Kayri, Murat
2015-01-01
The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…
International Nuclear Information System (INIS)
Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam
2007-01-01
Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported
Directory of Open Access Journals (Sweden)
Ali Mansourkhaki
2018-01-01
Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
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Agus Arif
2014-11-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.
Radial basis function neural network for power system load-flow
International Nuclear Information System (INIS)
Karami, A.; Mohammadi, M.S.
2008-01-01
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
The Matlab Radial Basis Function Toolbox
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Scott A. Sarra
2017-03-01
Full Text Available Radial Basis Function (RBF methods are important tools for scattered data interpolation and for the solution of Partial Differential Equations in complexly shaped domains. The most straight forward approach used to evaluate the methods involves solving a linear system which is typically poorly conditioned. The Matlab Radial Basis Function toolbox features a regularization method for the ill-conditioned system, extended precision floating point arithmetic, and symmetry exploitation for the purpose of reducing flop counts of the associated numerical linear algebra algorithms.
Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar
2018-06-01
The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.
Surface interpolation with radial basis functions for medical imaging
International Nuclear Information System (INIS)
Carr, J.C.; Beatson, R.K.; Fright, W.R.
1997-01-01
Radial basis functions are presented as a practical solution to the problem of interpolating incomplete surfaces derived from three-dimensional (3-D) medical graphics. The specific application considered is the design of cranial implants for the repair of defects, usually holes, in the skull. Radial basis functions impose few restrictions on the geometry of the interpolation centers and are suited to problems where interpolation centers do not form a regular grid. However, their high computational requirements have previously limited their use to problems where the number of interpolation centers is small (<300). Recently developed fast evaluation techniques have overcome these limitations and made radial basis interpolation a practical approach for larger data sets. In this paper radial basis functions are fitted to depth-maps of the skull's surface, obtained from X-ray computed tomography (CT) data using ray-tracing techniques. They are used to smoothly interpolate the surface of the skull across defect regions. The resulting mathematical description of the skull's surface can be evaluated at any desired resolution to be rendered on a graphics workstation or to generate instructions for operating a computer numerically controlled (CNC) mill
Fast radial basis functions for engineering applications
Biancolini, Marco Evangelos
2017-01-01
This book presents the first “How To” guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF: multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive – and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers’ cultural background, but also due to the numerical complex...
Point Set Denoising Using Bootstrap-Based Radial Basis Function.
Liew, Khang Jie; Ramli, Ahmad; Abd Majid, Ahmad
2016-01-01
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Point Set Denoising Using Bootstrap-Based Radial Basis Function.
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Khang Jie Liew
Full Text Available This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Mohri, Mehryar; Rostamizadeh, Afshin
2013-01-01
We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds generalize beyond standard margin-loss type bounds, allow for any convex and Lipschitz loss function, and admit a very simple proof.
Spherical radial basis functions, theory and applications
Hubbert, Simon; Morton, Tanya M
2015-01-01
This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging as one of the most promising techniques for solving problems where approximations are needed on the surface of a sphere. The aim of the book is to provide enough theoretical and practical details for the reader to be able to implement the SBF methods to solve real world problems. The authors stress the close connection between the theory of SBFs and that of the more well-known family of radial basis functions (RBFs), which are well-established tools for solving approximation theory problems on more general domains. The unique solvability of the SBF interpolation method for data fitting problems is established and an in-depth investigation of its accuracy is provided. Two chapters are devoted to partial differential equations (PDEs). One deals with the practical implementation of an SBF-based solution to an elliptic PDE and another which describes an SBF approach for solvi...
Energy Technology Data Exchange (ETDEWEB)
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
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.
Satisfiability of logic programming based on radial basis function neural networks
International Nuclear Information System (INIS)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-01-01
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems
Satisfiability of logic programming based on radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Doubly stochastic radial basis function methods
Yang, Fenglian; Yan, Liang; Ling, Leevan
2018-06-01
We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation. A careful operation count is provided in order to determine the ranges of all the parameters in our methods. The overhead cost for setting up the proposed DSRBF method is O (n2) for function recovery problems with n basis. Numerical experiments confirm that the proposed method not only outperforms constant shape parameter formulation (in terms of accuracy with comparable computational cost) but also the optimal LOOCV formulation (in terms of both accuracy and computational cost).
Learning Methods for Radial Basis Functions Networks
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Kudová, Petra
2005-01-01
Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005
Directory of Open Access Journals (Sweden)
Tatar Afshin
2016-03-01
Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.
Modeling multivariate time series on manifolds with skew radial basis functions.
Jamshidi, Arta A; Kirby, Michael J
2011-01-01
We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.
Radial Basis Function Based Quadrature over Smooth Surfaces
2016-03-24
Radial Basis Functions φ(r) Piecewise Smooth (Conditionally Positive Definite) MN Monomial |r|2m+1 TPS thin plate spline |r|2mln|r| Infinitely Smooth...smooth surfaces using polynomial interpolants, while [27] couples Thin - Plate Spline interpolation (see table 1) with Green’s integral formula [29
Pricing and simulation for real estate index options: Radial basis point interpolation
Gong, Pu; Zou, Dong; Wang, Jiayue
2018-06-01
This study employs the meshfree radial basis point interpolation (RBPI) for pricing real estate derivatives contingent on real estate index. This method combines radial and polynomial basis functions, which can guarantee the interpolation scheme with Kronecker property and effectively improve accuracy. An exponential change of variables, a mesh refinement algorithm and the Richardson extrapolation are employed in this study to implement the RBPI. Numerical results are presented to examine the computational efficiency and accuracy of our method.
Memristive Perceptron for Combinational Logic Classification
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Lidan Wang
2013-01-01
Full Text Available The resistance of the memristor depends upon the past history of the input current or voltage; so it can function as synapse in neural networks. In this paper, a novel perceptron combined with the memristor is proposed to implement the combinational logic classification. The relationship between the memristive conductance change and the synapse weight update is deduced, and the memristive perceptron model and its synaptic weight update rule are explored. The feasibility of the novel memristive perceptron for implementing the combinational logic classification (NAND, NOR, XOR, and NXOR is confirmed by MATLAB simulation.
Finite Size Scaling of Perceptron
Korutcheva, Elka; Tonchev, N.
2000-01-01
We study the first-order transition in the model of a simple perceptron with continuous weights and large, bit finite value of the inputs. Making the analogy with the usual finite-size physical systems, we calculate the shift and the rounding exponents near the transition point. In the case of a general perceptron with larger variety of inputs, the analysis only gives bounds for the exponents.
Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José
2014-02-01
The Direct Kernel Perceptron (DKP) (Fernández-Delgado et al., 2010) is a very simple and fast kernel-based classifier, related to the Support Vector Machine (SVM) and to the Extreme Learning Machine (ELM) (Huang, Wang, & Lan, 2011), whose α-coefficients are calculated directly, without any iterative training, using an analytical closed-form expression which involves only the training patterns. The DKP, which is inspired by the Direct Parallel Perceptron, (Auer et al., 2008), uses a Gaussian kernel and a linear classifier (perceptron). The weight vector of this classifier in the feature space minimizes an error measure which combines the training error and the hyperplane margin, without any tunable regularization parameter. This weight vector can be translated, using a variable change, to the α-coefficients, and both are determined without iterative calculations. We calculate solutions using several error functions, achieving the best trade-off between accuracy and efficiency with the linear function. These solutions for the α coefficients can be considered alternatives to the ELM with a new physical meaning in terms of error and margin: in fact, the linear and quadratic DKP are special cases of the two-class ELM when the regularization parameter C takes the values C=0 and C=∞. The linear DKP is extremely efficient and much faster (over a vast collection of 42 benchmark and real-life data sets) than 12 very popular and accurate classifiers including SVM, Multi-Layer Perceptron, Adaboost, Random Forest and Bagging of RPART decision trees, Linear Discriminant Analysis, K-Nearest Neighbors, ELM, Probabilistic Neural Networks, Radial Basis Function neural networks and Generalized ART. Besides, despite its simplicity and extreme efficiency, DKP achieves higher accuracies than 7 out of 12 classifiers, exhibiting small differences with respect to the best ones (SVM, ELM, Adaboost and Random Forest), which are much slower. Thus, the DKP provides an easy and fast way
On-line learning in radial basis functions networks
Freeman, Jason; Saad, David
1997-01-01
An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...
Exponential Convergence for Numerical Solution of Integral Equations Using Radial Basis Functions
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Zakieh Avazzadeh
2014-01-01
Full Text Available We solve some different type of Urysohn integral equations by using the radial basis functions. These types include the linear and nonlinear Fredholm, Volterra, and mixed Volterra-Fredholm integral equations. Our main aim is to investigate the rate of convergence to solve these equations using the radial basis functions which have normic structure that utilize approximation in higher dimensions. Of course, the use of this method often leads to ill-posed systems. Thus we propose an algorithm to improve the results. Numerical results show that this method leads to the exponential convergence for solving integral equations as it was already confirmed for partial and ordinary differential equations.
Radial basis function neural network in fault detection of automotive ...
African Journals Online (AJOL)
Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection
Complexity of Gaussian-Radial-Basis Networks Approximating Smooth Functions
Czech Academy of Sciences Publication Activity Database
Kainen, P.C.; Kůrková, Věra; Sanguineti, M.
2009-01-01
Roč. 25, č. 1 (2009), s. 63-74 ISSN 0885-064X R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : Gaussian-radial-basis-function networks * rates of approximation * model complexity * variation norms * Bessel and Sobolev norms * tractability of approximation Subject RIV: IN - Informatics, Computer Science Impact factor: 1.227, year: 2009
Application of radial basis neural network for state estimation of ...
African Journals Online (AJOL)
An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...
Organisms modeling: The question of radial basis function networks
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Muzy Alexandre
2014-01-01
Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.
On-line learning through simple perceptron learning with a margin.
Hara, Kazuyuki; Okada, Masato
2004-03-01
We analyze a learning method that uses a margin kappa a la Gardner for simple perceptron learning. This method corresponds to the perceptron learning when kappa = 0 and to the Hebbian learning when kappa = infinity. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the asymptotic property of the learning curve of this method through computer simulation and found that it was the same as for perceptron learning. We also investigated an adaptive margin control method.
New Method for Mesh Moving Based on Radial Basis Function Interpolation
De Boer, A.; Van der Schoot, M.S.; Bijl, H.
2006-01-01
A new point-by-point mesh movement algorithm is developed for the deformation of unstructured grids. The method is based on using radial basis function, RBFs, to interpolate the displacements of the boundary nodes to the whole flow mesh. A small system of equations has to be solved, only involving
Optimal properties of analog perceptrons with excitatory weights.
Directory of Open Access Journals (Sweden)
Claudia Clopath
Full Text Available The cerebellum is a brain structure which has been traditionally devoted to supervised learning. According to this theory, plasticity at the Parallel Fiber (PF to Purkinje Cell (PC synapses is guided by the Climbing fibers (CF, which encode an 'error signal'. Purkinje cells have thus been modeled as perceptrons, learning input/output binary associations. At maximal capacity, a perceptron with excitatory weights expresses a large fraction of zero-weight synapses, in agreement with experimental findings. However, numerous experiments indicate that the firing rate of Purkinje cells varies in an analog, not binary, manner. In this paper, we study the perceptron with analog inputs and outputs. We show that the optimal input has a sparse binary distribution, in good agreement with the burst firing of the Granule cells. In addition, we show that the weight distribution consists of a large fraction of silent synapses, as in previously studied binary perceptron models, and as seen experimentally.
On-line learning through simple perceptron with a margin
Hara, Kazuyuki; Okada, Masato
2003-01-01
We analyze a learning method that uses a margin $\\kappa$ {\\it a la} Gardner for simple perceptron learning. This method corresponds to the perceptron learning when $\\kappa=0$, and to the Hebbian learning when $\\kappa \\to \\infty$. Nevertheless, we found that the generalization ability of the method was superior to that of the perceptron and the Hebbian methods at an early stage of learning. We analyzed the asymptotic property of the learning curve of this method through computer simulation and...
Radial basis function and its application in tourism management
Hu, Shan-Feng; Zhu, Hong-Bin; Zhao, Lei
2018-05-01
In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.
An enhanced radial basis function network for short-term electricity price forecasting
International Nuclear Information System (INIS)
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
2010-01-01
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)
Parallel strategy for optimal learning in perceptrons
International Nuclear Information System (INIS)
Neirotti, J P
2010-01-01
We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.
ERROR VS REJECTION CURVE FOR THE PERCEPTRON
PARRONDO, JMR; VAN DEN BROECK, Christian
1993-01-01
We calculate the generalization error epsilon for a perceptron J, trained by a teacher perceptron T, on input patterns S that form a fixed angle arccos (J.S) with the student. We show that the error is reduced from a power law to an exponentially fast decay by rejecting input patterns that lie within a given neighbourhood of the decision boundary J.S = 0. On the other hand, the error vs. rejection curve epsilon(rho), where rho is the fraction of rejected patterns, is shown to be independent ...
Perceptron Genetic to Recognize Openning Strategy Ruy Lopez
Azmi, Zulfian; Mawengkang, Herman
2018-01-01
The application of Perceptron method is not effective for coding on hardware based systems because it is not real time learning. With Genetic algorithm approach in calculating and searching the best weight (fitness value) system will do learning only one iteration. And the results of this analysis were tested in the case of the introduction of the opening pattern of chess Ruy Lopez. The Analysis with Perceptron Model with Algorithm Approach Genetics from group Artificial Neural Network for open Ruy Lopez. The data is processed with base open chess, with step eight a position white Pion from end open chess. Using perceptron method have many input and one output process many weight and refraction until output equal goal. Data trained and test with software Matlab and system can recognize the chess opening Ruy Lopez or Not open Ruy Lopez with Real time.
Directory of Open Access Journals (Sweden)
Eyad K Almaita
2017-03-01
Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application. International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17
Learning unlearnable problems with perceptrons
Watkin, Timothy L. H.; Rau, Albrecht
1992-03-01
We study how well perceptrons learn to solve problems for which there is no perfect answer (the usual case), taking as examples a rule with a threshold, a rule in which the answer is not a monotonic function of the overlap between question and teacher, and a rule with many teachers (a ``hard'' unlearnable problem). In general there is a tendency for first-order transitions, even using spherical perceptrons, as networks compromise between conflicting requirements. Some existing learning schemes fail completely-occasionally even finding the worst possible solution; others are more successful. High-temperature learning seems more satisfactory than zero-temperature algorithms and avoids ``overlearning'' and ``overfitting,'' but care must be taken to avoid ``trapping'' in spurious free-energy minima. For some rules examples alone are not enough to learn from, and some prior information is required.
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
International Nuclear Information System (INIS)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-01-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
A metric for the Radial Basis Function Network - Application on Real Radar Data
Heiden, R. van der; Groen, F.C.A.
1996-01-01
A Radial Basis Functions (RBF) network for pattern recognition is considered. Classification with such a network is based on distances between patterns, so a metric is always present. Using real radar data, the Euclidean metric is shown to perform poorly - a metric based on the so called Box-Cox
A prediction method for the wax deposition rate based on a radial basis function neural network
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Ying Xie
2017-06-01
Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.
"Accelerated Perceptron": A Self-Learning Linear Decision Algorithm
Zuev, Yu. A.
2003-01-01
The class of linear decision rules is studied. A new algorithm for weight correction, called an "accelerated perceptron", is proposed. In contrast to classical Rosenblatt's perceptron this algorithm modifies the weight vector at each step. The algorithm may be employed both in learning and in self-learning modes. The theoretical aspects of the behaviour of the algorithm are studied when the algorithm is used for the purpose of increasing the decision reliability by means of weighted voting. I...
Learning and generalization errors for the 2D binary perceptron
Klymovskiy, A.
2005-01-01
The statistical mechanics model of the binary perceptron learning is considered. It is proved that under the regularity conditions learning and generalization errors for the binary perceptron with two inputs tend to 0 at the average; the first term of the asymptotics is provided; its behavior with
Neuronal spike sorting based on radial basis function neural networks
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Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Simulation of a Multidimensional Input Quantum Perceptron
Yamamoto, Alexandre Y.; Sundqvist, Kyle M.; Li, Peng; Harris, H. Rusty
2018-06-01
In this work, we demonstrate the improved data separation capabilities of the Multidimensional Input Quantum Perceptron (MDIQP), a fundamental cell for the construction of more complex Quantum Artificial Neural Networks (QANNs). This is done by using input controlled alterations of ancillary qubits in combination with phase estimation and learning algorithms. The MDIQP is capable of processing quantum information and classifying multidimensional data that may not be linearly separable, extending the capabilities of the classical perceptron. With this powerful component, we get much closer to the achievement of a feedforward multilayer QANN, which would be able to represent and classify arbitrary sets of data (both quantum and classical).
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. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
The construction of minimal multilayered perceptrons : a case study for sorting
Zwietering, P.J.; Aarts, E.H.L.; Wessels, J.
1993-01-01
We consider the construction of minimal multilayered perceptrons for solving combinatorial optimization problems. Though general in nature, the proposed construction method is presented as a case study for the sorting problem. The presentation starts with an O((n!)2) three-layered perceptron based
Directory of Open Access Journals (Sweden)
Jian Hu
2016-05-01
Full Text Available Uncertainties, including parametric uncertainties and uncertain nonlinearities, always exist in positioning servo systems driven by a hydraulic actuator, which would degrade their tracking accuracy. In this article, an integrated control scheme, which combines adaptive robust control together with radial basis function neural network–based disturbance observer, is proposed for high-accuracy motion control of hydraulic systems. Not only parametric uncertainties but also uncertain nonlinearities (i.e. nonlinear friction, external disturbances, and/or unmodeled dynamics are taken into consideration in the proposed controller. The above uncertainties are compensated, respectively, by adaptive control and radial basis function neural network, which are ultimately integrated together by applying feedforward compensation technique, in which the global stabilization of the controller is ensured via a robust feedback path. A new kind of parameter and weight adaptation law is designed on the basis of Lyapunov stability theory. Furthermore, the proposed controller obtains an expected steady performance even if modeling uncertainties exist, and extensive simulation results in various working conditions have proven the high performance of the proposed control scheme.
KLASIFIKASI WEBSITE MENGGUNAKAN ALGORITMA MULTILAYER PERCEPTRON
Directory of Open Access Journals (Sweden)
Nyoman Purnama
2014-12-01
Full Text Available Sistem klasifikasi merupakan proses temu balik informasi yang sangat bergantung dari elemen-elemen penyusunnya.Sistem ini banyak digunakan untuk mengatasi permasalahan segmentasi data. Klasifikasi dapat digunakan pada website sebagaimetode untuk mengelompokkan website. Website merupakan salah satu data yang memiliki informasi yang beraneka-ragam,sehingga pengelompokan data ini penting untuk diteliti. Sistem klasifikasi dimulai dengan melakukan proses pengumpulaninformasi dari halaman website (parsing dan untuk setiap hasil parsing dilakukan proses penghapusan kata henti, stemming,feature selection dengan tf-idf. Hasil dari proses ini berupa fitur yang menjadi inputan algoritma Multilayer Perceptron. Dalamalgoritma ini terjadi proses pembelajaran terhadap pola input masukan dan pembuatan bobot pelatihan. Bobot ini akandigunakan pada proses klasifikasi. Hasil dari penelitian menunjukkan bahwa algoritma Multilayer Perceptron dapatmenghasilkan klasifikasi website dengan akurasi yang bagus. Hal ini dibuktikan dengan beberapa tahapan penelitian yangberbeda dan didapatkan nilai akurasi rata-rata diatas 70%.
Recent advances in radial basis function collocation methods
Chen, Wen; Chen, C S
2014-01-01
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...
On-line learning of non-monotonic rules by simple perceptron
Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki
1997-01-01
We study the generalization ability of a simple perceptron which learns unlearnable rules. The rules are presented by a teacher perceptron with a non-monotonic transfer function. The student is trained in the on-line mode. The asymptotic behaviour of the generalization error is estimated under various conditions. Several learning strategies are proposed and improved to obtain the theoretical lower bound of the generalization error.
Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
2013-01-01
be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle
Institute of Scientific and Technical Information of China (English)
ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang
2012-01-01
Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function （Multiquadric, MQ for short） to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.
Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.
Directory of Open Access Journals (Sweden)
Michael R W Dawson
Full Text Available Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.
Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability
2017-01-01
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent’s environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned. PMID:28212422
Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.
Dawson, Michael R W; Gupta, Maya
2017-01-01
Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.
Machine learning of radial basis function neural network based on Kalman filter: Introduction
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Vuković Najdan L.
2014-01-01
Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.
Radial basis function neural networks with sequential learning MRAN and its applications
Sundararajan, N; Wei Lu Ying
1999-01-01
This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t
A learning rule for very simple universal approximators consisting of a single layer of perceptrons.
Auer, Peter; Burgsteiner, Harald; Maass, Wolfgang
2008-06-01
One may argue that the simplest type of neural networks beyond a single perceptron is an array of several perceptrons in parallel. In spite of their simplicity, such circuits can compute any Boolean function if one views the majority of the binary perceptron outputs as the binary output of the parallel perceptron, and they are universal approximators for arbitrary continuous functions with values in [0,1] if one views the fraction of perceptrons that output 1 as the analog output of the parallel perceptron. Note that in contrast to the familiar model of a "multi-layer perceptron" the parallel perceptron that we consider here has just binary values as outputs of gates on the hidden layer. For a long time one has thought that there exists no competitive learning algorithm for these extremely simple neural networks, which also came to be known as committee machines. It is commonly assumed that one has to replace the hard threshold gates on the hidden layer by sigmoidal gates (or RBF-gates) and that one has to tune the weights on at least two successive layers in order to achieve satisfactory learning results for any class of neural networks that yield universal approximators. We show that this assumption is not true, by exhibiting a simple learning algorithm for parallel perceptrons - the parallel delta rule (p-delta rule). In contrast to backprop for multi-layer perceptrons, the p-delta rule only has to tune a single layer of weights, and it does not require the computation and communication of analog values with high precision. Reduced communication also distinguishes our new learning rule from other learning rules for parallel perceptrons such as MADALINE. Obviously these features make the p-delta rule attractive as a biologically more realistic alternative to backprop in biological neural circuits, but also for implementations in special purpose hardware. We show that the p-delta rule also implements gradient descent-with regard to a suitable error measure
Learning rate and attractor size of the single-layer perceptron
International Nuclear Information System (INIS)
Singleton, Martin S.; Huebler, Alfred W.
2007-01-01
We study the simplest possible order one single-layer perceptron with two inputs, using the delta rule with online learning, in order to derive closed form expressions for the mean convergence rates. We investigate the rate of convergence in weight space of the weight vectors corresponding to each of the 14 out of 16 linearly separable rules. These vectors follow zigzagging lines through the piecewise constant vector field to their respective attractors. Based on our studies, we conclude that a single-layer perceptron with N inputs will converge in an average number of steps given by an Nth order polynomial in (t/l), where t is the threshold, and l is the size of the initial weight distribution. Exact values for these averages are provided for the five linearly separable classes with N=2. We also demonstrate that the learning rate is determined by the attractor size, and that the attractors of a single-layer perceptron with N inputs partition R N +R N
Directory of Open Access Journals (Sweden)
Jaime A. Echeverri A.
2007-07-01
Full Text Available En este trabajo se muestra la utilización de funciones de base radial de soporte compacto para la reconstrucción tridimensional de rostros. En trabajos anteriores se habían explorado diferentes técnicas y diferentes funciones de base radial para reconstrucción de superficies; ahora presentamos los algoritmos y los resultados de la utilización de funciones de base radial de soporte compacto las cuales presentan ventajas comparativas en términos del tiempo de construcción de un interpolante para la reconstrucción. Se presentan comparaciones con técnicas ampliamente utilizadas en este campo y se detalla el proceso global de reconstrucción de superficies.In previous works, we have explored several radial basis techniques and functions for the reconstruction of surfaces. We now present the use of compact support radial basis functions for the tri-dimensional reconstruction of human faces. Therefore, we present algorithms and results coming from the application of compact support radial basis functions which have revealed comparative advantages in terms of the amount of time needed for the construction of an interpolant to be used in the reconstruction. We are also presenting some comparisons with techniques widely used in this field and we explain in detail the global process for the surfaces reconstruction.
Piret, Cé cile
2012-01-01
Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper
Radial Basis Function Networks for Conversion of Sound Spectra
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Carlo Drioli
2001-03-01
Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
International Nuclear Information System (INIS)
Proriol, J.
1993-01-01
A cooperative multi-modular neural network architecture is presented: a Multi-Layer Perceptron (MLP), followed by a Radial Basis Function network (RBF). It is shown that, in the LEP experiment of electron-positron collision run at CERN, this architecture was able to outperform both a simple multi-layer perceptron, a multi-modular MLP+LVQ (LVQ: Learning Vector Quantization) and MLP+RBF trained sequentially and a conventional technique (Discriminant Analysis). (author). 10 refs., 2 figs
Adaptive radial basis function mesh deformation using data reduction
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
Perceptron-like computation based on biologically-inspired neurons with heterosynaptic mechanisms
Kaluza, Pablo; Urdapilleta, Eugenio
2014-10-01
Perceptrons are one of the fundamental paradigms in artificial neural networks and a key processing scheme in supervised classification tasks. However, the algorithm they provide is given in terms of unrealistically simple processing units and connections and therefore, its implementation in real neural networks is hard to be fulfilled. In this work, we present a neural circuit able to perform perceptron's computation based on realistic models of neurons and synapses. The model uses Wang-Buzsáki neurons with coupling provided by axodendritic and axoaxonic synapses (heterosynapsis). The main characteristics of the feedforward perceptron operation are conserved, which allows to combine both approaches: whereas the classical artificial system can be used to learn a particular problem, its solution can be directly implemented in this neural circuit. As a result, we propose a biologically-inspired system able to work appropriately in a wide range of frequencies and system parameters, while keeping robust to noise and error.
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.
Application of the recurrent multilayer perceptron in modeling complex process dynamics.
Parlos, A G; Chong, K T; Atiya, A F
1994-01-01
A nonlinear dynamic model is developed for a process system, namely a heat exchanger, using the recurrent multilayer perceptron network as the underlying model structure. The perceptron is a dynamic neural network, which appears effective in the input-output modeling of complex process systems. Dynamic gradient descent learning is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over a static learning algorithm used to train the same network. In developing the empirical process model the effects of actuator, process, and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response of various testing sets. Extensive model validation studies with signals that are encountered in the operation of the process system modeled, that is steps and ramps, indicate that the empirical model can substantially generalize operational transients, including accurate prediction of instabilities not in the training set. However, the accuracy of the model beyond these operational transients has not been investigated. Furthermore, online learning is necessary during some transients and for tracking slowly varying process dynamics. Neural networks based empirical models in some cases appear to provide a serious alternative to first principles models.
The Gaussian radial basis function method for plasma kinetic theory
Energy Technology Data Exchange (ETDEWEB)
Hirvijoki, E., E-mail: eero.hirvijoki@chalmers.se [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden); Candy, J.; Belli, E. [General Atomics, PO Box 85608, San Diego, CA 92186-5608 (United States); Embréus, O. [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden)
2015-10-30
Description of a magnetized plasma involves the Vlasov equation supplemented with the non-linear Fokker–Planck collision operator. For non-Maxwellian distributions, the collision operator, however, is difficult to compute. In this Letter, we introduce Gaussian Radial Basis Functions (RBFs) to discretize the velocity space of the entire kinetic system, and give the corresponding analytical expressions for the Vlasov and collision operator. Outlining the general theory, we also highlight the connection to plasma fluid theories, and give 2D and 3D numerical solutions of the non-linear Fokker–Planck equation. Applications are anticipated in both astrophysical and laboratory plasmas. - Highlights: • A radically new method to address the velocity space discretization of the non-linear kinetic equation of plasmas. • Elegant and physically intuitive, flexible and mesh-free. • Demonstration of numerical solution of both 2-D and 3-D non-linear Fokker–Planck relaxation problem.
A novel single neuron perceptron with universal approximation and XOR computation properties.
Lotfi, Ehsan; Akbarzadeh-T, M-R
2014-01-01
We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.
Using the Perceptron Algorithm to Find Consistent Hypotheses
Anthony, M.; Shawe-Taylor, J.
1993-01-01
The perceptron learning algorithm yields quite naturally an algorithm for finding a linearly separable boolean function consistent with a sample of such a function. Using the idea of a specifying sample, we give a simple proof that this algorithm is not efficient, in general.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Bae, C.H.; Blaabjerg, Frede
2005-01-01
A scheme to estimate the moment of inertia in a servo motor drive system at very low speed is proposed. The typical speed estimation scheme used in most servo systems operated at low speed is highly sensitive to variations in the moment of inertia. An observer that uses a radial basis function...
Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA
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Alisson C. D. de Souza
2014-09-01
Full Text Available This paper proposes a parallel fixed point radial basis function (RBF artificial neural network (ANN, implemented in a field programmable gate array (FPGA trained online with a least mean square (LMS algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx, with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.
SISTEM CERDAS DIAGNOSA PENYAKIT DALAM MENGGUNAKAN JARINGAN SYARAF TIRUAN DENGAN METODE PERCEPTRON
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Usman Usman
2017-12-01
Full Text Available Aplikasi Jaringan Syaraf Tiruan (JST untuk diagnosa Penyakit Dalam dengan metode perceptron. Aplikasi ini dibuat untuk mengetahui keakuratan diagnosa Penyakit Dalam menggunakan JST perceptron dan mengimplementasikan JST perceptron berdasarkan gejala-gejala Penyakit Dalam ke dalam matlab dengan tampilan Graphical User Interface (GUI. Penyakit Dalam yang dibahas sebanyak 9 Penyakit Dalam. Yaitu penyakit Asma bronchial, Anemia, Demam berdarah, Diabetes mellitus, Gagal Jantung, Tetanus, Hipertensi, Hepatitis, dan Tuberkolosis paru. Gejala penyakit dalam yang diambil berdasarkan data rekam medis penderita Penyakit Dalam dan data pasien yang berobat di Poli Penyakit Dalam RSUD Puri Husada Tembilahan. Dari hasil pelatihan (training terhadap 48 data, kecocokan keluaran jaringan dan target yang di inginkan yaitu penyakit dalam 100% dapat dikenali/sesuai dengan target yang di inginkan. Dan hasil pengujian data baru sebanyak 10 kali pengujian menghasilkan keluaran sekitar 78,9 % yang sesuai dengan target dan 21,1% yang tidak sesuai dengan target. Hasil pengujian dan pelatihan di implementasikan ke dalam GUI sebagai aplikasi dan user interface.
Czech Academy of Sciences Publication Activity Database
Bucha, B.; Bezděk, Aleš; Sebera, Josef; Janak, J.
2015-01-01
Roč. 36, č. 6 (2015), s. 773-801 ISSN 0169-3298 R&D Projects: GA ČR GA13-36843S Grant - others:SAV(SK) VEGA 1/0954/15 Institutional support: RVO:67985815 Keywords : spherical radial basis functions * spherical harmonics * geopotential Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 3.622, year: 2015
Quaternionic Multilayer Perceptron with Local Analyticity
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Nobuyuki Matsui
2012-11-01
Full Text Available A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this network. An error back-propagation algorithm is introduced for modifying the connection weights of the network.
Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks
Ali Reza Zirak; Sobhan Roshani
2016-01-01
A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...
Experimental characterization of the perceptron laser rangefinder
Kweon, I. S.; Hoffman, Regis; Krotkov, Eric
1991-01-01
In this report, we characterize experimentally a scanning laser rangefinder that employs active sensing to acquire three-dimensional images. We present experimental techniques applicable to a wide variety of laser scanners, and document the results of applying them to a device manufactured by Perceptron. Nominally, the sensor acquires data over a 60 deg x 60 deg field of view in 256 x 256 pixel images at 2 Hz. It digitizes both range and reflectance pixels to 12 bits, providing a maximum range of 40 m and a depth resolution of 1 cm. We present methods and results from experiments to measure geometric parameters including the field of view, angular scanning increments, and minimum sensing distance. We characterize qualitatively problems caused by implementation flaws, including internal reflections and range drift over time, and problems caused by inherent limitations of the rangefinding technology, including sensitivity to ambient light and surface material. We characterize statistically the precision and accuracy of the range measurements. We conclude that the performance of the Perceptron scanner does not compare favorably with the nominal performance, that scanner modifications are required, and that further experimentation must be conducted.
Out-of-equilibrium dynamical mean-field equations for the perceptron model
Agoritsas, Elisabeth; Biroli, Giulio; Urbani, Pierfrancesco; Zamponi, Francesco
2018-02-01
Perceptrons are the building blocks of many theoretical approaches to a wide range of complex systems, ranging from neural networks and deep learning machines, to constraint satisfaction problems, glasses and ecosystems. Despite their applicability and importance, a detailed study of their Langevin dynamics has never been performed yet. Here we derive the mean-field dynamical equations that describe the continuous random perceptron in the thermodynamic limit, in a very general setting with arbitrary noise and friction kernels, not necessarily related by equilibrium relations. We derive the equations in two ways: via a dynamical cavity method, and via a path-integral approach in its supersymmetric formulation. The end point of both approaches is the reduction of the dynamics of the system to an effective stochastic process for a representative dynamical variable. Because the perceptron is formally very close to a system of interacting particles in a high dimensional space, the methods we develop here can be transferred to the study of liquid and glasses in high dimensions. Potentially interesting applications are thus the study of the glass transition in active matter, the study of the dynamics around the jamming transition, and the calculation of rheological properties in driven systems.
Analysis of ensemble learning using simple perceptrons based on online learning theory
Miyoshi, Seiji; Hara, Kazuyuki; Okada, Masato
2005-03-01
Ensemble learning of K nonlinear perceptrons, which determine their outputs by sign functions, is discussed within the framework of online learning and statistical mechanics. One purpose of statistical learning theory is to theoretically obtain the generalization error. This paper shows that ensemble generalization error can be calculated by using two order parameters, that is, the similarity between a teacher and a student, and the similarity among students. The differential equations that describe the dynamical behaviors of these order parameters are derived in the case of general learning rules. The concrete forms of these differential equations are derived analytically in the cases of three well-known rules: Hebbian learning, perceptron learning, and AdaTron (adaptive perceptron) learning. Ensemble generalization errors of these three rules are calculated by using the results determined by solving their differential equations. As a result, these three rules show different characteristics in their affinity for ensemble learning, that is “maintaining variety among students.” Results show that AdaTron learning is superior to the other two rules with respect to that affinity.
Li, Bo; Rui, Xiaoting
2018-01-01
Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.
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Alireza Taravat
2015-02-01
Full Text Available A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.
Global sensitivity analysis using a Gaussian Radial Basis Function metamodel
International Nuclear Information System (INIS)
Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua
2016-01-01
Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.
Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics
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F. Cadini
2008-01-01
Full Text Available Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control applications in complex technologies such as those employed in the nuclear industry. In this paper, the nonlinear modeling capabilities of an infinite impulse response multilayer perceptron (IIR-MLP for nuclear dynamics are considered in comparison to static modeling by a finite impulse response multilayer perceptron (FIR-MLP and a conventional static MLP. The comparison is made with respect to the nonlinear dynamics of a nuclear reactor as investigated by IIR-MLP in a previous paper. The superior performance of the locally recurrent scheme is demonstrated.
Big geo data surface approximation using radial basis functions: A comparative study
Majdisova, Zuzana; Skala, Vaclav
2017-12-01
Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.
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Gisele Tessari Santos
2009-08-01
Full Text Available A large number of financial engineering problems involve non-linear equations with non-linear or time-dependent boundary conditions. Despite available analytical solutions, many classical and modified forms of the well-known Black-Scholes (BS equation require fast and accurate numerical solutions. This work introduces the radial basis function (RBF method as applied to the solution of the BS equation with non-linear boundary conditions, related to path-dependent barrier options. Furthermore, the diffusional method for solving advective-diffusive equations is explored as to its effectiveness to solve BS equations. Cubic and Thin-Plate Spline (TPS radial basis functions were employed and evaluated as to their effectiveness to solve barrier option problems. The numerical results, when compared against analytical solutions, allow affirming that the RBF method is very accurate and easy to be implemented. When the RBF method is applied, the diffusional method leads to the same results as those obtained from the classical formulation of Black-Scholes equation.Muitos problemas de engenharia financeira envolvem equações não-lineares com condições de contorno não-lineares ou dependentes do tempo. Apesar de soluções analíticas disponíveis, várias formas clássicas e modificadas da conhecida equação de Black-Scholes (BS requerem soluções numéricas rápidas e acuradas. Este trabalho introduz o método de função de base radial (RBF aplicado à solução da equação BS com condições de contorno não-lineares relacionadas a opções de barreira dependentes da trajetória. Além disso, explora-se o método difusional para solucionar equações advectivo-difusivas quanto à sua efetividade para solucionar equações BS. Utilizam-se funções de base radial Cúbica e Thin-Plate Spline (TPS, aplicadas à solução de problemas de opções de barreiras. Os resultados numéricos, quando comparados com as soluções analíticas, permitem afirmar
A perceptron network theorem prover for the propositional calculus
Drossaers, M.F.J.
In this paper a short introduction to neural networks and a design for a perceptron network theorem prover for the propositional calculus are presented. The theorem prover is a representation of a variant of the semantic tableau method, called the parallel tableau method, by a network of
Radial basis functions in mathematical modelling of flow boiling in minichannels
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Hożejowska Sylwia
2017-01-01
Full Text Available The paper addresses heat transfer processes in flow boiling in a vertical minichannel of 1.7 mm depth with a smooth heated surface contacting fluid. The heated element for FC-72 flowing in a minichannel was a 0.45 mm thick plate made of Haynes-230 alloy. An infrared camera positioned opposite the central, axially symmetric part of the channel measured the plate temperature. K-type thermocouples and pressure converters were installed at the inlet and outlet of the minichannel. In the study radial basis functions were used to solve a problem concerning heat transfer in a heated plate supplied with the controlled direct current. According to the model assumptions, the problem is treated as twodimensional and governed by the Poisson equation. The aim of the study lies in determining the temperature field and the heat transfer coefficient. The results were verified by comparing them with those obtained by the Trefftz method.
Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
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Wei Liu
2013-01-01
Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.
Quantum perceptron over a field and neural network architecture selection in a quantum computer.
da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa
2016-04-01
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.
Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.
1992-01-01
This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.
Radial Basis Function (RBF Interpolation and Investigating its Impact on Rainfall Duration Mapping
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Hassan Derakhshan
2012-01-01
Full Text Available The missing data in database must be reproduced primarily by appropriate interpolation techniques. Radial basis function (RBF interpolators can play a significant role in data completion of precipitation mapping. Five RBF techniques were engaged to be employed in compensating the missing data in event-wised dataset of Upper Paramatta River Catchment in the western suburbs of Sydney, Australia. The related shape parameter, C, of RBFs was optimized for first event of database during a cross-validation process. The Normalized mean square error (NMSE, percent average estimation error (PAEE and coefficient of determination (R2 were the statistics used as validation tools. Results showed that the multiquadric RBF technique with the least error, best suits compensation of the related database.
Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.
Hu, Yi-Chung
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.
Meshfree Local Radial Basis Function Collocation Method with Image Nodes
Energy Technology Data Exchange (ETDEWEB)
Baek, Seung Ki; Kim, Minjae [Pukyong National University, Busan (Korea, Republic of)
2017-07-15
We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem domain, similarly to the method of images in electrostatics, to perform collocation on the domain boundaries. We can thereby take into account the temperature profile as well as its gradients specified by boundary conditions at the same time, which holds true even for a node where two or more boundaries meet with different boundary conditions. We argue that the image method is computationally efficient when combined with the local RBF collocation method, whereas the addition of image nodes becomes very costly in case of the global collocation. We apply our modified method to a benchmark test of a boundary value problem, and find that this simple modification reduces the maximum error from the analytic solution significantly. The reduction is small for an initial value problem with simpler boundary conditions. We observe increased numerical instability, which has to be compensated for by a sufficient number of sample nodes and/or more careful parameter choices for time integration.
Multilayer Perceptron: Architecture Optimization and Training
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Hassan Ramchoun
2016-09-01
Full Text Available The multilayer perceptron has a large wide of classification and regression applications in many fields: pattern recognition, voice and classification problems. But the architecture choice has a great impact on the convergence of these networks. In the present paper we introduce a new approach to optimize the network architecture, for solving the obtained model we use the genetic algorithm and we train the network with a back-propagation algorithm. The numerical results assess the effectiveness of the theoretical results shown in this paper, and the advantages of the new modeling compared to the previous model in the literature.
International Nuclear Information System (INIS)
Vrankar, L.; Turk, G.; Runovc, F.; Kansa, E.J.
2006-01-01
Many heat-transfer problems involve a change of phase of material due to solidification or melting. Applications include: the safety studies of nuclear reactors (molten core concrete interaction), the drilling of high ice-content soil, the storage of thermal energy, etc. These problems are often called Stefan's or moving boundary value problems. Mathematically, the interface motion is expressed implicitly in an equation for the conservation of thermal energy at the interface (Stefan's conditions). This introduces a non-linear character to the system which treats each problem somewhat uniquely. The exact solution of phase change problems is limited exclusively to the cases in which e.g. the heat transfer regions are infinite or semi-infinite one dimensional-space. Therefore, solution is obtained either by approximate analytical solution or by numerical methods. Finite-difference methods and finite-element techniques have been used extensively for numerical solution of moving boundary problems. Recently, the numerical methods have focused on the idea of using a mesh-free methodology for the numerical solution of partial differential equations based on radial basis functions. In our case we will study solid-solid transformation. The numerical solutions will be compared with analytical solutions. Actually, in our work we will examine usefulness of radial basis functions (especially multiquadric-MQ) for one-dimensional Stefan's problems. The position of the moving boundary will be simulated by moving grid method. The resultant system of RBF-PDE will be solved by affine space decomposition. (author)
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Huaiqing Zhang
2014-01-01
Full Text Available The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.
