENTATION OF OBJECT TRACKING ALGORITHMS ON THE BASIS OF CUDA TECHNOLOGY

Directory of Open Access Journals (Sweden)

B. A. Zalesky

2014-01-01

Full Text Available A fast version of correlation algorithm to track objects on video-sequences made by a nonstabilized camcorder is presented. The algorithm is based on comparison of local correlations of the object image and regions of video-frames. The algorithm is implemented in programming technology CUDA. Application of CUDA allowed to attain real time execution of the algorithm. To improve its precision and stability, a robust version of the Kalman filter has been incorporated into the flowchart. Tests showed applicability of the algorithm to practical object tracking.

Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography.

Science.gov (United States)

Picek, Stjepan; Carlet, Claude; Guilley, Sylvain; Miller, Julian F; Jakobovic, Domagoj

2016-01-01

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm

International Nuclear Information System (INIS)

Muehlenbruch, Georg; Das, Marco; Hohl, Christian; Wildberger, Joachim E.; Guenther, Rolf W.; Mahnken, Andreas H.; Rinck, Daniel; Flohr, Thomas G.; Koos, Ralf; Knackstedt, Christian

2006-01-01

The purpose was to evaluate a new semi-automated 3D region-growing segmentation algorithm for functional analysis of the left ventricle in multislice CT (MSCT) of the heart. Twenty patients underwent contrast-enhanced MSCT of the heart (collimation 16 x 0.75 mm; 120 kV; 550 mAseff). Multiphase image reconstructions with 1-mm axial slices and 8-mm short-axis slices were performed. Left ventricular volume measurements (end-diastolic volume, end-systolic volume, ejection fraction and stroke volume) from manually drawn endocardial contours in the short axis slices were compared to semi-automated region-growing segmentation of the left ventricle from the 1-mm axial slices. The post-processing-time for both methods was recorded. Applying the new region-growing algorithm in 13/20 patients (65%), proper segmentation of the left ventricle was feasible. In these patients, the signal-to-noise ratio was higher than in the remaining patients (3.2±1.0 vs. 2.6±0.6). Volume measurements of both segmentation algorithms showed an excellent correlation (all P≤0.0001); the limits of agreement for the ejection fraction were 2.3±8.3 ml. In the patients with proper segmentation the mean post-processing time using the region-growing algorithm was diminished by 44.2%. On the basis of a good contrast-enhanced data set, a left ventricular volume analysis using the new semi-automated region-growing segmentation algorithm is technically feasible, accurate and more time-effective. (orig.)

Application of Trapezoidal-Shaped Characteristic Basis Functions to Arrays of Electrically Interconnected Antenna Elements

NARCIS (Netherlands)

Maaskant, R.; Mittra, R.; Tijhuis, A.G.; Graglia, R.D.

2007-01-01

This paper describes a novel technique for generating the characteristic basis functions (CBFs) used to represent the surface currents on finite arrays of electrically interconnected antenna elements. The CBFs are high-level basis functions, defined on subdomains in which the original problem is

A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering.

Science.gov (United States)

Yang, Shangming; Yi, Zhang; He, Xiaofei; Li, Xuelong

2015-12-01

Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.

A comparison of the behavior of functional/basis set combinations for hydrogen-bonding in the water dimer with emphasis on basis set superposition error.

Science.gov (United States)

Plumley, Joshua A; Dannenberg, J J

2011-06-01

We evaluate the performance of ten functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D, and MPWB1K) in combination with 16 basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of functionals and basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-density functional theory (non-DFT) molecular orbital (MO) calculations and to experimental results. Several of the smaller basis sets lead to qualitatively incorrect geometries when optimized on a normal potential energy surface (PES). This problem disappears when the optimization is performed on a counterpoise (CP) corrected PES. The calculated interaction energies (ΔEs) with the largest basis sets vary from -4.42 (B97D) to -5.19 (B2PLYPD) kcal/mol for the different functionals. Small basis sets generally predict stronger interactions than the large ones. We found that, because of error compensation, the smaller basis sets gave the best results (in comparison to experimental and high-level non-DFT MO calculations) when combined with a functional that predicts a weak interaction with the largest basis set. As many applications are complex systems and require economical calculations, we suggest the following functional/basis set combinations in order of increasing complexity and cost: (1) D95(d,p) with B3LYP, B97D, M06, or MPWB1k; (2) 6-311G(d,p) with B3LYP; (3) D95++(d,p) with B3LYP, B97D, or MPWB1K; (4) 6-311++G(d,p) with B3LYP or B97D; and (5) aug-cc-pVDZ with M05-2X, M06-2X, or X3LYP. Copyright © 2011 Wiley Periodicals, Inc.

Pricing and simulation for real estate index options: Radial basis point interpolation

Science.gov (United States)

Gong, Pu; Zou, Dong; Wang, Jiayue

2018-06-01

This study employs the meshfree radial basis point interpolation (RBPI) for pricing real estate derivatives contingent on real estate index. This method combines radial and polynomial basis functions, which can guarantee the interpolation scheme with Kronecker property and effectively improve accuracy. An exponential change of variables, a mesh refinement algorithm and the Richardson extrapolation are employed in this study to implement the RBPI. Numerical results are presented to examine the computational efficiency and accuracy of our method.

Zombie algorithms: a timesaving remote sensing systems engineering tool

Science.gov (United States)

Ardanuy, Philip E.; Powell, Dylan C.; Marley, Stephen

2008-08-01

In modern horror fiction, zombies are generally undead corpses brought back from the dead by supernatural or scientific means, and are rarely under anyone's direct control. They typically have very limited intelligence, and hunger for the flesh of the living [1]. Typical spectroradiometric or hyperspectral instruments providess calibrated radiances for a number of remote sensing algorithms. The algorithms typically must meet specified latency and availability requirements while yielding products at the required quality. These systems, whether research, operational, or a hybrid, are typically cost constrained. Complexity of the algorithms can be high, and may evolve and mature over time as sensor characterization changes, product validation occurs, and areas of scientific basis improvement are identified and completed. This suggests the need for a systems engineering process for algorithm maintenance that is agile, cost efficient, repeatable, and predictable. Experience on remote sensing science data systems suggests the benefits of "plug-n-play" concepts of operation. The concept, while intuitively simple, can be challenging to implement in practice. The use of zombie algorithms-empty shells that outwardly resemble the form, fit, and function of a "complete" algorithm without the implemented theoretical basis-provides the ground systems advantages equivalent to those obtained by integrating sensor engineering models onto the spacecraft bus. Combined with a mature, repeatable process for incorporating the theoretical basis, or scientific core, into the "head" of the zombie algorithm, along with associated scripting and registration, provides an easy "on ramp" for the rapid and low-risk integration of scientific applications into operational systems.

Estimating the Partition Function Zeros by Using the Wang-Landau Monte Carlo Algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Seung-Yeon [Korea National University of Transportation, Chungju (Korea, Republic of)

2017-03-15

The concept of the partition function zeros is one of the most efficient methods for investigating the phase transitions and the critical phenomena in various physical systems. Estimating the partition function zeros requires information on the density of states Ω(E) as a function of the energy E. Currently, the Wang-Landau Monte Carlo algorithm is one of the best methods for calculating Ω(E). The partition function zeros in the complex temperature plane of the Ising model on an L × L square lattice (L = 10 ∼ 80) with a periodic boundary condition have been estimated by using the Wang-Landau Monte Carlo algorithm. The efficiency of the Wang-Landau Monte Carlo algorithm and the accuracies of the partition function zeros have been evaluated for three different, 5%, 10%, and 20%, flatness criteria for the histogram H(E).

High-frequency asymptotics of the local vertex function. Algorithmic implementations

Energy Technology Data Exchange (ETDEWEB)

Tagliavini, Agnese; Wentzell, Nils [Institut fuer Theoretische Physik, Eberhard Karls Universitaet, 72076 Tuebingen (Germany); Institute for Solid State Physics, Vienna University of Technology, 1040 Vienna (Austria); Li, Gang; Rohringer, Georg; Held, Karsten; Toschi, Alessandro [Institute for Solid State Physics, Vienna University of Technology, 1040 Vienna (Austria); Taranto, Ciro [Institute for Solid State Physics, Vienna University of Technology, 1040 Vienna (Austria); Max Planck Institute for Solid State Research, D-70569 Stuttgart (Germany); Andergassen, Sabine [Institut fuer Theoretische Physik, Eberhard Karls Universitaet, 72076 Tuebingen (Germany)

2016-07-01

Local vertex functions are a crucial ingredient of several forefront many-body algorithms in condensed matter physics. However, the full treatment of their frequency dependence poses a huge limitation to the numerical performance. A significant advancement requires an efficient treatment of the high-frequency asymptotic behavior of the vertex functions. We here provide a detailed diagrammatic analysis of the high-frequency asymptotic structures and their physical interpretation. Based on these insights, we propose a frequency parametrization, which captures the whole high-frequency asymptotics for arbitrary values of the local Coulomb interaction and electronic density. We present its algorithmic implementation in many-body solvers based on parquet-equations as well as functional renormalization group schemes and assess its validity by comparing our results for the single impurity Anderson model with exact diagonalization calculations.

Algebraic Factoring algorithm to recognise read-once functions.

NARCIS (Netherlands)

Naidu, S.R.

2003-01-01

A fast polynomial-time algorithm was recently proposed to determine whether a logic function expressed as a unate DNF (disjunctive normal form) can be expressed as a read-once formula where each variable appears no more than once. The paper uses a combinatorial characterisation of read-once formulas

An Elementary Algorithm to Evaluate Trigonometric Functions to High Precision

Science.gov (United States)

Johansson, B. Tomas

2018-01-01

Evaluation of the cosine function is done via a simple Cordic-like algorithm, together with a package for handling arbitrary-precision arithmetic in the computer program Matlab. Approximations to the cosine function having hundreds of correct decimals are presented with a discussion around errors and implementation.

Radial basis function and its application in tourism management

Science.gov (United States)

Hu, Shan-Feng; Zhu, Hong-Bin; Zhao, Lei

2018-05-01

In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.

Energy functions for regularization algorithms

Science.gov (United States)

Delingette, H.; Hebert, M.; Ikeuchi, K.

1991-01-01

Regularization techniques are widely used for inverse problem solving in computer vision such as surface reconstruction, edge detection, or optical flow estimation. Energy functions used for regularization algorithms measure how smooth a curve or surface is, and to render acceptable solutions these energies must verify certain properties such as invariance with Euclidean transformations or invariance with parameterization. The notion of smoothness energy is extended here to the notion of a differential stabilizer, and it is shown that to void the systematic underestimation of undercurvature for planar curve fitting, it is necessary that circles be the curves of maximum smoothness. A set of stabilizers is proposed that meet this condition as well as invariance with rotation and parameterization.

On-line learning in radial basis functions networks

OpenAIRE

Freeman, Jason; Saad, David

1997-01-01

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...

On constructing optimistic simulation algorithms for the discrete event system specification

International Nuclear Information System (INIS)

Nutaro, James J.

2008-01-01

This article describes a Time Warp simulation algorithm for discrete event models that are described in terms of the Discrete Event System Specification (DEVS). The article shows how the total state transition and total output function of a DEVS atomic model can be transformed into an event processing procedure for a logical process. A specific Time Warp algorithm is constructed around this logical process, and it is shown that the algorithm correctly simulates a DEVS coupled model that consists entirely of interacting atomic models. The simulation algorithm is presented abstractly; it is intended to provide a basis for implementing efficient and scalable parallel algorithms that correctly simulate DEVS models

Sensitivity of Calibrated Parameters and Water Resource Estimates on Different Objective Functions and Optimization Algorithms

Directory of Open Access Journals (Sweden)

Delaram Houshmand Kouchi

2017-05-01

Full Text Available The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO, and eight different objective functions (R2, bR2, NSE, MNS, RSR, SSQR, KGE, and PBIAS in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are “conditioned” on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area.

An efficient parallel algorithm for the calculation of canonical MP2 energies.

Science.gov (United States)

Baker, Jon; Pulay, Peter

2002-09-01

We present the parallel version of a previous serial algorithm for the efficient calculation of canonical MP2 energies (Pulay, P.; Saebo, S.; Wolinski, K. Chem Phys Lett 2001, 344, 543). It is based on the Saebo-Almlöf direct-integral transformation, coupled with an efficient prescreening of the AO integrals. The parallel algorithm avoids synchronization delays by spawning a second set of slaves during the bin-sort prior to the second half-transformation. Results are presented for systems with up to 2000 basis functions. MP2 energies for molecules with 400-500 basis functions can be routinely calculated to microhartree accuracy on a small number of processors (6-8) in a matter of minutes with modern PC-based parallel computers. Copyright 2002 Wiley Periodicals, Inc. J Comput Chem 23: 1150-1156, 2002

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

Science.gov (United States)

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

Compactly Supported Basis Functions as Support Vector Kernels for Classification.

Science.gov (United States)

Wittek, Peter; Tan, Chew Lim

2011-10-01

Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

New algorithms for motion error detection of numerical control machine tool by laser tracking measurement on the basis of GPS principle

Science.gov (United States)

Wang, Jindong; Chen, Peng; Deng, Yufen; Guo, Junjie

2018-01-01

As a three-dimensional measuring instrument, the laser tracker is widely used in industrial measurement. To avoid the influence of angle measurement error on the overall measurement accuracy, the multi-station and time-sharing measurement with a laser tracker is introduced on the basis of the global positioning system (GPS) principle in this paper. For the proposed method, how to accurately determine the coordinates of each measuring point by using a large amount of measured data is a critical issue. Taking detecting motion error of a numerical control machine tool, for example, the corresponding measurement algorithms are investigated thoroughly. By establishing the mathematical model of detecting motion error of a machine tool with this method, the analytical algorithm concerning on base station calibration and measuring point determination is deduced without selecting the initial iterative value in calculation. However, when the motion area of the machine tool is in a 2D plane, the coefficient matrix of base station calibration is singular, which generates a distortion result. In order to overcome the limitation of the original algorithm, an improved analytical algorithm is also derived. Meanwhile, the calibration accuracy of the base station with the improved algorithm is compared with that with the original analytical algorithm and some iterative algorithms, such as the Gauss-Newton algorithm and Levenberg-Marquardt algorithm. The experiment further verifies the feasibility and effectiveness of the improved algorithm. In addition, the different motion areas of the machine tool have certain influence on the calibration accuracy of the base station, and the corresponding influence of measurement error on the calibration result of the base station depending on the condition number of coefficient matrix are analyzed.

Application of natural basis functions to soft x-ray tomography

International Nuclear Information System (INIS)

Ingesson, L.

2000-03-01

Natural basis functions (NBFs), also known as natural pixels in the literature, have been applied in tomographic reconstructions of simulated measurements for the JET soft x-ray system, which has a total of about 200 detectors spread over 6 directions. Various types of NBFs, i.e. normal, generalized and orthonormal NBFs, are reviewed. The number of basis functions is roughly equal to the number of measurements. Therefore, little a priori information is required as regularization and truncated singular-value decomposition can be used for the tomographic inversion. The results of NBFs are compared with reconstructions by the same solution technique using local basis functions (LBFs), and with the reconstructions of a conventional constrained-optimization tomography method with many more LBFs that requires more a priori information. Although the results of the conventional method are superior due to the a priori information, the results of the NBF and other LBF methods are reasonable and show the main features. Therefore, NBFs are a promising way to assess whether features in reconstructions are real or artefacts resulting from the a priori information. Of the NBFs, regular triangular (generalized) NBFs give the most acceptable reconstructions, much better than traditional square pixels, although the reconstructions with pyramid-shaped LBFs are also reasonable and have slightly smaller reconstruction errors. A more-regular (virtual) viewing geometry improves the reconstructions. However, simulations with a viewing geometry with a total of 480 channels spread over 12 directions clearly show that a priori information still improves the reconstructions considerably. (author)

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

Configuration space analysis of common cost functions in radiotherapy beam-weight optimization algorithms

Energy Technology Data Exchange (ETDEWEB)

Rowbottom, Carl Graham [Joint Department of Physics, Institute of Cancer Research and the Royal Marsden NHS Trust, Sutton, Surrey (United Kingdom); Webb, Steve [Joint Department of Physics, Institute of Cancer Research and the Royal Marsden NHS Trust, Sutton, Surrey (United Kingdom)

2002-01-07

The successful implementation of downhill search engines in radiotherapy optimization algorithms depends on the absence of local minima in the search space. Such techniques are much faster than stochastic optimization methods but may become trapped in local minima if they exist. A technique known as 'configuration space analysis' was applied to examine the search space of cost functions used in radiotherapy beam-weight optimization algorithms. A downhill-simplex beam-weight optimization algorithm was run repeatedly to produce a frequency distribution of final cost values. By plotting the frequency distribution as a function of final cost, the existence of local minima can be determined. Common cost functions such as the quadratic deviation of dose to the planning target volume (PTV), integral dose to organs-at-risk (OARs), dose-threshold and dose-volume constraints for OARs were studied. Combinations of the cost functions were also considered. The simple cost function terms such as the quadratic PTV dose and integral dose to OAR cost function terms are not susceptible to local minima. In contrast, dose-threshold and dose-volume OAR constraint cost function terms are able to produce local minima in the example case studied. (author)

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

Algorithm for determining two-periodic steady-states in AC machines directly in time domain

Directory of Open Access Journals (Sweden)

Sobczyk Tadeusz J.

2016-09-01

Full Text Available This paper describes an algorithm for finding steady states in AC machines for the cases of their two-periodic nature. The algorithm enables to specify the steady-state solution identified directly in time domain despite of the fact that two-periodic waveforms are not repeated in any finite time interval. The basis for such an algorithm is a discrete differential operator that specifies the temporary values of the derivative of the two-periodic function in the selected set of points on the basis of the values of that function in the same set of points. It allows to develop algebraic equations defining the steady state solution reached in a chosen point set for the nonlinear differential equations describing the AC machines when electrical and mechanical equations should be solved together. That set of those values allows determining the steady state solution at any time instant up to infinity. The algorithm described in this paper is competitive with respect to the one known in literature an approach based on the harmonic balance method operated in frequency domain.

Polarization functions for the modified m6-31G basis sets for atoms Ga through Kr.

Science.gov (United States)

Mitin, Alexander V

2013-09-05

The 2df polarization functions for the modified m6-31G basis sets of the third-row atoms Ga through Kr (Int J Quantum Chem, 2007, 107, 3028; Int J. Quantum Chem, 2009, 109, 1158) are proposed. The performances of the m6-31G, m6-31G(d,p), and m6-31G(2df,p) basis sets were examined in molecular calculations carried out by the density functional theory (DFT) method with B3LYP hybrid functional, Møller-Plesset perturbation theory of the second order (MP2), quadratic configuration interaction method with single and double substitutions and were compared with those for the known 6-31G basis sets as well as with the other similar 641 and 6-311G basis sets with and without polarization functions. Obtained results have shown that the performances of the m6-31G, m6-31G(d,p), and m6-31G(2df,p) basis sets are better in comparison with the performances of the known 6-31G, 6-31G(d,p) and 6-31G(2df,p) basis sets. These improvements are mainly reached due to better approximations of different electrons belonging to the different atomic shells in the modified basis sets. Applicability of the modified basis sets in thermochemical calculations is also discussed. © 2013 Wiley Periodicals, Inc.

Problem-Matched Basis Functions for Microstrip Coupled Slot Arrays based on Transmission Line Green+s Functions (TLGF)

NARCIS (Netherlands)

Bruni, S.; Llombart, N.; Neto, A.; Gerini, G.; Maci, S.

2004-01-01

A method is proposed for the analysis of arrays of linear printed antennas. After the formulation of pertinent set of integral equations, the appropriate equivalent currents of the Method of Moments are represented in terms of two sets of entire domain basis functions. These functions synthesize on

A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

Energy Technology Data Exchange (ETDEWEB)

Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

2015-07-01

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

Automation of Algorithmic Tasks for Virtual Laboratories Based on Automata Theory

Directory of Open Access Journals (Sweden)

Evgeniy A. Efimchik

2016-03-01

Full Text Available In the work a description of an automata model of standard algorithm for constructing a correct solution of algorithmic tests is given. The described model allows a formal determination of the variant complexity of algorithmic test and serves as a basis for determining the complexity functions, including the collision concept – the situation of uncertainty, when a choice must be made upon fulfilling the task between the alternatives with various priorities. The influence of collisions on the automata model and its inner structure is described. The model and complexity functions are applied for virtual laboratories upon designing the algorithms of constructing variant with a predetermined complexity in real time and algorithms of the estimation procedures of students’ solution with respect to collisions. The results of the work are applied to the development of virtual laboratories, which are used in the practical part of massive online course on graph theory.

Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

Science.gov (United States)

Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

2018-06-01

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.

An Allometric Algorithm for Fractal-Based Cobb-Douglas Function of Geographical Systems

Directory of Open Access Journals (Sweden)

Hongyu Luo

2014-01-01

Full Text Available The generalized Cobb-Douglas production function has been derived from a general input-output relation based on fractality assumptions. It was proved to be a useful self-affine model for geographical analysis. However, the ordinary least square calculation is always an ineffectual method for the Cobb-Douglas modeling because of the multicollinearity in the logarithmic linear regression. In this paper, a novel approach is proposed to build the geographical Cobb-Douglas models. Combining the concept of allometric scaling with the linear regression technique, we obtain a simple algorithm that can be employed to estimate the parameters of the Cobb-Douglas function. As a case, the algorithm and models are applied to the public transportation of China’s cities, and the results validate the allometric algorithm. A conclusion can be drawn that the allometric analysis is an effective way of modeling geographical systems with the general Cobb-Douglas function. This study is significant for integrating the notions of allometry, fractals, and scaling into a new framework to form a quantitative methodology of spatial analysis.

Projection pursuit water quality evaluation model based on chicken swam algorithm

Science.gov (United States)

Hu, Zhe

2018-03-01

In view of the uncertainty and ambiguity of each index in water quality evaluation, in order to solve the incompatibility of evaluation results of individual water quality indexes, a projection pursuit model based on chicken swam algorithm is proposed. The projection index function which can reflect the water quality condition is constructed, the chicken group algorithm (CSA) is introduced, the projection index function is optimized, the best projection direction of the projection index function is sought, and the best projection value is obtained to realize the water quality evaluation. The comparison between this method and other methods shows that it is reasonable and feasible to provide decision-making basis for water pollution control in the basin.

A novel algorithm of artificial immune system for high-dimensional function numerical optimization

Institute of Scientific and Technical Information of China (English)

DU Haifeng; GONG Maoguo; JIAO Licheng; LIU Ruochen

2005-01-01

Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed.

An algorithm for calculating exam quality as a basis for performance-based allocation of funds at medical schools.

Science.gov (United States)

Kirschstein, Timo; Wolters, Alexander; Lenz, Jan-Hendrik; Fröhlich, Susanne; Hakenberg, Oliver; Kundt, Günther; Darmüntzel, Martin; Hecker, Michael; Altiner, Attila; Müller-Hilke, Brigitte

2016-01-01

The amendment of the Medical Licensing Act (ÄAppO) in Germany in 2002 led to the introduction of graded assessments in the clinical part of medical studies. This, in turn, lent new weight to the importance of written tests, even though the minimum requirements for exam quality are sometimes difficult to reach. Introducing exam quality as a criterion for the award of performance-based allocation of funds is expected to steer the attention of faculty members towards more quality and perpetuate higher standards. However, at present there is a lack of suitable algorithms for calculating exam quality. In the spring of 2014, the students' dean commissioned the "core group" for curricular improvement at the University Medical Center in Rostock to revise the criteria for the allocation of performance-based funds for teaching. In a first approach, we developed an algorithm that was based on the results of the most common type of exam in medical education, multiple choice tests. It included item difficulty and discrimination, reliability as well as the distribution of grades achieved. This algorithm quantitatively describes exam quality of multiple choice exams. However, it can also be applied to exams involving short assay questions and the OSCE. It thus allows for the quantitation of exam quality in the various subjects and - in analogy to impact factors and third party grants - a ranking among faculty. Our algorithm can be applied to all test formats in which item difficulty, the discriminatory power of the individual items, reliability of the exam and the distribution of grades are measured. Even though the content validity of an exam is not considered here, we believe that our algorithm is suitable as a general basis for performance-based allocation of funds.

