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1

Multilayer perceptron for nonlinear programming

International Nuclear Information System (INIS)

A new method for solving nonlinear programming problems within the framework of a multilayer neural network perceptron is proposed. The method employs the Penalty Function method to transform a constrained optimization problem into a sequence of unconstrained optimization problems and then solves the sequence of unconstrained optimizations of the transformed problem by training a series of multilayer perceptrons. The neural network formulation is represented in such a way that the multilayer perceptron prediction error to be minimized mimics the objective function of the unconstrained problem, and therefore, the minimization of the objective function for each unconstrained optimization is attained by training a single perceptron. The multilayer perceptron allows for the transformation of problems with two-sided bounding constraints on the decision variables x, e.g., a?xn?b, into equivalent optimization problems in which these constraints do not explicitly appear. Hence, when these are the only constraints in the problem, the transformed problem is constraint free (i.e., the transformed objective function contains no penalty terms) and is solved by training a multilayer perceptron only once. In addition, we present a new Penalty Function method for solving nonlinear programming problems that is parameter free and guarantees that feasible solutions are obtained when the optimal solution is on the boundary of the feasible region. Simulation results, includble region. Simulation results, including an example from operations research, illustrate the proposed methods.

2

Multidimensional scaling using multilayer perceptron

The objective of this thesis is to introduce the reader to the concepts of neural network and multidimensional scaling and to demonstrate how these two can be used together. The thesis introduces a construction in which a multilayer perceptron is trained by means of multidimensional scaling in order to perform dimensionality reduction. The algorithm is tested in four different test experiments.

Tuovinen, Tommi

2013-01-01

3

Comparison of sinusoidal perceptron with multilayer classical perceptron

A new multi-threshold Perceptron capable of handling both binary and analog input is presented and discussed. The modified Perceptron replaces the sigmoid function with sinusoidal function. A computer program has been developed to simulate behavior of a network utilizing the modified Perceptron. Both XOR and Parity Check problems were solved using a single-layer network utilizing this modified Perceptron. Based on the results obtained from the simulation the modified Perceptron is capable of solving problems (such as XOR) that can not be solved using a single-layer of the classical Perceptron. Also a network utilizing this modified Perceptron requires fewer number of iterations to converge to a solution than that of a multi-layer Perceptron network using back propagation. 1.

Karimi, B.; Baradaran, T.; Ashenayi, Kaveh; Vogh, James

1991-03-01

4

Efficient block training of multilayer perceptrons.

The attractive possibility of applying layerwise block training algorithms to multilayer perceptrons MLP, which offers initial advantages in computational effort, is refined in this article by means of introducing a sensitivity correction factor in the formulation. This results in a clear performance advantage, which we verify in several applications. The reasons for this advantage are discussed and related to implicit relations with second-order techniques, natural gradient formulations through Fisher's information matrix, and sample selection. Extensions to recurrent networks and other research lines are suggested at the close of the article. PMID:10935721

Navia-Vázquez, A; Figueiras-Vidal, A R

2000-06-01

5

Auto-kernel using multilayer perceptron

This work presents a constructive method to train the multilayer perceptron layer after layer successively and to accomplish the kernel used in the support vector machine. Data in different classes will be trained to map to distant points in each layer. This will ease the mapping of the next layer. A perfect mapping kernel can be accomplished successively. Those distant mapped points can be discriminated easily by a single perceptron.

Wei-Chen Cheng

2012-01-01

6

Quaternionic Multilayer Perceptron with Local Analyticity

Directory of Open Access Journals (Sweden)

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.

Nobuyuki Matsui

2012-11-01

7

Quaternionic Multilayer Perceptron with Local Analyticity

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...

Nobuyuki Matsui; Haruhiko Nishimura; Teijiro Isokawa

2012-01-01

8

Hierarchical Multilayer Perceptron based Language Identification

Automatic language identification (LID) systems generally exploit acoustic knowledge, possibly enriched by explicit language specific phonotactic or lexical constraints. This paper investigates a new LID approach based on hierarchical multilayer perceptron (MLP) classifiers, where the first layer is a ``universal phoneme set MLP classifier''. The resulting (multilingual) phoneme posterior sequence is fed into a second MLP taking a larger temporal context into account. The second MLP can learn...

Imseng, David; Magimai -doss, Mathew; Bourlard, Herve?

2010-01-01

9

Fourier-Lapped Multilayer Perceptron Method for Speech Quality Assessment

The paper introduces a new objective method for speech quality assessment called Fourier-lapped multilayer perceptron (FLMLP). This method uses an overcomplete transform based on the discrete Fourier transform (DFT) and modulated lapped transform (MLT). This transform generates the DFT and the MLT speech spectral domains from which several relevant perceptual parameters are extracted. The proposed method also employs a multilayer perceptron neural network trained by a modified version of the...

Amauri Lopes; Marcos Travassos Romano, Jo O.; Jayme Garcia Arnal Barbedo; Vidal Ribeiro, Mois S.

2005-01-01

10

Wind speed estimation using multilayer perceptron

International Nuclear Information System (INIS)

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%

11

Efficient Estimation of Multidimensional Regression Model with Multilayer Perceptron

This work concerns estimation of multidimensional nonlinear regression models using multilayer perceptron (MLP). The main problem with such model is that we have to know the covariance matrix of the noise to get optimal estimator. however we show that, if we choose as cost function the logarithm of the determinant of the empirical error covariance matrix, we get an asymptotically optimal estimator.

Rynkiewicz, Joseph

2008-01-01

12

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

International Nuclear Information System (INIS)

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

13

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

Directory of Open Access Journals (Sweden)

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.

N. Pedroni

2008-03-01

14

Asymptotic law of likelihood ratio for multilayer perceptron models

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The data are assumed to be generated by a true MLP model and the estimation of the parameters of the MLP is done by maximizing the likelihood of the model. When the number of hidden units of the true model is known, the asymptotic distribution of the maximum likelihood estimator (MLE) and the likelihood ratio (LR) statistic is easy to compute and converge to a $\\c...

Rynkiewicz, Joseph

2010-01-01

15

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

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 ...

Mi?guez Gonza?lez, M.; Lo?pez Pen?a, F.; Di?az Casa?s, V.; Galeazzi, Roberto; Blanke, Mogens

2011-01-01

16

Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron

In this paper we present a simple novel approach to tackle the challenges of scaling and rotation of face images in face recognition. The proposed approach registers the training and testing visual face images by log-polar transformation, which is capable to handle complicacies introduced by scaling and rotation. Log-polar images are projected into eigenspace and finally classified using an improved multi-layer perceptron. In the experiments we have used ORL face database an...

Bhowmik, Mrinal Kanti; Bhattacharjee, Debotosh; Nasipuri, Mita; Kundu, Mahantapas; Basu, Dipak Kumar

2010-01-01

17

A Parallel Framework for Multilayer Perceptron for Human Face Recognition

Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and ...

Bhowmik, M K; Nasipuri, M; Basu, D K; Kundu, M

2010-01-01

18

Key Generation and Certification using Multilayer Perceptron in Wireless communication(KGCMLP)

In this paper, a key generation and certification technique using multilayer perceptron (KGCMLP) has been proposed in wireless communication of data/information. In this proposed KGCMLP technique both sender and receiver uses an identical multilayer perceptrons. Both perceptrons are start synchronization by exchanging some control frames. During synchronization process message integrity test and synchronization test has been carried out. Only the synchronization test does no...

Sarkar, Arindam; Mandal, J. K.

2012-01-01

19

A Choice of Input Variables for a Multilayer Perceptron

International Nuclear Information System (INIS)

In the paper some aspects of multilayer perceptron (MLP) application to the problem of classifying the events presented by empirical samples of a finite volume are considered. The results of the MLP learning for various forms of the input data are analyzed and the reasons leading to the effect of an instantaneous learning of the MLP and rise of the neural network are investigated for the case when the input data are presented in a form of variational series. The problem of hidden layer neuron reduction without raising the recognition error is discussed. (author). 13 refs., 6 figs., 1 tab

20

Error correcting code using tree-like multilayer perceptron

An error correcting code using a tree-like multilayer perceptron is proposed. An original message $\\mbi{s}^0$ is encoded into a codeword $\\boldmath{y}_0$ using a tree-like committee machine (committee tree) or a tree-like parity machine (parity tree). Based on these architectures, several schemes featuring monotonic or non-monotonic units are introduced. The codeword $\\mbi{y}_0$ is then transmitted via a Binary Asymmetric Channel (BAC) where it is corrupted by noise. The ana...

Cousseau, Florent; Mimura, Kazushi; Okada, Masato

2008-01-01

21

Dynamics of learning in multilayer perceptrons near singularities.

The dynamical behavior of learning is known to be very slow for the multilayer perceptron, being often trapped in the "plateau." It has been recently understood that this is due to the singularity in the parameter space of perceptrons, in which trajectories of learning are drawn. The space is Riemannian from the point of view of information geometry and contains singular regions where the Riemannian metric or the Fisher information matrix degenerates. This paper analyzes the dynamics of learning in a neighborhood of the singular regions when the true teacher machine lies at the singularity. We give explicit asymptotic analytical solutions (trajectories) both for the standard gradient (SGD) and natural gradient (NGD) methods. It is clearly shown, in the case of the SGD method, that the plateau phenomenon appears in a neighborhood of the critical regions, where the dynamical behavior is extremely slow. The analysis of the NGD method is much more difficult, because the inverse of the Fisher information matrix diverges. We conquer the difficulty by introducing the "blow-down" technique used in algebraic geometry. The NGD method works efficiently, and the state converges directly to the true parameters very quickly while it staggers in the case of the SGD method. The analytical results are compared with computer simulations, showing good agreement. The effects of singularities on learning are thus qualitatively clarified for both standard and NGD methods. PMID:18701364

Cousseau, Florent; Ozeki, Tomoko; Amari, Shun-Ichi

2008-08-01

22

A Parallel Framework for Multilayer Perceptron for Human Face Recognition

Directory of Open Access Journals (Sweden)

Full Text Available Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP have been demonstrated. The first architecture is All-Class-in-One-Network (ACON where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.

Mita Nasipuri

2010-01-01

23

Online learning dynamics of multilayer perceptrons with unidentifiable parameters

International Nuclear Information System (INIS)

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

24

Classification of fused face images using multilayer perceptron neural network

This paper presents a concept of image pixel fusion of visual and thermal faces, which can significantly improve the overall performance of a face recognition system. Several factors affect face recognition performance including pose variations, facial expression changes, occlusions, and most importantly illumination changes. So, image pixel fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Fused images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 95.07%. The main objective of employing fusion is to produce a fused image that provides the most detailed and reliable information. Fusion of multip...

Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

2010-01-01

25

Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network

DEFF Research Database (Denmark)

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 is shown to be capable of forecasting the ship’s roll motion in realistic scenarios.

Míguez González, M; López Peña, F.

2011-01-01

26

Optical proximity correction using a multilayer perceptron neural network

International Nuclear Information System (INIS)

Optical proximity correction (OPC) is one of the resolution enhancement techniques (RETs) in optical lithography, where the mask pattern is modified to improve the output pattern fidelity. Algorithms are needed to generate the modified mask pattern automatically and efficiently. In this paper, a multilayer perceptron (MLP) neural network (NN) is used to synthesize the mask pattern. We employ the pixel-based approach in this work. The MLP takes the pixel values of the desired output wafer pattern as input, and outputs the optimal mask pixel values. The MLP is trained with the backpropagation algorithm, with a training set retrieved from the desired output pattern, and the optimal mask pattern obtained by the model-based method. After training, the MLP is able to generate the optimal mask pattern non-iteratively with good pattern fidelity. (paper)

27

Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs

Many real world data are sampled functions. As shown by Functional Data Analysis (FDA) methods, spectra, time series, images, gesture recognition data, etc. can be processed more efficiently if their functional nature is taken into account during the data analysis process. This is done by extending standard data analysis methods so that they can apply to functional inputs. A general way to achieve this goal is to compute projections of the functional data onto a finite dimensional sub-space of the functional space. The coordinates of the data on a basis of this sub-space provide standard vector representations of the functions. The obtained vectors can be processed by any standard method. In our previous work, this general approach has been used to define projection based Multilayer Perceptrons (MLPs) with functional inputs. We study in this paper important theoretical properties of the proposed model. We show in particular that MLPs with functional inputs are universal approximators: they can approximate to ...

