A radial basis function Galerkin method for inhomogeneous nonlocal diffusion
Lehoucq, Richard B.; Rowe, Stephen T.
2016-02-01
We introduce a discretization for a nonlocal diffusion problem using a localized basis of radial basis functions. The stiffness matrix entries are assembled by a special quadrature routine unique to the localized basis. Combining the quadrature method with the localized basis produces a well-conditioned, sparse, symmetric positive definite stiffness matrix. We demonstrate that both the continuum and discrete problems are well-posed and present numerical results for the convergence behavior of the radial basis function method. As a result, we explore approximating the solution to anisotropic differential equations by solving anisotropic nonlocal integral equations using the radial basis function method.
Radial basis function neural networks applied to NASA SSME data
NASA Technical Reports Server (NTRS)
Wheeler, Kevin R.; Dhawan, Atam P.
1993-01-01
This paper presents a brief report on the application of Radial Basis Function Neural Networks (RBFNN) to the prediction of sensor values for fault detection and diagnosis of the Space Shuttle's Main Engines (SSME). The location of the Radial Basis Function (RBF) node centers was determined with a K-means clustering algorithm. A neighborhood operation about these center points was used to determine the variances of the individual processing notes.
Radial basis function neural networks applied to NASA SSME data
NASA Astrophysics Data System (ADS)
Wheeler, Kevin R.; Dhawan, Atam P.
1993-06-01
This paper presents a brief report on the application of Radial Basis Function Neural Networks (RBFNN) to the prediction of sensor values for fault detection and diagnosis of the Space Shuttle's Main Engines (SSME). The location of the Radial Basis Function (RBF) node centers was determined with a K-means clustering algorithm. A neighborhood operation about these center points was used to determine the variances of the individual processing notes.
Point Set Denoising Using Bootstrap-Based Radial Basis Function
Ramli, Ahmad; Abd. Majid, Ahmad
2016-01-01
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study. PMID:27315105
A radial basis function neurocomputer implemented with analog VLSI circuits
NASA Technical Reports Server (NTRS)
Watkins, Steven S.; Chau, Paul M.; Tawel, Raoul
1992-01-01
An electronic neurocomputer which implements a radial basis function neural network (RBFNN) is described. The RBFNN is a network that utilizes a radial basis function as the transfer function. The key advantages of RBFNNs over existing neural network architectures include reduced learning time and the ease of VLSI implementation. This neurocomputer is based on an analog/digital hybrid design and has been constructed with both custom analog VLSI circuits and a commercially available digital signal processor. The hybrid architecture is selected because it offers high computational performance while compensating for analog inaccuracies, and it features the ability to model large problems.
Surface interpolation with radial basis functions for medical imaging
Carr, J.C.; Beatson, R.K.; Fright, W.R.
1997-02-01
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.
Shakespeare vs. Fletcher: A Stylometric Analysis by Radial Basis Functions.
ERIC Educational Resources Information Center
Lowe, David; Matthews, Robert
1995-01-01
Illustrates how Radial Basis Function (RBF) network techniques can be used to explore questions concerning authorship of historic documents. Demonstrates the utility and potential for using quantitative techniques to assist in the decision-making process in relatively subjective disciplines. Compares RBF neural network techniques with more…
Precision of a radial basis function neural network tracking method
NASA Technical Reports Server (NTRS)
Hanan, J.; Zhou, H.; Chao, T. H.
2003-01-01
The precision of a radial basis function (RBF) neural network based tracking method has been assessed against real targets. Precision was assessed against traditionally measured frame-by-frame measurements from the recorded data set. The results show the potential limit for the technique and reveal intricacies associated with empirical data not necessarily observed in simulations.
Use of Normalized Radial Basis Function in Hydrology
Cotar, Anton; Brilly, Mitja
2008-11-13
In this article we will present a use of normalized radial basis function in hydrology for prediction of missing river Reka runoff data. The method is based on multidimensional normal distribution, where standard deviation is first optimized and later the whole prediction process is learned on existing data [5]. We can conclude, that the method works very well for middle ranges of data, but not so well for extremes because of its interpolating nature.
Dynamics of learning near singularities in radial basis function networks.
Wei, Haikun; Amari, Shun-Ichi
2008-09-01
The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation. PMID:18693082
Adaptive radial basis function mesh deformation using data reduction
NASA Astrophysics Data System (ADS)
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
Reconstructing the magnetosphere from data using radial basis functions
NASA Astrophysics Data System (ADS)
Andreeva, Varvara A.; Tsyganenko, Nikolai A.
2016-03-01
A new method is proposed to derive from data magnetospheric magnetic field configurations without any a priori assumptions on the geometry of electric currents. The approach utilizes large sets of archived satellite data and uses an advanced technique to represent the field as a sum of toroidal and poloidal parts, whose generating potentials Ψ1 and Ψ2 are expanded into series of radial basis functions (RBFs) with their nodes regularly distributed over the 3-D modeling domain. The method was tested by reconstructing the inner and high-latitude field within geocentric distances up to 12RE on the basis of magnetometer data of Geotail, Polar, Cluster, Time History of Events and Macroscale Interactions during Substorms, and Van Allen space probes, taken during 1995-2015. Four characteristic states of the magnetosphere before and during a disturbance have been modeled: a quiet prestorm period, storm deepening phase with progressively decreasing SYM-H index, the storm maximum around the negative peak of SYM-H, and the recovery phase. Fitting the RBF model to data faithfully resolved contributions to the total magnetic field from all principal sources, including the westward and eastward ring current, the tail current, diamagnetic currents associated with the polar cusps, and the large-scale effect of the field-aligned currents. For two main phase conditions, the model field exhibited a strong dawn-dusk asymmetry of the low-latitude magnetic depression, extending to low altitudes and partly spreading sunward from the terminator plane in the dusk sector. The RBF model was found to resolve even finer details, such as the bifurcation of the innermost tail current. The method can be further developed into a powerful tool for data-based studies of the magnetospheric currents.
Texture image classification using modular radial basis function neural networks
NASA Astrophysics Data System (ADS)
Chang, Chuan-Yu; Wang, Hung-Jen; Fu, Shih-Yu
2010-01-01
Image classification has become an important topic in multimedia processing. Recently, neural network-based methods have been proposed to solve the classification problem. Among them, the radial basis function neural network (RBFNN) is the most popular architecture, because it has good learning and approximation capabilities. However, traditional RBFNNs are sensitive to center initialization. To obtain appropriate centers, it needs to find significant features for further RBF clustering. In addition, the training procedure of a traditional RBFNN is time consuming. Therefore, in this work, a combination of a self-organizing map (SOM) and learning vector quantization (LVQ) neural networks is proposed to select more appropriate centers for an RBFNN, and a modular RBF neural network (MRBFNN) is proposed to improve the classification rate and to speed up the training time. Experimental results show that the proposed MRBFNN has better performance than those of the traditional RBFNN, the discrete wavelength transform (DWT)-based method, the tree structured wavelet (TWS), the discrete wavelet frame (DWF), the rotated wavelet filter (RWF), and the wavelet neural network based on adaptive norm entropy (WNN-ANE) methods.
3-D Spherical Mantle Convection with Radial Basis Functions
NASA Astrophysics Data System (ADS)
Flyer, N.; Wright, G. B.; Yuen, D.
2008-12-01
In the past 25 years a wide variety of numerical methods, such as finite-difference, finite-volume , finite- elements, and pseudospectral methods have been employed to study the problem of 3-D mantle convection. All have specialized strengths but also serious weaknesses. The first three methods are generally considered low-order and can involve high algorithmic complexity (as in triangular elements). Spectrally accurate methods do not practically allow for local mesh refinement and often involve cumbersome algebra. Here, we introduce a new grid/mesh-free approach using radial basis functions (RBFs). It has the advantage of being spectrally accurate for arbitrary node layouts in multi-dimensions with extreme algorithmic simplicity, and naturally permits local node refinement. It has been shown for shallow-water equations and vortex flows that RBFs outperform other numerical methods in the sense that they obtain a much higher accuracy for the same spatial resolution while being able to take unusually large time steps. One virtue of the RBF scheme is the ability to use a simple Cartesian geometry while implementing the required boundary conditions for the temperature, velocity and stresses on a spherical surface of both the outer( planetary surface ) and inner shell ( core-mantle boundary ). The velocity and stress components are expressed in terms of the scalar potential approach (Zebib and Schubert, 1982) and the other remaining variable is the perturbed temperature field. We have studied the problem from the onset of convection to a modest nonlinear regime.
CAD and mesh repair with Radial Basis Functions
NASA Astrophysics Data System (ADS)
Marchandise, E.; Piret, C.; Remacle, J.-F.
2012-03-01
In this paper we present a process that includes both model/mesh repair and mesh generation. The repair algorithm is based on an initial mesh that may be either an initial mesh of a dirty CAD model or STL triangulation with many errors such as gaps, overlaps and T-junctions. This initial mesh is then remeshed by computing a discrete parametrization with Radial Basis Functions (RBF's). We showed in [1] that a discrete parametrization can be computed by solving Partial Differential Equations (PDE's) on an initial correct mesh using finite elements. Paradoxically, the meshless character of the RBF's makes it an attractive numerical method for solving the PDE's for the parametrization in the case where the initial mesh contains errors or holes. In this work, we implement the Orthogonal Gradients method to be described in [2], as a RBF solution method for solving PDE's on arbitrary surfaces. Different examples show that the presented method is able to deal with errors such as gaps, overlaps, T-junctions and that the resulting meshes are of high quality. Moreover, the presented algorithm can be used as a hole-filling algorithm to repair meshes with undesirable holes. The overall procedure is implemented in the open-source mesh generator Gmsh [3].
Radial basis function interpolation in the limit of increasingly flat basis functions
NASA Astrophysics Data System (ADS)
Kindelan, Manuel; Moscoso, Miguel; González-Rodríguez, Pedro
2016-02-01
We propose a new approach to study Radial Basis Function (RBF) interpolation in the limit of increasingly flat functions. The new approach is based on the semi-analytical computation of the Laurent series of the inverse of the RBF interpolation matrix described in a previous paper [3]. Once the Laurent series is obtained, it can be used to compute the limiting polynomial interpolant, the optimal shape parameter of the RBFs used for interpolation, and the weights of RBF finite difference formulas, among other things.
A Cubic Radial Basis Function in the MLPG Method for Beam Problems
NASA Technical Reports Server (NTRS)
Raju, I. S.; Phillips, D. R.
2002-01-01
A non-compactly supported cubic radial basis function implementation of the MLPG method for beam problems is presented. The evaluation of the derivatives of the shape functions obtained from the radial basis function interpolation is much simpler than the evaluation of the moving least squares shape function derivatives. The radial basis MLPG yields results as accurate or better than those obtained by the conventional MLPG method for problems with discontinuous and other complex loading conditions.
Three learning phases for radial-basis-function networks.
Schwenker, F; Kestler, H A; Palm, G
2001-05-01
In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (ID objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where
Meshless Local Petrov-Galerkin Euler-Bernoulli Beam Problems: A Radial Basis Function Approach
NASA Technical Reports Server (NTRS)
Raju, I. S.; Phillips, D. R.; Krishnamurthy, T.
2003-01-01
A radial basis function implementation of the meshless local Petrov-Galerkin (MLPG) method is presented to study Euler-Bernoulli beam problems. Radial basis functions, rather than generalized moving least squares (GMLS) interpolations, are used to develop the trial functions. This choice yields a computationally simpler method as fewer matrix inversions and multiplications are required than when GMLS interpolations are used. Test functions are chosen as simple weight functions as in the conventional MLPG method. Compactly and noncompactly supported radial basis functions are considered. The non-compactly supported cubic radial basis function is found to perform very well. Results obtained from the radial basis MLPG method are comparable to those obtained using the conventional MLPG method for mixed boundary value problems and problems with discontinuous loading conditions.
Free vibrations and buckling analysis of laminated plates by oscillatory radial basis functions
NASA Astrophysics Data System (ADS)
Neves, A. M. A.; Ferreira, A. J. M.
2015-12-01
In this paper the free vibrations and buckling analysis of laminated plates is performed using a global meshless method. A refined version of Kant's theorie which accounts for transverse normal stress and through-the-thickness deformation is used. The innovation is the use of oscillatory radial basis functions. Numerical examples are performed and results are presented and compared to available references. Such functions proved to be an alternative to the tradicional nonoscillatory radial basis functions.
Structural basis for radial ornamentation in orthid brachiopods
Ackerly, S.C.
1985-01-01
Radial ornamentation patterns in brachiopods (eg. ribs, costellae) result from accretionary growth of a crenulated shell margin. The direction of rib growth represents the orientation of the crenulated fabric at the time of shell formation. Morphologic analysis reveals a close relationship between rib growth patterns and the position of adductor muscle attachment sites in the shell. In the brachial valves of most orthid brachiopods, the directions of rib growth, when projected backwards into the shell, converge on the anterior, or catch, adductor muscle scars. One explanation for the observed relationship is that the crenulated surface provides rigidity to the thin growing margin of the shell thereby resisting deformations caused by the adductor loadings. Alternatively, calcite secretion may be mediated by strain-induced growth mechanisms as observed in vertebrate bone growth patterns. Correspondence of muscle position with shell geometry indicates that muscle placement may be constrained by mechanical properties of the shell rather than by requirements of the hinge mechanism. Morphologic diversity among brachiopods is discussed in terms of structural and mechanical constraints on form.
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Satisfiability of logic programming based on radial basis function neural networks
NASA Astrophysics Data System (ADS)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-07-01
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Finite element basis for the expansion of radial wavefunction in quantum scattering calculations
NASA Astrophysics Data System (ADS)
Hwang, Woonglin; Sup Lee, Yoon; Park, Seung C.
1991-11-01
Radial wavefunctions in quantum scattering calculations are expanded in terms of two shape functions for each finite element. This approach is the R matrix version of Kohn's variational method and also directly applicable to S matrix in the log-derivative version. The linear algebra involved amounts to solving definite banded systems. In this basis set method, R matrix or log-derivative matrix is greatly simplified and the computational effort is linearly proportional to the number of radial basis functions, promising computational efficiencies for large scale calculations. Convergences for test vases are also reasonably rapid.
Regional ice mass balance for Greenland from GRACE and ICESat modelled by radial basis functions
NASA Astrophysics Data System (ADS)
Eicker, A.; Springer, A.; Jensen, L.; Kusche, J.
2012-04-01
This contribution presents a tailored regional mass balance for the Greenland ice sheet from GRACE and ICESat observations. A regional gravity field trend model is calculated directly from the GRACE level 1B observations using the short arc method. The gravity field model is parameterized by harmonic space localizing radial basis functions that can be tailored to the specific signal characteristics in Greenland. The ICESat along-track ice elevation changes are co-estimated together with the local topography in order to be independent from external elevation models. The along-track observations are then evaluated without any necessary gridding consistently with the GRACE processing in the same basis of radial basis functions. This allows further joint analysis of the two data sets in this same basis.
A Meshless Method Using Radial Basis Functions for Beam Bending Problems
NASA Technical Reports Server (NTRS)
Raju, I. S.; Phillips, D. R.; Krishnamurthy, T.
2004-01-01
A meshless local Petrov-Galerkin (MLPG) method that uses radial basis functions (RBFs) as trial functions in the study of Euler-Bernoulli beam problems is presented. RBFs, rather than generalized moving least squares (GMLS) interpolations, are used to develop the trial functions. This choice yields a computationally simpler method as fewer matrix inversions and multiplications are required than when GMLS interpolations are used. Test functions are chosen as simple weight functions as they are in the conventional MLPG method. Compactly and noncompactly supported RBFs are considered. Noncompactly supported cubic RBFs are found to be preferable. Patch tests, mixed boundary value problems, and problems with complex loading conditions are considered. Results obtained from the radial basis MLPG method are either of comparable or better accuracy than those obtained when using the conventional MLPG method.
Optical design and tolerancing of freeform surfaces using anisotropic radial basis functions
NASA Astrophysics Data System (ADS)
Maksimovic, Milan
2016-07-01
We investigate use of the anisotropic radial basis functions expansion as a means to represent surface errors on aspheric and freeform surfaces. We show how the optimal choice of the shape parameter and the placement of radial basis function (RBF) nodes can increase accuracy of the surface approximation. We show an example of the adaptive grid refinement. In our approach, complex surfaces are modeled with general arbitrary representation while the anisotropic RBFs expansion models perturbation of the base surface. We show how both the global and the localized surface errors can be modeled across a wide spatial frequency range. With our method, the impact of the structured surface errors on the arbitrary surfaces when applied on the standardized image quality metrics can be assessed for the purpose of optical tolerancing.
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa
2016-08-01
Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination ( R 2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.
An efficient ensemble of radial basis functions method based on quadratic programming
NASA Astrophysics Data System (ADS)
Shi, Renhe; Liu, Li; Long, Teng; Liu, Jian
2016-07-01
Radial basis function (RBF) surrogate models have been widely applied in engineering design optimization problems to approximate computationally expensive simulations. Ensemble of radial basis functions (ERBF) using the weighted sum of stand-alone RBFs improves the approximation performance. To achieve a good trade-off between the accuracy and efficiency of the modelling process, this article presents a novel efficient ERBF method to determine the weights through solving a quadratic programming subproblem, denoted ERBF-QP. Several numerical benchmark functions are utilized to test the performance of the proposed ERBF-QP method. The results show that ERBF-QP can significantly improve the modelling efficiency compared with several existing ERBF methods. Moreover, ERBF-QP also provides satisfactory performance in terms of approximation accuracy. Finally, the ERBF-QP method is applied to a satellite multidisciplinary design optimization problem to illustrate its practicality and effectiveness for real-world engineering applications.
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa
2015-06-01
Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R 2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.
Multi-dimensional option pricing using radial basis functions and the generalized Fourier transform
NASA Astrophysics Data System (ADS)
Larsson, Elisabeth; Ahlander, Krister; Hall, Andreas
2008-12-01
We show that the generalized Fourier transform can be used for reducing the computational cost and memory requirements of radial basis function methods for multi-dimensional option pricing. We derive a general algorithm, including a transformation of the Black-Scholes equation into the heat equation, that can be used in any number of dimensions. Numerical experiments in two and three dimensions show that the gain is substantial even for small problem sizes. Furthermore, the gain increases with the number of dimensions.
NASA Technical Reports Server (NTRS)
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.
NASA Technical Reports Server (NTRS)
Clancy, Daniel J.; Oezguener, Uemit; Graham, Ronald E.
1994-01-01
The potential for excessive plume impingement loads on Space Station Freedom solar arrays, caused by jet firings from an approaching Space Shuttle, is addressed. An artificial neural network is designed to determine commanded solar array beta gimbal angle for minimum plume loads. The commanded angle would be determined dynamically. The network design proposed involves radial basis functions as activation functions. Design, development, and simulation of this network design are discussed.
Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network
NASA Astrophysics Data System (ADS)
Gómez-Mayorga, Margarita E.; Gallegos-Funes, Francisco J.; de-La-Rosa-Vázquez, José M.; Cruz-Santiago, Rene; Ponomaryov, Volodymyr
The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.
Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography
Compas, Colin B.; Wong, Emily Y.; Huang, Xiaojie; Sampath, Smita; Lin, Ben A.; Pal, Prasanta; Papademetris, Xenophon; Thiele, Karl; Dione, Donald P.; Stacy, Mitchel; Staib, Lawrence H.; Sinusas, Albert J.; O'Donnell, Matthew; Duncan, James S.