An algorithm for calculating exam quality as a basis for performance-based allocation of funds at medical schools

Directory of Open Access Journals (Sweden)

Kirschstein, Timo

2016-05-01

Full Text Available Objective: The amendment of the Medical Licensing Act (ÄAppO in Germany in 2002 led to the introduction of graded assessments in the clinical part of medical studies. This, in turn, lent new weight to the importance of written tests, even though the minimum requirements for exam quality are sometimes difficult to reach. Introducing exam quality as a criterion for the award of performance-based allocation of funds is expected to steer the attention of faculty members towards more quality and perpetuate higher standards. However, at present there is a lack of suitable algorithms for calculating exam quality.Methods: In the spring of 2014, the students‘ dean commissioned the „core group“ for curricular improvement at the University Medical Center in Rostock to revise the criteria for the allocation of performance-based funds for teaching. In a first approach, we developed an algorithm that was based on the results of the most common type of exam in medical education, multiple choice tests. It included item difficulty and discrimination, reliability as well as the distribution of grades achieved. Results: This algorithm quantitatively describes exam quality of multiple choice exams. However, it can also be applied to exams involving short assay questions and the OSCE. It thus allows for the quantitation of exam quality in the various subjects and – in analogy to impact factors and third party grants – a ranking among faculty. Conclusion: Our algorithm can be applied to all test formats in which item difficulty, the discriminatory power of the individual items, reliability of the exam and the distribution of grades are measured. Even though the content validity of an exam is not considered here, we believe that our algorithm is suitable as a general basis for performance-based allocation of funds.

Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.

Science.gov (United States)

Xie, Jianwen; Douglas, Pamela K; Wu, Ying Nian; Brody, Arthur L; Anderson, Ariana E

2017-04-15

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet other mathematical constraints provide alternate biologically-plausible frameworks for generating brain networks. Non-negative matrix factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms (L1 Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks within scan for different constraints are used as basis functions to encode observed functional activity. These encodings are then decoded using machine learning, by using the time series weights to predict within scan whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. The sparse coding algorithm of L1 Regularized Learning outperformed 4 variations of ICA (pcoding algorithms. Holding constant the effect of the extraction algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (pcoding algorithms suggests that algorithms which enforce sparsity, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA. Negative BOLD signal may capture task-related activations. Copyright © 2017 Elsevier B.V. All rights reserved.

Generalization of some hidden subgroup algorithms for input sets of arbitrary size

Science.gov (United States)

Poslu, Damla; Say, A. C. Cem

2006-05-01

We consider the problem of generalizing some quantum algorithms so that they will work on input domains whose cardinalities are not necessarily powers of two. When analyzing the algorithms we assume that generating superpositions of arbitrary subsets of basis states whose cardinalities are not necessarily powers of two perfectly is possible. We have taken Ballhysa's model as a template and have extended it to Chi, Kim and Lee's generalizations of the Deutsch-Jozsa algorithm and to Simon's algorithm. With perfectly equal superpositions of input sets of arbitrary size, Chi, Kim and Lee's generalized Deutsch-Jozsa algorithms, both for evenly-distributed and evenly-balanced functions, worked with one-sided error property. For Simon's algorithm the success probability of the generalized algorithm is the same as that of the original for input sets of arbitrary cardinalities with equiprobable superpositions, since the property that the measured strings are all those which have dot product zero with the string we search, for the case where the function is 2-to-1, is not lost.

Radial Basis Function Networks for Conversion of Sound Spectra

Directory of Open Access Journals (Sweden)

Carlo Drioli

2001-03-01

Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.

Efficient quantum algorithm for computing n-time correlation functions.

Science.gov (United States)

Pedernales, J S; Di Candia, R; Egusquiza, I L; Casanova, J; Solano, E

2014-07-11

We propose a method for computing n-time correlation functions of arbitrary spinorial, fermionic, and bosonic operators, consisting of an efficient quantum algorithm that encodes these correlations in an initially added ancillary qubit for probe and control tasks. For spinorial and fermionic systems, the reconstruction of arbitrary n-time correlation functions requires the measurement of two ancilla observables, while for bosonic variables time derivatives of the same observables are needed. Finally, we provide examples applicable to different quantum platforms in the frame of the linear response theory.

A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

Directory of Open Access Journals (Sweden)

Daqing Wu

2012-01-01

Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions

KAUST Repository

Fowkes, Jaroslav M.; Gould, Nicholas I. M.; Farmer, Chris L.

2012-01-01

We present a branch and bound algorithm for the global optimization of a twice differentiable nonconvex objective function with a Lipschitz continuous Hessian over a compact, convex set. The algorithm is based on applying cubic regularisation

An algorithm for solving the optical problem for stratified anisotropic media

Energy Technology Data Exchange (ETDEWEB)

Palto, S P [Shubnikov Institute of Crystallography, Russian Academy of Sciences, Leninskii pr. 59, Moscow, 117333 (Russian Federation)

2001-04-01

An algorithm for solving the Maxwell equations for propagation of light through anisotropic stratified media is considered. The algorithm uses the Berreman matrices of order 4 x 4. In contrast to the numerical methods suggested by Berreman, the new method is exact. The Sylvester theorem for calculating functions of a matrix and the Laguerre method for determining eigenvalues provide the basis for an algorithm with an efficiency comparable to that of the algorithms based on analytic solutions, which exist only in the case of uniaxial media. The method suggested in this paper allows for the analysis of complex optical systems where the effects of biaxiality, magnetic anisotropy, and optical activity play an important role.

Probability Density Functions for the CALIPSO Lidar Version 4 Cloud-Aerosol Discrimination (CAD) Algorithm

Science.gov (United States)

Liu, Z.; Kar, J.; Zeng, S.; Tackett, J. L.; Vaughan, M.; Trepte, C. R.; Omar, A. H.; Hu, Y.; Winker, D. M.

2017-12-01

In the CALIPSO retrieval algorithm, detection layers in the lidar measurements is followed by their classification as a "cloud" or "aerosol" using 5-dimensional probability density functions (PDFs). The five dimensions are the mean attenuated backscatter at 532 nm, the layer integrated total attenuated color ratio, the mid-layer altitude, integrated volume depolarization ratio and latitude. The new version 4 (V4) level 2 (L2) data products, released in November 2016, are the first major revision to the L2 product suite since May 2010. Significant calibration changes in the V4 level 1 data necessitated substantial revisions to the V4 L2 CAD algorithm. Accordingly, a new set of PDFs was generated to derive the V4 L2 data products. The V4 CAD algorithm is now applied to layers detected in the stratosphere, where volcanic layers and occasional cloud and smoke layers are observed. Previously, these layers were designated as `stratospheric', and not further classified. The V4 CAD algorithm is also applied to all layers detected at single shot (333 m) resolution. In prior data releases, single shot detections were uniformly classified as clouds. The CAD PDFs used in the earlier releases were generated using a full year (2008) of CALIPSO measurements. Because the CAD algorithm was not applied to stratospheric features, the properties of these layers were not incorporated into the PDFs. When building the V4 PDFs, the 2008 data were augmented with additional data from June 2011, and all stratospheric features were included. The Nabro and Puyehue-Cordon volcanos erupted in June 2011, and volcanic aerosol layers were observed in the upper troposphere and lower stratosphere in both the northern and southern hemispheres. The June 2011 data thus provides the stratospheric aerosol properties needed for comprehensive PDF generation. In contrast to earlier versions of the PDFs, which were generated based solely on observed distributions, construction of the V4 PDFs considered the

Application of Radial Basis Function Methods in the Development of a 95th Percentile Male Seated FEA Model.

Science.gov (United States)

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.

Influence of the Training Methods in the Diagnosis of Multiple Sclerosis Using Radial Basis Functions Artificial Neural Networks

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.

Metropolis-Hastings Algorithms in Function Space for Bayesian Inverse Problems

KAUST Repository

Ernst, Oliver

2015-01-07

We consider Markov Chain Monte Carlo methods adapted to a Hilbert space setting. Such algorithms occur in Bayesian inverse problems where the solution is a probability measure on a function space according to which one would like to integrate or sample. We focus on Metropolis-Hastings algorithms and, in particular, we introduce and analyze a generalization of the existing pCN-proposal. This new proposal allows to exploit the geometry or anisotropy of the target measure which in turn might improve the statistical efficiency of the corresponding MCMC method. Numerical experiments for a real-world problem confirm the improvement.

Approximated Function Based Spectral Gradient Algorithm for Sparse Signal Recovery

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Weifeng Wang

2014-02-01

Full Text Available Numerical algorithms for the l0-norm regularized non-smooth non-convex minimization problems have recently became a topic of great interest within signal processing, compressive sensing, statistics, and machine learning. Nevertheless, the l0-norm makes the problem combinatorial and generally computationally intractable. In this paper, we construct a new surrogate function to approximate l0-norm regularization, and subsequently make the discrete optimization problem continuous and smooth. Then we use the well-known spectral gradient algorithm to solve the resulting smooth optimization problem. Experiments are provided which illustrate this method is very promising.

Metropolis-Hastings Algorithms in Function Space for Bayesian Inverse Problems

KAUST Repository

Ernst, Oliver

2015-01-01

We consider Markov Chain Monte Carlo methods adapted to a Hilbert space setting. Such algorithms occur in Bayesian inverse problems where the solution is a probability measure on a function space according to which one would like to integrate or sample. We focus on Metropolis-Hastings algorithms and, in particular, we introduce and analyze a generalization of the existing pCN-proposal. This new proposal allows to exploit the geometry or anisotropy of the target measure which in turn might improve the statistical efficiency of the corresponding MCMC method. Numerical experiments for a real-world problem confirm the improvement.

A fast and accurate online sequential learning algorithm for feedforward networks.

Science.gov (United States)

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

Application of the Characteristic Basis Function Method Using CUDA

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Juan Ignacio Pérez

2014-01-01

Full Text Available The characteristic basis function method (CBFM is a popular technique for efficiently solving the method of moments (MoM matrix equations. In this work, we address the adaptation of this method to a relatively new computing infrastructure provided by NVIDIA, the Compute Unified Device Architecture (CUDA, and take into account some of the limitations which appear when the geometry under analysis becomes too big to fit into the Graphics Processing Unit’s (GPU’s memory.

Novel Polynomial Basis with Fast Fourier Transform and Its Application to Reed-Solomon Erasure Codes

KAUST Repository

Lin, Sian-Jheng

2016-09-13

In this paper, we present a fast Fourier transform (FFT) algorithm over extension binary fields, where the polynomial is represented in a non-standard basis. The proposed Fourier-like transform requires O(h lg(h)) field operations, where h is the number of evaluation points. Based on the proposed Fourier-like algorithm, we then develop the encoding/ decoding algorithms for (n = 2m; k) Reed-Solomon erasure codes. The proposed encoding/erasure decoding algorithm requires O(n lg(n)), in both additive and multiplicative complexities. As the complexity leading factor is small, the proposed algorithms are advantageous in practical applications. Finally, the approaches to convert the basis between the monomial basis and the new basis are proposed.

Simulation of 4-turn algorithms for reconstructing lattice optic functions from orbit measurements

International Nuclear Information System (INIS)

Koscielniak, S.; Iliev, A.

1994-06-01

We describe algorithms for reconstructing tune, closed-orbit, beta-function and phase advance from four individual turns of beam orbit acquisition data, under the assumption of coherent, almost linear and uncoupled betatron oscillations. To estimate the beta-function at, and phase advance between, position monitors, we require at least one anchor location consisting of two monitors separated by a drift. The algorithms were submitted to a Monte Carlo analysis to find the likely measurement accuracy of the optics functions in the KAON Factory Booster ring racetrack lattice, assuming beam position monitors with surveying and reading errors, and assuming an imperfect lattice with gradient and surveying errors. Some of the results of this study are reported. (author)

Density functional theory calculations of the lowest energy quintet and triplet states of model hemes: role of functional, basis set, and zero-point energy corrections.

Science.gov (United States)

Khvostichenko, Daria; Choi, Andrew; Boulatov, Roman

2008-04-24

We investigated the effect of several computational variables, including the choice of the basis set, application of symmetry constraints, and zero-point energy (ZPE) corrections, on the structural parameters and predicted ground electronic state of model 5-coordinate hemes (iron(II) porphines axially coordinated by a single imidazole or 2-methylimidazole). We studied the performance of B3LYP and B3PW91 with eight Pople-style basis sets (up to 6-311+G*) and B97-1, OLYP, and TPSS functionals with 6-31G and 6-31G* basis sets. Only hybrid functionals B3LYP, B3PW91, and B97-1 reproduced the quintet ground state of the model hemes. With a given functional, the choice of the basis set caused up to 2.7 kcal/mol variation of the quintet-triplet electronic energy gap (DeltaEel), in several cases, resulting in the inversion of the sign of DeltaEel. Single-point energy calculations with triple-zeta basis sets of the Pople (up to 6-311G++(2d,2p)), Ahlrichs (TZVP and TZVPP), and Dunning (cc-pVTZ) families showed the same trend. The zero-point energy of the quintet state was approximately 1 kcal/mol lower than that of the triplet, and accounting for ZPE corrections was crucial for establishing the ground state if the electronic energy of the triplet state was approximately 1 kcal/mol less than that of the quintet. Within a given model chemistry, effects of symmetry constraints and of a "tense" structure of the iron porphine fragment coordinated to 2-methylimidazole on DeltaEel were limited to 0.3 kcal/mol. For both model hemes the best agreement with crystallographic structural data was achieved with small 6-31G and 6-31G* basis sets. Deviation of the computed frequency of the Fe-Im stretching mode from the experimental value with the basis set decreased in the order: nonaugmented basis sets, basis sets with polarization functions, and basis sets with polarization and diffuse functions. Contraction of Pople-style basis sets (double-zeta or triple-zeta) affected the results

Spectrums Transform Operators in Bases of Fourier and Walsh Functions

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V. V. Syuzev

2017-01-01

Full Text Available The problems of synthesis of the efficient algorithms for digital processing of discrete signals require transforming the signal spectra from one basis system into other. The rational solution to this problem is to construct the Fourier kernel, which is a spectrum of some basis functions, according to the system of functions of the other basis. However, Fourier kernel properties are not equally studied and described for all basis systems of practical importance. The article sets a task and presents an original way to solve the problem of mutual transformation of trigonometric Fourier spectrum into Walsh spectrum of different basis systems.The relevance of this theoretical and applied problem is stipulated, on the one hand, by the prevalence of trigonometric Fourier basis for harmonic representation of digital signals, and, on the other hand, by the fact that Walsh basis systems allow us to have efficient algorithms to simulate signals. The problem solution is achieved through building the Fourier kernel of a special structure that allows us to establish independent groups of Fourier and Walsh spectrum coefficients for further reducing the computational complexity of the transform algorithms.The article analyzes the properties of the system of trigonometric Fourier functions and shows its completeness. Considers the Walsh function basis systems in three versions, namely those of Hadamard, Paley, and Hartmut giving different ordering and analytical descriptions of the functions that make up the basis. Proves a completeness of these systems.Sequentially, for each of the three Walsh systems the analytical curves for the Fourier kernel components are obtained, and Fourier kernel themselves are built with binary rational number of samples of basis functions. The kernels are presented in matrix form and, as an example, recorded for a particular value of the discrete interval of N, equal to 8. The analysis spectral coefficients of the Fourier kernel

The tensor hypercontracted parametric reduced density matrix algorithm: coupled-cluster accuracy with O(r(4)) scaling.

Science.gov (United States)

Shenvi, Neil; van Aggelen, Helen; Yang, Yang; Yang, Weitao; Schwerdtfeger, Christine; Mazziotti, David

2013-08-07

Tensor hypercontraction is a method that allows the representation of a high-rank tensor as a product of lower-rank tensors. In this paper, we show how tensor hypercontraction can be applied to both the electron repulsion integral tensor and the two-particle excitation amplitudes used in the parametric 2-electron reduced density matrix (p2RDM) algorithm. Because only O(r) auxiliary functions are needed in both of these approximations, our overall algorithm can be shown to scale as O(r(4)), where r is the number of single-particle basis functions. We apply our algorithm to several small molecules, hydrogen chains, and alkanes to demonstrate its low formal scaling and practical utility. Provided we use enough auxiliary functions, we obtain accuracy similar to that of the standard p2RDM algorithm, somewhere between that of CCSD and CCSD(T).

An integral conservative gridding--algorithm using Hermitian curve interpolation.

Science.gov (United States)

Volken, Werner; Frei, Daniel; Manser, Peter; Mini, Roberto; Born, Ernst J; Fix, Michael K

2008-11-07

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to

An integral conservative gridding-algorithm using Hermitian curve interpolation

International Nuclear Information System (INIS)

Volken, Werner; Frei, Daniel; Manser, Peter; Mini, Roberto; Born, Ernst J; Fix, Michael K

2008-01-01

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to

Adaptive local basis set for Kohn–Sham density functional theory in a discontinuous Galerkin framework I: Total energy calculation

International Nuclear Information System (INIS)

Lin Lin; Lu Jianfeng; Ying Lexing; Weinan, E

2012-01-01

Kohn–Sham density functional theory is one of the most widely used electronic structure theories. In the pseudopotential framework, uniform discretization of the Kohn–Sham Hamiltonian generally results in a large number of basis functions per atom in order to resolve the rapid oscillations of the Kohn–Sham orbitals around the nuclei. Previous attempts to reduce the number of basis functions per atom include the usage of atomic orbitals and similar objects, but the atomic orbitals generally require fine tuning in order to reach high accuracy. We present a novel discretization scheme that adaptively and systematically builds the rapid oscillations of the Kohn–Sham orbitals around the nuclei as well as environmental effects into the basis functions. The resulting basis functions are localized in the real space, and are discontinuous in the global domain. The continuous Kohn–Sham orbitals and the electron density are evaluated from the discontinuous basis functions using the discontinuous Galerkin (DG) framework. Our method is implemented in parallel and the current implementation is able to handle systems with at least thousands of atoms. Numerical examples indicate that our method can reach very high accuracy (less than 1 meV) with a very small number (4–40) of basis functions per atom.

Practical auxiliary basis implementation of Rung 3.5 functionals

International Nuclear Information System (INIS)

Janesko, Benjamin G.; Scalmani, Giovanni; Frisch, Michael J.

2014-01-01

Approximate exchange-correlation functionals for Kohn-Sham density functional theory often benefit from incorporating exact exchange. Exact exchange is constructed from the noninteracting reference system's nonlocal one-particle density matrix γ(r -vector ,r -vector ′). Rung 3.5 functionals attempt to balance the strengths and limitations of exact exchange using a new ingredient, a projection of γ(r -vector ,r -vector ′) onto a semilocal model density matrix γ SL (ρ(r -vector ),∇ρ(r -vector ),r -vector −r -vector ′). γ SL depends on the electron density ρ(r -vector ) at reference point r -vector , and is closely related to semilocal model exchange holes. We present a practical implementation of Rung 3.5 functionals, expanding the r -vector −r -vector ′ dependence of γ SL in an auxiliary basis set. Energies and energy derivatives are obtained from 3D numerical integration as in standard semilocal functionals. We also present numerical tests of a range of properties, including molecular thermochemistry and kinetics, geometries and vibrational frequencies, and bandgaps and excitation energies. Rung 3.5 functionals typically provide accuracy intermediate between semilocal and hybrid approximations. Nonlocal potential contributions from γ SL yield interesting successes and failures for band structures and excitation energies. The results enable and motivate continued exploration of Rung 3.5 functional forms

Quantized kernel least mean square algorithm.

Science.gov (United States)

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

2012-01-01

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

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

Science.gov (United States)

Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

2015-01-01

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

Soft functions for generic jet algorithms and observables at hadron colliders

Energy Technology Data Exchange (ETDEWEB)

Bertolini, Daniele [Lawrence Berkeley National Laboratory, Berkeley, CA (United States). Theoretical Physics Group; California Univ., Berkeley, CA (United States). Berkeley Center for Theoretical Physics; Kolodrubetz, Daniel; Stewart, Iain W. [Massachusetts Institute of Technology, Cambridge, MA (United States). Center for Theoretical Physics; Duff, Neill [Los Alamos National Laboratory, NM (United States). Theoretical Div.; Massachusetts Institute of Technology, Cambridge, MA (United States). Center for Theoretical Physics; Pietrulewicz, Piotr; Tackmann, Frank J. [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany). Theory Group; Waalewijn, Wouter J. [NIKHEF, Amsterdam (Netherlands). Theory Group; Amsterdam Univ. (Netherlands). Inst. for Theoretical Physics Amsterdam and Delta Inst. for Theoretical Physics

2017-07-15

We introduce a method to compute one-loop soft functions for exclusive N-jet processes at hadron colliders, allowing for different definitions of the algorithm that determines the jet regions and of the measurements in those regions. In particular, we generalize the N-jettiness hemisphere decomposition of T. T. Joutennus et al. (2011) in a manner that separates the dependence on the jet boundary from the observables measured inside the jet and beam regions. Results are given for several factorizable jet definitions, including anti-k{sub T}, XCone, and other geometric partitionings. We calculate explicitly the soft functions for angularity measurements, including jet mass and jet broadening, in pp→L+1 jet and explore the differences for various jet vetoes and algorithms. This includes a consistent treatment of rapidity divergences when applicable. We also compute analytic results for these soft functions in an expansion for a small jet radius R. We find that the small-R results, including corrections up to O(R{sup 2}), accurately capture the full behavior over a large range of R.

Preconditioners based on the Alternating-Direction-Implicit algorithm for the 2D steady-state diffusion equation with orthotropic heterogeneous coefficients

KAUST Repository

Gao, Longfei; Calo, Victor M.

2015-01-01

In this paper, we combine the Alternating Direction Implicit (ADI) algorithm with the concept of preconditioning and apply it to linear systems discretized from the 2D steady-state diffusion equations with orthotropic heterogeneous coefficients by the finite element method assuming tensor product basis functions. Specifically, we adopt the compound iteration idea and use ADI iterations as the preconditioner for the outside Krylov subspace method that is used to solve the preconditioned linear system. An efficient algorithm to perform each ADI iteration is crucial to the efficiency of the overall iterative scheme. We exploit the Kronecker product structure in the matrices, inherited from the tensor product basis functions, to achieve high efficiency in each ADI iteration. Meanwhile, in order to reduce the number of Krylov subspace iterations, we incorporate partially the coefficient information into the preconditioner by exploiting the local support property of the finite element basis functions. Numerical results demonstrated the efficiency and quality of the proposed preconditioner. © 2014 Elsevier B.V. All rights reserved.

The reduced basis method for the electric field integral equation

International Nuclear Information System (INIS)

Fares, M.; Hesthaven, J.S.; Maday, Y.; Stamm, B.

2011-01-01

We introduce the reduced basis method (RBM) as an efficient tool for parametrized scattering problems in computational electromagnetics for problems where field solutions are computed using a standard Boundary Element Method (BEM) for the parametrized electric field integral equation (EFIE). This combination enables an algorithmic cooperation which results in a two step procedure. The first step consists of a computationally intense assembling of the reduced basis, that needs to be effected only once. In the second step, we compute output functionals of the solution, such as the Radar Cross Section (RCS), independently of the dimension of the discretization space, for many different parameter values in a many-query context at very little cost. Parameters include the wavenumber, the angle of the incident plane wave and its polarization.

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

International Nuclear Information System (INIS)

Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine; Minca, Eugenia; Filip, Florin

2009-01-01

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

Function-Oriented Networking and On-Demand Routing System in Network Using Ant Colony Optimization Algorithm

Directory of Open Access Journals (Sweden)

Young-Bo Sim

2017-11-01

Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

Science.gov (United States)

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Symmetry-Adapted Ro-vibrational Basis Functions for Variational Nuclear Motion Calculations: TROVE Approach.

Science.gov (United States)

Yurchenko, Sergei N; Yachmenev, Andrey; Ovsyannikov, Roman I

2017-09-12

We present a general, numerically motivated approach to the construction of symmetry-adapted basis functions for solving ro-vibrational Schrödinger equations. The approach is based on the property of the Hamiltonian operator to commute with the complete set of symmetry operators and, hence, to reflect the symmetry of the system. The symmetry-adapted ro-vibrational basis set is constructed numerically by solving a set of reduced vibrational eigenvalue problems. In order to assign the irreducible representations associated with these eigenfunctions, their symmetry properties are probed on a grid of molecular geometries with the corresponding symmetry operations. The transformation matrices are reconstructed by solving overdetermined systems of linear equations related to the transformation properties of the corresponding wave functions on the grid. Our method is implemented in the variational approach TROVE and has been successfully applied to many problems covering the most important molecular symmetry groups. Several examples are used to illustrate the procedure, which can be easily applied to different types of coordinates, basis sets, and molecular systems.

epsilon : A tool to find a canonical basis of master integrals

Science.gov (United States)

Prausa, Mario

2017-10-01

In 2013, Henn proposed a special basis for a certain class of master integrals, which are expressible in terms of iterated integrals. In this basis, the master integrals obey a differential equation, where the right hand side is proportional to ɛ in d = 4 - 2 ɛ space-time dimensions. An algorithmic approach to find such a basis was found by Lee. We present the tool epsilon, an efficient implementation of Lee's algorithm based on the Fermat computer algebra system as computational back end.