Rossi, F; Rossi, Fabrice; Conan-Guez, Brieuc

2006-01-01

28

Fast parallel off-line training of multilayer perceptrons.

Various approaches to the parallel implementation of second-order gradient-based multilayer perceptron training algorithms are described. Two main classes of algorithm are defined involving Hessian and conjugate gradient-based methods. The limited- and full-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms are selected as representative examples and used to show that the step size and gradient calculations are critical components. For larger problems the matrix calculations in the full-memory algorithm are also significant. Various strategies are considered for parallelization, the best of which is implemented on parallel virtual machine (PVM) and transputer-based architectures. Results from a range of problems are used to demonstrate the performance achievable with each architecture. The transputer implementation is found to give excellent speed-ups but the problem size is limited by memory constraints. The speed-ups achievable with the PVM implementation are much poorer because of inefficient communication, but memory is not a difficulty. PMID:18255667

McLoone, S; Irwin, G W

1997-01-01

29

Consistent estimation of the architecture of multilayer perceptrons

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The estimation of the parameters of the MLP can be done by maximizing the likelihood of the model. In this framework, it is difficult to determine the true number of hidden units using an information criterion, like the Bayesian information criteria (BIC), because the information matrix of Fisher is not invertible if the number of hidden units is overestimated. Indeed, the classical theoretical justification of information criteria relies entirely on the invertibility of this matrix. However, using recent methodology introduced to deal with models with a loss of identifiability, we prove that suitable information criterion leads to consistent estimation of the true number of hidden units.

Rynkiewicz, Joseph

2008-01-01

30

Inversion of Self Potential Anomalies with Multilayer Perceptron Neural Networks

This study investigates the inverse solution on a buried and polarized sphere-shaped body using the self-potential method via multilayer perceptron neural networks (MLPNN). The polarization angle ( ?), depth to the centre of sphere ( h), electrical dipole moment ( K) and the zero distance from the origin ( x 0) were estimated. For testing the success of the MLPNN for sphere model, parameters were also estimated by the traditional Damped Least Squares (Levenberg-Marquardt) inversion technique (DLS). The MLPNN was first tested on a synthetic example. The performance of method was also tested for two S/N ratios (5 % and 10 %) by adding noise to the same synthetic data, the estimated model parameters with MLPNN and DLS method are satisfactory. The MLPNN also applied for the field data example in ?zmir, Urla district, Turkey, with two cross-section data evaluated by MLPNN and DLS, and the two methods showed good agreement.

Kaftan, Ilknur; S?nd?rg?, Petek; Akdemir, Özer

2014-08-01

31

Dynamics of a multi-layered perceptron model : a rigorous result

We derive exactly and rigorously the system of dynamical equations for a multi-layered perceptron proposed by Domany, Meir and Kinzel (DMK-model). They describes both the main and the residual overlaps evolution.

Patrick, A. E.; Zagrebnov, V. A.

1990-01-01

32

In this paper, a multilayer perceptron guided key generation for encryption/decryption (MLPKG) has been proposed through recursive replacement using mutated character code generation for wireless communication of data/information. Multilayer perceptron transmitting systems at both ends accept an identical input vector, generate an output bit and the network are trained based on the output bit which is used to form a protected variable length secret-key. For each session, dif...

Sarkar, Arindam; Mandal, J. K.

2012-01-01

33

International Nuclear Information System (INIS)

A nonlinear multivariable empirical model is developed for a U-tube steam generator using the recurrent multilayer perceptron network as the underlying model structure. The recurrent multilayer perceptron is a dynamic neural network, very effective in the input-output modeling of complex process systems. A dynamic gradient descent learning algorithm is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over static learning algorithms. In developing the U-tube steam generator empirical 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. Extensive model validation studies indicate that the empirical model can substantially generalize (extrapolate), though online learning becomes necessary for tracking transients significantly different than the ones included in the training set and slowly varying U-tube steam generator dynamics. In view of the satisfactory modeling accuracy and the associated short development time, neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. Caution, however, must be exercisemodels. Caution, however, must be exercised because extensive on-line validation of these models is still warranted

34

In this paper, a group session Key Exchange multilayer Perceptron based Simulated Annealing guidedAutomata and Comparison based Metamorphosed encryption technique (GSMLPSA) has been proposed inwireless communication of data/information. Both sender and receiver uses identical multilayer perceptronand depending on the final output of the both side multilayer perceptron, weights vector of hidden layer gettuned in both ends. As a results both perceptrons generates identical weight vectors which ...

Arindam Sarkar; Mandal, J. K.

2013-01-01

35

Asymptotic law of likelihood ratio for multilayer perceptron models

We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The data are assumed to be generated by a true MLP model and the estimation of the parameters of the MLP is done by maximizing the likelihood of the model. When the number of hidden units of the true model is known, the asymptotic distribution of the maximum likelihood estimator (MLE) and the likelihood ratio (LR) statistic is easy to compute and converge to a $\\chi^2$ law. However, if the number of hidden unit is over-estimated the Fischer information matrix of the model is singular and the asymptotic behavior of the MLE is unknown. This paper deals with this case, and gives the exact asymptotic law of the LR statistics. Namely, if the parameters of the MLP lie in a suitable compact set, we show that the LR statistics is the supremum of the square of a Gaussian process indexed by a class of limit score functions.

Rynkiewicz, Joseph

2010-01-01

36

Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron

In this paper we present a simple novel approach to tackle the challenges of scaling and rotation of face images in face recognition. The proposed approach registers the training and testing visual face images by log-polar transformation, which is capable to handle complicacies introduced by scaling and rotation. Log-polar images are projected into eigenspace and finally classified using an improved multi-layer perceptron. In the experiments we have used ORL face database and Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database for visual face images. Experimental results show that the proposed approach significantly improves the recognition performances from visual to log-polar-visual face images. In case of ORL face database, recognition rate for visual face images is 89.5% and that is increased to 97.5% for log-polar-visual face images whereas for OTCBVS face database recognition rate for visual images is 87.84% and 96.36% for log-polar-visual face images.

Bhowmik, Mrinal Kanti; Nasipuri, Mita; Kundu, Mahantapas; Basu, Dipak Kumar

2010-01-01

37

Multi-objective turbomachinery optimization using a gradient-enhanced multi-layer perceptron

Response surface models (RSMs) have found widespread use to reduce the overall computational cost of turbomachinery blading design optimization. Recent developments have seen the successful use of gradient information alongside sampled response values in building accurate response surfaces. This paper describes the use of gradients to enhance the performance of the RSM provided by a multi-layer perceptron. Gradient information is included in the perceptron by modifying the error function such...

Duta, Mc; Duta, Md

2009-01-01

38

DEFF Research Database (Denmark)

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 ?m) 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.

Proud, Simon Richard

2015-01-01

39

The Multi-layer Perceptron Neural Networks (MLP NN) are well known for their simplicity, ease of training for small-scale problems, and suitability for online implementation. This paper presents the methodology and challenges in the design of near-optimal MLP NN based classifier with maximize classification accuracy under the constraints of minimum network dimension for implementation intelligent sensors.

Charniya, Nadir N.

2013-01-01

40

International Nuclear Information System (INIS)

In the thesis the results of verification of multilayer perceptron (MLP) {20–41–1} application with sigmoid activation function for prediction of lateral radionuclide migration are presented. The calculated values of Cs 137 and Sr 90 volumetric activity are close to experimental measurement limits, indicating the possibility of MLP application for the solving problem. (authors)

41

Estimating the Number of Components in a Mixture of Multilayer Perceptrons

BIC criterion is widely used by the neural-network community for model selection tasks, although its convergence properties are not always theoretically established. In this paper we will focus on estimating the number of components in a mixture of multilayer perceptrons and proving the convergence of the BIC criterion in this frame. The penalized marginal-likelihood for mixture models and hidden Markov models introduced by Keribin (2000) and, respectively, Gassiat (2002) is extended to mixtures of multilayer perceptrons for which a penalized-likelihood criterion is proposed. We prove its convergence under some hypothesis which involve essentially the bracketing entropy of the generalized score-functions class and illustrate it by some numerical examples.

Olteanu, Madalina

2008-01-01

42

Multilayer perceptron (MLP), normally trained by the offline backpropagation algorithm, could not adapt to the changing air quality system and subsequently underperforms. To improve this, the extended Kalman filter is adopted into the learning algorithm to build a time-varying multilayer perceptron (TVMLP) in this study. Application of the TVMLP to model the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 µm (PM10) in Macau shows statistically significant improvement on the performance indicators over the MLP counterpart. In addition, the adaptive learning algorithm could also address explicitly the uncertainty of the prediction so that confidence intervals can be provided. More importantly, the adaptiveness of the TVMLP gives prediction improvement on the region of higher particulate concentrations that the public concerns.

Hoi, K. I.; Yuen, K. V.; Mok, K. M.

2013-09-01

43

Approximations of Functions by a Multilayer Perceptron: a New Approach.

We provide a radically elementary proof of the universal approximation property of the one-hidden layer perceptron based on the Taylor expansion and the Vandermonde determinant. It works for both L(q) and uniform approximation on compact sets. This approach naturally yields some bounds for the design of the hidden layer and convergence results (including some rates) for the derivatives. A partial answer to Hornik's conjecture on the universality of the bias is proposed. An extension to vector valued functions is also carried out. Copyright 1997 Elsevier Science Ltd. PMID:12662500

Pagès, Gilles; Attali, Jean Gabriel

1997-08-01

44

Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics

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 mo...

Pedroni, N.; Zio, E.; Cadini, F.

2008-01-01

45

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis

This paper is an improved version of \\cit in which we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally ...

Rossi, Fabrice; Conan-guez, Brieuc

2007-01-01

46

We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep multicolor catalog. Various possible approaches for the training of the neural network are explored, including the deepest and most complete spectroscopic redshift catalog currently available (the Hubble Deep Field North dataset) and models o...

Vanzella, E.; Cristiani, S.; Fontana, A.; Nonino, M.; Arnouts, S.; Giallongo, E.; Grazian, A.; Fasano, G.; Popesso, P.; Saracco, P.; Zaggia, S.

2003-01-01

47

International Nuclear Information System (INIS)

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.

48

Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account ...

Martin, Arnaud; Osswald, Christophe

2008-01-01

49

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 ...

Flávio Clésio Silva de Souza; Renato José Sassi

2014-01-01

50

In this paper, a simple, general method of adding auxiliary stochastic neurons to a multi-layer perceptron is proposed. It is shown that the proposed method is a generalization of recently successful methods of dropout (Hinton et al., 2012), explicit noise injection (Vincent et al., 2010; Bishop, 1995) and semantic hashing (Salakhutdinov & Hinton, 2009). Under the proposed framework, an extension of dropout which allows using separate dropping probabilities for different hid...

Cho, Kyunghyun

2013-01-01

51

Image Binarization Using Multi-Layer Perceptron: A Semi-Supervised Approach

In this paper, we have discussed the Image Binarization technique using Multilayer Perceptron (MLP). The purpose of Image Binarization is to extract the lightness (brightness, density) as a feature amount from the Image. It converts a gray-scale image of up to 256 gray levels to a black and white image. We use Backpropagation algorithm for training MLP. It is a supervised learning technique. Here Kmeans clustering algorithm has been used for clustering a 256 × 256 gray-level image. The datas...

Amlan Raychaudhuri; Jayanta Dutta

2012-01-01

52

Belief Propagation for Error Correcting Codes and Lossy Compression Using Multilayer Perceptrons

The belief propagation (BP) based algorithm is investigated as a potential decoder for both of error correcting codes and lossy compression, which are based on non-monotonic tree-like multilayer perceptron encoders. We discuss that whether the BP can give practical algorithms or not in these schemes. The BP implementations in those kind of fully connected networks unfortunately shows strong limitation, while the theoretical results seems a bit promising. Instead, it reveals ...

Mimura, Kazushi; Cousseau, Florent; Okada, Masato

2011-01-01

53

Second-Order Learning Methods for a Multilayer Perceptron

International Nuclear Information System (INIS)

First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs

54

Directory of Open Access Journals (Sweden)

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 ?m 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.