Adiabatic superconducting cells for ultra-low-power artificial neural networks
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Andrey E. Schegolev
2016-10-01
Full Text Available We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.
ASSESSMENT OF LIBRARY USERS’ FEEDBACK USING MODIFIED MULTILAYER PERCEPTRON NEURAL NETWORKS
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K G Nandha Kumar
2017-07-01
Full Text Available An attempt has been made to evaluate the feedbacks of library users of four different libraries by using neural network based data mining techniques. This paper presents the results of a survey of users’ satisfactory level on four different libraries. The survey has been conducted among the users of four libraries of educational institutions of Kovai Medical Center Research and Educational Trust. Data were collected through questionnaires. Artificial neural network based data mining techniques are proposed and applied to assess the libraries in terms of level of satisfaction of users. In order to assess the users’ satisfaction level, two neural network techniques: Modified Multilayer Perceptron Network-Supervised and Modified Multilayer Perceptron Network-Unsupervised are proposed. The proposed techniques are compared with the conventional classification algorithm Multilayer Perceptron Neural Network and found better in overall performance. It is found that the quality of service provided by the libraries is highly good and users are highly satisfied with various aspects of library service. The Arts and Science College Library secured the maximum percent in terms of user satisfaction. This shows that the users’ satisfaction of ASCL is better than the other libraries. This study provides an insight into the actual quality and satisfactory level of users of libraries after proper assessment. It is strongly expected that the results will help library authorities to enhance services and quality in the near future.
First steps towards the realization of a double layer perceptron based on organic memristive devices
Emelyanov, A. V.; Lapkin, D. A.; Demin, V. A.; Erokhin, V. V.; Battistoni, S.; Baldi, G.; Dimonte, A.; Korovin, A. N.; Iannotta, S.; Kashkarov, P. K.; Kovalchuk, M. V.
2016-11-01
Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs) since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron) based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task) using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.
First steps towards the realization of a double layer perceptron based on organic memristive devices
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A. V. Emelyanov
2016-11-01
Full Text Available Memristors are widely considered as promising elements for the efficient implementation of synaptic weights in artificial neural networks (ANNs since they are resistors that keep memory of their previous conductive state. Whereas demonstrations of simple neural networks (e.g., a single-layer perceptron based on memristors already exist, the implementation of more complicated networks is more challenging and has yet to be reported. In this study, we demonstrate linearly nonseparable combinational logic classification (XOR logic task using a network implemented with CMOS-based neurons and organic memrisitive devices that constitutes the first step toward the realization of a double layer perceptron. We also show numerically the ability of such network to solve a principally analogue task which cannot be realized by digital devices. The obtained results prove the possibility to create a multilayer ANN based on memristive devices that paves the way for designing a more complex network such as the double layer perceptron.
Abdelkarim M. Ertiame; D. W. Yu; D. L. Yu; J. B. Gomm
2015-01-01
In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is emplo...
Generalization and capacity of extensively large two-layered perceptrons
International Nuclear Information System (INIS)
Rosen-Zvi, Michal; Kanter, Ido; Engel, Andreas
2002-01-01
The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, α c , at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different
THE ALGORITHM OF MESHFREE METHOD OF RADIAL BASIS FUNCTIONS IN TASKS OF UNDERGROUND HYDROMECHANICS
Directory of Open Access Journals (Sweden)
N. V. Medvid
2016-01-01
Full Text Available A Mathematical model of filtering consolidation in the body of soil dam with conduit andwashout zone in two-dimensional case is considered. The impact of such technogenic factors as temperature, salt concentration, subsidence of upper boundary and interior points of the dam with time is taken into account. The software to automate the calculation of numerical solution of the boundary problem by radial basis functions has been created, which enables to conduct numerical experiments by varying the input parameters and shape. The influence of the presence of conduit and washout zone on the pressure, temperature and concentration of salts in the dam body at different time intervals isinvestigated. A number of numerical experiments is conducted and the analysis of dam accidents is performed.
International Nuclear Information System (INIS)
Dalmasse, Kevin; Nychka, Douglas W.; Gibson, Sarah E.; Fan, Yuhong; Flyer, Natasha
2016-01-01
The Coronal Multichannel Polarimeter (CoMP) routinely performs coronal polarimetric measurements using the Fe XIII 10747 and 10798 lines, which are sensitive to the coronal magnetic field. However, inverting such polarimetric measurements into magnetic field data is a difficult task because the corona is optically thin at these wavelengths and the observed signal is therefore the integrated emission of all the plasma along the line of sight. To overcome this difficulty, we take on a new approach that combines a parameterized 3D magnetic field model with forward modeling of the polarization signal. For that purpose, we develop a new, fast and efficient, optimization method for model-data fitting: the Radial-basis-functions Optimization Approximation Method (ROAM). Model-data fitting is achieved by optimizing a user-specified log-likelihood function that quantifies the differences between the observed polarization signal and its synthetic/predicted analog. Speed and efficiency are obtained by combining sparse evaluation of the magnetic model with radial-basis-function (RBF) decomposition of the log-likelihood function. The RBF decomposition provides an analytical expression for the log-likelihood function that is used to inexpensively estimate the set of parameter values optimizing it. We test and validate ROAM on a synthetic test bed of a coronal magnetic flux rope and show that it performs well with a significantly sparse sample of the parameter space. We conclude that our optimization method is well-suited for fast and efficient model-data fitting and can be exploited for converting coronal polarimetric measurements, such as the ones provided by CoMP, into coronal magnetic field data.
Efficient learning algorithm for quantum perceptron unitary weights
Seow, Kok-Leong; Behrman, Elizabeth; Steck, James
2015-01-01
For the past two decades, researchers have attempted to create a Quantum Neural Network (QNN) by combining the merits of quantum computing and neural computing. In order to exploit the advantages of the two prolific fields, the QNN must meet the non-trivial task of integrating the unitary dynamics of quantum computing and the dissipative dynamics of neural computing. At the core of quantum computing and neural computing lies the qubit and perceptron, respectively. We see that past implementat...
Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks
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Zhihong Liao
2017-11-01
Full Text Available A radial basis function network (RBFN method is proposed to reconstruct daily Sea surface temperatures (SSTs with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI v2 daily 0.25° SST (OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.
Zhang, Zhuoyong; Wang, Yamin; Fan, Guoqiang; Harrington, Peter de B
2007-01-01
Artificial neural networks have gained much attention in recent years as fast and flexible methods for quality control in traditional medicine. Near-infrared (NIR) spectroscopy has become an accepted method for the qualitative and quantitative analyses of traditional Chinese medicine since it is simple, rapid, and non-destructive. The present paper describes a method by which to discriminate official and unofficial rhubarb samples using three layer perceptron neural networks applied to NIR data. Multilayer perceptron neural networks were trained with back propagation, delta-bar-delta and quick propagation algorithms. Results obtained using these methods were all satisfactory, but the best outcomes were obtained with the delta-bar-delta algorithm.
Higher-order probabilistic perceptrons as Bayesian inference engines
International Nuclear Information System (INIS)
Clark, J.W.; Ristig, M.L.
1994-08-01
This letter makes explicit a structural connection between the Bayes optimal classifier operating on K binary input variables and corresponding two-layer perceptron having normalized output activities and couplings from input to output units of all orders up to K. Given a large and unbiased training set and an effective learning algorithm, such a neural network should be able to learn the statistics of the classification problem and match the a posteriori probabilities given by the Bayes optimal classifier. (author). 18 refs
Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks
Czech Academy of Sciences Publication Activity Database
Kůrková, Věra
2018-01-01
Roč. 29, č. 7 (2018), s. 305-315 ISSN 0941-0643 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : shallow and deep networks * model complexity and sparsity * signum perceptron networks * finite mappings * variational norms * Hadamard matrices Subject RIV: IN - Informatics, Computer Science Impact factor: 2.505, year: 2016
Directory of Open Access Journals (Sweden)
Jaime Alberto Echeverri Arias
2009-07-01
Full Text Available La eliminación del ruido impulsivo es un problema clásico del procesado no lineal para el mejoramiento de imágenes y las funciones de base radial de soporte global son útiles para enfrentarlo. Este trabajo presenta una técnica de interpolación que disminuye eficientemente el ruido impulsivo en imágenes, mediante el uso de interpolante obtenido por funciones de base radial en el marco de la investigación enfocada en el desarrollo de un Sistema de recuperación de imágenes de recursos acuáticos amazónicos. Esta técnica primero etiqueta los píxeles de la imagen que son ruidosos y, mediante la interpolación, genera un valor de reconstrucción de dicho píxel usando sus vecinos. Los resultados obtenidos son comparables y muchas veces mejores que otras técnicas ya publicadas y reconocidas. Según el análisis de resultados, se puede aplicar a imágenes con altas tasas de ruido, manteniendo un bajo error de reconstrucción de los píxeles "ruidosos", así como la calidad visual.Global support radial base functions are effective in eliminating impulsive noise in non-linear processing. This paper introduces an interpolation technique which efficiently reduces image impulsive noise by means of an interpolant obtained through radial base functions. These functions have been used in a research project designed to develop a system for the recovery of images of Amazonian aquatic resources. This technique starts with the tagging by interpolation of noisy image pixels. Thus, a value of reconstruction for the noisy pixels is generated using neighboring pixels. The results obtained with this technique have proved comparable and often better than those obtained with previously known techniques. According to results analysis, this technique can be successfully applied on images with high noise levels. The results are low error in noisy pixel reconstruction and better visual quality.
Probabilistic Lower Bounds for Approximation by Shallow Perceptron Networks
Czech Academy of Sciences Publication Activity Database
Kůrková, Věra; Sanguineti, M.
2017-01-01
Roč. 91, July (2017), s. 34-41 ISSN 0893-6080 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : shallow networks * perceptrons * model complexity * lower bounds on approximation rates * Chernoff-Hoeffding bounds Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 5.287, year: 2016
Channel Equalization Using Multilayer Perceptron Networks
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Saba Baloch
2012-07-01
Full Text Available In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks. The simulated network is a multilayer feedforward Perceptron ANN, which has been trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network. This paper presents a very effective method for blind channel equalization, being more efficient than the pre-existing algorithms. The obtained results show a visible reduction in the noise content.
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Flávio Clésio Silva de Souza
2014-06-01
Full Text Available The purpose of the present research is to apply a Multilayer Perceptron (MLP neural network technique to create classification models from a portfolio of Non-Performing Loans (NPLs to classify this type of credit derivative. These credit derivatives are characterized as the amount of loans that were not paid and are already overdue more than 90 days. Since these titles are, because of legislative motives, moved by losses, Credit Rights Investment Funds (FDIC performs the purchase of these debts and the recovery of the credits. Using the Multilayer Perceptron (MLP architecture of Artificial Neural Network (ANN, classification models regarding the posterior recovery of these debts were created. To evaluate the performance of the models, evaluation metrics of classification relating to the neural networks with different architectures were presented. The results of the classifications were satisfactory, given the classification models were successful in the presented economics costs structure.
Multi-modular neural networks for the classification of e+e- hadronic events
International Nuclear Information System (INIS)
Proriol, J.
1994-01-01
Some multi-modular neural network methods of classifying e + e - hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)
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S.M. Hosseini-Moghari
2016-10-01
Full Text Available Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding drought management and its consequences alleviation, drought forecasting must be taken into account (11. The current research employed multi-layer perceptron (MLP, adaptive neuro-fuzzy inference system (ANFIS, radial basis function (RBF and general regression neural network (GRNN. It is interesting to note that, there has not been any record of applying GRNN in drought forecasting. Materials and Methods: Throughout this paper, Standard Precipitation Index (SPI was the basis of drought forecasting. To do so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. To provide short-term, mid-term, and long-term drought analysis; SPI for 1, 3, 6, 9, 12, and 24 months was evaluated. SPI evaluation benefited from four statistical distributions, namely, Gamma, Normal, Log-normal, and Weibull along with Kolmogrov-Smirnov (K-S test. Later, to compare the capabilities of four utilized neural networks for drought forecasting; MLP, ANFIS, RBF, and GRNN were applied. MLP as a multi-layer network, which has a sigmoid activation function in hidden layer plus linear function in output layer, can be considered as a powerful regressive tool. ANFIS besides adaptive neuro networks, employed fuzzy logic. RBF, the foundation of radial basis networks, is a three-layer network with Gaussian function in its hidden layer, and a linear function in the output layer. GRNN is another type of RBF which is used for radial basis regressive problems. The performance criteria of the research were as follows: Correlation (R2, Root Mean Square Error (RMSE, Mean Absolute Error (MAE. Results Discussion: According to statistical distribution
A radial basis classifier for the automatic detection of aspiration in children with dysphagia
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Blain Stefanie
2006-07-01
Full Text Available Abstract Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion
Time-scale invariance as an emergent property in a perceptron with realistic, noisy neurons.
Buhusi, Catalin V; Oprisan, Sorinel A
2013-05-01
In most species, interval timing is time-scale invariant: errors in time estimation scale up linearly with the estimated duration. In mammals, time-scale invariance is ubiquitous over behavioral, lesion, and pharmacological manipulations. For example, dopaminergic drugs induce an immediate, whereas cholinergic drugs induce a gradual, scalar change in timing. Behavioral theories posit that time-scale invariance derives from particular computations, rules, or coding schemes. In contrast, we discuss a simple neural circuit, the perceptron, whose output neurons fire in a clockwise fashion based on the pattern of coincidental activation of its input neurons. We show numerically that time-scale invariance emerges spontaneously in a perceptron with realistic neurons, in the presence of noise. Under the assumption that dopaminergic drugs modulate the firing of input neurons, and that cholinergic drugs modulate the memory representation of the criterion time, we show that a perceptron with realistic neurons reproduces the pharmacological clock and memory patterns, and their time-scale invariance, in the presence of noise. These results suggest that rather than being a signature of higher order cognitive processes or specific computations related to timing, time-scale invariance may spontaneously emerge in a massively connected brain from the intrinsic noise of neurons and circuits, thus providing the simplest explanation for the ubiquity of scale invariance of interval timing. Copyright © 2013 Elsevier B.V. All rights reserved.
Learning from correlated patterns by simple perceptrons
Energy Technology Data Exchange (ETDEWEB)
Shinzato, Takashi; Kabashima, Yoshiyuki [Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8502 (Japan)], E-mail: shinzato@sp.dis.titech.ac.jp, E-mail: kaba@dis.titech.ac.jp
2009-01-09
Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance.
Learning from correlated patterns by simple perceptrons
Shinzato, Takashi; Kabashima, Yoshiyuki
2009-01-01
Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance.
Learning from correlated patterns by simple perceptrons
International Nuclear Information System (INIS)
Shinzato, Takashi; Kabashima, Yoshiyuki
2009-01-01
Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance
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.
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Ángel Gutiérrez
2015-04-01
Full Text Available The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space. Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network. The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique. A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.
General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results
Czech Academy of Sciences Publication Activity Database
Šíma, Jiří; Orponen, P.
2003-01-01
Roč. 15, č. 12 (2003), s. 2727-2778 ISSN 0899-7667 R&D Projects: GA AV ČR IAB2030007; GA ČR GA201/02/1456 Institutional research plan: AV0Z1030915 Keywords : computational power * computational complexity * perceptrons * radial basis functions * spiking neurons * feedforward networks * reccurent networks * probabilistic computation * analog computation Subject RIV: BA - General Mathematics Impact factor: 2.747, year: 2003
Gaussian Radial Basis Function for Efficient Computation of Forest Indirect Illumination
Abbas, Fayçal; Babahenini, Mohamed Chaouki
2018-06-01
Global illumination of natural scenes in real time like forests is one of the most complex problems to solve, because the multiple inter-reflections between the light and material of the objects composing the scene. The major problem that arises is the problem of visibility computation. In fact, the computing of visibility is carried out for all the set of leaves visible from the center of a given leaf, given the enormous number of leaves present in a tree, this computation performed for each leaf of the tree which also reduces performance. We describe a new approach that approximates visibility queries, which precede in two steps. The first step is to generate point cloud representing the foliage. We assume that the point cloud is composed of two classes (visible, not-visible) non-linearly separable. The second step is to perform a point cloud classification by applying the Gaussian radial basis function, which measures the similarity in term of distance between each leaf and a landmark leaf. It allows approximating the visibility requests to extract the leaves that will be used to calculate the amount of indirect illumination exchanged between neighbor leaves. Our approach allows efficiently treat the light exchanges in the scene of a forest, it allows a fast computation and produces images of good visual quality, all this takes advantage of the immense power of computation of the GPU.
International Nuclear Information System (INIS)
Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin
2006-01-01
High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)
Wang, Zhiheng
2014-12-10
A meshless local radial basis function method is developed for two-dimensional incompressible Navier-Stokes equations. The distributed nodes used to store the variables are obtained by the philosophy of an unstructured mesh, which results in two main advantages of the method. One is that the unstructured nodes generation in the computational domain is quite simple, without much concern about the mesh quality; the other is that the localization of the obtained collocations for the discretization of equations is performed conveniently with the supporting nodes. The algebraic system is solved by a semi-implicit pseudo-time method, in which the convective and source terms are explicitly marched by the Runge-Kutta method, and the diffusive terms are implicitly solved. The proposed method is validated by several benchmark problems, including natural convection in a square cavity, the lid-driven cavity flow, and the natural convection in a square cavity containing a circular cylinder, and very good agreement with the existing results are obtained.
Hong, Xia
2006-07-01
In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.
Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs
International Nuclear Information System (INIS)
Bollig, Evan F.; Flyer, Natasha; Erlebacher, Gordon
2012-01-01
This paper presents parallelization strategies for the radial basis function-finite difference (RBF-FD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation and scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the first implementation of the RBF-FD method to leverage GPU accelerators for the solution of PDEs. Additionally, this implementation is the first to span both multiple CPUs and multiple GPUs. OpenCL kernels target the GPUs and inter-processor communication and synchronization is managed by the Message Passing Interface (MPI). We verify our implementation of the RBF-FD method with two hyperbolic PDEs on the sphere, and demonstrate up to 9x speedup on a commodity GPU with unoptimized kernel implementations. On a high performance cluster, the method achieves up to 7x speedup for the maximum problem size of 27,556 nodes.
Learning by random walks in the weight space of the Ising perceptron
International Nuclear Information System (INIS)
Huang, Haiping; Zhou, Haijun
2010-01-01
Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- or double-weight flips within the compatible weight configuration space of the earlier learned patterns. This process is able to reach a storage capacity of α≈0.63 for pattern length N = 101 and α≈0.41 for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of α≈0.80 for N = 101 and α≈0.42 for N = 1001. We found that, for a given learning task, the solutions constructed by the random walk learning process are separated by a typical Hamming distance, which decreases with the constraint density α of the learning task; at a fixed value of α, the width of the Hamming distance distribution decreases with N
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.
Kumudha, P; Venkatesan, R
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.
Analysis of 7Be behaviour in the air by using a multilayer perceptron neural network
International Nuclear Information System (INIS)
Samolov, A.; Dragović, S.; Daković, M.; Bačić, G.
2014-01-01
A multilayer perceptron artificial neural network (ANN) model for the prediction of the 7 Be behaviour in the air as the function of meteorological parameters was developed. The model was optimized and tested using 7 Be activity concentrations obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009–2011 and meteorological data for the same period. Good correlation (r = 0.91) between experimental values of 7 Be activity concentrations and those predicted by ANN was obtained. The good performance of the model in prediction of 7 Be activity concentrations could provide basis for construction of models which would forecast behaviour of other airborne radionuclides. - Highlights: • Neural network analysis was used to predict airborne 7 Be activity using meteorological parameters as inputs. • Strong correlation between calculated and measured activities was found. • Obtained results can help in construction of a general model of 7 Be activity variation in air
Directory of Open Access Journals (Sweden)
Yunfeng Wu
2014-01-01
Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.
Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
Directory of Open Access Journals (Sweden)
Murat Cuhadar
2014-03-01
Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.
Directory of Open Access Journals (Sweden)
Iman Aghayan
2012-11-01
Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.
Radial basis function networks applied to DNBR calculation in digital core protection systems
International Nuclear Information System (INIS)
Lee, Gyu-Cheon; Heung Chang, Soon
2003-01-01
The nuclear power plant has to be operated with sufficient margin from the specified DNBR limit for assuring its safety. The digital core protection system calculates on-line real-time DNBR by using a complex subchannel analysis program, and triggers a reliable reactor shutdown if the calculated DNBR approaches the specified limit. However, it takes a relatively long calculation time even for a steady state condition, which may have an adverse effect on the operation flexibility. To overcome the drawback, a new method using a radial basis function network is presented in this paper. Nonparametric training approach is utilized, which shows dramatic reduction of the training time, no tedious heuristic process for optimizing parameters, and no local minima problem during the training. The test results show that the predicted DNBR is within about ±2% deviation from the target DNBR for the fixed axial flux shape case. For the variable axial flux case including severely skewed shapes that appeared during accidents, the deviation is within about ±10%. The suggested method could be the alternative that can calculate DNBR very quickly while guaranteeing the plant safety
Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko
2018-04-01
Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.
Efficient training of multilayer perceptrons using principal component analysis
International Nuclear Information System (INIS)
Bunzmann, Christoph; Urbanczik, Robert; Biehl, Michael
2005-01-01
A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior
Chien, Ying-Ren; Chen, Jie-Wei; Xu, Sendren Sheng-Dong
2018-01-01
For power-line-based sensor networks, impulsive noise (IN) will dramatically degrade the data transmission rate in the power line. In this paper, we present a multilayer perceptron (MLP)-based approach to detect IN in orthogonal frequency
Low-cost autonomous perceptron neural network inspired by quantum computation
Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud
2017-11-01
Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.
GRACE L1b inversion through a self-consistent modified radial basis function approach
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
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Piret, Cécile
2012-05-01
Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper, we investigate methods to solve PDEs on arbitrary stationary surfaces embedded in . R3 using the RBF method. We present three RBF-based methods that easily discretize surface differential operators. We take advantage of the meshfree character of RBFs, which give us a high accuracy and the flexibility to represent the most complex geometries in any dimension. Two out of the three methods, which we call the orthogonal gradients (OGr) methods are the result of our work and are hereby presented for the first time. © 2012 Elsevier Inc.
Energy Technology Data Exchange (ETDEWEB)
Permoon, M. R.; Haddadpour, H. [Sharif University of Tech, Tehran (Iran, Islamic Republic of); Rashidinia, J.; Parsa, A.; Salehi, R. [Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)
2016-07-15
In this paper, the forced vibrations of the fractional viscoelastic beam with the Kelvin-Voigt fractional order constitutive relationship is studied. The equation of motion is derived from Newton's second law and the Galerkin method is used to discretize the equation of motion in to a set of linear ordinary differential equations. For solving the discretized equations, the radial basis functions and Sinc quadrature rule are used. In order to show the effectiveness and accuracy of this method, some test problem are considered, and it is shown that the obtained results are in very good agreement with exact solution. In the following, the proposed numerical solution is applied to exploring the effects of fractional parameters on the response of the beam and finally some conclusions are outlined.
Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm
Directory of Open Access Journals (Sweden)
G. Trejo-Caballero
2015-01-01
Full Text Available Curve fitting is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of data points, possibly noisy, the goal is to build a compact representation of the curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Despite the large number of methods available to tackle this problem, it remains challenging and elusive. In this paper, a new method to tackle such problem using strictly a linear combination of radial basis functions (RBFs is proposed. To be more specific, we divide the parameter search space into linear and nonlinear parameter subspaces. We use a hierarchical genetic algorithm (HGA to minimize a model selection criterion, which allows us to automatically and simultaneously determine the nonlinear parameters and then, by the least-squares method through Singular Value Decomposition method, to compute the linear parameters. The method is fully automatic and does not require subjective parameters, for example, smooth factor or centre locations, to perform the solution. In order to validate the efficacy of our approach, we perform an experimental study with several tests on benchmarks smooth functions. A comparative analysis with two successful methods based on RBF networks has been included.
METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS
Directory of Open Access Journals (Sweden)
L. V. Serebryanaya
2016-01-01
Full Text Available The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.
Chen, Zhaoxue; Chen, Hao
2014-01-01
A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.
A hybrid radial basis function-pseudospectral method for thermal convection in a 3-D spherical shell
Wright, G. B.
2010-07-01
A novel hybrid spectral method that combines radial basis function (RBF) and Chebyshev pseudospectral methods in a "2 + 1" approach is presented for numerically simulating thermal convection in a 3-D spherical shell. This is the first study to apply RBFs to a full 3-D physical model in spherical geometry. In addition to being spectrally accurate, RBFs are not defined in terms of any surface-based coordinate system such as spherical coordinates. As a result, when used in the lateral directions, as in this study, they completely circumvent the pole issue with the further advantage that nodes can be "scattered" over the surface of a sphere. In the radial direction, Chebyshev polynomials are used, which are also spectrally accurate and provide the necessary clustering near the boundaries to resolve boundary layers. Applications of this new hybrid methodology are given to the problem of convection in the Earth\\'s mantle, which is modeled by a Boussinesq fluid at infinite Prandtl number. To see whether this numerical technique warrants further investigation, the study limits itself to an isoviscous mantle. Benchmark comparisons are presented with other currently used mantle convection codes for Rayleigh number (Ra) 7 × 10^{3} and 10^{5}. Results from a Ra = 10^{6} simulation are also given. The algorithmic simplicity of the code (mostly due to RBFs) allows it to be written in less than 400 lines of MATLAB and run on a single workstation. We find that our method is very competitive with those currently used in the literature. Copyright 2010 by the American Geophysical Union.
Chen, Fangyue; Chen, Guanrong Ron; He, Guolong; Xu, Xiubin; He, Qinbin
2009-10-01
Universal perceptron (UP), a generalization of Rosenblatt's perceptron, is considered in this paper, which is capable of implementing all Boolean functions (BFs). In the classification of BFs, there are: 1) linearly separable Boolean function (LSBF) class, 2) parity Boolean function (PBF) class, and 3) non-LSBF and non-PBF class. To implement these functions, UP takes different kinds of simple topological structures in which each contains at most one hidden layer along with the smallest possible number of hidden neurons. Inspired by the concept of DNA sequences in biological systems, a novel learning algorithm named DNA-like learning is developed, which is able to quickly train a network with any prescribed BF. The focus is on performing LSBF and PBF by a single-layer perceptron (SLP) with the new algorithm. Two criteria for LSBF and PBF are proposed, respectively, and a new measure for a BF, named nonlinearly separable degree (NLSD), is introduced. In the sense of this measure, the PBF is the most complex one. The new algorithm has many advantages including, in particular, fast running speed, good robustness, and no need of considering the convergence property. For example, the number of iterations and computations in implementing the basic 2-bit logic operations such as AND, OR, and XOR by using the new algorithm is far smaller than the ones needed by using other existing algorithms such as error-correction (EC) and backpropagation (BP) algorithms. Moreover, the synaptic weights and threshold values derived from UP can be directly used in designing of the template of cellular neural networks (CNNs), which has been considered as a new spatial-temporal sensory computing paradigm.
Chen, Qian; Liu, Guohai; Xu, Dezhi; Xu, Liang; Xu, Gaohong; Aamir, Nazir
2018-05-01
This paper proposes a new decoupled control for a five-phase in-wheel fault-tolerant permanent magnet (IW-FTPM) motor drive, in which radial basis function neural network inverse (RBF-NNI) and internal model control (IMC) are combined. The RBF-NNI system is introduced into original system to construct a pseudo-linear system, and IMC is used as a robust controller. Hence, the newly proposed control system incorporates the merits of the IMC and RBF-NNI methods. In order to verify the proposed strategy, an IW-FTPM motor drive is designed based on dSPACE real-time control platform. Then, the experimental results are offered to verify that the d-axis current and the rotor speed are successfully decoupled. Besides, the proposed motor drive exhibits strong robustness even under load torque disturbance.
Estimation of effective connectivity using multi-layer perceptron artificial neural network.
Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman
2018-02-01
Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.
Lima, C S; Barbosa, D; Ramos, J; Tavares, A; Monteiro, L; Carvalho, L
2008-01-01
This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.
Directory of Open Access Journals (Sweden)
A S Yogesh
2011-01-01
Full Text Available In the present case, we have reported a unilateral variation of the radial and musculocutaneous nerves on the left side in a 64-year-old male cadaver. The radial nerve supplied all the heads of the triceps brachii muscle and gave cutaneous branches such as lower lateral cutaneous nerve of the arm and posterior cutaneous nerve of forearm. The radial nerve ended without continuing further. The musculocutaneous nerve supplied the brachioradialis, extensor carpi radialis longus and extensor carpi radialis brevis muscles. The musculocutaneous nerve divided terminally into two branches, superficial and deep. The deep branch of musculocutaneous nerve corresponded to usual deep branch of the radial nerve while the superficial branch of musculocutaneous nerve corresponded to usual superficial branch of the radial nerve. The dissection was continued to expose the entire brachial plexus from its origin and it was found to be normal. The structures on the right upper limb were found to be normal. Surgeons should keep such variations in mind while performing the surgeries of the upper limb.
Nimura, Yukitaka; Hayashi, Yuichiro; Kitasaka, Takayuki; Mori, Kensaku
2015-03-01
This paper presents a method for torso organ segmentation from abdominal CT images using structured perceptron and dual decomposition. A lot of methods have been proposed to enable automated extraction of organ regions from volumetric medical images. However, it is necessary to adjust empirical parameters of them to obtain precise organ regions. This paper proposes an organ segmentation method using structured output learning. Our method utilizes a graphical model and binary features which represent the relationship between voxel intensities and organ labels. Also we optimize the weights of the graphical model by structured perceptron and estimate the best organ label for a given image by dynamic programming and dual decomposition. The experimental result revealed that the proposed method can extract organ regions automatically using structured output learning. The error of organ label estimation was 4.4%. The DICE coefficients of left lung, right lung, heart, liver, spleen, pancreas, left kidney, right kidney, and gallbladder were 0.91, 0.95, 0.77, 0.81, 0.74, 0.08, 0.83, 0.84, and 0.03, respectively.
Directory of Open Access Journals (Sweden)
Shuang Wang
2012-01-01
Full Text Available As an efficient tool, radial basis function (RBF has been widely used for the multivariate approximation, interpolating continuous, and the solution of the particle differential equations. However, ill-conditioned interpolation matrix may be encountered when the interpolation points are very dense or irregularly arranged. To avert this problem, RBFs with variable shape parameters are introduced, and several new variation strategies are proposed. Comparison with the RBF with constant shape parameters are made, and the results show that the condition number of the interpolation matrix grows much slower with our strategies. As an application, an improved collocation meshless method is formulated by employing the new RBF. In addition, the Hermite-type interpolation is implemented to handle the Neumann boundary conditions and an additional sine/cosine basis is introduced for the Helmlholtz equation. Then, two interior acoustic problems are solved with the presented method; the results demonstrate the robustness and effectiveness of the method.
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.
Directory of Open Access Journals (Sweden)
M. Safish Mary
2012-04-01
Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2005-01-01
A new scheme to estimate the moment of inertia in the servo motor drive system in very low speed is proposed in this paper. The speed estimation scheme in most servo drive systems for low speed operation is sensitive to the variation of machine parameter, especially the moment of inertia....... To estimate the motor inertia value, the observer using the Radial Basis Function Network (RBFN) is applied. A control law for stabilizing the system and adaptive laws for updating both of the weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable...... in the sense of Lyapunov. The effectiveness of the proposed inertia estimation is verified by simulations and experiments. It is concluded that the speed control performance in low speed region is improved with the proposed disturbance observer using RBFN....
Energy Technology Data Exchange (ETDEWEB)
Martin, Bradley, E-mail: brma7253@colorado.edu; Fornberg, Bengt, E-mail: Fornberg@colorado.edu
2017-04-15
In a previous study of seismic modeling with radial basis function-generated finite differences (RBF-FD), we outlined a numerical method for solving 2-D wave equations in domains with material interfaces between different regions. The method was applicable on a mesh-free set of data nodes. It included all information about interfaces within the weights of the stencils (allowing the use of traditional time integrators), and was shown to solve problems of the 2-D elastic wave equation to 3rd-order accuracy. In the present paper, we discuss a refinement of that method that makes it simpler to implement. It can also improve accuracy for the case of smoothly-variable model parameter values near interfaces. We give several test cases that demonstrate the method solving 2-D elastic wave equation problems to 4th-order accuracy, even in the presence of smoothly-curved interfaces with jump discontinuities in the model parameters.
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.
The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation
Radouane Iqdour; Abdelouhab Zeroual
2007-01-01
The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also perfo...
DEVELOPMENT OF WEARABLE HUMAN FALL DETECTION SYSTEM USING MULTILAYER PERCEPTRON NEURAL NETWORK
Directory of Open Access Journals (Sweden)
Hamideh Kerdegari
2013-02-01
Full Text Available This paper presents an accurate wearable fall detection system which can identify the occurrence of falls among elderly population. A waist worn tri-axial accelerometer was used to capture the movement signals of human body. A set of laboratory-based falls and activities of daily living (ADL were performed by volunteers with different physical characteristics. The collected acceleration patterns were classified precisely to fall and ADL using multilayer perceptron (MLP neural network. This work was resulted to a high accuracy wearable fall-detection system with the accuracy of 91.6%.
Directory of Open Access Journals (Sweden)
DT Wiyanti
2013-07-01
Full Text Available Salah satu metode peramalan yang paling dikembangkan saat ini adalah time series, yakni menggunakan pendekatan kuantitatif dengan data masa lampau yang dijadikan acuan untuk peramalan masa depan. Berbagai penelitian telah mengusulkan metode-metode untuk menyelesaikan time series, di antaranya statistik, jaringan syaraf, wavelet, dan sistem fuzzy. Metode-metode tersebut memiliki kekurangan dan keunggulan yang berbeda. Namun permasalahan yang ada dalam dunia nyata merupakan masalah yang kompleks. Satu metode saja mungkin tidak mampu mengatasi masalah tersebut dengan baik. Dalam artikel ini dibahas penggabungan dua buah metode yaitu Auto Regressive Integrated Moving Average (ARIMA dan Radial Basis Function (RBF. Alasan penggabungan kedua metode ini adalah karena adanya asumsi bahwa metode tunggal tidak dapat secara total mengidentifikasi semua karakteristik time series. Pada artikel ini dibahas peramalan terhadap data Indeks Harga Perdagangan Besar (IHPB dan data inflasi komoditi Indonesia; kedua data berada pada rentang tahun 2006 hingga beberapa bulan di tahun 2012. Kedua data tersebut masing-masing memiliki enam variabel. Hasil peramalan metode ARIMA-RBF dibandingkan dengan metode ARIMA dan metode RBF secara individual. Hasil analisa menunjukkan bahwa dengan metode penggabungan ARIMA dan RBF, model yang diberikan memiliki hasil yang lebih akurat dibandingkan dengan penggunaan salah satu metode saja. Hal ini terlihat dalam visual plot, MAPE, dan RMSE dari semua variabel pada dua data uji coba.Â The accuracy of time series forecasting is the subject of many decision-making processes. Time series use a quantitative approach to employ data from the past to make forecast for the future. Many researches have proposed several methods to solve time series, such as using statistics, neural networks, wavelets, and fuzzy systems. These methods have different advantages and disadvantages. But often the problem in the real world is just too complex that a
Reliability analysis of C-130 turboprop engine components using artificial neural network
Qattan, Nizar A.
In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine
Minimizing Hexapod Robot Foot Deviations Using Multilayer Perceptron
Directory of Open Access Journals (Sweden)
Vytautas Valaitis
2015-12-01
Full Text Available Rough-terrain traversability is one of the most valuable characteristics of walking robots. Even despite their slower speeds and more complex control algorithms, walking robots have far wider usability than wheeled or tracked robots. However, efficient movement over irregular surfaces can only be achieved by eliminating all possible difficulties, which in many cases are caused by a high number of degrees of freedom, feet slippage, frictions and inertias between different robot parts or even badly developed inverse kinematics (IK. In this paper we address the hexapod robot-foot deviation problem. We compare the foot-positioning accuracy of unconfigured inverse kinematics and Multilayer Perceptron-based (MLP methods via theory, computer modelling and experiments on a physical robot. Using MLP-based methods, we were able to significantly decrease deviations while reaching desired positions with the hexapod's foot. Furthermore, this method is able to compensate for deviations of the robot arising from any possible reason.
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-09-18
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.
Directory of Open Access Journals (Sweden)
Misganaw Abebe
2017-11-01
Full Text Available Springback in multi-point dieless forming (MDF is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE. Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.
International Nuclear Information System (INIS)
Orozco-Monteagudo, Maykel; Taboada-Crispi, Alberto; Gutierrez-Hernandez, Liliana
2008-01-01
This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.
Directory of Open Access Journals (Sweden)
HU Qi-guo
2017-01-01
Full Text Available For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel’s thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver’s head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively. The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly.
International Nuclear Information System (INIS)
Farivar, Faezeh; Aliyari Shoorehdeli, Mahdi; Nekoui, Mohammad Ali; Teshnehlab, Mohammad
2012-01-01
Highlights: ► A systematic procedure for GPS of unknown heavy chaotic gyroscope systems. ► Proposed methods are based on Lyapunov stability theory. ► Without calculating Lyapunov exponents and Eigen values of the Jacobian matrix. ► Capable to extend for a variety of chaotic systems. ► Useful for practical applications in the future. - Abstract: This paper proposes the chaos control and the generalized projective synchronization methods for heavy symmetric gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic motions of symmetric gyroscopes. In this paper, the switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. Using the neural variable structure control technique, control laws are established which guarantees the chaos control and the generalized projective synchronization of unknown gyroscope systems. In the neural variable structure control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimator are derived in the sense of Lyapunov function. Thus, the unknown gyro systems can be guaranteed to be asymptotically stable. Also, the proposed method can achieve the control objectives. Numerical simulations are presented to
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.
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.
Generation of hourly irradiation synthetic series using the neural network multilayer perceptron
Energy Technology Data Exchange (ETDEWEB)
Hontoria, L.; Aguilera, J. [Universidad de Jaen, Linares-Jaen (Spain). Dpto. de Electronica; Zufiria, P. [Ciudad Universitaria, Madrid (Spain). Grupo de Redes Neuronales
2002-05-01
In this work, a methodology based on the neural network model called multilayer perceptron (MLP) to solve a typical problem in solar energy is presented. This methodology consists of the generation of synthetic series of hourly solar irradiation. The model presented is based on the capacity of the MLP for finding relations between variables for which interrelation is unknown explicitly. The information available can be included progressively at the series generator at different stages. A comparative study with other solar irradiation synthetic generation methods has been done in order to demonstrate the validity of the one proposed. (author)
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
Mayra Luiza Marques da Silva Binoti
2015-03-01
Full Text Available The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric variables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80% and generalization (20%. Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems.
Online learning dynamics of multilayer perceptrons with unidentifiable parameters
Energy Technology Data Exchange (ETDEWEB)
Park, Hyeyoung [Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Inoue, Masato [Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan); Department of Otolaryngology, Head and Neck Surgery, Graduate School of Medicine, Kyoto University, Kyoto 606-8507 (Japan); ' Intelligent Cooperation and Control' , PRESTO, JST, c/o RIKEN BSI, Saitama 351-0198 (Japan); Okada, Masato [Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198 (Japan)
2003-11-28
In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures.
Online learning dynamics of multilayer perceptrons with unidentifiable parameters
International Nuclear Information System (INIS)
Park, Hyeyoung; Inoue, Masato; Okada, Masato
2003-01-01
In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures
Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M
2011-11-01
Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.
International Nuclear Information System (INIS)
Casa, L D C; Krueger, P S
2013-01-01
Unstructured three-dimensional fluid velocity data were interpolated using Gaussian radial basis function (RBF) interpolation. Data were generated to imitate the spatial resolution and experimental uncertainty of a typical implementation of defocusing digital particle image velocimetry. The velocity field associated with a steadily rotating infinite plate was simulated to provide a bounded, fully three-dimensional analytical solution of the Navier–Stokes equations, allowing for robust analysis of the interpolation accuracy. The spatial resolution of the data (i.e. particle density) and the number of RBFs were varied in order to assess the requirements for accurate interpolation. Interpolation constraints, including boundary conditions and continuity, were included in the error metric used for the least-squares minimization that determines the interpolation parameters to explore methods for improving RBF interpolation results. Even spacing and logarithmic spacing of RBF locations were also investigated. Interpolation accuracy was assessed using the velocity field, divergence of the velocity field, and viscous torque on the rotating boundary. The results suggest that for the present implementation, RBF spacing of 0.28 times the boundary layer thickness is sufficient for accurate interpolation, though theoretical error analysis suggests that improved RBF positioning may yield more accurate results. All RBF interpolation results were compared to standard Gaussian weighting and Taylor expansion interpolation methods. Results showed that RBF interpolation improves interpolation results compared to the Taylor expansion method by 60% to 90% based on the average squared velocity error and provides comparable velocity results to Gaussian weighted interpolation in terms of velocity error. RMS accuracy of the flow field divergence was one to two orders of magnitude better for the RBF interpolation compared to the other two methods. RBF interpolation that was applied to
Spectral problem for the radial Schroedinger equation
International Nuclear Information System (INIS)
Vshivtsev, A.S.; Tatarintsev, A.V.; Prokopov, A.V.; Sorokin, V. N.