2014-01-01
Quantitative analysis of left ventricular deformation can provide valuable information about the extent of disease as well as the efficacy of treatment. In this work, we develop an adaptive multi-level compactly supported radial basis approach for deformation analysis in 3D+time echocardiography. Our method combines displacement information from shape tracking of myocardial boundaries (derived from B-mode data) with mid-wall displacements from radio-frequency-based ultrasound speckle tracking. We evaluate our methods on open-chest canines (N=8) and show that our combined approach is better correlated to magnetic resonance tagging-derived strains than either individual method. We also are able to identify regions of myocardial infarction (confirmed by postmortem analysis) using radial strain values obtained with our approach. PMID:24893257
(abstract) Ulysses Solar Wind Ion Temperatures: Radial, Latitudinal, and Dynamical Dependencies
NASA Technical Reports Server (NTRS)
Goldstein, B. E.; Smith, E. J.; Gosling, J. T.; McComas, D. J.; Balogh, A.
1996-01-01
Observations of the Ulysses SWOOPS plasma experiment are used to determine the dependencies of solar wind ion temperatures upon radial distance, speed, and other parameters, and to estimate solar wind heating. Comparisons with three dimensional temperature estimates determined from the ion spectra by a least squares fitting program will be provided (only small samples of data have been reduced with this program).
NASA Astrophysics Data System (ADS)
Roohani Ghehsareh, Hadi; Kamal Etesami, Seyed; Hajisadeghi Esfahani, Maryam
2016-08-01
In the current work, the electromagnetic (EM) scattering from infinite perfectly conducting cylinders with arbitrary cross sections in both transverse magnetic (TM) and transverse electric (TE) modes is numerically investigated. The problems of TE and TM EM scattering can be mathematically modelled via the magnetic field integral equation (MFIE) and the electric field integral equation (EFIE), respectively. An efficient technique is performed to approximate the solution of these surface integral equations. In the proposed numerical method, compactly supported radial basis functions (RBFs) are employed as the basis functions. The radial and compactly supported properties of these basis functions substantially reduce the computational cost and improve the efficiency of the method. To show the accuracy of the proposed technique, it has been applied to solve three interesting test problems. Moreover, the method is well used to compute the electric current density and also the radar cross section (RCS) for some practical scatterers with different cross section geometries. The reported numerical results through the tables and figures demonstrate the efficiency and accuracy of the proposed technique.
Optimization of global model composed of radial basis functions using the term-ranking approach
Cai, Peng; Tao, Chao Liu, Xiao-Jun
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
Ni, Shengqiao; Lv, Jiancheng; Cheng, Zhehao; Li, Mao
2015-01-01
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method. PMID:26161960
An improved radial basis function network for visual autonomous road following.
Rosenblum, M; Davis, L S
1996-01-01
We have developed a radial basis function network (RBFN) for visual autonomous road following. Preliminary testing of the RBFN was done using a driving simulator, and the RBFN was then installed on an actual vehicle at Carnegie Mellon University for testing in an outdoor road-following application. In our first attempts, the RBFN had some success, but it experienced some significant problems such as jittery control and driving failure. Several improvements have been made to the original RBFN architecture to overcome these problems in simulation and more importantly in actual road following, and the improvements are described in this paper. PMID:18263508
Parallel fixed point implementation of a radial basis function network in an FPGA.
de Souza, Alisson C D; Fernandes, Marcelo A C
2014-01-01
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. PMID:25268918
Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA
de Souza, Alisson C. D.; Fernandes, Marcelo A. C.
2014-01-01
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. PMID:25268918
Estimation of river pollution source using the space-time radial basis collocation method
NASA Astrophysics Data System (ADS)
Li, Zi; Mao, Xian-Zhong; Li, Tak Sing; Zhang, Shiyan
2016-02-01
River contaminant source identification problems can be formulated as an inverse model to estimate the missing source release history from the observed contaminant plume. In this study, the identification of pollution sources in rivers, where strong advection is dominant, is solved by the global space-time radial basis collocation method (RBCM). To search for the optimal shape parameter and scaling factor which strongly determine the accuracy of the RBCM method, a new cost function based on the residual errors of not only the observed data but also the specified governing equation, the initial and boundary conditions, was constructed for the k-fold cross-validation technique. The performance of three global radial basis functions, Hardy's multiquadric, inverse multiquadric and Gaussian, were also compared in the test cases. The numerical results illustrate that the new cost function is a good indicator to search for near-optimal solutions. Application to a real polluted river shows that the source release history is reasonably recovered, demonstrating that the RBCM with the k-fold cross-validation is a powerful tool for source identification problems in advection-dominated rivers.
Computing single step operators of logic programming in radial basis function neural networks
NASA Astrophysics Data System (ADS)
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.
Combustion monitoring of a water tube boiler using a discriminant radial basis network.
Sujatha, K; Pappa, N
2011-01-01
This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. PMID:20864104
On the influence of spread constant in radial basis networks for electrical impedance tomography.
Martin, Sébastien; Choi, Charles T M
2016-06-01
Electrical impedance tomography (EIT) is a non-invasive imaging technique. The main task of this work is to solve a non-linear inverse problem, for which several techniques have been suggested, but none of which gives a very high degree of accuracy. This paper introduces a novel approach, based on radial basis function (RBF) artificial neural networks (ANNs), to solve this problem, and uses several ANNs to obtain the best solution to the EIT inverse problem. ANNs have the potential to directly estimate the solution of the inverse problem with a high degree of accuracy. While different radial basis neural networks do not always perform well on different problems, they usually give good results on some specific problems. This paper evidences a strong correlation between the area of the target and the spread constant of the RBF network that gives the best reconstruction. A solution to automatically estimate the size of the target and pick the best neural network directly from voltage measurements is presented, making the reconstruction process automatic. By automatically selecting the best ANN for each specific set of voltage measurements, the proposed solution gives a more accurate reconstruction of both small and large targets. PMID:27203367
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
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.
The neural basis of a deficit in abstract thinking in patients with schizophrenia.
Oh, Jooyoung; Chun, Ji-Won; Joon Jo, Hang; Kim, Eunseong; Park, Hae-Jeong; Lee, Boreom; Kim, Jae-Jin
2015-10-30
Abnormal abstract thinking is a major cause of social dysfunction in patients with schizophrenia, but little is known about its neural basis. In this study, we aimed to determine the characteristic abstract thinking-related brain responses in patients using a task reflecting social situations. We conducted functional magnetic resonance imaging while 16 patients with schizophrenia and 16 healthy controls performed a theme-identification task, in which various emotional pictures depicting social situations were presented. Compared with healthy controls, the patients showed significantly decreased activity in the left frontopolar and right orbitofrontal cortices during theme identification. Activity in these two regions correlated well in the controls, but not in patients. Instead, the patients exhibited a close correlation between activity in both sides of the frontopolar cortex, and a positive correlation between the right orbitofrontal cortex activity and degrees of theme identification. Reduced activity in the left frontopolar and right orbitofrontal cortices and the underlying aberrant connectivity may be implicated in the patients' deficits in abstract thinking. These newly identified features of the neural basis of abnormal abstract thinking are important as they have implications for the impaired social behavior of patients with schizophrenia during real-life situations. PMID:26329118
On fast computation of finite-time coherent sets using radial basis functions
NASA Astrophysics Data System (ADS)
Froyland, Gary; Junge, Oliver
2015-08-01
Finite-time coherent sets inhibit mixing over finite times. The most expensive part of the transfer operator approach to detecting coherent sets is the construction of the operator itself. We present a numerical method based on radial basis function collocation and apply it to a recent transfer operator construction [G. Froyland, "Dynamic isoperimetry and the geometry of Lagrangian coherent structures," Nonlinearity (unpublished); preprint arXiv:1411.7186] that has been designed specifically for purely advective dynamics. The construction [G. Froyland, "Dynamic isoperimetry and the geometry of Lagrangian coherent structures," Nonlinearity (unpublished); preprint arXiv:1411.7186] is based on a "dynamic" Laplace operator and minimises the boundary size of the coherent sets relative to their volume. The main advantage of our new approach is a substantial reduction in the number of Lagrangian trajectories that need to be computed, leading to large speedups in the transfer operator analysis when this computation is costly.
On fast computation of finite-time coherent sets using radial basis functions.
Froyland, Gary; Junge, Oliver
2015-08-01
Finite-time coherent sets inhibit mixing over finite times. The most expensive part of the transfer operator approach to detecting coherent sets is the construction of the operator itself. We present a numerical method based on radial basis function collocation and apply it to a recent transfer operator construction [G. Froyland, "Dynamic isoperimetry and the geometry of Lagrangian coherent structures," Nonlinearity (unpublished); preprint arXiv:1411.7186] that has been designed specifically for purely advective dynamics. The construction [G. Froyland, "Dynamic isoperimetry and the geometry of Lagrangian coherent structures," Nonlinearity (unpublished); preprint arXiv:1411.7186] is based on a "dynamic" Laplace operator and minimises the boundary size of the coherent sets relative to their volume. The main advantage of our new approach is a substantial reduction in the number of Lagrangian trajectories that need to be computed, leading to large speedups in the transfer operator analysis when this computation is costly. PMID:26328580
Artificial neural network modeling of fixed bed biosorption using radial basis approach
NASA Astrophysics Data System (ADS)
Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha
2010-04-01
In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.
NASA Astrophysics Data System (ADS)
Florez, W. F.; Portapila, M.; Hill, A. F.; Power, H.; Orsini, P.; Bustamante, C. A.
2015-03-01
The aim of this paper is to present how to implement a control volume approach improved by Hermite radial basis functions (CV-RBF) for geochemical problems. A multi-step strategy based on Richardson extrapolation is proposed as an alternative to the conventional dual step sequential non-iterative approach (SNIA) for coupling the transport equations with the chemical model. Additionally, this paper illustrates how to use PHREEQC to add geochemical reaction capabilities to CV-RBF transport methods. Several problems with different degrees of complexity were solved including cases of cation exchange, dissolution, dissociation, equilibrium and kinetics at different rates for mineral species. The results show that the solution and strategies presented here are effective and in good agreement with other methods presented in the literature for the same cases.
Vibration measurement based on electronic speckle pattern interferometry and radial basis function
NASA Astrophysics Data System (ADS)
Dai, Xiangjun; Shao, Xinxing; Geng, Zhencen; Yang, Fujun; Jiang, Yijun; He, Xiaoyuan
2015-11-01
A method incorporating amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) with radial basis function (RBF) was proposed to investigate vibration characteristics of structures. The vibration patterns were obtained by AF-ESPI. A novel pre-filtering RBF method was presented to improve the quality of patterns. The out-of-plane vibration amplitude was rebuilt after fringe analysis. Ideal pre-filtering widow sizes for the presented RBF were given based on numerical experiments. For validation, an aluminum circular plate with fixed boundary was determined and compared with FEM, confirming the effectiveness of the proposed method. Finally, vibration characteristics of sandwich panels with honeycomb core were measured. The influence of presence of a pre-notch at different location was also investigated.
Dynamics of on-line learning in radial basis function networks
NASA Astrophysics Data System (ADS)
Freeman, Jason A. S.; Saad, David
1997-07-01
On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations.
NASA Astrophysics Data System (ADS)
Chen, Zhaoxue; Chen, Hao
2014-07-01
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.
Inf-sup condition for spherical polynomials and radial basis functions on spheres
NASA Astrophysics Data System (ADS)
Sloan, Ian H.; Wendland, Holger
2009-09-01
Interpolation by radial basis functions and interpolation by polynomials are both popular methods for function reconstruction from discrete data given on spheres. Recently, there has been an increasing interest in employing these function families together in hybrid schemes for scattered data modeling and the solution of partial differential equations on spheres. For the theoretical analysis of numerical methods for the associated discretized systems, a so-called inf-sup condition is crucial. In this paper, we derive such an inf-sup condition, and show that the constant in the inf-sup condition is independent of the polynomial degree and of the chosen point set, provided the mesh norm of the point set is sufficiently small. We then use the inf-sup condition to derive a new error analysis for the hybrid interpolation scheme of Sloan and Sommariva.
Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong
2009-01-01
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model. PMID:19964991
Improved radial basis function methods for multi-dimensional option pricing
NASA Astrophysics Data System (ADS)
Pettersson, Ulrika; Larsson, Elisabeth; Marcusson, Gunnar; Persson, Jonas
2008-12-01
In this paper, we have derived a radial basis function (RBF) based method for the pricing of financial contracts by solving the Black-Scholes partial differential equation. As an example of a financial contract that can be priced with this method we have chosen the multi-dimensional European basket call option. We have shown numerically that our scheme is second-order accurate in time and spectrally accurate in space for constant shape parameter. For other non-optimal choices of shape parameter values, the resulting convergence rate is algebraic. We propose an adapted node point placement that improves the accuracy compared with a uniform distribution. Compared with an adaptive finite difference method, the RBF method is 20-40 times faster in one and two space dimensions and has approximately the same memory requirements.
Radial Basis Function Neural Network Application to Power System Restoration Studies
Sadeghkhani, Iman; Ketabi, Abbas; Feuillet, Rene
2012-01-01
One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy. PMID:22792093
NASA Astrophysics Data System (ADS)
Kan, P.
2011-12-01
This study employs radial basis function network (RBFNN) to simulate regional runoff in the future climate condition in Taiwan and bootstrap sampling technique to evaluate uncertainties of RBFNN. The hydrological and meteorological data (such as rainfall, river flow) in northern area of Taiwan during 1981 to 1999 are adopted as the training dataset to RBFNN, in which the parameters of RBFNN are optimized with genetic algorithm (GA). Meanwhile, the bootstrap sampling technique is applied for uncertainty analysis of RBFNN. The simulated results show that RBFNN with GA simulating the regional runoff reveals good performance and corresponding uncertainty can be evaluated by the bootstrap sampling technique. The results also illustrate that selecting training datasets randomly and repeatedly can reduce the possibility of model over-fitting of RBFNN. The regional runoff in the future can be estimated into an interval representing the possibility of the runoff by the proposed approach.
Radial basis function network learns ceramic processing and predicts related strength and density
NASA Technical Reports Server (NTRS)
Cios, Krzysztof J.; Baaklini, George Y.; Vary, Alex; Tjia, Robert E.
1993-01-01
Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.
Hancock, M.F. Jr.
1995-12-31
The National Council on Compensation Insurance (NCCI) maintains a national data base of outcomes of workers` compensation claims. We consider whether a radial basis function network can predict the total dollar value of a claim based upon medical and demographic indicators (MDI`s). This work used data from 12,130 workers` compensation claims collected over a period of four years from the state of New Mexico. Two problems were addressed: (1) How well can the total incurred medical expense for all claims be predicted from available MDI`s? For individual claims? (2) How well can the duration of disability be predicted from available MDI`s? The available features intuitively correlated with total medical cost were selected, including type of injury, part of body injured, person`s age at time of injury, gender, marital status, etc. These features were statistically standardized and sorted by correlation with outcome valuation. Principal component analysis was applied. A radial basis function neural network was applied to the feature sets in both supervised and unsupervised training modes. For sets used in training, individual case valuations could consistently be predicted to within $1000 over 98% of the time. For these sets, it was possible to predict total medical expense for the training sets themselves to within 10%. When applied as blind tests against sets which were NOT part of the training data, the prediction was within 15% on the whole sets. Results on individual cases were very poor in only 30% of the cases were the predictions for the training sets within $1000 of their actual valuations. Single-factor analysis suggested that the presence of an attorney strongly decorrelated the data. A simple stratification was performed to remove cases involving attorneys and contested claims, and the procedures above repeated. Preliminary results based upon the very limited effort applied indicate that NCCI data support population estimates, but not single-point estimates.
Numerical solution of differential equations using multiquadric radial basis functions networks.
Mai-Duy, N; Tran-Cong, T
2001-03-01
This paper presents mesh-free procedures for solving linear differential equations (ODEs and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs). Based on our study of approximation of function and its derivatives using RBFNs that was reported in an earlier paper (Mai-Duy, N. & Tran-Cong, T. (1999). Approximation of function and its derivatives using radial basis function networks. Neural networks, submitted), new RBFN approximation procedures are developed in this paper for solving DEs, which can also be classified into two types: a direct (DRBFN) and an indirect (IRBFN) RBFN procedure. In the present procedures, the width of the RBFs is the only adjustable parameter according to a(i) = betad(i), where d(i) is the distance from the ith centre to the nearest centre. The IRBFN method is more accurate than the DRBFN one and experience so far shows that beta can be chosen in the range 7 < or = beta 10 for the former. Different combinations of RBF centres and collocation points (uniformly and randomly distributed) are tested on both regularly and irregularly shaped domains. The results for a 1D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-4)) and O(1.0 x 10(-8)), respectively, with a centre density of 50. Similarly, the results for a 2D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-3)) and O(1.0 x10(-6)) respectively, with a centre density of 12 X 12. PMID:11316233
GRACE L1b inversion through a self-consistent modified radial basis function approach
NASA Astrophysics Data System (ADS)
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
A radial basis classifier for the automatic detection of aspiration in children with dysphagia
Lee, Joon; Blain, Stefanie; Casas, Mike; Kenny, Dave; Berall, Glenn; Chau, Tom
2006-01-01
Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion The proposed aspiration
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-01-01
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. PMID:25237902
NASA Astrophysics Data System (ADS)
Johnsson, Roger
2006-11-01
Methods to measure and monitor the cylinder pressure in internal combustion engines can contribute to reduced fuel consumption, noise and exhaust emissions. As direct measurements of the cylinder pressure are expensive and not suitable for measurements in vehicles on the road indirect methods which measure cylinder pressure have great potential value. In this paper, a non-linear model based on complex radial basis function (RBF) networks is proposed for the reconstruction of in-cylinder pressure pulse waveforms. Input to the network is the Fourier transforms of both engine structure vibration and crankshaft speed fluctuation. The primary reason for the use of Fourier transforms is that different frequency regions of the signals are used for the reconstruction process. This approach also makes it easier to reduce the amount of information that is used as input to the RBF network. The complex RBF network was applied to measurements from a 6-cylinder ethanol powered diesel engine over a wide range of running conditions. Prediction accuracy was validated by comparing a number of parameters between the measured and predicted cylinder pressure waveform such as maximum pressure, maximum rate of pressure rise and indicated mean effective pressure. The performance of the network was also evaluated for a number of untrained running conditions that differ both in speed and load from the trained ones. The results for the validation set were comparable to the trained conditions.
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-01-01
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. PMID:25237902
High Rayleigh Number 3-D Spherical Mantle Convection with Radial Basis Functions
NASA Astrophysics Data System (ADS)
Flyer, N.; Yuen (3), G. Wright, D.
2009-04-01
In the last quarter of a century many numerical methods, such as finite-differences, finite-volume, their yin-yang variants, finite-elements and pseudo-spectral methods have been used to study the problem of 3-D spherical convection. All have their respective strengths but also serious weaknesses, such as low-order and can involve high algorithmic complexity, as in triangular elements. Spectrally accurate methods do not practically allow for local mesh refinement and often involve cumbersome algebra. We have recently introduced a new grid/mesh-free approach, using radial basis functions ( RBFs) . It has the advantage of being spectrally accurate for arbitrary node layouts in multi-dimensions with extreme algorithmic simplicity, and allows naturally node-refinement. One virtue of the RBF scheme is the ability to use a simple Cartesian geometry while implementing the required boundary conditions for the temperature, velocity and stresses on a spherical surface of both the outer( planetary surface ) and inner shell ( core-mantle boundary ). The velocity and stress components are expressed in terms of the scalar potential approach and the other remaining variable is the perturbed temperature field. We have studied the problem from the weakly nonlinear to a moderately nonlinear regime involving a Rayleigh number, about 1000 times super-critical. Both purely basal and partially internal -heating cases have been considered
High Rayleigh Number 3-D Spherical Mantle Convection with Radial Basis Functions
NASA Astrophysics Data System (ADS)
Flyer, N.; Wright, G.; Yuen, D. A.