Alireza Taravat

2015-02-01

55

Multi-layer perceptrons in filtering geophysical signals

Multilayer neural networks are used to model the influence of noise sources on some types of geophysical signals. The role of neural networks is to identify nonlinear time-invariant NARMAX (nonlinear autoregressive moving average with exogenous inputs) models which are used to filter corrupted data using a model-based approach, avoiding the drawbacks of the classical digital filtering techniques. The effectiveness of the proposed approach is shown by considering a case study concerning the filtering of tilt signals recorded in volcanically active areas.

Fortuna, L.; Graziani, S.; Lo Presti, M.; Nunnari, G.

56

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 ac...

Mohammad Fathian,; Kia, Arash N.

2012-01-01

57

Generation of hourly irradiation synthetic series using the neural network multilayer perceptron

Energy Technology Data Exchange (ETDEWEB)

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)

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

58

In this paper, a tentative of geomagnetic storms prediction is implanted by analyzing the International Real-Time Magnetic Observatory Network data using the Artificial Neural Network (ANN). The implanted method is based on the prediction of future horizontal geomagnetic field component using a Multilayer Perceptron (MLP) neural network model. The input is the time and the output is the X and Y magnetic field components. Application to geomagnetic data of Mai 2002 shows that the implanted ANN model can greatly help the geomagnetic storms prediction.

Ouadfeul, S.; Aliouane, L.; Tourtchine, V.

2013-09-01

59

Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis

In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.

Rossi, Fabrice

2005-01-01

60

Belief Propagation for Error Correcting Codes and Lossy Compression Using Multilayer Perceptrons

The belief propagation (BP) based algorithm is investigated as a potential decoder for both of error correcting codes and lossy compression, which are based on non-monotonic tree-like multilayer perceptron encoders. We discuss that whether the BP can give practical algorithms or not in these schemes. The BP implementations in those kind of fully connected networks unfortunately shows strong limitation, while the theoretical results seems a bit promising. Instead, it reveals it might have a rich and complex structure of the solution space via the BP-based algorithms.

Mimura, Kazushi; Okada, Masato

2011-01-01

61

Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.

Martin, Arnaud

2008-01-01

62

Missing value imputation on missing completely at random data using multilayer perceptrons.

Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of missing values. These data set sizes range from 47 to 1389 records. A perturbation experiment was performed for each data set where the probability of missing value was set to 0.05. Several architectures and learning algorithms for the multilayer perceptron are tested and compared with three classic imputation procedures: mean/mode imputation, regression and hot-deck. The obtained results, considering different performance measures, not only suggest this approach improves the quality of a database with missing values, but also the best results are clearly obtained using the Multilayer Perceptron model in data sets with categorical variables. Three learning rules (Levenberg-Marquardt, BFGS Quasi-Newton and Conjugate Gradient Fletcher-Reeves Update) and a small number of hidden nodes are recommended. PMID:20875726

Silva-Ramírez, Esther-Lydia; Pino-Mejías, Rafael; López-Coello, Manuel; Cubiles-de-la-Vega, María-Dolores

2011-01-01

63

Approximating Gaussian mixture model or radial basis function network with multilayer perceptron.

Gaussian mixture models (GMMs) and multilayer perceptron (MLP) are both popular pattern classification techniques. This brief shows that a multilayer perceptron with quadratic inputs (MLPQ) can accurately approximate GMMs with diagonal covariance matrices. The mapping equations between the parameters of GMM and the weights of MLPQ are presented. A similar approach is applied to radial basis function networks (RBFNs) to show that RBFNs with Gaussian basis functions and Euclidean norm can be approximated accurately with MLPQ. The mapping equations between RBFN and MLPQ weights are presented. There are well-established training procedures for GMMs, such as the expectation maximization (EM) algorithm. The GMM parameters obtained by the EM algorithm can be used to generate a set of initial weights of MLPQ. Similarly, a trained RBFN can be used to generate a set of initial weights of MLPQ. MLPQ training can be continued further with gradient-descent based methods, which can lead to improvement in performance compared to the GMM or RBFN from which it is initialized. Thus, the MLPQ can always perform as well as or better than the GMM or RBFN. PMID:24808530

Patrikar, Ajay M

2013-07-01

64

Neural Networks for Gas Turbine Fault Identification: Multilayer Perceptron or Radial Basis Network?

Efficiency of gas turbine condition monitoring systems depends on quality of diagnostic analysis at all its stages such as feature extraction (from raw input data), fault detection, fault identification, and prognosis. Fault identification algorithms based on the gas path analysis may be considered as an important and sophisticated component of these systems. These algorithms widely use pattern recognition techniques, mostly different artificial neural networks. In order to choose the best technique, the present paper compares two network types: a multilayer perceptron and a radial basis network. The first network is being commonly applied to recognize gas turbine faults. However, some studies note high recognition capabilities of the second network. For the purpose of the comparison, both networks were included into a special testing procedure that computes for each network the true positive rate that is the probability of a correct diagnosis. Networks were first tuned and then compared using this criterion. Same procedure input data were fed to both networks during the comparison. However, to draw firm conclusions on the networks' applicability, comparative calculations were repeated with different variations of these data. In particular, two engines that differ in an application and gas path structure were chosen as a test case. By way of summing up comparison results, the conclusion is that the radial basis network is a little more accurate than the perceptron, however the former needs much more available computer memory and computation time.

Loboda, Igor; Feldshteyn, Yakov; Ponomaryov, Volodymyr

2012-03-01

65

Recognition of Epileptiform Patterns in the Human Electroencephalogram Using Multi-Layer Perceptron

Directory of Open Access Journals (Sweden)

Full Text Available Automatic detection of epileptiform patterns is highly desirable during continuous monitoring of patients with epilepsy. This paper describes an unconvential system for automatic off-line recognition of epileptic sharp transients in the human electroencephalogram (EEG, based on a standard neural network architecture - multi-layer perceptron (MLP, and implemented on a Silicon Graphics Indigo workstation. The system makes comprehensive use of wide spatial contextual information available on 12 channels of EEG and takes advantage of discrete dyadic wavelet transform (DDWT for efficient parameterisation of EEG data. The EEG database consists of 12 patients, 7 of which are used in the process of training of MLP. The resulting MLP is presented with the testing data set consisting of all data vectors from all 12 patients, and is shown to be capable to recognise a wide variety of epileptic signals.

V. Mokran

1995-06-01

66

An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose

Directory of Open Access Journals (Sweden)

Full Text Available This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN. This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 ?m standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.

Chih-Heng Pan

2012-12-01

67

An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose

This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN). This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 ?m standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy. PMID:23262482

Pan, Chih-Heng; Hsieh, Hung-Yi; Tang, Kea-Tiong

2013-01-01

68

A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron

Directory of Open Access Journals (Sweden)

Full Text Available The question of financial health and sustenance of a firm is so intriguing that it has spanned numerous studies. For investors,stakeholders and lenders, assessing the risk associated with an enterprise is vital. Several tools have been formulated to deal with predicting the solvency of a firm. This paper attempts to combine Data Envelopment Analysis and Multi-Layer Perceptron (MLP to suggest a new method for prediction of bankruptcy that not only focusses on historical financial data of firms that filed for bankruptcy like other past studies but also takes into account the data of those firms that were likely to do so. This method thus identifies firms that have a high chance of facing bankruptcy along with those that have filed for bankruptcy. The performance of this procedure is compared with MLP. The suggested method outperforms MLP in prediction of bankruptcy.

Ayan Mukhopadhyay

2012-07-01

69

Highly Accurate Multi-layer Perceptron Neural Network for Air Data System

Directory of Open Access Journals (Sweden)

Full Text Available The error backpropagation multi-layer perceptron algorithm is revisited. This algorithm is used to train and validate two models of three-layer neural networks that can be used to calibrate a 5-hole pressure probe. This paper addresses Occam's Razor problem as it describes the adhoc training methodology applied to improve accuracy and sensitivity. The trained outputs from 5-4-3 feed-forward network architecture with jump connection are comparable to second decimal digit (~0.05 accuracy, hitherto unreported in literature.Defence Science Journal, 2009, 59(6, pp.670-674, DOI:http://dx.doi.org/10.14429/dsj.59.1574

H. S. Krishna

2009-11-01

70

Artificial neural networks (ANNs) have recently been utilized in the nuclear technology applications since they are fast, precise and flexible vehicles to modeling, simulation and optimization. This paper presents a new approach based on multilayer perceptron neural networks (MLPNNs) for the estimation of some important neutronic parameters (net 239Pu production, tritium breeding ratio, cumulative fissile fuel enrichment, and fission rate) of a high power density fusion-fission (hybrid) reactor using light water reactor (LWR) spent fuel. A comparison of the results obtained by the MLPNNs and those found by using the code (Scale 4.3) was carried out. The results pointed out that the MLPNNs trained with least mean squares (LMS) algorithm could provide an accurate computation of the main neutronic parameters for the high power density reactor.

Übeyli, Mustafa; Übeyli, Elif Derya

2008-12-01

71

Directory of Open Access Journals (Sweden)

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.

Mohammad Fathian

2012-04-01

72

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). Thes...

Voyant, Cyril; Tamas, Wani; Nivet, Marie Laure; Notton, Gilles; Paoli, Christophe; Balu, Aure?lia; Muselli, Marc

2014-01-01

73

Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method

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). Thes...

Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aure?lia; Muselli, Marc; Nivet, Marie Laure; Notton, Gilles

2013-01-01

74

This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land ...

Salmon, Brian Paxton; Olivier, Jan Corne; Kleynhans, Waldo; Wessels, Konrad J.; Den Bergh, Frans; Steenkamp, Karen C.

2011-01-01

75

This paper presents discrete wavelet transform and the S-transform based neural classifier scheme used for time series data mining of power quality events occurring due to power signal disturbances. The DWT and the S –transform are used for feature extraction and then the extracted features are classified with neural classifiers such as multilayered perceptron network (MLP) for pattern classification, data mining and subsequent knowledge discovery.

LALIT KUMAR BEHERA; MAYA NAYAK; SAREETA MOHANTY

2011-01-01

76

The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on l...

Piotrowski, A.; Wallis, S. G.; Napio?rkowski, J. J.; Rowin?ski, P. M.

2007-01-01

77

The paper presents a novel method of initial weights optimization method in Multi-Layer Perceptron Network(MLPN). Firstly, the sample sets should be transformed by K-L Transform. Secondly, use K-L Converting Matrix to initialize the weights between input and hidden layer. Thirdly the MLPN is trained by BP algorithm, and the convergence speed of MLPN is improved evidently. The ultimate test shows the new algorithm is suitable for the situation of low-dimensional data.

Xiao, Wei; Pu, Dun; Dong, Zhicheng; Liu, Cungen

2013-07-01

78

An application of the multilayer perceptron: Solar radiation maps in Spain

Energy Technology Data Exchange (ETDEWEB)

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)

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

79

Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

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. PMID:25110755

Hu, Yi-Chung

2014-01-01

80

Analog Multilayer Perceptron Circuit with On-chip Learning: Portable Electronic Nose

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 ?m 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.