Optimized Bayesian dynamic advising theory and algorithms

CERN Document Server

Karny, Miroslav

2006-01-01

Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture models

The algorithm of individualization in physical education students

Directory of Open Access Journals (Sweden)

Barybina L.N.

2012-11-01

Full Text Available The algorithm of individualization of process of physical education is offered in higher educational establishment. Basis of algorithm is made by the physical, functional and psychophysiological features of students. In research took part 413 students (177 girls and 236 youths. The stages of algorithm of the author system of individualization of physical education of students are presented. It is set that youths (a type of sport is basketball and volleyball have a similar structure of indexes of psycho-physiological possibilities, physical preparedness and progress. High meaningfulness of the computer programs which are instrumental in perfection of the system of physical education is set. Also the programs allow quickly and effectively to determine the psycho-physiological features of students. It is recommended to distribute students on sporting specializations in obedience to their individual features.

Wavelets as basis functions in electronic structure calculations; Les ondelettes comme fonction de base dans le calcul de structures electroniques

Energy Technology Data Exchange (ETDEWEB)

Chauvin, C

2005-11-15

This thesis is devoted to the definition and the implementation of a multi-resolution method to determine the fundamental state of a system composed of nuclei and electrons. In this work, we are interested in the Density Functional Theory (DFT), which allows to express the Hamiltonian operator with the electronic density only, by a Coulomb potential and a non-linear potential. This operator acts on orbitals, which are solutions of the so-called Kohn-Sham equations. Their resolution needs to express orbitals and density on a set of functions owing both physical and numerical properties, as explained in the second chapter. One can hardly satisfy these two properties simultaneously, that is why we are interested in orthogonal and bi-orthogonal wavelets basis, whose properties of interpolation are presented in the third chapter. We present in the fourth chapter three dimensional solvers for the Coulomb's potential, using not only the preconditioning property of wavelets, but also a multigrid algorithm. Determining this potential allows us to solve the self-consistent Kohn-Sham equations, by an algorithm presented in chapter five. The originality of our method consists in the construction of the stiffness matrix, combining a Galerkin formulation and a collocation scheme. We analyse the approximation properties of this method in case of linear Hamiltonian, such as harmonic oscillator and hydrogen, and present convergence results of the DFT for small electrons. Finally we show how orbital compression reduces considerably the number of coefficients to keep, while preserving a good accuracy of the fundamental energy. (author)

Space-time spectral collocation algorithm for solving time-fractional Tricomi-type equations

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Abdelkawy M.A.

2016-01-01

Full Text Available We introduce a new numerical algorithm for solving one-dimensional time-fractional Tricomi-type equations (T-FTTEs. We used the shifted Jacobi polynomials as basis functions and the derivatives of fractional is evaluated by the Caputo definition. The shifted Jacobi Gauss-Lobatt algorithm is used for the spatial discretization, while the shifted Jacobi Gauss-Radau algorithmis applied for temporal approximation. Substituting these approximations in the problem leads to a system of algebraic equations that greatly simplifies the problem. The proposed algorithm is successfully extended to solve the two-dimensional T-FTTEs. Extensive numerical tests illustrate the capability and high accuracy of the proposed methodologies.

A quantitative performance evaluation of the EM algorithm applied to radiographic images

International Nuclear Information System (INIS)

Brailean, J.C.; Sullivan, B.J.; Giger, M.L.; Chen, C.T.

1991-01-01

In this paper, the authors quantitatively evaluate the performance of the Expectation Maximization (EM) algorithm as a restoration technique for radiographic images. The perceived signal-to-noise ratio (SNR), of simple radiographic patterns processed by the EM algorithm are calculated on the basis of a statistical decision theory model that includes both the observer's visual response function and a noise component internal to the eye-brain system. The relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to quantitatively compare the effects of the EM algorithm to two popular image enhancement techniques: contrast enhancement (windowing) and unsharp mask filtering

Structure-Based Algorithms for Microvessel Classification

KAUST Repository

Smith, Amy F.

2015-02-01

© 2014 The Authors. Microcirculation published by John Wiley & Sons Ltd. Objective: Recent developments in high-resolution imaging techniques have enabled digital reconstruction of three-dimensional sections of microvascular networks down to the capillary scale. To better interpret these large data sets, our goal is to distinguish branching trees of arterioles and venules from capillaries. Methods: Two novel algorithms are presented for classifying vessels in microvascular anatomical data sets without requiring flow information. The algorithms are compared with a classification based on observed flow directions (considered the gold standard), and with an existing resistance-based method that relies only on structural data. Results: The first algorithm, developed for networks with one arteriolar and one venular tree, performs well in identifying arterioles and venules and is robust to parameter changes, but incorrectly labels a significant number of capillaries as arterioles or venules. The second algorithm, developed for networks with multiple inlets and outlets, correctly identifies more arterioles and venules, but is more sensitive to parameter changes. Conclusions: The algorithms presented here can be used to classify microvessels in large microvascular data sets lacking flow information. This provides a basis for analyzing the distinct geometrical properties and modelling the functional behavior of arterioles, capillaries, and venules.

A Comparison of the Behavior of Functional/Basis Set Combinations for Hydrogen-Bonding in the Water Dimer with Emphasis on Basis Set Superposition Error

OpenAIRE

Plumley, Joshua A.; Dannenberg, J. J.

2011-01-01

We evaluate the performance of nine functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D and MPWB1K) in combination with 16 basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of functionals and basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-DFT molecular orbital calculations and to experimenta...

Method of applying single higher order polynomial basis function over multiple domains

CSIR Research Space (South Africa)

Lysko, AA

2010-03-01

Full Text Available A novel method has been devised where one set of higher order polynomial-based basis functions can be applied over several wire segments, thus permitting to decouple the number of unknowns from the number of segments, and so from the geometrical...

A multilevel search algorithm for the maximization of submodular functions applied to the quadratic cost partition problem

NARCIS (Netherlands)

Goldengorin, B.; Ghosh, D.

Maximization of submodular functions on a ground set is a NP-hard combinatorial optimization problem. Data correcting algorithms are among the several algorithms suggested for solving this problem exactly and approximately. From the point of view of Hasse diagrams data correcting algorithms use

Method of moments solution of volume integral equations using higher-order hierarchical Legendre basis functions

DEFF Research Database (Denmark)

Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter

2004-01-01

An efficient higher-order method of moments (MoM) solution of volume integral equations is presented. The higher-order MoM solution is based on higher-order hierarchical Legendre basis functions and higher-order geometry modeling. An unstructured mesh composed of 8-node trilinear and/or curved 27...... of magnitude in comparison to existing higher-order hierarchical basis functions. Consequently, an iterative solver can be applied even for high expansion orders. Numerical results demonstrate excellent agreement with the analytical Mie series solution for a dielectric sphere as well as with results obtained...

STAR Algorithm Integration Team - Facilitating operational algorithm development

Science.gov (United States)

Mikles, V. J.

2015-12-01

The NOAA/NESDIS Center for Satellite Research and Applications (STAR) provides technical support of the Joint Polar Satellite System (JPSS) algorithm development and integration tasks. Utilizing data from the S-NPP satellite, JPSS generates over thirty Environmental Data Records (EDRs) and Intermediate Products (IPs) spanning atmospheric, ocean, cryosphere, and land weather disciplines. The Algorithm Integration Team (AIT) brings technical expertise and support to product algorithms, specifically in testing and validating science algorithms in a pre-operational environment. The AIT verifies that new and updated algorithms function in the development environment, enforces established software development standards, and ensures that delivered packages are functional and complete. AIT facilitates the development of new JPSS-1 algorithms by implementing a review approach based on the Enterprise Product Lifecycle (EPL) process. Building on relationships established during the S-NPP algorithm development process and coordinating directly with science algorithm developers, the AIT has implemented structured reviews with self-contained document suites. The process has supported algorithm improvements for products such as ozone, active fire, vegetation index, and temperature and moisture profiles.

Optimization of vehicle compartment low frequency noise based on Radial Basis Function Neuro-Network Approximation Model

Directory of Open Access Journals (Sweden)

HU Qi-guo

2017-01-01

Full Text Available For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel’s thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver’s head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively. The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly.

A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

Science.gov (United States)

Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

Development of fuzzy algorithm with learning function for nuclear steam generator level control

International Nuclear Information System (INIS)

Park, Gee Yong; Seong, Poong Hyun

1993-01-01

A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)

Density Functional Theory and the Basis Set Truncation Problem with Correlation Consistent Basis Sets: Elephant in the Room or Mouse in the Closet?

Science.gov (United States)

Feller, David; Dixon, David A

2018-03-08

Two recent papers in this journal called into question the suitability of the correlation consistent basis sets for density functional theory (DFT) calculations, because the sets were designed for correlated methods such as configuration interaction, perturbation theory, and coupled cluster theory. These papers focused on the ability of the correlation consistent and other basis sets to reproduce total energies, atomization energies, and dipole moments obtained from "quasi-exact" multiwavelet results. Undesirably large errors were observed for the correlation consistent basis sets. One of the papers argued that basis sets specifically optimized for DFT methods were "essential" for obtaining high accuracy. In this work we re-examined the performance of the correlation consistent basis sets by resolving problems with the previous calculations and by making more appropriate basis set choices for the alkali and alkaline-earth metals and second-row elements. When this is done, the statistical errors with respect to the benchmark values and with respect to DFT optimized basis sets are greatly reduced, especially in light of the relatively large intrinsic error of the underlying DFT method. When judged with respect to high-quality Feller-Peterson-Dixon coupled cluster theory atomization energies, the PBE0 DFT method used in the previous studies exhibits a mean absolute deviation more than a factor of 50 larger than the quintuple zeta basis set truncation error.

SYNTHESIS AND REDUCED LOGIC GATE REALIZATION OF MULTI-VALUED LOGIC FUNCTIONS USING NEURAL NETWORK DEPLOYMENT ALGORITHM

Directory of Open Access Journals (Sweden)

A. K. CHOWDHURY

2016-02-01

Full Text Available In this paper an evolutionary technique for synthesizing Multi-Valued Logic (MVL functions using Neural Network Deployment Algorithm (NNDA is presented. The algorithm is combined with back-propagation learning capability and neural MVL operators. This research article is done to observe the anomalistic characteristics of MVL neural operators and their role in synthesis. The advantages of NNDA-MVL algorithm is demonstrated with realization of synthesized many valued functions with lesser MVL operators. The characteristic feature set consists of MVL gate count, network link count, network propagation delay and accuracy achieved in training. In brief, this paper depicts an effort of reduced network size for synthesized MVL functions. Trained MVL operators improve the basic architecture by reducing MIN gate and interlink connection by 52.94% and 23.38% respectively.

Beam-hardening correction in CT based on basis image and TV model

International Nuclear Information System (INIS)

Li Qingliang; Yan Bin; Li Lei; Sun Hongsheng; Zhang Feng

2012-01-01

In X-ray computed tomography, the beam hardening leads to artifacts and reduces the image quality. It analyzes how beam hardening influences on original projection. According, it puts forward a kind of new beam-hardening correction method based on the basis images and TV model. Firstly, according to physical characteristics of the beam hardening an preliminary correction model with adjustable parameters is set up. Secondly, using different parameters, original projections are operated by the correction model. Thirdly, the projections are reconstructed to obtain a series of basis images. Finally, the linear combination of basis images is the final reconstruction image. Here, with total variation for the final reconstruction image as the cost function, the linear combination coefficients for the basis images are determined according to iterative method. To verify the effectiveness of the proposed method, the experiments are carried out on real phantom and industrial part. The results show that the algorithm significantly inhibits cup and strip artifacts in CT image. (authors)

Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation

Directory of Open Access Journals (Sweden)

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.

A deterministic algorithm for fitting a step function to a weighted point-set

KAUST Repository

Fournier, Hervé

2013-02-01

Given a set of n points in the plane, each point having a positive weight, and an integer k>0, we present an optimal O(nlogn)-time deterministic algorithm to compute a step function with k steps that minimizes the maximum weighted vertical distance to the input points. It matches the expected time bound of the best known randomized algorithm for this problem. Our approach relies on Coles improved parametric searching technique. As a direct application, our result yields the first O(nlogn)-time algorithm for computing a k-center of a set of n weighted points on the real line. © 2012 Elsevier B.V.

MANAGER PRINCIPLES AS BASIS OF MANAGEMENT STYLE TRANSFORMATION

OpenAIRE

R. A. Kopytov

2011-01-01

The paper considers an approach which is based on non-conventional mechanisms of management style formation. The preset level of sustainable management is maintained by self-organized environment created in the process of management style transformation in efficient management principles. Their efficiency is checked within an adaptive algorithm. The algorithm is developed on the basis of combination of evaluative tools  and base of operational  proves. The operating algorithm capability is te...

Basis reduction for layered lattices

NARCIS (Netherlands)

E.L. Torreão Dassen (Erwin)

2011-01-01

htmlabstractWe develop the theory of layered Euclidean spaces and layered lattices. With this new theory certain problems that usually are solved by using classical lattices with a "weighting" gain a new, more natural form. Using the layered lattice basis reduction algorithms introduced here these

A comparison of global optimization algorithms with standard benchmark functions and real-world applications using Energy Plus

Energy Technology Data Exchange (ETDEWEB)

Kamph, Jerome Henri; Robinson, Darren; Wetter, Michael

2009-09-01

There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimise the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility.

Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1

Energy Technology Data Exchange (ETDEWEB)

Luna, Antonio [Health Time Group, Jaen (Spain); University Hospitals, Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Radiology; Vilanova, Joan C. [Clinica Girona - Hospital Sta. Caterina, Girona (Spain); Hygino da Cruz, L. Celso Jr. [CDPI and IRM, Rio de Janeiro, RJ (Brazil). Dept. of Radiology; Rossi, Santiago E. (ed.) [Centro de Diagnostico, Buenos Aires (Argentina)

2014-07-01

Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.

Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1

International Nuclear Information System (INIS)

Luna, Antonio; Hygino da Cruz, L. Celso Jr.

2014-01-01

Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.

Molecular basis of the functional heterogeneity of the muscarinic acetylcholine receptor

International Nuclear Information System (INIS)

Numa, S.; Fukuda, K.; Kubo, T.; Maeda, A.; Akiba, I.; Bujo, H.; Nakai, J.; Mishina, M.; Higashida, H.

1988-01-01

The muscarinic acetylcholine receptor (mAChR) mediates a variety of cellular responses, including inhibition of adenylate cyclase, breakdown of phosphoinositides, and modulation of potassium channels, through the action of guanine-nucleotide-binding regulatory proteins (G proteins). The question then arises as to whether multiple mAChR species exist that are responsible for the various biochemical and physiological effects. In fact, pharmacologically distinguishable forms of the mAChR occur in different tissues and have been provisionally classified into M 1 (I), M 2 cardiac (II), and M 2 glandular (III) subtypes on the basis of their difference in apparent affinity for antagonists. Here, the authors have made attempts to understand the molecular basis of the functional heterogeneity of the mAChR, using recombinant DNA technology

The static response function in Kohn-Sham theory: An appropriate basis for its matrix representation in case of finite AO basis sets

International Nuclear Information System (INIS)

Kollmar, Christian; Neese, Frank

2014-01-01

The role of the static Kohn-Sham (KS) response function describing the response of the electron density to a change of the local KS potential is discussed in both the theory of the optimized effective potential (OEP) and the so-called inverse Kohn-Sham problem involving the task to find the local KS potential for a given electron density. In a general discussion of the integral equation to be solved in both cases, it is argued that a unique solution of this equation can be found even in case of finite atomic orbital basis sets. It is shown how a matrix representation of the response function can be obtained if the exchange-correlation potential is expanded in terms of a Schmidt-orthogonalized basis comprising orbitals products of occupied and virtual orbitals. The viability of this approach in both OEP theory and the inverse KS problem is illustrated by numerical examples

General Fit-Basis Functions and Specialized Coordinates in an Adaptive Density-Guided Approach to Potential Energy Surfaces

DEFF Research Database (Denmark)

Klinting, Emil Lund; Thomsen, Bo; Godtliebsen, Ian Heide

. This results in a decreased number of single point calculations required during the potential construction. Especially the Morse-like fit-basis functions are of interest, when combined with rectilinear hybrid optimized and localized coordinates (HOLCs), which can be generated as orthogonal transformations......The overall shape of a molecular energy surface can be very different for different molecules and different vibrational coordinates. This means that the fit-basis functions used to generate an analytic representation of a potential will be met with different requirements. It is therefore worthwhile...... single point calculations when constructing the molecular potential. We therefore present a uniform framework that can handle general fit-basis functions of any type which are specified on input. This framework is implemented to suit the black-box nature of the ADGA in order to avoid arbitrary choices...

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)

A reconstruction algorithm for electrical impedance tomography based on sparsity regularization

KAUST Repository

Jin, Bangti

2011-08-24

This paper develops a novel sparse reconstruction algorithm for the electrical impedance tomography problem of determining a conductivity parameter from boundary measurements. The sparsity of the \\'inhomogeneity\\' with respect to a certain basis is a priori assumed. The proposed approach is motivated by a Tikhonov functional incorporating a sparsity-promoting ℓ 1-penalty term, and it allows us to obtain quantitative results when the assumption is valid. A novel iterative algorithm of soft shrinkage type was proposed. Numerical results for several two-dimensional problems with both single and multiple convex and nonconvex inclusions were presented to illustrate the features of the proposed algorithm and were compared with one conventional approach based on smoothness regularization. © 2011 John Wiley & Sons, Ltd.

Accuracy of Lagrange-sinc functions as a basis set for electronic structure calculations of atoms and molecules

International Nuclear Information System (INIS)

Choi, Sunghwan; Hong, Kwangwoo; Kim, Jaewook; Kim, Woo Youn

2015-01-01

We developed a self-consistent field program based on Kohn-Sham density functional theory using Lagrange-sinc functions as a basis set and examined its numerical accuracy for atoms and molecules through comparison with the results of Gaussian basis sets. The result of the Kohn-Sham inversion formula from the Lagrange-sinc basis set manifests that the pseudopotential method is essential for cost-effective calculations. The Lagrange-sinc basis set shows faster convergence of the kinetic and correlation energies of benzene as its size increases than the finite difference method does, though both share the same uniform grid. Using a scaling factor smaller than or equal to 0.226 bohr and pseudopotentials with nonlinear core correction, its accuracy for the atomization energies of the G2-1 set is comparable to all-electron complete basis set limits (mean absolute deviation ≤1 kcal/mol). The same basis set also shows small mean absolute deviations in the ionization energies, electron affinities, and static polarizabilities of atoms in the G2-1 set. In particular, the Lagrange-sinc basis set shows high accuracy with rapid convergence in describing density or orbital changes by an external electric field. Moreover, the Lagrange-sinc basis set can readily improve its accuracy toward a complete basis set limit by simply decreasing the scaling factor regardless of systems

A sparse matrix based full-configuration interaction algorithm

International Nuclear Information System (INIS)

Rolik, Zoltan; Szabados, Agnes; Surjan, Peter R.

2008-01-01

We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers

Developing the algorithm for assessing the competitive abilities of functional foods in marketing

Directory of Open Access Journals (Sweden)

Nilova Liudmila

2017-01-01

Full Text Available A thorough analysis of competitive factors of functional foods has made it possible to develop an algorithm for assessing the competitive factors of functional food products, with respect to their essential consumer features — quality, safety and functionality. Questionnaires filled in by experts and the published results of surveys of consumers from different countries were used to help select the essential consumer features in functional foods. A “desirability of consumer features” model triangle, based on functional bread and bakery products, was constructed with the use of the Harrington function.

A Particle Swarm Optimization Algorithm with Variable Random Functions and Mutation

Institute of Scientific and Technical Information of China (English)

ZHOU Xiao-Jun; YANG Chun-Hua; GUI Wei-Hua; DONG Tian-Xue

2014-01-01

The convergence analysis of the standard particle swarm optimization (PSO) has shown that the changing of random functions, personal best and group best has the potential to improve the performance of the PSO. In this paper, a novel strategy with variable random functions and polynomial mutation is introduced into the PSO, which is called particle swarm optimization algorithm with variable random functions and mutation (PSO-RM). Random functions are adjusted with the density of the population so as to manipulate the weight of cognition part and social part. Mutation is executed on both personal best particle and group best particle to explore new areas. Experiment results have demonstrated the effectiveness of the strategy.

Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

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.

Quantum Chemistry on Quantum Computers: A Polynomial-Time Quantum Algorithm for Constructing the Wave Functions of Open-Shell Molecules.

Science.gov (United States)

Sugisaki, Kenji; Yamamoto, Satoru; Nakazawa, Shigeaki; Toyota, Kazuo; Sato, Kazunobu; Shiomi, Daisuke; Takui, Takeji

2016-08-18

Quantum computers are capable to efficiently perform full configuration interaction (FCI) calculations of atoms and molecules by using the quantum phase estimation (QPE) algorithm. Because the success probability of the QPE depends on the overlap between approximate and exact wave functions, efficient methods to prepare accurate initial guess wave functions enough to have sufficiently large overlap with the exact ones are highly desired. Here, we propose a quantum algorithm to construct the wave function consisting of one configuration state function, which is suitable for the initial guess wave function in QPE-based FCI calculations of open-shell molecules, based on the addition theorem of angular momentum. The proposed quantum algorithm enables us to prepare the wave function consisting of an exponential number of Slater determinants only by a polynomial number of quantum operations.

UAV Control on the Basis of 3D Landmark Bearing-Only Observations.

Science.gov (United States)

Karpenko, Simon; Konovalenko, Ivan; Miller, Alexander; Miller, Boris; Nikolaev, Dmitry

2015-11-27

The article presents an approach to the control of a UAV on the basis of 3D landmark observations. The novelty of the work is the usage of the 3D RANSAC algorithm developed on the basis of the landmarks' position prediction with the aid of a modified Kalman-type filter. Modification of the filter based on the pseudo-measurements approach permits obtaining unbiased UAV position estimation with quadratic error characteristics. Modeling of UAV flight on the basis of the suggested algorithm shows good performance, even under significant external perturbations.

The algorithms for calculating synthetic seismograms from a dipole source using the derivatives of Green's function

Science.gov (United States)

Pavlov, V. M.

2017-07-01

The problem of calculating complete synthetic seismograms from a point dipole with an arbitrary seismic moment tensor in a plane parallel medium composed of homogeneous elastic isotropic layers is considered. It is established that the solutions of the system of ordinary differential equations for the motion-stress vector have a reciprocity property, which allows obtaining a compact formula for the derivative of the motion vector with respect to the source depth. The reciprocity theorem for Green's functions with respect to the interchange of the source and receiver is obtained for a medium with cylindrical boundary. The differentiation of Green's functions with respect to the coordinates of the source leads to the same calculation formulas as the algorithm developed in the previous work (Pavlov, 2013). A new algorithm appears when the derivatives with respect to the horizontal coordinates of the source is replaced by the derivatives with respect to the horizontal coordinates of the receiver (with the minus sign). This algorithm is more transparent, compact, and economic than the previous one. It requires calculating the wavenumbers associated with Bessel function's roots of order 0 and order 1, whereas the previous algorithm additionally requires the second order roots.

Model's sparse representation based on reduced mixed GMsFE basis methods

Energy Technology Data Exchange (ETDEWEB)

Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn [Institute of Mathematics, Hunan University, Changsha 410082 (China); Li, Qiuqi, E-mail: qiuqili@hnu.edu.cn [College of Mathematics and Econometrics, Hunan University, Changsha 410082 (China)

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

Science.gov (United States)

Hajri, S; Liouane, N; Hammadi, S; Borne, P

2000-01-01

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

Efficient and Flexible Computation of Many-Electron Wave Function Overlaps.