2009-04-01
In the last quarter of a century many numerical methods, such as finite-differences, finite-volume, their yin-yang variants, finite-elements and pseudo-spectral methods have been used to study the problem of 3-D spherical convection. All have their respective strengths but also serious weaknesses, such as low-order and can involve high algorithmic complexity, as in triangular elements. Spectrally accurate methods do not practically allow for local mesh refinement and often involve cumbersome algebra. We have recently introduced a new grid/mesh-free approach, using radial basis functions (RBFs). It has the advantage of being spectrally accurate for arbitrary node layouts in multi-dimensions with extreme algorithmic simplicity, and allows naturally node-refinement. One virtue of the RBF scheme is the ability to use a simple Cartesian geometry while implementing the required boundary conditions for the temperature, velocity and stresses on a spherical surface of both the outer(planetary surface) and inner shell (core-mantle boundary). The velocity and stress components are expressed in terms of the scalar potential approach and the other remaining variable is the perturbed temperature field. We have studied the problem from the weakly onlinear to a moderately nonlinear regime involving a Rayleigh number, about 1000 times super-critical. Both purely basal and partially internal-heating cases have been considered.
Classification of gear faults using cumulants and the radial basis function network
NASA Astrophysics Data System (ADS)
Wuxing, Lai; Tse, Peter W.; Guicai, Zhang; Tielin, Shi
2004-03-01
Every tooth in a gearbox is alternately meshing and detaching during its operation. Hence, the loading condition of the tooth is alternately changing. Such a condition will make the tooth easily subject to spalling and worn. Moreover, Gaussian type of noise which is always embedded in the measurements makes the signal-to-noise ratio (SNR) of the collected data low and difficult to extract in fault-related features. This paper aims to propose an approach for gear fault classification by using cumulants and the radial basis function (BRF) network. The use of cumulants can minimize Gaussian noise and increase the SNR. The RBF network has proven to be superior to back-propagation networks. The RBF network provides better functions to approximate non-linear inputs and faster in convergence. In this paper, experiments have been conducted on a real gearbox. The cumulants calculated from the vibration signal collected from the inspected gearbox are used as input features. The RBF network is then used as a classifier for various kinds of operating conditions of the gearbox. Results show that the method of classification by combining cumulants and the RBF network is promising and achieved better accuracy.
Behnia, Pouran
2007-06-15
The metallogeny of Central Iran is characterized mainly by the presence of several iron, apatite, and uranium deposits of Proterozoic age. Radial Basis Function Link Networks (RBFLN) were used as a data-driven method for GIS-based predictive mapping of Proterozoic mineralization in this area. To generate the input data for RBFLN, the evidential maps comprising stratigraphic, structural, geophysical, and geochemical data were used. Fifty-eight deposits and 58 'nondeposits' were used to train the network. The operations for the application of neural networks employed in this study involve both multiclass and binary representation of evidential maps. Running RBFLN on different input data showed that an increase in the number of evidential maps and classes leads to a larger classification sum of squared error (SSE). As a whole, an increase in the number of iterations resulted in the improvement of training SSE. The results of applying RBFLN showed that a successful classification depends on the existence of spatially well distributed deposits and nondeposits throughout the study area.
NASA Astrophysics Data System (ADS)
Markopoulos, Angelos P.; Georgiopoulos, Sotirios; Manolakos, Dimitrios E.
2016-03-01
Various artificial neural networks types are examined and compared for the prediction of surface roughness in manufacturing technology. The aim of the study is to evaluate different kinds of neural networks and observe their performance and applicability on the same problem. More specifically, feed-forward artificial neural networks are trained with three different back propagation algorithms, namely the adaptive back propagation algorithm of the steepest descent with the use of momentum term, the back propagation Levenberg-Marquardt algorithm and the back propagation Bayesian algorithm. Moreover, radial basis function neural networks are examined. All the aforementioned algorithms are used for the prediction of surface roughness in milling, trained with the same input parameters and output data so that they can be compared. The advantages and disadvantages, in terms of the quality of the results, computational cost and time are identified. An algorithm for the selection of the spread constant is applied and tests are performed for the determination of the neural network with the best performance. The finally selected neural networks can satisfactorily predict the quality of the manufacturing process performed, through simulation and input-output surfaces for combinations of the input data, which correspond to milling cutting conditions.
Ensembles of radial basis function networks for spectroscopic detection of cervical precancer
NASA Technical Reports Server (NTRS)
Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.
1998-01-01
The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.
Fire Risk Assessment of Some Indian Coals Using Radial Basis Function (RBF) Technique
NASA Astrophysics Data System (ADS)
Nimaje, Devidas; Tripathy, Debi Prasad
2016-03-01
Fires, whether surface or underground, pose serious and environmental problems in the global coal mining industry. It is causing huge loss of coal due to burning and loss of lives, sterilization of coal reserves and environmental pollution. Most of the instances of coal mine fires happening worldwide are mainly due to the spontaneous combustion. Hence, attention must be paid to take appropriate measures to prevent occurrence and spread of fire. In this paper, to evaluate the different properties of coals for fire risk assessment, forty-nine in situ coal samples were collected from major coalfields of India. Intrinsic properties viz. proximate and ultimate analysis; and susceptibility indices like crossing point temperature, flammability temperature, Olpinski index and wet oxidation potential method of Indian coals were carried out to ascertain the liability of coal to spontaneous combustion. Statistical regression analysis showed that the parameters of ultimate analysis provide significant correlation with all investigated susceptibility indices as compared to the parameters of proximate analysis. Best correlated parameters (ultimate analysis) were used as inputs to the radial basis function network model. The model revealed that Olpinski index can be used as a reliable method to assess the liability of Indian coals to spontaneous combustion.
Space shuttle main engine sensor modeling using radial-basis-function neural networks
NASA Astrophysics Data System (ADS)
Wheeler, Kevin R.; Dhawan, Atam P.; Meyer, Claudia M.
1994-11-01
An efficient method of parameter prediction is needed for sensor validation of space shuttle main-engine (SSME) parameters during real-time safety monitoring and post-test analysis. Feedforward neural networks (FFNN) have been used to model the highly nonlinear and dynamic SSME parameters during startup. Due to several problems associated with the use of feedforward networks, radial-basis-function neural networks (RBFNN) were investigated in modeling SSME parameters. In this paper, RBFNNs are used to predict the high-pressure oxidizer turbine discharge temperature, a redlined parameter, during the startup transient. Data from SSME ground test firings were used to train and validate the RBFNNs. The performance of the RBFNN model is compared with that of a FFNN model, trained with the Quickprop learning algorithm. In comparison with the FFNN model, the RBFNN-based model was found to be more robust against variations in architecture and network parameters, and was faster to train. In addition, the performance of the RBFNN model during nominal operation and during simulated input sensor failures was found to be robust in the presence of small deviations in the input.
An, Yu; Liu, Jie; Zhang, Guanglei; Ye, Jinzuo; Mao, Yamin; Jiang, Shixin; Shang, Wenting; Du, Yang; Chi, Chongwei; Tian, Jie
2015-10-01
Fluorescence molecular tomography (FMT) is a promising tool in the study of cancer, drug discovery, and disease diagnosis, enabling noninvasive and quantitative imaging of the biodistribution of fluorophores in deep tissues via image reconstruction techniques. Conventional reconstruction methods based on the finite-element method (FEM) have achieved acceptable stability and efficiency. However, some inherent shortcomings in FEM meshes, such as time consumption in mesh generation and a large discretization error, limit further biomedical application. In this paper, we propose a meshless method for reconstruction of FMT (MM-FMT) using compactly supported radial basis functions (CSRBFs). With CSRBFs, the image domain can be accurately expressed by continuous CSRBFs, avoiding the discretization error to a certain degree. After direct collocation with CSRBFs, the conventional optimization techniques, including Tikhonov, L1-norm iteration shrinkage (L1-IS), and sparsity adaptive matching pursuit, were adopted to solve the meshless reconstruction. To evaluate the performance of the proposed MM-FMT, we performed numerical heterogeneous mouse experiments and in vivo bead-implanted mouse experiments. The results suggest that the proposed MM-FMT method can reduce the position error of the reconstruction result to smaller than 0.4 mm for the double-source case, which is a significant improvement for FMT. PMID:26451513
Online dimensionality reduction using competitive learning and Radial Basis Function network.
Tomenko, Vladimir
2011-06-01
The general purpose dimensionality reduction method should preserve data interrelations at all scales. Additional desired features include online projection of new data, processing nonlinearly embedded manifolds and large amounts of data. The proposed method, called RBF-NDR, combines these features. RBF-NDR is comprised of two modules. The first module learns manifolds by utilizing modified topology representing networks and geodesic distance in data space and approximates sampled or streaming data with a finite set of reference patterns, thus achieving scalability. Using input from the first module, the dimensionality reduction module constructs mappings between observation and target spaces. Introduction of specific loss function and synthesis of the training algorithm for Radial Basis Function network results in global preservation of data structures and online processing of new patterns. The RBF-NDR was applied for feature extraction and visualization and compared with Principal Component Analysis (PCA), neural network for Sammon's projection (SAMANN) and Isomap. With respect to feature extraction, the method outperformed PCA and yielded increased performance of the model describing wastewater treatment process. As for visualization, RBF-NDR produced superior results compared to PCA and SAMANN and matched Isomap. For the Topic Detection and Tracking corpus, the method successfully separated semantically different topics. PMID:21420831
NASA Astrophysics Data System (ADS)
An, Yu; Liu, Jie; Zhang, Guanglei; Ye, Jinzuo; Mao, Yamin; Jiang, Shixin; Shang, Wenting; Du, Yang; Chi, Chongwei; Tian, Jie
2015-10-01
Fluorescence molecular tomography (FMT) is a promising tool in the study of cancer, drug discovery, and disease diagnosis, enabling noninvasive and quantitative imaging of the biodistribution of fluorophores in deep tissues via image reconstruction techniques. Conventional reconstruction methods based on the finite-element method (FEM) have achieved acceptable stability and efficiency. However, some inherent shortcomings in FEM meshes, such as time consumption in mesh generation and a large discretization error, limit further biomedical application. In this paper, we propose a meshless method for reconstruction of FMT (MM-FMT) using compactly supported radial basis functions (CSRBFs). With CSRBFs, the image domain can be accurately expressed by continuous CSRBFs, avoiding the discretization error to a certain degree. After direct collocation with CSRBFs, the conventional optimization techniques, including Tikhonov, L1-norm iteration shrinkage (L1-IS), and sparsity adaptive matching pursuit, were adopted to solve the meshless reconstruction. To evaluate the performance of the proposed MM-FMT, we performed numerical heterogeneous mouse experiments and in vivo bead-implanted mouse experiments. The results suggest that the proposed MM-FMT method can reduce the position error of the reconstruction result to smaller than 0.4 mm for the double-source case, which is a significant improvement for FMT.
Estuary water-stage forecasting by using radial basis function neural network
NASA Astrophysics Data System (ADS)
Chang, Fi-John; Chen, Yen-Chang
2003-01-01
The Radial basis function neural network (RBFNN) has been successfully applied to many tasks due to its powerful properties in classification and functional approximation. This paper presents a novel RBFNN for water-stage forecasting in an estuary under high flood and tidal effects. The RBFNN adopts a hybrid two-stage learning scheme, unsupervised and supervised learning. In the first scheme, fuzzy min-max clustering is proposed for choosing best patterns for cluster representation in an efficient and automatic way. The second scheme uses supervised learning, which is a multivariate linear regression method to produce a weighted sum of the output from the hidden layer. Since this network has only one layer using a supervised learning algorithm, its training process is much faster than the error back propagation based multilayer perceptrons. Moreover, only one parameter, θ, must be determined manually. The other parameters used in this model can be adjusted automatically by model training. The water-stage data of the Tanshui River under tidal effect are used to construct a water-stage forecasting model that can also be used during flood. The results show that the RBFNN can be applied successfully and provide high accuracy and reliability of water-stage forecasting in an estuary.
Lu, Y; Sundararajan, N; Saratchandran, P
1998-01-01
This paper presents a detailed performance analysis of the minimal resource allocation network (M-RAN) learning algorithm, M-RAN is a sequential learning radial basis function neural network which combines the growth criterion of the resource allocating network (RAN) of Platt (1991) with a pruning strategy based on the relative contribution of each hidden unit to the overall network output. The resulting network leads toward a minimal topology for the RAN. The performance of this algorithm is compared with the multilayer feedforward networks (MFNs) trained with 1) a variant of the standard backpropagation algorithm, known as RPROP and 2) the dependence identification (DI) algorithm of Moody and Antsaklis on several benchmark problems in the function approximation and pattern classification areas. For all these problems, the M-RAN algorithm is shown to realize networks with far fewer hidden neurons with better or same approximation/classification accuracy. Further, the time taken for learning (training) is also considerably shorter as M-RAN does not require repeated presentation of the training data. PMID:18252454
Enhancing finite differences with radial basis functions: Experiments on the Navier-Stokes equations
NASA Astrophysics Data System (ADS)
Flyer, Natasha; Barnett, Gregory A.; Wicker, Louis J.
2016-07-01
Polynomials are used together with polyharmonic spline (PHS) radial basis functions (RBFs) to create local RBF-finite-difference (RBF-FD) weights on different node layouts for spatial discretizations that can be viewed as enhancements of the classical finite differences (FD). The presented method replicates the convergence properties of FD but for arbitrary node layouts. It is tested on the 2D compressible Navier-Stokes equations at low Mach number, relevant to atmospheric flows. Test cases are taken from the numerical weather prediction community and solved on bounded domains. Thus, attention is given on how to handle boundaries with the RBF-FD method, as well as a novel implementation for hyperviscosity. Comparisons are done on Cartesian, hexagonal, and quasi-uniform node layouts. Consideration and guidelines are given on PHS order, polynomial degree and stencil size. The main advantages of the present method are: 1) capturing the basic physics of the problem surprisingly well, even at very coarse resolutions, 2) high-order accuracy without the need of tuning a shape parameter, and 3) the inclusion of polynomials eliminates stagnation (saturation) errors. A MATLAB code is given to calculate the differentiation weights for this novel approach.
Radial Basis Function Based Neural Network for Motion Detection in Dynamic Scenes.
Huang, Shih-Chia; Do, Ben-Hsiang
2014-01-01
Motion detection, the process which segments moving objects in video streams, is the first critical process and plays an important role in video surveillance systems. Dynamic scenes are commonly encountered in both indoor and outdoor situations and contain objects such as swaying trees, spouting fountains, rippling water, moving curtains, and so on. However, complete and accurate motion detection in dynamic scenes is often a challenging task. This paper presents a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. The proposed method involves two important modules: a multibackground generation module and a moving object detection module. The multibackground generation module effectively generates a flexible probabilistic model through an unsupervised learning process to fulfill the property of either dynamic background or static background. Next, the moving object detection module achieves complete and accurate detection of moving objects by only processing blocks that are highly likely to contain moving objects. This is accomplished by two procedures: the block alarm procedure and the object extraction procedure. The detection results of our method were evaluated by qualitative and quantitative comparisons with other state-of-the-art methods based on a wide range of natural video sequences. The overall results show that the proposed method substantially outperforms existing methods with Similarity and F1 accuracy rates of 69.37% and 65.50%, respectively. PMID:24108721
Solution of the quantum fluid dynamical equations with radial basis function interpolation
Hu, Xu-Guang; Ho, Tak-San; Rabitz, Herschel; Askar, Attila
2000-05-01
The paper proposes a numerical technique within the Lagrangian description for propagating the quantum fluid dynamical (QFD) equations in terms of the Madelung field variables R and S, which are connected to the wave function via the transformation {psi}=exp{l_brace}(R+iS)/({Dirac_h}/2{pi})(right brace). The technique rests on the QFD equations depending only on the form, not the magnitude, of the probability density {rho}=|{psi}|{sup 2} and on the structure of R=({Dirac_h}/2{pi})/2 ln {rho} generally being simpler and smoother than {rho}. The spatially smooth functions R and S are especially suitable for multivariate radial basis function interpolation to enable the implementation of a robust numerical scheme. Examples of two-dimensional model systems show that the method rivals, in both efficiency and accuracy, the split-operator and Chebychev expansion methods. The results on a three-dimensional model system indicates that the present method is superior to the existing ones, especially, for its low storage requirement and its uniform accuracy. The advantage of the new algorithm is expected to increase for higher dimensional systems to provide a practical computational tool. (c) 2000 The American Physical Society.
NASA Astrophysics Data System (ADS)
Shankar, Praveen
The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network
Unification of Plasma Fluid and Kinetic Theory via Gaussian Radial Basis Functions
NASA Astrophysics Data System (ADS)
Candy, J. M.
2015-11-01
A fundamental macroscopic description of a magnetized plasma is the Vlasov equation supplemented by the nonlinear inverse-square force Fokker-Planck collision operator [Rosenbluth et al., Phys. Rev. 107, 1957]. The Vlasov part describes advection in a six-dimensional phase space whereas the collision operator contains friction and diffusion coefficients that are weighted velocity-space integrals of the particle distribution function. The Fokker-Planck collision operator is an integro-differential, nonlinear (bilinear) operator. Numerical discretization of the operator, in particular for collisions of unlike species, is extremely challenging. In this work, we describe a new approach to discretize the entire kinetic system based on an expansion in Gaussian Radial Basis functions (RBFs). This approach is particularly well-suited to treat the collision operator because the friction and diffusion coefficients can be analytically calculated. Although the RBF method is known to be a powerful scheme for the interpolation of scattered multidimensional data, Gaussian RBFs also have a deep physical interpretation in statistical mechanics and plasma physics as local thermodynamic equilibria. We outline the general theory, highlight the connection to plasma fluid theories, and also give 2D and 3D numerical solutions of the nonlinear Fokker-Planck equation. A broad spectrum of applications for the new method is anticipated in both astrophysical and laboratory plasmas. In particular, we believe that the RBF method may provide a new bridge between fluid and kinetic descriptions of magnetized plasma. Work supported in part by US DOE under DE-FG02-08ER54963.
Thoracic non-rigid registration combining self-organizing maps and radial basis functions.
Matsopoulos, George K; Mouravliansky, Nikolaos A; Asvestas, Pantelis A; Delibasis, Konstantinos K; Kouloulias, Vassilis
2005-06-01
An automatic three-dimensional non-rigid registration scheme is proposed in this paper and applied to thoracic computed tomography (CT) data of patients with stage III non-small cell lung cancer (NSCLC). According to the registration scheme, initially anatomical set of points such as the vertebral spine, the ribs, and shoulder blades are automatically segmented slice by slice from the two CT scans of the same patient in order to serve as interpolant points. Based on these extracted features, a rigid-body transformation is then applied to provide a pre-registration of the data. To establish correspondence between the feature points, the novel application of the self-organizing maps (SOMs) is adopted. In particular, the automatic correspondence of the interpolant points is based on the initialization of the Kohonen neural network model capable to identify 500 corresponding pairs of points approximately in the two CT sets. Then, radial basis functions (RBFs) using the shifted log function is subsequently employed for elastic warping of the image volume, using the correspondence between the interpolant points, as obtained in the previous phase. Quantitative and qualitative results are also presented to validate the performance of the proposed elastic registration scheme resulting in an alignment error of 6 mm, on average, over 15 CT paired datasets. Finally, changes of the tumor volume in respect to each reference dataset are estimated for all patients, which indicate inspiration and/or movement of the patient during acquisition of the data. Thus, the practical implementation of this scheme could provide estimations of lung tumor volumes during radiotherapy treatment planning. PMID:15854844
Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold
2015-09-01
In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. PMID:26163042
Near and long-term load prediction using radial basis function networks
Hancock, M.F.