1998-01-01
For the first time, a procedure for determining spectra on the basis of generalized integral transformations is implemented for a wide class of radial Schroedinger equations. It is shown that this procedure works well for known types of potentials. Concurrently, this method makes it possible to obtain new analytic results for the Cornell potential. This may prove important for hadron physics
International Nuclear Information System (INIS)
Yu, Shiwei; Wang, Ke; Wei, Yi-Ming
2015-01-01
Highlights: • A hybrid self-adaptive PSO–GA-RBF model is proposed for electricity demand prediction. • Each mixed-coding particle is composed by two coding parts of binary and real. • Five independent variables have been selected to predict future electricity consumption in Wuhan. • The proposed model has a simpler structure or higher estimating precision than other ANN models. • No matter what the scenario, the electricity consumption of Wuhan will grow rapidly. - Abstract: The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO–GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the RBF by GA operation and the parameters of the basis and weights by a PSO–GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO–GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models; and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7–11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85 billion kW h. The lowest demand is estimated for the business-as-usual scenario, and will be 88.45 billion kW h
Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network
DEFF Research Database (Denmark)
Míguez González, M; López Peña, F.; Díaz Casás, V.
2011-01-01
Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been acknowle......Parametric roll resonance is a ship stability related phenomenon that generates sudden large amplitude oscillations up to 30-40 degrees of roll. This can cause severe damage, and it can put the crew in serious danger. The need for a parametric rolling real time prediction system has been...... acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network...
Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.
2012-04-01
Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.
International Nuclear Information System (INIS)
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
In this paper, a novel method is proposed for predicting the density of liquid phase in stratified regime of liquid-gas two phase flows by utilizing dual modality densitometry technique and artificial neural network (ANN) model of radial basis function (RBF). The detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors for registering transmitted and scattered photons. At the first step, a Monte Carlo simulation model was utilized to obtain the optimum position for the scattering detector in dual modality densitometry configuration. At the next step, an experimental setup was designed based on obtained optimum position for detectors from simulation in order to generate the required data for training and testing the ANN. The results show that the proposed approach could be successfully applied for predicting the density of liquid phase in stratified regime of gas-liquid two phase flows with mean relative error (MRE) of less than 0.701. - Highlights: • Density of liquid phase in stratified regime of two phase flows was predicted. • Combination of dual modality densitometry technique and ANN was utilized. • Detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors. • MCNP simulation was done to obtain the optimum position for the scattering detector. • An experimental setup was designed to generate the required data for training the ANN.
Wind speed estimation using multilayer perceptron
International Nuclear Information System (INIS)
Velo, Ramón; López, Paz; Maseda, Francisco
2014-01-01
Highlights: • We present a method for determining the average wind speed using neural networks. • We use data from that site in the short term and data from other nearby stations. • The inputs used in the ANN were wind speed and direction data from a station. • The method allows knowing the wind speed without topographical data. - Abstract: Wind speed knowledge is prerequisite in the siting of wind turbines. In consequence the wind energy use requires meticulous and specified knowledge of the wind characteristics at a location. This paper presents a method for determining the annual average wind speed at a complex terrain site by using neural networks, when only short term data are available for that site. This information is useful for preliminary calculations of the wind resource at a remote area having only a short time period of wind measurements measurement in a site. Artificial neural networks are useful for implementing non-linear process variables over time, and therefore are a useful tool for estimating the wind speed. The neural network used is multilayer perceptron with three layers and the supervised learning algorithm used is backpropagation. The inputs used in the neural network were wind speed and direction data from a single station, and the training patterns used correspond to sixty days data. The results obtained by simulating the annual average wind speed at the selected site based on data from nearby stations with correlation coefficients above 0.5 were satisfactory, compared with actual values. Reliable estimations were obtained, with errors below 6%
Nourani, Vahid; Mousavi, Shahram; Dabrowska, Dominika; Sadikoglu, Fahreddin
2017-05-01
As an innovation, both black box and physical-based models were incorporated into simulating groundwater flow and contaminant transport. Time series of groundwater level (GL) and chloride concentration (CC) observed at different piezometers of study plain were firstly de-noised by the wavelet-based de-noising approach. The effect of de-noised data on the performance of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) was evaluated. Wavelet transform coherence was employed for spatial clustering of piezometers. Then for each cluster, ANN and ANFIS models were trained to predict GL and CC values. Finally, considering the predicted water heads of piezometers as interior conditions, the radial basis function as a meshless method which solves partial differential equations of GFCT, was used to estimate GL and CC values at any point within the plain where there is not any piezometer. Results indicated that efficiency of ANFIS based spatiotemporal model was more than ANN based model up to 13%.
Directory of Open Access Journals (Sweden)
José Antonio Vázquez-López
2012-06-01
Full Text Available In this article the Perceptron artificial neural network is applied as a classifier system of out of control points, in the field of contrlol chart for individual measurements. The use of geometric properties of the Perceptron as a training method is introduced, replacing in consequence to the known training methods. Some experiments with numerical databases contaminated with altered data in global average was performed, and the ability of the detection of \\out of control points" of the control chart with the implementation of the Perceptron trained by geometry was compared. The results reveal greater capacity in the Perceptron. This approach can be generalized to other types of control charts and patterns of natural and special variation, not considered in this research. // RESUMEN: En este artículo se aplica la red neuronal artificial Perceptrón como sistema clasificador de puntos fuera de control en el ámbito de la carta de control de mediciones individuales. Se introduce el uso de las propiedades geométricas de la Perceptrón como método de entrenamiento para sustituir, en consecuencia, a los métodos de entrenamiento conocidos. Se experimentó con bases de datos numéricas contaminadas con datos alterados en su media global y se comparó la capacidad de la detección de puntos fuera de control de la carta de control con la aplicación de la Perceptrón entrenada por geometría. Los resultados revelan mayor capacidad en la Perceptrón en diferentes porcentajes de contaminación. Esta propuesta puede ser generalizada a otros tipos de gráficos de control y a patrones de variación especial y natural no considerados en esta investigación.
Directory of Open Access Journals (Sweden)
Seng-Chi Chen
2014-01-01
Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.
Intelligent control of HVAC systems. Part II: perceptron performance analysis
Directory of Open Access Journals (Sweden)
Ioan URSU
2013-09-01
Full Text Available This is the second part of a paper on intelligent type control of Heating, Ventilating, and Air-Conditioning (HVAC systems. The whole study proposes a unified approach in the design of intelligent control for such systems, to ensure high energy efficiency and air quality improving. In the first part of the study it is considered as benchmark system a single thermal space HVAC system, for which it is assigned a mathematical model of the controlled system and a mathematical model(algorithm of intelligent control synthesis. The conception of the intelligent control is of switching type, between a simple neural network, a perceptron, which aims to decrease (optimize a cost index,and a fuzzy logic component, having supervisory antisaturating role for neuro-control. Based on numerical simulations, this Part II focuses on the analysis of system operation in the presence only ofthe neural control component. Working of the entire neuro-fuzzy system will be reported in a third part of the study.
Neuropathy - radial nerve; Radial nerve palsy; Mononeuropathy ... Damage to one nerve group, such as the radial nerve, is called mononeuropathy . Mononeuropathy means there is damage to a single nerve. Both ...
Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman
2017-02-01
The soil sorption partition coefficient logK oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Khuat Thanh Tung
2016-11-01
Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.
Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.
2015-06-01
Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.
Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China
Directory of Open Access Journals (Sweden)
C. W. Dawson
2002-01-01
Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting
International Nuclear Information System (INIS)
Hosseini-Golgoo, S M; Bozorgi, H; Saberkari, A
2015-01-01
Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively. (paper)
Radial head button holing: a cause of irreducible anterior radial head dislocation
Energy Technology Data Exchange (ETDEWEB)
Shin, Su-Mi; Chai, Jee Won; You, Ja Yeon; Park, Jina [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Radiology, Seoul (Korea, Republic of); Bae, Kee Jeong [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Orthopedic Surgery, Seoul (Korea, Republic of)
2016-10-15
''Buttonholing'' of the radial head through the anterior joint capsule is a known cause of irreducible anterior radial head dislocation associated with Monteggia injuries in pediatric patients. To the best of our knowledge, no report has described an injury consisting of buttonholing of the radial head through the annular ligament and a simultaneous radial head fracture in an adolescent. In the present case, the radiographic findings were a radial head fracture with anterior dislocation and lack of the anterior fat pad sign. Magnetic resonance imaging (MRI) clearly demonstrated anterior dislocation of the fractured radial head through the torn annular ligament. The anterior joint capsule and proximal portion of the annular ligament were interposed between the radial head and capitellum, preventing closed reduction of the radial head. Familiarity with this condition and imaging findings will aid clinicians to make a proper diagnosis and fast decision to perform an open reduction. (orig.)
International Nuclear Information System (INIS)
Gomes, Carla Regina; Canedo Medeiros, Jose Antonio Carlos
2015-01-01
Highlights: • It is presented a new method based on Artificial Neural Network (ANN) developed to deal with accident identification in PWR nuclear power plants. • Obtained results have shown the efficiency of the referred technique. • Results obtained with this method are as good as or even better to similar optimization tools available in the literature. - Abstract: The task of monitoring a nuclear power plant consists on determining, continuously and in real time, the state of the plant’s systems in such a way to give indications of abnormalities to the operators and enable them to recognize anomalies in system behavior. The monitoring is based on readings of a large number of meters and alarm indicators which are located in the main control room of the facility. On the occurrence of a transient or of an accident on the nuclear power plant, even the most experienced operators can be confronted with conflicting indications due to the interactions between the various components of the plant systems; since a disturbance of a system can cause disturbances on another plant system, thus the operator may not be able to distinguish what is cause and what is the effect. This cognitive overload, to which operators are submitted, causes a difficulty in understanding clearly the indication of an abnormality in its initial phase of development and in taking the appropriate and immediate corrective actions to face the system failure. With this in mind, computerized monitoring systems based on artificial intelligence that could help the operators to detect and diagnose these failures have been devised and have been the subject of research. Among the techniques that can be used in such development, radial basis functions (RBFs) neural networks play an important role due to the fact that they are able to provide good approximations to functions of a finite number of real variables. This paper aims to present an application of a neural network of Gaussian radial basis
Directory of Open Access Journals (Sweden)
Nan YU
2014-09-01
Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.
Das, Nibaran; Mollah, Ayatullah Faruk; Sarkar, Ram; Basu, Subhadip
2010-01-01
The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a database of 3000 samples, the maximum recognition rate of 95.80% is observed with both of two separat...
Stability of radial and non-radial pulsation modes of massive ZAMS models
International Nuclear Information System (INIS)
Odell, A.P.; Pausenwein, A.; Weiss, W.W.; Hajek, A.
1987-01-01
The authors have computed non-adiabatic eigenvalues for radial and non-radial pulsation modes of star models between 80 and 120 M solar with composition of chi=0.70 and Z=0.02. The radial fundamental mode is unstable in models with mass greater than 95 M solar , but the first overtone mode is always stable. The non-radial modes are all stable for all models, but the iota=2 f-mode is the closest to being driven. The non-radial modes are progressively more stable with higher iota and with higher n (for both rho- and g-modes). Thus, their results indicate that radial pulsation limits the upper mass of a star
International Nuclear Information System (INIS)
Kim, Kyung-O; Jeong, Hae Sun; Jo, Daeseong
2017-01-01
Highlights: • Employing the Radial Point Interpolation Method (RPIM) in numerical analysis of multi-group neutron-diffusion equation. • Establishing mathematical formation of modified multi-group neutron-diffusion equation by RPIM. • Performing the numerical analysis for 2D critical problem. - Abstract: A mesh-free method is introduced to overcome the drawbacks (e.g., mesh generation and connectivity definition between the meshes) of mesh-based (nodal) methods such as the finite-element method and finite-difference method. In particular, the Point Interpolation Method (PIM) using a radial basis function is employed in the numerical analysis for the multi-group neutron-diffusion equation. The benchmark calculations are performed for the 2D homogeneous and heterogeneous problems, and the Multiquadrics (MQ) and Gaussian (EXP) functions are employed to analyze the effect of the radial basis function on the numerical solution. Additionally, the effect of the dimensionless shape parameter in those functions on the calculation accuracy is evaluated. According to the results, the radial PIM (RPIM) can provide a highly accurate solution for the multiplication eigenvalue and the neutron flux distribution, and the numerical solution with the MQ radial basis function exhibits the stable accuracy with respect to the reference solutions compared with the other solution. The dimensionless shape parameter directly affects the calculation accuracy and computing time. Values between 1.87 and 3.0 for the benchmark problems considered in this study lead to the most accurate solution. The difference between the analytical and numerical results for the neutron flux is significantly increased in the edge of the problem geometry, even though the maximum difference is lower than 4%. This phenomenon seems to arise from the derivative boundary condition at (x,0) and (0,y) positions, and it may be necessary to introduce additional strategy (e.g., the method using fictitious points and
MIMO transmit scheme based on morphological perceptron with competitive learning.
Valente, Raul Ambrozio; Abrão, Taufik
2016-08-01
This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The proposed MIMO transmission scheme is able to achieve double spectral efficiency; hence, in each time-slot the receiver decodes two symbols at a time instead one as Alamouti scheme. Other advantage of the proposed transmit scheme with MP/CL-aided detector is its polynomial complexity according to modulation order, while it becomes linear when the data stream length is greater than modulation order. The performance of the proposed scheme is compared to the traditional MIMO schemes, namely Alamouti scheme and maximum-likelihood MIMO (ML-MIMO) detector. Also, the proposed scheme is evaluated in a scenario with variable channel information along the frame. Numerical results have shown that the diversity gain under space-time coding Alamouti scheme is partially lost, which slightly reduces the bit-error rate (BER) performance of the proposed MP/CL-NN MIMO scheme. Copyright © 2016 Elsevier Ltd. All rights reserved.
Moussaoui, Ahmed; Bouziane, Touria
2016-01-01
The method LRPIM is a Meshless method with properties of simple implementation of the essential boundary conditions and less costly than the moving least squares (MLS) methods. This method is proposed to overcome the singularity associated to polynomial basis by using radial basis functions. In this paper, we will present a study of a 2D problem of an elastic homogenous rectangular plate by using the method LRPIM. Our numerical investigations will concern the influence of different shape parameters on the domain of convergence,accuracy and using the radial basis function of the thin plate spline. It also will presents a comparison between numerical results for different materials and the convergence domain by precising maximum and minimum values as a function of distribution nodes number. The analytical solution of the deflection confirms the numerical results. The essential points in the method are: •The LRPIM is derived from the local weak form of the equilibrium equations for solving a thin elastic plate.•The convergence of the LRPIM method depends on number of parameters derived from local weak form and sub-domains.•The effect of distributions nodes number by varying nature of material and the radial basis function (TPS).
Energy Technology Data Exchange (ETDEWEB)
Kabashima, Y [Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama 226-8502 (Japan)], E-mail: kaba@dis.titech.ac.jp
2008-01-15
A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown.
International Nuclear Information System (INIS)
Kabashima, Y
2008-01-01
A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown
Kabashima, Y.
2008-01-01
A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown.
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.
International Nuclear Information System (INIS)
Draz, U.; Jahanzaib, M.; Asghar, G.
2016-01-01
SMEs (Small and Medium Sized Enterprises) sector is facing problems relating to implementation of international quality standards. These SMEs need to identify factors affecting business success abroad for intelligent allocation of resources to the process of internationalization. In this paper, MLP NN (Multi-Layer Perceptron Neural Network) has been used for identifying relative importance of key variables related to firm basics, manufacturing, quality inspection labs and level of education in determining the exporting status of Pakistani SMEs. A survey has been conducted for scoring out the pertinent variables in SMEs and coded in MLP NNs. It is found that firm registered with OEM (Original Equipment Manufacturer) and size of firm are the most important in determining exporting status of SMEs followed by other variables. For internationalization, the results aid policy makers in formulating strategies. (author)
Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong
2008-06-01
A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.
Directory of Open Access Journals (Sweden)
Jingwen Tian
2013-02-01
Full Text Available Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro-mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real-time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.
Radial electromagnetic force calculation of induction motor based on multi-loop theory
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HE Haibo
2017-12-01
Full Text Available [Objectives] In order to study the vibration and noise of induction motors, a method of radial electromagnetic force calculation is established on the basis of the multi-loop model.[Methods] Based on the method of calculating air-gap magneto motive force according to stator and rotor fundamental wave current, the analytic formulas are deduced for calculating the air-gap magneto motive force and radial electromagnetic force generated in accordance with any stator winding and rotor conducting bar current. The multi-loop theory and calculation method for the electromagnetic parameters of a motor are introduced, and a dynamic simulation model of an induction motor built to achieve the current of the stator winding and rotor conducting bars, and obtain the calculation formula of radial electromagnetic force. The radial electromagnetic force and vibration are then estimated.[Results] The experimental results indicate that the vibration acceleration frequency and amplitude of the motor are consistent with the experimental results.[Conclusions] The results and calculation method can support the low noise design of converters.
River flow simulation using a multilayer perceptron-firefly algorithm model
Darbandi, Sabereh; Pourhosseini, Fatemeh Akhoni
2018-06-01
River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP-ANN and hybrid multilayer perceptron (MLP-FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP-ANN and MLP-FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP-ANN to MLP-FFA models. The results show that MLP-FFA model is satisfactory for monthly river flow simulation in study area.
Analog Multilayer Perceptron Circuit with On-chip Learning: Portable Electronic Nose
Pan, Chih-Heng; Tang, Kea-Tiong
2011-09-01
This article presents an analog multilayer perceptron (MLP) neural network circuit with on-chip back propagation learning. This low power and small area analog MLP circuit is proposed to implement as a classifier in an electronic nose (E-nose). Comparing with the E-nose using microprocessor or FPGA as a classifier, the E-nose applying analog circuit as a classifier can be faster and much smaller, demonstrate greater power efficiency and be capable of developing a portable E-nose [1]. The system contains four inputs, four hidden neurons, and only one output neuron; this simple structure allows the circuit to have a smaller area and less power consumption. The circuit is fabricated using TSMC 0.18 μm 1P6M CMOS process with 1.8 V supply voltage. The area of this chip is 1.353×1.353 mm2 and the power consumption is 0.54 mW. Post-layout simulations show that the proposed analog MLP circuit can be successively trained to identify three kinds of fruit odors.
Assessing the suitability of soft computing approaches for forest fires prediction
Directory of Open Access Journals (Sweden)
Samaher Al_Janabi
2018-07-01
Full Text Available Forest fires present one of the main causes of environmental hazards that have many negative results in different aspect of life. Therefore, early prediction, fast detection and rapid action are the key elements for controlling such phenomenon and saving lives. Through this work, 517 different entries were selected at different times for montesinho natural park (MNP in Portugal to determine the best predictor that has the ability to detect forest fires, The principle component analysis (PCA was applied to find the critical patterns and particle swarm optimization (PSO technique was used to segment the fire regions (clusters. In the next stage, five soft computing (SC Techniques based on neural network were used in parallel to identify the best technique that would potentially give more accurate and optimum results in predicting of forest fires, these techniques namely; cascade correlation network (CCN, multilayer perceptron neural network (MPNN, polynomial neural network (PNN, radial basis function (RBF and support vector machine (SVM In the final stage, the predictors and their performance were evaluated based on five quality measures including root mean squared error (RMSE, mean squared error (MSE, relative absolute error (RAE, mean absolute error (MAE and information gain (IG. The results indicate that SVM technique was more effective and efficient than the RBF, MPNN, PNN and CCN predictors. The results also show that the SVM algorithm provides more precise predictions compared with other predictors with small estimation error. The obtained results confirm that the SVM improves the prediction accuracy and suitable for forest fires prediction compared to other methods. Keywords: Forest fires, Soft computing, Prediction, Principle component analysis, Particle swarm optimization, Cascade correlation network, Multilayer perceptron neural network, Polynomial neural networks, Radial basis function, Support vector machine
Altieri, Ada
2018-01-01
In view of the results achieved in a previously related work [A. Altieri, S. Franz, and G. Parisi, J. Stat. Mech. (2016) 093301], 10.1088/1742-5468/2016/09/093301, regarding a Plefka-like expansion of the free energy up to the second order in the perceptron model, we improve the computation here focusing on the role of third-order corrections. The perceptron model is a simple example of constraint satisfaction problem, falling in the same universality class as hard spheres near jamming and hence allowing us to get exact results in high dimensions for more complex settings. Our method enables to define an effective potential (or Thouless-Anderson-Palmer free energy), namely a coarse-grained functional, which depends on the generalized forces and the effective gaps between particles. The analysis of the third-order corrections to the effective potential reveals that, albeit irrelevant in a mean-field framework in the thermodynamic limit, they might instead play a fundamental role in considering finite-size effects. We also study the typical behavior of generalized forces and we show that two kinds of corrections can occur. The first contribution arises since the system is analyzed at a finite distance from jamming, while the second one is due to finite-size corrections. We nevertheless show that third-order corrections in the perturbative expansion vanish in the jamming limit both for the potential and the generalized forces, in agreement with the isostaticity argument proposed by Wyart and coworkers. Finally, we analyze the relevant scaling solutions emerging close to the jamming line, which define a crossover regime connecting the control parameters of the model to an effective temperature.
Estimates of Storage Capacity of Multilayer Perceptron with Threshold Logic Hidden Units.
Kowalczyk, Adam
1997-11-01
We estimate the storage capacity of multilayer perceptron with n inputs, h(1) threshold logic units in the first hidden layer and 1 output. We show that if the network can memorize 50% of all dichotomies of a randomly selected N-tuple of points of R(n) with probability 1, then Nmemory capacity (in the sense of Cover) between nh(1)+1 and 2(nh(1)+1) input patterns and for the most efficient networks in this class between 1 and 2 input patterns per connection. Comparing these results with the recent estimates of VC-dimension we find that in contrast to a single neuron case, the VC-dimension exceeds the capacity for a sufficiently large n and h(1). The results are based on the derivation of an explicit expression for the number of dichotomies which can be implemented by such a network for a special class of N-tuples of input patterns which has a positive probability of being randomly chosen.
Govorov, Michael; Gienko, Gennady; Putrenko, Viktor
2018-05-01
In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.
International Nuclear Information System (INIS)
Guerra, Fabio A.; Coelho, Leandro dos S.
2008-01-01
An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting
Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun
2013-10-01
Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.
International Nuclear Information System (INIS)
Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan
2011-01-01
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)
Aid decision algorithms to estimate the risk in congenital heart surgery.
Ruiz-Fernández, Daniel; Monsalve Torra, Ana; Soriano-Payá, Antonio; Marín-Alonso, Oscar; Triana Palencia, Eddy
2016-04-01
In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks
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Shalin Savalia
2018-05-01
Full Text Available The electrocardiogram (ECG plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP and convolution neural network (CNN. The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.
Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.
Savalia, Shalin; Emamian, Vahid
2018-05-04
The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.
International Nuclear Information System (INIS)
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137 Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%. - Highlights: • Flow regime and void fraction were determined in two phase flows independent of the liquid phase density changes. • An experimental structure was set up and the required data was obtained. • 3 detectors and one gamma source were used in detection geometry. • RBF networks were utilized for flow regime and void fraction determination.
D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R
2010-03-09
It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.
Moment methods with effective nuclear Hamiltonians; calculations of radial moments
International Nuclear Information System (INIS)
Belehrad, R.H.
1981-02-01
A truncated orthogonal polynomial expansion is used to evaluate the expectation value of the radial moments of the one-body density of nuclei. The expansion contains the configuration moments, , , and 2 >, where R/sup (k)/ is the operator for the k-th power of the radial coordinate r, and H is the effective nuclear Hamiltonian which is the sum of the relative kinetic energy operator and the Bruckner G matrix. Configuration moments are calculated using trace reduction formulae where the proton and neutron orbitals are treated separately in order to find expectation values of good total isospin. The operator averages are taken over many-body shell model states in the harmonic oscillator basis where all particles are active and single-particle orbitals through six major shells are included. The radial moment expectation values are calculated for the nuclei 16 O, 40 Ca, and 58 Ni and find that is usually the largest term in the expansion giving a large model space dependence to the results. For each of the 3 nuclei, a model space is found which gives the desired rms radius and then we find that the other 5 lowest moments compare favorably with other theoretical predictions. Finally, we use a method of Gordon (5) to employ the lowest 6 radial moment expectation values in the calculation of elastic electron scattering from these nuclei. For low to moderate momentum transfer, the results compare favorably with the experimental data
Moritomo, Hisao; Arimitsu, Sayuri; Kubo, Nobuyuki; Masatomi, Takashi; Yukioka, Masao
2015-02-01
To classify triangular fibrocartilage complex (TFCC) foveal lesions on the basis of computed tomography (CT) arthrography using a radial plane view and to correlate the CT arthrography results with surgical findings. We also tested the interobserver and intra-observer reliability of the radial plane view. A total of 33 patients with a suspected TFCC foveal tear who had undergone wrist CT arthrography and subsequent surgical exploration were enrolled. We classified the configurations of TFCC foveal lesions into 5 types on the basis of CT arthrography with the radial plane view in which the image slices rotate clockwise centered on the ulnar styloid process. Sensitivity, specificity, and positive predictive values were calculated for each type of foveal lesion in CT arthrography to detect foveal tears. We determined interobserver and intra-observer agreements using kappa statistics. We also compared accuracies with the radial plane views with those with the coronal plane views. Among the tear types on CT arthrography, type 3, a roundish defect at the fovea, and type 4, a large defect at the overall ulnar insertion, had high specificity and positive predictive value for the detection of foveal tears. Specificity and positive predictive values were 90% and 89% for type 3 and 100% and 100% for type 4, respectively, whereas sensitivity was 35% for type 3 and 22% for type 4. Interobserver and intra-observer agreement was substantial and almost perfect, respectively. The radial plane view identified foveal lesion of each palmar and dorsal radioulnar ligament separately, but accuracy results with the radial plane views were not statistically different from those with the coronal plane views. Computed tomography arthrography with a radial plane view exhibited enhanced specificity and positive predictive value when a type 3 or 4 lesion was identified in the detection of a TFCC foveal tear compared with historical controls. Diagnostic II. Copyright © 2015 American Society for
Antiproton compression and radial measurements
Andresen, G B; Bowe, P D; Bray, C C; Butler, E; Cesar, C L; Chapman, S; Charlton, M; Fajans, J; Fujiwara, M C; Funakoshi, R; Gill, D R; Hangst, J S; Hardy, W N; Hayano, R S; Hayden, M E; Humphries, A J; Hydomako, R; Jenkins, M J; Jorgensen, L V; Kurchaninov, L; Lambo, R; Madsen, N; Nolan, P; Olchanski, K; Olin, A; Page R D; Povilus, A; Pusa, P; Robicheaux, F; Sarid, E; Seif El Nasr, S; Silveira, D M; Storey, J W; Thompson, R I; Van der Werf, D P; Wurtele, J S; Yamazaki, Y
2008-01-01
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.
Short-Term Load Forecast in Electric Energy System in Bulgaria
Directory of Open Access Journals (Sweden)
Irina Asenova
2010-01-01
Full Text Available As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented.
Intelligent harmonic load model based on neural networks
Ji, Pyeong-Shik; Lee, Dae-Jong; Lee, Jong-Pil; Park, Jae-Won; Lim, Jae-Yoon
2007-12-01
In this study, we developed a RBFNs(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method implemented by using harmonic information as well as fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. The RBFNs have certain advantage such as simple structure and rapid computation ability compared with multilayer perceptron which is extensively applied for load modeling. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynominal 2nd equation method, MLP and RBF without considering harmonic components.
Directory of Open Access Journals (Sweden)
P. Hashemi
2018-01-01
Full Text Available Construction sites are accident-prone locations and therefore safety management plays an im-portant role in these workplaces. This study presents an adaptive algorithm for performance as-sessment of project management with respect to resilience engineering and job security in a large construction site. The required data are collected using questionnaires in a large construction site. The presented algorithm is composed of radial basis function (RBF, artificial neural networks multi-layer perceptron (ANN-MLP, and statistical tests. The results indicate that preparedness, fault-tolerance, and flexibility are the most effective factors on overall efficiency. Moreover, job security and resilience engineering have similar statistical impacts on overall system efficiency. The results are verified and validated by the proposed algorithm.
International Nuclear Information System (INIS)
Zou, Zhichao; Wang, Fujun; Yao, Zhifeng; Tao, Ran; Xiao, Ruofu; Li, Huaicheng
2016-01-01
Highlights: • Conclude the characteristics of transient radial force in the startup process for a large double-suction centrifugal pump. • The overall direction of the radial force during startup process is also confirmed. • A formula used to calculate the transient radial force during startup process is proposed. • A relationship between radial force variation and axial vortex development in blade channel during the startup process is established. The mechanism of the radial force evolution is revealed. - Abstract: Double-suction centrifugal pumps play an important role in the main feedwater systems of nuclear power plant. The impeller radial force in a centrifugal pump varies dramatically during startup at the shut-off condition. In this study, the startup process of a large double-suction centrifugal pump is investigated using CFD. During testing, the impeller speed is accelerated from zero to its rated speed in 1.0 s (marked as t_0) and is then maintained at the rated speed. The results show that the radial force increase lags behind the impeller speed increase. At 0–0.4t_0, the radial force is small (approaching zero). At 0.4–1.4t_0, the radial force increases rapidly. After 1.4t_0, the average radial force stabilizes and reaches its maximum value of 55,619 N. The observed maximum radial force value during startup is approximately nine times as high as the radial force under rated condition. During startup, the overall radial force direction is proximate to the radial line located 25° from the volute tongue along circumferential direction. A transient radial force formula is proposed to evaluate the changes in radial force during startup. The streamline distribution in impeller passages and the impeller outlet pressure profile varying over time are produced. The relationship between radial force evolution and the varying axial-to-spiral vortex structure is analyzed. The radial force change mechanism is revealed. This research provides a scientific
Energy Technology Data Exchange (ETDEWEB)
Zou, Zhichao [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Wang, Fujun, E-mail: wangfj@cau.edu.cn [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Yao, Zhifeng [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Tao, Ran [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Xiao, Ruofu [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Li, Huaicheng [Shanghai Liancheng (Group) Co., Ltd., Shanghai 201812 (China)
2016-12-15
Highlights: • Conclude the characteristics of transient radial force in the startup process for a large double-suction centrifugal pump. • The overall direction of the radial force during startup process is also confirmed. • A formula used to calculate the transient radial force during startup process is proposed. • A relationship between radial force variation and axial vortex development in blade channel during the startup process is established. The mechanism of the radial force evolution is revealed. - Abstract: Double-suction centrifugal pumps play an important role in the main feedwater systems of nuclear power plant. The impeller radial force in a centrifugal pump varies dramatically during startup at the shut-off condition. In this study, the startup process of a large double-suction centrifugal pump is investigated using CFD. During testing, the impeller speed is accelerated from zero to its rated speed in 1.0 s (marked as t{sub 0}) and is then maintained at the rated speed. The results show that the radial force increase lags behind the impeller speed increase. At 0–0.4t{sub 0}, the radial force is small (approaching zero). At 0.4–1.4t{sub 0}, the radial force increases rapidly. After 1.4t{sub 0}, the average radial force stabilizes and reaches its maximum value of 55,619 N. The observed maximum radial force value during startup is approximately nine times as high as the radial force under rated condition. During startup, the overall radial force direction is proximate to the radial line located 25° from the volute tongue along circumferential direction. A transient radial force formula is proposed to evaluate the changes in radial force during startup. The streamline distribution in impeller passages and the impeller outlet pressure profile varying over time are produced. The relationship between radial force evolution and the varying axial-to-spiral vortex structure is analyzed. The radial force change mechanism is revealed. This research
DEFF Research Database (Denmark)
Kucuk, Nil; Manohara, S.R.; Hanagodimath, S.M.
2013-01-01
In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca3(PO4)2] in the energy region 0.015–15Me......V, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg–Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.4.3 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA...
Stability of radial swirl flows
International Nuclear Information System (INIS)
Dou, H S; Khoo, B C
2012-01-01
The energy gradient theory is used to examine the stability of radial swirl flows. It is found that the flow of free vortex is always stable, while the introduction of a radial flow will induce the flow to be unstable. It is also shown that the pure radial flow is stable. Thus, there is a flow angle between the pure circumferential flow and the pure radial flow at which the flow is most unstable. It is demonstrated that the magnitude of this flow angle is related to the Re number based on the radial flow rate, and it is near the pure circumferential flow. The result obtained in this study is useful for the design of vaneless diffusers of centrifugal compressors and pumps as well as other industrial devices.
International Nuclear Information System (INIS)
Akishina, T.P.; Denisova, O.Yu.; Ivanov, V.V.
2009-01-01
The problem of pion-electron identification based on their energy losses in the TRD is considered in the frame of the CBM experiment. For particles identification an artificial neural network (ANN) was used, a multilayer perceptron realized in JETNET and ROOT packages. It is demonstrated that, in order to get correct and comparable results, it is important to define the network structure correctly. The recommendations for such a selection are given. In order to achieve an acceptable level of pions suppression, the energy losses need to be transformed to more 'effective' variables. The dependency of ANN output threshold for a fixed portion of electron loss on the particle momentum is presented
Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron
Directory of Open Access Journals (Sweden)
Mohammad Subhi Al-batah
2015-01-01
Full Text Available Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP and Cascade Forward Neural Network (CFNN, are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.
Xie, Cai-Xia; Zhang, Miao; Li, Ya-Jing; Geng, Xiao-Tong; Wang, Feng-Qing; Zhang, Zhong-Yi
2017-11-01
An HPLC method was established to determine the contents of catalpol, acteoside, rehmaionoside A, rehmaionoside D, leonuride in three part of Rehmanni glutinosa in Beijing No.1 variety R. glutinosa during the growth period, This method, in combination with its HPLC fingerprint was used to evaluate its overall quality characteristics.The results showed that：① the content of main components of R. glutinosa varied in different growth stages ;② there was a great difference of the content of main components between theradial striations and the non-radial striations; ③ the two sections almost have the same content distribution of catalpol, acteoside and rehmaionoside D; ④the content of rehmaionoside A in non-radial striations was higher than that in radial striations,while the content of leonuride in radial striations was higher than that in non-radial striations.; ⑤the HPLC fingerprint of radial striations, non-radial striations and whole root tuber were basically identical, except for the big difference in the content of chemical components. The result of clustering displayed that the radial striations, non-radial striations, and whole root were divided into two groups. In conclusion, there was a significant difference in the quality characteristics of radial striations and non-radial striations of R. glutinosa. This research provides a reference for quality evaluation and geoherbalism of R. glutinosa. Copyright© by the Chinese Pharmaceutical Association.
Experimental feasibility study of radial injection cooling of three-pad radial air foil bearings
Shrestha, Suman K.
Air foil bearings use ambient air as a lubricant allowing environment-friendly operation. When they are designed, installed, and operated properly, air foil bearings are very cost effective and reliable solution to oil-free turbomachinery. Because air is used as a lubricant, there are no mechanical contacts between the rotor and bearings and when the rotor is lifted off the bearing, near frictionless quiet operation is possible. However, due to the high speed operation, thermal management is one of the very important design factors to consider. Most widely accepted practice of the cooling method is axial cooling, which uses cooling air passing through heat exchange channels formed underneath the bearing pad. Advantage is no hardware modification to implement the axial cooling because elastic foundation structure of foil bearing serves as a heat exchange channels. Disadvantage is axial temperature gradient on the journal shaft and bearing. This work presents the experimental feasibility study of alternative cooling method using radial injection of cooling air directly on the rotor shaft. The injection speeds, number of nozzles, location of nozzles, total air flow rate are important factors determining the effectiveness of the radial injection cooling method. Effectiveness of the radial injection cooling was compared with traditional axial cooling method. A previously constructed test rig was modified to accommodate a new motor with higher torque and radial injection cooling. The radial injection cooling utilizes the direct air injection to the inlet region of air film from three locations at 120° from one another with each location having three axially separated holes. In axial cooling, a certain axial pressure gradient is applied across the bearing to induce axial cooling air through bump foil channels. For the comparison of the two methods, the same amount of cooling air flow rate was used for both axial cooling and radial injection. Cooling air flow rate was
Algorithm for detection of the broken phase conductor in the radial networks
Directory of Open Access Journals (Sweden)
Ostojić Mladen M.
2016-01-01
Full Text Available The paper presents an algorithm for a directional relay to be used for a detection of the broken phase conductor in the radial networks. The algorithm would use synchronized voltages, measured at the beginning and at the end of the line, as input signals. During the process, the measured voltages would be phase-compared. On the basis of the normalized energy, the direction of the phase conductor, with a broken point, would be detected. Software tool Matlab/Simulink package has developed a radial network model which simulates the broken phase conductor. The simulations generated required input signals by which the algorithm was tested. Development of the algorithm along with the formation of the simulation model and the test results of the proposed algorithm are presented in this paper.
Schmidt, J.; Piret, C.; Zhang, N.; Kadlec, B. J.; Liu, Y.; Yuen, D. A.; Wright, G. B.; Sevre, E. O.
2008-12-01
The faster growth curves in the speed of GPUs relative to CPUs in recent years and its rapidly gained popularity has spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations and producing the attendant visualization. We are concerned with modeling tsunami waves, where computational time is of extreme essence, for broadcasting warnings. In order to test the efficacy of the GPU on the set of shallow-water equations, we employed the NVIDIA board 8600M GT on a MacBook Pro. We have compared the relative speeds between the CPU and the GPU on a single processor for two types of spatial discretization based on second-order finite-differences and radial basis functions. RBFs are a more novel method based on a gridless and a multi- scale, adaptive framework. Using the NVIDIA 8600M GT, we received a speed up factor of 8 in favor of GPU for the finite-difference method and a factor of 7 for the RBF scheme. We have also studied the atmospheric dynamics problem of swirling flows over a spherical surface and found a speed-up of 5.3 using the GPU. The time steps employed for the RBF method are larger than those used in finite-differences, because of the much fewer number of nodal points needed by RBF. Thus, in modeling the same physical time, RBF acting in concert with GPU would be the fastest way to go.
On the stability of a homogeneous barrier discharge in nitrogen relative to radial perturbations
Golubovskii, Y B; Behnke, J; Behnke, J F
2003-01-01
The influence of small radial perturbations of the cathode current on the characteristics of a homogeneous barrier discharge in nitrogen is investigated on the basis of a two-dimensional fluid model. In a Townsend discharge, radial fluctuations are substantially suppressed, which is the evidence of its stability. The oscillative mode of the Townsend discharge is also stable with regard to radial perturbations. As the discharge turns into a form controlled by spatial charge (a streamer is developed), disturbances of all radii grow in time. Such a behaviour testifies the instability of a streamer front and may cause the discharge filamentation. Since only the Townsend discharge is stable, it is possible to use a one-dimensional model to determine the domain of existence for a homogeneous discharge. The study of homogeneity domains by means of the one-dimensional model shows that at relatively large values of the voltage growth rate, discharge gap width, or capacitance of dielectric barriers the discharge tends ...
International Nuclear Information System (INIS)
Ida, Katsumi; Miura, Yukitoshi; Itoh, Sanae
1994-10-01
Radial structures of plasma rotation and radial electric field are experimentally studied in tokamak, heliotron/torsatron and stellarator devices. The perpendicular and parallel viscosities are measured. The parallel viscosity, which is dominant in determining the toroidal velocity in heliotron/torsatron and stellarator devices, is found to be neoclassical. On the other hand, the perpendicular viscosity, which is dominant in dictating the toroidal rotation in tokamaks, is anomalous. Even without external momentum input, both a plasma rotation and a radial electric field exist in tokamaks and heliotrons/torsatrons. The observed profiles of the radial electric field do not agree with the theoretical prediction based on neoclassical transport. This is mainly due to the existence of anomalous perpendicular viscosity. The shear of the radial electric field improves particle and heat transport both in bulk and edge plasma regimes of tokamaks. (author) 95 refs
Radial retinotomy in the macula.
Bovino, J A; Marcus, D F
1984-01-01
Radial retinotomy is an operative procedure usually performed in the peripheral or equatorial retina. To facilitate retinal attachment, the authors used intraocular scissors to perform radial retinotomy in the macula of two patients during vitrectomy surgery. In the first patient, a retinal detachment complicated by periretinal proliferation and macula hole formation was successfully reoperated with the aid of three radial cuts in the retina at the edges of the macular hole. In the second patient, an intraoperative retinal tear in the macula during diabetic vitrectomy was also successfully repaired with the aid of radial retinotomy. In both patients, retinotomy in the macula was required because epiretinal membranes, which could not be easily delaminated, were hindering retinal reattachment.
Radial head dislocation during proximal radial shaft osteotomy.