Pan, Chih-Heng; Tang, Kea-Tiong

2011-09-01

81

Energy Technology Data Exchange (ETDEWEB)

In the past two decades, artificial neural networks (ANN) have been applied to quickly compute the critical clearing time (CCT), a frequently quoted measurement for power systems transient stability. This kind of applications mainly concerns the CCT prediction rather than the explanation because ANN was commonly considered as a black box. This paper will challenge this myth. In this paper, we describe the procedures for explaining CCT by means of a multilayer perceptron (MLP) artificial neural network. The explanation is expressed in terms of ''IF antecedent THEN consequent'' rules, where the antecedent indicates the power system operating conditions and the consequent refers to whether the CCT is high or low. We can accordingly explain CCT, and in turn we can observe under what circumstances will cause the power system CCT to be high or low. To justify the proposed method, the CCTs of two contingencies in 39-bus power systems are investigated. The results have demonstrated that the CCT can be explained by MLP very well. (author)

Lin, Yu-Jen [Dept. of Electrical Engineering, I-Shou University, Kaohsiung County (China)

2010-10-15

82

Face Recognition through Multilayer Perceptron (MLP and Learning Vector Quantization (LVQ

Directory of Open Access Journals (Sweden)

Full Text Available Face recognition is challenging problems and there is still a lot of work that needs to be done in this area. Over the past ten years, face recognition has received substantial attention from researchers in biometrics, pattern recognition, computer vision, and cognitive psychology communities. This common interest in facial recognition technology among researchers working in diverse fields is motivated both by the remarkable ability to recognize people and by the increased attention being devoted to security applications. Applications of face recognition can be found in security, tracking, multimedia, and entertainment domains.This paper presents a face recognition system using artificial neural network. Here, we have designed a neural network with some own set network parameters. The results presented here have been obtained using two basic methods: multilayer perceptron (MLP, and learning vector quantization (LVQ. In both cases, two kinds of data have been fed to the classifiers: reduced resolution images (gray level or segmented, and feature vectors. The experimental results also show that, for the approaches considered here, analyzing gray level images produced better results than analyzing geometrical features, either because of the errors introduced during their extraction or because the original images have a richer information content. Furthermore, training times were much shorter for LVQ than for MLP. On the other hand, MLP achieved lower error rates when dealing with geometrical features.

Dr. Ikvinderpal Singh

2012-12-01

83

We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep multicolor catalog. Various possible approaches for the training of the neural network are explored, including the deepest and most complete spectroscopic redshift catalog currently available (the Hubble Deep Field North dataset) and models of the spectral energy distribution of galaxies available in the literature. The MLP can be trained on observed data, theoretical data and mixed samples. The prediction of the method is tested on the spectroscopic sample in the HDF-S (44 galaxies). Over the entire redshift range, $0.1

Vanzella, E; Fontana, A; Nonino, M; Arnouts, S; Giallongo, E; Grazian, A; Fasano, G; Popesso, P; Saracco, P; Zaggia, S R

2003-01-01

84

Geomagnetic Dst index forecast using a multilayer perceptrons artificial neural network

International Nuclear Information System (INIS)

Complete text of publication follows. The best known manifestations of the impact of solar wind on the magnetosphere are the geomagnetic storms. The prediction of geomagnetic field behavior allows the alert of geomagnetic storms occurrence, as those phenomena can cause many damages in the planet. The Artificial Intelligence tools have been applied in many multidisciplinary studies, covering several areas of knowledge, as a choice of approach to the solution of problems with characteristics like non-linearity, imprecision, and other features that can not be easily solved with conventional computational models. Techniques such as Artificial Neural Networks, Expert Systems and Decision Trees have been used in the Space Weather studies to perform tasks such as forecasting geomagnetic storms and the investigation of rules and parameters related on its occurrence. The main focus of this work is on forecasting the geomagnetic field behavior, represented this time by the Dst index, using for that task, mainly, the interplanetary magnetic field components and solar wind data. The tool chosen here to solve the non-linear problem was a Multi-layer Perceptrons Artificial Neural Network, trained with the backpropagation algorithm. Unlike what was done in other studies, we chose to predict calm and disturbed periods like, for example, a full month of data, for application in a real time forecasting system. It was possible to predict the geomagnetic Dst index one or two hours beforemagnetic Dst index one or two hours before with great percentage efficiency.

85

A multilayered perceptron approach to prediction of the SEC's investigation targets.

In the fields of accounting and auditing, detection of firms engaged in fraudulent financial reporting has become increasingly important, due to the increased frequency of such events and the attendant costs of litigation. The neural-network approach sheds some light on this problem due to the attributes that it requires minimum prior knowledge of the data and achieves a highly nonlinear computational model based on past experience (training). In this study, we employ seven red flags which are composed of four financial red flags and three turnover red flags in order to detect targets of the Securities and Exchange Commission's (SECs) investigation of fraudulent financial reporting. The red flags are computed over 70 firms spread among various industrial sectors, and form the base data that is used for developing the computational prediction model. Multilayered perceptron computation of this data was able to predict the targets of the SEC investigated firms with an average of 88% accuracy in the cross-validation test. On the other hand, the same data computed by the logit program gave an average prediction rate of 47% PMID:18263521

Kwon, T M; Feroz, E H

1996-01-01

86

Directory of Open Access Journals (Sweden)

Full Text Available This paper presents the results obtained by developing a methodology to detect 5 types of heartbeats (Normal (N, Right bundle branch block (RBBB, Left bundle branch block (LBBB, Premature atrial contraction (APC and Premature ventricular contraction (PVC, using Wavelet transform packets with non-adaptative mode applied on features extraction from heartbeats. It was used the Shannon function to calculate the entropy and It was added an identification nodes stage per every type of cardiac signal in the Wavelet tree. The using of Wavelet packets transform allows the access to information which results of decomposition of low and high frecuency, giving providing a more integral analysis than achieved by the discrete Wavelet transform. Three families of mother Wavelet were evaluated on transformation: Daubechies, Symlet and Reverse Biorthogonal, which were results from a previous research in that were identified the mother Wavelet that had higher entropy with the cardiac signals. With non-adaptive mode, the computational cost is reduced when Wavelet packets are used; this cost represents the most marked disadvantage from the transform. To classify the heartbeats were used Support Vector Machines and Multilayer Perceptron. The best classification error was achieved employing Support Vector Machine and a radial basis function; it was 2.57 %.

Alejandro J. Orozco-Naranjo

2013-11-01

87

In this paper, we propose a wideband dynamic behavioral model for a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in colorless radio over fiber (RoF) systems using a tapped-delay multilayer perceptron (TDMLP). 64 quadrature amplitude modulation (QAM) signals with 20 Msymbol/s were used to train, validate and test the model. Nonlinear distortion and dynamic effects induced by the RSOA modulator are demonstrated. The parameters of the model such as the number of nodes in the hidden layer and memory depth were optimized to ensure the generality and accuracy. The normalized mean square error (NMSE) is used as a figure of merit. The NMSE was up to -44.33 dB when the number of nodes in the hidden layer and memory depth were set to 20 and 3, respectively. The TDMLP model can accurately approximate to the dynamic characteristics of the RSOA modulator. The dynamic AM-AM and dynamic AM-PM distortions of the RSOA modulator are drawn. The results show that the single hidden layer TDMLP can provide accurate approximation for behaviors of the RSOA modulator. PMID:23481795

Liu, Zhansheng; Violas, Manuel Alberto; Carvalho, Nuno Borges

2013-02-11

88

Estimate of significant wave height from non-coherent marine radar images by multilayer perceptrons

One of the most relevant parameters to characterize the severity of ocean waves is the significant wave height ( H s ). The estimate of H s from remotely sensed data acquired by non-coherent X-band marine radars is a problem not completely solved nowadays. A method commonly used in the literature (standard method) uses the square root of the signal-to-noise ratio (SNR) to linearly estimate H s . This method has been widely used during the last decade, but it presents some limitations, especially when swell-dominated sea states are present. To overcome these limitations, a new non-linear method incorporating additional sea state information is proposed in this article. This method is based on artificial neural networks (ANNs), specifically on multilayer perceptrons (MLPs). The information incorporated in the proposed MLP-based method is given by the wave monitoring system (WaMoS II) and concerns not only to the square root of the SNR, as in the standard method, but also to the peak wave length and mean wave period. Results for two different platforms (Ekofisk and FINO 1) placed in different locations of the North Sea are presented to analyze whether the proposed method works regardless of the sea states observed in each location or not. The obtained results empirically demonstrate how the proposed non-linear solution outperforms the standard method regardless of the environmental conditions (platform), maintaining real-time properties.

Vicen-Bueno, Raúl; Lido-Muela, Cristina; Nieto-Borge, José Carlos

2012-12-01

89

Cross Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural Networks

Directory of Open Access Journals (Sweden)

Full Text Available Problem statement: The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers have prohibited an accurate prognosis. Approach: The aim of this study is to investigate the effectiveness of a Multilayer Perceptron (MLP for predicting breast cancer progression using a set of four biomarkers of breast tumors. The biomarkers include DNA ploidy, cell cycle distribution (G0G1/G2M, steroid receptors (ER/PR and S-Phase Fraction (SPF. A further objective of the study is to explore the predictive potential of these markers in defining the state of nodal involvement in breast cancer. Two methods of outcome evaluation viz. stratified and simple k-fold Cross Validation (CV are studied in order to assess their accuracy and reliability for neural network validation. Criteria such as output accuracy, sensitivity and specificity are used for selecting the best validation technique besides evaluating the network outcome for different combinations of markers. Results: The results show that stratified 2-fold CV is more accurate and reliable compared to simple k-fold CV as it obtains a higher accuracy and specificity and also provides a more stable network validation in terms of sensitivity. Best prediction results are obtained by using an individual marker-SPF which obtains an accuracy of 65%. Conclusion/Recommendations: Our findings suggest that MLP-based analysis provides an accurate and reliable platform for breast cancer prediction given that an appropriate design and validation method is employed.

Shirin A. Mojarad

2011-01-01

90

This study uses multilayer perceptron (MP) methods to develop classification models for predicting cascade, step-pool, plane bed, and pool-riffle type reach morphologies in mountain streams. Several models were developed with MP and classical linear regression methods on the basis of the following input variables: channel slope (S), sediment size (d84), bankfull depth (h), and bankfull width (w). Data for model calibration and testing were compiled from previous studies in mountain environments. The data were divided into separate calibration (training) and testing (prediction) sets for both the MP and classical linear regression methods; model performance was based on the percentage of accurately predicted reach morphologies using the testing portion of the data. The results indicate that (1) the MP models outperformed the linear regression models for reach morphology classification; (2) relative submergence (h/d84) was useful for classifying step-pool and pool-riffle reaches but performed poorly in discriminating cascade and plane bed type reaches; (3) inclusion of channel slope in models was important for classifying cascade type reaches; and (4) plane bed reaches were the most difficult to classify and delineate from pool-riffle reaches. The two best performing MP models included the input variables (S, h/d84) and (S, h/d84, w). The overall predictive accuracy for classification of reach type for the two models was 81% and 83%, respectively, with predictive accuracies by reach type as follows: cascade, 100%; step-pool, 81%; plane bed, 67%; pool-riffle, 88% (first model) and cascade, 100%; step-pool, 87%; plane bed, 70%; pool-riffle, 90% (second model).

Altunkaynak, Abdüsselam; Strom, Kyle B.

2009-12-01

91

International Nuclear Information System (INIS)

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

92

Replica Symmetry Breaking and the Kuhn-Tucker Cavity Method in simple and multilayer Perceptrons

Within a Kuhn-Tucker cavity method introduced in a former paper, we study optimal stability learning for situations, where in the replica formalism the replica symmetry may be broken, namely (i) the case of a simple perceptron above the critical loading, and (ii) the case of two-layer AND-perceptrons, if one learns with maximal stability. We find that the deviation of our cavity solution from the replica symmetric one in these cases is a clear indication of the nece...

Gerl, F.; Krey, U.

1996-01-01

93

Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise

Energy Technology Data Exchange (ETDEWEB)

Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLP-start-LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trained with noise perform better than the MLP trained without noise, and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location. (author)

Jun, Sung Chan [Department of Computer Science, University of New Mexico, Albuquerque, NM (Mexico)]. E-mail: junsc@cs.unm.edu; Pearlmutter, Barak A.; Nolte, Guido [Department of Computer Science, University of New Mexico, Albuquerque, NM (Mexico)

2002-07-21

94

An Optical Thresholding Perceptron

An implementation of an optical perceptron with a soft optical threshold trained with an adapted BP algorithm is described as a precursor to an optical multilayer perceptron (MLP). It has 64 inputs and ten outputs. The soft threshold is implemented by a liquid crystal light valve. Experimental results on perceptron recall are also reported. The effect of a modified grey-scale to weight mapping for weight levels implemented by LCTVs is evaluated based on the results of handwritten digit recogn...

Saxena, Indu; Moerland, Perry; Fiesler, Emile; Pourzand, A. R.; Collings, N.