1995-12-31
A number of researchers have investigated the application of multi-layer perceptrons (MLP`s), a variety of neural network, to the problem of short-term load forecasting for electric utilities (e.g., Rahman & Hazin, IEEE Trans. Power Systems, May 1993). {open_quotes}Short-term{close_quotes} in this context typically means {open_quotes}next day{close_quotes}. These forecasts have been based upon previous day actual loads and meteorological factors (e.g., max-min temperature, relative humidity). We describe the application of radial basis function networks (RBF`s) to the {open_quotes}long-term{close_quotes} (next year) load forecasting problem. The RBF network performs a two-stage classification based upon annual average loads and meteorological data. During stage 1, discrete classification is performed using radius-limited elements. During stage 2, a multi-layer perceptron may be applied. The quantized output is used to correct a prediction template. The stage 1 classifier is trained by maximizing an objective function (the {open_quotes}disambiguity{close_quotes}). The stage 2 MLP`s are trained by standard back-propagation. This work uses 12 months of hourly meteorological data, and the corresponding hourly load data for both commercial and residential feeders. At the current stage of development, the RBF machine can train on 20% of the weather/load data (selected by simple linear sampling), and estimate the hourly load for an entire year (8,760 data points) with 9.1% error (RMS, relative to daily peak load). (By comparison, monthly mean profiles perform at c. 12% error.) The best short-term load forecasters operate in the 2% error range. The current system is an engineering prototype, and development is continuing.
NASA Astrophysics Data System (ADS)
Yan, Qin; Zhong, Yanfei
2008-12-01
The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre
Application of Polynomial and Radial Basis Function Maps to Signal Masking
Damiano, B.
1998-01-01
The objective of this research was to develop and demonstrate a technique for encrypting information by using a masking signal that closely approximates local ambient noise. Signal masking techniques developed to date have used nonlinear differential equations, spread spectrum, and various modulation schemes to encode information. While these techniques can effectively hide a signal, the resulting masks may not appear as ambient noise to an observer. The advantage of the proposed technique over commonly used masking methods is that the transmitted signal will appear as normal background noise, thus greatly reducing the probability of detection and exploitation. A promising near-term application of this technology presents itself in the area of clandestine minefield reconnaissance in shallow water areas. Shallow water mine-counter-mine (SWMCM) activity is essential for minefield avoidance, efficient minefield clearance, and effective selection of transit lanes within minefields. A key technology area for SWMCM is the development of special sonar waveforms with low probability of exploitation/intercept (LPE/LPI) attributes. In addition to LPE/LPI sonar, this technology has the potential to enable significant improvements in underwater acoustic communications. For SWMCM, the chaotic waveform research provides a mechanism for encrypted communications between a submarine (SSN) and an unmanned underwater vehicle (UUV) via an acoustic channel. Acoustic SSN/UUV communications would eliminate the need for a fiberoptic link between the two vessels, thus increasing the robustness of SWMCM. Similar applications may exist in the areas of radar masking and secure communications. The original approach called for the use of polynomial maps to generate a masking signal. Because polynomial maps were found to have highly restrictive stability criteria, the approach was modified to use radial basis function (RBF) maps. they have shown that stable RBF maps that closely approximate an
NASA Astrophysics Data System (ADS)
Rashid, Kashif; Ambani, Saumil; Cetinkaya, Eren
2013-02-01
Many real-world optimization problems comprise objective functions that are based on the output of one or more simulation models. As these underlying processes can be time and computation intensive, the objective function is deemed expensive to evaluate. While methods to alleviate this cost in the optimization procedure have been explored previously, less attention has been given to the treatment of expensive constraints. This article presents a methodology for treating expensive simulation-based nonlinear constraints alongside an expensive simulation-based objective function using adaptive radial basis function techniques. Specifically, a multiquadric radial basis function approximation scheme is developed, together with a robust training method, to model not only the costly objective function, but also each expensive simulation-based constraint defined in the problem. The article presents the methodology developed for expensive nonlinear constrained optimization problems comprising both continuous and integer variables. Results from various test cases, both analytical and simulation-based, are presented.
Liu, Yan; Lu, Chengyu; Meng, Qingfan; Lu, Jiahui; Fu, Yao; Liu, Botong; Zhou, Yongcan; Guo, Weiliang; Teng, Lesheng
2015-01-01
In our previous work, partial least squares (PLSs) were employed to develop the near infrared spectroscopy (NIRs) models for at-line (fast off-line) monitoring key parameters of Lactococcus lactis subsp. fermentation. In this study, radial basis function neural network (RBFNN) as a non-linear modeling method was investigated to develop NIRs models instead of PLS. A method named moving window radial basis function neural network (MWRBFNN) was applied to select the characteristic wavelength variables by using the degree approximation (Da) as criterion. Next, the RBFNN models with selected wavelength variables were optimized by selecting a suitable constant spread. Finally, the effective spectra pretreatment methods were selected by comparing the robustness of the optimum RBFNN models developed with pretreated spectra. The results demonstrated that the robustness of the optimal RBFNN models were better than the PLS models for at-line monitoring of glucose and pH of L. lactis subsp. fermentation. PMID:26858554
NASA Technical Reports Server (NTRS)
Krishnamurthy, Thiagarajan
2005-01-01
Response construction methods using Moving Least Squares (MLS), Kriging and Radial Basis Functions (RBF) are compared with the Global Least Squares (GLS) method in three numerical examples for derivative generation capability. Also, a new Interpolating Moving Least Squares (IMLS) method adopted from the meshless method is presented. It is found that the response surface construction methods using the Kriging and RBF interpolation yields more accurate results compared with MLS and GLS methods. Several computational aspects of the response surface construction methods also discussed.
NASA Astrophysics Data System (ADS)
Paranin, Vyacheslav D.; Karpeev, Sergey V.; Khonina, Svetlana N.
2016-03-01
The calculation and simulation of interference polarizer to generate radially polarized light is made. The method is based on converting the conical wavefront passing through the interference polarizer. The multilayer optical coating can be applied on the surface of the axicon. It is shown that in this way we noticeably reduce both the operating angle of incidence and achieve practically significant degree of polarization of the beam generated at much lower energy losses.
The Molecular Basis of Radial Intercalation during Tissue Spreading in Early Development
Szabó, András; Cobo, Isidoro; Omara, Sharif; McLachlan, Sophie; Keller, Ray; Mayor, Roberto
2016-01-01
Summary Radial intercalation is a fundamental process responsible for the thinning of multilayered tissues during large-scale morphogenesis; however, its molecular mechanism has remained elusive. Using amphibian epiboly, the thinning and spreading of the animal hemisphere during gastrulation, here we provide evidence that radial intercalation is driven by chemotaxis of cells toward the external layer of the tissue. This role of chemotaxis in tissue spreading and thinning is unlike its typical role associated with large-distance directional movement of cells. We identify the chemoattractant as the complement component C3a, a factor normally linked with the immune system. The mechanism is explored by computational modeling and tested in vivo, ex vivo, and in vitro. This mechanism is robust against fluctuations of chemoattractant levels and expression patterns and explains expansion during epiboly. This study provides insight into the fundamental process of radial intercalation and could be applied to a wide range of morphogenetic events. PMID:27165554
The Molecular Basis of Radial Intercalation during Tissue Spreading in Early Development.
Szabó, András; Cobo, Isidoro; Omara, Sharif; McLachlan, Sophie; Keller, Ray; Mayor, Roberto
2016-05-01
Radial intercalation is a fundamental process responsible for the thinning of multilayered tissues during large-scale morphogenesis; however, its molecular mechanism has remained elusive. Using amphibian epiboly, the thinning and spreading of the animal hemisphere during gastrulation, here we provide evidence that radial intercalation is driven by chemotaxis of cells toward the external layer of the tissue. This role of chemotaxis in tissue spreading and thinning is unlike its typical role associated with large-distance directional movement of cells. We identify the chemoattractant as the complement component C3a, a factor normally linked with the immune system. The mechanism is explored by computational modeling and tested in vivo, ex vivo, and in vitro. This mechanism is robust against fluctuations of chemoattractant levels and expression patterns and explains expansion during epiboly. This study provides insight into the fundamental process of radial intercalation and could be applied to a wide range of morphogenetic events. PMID:27165554
NASA Astrophysics Data System (ADS)
Zabihi, F.; Saffarian, M.
2016-07-01
The aim of this article is to obtain the numerical solution of the two-dimensional KdV-Burgers equation. We construct the solution by using a different approach, that is based on using collocation points. The solution is based on using the thin plate splines radial basis function, which builds an approximated solution with discretizing the time and the space to small steps. We use a predictor-corrector scheme to avoid solving the nonlinear system. The results of numerical experiments are compared with analytical solutions to confirm the accuracy and efficiency of the presented scheme.
Cho, Baek Hwan; Yu, Hwanjo; Lee, Jongshill; Chee, Young Joon; Kim, In Young; Kim, Sun I
2008-03-01
Nonlinear classifiers, e.g., support vector machines (SVMs) with radial basis function (RBF) kernels, have been used widely for automatic diagnosis of diseases because of their high accuracies. However, it is difficult to visualize the classifiers, and thus difficult to provide intuitive interpretation of results to physicians. We developed a new nonlinear kernel, the localized radial basis function (LRBF) kernel, and new visualization system visualization for risk factor analysis (VRIFA) that applies a nomogram and LRBF kernel to visualize the results of nonlinear SVMs and improve the interpretability of results while maintaining high prediction accuracy. Three representative medical datasets from the University of California, Irvine repository and Statlog dataset-breast cancer, diabetes, and heart disease datasets-were used to evaluate the system. The results showed that the classification performance of the LRBF is comparable with that of the RBF, and the LRBF is easy to visualize via a nomogram. Our study also showed that the LRBF kernel is less sensitive to noise features than the RBF kernel, whereas the LRBF kernel degrades the prediction accuracy more when important features are eliminated. We demonstrated the VRIFA system, which visualizes the results of linear and nonlinear SVMs with LRBF kernels, on the three datasets. PMID:18348954
The neural basis of conceptualizing the same action at different levels of abstraction.
Spunt, Robert P; Kemmerer, David; Adolphs, Ralph
2016-07-01
People can conceptualize the same action (e.g. 'riding a bike') at different levels of abstraction (LOA), where higher LOAs specify the abstract motives that explain why the action is performed (e.g. 'getting exercise'), while lower LOAs specify the concrete steps that indicate how the action is performed (e.g. 'gripping handlebars'). Prior neuroimaging studies have shown that why and how questions about actions differentially activate two cortical networks associated with mental-state reasoning and action representation, respectively; however, it remains unknown whether this is due to the differential demands of the questions per se or to the shifts in LOA those questions produce. We conducted functional magnetic resonance imaging while participants judged pairs of action phrases that varied in LOA and that could be framed either as a why question (Why ride a bike? Get exercise.) or a how question (How to get exercise? Ride a bike.). Question framing (why vs how) had no effect on activity in regions of the two networks. Instead, these regions uniquely tracked parametric variation in LOA, both across and within trials. This suggests that the human capacity to understand actions at different LOA is based in the relative activity of two cortical networks. PMID:26117505
Use of Structure as a Basis for Abstraction in Air Traffic Control
NASA Technical Reports Server (NTRS)
Davison, Hayley J.; Hansman, R. John
2004-01-01
The safety and efficiency of the air traffic control domain is highly dependent on the capabilities and limitations of its human controllers. Past research has indicated that structure provided by the airspace and procedures could aid in simplifying the controllers cognitive tasks. In this paper, observations, interviews, voice command data analyses, and radar analyses were conducted at the Boston Terminal Route Control (TRACON) facility to determine if there was evidence of controllers using structure to simplify their cognitive processes. The data suggest that controllers do use structure-based abstractions to simplify their cognitive processes, particularly the projection task. How structure simplifies the projection task and the implications of understanding the benefits structure provides to the projection task was discussed.
A hybrid radial basis function-pseudospectral method for thermal convection in a 3-D spherical shell
NASA Astrophysics Data System (ADS)
Wright, G. B.; Flyer, N.; Yuen, D. A.
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.
NASA Astrophysics Data System (ADS)
Dai, Xiangjun; Shao, Xinxing; Yang, Fujun; Yun, Hai
2016-06-01
In-plane electronic speckle pattern interferometry (ESPI) was developed to determine the thickness uniformity of a transparent film. The method is based on the subsequent spatial carrier patterns caused by the change of the rotation angle. Full-field thickness distribution can be obtained according to the relation between the phase difference and optical path difference generated by film rotation. Moreover, radial basis function was applied to improve the image quality of interference patterns. The main principle and experimental procedure of the method were presented. The errors of measurement results were analyzed. It is shown that the thickness uniformity of the thin film can be measured rapidly and accurately. Also, the refractive index can be determined by the developed method simultaneously.
Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun
2015-01-01
Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm. PMID:26367382
Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun
2015-01-01
Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm. PMID:26367382
NASA Astrophysics Data System (ADS)
Dou, Ying; Mi, Hong; Zhao, Lingzhi; Ren, Yuqiu; Ren, Yulin
2006-09-01
The application of the second most popular artificial neural networks (ANNs), namely, the radial basis function (RBF) networks, has been developed for quantitative analysis of drugs during the last decade. In this paper, the two components (aspirin and phenacetin) were simultaneously determined in compound aspirin tablets by using near-infrared (NIR) spectroscopy and RBF networks. The total database was randomly divided into a training set (50) and a testing set (17). Different preprocessing methods (standard normal variate (SNV), multiplicative scatter correction (MSC), first-derivative and second-derivative) were applied to two sets of NIR spectra of compound aspirin tablets with different concentrations of two active components and compared each other. After that, the performance of RBF learning algorithm adopted the nearest neighbor clustering algorithm (NNCA) and the criterion for selection used a cross-validation technique. Results show that using RBF networks to quantificationally analyze tablets is reliable, and the best RBF model was obtained by first-derivative spectra.
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. PMID:26192960
NASA Astrophysics Data System (ADS)
Stevens, D.; Power, H.; Meng, C. Y.; Howard, D.; Cliffe, K. A.
2013-12-01
, the stress fields are reproduced to the same degree of accuracy as the displacement field, and exhibit the same order of convergence. The method is also highly stable towards variations in basis function flatness, demonstrating significantly improved stability in comparison to finite-difference type RBF collocation methods.
García-Pérez, Verónica; Tristán-Vega, Antonio; Aja-Fernández, Santiago
2010-01-01
In this paper we propose a novel approach to design a family of Radial Basis Functions with Compact Support applied to elastic registration of medical images. The proposed method is based on Non-Uniform Rational B-Spline theory, which introduce a number of practical properties. The proposed method allows to design almost perfect equally distributed functions which fulfill most of the requirements identified in the recent literature. The Radial Basis Function is merely parametrized by the symmetric desired curvature at peak-and-tails. Properties of the function are numerically compared with foregoing RBFs. Preliminary experimental results indicate its suitability and benefits in registration of medical images. PMID:21097344
NASA Astrophysics Data System (ADS)
Regis, Rommel G.; Shoemaker, Christine A.
2013-05-01
This article presents the DYCORS (DYnamic COordinate search using Response Surface models) framework for surrogate-based optimization of HEB (High-dimensional, Expensive, and Black-box) functions that incorporates an idea from the DDS (Dynamically Dimensioned Search) algorithm. The iterate is selected from random trial solutions obtained by perturbing only a subset of the coordinates of the current best solution. Moreover, the probability of perturbing a coordinate decreases as the algorithm reaches the computational budget. Two DYCORS algorithms that use RBF (Radial Basis Function) surrogates are developed: DYCORS-LMSRBF is a modification of the LMSRBF algorithm while DYCORS-DDSRBF is an RBF-assisted DDS. Numerical results on a 14-D watershed calibration problem and on eleven 30-D and 200-D test problems show that DYCORS algorithms are generally better than EGO, DDS, LMSRBF, MADS with kriging, SQP, an RBF-assisted evolution strategy, and a genetic algorithm. Hence, DYCORS is a promising approach for watershed calibration and for HEB optimization.
NASA Astrophysics Data System (ADS)
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.
Mahdi, Rami N; Rouchka, Eric C
2009-01-01
Accurate identification of promoter regions and transcription start sites (TSS) in genomic DNA allows for a more complete understanding of the structure of genes and gene regulation within a given genome. Many recently published methods have achieved high identification accuracy of TSS. However, models providing more accurate modeling of promoters and TSS are needed. A novel identification method for identifying transcription start sites that improves the accuracy of TSS recognition for recently published methods is proposed. This method incorporates a metric feature based on oligonucleotide positional frequencies, taking into account the nature of promoters. A radial basis function neural network for identifying transcription start sites (RBF-TSS) is proposed and employed as a classification algorithm. Using non-overlapping chunks (windows) of size 50 and 500 on the human genome, the proposed method achieves an area under the Receiver Operator Characteristic curve (auROC) of 94.75% and 95.08% respectively, providing increased performance over existing TSS prediction methods. PMID:19287502
NASA Astrophysics Data System (ADS)
Yan, Zhi-zhong; Wei, Chun-qiu; Zheng, Hui; Zhang, Chuanzeng
2016-05-01
In this paper, a meshless radial basis function (RBF) collocation method is developed to calculate the phononic band structures taking account of different interface models. The present method is validated by using the analytical results in the case of perfect interfaces. The stability is fully discussed based on the types of RBFs, the shape parameters and the node numbers. And the advantages of the proposed RBF method compared to the finite element method (FEM) are also illustrated. In addition, the influences of the spring-interface model and the three-phase model on the wave band gaps are investigated by comparing with the perfect interfaces. For different interface models, the effects of various interface conditions, length ratios and density ratios on the band gap width are analyzed. The comparison results of the two models show that the weakly bonded interface has a significant effect on the properties of phononic crystals. Besides, the band structures of the spring-interface model have certain similarities and differences with those of the three-phase model.
NASA Astrophysics Data System (ADS)
Tyan, Maxim; Van Nguyen, Nhu; Lee, Jae-Woo
2015-07-01
The global variable-fidelity modelling (GVFM) method presented in this article extends the original variable-complexity modelling (VCM) algorithm that uses a low-fidelity and scaling function to approximate a high-fidelity function for efficiently solving design-optimization problems. GVFM uses the design of experiments to sample values of high- and low-fidelity functions to explore global design space and to initialize a scaling function using the radial basis function (RBF) network. This approach makes it possible to remove high-fidelity-gradient evaluation from the process, which makes GVFM more efficient than VCM for high-dimensional design problems. The proposed algorithm converges with 65% fewer high-fidelity function calls for a one-dimensional problem than VCM and approximately 80% fewer for a two-dimensional numerical problem. The GVFM method is applied for the design optimization of transonic and subsonic aerofoils. Both aerofoil design problems show design improvement with a reasonable number of high- and low-fidelity function evaluations.
NASA Astrophysics Data System (ADS)
Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli
2013-03-01
Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.
NASA Astrophysics Data System (ADS)
Ayala, Helon Vicente Hultmann; Coelho, Leandro dos Santos
2016-02-01
The present work introduces a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models with an improved objective function based on the residuals and its correlation function coefficients. We show the results when the proposed methodology is applied to model a magnetorheological damper, with real acquired data, and other two well-known benchmarks. The canonical genetic and differential evolution algorithms are used in cascade to decompose the problem of defining the lags taken as the inputs of the model and its related parameters based on the simultaneous minimization of the residuals and higher orders correlation functions. The inner layer of the cascaded approach is composed of a population which represents the lags on the inputs and outputs of the system and an outer layer represents the corresponding parameters of the RBFNN. The approach is able to define both the inputs of the model and its parameters. This is interesting as it frees the designer of manual procedures, which are time consuming and prone to error, usually done to define the model inputs. We compare the proposed methodology with other works found in the literature, showing overall better results for the cascaded approach.
NASA Astrophysics Data System (ADS)
Bucha, Blažej; Bezděk, Aleš; Sebera, Josef; Janák, Juraj
2015-11-01
We present global and regional gravity field models to degree 130 based on the GOCE kinematic orbit from the period 01 November 2009 to 11 January 2010. The gravity field models are parameterized in terms of the Shannon and Kaula's spherical radial basis functions. The relation between the unknown expansion coefficients and the kinematic orbit of the satellite is established by the acceleration approach. We show that our global GOCE-only solutions free from prior information can compete with unconstrained spherical harmonic models in terms of accuracy. Furthermore, we utilize our low-degree global GOCE-based models to introduce prior information into the least-squares adjustment. This procedure substantially improves the zonal and near-zonal spherical harmonic coefficients, which are usually degraded due to the polar gap problem. As an unwanted side effect, low-pass filtering of the geopotential may occur, but this can be adjusted by the spectral content of the prior information. We show that the regional enhancement of the global solutions reduces noise in the final model between degrees 70 and 130 by ~10 % in terms of RMS error. In general, our Shannon-based solutions systematically outperform the Kaula-based ones. To validate our results, we use the EIGEN-6S model, which is superior to the solutions from kinematic orbits at least by one order of magnitude. Both the global and the regional models satisfy the GOCE-only strategy.