Science.gov (United States)

Plasser, Felix; Ruckenbauer, Matthias; Mai, Sebastian; Oppel, Markus; Marquetand, Philipp; González, Leticia

2016-03-08

A new algorithm for the computation of the overlap between many-electron wave functions is described. This algorithm allows for the extensive use of recurring intermediates and thus provides high computational efficiency. Because of the general formalism employed, overlaps can be computed for varying wave function types, molecular orbitals, basis sets, and molecular geometries. This paves the way for efficiently computing nonadiabatic interaction terms for dynamics simulations. In addition, other application areas can be envisaged, such as the comparison of wave functions constructed at different levels of theory. Aside from explaining the algorithm and evaluating the performance, a detailed analysis of the numerical stability of wave function overlaps is carried out, and strategies for overcoming potential severe pitfalls due to displaced atoms and truncated wave functions are presented.

Vector Green's function algorithm for radiative transfer in plane-parallel atmosphere

International Nuclear Information System (INIS)

Qin Yi; Box, Michael A.

2006-01-01

Green's function is a widely used approach for boundary value problems. In problems related to radiative transfer, Green's function has been found to be useful in land, ocean and atmosphere remote sensing. It is also a key element in higher order perturbation theory. This paper presents an explicit expression of the Green's function, in terms of the source and radiation field variables, for a plane-parallel atmosphere with either vacuum boundaries or a reflecting (BRDF) surface. Full polarization state is considered but the algorithm has been developed in such way that it can be easily reduced to solve scalar radiative transfer problems, which makes it possible to implement a single set of code for computing both the scalar and the vector Green's function

Efficient second order Algorithms for Function Approximation with Neural Networks. Application to Sextic Potentials

International Nuclear Information System (INIS)

Gougam, L.A.; Taibi, H.; Chikhi, A.; Mekideche-Chafa, F.

2009-01-01

The problem of determining the analytical description for a set of data arises in numerous sciences and applications and can be referred to as data modeling or system identification. Neural networks are a convenient means of representation because they are known to be universal approximates that can learn data. The desired task is usually obtained by a learning procedure which consists in adjusting the s ynaptic weights . For this purpose, many learning algorithms have been proposed to update these weights. The convergence for these learning algorithms is a crucial criterion for neural networks to be useful in different applications. The aim of the present contribution is to use a training algorithm for feed forward wavelet networks used for function approximation. The training is based on the minimization of the least-square cost function. The minimization is performed by iterative second order gradient-based methods. We make use of the Levenberg-Marquardt algorithm to train the architecture of the chosen network and, then, the training procedure starts with a simple gradient method which is followed by a BFGS (Broyden, Fletcher, Glodfarb et Shanno) algorithm. The performances of the two algorithms are then compared. Our method is then applied to determine the energy of the ground state associated to a sextic potential. In fact, the Schrodinger equation does not always admit an exact solution and one has, generally, to solve it numerically. To this end, the sextic potential is, firstly, approximated with the above outlined wavelet network and, secondly, implemented into a numerical scheme. Our results are in good agreement with the ones found in the literature.

Fast generation of macro basis functions for LEGO through the adaptive cross approximation

NARCIS (Netherlands)

Lancellotti, V.

2015-01-01

We present a method for the fast generation of macro basis functions in the context of the linear embedding via Green's operators approach (LEGO) which is a domain decomposition technique based on the combination of electromagnetic bricks in turn described by means of scattering operators. We show

Application of regularization technique in image super-resolution algorithm via sparse representation

Science.gov (United States)

Huang, De-tian; Huang, Wei-qin; Huang, Hui; Zheng, Li-xin

2017-11-01

To make use of the prior knowledge of the image more effectively and restore more details of the edges and structures, a novel sparse coding objective function is proposed by applying the principle of the non-local similarity and manifold learning on the basis of super-resolution algorithm via sparse representation. Firstly, the non-local similarity regularization term is constructed by using the similar image patches to preserve the edge information. Then, the manifold learning regularization term is constructed by utilizing the locally linear embedding approach to enhance the structural information. The experimental results validate that the proposed algorithm has a significant improvement compared with several super-resolution algorithms in terms of the subjective visual effect and objective evaluation indices.

A filtered backprojection algorithm with characteristics of the iterative landweber algorithm

OpenAIRE

L. Zeng, Gengsheng

2012-01-01

Purpose: In order to eventually develop an analytical algorithm with noise characteristics of an iterative algorithm, this technical note develops a window function for the filtered backprojection (FBP) algorithm in tomography that behaves as an iterative Landweber algorithm.

Comparison between Genetic Algorithms and Particle Swarm Optimization Methods on Standard Test Functions and Machine Design

DEFF Research Database (Denmark)

Nica, Florin Valentin Traian; Ritchie, Ewen; Leban, Krisztina Monika

2013-01-01

, genetic algorithm and particle swarm are shortly presented in this paper. These two algorithms are tested to determine their performance on five different benchmark test functions. The algorithms are tested based on three requirements: precision of the result, number of iterations and calculation time....... Both algorithms are also tested on an analytical design process of a Transverse Flux Permanent Magnet Generator to observe their performances in an electrical machine design application.......Nowadays the requirements imposed by the industry and economy ask for better quality and performance while the price must be maintained in the same range. To achieve this goal optimization must be introduced in the design process. Two of the best known optimization algorithms for machine design...

A metric for the Radial Basis Function Network - Application on Real Radar Data

NARCIS (Netherlands)

Heiden, R. van der; Groen, F.C.A.

1996-01-01

A Radial Basis Functions (RBF) network for pattern recognition is considered. Classification with such a network is based on distances between patterns, so a metric is always present. Using real radar data, the Euclidean metric is shown to perform poorly - a metric based on the so called Box-Cox

A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction

International Nuclear Information System (INIS)

Yu, Shiwei; Wang, Ke; Wei, Yi-Ming

2015-01-01

Highlights: • A hybrid self-adaptive PSO–GA-RBF model is proposed for electricity demand prediction. • Each mixed-coding particle is composed by two coding parts of binary and real. • Five independent variables have been selected to predict future electricity consumption in Wuhan. • The proposed model has a simpler structure or higher estimating precision than other ANN models. • No matter what the scenario, the electricity consumption of Wuhan will grow rapidly. - Abstract: The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO–GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the RBF by GA operation and the parameters of the basis and weights by a PSO–GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO–GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models; and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7–11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85 billion kW h. The lowest demand is estimated for the business-as-usual scenario, and will be 88.45 billion kW h

Algorithms: economical computation of functions of real matrices

International Nuclear Information System (INIS)

Weiss, Z.

1991-01-01

An algorithm is presented which economizes on the calculation of F(a), where A is a real matrix and F(x) a real valued function of x, using spectral analysis. Assuming the availability of the software for the calculation of the complete set of eigenvalues and eigen vectors of A, it is shown that the complex matrix arithmetics involved in subsequent operations leading from A to F(A) can be reduced to the size comparable with the analogous problem in real matrix arithmetics. Saving in CPU time and storage has been achieved by utilizing explicitly the property that complex eigenvalues of a real matrix appear in pairs of complex conjugated numbers. (author)

A conceptual basis to encode and detect organic functional groups in XML.

Science.gov (United States)

Sankar, Punnaivanam; Krief, Alain; Vijayasarathi, Durairaj

2013-06-01

A conceptual basis to define and detect organic functional groups is developed. The basic model of a functional group is termed as a primary functional group and is characterized by a group center composed of one or more group center atoms bonded to terminal atoms and skeletal carbon atoms. The generic group center patterns are identified from the structures of known functional groups. Accordingly, a chemical ontology 'Font' is developed to organize the existing functional groups as well as the new ones to be defined by the chemists. The basic model is extended to accommodate various combinations of primary functional groups as functional group assemblies. A concept of skeletal group is proposed to define the characteristic groups composed of only carbon atoms to be regarded as equivalent to functional groups. The combination of primary functional groups with skeletal groups is categorized as skeletal group assembly. In order to make the model suitable for reaction modeling purpose, a Graphical User Interface (GUI) is developed to define the functional groups and to encode in XML format appropriate to detect them in chemical structures. The system is capable of detecting multiple instances of primary functional groups as well as the overlapping poly-functional groups as the respective assemblies. Copyright © 2013 Elsevier Inc. All rights reserved.

The matrix elements of the potential energy operator between the Sp(2,R) basis generating functions. Near-magic nuclei

International Nuclear Information System (INIS)

Filippov, G.F.; Ovcharenko, V.I.; Teryoshin, Yu.V.

1980-01-01

For near-magnetic nuclei, the matrix elements of the central exchange nucleon-nucleon interaction potential energy operator between the generating functions of the total basis of the Sn are obtained. The basis states are highest weigt vectorsp(2,R) irreducible representatio of the SO(3) irredicible representation and in addition, have a definite O(A-1) symmetry. The Sp(2,R) basis generating matrix elements simplify essentially the problem of calculating the spectrum of collective excitations of the atomic nucleus over an intrinsic function of definite O(A-1) symmetry

An Alternative Route to Teaching Fraction Division: Abstraction of Common Denominator Algorithm

Directory of Open Access Journals (Sweden)

İsmail Özgür ZEMBAT

2015-06-01

Full Text Available From a curricular stand point, the traditional invert and multiply algorithm for division of fractions provides few affordances for linking to a rich understanding of fractions. On the other hand, an alternative algorithm, called common denominator algorithm, has many such affordances. The current study serves as an argument for shifting curriculum for fraction division from use of invert and multiply algorithm as a basis to the use of common denominator algorithm as a basis. This was accomplished with the analysis of learning of two prospective elementary teachers being an illustration of how to realize those conceptual affordances. In doing so, the article proposes an instructional sequence and details it by referring to both the (mathematical and pedagogical advantages and the disadvantages. As a result, this algorithm has a conceptual basis depending on basic operations of partitioning, unitizing, and counting, which make it accessible to learners. Also, when participants are encouraged to construct this algorithm based on their work with diagrams, common denominator algorithm formalizes the work that they do with diagrams.

An alternative route to teaching fraction division: Abstraction of common denominator algorithm

Directory of Open Access Journals (Sweden)

İsmail Özgür Zembat

2015-07-01

Full Text Available From a curricular stand point, the traditional invert and multiply algorithm for division of fractions provides few affordances for linking to a rich understanding of fractions. On the other hand, an alternative algorithm, called common denominator algorithm, has many such affordances. The current study serves as an argument for shifting curriculum for fraction division from use of invert and multiply algorithm as a basis to the use of common denominator algorithm as a basis. This was accomplished with the analysis of learning of two prospective elementary teachers being an illustration of how to realize those conceptual affordances. In doing so, the article proposes an instructional sequence and details it by referring to both the (mathematical and pedagogical advantages and the disadvantages. As a result, this algorithm has a conceptual basis depending on basic operations of partitioning, unitizing, and counting, which make it accessible to learners. Also, when participants are encouraged to construct this algorithm based on their work with diagrams, common denominator algorithm formalizes the work that they do with diagrams.

Performance-oriented asymptotic tracking control of hydraulic systems with radial basis function network disturbance observer

Directory of Open Access Journals (Sweden)

Jian Hu

2016-05-01

Full Text Available Uncertainties, including parametric uncertainties and uncertain nonlinearities, always exist in positioning servo systems driven by a hydraulic actuator, which would degrade their tracking accuracy. In this article, an integrated control scheme, which combines adaptive robust control together with radial basis function neural network–based disturbance observer, is proposed for high-accuracy motion control of hydraulic systems. Not only parametric uncertainties but also uncertain nonlinearities (i.e. nonlinear friction, external disturbances, and/or unmodeled dynamics are taken into consideration in the proposed controller. The above uncertainties are compensated, respectively, by adaptive control and radial basis function neural network, which are ultimately integrated together by applying feedforward compensation technique, in which the global stabilization of the controller is ensured via a robust feedback path. A new kind of parameter and weight adaptation law is designed on the basis of Lyapunov stability theory. Furthermore, the proposed controller obtains an expected steady performance even if modeling uncertainties exist, and extensive simulation results in various working conditions have proven the high performance of the proposed control scheme.

Reconstruction of tissue dynamics in the compressed breast using multiplexed measurements and temporal basis functions

Science.gov (United States)

Boverman, Gregory; Miller, Eric L.; Brooks, Dana H.; Fang, Qianqian; Carp, S. A.; Selb, J. J.; Boas, David A.

2007-02-01

In the course of our experiments imaging the compressed breast in conjunction with digital tomosynthesis, we have noted that significant changes in tissue optical properties, on the order of 5%, occur during our imaging protocol. These changes seem to consistent with changes both in total Hemoglobin concentration as well as in oxygen saturation, as was the case for our standalone breast compression study, which made use of reflectance measurements. Simulation experiments show the importance of taking into account the temporal dynamics in the image reconstruction, and demonstrate the possibility of imaging the spatio-temporal dynamics of oxygen saturation and total Hemoglobin in the breast. In the image reconstruction, we make use of spatio-temporal basis functions, specifically a voxel basis for spatial imaging, and a cubic spline basis in time, and we reconstruct the spatio-temporal images using the entire data set simultaneously, making use of both absolute and relative measurements in the cost function. We have modified the sequence of sources used in our imaging acquisition protocol to improve our temporal resolution, and preliminary results are shown for normal subjects.

An interactive algorithm for identifying multiattribute measurable value functions based on finite-order independence of structural difference

International Nuclear Information System (INIS)

Tamura, Hiroyuki; Hikita, Shiro

1985-01-01

In this paper, we develop an interactive algorithm for identifying multiattribute measurable value functions based on the concept of finite-order independence of structural difference. This concept includes Dyer and Sarin's weak difference independence as special cases. The algorithm developed is composed of four major parts: 1) formulation of the problem 2) assessment of normalized conditional value functions and structural difference functions 3) assessment of corner values 4) assessment of the order of independence of structural difference and selection of the model. A hypothetical numerical example of a trade-off analysis for siting a nuclear power plant is included. (author)

An algorithm of computing inhomogeneous differential equations for definite integrals

OpenAIRE

Nakayama, Hiromasa; Nishiyama, Kenta

2010-01-01

We give an algorithm to compute inhomogeneous differential equations for definite integrals with parameters. The algorithm is based on the integration algorithm for $D$-modules by Oaku. Main tool in the algorithm is the Gr\\"obner basis method in the ring of differential operators.

Algorithm for removing scalp signals from functional near-infrared spectroscopy signals in real time using multidistance optodes.

Science.gov (United States)

Kiguchi, Masashi; Funane, Tsukasa

2014-11-01

A real-time algorithm for removing scalp-blood signals from functional near-infrared spectroscopy signals is proposed. Scalp and deep signals have different dependencies on the source-detector distance. These signals were separated using this characteristic. The algorithm was validated through an experiment using a dynamic phantom in which shallow and deep absorptions were independently changed. The algorithm for measurement of oxygenated and deoxygenated hemoglobins using two wavelengths was explicitly obtained. This algorithm is potentially useful for real-time systems, e.g., brain-computer interfaces and neuro-feedback systems.

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.

Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

Directory of Open Access Journals (Sweden)

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.

Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

Institute of Scientific and Technical Information of China (English)

Haidong Xu; Mingyan Jiang; Kun Xu

2015-01-01

The artificial bee colony (ABC) algorithm is a com-petitive stochastic population-based optimization algorithm. How-ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in-sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA cal ed Archimedean copula estima-tion of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench-mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen-tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

Science.gov (United States)

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Optimal design of the rotor geometry of line-start permanent magnet synchronous motor using the bat algorithm

Science.gov (United States)

Knypiński, Łukasz

2017-12-01

In this paper an algorithm for the optimization of excitation system of line-start permanent magnet synchronous motors will be presented. For the basis of this algorithm, software was developed in the Borland Delphi environment. The software consists of two independent modules: an optimization solver, and a module including the mathematical model of a synchronous motor with a self-start ability. The optimization module contains the bat algorithm procedure. The mathematical model of the motor has been developed in an Ansys Maxwell environment. In order to determine the functional parameters of the motor, additional scripts in Visual Basic language were developed. Selected results of the optimization calculation are presented and compared with results for the particle swarm optimization algorithm.

ROAD DETECTION BY NEURAL AND GENETIC ALGORITHM IN URBAN ENVIRONMENT

Directory of Open Access Journals (Sweden)

A. Barsi

2012-07-01

Full Text Available In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.

Generation of Optimal Basis Functions for Reconstruction of Power Distribution

Energy Technology Data Exchange (ETDEWEB)

Park, Moonghu [Sejong Univ., Seoul (Korea, Republic of)

2014-05-15

This study proposes GMDH to find not only the best functional form but also the optimal parameters those describe the power distribution most accurately. A total of 1,060 cases of axially 1-dimensional core power distributions of 20-nodes are generated by 3-dimensional core analysis code covering BOL to EOL core burnup histories to validate the method. Axially five-point box powers at in-core detectors are considered as measurements. The reconstructed axial power shapes using GMDH method are compared to the reference power shapes. The results show that the proposed method is very robust and accurate compared with spline fitting method. It is shown that the GMDH analysis can give optimal basis functions for core power shape reconstruction. The in-core measurements are the 5 detector snapshots and the 20-node power distribution is successfully reconstructed. The effectiveness of the method is demonstrated by comparing the results of spline fitting for BOL, saddle and top-skewed power shapes.

Focuss algorithm application in kinetic compartment modeling for PET tracer

International Nuclear Information System (INIS)

Huang Xinrui; Bao Shanglian

2004-01-01

Molecular imaging is in the process of becoming. Its application mostly depends on the molecular discovery process of imaging probes and drugs, from the mouse to the patient, from research to clinical practice. Positron emission tomography (PET) can non-invasively monitor . pharmacokinetic and functional processes of drugs in intact organisms at tracer concentrations by kinetic modeling. It has been known that for all biological systems, linear or nonlinear, if the system is injected by a tracer in a steady state, the distribution of the tracer follows the kinetics of a linear compartmental system, which has sums of exponential solutions. Based on the general compartmental description of the tracer's fate in vivo, we presented a novel kinetic modeling approach for the quantification of in vivo tracer studies with dynamic positron emission tomography (PET), which can determine a parsimonious model consisting with the measured data. This kinetic modeling technique allows for estimation of parametric images from a voxel based analysis and requires no a priori decision about the tracer's fate in vivo, instead determining the most appropriate model from the information contained within the kinetic data. Choosing a set of exponential functions, convolved with the plasma input function, as basis functions, the time activity curve of a region or a pixel can be written as a linear combination of the basis functions with corresponding coefficients. The number of non-zero coefficients returned corresponds to the model order which is related to the number of tissue compartments. The system macro parameters are simply determined using the focal underdetermined system solver (FOCUSS) algorithm. The FOCUSS algorithm is a nonparametric algorithm for finding localized energy solutions from limited data and is a recursive linear estimation procedure. FOCUSS algorithm usually converges very fast, so demands a few iterations. The effectiveness is verified by simulation and clinical

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

Science.gov (United States)

Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.

1991-01-01

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Maximize Minimum Utility Function of Fractional Cloud Computing System Based on Search Algorithm Utilizing the Mittag-Leffler Sum

Directory of Open Access Journals (Sweden)

Rabha W. Ibrahim

2018-01-01

Full Text Available The maximum min utility function (MMUF problem is an important representative of a large class of cloud computing systems (CCS. Having numerous applications in practice, especially in economy and industry. This paper introduces an effective solution-based search (SBS algorithm for solving the problem MMUF. First, we suggest a new formula of the utility function in term of the capacity of the cloud. We formulate the capacity in CCS, by using a fractional diffeo-integral equation. This equation usually describes the flow of CCS. The new formula of the utility function is modified recent active utility functions. The suggested technique first creates a high-quality initial solution by eliminating the less promising components, and then develops the quality of the achieved solution by the summation search solution (SSS. This method is considered by the Mittag-Leffler sum as hash functions to determine the position of the agent. Experimental results commonly utilized in the literature demonstrate that the proposed algorithm competes approvingly with the state-of-the-art algorithms both in terms of solution quality and computational efficiency.

A new algorithm to compute conjectured supply function equilibrium in electricity markets

International Nuclear Information System (INIS)

Diaz, Cristian A.; Villar, Jose; Campos, Fco Alberto; Rodriguez, M. Angel

2011-01-01

Several types of market equilibria approaches, such as Cournot, Conjectural Variation (CVE), Supply Function (SFE) or Conjectured Supply Function (CSFE) have been used to model electricity markets for the medium and long term. Among them, CSFE has been proposed as a generalization of the classic Cournot. It computes the equilibrium considering the reaction of the competitors against changes in their strategy, combining several characteristics of both CVE and SFE. Unlike linear SFE approaches, strategies are linearized only at the equilibrium point, using their first-order Taylor approximation. But to solve CSFE, the slope or the intercept of the linear approximations must be given, which has been proved to be very restrictive. This paper proposes a new algorithm to compute CSFE. Unlike previous approaches, the main contribution is that the competitors' strategies for each generator are initially unknown (both slope and intercept) and endogenously computed by this new iterative algorithm. To show the applicability of the proposed approach, it has been applied to several case examples where its qualitative behavior has been analyzed in detail. (author)

A deterministic algorithm for fitting a step function to a weighted point-set

KAUST Repository

Fournier, Hervé ; Vigneron, Antoine E.

2013-01-01

Given a set of n points in the plane, each point having a positive weight, and an integer k>0, we present an optimal O(nlogn)-time deterministic algorithm to compute a step function with k steps that minimizes the maximum weighted vertical distance

Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.

Science.gov (United States)

Ullah, Azmat; Malik, Suheel Abdullah; Alimgeer, Khurram Saleem

2018-01-01

In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.

Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.

Directory of Open Access Journals (Sweden)

Azmat Ullah

Full Text Available In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA with Interior Point Algorithm (IPA is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.

Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics

Energy Technology Data Exchange (ETDEWEB)

Makhov, Dmitry V.; Shalashilin, Dmitrii V. [Department of Chemistry, University of Leeds, Leeds LS2 9JT (United Kingdom); Glover, William J.; Martinez, Todd J. [Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA and SLAC National Accelerator Laboratory, Menlo Park, California 94025 (United States)

2014-08-07

We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as “cloning,” in analogy to the “spawning” procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, “trains,” as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions.

Generalized phase retrieval algorithm based on information measures

OpenAIRE

Shioya, Hiroyuki; Gohara, Kazutoshi

2006-01-01

An iterative phase retrieval algorithm based on the maximum entropy method (MEM) is presented. Introducing a new generalized information measure, we derive a novel class of algorithms which includes the conventionally used error reduction algorithm and a MEM-type iterative algorithm which is presented for the first time. These different phase retrieval methods are unified on the basis of the framework of information measures used in information theory.

KARHUNEN-LOÈVE Basis Functions of Kolmogorov Turbulence in the Sphere

Science.gov (United States)

Mathar, Richard J.

In support of modeling atmospheric turbulence, the statistically independent Karhunen-Loève modes of refractive indices with isotropic Kolmogorov spectrum of the covariance are calculated inside a sphere of fixed radius, rendered as series of 3D Zernike functions. Many of the symmetry arguments of the well-known associated 2D problem for the circular input pupil remain valid. The technique of efficient diagonalization of the eigenvalue problem in wavenumber space is founded on the Fourier representation of the 3D Zernike basis, and extensible to the von-Kármán power spectrum.

Machine learning (ML)-guided OPC using basis functions of polar Fourier transform

Science.gov (United States)

Choi, Suhyeong; Shim, Seongbo; Shin, Youngsoo

2016-03-01

With shrinking feature size, runtime has become a limitation of model-based OPC (MB-OPC). A few machine learning-guided OPC (ML-OPC) have been studied as candidates for next-generation OPC, but they all employ too many parameters (e.g. local densities), which set their own limitations. We propose to use basis functions of polar Fourier transform (PFT) as parameters of ML-OPC. Since PFT functions are orthogonal each other and well reflect light phenomena, the number of parameters can significantly be reduced without loss of OPC accuracy. Experiments demonstrate that our new ML-OPC achieves 80% reduction in OPC time and 35% reduction in the error of predicted mask bias when compared to conventional ML-OPC.

Algorithm for study on the stressed-strained state of thermonuclear device vacuum chambers under dynamic loads

International Nuclear Information System (INIS)

Zhuravleva, A.M.; Litvinov, V.B.