1997-01-01

95

Directory of Open Access Journals (Sweden)

Full Text Available Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs, namely Radial Basis Function (RBF and Multi-Layer Perceptron (MLP were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE, Normalized Mean Square Error (NMSE and correlation coefficient (r were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

Hadi Memarian

2012-10-01

96

The random initialization of weights of a multilayer perceptron makes it possible to model its training process as a Las Vegas algorithm, i.e. a randomized algorithm which stops when some required training error is obtained, and whose execution time is a random variable. This modelling is used to perform a case study on a well-known pattern recognition benchmark: the UCI Thyroid Disease Database. Empirical evidence is presented of the training time probability distribution exhibiting a heavy tail behavior, meaning a big probability mass of long executions. This fact is exploited to reduce the training time cost by applying two simple restart strategies. The first assumes full knowledge of the distribution yielding a 40% cut down in expected time with respect to the training without restarts. The second, assumes null knowledge, yielding a reduction ranging from 9% to 23%.

Cebrian, Manuel

2007-01-01

97

Feature-Based Facial Expression Recognition: Experiments With a Multi-Layer Perceptron

In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducialpoints on a face, and a set of multi-scale and multi-orientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been comp...

Zhang, Zhengyou

1998-01-01

98

In order to perceive of rainfall- runoff process, essential prediction for water surface source management has special importance. Thereby in this paper, Tang-e Karzin hydrometric station which is located in sub-domain of salman-farsi dam had been considered. By utilizing of weekly statistical discharge information related to past 36 years, multilayer perceptron neural network model was used to predict the average weekly discharge of Tang-e Karzin station through the discharge information of ...

Saleh Salimi; Hamid Mahmoodi; Nader Barahmand

2013-01-01

99

There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parallelism of neural networks and allow on-thefly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any app...

Rodrigo Martins da Silva; Luiza de Macedo Mourelle; Nadia Nedjah

2011-01-01

100

International Nuclear Information System (INIS)

Highlights: • Multilayer perceptrons are used to simulate the I–V curve of thin-film PV modules. • APE from the spectral irradiance was added as an input variable to the network. • A self-organised map is used to select the curves used for training the network. • Curve error and maximum power error decrease when using this technique. • This method could provide accurate estimation of the output of a PV plant. - Abstract: In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I–V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I–V curve to the measured one and the peak power point estimation

101

International Nuclear Information System (INIS)

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

102

Energy Technology Data Exchange (ETDEWEB)

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.

Vaziri, Nima [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of)]. E-mail: n.vaziri@gmail.com; Hojabri, Alireza [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Erfani, Ali [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Monsefi, Mehrdad [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of); Nilforooshan, Behnam [Department of Physics, Islamic Azad University, Karaj Branch, Moazen Blvd., Rajaee shahr (Iran, Islamic Republic of)

2007-02-15

103

Directory of Open Access Journals (Sweden)

Full Text Available Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR, as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM and MultiLayer Perceptron (MLP neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN model generates poor accuracies.

Alireza Taravat

2014-12-01

104

Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method

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.

Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles

2014-03-01

105

This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land cover change examples. The simulated land cover change comprises of concatenated time series that are produced by blending actual time series of pixels from human settlements to those from adjacent areas covered by natural vegetation. The method employs an iteratively retrained MLP to capture all local patterns and to compensate for the time-varying climate change in the geographical area. The iteratively retrained MLP was compared to a classical batch mode trained MLP. Depending on the length of the temporal sliding window used, an overall change detection accuracy between 83% and 90% was achieved. It is shown that a sliding window of 6 months using all 7 bands of MODIS data is sufficient to detect land cover change reliably. Window sizes of 18 months and longer provide minor improvements to classification accuracy and change detection performance at the cost of longer time delays.

Salmon, B. P.; Olivier, J. C.; Kleynhans, W.; Wessels, K. J.; van den Bergh, F.; Steenkamp, K. C.

2011-12-01

106

Cross Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural Networks

Problem statement: The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers have prohibited an accurate prognosis. Approach: The aim of this study is to investigate the effectiveness of a Multilayer Perc...

Mojarad, Shirin A.; Dlay, Satnam S.; Woo, Wai L.; Sherbet, Gajanan V.

2011-01-01

107

This paper presents a multi-layered perceptronneural network (MLPNN) method to solve the combinedeconomic and emission dispatch (CEED) problem. The harmfulecological effects caused by the emission of particulate andgaseous pollutants like sulfur dioxide (SO2) and oxides ofnitrogen ( NOx ) can be reduced by adequate distribution ofload between the plants of a power system. However, this leadsto a noticeable increase in the operating cost of the plants. Thispaper presents the (MLPNN) method app...

Sarakhs branch; Sarakhs, Iran

2012-01-01

108

Directory of Open Access Journals (Sweden)

Full Text Available In order to perceive of rainfall- runoff process, essential prediction for water surface source management has special importance. Thereby in this paper, Tang-e Karzin hydrometric station which is located in sub-domain of salman-farsi dam had been considered. By utilizing of weekly statistical discharge information related to past 36 years, multilayer perceptron neural network model was used to predict the average weekly discharge of Tang-e Karzin station through the discharge information of its two upside stations. In order to optimize the weights and biases of the MLP network, we tried to use Artificial Bee Colony (ABC algorithm within training phase of the ANN network. The results indicated that by changing of different parameters of hidden layer of perceptron model, ABC can well optimize ANN’s weights and biases. Among five activation function Log-sigmoid was performed better than others with 9 neurons in hidden layer

Saleh Salimi

2013-10-01

109

Directory of Open Access Journals (Sweden)

Full Text Available The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections.

The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept.

In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.

A. Piotrowski

2007-08-01

110

Directory of Open Access Journals (Sweden)

Full Text Available The prediction of temporal concentration profiles of a transported pollutant in a river is still a subject of ongoing research efforts worldwide. The present paper is aimed at studying the possibility of using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under various flow conditions, using as little information about the river system as possible. In contrast with the earlier neural networks based work on longitudinal dispersion coefficients, this new approach relies more heavily on measurements of concentration collected during tracer tests over a range of flow conditions, but fewer hydraulic and morphological data are needed. The study is based upon 26 tracer experiments performed in a small river in Edinburgh, UK (Murray Burn at various flow rates in a 540 m long reach. The only data used in this study were concentration measurements collected at 4 cross-sections, distances between the cross-sections and the injection site, time, as well as flow rate and water velocity, obtained according to the data measured at the 1st and 2nd cross-sections.

The four main features of concentration versus time profiles at a particular cross-section, namely the peak concentration, the arrival time of the peak at the cross-section, and the shapes of the rising and falling limbs of the profile are modeled, and for each of them a separately designed neural network was used. There was also a variant investigated in which the conservation of the injected mass was assured by adjusting the predicted peak concentration. The neural network methods were compared with the unit peak attenuation curve concept.

In general the neural networks predicted the main features of the concentration profiles satisfactorily. The predicted peak concentrations were generally better than those obtained using the unit peak attenuation method, and the method with mass-conservation assured generally performed better than the method that did not account for mass-conservation. Predictions of peak travel time were also better using the neural networks than the unit peak attenuation method. Including more data into the neural network training set clearly improved the prediction of the shapes of the concentration profiles. Similar improvements in peak concentration were less significant and the travel time prediction appeared to be largely unaffected.

A. Piotrowski

2007-12-01

111

Directory of Open Access Journals (Sweden)

Full Text Available Normalization is important for Electrical Capacitance Tomography (ECT data due to the very small capacitance values obtained either from the physical or simulated ECT system. Thus far, there are two commonly used normalization methods for ECT, but their suitability has not been investigated. This paper presents the work on comparing the performances of two Multilayer Perceptron (MLP neural networks; one trained based on ECT data normalized using the conventional equation and the other normalized using the improved equation, to recognize gas-oil flow patterns. The correct pattern recognition percentages for both MLPs were calculated and compared. The results showed that the MLP trained with the conventional ECT normalization equation out-performed the ones trained with the improved normalization data for the task of gas-oil pattern recognition.

Hafizah Talib

2009-02-01

112

Optical scattering spectra obtained in the clinical trials of breast cancer diagnostic system were analyzed for the purpose to detect in the dataflow the segments corresponding to malignant tissues. Minimal invasive probe with optical fibers inside delivers white light from the source and collects the scattering light while being moved through the tissue. The sampling rate is 100 Hz and each record contains the results of measurements of scattered light intensity at 184 fixed wavelength points. Large amount of information acquired in each procedure, fuzziness in criteria of 'cancer' family membership and data noisiness make neural networks to be an attractive tool for analysis of these data. To define the dividing rule between 'cancer' and 'non-cancer' spectral families a three-layer perceptron was applied. In the process of perceptron learning back propagation method was used to minimize the learning error. Regularization was done using the Bayesian approach. The learning sample was formed by the experts. End-to-end probability calculation throughout the procedure dataset showed reliable detection of the 'cancer' segments. Much attention was paid on the spectra of the tissues with high blood content. Often the reason is vessel injury caused by the penetrating optical probe. But also it can be a dense vessel net surrounding the malignant tumor. To make the division into 'cancer' and 'non-cancer' families for the tissues with high blood content a special perceptron was learnt exceptionally on such spectra.

Nuzhny, Anton S.; Shumsky, Sergey A.; Korzhov, Alexey G.; Lyubynskaya, Tatiana E.

2008-02-01

113

Scientific Electronic Library Online (English)

Full Text Available SciELO Colombia | Language: Spanish Abstract in spanish En este trabajo se aplica un método constructivo aproximado para encontrar arquitecturas de redes neuronales artificiales (RNA) de tipo perceptrón multicapa (PMC). El método se complementa con la técnica de la búsqueda forzada de mejores mínimos locales. El entrenamiento de la red se lleva a cabo a [...] través del algoritmo gradiente descendente básico (GDB); se aplican técnicas como la repetición del entrenamiento y la detención temprana (validación cruzada), para mejorar los resultados. El criterio de evaluación se basa en las habilidades de aprendizaje y de generalización de las arquitecturas generadas específicas de un dominio. Se presentan resultados experimentales con los cuales se demuestra la efectividad del método propuesto y comparan con las arquitecturas halladas por otros métodos. Abstract in english This paper deals with an approximate constructive method to find architectures of artificial neuronal network (ANN) of the type MultiLayer Percetron (MLP) which solves a particular problem. This method is supplemented with the technique of the Forced search of better local minima. The training of th [...] e net uses an algorithm basic descending gradient (BDG). Techniques such as repetition of the training and the early stopping (cross validation) are used to improve the results. The evaluation approach is based not only on the learning abilities but also on the generalization of the specific generated architectures of a domain. Experimental results are presented in order to prove the effectiveness of the proposed method. These are compared with architectures found by other methods.

Héctor, Tabares; John, Branch; Jaime, Valencia.

2006-09-01

114

Reliable results are crucial when working with medical decision support systems. A decision support system should be reliable but also be interpretable, i.e. able to show how it has inferred its conclusions. In this thesis, the preprocessing perceptron is presented as a simple but effective and efficient analysis method to consider when creating medical decision support systems. The preprocessing perceptron has the simplicity of a perceptron combined with a performance comparable to the multi...

Kallin Westin, Lena

2004-01-01

115

This study proposes the use of multi-layer perceptron neural networks (MLPNN) to invert dispersion curves obtained via multi-channel analysis of surface waves (MASW) for shear S-wave velocity profile. The dispersion curve used in inversion includes the fundamental-mode dispersion data. In order to investigate the applicability and performance of the proposed MLPNN algorithm, test studies were performed using both synthetic and field examples. Gaussian random noise with a standard deviation of 4 and 8% was added to the noise-free test data to make the synthetic test more realistic. The model parameters, such as S-wave velocities and thicknesses of the synthetic layered-earth model, were obtained for different S/N ratios and noise-free data. The field survey was performed over the natural gas pipeline, located in the Germencik district of Ayd?n city, western Turkey. The results show that depth, velocity, and location of the embedded natural gas pipe are successfully estimated with reasonably good approximation.