Hazel, Antony; Bindra, Randy R
2014-03-01
The following case report describes a 48-year-old female patient with a longstanding both-bone forearm malunion, who underwent osteotomies of both the radius and ulna to improve symptoms of pain and lack of rotation at the wrist. The osteotomies were templated preoperatively. During surgery, after performing the planned radial shaft osteotomy, the authors recognized that the radial head was subluxated. The osteotomy was then revised from an opening wedge to a closing wedge with improvement of alignment and rotation. The case report discusses the details of the operation, as well as ways in which to avoid similar shortcomings in the future. Copyright © 2014 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.
Improved WKB radial wave functions in several bases
International Nuclear Information System (INIS)
Durand, B.; Durand, L.; Department of Physics, University of Wisconsin, Madison, Wisconsin 53706)
1986-01-01
We develop approximate WKB-like solutions to the radial Schroedinger equation for problems with an angular momentum barrier using Riccati-Bessel, Coulomb, and harmonic-oscillator functions as basis functions. The solutions treat the angular momentum singularity near the origin more accurately in leading approximation than the standard WKB solutions based on sine waves. The solutions based on Riccati-Bessel and free Coulomb wave functions continue smoothly through the inner turning point and are appropriate for scattering problems. The solutions based on oscillator and bound Coulomb wave functions incorporate both turning points smoothly and are particularly appropriate for bound-state problems; no matching of piecewise solutions using Airy functions is necessary
Tamouridou, Afroditi A; Alexandridis, Thomas K; Pantazi, Xanthoula E; Lagopodi, Anastasia L; Kashefi, Javid; Kasampalis, Dimitris; Kontouris, Georgios; Moshou, Dimitrios
2017-10-11
Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.
Radial cracks and fracture mechanism of radially oriented ring 2:17 type SmCo magnets
International Nuclear Information System (INIS)
Tian Jianjun; Pan Dean; Zhou Hao; Yin Fuzheng; Tao Siwu; Zhang Shengen; Qu Xuanhui
2009-01-01
Radially oriented ring 2:17 type SmCo magnets have different microstructure in the radial direction (easy magnetization) and axial direction (hard magnetization). The structure of the cross-section in radial direction is close-packed atomic plane, which shows cellular microstructure. The microstructure of the cross-section in axial direction consists of a mixture of rhombic microstructure and parallel lamella phases. So the magnets have obvious anisotropy of thermal expansion in different directions. The difference of the thermal expansion coefficients reaches the maximum value at 830-860 deg. C, which leads to radial cracks during quenching. The magnets have high brittlement because there are fewer slip systems in crystal structure. The fracture is brittle cleavage fracture.
Directory of Open Access Journals (Sweden)
M.L.B. Simas
2005-03-01
Full Text Available An assumption commonly made in the study of visual perception is that the lower the contrast threshold for a given stimulus, the more sensitive and selective will be the mechanism that processes it. On the basis of this consideration, we investigated contrast thresholds for two classes of stimuli: sine-wave gratings and radial frequency stimuli (i.e., j0 targets or stimuli modulated by spherical Bessel functions. Employing a suprathreshold summation method, we measured the selectivity of spatial and radial frequency filters using either sine-wave gratings or j0 target contrast profiles at either 1 or 4 cycles per degree of visual angle (cpd, as the test frequencies. Thus, in a forced-choice trial, observers chose between a background spatial (or radial frequency alone and the given background stimulus plus the test frequency (1 or 4 cpd sine-wave grating or radial frequency. Contrary to our expectations, the results showed elevated thresholds (i.e., inhibition for sine-wave gratings and decreased thresholds (i.e., summation for radial frequencies when background and test frequencies were identical. This was true for both 1- and 4-cpd test frequencies. This finding suggests that sine-wave gratings and radial frequency stimuli are processed by different quasi-linear systems, one working at low luminance and contrast level (sine-wave gratings and the other at high luminance and contrast levels (radial frequency stimuli. We think that this interpretation is consistent with distinct foveal only and foveal-parafoveal mechanisms involving striate and/or other higher visual areas (i.e., V2 and V4.
Perceived radial translation during centrifugation
Bos, J.E.; Correia Grácio, B.J.
2015-01-01
BACKGROUND: Linear acceleration generally gives rise to translation perception. Centripetal acceleration during centrifugation, however, has never been reported giving rise to a radial, inward translation perception. OBJECTIVE: To study whether centrifugation can induce a radial translation
Temperature and boron dependencies of buckling and radial reflector saving for VVER lattices
International Nuclear Information System (INIS)
Alvarez, C.
1990-01-01
The temperature and boron dependencies of buckling and radial reflectors savings are analyzed in this paper on the basis of the results from the calculations ZR-6M critical assembly. These dependencies are related to the physical behavior of temperature and boron reactivity coefficients for the cores of VVER-type critical facilities. As a byproduct, the parameter was also investigated and its dependence on water density was determined
Directory of Open Access Journals (Sweden)
Kresno Wikan Sadono
2016-12-01
Full Text Available Persamaan differensial banyak digunakan untuk menggambarkan berbagai fenomena dalam bidang sains dan rekayasa. Berbagai masalah komplek dalam kehidupan sehari-hari dapat dimodelkan dengan persamaan differensial dan diselesaikan dengan metode numerik. Salah satu metode numerik, yaitu metode meshfree atau meshless berkembang akhir-akhir ini, tanpa proses pembuatan elemen pada domain. Penelitian ini menggabungkan metode meshless yaitu radial basis point interpolation method (RPIM dengan integrasi waktu discontinuous Galerkin method (DGM, metode ini disebut RPIM-DGM. Metode RPIM-DGM diaplikasikan pada advection equation pada satu dimensi. RPIM menggunakan basis function multiquadratic function (MQ dan integrasi waktu diturunkan untuk linear-DGM maupun quadratic-DGM. Hasil simulasi menunjukkan, metode ini mendekati hasil analitis dengan baik. Hasil simulasi numerik dengan RPIM DGM menunjukkan semakin banyak node dan semakin kecil time increment menunjukkan hasil numerik semakin akurat. Hasil lain menunjukkan, integrasi numerik dengan quadratic-DGM untuk suatu time increment dan jumlah node tertentu semakin meningkatkan akurasi dibandingkan dengan linear-DGM. [Title: Numerical solution of advection equation with radial basis interpolation method and discontinuous Galerkin method for time integration] Differential equation is widely used to describe a variety of phenomena in science and engineering. A variety of complex issues in everyday life can be modeled with differential equations and solved by numerical method. One of the numerical methods, the method meshfree or meshless developing lately, without making use of the elements in the domain. The research combines methods meshless, i.e. radial basis point interpolation method with discontinuous Galerkin method as time integration method. This method is called RPIM-DGM. The RPIM-DGM applied to one dimension advection equation. The RPIM using basis function multiquadratic function and time
Chien, Ying-Ren
2018-04-10
For power-line-based sensor networks, impulsive noise (IN) will dramatically degrade the data transmission rate in the power line. In this paper, we present a multilayer perceptron (MLP)-based approach to detect IN in orthogonal frequency-division multiplexing (OFDM)-based baseband power line communications (PLCs). Combining the MLP-based IN detection method with the outlier detection theory allows more accurate identification of the harmful residual IN. For OFDM-based PLC systems, the high peak-to-average power ratio (PAPR) of the received signal makes detection of harmful residual IN more challenging. The detection mechanism works in an iterative receiver that contains a pre-IN mitigation and a post-IN mitigation. The pre-IN mitigation is meant to null the stronger portion of IN, while the post-IN mitigation suppresses the residual portion of IN using an iterative process. Compared with previously reported IN detectors, the simulation results show that our MLP-based IN detector improves the resulting bit error rate (BER) performance.
International Nuclear Information System (INIS)
Hazeltine, R.D.
1988-12-01
The boundary layer arising in the radial vicinity of a tokamak limiter is examined, with special reference to the TEXT tokamak. It is shown that sheath structure depends upon the self-consistent effects of ion guiding-center orbit modification, as well as the radial variation of E /times/ B-induced toroidal rotation. Reasonable agreement with experiment is obtained from an idealized model which, however simplified, preserves such self-consistent effects. It is argued that the radial sheath, which occurs whenever confining magnetic field-lines lie in the plasma boundary surface, is an object of some intrinsic interest. It differs from the more familiar axial sheath because magnetized charges respond very differently to parallel and perpendicular electric fields. 11 refs., 1 fig
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.
DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel
2016-03-29
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
Directory of Open Access Journals (Sweden)
Mohammad Fathian
2012-04-01
Full Text Available In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.
Jäger, Jörg M; Schöllhorn, Wolfgang I
2012-04-01
Offensive and defensive systems of play represent important aspects of team sports. They include the players' positions at certain situations during a match, i.e., when players have to be on specific positions on the court. Patterns of play emerge based on the formations of the players on the court. Recognition of these patterns is important to react adequately and to adjust own strategies to the opponent. Furthermore, the ability to apply variable patterns of play seems to be promising since they make it harder for the opponent to adjust. The purpose of this study is to identify different team tactical patterns in volleyball and to analyze differences in variability. Overall 120 standard situations of six national teams in women's volleyball are analyzed during a world championship tournament. Twenty situations from each national team are chosen, including the base defence position (start configuration) and the two players block with middle back deep (end configuration). The shapes of the defence formations at the start and end configurations during the defence of each national team as well as the variability of these defence formations are statistically analyzed. Furthermore these shapes data are used to train multilayer perceptrons in order to test whether artificial neural networks can recognize the teams by their tactical patterns. Results show significant differences between the national teams in both the base defence position at the start and the two players block with middle back deep at the end of the standard defence situation. Furthermore, the national teams show significant differences in variability of the defence systems and start-positions are more variable than the end-positions. Multilayer perceptrons are able to recognize the teams at an average of 98.5%. It is concluded that defence systems in team sports are highly individual at a competitive level and variable even in standard situations. Artificial neural networks can be used to recognize
Radial pseudoaneurysm following diagnostic coronary angiography
Directory of Open Access Journals (Sweden)
Shankar Laudari
2015-06-01
Full Text Available The radial artery access has gained popularity as a method of diagnostic coronary catheterization compared to femoral artery puncture in terms of vascular complications and early ambulation. However, very rare complication like radial artery pseudoaneurysm may occur following cardiac catheterization which may give rise to serious consequences. Here, we report a patient with radial pseudoaneurysm following diagnostic coronary angiography. Adequate and correct methodology of compression of radial artery following puncture for maintaining hemostasis is the key to prevention.DOI: http://dx.doi.org/10.3126/jcmsn.v10i3.12776 Journal of College of Medical Sciences-Nepal, 2014, Vol-10, No-3, 48-50
DEFF Research Database (Denmark)
Carranza, Christian L; Ballegaard, Martin; Werner, Mads U
2014-01-01
the postoperative complications will be registered, and we will evaluate muscular function, scar appearance, vascular supply to the hand, and the graft patency including the patency of the central radial artery anastomosis. A patency evaluation by multi-slice computer tomography will be done at one year...... to aorto-radial revascularisation techniques but this objective is exploratory. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01848886.Danish Ethics committee number: H-3-2012-116.Danish Data Protection Agency: 2007-58-0015/jr.n:30-0838....
Temperature and boron dependencies of buckling and radial reflector savings for VVER lattices
International Nuclear Information System (INIS)
Alvarez, C.
1990-01-01
The temperature and boron dependencies of buckling and radial reflector savings are analyzed in this paper on the basis of the results from the calculations for the ZR-6M critical assembly. These dependencies are related to he physical behaviour of temperature and boron reactivity coefficients for the cores of VVER-type critical facilities. As a byproduct, the dp/dBg 2 parameter was also investigated and its dependence on water density was determined
Numerical simulation of liquid-metal-flows in radial-toroidal-radial bends
International Nuclear Information System (INIS)
Molokov, S.; Buehler, L.
1993-09-01
Magnetohydrodynamic flows in a U-bend and right-angle bend are considered with reference to the radial-toroidal-radial concept of a self-cooled liquid-metal blanket. The ducts composing bends have rectangular cross-section. The applied magnetic field is aligned with the toroidal duct and perpendicular to the radial ones. At high Hartmann number the flow region is divided into cores and boundary layers of different types. The magnetohydrodynamic equations are reduced to a system of partial differential equations governing wall electric potentials and the core pressure. The system is solved numerically by two different methods. The first method is iterative with iteration between wall potential and the core pressure. The second method is a general one for the solution of the core flow equations in curvilinear coordinates generated by channel geometry and magnetic field orientation. Results obtained are in good agreement. They show, that the 3D-pressure drop of MHD flows in a U-bend is not a critical issue for blanket applications. (orig./HP) [de
Dedicated radial ventriculography pigtail catheter
Energy Technology Data Exchange (ETDEWEB)
Vidovich, Mladen I., E-mail: miv@uic.edu
2013-05-15
A new dedicated cardiac ventriculography catheter was specifically designed for radial and upper arm arterial access approach. Two catheter configurations have been developed to facilitate retrograde crossing of the aortic valve and to conform to various subclavian, ascending aortic and left ventricular anatomies. The “short” dedicated radial ventriculography catheter is suited for horizontal ascending aortas, obese body habitus, short stature and small ventricular cavities. The “long” dedicated radial ventriculography catheter is suited for vertical ascending aortas, thin body habitus, tall stature and larger ventricular cavities. This new design allows for improved performance, faster and simpler insertion in the left ventricle which can reduce procedure time, radiation exposure and propensity for radial artery spasm due to excessive catheter manipulation. Two different catheter configurations allow for optimal catheter selection in a broad range of patient anatomies. The catheter is exceptionally stable during contrast power injection and provides equivalent cavity opacification to traditional femoral ventriculography catheter designs.
Torrent, Daniel; Sánchez-Dehesa, José
2009-08-07
We demonstrate that metamaterials with anisotropic properties can be used to develop a new class of periodic structures that has been named radial wave crystals. They can be sonic or photonic, and wave propagation along the radial directions is obtained through Bloch states like in usual sonic or photonic crystals. The band structure of the proposed structures can be tailored in a large amount to get exciting novel wave phenomena. For example, it is shown that acoustical cavities based on radial sonic crystals can be employed as passive devices for beam forming or dynamically orientated antennas for sound localization.
Directory of Open Access Journals (Sweden)
Luiz Ernani Meira Jr.
2011-12-01
Full Text Available Os aneurismas da artéria radial são extremamente raros. Em sua maioria, consistem de pseudoaneurismas pós-traumáticos. Os aneurismas da artéria radial verdadeiros podem ser idiopáticos, congênitos, pós-estenóticos ou associados a patologias, tais como vasculites e doenças do tecido conjuntivo. Foi relatado um caso de aneurisma idiopático de artéria radial em uma criança de três anos, que, após completa investigação diagnóstica complementar, foi submetida à ressecção cirúrgica.Radial artery aneurysms are extremely rare. Post-traumatic pseudoaneurysms are the vast majority. True radial artery aneurysms can be idiopathic, congenital, poststenotic, or associated with some pathologies, such as vasculitis and conjunctive tissue diseases. We report a case of an idiopathic aneurysm of the radial artery in a three-year-old child who was submitted to surgical resection after a complete diagnostic approach.
Energy Technology Data Exchange (ETDEWEB)
Sun, W; Jiang, M; Yin, F [Duke University Medical Center, Durham, NC (United States)
2016-06-15
Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, a Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results
Ulnar nerve entrapment complicating radial head excision
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Kevin Parfait Bienvenu Bouhelo-Pam
Full Text Available Introduction: Several mechanisms are involved in ischemia or mechanical compression of ulnar nerve at the elbow. Presentation of case: We hereby present the case of a road accident victim, who received a radial head excision for an isolated fracture of the radial head and complicated by onset of cubital tunnel syndrome. This outcome could be the consequence of an iatrogenic valgus of the elbow due to excision of the radial head. Hitherto the surgical treatment of choice it is gradually been abandoned due to development of radial head implant arthroplasty. However, this management option is still being performed in some rural centers with low resources. Discussion: The radial head plays an important role in the stability of the elbow and his iatrogenic deformity can be complicated by cubital tunnel syndrome. Conclusion: An ulnar nerve release was performed with favorable outcome. Keywords: Cubital tunnel syndrome, Peripheral nerve palsy, Radial head excision, Elbow valgus
Radial lean direct injection burner
Khan, Abdul Rafey; Kraemer, Gilbert Otto; Stevenson, Christian Xavier
2012-09-04
A burner for use in a gas turbine engine includes a burner tube having an inlet end and an outlet end; a plurality of air passages extending axially in the burner tube configured to convey air flows from the inlet end to the outlet end; a plurality of fuel passages extending axially along the burner tube and spaced around the plurality of air passage configured to convey fuel from the inlet end to the outlet end; and a radial air swirler provided at the outlet end configured to direct the air flows radially toward the outlet end and impart swirl to the air flows. The radial air swirler includes a plurality of vanes to direct and swirl the air flows and an end plate. The end plate includes a plurality of fuel injection holes to inject the fuel radially into the swirling air flows. A method of mixing air and fuel in a burner of a gas turbine is also provided. The burner includes a burner tube including an inlet end, an outlet end, a plurality of axial air passages, and a plurality of axial fuel passages. The method includes introducing an air flow into the air passages at the inlet end; introducing a fuel into fuel passages; swirling the air flow at the outlet end; and radially injecting the fuel into the swirling air flow.
Baasch, B.; M"uller, H.; von Dobeneck, T.
2018-04-01
In this work we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine learning techniques. Nonnegative matrix factorisation is used to determine grain-size end-members from sediment surface samples. Four end-members were found which well represent the variety of sediments in the study area. A radial-basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.
Roshani, G H; Karami, A; Salehizadeh, A; Nazemi, E
2017-11-01
The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152 Eu and 137 Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Zaqoot, Hossam Adel; Ansari, Abdul Khalique; Unar, Mukhtiar Ali; Khan, Shaukat Hyat
2009-01-01
Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.
Przednowek, Krzysztof; Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam
2017-12-01
This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.
Directory of Open Access Journals (Sweden)
Przednowek Krzysztof
2017-12-01
Full Text Available This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.
Anomalies of radial and ulnar arteries
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Rajani Singh
Full Text Available Abstract During dissection conducted in an anatomy department of the right upper limb of the cadaver of a 70-year-old male, both origin and course of the radial and ulnar arteries were found to be anomalous. After descending 5.5 cm from the lower border of the teres major, the brachial artery anomalously bifurcated into a radial artery medially and an ulnar artery laterally. In the arm, the ulnar artery lay lateral to the median nerve. It followed a normal course in the forearm. The radial artery was medial to the median nerve in the arm and then, at the level of the medial epicondyle, it crossed from the medial to the lateral side of the forearm, superficial to the flexor muscles. The course of the radial artery was superficial and tortuous throughout the arm and forearm. The variations of radial and ulnar arteries described above were associated with anomalous formation and course of the median nerve in the arm. Knowledge of neurovascular anomalies are important for vascular surgeons and radiologists.
LENUS (Irish Health Repository)
Pate, G
2011-10-01
A survey was conducted of medication administered during radial artery cannulation for coronary angiography in 2009 in Ireland; responses were obtained for 15 of 20 centres, in 5 of which no radial access procedures were undertaken. All 10 (100%) centres which provided data used heparin and one or more anti-spasmodics; verapamil in 9 (90%), nitrate in 1 (10%), both in 2 (20%). There were significant variations in the doses used. Further work needs to be done to determine the optimum cocktail to prevent radial artery injury following coronary angiography.
Design of radial reinforcement for prestressed concrete containments
Energy Technology Data Exchange (ETDEWEB)
Wang, Shen, E-mail: swang@bechtel.com [Bechtel Power Corporation, 5275 Westview Drive, BP2-2C3, Frederick, MD 21703 (United States); Munshi, Javeed A., E-mail: jamunshi@bechtel.com [Bechtel Power Corporation, 5275 Westview Drive, BP2-2C3, Frederick, MD 21703 (United States)
2013-02-15
Highlights: ► A rigorous formulae is proposed to calculate radial stress within prestressed concrete containments. ► The proposed method is validated by finite element analysis in an illustrative practical example. ► A partially prestressed condition is more critical than a fully prestressed condition for radial tension. ► Practical design consideration is provided for detailing of radial reinforcement. -- Abstract: Nuclear containments are critical components for safety of nuclear power plants. Failure can result in catastrophic safety consequences as a result of leakage of radiation. Prestressed concrete containments have been used in large nuclear power plants with significant design internal pressure. These containments are generally reinforced with prestressing tendons in the circumferential (hoop) and meridional (vertical) directions. The curvature effect of the tendons introduces radial tensile stresses in the concrete shell which are generally neglected in the design of such structures. It is assumed that such tensile radial stresses are small as such no radial reinforcement is provided for this purpose. But recent instances of significant delaminations in Crystal River Unit 3 in Florida have elevated the need for reevaluation of the radial tension issue in prestressed containment. Note that currently there are no well accepted industry standards for design and detailing of radial reinforcement. This paper discusses the issue of radial tension in prestressed cylindrical and dome shaped structures and proposes formulae to calculate radial stresses. A practical example is presented to illustrate the use of the proposed method which is then verified by using state of art finite element analysis. This paper also provides some practical design consideration for detailing of radial reinforcement in prestressed containments.
Methods and apparatus for radially compliant component mounting
Bulman, David Edward [Cincinnati, OH; Darkins, Jr., Toby George; Stumpf, James Anthony [Columbus, IN; Schroder, Mark S [Greenville, SC; Lipinski, John Joseph [Simpsonville, SC
2012-03-27
Methods and apparatus for a mounting assembly for a liner of a gas turbine engine combustor are provided. The combustor includes a combustor liner and a radially outer annular flow sleeve. The mounting assembly includes an inner ring surrounding a radially outer surface of the liner and including a plurality of axially extending fingers. The mounting assembly also includes a radially outer ring coupled to the inner ring through a plurality of spacers that extend radially from a radially outer surface of the inner ring to the outer ring.
Computer model analysis of the radial artery pressure waveform.
Schwid, H A; Taylor, L A; Smith, N T
1987-10-01
Simultaneous measurements of aortic and radial artery pressures are reviewed, and a model of the cardiovascular system is presented. The model is based on resonant networks for the aorta and axillo-brachial-radial arterial system. The model chosen is a simple one, in order to make interpretation of the observed relationships clear. Despite its simplicity, the model produces realistic aortic and radial artery pressure waveforms. It demonstrates that the resonant properties of the arterial wall significantly alter the pressure waveform as it is propagated from the aorta to the radial artery. Although the mean and end-diastolic radial pressures are usually accurate estimates of the corresponding aortic pressures, the systolic pressure at the radial artery is often much higher than that of the aorta due to overshoot caused by the resonant behavior of the radial artery. The radial artery dicrotic notch is predominantly dependent on the axillo-brachial-radial arterial wall properties, rather than on the aortic valve or peripheral resistance. Hence the use of the radial artery dicrotic notch as an estimate of end systole is unreliable. The rate of systolic upstroke, dP/dt, of the radial artery waveform is a function of many factors, making it difficult to interpret. The radial artery waveform usually provides accurate estimates for mean and diastolic aortic pressures; for all other measurements it is an inadequate substitute for the aortic pressure waveform. In the presence of low forearm peripheral resistance the mean radial artery pressure may significantly underestimate the mean aortic pressure, as explained by a voltage divider model.
Neural networks and statistical learning
Du, Ke-Lin
2014-01-01
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...
MODELS TO ESTIMATE BRAZILIAN INDIRECT TENSILE STRENGTH OF LIMESTONE IN SATURATED STATE
Directory of Open Access Journals (Sweden)
Zlatko Briševac
2016-06-01
Full Text Available There are a number of methods of estimating physical and mechanical characteristics. Principally, the most widely used is the regression, but recently the more sophisticated methods such as neural networks has frequently been applied, as well. This paper presents the models of a simple and a multiple regression and the neural networks – types Radial Basis Function and Multiple Layer Perceptron, which can be used for the estimate of the Brazilian indirect tensile strength in saturated conditions. The paper includes the issues of collecting the data for the analysis and modelling and the overview of the performed analysis of the efficacy assessment of the estimate of each model. After the assessment, the model which provides the best estimate was selected, including the model which could have the most wide-spread application in the engineering practice.
Chaotic time series. Part II. System Identification and Prediction
Directory of Open Access Journals (Sweden)
Bjørn Lillekjendlie
1994-10-01
Full Text Available This paper is the second in a series of two, and describes the current state of the art in modeling and prediction of chaotic time series. Sample data from deterministic non-linear systems may look stochastic when analysed with linear methods. However, the deterministic structure may be uncovered and non-linear models constructed that allow improved prediction. We give the background for such methods from a geometrical point of view, and briefly describe the following types of methods: global polynomials, local polynomials, multilayer perceptrons and semi-local methods including radial basis functions. Some illustrative examples from known chaotic systems are presented, emphasising the increase in prediction error with time. We compare some of the algorithms with respect to prediction accuracy and storage requirements, and list applications of these methods to real data from widely different areas.
The effect of texture on the shaft surface on the sealing performance of radial lip seals
Guo, Fei; Jia, XiaoHong; Gao, Zhi; Wang, YuMing
2014-07-01
On the basis of elastohydrodynamic model, the present study numerically analyzes the effect of various microdimple texture shapes, namely, circular, square, oriented isosceles triangular, on the pumping rate and the friction torque of radial lip seals, and determines the microdimple texture shape that can produce positive pumping rate. The area ratio, depth and shape dimension of a single texture are the most important geometric parameters which influence the tribological performance. According to the selected texture shape, parameter analysis is conducted to determine the optimal combination for the above three parameters. Simultaneously, the simulated performances of radial lip seal with texture on the shaft surface are compared with those of the conventional lip seal without any texture on the shaft surface.
Radial rescaling approach for the eigenvalue problem of a particle in an arbitrarily shaped box.
Lijnen, Erwin; Chibotaru, Liviu F; Ceulemans, Arnout
2008-01-01
In the present work we introduce a methodology for solving a quantum billiard with Dirichlet boundary conditions. The procedure starts from the exactly known solutions for the particle in a circular disk, which are subsequently radially rescaled in such a way that they obey the new boundary conditions. In this way one constructs a complete basis set which can be used to obtain the eigenstates and eigenenergies of the corresponding quantum billiard to a high level of precision. Test calculations for several regular polygons show the efficiency of the method which often requires one or two basis functions to describe the lowest eigenstates with high accuracy.
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Ali Tarighatnia
2016-06-01
Conclusion: On the basis of the results obtained in this study, no differences were found in patients’ radiation dose in both access groups, therefore with regard to comparatively more clinical advantages associated with the Trans-radial access technique it might be a good substitute for Trans-femoral access.
MR accuracy and arthroscopic incidence of meniscal radial tears
Energy Technology Data Exchange (ETDEWEB)
Magee, Thomas; Shapiro, Marc; Williams, David [Department of Radiology, Neuroimaging Institute, 27 East Hibiscus Blvd., Melbourne, FL 32901 (United States)
2002-12-01
A meniscal radial tear is a vertical tear that involves the inner meniscal margin. The tear is most frequent in the middle third of the lateral meniscus and may extend outward in any direction. We report (1) the arthroscopic incidence of radial tears, (2) MR signs that aid in the detection of radial tears and (3) our prospective accuracy in detection of radial tears. Design and patients. Three musculoskeletal radiologists prospectively read 200 consecutive MR examinations of the knee that went on to arthroscopy by one orthopedic surgeon. MR images were assessed for location and MR characteristics of radial tears. MR criteria used for diagnosis of a radial tear were those outlined by Tuckman et al.: truncation, abnormal morphology and/or lack of continuity or absence of the meniscus on one or more MR images. An additional criterion used was abnormal increased signal in that area on fat-saturated proton density or T2-weighted coronal and sagittal images. Prospective MR readings were correlated with the arthroscopic findings.Results. Of the 200 consecutive knee arthroscopies, 28 patients had radial tears reported arthroscopically (14% incidence). MR readings prospectively demonstrated 19 of the 28 radial tears (68% sensitivity) when the criteria for diagnosis of a radial tear were truncation or abnormal morphology of the meniscus. With the use of the additional criterion of increased signal in the area of abnormal morphology on fat-saturated T2-weighted or proton density weighted sequences, the prospective sensitivity was 25 of 28 radial tears (89% sensitivity). There were no radial tears described in MR reports that were not demonstrated on arthroscopy (i.e., there were no false positive MR readings of radial tears in these 200 patients). Radial tears are commonly seen at arthroscopy. There was a 14% incidence in this series of 200 patients who underwent arthroscopy. Prospective detection of radial tears was 68% as compared with arthroscopy when the criteria as
MR accuracy and arthroscopic incidence of meniscal radial tears
International Nuclear Information System (INIS)
Magee, Thomas; Shapiro, Marc; Williams, David
2002-01-01
A meniscal radial tear is a vertical tear that involves the inner meniscal margin. The tear is most frequent in the middle third of the lateral meniscus and may extend outward in any direction. We report (1) the arthroscopic incidence of radial tears, (2) MR signs that aid in the detection of radial tears and (3) our prospective accuracy in detection of radial tears. Design and patients. Three musculoskeletal radiologists prospectively read 200 consecutive MR examinations of the knee that went on to arthroscopy by one orthopedic surgeon. MR images were assessed for location and MR characteristics of radial tears. MR criteria used for diagnosis of a radial tear were those outlined by Tuckman et al.: truncation, abnormal morphology and/or lack of continuity or absence of the meniscus on one or more MR images. An additional criterion used was abnormal increased signal in that area on fat-saturated proton density or T2-weighted coronal and sagittal images. Prospective MR readings were correlated with the arthroscopic findings.Results. Of the 200 consecutive knee arthroscopies, 28 patients had radial tears reported arthroscopically (14% incidence). MR readings prospectively demonstrated 19 of the 28 radial tears (68% sensitivity) when the criteria for diagnosis of a radial tear were truncation or abnormal morphology of the meniscus. With the use of the additional criterion of increased signal in the area of abnormal morphology on fat-saturated T2-weighted or proton density weighted sequences, the prospective sensitivity was 25 of 28 radial tears (89% sensitivity). There were no radial tears described in MR reports that were not demonstrated on arthroscopy (i.e., there were no false positive MR readings of radial tears in these 200 patients). Radial tears are commonly seen at arthroscopy. There was a 14% incidence in this series of 200 patients who underwent arthroscopy. Prospective detection of radial tears was 68% as compared with arthroscopy when the criteria as
Radial velocities of RR Lyrae stars
International Nuclear Information System (INIS)
Hawley, S.L.; Barnes, T.G. III
1985-01-01
283 spectra of 57 RR Lyrae stars have been obtained using the 2.1-m telescope at McDonald Observatory. Radial velocities were determined using a software cross-correlation technique. New mean radial velocities were determined for 46 of the stars. 11 references
A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.
Araújo, Ricardo de A
2012-04-01
Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature. Copyright © 2011 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Hewes, R.C.; Miller, T.R.
1988-01-01
To profile optimally each portion of the meniscus, the authors use the multiangle, multisection feature of a General Electric SIGNA 1.5-T imager to produce radial images centered on each meniscus. A total of 12-15 sections are imaged at 10 0 -15 0 intervals of each meniscus, yielding perpendicular images of the entire meniscus, comparable with the arthrographic tangential views. The authors review their technique and demonstrate correlation cases between the radial gradient recalled acquisition in a steady state sequences, sagittal and coronal MR images, and arthrograms. Radial images should be a routine part of knee MR imaging
21 CFR 866.4800 - Radial immunodiffusion plate.
2010-04-01
...) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Immunology Laboratory Equipment and Reagents § 866.4800 Radial immunodiffusion plate. (a) Identification. A radial immunodiffusion plate for clinical use...
Stirling Engine With Radial Flow Heat Exchangers
Vitale, N.; Yarr, George
1993-01-01
Conflict between thermodynamical and structural requirements resolved. In Stirling engine of new cylindrical configuration, regenerator and acceptor and rejector heat exchangers channel flow of working gas in radial direction. Isotherms in regenerator ideally concentric cylinders, and gradient of temperature across regenerator radial rather than axial. Acceptor and rejector heat exchangers located radially inward and outward of regenerator, respectively. Enables substantial increase in power of engine without corresponding increase in diameter of pressure vessel.
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.
Using multilayer perceptron and a satellite image for the estimation of soil salinity
International Nuclear Information System (INIS)
Lau, A.; Ruiz, M.E.; Garcia, E.
2008-01-01
Applying the model of the Perceptron multilayer with momentum of an artificial neural network particularly and a multispectral image of high resolution spatial and radiometric, for the first time estimated the salinity of the soil cultivated with sugar cane. The study area is the UBPC 'Lazaro Romero' of the sugar company 'Hector Molina' of the locality San Nicolas de Bari, Havana province, located at 22° 44' North latitude and 81 ° 56' longitude West. The experiments were made in the framework of the El-479 project funded by the Inter universities Council of Flanders, Belgium. 36 samples geo referenced of soils were taken at 3 depths in each of the 4 sugar cane selected blocks, which determined the electrical conductivity of the saturation extract; half of that amount of data was used for the training of the network and the other half for control in a computer program of the artificial neural network created to that effect, together with the reflectance of vegetation indexes for the image, which were maps of electrical conductivity of each block and bands. They were compared with those obtained by simple linear regression between the normalized difference vegetation index and electrical conductivity, Ndv with the approach of the neuronal network, the correlation coefficient was 0.78 to 0.83, while the linear regression was between 0.65 to 0.75
Radial pattern of nuclear decay processes
International Nuclear Information System (INIS)
Iskra, W.; Mueller, M.; Rotter, I.; Technische Univ. Dresden
1994-05-01
At high level density of nuclear states, a separation of different time scales is observed (trapping effect). We calculate the radial profile of partial widths in the framework of the continuum shell model for some 1 - resonances with 2p-2h nuclear structure in 16 O as a function of the coupling strength to the continuum. A correlation between the lifetime of a nuclear state and the radial profile of the corresponding decay process is observed. We conclude from our numerical results that the trapping effect creates structures in space and time characterized by a small radial extension and a short lifetime. (orig.)
Intraluminal milrinone for dilation of the radial artery graft.
García-Rinaldi, R; Soltero, E R; Carballido, J; Mojica, J
1999-01-01
There is renewed interest in the use of the radial artery as a conduit for coronary artery bypass surgery. The radial artery is, however, a very muscular artery, prone to vasospasm. Milrinone, a potent vasodilator, has demonstrated vasodilatory properties superior to those of papaverine. In this report, we describe our technique of radial artery harvesting and the adjunctive use of intraluminal milrinone as a vasodilator in the preparation of this conduit for coronary artery bypass grafting. We have used these techniques in 25 patients who have undergone coronary artery bypass grafting using the radial artery. No hand ischemic complications have been observed in this group. Intraluminal milrinone appears to dilate and relax the radial artery, rendering this large conduit spasm free and very easy to use. We recommend the skeletonization technique for radial artery harvesting and the use of intraluminal milrinone as a radial artery vasodilator in routine myocardial revascularization. PMID:10524740
Tien Bui, Dieu; Hoang, Nhat-Duc
2017-09-01
In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.
Su, Feng-Chun; Ho, Chung-Ru; Kuo, Nan-Jung
2005-01-01
The multilayer perceptron (MLP) neural network have been widely used to fit non-linear transfer function and performed well. In this study, we use MLP to estimate chlorophyll-a concentrations from marine reflectance measures. The optical data were assembled from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm Mini-workshop (SeaBAM). Most bio-optical algorithms use simple ratios of reflectance in blue and green bands or combinations of ratios as parameters for regression analysis. Regression analysis has limitations for nonlinear function. Neural network, however, have been shown better performance for nonlinear problems. The result showed that accuracy of chlorophyll-a concentration using MLP is much higher than that of regression method. Nevertheless, using all of the five bands as input can derive the best performance. The results showed that each band could carry some useful messages for ocean color remote sensing. Only using band ratio (OC2) or band switch (OC4) might lose some available information. By preprocessing reflectance data with the principle component analysis (PCA), MLP could derive much better accuracy than traditional methods. The result showed that the reflectance of all bands should not be ignored for deriving the chlorophyll-a concentration because each band carries different useful ocean color information.
Whittemore, Jacqueline C; Nystrom, Michael R; Mawby, Dianne I
2017-04-01
OBJECTIVE To assess the effects of age, body condition score (BCS), and muscle condition score (MCS) on radial and coccygeal systolic arterial blood pressure (SAP) in cats. DESIGN Prospective randomized trial. ANIMALS 66 privately owned cats enrolled between May and December 2010. PROCEDURES BCS and MCS of cats were assessed by 2 investigators; SAP was measured via Doppler ultrasonic flow detector, with cats positioned in right lateral or sternal recumbency for measurements at the radial or coccygeal artery, respectively, with order of site randomized. Associations among variables were assessed through correlation coefficients, partial correlation coefficients, and ANCOVA. RESULTS Interrater reliability for BCS and MCS assessment was high (correlation coefficients, 0.95 and 0.83, respectively). No significant effect was identified for order of SAP measurement sites. Coccygeal and radial SAP were positively correlated (ρ = 0.45). The median difference in coccygeal versus radial SAP was 19 mm Hg, but differences were not consistently positive or negative. Radial SAP was positively correlated with age (ρ = 0.48) and negatively correlated with MCS (ρ = -0.30). On the basis of the correlation analysis, the association between radial SAP and MCS reflected the confounding influence of age. Coccygeal SAP was not significantly correlated with age, BCS, or MCS. CONCLUSIONS AND CLINICAL RELEVANCE Use of the coccygeal artery is recommended to reduce the confounding effects of age and sarcopenia on Doppler ultrasonographic SAP measurements in cats. Additionally, monitoring for changes in MCS is recommended for cats undergoing serial SAP measurement.
Channeling of protons through radial deformed carbon nanotubes
Energy Technology Data Exchange (ETDEWEB)
Borka Jovanović, V., E-mail: vborka@vinca.rs [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Borka, D. [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Galijaš, S.M.D. [Faculty of Physics, University of Belgrade, P.O. Box 368, 11001 Belgrade (Serbia)
2017-05-18
Highlights: • For the first time we presented theoretically obtained distributions of channeled protons with radially deformed SWNT. • Our findings indicate that influence of the radial deformation is very strong and it should not be omitted in simulations. • We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. • Our obtained results can be compared with measured distributions to reveal the presence of various types of defects in SWNT. - Abstract: In this paper we have presented a theoretical investigation of the channeling of 1 GeV protons with the radial deformed (10, 0) single-wall carbon nanotubes (SWNTs). We have calculated channeling potential within the deformed nanotubes. For the first time we presented theoretically obtained spatial and angular distributions of channeled protons with radially deformed SWNT. We used a Monte Carlo (MC) simulation technique. We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. These results may be useful for nanotube characterization and production and guiding of nanosized ion beams.
Radial electric fields for improved tokamak performance
International Nuclear Information System (INIS)
Downum, W.B.
1981-01-01
The influence of externally-imposed radial electric fields on the fusion energy output, energy multiplication, and alpha-particle ash build-up in a TFTR-sized, fusing tokamak plasma is explored. In an idealized tokamak plasma, an externally-imposed radial electric field leads to plasma rotation, but no charge current flows across the magnetic fields. However, a realistically-low neutral density profile generates a non-zero cross-field conductivity and the species dependence of this conductivity allows the electric field to selectively alter radial particle transport
CSIR Research Space (South Africa)
Bogaers, Alfred EJ
2016-10-01
Full Text Available words, gB = [ φBA PB ] [ MAA PA P TA 0 ]−1 [ gA 0 ] . (15) NAME: DEFINITION C0 compactly supported piecewise polynomial (C0): (1− (||x|| /r))2+ C2 compactly supported piecewise polynomial (C2): (1− (||x|| /r))4+ (4 (||x|| /r) + 1) Thin-plate spline (TPS... a numerical comparison to Kriging and the moving least-squares method, see Krishnamurthy [16]). RBF interpolation is based on fitting a series of splines, or basis functions to interpolate information from one point cloud to another. Let us assume we...
Seismic activity prediction using computational intelligence techniques in northern Pakistan
Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat
2017-10-01
Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.
A statistical framework for evaluating neural networks to predict recurrent events in breast cancer
Gorunescu, Florin; Gorunescu, Marina; El-Darzi, Elia; Gorunescu, Smaranda
2010-07-01
Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.
Interactions between Radial Electric Field, Transport and Structure in Helical Plasmas
International Nuclear Information System (INIS)
Ida, Katsumi and others
2006-01-01
Control of the radial electric field is considered to be important in helical plasmas, because the radial electric field and its shear are expected to reduce neoclassical and anomalous transport, respectively. Particle and heat transport, that determines the radial structure of density and electron profiles, sensitive to the structure of radial electric field. On the other hand, the radial electric field itself is determined by the plasma parameters. In general, the sign of the radial electric field is determined by the plasma collisionality, while the magnitude of the radial electric field is determined by the temperature and/or density gradients. Therefore the structure of radial electric field and temperature and density are strongly coupled through the particle and heat transport and formation mechanism of radial electric field. Interactions between radial electric field, transport and structure in helical plasmas is discussed based on the experiments on Large Helical Device
Anomalous Medial Branch of Radial Artery: A Rare Variant
Directory of Open Access Journals (Sweden)
Surbhi Wadhwa
2016-10-01
Full Text Available Radial artery is an important consistent vessel of the upper limb. It is a useful vascular access site for coronary procedures and its reliable anatomy has resulted in an elevation of radial forearm flaps for reconstructive surgeries of head and neck. Technical failures, in both the procedures, are mainly due to anatomical variations, such as radial loops, ectopic radial arteries or tortuosity in the vessel. We present a rare and a unique anomalous medial branch of the radial artery spiraling around the flexor carpi radialis muscle in the forearm with a high rising superficial palmar branch of radial artery. Developmentally it probably is a remanent of the normal pattern of capillary vessel maintenance and regression. Such a case is of importance for reconstructive surgeons and coronary interventionists, especially in view of its unique medial and deep course.