1982-01-01

The problem of dynamic analysis of stressed-strained state of vacuum chambers is vital for large thermonuclear devices during the stall of the plasma-filament apd other tpansitional operation regimes when loading for a chamber are nonstationary. To plot a mathematical model the design of the vacuum chamber is discreted on the basis of the method of final elements. To approximate vacuum shell, a plate triangular element with 3 joint points and 5 parameters in the joint is used. It is obtained due to the unity of the bemded element and the element for the flat problem. To investigate nonstationary oscillations of vacuum chambers discreted on the basis of the method of final elements, it is suggested to use the numeric conversion of the Japlace transformation. On the basis of the algorithm suggested a program of numerical function conversion is developed. Test calculations have shown a good stability of the algorithm when selecting the values of transformation parameter in the range of lower intrinsic system frequencies. The advantage of the above method is in the fact that the time-structure shift function is found instantly in the form of the series for the whole time interval and does not require temporary steps, which bring about large expenses of counting time and error accumulation

Research on compressive sensing reconstruction algorithm based on total variation model

Science.gov (United States)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

A Regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data

Directory of Open Access Journals (Sweden)

Ruzzo Walter L

2006-03-01

Full Text Available Abstract Background As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. Methods In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. Results We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly. Conclusion Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.

Self-adaptive numerical integrator for analytic functions

International Nuclear Information System (INIS)

Garribba, S.; Quartapelle, L.; Reina, G.

1978-01-01

A new adaptive algorithm for the integration of analytical functions is presented. The algorithm processes the integration interval by generating local subintervals whose length is controlled through a feedback loop. The control is obtained by means of a relation derived on an analytical basis and valid for an arbitrary integration rule: two different estimates of an integral are used to compute the interval length necessary to obtain an integral estimate with accuracy within the assigned error bounds. The implied method for local generation of subintervals and an effective assumption of error partition among subintervals give rise to an adaptive algorithm provided with a highly accurate and very efficient integration procedure. The particular algorithm obtained by choosing the 6-point Gauss-Legendre integration rule is considered and extensive comparisons are made with other outstanding integration algorithms

Static Load Balancing Algorithms In Cloud Computing Challenges amp Solutions

Directory of Open Access Journals (Sweden)

Nadeem Shah

2015-08-01

Full Text Available Abstract Cloud computing provides on-demand hosted computing resources and services over the Internet on a pay-per-use basis. It is currently becoming the favored method of communication and computation over scalable networks due to numerous attractive attributes such as high availability scalability fault tolerance simplicity of management and low cost of ownership. Due to the huge demand of cloud computing efficient load balancing becomes critical to ensure that computational tasks are evenly distributed across servers to prevent bottlenecks. The aim of this review paper is to understand the current challenges in cloud computing primarily in cloud load balancing using static algorithms and finding gaps to bridge for more efficient static cloud load balancing in the future. We believe the ideas suggested as new solution will allow researchers to redesign better algorithms for better functionalities and improved user experiences in simple cloud systems. This could assist small businesses that cannot afford infrastructure that supports complex amp dynamic load balancing algorithms.

Rational quadratic trigonometric Bézier curve based on new basis with exponential functions

Directory of Open Access Journals (Sweden)

Wu Beibei

2017-06-01

Full Text Available We construct a rational quadratic trigonometric Bézier curve with four shape parameters by introducing two exponential functions into the trigonometric basis functions in this paper. It has the similar properties as the rational quadratic Bézier curve. For given control points, the shape of the curve can be flexibly adjusted by changing the shape parameters and the weight. Some conics can be exactly represented when the control points, the shape parameters and the weight are chosen appropriately. The C0, C1 and C2 continuous conditions for joining two constructed curves are discussed. Some examples are given.

An efficient parallel algorithm for the calculation of unrestricted canonical MP2 energies.

Science.gov (United States)

Baker, Jon; Wolinski, Krzysztof

2011-11-30

We present details of our efficient implementation of full accuracy unrestricted open-shell second-order canonical Møller-Plesset (MP2) energies, both serial and parallel. The algorithm is based on our previous restricted closed-shell MP2 code using the Saebo-Almlöf direct integral transformation. Depending on system details, UMP2 energies take from less than 1.5 to about 3.0 times as long as a closed-shell RMP2 energy on a similar system using the same algorithm. Several examples are given including timings for some large stable radicals with 90+ atoms and over 3600 basis functions. Copyright © 2011 Wiley Periodicals, Inc.

A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization

Directory of Open Access Journals (Sweden)

Soroor Sarafrazi

2015-07-01

Full Text Available It is now well recognized that pure algorithms can be promisingly improved by hybridization with other techniques. One of the relatively new metaheuristic algorithms is Gravitational Search Algorithm (GSA which is based on the Newton laws. In this paper, to enhance the performance of GSA, a novel algorithm called “Kepler”, inspired by the astrophysics, is introduced. The Kepler algorithm is based on the principle of the first Kepler law. The hybridization of GSA and Kepler algorithm is an efficient approach to provide much stronger specialization in intensification and/or diversification. The performance of GSA–Kepler is evaluated by applying it to 14 benchmark functions with 20–1000 dimensions and the optimal approximation of linear system as a practical optimization problem. The results obtained reveal that the proposed hybrid algorithm is robust enough to optimize the benchmark functions and practical optimization problems.

The Algorithm Theoretical Basis Document for Level 1A Processing

Science.gov (United States)

Jester, Peggy L.; Hancock, David W., III

2012-01-01

The first process of the Geoscience Laser Altimeter System (GLAS) Science Algorithm Software converts the Level 0 data into the Level 1A Data Products. The Level 1A Data Products are the time ordered instrument data converted from counts to engineering units. This document defines the equations that convert the raw instrument data into engineering units. Required scale factors, bias values, and coefficients are defined in this document. Additionally, required quality assurance and browse products are defined in this document.

Groebner basis, resultants and the generalized Mandelbrot set

Energy Technology Data Exchange (ETDEWEB)

Geum, Young Hee [Centre of Research for Computational Sciences and Informatics in Biology, Bioindustry, Environment, Agriculture and Healthcare, University of Malaya, 50603 Kuala Lumpur (Malaysia)], E-mail: conpana@empal.com; Hare, Kevin G. [Department of Pure Mathematics, University of Waterloo, Waterloo, Ont., N2L 3G1 (Canada)], E-mail: kghare@math.uwaterloo.ca

2009-10-30

This paper demonstrates how the Groebner basis algorithm can be used for finding the bifurcation points in the generalized Mandelbrot set. It also shows how resultants can be used to find components of the generalized Mandelbrot set.

Groebner basis, resultants and the generalized Mandelbrot set

International Nuclear Information System (INIS)

Geum, Young Hee; Hare, Kevin G.

2009-01-01

This paper demonstrates how the Groebner basis algorithm can be used for finding the bifurcation points in the generalized Mandelbrot set. It also shows how resultants can be used to find components of the generalized Mandelbrot set.

Performance assessment of density functional methods with Gaussian and Slater basis sets using 7σ orbital momentum distributions of N2O

Science.gov (United States)

Wang, Feng; Pang, Wenning; Duffy, Patrick

2012-12-01

Performance of a number of commonly used density functional methods in chemistry (B3LYP, Bhandh, BP86, PW91, VWN, LB94, PBe0, SAOP and X3LYP and the Hartree-Fock (HF) method) has been assessed using orbital momentum distributions of the 7σ orbital of nitrous oxide (NNO), which models electron behaviour in a chemically significant region. The density functional methods are combined with a number of Gaussian basis sets (Pople's 6-31G*, 6-311G**, DGauss TZVP and Dunning's aug-cc-pVTZ as well as even-tempered Slater basis sets, namely, et-DZPp, et-QZ3P, et-QZ+5P and et-pVQZ). Orbital momentum distributions of the 7σ orbital in the ground electronic state of NNO, which are obtained from a Fourier transform into momentum space from single point electronic calculations employing the above models, are compared with experimental measurement of the same orbital from electron momentum spectroscopy (EMS). The present study reveals information on performance of (a) the density functional methods, (b) Gaussian and Slater basis sets, (c) combinations of the density functional methods and basis sets, that is, the models, (d) orbital momentum distributions, rather than a group of specific molecular properties and (e) the entire region of chemical significance of the orbital. It is found that discrepancies of this orbital between the measured and the calculated occur in the small momentum region (i.e. large r region). In general, Slater basis sets achieve better overall performance than the Gaussian basis sets. Performance of the Gaussian basis sets varies noticeably when combining with different Vxc functionals, but Dunning's augcc-pVTZ basis set achieves the best performance for the momentum distributions of this orbital. The overall performance of the B3LYP and BP86 models is similar to newer models such as X3LYP and SAOP. The present study also demonstrates that the combinations of the density functional methods and the basis sets indeed make a difference in the quality of the

Modified Firefly Algorithm

Directory of Open Access Journals (Sweden)

Surafel Luleseged Tilahun

2012-01-01

Full Text Available Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behavior of fireflies. In the algorithm, randomly generated solutions will be considered as fireflies, and brightness is assigned depending on their performance on the objective function. One of the rules used to construct the algorithm is, a firefly will be attracted to a brighter firefly, and if there is no brighter firefly, it will move randomly. In this paper we modify this random movement of the brighter firefly by generating random directions in order to determine the best direction in which the brightness increases. If such a direction is not generated, it will remain in its current position. Furthermore the assignment of attractiveness is modified in such a way that the effect of the objective function is magnified. From the simulation result it is shown that the modified firefly algorithm performs better than the standard one in finding the best solution with smaller CPU time.

A Sparse Self-Consistent Field Algorithm and Its Parallel Implementation: Application to Density-Functional-Based Tight Binding.

Science.gov (United States)

Scemama, Anthony; Renon, Nicolas; Rapacioli, Mathias

2014-06-10

We present an algorithm and its parallel implementation for solving a self-consistent problem as encountered in Hartree-Fock or density functional theory. The algorithm takes advantage of the sparsity of matrices through the use of local molecular orbitals. The implementation allows one to exploit efficiently modern symmetric multiprocessing (SMP) computer architectures. As a first application, the algorithm is used within the density-functional-based tight binding method, for which most of the computational time is spent in the linear algebra routines (diagonalization of the Fock/Kohn-Sham matrix). We show that with this algorithm (i) single point calculations on very large systems (millions of atoms) can be performed on large SMP machines, (ii) calculations involving intermediate size systems (1000-100 000 atoms) are also strongly accelerated and can run efficiently on standard servers, and (iii) the error on the total energy due to the use of a cutoff in the molecular orbital coefficients can be controlled such that it remains smaller than the SCF convergence criterion.

Estimated Quality of Multistage Process on the Basis of Probabilistic Approach with Continuous Response Functions

Directory of Open Access Journals (Sweden)

Yuri B. Tebekin

2011-11-01

Full Text Available The article is devoted to the problem of the quality management for multiphase processes on the basis of the probabilistic approach. Method with continuous response functions is offered from the application of the method of Lagrange multipliers.

The Algorithmic Imaginary

DEFF Research Database (Denmark)

Bucher, Taina

2017-01-01

the notion of the algorithmic imaginary. It is argued that the algorithmic imaginary – ways of thinking about what algorithms are, what they should be and how they function – is not just productive of different moods and sensations but plays a generative role in moulding the Facebook algorithm itself...... of algorithms affect people's use of these platforms, if at all? To help answer these questions, this article examines people's personal stories about the Facebook algorithm through tweets and interviews with 25 ordinary users. To understand the spaces where people and algorithms meet, this article develops...

Global sensitivity analysis using a Gaussian Radial Basis Function metamodel

International Nuclear Information System (INIS)

Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua

2016-01-01

Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.

Minimum Cycle Basis and All-Pairs Min Cut of a Planar Graph in Subquadratic Time

DEFF Research Database (Denmark)

Wulff-Nilsen, Christian

2009-01-01

equivalent to the minimum cycle basis problem for planar graphs. We also obtain O(n3/2 log n) time and O(n3/2) space algorithms for finding, respectively, the weight vector and a Gomory-Hu tree of G. The previous best time and space bound for these two problems was quadratic. From our Gomory-Hu tree...... show that this is optimal if an explicit represen- tation of the basis is required. We then present an O(n3/2 log n) time and O(n3/2) space algorithm that computes a minimum cycle basis implicitly. From this result, we obtain an output-sensitive algorithm that explicitly computes a minimum cycle basis...... in O(n3/2 log n + C) time and O(n3/2 + C) space, where C is the total size (number of edges and vertices) of the cycles in the basis. These bounds reduce to O(n3/2 log n) and O(n3/2), respectively, when G is unweighted. We get similar results for the all-pairs min cut problem since it is dual...

Solution of volume-surface integral equations using higher-order hierarchical Legendre basis functions

DEFF Research Database (Denmark)

Kim, Oleksiy S.; Meincke, Peter; Breinbjerg, Olav

2007-01-01

The problem of electromagnetic scattering by composite metallic and dielectric objects is solved using the coupled volume-surface integral equation (VSIE). The method of moments (MoM) based on higher-order hierarchical Legendre basis functions and higher-order curvilinear geometrical elements...... with the analytical Mie series solution. Scattering by more complex metal-dielectric objects are also considered to compare the presented technique with other numerical methods....

Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

KAUST Repository

Ryu, Duchwan; Liang, Faming; Mallick, Bani K.

2013-01-01

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

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

Science.gov (United States)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

RECONSTRUCCIÓN TRIDIMENSIONAL DE ROSTROS A PARTIR DE IMÁGENES DE RANGO POR MEDIO DE FUNCIONES DE BASE RADIAL DE SOPORTE COMPACTO TRI-DIMENSIONAL RECONSTRUCTION OF FACES FROM RANGE IMAGES THROUGH COMPACT SUPPORT RADIAL BASIS FUNCTIONS

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.

Characterization and prediction of the backscattered form function of an immersed cylindrical shell using hybrid fuzzy clustering and bio-inspired algorithms.

Science.gov (United States)

Agounad, Said; Aassif, El Houcein; Khandouch, Younes; Maze, Gérard; Décultot, Dominique

2018-02-01

The acoustic scattering of a plane wave by an elastic cylindrical shell is studied. A new approach is developed to predict the form function of an immersed cylindrical shell of the radius ratio b/a ('b' is the inner radius and 'a' is the outer radius). The prediction of the backscattered form function is investigated by a combined approach between fuzzy clustering algorithms and bio-inspired algorithms. Four famous fuzzy clustering algorithms: the fuzzy c-means (FCM), the Gustafson-Kessel algorithm (GK), the fuzzy c-regression model (FCRM) and the Gath-Geva algorithm (GG) are combined with particle swarm optimization and genetic algorithm. The symmetric and antisymmetric circumferential waves A, S 0 , A 1 , S 1 and S 2 are investigated in a reduced frequency (k 1 a) range extends over 0.1functions. This representation is used as a comparison criterion between the calculated form function by the analytical method and that predicted by the proposed approach on the one hand and is used to extract the predicted cut-off frequencies on the other hand. Moreover, the transverse velocity of the material constituting the cylindrical shell is extracted. The computational results show that the proposed approach is very efficient to predict the form function and consequently, for acoustic characterization purposes. Copyright © 2017 Elsevier B.V. All rights reserved.

Total Positivity of the Cubic Trigonometric Bézier Basis

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Xuli Han

2014-01-01

Full Text Available Within the general framework of Quasi Extended Chebyshev space, we prove that the cubic trigonometric Bézier basis with two shape parameters λ and μ given in Han et al. (2009 forms an optimal normalized totally positive basis for λ,μ∈(-2,1]. Moreover, we show that for λ=-2 or μ=-2 the basis is not suited for curve design from the blossom point of view. In order to compute the corresponding cubic trigonometric Bézier curves stably and efficiently, we also develop a new corner cutting algorithm.

Predicting availability functions in time-dependent complex systems with SAEDES simulation algorithms

International Nuclear Information System (INIS)

Faulin, Javier; Juan, Angel A.; Serrat, Carles; Bargueno, Vicente

2008-01-01

In this paper, we propose the use of discrete-event simulation (DES) as an efficient methodology to obtain estimates of both survival and availability functions in time-dependent real systems-such as telecommunication networks or distributed computer systems. We discuss the use of DES in reliability and availability studies, not only as an alternative to the use of analytical and probabilistic methods, but also as a complementary way to: (i) achieve a better understanding of the system internal behavior and (ii) find out the relevance of each component under reliability/availability considerations. Specifically, this paper describes a general methodology and two DES algorithms, called SAEDES, which can be used to analyze a wide range of time-dependent complex systems, including those presenting multiple states, dependencies among failure/repair times or non-perfect maintenance policies. These algorithms can provide valuable information, specially during the design stages, where different scenarios can be compared in order to select a system design offering adequate reliability and availability levels. Two case studies are discussed, using a C/C++ implementation of the SAEDES algorithms, to show some potential applications of our approach

Predicting availability functions in time-dependent complex systems with SAEDES simulation algorithms

Energy Technology Data Exchange (ETDEWEB)

Faulin, Javier [Department of Statistics and Operations Research, Los Magnolios Building, First Floor, Campus Arrosadia, Public University of Navarre, 31006 Pamplona, Navarre (Spain)], E-mail: javier.faulin@unavarra.es; Juan, Angel A. [Department of Applied Mathematics I, Av. Doctor Maranon 44-50, Technical University of Catalonia, 08028 Barcelona (Spain)], E-mail: angel.alejandro.juan@upc.edu; Serrat, Carles [Department of Applied Mathematics I, Av. Doctor Maranon 44-50, Technical University of Catalonia, 08028 Barcelona (Spain)], E-mail: carles.serrat@upc.edu; Bargueno, Vicente [Department of Applied Mathematics I, ETS Ingenieros Industriales, Universidad Nacional de Educacion a Distancia, 28080 Madrid (Spain)], E-mail: vbargueno@ind.uned.es

2008-11-15

In this paper, we propose the use of discrete-event simulation (DES) as an efficient methodology to obtain estimates of both survival and availability functions in time-dependent real systems-such as telecommunication networks or distributed computer systems. We discuss the use of DES in reliability and availability studies, not only as an alternative to the use of analytical and probabilistic methods, but also as a complementary way to: (i) achieve a better understanding of the system internal behavior and (ii) find out the relevance of each component under reliability/availability considerations. Specifically, this paper describes a general methodology and two DES algorithms, called SAEDES, which can be used to analyze a wide range of time-dependent complex systems, including those presenting multiple states, dependencies among failure/repair times or non-perfect maintenance policies. These algorithms can provide valuable information, specially during the design stages, where different scenarios can be compared in order to select a system design offering adequate reliability and availability levels. Two case studies are discussed, using a C/C++ implementation of the SAEDES algorithms, to show some potential applications of our approach.

Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

Science.gov (United States)

Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.

2012-04-01

Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.

Synthesis of Greedy Algorithms Using Dominance Relations

Science.gov (United States)

Nedunuri, Srinivas; Smith, Douglas R.; Cook, William R.

2010-01-01

Greedy algorithms exploit problem structure and constraints to achieve linear-time performance. Yet there is still no completely satisfactory way of constructing greedy algorithms. For example, the Greedy Algorithm of Edmonds depends upon translating a problem into an algebraic structure called a matroid, but the existence of such a translation can be as hard to determine as the existence of a greedy algorithm itself. An alternative characterization of greedy algorithms is in terms of dominance relations, a well-known algorithmic technique used to prune search spaces. We demonstrate a process by which dominance relations can be methodically derived for a number of greedy algorithms, including activity selection, and prefix-free codes. By incorporating our approach into an existing framework for algorithm synthesis, we demonstrate that it could be the basis for an effective engineering method for greedy algorithms. We also compare our approach with other characterizations of greedy algorithms.

Efficient, approximate and parallel Hartree-Fock and hybrid DFT calculations. A 'chain-of-spheres' algorithm for the Hartree-Fock exchange

International Nuclear Information System (INIS)

Neese, Frank; Wennmohs, Frank; Hansen, Andreas; Becker, Ute

2009-01-01

In this paper, the possibility is explored to speed up Hartree-Fock and hybrid density functional calculations by forming the Coulomb and exchange parts of the Fock matrix by different approximations. For the Coulomb part the previously introduced Split-RI-J variant (F. Neese, J. Comput. Chem. 24 (2003) 1740) of the well-known 'density fitting' approximation is used. The exchange part is formed by semi-numerical integration techniques that are closely related to Friesner's pioneering pseudo-spectral approach. Our potentially linear scaling realization of this algorithm is called the 'chain-of-spheres exchange' (COSX). A combination of semi-numerical integration and density fitting is also proposed. Both Split-RI-J and COSX scale very well with the highest angular momentum in the basis sets. It is shown that for extended basis sets speed-ups of up to two orders of magnitude compared to traditional implementations can be obtained in this way. Total energies are reproduced with an average error of <0.3 kcal/mol as determined from extended test calculations with various basis sets on a set of 26 molecules with 20-200 atoms and up to 2000 basis functions. Reaction energies agree to within 0.2 kcal/mol (Hartree-Fock) or 0.05 kcal/mol (hybrid DFT) with the canonical values. The COSX algorithm parallelizes with a speedup of 8.6 observed for 10 processes. Minimum energy geometries differ by less than 0.3 pm in the bond distances and 0.5 deg. in the bond angels from their canonical values. These developments enable highly efficient and accurate self-consistent field calculations including nonlocal Hartree-Fock exchange for large molecules. In combination with the RI-MP2 method and large basis sets, second-order many body perturbation energies can be obtained for medium sized molecules with unprecedented efficiency. The algorithms are implemented into the ORCA electronic structure system

Image Encryption Algorithm Based on a Novel Improper Fractional-Order Attractor and a Wavelet Function Map

Directory of Open Access Journals (Sweden)

Jian-feng Zhao

2017-01-01

Full Text Available This paper presents a three-dimensional autonomous chaotic system with high fraction dimension. It is noted that the nonlinear characteristic of the improper fractional-order chaos is interesting. Based on the continuous chaos and the discrete wavelet function map, an image encryption algorithm is put forward. The key space is formed by the initial state variables, parameters, and orders of the system. Every pixel value is included in secret key, so as to improve antiattack capability of the algorithm. The obtained simulation results and extensive security analyses demonstrate the high level of security of the algorithm and show its robustness against various types of attacks.

A Swarm Optimization Algorithm for Multimodal Functions and Its Application in Multicircle Detection

Directory of Open Access Journals (Sweden)

Erik Cuevas

2013-01-01

Full Text Available In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global are known, the implementation can be quickly switched to another solution without much interrupting the design process. This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents emulate a group of animals which interact with each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi-circle detection, achieving satisfactory results.

Application of GA package in functional packaging

Science.gov (United States)

Belousova, D. A.; Noskova, E. E.; Kapulin, D. V.

2018-05-01

The approach to application program for the task of configuration of the elements of the commutation circuit for design of the radio-electronic equipment on the basis of the genetic algorithm is offered. The efficiency of the used approach for commutation circuits with different characteristics for computer-aided design on radio-electronic manufacturing is shown. The prototype of the computer-aided design subsystem on the basis of a package GA for R with a set of the general functions for optimization of multivariate models is programmed.

Fast and accurate computation of projected two-point functions

Science.gov (United States)

Grasshorn Gebhardt, Henry S.; Jeong, Donghui

2018-01-01

We present the two-point function from the fast and accurate spherical Bessel transformation (2-FAST) algorithm1Our code is available at https://github.com/hsgg/twoFAST. for a fast and accurate computation of integrals involving one or two spherical Bessel functions. These types of integrals occur when projecting the galaxy power spectrum P (k ) onto the configuration space, ξℓν(r ), or spherical harmonic space, Cℓ(χ ,χ'). First, we employ the FFTLog transformation of the power spectrum to divide the calculation into P (k )-dependent coefficients and P (k )-independent integrations of basis functions multiplied by spherical Bessel functions. We find analytical expressions for the latter integrals in terms of special functions, for which recursion provides a fast and accurate evaluation. The algorithm, therefore, circumvents direct integration of highly oscillating spherical Bessel functions.

Modelling of coil-loaded wire antenna using composite multiple domain basis functions

CSIR Research Space (South Africa)

Lysko, AA

2010-03-01

Full Text Available - tional Electromagnetics, Artech House, 2001. 3. Rogers, S. D. and C. M. Butler, \\An e–cient curved-wire integral equation solution technique," IEEE Trans. Ant. and Propag., 70{79, Vol. 49, Jan. 2001. 4. Mosig, J. and E. Suter, \\A multilevel divide.... 8. Wan, J. X., J. Lei, and C.-H. Liang, \\An e–cient analysis of large-scale periodic microstrip antenna arrays using the characteristic basis function method," Progress In Electromagnetics Research, PIER 50, 61{81, 2005. 9. Taguchi, M., K...