Kuo, R J.; Cohen, P H.
1999-03-01
On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile. PMID:12662710
NASA Astrophysics Data System (ADS)
Oh, Sung-Kwun; Kim, Wook-Dong; Pedrycz, Witold
2016-05-01
In this paper, we introduce a new architecture of optimized Radial Basis Function neural network classifier developed with the aid of fuzzy clustering and data preprocessing techniques and discuss its comprehensive design methodology. In the preprocessing part, the Linear Discriminant Analysis (LDA) or Principal Component Analysis (PCA) algorithm forms a front end of the network. The transformed data produced here are used as the inputs of the network. In the premise part, the Fuzzy C-Means (FCM) algorithm determines the receptive field associated with the condition part of the rules. The connection weights of the classifier are of functional nature and come as polynomial functions forming the consequent part. The Particle Swarm Optimization algorithm optimizes a number of essential parameters needed to improve the accuracy of the classifier. Those optimized parameters include the type of data preprocessing, the dimensionality of the feature vectors produced by the LDA (or PCA), the number of clusters (rules), the fuzzification coefficient used in the FCM algorithm and the orders of the polynomials of networks. The performance of the proposed classifier is reported for several benchmarking data-sets and is compared with the performance of other classifiers reported in the previous studies.
NASA Astrophysics Data System (ADS)
Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.
2014-01-01
This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.
Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M
2016-01-01
This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600
NASA Astrophysics Data System (ADS)
Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude
2010-02-01
Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.
NASA Astrophysics Data System (ADS)
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.
Ebtehaj, Isa; Bonakdari, Hossein; Zaji, Amir Hossein
2016-01-01
In this study, an expert system with a radial basis function neural network (RBF-NN) based on decision trees (DT) is designed to predict sediment transport in sewer pipes at the limit of deposition. First, sensitivity analysis is carried out to investigate the effect of each parameter on predicting the densimetric Froude number (Fr). The results indicate that utilizing the ratio of the median particle diameter to pipe diameter (d/D), ratio of median particle diameter to hydraulic radius (d/R) and volumetric sediment concentration (C(V)) as the input combination leads to the best Fr prediction. Subsequently, the new hybrid DT-RBF method is presented. The results of DT-RBF are compared with RBF and RBF-particle swarm optimization (PSO), which uses PSO for RBF training. It appears that DT-RBF is more accurate (R(2) = 0.934, MARE = 0.103, RMSE = 0.527, SI = 0.13, BIAS = -0.071) than the two other RBF methods. Moreover, the proposed DT-RBF model offers explicit expressions for use by practicing engineers. PMID:27386995
NASA Astrophysics Data System (ADS)
Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Yue, Chen
2015-11-01
The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.
Adetiba, Emmanuel; Olugbara, Oludayo O.
2015-01-01
Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error. PMID:26625358
Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.
2016-01-01
This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600
ERIC Educational Resources Information Center
Meghabghab, George
2001-01-01
Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…
Lyons, Ian M; Ansari, Daniel
2009-09-01
Although significant insights into the neural basis of numerical and mathematical processing have been made, the neural processes that enable abstract symbols to become numerical remain largely unexplored in humans. In the present study, adult participants were trained to associate novel symbols with nonsymbolic numerical magnitudes (arrays of dots). Functional magnetic resonance imaging was used to examine the neural correlates of numerical comparison versus recognition of the novel symbols after each of two training stages. A left-lateralized fronto-parietal network, including the intraparietal sulcus, the precuneus, and the dorsal prefrontal cortex, was more active during numerical comparison than during perceptual recognition. In contrast, a network including bilateral temporal-occipital regions was more active during recognition than comparison. A whole-brain three-way interaction revealed that those individuals who had higher scores on a postscan numerical task (measuring their understanding of the global numerical organization of the novel symbols) exhibited increasing segregation between the two tasks in the bilateral intraparietal sulci as a function of increased training. Furthermore, whole-brain regression analysis showed that activity in the left intraparietal sulcus was systematically related to the effect of numerical distance on accuracy. These data provide converging evidence that parietal and left prefrontal cortices are involved in learning to map numerical quantities onto visual symbols. Only the parietal cortex, however, appeared systematically related to the degree to which individuals learned to associate novel symbols with their numerical referents. We conclude that the left parietal cortex, in particular, may play a central role in imbuing visual symbols with numerical meaning. PMID:18823231
Xie, Li-juan; Ye, Xing-qian; Liu, Dong-hong; Ying, Yi-bin
2008-01-01
Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice. PMID:19067467
NASA Astrophysics Data System (ADS)
Pamosoaji, Anugrah K.; Thuong Cat, Pham; Hong, Keum-Shik
2014-12-01
An obstacle avoidance problem of rear-steered wheeled vehicles in consideration of the presence of uncertainties is addressed. Modelling errors and additional uncertainties are taken into consideration. Controller designs for driving and steering motors are designed. A proportional-derivative-type driving motor controller and a sliding-mode steering controller combined with radial basis function neural network (RBFNN) based estimators are proposed. The convergence properties of the RBFNN-based estimators are proven by the Stone-Weierstrass theorem. The stability of the proposed control law is proven using Lyapunov stability analysis. The obstacle avoidance strategy utilising the sliding surface adjustment to an existing navigation method is presented. It is concluded that the driving velocity and steering-angle performances of the proposed control system are satisfactory.
Heidari, Mohammad; Heidari, Ali; Homaei, Hadi
2014-01-01
The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS. PMID:24860602
Heidari, Mohammad; Heidari, Ali; Homaei, Hadi
2014-01-01
The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS. PMID:24860602
NASA Astrophysics Data System (ADS)
Eicker, Annette; Schall, Judith; Lieb, Verena; Bentel, Katrin; Schmidt, Michael; Buße, Kirsten; Kusche, Jürgen; Gerlach, Christian
2014-05-01
Traditionally, the gravity field of the Earth is modeled as a series expansion into globally defined spherical harmonic basis functions. However, it is well-known that spherical harmonic approaches have problems to properly represent data of heterogeneous density and quality. These and other deficiencies can be overcome using regional modeling approaches, which allow to more flexibly adjust the analysis procedure to the gravity field signal in certain geographical areas. Therefore, different sophisticated regional gravity field modeling approaches have been developed in recent years. In order to systematically compare the different approaches, the IAG ICCT Joint Study Group JSG0.3 "Comparison of Current Methodologies in Regional Gravity Field Modeling" has recently created synthetic test data sets. In this presentation we will discuss and compare the results obtained from the test data sets using a parameterization by different types of radial basis functions as provided by the groups of the University of Bonn, the German Geodetic Research Institute (DGFI) and the Norwegian University of Life Sciences. Furthermore, we will present the improvements that can be obtained by regional processing techniques compared to global spherical harmonic modeling at the example of GOCE real data applications.
NASA Astrophysics Data System (ADS)
Rezaeian-Zadeh, Mehdi; Zand-Parsa, Shahrookh; Abghari, Hirad; Zolghadr, Masih; Singh, Vijay P.
2012-08-01
This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg-Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination ( R 2) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.
NASA Astrophysics Data System (ADS)
Owusu-Banson, Derek
In recent times, a variety of industries, applications and numerical methods including the meshless method have enjoyed a great deal of success by utilizing the graphical processing unit (GPU) as a parallel coprocessor. These benefits often include performance improvement over the previous implementations. Furthermore, applications running on graphics processors enjoy superior performance per dollar and performance per watt than implementations built exclusively on traditional central processing technologies. The GPU was originally designed for graphics acceleration but the modern GPU, known as the General Purpose Graphical Processing Unit (GPGPU) can be used for scientific and engineering calculations. The GPGPU consists of massively parallel array of integer and floating point processors. There are typically hundreds of processors per graphics card with dedicated high-speed memory. This work describes an application written by the author, titled GaussianRBF to show the implementation and results of a novel meshless method that in-cooperates the collocation of the Gaussian radial basis function by utilizing the GPU as a parallel co-processor. Key phases of the proposed meshless method have been executed on the GPU using the NVIDIA CUDA software development kit. Especially, the matrix fill and solution phases have been carried out on the GPU, along with some post processing. This approach resulted in a decreased processing time compared to similar algorithm implemented on the CPU while maintaining the same accuracy.
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. PMID:25233483
Kong, Chunli; Wang, Huiyi; Li, Dapeng; Zhang, Yuemei; Pan, Jinfeng; Zhu, Beiwei; Luo, Yongkang
2016-06-15
To investigate and predict quality of 2% brined common carp (Cyprinus carpio) fillets during frozen storage, free fatty acids (FFA), salt extractable protein (SEP), total sulfhydryl (SH) content, and Ca(2+)-ATPase activity were determined at 261 K, 253 K, and 245 K, respectively. There was a dramatic increase (P<0.05) in FFA and a sharp decrease (P<0.05) in SH at 261 K during the first 3 weeks. SEP decreased to 67.31% after 17 weeks at 245 K, whereas it took about 7 weeks and 13 weeks to decrease to the same extent at 261 K and 253 K, respectively. Ca(2+)-ATPase activity kept decreasing to 18.28% after 7 weeks at 261 K. Furthermore, radial basis function neural networks (RBFNNs) were developed to predict quality (FFA, SEP, SH, and Ca(2+)-ATPase activity) of brined carp fillets during frozen storage with relative errors all within ±5%. Thus, RBFNN is a promising method to predict quality of carp fillets during storage at 245-261 K. PMID:26868584
NASA Astrophysics Data System (ADS)
D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel
2016-03-01
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
NASA Astrophysics Data System (ADS)
Chanthawara, Krittidej; Kaennakham, Sayan; Toutip, Wattana
2016-02-01
The methodology of Dual Reciprocity Boundary Element Method (DRBEM) is applied to the convection-diffusion problems and investigating its performance is our first objective of the work. Seven types of Radial Basis Functions (RBF); Linear, Thin-plate Spline, Cubic, Compactly Supported, Inverse Multiquadric, Quadratic, and that proposed by [12], were closely investigated in order to numerically compare their effectiveness drawbacks etc. and this is taken as our second objective. A sufficient number of simulations were performed covering as many aspects as possible. Varidated against both exacts and other numerical works, the final results imply strongly that the Thin-Plate Spline and Linear type of RBF are superior to others in terms of both solutions' quality and CPU-time spent while the Inverse Multiquadric seems to poorly yield the results. It is also found that DRBEM can perform relatively well at moderate level of convective force and as anticipated becomes unstable when the problem becomes more convective-dominated, as normally found in all classical mesh-dependence methods.
Neelambike, Sumana M.
2016-01-01
Introduction Human Immunodeficiency Virus (HIV) infects and cripples the immune system of the body. The two important marker CD4+T cells and Plasma viral load are crucial not only in understanding the disease progression but also in starting the antiretroviral therapy. A lot of research is going on in understanding the dynamic nature of HIV. Aim To find the correlation between CD4+ count and Plasma Viral Load (PVL) measured by two different technologies; with the help of correlation technique in conjunction with the three dimensional HIV model with a purpose of establishing a mathematical model between the CD4+ cells and PVL using a sinusoidal function as well as Radial Basis Function (RBF) neural network. Materials and Methods Plasma Viral Load were determined by two different methods viz Exavir CavidiTM and Abbott Real time HIV-1 assay and then they were correlated with the CD4+ count with the help of computational intelligence in predicting viral load. Results It was found that there exists a positive correlation between the CD4+ cells and viral loads. A correlation value of 0.4082 and 0.3652 was observed between CD4+ cells and viral measured using Exavir CavidiTM and Abbott Real time HIV-1 assay respectively. Conclusion The existence of positive correlation had helped us to understand the nature and dynamic of the existence of HIV and how the CD4 + and PVL act. PMID:27190799
NASA Astrophysics Data System (ADS)
Sabetghadam, Fereidoun; Soltani, Elshan
2015-10-01
The moving boundary conditions are implemented into the Fourier pseudo-spectral solution of the two-dimensional incompressible Navier-Stokes equations (NSE) in the vorticity-velocity form, using the radial basis functions (RBF). Without explicit definition of an external forcing function, the desired immersed boundary conditions are imposed by direct modification of the convection and diffusion terms. At the beginning of each time-step the solenoidal velocities, satisfying the desired moving boundary conditions, along with a modified vorticity are obtained and used in modification of the convection and diffusion terms of the vorticity evolution equation. Time integration is performed by the explicit fourth-order Runge-Kutta method and the boundary conditions are set at the beginning of each sub-step. The method is applied to a couple of moving boundary problems and more than second-order of accuracy in space is demonstrated for the Reynolds numbers up to Re = 550. Moreover, performance of the method is shown in comparison with the classical Fourier pseudo-spectral method.
NASA Astrophysics Data System (ADS)
Talebpour, Zahra; Tavallaie, Roya; Ahmadi, Seyyed Hamid; Abdollahpour, Assem
2010-09-01
In this study, a new method for the simultaneous determination of penicillin G salts in pharmaceutical mixture via FT-IR spectroscopy combined with chemometrics was investigated. The mixture of penicillin G salts is a complex system due to similar analytical characteristics of components. Partial least squares (PLS) and radial basis function-partial least squares (RBF-PLS) were used to develop the linear and nonlinear relation between spectra and components, respectively. The orthogonal signal correction (OSC) preprocessing method was used to correct unexpected information, such as spectral overlapping and scattering effects. In order to compare the influence of OSC on PLS and RBF-PLS models, the optimal linear (PLS) and nonlinear (RBF-PLS) models based on conventional and OSC preprocessed spectra were established and compared. The obtained results demonstrated that OSC clearly enhanced the performance of both RBF-PLS and PLS calibration models. Also in the case of some nonlinear relation between spectra and component, OSC-RBF-PLS gave satisfactory results than OSC-PLS model which indicated that the OSC was helpful to remove extrinsic deviations from linearity without elimination of nonlinear information related to component. The chemometric models were tested on an external dataset and finally applied to the analysis commercialized injection product of penicillin G salts.
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J.
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483
Mirbagheri, Seyed Ahmad; Bagheri, Majid; Boudaghpour, Siamak; Ehteshami, Majid; Bagheri, Zahra
2015-01-01
Treatment process models are efficient tools to assure proper operation and better control of wastewater treatment systems. The current research was an effort to evaluate performance of a submerged membrane bioreactor (SMBR) treating combined municipal and industrial wastewater and to simulate effluent quality parameters of the SMBR using a radial basis function artificial neural network (RBFANN). The results showed that the treatment efficiencies increase and hydraulic retention time (HRT) decreases for combined wastewater compared with municipal and industrial wastewaters. The BOD, COD, [Formula: see text] and total phosphorous (TP) removal efficiencies for combined wastewater at HRT of 7 hours were 96.9%, 96%, 96.7% and 92%, respectively. As desirable criteria for treating wastewater, the TBOD/TP ratio increased, the BOD and COD concentrations decreased to 700 and 1000 mg/L, respectively and the BOD/COD ratio was about 0.5 for combined wastewater. The training procedures of the RBFANN models were successful for all predicted components. The train and test models showed an almost perfect match between the experimental and predicted values of effluent BOD, COD, [Formula: see text] and TP. The coefficient of determination (R(2)) values were higher than 0.98 and root mean squared error (RMSE) values did not exceed 7% for train and test models. PMID:25798288
NASA Astrophysics Data System (ADS)
Boyd, John P.
2011-02-01
Radial basis function (RBF) interpolants have become popular in computer graphics, neural networks and for solving partial differential equations in many fields of science and engineering. In this article, we compare five different species of RBFs: Gaussians, hyperbolic secant (sech's), inverse quadratics, multiquadrics and inverse multiquadrics. We show that the corresponding cardinal functions for a uniform, unbounded grid are all approximated by the same function: C(X) ∼ (1/(ρ)) sin (πX)/sinh (πX/ρ) for some constant ρ(α) which depends on the inverse width parameter (“shape parameter”) α of the RBF and also on the RBF species. The error in this approximation is exponentially small in 1/α for sech's and inverse quadratics and exponentially small in 1/α2 for Gaussians; the error is proportional to α4 for multiquadrics and inverse multiquadrics. The error in all cases is small even for α ∼ O(1). These results generalize to higher dimensions. The Gaussian RBF cardinal functions in any number of dimensions d are, without approximation, the tensor product of one dimensional Gaussian cardinal functions: Cd(x1,x2…,xd)=∏j=1dC(xj). For other RBF species, we show that the two-dimensional cardinal functions are well approximated by the products of one-dimensional cardinal functions; again the error goes to zero as α → 0. The near-identity of the cardinal functions implies that all five species of RBF interpolants are (almost) the same, despite the great differences in the RBF ϕ's themselves.
NASA Astrophysics Data System (ADS)
Hassanzadeh, Zeinabe; Kompany-Zareh, Mohsen; Ghavami, Raouf; Gholami, Somayeh; Malek-Khatabi, Atefe
2015-10-01
The configuring of a radial basis function neural network (RBFN) consists of optimizing the architecture and the network parameters (centers, widths, and weights). Methods such as genetic algorithm (GA), K-means and cluster analysis (CA) are among center selection methods. In the most of reports on RBFN modeling optimum centers are selected among rows of descriptors matrix. A combination of RBFN and GA is introduced for better description of quantitative structure-property relationships (QSPR) models. In this method, centers are not exactly rows of the independent matrix and can be located in any point of the samples space. In the proposed approach, initial centers are randomly selected from the calibration set. Then GA changes the locations of the initially selected centers to find the optimum positions of centers from the whole space of scores matrix, in order to obtain highest prediction ability. This approach is called whole space GA-RBFN (wsGA-RBFN) and applied to predict the adsorption coefficients (logk), of 40 small molecules on the surface of multi-walled carbon nanotubes (MWCNTs). The data consists of five solute descriptors [R, π, α, β, V] of the molecules and known as data set1. Prediction ability of wsGA-RBFN is compared to GA-RBFN and MLR models. The obtained Q2 values for wsGA-RBFN, GA-RBFN and MLR are 0.95, 0.85, and 0.78, respectively, which shows the merit of wsGA-RBFN. The method is also applied on the logarithm of surface area normalized adsorption coefficients (logKSA), of organic compounds (OCs) on MWCNTs surface. The data set2 includes 69 aromatic molecules with 13 physicochemical properties of the OCs. Thirty-nine of these molecules were similar to those of data set1 and the others were aromatic compounds included of small and big molecules. Prediction ability of wsGA-RBFN for second data set was compared to GA-RBF. The Q2 values for wsGA-RBFN and GA-RBF are obtained as 0.89 and 0.80, respectively.
NASA Astrophysics Data System (ADS)
Wright, G.; Flyer, N.; Yuen, D. A.; Monnereau, M.; Zhang, S.; Wang, S. M.
2009-05-01
Many numerical methods, such as finite-differences, finite-volume, their yin-yang variants, finite-elements and spectral methods have been employed to study 3-D mantle convection. All have their own strengths, but also serious weaknesses. Spectrally accurate methods do not practically allow for node refinement and often involve cumbersome algebra while finite difference, volume, or element methods are generally low-order, adding excessive numerical diffusion to the model. For the 3-D mantle convection problem, we have introduced a new mesh-free approach, using radial basis functions (RBF). This method has the advantage of being algorithmic simple, spectrally accurate for arbitrary node layouts in multi-dimensions and naturally allows for node-refinement. One virtue of the RBF scheme allows the user to use a simple Cartesian geometry, while implementing the required boundary conditions for the temperature, velocities and stress components on a spherical surface at both the planetary surface and the core-mantle boundary. We have studied time- dependent mantle convection, using both a RBF-pseudospectral code and a code which uses spherical- harmonics in the angular direction and second-order finite volume in the radial direction. We have employed a third code , which uses spherical harmonics and higher-order finite-difference method a la Fornberg in the radial coordinate.We first focus on the onset of time-dependence at Rayleigh number Ra of 70,000. We follow the development of stronger time-dependence to a Ra of one million, using high enough resolution with 120 to 200 points in the radial direction and 128 to 256 spherical harmonics.