Fuel radial design using Path Relinking; Diseno radial de combustible usando Path Relinking
Energy Technology Data Exchange (ETDEWEB)
Campos S, Y. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico)
2007-07-01
The present work shows the obtained results when implementing the combinatory optimization technique well-known as Path Re linking (Re-linkage of Trajectories), to the problem of the radial design of nuclear fuel assemblies, for boiling water reactors (BWR Boiling Water Reactor by its initials in English), this type of reactors is those that are used in the Laguna Verde Nucleo electric Central, Veracruz. As in any other electric power generation plant of that make use of some fuel to produce heat and that it needs each certain time (from 12 to 14 months) to make a supply of the same one, because this it wears away or it burns, in the nucleolectric plants to this activity is denominated fuel reload. In this reload different activities intervene, among those which its highlight the radial and axial designs of fuel assemblies, the patterns of control rods and the multi cycles study, each one of these stages with their own complexity. This work was limited to study in independent form the radial design, without considering the other activities. These phases are basic for the fuel reload design and of reactor operation strategies. (Author)
Chen, Hongwei; Wang, Ziyang; Shang, Yongjun
2018-06-01
To compare clinical outcomes of unipolar and bipolar radial head prosthesis in the treatment of patients with radial head fracture. Medline, Cochrane, EMBASE, Google Scholar databases were searched until April 18, 2016 using the following search terms: radial head fracture, elbow fracture, radial head arthroplasty, implants, prosthesis, unipolar, bipolar, cemented, and press-fit. Randomized controlled trials, retrospective, and cohort studies were included. The Mayo elbow performance score (MEPS), disabilities of the arm, shoulder, and hand (DASH) score, radiologic assessment, ROM, and grip strength following elbow replacement were similar between prosthetic devices. The pooled mean excellent/good ranking of MEPS was 0.78 for unipolar and 0.73 for bipolar radial head arthroplasty, and the pooled mean MEPS was 86.9 and 79.9, respectively. DASH scores for unipolar and bipolar prosthesis were 19.0 and 16.3, respectively. Range of motion outcomes were similar between groups, with both groups have comparable risk of flexion arc, flexion, extension deficit, rotation arc, pronation, and supination (p values bipolar prosthesis). However, bipolar radial head prosthesis was associated with an increased chance of heterotopic ossification and lucency (p values ≤0.049) while unipolar prosthesis was not (p values ≥0.088). Both groups had risk for development of capitellar osteopenia or erosion/wear (p values ≤0.039). Unipolar and bipolar radial head prostheses were similar with respect to clinical outcomes. Additional comparative studies are necessary to further compare different radial head prostheses used to treat radial head fracture.
Vitreous veils and radial lattice in Marshall syndrome.
Brubaker, Jacob W; Mohney, Brian G; Pulido, Jose S; Babovic-Vuksanovic, Dusica
2008-12-01
To report the findings of membranous vitreous veils and radial lattice in a child with Marshall syndrome. Observational case report. Retrospective review of medical records and fundus photograph of a 6-year-old boy with Marshall syndrome. Vitreoretinal findings were significant for bilateral membranous vitreous veils and radial lattice degeneration. This case demonstrates the occurrence of vitreous veils and radial lattice degeneration in patients with Marshall syndrome.
Mansat, Pierre
2016-01-01
Radial head arthroplasty is used to stabilize the joint after a complex acute radial head fracture that is not amenable for fixation or to treat sequelae of radial head fractures. Most of the currently used radial head prostheses are metallic monoblock implants that are not consistently adaptable and raise technical challenges since their implantation requires lateral elbow subluxation. Metallic modular radial head arthroplasty implants available in various head and stem sizes have been devel...
Radial head fracture associated with posterior interosseous nerve injury
Directory of Open Access Journals (Sweden)
Bernardo Barcellos Terra
Full Text Available ABSTRACT Fractures of the radial head and radial neck correspond to 1.7-5.4% of all fractures and approximately 30% may present associated injuries. In the literature, there are few reports of radial head fracture with posterior interosseous nerve injury. This study aimed to report a case of radial head fracture associated with posterior interosseous nerve injury. CASE REPORT: A male patient, aged 42 years, sought medical care after falling from a skateboard. The patient related pain and limitation of movement in the right elbow and difficulty to extend the fingers of the right hand. During physical examination, thumb and fingers extension deficit was observed. The wrist extension showed a slight radial deviation. After imaging, it became evident that the patient had a fracture of the radial head that was classified as grade III in the Mason classification. The patient underwent fracture fixation; at the first postoperative day, thumb and fingers extension was observed. Although rare, posterior interosseous nerve branch injury may be associated with radial head fractures. In the present case, the authors believe that neuropraxia occurred as a result of the fracture hematoma and edema.
Radial Field Piezoelectric Diaphragms
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.
Directory of Open Access Journals (Sweden)
D. Tien Bui
2017-09-01
Full Text Available In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM, radial-basis-function Fisher discriminant analysis (RBFDA, and a geographic information system (GIS database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.
Radial transport with perturbed magnetic field
Energy Technology Data Exchange (ETDEWEB)
Hazeltine, R. D. [Institute for Fusion Studies, University of Texas at Austin, Austin, Texas 78712 (United States)
2015-05-15
It is pointed out that the viscosity coefficient describing radial transport of toroidal angular momentum is proportional to the second power of the gyro-radius—like the corresponding coefficients for particle and heat transport—regardless of any geometrical symmetry. The observation is widely appreciated, but worth emphasizing because some literature gives the misleading impression that asymmetry can allow radial moment transport in first-order.
Radial transport with perturbed magnetic field
International Nuclear Information System (INIS)
Hazeltine, R. D.
2015-01-01
It is pointed out that the viscosity coefficient describing radial transport of toroidal angular momentum is proportional to the second power of the gyro-radius—like the corresponding coefficients for particle and heat transport—regardless of any geometrical symmetry. The observation is widely appreciated, but worth emphasizing because some literature gives the misleading impression that asymmetry can allow radial moment transport in first-order
Energy Technology Data Exchange (ETDEWEB)
Feller, D.F.
1979-01-01
The behavior of the two exponential parameters in an even-tempered gaussian basis set is investigated as the set optimally approaches an integral transform representation of the radial portion of atomic and molecular orbitals. This approach permits a highly accurate assessment of the Hartree-Fock limit for atoms and molecules.
DEFF Research Database (Denmark)
Johansen, Per; Roemer, Daniel Beck; Andersen, Torben O.
2015-01-01
-stroke displacement simulations are used as basis for the parametric analysis. From the parametric analysis a change, in the minimum film thickness as function of piston and cylinder density, is shown for certain operating modes of the digital fluid power displacement motor. This indicate a need for careful....... In this paper the influence of the inertia term on the lubrication gaps of a radial piston motor are studied by a parametric analysis of the piston and cylinder density in a multibody tribodynamic simulation model. The motor is modeled as a digital fluid power displacement machine and a series of full...... assessment of the applicability, of the force balance condition, if it is used in multibody tribodynamic simulations of radial piston digital fluid power displacement motors....
File access prediction using neural networks.
Patra, Prashanta Kumar; Sahu, Muktikanta; Mohapatra, Subasish; Samantray, Ronak Kumar
2010-06-01
One of the most vexing issues in design of a high-speed computer is the wide gap of access times between the memory and the disk. To solve this problem, static file access predictors have been used. In this paper, we propose dynamic file access predictors using neural networks to significantly improve upon the accuracy, success-per-reference, and effective-success-rate-per-reference by using neural-network-based file access predictor with proper tuning. In particular, we verified that the incorrect prediction has been reduced from 53.11% to 43.63% for the proposed neural network prediction method with a standard configuration than the recent popularity (RP) method. With manual tuning for each trace, we are able to improve upon the misprediction rate and effective-success-rate-per-reference using a standard configuration. Simulations on distributed file system (DFS) traces reveal that exact fit radial basis function (RBF) gives better prediction in high end system whereas multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) backpropagation outperforms in system having good computational capability. Probabilistic and competitive predictors are the most suitable for work stations having limited resources to deal with and the former predictor is more efficient than the latter for servers having maximum system calls. Finally, we conclude that MLP with LM backpropagation algorithm has better success rate of file prediction than those of simple perceptron, last successor, stable successor, and best k out of m predictors.
Manufacturing of small scale W monoblock mockups by hot radial pressing
International Nuclear Information System (INIS)
Visca, Eliseo; Testani, C.; Libera, S.; Sacchetti, M.
2003-01-01
In the frame of the European Technology R and D programme for International thermonuclear experimental reactor (ITER) and in the area of high heat flux plasma facing components (HHFC), representative small-scale mock-ups were manufactured and tested to compare different concepts and joining technologies (i.e. active brazing, hot isostatic pressing (HIPping), diffusion bonding, etc.). On the basis of the results obtained by thermal fatigue tests, the monoblock concept resulted to be the most robust one, particularly when the HIPping manufacturing technology is used. Within this programme, ENEA developed an alternative technique for manufacturing plasma-facing components with a monoblock geometry of the ITER machine. The basic idea of this technique, named hot radial pressing (HRP), is to perform a radial diffusion bonding between the cooling tube and the armour tile by pressurising the internal tube only and by keeping the process parameters within the range in which the thermo-mechanical properties of the copper alloys are not yet degraded. The HRP is performed by a standard furnace, in which only a section of the canister is heated. The manufacturing procedure and the results of the screening and fatigue thermal tests performed on the ENEA mock-ups are reported in this paper
International Nuclear Information System (INIS)
Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice
2017-01-01
As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.
Chinsamy, Anusuya; Tumarkin-Deratzian, Allison
2009-09-01
We report on similar pathological bone microstructure in an extant turkey vulture (Cathartes aura) and a nonavian dinosaur from Transylvania. Both these individuals exhibit distinctive periosteal reactive bone deposition accompanied by endosteal bone deposits in the medullary cavity. Our findings have direct implications on the two novel bone tissues recently described among nonavian dinosaurs, radial fibrolamellar bone tissue and medullary bone tissue. On the basis of the observed morphology of the periosteal reactive bone in the turkey vulture and the Transylvanian dinosaur, we propose that the radial fibrolamellar bone tissues observed in mature dinosaurs may have had a pathological origin. Our analysis also shows that on the basis of origin, location, and morphology, pathologically derived endosteal bone tissue can be similar to medullary bone tissues described in nonavian dinosaurs. As such, we caution the interpretation of all endosteally derived bone tissue as homologous to avian medullary bone. (c) 2009 Wiley-Liss, Inc.
Concepts of radial and angular kinetic energies
DEFF Research Database (Denmark)
Dahl, Jens Peder; Schleich, W.P.
2002-01-01
We consider a general central-field system in D dimensions and show that the division of the kinetic energy into radial and angular parts proceeds differently in the wave-function picture and the Weyl-Wigner phase-space picture, Thus, the radial and angular kinetic energies are different quantities...
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method
International Nuclear Information System (INIS)
Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles
2014-01-01
A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its ''black box'' aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where ''all'' configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA
Tunali, Ilke; Stringfield, Olya; Guvenis, Albert; Wang, Hua; Liu, Ying; Balagurunathan, Yoganand; Lambin, Philippe; Gillies, Robert J; Schabath, Matthew B
2017-11-10
The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.
Illumination Profile & Dispersion Variation Effects on Radial Velocity Measurements
Grieves, Nolan; Ge, Jian; Thomas, Neil B.; Ma, Bo; Li, Rui; SDSS-III
2015-01-01
The Multi-object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS) measures radial velocities using a fiber-fed dispersed fixed-delay interferometer (DFDI) with a moderate dispersion spectrograph. This setup allows a unique insight into the 2D illumination profile from the fiber on to the dispersion grating. Illumination profile investigations show large changes in the profile over time and fiber location. These profile changes are correlated with dispersion changes and long-term radial velocity offsets, a major problem within the MARVELS radial velocity data. Characterizing illumination profiles creates a method to both detect and correct radial velocity offsets, allowing for better planet detection. Here we report our early results from this study including improvement of radial velocity data points from detected giant planet candidates. We also report an illumination profile experiment conducted at the Kitt Peak National Observatory using the EXPERT instrument, which has a DFDI mode similar to MARVELS. Using profile controlling octagonal-shaped fibers, long term offsets over a 3 month time period were reduced from ~50 m/s to within the photon limit of ~4 m/s.
Radial transfer effects for poloidal rotation
Hallatschek, Klaus
2010-11-01
Radial transfer of energy or momentum is the principal agent responsible for radial structures of Geodesic Acoustic Modes (GAMs) or stationary Zonal Flows (ZF) generated by the turbulence. For the GAM, following a physical approach, it is possible to find useful expressions for the individual components of the Poynting flux or radial group velocity allowing predictions where a mathematical full analysis is unfeasible. Striking differences between up-down symmetric flux surfaces and asymmetric ones have been found. For divertor geometries, e.g., the direction of the propagation depends on the sign of the ion grad-B drift with respect to the X-point, reminiscent of a sensitive determinant of the H-mode threshold. In nonlocal turbulence computations it becomes obvious that the linear energy transfer terms can be completely overwhelmed by the action of the turbulence. In contrast, stationary ZFs are governed by the turbulent radial transfer of momentum. For sufficiently large systems, the Reynolds stress becomes a deterministic functional of the flows, which can be empirically determined from the stress response in computational turbulence studies. The functional allows predictions even on flow/turbulence states not readily obtainable from small amplitude noise, such as certain transport bifurcations or meta-stable states.
Evaluation of multilayer perceptron algorithms for an analysis of network flow data
Bieniasz, Jedrzej; Rawski, Mariusz; Skowron, Krzysztof; Trzepiński, Mateusz
2016-09-01
The volume of exchanged information through IP networks is larger than ever and still growing. It creates a space for both benign and malicious activities. The second one raises awareness on security network devices, as well as network infrastructure and a system as a whole. One of the basic tools to prevent cyber attacks is Network Instrusion Detection System (NIDS). NIDS could be realized as a signature-based detector or an anomaly-based one. In the last few years the emphasis has been placed on the latter type, because of the possibility of applying smart and intelligent solutions. An ideal NIDS of next generation should be composed of self-learning algorithms that could react on known and unknown malicious network activities respectively. In this paper we evaluated a machine learning approach for detection of anomalies in IP network data represented as NetFlow records. We considered Multilayer Perceptron (MLP) as the classifier and we used two types of learning algorithms - Backpropagation (BP) and Particle Swarm Optimization (PSO). This paper includes a comprehensive survey on determining the most optimal MLP learning algorithm for the classification problem in application to network flow data. The performance, training time and convergence of BP and PSO methods were compared. The results show that PSO algorithm implemented by the authors outperformed other solutions if accuracy of classifications is considered. The major disadvantage of PSO is training time, which could be not acceptable for larger data sets or in real network applications. At the end we compared some key findings with the results from the other papers to show that in all cases results from this study outperformed them.
Anterior transposition of the radial nerve--a cadaveric study.
Yakkanti, Madhusudhan R; Roberts, Craig S; Murphy, Joshua; Acland, Robert D
2008-01-01
The radial nerve is at risk during the posterior plating of the humerus. The purpose of this anatomic study was to assess the extent of radial nerve dissection required for anterior transposition through the fracture site (transfracture anterior transposition). A cadaver study was conducted approaching the humerus by a posterior midline incision. The extent of dissection of the nerve necessary for plate fixation of the humerus fracture was measured. An osteotomy was created to model a humeral shaft fracture at the spiral groove (OTA classification 12-A2, 12-A3). The radial nerve was then transposed anterior to the humeral shaft through the fracture site. The additional dissection of the radial nerve and the extent of release of soft tissue from the humerus shaft to achieve the transposition were measured. Plating required a dissection of the radial nerve 1.78 cm proximal and 2.13 cm distal to the spiral groove. Transfracture anterior transposition of the radial nerve required an average dissection of 2.24 cm proximal and 2.68 cm distal to the spiral groove. The lateral intermuscular septum had to be released for 2.21 cm on the distal fragment to maintain laxity of the transposed nerve. Transfracture anterior transposition of the radial nerve before plating is feasible with dissection proximal and distal to the spiral groove and elevation of the lateral intermuscular septum. Potential clinical advantages of this technique include enhanced fracture site visualization, application of broader plates, and protection of the radial nerve during the internal fixation.
A New Filtering Algorithm Utilizing Radial Velocity Measurement
Institute of Scientific and Technical Information of China (English)
LIU Yan-feng; DU Zi-cheng; PAN Quan
2005-01-01
Pulse Doppler radar measurements consist of range, azimuth, elevation and radial velocity. Most of the radar tracking algorithms in engineering only utilize position measurement. The extended Kalman filter with radial velocity measureneut is presented, then a new filtering algorithm utilizing radial velocity measurement is proposed to improve tracking results and the theoretical analysis is also given. Simulation results of the new algorithm, converted measurement Kalman filter, extended Kalman filter are compared. The effectiveness of the new algorithm is verified by simulation results.
Photoelectric Radial Velocities, Paper XIX Additional Spectroscopic ...
Indian Academy of Sciences (India)
ian velocity curve that does justice to the measurements, but it cannot be expected to have much predictive power. Key words. Stars: late-type—stars: radial velocities—spectroscopic binaries—orbits. 0. Preamble. The 'Redman K stars' are a lot of seventh-magnitude K stars whose radial velocities were first observed by ...
Linear theory radial and nonradial pulsations of DA dwarf stars
International Nuclear Information System (INIS)
Starrfield, S.; Cox, A.N.; Hodson, S.; Pesnell, W.D.
1982-01-01
The Los Alamos stellar envelope and radial linear non-adiabatic computer code, along with a new Los Alamos non-radial code are used to investigate the total hydrogen mass necessary to produce the non-radial instability of DA dwarfs
Evaporation Limited Radial Capillary Penetration in Porous Media.
Liu, Mingchao; Wu, Jian; Gan, Yixiang; Hanaor, Dorian A H; Chen, C Q
2016-09-27
The capillary penetration of fluids in thin porous layers is of fundamental interest in nature and various industrial applications. When capillary flows occur in porous media, the extent of penetration is known to increase with the square root of time following the Lucas-Washburn law. In practice, volatile liquid evaporates at the surface of porous media, which restricts penetration to a limited region. In this work, on the basis of Darcy's law and mass conservation, a general theoretical model is developed for the evaporation-limited radial capillary penetration in porous media. The presented model predicts that evaporation decreases the rate of fluid penetration and limits it to a critical radius. Furthermore, we construct a unified phase diagram that describes the limited penetration in an annular porous medium, in which the boundaries of outward and inward liquid are predicted quantitatively. It is expected that the proposed theoretical model will advance the understanding of penetration dynamics in porous media and facilitate the design of engineered porous architectures.
Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata
2015-04-01
The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.
Zernike Basis to Cartesian Transformations
Mathar, R. J.
2009-12-01
The radial polynomials of the 2D (circular) and 3D (spherical) Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle) defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.
Effect of the radial electric field on turbulence
International Nuclear Information System (INIS)
Carreras, B.A.; Lynch, V.E.
1990-01-01
For many years, the neoclassical transport theory for three- dimensional magnetic configurations, such as magnetic mirrors, ELMO Bumpy Tori (EBTs), and stellarators, has recognized the critical role of the radial electric field in the confinement. It was in these confinement devices that the first experimental measurements of the radial electric field were made and correlated with confinement losses. In tokamaks, the axisymmetry implies that the neoclassical fluxes are ambipolar and, as a consequence, independent of the radial electric field. However, axisymmetry is not strict in a tokamak with turbulent fluctuations, and near the limiter ambipolarity clearly breaks down. Therefore, the question of the effect of the radial electric field on tokamak confinement has been raised in recent years. In particular, the radial electric field has been proposed to explain the transition from L-mode to H-mode confinement. There is some initial experimental evidence supporting this type of explanation, although there is not yet a self-consistent theory explaining the generation of the electric field and its effect on the transport. Here, a brief review of recent results is presented. 27 refs., 4 figs
A user's evaluation of radial flow HEPA filters
International Nuclear Information System (INIS)
Purcell, J.A.
1992-07-01
High efficiency particulate air (HEPA) filters of rectangular cross section have been used to remove particulates and the associated radioactivity from air ventilation streams since the advent of nuclear materials processing. Use of round axial flow HEPA filters is also longstanding. The advantages of radial flow filters in a circular configuration have been well demonstrated in UKAEA during the last 5--7 years. An evaluation of radial flow filters for fissile process gloveboxes reveals several substantial benefits in addition to the advantages claimed in UKAEA Facilities. The radial flow filter may be provided in a favorable geometry resulting in improved criticality safety. The filter configuration lends to in-place testing at the glovebox to exhaust duct interface. This will achieve compliance with DOE Order 6430.1A, Section 99.0.2. Preliminary testing at SRS for radial flow filters manufactured by Flanders Filters, Inc. revealed compliance in all the usual specifications for filtration efficiency, pressure differential and materials of construction. An evaluation, further detailed in this report, indicates that the radial flow HEPA filter should be considered for inclusion in new ventilation system designs
Radial extension of drift waves in presence of velocity profiles
International Nuclear Information System (INIS)
Sen, S.; Weiland, J.
1994-01-01
The effect of a radially varying poloidal velocity field on the recently found radially extended toroidal drift waves is investigated analytically. The role of velocity curvature (υ φ '') is found to have robust effects on the radial model structure of the mode. For a positive value of the curvature (Usually found in the H-mode edges) the radial model envelope, similar to the sheared slab case, becomes fully outgoing. The mode is therefore stable. On the other hand, for a negative value of the curvature (usually observed in the L-mode edges) all the characteristics of conventional drift waves return back. The radial mode envelope reduces to a localized Gaussian shape and the mode is therefore unstable again for typical (magnetic) shear values in tokamaks. Velocity shear (υ φ ??) on the other hand is found to have rather insignificant role both in determining the radial model structure and stability
Hanna, Elias B; Mogabgab, Owen N; Baydoun, Hassan
2018-01-01
We present cases of complex, calcified iliac occlusive disease revascularized via a combined radial-femoral access strategy. Through a 6-French, 125-cm transradial guiding catheter, antegrade guidewires and catheters are advanced into the iliac occlusion, while retrograde devices are advanced transfemorally. The transradial and transfemoral channels communicate, allowing the devices to cross the occlusion into the true lumen (radial-femoral antegrade-retrograde rendezvous).
Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network
Directory of Open Access Journals (Sweden)
Kemal Fidanboylu
2009-09-01
Full Text Available Artificial neural network (ANN based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP with different training algorithms, Radial Basis Function (RBF network and General Regression Neural Network (GRNN are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.
Modelling a variable valve timing spark ignition engine using different neural networks
Energy Technology Data Exchange (ETDEWEB)
Beham, M. [BMW AG, Munich (Germany); Yu, D.L. [John Moores University, Liverpool (United Kingdom). Control Systems Research Group
2004-10-01
In this paper different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems. (author)
Advanced approach to numerical forecasting using artificial neural networks
Directory of Open Access Journals (Sweden)
Michael Štencl
2009-01-01
Full Text Available Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.
Failure and reliability prediction by support vector machines regression of time series data
International Nuclear Information System (INIS)
Chagas Moura, Marcio das; Zio, Enrico; Lins, Isis Didier; Droguett, Enrique
2011-01-01
Support Vector Machines (SVMs) are kernel-based learning methods, which have been successfully adopted for regression problems. However, their use in reliability applications has not been widely explored. In this paper, a comparative analysis is presented in order to evaluate the SVM effectiveness in forecasting time-to-failure and reliability of engineered components based on time series data. The performance on literature case studies of SVM regression is measured against other advanced learning methods such as the Radial Basis Function, the traditional MultiLayer Perceptron model, Box-Jenkins autoregressive-integrated-moving average and the Infinite Impulse Response Locally Recurrent Neural Networks. The comparison shows that in the analyzed cases, SVM outperforms or is comparable to other techniques. - Highlights: → Realistic modeling of reliability demands complex mathematical formulations. → SVM is proper when the relation input/output is unknown or very costly to be obtained. → Results indicate the potential of SVM for reliability time series prediction. → Reliability estimates support the establishment of adequate maintenance strategies.
Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation
Directory of Open Access Journals (Sweden)
M. Agatonović
2012-12-01
Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.
Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon
2017-07-03
Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.
An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost
Directory of Open Access Journals (Sweden)
Yinan Zhang
2016-01-01
Full Text Available Diabetic retinopathy (DR screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and K Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.
Time series prediction: statistical and neural techniques
Zahirniak, Daniel R.; DeSimio, Martin P.
1996-03-01
In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.
Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network
Taghipour-Gorjikolaie, Mehran; Valipour Motlagh, Naser
2018-02-01
The interaction between variables, which are effective on the surface wettability, is very complex to predict the contact angles and sliding angles of liquid drops. In this paper, in order to solve this complexity, artificial neural network was used to develop reliable models for predicting the angles of liquid drops. Experimental data are divided into training data and testing data. By using training data and feed forward structure for the neural network and using particle swarm optimization for training the neural network based models, the optimum models were developed. The obtained results showed that regression index for the proposed models for the contact angles and sliding angles are 0.9874 and 0.9920, respectively. As it can be seen, these values are close to unit and it means the reliable performance of the models. Also, it can be inferred from the results that the proposed model have more reliable performance than multi-layer perceptron and radial basis function based models.
Reassessment of the basis for NRC fuel damage criteria for reactivity transients
International Nuclear Information System (INIS)
McCardell, R.K.
1994-01-01
The present basis for NRC Fuel Damage Criteria was obtained from experiments performed in the Special Power Excursion Reactor Test (SPERT) IV Reactor Capsule Driver Core (CDC) at the Idaho National Engineering Laboratory (INEL) between 1967 and 1970. Most of the CDC test fuel rods were previously unirradiated and the failure threshold for these unirradiated fuel rods was measured to be about 200 calories per gram of UO 2 radially averaged fuel enthalpy at the axial peak
A novel structure of permanent-magnet-biased radial hybrid magnetic bearing
International Nuclear Information System (INIS)
Sun Jinji; Fang Jiancheng
2011-01-01
The paper proposes a novel structure for a permanent-magnet-biased radial hybrid magnetic bearing. Based on the air gap between the rotor and stator of traditional radial hybrid magnetic bearings, a subsidiary air gap is first constructed between the permanent magnets and the inner magnetic parts. Radial magnetic bearing makes X and Y magnetic fields independent of each other with separate stator poles, and the subsidiary air gap makes control flux to a close loop. As a result, magnetic field coupling of the X and Y channels is decreased significantly by the radial hybrid magnetic bearing and makes it easier to design control systems. Then an external rotor structure is designed into the radial hybrid magnetic bearing. The working principle of the radial hybrid magnetic bearing and its mathematical model is discussed. Finally, a non-linear magnetic network method is proposed to analyze the radial hybrid magnetic bearing. Simulation results indicate that magnetic fields in the two channels of the proposed radial hybrid magnetic bearing decouple well from each other.
A novel structure of permanent-magnet-biased radial hybrid magnetic bearing
Energy Technology Data Exchange (ETDEWEB)
Sun Jinji, E-mail: sunjinji@aspe.buaa.edu.c [Key Laboratory of Fundamental Science for National Defense, Novel Inertial Instrument and Navigation System Technology, School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, 100191 (China); Fang Jiancheng [Key Laboratory of Fundamental Science for National Defense, Novel Inertial Instrument and Navigation System Technology, School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, 100191 (China)
2011-01-15
The paper proposes a novel structure for a permanent-magnet-biased radial hybrid magnetic bearing. Based on the air gap between the rotor and stator of traditional radial hybrid magnetic bearings, a subsidiary air gap is first constructed between the permanent magnets and the inner magnetic parts. Radial magnetic bearing makes X and Y magnetic fields independent of each other with separate stator poles, and the subsidiary air gap makes control flux to a close loop. As a result, magnetic field coupling of the X and Y channels is decreased significantly by the radial hybrid magnetic bearing and makes it easier to design control systems. Then an external rotor structure is designed into the radial hybrid magnetic bearing. The working principle of the radial hybrid magnetic bearing and its mathematical model is discussed. Finally, a non-linear magnetic network method is proposed to analyze the radial hybrid magnetic bearing. Simulation results indicate that magnetic fields in the two channels of the proposed radial hybrid magnetic bearing decouple well from each other.
Detonation in supersonic radial outflow
Kasimov, Aslan R.
2014-11-07
We report on the structure and dynamics of gaseous detonation stabilized in a supersonic flow emanating radially from a central source. The steady-state solutions are computed and their range of existence is investigated. Two-dimensional simulations are carried out in order to explore the stability of the steady-state solutions. It is found that both collapsing and expanding two-dimensional cellular detonations exist. The latter can be stabilized by putting several rigid obstacles in the flow downstream of the steady-state sonic locus. The problem of initiation of standing detonation stabilized in the radial flow is also investigated numerically. © 2014 Cambridge University Press.
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 ...
Zernike Basis to Cartesian Transformations
Directory of Open Access Journals (Sweden)
Mathar, R. J.
2009-12-01
Full Text Available The radial polynomials of the 2D (circular and 3D (spherical Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.
Zernike basis to cartesian transformations
Directory of Open Access Journals (Sweden)
Mathar R.J.
2009-01-01
Full Text Available The radial polynomials of the 2D (circular and 3D (spherical Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.
Variable stator radial turbine
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.
The investigation of the non-orthogonal basis expansion method for a three-fermion system
International Nuclear Information System (INIS)
Baoqiu Chen; Kentucky Univ., Lexington, KY
1992-01-01
In this paper, the non-orthogonal basis expansion method has been extended to solve a three-fermion system. The radial wavefunction of such a system is expanded in terms of a non-orthogonal Gaussian basis. All matrix elements of the Hamiltonian, including the central, tensor and spin-orbit potentials are derived in analytical forms. The new method simplifies the three-body system calculations, which are usually rather tedious by other methods. The method can be used to calculate energies for both the ground state and low excited states and has been used further to investigate the other nuclear properties of a three-body system such as Λ 3 H. (Author)
Radial-probe EBUS for the diagnosis of peripheral pulmonary lesions
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Marcia Jacomelli
Full Text Available ABSTRACT Objective: Conventional bronchoscopy has a low diagnostic yield for peripheral pulmonary lesions. Radial-probe EBUS employs a rotating ultrasound transducer at the end of a probe that is passed through the working channel of the bronchoscope. Radial-probe EBUS facilitates the localization of peripheral pulmonary nodules, thus increasing the diagnostic yield. The objective of this study was to present our initial experience using radial-probe EBUS in the diagnosis of peripheral pulmonary lesions at a tertiary hospital. Methods: We conducted a retrospective analysis of 54 patients who underwent radial-probe EBUS-guided bronchoscopy for the investigation of pulmonary nodules or masses between February of 2012 and September of 2013. Radial-probe EBUS was performed with a flexible 20-MHz probe, which was passed through the working channel of the bronchoscope and advanced through the bronchus to the target lesion. For localization of the lesion and for collection procedures (bronchial brushing, transbronchial needle aspiration, and transbronchial biopsy, we used fluoroscopy. Results: Radial-probe EBUS identified 39 nodules (mean diameter, 1.9 ± 0.7 cm and 19 masses (mean diameter, 4.1 ± 0.9 cm. The overall sensitivity of the method was 66.7% (79.5% and 25.0%, respectively, for lesions that were visible and not visible by radial-probe EBUS. Among the lesions that were visible by radial-probe EBUS, the sensitivity was 91.7% for masses and 74.1% for nodules. The complications were pneumothorax (in 3.7% and bronchial bleeding, which was controlled bronchoscopically (in 9.3%. Conclusions: Radial-probe EBUS shows a good safety profile, a low complication rate, and high sensitivity for the diagnosis of peripheral pulmonary lesions.
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Jiří Fejfar
2012-01-01
Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.
Microinstabilities in a radially contracting inhomogeneous cylindrical plasma slab
International Nuclear Information System (INIS)
Deutsch, R.; Kaeppeler, H.J.
1980-07-01
In order to study the development of microinstabilities in a collapsing cylindrical plasma sheath, corresponding to the situations in a z-pinch or a plasma focus, the dispersion relation for electromagnetic perturbations is derived with the aid of a newly established slab-model for an inhomogeneous, radially contracting plasma. In contrast to previously used slab-models, the orientation of the electric field is in direction of the cylinder axis and the azimuthal magnetic field is induced by the current flowing through the cylindrical plasma slab. The Vlasov equation is used together with the Krook collision term in order to include the influence of collisions. The results of this theory presented in this report will be used to calculate the growth of drift instabilities in the compression phase of a plasma focus, and shall serve as a basis for further development of a more general dispersion relation including runaway-effects. (orig.)
Sharp Dissection versus Electrocautery for Radial Artery Harvesting
Marzban, Mehrab; Arya, Reza; Mandegar, Mohammad Hossein; Karimi, Abbas Ali; Abbasi, Kiomars; Movahed, Namvar; Abbasi, Seyed Hesameddin
2006-01-01
Radial arteries have been increasingly used during the last decade as conduits for coronary artery revascularization. Although various harvesting techniques have been described, there has been little comparative study of arterial damage and patency. A radial artery graft was used in 44 consecutive patients, who were randomly divided into 2 groups. In the 1st group, the radial artery was harvested by sharp dissection and in the 2nd, by electrocautery. These groups were compared with regard to radial artery free flow, harvest time, number of clips used, complications, and endothelial damage. Radial artery free flow before and after intraluminal administration of papaverine was significantly greater in the electrocautery group (84.3 ± 50.7 mL/min and 109.7 ± 68.5 mL/min) than in the sharp-dissection group (52.9 ± 18.3 mL/min and 69.6 ± 28.2 mL/ min) (P =0.003). Harvesting time by electrocautery was significantly shorter (25.4 ± 4.3 min vs 34.4 ± 5.9 min) (P =0.0001). Electrocautery consumed an average of 9.76 clips, versus 22.45 clips consumed by sharp dissection. The 2 groups were not different regarding postoperative complications, except for 3 cases of temporary paresthesia of the thumb in the electrocautery group; histopathologic examination found no endothelial damage. We conclude that radial artery harvesting by electrocautery is faster and more economical than harvesting by sharp dissection and is associated with better intraoperative flow and good preservation of endothelial integrity. PMID:16572861
Radial distribution of ions in pores with a surface charge
Stegen, J.H.G. van der; Görtzen, J.; Kuipers, J.A.M.; Hogendoorn, J.A.; Versteeg, G.F.
2001-01-01
A sorption model applicable to calculate the radial equilibrium concentrations of ions in the pores of ion-selective membranes with a pore structure is developed. The model is called the radial uptake model. Because the model is applied to a Nafion sulfonic layer with very small pores and the radial
Stellar Angular Momentum Distributions and Preferential Radial Migration
Wyse, Rosemary; Daniel, Kathryne J.
2018-04-01
I will present some results from our recent investigations into the efficiency of radial migration in stellar disks of differing angular momentum distributions, within a given adopted 2D spiral disk potential. We apply to our models an analytic criterion that determines whether or not individual stars are in orbits that could lead to radial migration around the corotation resonance. We couch our results in terms of the local stellar velocity dispersion and find that the fraction of stars that could migrate radially decreases as the velocity dispersion increases. I will discuss implications and comparisons with the results of other approaches.
Research on Radial Vibration of a Circular Plate
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Wei Liu
2016-01-01
Full Text Available Radial vibration of the circular plate is presented using wave propagation approach and classical method containing Bessel solution and Hankel solution for calculating the natural frequency theoretically. In cylindrical coordinate system, in order to obtain natural frequency, propagation and reflection matrices are deduced at the boundaries of free-free, fixed-fixed, and fixed-free using wave propagation approach. Furthermore, radial phononic crystal is constructed by connecting two materials periodically for the analysis of band phenomenon. Also, Finite Element Simulation (FEM is adopted to verify the theoretical results. Finally, the radial and piezoelectric effects on the band are also discussed.
Radial-probe EBUS for the diagnosis of peripheral pulmonary lesions.
Jacomelli, Marcia; Demarzo, Sergio Eduardo; Cardoso, Paulo Francisco Guerreiro; Palomino, Addy Lidvina Mejia; Figueiredo, Viviane Rossi
2016-01-01
Conventional bronchoscopy has a low diagnostic yield for peripheral pulmonary lesions. Radial-probe EBUS employs a rotating ultrasound transducer at the end of a probe that is passed through the working channel of the bronchoscope. Radial-probe EBUS facilitates the localization of peripheral pulmonary nodules, thus increasing the diagnostic yield. The objective of this study was to present our initial experience using radial-probe EBUS in the diagnosis of peripheral pulmonary lesions at a tertiary hospital. We conducted a retrospective analysis of 54 patients who underwent radial-probe EBUS-guided bronchoscopy for the investigation of pulmonary nodules or masses between February of 2012 and September of 2013. Radial-probe EBUS was performed with a flexible 20-MHz probe, which was passed through the working channel of the bronchoscope and advanced through the bronchus to the target lesion. For localization of the lesion and for collection procedures (bronchial brushing, transbronchial needle aspiration, and transbronchial biopsy), we used fluoroscopy. Radial-probe EBUS identified 39 nodules (mean diameter, 1.9 ± 0.7 cm) and 19 masses (mean diameter, 4.1 ± 0.9 cm). The overall sensitivity of the method was 66.7% (79.5% and 25.0%, respectively, for lesions that were visible and not visible by radial-probe EBUS). Among the lesions that were visible by radial-probe EBUS, the sensitivity was 91.7% for masses and 74.1% for nodules. The complications were pneumothorax (in 3.7%) and bronchial bleeding, which was controlled bronchoscopically (in 9.3%). Radial-probe EBUS shows a good safety profile, a low complication rate, and high sensitivity for the diagnosis of peripheral pulmonary lesions. A broncoscopia convencional possui baixo rendimento diagnóstico para lesões pulmonares periféricas. A ecobroncoscopia radial (EBUS radial) emprega um transdutor ultrassonográfico rotatório na extremidade de uma sonda que é inserida no canal de trabalho do broncoscópio. O EBUS
Radial vibration and ultrasonic field of a long tubular ultrasonic radiator.
Shuyu, Lin; Zhiqiang, Fu; Xiaoli, Zhang; Yong, Wang; Jing, Hu
2013-09-01
The radial vibration of a metal long circular tube is studied analytically and its electro-mechanical equivalent circuit is obtained. Based on the equivalent circuit, the radial resonance frequency equation is derived. The theoretical relationship between the radial resonance frequency and the geometrical dimensions is studied. Finite element method is used to simulate the radial vibration and the radiated ultrasonic field and the results are compared with those from the analytical method. It is concluded that the radial resonance frequency for a solid metal rod is larger than that for a metal tube with the same outer radius. The radial resonance frequencies from the analytical method are in good agreement with those from the numerical method. Based on the acoustic field analysis, it is concluded that the long metal tube with small wall thickness is superior to that with large wall thickness in producing radial vibration and ultrasonic radiation. Therefore, it is expected to be used as an effective radial ultrasonic radiator in ultrasonic sewage treatment, ultrasonic antiscale and descaling and other ultrasonic liquid handling applications. Copyright © 2013 Elsevier B.V. All rights reserved.
An application of the multilayer perceptron: Solar radiation maps in Spain
Energy Technology Data Exchange (ETDEWEB)
Hontoria, L.; Aguilera, J. [Grupo Investigacion y Desarrollo en Energia Solar y Automatica, Dpto. de Ingenieria Electronica, de Telecomunicaciones y Automatica, Escuela Politecnica Superior de Jaen, Campus de las Lagunillas, Universidad de Jaen, 23071 Jaen (Spain); Zufiria, P. [Grupo de Redes Neuronales, Dpto. de Matematica Aplicada a las Tecnologias de la Informacion, ETSI Telecomunicaciones, UPM Ciudad Universitaria s/n, 28040 Madrid (Spain)
2005-11-01
In this work an application of a methodology to obtain solar radiation maps is presented. This methodology is based on a neural network system [Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22] called Multi-Layer Perceptron (MLP) [Haykin, S., 1994. Neural Networks. A Comprehensive Foundation. Macmillan Publishing Company; Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366]. To obtain a solar radiation map it is necessary to know the solar radiation of many points spread wide across the zone of the map where it is going to be drawn. For most of the locations all over the world the records of these data (solar radiation in whatever scale, daily or hourly values) are non-existent. Only very few locations have the privilege of having good meteorological stations where records of solar radiation have being registered. But even in those locations with historical records of solar data, the quality of these solar series is not as good as it should be for most purposes. In addition, to draw solar radiation maps the number of points on the maps (real sites) that it is necessary to work with makes this problem difficult to solve. Nevertheless, with the application of the methodology proposed in this paper, this problem has been solved and solar radiation maps have been obtained for a small region of Spain: Jaen province, a southern province of Spain between parallels 38{sup o}25' N and 37{sup o}25' N, and meridians 4{sup o}10' W and 2{sup o}10' W, and for a larger region: Andalucia, the most southern region of Spain situated between parallels 38{sup o}40' N and 36{sup o}00' N, and meridians 7{sup o}30' W and 1{sup o}40' W. (author)
Directory of Open Access Journals (Sweden)
Mawloud GUERMOUI
2016-07-01
Full Text Available Accurate estimation of Daily Global Solar Radiation (DGSR has been a major goal for solar energy application. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly of the search for relationships between weather variables, such as temperature, humidity, sunshine duration, etc. In this respect, the present study focuses on the development of artificial neural network (ANN model for estimation of daily global solar radiation on horizontal surface in Ghardaia city (South Algeria. In this analysis back-propagation algorithm is applied. Daily mean air temperature, relative humidity and sunshine duration was used as climatic inputs parameters, while the daily global solar radiation (DGSR was the only output of the ANN. We have evaluated Multi-Layer Perceptron (MLP models to estimate DGSR using three year of measurement (2005-2008. It was found that MLP-model based on sunshine duration and mean air temperature give accurate results in term of Mean Absolute Bias Error, Root Mean Square Error, Relative Square Error and Correlation Coefficient. The obtained values of these indicators are 0.67 MJ/m², 1.28 MJ/m², 6.12%and 98.18%, respectively which shows that MLP is highly qualified for DGSR estimation in semi-arid climates.