Energy and energy gradient matrix elements with N-particle explicitly correlated complex Gaussian basis functions with L =1

Science.gov (United States)

Bubin, Sergiy; Adamowicz, Ludwik

2008-03-01

In this work we consider explicitly correlated complex Gaussian basis functions for expanding the wave function of an N-particle system with the L =1 total orbital angular momentum. We derive analytical expressions for various matrix elements with these basis functions including the overlap, kinetic energy, and potential energy (Coulomb interaction) matrix elements, as well as matrix elements of other quantities. The derivatives of the overlap, kinetic, and potential energy integrals with respect to the Gaussian exponential parameters are also derived and used to calculate the energy gradient. All the derivations are performed using the formalism of the matrix differential calculus that facilitates a way of expressing the integrals in an elegant matrix form, which is convenient for the theoretical analysis and the computer implementation. The new method is tested in calculations of two systems: the lowest P state of the beryllium atom and the bound P state of the positronium molecule (with the negative parity). Both calculations yielded new, lowest-to-date, variational upper bounds, while the number of basis functions used was significantly smaller than in previous studies. It was possible to accomplish this due to the use of the analytic energy gradient in the minimization of the variational energy.

Energy and energy gradient matrix elements with N-particle explicitly correlated complex Gaussian basis functions with L=1.

Science.gov (United States)

Bubin, Sergiy; Adamowicz, Ludwik

2008-03-21

In this work we consider explicitly correlated complex Gaussian basis functions for expanding the wave function of an N-particle system with the L=1 total orbital angular momentum. We derive analytical expressions for various matrix elements with these basis functions including the overlap, kinetic energy, and potential energy (Coulomb interaction) matrix elements, as well as matrix elements of other quantities. The derivatives of the overlap, kinetic, and potential energy integrals with respect to the Gaussian exponential parameters are also derived and used to calculate the energy gradient. All the derivations are performed using the formalism of the matrix differential calculus that facilitates a way of expressing the integrals in an elegant matrix form, which is convenient for the theoretical analysis and the computer implementation. The new method is tested in calculations of two systems: the lowest P state of the beryllium atom and the bound P state of the positronium molecule (with the negative parity). Both calculations yielded new, lowest-to-date, variational upper bounds, while the number of basis functions used was significantly smaller than in previous studies. It was possible to accomplish this due to the use of the analytic energy gradient in the minimization of the variational energy.

METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS

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L. V. Serebryanaya

2016-01-01

Full Text Available The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.

Analysis of the Chirplet Transform-Based Algorithm for Radar Detection of Accelerated Targets

Science.gov (United States)

Galushko, V. G.; Vavriv, D. M.

2017-06-01

Purpose: Efficiency analysis of an optimal algorithm of chirp signal processing based on the chirplet transform as applied to detection of radar targets in uniformly accelerated motion. Design/methodology/approach: Standard methods of the optimal filtration theory are used to investigate the ambiguity function of chirp signals. Findings: An analytical expression has been derived for the ambiguity function of chirp signals that is analyzed with respect to detection of radar targets moving at a constant acceleration. Sidelobe level and characteristic width of the ambiguity function with respect to the coordinates frequency and rate of its change have been estimated. The gain in the signal-to-noise ratio has been assessed that is provided by the algorithm under consideration as compared with application of the standard Fourier transform to detection of chirp signals against a “white” noise background. It is shown that already with a comparatively small (processing channels (elementary filters with respect to the frequency change rate) the gain in the signal-tonoise ratio exceeds 10 dB. A block diagram of implementation of the algorithm under consideration is suggested on the basis of a multichannel weighted Fourier transform. Recommendations as for selection of the detection algorithm parameters have been developed. Conclusions: The obtained results testify to efficiency of application of the algorithm under consideration to detection of radar targets moving at a constant acceleration. Nevertheless, it seems expedient to perform computer simulations of its operability with account for the noise impact along with trial measurements in real conditions.

A Direct Search Algorithm for Global Optimization

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Enrique Baeyens

2016-06-01

Full Text Available A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.

Using protein design algorithms to understand the molecular basis of disease caused by protein-DNA interactions: the Pax6 example

DEFF Research Database (Denmark)

Alibes, A.; Nadra, A.; De Masi, Federico

2010-01-01

diseases such as aniridia. The validity of FoldX to deal with protein-DNA interactions was demonstrated by showing that high levels of accuracy can be achieved for mutations affecting these interactions. Also we showed that protein-design algorithms can accurately reproduce experimental DNA-binding logos......Quite often a single or a combination of protein mutations is linked to specific diseases. However, distinguishing from sequence information which mutations have real effects in the protein's function is not trivial. Protein design tools are commonly used to explain mutations that affect protein...... stability, or protein-protein interaction, but not for mutations that could affect protein-DNA binding. Here, we used the protein design algorithm FoldX to model all known missense mutations in the paired box domain of Pax6, a highly conserved transcription factor involved in eye development and in several...

Intelligent Control of Welding Gun Pose for Pipeline Welding Robot Based on Improved Radial Basis Function Network and Expert System

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.

Relaxation of functions of STO-3G and 6-31G* basis sets in the series of isoelectronic to LiF molecule

International Nuclear Information System (INIS)

Ermakov, A.I.; Belousov, V.V.

2007-01-01

Relaxation effect of functions of the basis sets (BS) STO-3G and 6-31G* on their equilibration in the series of isoelectron molecules: LiF, BeO, BN and C 2 is considered. Values of parameters (exponential factor of basis functions, orbital exponents of Gauss primitives and coefficients of their grouping) of basis functions in molecules are discovered using the criterion of minimum of energy by the unlimited Hartree-Fock method calculations (UHF) with the help of direct optimization of parameters: the simplex-method and Rosenbrock method. Certain schemes of optimization differing by the amount of varying parameters have been done. Interaction of basis functions parameters of concerned sets through medium values of the Gauss exponents is established. Effects of relaxation on the change of full energy and relative errors of the calculations of interatomic distances, normal oscillations frequencies, dissociation energy and other properties of molecules are considered. Change of full energy during the relaxation of basis functions (RBF) STO-3G and 6-31G* amounts 1100 and 80 kJ/mol correspondingly, and it is in need of the account during estimation of energetic characteristics, especially for systems with high-polar chemical bonds. The relaxation BS STO-3G practically in all considered cases improves description of molecular properties, whereas the relaxation BS 6-31G* lightly effects on its equilibration [ru

Vectorization of a penalty function algorithm for well scheduling

Science.gov (United States)

Absar, I.

1984-01-01

In petroleum engineering, the oil production profiles of a reservoir can be simulated by using a finite gridded model. This profile is affected by the number and choice of wells which in turn is a result of various production limits and constraints including, for example, the economic minimum well spacing, the number of drilling rigs available and the time required to drill and complete a well. After a well is available it may be shut in because of excessive water or gas productions. In order to optimize the field performance a penalty function algorithm was developed for scheduling wells. For an example with some 343 wells and 15 different constraints, the scheduling routine vectorized for the CYBER 205 averaged 560 times faster performance than the scalar version.

A Functional Programming Approach to AI Search Algorithms

Science.gov (United States)

Panovics, Janos

2012-01-01

The theory and practice of search algorithms related to state-space represented problems form the major part of the introductory course of Artificial Intelligence at most of the universities and colleges offering a degree in the area of computer science. Students usually meet these algorithms only in some imperative or object-oriented language…

Redesigned-Scale-Free CORDIC Algorithm Based FPGA Implementation of Window Functions to Minimize Area and Latency

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Supriya Aggarwal

2012-01-01

Full Text Available One of the most important steps in spectral analysis is filtering, where window functions are generally used to design filters. In this paper, we modify the existing architecture for realizing the window functions using CORDIC processor. Firstly, we modify the conventional CORDIC algorithm to reduce its latency and area. The proposed CORDIC algorithm is completely scale-free for the range of convergence that spans the entire coordinate space. Secondly, we realize the window functions using a single CORDIC processor as against two serially connected CORDIC processors in existing technique, thus optimizing it for area and latency. The linear CORDIC processor is replaced by a shift-add network which drastically reduces the number of pipelining stages required in the existing design. The proposed design on an average requires approximately 64% less pipeline stages and saves up to 44.2% area. Currently, the processor is designed to implement Blackman windowing architecture, which with slight modifications can be extended to other widow functions as well. The details of the proposed architecture are discussed in the paper.

Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization

Science.gov (United States)

Lin, Chuang; Wang, Binghui; Jiang, Ning; Farina, Dario

2018-04-01

Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.

Finite element transport using Wachspress rational basis functions on quadrilaterals in diffusive regions

International Nuclear Information System (INIS)

Davidson, G.; Palmer, T.S.

2005-01-01

In 1975, Wachspress developed basis functions that can be constructed upon very general zone shapes, including convex polygons and polyhedra, as well as certain zone shapes with curved sides and faces. Additionally, Adams has recently shown that weight functions with certain properties will produce solutions with full-resolution. Wachspress rational functions possess those necessary properties. Here we present methods to construct and integrate Wachspress rational functions on quadrilaterals. We also present an asymptotic analysis of a discontinuous finite element discretization on quadrilaterals, and we present 3 numerical results that confirm the predictions of our analysis. In the first test problem, we showed that Wachspress rational functions could give robust solutions for a strongly heterogeneous problem with both orthogonal and skewed meshes. This strongly heterogenous problem contained thick, diffusive regions, and the discretization provided full-resolution solutions. In the second test problem, we confirmed our asymptotic analysis by demonstrating that the transport solution will converge to the diffusion solution as the problem is made increasingly thick and diffusive. In the third test problem, we demonstrated that bilinear discontinuous based transport and Wachspress rational function based transport converge in the one-mesh limit

Efficient fractal-based mutation in evolutionary algorithms from iterated function systems

Science.gov (United States)

Salcedo-Sanz, S.; Aybar-Ruíz, A.; Camacho-Gómez, C.; Pereira, E.

2018-03-01

In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs). The new mutation procedure proposed consists of considering a set of IFS which are able to generate fractal structures in a two-dimensional phase space, and use them to modify a current individual of the EP algorithm, instead of using random numbers from different probability density functions. We test this new proposal in a set of benchmark functions for continuous optimization problems. In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps. We also include a discussion on the IFS-based mutation in a real application of Tuned Mass Dumper (TMD) location and optimization for vibration cancellation in buildings. In both practical cases, the proposed EP with the IFS-based mutation obtained extremely competitive results compared to alternative classical mutation operators.

New real space correlated-basis-functions approach for the electron correlations of the semiconductor inversion layer

International Nuclear Information System (INIS)

Feng Weiguo; Wang Hongwei; Wu Xiang

1989-12-01

Based on the real space Correlated-Basis-Functions theory and the collective oscillation behaviour of the electron gas with effective Coulomb interaction, the many body wave function is obtained for the quasi-two-dimensional electron system in the semiconductor inversion layer. The pair-correlation function and the correlation energy of the system have been calculated by the integro-differential method in this paper. The comparison with the other previous theoretical results is also made. The new theoretical approach and its numerical results show that the pair-correlation functions are definitely positive and satisfy the normalization condition. (author). 10 refs, 2 figs

Composite Differential Search Algorithm

Directory of Open Access Journals (Sweden)

Bo Liu

2014-01-01

Full Text Available Differential search algorithm (DS is a relatively new evolutionary algorithm inspired by the Brownian-like random-walk movement which is used by an organism to migrate. It has been verified to be more effective than ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES. In this paper, we propose four improved solution search algorithms, namely “DS/rand/1,” “DS/rand/2,” “DS/current to rand/1,” and “DS/current to rand/2” to search the new space and enhance the convergence rate for the global optimization problem. In order to verify the performance of different solution search methods, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, the original algorithm when considering the quality of the solution obtained. However, these schemes cannot still achieve the best solution for all functions. In order to further enhance the convergence rate and the diversity of the algorithm, a composite differential search algorithm (CDS is proposed in this paper. This new algorithm combines three new proposed search schemes including “DS/rand/1,” “DS/rand/2,” and “DS/current to rand/1” with three control parameters using a random method to generate the offspring. Experiment results show that CDS has a faster convergence rate and better search ability based on the 23 benchmark functions.

A Family of Algorithms for Computing Consensus about Node State from Network Data

Science.gov (United States)

Brush, Eleanor R.; Krakauer, David C.; Flack, Jessica C.

2013-01-01

Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes–from ranking websites to determining critical species in ecosystems–yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus–through breadth or depth– impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes “form opinions” about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which

Object-Oriented Implementation of Adaptive Mesh Refinement Algorithms

Directory of Open Access Journals (Sweden)

William Y. Crutchfield

1993-01-01

Full Text Available We describe C++ classes that simplify development of adaptive mesh refinement (AMR algorithms. The classes divide into two groups, generic classes that are broadly useful in adaptive algorithms, and application-specific classes that are the basis for our AMR algorithm. We employ two languages, with C++ responsible for the high-level data structures, and Fortran responsible for low-level numerics. The C++ implementation is as fast as the original Fortran implementation. Use of inheritance has allowed us to extend the original AMR algorithm to other problems with greatly reduced development time.

Adaptive local basis set for Kohn–Sham density functional theory in a discontinuous Galerkin framework II: Force, vibration, and molecular dynamics calculations

Energy Technology Data Exchange (ETDEWEB)

Zhang, Gaigong [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Lin, Lin, E-mail: linlin@math.berkeley.edu [Department of Mathematics, University of California, Berkeley, Berkeley, CA 94720 (United States); Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Hu, Wei, E-mail: whu@lbl.gov [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Yang, Chao, E-mail: cyang@lbl.gov [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States); Pask, John E., E-mail: pask1@llnl.gov [Physics Division, Lawrence Livermore National Laboratory, Livermore, CA 94550 (United States)

2017-04-15

Recently, we have proposed the adaptive local basis set for electronic structure calculations based on Kohn–Sham density functional theory in a pseudopotential framework. The adaptive local basis set is efficient and systematically improvable for total energy calculations. In this paper, we present the calculation of atomic forces, which can be used for a range of applications such as geometry optimization and molecular dynamics simulation. We demonstrate that, under mild assumptions, the computation of atomic forces can scale nearly linearly with the number of atoms in the system using the adaptive local basis set. We quantify the accuracy of the Hellmann–Feynman forces for a range of physical systems, benchmarked against converged planewave calculations, and find that the adaptive local basis set is efficient for both force and energy calculations, requiring at most a few tens of basis functions per atom to attain accuracies required in practice. Since the adaptive local basis set has implicit dependence on atomic positions, Pulay forces are in general nonzero. However, we find that the Pulay force is numerically small and systematically decreasing with increasing basis completeness, so that the Hellmann–Feynman force is sufficient for basis sizes of a few tens of basis functions per atom. We verify the accuracy of the computed forces in static calculations of quasi-1D and 3D disordered Si systems, vibration calculation of a quasi-1D Si system, and molecular dynamics calculations of H{sub 2} and liquid Al–Si alloy systems, where we show systematic convergence to benchmark planewave results and results from the literature.

Vector Green's function algorithm for radiative transfer in plane-parallel atmosphere

Energy Technology Data Exchange (ETDEWEB)

Qin Yi [School of Physics, University of New South Wales (Australia)]. E-mail: yi.qin@csiro.au; Box, Michael A. [School of Physics, University of New South Wales (Australia)

2006-01-15

Green's function is a widely used approach for boundary value problems. In problems related to radiative transfer, Green's function has been found to be useful in land, ocean and atmosphere remote sensing. It is also a key element in higher order perturbation theory. This paper presents an explicit expression of the Green's function, in terms of the source and radiation field variables, for a plane-parallel atmosphere with either vacuum boundaries or a reflecting (BRDF) surface. Full polarization state is considered but the algorithm has been developed in such way that it can be easily reduced to solve scalar radiative transfer problems, which makes it possible to implement a single set of code for computing both the scalar and the vector Green's function.

Functional Generalized Structured Component Analysis.

Science.gov (United States)

Suk, Hye Won; Hwang, Heungsun

2016-12-01

An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.

Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm

Science.gov (United States)

Gilat Schmidt, Taly; Barber, Rina F.; Sidky, Emil Y.

2017-03-01

Metal objects cause artifacts in computed tomography (CT) images. This work investigated the feasibility of a spectral CT method to reduce metal artifacts. Spectral CT acquisition combined with optimization-based reconstruction is proposed to reduce artifacts by modeling the physical effects that cause metal artifacts and by providing the flexibility to selectively remove corrupted spectral measurements in the spectral-sinogram space. The proposed Constrained `One-Step' Spectral CT Image Reconstruction (cOSSCIR) algorithm directly estimates the basis material maps while enforcing convex constraints. The incorporation of constraints on the reconstructed basis material maps is expected to mitigate undersampling effects that occur when corrupted data is excluded from reconstruction. The feasibility of the cOSSCIR algorithm to reduce metal artifacts was investigated through simulations of a pelvis phantom. The cOSSCIR algorithm was investigated with and without the use of a third basis material representing metal. The effects of excluding data corrupted by metal were also investigated. The results demonstrated that the proposed cOSSCIR algorithm reduced metal artifacts and improved CT number accuracy. For example, CT number error in a bright shading artifact region was reduced from 403 HU in the reference filtered backprojection reconstruction to 33 HU using the proposed algorithm in simulation. In the dark shading regions, the error was reduced from 1141 HU to 25 HU. Of the investigated approaches, decomposing the data into three basis material maps and excluding the corrupted data demonstrated the greatest reduction in metal artifacts.

The neural basis of human social values: evidence from functional MRI.

Science.gov (United States)

Zahn, Roland; Moll, Jorge; Paiva, Mirella; Garrido, Griselda; Krueger, Frank; Huey, Edward D; Grafman, Jordan

2009-02-01

Social values are composed of social concepts (e.g., "generosity") and context-dependent moral sentiments (e.g., "pride"). The neural basis of this intricate cognitive architecture has not been investigated thus far. Here, we used functional magnetic resonance imaging while subjects imagined their own actions toward another person (self-agency) which either conformed or were counter to a social value and were associated with pride or guilt, respectively. Imagined actions of another person toward the subjects (other-agency) in accordance with or counter to a value were associated with gratitude or indignation/anger. As hypothesized, superior anterior temporal lobe (aTL) activity increased with conceptual detail in all conditions. During self-agency, activity in the anterior ventromedial prefrontal cortex correlated with pride and guilt, whereas activity in the subgenual cingulate solely correlated with guilt. In contrast, indignation/anger activated lateral orbitofrontal-insular cortices. Pride and gratitude additionally evoked mesolimbic and basal forebrain activations. Our results demonstrate that social values emerge from coactivation of stable abstract social conceptual representations in the superior aTL and context-dependent moral sentiments encoded in fronto-mesolimbic regions. This neural architecture may provide the basis of our ability to communicate about the meaning of social values across cultural contexts without limiting our flexibility to adapt their emotional interpretation.

Algorithm of Data Reduce in Determination of Aerosol Particle Size Distribution at Damps/C

International Nuclear Information System (INIS)

Muhammad-Priyatna; Otto-Pribadi-Ruslanto

2001-01-01

The analysis had to do for algorithm of data reduction on Damps/C (Differential Mobility Particle Sizer with Condensation Particle Counter) system, this is for determine aerosol particle size distribution with range 0,01 μm to 1 μm in diameter. Damps/C (Differential Mobility Particle Sizer with Condensation Particle Counter) system contents are software and hardware. The hardware used determine of mobilities of aerosol particle and so the software used determine aerosol particle size distribution in diameter. The mobilities and diameter particle had connection in the electricity field. That is basic program for reduction of data and particle size conversion from particle mobility become particle diameter. The analysis to get transfer function value, Ω, is 0.5. The data reduction program to do conversation mobility basis become diameter basis with number efficiency correction, transfer function value, and poly charge particle. (author)

A roadmap of clustering algorithms: finding a match for a biomedical application.

Science.gov (United States)

Andreopoulos, Bill; An, Aijun; Wang, Xiaogang; Schroeder, Michael

2009-05-01

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as grid-based, density-based and model-based clustering. For improved bioinformatics analysis of data, it is important to match clusterings to the requirements of a biomedical application. In this article, we present a set of desirable clustering features that are used as evaluation criteria for clustering algorithms. We review 40 different clustering algorithms of all approaches and datatypes. We compare algorithms on the basis of desirable clustering features, and outline algorithms' benefits and drawbacks as a basis for matching them to biomedical applications.

Recursive automatic classification algorithms

Energy Technology Data Exchange (ETDEWEB)

Bauman, E V; Dorofeyuk, A A

1982-03-01

A variational statement of the automatic classification problem is given. The dependence of the form of the optimal partition surface on the form of the classification objective functional is investigated. A recursive algorithm is proposed for maximising a functional of reasonably general form. The convergence problem is analysed in connection with the proposed algorithm. 8 references.

A new chaotic algorithm for image encryption

International Nuclear Information System (INIS)

Gao Haojiang; Zhang Yisheng; Liang Shuyun; Li Dequn

2006-01-01

Recent researches of image encryption algorithms have been increasingly based on chaotic systems, but the drawbacks of small key space and weak security in one-dimensional chaotic cryptosystems are obvious. This paper presents a new nonlinear chaotic algorithm (NCA) which uses power function and tangent function instead of linear function. Its structural parameters are obtained by experimental analysis. And an image encryption algorithm in a one-time-one-password system is designed. The experimental results demonstrate that the image encryption algorithm based on NCA shows advantages of large key space and high-level security, while maintaining acceptable efficiency. Compared with some general encryption algorithms such as DES, the encryption algorithm is more secure

Law and Order in Algorithmics

NARCIS (Netherlands)

Fokkinga, M.M.

1992-01-01

An algorithm is the input-output effect of a computer program; mathematically, the notion of algorithm comes close to the notion of function. Just as arithmetic is the theory and practice of calculating with numbers, so is ALGORITHMICS the theory and practice of calculating with algorithms. Just as

Sturmian functions in a L{sup 2} basis: Critical nuclear charge for N-electron atoms

Energy Technology Data Exchange (ETDEWEB)

Frapiccini, A.L. [Departamento de Fisica, Universidad Nacional del Sur, Bahia Blanca (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina)], E-mail: afrapic@uns.edu.ar; Gasaneo, G. [Departamento de Fisica, Universidad Nacional del Sur, Bahia Blanca (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina); Colavecchia, F.D. [Centro Atomico Bariloche, San Carlos de Bariloche, Rio Negro (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina); Mitnik, D. [Instituto de Astronomia y Fisica del Espacio and, Departamento de Fisica, Universidad de Buenos Aires (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina)

2007-10-15

Two particle Sturmian functions [M. Rotenberg, Ann. Phys., NY 19 (1962) 262; S.V. Khristenko, Theor. Math. Fiz. 22 (1975) 31 (Engl. Transl. Theor. Math. Phys. 22, 21)] for a short range potentials are obtained by expanding the solution of the Schroedinger equation in a finite L{sup 2}Laguerre-type basis. These functions are chosen to satisfy certain boundary conditions, such as regularity at the origin and the correct asymptotic behavior according to the energy domain: exponential decay for negative energy and outgoing (incoming or standing wave) for positive energy. The set of eigenvalues obtained is discrete for both positive and negative energies. This Sturmian basis is used to solve the Schroedinger equation for a one-particle model potential [A.V. Sergeev, S. Kais, J. Quant. Chem. 75 (1999) 533] to describe the motion of a loosely bound electron in a multielectron atom. Values of the two parameters of the potential are computed to represent the Helium isoelectronic series and the critical nuclear charge Z{sub c} is found, in good agreement with previous calculations.

An Intelligent Broadcasting Algorithm for Early Warning Message Dissemination in VANETs

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Ihn-Han Bae

2015-01-01

Full Text Available Vehicular ad hoc network (VANET has gained much attention recently to improve road safety, reduce traffic congestion, and enable efficient traffic management because of its many important applications in transportation. In this paper, an early warning intelligence broadcasting algorithm is proposed, EW-ICAST, to disseminate a safety message for VANETs. The proposed EW-ICAST uses not only the early warning system on the basis of time to collision (TTC but also the intelligent broadcasting algorithm on the basis of fuzzy logic. Thus, the EW-ICAST resolves effectively broadcast storm problem and meets time-critical requirement. The performance of EW-ICAST is evaluated through simulation and compared with that of other alert message dissemination algorithms. From the simulation results, we know that EW-ICAST is superior to Simple, P-persistence, and EDB algorithms.