G. Ragan
2001-12-19
The purpose of the inventory abstraction, which has been prepared in accordance with a technical work plan (CRWMS M&O 2000e for ICN 02 of the present analysis, and BSC 2001e for ICN 03 of the present analysis), is to: (1) Interpret the results of a series of relative dose calculations (CRWMS M&O 2000c, 2000f). (2) Recommend, including a basis thereof, a set of radionuclides that should be modeled in the Total System Performance Assessment in Support of the Site Recommendation (TSPA-SR) and the Total System Performance Assessment in Support of the Final Environmental Impact Statement (TSPA-FEIS). (3) Provide initial radionuclide inventories for the TSPA-SR and TSPA-FEIS models. (4) Answer the U.S. Nuclear Regulatory Commission (NRC)'s Issue Resolution Status Report ''Key Technical Issue: Container Life and Source Term'' (CLST IRSR) key technical issue (KTI): ''The rate at which radionuclides in SNF [spent nuclear fuel] are released from the EBS [engineered barrier system] through the oxidation and dissolution of spent fuel'' (NRC 1999, Subissue 3). The scope of the radionuclide screening analysis encompasses the period from 100 years to 10,000 years after the potential repository at Yucca Mountain is sealed for scenarios involving the breach of a waste package and subsequent degradation of the waste form as required for the TSPA-SR calculations. By extending the time period considered to one million years after repository closure, recommendations are made for the TSPA-FEIS. The waste forms included in the inventory abstraction are Commercial Spent Nuclear Fuel (CSNF), DOE Spent Nuclear Fuel (DSNF), High-Level Waste (HLW), naval Spent Nuclear Fuel (SNF), and U.S. Department of Energy (DOE) plutonium waste. The intended use of this analysis is in TSPA-SR and TSPA-FEIS. Based on the recommendations made here, models for release, transport, and possibly exposure will be developed for the isotopes that would be the highest contributors to the dose given a release to the
ERIC Educational Resources Information Center
Henkes, Robert
1978-01-01
Abstract art provokes numerous interpretations, and as many misunderstandings. The adolescent reaction is no exception. The procedure described here can help the student to understand the abstract from at least one direction. (Author/RK)
Radial systems of dark globules
Gyul'budagyn, A.L.
1986-03-01
The author gives examples of radial systems consisting of dark globules and ''elephant trunks''. Besides already known systems, which contain hot stars at their center, data are given on three radial systems of a new kind, at the center of which there are stars of spectral types later than B. Data are given on 32 globules of radial systems of the association Cep OB2. On the basis of the observational data, it is concluded that at least some of the isolated Bok globules derive from elephant trunks and dark globules forming radial systems around hot stars. It is also suggested that the two molecular clouds situated near the Rosette nebula and possessing velocities differing by ca 20 km/sec from the velocity of the nebula could have been ejected in opposite directions from the center of the nebula. One of these clouds consists of dark globules forming the radial system of the Rosette nebula.
NASA Technical Reports Server (NTRS)
2005-01-01
[figure removed for brevity, see original site]
The ejecta surrounding the crater (off image to the left) in this image has undergone significant erosion by the wind. The wind has stripped the surface features from the ejecta and has started to winnow away the ejecta blanket. Near the margin of the ejecta the wind is eroding along a radial pattern -- taking advantage of radial emplacement. Note the steep margin of the ejecta blanket. Most, if not all, of the fine ejecta material has been removed and the wind in now working on the more massive continuous ejecta blanket.
Image information: VIS instrument. Latitude 12.5, Longitude 197.4 East (162.6 West). 37 meter/pixel resolution.
Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.
NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.
Radial-radial single rotor turbine
Platts, David A.
2006-05-16
A rotor for use in turbine applications has a radial compressor/pump having radially disposed spaced apart fins forming passages and a radial turbine having hollow turbine blades interleaved with the fins and through which fluid from the radial compressor/pump flows. The rotor can, in some applications, be used to produce electrical power.
ERIC Educational Resources Information Center
Pietropola, Anne
1998-01-01
Describes a lesson designed to culminate a year of eighth-grade art classes in which students explore elements of design and space by creating 3-D abstract constructions. Outlines the process of using foam board and markers to create various shapes and optical effects. (DSK)
Radially inhomogeneous bounded plasmas
NASA Astrophysics Data System (ADS)
Zakeri-Khatir, H.; Aghamir, F. M.
2016-07-01
On the basis of kinetic theory along with self-consistent field equations, the expressions for dielectric tensor of radially inhomogeneous magnetized plasma columns are obtained. The study of dielectric tensor characteristics allows the accurate analysis of the inhomogeneous properties, beyond limitations that exist in the conventional method. Through the Bessel–Fourier transformation, the localized form of material equations in a radially inhomogeneous medium are obtained. In order to verify the integrity of the model and reveal the effect of inhomogeneity, a special case of a cylindrical plasma waveguide completely filled with inhomogeneous magnetized cold plasma was considered. The dispersion relation curves for four families of electromagnetic (EH and HE) and electrostatic (SC and C) modes are obtained and compared with the findings of the conventional model. The numerical analysis indicates that the inhomogeneity effect leads to coupling of electromagnetic and electrostatic modes each having different radial eigen numbers. The study also reveals that the electrostatic modes are more sensitive to inhomogeneous effects than the electromagnetic modes.
Radial head fracture - aftercare
Elbow fracture - radial head - aftercare ... the radius bone, just below your elbow. A fracture is a break in your bone. The most common cause of a radial head fracture is falling with an outstretched arm.
McKeown, Mark H.; Beason, Steven C.
1991-01-01
The radial arm strike rail assembly is a system for measurement of bearings, directions, and stereophotography for geologic mapping, particularly where magnetic compasses are not appropriate. The radial arm, pivoting around a shaft axis, provides a reference direction determination for geologic mapping and bearing or direction determination. The centerable and levelable pedestal provide a base for the radial arm strike rail and the telescoping camera pedestal. The telescoping feature of the radial arm strike rail allows positioning the end of the rail for strike direction or bearing measurement with a goniometer.
[Zaidemberg's vascularized radial graft].
Saint-Cast, Y
2010-12-01
In 1991, Carlos Zaidemberg described a new technique to repair scaphoid non-unions with a vascularized bone graft harvested from the radial styloid process. An anatomic study based on 30 dissections after colorized latex injection established the constancy of the radial styloid process's artery, while showing that its origin, course and length were subject to variations. In a retrospective series of 38 cases over a period of 10 years, the vascularized bone graft was indicated for: (1) scaphoid non-union with the presence of avascular changes of the proximal fragment (23 cases); (2) failed prior reconstruction with bone graft and internal fixation (nine cases); (3) degenerative styloid-scaphoid arthritis (three cases); (4) fracture on Preiser dystrophy (three cases). The five steps of the simplified operative technique without dissection of the vascular pedicle include: (1) longitudinal dorso-radial approach, identification of the periosteal portion of the radial styloid process artery; (2) incision of the first and second compartments, longitudinal arthrotomy under the second compartment; (3) styloidectomy and transversal resection of the scaphoid non-union and sclerotic bone; (4) elevation of the vascularized bone graft; (5) transversal and radial insertion of the vascularized bone graft, osteosynthesis by two or three K-wire touching the scaphoid's radial edge. Scaphoid union was obtained in 33 cases out of 38. The only postoperative complications were two transient radial paresthesia. The standardized surgical procedure using vascularized bone graft harvested from the radial styloid process provides an efficient scaphoid reconstruction. PMID:21087882
Venugopalan, Poothirikovil; Sivakumar, Puthuval; Ardley, Robert G.; Oates, Crispian
2012-01-01
We present an 11year-old boy with a weak right radial pulse, and describe the successful application of vascular ultrasound to identify the ulnar artery dominance and a thin right radial artery with below normal Doppler flow velocity that could explain the discrepancy. The implications of identifying this anomaly are discussed. PMID:22375269
Ebert, Todd A; Carella, John A
2012-03-13
A triple acting radial seal used as an interstage seal assembly in a gas turbine engine, where the seal assembly includes an interstage seal support extending from a stationary inner shroud of a vane ring, the interstage seal support includes a larger annular radial inward facing groove in which an outer annular floating seal assembly is secured for radial displacement, and the outer annular floating seal assembly includes a smaller annular radial inward facing groove in which an inner annular floating seal assembly is secured also for radial displacement. A compliant seal is secured to the inner annular floating seal assembly. The outer annular floating seal assembly encapsulates the inner annular floating seal assembly which is made from a very low alpha material in order to reduce thermal stress.
NASA Astrophysics Data System (ADS)
Salim, S.; Gould, A.
2000-12-01
Full-Sky Astrometric Mapping Explorer (FAME) belongs to a new generation of astrometry satellites and will probe the surrounding space some 20 times deeper than its predecessor Hipparcos. As a result we will acquire precise knowledge of 5 out of 6 components of phase-space for millions of stars. The remaining coordinate, radial velocity, will remain unknown. In this study, we look at how the knowledge of radial velocity affects the determination of the structure of the Galaxy, and its gravitational potential. We therefore propose a radial velocity survey of FAME stars, and discuss its feasibility and technical requirements.
ERIC Educational Resources Information Center
Moessinger, Pierre; Poulin-Dubois, Diane
1981-01-01
Reviews and discusses Piaget's recent work on abstract reasoning. Piaget's distinction between empirical and reflective abstraction is presented; his hypotheses are considered to be metaphorical. (Author/DB)
Radial nerve dysfunction (image)
The radial nerve travels down the arm and supplies movement to the triceps muscle at the back of the upper arm. ... the wrist and hand. The usual causes of nerve dysfunction are direct trauma, prolonged pressure on the ...
NASA Technical Reports Server (NTRS)
Basiulis, A.; Buzzard, R. J.
1971-01-01
Unit moves heat radially from small diameter shell to larger diameter shell, or vice versa, with negligible temperature drop, making device useful wherever heating or cooling of concentrically arranged materials, substances, and structures is desired.
... may occur: Abnormal sensations to the hand or forearm ("back" of the hand), "thumb side" (radial surface) ... wrist or fingers Muscle loss ( atrophy ) in the forearm Weakness of the wrist and finger Wrist or ...
... nerve leads to problems with movement in the arm and wrist and with sensation in the back of the arm or hand. ... to the radial nerve, which travels down the arm and controls movement of the triceps muscle at ...
NASA Astrophysics Data System (ADS)
Roelke, Richard J.
The technology of high temperature cooled radial turbines is reviewed. Aerodynamic performance considerations are described. Heat transfer and structural analysis are addressed, and in doing so the following topics are covered: cooling considerations, hot side convection, coolant side convection, and rotor mechanical analysis. Cooled rotor concepts and fabrication are described, and the following are covered in this context: internally cooled rotor, hot isostatic pressure bonded rotor, laminated rotor, split blade rotor, and the NASA radial turbine program.
NASA Technical Reports Server (NTRS)
Roelke, Richard J.
1992-01-01
The technology of high temperature cooled radial turbines is reviewed. Aerodynamic performance considerations are described. Heat transfer and structural analysis are addressed, and in doing so the following topics are covered: cooling considerations, hot side convection, coolant side convection, and rotor mechanical analysis. Cooled rotor concepts and fabrication are described, and the following are covered in this context: internally cooled rotor, hot isostatic pressure bonded rotor, laminated rotor, split blade rotor, and the NASA radial turbine program.
Bartoníček, J; Naňka, O; Tuček, M
2015-10-01
In the clinical practice, radial shaft may be exposed via two approaches, namely the posterolateral Thompson and volar (anterior) Henry approaches. A feared complication of both of them is the injury to the deep branch of the radial nerve. No consensus has been reached, yet, as to which of the two approaches is more beneficial for the proximal half of radius. According to our anatomical studies and clinical experience, Thompson approach is safe only in fractures of the middle and distal thirds of the radial shaft, but highly risky in fractures of its proximal third. Henry approach may be used in any fracture of the radial shaft and provides a safe exposure of the entire lateral and anterior surfaces of the radius.The Henry approach has three phases. In the first phase, incision is made along the line connecting the biceps brachii tendon and the styloid process of radius. Care must be taken not to damage the lateral cutaneous nerve of forearm.In the second phase, fascia is incised and the brachioradialis identified by the typical transition from the muscle belly to tendon and the shape of the tendon. On the lateral side, the brachioradialis lines the space with the radial artery and veins and the superficial branch of the radial nerve running at its bottom. On the medial side, the space is defined by the pronator teres in the proximal part and the flexor carpi radialis in the distal part. The superficial branch of the radial nerve is retracted together with the brachioradialis laterally, and the radial artery medially.In the third phase, the attachment of the pronator teres is identified by its typical tendon in the middle of convexity of the lateral surface of the radial shaft. The proximal half of the radius must be exposed very carefully in order not to damage the deep branch of the radial nerve. Dissection starts at the insertion of the pronator teres and proceeds proximally along its lateral border in interval between this muscle and insertion of the supinator
NASA Technical Reports Server (NTRS)
Roelke, Richard J.
1992-01-01
Radial turbines have been used extensively in many applications including small ground based electrical power generators, automotive engine turbochargers and aircraft auxiliary power units. In all of these applications the turbine inlet temperature is limited to a value commensurate with the material strength limitations and life requirements of uncooled metal rotors. To take advantage of all the benefits that higher temperatures offer, such as increased turbine specific power output or higher cycle thermal efficiency, requires improved high temperature materials and/or blade cooling. Extensive research is on-going to advance the material properties of high temperature superalloys as well as composite materials including ceramics. The use of ceramics with their high temperature potential and low cost is particularly appealing for radial turbines. However until these programs reach fruition the only way to make significant step increases beyond the present material temperature barriers is to cool the radial blading.
Radial Nerve Tendon Transfers.
Cheah, Andre Eu-Jin; Etcheson, Jennifer; Yao, Jeffrey
2016-08-01
Radial nerve palsy typically occurs as a result of trauma or iatrogenic injury and leads to the loss of wrist extension, finger extension, thumb extension, and a reduction in grip strength. In the absence of nerve recovery, reconstruction of motor function involves tendon transfer surgery. The most common donor tendons include the pronator teres, wrist flexors, and finger flexors. The type of tendon transfer is classified based on the donor for the extensor digitorum communis. Good outcomes have been reported for most methods of radial nerve tendon transfers as is typical for positional tendon transfers not requiring significant power. PMID:27387076
Radially uniform electron source
NASA Technical Reports Server (NTRS)
Mccomas, D.; Bame, S. J.
1982-01-01
A thermionic electron source capable of producing uniform count rates in a number of channel electron multipliers simultaneously was required for conditioning multipliers for an extended space mission. It was found that a straight tungsten filament in the center of a cylindrically symmetric geometry surrounded by an array of multipliers emits a radially asymmetric distribution of electrons that changes with time. A source was developed which successfully produces a time-independent radially uniform distribution of electrons by moving the filament out of the direct line of sight and replacing it with a centrally located electron 'cloud.'
Smith, Karl H.
2002-01-01
A radial wedge flange clamp comprising a pair of flanges each comprising a plurality of peripheral flat wedge facets having flat wedge surfaces and opposed and mating flat surfaces attached to or otherwise engaged with two elements to be joined and including a series of generally U-shaped wedge clamps each having flat wedge interior surfaces and engaging one pair of said peripheral flat wedge facets. Each of said generally U-shaped wedge clamps has in its opposing extremities apertures for the tangential insertion of bolts to apply uniform radial force to said wedge clamps when assembled about said wedge segments.
ERIC Educational Resources Information Center
Engineering Education, 1975
1975-01-01
Papers abstracted represent those submitted to the distribution center at the 83rd American Society for Engineering Education Convention. Abstracts are grouped under headings corresponding to the main topic of the paper. (Editor/CP)
ERIC Educational Resources Information Center
Monaghan, John; Ozmantar, Mehmet Fatih
2006-01-01
The framework for this paper is a recently developed theory of abstraction in context. The paper reports on data collected from one student working on tasks concerned with absolute value functions. It examines the relationship between mathematical constructions and abstractions. It argues that an abstraction is a consolidated construction that can…
Abstraction and Problem Reformulation
NASA Technical Reports Server (NTRS)
Giunchiglia, Fausto
1992-01-01
In work done jointly with Toby Walsh, the author has provided a sound theoretical foundation to the process of reasoning with abstraction (GW90c, GWS9, GW9Ob, GW90a). The notion of abstraction formalized in this work can be informally described as: (property 1), the process of mapping a representation of a problem, called (following historical convention (Sac74)) the 'ground' representation, onto a new representation, called the 'abstract' representation, which, (property 2) helps deal with the problem in the original search space by preserving certain desirable properties and (property 3) is simpler to handle as it is constructed from the ground representation by "throwing away details". One desirable property preserved by an abstraction is provability; often there is a relationship between provability in the ground representation and provability in the abstract representation. Another can be deduction or, possibly inconsistency. By 'throwing away details' we usually mean that the problem is described in a language with a smaller search space (for instance a propositional language or a language without variables) in which formulae of the abstract representation are obtained from the formulae of the ground representation by the use of some terminating rewriting technique. Often we require that the use of abstraction results in more efficient .reasoning. However, it might simply increase the number of facts asserted (eg. by allowing, in practice, the exploration of deeper search spaces or by implementing some form of learning). Among all abstractions, three very important classes have been identified. They relate the set of facts provable in the ground space to those provable in the abstract space. We call: TI abstractions all those abstractions where the abstractions of all the provable facts of the ground space are provable in the abstract space; TD abstractions all those abstractions wllere the 'unabstractions' of all the provable facts of the abstract space are
Radial forces in a misaligned radial face seal
NASA Technical Reports Server (NTRS)
Etsion, I.
1977-01-01
Radial forces on the primary seal ring of a flat misaligned seal are analyzed, taking into account the radial variation in seal clearance. An analytical solution for both hydrostatic and hydrodynamic effects is presented that covers the whole range from zero to full angular misalignment. The net radial force on the primary seal ring is always directed so as to produce a radial eccentricity which generates inward pumping. Although the radial force is usually very small, in some cases it may be one of the reasons for excessive leakage through both the primary and secondary seals of a radial face seal.
Radial forces in a misaligned radial face seal
NASA Technical Reports Server (NTRS)
Etsion, I.
1978-01-01
Radial forces on the primary seal ring of a flat misaligned seal are analyzed, taking into account the radial variation in seal clearance. An analytical solution for both hydrostatic and hydrodynamic effects is presented that covers the whole range from zero to full angular misalignment. The net radial force on the primary seal ring is always directed so as to produce a radial eccentricity which generates inward pumping. Although the radial force is usually very small, in some cases it may be one of the reasons for excessive leakage through both the primary and secondary seals of a radial face seal.
Ferrari, Pier Luigi
2003-07-29
Some current interpretations of abstraction in mathematical settings are examined from different perspectives, including history and learning. It is argued that abstraction is a complex concept and that it cannot be reduced to generalization or decontextualization only. In particular, the links between abstraction processes and the emergence of new objects are shown. The role that representations have in abstraction is discussed, taking into account both the historical and the educational perspectives. As languages play a major role in mathematics, some ideas from functional linguistics are applied to explain to what extent mathematical notations are to be considered abstract. Finally, abstraction is examined from the perspective of mathematics education, to show that the teaching ideas resulting from one-dimensional interpretations of abstraction have proved utterly unsuccessful. PMID:12903658
Radial Halbach Magnetic Bearings
NASA Technical Reports Server (NTRS)
Eichenberg, Dennis J.; Gallo, Christopher A.; Thompson, William K.