Czech Academy of Sciences Publication Activity Database
Coufal, David
2017-01-01
Roč. 319, 15 July (2017), s. 1-27 ISSN 0165-0114 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : fuzzy systems * radial functions * coherence Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.718, year: 2016
Directory of Open Access Journals (Sweden)
Pedro Beraldo de Andrade
2011-04-01
Full Text Available FUNDAMENTO: Embora a técnica radial exiba resultados incontestáveis na redução de complicações vasculares e ocorrência de sangramento grave quando comparada à técnica femoral, seu emprego permanece restrito a poucos centros que a elegeram como via de acesso preferencial. OBJETIVO: Avaliar o cenário atual das intervenções coronarianas percutâneas no Brasil quanto à utilização da via de acesso radial. MÉTODOS: Análise dos dados cadastrados de forma espontânea na Central Nacional de Intervenções Cardiovasculares (CENIC durante o quadriênio de 2005-2008, o que totaliza 83.376 procedimentos. RESULTADOS: A técnica radial foi utilizada em 12,6% dos procedimentos efetivados, e a técnica femoral, em 84,3%. Os 3,1% restantes foram representados pela dissecção ou punção braquial. Com uma taxa de sucesso de 97,5%, a opção pelo acesso radial associou-se à redução significativa de complicações vasculares quando comparado ao femoral (2,5% versus 3,6%, p FUNDAMENTO: Aunque la técnica radial exhiba resultados incontestables en la reducción de complicaciones vasculares y ocurrencia de sangrado grave cuando es comparada a la técnica femoral, su empleo permanece restringido a pocos centros que la eligieron como vía de acceso preferencial. OBJETIVO:Evaluar el escenario actual de las intervenciones coronarias percutáneas en el Brasil en cuanto a la utilización de la vía de acceso radial. MÉTODOS:Análisis de los datos registrados de forma espontánea en la Central Nacional de Intervenciones Cardiovasculares (CENIC durante el cuatrienio de 2005-2008, lo que totaliza 83.376 procedimientos. RESULTADOS:La técnica radial fue utilizada en 12,6% de los procedimientos efectuados, y la técnica femoral, en 84,3%. Los 3,1% restantes fueron representados por la disección o punción braquial. Con una tasa de éxito de 97,5%, la opción por el acceso radial se asoció a la reducción significativa de complicaciones vasculares cuando
Velocidades radiales en Collinder 121
Arnal, M.; Morrell, N.
Se han llevado a cabo observaciones espectroscópicas de unas treinta estrellas que son posibles miembros del cúmulo abierto Collinder 121. Las mismas fueron realizadas con el telescopio de 2.15m del Complejo Astronómico El Leoncito (CASLEO). El análisis de las velocidades radiales derivadas del material obtenido, confirma la realidad de Collinder 121, al menos desde el punto de vista cinemático. La velocidad radial baricentral (LSR) del cúmulo es de +17 ± 3 km.s-1. Esta velocidad coincide, dentro de los errores, con la velocidad radial (LSR) de la nebulosa anillo S308, la cual es de ~20 ± 10 km.s-1. Como S308 se encuentra físicamente asociada a la estrella Wolf-Rayet HD~50896, es muy probable que esta última sea un miembro de Collinder 121. Desde un punto de vista cinemático, la supergigante roja HD~50877 (K3Iab) también pertenecería a Collinder 121. Basándonos en la pertenencia de HD~50896 a Collinder 121, y en la interacción encontrada entre el viento de esta estrella y el medio interestelar circundante a la misma, se estima para este cúmulo una distancia del orden de 1 kpc.
Radial supports of face motors with slack compensation
Energy Technology Data Exchange (ETDEWEB)
Kuznetsova, I I; Gelman, A B; Krekina, T V
1982-01-01
The design of a radial support of a face motor with slack compensation is described, and gives the results of field tests which confirm the performance capacity of the experimental support both from the viewpoint of durability, and in relation to preventing radial slack of the face motor shaft.
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José Edilberto Pacheco Serrano
2000-06-01
Full Text Available La miniqueratotomía radial se viene realizando desde 1995. Se plantea que incisiones más cortas tienen el mismo efecto y producen menos debilidad corneal, ya que disminuye la susceptibilidad a sufrir complicaciones graves provenientes de traumas de la vida cotidiana. Esta idea nos motivó a realizar un estudio para observar el comportamiento de incisiones más cortas en nuestro centro y, en caso de resultados positivos, implementar la técnica de manera que nuestros pacientes puedan beneficiarse de ella. Se comparan resultados de la aplicación de dos técnicas quirúrgicas refractivas para corrección de miopía entre leve y moderada. Se seleccionaron 38 pacientes entre 20 y 40 años de edad, con miopías entre -2 y -6 dioptrías y astigmatismo no mayor a -0,75 dioptrías. Se realizó queratotomía radial convencional en el ojo derecho y miniqueratotomía radial en el ojo izquierdo del mismo paciente. Las variaciones obtenidas en promedio fueron, en el ojo derecho: la esfera (en dioptrías D de -3,38 a -0,32, cilindro de -0,48 a -0,45 D, la queratometría de 44,75 a 41,21 D. En el ojo izquierdo: la esfera de -3,38 D a -0,44 D, cilindro de -0,44 D a -0,38 D, la queratometría de 44,83 a 41,80 D. Hubo una mejoría de la agudeza visual sin cristales de 0,61 en el ojo derecho y 0,59 en el ojo izquierdo. Las dos técnicas no mostraron diferencias estadísticamente significativas, con el beneficio de que la nueva técnica disminuye el riesgo de ruptura postraumática, según la bibliografía revisada, a causa de la menor injuria corneal.In this hospital, radial keratotomy is performed sice 1995. We propose that shorter incisions have some effect and cause less corneal weakness, since dicreases susceptibility to severe complications from traumata of daily life. This notion encouraged us to carry out a study to observe behaviour of shorter incisions in our service, and in the event of positive results, implementation of the technique so that our
Pseudarthrosis of radial shaft with dislocation of heads of radial and ulnar bones (case report
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M. E. Puseva
2013-01-01
Full Text Available The authors presented a rare clinical case - the injury of forearm complicated by the formation of the pseudarthrosis of the radial shaft in combination with old dislocation of heads the radius and ulna. The differentiated approach to the choice of surgical tactics was proposed, which consists of several consistent stages: taking free autotransplant from the crest of iliac bone, resection of pseudarthrosis of radius with replacement of the bone defect by the graft for restoration of anatomic length, conducting combined strained osteosynthesis and elimination of dislocation of a head of radial and ulnar bones by transosseous osteosynthesis. The chosen treatment strategy allowed to restore the anatomy and function of the upper extremity.
Compactly Supported Basis Functions as Support Vector Kernels for Classification.
Wittek, Peter; Tan, Chew Lim
2011-10-01
Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.
Manufacturing of Precision Forgings by Radial Forging
International Nuclear Information System (INIS)
Wallner, S.; Harrer, O.; Buchmayr, B.; Hofer, F.
2011-01-01
Radial forging is a multi purpose incremental forging process using four tools on the same plane. It is widely used for the forming of tool steels, super alloys as well as titanium- and refractory metals. The range of application goes from reducing the diameters of shafts, tubes, stepped shafts and axels, as well as for creating internal profiles for tubes in Near-Net-Shape and Net-Shape quality. Based on actual development of a weight optimized transmission input shaft, the specific features of radial forging technology is demonstrated. Also a Finite Element Model for the simulation of the process is shown which leads to reduced pre-processing effort and reduced computing time compared to other published simulation methods for radial forging. The finite element model can be applied to quantify the effects of different forging strategies.
Radially truncated galactic discs
Grijs, R. de; Kregel, M.; Wesson, K H
2000-01-01
Abstract: We present the first results of a systematic analysis of radially truncatedexponential discs for four galaxies of a sample of disc-dominated edge-onspiral galaxies. Edge-on galaxies are very useful for the study of truncatedgalactic discs, since we can follow their light distributions out
Radial-arrayed rotary electrification for high performance triboelectric generator.
Zhu, Guang; Chen, Jun; Zhang, Tiejun; Jing, Qingshen; Wang, Zhong Lin
2014-03-04
Harvesting mechanical energy is an important route in obtaining cost-effective, clean and sustainable electric energy. Here we report a two-dimensional planar-structured triboelectric generator on the basis of contact electrification. The radial arrays of micro-sized sectors on the contact surfaces enable a high output power of 1.5 W (area power density of 19 mW cm(-2)) at an efficiency of 24%. The triboelectric generator can effectively harness various ambient motions, including light wind, tap water flow and normal body movement. Through a power management circuit, a triboelectric-generator-based power-supplying system can provide a constant direct-current source for sustainably driving and charging commercial electronics, immediately demonstrating the feasibility of the triboelectric generator as a practical power source. Given exceptional power density, extremely low cost and unique applicability resulting from distinctive mechanism and structure, the triboelectric generator can be applied not only to self-powered electronics but also possibly to power generation at a large scale.
Radial velocity observations of VB10
Deshpande, R.; Martin, E.; Zapatero Osorio, M. R.; Del Burgo, C.; Rodler, F.; Montgomery, M. M.
2011-07-01
VB 10 is the smallest star known to harbor a planet according to the recent astrometric study of Pravdo & Shaklan [1]. Here we present near-infrared (J-band) radial velocity of VB 10 performed from high resolution (R~20,000) spectroscopy (NIRSPEC/KECK II). Our results [2] suggest radial velocity variability with amplitude of ~1 km/s, a result that is consistent with the presence of a massive planet companion around VB10 as found via long-term astrometric monitoring of the star by Pravdo & Shaklan. Employing an entirely different technique we verify the results of Pravdo & Shaklan.
Radial electron beam laser excitation: the REBLE report
International Nuclear Information System (INIS)
Ramirez, J.J.; Prestwich, K.R.
1978-10-01
The results of an investigation of techniques to generate high-power radially converging electron beams and the application of these beams to gas lasers is discussed. The design and performance of the REBLE accelerator that was developed for this program is presented. Reliable operation of the radial diode has been obtained at levels up to 1 MV, 200 kA, and 20 ns. It has been demonstrated that the anode current density can be made uniform to better than 15% over 1000 cm 2 areas with 100 to 250 A/cm 2 intensities. The measured total and spatially resolved energy deposition of this radial electron beam in various gases is compared with Monte Carlo calculations. In most cases, these codes give an accurate description of the beam transport and energy deposition. With the electron beam pumping xenon gas, the amplitude of xenon excimer radiation (1720 A 0 ) was radially uniform to within the experimental uncertainty. The efficiency of converting deposited electron beam energy to xenon excimer radiation was 20%
Radial scar/complex sclerosing lesion of the breast--value of ultrasound.
Grunwald, S; Heyer, H; Kühl, A; Schwesinger, G; Schimming, A; Köhler, G; Ohlinger, R
2007-04-01
Although benign, radial scar/complex sclerosing adenosis is a lesion which histopathologically resembles tubular carcinoma. On physical examination, it is difficult to distinguish radial scar from a malignant tumour. Mammography cannot differentiate radial scar from malignancy. This clinical study aims to delineate the role of preoperative ultrasonography with emphasis on the question whether ultrasonography could lower the number of false-positive readings and therefore the number of open biopsies required. In this examination, we present the clinical, mammographic, ultrasonographic, and histopathological features of 6 cases of radial scars. Although most authors describe radial scars as non-palpable, 2 of 6 lesions were indeed palpable. On mammograms, radial scars have a spiculated appearance, a feature observed in all of our cases. Numerous ultrasonographic characteristics are listed in the literature, but ultrasonography is not reported to have clear-cut advantages. Although this study did not elucidate any unique ultrasonographic features to characterise these lesions, the analysis of all ultrasonographic results made us recognise a set of "nearly specific ultrasonographic features" of radial scars. Current B-mode imaging does not appear to lead to the desirable reduction of the rate of unnecessary open biopsies.
Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex
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Mir Jalil Razavi
2017-08-01
Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.
Global and radial variations in the efficiency of massive star formation among galaxies
International Nuclear Information System (INIS)
Allen, L.E.; Young, J.S.
1990-01-01
In order to determine the regions within galaxies which give rise to the most efficient star formation and to test the hypothesis that galaxies with high infrared luminosities per unit molecular mass are efficiently producing high mass stars, researchers have undertaken an H alpha imaging survey in galaxies whose CO distributions have been measured as part of the Five College Radio Astronomy Observatory (FCRAO) Extragalactic CO Survey. From these images researchers have derived global H alpha fluxes and distributions for comparison with far infrared radiation (FIR) fluxes and CO fluxes and distributions. Here, researchers present results on the global massive star formation efficiency (SFE = L sub H sub alpha/M(H2)) as a function of morphological type and environment, and on the radial distribution of the SFE within both peculiar and isolated galaxies. On the basis of comparison of the global L sub H sub alpha/M(H2) and L sub FIR/M(H2) for 111 galaxies, researchers conclude that environment rather than morphological type has the strongest effect on the global efficiency of massive star formation. Based on their study of a small sample, they find that the largest radial gradients are observed in the interacting/peculiar galaxies, indicating that environment affects the star formation efficiency within galaxies as well
Effects of the radial electrical field on the drifts, trapping and particle orbits in TJ-II
International Nuclear Information System (INIS)
Guasp, J.; Liniers, M.
1997-01-01
In this study a detailed analysis of the effect of radial electric fields on drifts, trapping and trajectories for ions of low and intermediate energy (0.1-1 keV) in the helical axis stellarator TJ-II has been performed. In TJ-II the drift velocities have the same rotation direction than the Hard Core (HC, the same than the plasma) with predominance of the vertical downwards component. The intensity is higher near the HC and in the outwards direction. These trends create strong asymmetries in losses even in the absence of electric field. When an electric field is present the poloidal components of the drift velocity predominates modifying deeply the orbit behaviour. Positive electric fields produce internal radial trapping barriers and have a tendency to eliminate the external ones. The opposite happens for negative fields. These facts alterate deeply the tapping and confinement properties of the particles. All these analysis will be used as a basis for the understanding of the modifications on the loss distribution, trapping regions and loss cones for TJ-II that will be addressed in forthcoming studies. (Author)
Fuel radial design using Path Relinking
International Nuclear Information System (INIS)
Campos S, Y.
2007-01-01
The present work shows the obtained results when implementing the combinatory optimization technique well-known as Path Re linking (Re-linkage of Trajectories), to the problem of the radial design of nuclear fuel assemblies, for boiling water reactors (BWR Boiling Water Reactor by its initials in English), this type of reactors is those that are used in the Laguna Verde Nucleo electric Central, Veracruz. As in any other electric power generation plant of that make use of some fuel to produce heat and that it needs each certain time (from 12 to 14 months) to make a supply of the same one, because this it wears away or it burns, in the nucleolectric plants to this activity is denominated fuel reload. In this reload different activities intervene, among those which its highlight the radial and axial designs of fuel assemblies, the patterns of control rods and the multi cycles study, each one of these stages with their own complexity. This work was limited to study in independent form the radial design, without considering the other activities. These phases are basic for the fuel reload design and of reactor operation strategies. (Author)
Numerical Simulation of Hydraulic Fracture Propagation Guided by Single Radial Boreholes
Directory of Open Access Journals (Sweden)
Tiankui Guo
2017-10-01
Full Text Available Conventional hydraulic fracturing is not effective in target oil development zones with available wellbores located in the azimuth of the non-maximum horizontal in-situ stress. To some extent, we think that the radial hydraulic jet drilling has the function of guiding hydraulic fracture propagation direction and promoting deep penetration, but this notion currently lacks an effective theoretical support for fracture propagation. In order to verify the technology, a 3D extended finite element numerical model of hydraulic fracturing promoted by the single radial borehole was established, and the influences of nine factors on propagation of hydraulic fracture guided by the single radial borehole were comprehensively analyzed. Moreover, the term ‘Guidance factor (Gf’ was introduced for the first time to effectively quantify the radial borehole guidance. The guidance of nine factors was evaluated through gray correlation analysis. The experimental results were consistent with the numerical simulation results to a certain extent. The study provides theoretical evidence for the artificial control technology of directional propagation of hydraulic fracture promoted by the single radial borehole, and it predicts the guidance effect of a single radial borehole on hydraulic fracture to a certain extent, which is helpful for planning well-completion and fracturing operation parameters in radial borehole-promoted hydraulic fracturing technology.
Radial collective flow in heavy-ion collisions at intermediate energies
International Nuclear Information System (INIS)
Borderie, B.
1996-11-01
The production of radial collective flow is associated with collisions leading to sources which undergo multifragmentation/explosion processes. After a theoretical survey of possible causes of production of radial flow, methods used to derive experimental values are discussed. Finally, a large set of data is presented which can be used to study and disentangle the different effects leading to radial collective flow. The dominant role of compression in the lower energy domain is emphasized. (author)
Rotary and radial forcing effects on center-of-mass locomotion dynamics.
Shen, Z H; Larson, P L; Seipel, J E
2014-09-01
Rotary and radial forcing are two common actuation methods for legged robots. However, these two orthogonal methods of center-of-mass (CoM) forcing have not been compared as potentially alternative strategies of actuation. In this paper, we compare the CoM stability and energetics of running with rotary and radial actuation through the simulation of two models: the rotary-forced spring-loaded inverted pendulum (rotary-forced-SLIP), and the radially-forced-SLIP. We model both radial and rotary actuation in the simplest way, applying them as a constant force during the stance portion of the gait. A simple application of constant rotary forcing throughout stance is capable of producing fully-asymptotically stable motion; however, a similarly constant application of radial forcing throughout the stance is not capable of producing stable solutions. We then allow both the applied rotary and radial forcing functions to turn on or off based on the occurrence of the mid-stance event, which breaks the symmetry of actuation during stance towards a net forward propulsion. We find that both a rotary force applied in the first half of stance and a radial force applied in the second half of stance, are capable of stabilizing running. Interestingly, these two forcing methods improve the motion stability in different ways. Rotary forcing first reduces then greatly increases the size of the stable parameter region when gradually increased. Radial forcing expands the stable parameter region, but only in a moderate way. Also, it is found that parameter region stabilized by rotary and radial forcing are largely complementary. Overall, rotary forcing can better stabilize running for both constant and event-based forcing functions that were attempted. This indicates that rotary forcing has an inherent capability of stabilizing running, even when minimal time-or-event-or-state feedback is present. Radial forcing, however, tends to be more energy efficient when compared to rotary forcing
On improved confinement in mirror plasmas by a radial electric field
Ågren, O.; Moiseenko, V. E.
2017-11-01
A weak radial electric field can suppress radial excursions of a guiding center from its mean magnetic surface. The physical origin of this effect is the smearing action by a poloidal E × B rotation, which tend to cancel out the inward and outward radial drifts. A use of this phenomenon may provide larger margins for magnetic field shaping with radial confinement of particles maintained in the collision free idealization. Mirror fields, stabilized by a quadrupolar field component, are of particular interest for their MHD stability and the possibility to control the quasi neutral radial electric field by biased potential plates outside the confinement region. Flux surface footprints on the end tank wall have to be traced to avoid short-circuiting between biased plates. Assuming a robust biasing procedure, moderate voltage demands for the biased plates seems adequate to cure even the radial excursions of Yushmanov ions which could be locally trapped near the mirrors. Analytical expressions are obtained for a magnetic quadrupolar mirror configuration which possesses minimal radial magnetic drifts in the central confinement region. By adding a weak controlled radial quasi-neutral electric field, the majority of gyro centers are predicted to be forced to move even closer to their respective mean magnetic surface. The gyro center radial coordinate is in such a case an accurate approximation for a constant of motion. By using this constant of motion, the analysis is in a Vlasov description extended to finite β. A correspondence between that Vlasov system and a fluid description with a scalar pressure and an electric potential is verified. The minimum B criterion is considered and implications for flute mode stability in the considered magnetic field is analyzed. By carrying out a long-thin expansion to a higher order, the validity of the calculations are extended to shorter and more compact device designs.
Radial Transport and Meridional Circulation in Accretion Disks
Energy Technology Data Exchange (ETDEWEB)
Philippov, Alexander A. [Department of Astrophysical Sciences, Princeton University, Ivy Lane, Princeton, NJ 08540 (United States); Rafikov, Roman R., E-mail: sashaph@princeton.edu [Institute for Advanced Study, Einstein Drive, Princeton, NJ 08540 (United States)
2017-03-10
Radial transport of particles, elements and fluid driven by internal stresses in three-dimensional (3D) astrophysical accretion disks is an important phenomenon, potentially relevant for the outward dust transport in protoplanetary disks, origin of the refractory particles in comets, isotopic equilibration in the Earth–Moon system, etc. To gain better insight into these processes, we explore the dependence of meridional circulation in 3D disks with shear viscosity on their thermal stratification, and demonstrate a strong effect of the latter on the radial flow. Previous locally isothermal studies have normally found a pattern of the radial outflow near the midplane, switching to inflow higher up. Here we show, both analytically and numerically, that a flow that is inward at all altitudes is possible in disks with entropy and temperature steeply increasing with height. Such thermodynamic conditions may be typical in the optically thin, viscously heated accretion disks. Disks in which these conditions do not hold should feature radial outflow near the midplane, as long as their internal stress is provided by the shear viscosity. Our results can also be used for designing hydrodynamical disk simulations with a prescribed pattern of the meridional circulation.
Detonation in supersonic radial outflow
Kasimov, Aslan R.; Korneev, Svyatoslav
2014-01-01
We report on the structure and dynamics of gaseous detonation stabilized in a supersonic flow emanating radially from a central source. The steady-state solutions are computed and their range of existence is investigated. Two-dimensional simulations
THE OCCURRENCE OF THE RADIAL CLUB HAND IN CHILDREN WITH DIFFERENT SYNDROMES
Directory of Open Access Journals (Sweden)
Sergey Ivanovich Golyana
2013-03-01
Full Text Available Radial club hand is a developmental anomaly of the upper extremity, being characterized as a longitudinal underdevelopment of a forearm and a hand on the radial surface, consisting in a hypo-/ aplazy radial bone and the thumb of various degree of expressiveness. Characteristic symptoms of this developmental anomaly are: shortening and bow-shaped curvature of a forearm, palmar and radial deviation of a hand, underdevelopment of the thumb from its proximal departments and structures, anomaly of development of three-phalanx fingers of a hand (is more often than the 2-4th, violation of a cosmetic condition and functionality of the affected segment. From 2000 for 2012 in FSI SRICO n.a. H.Turner examination and treatment of 23 children with various syndromes at which the radial club hand was revealed are conducted. The main syndromes at which it is revealed radial club hand - Holt-Orama syndrome, TAR- syndrome and VACTERL syndrome. Tactics and techniques of surgical treatment of a radial club hand it various syndromes most often don’t differ from treatment of other types of a radial club hand though demand an individual approach depending on severity and a type of deformation of the upper extremity.
The effect of radial migration on galactic disks
International Nuclear Information System (INIS)
Vera-Ciro, Carlos; D'Onghia, Elena; Navarro, Julio; Abadi, Mario
2014-01-01
We study the radial migration of stars driven by recurring multi-arm spiral features in an exponential disk embedded in a dark matter halo. The spiral perturbations redistribute angular momentum within the disk and lead to substantial radial displacements of individual stars, in a manner that largely preserves the circularity of their orbits and that results, after 5 Gyr (∼40 full rotations at the disk scale length), in little radial heating and no appreciable changes to the vertical or radial structure of the disk. Our results clarify a number of issues related to the spatial distribution and kinematics of migrators. In particular, we find that migrators are a heavily biased subset of stars with preferentially low vertical velocity dispersions. This 'provenance bias' for migrators is not surprising in hindsight, for stars with small vertical excursions spend more time near the disk plane, and thus respond more readily to non-axisymmetric perturbations. We also find that the vertical velocity dispersion of outward migrators always decreases, whereas the opposite holds for inward migrators. To first order, newly arrived migrators simply replace stars that have migrated off to other radii, thus inheriting the vertical bias of the latter. Extreme migrators might therefore be recognized, if present, by the unexpectedly small amplitude of their vertical excursions. Our results show that migration, understood as changes in angular momentum that preserve circularity, can strongly affect the thin disk, but cast doubts on models that envision the Galactic thick disk as a relic of radial migration.
Rayleigh-Taylor instability of cylindrical jets with radial motion
International Nuclear Information System (INIS)
Chen, X.M.; Schrock, V.E.; Peterson, P.F.
1997-01-01
Rayleigh-Taylor instability of an interface between fluids with different densities subjected to acceleration normal to itself has interested researchers for almost a century. The classic analyses of a flat interface by Rayleigh and Taylor have shown that this type of instability depends on the direction of acceleration and the density differences of the two fluids. Plesset later analyzed the stability of a spherically symmetric flows (and a spherical interface) and concluded that the instability also depends on the velocity of the interface as well as the direction and magnitude of radial acceleration. The instability induced by radial motion in cylindrical systems seems to have been neglected by previous researchers. This paper analyzes the Rayleigh-Taylor type of instability for a cylindrical surface with radial motions. The results of the analysis show that, like the spherical case, the radial velocity also plays an important role. As an application, the example of a liquid jet surface in an Inertial Confinement Fusion (ICF) reactor design is analyzed. (orig.)
Directory of Open Access Journals (Sweden)
Jinji Sun
2014-01-01
Full Text Available In this paper, a novel integrated structure is proposed in order to reduce the axial length of the high speed of a magnetically suspended motor (HSMSM to ensure the maximum speed, which combines radial displacement sensor probes and the permanent magnet biased radial magnetic bearing in HSMSM. The sensor probes are integrated in the magnetic bearing, and the sensor preamplifiers are placed in the control system of the HSMSM, separate from the sensor probes. The proposed integrated structure can save space in HSMSMs, improve the working frequency, reduce the influence of temperature on the sensor circuit, and improve the stability of HSMSMs.
Radial-piston pump for drive of test machines
Nizhegorodov, A. I.; Gavrilin, A. N.; Moyzes, B. B.; Cherkasov, A. I.; Zharkevich, O. M.; Zhetessova, G. S.; Savelyeva, N. A.
2018-01-01
The article reviews the development of radial-piston pump with phase control and alternating-flow mode for seismic-testing platforms and other test machines. The prospects for use of the developed device are proved. It is noted that the method of frequency modulation with the detection of the natural frequencies is easily realized by using the radial-piston pump. The prospects of further research are given proof.
Douma, M.; Ligierko, G.; Angelov, I.
2008-10-01
The need for information has increased exponentially over the past decades. The current systems for constructing, exploring, classifying, organizing, and searching information face the growing challenge of enabling their users to operate efficiently and intuitively in knowledge-heavy environments. This paper presents SpicyNodes, an advanced user interface for difficult interaction contexts. It is based on an underlying structure known as a radial map, which allows users to manipulate and interact in a natural manner with entities called nodes. This technology overcomes certain limitations of existing solutions and solves the problem of browsing complex sets of linked information. SpicyNodes is also an organic system that projects users into a living space, stimulating exploratory behavior and fostering creative thought. Our interactive radial layout is used for educational purposes and has the potential for numerous other applications.
Modelling and analysis of radial thermal stresses and temperature ...
African Journals Online (AJOL)
A theoretical investigation has been undertaken to study operating temperatures, heat fluxes and radial thermal stresses in the valves of a modern diesel engine with and without air-cavity. Temperatures, heat fluxes and radial thermal stresses were measured theoretically for both cases under all four thermal loading ...
A novel integrated 4-DOF radial hybrid magnetic bearing for MSCMG
Energy Technology Data Exchange (ETDEWEB)
Jinji, Sun; Ziyan, Ju [School of Instrumentation Science & Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Science and Technology on Inertial Laboratory, Beijing 100191 (China); Weitao, Han, E-mail: hanweitaotao@163.com [CRRC Qingdao Sifang CO., LTD, Qingdao 266111 (China); Gang, Liu [School of Instrumentation Science & Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Science and Technology on Inertial Laboratory, Beijing 100191 (China)
2017-01-01
This paper proposes a novel integrated radial hybrid magnetic bearing (RHMB) for application with the small-sized magnetically suspended control moment gyroscope (MSCMG), which can control four degrees of freedom (4-DOFs), including two radial translational DOFs and two radial tilting DOFs, and provide the axial passive resilience. The configuration and working principle of the RHMB are introduced. Mathematical models of radial force, axial resilience and moment are established by using equivalent magnetic circuit method (EMCM), from which the radial force–radial displacement, radial force–current relationships are derived, as well as axial resilience–axial displacement, moment–tilting angle and moment–current. Finite element method (FEM) is also applied to analyze the performance and characteristics of the RHMB. The analysis results are in good agreement with that calculated by the EMCM, which is helpful in designing, optimizing and controlling the RHMB. The comparisons between the performances of the integrated 4-DOF RHMB and the traditional 4-DOF RHMB are made. The contrast results indicate that the proposed integrated 4-DOF RHMB possesses better performance compared to the traditional structure, such as copper loss, current stiffness, and tilting current stiffness. - Highlights: • An integrated 4-DOF RHMB is proposed for the small-sized MSCMG. • The 4-DOF RHMB has good linear force–displacement and force–current characteristics. • The RHMB has good linear moment–current and the moment–tilting angle characteristic.
MRI of radial displacement of the meniscus in the knee
International Nuclear Information System (INIS)
Chen Jian; Lv Houshan; Lao Shan; Guan Zhenpeng; Hong Nan; Liang Hao
2006-01-01
Objective: To describe the phenomenon of radial displacement of the meniscus of the knees in the study population with MR imaging, and to establish MRI diagnostic criteria for radial displacement of the meniscus and displacement index. Methods: MR signs of radial displacement of the meniscus were evaluated retrospectively in 398 patients with knee symptoms who were examined with non- weight bearing MR images from Jan. 2000 to Feb. 2004. The patients younger than 18 years old, with joint effusion or serious arthropathy were excluded and 312 patients were eligible to be enrolled in this study. The criterion for radial displacement of the meniscus was defined as the location of the edge of meniscal body beyond the femoral and tibial outer border line. A displacement index, defined as the ratio of meniscal overhang to meniscal width, was used to quantify meniscal displacement. Results: The prevalence of radial displacement of the meniscus was 16.7% (52/312) and 13.9% (21/151) in right knee and 19.3% (31/161 )in left knee, respectively. There was no significant difference between left and right knee (χ 2 =1.60, P>0.05) and the ratio between medial and lateral meniscus was 7.8:1. The average displacement index was 0.54±0.24. The displacement indices were significant higher in older group (F=3.63, P<0.05). The incidence and indices of radial displacement of the meniscus for patients under or above 50 year older were 12.0%(17/142), 0.46±0.22 and 20.6% (35/170), 0.64±0.20, respectively. Difference was highly significant (t=0.84, P<0.01). Conclusion: It was concluded that radial displacement of the meniscus in knees was not a rare finding with MR imaging in patients with knee symptoms. The incidence increased in older age group. Further investigations were recommended to understand the etiology and clinical significance of the phenomenon of radial displacement of the meniscus. (authors)
Vortex Whistle in Radial Intake
National Research Council Canada - National Science Library
Tse, Man-Chun
2004-01-01
In a radial-to-axial intake with inlet guide vanes (IGV) at the entry, a strong flow circulation Gamma can be generated from the tangential flow components created by the IGVs when their setting exceed about halfclosing (approx. 45 deg...
Radial force measurement of endovascular stents: Influence of stent design and diameter.
Matsumoto, Takuya; Matsubara, Yutaka; Aoyagi, Yukihiko; Matsuda, Daisuke; Okadome, Jun; Morisaki, Koichi; Inoue, Kentarou; Tanaka, Shinichi; Ohkusa, Tomoko; Maehara, Yoshihiko
2016-04-01
Angioplasty and endovascular stent placement is used in case to rescue the coverage of main branches to supply blood to brain from aortic arch in thoracic endovascular aortic repair. This study assessed mechanical properties, especially differences in radial force, of different endovascular and thoracic stents. We analyzed the radial force of three stent models (Epic, E-Luminexx and SMART) stents using radial force-tester method in single or overlapping conditions. We also analyzed radial force in three thoracic stents using Mylar film testing method: conformable Gore-TAG, Relay, and Valiant Thoracic Stent Graft. Overlapping SMART stents had greater radial force than overlapping Epic or Luminexx stents (P stents was greater than that of all three endovascular stents (P stents, site of deployment, and layer characteristics. In clinical settings, an understanding of the mechanical characteristics, including radial force, is important in choosing a stent for each patient. © The Author(s) 2015.
Energy Technology Data Exchange (ETDEWEB)
Martin del Campo M, C.; Carmona H, R.; Oropeza C, I.P. [UNAM, Paseo Cuauhnahuac 8532, 62550 Jiutepec, Morelos (Mexico)]. e-mail: cmcm@fi-b.unam.mx
2006-07-01
The development of the application of the Genetic Algorithms (GA) to the optimization of the radial distribution of enrichment in a cell of fuel of a BWR (Boiling Water Reactor) is presented. The optimization process it was ties to the HELIOS simulator, which is a transport code of neutron simulation of fuel cells that has been validated for the calculation of nuclear banks for BWRs. With heterogeneous radial designs can improve the radial distribution of the power, for what the radial design of fuel has a strong influence in the global design of fuel recharges. The optimum radial distribution of fuel bars is looked for with different enrichments of U{sup 235} and contents of consumable poison. For it is necessary to define the representation of the solution, the objective function and the implementation of the specific optimization process to the solution of the problem. The optimization process it was coded in 'C' language, it was automated the creation of the entrances to the simulator, the execution of the simulator and the extraction, in the exit of the simulator, of the parameters that intervene in the objective function. The objective function includes four parameters: average enrichment of the cell, average gadolinia concentration of the cell, peak factor of radial power and k-infinite multiplication factor. To be able to calculate the parameters that intervene in the objective function, the one evaluation process of GA was ties to the HELIOS code executed in a Compaq Alpha workstation. It was applied to the design of a fuel cell of 10 x 10 that it can be employee in the fuel assemble designs that are used at the moment in the Laguna Verde Nucleo electric Central. Its were considered 10 different fuel compositions which four contain gadolinia. Three heuristic rules that consist in prohibiting the placement of bars with gadolinia in the ends of the cell, to place the compositions with the smallest enrichment in the corners of the cell and to fix
MARVELS Radial Velocity Solutions to Seven Kepler Eclipsing Binaries
Heslar, Michael Francis; Thomas, Neil B.; Ge, Jian; Ma, Bo; Herczeg, Alec; Reyes, Alan; SDSS-III MARVELS Team
2016-01-01
Eclipsing binaries serve momentous purposes to improve the basis of understanding aspects of stellar astrophysics, such as the accurate calculation of the physical parameters of stars and the enigmatic mass-radius relationship of M and K dwarfs. We report the investigation results of 7 eclipsing binary candidates, initially identified by the Kepler mission, overlapped with the radial velocity observations from the SDSS-III Multi-Object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS). The RV extractions and spectroscopic solutions of these eclipsing binaries were generated by the University of Florida's 1D data pipeline with a median RV precision of ~60-100 m/s, which was utilized for the DR12 data release. We performed the cross-reference fitting of the MARVELS RV data and the Kepler photometric fluxes obtained from the Kepler Eclipsing Binary Catalog (V2) and modelled the 7 eclipsing binaries in the BinaryMaker3 and PHOEBE programs. This analysis accurately determined the absolute physical and orbital parameters of each binary. Most of the companion stars were determined to have masses of K and M dwarf stars (0.3-0.8 M⊙), and allowed for an investigation into the mass-radius relationship of M and K dwarfs. Among the cases are KIC 9163796, a 122.2 day period "heartbeat star", a recently-discovered class of eccentric binaries known for tidal distortions and pulsations, with a high eccentricity (e~0.75) and KIC 11244501, a 0.29 day period, contact binary with a double-lined spectrum and mass ratio (q~0.45). We also report on the possible reclassification of 2 Kepler eclipsing binary candidates as background eclipsing binaries based on the analysis of the flux measurements, flux ratios of the spectroscopic and photometric solutions, the differences in the FOVs, the image processing of Kepler, and RV and spectral analysis of MARVELS.
Directory of Open Access Journals (Sweden)
Krishna Kumar
2010-11-01
Full Text Available Radial spasm is often very prolonged and painful to the patient. Here, we describe a novel way to deal with the same. The total spasm lasted over 4 hours. A 3.4 6 JR catheter was introduced via the femoral route and papav arine one ampoule was injected directly into the right subclavian artery. After about 10 min we were able to pull out the radial catheter. Radial angiography is a simple procedure with reportedly less complications 1,2. How ever ,it has one major complication radial spasm. We describe here a patient with radial spasm that persisted for more than 2 hours and how we dealt with it.
Rotary and radial forcing effects on center-of-mass locomotion dynamics
International Nuclear Information System (INIS)
Shen, Z H; Larson, P L; Seipel, J E
2014-01-01
Rotary and radial forcing are two common actuation methods for legged robots. However, these two orthogonal methods of center-of-mass (CoM) forcing have not been compared as potentially alternative strategies of actuation. In this paper, we compare the CoM stability and energetics of running with rotary and radial actuation through the simulation of two models: the rotary-forced spring-loaded inverted pendulum (rotary-forced-SLIP), and the radially-forced-SLIP. We model both radial and rotary actuation in the simplest way, applying them as a constant force during the stance portion of the gait. A simple application of constant rotary forcing throughout stance is capable of producing fully-asymptotically stable motion; however, a similarly constant application of radial forcing throughout the stance is not capable of producing stable solutions. We then allow both the applied rotary and radial forcing functions to turn on or off based on the occurrence of the mid-stance event, which breaks the symmetry of actuation during stance towards a net forward propulsion. We find that both a rotary force applied in the first half of stance and a radial force applied in the second half of stance, are capable of stabilizing running. Interestingly, these two forcing methods improve the motion stability in different ways. Rotary forcing first reduces then greatly increases the size of the stable parameter region when gradually increased. Radial forcing expands the stable parameter region, but only in a moderate way. Also, it is found that parameter region stabilized by rotary and radial forcing are largely complementary. Overall, rotary forcing can better stabilize running for both constant and event-based forcing functions that were attempted. This indicates that rotary forcing has an inherent capability of stabilizing running, even when minimal time-or-event-or-state feedback is present. Radial forcing, however, tends to be more energy efficient when compared to rotary forcing
The First Experience of Triple Nerve Transfer in Proximal Radial Nerve Palsy.
Emamhadi, Mohammadreza; Andalib, Sasan
2018-01-01
Injury to distal portion of posterior cord of brachial plexus leads to palsy of radial and axillary nerves. Symptoms are usually motor deficits of the deltoid muscle; triceps brachii muscle; and extensor muscles of the wrist, thumb, and fingers. Tendon transfers, nerve grafts, and nerve transfers are options for surgical treatment of proximal radial nerve palsy to restore some motor functions. Tendon transfer is painful, requires a long immobilization, and decreases donor muscle strength; nevertheless, nerve transfer produces promising outcomes. We present a patient with proximal radial nerve palsy following a blunt injury undergoing triple nerve transfer. The patient was involved in a motorcycle accident with complete palsy of the radial and axillary nerves. After 6 months, on admission, he showed spontaneous recovery of axillary nerve palsy, but radial nerve palsy remained. We performed triple nerve transfer, fascicle of ulnar nerve to long head of the triceps branch of radial nerve, flexor digitorum superficialis branch of median nerve to extensor carpi radialis brevis branch of radial nerve, and flexor carpi radialis branch of median nerve to posterior interosseous nerve, for restoration of elbow, wrist, and finger extensions, respectively. Our experience confirmed functional elbow, wrist, and finger extensions in the patient. Triple nerve transfer restores functions of the upper limb in patients with debilitating radial nerve palsy after blunt injuries. Copyright © 2017 Elsevier Inc. All rights reserved.