GRACE L1b inversion through a self-consistent modified radial basis function approach

Science.gov (United States)

Yang, Fan; Kusche, Juergen; Rietbroek, Roelof; Eicker, Annette

2016-04-01

Implementing a regional geopotential representation such as mascons or, more general, RBFs (radial basis functions) has been widely accepted as an efficient and flexible approach to recover the gravity field from GRACE (Gravity Recovery and Climate Experiment), especially at higher latitude region like Greenland. This is since RBFs allow for regionally specific regularizations over areas which have sufficient and dense GRACE observations. Although existing RBF solutions show a better resolution than classical spherical harmonic solutions, the applied regularizations cause spatial leakage which should be carefully dealt with. It has been shown that leakage is a main error source which leads to an evident underestimation of yearly trend of ice-melting over Greenland. Unlike some popular post-processing techniques to mitigate leakage signals, this study, for the first time, attempts to reduce the leakage directly in the GRACE L1b inversion by constructing an innovative modified (MRBF) basis in place of the standard RBFs to retrieve a more realistic temporal gravity signal along the coastline. Our point of departure is that the surface mass loading associated with standard RBF is smooth but disregards physical consistency between continental mass and passive ocean response. In this contribution, based on earlier work by Clarke et al.(2007), a physically self-consistent MRBF representation is constructed from standard RBFs, with the help of the sea level equation: for a given standard RBF basis, the corresponding MRBF basis is first obtained by keeping the surface load over the continent unchanged, but imposing global mass conservation and equilibrium response of the oceans. Then, the updated set of MRBFs as well as standard RBFs are individually employed as the basis function to determine the temporal gravity field from GRACE L1b data. In this way, in the MRBF GRACE solution, the passive (e.g. ice melting and land hydrology response) sea level is automatically

Analysis and Application of High Resolution Numerical Perturbation Algorithm for Convective-Diffusion Equation

International Nuclear Information System (INIS)

Gao Zhi; Shen Yi-Qing

2012-01-01

The high resolution numerical perturbation (NP) algorithm is analyzed and tested using various convective-diffusion equations. The NP algorithm is constructed by splitting the second order central difference schemes of both convective and diffusion terms of the convective-diffusion equation into upstream and downstream parts, then the perturbation reconstruction functions of the convective coefficient are determined using the power-series of grid interval and eliminating the truncated errors of the modified differential equation. The important nature, i.e. the upwind dominance nature, which is the basis to ensuring that the NP schemes are stable and essentially oscillation free, is firstly presented and verified. Various numerical cases show that the NP schemes are efficient, robust, and more accurate than the original second order central scheme

Application of Deconvolution Algorithm of Point Spread Function in Improving Image Quality: An Observer Preference Study on Chest Radiography.

Science.gov (United States)

Chae, Kum Ju; Goo, Jin Mo; Ahn, Su Yeon; Yoo, Jin Young; Yoon, Soon Ho

2018-01-01

To evaluate the preference of observers for image quality of chest radiography using the deconvolution algorithm of point spread function (PSF) (TRUVIEW ART algorithm, DRTECH Corp.) compared with that of original chest radiography for visualization of anatomic regions of the chest. Prospectively enrolled 50 pairs of posteroanterior chest radiographs collected with standard protocol and with additional TRUVIEW ART algorithm were compared by four chest radiologists. This algorithm corrects scattered signals generated by a scintillator. Readers independently evaluated the visibility of 10 anatomical regions and overall image quality with a 5-point scale of preference. The significance of the differences in reader's preference was tested with a Wilcoxon's signed rank test. All four readers preferred the images applied with the algorithm to those without algorithm for all 10 anatomical regions (mean, 3.6; range, 3.2-4.0; p chest anatomical structures applied with the deconvolution algorithm of PSF was superior to the original chest radiography.

Explicit symplectic algorithms based on generating functions for relativistic charged particle dynamics in time-dependent electromagnetic field

Science.gov (United States)

Zhang, Ruili; Wang, Yulei; He, Yang; Xiao, Jianyuan; Liu, Jian; Qin, Hong; Tang, Yifa

2018-02-01

Relativistic dynamics of a charged particle in time-dependent electromagnetic fields has theoretical significance and a wide range of applications. The numerical simulation of relativistic dynamics is often multi-scale and requires accurate long-term numerical simulations. Therefore, explicit symplectic algorithms are much more preferable than non-symplectic methods and implicit symplectic algorithms. In this paper, we employ the proper time and express the Hamiltonian as the sum of exactly solvable terms and product-separable terms in space-time coordinates. Then, we give the explicit symplectic algorithms based on the generating functions of orders 2 and 3 for relativistic dynamics of a charged particle. The methodology is not new, which has been applied to non-relativistic dynamics of charged particles, but the algorithm for relativistic dynamics has much significance in practical simulations, such as the secular simulation of runaway electrons in tokamaks.

Least square fitting of low resolution gamma ray spectra with cubic B-spline basis functions

International Nuclear Information System (INIS)

Zhu Menghua; Liu Lianggang; Qi Dongxu; You Zhong; Xu Aoao

2009-01-01

In this paper, the least square fitting method with the cubic B-spline basis functions is derived to reduce the influence of statistical fluctuations in the gamma ray spectra. The derived procedure is simple and automatic. The results show that this method is better than the convolution method with a sufficient reduction of statistical fluctuation. (authors)

Complex basis functions for molecular resonances: Methodology and applications

Science.gov (United States)

White, Alec; McCurdy, C. William; Head-Gordon, Martin

The computation of positions and widths of metastable electronic states is a challenge for molecular electronic structure theory because, in addition to the difficulty of the many-body problem, such states obey scattering boundary conditions. These resonances cannot be addressed with naïve application of traditional bound state electronic structure theory. Non-Hermitian electronic structure methods employing complex basis functions is one way that we may rigorously treat resonances within the framework of traditional electronic structure theory. In this talk, I will discuss our recent work in this area including the methodological extension from single determinant SCF-based approaches to highly correlated levels of wavefunction-based theory such as equation of motion coupled cluster and many-body perturbation theory. These approaches provide a hierarchy of theoretical methods for the computation of positions and widths of molecular resonances. Within this framework, we may also examine properties of resonances including the dependence of these parameters on molecular geometry. Some applications of these methods to temporary anions and dianions will also be discussed.

SYNTHESIS METHODS OF ALGEBRAIC NORMAL FORM OF MANY-VALUED LOGIC FUNCTIONS

Directory of Open Access Journals (Sweden)

A. V. Sokolov

2016-01-01

Full Text Available The rapid development of methods of error-correcting coding, cryptography, and signal synthesis theory based on the principles of many-valued logic determines the need for a more detailed study of the forms of representation of functions of many-valued logic. In particular the algebraic normal form of Boolean functions, also known as Zhegalkin polynomial, that well describe many of the cryptographic properties of Boolean functions is widely used. In this article, we formalized the notion of algebraic normal form for many-valued logic functions. We developed a fast method of synthesis of algebraic normal form of 3-functions and 5-functions that work similarly to the Reed-Muller transform for Boolean functions: on the basis of recurrently synthesized transform matrices. We propose the hypothesis, which determines the rules of the synthesis of these matrices for the transformation from the truth table to the coefficients of the algebraic normal form and the inverse transform for any given number of variables of 3-functions or 5-functions. The article also introduces the definition of algebraic degree of nonlinearity of the functions of many-valued logic and the S-box, based on the principles of many-valued logic. Thus, the methods of synthesis of algebraic normal form of 3-functions applied to the known construction of recurrent synthesis of S-boxes of length N = 3k, whereby their algebraic degrees of nonlinearity are computed. The results could be the basis for further theoretical research and practical applications such as: the development of new cryptographic primitives, error-correcting codes, algorithms of data compression, signal structures, and algorithms of block and stream encryption, all based on the perspective principles of many-valued logic. In addition, the fast method of synthesis of algebraic normal form of many-valued logic functions is the basis for their software and hardware implementation.

Two-loop QED corrections to the Altarelli-Parisi splitting functions

Energy Technology Data Exchange (ETDEWEB)

Florian, Daniel de [International Center for Advanced Studies (ICAS), UNSAM,Campus Miguelete, 25 de Mayo y Francia (1650) Buenos Aires (Argentina); Sborlini, Germán F.R.; Rodrigo, Germán [Instituto de Física Corpuscular, Universitat de València,Consejo Superior de Investigaciones Científicas,Parc Científic, E-46980 Paterna, Valencia (Spain)

2016-10-11

We compute the two-loop QED corrections to the Altarelli-Parisi (AP) splitting functions by using a deconstructive algorithmic Abelianization of the well-known NLO QCD corrections. We present explicit results for the full set of splitting kernels in a basis that includes the leptonic distribution functions that, starting from this order in the QED coupling, couple to the partonic densities. Finally, we perform a phenomenological analysis of the impact of these corrections in the splitting functions.

Extension and optimization of the FIND algorithm: Computing Green’s and less-than Green’s functions

International Nuclear Information System (INIS)

Li, S.; Darve, E.

2012-01-01

Highlights: ► FIND is an algorithm for calculating entries of the inverse of a sparse matrix. ► We extend the algorithm to other matrix inverse related calculations. ► We exploit sparsity and symmetry to improve performance. - Abstract: The FIND algorithm is a fast algorithm designed to calculate certain entries of the inverse of a sparse matrix. Such calculation is critical in many applications, e.g., quantum transport in nano-devices. We extended the algorithm to other matrix inverse related calculations. Those are required for example to calculate the less-than Green’s function and the current density through the device. For a 2D device discretized as an N x × N y mesh, the best known algorithms have a running time of O(N x 3 N y ), whereas FIND only requires O(N x 2 N y ). Even though this complexity has been reduced by an order of magnitude, the matrix inverse calculation is still the most time consuming part in the simulation of transport problems. We could not reduce the order of complexity, but we were able to significantly reduce the constant factor involved in the computation cost. By exploiting the sparsity and symmetry, the size of the problem beyond which FIND is faster than other methods typically decreases from a 130 × 130 2D mesh down to a 40 × 40 mesh. These improvements make the optimized FIND algorithm even more competitive for real-life applications.

Functional Basis of Microorganism Classification.

Science.gov (United States)

Zhu, Chengsheng; Delmont, Tom O; Vogel, Timothy M; Bromberg, Yana

2015-08-01

Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with

Parameter Search Algorithms for Microwave Radar-Based Breast Imaging: Focal Quality Metrics as Fitness Functions.

Science.gov (United States)

O'Loughlin, Declan; Oliveira, Bárbara L; Elahi, Muhammad Adnan; Glavin, Martin; Jones, Edward; Popović, Milica; O'Halloran, Martin

2017-12-06

Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric properties, this paper shows how these metrics can be used as suitable fitness functions in parameter search algorithms to reconstruct clear and focused microwave radar images.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

Science.gov (United States)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Unsteady Solution of Non-Linear Differential Equations Using Walsh Function Series

Science.gov (United States)

Gnoffo, Peter A.

2015-01-01

Walsh functions form an orthonormal basis set consisting of square waves. The discontinuous nature of square waves make the system well suited for representing functions with discontinuities. The product of any two Walsh functions is another Walsh function - a feature that can radically change an algorithm for solving non-linear partial differential equations (PDEs). The solution algorithm of non-linear differential equations using Walsh function series is unique in that integrals and derivatives may be computed using simple matrix multiplication of series representations of functions. Solutions to PDEs are derived as functions of wave component amplitude. Three sample problems are presented to illustrate the Walsh function series approach to solving unsteady PDEs. These include an advection equation, a Burgers equation, and a Riemann problem. The sample problems demonstrate the use of the Walsh function solution algorithms, exploiting Fast Walsh Transforms in multi-dimensions (O(Nlog(N))). Details of a Fast Walsh Reciprocal, defined here for the first time, enable inversion of aWalsh Symmetric Matrix in O(Nlog(N)) operations. Walsh functions have been derived using a fractal recursion algorithm and these fractal patterns are observed in the progression of pairs of wave number amplitudes in the solutions. These patterns are most easily observed in a remapping defined as a fractal fingerprint (FFP). A prolongation of existing solutions to the next highest order exploits these patterns. The algorithms presented here are considered a work in progress that provide new alternatives and new insights into the solution of non-linear PDEs.

Strength Pareto Evolutionary Algorithm using Self-Organizing Data Analysis Techniques

Directory of Open Access Journals (Sweden)

Ionut Balan

2015-03-01

Full Text Available Multiobjective optimization is widely used in problems solving from a variety of areas. To solve such problems there was developed a set of algorithms, most of them based on evolutionary techniques. One of the algorithms from this class, which gives quite good results is SPEA2, method which is the basis of the proposed algorithm in this paper. Results from this paper are obtained by running these two algorithms on a flow-shop problem.

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.

Function-Based Algorithms for Biological Sequences

Science.gov (United States)

Mohanty, Pragyan Sheela P.

2015-01-01

Two problems at two different abstraction levels of computational biology are studied. At the molecular level, efficient pattern matching algorithms in DNA sequences are presented. For gene order data, an efficient data structure is presented capable of storing all gene re-orderings in a systematic manner. A common characteristic of presented…

An adaptive algorithm for performance assessment of construction project management with respect to resilience engineering and job security

Directory of Open Access Journals (Sweden)

P. Hashemi

2018-01-01

Full Text Available Construction sites are accident-prone locations and therefore safety management plays an im-portant role in these workplaces. This study presents an adaptive algorithm for performance as-sessment of project management with respect to resilience engineering and job security in a large construction site. The required data are collected using questionnaires in a large construction site. The presented algorithm is composed of radial basis function (RBF, artificial neural networks multi-layer perceptron (ANN-MLP, and statistical tests. The results indicate that preparedness, fault-tolerance, and flexibility are the most effective factors on overall efficiency. Moreover, job security and resilience engineering have similar statistical impacts on overall system efficiency. The results are verified and validated by the proposed algorithm.

Functional Basis of Microorganism Classification

Science.gov (United States)

Zhu, Chengsheng; Delmont, Tom O.; Vogel, Timothy M.; Bromberg, Yana

2015-01-01

Correctly identifying nearest “neighbors” of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned

Neural Basis of Enhanced Executive Function in Older Video Game Players: An fMRI Study.

Science.gov (United States)

Wang, Ping; Zhu, Xing-Ting; Qi, Zhigang; Huang, Silin; Li, Hui-Jie

2017-01-01

Video games have been found to have positive influences on executive function in older adults; however, the underlying neural basis of the benefits from video games has been unclear. Adopting a task-based functional magnetic resonance imaging (fMRI) study targeted at the flanker task, the present study aims to explore the neural basis of the improved executive function in older adults with video game experiences. Twenty video game players (VGPs) and twenty non-video game players (NVGPs) of 60 years of age or older participated in the present study, and there are no significant differences in age ( t = 0.62, p = 0.536), gender ratio ( t = 1.29, p = 0.206) and years of education ( t = 1.92, p = 0.062) between VGPs and NVGPs. The results show that older VGPs present significantly better behavioral performance than NVGPs. Older VGPs activate greater than NVGPs in brain regions, mainly in frontal-parietal areas, including the right dorsolateral prefrontal cortex, the left supramarginal gyrus, the right angular gyrus, the right precuneus and the left paracentral lobule. The present study reveals that video game experiences may have positive influences on older adults in behavioral performance and the underlying brain activation. These results imply the potential role that video games can play as an effective tool to improve cognitive ability in older adults.

Neural Basis of Enhanced Executive Function in Older Video Game Players: An fMRI Study

Directory of Open Access Journals (Sweden)

Ping Wang

2017-11-01

Full Text Available Video games have been found to have positive influences on executive function in older adults; however, the underlying neural basis of the benefits from video games has been unclear. Adopting a task-based functional magnetic resonance imaging (fMRI study targeted at the flanker task, the present study aims to explore the neural basis of the improved executive function in older adults with video game experiences. Twenty video game players (VGPs and twenty non-video game players (NVGPs of 60 years of age or older participated in the present study, and there are no significant differences in age (t = 0.62, p = 0.536, gender ratio (t = 1.29, p = 0.206 and years of education (t = 1.92, p = 0.062 between VGPs and NVGPs. The results show that older VGPs present significantly better behavioral performance than NVGPs. Older VGPs activate greater than NVGPs in brain regions, mainly in frontal-parietal areas, including the right dorsolateral prefrontal cortex, the left supramarginal gyrus, the right angular gyrus, the right precuneus and the left paracentral lobule. The present study reveals that video game experiences may have positive influences on older adults in behavioral performance and the underlying brain activation. These results imply the potential role that video games can play as an effective tool to improve cognitive ability in older adults.

A direct method to transform between expansions in the configuration state function and Slater determinant bases

International Nuclear Information System (INIS)

Olsen, Jeppe

2014-01-01

A novel algorithm is introduced for the transformation of wave functions between the bases of Slater determinants (SD) and configuration state functions (CSF) in the genealogical coupling scheme. By modifying the expansion coefficients as each electron is spin-coupled, rather than performing a single many-electron transformation, the large transformation matrix that plagues previous approaches is avoided and the required number of operations is drastically reduced. As an example of the efficiency of the algorithm, the transformation for a configuration with 30 unpaired electrons and singlet spin is discussed. For this case, the 10 × 10 6 coefficients in the CSF basis is obtained from the 150 × 10 6 coefficients in the SD basis in 1 min, which should be compared with the seven years that the previously employed method is estimated to require

VISUALIZATION OF PAGERANK ALGORITHM

OpenAIRE

Perhaj, Ervin

2013-01-01

The goal of the thesis is to develop a web application that help users understand the functioning of the PageRank algorithm. The thesis consists of two parts. First we develop an algorithm to calculate PageRank values of web pages. The input of algorithm is a list of web pages and links between them. The user enters the list through the web interface. From the data the algorithm calculates PageRank value for each page. The algorithm repeats the process, until the difference of PageRank va...

MVDR Algorithm Based on Estimated Diagonal Loading for Beamforming

Directory of Open Access Journals (Sweden)

Yuteng Xiao

2017-01-01

Full Text Available Beamforming algorithm is widely used in many signal processing fields. At present, the typical beamforming algorithm is MVDR (Minimum Variance Distortionless Response. However, the performance of MVDR algorithm relies on the accurate covariance matrix. The MVDR algorithm declines dramatically with the inaccurate covariance matrix. To solve the problem, studying the beamforming array signal model and beamforming MVDR algorithm, we improve MVDR algorithm based on estimated diagonal loading for beamforming. MVDR optimization model based on diagonal loading compensation is established and the interval of the diagonal loading compensation value is deduced on the basis of the matrix theory. The optimal diagonal loading value in the interval is also determined through the experimental method. The experimental results show that the algorithm compared with existing algorithms is practical and effective.

Approaching the basis set limit for DFT calculations using an environment-adapted minimal basis with perturbation theory: Formulation, proof of concept, and a pilot implementation

International Nuclear Information System (INIS)

Mao, Yuezhi; Horn, Paul R.; Mardirossian, Narbe; Head-Gordon, Teresa; Skylaris, Chris-Kriton; Head-Gordon, Martin

2016-01-01

Recently developed density functionals have good accuracy for both thermochemistry (TC) and non-covalent interactions (NC) if very large atomic orbital basis sets are used. To approach the basis set limit with potentially lower computational cost, a new self-consistent field (SCF) scheme is presented that employs minimal adaptive basis (MAB) functions. The MAB functions are optimized on each atomic site by minimizing a surrogate function. High accuracy is obtained by applying a perturbative correction (PC) to the MAB calculation, similar to dual basis approaches. Compared to exact SCF results, using this MAB-SCF (PC) approach with the same large target basis set produces <0.15 kcal/mol root-mean-square deviations for most of the tested TC datasets, and <0.1 kcal/mol for most of the NC datasets. The performance of density functionals near the basis set limit can be even better reproduced. With further improvement to its implementation, MAB-SCF (PC) is a promising lower-cost substitute for conventional large-basis calculations as a method to approach the basis set limit of modern density functionals.

The orthogonal gradients method: A radial basis functions method for solving partial differential equations on arbitrary surfaces

KAUST Repository

Piret, Cé cile

2012-01-01

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

Firefly Mating Algorithm for Continuous Optimization Problems

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Amarita Ritthipakdee

2017-01-01

Full Text Available This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA, for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i the mutual attraction between males and females causes them to mate and (ii fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.

Comparison of genetic algorithms with conjugate gradient methods

Science.gov (United States)

Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

1972-01-01

Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

Analog Circuit Design Optimization Based on Evolutionary Algorithms

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Mansour Barari

2014-01-01

Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

Basis set construction for molecular electronic structure theory: natural orbital and Gauss-Slater basis for smooth pseudopotentials.

Science.gov (United States)

Petruzielo, F R; Toulouse, Julien; Umrigar, C J

2011-02-14

A simple yet general method for constructing basis sets for molecular electronic structure calculations is presented. These basis sets consist of atomic natural orbitals from a multiconfigurational self-consistent field calculation supplemented with primitive functions, chosen such that the asymptotics are appropriate for the potential of the system. Primitives are optimized for the homonuclear diatomic molecule to produce a balanced basis set. Two general features that facilitate this basis construction are demonstrated. First, weak coupling exists between the optimal exponents of primitives with different angular momenta. Second, the optimal primitive exponents for a chosen system depend weakly on the particular level of theory employed for optimization. The explicit case considered here is a basis set appropriate for the Burkatzki-Filippi-Dolg pseudopotentials. Since these pseudopotentials are finite at nuclei and have a Coulomb tail, the recently proposed Gauss-Slater functions are the appropriate primitives. Double- and triple-zeta bases are developed for elements hydrogen through argon. These new bases offer significant gains over the corresponding Burkatzki-Filippi-Dolg bases at various levels of theory. Using a Gaussian expansion of the basis functions, these bases can be employed in any electronic structure method. Quantum Monte Carlo provides an added benefit: expansions are unnecessary since the integrals are evaluated numerically.

Test of TEDA, Tsunami Early Detection Algorithm

Science.gov (United States)

Bressan, Lidia; Tinti, Stefano

2010-05-01

Tsunami detection in real-time, both offshore and at the coastline, plays a key role in Tsunami Warning Systems since it provides so far the only reliable and timely proof of tsunami generation, and is used to confirm or cancel tsunami warnings previously issued on the basis of seismic data alone. Moreover, in case of submarine or coastal landslide generated tsunamis, which are not announced by clear seismic signals and are typically local, real-time detection at the coastline might be the fastest way to release a warning, even if the useful time for emergency operations might be limited. TEDA is an algorithm for real-time detection of tsunami signal on sea-level records, developed by the Tsunami Research Team of the University of Bologna. The development and testing of the algorithm has been accomplished within the framework of the Italian national project DPC-INGV S3 and the European project TRANSFER. The algorithm is to be implemented at station level, and it is based therefore only on sea-level data of a single station, either a coastal tide-gauge or an offshore buoy. TEDA's principle is to discriminate the first tsunami wave from the previous background signal, which implies the assumption that the tsunami waves introduce a difference in the previous sea-level signal. Therefore, in TEDA the instantaneous (most recent) and the previous background sea-level elevation gradients are characterized and compared by proper functions (IS and BS) that are updated at every new data acquisition. Detection is triggered when the instantaneous signal function passes a set threshold and at the same time it is significantly bigger compared to the previous background signal. The functions IS and BS depend on temporal parameters that allow the algorithm to be adapted different situations: in general, coastal tide-gauges have a typical background spectrum depending on the location where the instrument is installed, due to local topography and bathymetry, while offshore buoys are

Neural basis of disgust perception in racial prejudice.

Science.gov (United States)

Liu, Yunzhe; Lin, Wanjun; Xu, Pengfei; Zhang, Dandan; Luo, Yuejia

2015-12-01

Worldwide racial prejudice is originated from in-group/out-group discrimination. This prejudice can bias face perception at the very beginning of social interaction. However, little is known about the neurocognitive mechanism underlying the influence of racial prejudice on facial emotion perception. Here, we examined the neural basis of disgust perception in racial prejudice using a passive viewing task and functional magnetic resonance imaging. We found that compared with the disgusted faces of in-groups, the disgusted faces of out-groups result in increased amygdala and insular engagement, positive coupling of the insula with amygdala-based emotional system, and negative coupling of the insula with anterior cingulate cortex (ACC)-based regulatory system. Furthermore, machine-learning algorithms revealed that the level of implicit racial prejudice could be predicted by functional couplings of the insula with both the amygdala and the ACC, which suggests that the insula is largely involved in racially biased disgust perception through two distinct neural circuits. In addition, individual difference in disgust sensitivity was found to be predictive of implicit racial prejudice. Taken together, our results suggest a crucial role of insula-centered circuits for disgust perception in racial prejudice. © 2015 Wiley Periodicals, Inc.