2009-01-01
Radial Halbach magnetic bearings have been investigated as part of an effort to develop increasingly reliable noncontact bearings for future high-speed rotary machines that may be used in such applications as aircraft, industrial, and land-vehicle power systems and in some medical and scientific instrumentation systems. Radial Halbach magnetic bearings are based on the same principle as that of axial Halbach magnetic bearings, differing in geometry as the names of these two types of bearings suggest. Both radial and axial Halbach magnetic bearings are passive in the sense that unlike most other magnetic bearings that have been developed in recent years, they effect stable magnetic levitation without need for complex active control. Axial Halbach magnetic bearings were described in Axial Halbach Magnetic Bearings (LEW-18066-1), NASA Tech Briefs, Vol. 32, No. 7 (July 2008), page 85. In the remainder of this article, the description of the principle of operation from the cited prior article is recapitulated and updated to incorporate the present radial geometry. In simplest terms, the basic principle of levitation in an axial or radial Halbach magnetic bearing is that of the repulsive electromagnetic force between (1) a moving permanent magnet and (2) an electric current induced in a stationary electrical conductor by the motion of the magnetic field. An axial or radial Halbach bearing includes multiple permanent magnets arranged in a Halbach array ("Halbach array" is defined below) in a rotor and multiple conductors in the form of wire coils in a stator, all arranged so the rotary motion produces an axial or radial repulsion that is sufficient to levitate the rotor. A basic Halbach array (see Figure 1) consists of a row of permanent magnets, each oriented so that its magnetic field is at a right angle to that of the adjacent magnet, and the right-angle turns are sequenced so as to maximize the magnitude of the magnetic flux density on one side of the row while
Variable stator radial turbine
NASA Technical Reports Server (NTRS)
Rogo, C.; Hajek, T.; Chen, A. G.
1984-01-01
A radial turbine stage with a variable area nozzle was investigated. A high work capacity turbine design with a known high performance base was modified to accept a fixed vane stagger angle moveable sidewall nozzle. The nozzle area was varied by moving the forward and rearward sidewalls. Diffusing and accelerating rotor inlet ramps were evaluated in combinations with hub and shroud rotor exit rings. Performance of contoured sidewalls and the location of the sidewall split line with respect to the rotor inlet was compared to the baseline. Performance and rotor exit survey data are presented for 31 different geometries. Detail survey data at the nozzle exit are given in contour plot format for five configurations. A data base is provided for a variable geometry concept that is a viable alternative to the more common pivoted vane variable geometry radial turbine.
Radial Inflow Turboexpander Redesign
William G. Price
2001-09-24
Steamboat Envirosystems, LLC (SELC) was awarded a grant in accordance with the DOE Enhanced Geothermal Systems Project Development. Atlas-Copco Rotoflow (ACR), a radial expansion turbine manufacturer, was responsible for the manufacturing of the turbine and the creation of the new computer program. SB Geo, Inc. (SBG), the facility operator, monitored and assisted ACR's activities as well as provided installation and startup assistance. The primary scope of the project is the redesign of an axial flow turbine to a radial inflow turboexpander to provide increased efficiency and reliability at an existing facility. In addition to the increased efficiency and reliability, the redesign includes an improved reduction gear design, and improved shaft seal design, and upgraded control system and a greater flexibility of application
ERIC Educational Resources Information Center
Stevens, Lori
2004-01-01
The author describes a lesson she did on abstract art with her high school art classes. She passed out a required step-by-step outline of the project process. She asked each of them to look at abstract art. They were to list five or six abstract artists they thought were interesting, narrow their list down to the one most personally intriguing,…
Batzer, T.H.; Call, W.R.
1989-01-24
This invention provides an all metal seal for vacuum or pressure vessels or systems. This invention does not use gaskets. The invention uses a flange which fits into a matching groove. Fluid pressure is applied in a chamber in the flange causing at least one of the flange walls to radially press against a side of the groove creating the seal between the flange wall and the groove side. 5 figs.
Batzer, Thomas H.; Call, Wayne R.
1989-01-01
This invention provides an all metal seal for vacuum or pressure vessels or systems. This invention does not use gaskets. The invention uses a flange which fits into a matching groove. Fluid pressure is applied in a chamber in the flange causing at least one of the flange walls to radially press against a side of the groove creating the seal between the flange wall and the groove side.
Radial Field Piezoelectric Diaphragms
NASA Technical Reports Server (NTRS)
Bryant, R. G.; Effinger, R. T., IV; Copeland, B. M., Jr.
2002-01-01
A series of active piezoelectric diaphragms were fabricated and patterned with several geometrically defined Inter-Circulating Electrodes "ICE" and Interdigitated Ring Electrodes "ICE". When a voltage potential is applied to the electrodes, the result is a radially distributed electric field that mechanically strains the piezoceramic along the Z-axis (perpendicular to the applied electric field). Unlike other piezoelectric bender actuators, these Radial Field Diaphragms (RFDs) strain concentrically yet afford high displacements (several times that of the equivalent Unimorph) while maintaining a constant circumference. One of the more intriguing aspects is that the radial strain field reverses itself along the radius of the RFD while the tangential strain remains relatively constant. The result is a Z-deflection that has a conical profile. This paper covers the fabrication and characterization of the 5 cm. (2 in.) diaphragms as a function of poling field strength, ceramic thickness, electrode type and line spacing, as well as the surface topography, the resulting strain field and displacement as a function of applied voltage at low frequencies. The unique features of these RFDs include the ability to be clamped about their perimeter with little or no change in displacement, the environmentally insulated packaging, and a highly repeatable fabrication process that uses commodity materials.
Antiproton compression and radial measurements
Andresen, G. B.; Bowe, P. D.; Hangst, J. S.; Bertsche, W.; Butler, E.; Charlton, M.; Humphries, A. J.; Jenkins, M. J.; Joergensen, L. V.; Madsen, N.; Werf, D. P. van der; Bray, C. C.; Chapman, S.; Fajans, J.; Povilus, A.; Wurtele, J. S.; Cesar, C. L.; Lambo, R.; Silveira, D. M.; Fujiwara, M. C.
2008-08-08
Control of the radial profile of trapped antiproton clouds is critical to trapping antihydrogen. We report detailed measurements of the radial manipulation of antiproton clouds, including areal density compressions by factors as large as ten, achieved by manipulating spatially overlapped electron plasmas. We show detailed measurements of the near-axis antiproton radial profile, and its relation to that of the electron plasma. We also measure the outer radial profile by ejecting antiprotons to the trap wall using an octupole magnet.
Community Development Abstracts.
ERIC Educational Resources Information Center
Agency for International Development (Dept. of State), Washington, DC.
This volume of 1,108 abstracts summarizes the majority of important works on community development during the last ten years. Part I contains abstracts of periodical literature and is classified into 19 sections, including general history, communications, community and area studies, decision-making, leadership, migration and settlement, social…
Leadership Abstracts, Volume 10.
ERIC Educational Resources Information Center
Milliron, Mark D., Ed.
1997-01-01
The abstracts in this series provide brief discussions of issues related to leadership, administration, professional development, technology, and education in community colleges. Volume 10 for 1997 contains the following 12 abstracts: (1) "On Community College Renewal" (Nathan L. Hodges and Mark D. Milliron); (2) "The Community College Niche in a…
Has Abstractness Been Resolved?
ERIC Educational Resources Information Center
Al-Omoush, Ahmad
1989-01-01
A discussion focusing on the abstractness of analysis in phonology, debated since the 1960s, describes the issue, reviews the literature on the subject, cites specific natural language examples, and examines the extent to which the issue has been resolved. An underlying representation is said to be abstract if it is different from the derived one,…
Designing for Mathematical Abstraction
ERIC Educational Resources Information Center
Pratt, Dave; Noss, Richard
2010-01-01
Our focus is on the design of systems (pedagogical, technical, social) that encourage mathematical abstraction, a process we refer to as "designing for abstraction." In this paper, we draw on detailed design experiments from our research on children's understanding about chance and distribution to re-present this work as a case study in designing…
ERIC Educational Resources Information Center
Black, William J.
1990-01-01
Discussion of automatic abstracting of technical papers focuses on a knowledge-based method that uses two sets of rules. Topics discussed include anaphora; text structure and discourse; abstracting techniques, including the keyword method and the indicator phrase method; and tools for text skimming. (27 references) (LRW)
ERIC Educational Resources Information Center
Johnson, Larry, Ed.
1995-01-01
The abstracts in this series provide two-page discussions of issues related to leadership, administration, and teaching in community colleges. The 12 abstracts for Volume 8, 1995, are: (1) "Redesigning the System To Meet the Workforce Training Needs of the Nation," by Larry Warford; (2) "The College President, the Board, and the Board Chair: A…
ERIC Educational Resources Information Center
Sutley, Jane
2010-01-01
Abstraction is, in effect, a simplification and reduction of shapes with an absence of detail designed to comprise the essence of the more naturalistic images being depicted. Without even intending to, young children consistently create interesting, and sometimes beautiful, abstract compositions. A child's creations, moreover, will always seem to…
ERIC Educational Resources Information Center
Kernan, Christine
2011-01-01
For this author, one of the most enjoyable aspects of teaching elementary art is the willingness of students to embrace the different styles of art introduced to them. In this article, she describes a project that allows upper-elementary students to learn about abstract art and the lives of some of the master abstract artists, implement the idea…
Journalism Abstracts. Vol. 15.
ERIC Educational Resources Information Center
Popovich, Mark N., Ed.
This book, the fifteenth volume of an annual publication, contains 373 abstracts of 52 doctoral and 321 master's theses from 50 colleges and universities. The abstracts are arranged alphabetically by author, with the doctoral dissertations appearing first. These cover such topics as advertising, audience analysis, content analysis of news issues…
ERIC Educational Resources Information Center
Johnson, Larry, Ed.
1996-01-01
The abstracts in this series provide two-page discussions of issues related to leadership, administration, professional development, technology, and education in community colleges. Volume 9 for 1996 includes the following 12 abstracts: (1) "Tech-Prep + School-To-Work: Working Together To Foster Educational Reform," (Roderick F. Beaumont); (2)…
Mathematical Abstraction through Scaffolding
ERIC Educational Resources Information Center
Ozmantar, Mehmet Fatih; Roper, Tom
2004-01-01
This paper examines the role of scaffolding in the process of abstraction. An activity-theoretic approach to abstraction in context is taken. This examination is carried out with reference to verbal protocols of two 17 year-old students working together on a task connected to sketching the graph of |f|x|)|. Examination of the data suggests that…
Rehder, B; Ross, B H
2001-09-01
Many studies have demonstrated the importance of the knowledge that interrelates features in people's mental representation of categories and that makes our conception of categories coherent. This article focuses on abstract coherent categories, coherent categories that are also abstract because they are defined by relations independently of any features. Four experiments demonstrate that abstract coherent categories are learned more easily than control categories with identical features and statistical structure, and also that participants induced an abstract representation of the category by granting category membership to exemplars with completely novel features. The authors argue that the human conceptual system is heavily populated with abstract coherent concepts, including conceptions of social groups, societal institutions, legal, political, and military scenarios, and many superordinate categories, such as classes of natural kinds. PMID:11550753
NASA Technical Reports Server (NTRS)
Owre, Sam; Shankar, Natarajan
1997-01-01
PVS (Prototype Verification System) is a general-purpose environment for developing specifications and proofs. This document deals primarily with the abstract datatype mechanism in PVS which generates theories containing axioms and definitions for a class of recursive datatypes. The concepts underlying the abstract datatype mechanism are illustrated using ordered binary trees as an example. Binary trees are described by a PVS abstract datatype that is parametric in its value type. The type of ordered binary trees is then presented as a subtype of binary trees where the ordering relation is also taken as a parameter. We define the operations of inserting an element into, and searching for an element in an ordered binary tree; the bulk of the report is devoted to PVS proofs of some useful properties of these operations. These proofs illustrate various approaches to proving properties of abstract datatype operations. They also describe the built-in capabilities of the PVS proof checker for simplifying abstract datatype expressions.
Abstract Interpreters for Free
NASA Astrophysics Data System (ADS)
Might, Matthew
In small-step abstract interpretations, the concrete and abstract semantics bear an uncanny resemblance. In this work, we present an analysis-design methodology that both explains and exploits that resemblance. Specifically, we present a two-step method to convert a small-step concrete semantics into a family of sound, computable abstract interpretations. The first step re-factors the concrete state-space to eliminate recursive structure; this refactoring of the state-space simultaneously determines a store-passing-style transformation on the underlying concrete semantics. The second step uses inference rules to generate an abstract state-space and a Galois connection simultaneously. The Galois connection allows the calculation of the "optimal" abstract interpretation. The two-step process is unambiguous, but nondeterministic: at each step, analysis designers face choices. Some of these choices ultimately influence properties such as flow-, field- and context-sensitivity. Thus, under the method, we can give the emergence of these properties a graph-theoretic characterization. To illustrate the method, we systematically abstract the continuation-passing style lambda calculus to arrive at two distinct families of analyses. The first is the well-known k-CFA family of analyses. The second consists of novel "environment-centric" abstract interpretations, none of which appear in the literature on static analysis of higher-order programs.
Radial Rydberg wavepacket maps
NASA Astrophysics Data System (ADS)
Zeibel, J. G.; Jones, R. R.
2001-04-01
Picosecond laser pulses have been used to excite radial Rydberg wavepackets in Ca. Time-delayed, unipolar, `half-cycle' electric field pulses are used to probe the evolution of the wavepackets as a continuous function of binding energy. The data provide three-dimensional maps of wavepacket recurrence probability versus binding energy versus time. A rescaling of the energy and time coordinate axes allows the visualization of the distinct difference between the initial oscillations of the wavepacket and those that occur at integer and fractional revivals.
Radial reflection diffraction tomography
Lehman, Sean K.
2012-12-18
A wave-based tomographic imaging method and apparatus based upon one or more rotating radially outward oriented transmitting and receiving elements have been developed for non-destructive evaluation. At successive angular locations at a fixed radius, a predetermined transmitting element can launch a primary field and one or more predetermined receiving elements can collect the backscattered field in a "pitch/catch" operation. A Hilbert space inverse wave (HSIW) algorithm can construct images of the received scattered energy waves using operating modes chosen for a particular application. Applications include, improved intravascular imaging, bore hole tomography, and non-destructive evaluation (NDE) of parts having existing access holes.
Radial Reflection diffraction tomorgraphy
Lehman, Sean K
2013-11-19
A wave-based tomographic imaging method and apparatus based upon one or more rotating radially outward oriented transmitting and receiving elements have been developed for non-destructive evaluation. At successive angular locations at a fixed radius, a predetermined transmitting element can launch a primary field and one or more predetermined receiving elements can collect the backscattered field in a "pitch/catch" operation. A Hilbert space inverse wave (HSIW) algorithm can construct images of the received scattered energy waves using operating modes chosen for a particular application. Applications include, improved intravascular imaging, bore hole tomography, and non-destructive evaluation (NDE) of parts having existing access holes.
Underground radial pipe network
Peterson, D.L.
1984-04-24
The network, useful in conducting fluids to underground sites, is an assembly of flexible pipes or tubes, suspended from and connected to a drill pipe. The flexible pipes, assembled in a bundle, are spring biased to flare outwardly in an arcuate manner when a releasable cap on the distal end of the bundle is removed. The assembled bundle is inserted into and lowered down a bore hole. When the cap is released, the pipes flare radially and outwardly. Fluid, pumped into and through the assembly, can be directed into the underground formation for various purposes.
Robertson, M.C.
1997-01-08
The project`s aim is to complete development of the Radial Cutting Torch, a pyrotechnic cutter, for use in all downhole tubular cutting operations in the petroleum industry. Project objectives are to redesign and pressure test nozzle seals to increase product quality, reliability, and manufacturability; improve the mechanical anchor to increase its temperature tolerance and its ability to function in a wider variety of wellbore fluids; and redesign and pressure test the RCT nozzle for operation at pressures from 10 to 20 ksi. The proposal work statement is included in the statement of work for the grant via this reference.
ERIC Educational Resources Information Center
Proceedings of the ASIS Annual Meeting, 1997
1997-01-01
Presents abstracts of SIG Sessions. Highlights include digital collections; information retrieval methods; public interest/fair use; classification and indexing; electronic publication; funding; globalization; information technology projects; interface design; networking in developing countries; metadata; multilingual databases; networked…
Automatic Abstraction in Planning
NASA Technical Reports Server (NTRS)
Christensen, J.
1991-01-01
Traditionally, abstraction in planning has been accomplished by either state abstraction or operator abstraction, neither of which has been fully automatic. We present a new method, predicate relaxation, for automatically performing state abstraction. PABLO, a nonlinear hierarchical planner, implements predicate relaxation. Theoretical, as well as empirical results are presented which demonstrate the potential advantages of using predicate relaxation in planning. We also present a new definition of hierarchical operators that allows us to guarantee a limited form of completeness. This new definition is shown to be, in some ways, more flexible than previous definitions of hierarchical operators. Finally, a Classical Truth Criterion is presented that is proven to be sound and complete for a planning formalism that is general enough to include most classical planning formalisms that are based on the STRIPS assumption.
1971 Annual Conference Abstracts
ERIC Educational Resources Information Center
Journal of Engineering Education, 1971
1971-01-01
Included are 112 abstracts listed under headings such as: acoustics, continuing engineering studies, educational research and methods, engineering design, libraries, liberal studies, and materials. Other areas include agricultural, electrical, mechanical, mineral, and ocean engineering. (TS)
2016-07-01
The peer-reviewed abstracts presented at the 73rd Annual Meeting of the ACPA are published as submitted by the authors. For financial conflict of interest disclosure, please visit http://meeting.acpa-cpf.org/disclosures.html. PMID:27447885
Abstracts of contributed papers
Not Available
1994-08-01
This volume contains 571 abstracts of contributed papers to be presented during the Twelfth US National Congress of Applied Mechanics. Abstracts are arranged in the order in which they fall in the program -- the main sessions are listed chronologically in the Table of Contents. The Author Index is in alphabetical order and lists each paper number (matching the schedule in the Final Program) with its corresponding page number in the book.
Radial Eigenmodes for a Toroidal Waveguide with Rectangular Cross Section
Rui Li
2012-07-01
In applying mode expansion to solve the CSR impedance for a section of toroidal vacuum chamber with rectangular cross section, we identify the eigenvalue problem for the radial eigenmodes which is different from that for cylindrical structures. In this paper, we present the general expressions of the radial eigenmodes, and discuss the properties of the eigenvalues on the basis of the Sturm-Liouville theory.
Radial evolution of the energy density of solar wind fluctuations
NASA Technical Reports Server (NTRS)
Zank, G. P.; Matthaeus, W. H.; Smith, C. W.
1995-01-01
On the basis of transport theories appropriate to a radially expanding solar wind, we describe new results for the radial evolution of the energy density in solar wind fluctuations at MHD scales. These models include the effects of 'mixing' and driving as well as the possibility of non-isotropic MHD turbulence. Implications of these results for solar wind heating, cosmic ray diffusion and interstellar pick-up ions will also be addressed.
Grundy, Brian R.
1981-01-01
The radial cold trap comprises a housing having a plurality of mesh bands disposed therein. The mesh bands comprise concentrically arranged bands of mesh with the mesh specific surface area of each band increasing from the outermost mesh band to the innermost mesh band. An inlet nozzle is attached to the outside section of the housing while an outlet nozzle is attached to the inner portion of the housing so as to be concentrically connected to the innermost mesh band. An inlet baffle having orifices therein may be disposed around the outermost mesh band and within the housing for directing the flow of the fluid from the inlet nozzle to the outermost mesh band in a uniform manner. The flow of fluid passes through each consecutive mesh band and into the outlet nozzle. The circular pattern of the symmetrically arranged mesh packing allows for better utilization of the entire cold trap volume.
Grundy, B.R.