Development of a Radial Deconsolidation Method
Energy Technology Data Exchange (ETDEWEB)
Helmreich, Grant W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Montgomery, Fred C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hunn, John D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-12-01
A series of experiments have been initiated to determine the retention or mobility of fission products* in AGR fuel compacts [Petti, et al. 2010]. This information is needed to refine fission product transport models. The AGR-3/4 irradiation test involved half-inch-long compacts that each contained twenty designed-to-fail (DTF) particles, with 20-μm thick carbon-coated kernels whose coatings were deliberately fabricated such that they would crack under irradiation, providing a known source of post-irradiation isotopes. The DTF particles in these compacts were axially distributed along the compact centerline so that the diffusion of fission products released from the DTF kernels would be radially symmetric [Hunn, et al. 2012; Hunn et al. 2011; Kercher, et al. 2011; Hunn, et al. 2007]. Compacts containing DTF particles were irradiated at Idaho National Laboratory (INL) at the Advanced Test Reactor (ATR) [Collin, 2015]. Analysis of the diffusion of these various post-irradiation isotopes through the compact requires a method to radially deconsolidate the compacts so that nested-annular volumes may be analyzed for post-irradiation isotope inventory in the compact matrix, TRISO outer pyrolytic carbon (OPyC), and DTF kernels. An effective radial deconsolidation method and apparatus appropriate to this application has been developed and parametrically characterized.
A visual study of radial inward choked flow of liquid nitrogen.
Hendricks, R. C.; Simoneau, R. J.; Hsu, Y. Y.
1973-01-01
Data and high speed movies were acquired on pressurized subcooled liquid nitrogen flowing radially inward through a 0.0076 cm gap. The stagnation pressure ranged from 0.7 to 4 MN/sq m. Steady radial inward choked flow appears equivalent to steady choked flow through axisymmetric nozzles. Transient choked flows through the radial gap are not uniform and the discharge pattern appears as nonuniform impinging jets. The critical mass flow rate data for the transient case appear different from those for the steady case. On the mass flow rate vs pressure map, the slope and separation of the isotherms appear to be less for transient than for steady radial choked flow.
Sirenomelia with radial dysplasia.
Kulkarni, M L; Abdul Manaf, K M; Prasannakumar, D G; Kulkarni, Preethi M
2004-05-01
Sirenomelia is a rare anomaly usually associated with other multiple malformations. In this communication the authors report a case of sirenomelia associated with multiple malformations, which include radial hypoplasia also. Though several theories have been proposed regarding the etiology of multiple malformation syndromes in the past, the recent theory of primary developmental defect during blastogenesis holds good in this case.
Comparison of Deterministic and Probabilistic Radial Distribution Systems Load Flow
Gupta, Atma Ram; Kumar, Ashwani
2017-12-01
Distribution system network today is facing the challenge of meeting increased load demands from the industrial, commercial and residential sectors. The pattern of load is highly dependent on consumer behavior and temporal factors such as season of the year, day of the week or time of the day. For deterministic radial distribution load flow studies load is taken as constant. But, load varies continually with a high degree of uncertainty. So, there is a need to model probable realistic load. Monte-Carlo Simulation is used to model the probable realistic load by generating random values of active and reactive power load from the mean and standard deviation of the load and for solving a Deterministic Radial Load Flow with these values. The probabilistic solution is reconstructed from deterministic data obtained for each simulation. The main contribution of the work is: Finding impact of probable realistic ZIP load modeling on balanced radial distribution load flow. Finding impact of probable realistic ZIP load modeling on unbalanced radial distribution load flow. Compare the voltage profile and losses with probable realistic ZIP load modeling for balanced and unbalanced radial distribution load flow.
Introducing radiality constraints in capacitated location-routing problems
Directory of Open Access Journals (Sweden)
Eliana Mirledy Toro Ocampo
2017-03-01
Full Text Available In this paper, we introduce a unified mathematical formulation for the Capacitated Vehicle Routing Problem (CVRP and for the Capacitated Location Routing Problem (CLRP, adopting radiality constraints in order to guarantee valid routes and eliminate subtours. This idea is inspired by formulations already employed in electric power distribution networks, which requires a radial topology in its operation. The results show that the proposed formulation greatly improves the convergence of the solver.
Analysis on Coupled Vibration of a Radially Polarized Piezoelectric Cylindrical Transducer
Directory of Open Access Journals (Sweden)
Jie Xu
2017-12-01
Full Text Available Coupled vibration of a radially polarized piezoelectric cylindrical transducer is analyzed with the mechanical coupling coefficient method. The method has been utilized to analyze the metal cylindrical transducer and the axially polarized piezoelectric cylindrical transducer. In this method, the mechanical coupling coefficient is introduced and defined as the stress ratio in different directions. Coupled vibration of the cylindrical transducer is regarded as the interaction of the plane radial vibration of a ring and the longitudinal vibration of a tube. For the radially polarized piezoelectric cylindrical transducer, the radial and longitudinal electric admittances as functions of mechanical coupling coefficients and angular frequencies are derived, respectively. The resonance frequency equations are obtained. The dependence of resonance frequency and mechanical coupling coefficient on aspect ratio is studied. Vibrational distributions on the surfaces of the cylindrical transducer are presented with experimental measurement. On the support of experiments, this work is verified and provides a theoretical foundation for the analysis and design of the radially polarized piezoelectric cylindrical transducer.
Heddam, Salim
2016-09-01
This paper proposes multilayer perceptron neural network (MLPNN) to predict phycocyanin (PC) pigment using water quality variables as predictor. In the proposed model, four water quality variables that are water temperature, dissolved oxygen, pH, and specific conductance were selected as the inputs for the MLPNN model, and the PC as the output. To demonstrate the capability and the usefulness of the MLPNN model, a total of 15,849 data measured at 15-min (15 min) intervals of time are used for the development of the model. The data are collected at the lower Charles River buoy, and available from the US Environmental Protection Agency (USEPA). For comparison purposes, a multiple linear regression (MLR) model that was frequently used for predicting water quality variables in previous studies is also built. The performances of the models are evaluated using a set of widely used statistical indices. The performance of the MLPNN and MLR models is compared with the measured data. The obtained results show that (i) the all proposed MLPNN models are more accurate than the MLR models and (ii) the results obtained are very promising and encouraging for the development of phycocyanin-predictive models.
Institute of Scientific and Technical Information of China (English)
朱国俊; 冯建军; 郭鹏程; 罗兴锜
2014-01-01
如何提高海流能水轮机的能量捕获效率是海洋能开发领域的重点研究课题，而提高海流能水轮机能量性能的关键在于其叶片几何的构造基础--水力翼型的性能提升。该文提出了一种水力翼型的多工况优化设计方法，该方法采用Bezier曲线参数化技术建立翼型的参数化表征方法，然后利用拉丁超立方试验设计技术在设计空间获取训练径向基（radial basis function，RBF）神经网络的样本点，通过计算流体动力学的方法获得每个翼型样本的性能参数后开展神经网络的学习训练，最后采用RBF神经网络与NSGA-II遗传算法相结合的现代优化技术数值求解水力翼型的多工况优化问题。基于上述优化方法对NACA63-815翼型进行了优化改进，重点研究了该翼型在3个攻角工况下（0，6°和12°）的优化问题及求解方法。优化结果表明，优化后的翼型在3个工况点下都具有更好的升阻比性能，同时也能更好地抑制失速现象的产生，验证了该优化方法的理论正确性和可行性。%In order to reduce the current dependence on fossil and nuclear-fueled power plants to cope with the growing demand of electrical energy, the ocean energy technologies must be improved to develop more energy. There are several types of ocean energy that can be feasible to exploit:wave energy, marine-current energy, tidal barrages, ocean thermal energy and so on. But the most promising in the short term may be wave and marine-current energy. Marine-current energy can be exploited by a marine current turbine. So how to improve the efficiency of mariner current turbine is the key research subject in ocean energy development. The key to efficiency improvement is the performance improvement of hydrofoil, which is used to establish the turbine blade. In order to improve the hydrofoil’s performance, a multi-point optimization method is presented in this paper. In this method
Energy Technology Data Exchange (ETDEWEB)
Payan, J
1994-05-01
After a review of turbulence and transport phenomena in tokamak plasmas and the radial electric field shear effect in various tokamaks, experimental measurements obtained at Tore Supra by the means of the ALTAIR plasma diagnostic technique, are presented. Electronic drift waves destabilization mechanisms, which are the main features that could describe the experimentally observed microturbulence, are then examined. The effect of a radial electric field shear on electronic drift waves is then introduced, and results with ohmic heating are studied together with relations between turbulence and transport. The possible existence of ionic waves is rejected, and a spectral frequency modelization is presented, based on the existence of an electric field sheared radial profile. The position of the inversion point of this field is calculated for different values of the mean density and the plasma current, and the modelization is applied to the TEXT tokamak. The radial electric field at Tore Supra is then estimated. The effect of the ergodic divertor on turbulence and abnormal transport is then described and the density fluctuation radial profile in presence of the ergodic divertor is modelled. 80 figs., 120 refs.
International Nuclear Information System (INIS)
Maslov, V.I.; Barchuk, S.V.; Lapshin, V.I.; Volkov, E.D.; Melentsov, Yu.V.
2006-01-01
It is shown, that at development of instability due to a radial gradient of density in the crossed electric and magnetic fields in nuclear fusion installations ordering convective cells can be excited. It provides anomalous particle transport. The spatial structures of these convective cells have been constructed. The radial dimensions of these convective cells depend on their amplitudes and on a radial gradient of density. The convective-diffusion equation for radial dynamics of the electrons has been derived. At the certain value of the universal controlling parameter, the convective cell excitation and the anomalous radial transport are suppressed. (author)
Plasma Signatures of Radial Field Power Dropouts
International Nuclear Information System (INIS)
Lucek, E.A.; Horbury, T.S.; Balogh, A.; McComas, D.J.
1998-01-01
A class of small scale structures, with a near-radial magnetic field and a drop in magnetic field fluctuation power, have recently been identified in the polar solar wind. An earlier study of 24 events, each lasting for 6 hours or more, identified no clear plasma signature. In an extension of that work, radial intervals lasting for 4 hours or more (89 in total), have been used to search for a statistically significant plasma signature. It was found that, despite considerable variations between intervals, there was a small but significant drop, on average, in plasma temperature, density and β during these events
Reble, a radially converging electron beam accelerator
International Nuclear Information System (INIS)
Ramirez, J.J.; Prestwich, K.R.
1976-01-01
The Reble accelerator at Sandia Laboratories is described. This accelerator was developed to provide an experimental source for studying the relevant diode physics, beam propagation, beam energy deposition in a gas using a radially converging e-beam. The nominal parameters for Reble are 1 MV, 200 kA, 20 ns e-beam pulse. The anode and cathode are concentric cylinders with the anode as the inner cylinder. The radial beam can be propagated through the thin foil anode into the laser gas volume. The design and performance of the various components of the accelerator are presented
Observational hints of radial migration in disc galaxies from CALIFA
Ruiz-Lara, T.; Pérez, I.; Florido, E.; Sánchez-Blázquez, P.; Méndez-Abreu, J.; Sánchez-Menguiano, L.; Sánchez, S. F.; Lyubenova, M.; Falcón-Barroso, J.; van de Ven, G.; Marino, R. A.; de Lorenzo-Cáceres, A.; Catalán-Torrecilla, C.; Costantin, L.; Bland-Hawthorn, J.; Galbany, L.; García-Benito, R.; Husemann, B.; Kehrig, C.; Márquez, I.; Mast, D.; Walcher, C. J.; Zibetti, S.; Ziegler, B.
2017-01-01
Context. According to numerical simulations, stars are not always kept at their birth galactocentric distances but they have a tendency to migrate. The importance of this radial migration in shaping galactic light distributions is still unclear. However, if radial migration is indeed important,
Radial mixing of material in the asterodial zone
International Nuclear Information System (INIS)
Ruzmaikina, T.V.; Safronov, V.S.; Weidenschilling, S.J.
1989-01-01
The asteroid belt shows radial zoning of compositional structure. The most abundant types are successively S, C and P types from the inner to the outer parts of the main belt, and D type in the Trojan clouds. Boundaries between compositional zones are not sharp, but gradual transitions over scales ∼1 AU in semimajor axis. The authors examine processes for producing this structure before, during and after the accretion of asteroids. The initial structure is established by temperature and composition gradients in the turbulent solar nebula during the collapse of the presolar cloud. The radial scale of the zoning, comparable to the disk thickness, favors disk models with relatively low turbulent viscosity. Radial decay of solid bodies due to gas drag during settling to the central plane and planetesimal formation probably causes only a small degree of mixing, due to the systematic nature of drag-induced motions
Mixed-Degree Spherical Simplex-Radial Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Shiyuan Wang
2017-01-01
Full Text Available Conventional low degree spherical simplex-radial cubature Kalman filters often generate low filtering accuracy or even diverge for handling highly nonlinear systems. The high-degree Kalman filters can improve filtering accuracy at the cost of increasing computational complexity; nevertheless their stability will be influenced by the negative weights existing in the high-dimensional systems. To efficiently improve filtering accuracy and stability, a novel mixed-degree spherical simplex-radial cubature Kalman filter (MSSRCKF is proposed in this paper. The accuracy analysis shows that the true posterior mean and covariance calculated by the proposed MSSRCKF can agree accurately with the third-order moment and the second-order moment, respectively. Simulation results show that, in comparison with the conventional spherical simplex-radial cubature Kalman filters that are based on the same degrees, the proposed MSSRCKF can perform superior results from the aspects of filtering accuracy and computational complexity.
Analysis of radial runout for symmetric and asymmetric HDD spindle motors with rotor eccentricity
International Nuclear Information System (INIS)
Kim, T.-J.; Kim, K.-T.; Hwang, S.-M.; Lee, S.-B.; Park, N.-G.
2001-01-01
Radial runout of disk drive spindle is one of the major limiting factors in achieving higher track densities in hard disk drives. Mechanical, magnetic and their coupled origins, such as unbalanced mass, reaction forces and magnetic forces, introduce radial runout of spindle motors. In this paper, radial magnetic forces are calculated with respect to the various rotor eccentricities using analytic method. Based on the results of the radial magnetic forces, the radial runout of the spindle motor is analyzed using finite element and transfer matrices. Results show that an asymmetric motor has a worse performance on unbalanced magnetic forces and radial runout when mechanical and magnetic coupling exists
The radial-hedgehog solution in Landau–de Gennes' theory for nematic liquid crystals
MAJUMDAR, APALA
2011-09-06
We study the radial-hedgehog solution in a three-dimensional spherical droplet, with homeotropic boundary conditions, within the Landau-de Gennes theory for nematic liquid crystals. The radial-hedgehog solution is a candidate for a global Landau-de Gennes minimiser in this model framework and is also a prototype configuration for studying isolated point defects in condensed matter physics. The static properties of the radial-hedgehog solution are governed by a non-linear singular ordinary differential equation. We study the analogies between Ginzburg-Landau vortices and the radial-hedgehog solution and demonstrate a Ginzburg-Landau limit for the Landau-de Gennes theory. We prove that the radial-hedgehog solution is not the global Landau-de Gennes minimiser for droplets of finite radius and sufficiently low temperatures and prove the stability of the radial-hedgehog solution in other parameter regimes. These results contain quantitative information about the effect of geometry and temperature on the properties of the radial-hedgehog solution and the associated biaxial instabilities. © Copyright Cambridge University Press 2011.
The radial-hedgehog solution in Landau–de Gennes' theory for nematic liquid crystals
MAJUMDAR, APALA
2011-01-01
We study the radial-hedgehog solution in a three-dimensional spherical droplet, with homeotropic boundary conditions, within the Landau-de Gennes theory for nematic liquid crystals. The radial-hedgehog solution is a candidate for a global Landau-de Gennes minimiser in this model framework and is also a prototype configuration for studying isolated point defects in condensed matter physics. The static properties of the radial-hedgehog solution are governed by a non-linear singular ordinary differential equation. We study the analogies between Ginzburg-Landau vortices and the radial-hedgehog solution and demonstrate a Ginzburg-Landau limit for the Landau-de Gennes theory. We prove that the radial-hedgehog solution is not the global Landau-de Gennes minimiser for droplets of finite radius and sufficiently low temperatures and prove the stability of the radial-hedgehog solution in other parameter regimes. These results contain quantitative information about the effect of geometry and temperature on the properties of the radial-hedgehog solution and the associated biaxial instabilities. © Copyright Cambridge University Press 2011.
Directory of Open Access Journals (Sweden)
Zhongbao Wang
2014-01-01
Full Text Available A computer-aided design model based on the artificial neural network (ANN is proposed to directly obtain patch physical dimensions of the single-feed corner-truncated circularly polarized microstrip antenna (CPMA with an air gap for wideband applications. To take account of the effect of the air gap, an equivalent relative permittivity is introduced and adopted to calculate the resonant frequency and Q-factor of square microstrip antennas for obtaining the training data sets. ANN architectures using multilayered perceptrons (MLPs and radial basis function networks (RBFNs are compared. Also, six learning algorithms are used to train the MLPs for comparison. It is found that MLPs trained with the Levenberg-Marquardt (LM algorithm are better than RBFNs for the synthesis of the CPMA. An accurate model is achieved by using an MLP with three hidden layers. The model is validated by the electromagnetic simulation and measurements. It is enormously useful to antenna engineers for facilitating the design of the single-feed CPMA with an air gap.
BELM: Bayesian extreme learning machine.
Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J
2011-03-01
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.
Panigrahi, Binay Kumar; Das, Soumya; Nath, Tushar Kumar; Senapati, Manas Ranjan
2018-05-01
In the present study, with a view to speculate the water flow of two rivers in eastern India namely river Daya and river Bhargavi, the focus was on developing Cascaded Functional Link Artificial Neural Network (C-FLANN) model. Parameters of C-FLANN architecture were updated using Harmony Search (HS) and Differential Evolution (DE). As the numbers of samples are very low, there is a risk of over fitting. To avoid this Map reduce based ANOVA technique is used to select important features. These features were used and provided to the architecture which is used to predict the water flow in both the rivers, one day, one week and two weeks ahead. The results of both the techniques were compared with Radial Basis Functional Neural Network (RBFNN) and Multilayer Perceptron (MLP), two widely used artificial neural network for prediction. From the result it was confirmed that C-FLANN trained through HS gives better prediction result than being trained through DE or RBFNN or MLP and can be used for predicting water flow in different rivers.
Macrocell path loss prediction using artificial intelligence techniques
Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.
2014-04-01
The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.
Statistical process control using optimized neural networks: a case study.
Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid
2014-09-01
The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
Robot Grasp Learning by Demonstration without Predefined Rules
Directory of Open Access Journals (Sweden)
César Fernández
2011-12-01
Full Text Available A learning-based approach to autonomous robot grasping is presented. Pattern recognition techniques are used to measure the similarity between a set of previously stored example grasps and all the possible candidate grasps for a new object. Two sets of features are defined in order to characterize grasps: point attributes describe the surroundings of a contact point; point-set attributes describe the relationship between the set of n contact points (assuming an n-fingered robot gripper is used. In the experiments performed, the nearest neighbour classifier outperforms other approaches like multilayer perceptrons, radial basis functions or decision trees, in terms of classification accuracy, while computational load is not excessive for a real time application (a grasp is fully synthesized in 0.2 seconds. The results obtained on a synthetic database show that the proposed system is able to imitate the grasping behaviour of the user (e.g. the system learns to grasp a mug by its handle. All the code has been made available for testing purposes.
Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models
International Nuclear Information System (INIS)
Nguyen, Hang T.; Nabney, Ian T.
2010-01-01
This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)
Feng, Yongjiu; Liu, Miaolong; Tong, Xiaohua
2007-06-01
An improved fractal measurement, the weighted radial dimension, is put forward for highway transportation networks distribution. The radial dimension (DL), originated from subway investigation in Stuttgart, is a fractal measurement for transportation systems under ideal assumption considering all the network lines to be homogeneous curves, ignoring the difference on spatial structure, quality and level, especially the highway networks. Considering these defects of radial dimension, an improved fractal measurement called weighted radial dimension (D WL) is introduced and the transportation system in Guangdong province is studied in detail using this novel method. Weighted radial dimensions are measured and calculated, and the spatial structure, intensity and connectivity of transportation networks are discussed in Guangdong province and the four sub-areas: the Pearl River Delta area, the East Costal area, the West Costal area and the Northern Guangdong area. In Guangdong province, the fractal spatial pattern characteristics of transportation system vary remarkably: it is the highest in the Pearl River Delta area, moderate in Costal area and lowest in the Northern Guangdong area. With the Pearl River Delta area as the centre, the weighted radial dimensions decrease with the distance increasing, while the decline level is smaller in the costal area and greater in the Northern Guangdong province. By analysis of the conic of highway density, it is recognized that the density decrease with the distance increasing from the calculation centre (Guangzhou), demonstrating the same trend as weighted radial dimensions shown. Evidently, the improved fractal measurement, weighted radial dimension, is an indictor describing the characteristics of highway transportation system more effectively and accurately.
Radial nerve palsy in mid/distal humeral fractures: is early exploration effective?
Keighley, Geffrey; Hermans, Deborah; Lawton, Vidya; Duckworth, David
2018-03-01
Radial nerve palsies are a common complication with displaced distal humeral fractures. This case series examines the outcomes of early operative exploration and decompression of the nerve with fracture fixation with the view that this provides a solid construct for optimisation of nerve recovery. A total of 10 consecutive patients with a displaced distal humeral fracture and an acute radial nerve palsy were treated by the senior author by open reduction and internal fixation of the distal humerus and exploration and decompression of the radial nerve. Motor function and sensation of the radial nerve was assessed in the post-operative period every 2 months or until full recovery of the radial nerve function had occurred. All patients (100%) had recovery of motor and sensation function of their upper limb in the radial nerve distribution over a 12-month period. Recovery times ranged between 4 and 32 weeks, with the median time to recovery occurring at 26 weeks and the average time to full recovery being 22.9 weeks. Wrist extension recovered by an average of 3 months (range 2-26 weeks) and then finger extension started to recover 2-6 weeks after this. Disability of the arm, shoulder and hand scores ranged from 0 to 11.8 at greater than 1 year post-operatively. Our study demonstrated that early operative exploration of the radial nerve when performing an open stabilization of displaced distal humeral fractures resulted in a 100% recovery of the radial nerve. © 2017 Royal Australasian College of Surgeons.
Observational hints of radial migration in disc galaxies from CALIFA
Ruiz-Lara, T.; Pérez, I.; Florido, E.; Sánchez-Blázquez, P.; Méndez-Abreu, J.; Sánchez-Menguiano, L.; Sánchez, S. F.; Lyubenova, M.; Falcón-Barroso, J.; van de Ven, G.; Marino, R. A.; de Lorenzo-Cáceres, A.; Catalán-Torrecilla, C.; Costantin, L.; Bland-Hawthorn, J.; Galbany, L.; García-Benito, R.; Husemann, B.; Kehrig, C.; Márquez, I.; Mast, D.; Walcher, C. J.; Zibetti, S.; Ziegler, B.; Califa Team
2017-07-01
Context. According to numerical simulations, stars are not always kept at their birth galactocentric distances but they have a tendency to migrate. The importance of this radial migration in shaping galactic light distributions is still unclear. However, if radial migration is indeed important, galaxies with different surface brightness (SB) profiles must display differences in their stellar population properties. Aims: We investigate the role of radial migration in the light distribution and radial stellar content by comparing the inner colour, age, and metallicity gradients for galaxies with different SB profiles. We define these inner parts, avoiding the bulge and bar regions and up to around three disc scale lengths (type I, pure exponential) or the break radius (type II, downbending; type III, upbending). Methods: We analysed 214 spiral galaxies from the CALIFA survey covering different SB profiles. We made use of GASP2D and SDSS data to characterise the light distribution and obtain colour profiles of these spiral galaxies. The stellar age and metallicity profiles were computed using a methodology based on full-spectrum fitting techniques (pPXF, GANDALF, and STECKMAP) to the Integral Field Spectroscopic CALIFA data. Results: The distributions of the colour, stellar age, and stellar metallicity gradients in the inner parts for galaxies displaying different SB profiles are unalike as suggested by Kolmogorov-Smirnov and Anderson-Darling tests. We find a trend in which type II galaxies show the steepest profiles of all, type III show the shallowest, and type I display an intermediate behaviour. Conclusions: These results are consistent with a scenario in which radial migration is more efficient for type III galaxies than for type I systems, where type II galaxies present the lowest radial migration efficiency. In such a scenario, radial migration mixes the stellar content, thereby flattening the radial stellar properties and shaping different SB profiles. However
Radially global δf computation of neoclassical phenomena in a tokamak pedestal
International Nuclear Information System (INIS)
Landreman, Matt; Parra, Felix I; Catto, Peter J; Ernst, Darin R; Pusztai, Istvan
2014-01-01
Conventional radially-local neoclassical calculations become inadequate if the radial gradient scale lengths of the H-mode pedestal become as small as the poloidal ion gyroradius. Here, we describe a radially global δf continuum code that generalizes neoclassical calculations to allow for stronger gradients. As with conventional neoclassical calculations, the formulation is time-independent and requires only the solution of a single sparse linear system. We demonstrate precise agreement with an asymptotic analytic solution of the radially global kinetic equation in the appropriate limits of aspect ratio and collisionality. This agreement depends crucially on accurate treatment of finite orbit width effects. (paper)
Directory of Open Access Journals (Sweden)
Mohammadjavad Hosseini
2017-07-01
Full Text Available A high-performance centrifugal compressor is needed for numerous industry applications nowadays. The radial gap ratio between the impeller and the diffuser vanes plays an important role in the improvement of the compressor performance. In this paper, the effects of the radial gap ratio on a high-pressure ratio centrifugal compressor are investigated using numerical simulations. The performance and the flow field are compared for six different radial gap ratios and five rotational speeds. The minimal radial gap ratio was 1.04 and the maximal was 1.14. Results showed that reducing the radial gap ratio decreases the choke mass flow rate. For the tip-speed Mach number (impeller inlet with Mu < 1, the pressure recovery and the loss coefficients are not sensitive to the radial gap ratio. However, for Mu ≥ 1, the best radial gap ratio is 1.08 for the pressure recovery and the loss coefficients. Furthermore, the impeller pressure ratio and efficiency are reduced by increasing the radial gap ratio. Finally, the compressor efficiency was compared for different radial gap ratios. For Mu < 1, the radial gap ratio does not have noticeable effects. In comparison, the radial gap ratio of 1.08 has the best performance for Mu ≥ 1.
Hyperspherical Coulomb spheroidal basis in the Coulomb three-body problem
International Nuclear Information System (INIS)
Abramov, D. I.
2013-01-01
A hyperspherical Coulomb spheroidal (HSCS) representation is proposed for the Coulomb three-body problem. This is a new expansion in the set of well-known Coulomb spheroidal functions. The orthogonality of Coulomb spheroidal functions on a constant-hyperradius surface ρ = const rather than on a constant-internuclear-distance surface R = const, as in the traditional Born-Oppenheimer approach, is a distinguishing feature of the proposed approach. Owing to this, the HSCS representation proves to be consistent with the asymptotic conditions for the scattering problem at energies below the threshold for three-body breakup: only a finite number of radial functions do not vanish in the limit of ρ→∞, with the result that the formulation of the scattering problem becomes substantially simpler. In the proposed approach, the HSCS basis functions are considerably simpler than those in the well-known adiabatic hyperspherical representation, which is also consistent with the asymptotic conditions. Specifically, the HSCS basis functions are completely factorized. Therefore, there arise no problems associated with avoided crossings of adiabatic hyperspherical terms.
Propagation of a radial phased-locked Lorentz beam array in turbulent atmosphere.
Zhou, Guoquan
2011-11-21
A radial phased-locked (PL) Lorentz beam array provides an appropriate theoretical model to describe a coherent diode laser array, which is an efficient radiation source for high-power beaming use. The propagation of a radial PL Lorentz beam array in turbulent atmosphere is investigated. Based on the extended Huygens-Fresnel integral and some mathematical techniques, analytical formulae for the average intensity and the effective beam size of a radial PL Lorentz beam array are derived in turbulent atmosphere. The average intensity distribution and the spreading properties of a radial PL Lorentz beam array in turbulent atmosphere are numerically calculated. The influences of the beam parameters and the structure constant of the atmospheric turbulence on the propagation of a radial PL Lorentz beam array in turbulent atmosphere are discussed in detail. © 2011 Optical Society of America
Study on the radial vibration and acoustic field of an isotropic circular ring radiator.
Lin, Shuyu; Xu, Long
2012-01-01
Based on the exact analytical theory, the radial vibration of an isotropic circular ring is studied and its electro-mechanical equivalent circuit is obtained. By means of the equivalent circuit model, the resonance frequency equation is derived; the relationship between the radial resonance frequency, the radial displacement amplitude magnification and the geometrical dimensions, the material property is analyzed. For comparison, numerical method is used to simulate the radial vibration of isotropic circular rings. The resonance frequency and the radial vibrational displacement distribution are obtained, and the radial radiation acoustic field of the circular ring in radial vibration is simulated. It is illustrated that the radial resonance frequencies from the analytical method and the numerical method are in good agreement when the height is much less than the radius. When the height becomes large relative to the radius, the frequency deviation from the two methods becomes large. The reason is that the exact analytical theory is limited to thin circular ring whose height must be much less than its radius. Copyright © 2011 Elsevier B.V. All rights reserved.
An axially averaged-radial transport model of tokamak edge plasmas
International Nuclear Information System (INIS)
Prinja, A.K.; Conn, R.W.
1984-01-01
A two-zone axially averaged-radial transport model for edge plasmas is described that incorporates parallel electron and ion conduction, localized recycling, parallel electron pressure gradient effects and sheath losses. Results for high recycling show that the radial electron temperature profile is determined by parallel electron conduction over short radial distances (proportional 3 cm). At larger radius where Tsub(e) has fallen appreciably, convective transport becomes equally important. The downstream density and ion temperature profiles are very flat over the region where electron conduction dominates. This is seen to result from a sharply decaying velocity profile that follows the radial electron temperature. A one-dimensional analytical recycling model shows that at high neutral pumping rates, the plasma density at the plate, nsub(ia), scales linearly with the unperturbed background density, nsub(io). When ionization dominates nsub(ia)/nsub(io) proportional exp(nsub(io)) while in the intermediate regime nsub(ia)/nsub(io) proportional exp(proportional nsub(io)). Such behavior is qualitatively in accord with experimental observations. (orig.)
Optical cage generated by azimuthal- and radial-variant vector beams.
Man, Zhongsheng; Bai, Zhidong; Li, Jinjian; Zhang, Shuoshuo; Li, Xiaoyu; Zhang, Yuquan; Ge, Xiaolu; Fu, Shenggui
2018-05-01
We propose a method to generate an optical cage using azimuthal- and radial-variant vector beams in a high numerical aperture optical system. A new kind of vector beam that has azimuthal- and radial-variant polarization states is proposed and demonstrated theoretically. Then, an integrated analytical model to calculate the electromagnetic field and Poynting vector distributions of the input azimuthal- and radial-variant vector beams is derived and built based on the vector diffraction theory of Richards and Wolf. From calculations, a full polarization-controlled optical cage is obtained by simply tailoring the radial index of the polarization, the uniformity U of which is up to 0.7748, and the cleanness C is zero. Additionally, a perfect optical cage can be achieved with U=1, and C=0 by introducing an amplitude modulation; its magnetic field and energy flow are also demonstrated in detail. Such optical cages may be helpful in applications such as optical trapping and high-resolution imaging.
Linear radial pulsation theory. Lecture 5
International Nuclear Information System (INIS)
Cox, A.N.
1983-01-01
We describe a method for getting an equilibrium stellar envelope model using as input the total mass, the envelope mass, the surface effective temperature, the total surface luminosity, and the composition of the envelope. Then wih the structure of the envelope model known, we present a method for obtaining the raidal pulsation periods and growth rates for low order modes. The large amplitude pulsations observed for the yellow and red giants and supergiants are always these radial models, but for the stars nearer the main sequence, as for all of our stars and for the white dwarfs, there frequently are nonradial modes occuring also. Application of linear theory radial pulsation theory is made to the giant star sigma Scuti variables, while the linear nonradial theory will be used for the B stars in later lectures
Advances in Artificial Neural Networks – Methodological Development and Application
Directory of Open Access Journals (Sweden)
Yanbo Huang
2009-08-01
Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological
Aliouane, Leila; Ouadfeul, Sid-Ali; Rabhi, Abdessalem; Rouina, Fouzi; Benaissa, Zahia; Boudella, Amar
2013-04-01
The main goal of this work is to realize a comparison between two lithofacies segmentation techniques of reservoir interval. The first one is based on the Kohonen's Self-Organizing Map neural network machine. The second technique is based on the Walsh transform decomposition. Application to real well-logs data of two boreholes located in the Algerian Sahara shows that the Self-organizing map is able to provide more lithological details that the obtained lithofacies model given by the Walsh decomposition. Keywords: Comparison, Lithofacies, SOM, Walsh References: 1)Aliouane, L., Ouadfeul, S., Boudella, A., 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network, Arabian Journal of geosciences, doi: 10.1007/s12517-011-0459-4 2) Aliouane, L., Ouadfeul, S., Djarfour, N., Boudella, A., 2012, Petrophysical Parameters Estimation from Well-Logs Data Using Multilayer Perceptron and Radial Basis Function Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 730-736, doi : 10.1007/978-3-642-34500-5_86 3)Ouadfeul, S. and Aliouane., L., 2011, Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data, International journal of applied physics and mathematics, Vol01 N01. 4) Ouadfeul, S., Aliouane, L., 2012, Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 737-744, doi : 10.1007/978-3-642-34500-5_87 5) Weisstein, Eric W. "Fast Walsh Transform." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/FastWalshTransform.html
Optimization of a radially cooled pebble bed reactor - HTR2008-58117
International Nuclear Information System (INIS)
Boer, B.; Kloosterman, J. L.; Lathouwers, D.; Van Der Hagen, T. H. J. J.; Van Dam, H.
2008-01-01
By altering the coolant flow direction in a pebble bed reactor from axial to radial, the pressure drop can be reduced tremendously. In this case the coolant flows from the outer reflector through the pebble bed and finally to flow paths in the inner reflector. As a consequence, the fuel temperatures are elevated due to the reduced heat transfer of the coolant. However, the power profile and pebble size in a radially cooled pebble bed reactor can be optimized to achieve lower fuel temperatures than current axially cooled designs, while the low pressure drop can be maintained. The radial power profile in the core can be altered by adopting multi-pass fuel management using several radial fuel zones in the core. The optimal power profile yielding a flat temperature profile is derived analytically and is approximated by radial fuel zoning. In this case, the pebbles pass through the outer region of the core first and each consecutive pass is located in a fuel zone closer to the inner reflector. Thereby, the resulting radial distribution of the fissile material in the core is influenced and the temperature profile is close to optimal. The fuel temperature in the pebbles can be further reduced by reducing the standard pebble diameter from 6 cm to a value as low as I cm. An analytical investigation is used to demonstrate the effects on the fuel temperature and pressure drop for both radial and axial cooling. Finally, two-dimensional numerical calculations were performed, using codes for neutronics, thermal-hydraulics and fuel depletion analysis, in order to validate the results for the optimized design that were obtained from the analytical investigations. It was found that for a radially cooled design with an optimized power profile and reduced pebble diameter (below 3.5 cm) both a reduction in the pressure drop (Δp = -2.6 bar), which increases the reactor efficiency with several percent, and a reduction in the maximum fuel temperature (ΔT = -50 deg. C) can be achieved
Radiographic study of distal radial physeal closure in thoroughbred horses
International Nuclear Information System (INIS)
Vulcano, L.C.; Mamprim, M.J.; Muniz, L.M.R.; Moreira, A.F.; Luna, S.P.L.
1997-01-01
Monthly radiography was performed to study distal radial physeal closure in ten male and ten female Throughbred horses. The height, thoracic circumference and metacarpus circumference were also measured, Distal radial physeal closure time was sooner in females than males, and took 701 +/- 37 and 748 +/- 55 days respectively
International Nuclear Information System (INIS)
Buffa, A.
1967-06-01
The effect of a circularly polarized wave on a cylindrical plasma in a axial magnetostatic field and a radial space-charge field proportional to r is studied. Single particle motion is considered. The electrostatic field produces a shift in the cyclotron resonance frequency and,in case of high charge density, a radial movement of the off-resonance particles. In these conditions a radio-frequency-particle resonance is also possible called 'drift-resonance'. The drift resonance can be produced, with whistler mode, and may be employed in ion acceleration. Afterwards parametrical resonances produced by space-charge field oscillations and collisional limits of theory are studied. Cases in which ion acceleration is possible are considered on the basis of a quantitative analysis of results. (author) [fr
Effects of relativistic small radial component on atomic photoionization cross sections
International Nuclear Information System (INIS)
Liu Xiaobin; Xing Yongzhong; Sun Xiaowei
2008-01-01
The effects of relativistic small radial component on atomic photoionization cross sections have been studied within relativistic average self-consistent field theory. Relativistic effects are relatively unimportant for low photon energy, along with a review of high-energy photoionization the relativistic effects are quite important. The effects of relativistic small radial component on photoionization process should show breakdown when the nuclear finite-size effects is taken into account. The compression of wavefunction into the space near nucleus is so strong in highly charged ions that the electronic radius greatly decreases, and the effects of relativistic small radial component on photoionization cross sections turn to stronger than ordinary atoms. Since relativistic effects are extremely sensitive to the behavior of small radial component, the results are in good agreement with relativistic effects on photoionization cross section. (authors)
Bessel-like beams modulated by arbitrary radial functions
Herman; Wiggins
2000-06-01
An approximate method for determining the radial and axial intensity of a Bessel-like beam is presented for the general case in which a radial Bessel distribution of any order is modulated by an arbitrary function. For Bessel-Gauss, generalized Bessel-Gauss, and Bessel-super-Gauss beams, this simple approximation yields results that are very close to the exact values, while they are exact for Bessel beams. A practical beam that can be generated with a combination of simple lenses is also analyzed and illustrated.
Bilateral radial neck fractures – A Case Report
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ABY Ng
2007-11-01
Full Text Available Radial head and neck fractures are the most frequently seen elbow fractures. The usual cause of this injury is a fall onto an outstretched hand with a partly flexed elbow. We report here an unusual case of bilateral non-displaced radial neck fractures in a patient who presented with complaints of pain in both elbows following a simple fall. This case highlights the need for a high index of suspicion in the diagnosis of multiple injuries, no matter how `trivial` the mechanism of injury.
Radial Color Gradient in a Globular Cluster 1. M68
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Sukyoung Yi
1990-12-01
Full Text Available Stars in M68 from the observed color-magnitude diagrams with CCD were integrated to find any radial gradient. The result shows that M68 has a slightly bluer core. The main cause of these calculated radial color variations seems to come from the random distribution of giants.
Heo, Muyoung; Kim, Suhkmann; Moon, Eun-Joung; Cheon, Mookyung; Chung, Kwanghoon; Chang, Iksoo
2005-07-01
Although a coarse-grained description of proteins is a simple and convenient way to attack the protein folding problem, the construction of a global pairwise energy function which can simultaneously recognize the native folds of many proteins has resulted in partial success. We have sought the possibility of a systematic improvement of this pairwise-contact energy function as we extended the parameter space of amino acids, incorporating local environments of amino acids, beyond a 20×20 matrix. We have studied the pairwise contact energy functions of 20×20 , 60×60 , and 180×180 matrices depending on the extent of parameter space, and compared their effect on the learnability of energy parameters in the context of a gapless threading, bearing in mind that a 20×20 pairwise contact matrix has been shown to be too simple to recognize the native folds of many proteins. In this paper, we show that the construction of a global pairwise energy function was achieved using 1006 training proteins of a homology of less than 30%, which include all representatives of different protein classes. After parametrizing the local environments of the amino acids into nine categories depending on three secondary structures and three kinds of hydrophobicity (desolvation), the 16290 pairwise contact energies (scores) of the amino acids could be determined by perceptron learning and protein threading. These could simultaneously recognize all the native folds of the 1006 training proteins. When these energy parameters were tested on the 382 test proteins of a homology of less than 90%, 370 (96.9%) proteins could recognize their native folds. We set up a simple thermodynamic framework in the conformational space of decoys to calculate the unfolded fraction and the specific heat of real proteins. The different thermodynamic stabilities of E.coli ribonuclease H (RNase H) and its mutants were well described in our calculation, agreeing with the experiment.
A Non-linear Predictive Model of Borderline Personality Disorder Based on Multilayer Perceptron.