Evaluation of adjustment functions in a heuristic algorithm; Evaluacion de funciones de ajuste en un algoritmo heuristico

Energy Technology Data Exchange (ETDEWEB)

Perusquia del Cueto, R.; Montes T, J. L.; Ortiz S, J. J.; Castillo M, A., E-mail: raul.perusquia@inin.gob.mx [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)

2011-11-15

At present the techniques of evolution al computation receive an increasing attention in the scientific and technological areas. This situation is due to its enormous potential in the optimization applied to problems of discussed computational complexity. In the nuclear area these techniques are used in diverse problems of combinatory optimization related with designing cores of power reactors. A distinctive characteristic of the evolution al and/or meta-heuristic algorithms is that appeal in each one from their applications to an adjustment function, fitness or of quality. This function allows to discriminate or to evaluate potentials solutions of the problem to solve. The definition of this situation is very important since it allows following the search of the algorithm toward different regions of the search space. In this work the impact that has the election of this function in the quality of the found solution is shown. The optimization technique by ant colonies or Acs (ant colony system) was used applied to the radial design of fuel cells for a boiling water power reactor. The notable results of the Acs allowed to propose the adjustment method of the importance and with this to obtain adjustment functions that guide the search of solutions of collective algorithms efficiently, basic capacity to develop the proposal of emulation of the natural selection and to investigate the possibility that on order of specify goals, to obtain the corresponding decision variables. A variety of re tro-exit (re tro-out) complementary process of feedback (re tro-in) that opens extended application perspectives of be feasible. (Author)

A hybrid electromagnetism-like algorithm for a multi-mode resource-constrained project scheduling problem

Directory of Open Access Journals (Sweden)

Mohammad Hossein Sadeghi

2013-08-01

Full Text Available In this paper, two different sub-problems are considered to solve a resource constrained project scheduling problem (RCPSP, namely i assignment of modes to tasks and ii scheduling of these tasks in order to minimize the makespan of the project. The modified electromagnetism-like algorithm deals with the first problem to create an assignment of modes to activities. This list is used to generate a project schedule. When a new assignment is made, it is necessary to fix all mode dependent requirements of the project activities and to generate a random schedule with the serial SGS method. A local search will optimize the sequence of the activities. Also in this paper, a new penalty function has been proposed for solutions which are infeasible with respect to non-renewable resources. Performance of the proposed algorithm has been compared with the best algorithms published so far on the basis of CPU time and number of generated schedules stopping criteria. Reported results indicate excellent performance of the algorithm.

A new diffusion nodal method based on analytic basis function expansion

International Nuclear Information System (INIS)

Noh, J.M.; Cho, N.Z.

1993-01-01

The transverse integration procedure commonly used in most advanced nodal methods results in some limitations. The first is that the transverse leakage term that appears in the transverse integration procedure must be appropriately approximated. In most advanced nodal methods, this term is expanded in a quadratic polynomial. The second arises when reconstructing the pinwise flux distribution within a node. The available one-dimensional flux shapes from nodal calculation in each spatial direction cannot be used directly in the flux reconstruction. Finally, the transverse leakage defined for a hexagonal node becomes so complicated as not to be easily handled and contains nonphysical singular terms. In this paper, a new nodal method called the analytic function expansion nodal (AFEN) method is described for both the rectangular geometry and the hexagonal geometry in order to overcome these limitations. This method does not solve the transverse-integrated one-dimensional diffusion equations but instead solves directly the original multidimensional diffusion equation within a node. This is a accomplished by expanding the solution (or the intranodal homogeneous flux distribution) in terms of nonseparable analytic basis functions satisfying the diffusion equation at any point in the node

ROAM: A Radial-Basis-Function Optimization Approximation Method for Diagnosing the Three-Dimensional Coronal Magnetic Field

International Nuclear Information System (INIS)

Dalmasse, Kevin; Nychka, Douglas W.; Gibson, Sarah E.; Fan, Yuhong; Flyer, Natasha

2016-01-01

The Coronal Multichannel Polarimeter (CoMP) routinely performs coronal polarimetric measurements using the Fe XIII 10747 and 10798 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 analog. 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.

Realization of Deutsch-like algorithm using ensemble computing

International Nuclear Information System (INIS)

Wei Daxiu; Luo Jun; Sun Xianping; Zeng Xizhi

2003-01-01

The Deutsch-like algorithm [Phys. Rev. A. 63 (2001) 034101] distinguishes between even and odd query functions using fewer function calls than its possible classical counterpart in a two-qubit system. But the similar method cannot be applied to a multi-qubit system. We propose a new approach for solving Deutsch-like problem using ensemble computing. The proposed algorithm needs an ancillary qubit and can be easily extended to multi-qubit system with one query. Our ensemble algorithm beginning with a easily-prepared initial state has three main steps. The classifications of the functions can be obtained directly from the spectra of the ancilla qubit. We also demonstrate the new algorithm in a four-qubit molecular system using nuclear magnetic resonance (NMR). One hydrogen and three carbons are selected as the four qubits, and one of carbons is ancilla qubit. We choice two unitary transformations, corresponding to two functions (one odd function and one even function), to validate the ensemble algorithm. The results show that our experiment is successfully and our ensemble algorithm for solving the Deutsch-like problem is virtual

Push it to the limit: Characterizing the convergence of common sequences of basis sets for intermolecular interactions as described by density functional theory

Energy Technology Data Exchange (ETDEWEB)

Witte, Jonathon [Department of Chemistry, University of California, Berkeley, California 94720 (United States); Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720 (United States); Neaton, Jeffrey B. [Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720 (United States); Department of Physics, University of California, Berkeley, California 94720 (United States); Kavli Energy Nanosciences Institute at Berkeley, Berkeley, California 94720 (United States); Head-Gordon, Martin, E-mail: mhg@cchem.berkeley.edu [Department of Chemistry, University of California, Berkeley, California 94720 (United States); Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720 (United States)

2016-05-21

With the aim of systematically characterizing the convergence of common families of basis sets such that general recommendations for basis sets can be made, we have tested a wide variety of basis sets against complete-basis binding energies across the S22 set of intermolecular interactions—noncovalent interactions of small and medium-sized molecules consisting of first- and second-row atoms—with three distinct density functional approximations: SPW92, a form of local-density approximation; B3LYP, a global hybrid generalized gradient approximation; and B97M-V, a meta-generalized gradient approximation with nonlocal correlation. We have found that it is remarkably difficult to reach the basis set limit; for the methods and systems examined, the most complete basis is Jensen’s pc-4. The Dunning correlation-consistent sequence of basis sets converges slowly relative to the Jensen sequence. The Karlsruhe basis sets are quite cost effective, particularly when a correction for basis set superposition error is applied: counterpoise-corrected def2-SVPD binding energies are better than corresponding energies computed in comparably sized Dunning and Jensen bases, and on par with uncorrected results in basis sets 3-4 times larger. These trends are exhibited regardless of the level of density functional approximation employed. A sense of the magnitude of the intrinsic incompleteness error of each basis set not only provides a foundation for guiding basis set choice in future studies but also facilitates quantitative comparison of existing studies on similar types of systems.

Accuracy Analysis of Lunar Lander Terminal Guidance Algorithm

Directory of Open Access Journals (Sweden)

E. K. Li

2017-01-01

Full Text Available This article studies a proposed analytical algorithm of the terminal guidance for the lunar lander. The analytical solution, which forms the basis of the algorithm, was obtained for a constant acceleration trajectory and thrust vector orientation programs that are essentially linear with time. The main feature of the proposed algorithm is a completely analytical solution to provide the lander terminal guidance to the desired spot in 3D space when landing on the atmosphereless body with no numerical procedures. To reach 6 terminal conditions (components of position and velocity vectors at the final time are used 6 guidance law parameters, namely time-to-go, desired value of braking deceleration, initial values of pitch and yaw angles and rates of their change. In accordance with the principle of flexible trajectories, this algorithm assumes the implementation of a regularly updated control program that ensures reaching terminal conditions from the current state that corresponds to the control program update time. The guidance law parameters, which ensure that terminal conditions are reached, are generated as a function of the current phase coordinates of a lander. The article examines an accuracy and reliability of the proposed analytical algorithm that provides the terminal guidance of the lander in 3D space through mathematical modeling of the lander guidance from the circumlunar pre-landing orbit to the desired spot near the lunar surface. A desired terminal position of the lunar lander is specified by the selenographic latitude, longitude and altitude above the lunar surface. The impact of variations in orbital parameters on the terminal guidance accuracy has been studied. By varying the five initial orbit parameters (obliquity, ascending node longitude, argument of periapsis, periapsis height, apoapsis height when the terminal spot is fixed the statistic characteristics of the terminal guidance algorithm error according to the terminal

Advanced Test Reactor Safety Basis Upgrade Lessons Learned Relative to Design Basis Verification and Safety Basis Management

International Nuclear Information System (INIS)

G. L. Sharp; R. T. McCracken

2004-01-01

The Advanced Test Reactor (ATR) is a pressurized light-water reactor with a design thermal power of 250 MW. The principal function of the ATR is to provide a high neutron flux for testing reactor fuels and other materials. The reactor also provides other irradiation services such as radioisotope production. The ATR and its support facilities are located at the Test Reactor Area of the Idaho National Engineering and Environmental Laboratory (INEEL). An audit conducted by the Department of Energy's Office of Independent Oversight and Performance Assurance (DOE OA) raised concerns that design conditions at the ATR were not adequately analyzed in the safety analysis and that legacy design basis management practices had the potential to further impact safe operation of the facility.1 The concerns identified by the audit team, and issues raised during additional reviews performed by ATR safety analysts, were evaluated through the unreviewed safety question process resulting in shutdown of the ATR for more than three months while these concerns were resolved. Past management of the ATR safety basis, relative to facility design basis management and change control, led to concerns that discrepancies in the safety basis may have developed. Although not required by DOE orders or regulations, not performing design basis verification in conjunction with development of the 10 CFR 830 Subpart B upgraded safety basis allowed these potential weaknesses to be carried forward. Configuration management and a clear definition of the existing facility design basis have a direct relation to developing and maintaining a high quality safety basis which properly identifies and mitigates all hazards and postulated accident conditions. These relations and the impact of past safety basis management practices have been reviewed in order to identify lessons learned from the safety basis upgrade process and appropriate actions to resolve possible concerns with respect to the current ATR safety

Modified Decoding Algorithm of LLR-SPA

Directory of Open Access Journals (Sweden)

Zhongxun Wang

2014-09-01

Full Text Available In wireless sensor networks, the energy consumption is mainly occurred in the stage of information transmission. The Low Density Parity Check code can make full use of the channel information to save energy. Because of the widely used decoding algorithm of the Low Density Parity Check code, this paper proposes a new decoding algorithm which is based on the LLR-SPA (Sum-Product Algorithm in Log-Likelihood-domain to improve the accuracy of the decoding algorithm. In the modified algorithm, a piecewise linear function is used to approximate the complicated Jacobi correction term in LLR-SPA decoding algorithm. Construct the tangent by the tangency point to the function of Jacobi correction term, which is based on the first order Taylor Series. In this way, the proposed piecewise linear approximation offers almost a perfect match to the function of Jacobi correction term. Meanwhile, the proposed piecewise linear approximation could avoid the operation of logarithmic which is more suitable for practical application. The simulation results show that the proposed algorithm could improve the decoding accuracy greatly without noticeable variation of the computational complexity.

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

Science.gov (United States)

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.

CATEGORIES OF COMPUTER SYSTEMS ALGORITHMS

Directory of Open Access Journals (Sweden)

A. V. Poltavskiy

2015-01-01

Full Text Available Philosophy as a frame of reference on world around and as the first science is a fundamental basis, "roots" (R. Descartes for all branches of the scientific knowledge accumulated and applied in all fields of activity of a human being person. The theory of algorithms as one of the fundamental sections of mathematics, is also based on researches of the gnoseology conducting cognition of a true picture of the world of the buman being. From gnoseology and ontology positions as fundamental sections of philosophy modern innovative projects are inconceivable without development of programs,and algorithms.

Quantum Dynamics with Short-Time Trajectories and Minimal Adaptive Basis Sets.

Science.gov (United States)

Saller, Maximilian A C; Habershon, Scott

2017-07-11

Methods for solving the time-dependent Schrödinger equation via basis set expansion of the wave function can generally be categorized as having either static (time-independent) or dynamic (time-dependent) basis functions. We have recently introduced an alternative simulation approach which represents a middle road between these two extremes, employing dynamic (classical-like) trajectories to create a static basis set of Gaussian wavepackets in regions of phase-space relevant to future propagation of the wave function [J. Chem. Theory Comput., 11, 8 (2015)]. Here, we propose and test a modification of our methodology which aims to reduce the size of basis sets generated in our original scheme. In particular, we employ short-time classical trajectories to continuously generate new basis functions for short-time quantum propagation of the wave function; to avoid the continued growth of the basis set describing the time-dependent wave function, we employ Matching Pursuit to periodically minimize the number of basis functions required to accurately describe the wave function. Overall, this approach generates a basis set which is adapted to evolution of the wave function while also being as small as possible. In applications to challenging benchmark problems, namely a 4-dimensional model of photoexcited pyrazine and three different double-well tunnelling problems, we find that our new scheme enables accurate wave function propagation with basis sets which are around an order-of-magnitude smaller than our original trajectory-guided basis set methodology, highlighting the benefits of adaptive strategies for wave function propagation.

Ship nonlinear-feedback course keeping algorithm based on MMG model driven by bipolar sigmoid function for berthing

Directory of Open Access Journals (Sweden)

Qiang Zhang

2017-09-01

Full Text Available Course keeping is hard to implement under the condition of the propeller stopping or reversing at slow speed for berthing due to the ship's dynamic motion becoming highly nonlinear. To solve this problem, a practical Maneuvering Modeling Group (MMG ship mathematic model with propeller reversing transverse forces and low speed correction is first discussed to be applied for the right-handed single-screw ship. Secondly, a novel PID-based nonlinear feedback algorithm driven by bipolar sigmoid function is proposed. The PID parameters are determined by a closed-loop gain shaping algorithm directly, while the closed-loop gain shaping theory was employed for effects analysis of this algorithm. Finally, simulation experiments were carried out on an LPG ship. It is shown that the energy consumption and the smoothness performance of the nonlinear feedback control are reduced by 4.2% and 14.6% with satisfactory control effects; the proposed algorithm has the advantages of robustness, energy saving and safety in berthing practice.

Algorithmic foundation of multi-scale spatial representation

CERN Document Server

Li, Zhilin

2006-01-01

With the widespread use of GIS, multi-scale representation has become an important issue in the realm of spatial data handling. However, no book to date has systematically tackled the different aspects of this discipline. Emphasizing map generalization, Algorithmic Foundation of Multi-Scale Spatial Representation addresses the mathematical basis of multi-scale representation, specifically, the algorithmic foundation.Using easy-to-understand language, the author focuses on geometric transformations, with each chapter surveying a particular spatial feature. After an introduction to the essential operations required for geometric transformations as well as some mathematical and theoretical background, the book describes algorithms for a class of point features/clusters. It then examines algorithms for individual line features, such as the reduction of data points, smoothing (filtering), and scale-driven generalization, followed by a discussion of algorithms for a class of line features including contours, hydrog...

Improved Bat Algorithm Applied to Multilevel Image Thresholding

Directory of Open Access Journals (Sweden)

Adis Alihodzic

2014-01-01

Full Text Available Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.

Optimal battery sizing in photovoltaic based distributed generation using enhanced opposition-based firefly algorithm for voltage rise mitigation.

Science.gov (United States)

Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul

2014-01-01

This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.

Optimal Battery Sizing in Photovoltaic Based Distributed Generation Using Enhanced Opposition-Based Firefly Algorithm for Voltage Rise Mitigation

Directory of Open Access Journals (Sweden)

Ling Ai Wong

2014-01-01

Full Text Available This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.

Initialization-free generalized Deutsche-Jazz's algorithm

Energy Technology Data Exchange (ETDEWEB)

Chi, Dong Pyo [School of Mathematical Sciences, Seoul National University, Seoul (Korea, Republic of)]. E-mail: dpchi@math.snu.ac.kr; Kim, Jinsoo [School of Electrical Engineering and Computer Science, Seoul National University, Seoul (Korea)]. E-mail: jkim@ee.snu.ac.kr; Lee, Soojoon [School of Mathematical Sciences, Seoul National University, Seoul (Korea)]. E-mail: level@math.snu.ac.kr

2001-06-29

We generalize the Deutsch-Jozsa algorithm by exploiting summations of the roots of unity. The generalized algorithm distinguishes a wider class of functions promised to be either constant or many to one and onto an evenly spaced range. As previously, the generalized quantum algorithm solves this problem using a single functional evaluation. We also consider the problem of distinguishing constant and evenly balanced functions and present a quantum algorithm for this problem that does not require any initialization of an auxiliary register involved in the process of functional evaluation and after solving the problem recovers the initial state of an auxiliary register. (author)

Unsupervised learning of a steerable basis for invariant image representations

Science.gov (United States)

Bethge, Matthias; Gerwinn, Sebastian; Macke, Jakob H.

2007-02-01

There are two aspects to unsupervised learning of invariant representations of images: First, we can reduce the dimensionality of the representation by finding an optimal trade-off between temporal stability and informativeness. We show that the answer to this optimization problem is generally not unique so that there is still considerable freedom in choosing a suitable basis. Which of the many optimal representations should be selected? Here, we focus on this second aspect, and seek to find representations that are invariant under geometrical transformations occuring in sequences of natural images. We utilize ideas of 'steerability' and Lie groups, which have been developed in the context of filter design. In particular, we show how an anti-symmetric version of canonical correlation analysis can be used to learn a full-rank image basis which is steerable with respect to rotations. We provide a geometric interpretation of this algorithm by showing that it finds the two-dimensional eigensubspaces of the average bivector. For data which exhibits a variety of transformations, we develop a bivector clustering algorithm, which we use to learn a basis of generalized quadrature pairs (i.e. 'complex cells') from sequences of natural images.

Online co-regularized algorithms

NARCIS (Netherlands)

Ruijter, T. de; Tsivtsivadze, E.; Heskes, T.

2012-01-01

We propose an online co-regularized learning algorithm for classification and regression tasks. We demonstrate that by sequentially co-regularizing prediction functions on unlabeled data points, our algorithm provides improved performance in comparison to supervised methods on several UCI benchmarks

A fitting algorithm based on simulated annealing techniques for efficiency calibration of HPGe detectors using different mathematical functions

Energy Technology Data Exchange (ETDEWEB)

Hurtado, S. [Servicio de Radioisotopos, Centro de Investigacion, Tecnologia e Innovacion (CITIUS), Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012 Sevilla (Spain)], E-mail: shurtado@us.es; Garcia-Leon, M. [Departamento de Fisica Atomica, Molecular y Nuclear, Facultad de Fisica, Universidad de Sevilla, Aptd. 1065, 41080 Sevilla (Spain); Garcia-Tenorio, R. [Departamento de Fisica Aplicada II, E.T.S.A. Universidad de Sevilla, Avda, Reina Mercedes 2, 41012 Sevilla (Spain)

2008-09-11

In this work several mathematical functions are compared in order to perform the full-energy peak efficiency calibration of HPGe detectors using a 126cm{sup 3} HPGe coaxial detector and gamma-ray energies ranging from 36 to 1460 keV. Statistical tests and Monte Carlo simulations were used to study the performance of the fitting curve equations. Furthermore the fitting procedure of these complex functional forms to experimental data is a non-linear multi-parameter minimization problem. In gamma-ray spectrometry usually non-linear least-squares fitting algorithms (Levenberg-Marquardt method) provide a fast convergence while minimizing {chi}{sub R}{sup 2}, however, sometimes reaching only local minima. In order to overcome that shortcoming a hybrid algorithm based on simulated annealing (HSA) techniques is proposed. Additionally a new function is suggested that models the efficiency curve of germanium detectors in gamma-ray spectrometry.

Maximum-entropy clustering algorithm and its global convergence analysis

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.

On the accuracy of density-functional theory exchange-correlation functionals for H bonds in small water clusters: Benchmarks approaching the complete basis set limit

Science.gov (United States)

Santra, Biswajit; Michaelides, Angelos; Scheffler, Matthias

2007-11-01

The ability of several density-functional theory (DFT) exchange-correlation functionals to describe hydrogen bonds in small water clusters (dimer to pentamer) in their global minimum energy structures is evaluated with reference to second order Møller-Plesset perturbation theory (MP2). Errors from basis set incompleteness have been minimized in both the MP2 reference data and the DFT calculations, thus enabling a consistent systematic evaluation of the true performance of the tested functionals. Among all the functionals considered, the hybrid X3LYP and PBE0 functionals offer the best performance and among the nonhybrid generalized gradient approximation functionals, mPWLYP and PBE1W perform best. The popular BLYP and B3LYP functionals consistently underbind and PBE and PW91 display rather variable performance with cluster size.

A hybrid radial basis function-pseudospectral method for thermal convection in a 3-D spherical shell

KAUST Repository

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 × 10³ and 10⁵. Results from a Ra = 10⁶ 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.

Two- and three-dimensional nonlocal density functional theory for inhomogeneous fluids. 1. Algorithms and parallelization

International Nuclear Information System (INIS)

Frink, L.J.D.; Salinger, A.G.

2000-01-01

Fluids adsorbed near surfaces, near macromolecules, and in porous materials are inhomogeneous, exhibiting spatially varying density distributions. This inhomogeneity in the fluid plays an important role in controlling a wide variety of complex physical phenomena including wetting, self-assembly, corrosion, and molecular recognition. One of the key methods for studying the properties of inhomogeneous fluids in simple geometries has been density functional theory (DFT). However, there has been a conspicuous lack of calculations in complex two- and three-dimensional geometries. The computational difficulty arises from the need to perform nested integrals that are due to nonlocal terms in the free energy functional. These integral equations are expensive both in evaluation time and in memory requirements; however, the expense can be mitigated by intelligent algorithms and the use of parallel computers. This paper details the efforts to develop efficient numerical algorithms so that nonlocal DFT calculations in complex geometries that require two or three dimensions can be performed. The success of this implementation will enable the study of solvation effects at heterogeneous surfaces, in zeolites, in solvated (bio)polymers, and in colloidal suspensions

Golden Sine Algorithm: A Novel Math-Inspired Algorithm

Directory of Open Access Journals (Sweden)

TANYILDIZI, E.

2017-05-01

Full Text Available In this study, Golden Sine Algorithm (Gold-SA is presented as a new metaheuristic method for solving optimization problems. Gold-SA has been developed as a new search algorithm based on population. This math-based algorithm is inspired by sine that is a trigonometric function. In the algorithm, random individuals are created as many as the number of search agents with uniform distribution for each dimension. The Gold-SA operator searches to achieve a better solution in each iteration by trying to bring the current situation closer to the target value. The solution space is narrowed by the golden section so that the areas that are supposed to give only good results are scanned instead of the whole solution space scan. In the tests performed, it is seen that Gold-SA has better results than other population based methods. In addition, Gold-SA has fewer algorithm-dependent parameters and operators than other metaheuristic methods, increasing the importance of this method by providing faster convergence of this new method.

Paired Pulse Basis Functions for the Method of Moments EFIE Solution of Electromagnetic Problems Involving Arbitrarily-shaped, Three-dimensional Dielectric Scatterers

Science.gov (United States)

MacKenzie, Anne I.; Rao, Sadasiva M.; Baginski, Michael E.

2007-01-01

A pair of basis functions is presented for the surface integral, method of moment solution of scattering by arbitrarily-shaped, three-dimensional dielectric bodies. Equivalent surface currents are represented by orthogonal unit pulse vectors in conjunction with triangular patch modeling. The electric field integral equation is employed with closed geometries for dielectric bodies; the method may also be applied to conductors. Radar cross section results are shown for dielectric bodies having canonical spherical, cylindrical, and cubic shapes. Pulse basis function results are compared to results by other methods.