1981-09-29
The radial cold trap comprises a housing having a plurality of mesh bands disposed therein. The mesh bands comprise concentrically arranged bands of mesh with the mesh specific surface area of each band increasing from the outermost mesh band to the innermost mesh band. An inlet nozzle is attached to the outside section of the housing while an outlet nozzle is attached to the inner portion of the housing so as to be concentrically connected to the innermost mesh band. An inlet baffle having orifices therein may be disposed around the outermost mesh band and within the housing for directing the flow of the fluid from the inlet nozzle to the outermost mesh band in a uniform manner. The flow of fluid passes through each consecutive mesh band and into the outlet nozzle. The circular pattern of the symmetrically arranged mesh packing allows for better utilization of the entire cold trap volume. 2 figs.
Metacognition and abstract reasoning.
Markovits, Henry; Thompson, Valerie A; Brisson, Janie
2015-05-01
The nature of people's meta-representations of deductive reasoning is critical to understanding how people control their own reasoning processes. We conducted two studies to examine whether people have a metacognitive representation of abstract validity and whether familiarity alone acts as a separate metacognitive cue. In Study 1, participants were asked to make a series of (1) abstract conditional inferences, (2) concrete conditional inferences with premises having many potential alternative antecedents and thus specifically conducive to the production of responses consistent with conditional logic, or (3) concrete problems with premises having relatively few potential alternative antecedents. Participants gave confidence ratings after each inference. Results show that confidence ratings were positively correlated with logical performance on abstract problems and concrete problems with many potential alternatives, but not with concrete problems with content less conducive to normative responses. Confidence ratings were higher with few alternatives than for abstract content. Study 2 used a generation of contrary-to-fact alternatives task to improve levels of abstract logical performance. The resulting increase in logical performance was mirrored by increases in mean confidence ratings. Results provide evidence for a metacognitive representation based on logical validity, and show that familiarity acts as a separate metacognitive cue. PMID:25416026
Thyra Abstract Interface Package
Energy Science and Technology Software Center (ESTSC)
2005-09-01
Thrya primarily defines a set of abstract C++ class interfaces needed for the development of abstract numerical atgorithms (ANAs) such as iterative linear solvers, transient solvers all the way up to optimization. At the foundation of these interfaces are abstract C++ classes for vectors, vector spaces, linear operators and multi-vectors. Also included in the Thyra package is C++ code for creating concrete vector, vector space, linear operator, and multi-vector subclasses as well as other utilitiesmore » to aid in the development of ANAs. Currently, very general and efficient concrete subclass implementations exist for serial and SPMD in-core vectors and multi-vectors. Code also currently exists for testing objects and providing composite objects such as product vectors.« less
Abstracting and indexing guide
U.S. Department of the Interior; Office of Water Resources Research
1974-01-01
These instructions have been prepared for those who abstract and index scientific and technical documents for the Water Resources Scientific Information Center (WRSIC). With the recent publication growth in all fields, information centers have undertaken the task of keeping the various scientific communities aware of current and past developments. An abstract with carefully selected index terms offers the user of WRSIC services a more rapid means for deciding whether a document is pertinent to his needs and professional interests, thus saving him the time necessary to scan the complete work. These means also provide WRSIC with a document representation or surrogate which is more easily stored and manipulated to produce various services. Authors are asked to accept the responsibility for preparing abstracts of their own papers to facilitate quick evaluation, announcement, and dissemination to the scientific community.
Radial gate hoist mechanisms mounted above radial gates, view to ...
Radial gate hoist mechanisms mounted above radial gates, view to the east - Wellton-Mohawk Irrigation System, Wasteway No. 1, Wellton-Mohawk Canal, North side of Wellton-Mohawk Canal, bounded by Gila River to North & the Union Pacific Railroad & Gila Mountains to south, Wellton, Yuma County, AZ
Gortais, Bernard
2003-01-01
In a given social context, artistic creation comprises a set of processes, which relate to the activity of the artist and the activity of the spectator. Through these processes we see and understand that the world is vaster than it is said to be. Artistic processes are mediated experiences that open up the world. A successful work of art expresses a reality beyond actual reality: it suggests an unknown world using the means and the signs of the known world. Artistic practices incorporate the means of creation developed by science and technology and change forms as they change. Artists and the public follow different processes of abstraction at different levels, in the definition of the means of creation, of representation and of perception of a work of art. This paper examines how the processes of abstraction are used within the framework of the visual arts and abstract painting, which appeared during a period of growing importance for the processes of abstraction in science and technology, at the beginning of the twentieth century. The development of digital platforms and new man-machine interfaces allow multimedia creations. This is performed under the constraint of phases of multidisciplinary conceptualization using generic representation languages, which tend to abolish traditional frontiers between the arts: visual arts, drama, dance and music. PMID:12903659
NASA Astrophysics Data System (ADS)
Silvis, G.
2015-12-01
(Abstract only) The Stanford/SARA SuperSid project offers an opportunity for adding data to the AAVSO SID Monitoring project. You can now build a SID antenna and monitoring setup for about $150. And with the SIDdatagrabber application you can easily re-purpose the data collected for the AAVSO.
ERIC Educational Resources Information Center
Potter, Lee Ann
2005-01-01
President Ronald Reagan nominated a woman to serve on the United States Supreme Court. He did so through a single-page form letter, completed in part by hand and in part by typewriter, announcing Sandra Day O'Connor as his nominee. While the document serves as evidence of a historic event, it is also a tangible illustration of abstract concepts…
ERIC Educational Resources Information Center
Wilson, Cynthia, Ed.
2001-01-01
Volume 4 of the League for Innovation in the Community College's Learning Abstracts include the following: (1) "Touching Students in the Digital Age: The Move Toward Learner Relationship Management (LRM)," by Mark David Milliron, which offers an overview of an organizing concept to help community colleges navigate the intersection between digital…
ERIC Educational Resources Information Center
Wilson, Cynthia, Ed.; Milliron, Mark David, Ed.
2002-01-01
This 2002 volume of Leadership Abstracts contains issue numbers 1-12. Articles include: (1) "Skills Certification and Workforce Development: Partnering with Industry and Ourselves," by Jeffrey A. Cantor; (2) "Starting Again: The Brookhaven Success College," by Alice W. Villadsen; (3) "From Digital Divide to Digital Democracy," by Gerardo E. de los…
ERIC Educational Resources Information Center
Doucette, Don, Ed.
1993-01-01
This document includes 10 issues of Leadership Abstracts (volume 6, 1993), a newsletter published by the League for Innovation in the Community College (California). The featured articles are: (1) "Reinventing Government" by David T. Osborne; (2) "Community College Workforce Training Programs: Expanding the Mission to Meet Critical Needs" by…
ERIC Educational Resources Information Center
Avraamidou, Antri; Monaghan, John; Walker, Aisha
2012-01-01
This paper examines the computer game play of an 11-year-old boy. In the course of building a virtual house he developed and used, without assistance, an artefact and an accompanying strategy to ensure that his house was symmetric. We argue that the creation and use of this artefact-strategy is a mathematical abstraction. The discussion…
ERIC Educational Resources Information Center
International Labour Office, Geneva (Switzerland).
The aim of the CIRF abstracts is to convey information about vocational training ideas, programs, experience, and experiments described in periodicals, books, and other publications and relating to operative personnel, supervisors, and technical and training staff in all sectors of economic activity. Information is also given on major trends in…
ERIC Educational Resources Information Center
Leadership Abstracts, 1999
1999-01-01
This document contains five Leadership Abstracts publications published February-December 1999. The article, "Teaching the Teachers: Meeting the National Teacher Preparation Challenge," authored by George R. Boggs and Sadie Bragg, examines the community college role and makes recommendations and a call to action for teacher education. "Chaos…
NASA Astrophysics Data System (ADS)
Simonsen, M.
2015-12-01
(Abstract only) Variable stars with close companions can be difficult to accurately measure and characterize. The companions can create misidentifications, which in turn can affect the perceived magnitudes, amplitudes, periods, and colors of the variable stars. We will show examples of these Double Trouble stars and the impact their close companions have had on our understanding of some of these variable stars.
ERIC Educational Resources Information Center
Levy, Steven
1985-01-01
Discusses Magazine Index's practice of assigning letter grades (sometimes inaccurate) to book, restaurant, and movie reviews, thus allowing patrons to get the point of the review from the index rather than the article itself, and argues that this situation is indicative of the larger problem of reliability of abstracts. (MBR)
ERIC Educational Resources Information Center
Engineering Education, 1976
1976-01-01
Presents the abstracts of 158 papers presented at the American Society for Engineering Education's annual conference at Knoxville, Tennessee, June 14-17, 1976. Included are engineering topics covering education, aerospace, agriculture, biomedicine, chemistry, computers, electricity, acoustics, environment, mechanics, and women. (SL)
Middlebrooks, E.J.
1982-01-01
Separate abstracts were prepared for the 31 chapters of this book which deals with all aspects of wastewater reuse. Design data, case histories, performance data, monitoring information, health information, social implications, legal and organizational structures, and background information needed to analyze the desirability of water reuse are presented. (KRM)
Reasoning abstractly about resources
NASA Technical Reports Server (NTRS)
Clement, B.; Barrett, A.
2001-01-01
r describes a way to schedule high level activities before distributing them across multiple rovers in order to coordinate the resultant use of shared resources regardless of how each rover decides how to perform its activities. We present an algorithm for summarizing the metric resource requirements of an abstract activity based n the resource usages of its potential refinements.
Humor, abstraction, and disbelief.
Hoicka, Elena; Jutsum, Sarah; Gattis, Merideth
2008-09-01
We investigated humor as a context for learning about abstraction and disbelief. More specifically, we investigated how parents support humor understanding during book sharing with their toddlers. In Study 1, a corpus analysis revealed that in books aimed at 1-to 2-year-olds, humor is found more often than other forms of doing the wrong thing including mistakes, pretense, lying, false beliefs, and metaphors. In Study 2, 20 parents read a book containing humorous and non-humorous pages to their 19-to 26-month-olds. Parents used a significantly higher percentage of high abstraction extra-textual utterances (ETUs) when reading the humorous pages. In Study 3, 41 parents read either a humorous or non-humorous book to their 18-to 24-month-olds. Parents reading the humorous book made significantly more ETUs coded for a specific form of high abstraction: those encouraging disbelief of prior utterances. Sharing humorous books thus increases toddlers' exposure to high abstraction and belief-based language. PMID:21585438
ERIC Educational Resources Information Center
Proceedings of the ASIS Annual Meeting, 1995
1995-01-01
Presents abstracts of 15 special interest group (SIG) sessions. Topics include navigation and information utilization in the Internet, natural language processing, automatic indexing, image indexing, classification, users' models of database searching, online public access catalogs, education for information professions, information services,…
2002 NASPSA Conference Abstracts.
ERIC Educational Resources Information Center
Journal of Sport & Exercise Psychology, 2002
2002-01-01
Contains abstracts from the 2002 conference of the North American Society for the Psychology of Sport and Physical Activity. The publication is divided into three sections: the preconference workshop, "Effective Teaching Methods in the Classroom;" symposia (motor development, motor learning and control, and sport psychology); and free…
ERIC Educational Resources Information Center
Journal of Engineering Education, 1972
1972-01-01
Includes abstracts of papers presented at the 80th Annual Conference of the American Society for Engineering Education. The broad areas include aerospace, affiliate and associate member council, agricultural engineering, biomedical engineering, continuing engineering studies, chemical engineering, civil engineering, computers, cooperative…
ERIC Educational Resources Information Center
League for Innovation in the Community Coll.
This document contains volume two of Learning Abstracts, a bimonthly newsletter from the League for Innovation in the Community College. Articles in these seven issues include: (1) "Get on the Fast Track to Learning: An Accelerated Associate Degree Option" (Gerardo E. de los Santos and Deborah J. Cruise); (2) "The Learning College: Both Learner…
ERIC Educational Resources Information Center
Nwabueze, Kenneth K.
2004-01-01
The current emphasis on flexible modes of mathematics delivery involving new information and communication technology (ICT) at the university level is perhaps a reaction to the recent change in the objectives of education. Abstract algebra seems to be one area of mathematics virtually crying out for computer instructional support because of the…
ERIC Educational Resources Information Center
Le Grice, Malcolm
A theoretical and historical account of the main preoccupations of makers of abstract films is presented in this book. The book's scope includes discussion of nonrepresentational forms as well as examination of experiments in the manipulation of time in films. The ten chapters discuss the following topics: art and cinematography, the first…
Radially composite piezoelectric ceramic tubular transducer in radial vibration.
Shuyu, Lin; Shuaijun, Wang
2011-11-01
The radially composite piezoelectric tubular transducer is studied. It is composed of radially poled piezoelectric and a long metal tube. The electro-mechanical equivalent circuit of the radially poled piezoelectric and metal tube in radial vibration is obtained. Based on the force and velocity boundary conditions, the six-port electro-mechanical equivalent circuit for the composite tubular transducer is given and the resonance/anti-resonance frequency equations are obtained. The relationship between the resonance frequency and the dimensions is analyzed. Numerically simulated results obtained by the finite element method are compared with those from the analytical method. Composite piezoelectric tubular transducers are designed and manufactured. The resonance/anti-resonance frequencies are measured, and it is shown that the theoretical results are in good agreement with the simulated and experimental results. It is expected that radially composite piezoelectric tubular transducers can be used as high-power ultrasonic radiators in ultrasonic applications, such as ultrasonic liquid processing. PMID:22083782
Valenzuela, Javier
2001-01-01
A radial flow heat exchanger (20) having a plurality of first passages (24) for transporting a first fluid (25) and a plurality of second passages (26) for transporting a second fluid (27). The first and second passages are arranged in stacked, alternating relationship, are separated from one another by relatively thin plates (30) and (32), and surround a central axis (22). The thickness of the first and second passages are selected so that the first and second fluids, respectively, are transported with laminar flow through the passages. To enhance thermal energy transfer between first and second passages, the latter are arranged so each first passage is in thermal communication with an associated second passage along substantially its entire length, and vice versa with respect to the second passages. The heat exchangers may be stacked to achieve a modular heat exchange assembly (300). Certain heat exchangers in the assembly may be designed slightly differently than other heat exchangers to address changes in fluid properties during transport through the heat exchanger, so as to enhance overall thermal effectiveness of the assembly.
Historical development of abstracting.
Skolnik, H
1979-11-01
The abstract, under a multitude of names, such as hypothesis, marginalia, abridgement, extract, digest, précis, resumé, and summary, has a long history, one which is concomitant with advancing scholarship. The progression of this history from the Sumerian civilization ca. 3600 B.C., through the Egyptian and Greek civilizations, the Hellenistic period, the Dark Ages, Middle Ages, Renaissance, and into the modern period is reviewed. PMID:399482
Generalized Abstract Symbolic Summaries
NASA Technical Reports Server (NTRS)
Person, Suzette; Dwyer, Matthew B.
2009-01-01
Current techniques for validating and verifying program changes often consider the entire program, even for small changes, leading to enormous V&V costs over a program s lifetime. This is due, in large part, to the use of syntactic program techniques which are necessarily imprecise. Building on recent advances in symbolic execution of heap manipulating programs, in this paper, we develop techniques for performing abstract semantic differencing of program behaviors that offer the potential for improved precision.
EBS Radionuclide Transport Abstraction
J. Prouty
2006-07-14
The purpose of this report is to develop and analyze the engineered barrier system (EBS) radionuclide transport abstraction model, consistent with Level I and Level II model validation, as identified in Technical Work Plan for: Near-Field Environment and Transport: Engineered Barrier System: Radionuclide Transport Abstraction Model Report Integration (BSC 2005 [DIRS 173617]). The EBS radionuclide transport abstraction (or EBS RT Abstraction) is the conceptual model used in the total system performance assessment (TSPA) to determine the rate of radionuclide releases from the EBS to the unsaturated zone (UZ). The EBS RT Abstraction conceptual model consists of two main components: a flow model and a transport model. Both models are developed mathematically from first principles in order to show explicitly what assumptions, simplifications, and approximations are incorporated into the models used in the TSPA. The flow model defines the pathways for water flow in the EBS and specifies how the flow rate is computed in each pathway. Input to this model includes the seepage flux into a drift. The seepage flux is potentially split by the drip shield, with some (or all) of the flux being diverted by the drip shield and some passing through breaches in the drip shield that might result from corrosion or seismic damage. The flux through drip shield breaches is potentially split by the waste package, with some (or all) of the flux being diverted by the waste package and some passing through waste package breaches that might result from corrosion or seismic damage. Neither the drip shield nor the waste package survives an igneous intrusion, so the flux splitting submodel is not used in the igneous scenario class. The flow model is validated in an independent model validation technical review. The drip shield and waste package flux splitting algorithms are developed and validated using experimental data. The transport model considers advective transport and diffusive transport
Reverse Radial Artery Flap Perforator Anatomy and Clinical Applications.
White, Colin P; Steve, Anna K; Buchel, Edward W; Hayakawa, Thomas E; Morris, Steven F
2016-09-01
The pedicled reverse radial forearm flap is a well-known option for the treatment of a variety of soft tissue wounds including dorsal hand wounds. We document the number, emerging diameter, length from origin, course, and location of all perforators of the radial artery in a series of 6 fresh human cadavers after whole body lead oxide and gelatin injection to confirm and comprehensively document the anatomy of the radial artery perforators. This data provide an anatomic basis for a modification to the reversed radial forearm flap used to decrease venous congestion in the postoperative period. Two case reports are presented to provide clinical demonstration of the importance of this modification. PMID:26678105
Resection arthroplasty after failure of a radial head prosthesis: a case report
Vanni, Stefania; Marenco, Stefano; Calò, Michel; Battiston, Bruno
2016-01-01
Abstract Radial head represents a secondary elbow stabilizer for varus-valgus and postero-lateral stress. In complex fractures, that cannot be synthesized, the presence of associated ligament injuries makes radial head replacement necessary to restore elbow stability. This study evaluates how the elbow responds to a prosthetic removal after a complex injury repair.
Focal spot analysis of radially polarized femtosecond laser pulses
NASA Astrophysics Data System (ADS)
Sun, Wenchao; Hu, Wenhua; Qi, Junli; Wang, Weiming; Liao, Jiali; Yi, Wenjun; Jia, Hui; Li, Xiujian
2014-09-01
When radially polarized light beams focus through high numerical-aperture lens, there will be a very strong longitudinal component of the light field near the focus. And, under the condition of certain system parameters, they can shape a spot which is over the focusing spot of the diffraction limit, which are the superiorities that linearly polarized light and circularly polarized light do not have. Besides, what we have found in the experiment is that radially polarized femtosecond laser pulses own the same superiorities, which provides the basis for using the focusing characteristics of radially polarized light beams under the condition of shorter and more powerful laser pulses. So far, although people have studied a lot on radially polarized light beams, this kind of light beams' focusing characters are rarely researched. What is worse, most research of its focusing characters still stays in the stage of theoretical simulation，and it seems that none of people have really studied it by the way of experiments. This article is precisely based on this. On the basis of predecessors' a lot of theoretical research, the article pays more attention on analyzing radially polarized light beams' focusing character through experiments. What's more, the article, based on femtosecond laser pulses, compares the differences of the focusing nature among linearly polarized light, circularly polarized light and radially polarized light. And it gets the conclusion that radially polarized femtosecond laser pulses have better focusing character in longitudinal light field, confirming the feasibility that radially polarized light beams can be used in the fields of pulling, catching, and accelerating particles, metal cutting and high-density storage.
Radial and azimuthal beam parameters.
Lumer, Yaakov; Moshe, Inon
2009-02-01
Global invariant parameters are introduced to characterize the radial and azimuthal content of totally polarized beams. Such parameters are written in terms of the second moments of the optical beam and are invariant in propagation through symmetric first-order optical systems described by the ABCD matrix. Since it was proven in the past that the usual definition for radial polarization is not invariant, such invariance is novel in characterizing the radial and azimuthal polarizations content of optical beams. The possibility of obtaining a pure mode from a given beam using the proposed parameters is discussed. PMID:19183626