Çaylak, Ça?r?; Kaftan, ?lknur

2014-12-01

116

The model organism, Drosophila melanogaster, and the mosquito Anopheles gambiae use 60 and 79 odorant receptors, respectively, to sense their olfactory world. However, a commercial "electronic nose" in the form of an insect olfactory biosensor demands very low numbers of receptors at its front end of detection due to the difficulties of receptor/sensor integration and functionalization. In this letter, we demonstrate how computation via artificial neural networks (ANNs), in the form of multilayer perceptrons (MLPs), can be successfully incorporated as the signal processing back end of the biosensor to drastically reduce the number of receptors to three while still retaining 100% performance of odorant detection to that of a full complement of receptors. In addition, we provide a detailed performance comparison between D. melanogaster and A. gambiae odorant receptors and demonstrate that A. gambiae receptors provide superior olfaction detection performance over D. melanogaster for very low receptor numbers. The results from this study present the possibility of using the computation of MLPs to discover ideal biological olfactory receptors for an olfactory biosensor device to provide maximum classification performance of unknown odorants. PMID:25380337

Bachtiar, Luqman R; Unsworth, Charles P; Newcomb, Richard D

2015-01-01

117

International Nuclear Information System (INIS)

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 0.015–15 MeV, 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 data for TLD materials have been given with penetration depth and incident photon energy as comparative to the results of the interpolation method using the Geometrical Progression (G-P) fitting formula. - Highlights: ? Gamma-ray energy absorption buildup factors estimation in TLD materials. ? The ANN approach can be alternative to G-P fitting method for BA calculations. ? The applied model is not time-consuming and easily predicted

118

In this work, a methodology based on the combined use of a multilayer perceptron model fed using selected spectral information is presented to invert the radiative transfer equation (RTE) and to recover the spatial temperature profile inside an axisymmetric flame. The spectral information is provided by the measurement of the infrared CO2 emission band in the 3-5 ?m spectral region. A guided spectral feature selection was carried out using a joint criterion of principal component analysis and a priori physical knowledge of the radiative problem. After applying this guided feature selection, a subset of 17 wavenumbers was selected. The proposed methodology was applied over synthetic scenarios. Also, an experimental validation was carried out by measuring the spectral emission of the exhaust hot gas plume in a microjet engine with a Fourier transform-based spectroradiometer. Temperatures retrieved using the proposed methodology were compared with classical thermocouple measurements, showing a good agreement between them. Results obtained using the proposed methodology are very promising and can encourage the use of sensor systems based on the spectral measurement of the CO2 emission band in the 3-5 ?m spectral window to monitor combustion processes in a nonintrusive way. PMID:25061791

García-Cuesta, Esteban; de Castro, Antonio J; Galván, Inés M; López, Fernando

2014-01-01

119

Scientific Electronic Library Online (English)

Full Text Available Em termos computacionais, uma rede neural artificial (RNA) pode ser implementada em software ou em hardware, ou ainda de maneira híbrida, combinando ambos os recursos. O presente trabalho propõe uma arquitetura de hardware para a computação de uma rede neural do tipo perceptron com múltiplas camadas [...] (MLP). Soluções em hardware tendem a ser mais eficientes do que soluções em software. O projeto em questão, além de explorar fortemente o paralelismo das redes neurais, permite alterações do número de entradas, número de camadas e de neurônios por camada, de modo que diversas aplicações de RNAs possam ser executadas no hardware proposto. Visando a uma redução de tempo do processamento aritmético, um número real é aproximado por uma fração de inteiros. Dessa forma, as operações aritméticas limitam-se a operações inteiras, executadas por circuitos combinacionais. Uma simples máquina de estados é demandada para controlar somas e produtos de frações. A função de ativação usada neste projeto é a sigmóide. Essa função é aproximada mediante o uso de polinômios, cujas operações são regidas por somas e produtos. Um teorema é introduzido e provado, permitindo a fundamentação da estratégia de cálculo da função de ativação. Dessa forma, reaproveita-se o circuito aritmético da soma ponderada para também computar a sigmóide. Essa re-utilização dos recursos levou a uma redução drástica de área total de circuito. Após modelagem e simulação para validação do bom funcionamento, a arquitetura proposta foi sintetizada utilizando recursos reconfiguráveis, do tipo FPGA. Os resultados são promissores. Abstract in english There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN), whose topology is the multilayer perceptron (MLP). In this paper, we explore the parall [...] elism of neural networks and allow on-thefly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetics is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. this resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.

Rodrigo Martins da, Silva; Luiza de Macedo, Mourelle; Nadia, Nedjah.

2011-12-01

120

Directory of Open Access Journals (Sweden)

Full Text Available There are several neural network implementations using either software, hardware-based or a hardware/software co-design. This work proposes a hardware architecture to implement an artificial neural network (ANN, whose topology is the multilayer perceptron (MLP. In this paper, we explore the parallelism of neural networks and allow on-thefly changes of the number of inputs, number of layers and number of neurons per layer of the net. This reconfigurability characteristic permits that any application of ANNs may be implemented using the proposed hardware. In order to reduce the processing time that is spent in arithmetic computation, a real number is represented using a fraction of integers. In this way, the arithmetics is limited to integer operations, performed by fast combinational circuits. A simple state machine is required to control sums and products of fractions. Sigmoid is used as the activation function in the proposed implementation. It is approximated by polynomials, whose underlying computation requires only sums and products. A theorem is introduced and proven so as to cover the arithmetic strategy of the computation of the activation function. Thus, the arithmetic circuitry used to implement the neuron weighted sum is reused for computing the sigmoid. this resource sharing decreased drastically the total area of the system. After modeling and simulation for functionality validation, the proposed architecture synthesized using reconfigurable hardware. The results are promising.Em termos computacionais, uma rede neural artificial (RNA pode ser implementada em software ou em hardware, ou ainda de maneira híbrida, combinando ambos os recursos. O presente trabalho propõe uma arquitetura de hardware para a computação de uma rede neural do tipo perceptron com múltiplas camadas (MLP. Soluções em hardware tendem a ser mais eficientes do que soluções em software. O projeto em questão, além de explorar fortemente o paralelismo das redes neurais, permite alterações do número de entradas, número de camadas e de neurônios por camada, de modo que diversas aplicações de RNAs possam ser executadas no hardware proposto. Visando a uma redução de tempo do processamento aritmético, um número real é aproximado por uma fração de inteiros. Dessa forma, as operações aritméticas limitam-se a operações inteiras, executadas por circuitos combinacionais. Uma simples máquina de estados é demandada para controlar somas e produtos de frações. A função de ativação usada neste projeto é a sigmóide. Essa função é aproximada mediante o uso de polinômios, cujas operações são regidas por somas e produtos. Um teorema é introduzido e provado, permitindo a fundamentação da estratégia de cálculo da função de ativação. Dessa forma, reaproveita-se o circuito aritmético da soma ponderada para também computar a sigmóide. Essa re-utilização dos recursos levou a uma redução drástica de área total de circuito. Após modelagem e simulação para validação do bom funcionamento, a arquitetura proposta foi sintetizada utilizando recursos reconfiguráveis, do tipo FPGA. Os resultados são promissores.

Rodrigo Martins da Silva

2011-12-01

121

Scientific Electronic Library Online (English)

Full Text Available En este trabajo se realizó un estudio estadístico de variables físico químicas asociadas al fenómeno de contaminación ambiental, en particular concentración media mensual de SO2 , medidas en la ciudad Salta Capital, Argentina, simultáneamente a concentraciones de NO2 y O3 . Las series bajo estudio p [...] resentaban comportamientos dinámicos no lineales, datos atípicos y cambios estructurales, lo que hizo imposible modelarlas con tipologías econométricas tradiciones (AR, MA, ARMA, ARIMA, entre otras). Una solución eficiente que se encontró, hace uso de la teoría de los perceptrones multicapa. Mediante el modelo estructural de series de tiempo, esta solución se presenta como un proceso matemático iterativo que permite obtener un modelado final el cual tiene una muy alta confiabilidad (95%), para realizar pronoósticos a futuro sobre el comportamiento de la variable estudiada. Abstract in english In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behavi [...] our, outliers and structural changes. Due to these it was impossible to use typical econometric typologies (AR, MA, ARMA, ARIMA, among others). An effective solution which uses multistep perceptrons theory was found. By using structural time series modelling, this solution is presented by an iterative mathematical process that allows us to obtain a final model with a high confidence level (95%) in order to do the forecasting step on the studied variable.

Haydeé Elena, Musso; Orlando José, Ávila Blas.

2013-01-01

122

Directory of Open Access Journals (Sweden)

Full Text Available En este trabajo se realizó un estudio estadístico de variables físico químicas asociadas al fenómeno de contaminación ambiental, en particular concentración media mensual de SO2 , medidas en la ciudad Salta Capital, Argentina, simultáneamente a concentraciones de NO2 y O3 . Las series bajo estudio presentaban comportamientos dinámicos no lineales, datos atípicos y cambios estructurales, lo que hizo imposible modelarlas con tipologías econométricas tradiciones (AR, MA, ARMA, ARIMA, entre otras. Una solución eficiente que se encontró, hace uso de la teoría de los perceptrones multicapa. Mediante el modelo estructural de series de tiempo, esta solución se presenta como un proceso matemático iterativo que permite obtener un modelado final el cual tiene una muy alta confiabilidad (95%, para realizar pronoósticos a futuro sobre el comportamiento de la variable estudiada.In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behaviour, outliers and structural changes. Due to these it was impossible to use typical econometric typologies (AR, MA, ARMA, ARIMA, among others. An effective solution which uses multistep perceptrons theory was found. By using structural time series modelling, this solution is presented by an iterative mathematical process that allows us to obtain a final model with a high confidence level (95% in order to do the forecasting step on the studied variable.

Haydeé Elena Musso

2013-01-01

123

Scientific Electronic Library Online (English)

Full Text Available En el presente artículo se da a conocer una alternativa algorítimica a los sistemas actuales de reconocimiento automático del habla (ASR), mediante una propuesta en la forma de realizar la caracterización de las palabras basada en una aproximación que usa la extracción de coeficientes de la codifica [...] ción de predicción lineal (LPC) y la correlación cruzada. La implementación consiste en extraer las características fonéticas mediante los coeficientes LPC, después se forman vectores de patrones de la pronunciación conformados por el promedio de los coeficientes LPC de las muestras de las palabras obteniendo un vector característico de cada pronunciación mediante la autocorrelación de las secuencias de coeficientes LPC; estos vectores se utilizan para entrenar un clasificador de tipo perceptrón multicapa (MLP). Se realizaron pruebas de desempeño previo entrenamiento con los diferentes patrones de las palabras a reconocer. Se utilizó la fonética de los dígitos del cero al nueve como vocabulario objetivo, debido a su amplia aplicación, y para estimar el desempeño de este método se utilizaron dos corpus de pronunciaciones: el corpus UPA, que contempla en su base de datos la pronuncación de la región occidente de México, y el corpus Tlatoa, que hace lo propio para la región centro de México. Las señales en ambos corpus fueron adquiridas en el lenguaje español, y a una frecuencia de muestreo de 8kHz. Los porcentajes de reconocimiento obtenidos fueron del 96.7 y 93.3% para las modalidades de mono-locutor para el corpus UPA y múltiple-locutor para el corpus Tlatoa, respectivamente. Asimismo, se realizó una comparación contra dos métodos clásicos del reconocimiento de voz y del habla, Dynamic Time Warping (DTW) y Hidden Markov Models (HMM). Abstract in english It this paper we present an algorithmic alternative to the current Automatic Speech Recognition (ASR) systems by proposing a way to characterize words based on approximations that use an extracted coefficient from Linear Predictive Coding (LPC). The method consists in extracting phonetic characteris [...] tics through the use of LPC coefficients, after which pattern vectors are formed from the LPC coefficient averages taken from the word sampling, thus creating a unique vector for each pronunciation through the auto correlation of the LPC coefficient sequences. These vectors are used to train a Multilayer Perceptron (MLP) classifier. After training performance trials were executed. The sounds from the digits zero through nine where used as a target vocabulary, given its general use, and to estimate the performance of this method two corpus where used: the UPA corpus, which in its vocabulary uses a pronunciation familiar to the western part of Mexico, and the Tlatoa corpus, who's vocabulary presents a pronunciation typical of the central region of Mexico. The signals from both corpus where sampled in the Spanish language, and at a sampling frequency of 8kHz. The recognition rate for the mono-speaker from the UPA corpus and the multiple-speaker from the Tlatoa corpus were 96.7% and 93.3% respectively. Additionally, there where comparisons done against two classic methods used for speech recognition, Dynamic Time Warping (DTW) and Hidden Markov Models (HMM).