Maldonato, Nelson M; Sperandeo, Raffaele; Moretto, Enrico; Dell'Orco, Silvia
2018-01-01
Borderline Personality Disorder is a serious mental disease, classified in Cluster B of DSM IV-TR personality disorders. People with this syndrome presents an anamnesis of traumatic experiences and shows dissociative symptoms. Since not all subjects who have been victims of trauma develop a Borderline Personality Disorder, the emergence of this serious disease seems to have the fragility of character as a predisposing condition. Infect, numerous studies show that subjects positive for diagnosis of Borderline Personality Disorder had scores extremely high or extremely low to some temperamental dimensions (harm Avoidance and reward dependence) and character dimensions (cooperativeness and self directedness). In a sample of 602 subjects, who have had consecutive access to an Outpatient Mental Health Service, it was evaluated the presence of Borderline Personality Disorder using the semi-structured interview for the DSM IV-TR personality disorders. In this population we assessed the presence of dissociative symptoms with the Dissociative Experiences Scale and the personality traits with the Temperament and Character Inventory developed by Cloninger. To assess the weight and the predictive value of these psychopathological dimensions in relation to the Borderline Personality Disorder diagnosis, a neural network statistical model called "multilayer perceptron," was implemented. This model was developed with a dichotomous dependent variable, consisting in the presence or absence of the diagnosis of borderline personality disorder and with five covariates. The first one is the taxonomic subscale of dissociative experience scale, the others are temperamental and characterial traits: Novelty-Seeking, Harm-Avoidance, Self-Directedness and Cooperativeness. The statistical model, that results satisfactory, showed a significance capacity (89%) to predict the presence of borderline personality disorder. Furthermore, the dissociative symptoms seem to have a greater influence than
A Non-linear Predictive Model of Borderline Personality Disorder Based on Multilayer Perceptron
Directory of Open Access Journals (Sweden)
Nelson M. Maldonato
2018-04-01
Full Text Available Borderline Personality Disorder is a serious mental disease, classified in Cluster B of DSM IV-TR personality disorders. People with this syndrome presents an anamnesis of traumatic experiences and shows dissociative symptoms. Since not all subjects who have been victims of trauma develop a Borderline Personality Disorder, the emergence of this serious disease seems to have the fragility of character as a predisposing condition. Infect, numerous studies show that subjects positive for diagnosis of Borderline Personality Disorder had scores extremely high or extremely low to some temperamental dimensions (harm Avoidance and reward dependence and character dimensions (cooperativeness and self directedness. In a sample of 602 subjects, who have had consecutive access to an Outpatient Mental Health Service, it was evaluated the presence of Borderline Personality Disorder using the semi-structured interview for the DSM IV-TR personality disorders. In this population we assessed the presence of dissociative symptoms with the Dissociative Experiences Scale and the personality traits with the Temperament and Character Inventory developed by Cloninger. To assess the weight and the predictive value of these psychopathological dimensions in relation to the Borderline Personality Disorder diagnosis, a neural network statistical model called “multilayer perceptron,” was implemented. This model was developed with a dichotomous dependent variable, consisting in the presence or absence of the diagnosis of borderline personality disorder and with five covariates. The first one is the taxonomic subscale of dissociative experience scale, the others are temperamental and characterial traits: Novelty-Seeking, Harm-Avoidance, Self-Directedness and Cooperativeness. The statistical model, that results satisfactory, showed a significance capacity (89% to predict the presence of borderline personality disorder. Furthermore, the dissociative symptoms seem to have a
Management of post-traumatic elbow instability after failed radial head excision: A case report
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Georgios Touloupakis
2017-02-01
Full Text Available Radial head excision has always been a safe commonly used surgical procedure with a satisfactory clinical outcome for isolated comminuted radial head fractures. However, diagnosis of elbow instability is still very challenging and often underestimated in routine orthopaedic evaluation. We present the case of a 21-years old female treated with excision after radial head fracture, resulting in elbow instability. The patient underwent revision surgery after four weeks. We believe that ligament reconstruction without radial head substitution is a safe alternative choice for Mason III radial head fractures accompanied by complex ligament lesions.
Autorefraction versus subjective refraction in a radially asymmetric multifocal intraocular lens
Linden, J.W.M. van der; Vrijman, V.; El-Saady, R.; Meulen, I.J. van der; Mourits, M.P.; Lapid-Gortzak, R.
2014-01-01
PURPOSE: To evaluate whether the automated refraction (AR) correlates with subjective manifest (MR) refraction in eyes implanted with radially asymmetric multifocal intraocular lens (IOLs). METHODS: This retrospective study evaluated 52 eyes (52 patients) implanted with a radially asymmetric
RTOD- RADIAL TURBINE OFF-DESIGN PERFORMANCE ANALYSIS
Glassman, A. J.
1994-01-01
The RTOD program was developed to accurately predict radial turbine off-design performance. The radial turbine has been used extensively in automotive turbochargers and aircraft auxiliary power units. It is now being given serious consideration for primary powerplant applications. In applications where the turbine will operate over a wide range of power settings, accurate off-design performance prediction is essential for a successful design. RTOD predictions have already illustrated a potential improvement in off-design performance offered by rotor back-sweep for high-work-factor radial turbines. RTOD can be used to analyze other potential performance enhancing design features. RTOD predicts the performance of a radial turbine (with or without rotor blade sweep) as a function of pressure ratio, speed, and stator setting. The program models the flow with the following: 1) stator viscous and trailing edge losses; 2) a vaneless space loss between the stator and the rotor; and 3) rotor incidence, viscous, trailing-edge, clearance, and disk friction losses. The stator and rotor viscous losses each represent the combined effects of profile, endwall, and secondary flow losses. The stator inlet and exit and the rotor inlet flows are modeled by a mean-line analysis, but a sector analysis is used at the rotor exit. The leakage flow through the clearance gap in a pivoting stator is also considered. User input includes gas properties, turbine geometry, and the stator and rotor viscous losses at a reference performance point. RTOD output includes predicted turbine performance over a specified operating range and any user selected flow parameters. The RTOD program is written in FORTRAN IV for batch execution and has been implemented on an IBM 370 series computer with a central memory requirement of approximately 100K of 8 bit bytes. The RTOD program was developed in 1983.
Turbulence in tokamak plasmas. Effect of a radial electric field shear
International Nuclear Information System (INIS)
Payan, J.
1994-05-01
After a review of turbulence and transport phenomena in tokamak plasmas and the radial electric field shear effect in various tokamaks, experimental measurements obtained at Tore Supra by the means of the ALTAIR plasma diagnostic technique, are presented. Electronic drift waves destabilization mechanisms, which are the main features that could describe the experimentally observed microturbulence, are then examined. The effect of a radial electric field shear on electronic drift waves is then introduced, and results with ohmic heating are studied together with relations between turbulence and transport. The possible existence of ionic waves is rejected, and a spectral frequency modelization is presented, based on the existence of an electric field sheared radial profile. The position of the inversion point of this field is calculated for different values of the mean density and the plasma current, and the modelization is applied to the TEXT tokamak. The radial electric field at Tore Supra is then estimated. The effect of the ergodic divertor on turbulence and abnormal transport is then described and the density fluctuation radial profile in presence of the ergodic divertor is modelled. 80 figs., 120 refs
Leakage Account for Radial Face Contact Seal in Aircraft Engine Support
Vinogradov, A. S.; Sergeeva, T. V.
2018-01-01
The article is dedicated to the development of a methodology for the radial face contact seal design taking into consideration the supporting elements deformations in different aircraft engine operating modes. Radial face contact seals are popular in the aircraft engines bearing support. However, there are no published leakage calculation methodologies of these seals. Radial face contact seal leakage is determined by the gap clearance in the carbon seal ring split. In turn, the size gap clearance depends on the deformation of the seal assembly parts and from the engine operation. The article shows the leakage detection sequence in the intershaft radial face contact seal of the compressor support for take-off and cruising modes. Evaluated calculated leakage values (2.4 g/s at takeoff and 0.75 g/s at cruising) go with experience in designing seals.
Clinical Outcomes following median to radial nerve transfers
Ray, Wilson Z.; Mackinnon, Susan E.
2010-01-01
Purpose In this study the authors evaluate the clinical outcomes in patients with radial nerve palsy who underwent nerve transfers utilizing redundant fascicles of median nerve (innervating the flexor digitorum superficialis and flexor carpi radialis muscles) to the posterior interosseous nerve and the nerve to the extensor carpi radialis brevis. Methods A retrospective review of the clinical records of 19 patients with radial nerve injuries who underwent nerve transfer procedures using the median nerve as a donor nerve were included. All patients were evaluated using the Medical Research Council (MRC) grading system. Results The mean age of patients was 41 years (range 17 – 78 years). All patients received at least 12 months of follow-up (20.3 ± 5.8 months). Surgery was performed at a mean of 5.7 ± 1.9 months post-injury. Post-operative functional evaluation was graded according to the following scale: grades MRC 0/5 - MRC 2/5 were considered poor outcomes, while MRC of 3/5 was a fair result, MRC grade 4/5 was a good result, and grade 4+/5 was considered an excellent outcome. Seventeen patients (89%) had a complete radial nerve palsy while two patients (11%) had intact wrist extension but no finger or thumb extension. Post-operatively all patients except one had good to excellent recovery of wrist extension. Twelve patients recovered good to excellent finger and thumb extension, two patients had fair recovery, five patients had a poor recovery. Conclusions The radial nerve is a commonly injured nerve, causing significant morbidity in affected patients. The median nerve provides a reliable source of donor nerve fascicles for radial nerve reinnervation. This transfer was first performed in 1999 and evolved over the subsequent decade. The important nuances of both surgical technique and motor re-education critical for to the success of this transfer have been identified and are discussed. PMID:21168979
Free radial forearm adiposo-fascial flap for inferior maxillectomy defect reconstruction
Thankappan, Krishnakumar; Trivedi, Nirav P.; Sharma, Mohit; Kuriakose, Moni A.; Iyer, Subramania
2009-01-01
A free radial forearm fascial flap has been described for intraoral reconstruction. Adiposo-fascial flap harvesting involves few technical modifications from the conventional radial forearm fascio-cutaneous free flap harvesting. We report a case of inferior maxillectomy defect reconstruction in a 42-year-old male with a free radial forearm adiposo-fascial flap with good aesthetic and functional outcome with minimal primary and donor site morbidity. The technique of raising the flap and closing the donor site needs to be meticulous in order to achieve good cosmetic and functional outcome. PMID:19881028
Free radial forearm adiposo-fascial flap for inferior maxillectomy defect reconstruction
Directory of Open Access Journals (Sweden)
Thankappan Krishnakumar
2009-01-01
Full Text Available A free radial forearm fascial flap has been described for intraoral reconstruction. Adiposo-fascial flap harvesting involves few technical modifications from the conventional radial forearm fascio-cutaneous free flap harvesting. We report a case of inferior maxillectomy defect reconstruction in a 42-year-old male with a free radial forearm adiposo-fascial flap with good aesthetic and functional outcome with minimal primary and donor site morbidity. The technique of raising the flap and closing the donor site needs to be meticulous in order to achieve good cosmetic and functional outcome.
The radial distribution of plutonium in high burnup UO2 fuels
International Nuclear Information System (INIS)
Lassmann, K.; O'Carroll, C.; Laar, J. van de; Walker, C.T.
1994-01-01
A new model (TUBRNP) is described which predicts the radial power density distribution as a function of burnup (and hence the radial burnup profile as a function of time) together with the radial profile of uranium and plutonium isotopes. Comparisons between measurements and the predictions of the TUBRNP model are made on fuels with enrichments in the range 2.9 to 8.25% and with burnups between 21 000 and 64 000 MWd/t. It is shown to be in excellent agreement with experimental measurements and is a marked improvement on earlier versions. (orig.)
Hanna, Elias B; Prout, Davey L
2016-04-01
To describe the combined use of radial-pedal access for recanalization of complex superficial femoral artery (SFA) occlusions unsuitable for transfemoral recanalization. Patients are selected for this strategy if they have a long (≥ 10 cm) SFA occlusion with unfavorable aortoiliac anatomy, an absent ostial stump, or severely diseased and calcified distal reconstitution. Left radial artery and distal anterior or posterior tibial artery are accessed with 6-F and 4-F sheaths, respectively. The SFA lesion is crossed retrogradely with a 0.035-inch wire system. If retrograde crossing is not immediately successful, transradial subintimal tracking and radial-pedal subintimal rendezvous are used to allow retrograde reentry. Fifteen patients (mean age 62 ± 5 years; 11 men) have been treated in this fashion, and frequently stented, through the tibiopedal access. Seven patients required radial-pedal rendezvous to facilitate retrograde reentry. Two patients underwent transradial iliac stenting during the same session, and 1 patient underwent transradial kissing angioplasty of the profunda. No major complication occurred in any patient. After the procedure, the pulse across the accessed tibial artery was palpable in all patients. In patients with long and complex SFA occlusion unsuitable for transfemoral recanalization, a radial-pedal strategy can overcome revascularization obstacles. © The Author(s) 2016.
Oculoauriculovertebral spectrum with radial anomaly in child.
Taksande, Amar; Vilhekar, Krishna
2013-01-01
Oculoauriculovertebral spectrum (OAVS) or Goldenhar syndrome is a wide spectrum of congenital anomalies that involves structures arising from the first and second branchial arches. It is characterized by a wide spectrum of symptoms and physical features. These abnormalities mainly involve the cheekbones, jaws, mouth, ears, eyes, or vertebrae. Other conditions with ear and/or radial involvement, such as, the Nager syndrome, Holt-Oram syndrome, Radial-renal syndrome, facioauriculoradial dysplasia, Fanconi anemia, and Vertebral, Anal atresia, Cardiac, Trachea, Esophageal, Renal, and Limb (VACTERL) association should be considered for differential diagnosis. Here we report a child who had facial asymmetry, microsomia, microtia, congenital facial nerve palsy, conductive hearing loss, skin tags, iris coloboma, and preaxial polydactyly.
Oculoauriculovertebral spectrum with radial anomaly in child
Directory of Open Access Journals (Sweden)
Amar Taksande
2013-01-01
Full Text Available Oculoauriculovertebral spectrum (OAVS or Goldenhar syndrome is a wide spectrum of congenital anomalies that involves structures arising from the first and second branchial arches. It is characterized by a wide spectrum of symptoms and physical features. These abnormalities mainly involve the cheekbones, jaws, mouth, ears, eyes, or vertebrae. Other conditions with ear and/or radial involvement, such as, the Nager syndrome, Holt-Oram syndrome, Radial-renal syndrome, facioauriculoradial dysplasia, Fanconi anemia, and Vertebral, Anal atresia, Cardiac, Trachea, Esophageal, Renal, and Limb (VACTERL association should be considered for differential diagnosis. Here we report a child who had facial asymmetry, microsomia, microtia, congenital facial nerve palsy, conductive hearing loss, skin tags, iris coloboma, and preaxial polydactyly.
WWER radial reflector modeling by diffusion codes
International Nuclear Information System (INIS)
Petkov, P. T.; Mittag, S.
2005-01-01
The two commonly used approaches to describe the WWER radial reflectors in diffusion codes, by albedo on the core-reflector boundary and by a ring of diffusive assembly size nodes, are discussed. The advantages and disadvantages of the first approach are presented first, then the Koebke's equivalence theory is outlined and its implementation for the WWER radial reflectors is discussed. Results for the WWER-1000 reactor are presented. Then the boundary conditions on the outer reflector boundary are discussed. The possibility to divide the library into fuel assembly and reflector parts and to generate each library by a separate code package is discussed. Finally, the homogenization errors for rodded assemblies are presented and discussed (Author)
Evidence for Radial Anisotropy in Earth's Upper Inner Core from Normal Modes
Lythgoe, K.; Deuss, A. F.
2017-12-01
The structure of the uppermost inner core is related to solidification of outer core material at the inner core boundary. Previous seismic studies using body waves indicate an isotropic upper inner core, although radial anisotropy has not been considered since it cannot be uniquely determined by body waves. Normal modes, however, do constrain radial anisotropy in the inner core. Centre frequency measurements indicate 2-5 % radial anisotropy in the upper 100 km of the inner core, with a fast direction radially outwards and a slow direction along the inner core boundary. This seismic structure provides constraints on solidification processes at the inner core boundary and appears consistent with texture predicted due to anisotropic inner core growth.
Blade bowing effects on radial equilibrium of inlet flow in axial compressor cascades
Directory of Open Access Journals (Sweden)
Han XU
2017-10-01
Full Text Available The circumferentially averaged equation of the inlet flow radial equilibrium in axial compressor was deduced. It indicates that the blade inlet radial pressure gradient is closely related to the radial component of the circumferential fluctuation (CF source item. Several simplified cascades with/without aerodynamic loading were numerically studied to investigate the effects of blade bowing on the inlet flow radial equilibrium. A data reduction program was conducted to obtain the CF source from three-dimensional (3D simulation results. Flow parameters at the passage inlet were focused on and each term in the radial equilibrium equation was discussed quantitatively. Results indicate that the inviscid blade force is the inducement of the inlet CF due to geometrical asymmetry. Blade bowing induces variation of the inlet CF, thus changes the radial pressure gradient and leads to flow migration before leading edge (LE in the cascades. Positive bowing drives the inlet flow to migrate from end walls to mid-span and negative bowing turns it to the reverse direction to build a new equilibrium. In addition, comparative studies indicate that the inlet Mach number and blade loading can efficiently impact the effectiveness of blade bowing on radial equilibrium in compressor design.
Radial Flow in a Multiphase Transport Model at FAIR Energies
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Soumya Sarkar
2018-01-01
Full Text Available Azimuthal distributions of radial velocities of charged hadrons produced in nucleus-nucleus (AB collisions are compared with the corresponding azimuthal distribution of charged hadron multiplicity in the framework of a multiphase transport (AMPT model at two different collision energies. The mean radial velocity seems to be a good probe for studying radial expansion. While the anisotropic parts of the distributions indicate a kind of collective nature in the radial expansion of the intermediate “fireball,” their isotropic parts characterize a thermal motion. The present investigation is carried out keeping the upcoming Compressed Baryonic Matter (CBM experiment to be held at the Facility for Antiproton and Ion Research (FAIR in mind. As far as high-energy heavy-ion interactions are concerned, CBM will supplement the Relativistic Heavy-Ion Collider (RHIC and Large Hadron Collider (LHC experiments. In this context our simulation results at high baryochemical potential would be interesting, when scrutinized from the perspective of an almost baryon-free environment achieved at RHIC and LHC.
Vertical, radial and drag force analysis of superconducting magnetic bearings
International Nuclear Information System (INIS)
Cansiz, Ahmet
2009-01-01
The behavior of the force between a permanent magnet (PM) and a high temperature superconductor (HTS) was tested with the frozen-image model based on flux pinning. It was found that the associated dipole moment assumptions of the method of the frozen image underestimate the force somewhat; thus a quadrupole moment analysis is proposed. The radial and drag forces associated with the rotation of the PM levitated above the HTS were measured by using a force transducer and by means of a cantilevered beam technique. The radial force was found not to be dependent on the radial direction, and the least radial force was found to be periodic with an angular displacement during the slow rotation of the PM relative to the HTS. The periodicity behavior of the force is attributed to the geometric eccentricity from the magnetization distribution of the PM and HTS. The drag force associated with the torsional stiffness of the levitated PM during the low and high rotational speeds was incorporated with the data from the literature.
Autorefraction versus subjective refraction in a radially asymmetric multifocal intraocular lens
van der Linden, Jan Willem; Vrijman, Violette; Al-Saady, Rana; El-Saady, Rana; van der Meulen, Ivanka J.; Mourits, Maarten P.; Lapid-Gortzak, Ruth
2014-01-01
To evaluate whether the automated refraction (AR) correlates with subjective manifest (MR) refraction in eyes implanted with radially asymmetric multifocal intraocular lens (IOLs). This retrospective study evaluated 52 eyes (52 patients) implanted with a radially asymmetric multifocal IOL (LS-312
Utility of the puncture of the radial artery in interventionist radiology
International Nuclear Information System (INIS)
Triana Rodriguez, Carlos Eduardo; Montes S, Mauricio; Barragan F, Jaime; Ucros Diaz Pablo; Ucros Diaz, Ignacio; Castillo, Luis Fernando
1998-01-01
We present the radial artery access, previous evaluation of collateral circulation with Allen's Test, as an alternative vascular access in patients with contraindications for femoral or axillary approaches. The radial artery puncture offers advantages, such as diminished bleeding and hematoma formation
Transverse and radial flow in intermediate energy nucleus-nucleus collisions
International Nuclear Information System (INIS)
Vestfall, D. Gary
1997-01-01
We have studied transverse and radial flow in nucleus-nucleus collisions ranging in energy from 15 to 155 MeV/nucleon. We have measured the impact parameter dependence of the balance energy for Ar + Sc and compared the results with Quantum Molecular Dynamics calculations with and without momentum dependence. We have shown that transverse flow and the balance energy dependence on the isospin of the system using the systems 58 Fe + 58 Fe, 58 Ni + 58 Ni, and 58 Mn + 58 Fe. These results are compared with Boltzmann-Uehling-Uehlenbeck calculations incorporating isospin-dependence. We have measured radial flow for Ar + Sc and find that about 50% of the observed energy is related to radial flow. (author)
Radial restricted solid-on-solid and etching interface-growth models
Alves, Sidiney G.
2018-03-01
An approach to generate radial interfaces is presented. A radial network recursively obtained is used to implement discrete model rules designed originally for the investigation in flat substrates. I used the restricted solid-on-solid and etching models as to test the proposed scheme. The results indicate the Kardar, Parisi, and Zhang conjecture is completely verified leading to a good agreement between the interface radius fluctuation distribution and the Gaussian unitary ensemble. The evolution of the radius agrees well with the generalized conjecture, and the two-point correlation function exhibits also a good agreement with the covariance of the Airy2 process. The approach can be used to investigate radial interfaces evolution for many other classes of universality.
BENCHMARK OF MACHINE LEARNING METHODS FOR CLASSIFICATION OF A SENTINEL-2 IMAGE
Directory of Open Access Journals (Sweden)
F. Pirotti
2016-06-01
random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.
Inward transport of a toroidally confined plasma subject to strong radial electric fields
Roth, J. R.; Krawczonek, W. M.; Powers, E. J.; Hong, J.; Kim, Y.
1977-01-01
The paper aims at showing that the density and confinement time of a toroidal plasma can be enhanced by radial electric fields far stronger than the ambipolar values, and that, if such electric fields point into the plasma, radially inward transport can result. The investigation deals with low-frequency fluctuation-induced transport using digitally implemented spectral analysis techniques and with the role of strong applied radial electric fields and weak vertical magnetic fields on plasma density and particle confinement times in a Bumpy Torus geometry. Results indicate that application of sufficiently strong radially inward electric fields results in radially inward fluctuation-induced transport into the toroidal electrostatic potential well; this inward transport gives rise to higher average electron densities and longer particle confinement times in the toroidal plasma.
Variation of Drying Strains between Tangential and Radial Directions in Asian White Birch
Directory of Open Access Journals (Sweden)
Zongying Fu
2016-03-01
Full Text Available In this study, wood disks of 30 mm in thickness cut from white birch (Betula platyphylla Suk logs were dried at a constant temperature (40 °C. The drying strains including practical shrinkage strain, elastic strain, viscoelastic creep strain and mechano-sorptive creep were measured both tangentially and radially. The effects of moisture content and radial position on each strain were also discussed qualitatively. Overall, the difference of the practical shrinkage strain between the tangential and radial directions was proportional to the distance from the pith. The tangential elastic strain and viscoelastic creep strain were higher than these strains in a radial direction, and they all decreased with the decrease of moisture content. Additionally, there were opposite mechano-sorptive creep between tangential and radial directions.
Kovtyukh, Alexander S.
2016-11-01
From the data on the fluxes and energy spectra of protons with an equatorial pitch angle of α0 ≈ 90° during quiet and slightly disturbed (Kp ≤ 2) periods, I directly calculated the value DLL, which is a measure of the rate of radial transport (diffusion) of trapped particles. This is done by successively solving the systems (chains) of integrodifferential equations which describe the balance of radial transport/acceleration and ionization losses of low-energy protons of the stationary belt. This was done for the first time. For these calculations, I used data of International Sun-Earth Explorer 1 (ISEE-1) for protons with an energy of 24 to 2081 keV at L = 2-10 and data of Explorer-45 for protons with an energy of 78.6 to 872 keV at L = 2-5. Ionization losses of protons (Coulomb losses and charge exchange) were calculated on the basis of modern models of the plasmasphere and the exosphere. It is shown that for protons with μ from ˜ 0.7 to ˜ 7 keV nT-1 at L ≈ 4.5-10, the functions of DLL can be approximated by the following equivalent expressions: DLL ≈ 4.9 × 10-14μ-4.1L8.2 or DLL ≈ 1.3 × 105(EL)-4.1 or DLL ≈ 1.2 × 10-9fd-4.1, where fd is the drift frequency of the protons (in mHz), DLL is measured in s-1, E is measured in kiloelectronvolt and μ is measured in kiloelectronvolt per nanotesla. These results are consistent with the radial diffusion of particles under the action of the electric field fluctuations (pulsations) in the range of Pc6 and contradict the mechanism of the radial diffusion of particles under the action of sudden impulses (SIs) of the magnetic field and also under the action of substorm impulses of the electric field. During magnetic storms DLL increases, and the expressions for DLL obtained here can change completely.
Energy Technology Data Exchange (ETDEWEB)
Johns, Jesse M., E-mail: jesse.johns@pnnl.gov; Burkes, Douglas, E-mail: douglas.burkes@pnnl.gov
2017-07-15
In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal time-temperature-transformation (TTT) models covering a range of U-Mo binary and ternary alloys. The selected ternary alloys for model development are U-Mo-Ru, U-Mo-Nb, U-Mo-Zr, U-Mo-Cr, and U-Mo-Re. These model's ability to predict 'novel' U-Mo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermal-mechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve follow-on testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improved material characterization and therefore improved fundamental understanding of TTT dynamics. As additional understanding of phenomena driving TTTs is acquired, this type of MLP model can be used to populate unknowns (such as material impurity and other thermal mechanical properties) from past literature sources.
Directory of Open Access Journals (Sweden)
Diercks Ron L
2011-10-01
Full Text Available Abstract Background Patellar tendinopathy is a chronic overuse injury of the patellar tendon that is especially prevalent in people who are involved in jumping activities. Extracorporeal Shockwave Therapy is a relatively new treatment modality for tendinopathies. It seems to be a safe and promising part of the rehabilitation program for patellar tendinopathy. Extracorporeal Shockwave Therapy originally used focused shockwaves. Several years ago a new kind of shockwave therapy was introduced: radial shockwave therapy. Studies that investigate the effectiveness of radial shockwave therapy as treatment for patellar tendinopathy are scarce. Therefore the aim of this study is to compare the effectiveness of focussed shockwave therapy and radial shockwave therapy as treatments for patellar tendinopathy. Methods/design The TOPSHOCK study (Tendinopathy Of Patella SHOCKwave is a two-armed randomised controlled trial in which the effectiveness of focussed shockwave therapy and radial shockwave therapy are directly compared. Outcome assessors and patients are blinded as to which treatment is given. Patients undergo three sessions of either focused shockwave therapy or radial shockwave therapy at 1-week intervals, both in combination with eccentric decline squat training. Follow-up measurements are scheduled just before treatments 2 and 3, and 1, 4, 7 and 12 weeks after the final treatment. The main outcome measure is the Dutch VISA-P questionnaire, which asks for pain, function and sports participation in subjects with patellar tendinopathy. Secondary outcome measures are pain determined with a VAS during ADL, sports and decline squats, rating of subjective improvement and overall satisfaction with the treatment. Patients will also record their sports activities, pain during and after these activities, and concurrent medical treatment on a weekly basis in a web-based diary. Results will be analysed according to the intention-to-treat principle. Discussion
van der Worp, Henk; Zwerver, Johannes; van den Akker-Scheek, Inge; Diercks, Ron L
2011-10-11
Patellar tendinopathy is a chronic overuse injury of the patellar tendon that is especially prevalent in people who are involved in jumping activities. Extracorporeal Shockwave Therapy is a relatively new treatment modality for tendinopathies. It seems to be a safe and promising part of the rehabilitation program for patellar tendinopathy. Extracorporeal Shockwave Therapy originally used focused shockwaves. Several years ago a new kind of shockwave therapy was introduced: radial shockwave therapy. Studies that investigate the effectiveness of radial shockwave therapy as treatment for patellar tendinopathy are scarce. Therefore the aim of this study is to compare the effectiveness of focussed shockwave therapy and radial shockwave therapy as treatments for patellar tendinopathy. The TOPSHOCK study (Tendinopathy Of Patella SHOCKwave) is a two-armed randomised controlled trial in which the effectiveness of focussed shockwave therapy and radial shockwave therapy are directly compared. Outcome assessors and patients are blinded as to which treatment is given. Patients undergo three sessions of either focused shockwave therapy or radial shockwave therapy at 1-week intervals, both in combination with eccentric decline squat training. Follow-up measurements are scheduled just before treatments 2 and 3, and 1, 4, 7 and 12 weeks after the final treatment. The main outcome measure is the Dutch VISA-P questionnaire, which asks for pain, function and sports participation in subjects with patellar tendinopathy. Secondary outcome measures are pain determined with a VAS during ADL, sports and decline squats, rating of subjective improvement and overall satisfaction with the treatment. Patients will also record their sports activities, pain during and after these activities, and concurrent medical treatment on a weekly basis in a web-based diary. Results will be analysed according to the intention-to-treat principle. The TOPSHOCK study is the first randomised controlled trial that
Dynamics of a radially expanding liquid sheet: Experiments
Majumdar, Nayanika; Tirumkudulu, Mahesh
2017-11-01
A recent theory predicts that sinuous waves generated at the center of a radially expanding liquid sheet grow spatially even in absence of a surrounding gas phase. Unlike flat liquid sheets, the thickness of a radially expanding liquid sheet varies inversely with distance from the center of the sheet. To test the predictions of the theory, experiments were carried out on a horizontal, radially expanding liquid sheet formed by collision of a single jet on a solid impactor. The latter was placed on a speaker-vibrator with controlled amplitude and frequency. The growth of sinuous waves was determined by measuring the wave surface inclination angle using reflected laser light under both atmospheric and sub-atmospheric pressure conditions. It is shown that the measured growth rate matches with the predictions of the theory over a large range of Weber numbers for both pressure conditions suggesting that the thinning of the liquid sheet plays a dominant role in setting the growth rate of sinuous waves with minimal influence of the surrounding gas phase on its dynamics. IIT Bombay.
Dynamic radial distribution function from inelastic neutron scattering
International Nuclear Information System (INIS)
McQueeney, R.J.
1998-01-01
A real-space, local dynamic structure function g(r,ω) is defined from the dynamic structure function S(Q,ω), which can be measured using inelastic neutron scattering. At any particular frequency ω, S(Q,ω) contains Q-dependent intensity oscillations which reflect the spatial distribution and relative displacement directions for the atoms vibrating at that frequency. Information about local and dynamic atomic correlations is obtained from the Fourier transform of these oscillations g(r,ω) at the particular frequency. g(r,ω) can be formulated such that the elastic and frequency-summed limits correspond to the average and instantaneous radial distribution function, respectively, and is thus called the dynamic radial distribution function. As an example, the dynamic radial distribution function is calculated for fcc nickel in a model which considers only the harmonic atomic displacements due to phonons. The results of these calculations demonstrate that the magnitude of the atomic correlations can be quantified and g(r,ω) is a well-defined correlation function. This leads to a simple prescription for investigating local lattice dynamics. copyright 1998 The American Physical Society
A Raikov-Type Theorem for Radial Poisson Distributions: A Proof of Kingman's Conjecture
Van Nguyen, Thu
2011-01-01
In the present paper we prove the following conjecture in Kingman, J.F.C., Random walks with spherical symmetry, Acta Math.,109, (1963), 11-53. concerning a famous Raikov's theorem of decomposition of Poisson random variables: "If a radial sum of two independent random variables X and Y is radial Poisson, then each of them must be radial Poisson."
Directory of Open Access Journals (Sweden)
Hosein Ghaffarzadeh
Full Text Available Abstract This paper investigates the numerical modeling of the flexural wave propagation in Euler-Bernoulli beams using the Hermite-type radial point interpolation method (HRPIM under the damage quantification approach. HRPIM employs radial basis functions (RBFs and their derivatives for shape function construction as a meshfree technique. The performance of Multiquadric(MQ RBF to the assessment of the reflection ratio was evaluated. HRPIM signals were compared with the theoretical and finite element responses. Results represent that MQ is a suitable RBF for HRPIM and wave propagation. However, the range of the proper shape parameters is notable. The number of field nodes is the main parameter for accurate wave propagation modeling using HRPIM. The size of support domain should be less thanan upper bound in order to prevent high error. With regard to the number of quadrature points, providing the minimum numbers of points are adequate for the stable solution, but the existence of more points in damage region does not leads to necessarily the accurate responses. It is concluded that the pure HRPIM, without any polynomial terms, is acceptable but considering a few terms will improve the accuracy; even though more terms make the problem unstable and inaccurate.
Radial Domany-Kinzel models with mutation and selection
Lavrentovich, Maxim O.; Korolev, Kirill S.; Nelson, David R.
2013-01-01
We study the effect of spatial structure, genetic drift, mutation, and selective pressure on the evolutionary dynamics in a simplified model of asexual organisms colonizing a new territory. Under an appropriate coarse-graining, the evolutionary dynamics is related to the directed percolation processes that arise in voter models, the Domany-Kinzel (DK) model, contact process, and so on. We explore the differences between linear (flat front) expansions and the much less familiar radial (curved front) range expansions. For the radial expansion, we develop a generalized, off-lattice DK model that minimizes otherwise persistent lattice artifacts. With both simulations and analytical techniques, we study the survival probability of advantageous mutants, the spatial correlations between domains of neutral strains, and the dynamics of populations with deleterious mutations. “Inflation” at the frontier leads to striking differences between radial and linear expansions. For a colony with initial radius R0 expanding at velocity v, significant genetic demixing, caused by local genetic drift, occurs only up to a finite time t*=R0/v, after which portions of the colony become causally disconnected due to the inflating perimeter of the expanding front. As a result, the effect of a selective advantage is amplified relative to genetic drift, increasing the survival probability of advantageous mutants. Inflation also modifies the underlying directed percolation transition, introducing novel scaling functions and modifications similar to a finite-size effect. Finally, we consider radial range expansions with deflating perimeters, as might arise from colonization initiated along the shores of an island.
Sparse Reconstruction of Electric Fields from Radial Magnetic Data
International Nuclear Information System (INIS)
Yeates, Anthony R.
2017-01-01
Accurate estimates of the horizontal electric field on the Sun’s visible surface are important not only for estimating the Poynting flux of magnetic energy into the corona but also for driving time-dependent magnetohydrodynamic models of the corona. In this paper, a method is developed for estimating the horizontal electric field from a sequence of radial-component magnetic field maps. This problem of inverting Faraday’s law has no unique solution. Unfortunately, the simplest solution (a divergence-free electric field) is not realistically localized in regions of nonzero magnetic field, as would be expected from Ohm’s law. Our new method generates instead a localized solution, using a basis pursuit algorithm to find a sparse solution for the electric field. The method is shown to perform well on test cases where the input magnetic maps are flux balanced in both Cartesian and spherical geometries. However, we show that if the input maps have a significant imbalance of flux—usually arising from data assimilation—then it is not possible to find a localized, realistic, electric field solution. This is the main obstacle to driving coronal models from time sequences of solar surface magnetic maps.
Sparse Reconstruction of Electric Fields from Radial Magnetic Data
Energy Technology Data Exchange (ETDEWEB)
Yeates, Anthony R. [Department of Mathematical Sciences, Durham University, Durham, DH1 3LE (United Kingdom)
2017-02-10
Accurate estimates of the horizontal electric field on the Sun’s visible surface are important not only for estimating the Poynting flux of magnetic energy into the corona but also for driving time-dependent magnetohydrodynamic models of the corona. In this paper, a method is developed for estimating the horizontal electric field from a sequence of radial-component magnetic field maps. This problem of inverting Faraday’s law has no unique solution. Unfortunately, the simplest solution (a divergence-free electric field) is not realistically localized in regions of nonzero magnetic field, as would be expected from Ohm’s law. Our new method generates instead a localized solution, using a basis pursuit algorithm to find a sparse solution for the electric field. The method is shown to perform well on test cases where the input magnetic maps are flux balanced in both Cartesian and spherical geometries. However, we show that if the input maps have a significant imbalance of flux—usually arising from data assimilation—then it is not possible to find a localized, realistic, electric field solution. This is the main obstacle to driving coronal models from time sequences of solar surface magnetic maps.
Stability of a radial immiscible drive
Energy Technology Data Exchange (ETDEWEB)
Bataille, J
1968-11-01
The stability of the displacement front between 2 immiscible fluids of radial flow between 2 parallel plates (Hele-Shaw model) is studied mathematically by superposing onto the circular displacement front a sinusoidal perturbation. The equations are reduced to dimensionless variables, and it is shown that the stable and unstable domains in a plot: dimensionless viscosity vs. dimensionless time are separated by a polygonal contour, each side of the contour being characterized by the (integer) number of perturbations along the circumference. There is a critical reduced time below which the perturbations are amortized but beyond which they are amplified. Experimental results have been in fair general agreement with theoretical results, the divergence between them being attributable to neglecting capillary phenomena, which may become very important at large radial distances. One test with miscible fluids has shown that even in this case, there is a critical time or an equivalent critical radius.
Elbow joint laxity after experimental radial head excision and lateral collateral ligament rupture
DEFF Research Database (Denmark)
Jensen, Steen Lund; Olsen, Bo Sanderhoff; Tyrdal, Stein
2005-01-01
The objectives of this experimental study were to investigate the effect of radial head excision and lateral collateral ligament (LCL) division on elbow joint laxity and to determine the efficacy of radial head prosthetic replacement and LCL repair. Valgus, varus, internal rotation, and external...... rotation of the ulna were measured during passive flexion-extension and application of a 0.75-Nm torque in 6 intact cadaveric elbows and after (1) either excision of the radial head or division of the LCL, (2) removal of both constraints, (3) isolated radial head prosthetic replacement, (4) isolated LCL...... normalized varus laxity but resulted in a 2.9 degrees increase in external rotatory laxity. The combined procedures restored laxity completely. The radial head is a constraint to varus and external rotation in the elbow joint, functioning by maintaining tension in the LCL. Still, removal of both constraints...
Rayleigh-Taylor instability of cylindrical jets with radial motion
Energy Technology Data Exchange (ETDEWEB)
Chen, Xiang M. [GE Nuclear, Wilmington, NC (United States); Schrock, V.E.; Peterson, P.F. [Univ. of California, Berkeley, CA (United States)
1995-09-01
Rayleigh-Taylor instability of an interface between fluids with different densities subjected to accelleration normal to itself has interested researchers for almost a century. The classic analyses of a flat interface by Rayleigh and Taylor have shown that this type of instability depends on the direction of acceleration and the density differences of the two fluids. Plesset later analyzed the stability of a spherically symmetric flows (and a spherical interface) and concluded that the instability also depends on the velocity of the interface as well as the direction and magnitude of radial acceleration. The instability induced by radial motion in cylindrical systems seems to have been neglected by previous researchers. This paper analyzes the Rayleigh-Taylor type of the spherical case, the radial velocity also plays an important role. As an application, the example of a liquid jet surface in an Inertial Confinement Fusion (ICF) reactor design is analyzed.
THE RADIAL VELOCITY EXPERIMENT (RAVE): THIRD DATA RELEASE
International Nuclear Information System (INIS)
Siebert, A.; Williams, M. E. K.; Siviero, A.; Boeche, C.; Steinmetz, M.; De Jong, R. S.; Enke, H.; Anguiano, B.; Reid, W.; Ritter, A.; Fulbright, J.; Wyse, R. F. G.; Munari, U.; Zwitter, T.; Watson, F. G.; Burton, D.; Cass, C. J. P.; Fiegert, K.; Hartley, M.; Russel, K. S.
2011-01-01
We present the third data release of the RAdial Velocity Experiment (RAVE) which is the first milestone of the RAVE project, releasing the full pilot survey. The catalog contains 83,072 radial velocity measurements for 77,461 stars in the southern celestial hemisphere, as well as stellar parameters for 39,833 stars. This paper describes the content of the new release, the new processing pipeline, as well as an updated calibration for the metallicity based upon the observation of additional standard stars. Spectra will be made available in a future release. The data release can be accessed via the RAVE Web site.
RADIALLY MAGNETIZED PROTOPLANETARY DISK: VERTICAL PROFILE
International Nuclear Information System (INIS)
Russo, Matthew; Thompson, Christopher
2015-01-01
This paper studies the response of a thin accretion disk to an external radial magnetic field. Our focus is on protoplanetary disks (PPDs), which are exposed during their later evolution to an intense, magnetized wind from the central star. A radial magnetic field is mixed into a thin surface layer, wound up by the disk shear, and pushed downward by a combination of turbulent mixing and ambipolar and ohmic drift. The toroidal field reaches much greater strengths than the seed vertical field that is usually invoked in PPD models, even becoming superthermal. Linear stability analysis indicates that the disk experiences the magnetorotational instability (MRI) at a higher magnetization than a vertically magnetized disk when both the effects of ambipolar and Hall drift are taken into account. Steady vertical profiles of density and magnetic field are obtained at several radii between 0.06 and 1 AU in response to a wind magnetic field B r ∼ (10 −4 –10 −2 )(r/ AU) −2 G. Careful attention is given to the radial and vertical ionization structure resulting from irradiation by stellar X-rays. The disk is more strongly magnetized closer to the star, where it can support a higher rate of mass transfer. As a result, the inner ∼1 AU of a PPD is found to evolve toward lower surface density. Mass transfer rates around 10 −8 M ⊙ yr −1 are obtained under conservative assumptions about the MRI-generated stress. The evolution of the disk and the implications for planet migration are investigated in the accompanying paper