Carlos A., de Luna-Ortega; Miguel, Mora-González; Julio C., Martínez-Romo; Francisco J., Luna-Rosas; Jesús, Muñoz-Maciel.

124

We propose a focus of attention mechanism to speed up the Perceptron algorithm. Focus of attention speeds up the Perceptron algorithm by lowering the number of features evaluated throughout training and prediction. Whereas the traditional Perceptron evaluates all the features of each example, the Attentive Perceptron evaluates less features for easy to classify examples, thereby achieving significant speedups and small losses in prediction accuracy. Focus of attention allows...

Pelossof, Raphael; Ying, Zhiliang

2010-01-01

125

We propose a focus of attention mechanism to speed up the Perceptron algorithm. Focus of attention speeds up the Perceptron algorithm by lowering the number of features evaluated throughout training and prediction. Whereas the traditional Perceptron evaluates all the features of each example, the Attentive Perceptron evaluates less features for easy to classify examples, thereby achieving significant speedups and small losses in prediction accuracy. Focus of attention allows the Attentive Perceptron to stop the evaluation of features at any interim point and filter the example. This creates an attentive filter which concentrates computation at examples that are hard to classify, and quickly filters examples that are easy to classify.

Pelossof, Raphael

2010-01-01

126

Scientific Electronic Library Online (English)

Full Text Available El perceptrón multicapa (PMC) figura dentro de los tipos de redes neuronales artificiales (RNA) con resultados útiles en los estudios de relación estructura-actividad. Dado que los volúmenes de datos en proyectos de Bioinformática son eventualmente grandes, se propuso evaluar algoritmos para acortar [...] el tiempo de entrenamiento de la red sin afectar su eficiencia. Se desarrolló un algoritmo para el entrenamiento local y distribuido del PMC con la posibilidad de variar las funciones de transferencias para lo cual se utilizaron el Weka y la Plataforma de Tareas Distribuidas Tarenal para distribuir el entrenamiento del perceptrón multicapa. Se demostró que en dependencia de la muestra de entrenamiento, la variación de las funciones de transferencia pueden reportar resultados mucho más eficientes que los obtenidos con la clásica función Sigmoidal, con incremento de la g-media entre el 4.5 y el 17 %. Se encontró además que en los entrenamientos distribuidos es posible alcanzar eventualmente mejores resultados que los logrados en ambiente local. Abstract in english The multilayer perceptron (PMC) ranks among the types of artificial neural networks (ANN), which has provided better results in studies of structure-activity relationship. As the data volumes in Bioinformatics' projects are eventually big, it was proposed to evaluate algorithms to shorten the traini [...] ng time of the network without affecting its efficiency. There were evaluated different tools that work with ANN and were selected Weka algorithm for extracting the network and the Platform for Distributed Task Tarenal to distribute the training of multilayer perceptron. Finally, it was developed a training algorithm for local and distributed the MLP with the possibility of varying transfer functions. It was shown that depending on the training sample, the change of transfer functions can yield results much more efficient than those obtained with the classic sigmoid function with increased g-media between 4.5 and 17 %. Moreover, it was found that with distributed training can be achieved eventually, better results than those achieved in the local environment.

Yuleidys, Mejías César; Ramón, Carrasco Velar; Isbel, Ochoa Izquierdo; Edel, Moreno Lemus.

2013-12-01

127

The task of a classical perceptron is to classify two classes of patterns by generating a separation hyperplane. Here, we give a complete description of a quantum perceptron. The quantum algorithms for classification and learning are formulated in terms of unitary quantum gates operators. In the quantum case, the concept of separable or non-separable classes is irrelevant because the quantum perceptron can learn a superposition of patterns which are not separable by a hyperplane.

Andrecut, M.; Ali, M. K.

128

Generalization ability of a perceptron with non-monotonic transfer function

We investigate the generalization ability of a perceptron with non-monotonic transfer function of a reversed-wedge type in on-line mode. This network is identical to a parity machine, a multilayer network. We consider several learning algorithms. By the perceptron algorithm the generalization error is shown to decrease by the ${\\alpha}^{-1/3}$-law similarly to the case of a simple perceptron in a restricted range of the parameter $a$ characterizing the non-monotonic transfer...

Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki

1997-01-01

129

The choice of architecture of artificial neuron network (ANN) is still a challenging task that users face every time. It greatly affects the accuracy of the built network. In fact there is no optimal method that is applicable to various implementations at the same time. In this paper we propose a method to construct ANN based on clustering, that resolves the problems of random and ad hoc approaches for multilayer ANN architecture. Our method can be applied to regression prob...

Arouri, Cyrine; Nguifo, Engelbert Mephu; Aridhi, Sabeur; Roucelle, Ce?cile; Bonnet-loosli, Gaelle; Tsopze?, Norbert

2014-01-01

130

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.

Mohri, Mehryar; Rostamizadeh, Afshin

2013-01-01

131

Introduction to Perceptron Networks

DEFF Research Database (Denmark)

When it is time-consuming or expensive to model a plant using the basic laws of physics, a neural network approach can be an alternative. From a control engineer's viewpoint a two-layer perceptron network is sufficient. It is indicated how to model a dynamic plant using a perceptron network.

Jantzen, Jan

1998-01-01

132

Ashkin-Teller type perceptron models are introduced. Their maximal capacity per number of couplings is calculated within a first-step replica-symmetry-breaking Gardner approach. The results are compared with extensive numerical simulations using several algorithms.

Bolle, D.; Kozlowski, P.

2000-01-01

133

Prediction of Bladder Cancer Recurrences Using Artificial Neural Networks

Even if considerable advances have been made in the field of early diagnosis, there is no simple, cheap and non-invasive method that can be applied to the clinical monitorisation of bladder cancer patients. Moreover, bladder cancer recurrences or the reappearance of the tumour after its surgical resection cannot be predicted in the current clinical setting. In this study, Artificial Neural Networks (ANN) were used to assess how different combinations of classical clinical parameters (stage-grade and age) and two urinary markers (growth factor and pro-inflammatory mediator) could predict post surgical recurrences in bladder cancer patients. Different ANN methods, input parameter combinations and recurrence related output variables were used and the resulting positive and negative prediction rates compared. MultiLayer Perceptron (MLP) was selected as the most predictive model and urinary markers showed the highest sensitivity, predicting correctly 50% of the patients that would recur in a 2 year follow-up period.

Zulueta Guerrero, Ekaitz; Garay, Naiara Telleria; Lopez-Guede, Jose Manuel; Vilches, Borja Ayerdi; Iragorri, Eider Egilegor; Castaños, David Lecumberri; de La Hoz Rastrollo, Ana Belén; Peña, Carlos Pertusa

134

Scientific Electronic Library Online (English)

Full Text Available Este artículo presenta los resultados obtenidos al desarrollar una metodología para la detección de 5 tipos de latidos cardiacos (Normal (N), Bloqueo de Rama Derecha (RBBB), Bloqueo de Rama Izquierda (LBBB), Contracción Auricular Prematura (APC) y Contracción Ventricular Prematura (PVC)) utilizando [...] la transformada por paquetes Wavelet de manera no adaptativa en la extracción de características de las señales cardiacas, empleando la función Shanon para cálculo de la entropía y adicionando una fase de identificación de nodos por cada tipo de señal cardiaca en el árbol Wavelet. La utilización de la transformada por paquetes Wavelet permite acceder a información obtenida de la descomposición tanto de baja como de alta frecuencia proporcionando un análisis más integral que el logrado con la transformada Wavelet discreta. Se evaluaron Wavelets madre de las familias Daubechies, Symlet 5 y Biortogonal inversa; que fueron resultado de una investigación previa en que se identificaron las Wavelet madre que mayor entropía presentaban con las señales cardiacas. Con la modalidad no adaptativa se reduce el costo computacional al utilizar los paquetes Wavelet, coste que representa la mayor desventaja de esta transformada, dando validez a la investigación realizada. Para la clasificación de los patrones cardiacos se emplearon las máquinas de soporte vectorial y el perceptrón multicapa. Con las máquinas de soporte vectorial empleando kernel de función de base radial, se logró un error de clasificación del 2,57 %. Abstract in english This paper presents the results obtained by developing a methodology to detect 5 types of heartbeats (Normal (N), Right bundle branch block (RBBB), Left bundle branch block (LBBB), Premature atrial contraction (APC) and Premature ventricular contraction (PVC)), using Wavelet transform packets with n [...] on-adaptative mode applied on features extraction from heartbeats. It was used the Shannon function to calculate the entropy and It was added an identification nodes stage per every type of cardiac signal in the Wavelet tree. The using of Wavelet packets transform allows the access to information which results of decomposition of low and high frecuency, giving providing a more integral analysis than achieved by the discrete Wavelet transform. Three families of mother Wavelet were evaluated on transformation: Daubechies, Symlet and Reverse Biorthogonal, which were results from a previous research in that were identified the mother Wavelet that had higher entropy with the cardiac signals. With non-adaptive mode, the computational cost is reduced when Wavelet packets are used; this cost represents the most marked disadvantage from the transform. To classify the heartbeats were used Support Vector Machines and Multilayer Perceptron. The best classification error was achieved employing Support Vector Machine and a radial basis function; it was 2.57 %.

Alejandro J., Orozco-Naranjo; Pablo A., Muñoz-Gutiérrez.

2013-12-30

135

Influence of different nonlinearity functions on perceptron performance

Influence of two new nonlinearity functions on Perceptron performance is studied. The two new functions under consideration are Gaussian and sinusoid functions. The new functions create multithreshold Perceptions capable of handling both binary and analog inputs. A computer program has been developed to simulate behavior of a network utilizing either of the two modified Perceptrons. Both XOR and Parity Check problems were solved using a single-layer network utilizing these modified Perceptions. Based on the results obtained from the simulation the modified Perceptions are capable of solving problems (such as XOR) that can not be solved using a single-layer of the classical Perceptron. Also networks utilizing these modified Perceptions require fewer number of iterations to converge to a solution than that of a multi-layer network of classical Perceptions using back propagation. In addition the results show that Sinusoidal Perceptronperforms better than Gaussian Perception. 1.

Kaveh, Ashenayi; Vogh, James

1991-03-01

136

Multilayer perceptron neural network for flow prediction.

Artificial neural networks (ANNs) have proven to be a tool for characterizing, modeling and predicting many of the non-linear hydrological processes such as rainfall-runoff, groundwater evaluation or simulation of water quality. After proper training they are able to generate satisfactory predictive results for many of these processes. In this paper they have been used to predict 1 or 2 days ahead the average and maximum daily flow of a river in a small forest headwaters in northwestern Spain. The inputs used were the flow and climate data (precipitation, temperature, relative humidity, solar radiation and wind speed) as recorded in the basin between 2003 and 2008. Climatic data have been utilized in a disaggregated form by considering each one as an input variable in ANN(1), or in an aggregated form by its use in the calculation of evapotranspiration and using this as input variable in ANN(2). Both ANN(1) and ANN(2), after being trained with the data for the period 2003-2007, have provided a good fit between estimated and observed data, with R(2) values exceeding 0.95. Subsequently, its operation has been verified making use of the data for the year 2008. The correlation coefficients obtained between the data estimated by ANNs and those observed were in all cases superior to 0.85, confirming the capacity of ANNs as a model for predicting average and maximum daily flow 1 or 2 days in advance. PMID:21088795

Araujo, P; Astray, G; Ferrerio-Lage, J A; Mejuto, J C; Rodriguez-Suarez, J A; Soto, B

2011-01-01

137

The Perceptron with Dynamic Margin

The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum margin classifier called the perceptron with dynamic margin (PDM). We demonstrate ...

Panagiotakopoulos, Constantinos; Tsampouka, Petroula

2011-01-01

138

In this paper we consider the classical spherical perceptron problem. This problem and its variants have been studied in a great detail in a broad literature ranging from statistical physics and neural networks to computer science and pure geometry. Among the most well known results are those created using the machinery of statistical physics in \\cite{Gar88}. They typically relate to various features ranging from the storage capacity to typical overlap of the optimal configu...

Stojnic, Mihailo

2013-01-01

139

The Perceptron with Dynamic Margin

The classical perceptron rule provides a varying upper bound on the maximum margin, namely the length of the current weight vector divided by the total number of updates up to that time. Requiring that the perceptron updates its internal state whenever the normalized margin of a pattern is found not to exceed a certain fraction of this dynamic upper bound we construct a new approximate maximum margin classifier called the perceptron with dynamic margin (PDM). We demonstrate that PDM converges in a finite number of steps and derive an upper bound on them. We also compare experimentally PDM with other perceptron-like algorithms and support vector machines on hard margin tasks involving linear kernels which are equivalent to 2-norm soft margin.

Panagiotakopoulos, Constantinos

2011-01-01

140

Finite Size Scaling of Perceptron

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.

Korutcheva, Elka; Tonchev, N.

2000-01-01

141

An artificial neural network can be used to generate a series of numbers. A boolean perceptron generates bit sequences with a periodic structure. The corresponding spectrum of cycle lengths is investigated analytically and numerically; it has similarities with properties of rational numbers.

Schroeder, M.; Kinzel, W.

1997-01-01

142

Core reactivity estimation in space reactors using recurrent dynamic networks

A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.

Parlos, Alexander G.; Tsai, Wei K.

1991-01-01

143

Polyhedrons and Perceptrons Are Functionally Equivalent

Mathematical definitions of polyhedrons and perceptron networks are discussed. The formalization of polyhedrons is done in a rather traditional way. For networks, previously proposed systems are developed. Perceptron networks in disjunctive normal form (DNF) and conjunctive normal forms (CNF) are introduced. The main theme is that single output perceptron neural networks and characteristic functions of polyhedrons are one and the same class of functions. A rigorous formulati...

Crespin, Daniel

2013-01-01

144

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

145

The Margitron: A Generalised Perceptron with Margin

We identify the classical Perceptron algorithm with margin as a member of a broader family of large margin classifiers which we collectively call the Margitron. The Margitron, (despite its) sharing the same update rule with the Perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. Experiments comparing the Margitron with decomposition SVMs on tasks involving linear kernel...

Panagiotakopoulos, Constantinos; Tsampouka, Petroula

2008-01-01

146

Robust chaos generation by a perceptron

The properties of time series generated by a perceptron with monotonic and non-monotonic transfer function, where the next input vector is determined from past output values, are examined. Analysis of the parameter space reveals the following main finding: a perceptron with a monotonic function can produce fragile chaos only whereas a non-monotonic function can generate robust chaos as well. For non-monotonic functions, the dimension of the attractor can be controlled monoto...

Priel, A.; Kanter, I.

2000-01-01

147

The Margitron: A Generalised Perceptron with Margin

We identify the classical Perceptron algorithm with margin as a member of a broader family of large margin classifiers which we collectively call the Margitron. The Margitron, (despite its) sharing the same update rule with the Perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. Experiments comparing the Margitron with decomposition SVMs on tasks involving linear kernels and 2-norm soft margin are also reported.

Panagiotakopoulos, Constantinos

2008-01-01

148

Hybrid Evolutionary Algorithm for Multilayer Perceptron Networks with Competetive Performance.

Czech Academy of Sciences Publication Activity Database

Los Alamitos : IEEE, 2007, s. 1620-1627. ISBN 978-1-4244-1339-3. [CEC 2007. Congress on Evolutionary Computation . Singapore (SG), 25.09.2007-28.09.2007] R&D Projects: GA AV ?R 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : hybrid algorithms * evolutionary learning * neural networks Subject RIV: IN - Informatics, Computer Science

Neruda, Roman

149

Multilayer Perceptron with Functional Inputs: an Inverse Regression Approach

Abstract. Functional data analysis is a growing research field as more and more practical applications involve functional data. In this paper, we focus on the problem of regression and classification with functional predictors: the model suggested combines an efficient dimension reduction procedure [functional sliced inverse regression, first introduced by Ferré & Yao (Statistics, 37, 2003, 475)], for which we give a regularized version, with the accuracy of a neural network. Some consistenc...

Ferre?, Louis; Villa, Nathalie

2007-01-01

150

Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings

The work proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The diagnosis uses dissolved gas analysis (DGA) data from bushings based on IEC60599 and IEEE C57-104 criteria for oil impregnated paper (OIP) bushings. FST and neural networks are compared in terms of accuracy and computational efficiency. Both FST and NN simulations were able to diagnose the bushings condition with 10% error. By using fuzzy theory, the maintenance department can classify bushings and know the extent of degradation in the component.

Dhlamini, Sizwe M; Majozi, Thokozani

2007-01-01

151

Fuzzy and Multilayer Perceptron for Evaluation of HV Bushings

The work proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The diagnosis uses dissolved gas analysis (DGA) data from bushings based on IEC60599 and IEEE C57-104 criteria for oil impregnated paper (OIP) bushings. FST and neural networks are compared in terms of accuracy and computational efficiency. Both FST and NN simulations were able to diagnose the bushings condition with 10% error. By using fuzzy theory, the maintenanc...

Dhlamini, Sizwe M.; Marwala, Tshilidzi; Majozi, Thokozani

2007-01-01

152

Classification of fused face images using multilayer perceptron neural network

This paper presents a concept of image pixel fusion of visual and thermal faces, which can significantly improve the overall performance of a face recognition system. Several factors affect face recognition performance including pose variations, facial expression changes, occlusions, and most importantly illumination changes. So, image pixel fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. F...

Bhattacharjee, Debotosh; Bhowmik, Mrinal Kanti; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

2010-01-01

153

Classification of fuels using multilayer perceptron neural networks

International Nuclear Information System (INIS)

Electrical impedance data obtained with an array of conducting polymer chemical sensors was used by a neural network (ANN) to classify fuel adulteration. Real samples were classified with accuracy greater than 90% in two groups: approved and adulterated.

154

FPGA Implementation of Multilayer Perceptron for Modeling of Photovoltaic panel

The Number of electronic applications using artificial neural network-based solutions has increased considerably in the last few years. However, their applications in photovoltaic systems are very limited. This paper introduces the preliminary result of the modeling and simulation of photovoltaic panel based on neural network and VHDL-language. In fact, an experimental database of meteorological data (irradiation, temperature) and output electrical generation signals of the PV-panel (current and voltage) has been used in this study. The inputs of the ANN-PV-panel are the daily total irradiation and mean average temperature while the outputs are the current and voltage generated from the panel. Firstly, a dataset of 4x364 have been used for training the network. Subsequently, the neural network (MLP) corresponding to PV-panel is simulated using VHDL language based on the saved weights and bias of the network. Simulation results of the trained MLP-PV panel based on Matlab and VHDL are presented. The proposed PV-panel model based ANN and VHDL permit to evaluate the performance PV-panel using only the environmental factors and involves less computational efforts, and it can be used for predicting the output electrical energy from the PV-panel.

Mekki, H.; Mellit, A.; Salhi, H.; Belhout, K.

2008-06-01

155

FPGA Implementation of Multilayer Perceptron for Modeling of Photovoltaic panel

International Nuclear Information System (INIS)

The Number of electronic applications using artificial neural network-based solutions has increased considerably in the last few years. However, their applications in photovoltaic systems are very limited. This paper introduces the preliminary result of the modeling and simulation of photovoltaic panel based on neural network and VHDL-language. In fact, an experimental database of meteorological data (irradiation, temperature) and output electrical generation signals of the PV-panel (current and voltage) has been used in this study. The inputs of the ANN-PV-panel are the daily total irradiation and mean average temperature while the outputs are the current and voltage generated from the panel. Firstly, a dataset of 4x364 have been used for training the network. Subsequently, the neural network (MLP) corresponding to PV-panel is simulated using VHDL language based on the saved weights and bias of the network. Simulation results of the trained MLP-PV panel based on Matlab and VHDL are presented. The proposed PV-panel model based ANN and VHDL permit to evaluate the performance PV-panel using only the environmental factors and involves less computational efforts, and it can be used for predicting the output electrical energy from the PV-panel

156

Classification of Parking Spots Using Multilayer Perceptron Networks

Directory of Open Access Journals (Sweden)

Full Text Available This project intends to develop a prototype for the identification of free spots in open air parking area where there is a good aerial view without obstacles, allowing for the identification of occupied and free spots. We used image processing techniques and pattern recognition using Artificial Neural Networks (ANN. In order to help implement the prototype, we used Matlab. In order to simulate the parking area, we created a model so that we could acquire the images using a webcam, process them, train the neural network, classify the spots and finally, show the results. The results show that it is viable to apply pattern recognition through image capture to classify parking spots

FALCAO, H. S.

2013-12-01

157

Classification of Parking Spots Using Multilayer Perceptron Networks

This project intends to develop a prototype for the identification of free spots in open air parking area where there is a good aerial view without obstacles, allowing for the identification of occupied and free spots. We used image processing techniques and pattern recognition using Artificial Neural Networks (ANN). In order to help implement the prototype, we used Matlab. In order to simulate the parking area, we created a model so that we could acquire the images using a webcam, process th...

Falcao, H. S.; Lovato, A. V.; Dos, Santos A. F.; Oliveira, L. S.

2013-01-01

158

The Normalized Radial Basis Function Neural Network and its Relation to the Perceptron

The normalized radial basis function neural network emerges in the statistical modeling of natural laws that relate components of multivariate data. The modeling is based on the kernel estimator of the joint probability density function pertaining to given data. From this function a governing law is extracted by the conditional average estimator. The corresponding nonparametric regression represents a normalized radial basis function neural network and can be related with the multi-layer perceptron equation. In this article an exact equivalence of both paradigms is demonstrated for a one-dimensional case with symmetric triangular basis functions. The transformation provides for a simple interpretation of perceptron parameters in terms of statistical samples of multivariate data.

Grabec, I

2007-01-01

159

Convergence Analysis of Adaptive Recurrent Neural Network

This paper presents analysis of a modified Feed Forward Multilayer Perceptron (FMP) by inserting an ARMA (Auto Regressive Moving Average) model at each neuron (processor node) with the Backp ropagation learning algorithm. The stability analysis is presented to establish the convergence theory of the Back propagation algorithm based on the Lyapunov function. Furthermore, the analysis extends the Back propagation learning rule by introducing the adaptive learning factors. A rang...

Hong Li; Ali Setoodehnia

2014-01-01

160

Perceptron beyond the limit of capacity

An input-output map in which the patterns are divided into classes is considered for the perceptron. The statistical mechanical analysis with a finite number of classes turns out to give the same results as the case of only one class of patterns ; the limit of capacity and the relevant order parameters are calculated in a mean field approach. The analysis is then extended to the Derrida Gardner canonical ensemble in which the perceptron can be studied beyond the limit of capacity. We complete...

Del Giudice, P.; Franz, S.; Virasoro, M. A.

1989-01-01

161

The margitron: a generalized perceptron with margin.

We identify the classical perceptron algorithm with margin as a member of a broader family of large margin classifiers, which we collectively call the margitron. The margitron, (despite its) sharing the same update rule with the perceptron, is shown in an incremental setting to converge in a finite number of updates to solutions possessing any desirable fraction of the maximum margin. We also report on experiments comparing the margitron with decomposition support vector machines, cutting-plane algorithms, and gradient descent methods on hard margin tasks involving linear kernels which are equivalent to 2-norm soft margin. Our results suggest that the margitron is very competitive. PMID:21216709

Panagiotakopoulos, Constantinos; Tsampouka, Petroula

2011-03-01

162

Parallel strategy for optimal learning in perceptrons

Energy Technology Data Exchange (ETDEWEB)

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.

Neirotti, J P [NCRG, Aston University, Birmingham (United Kingdom)], E-mail: j.p.neirotti@aston.ac.uk

2010-03-26

163

Parallel strategy for optimal learning in perceptrons

International Nuclear Information System (INIS)

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.

164

Jordan recurrent neural network versus IHACRES in modelling daily streamflows

SummaryA study of possible scenarios for modelling streamflow data from daily time series, using artificial neural networks (ANNs), is presented. Particular emphasis is devoted to the reconstruction of drought periods where water resource management and control are most critical. This paper considers two connectionist models: a feedforward multilayer perceptron (MLP) and a Jordan recurrent neural network (JNN), comparing network performance on real world data from two small catchments (192 and 69 km 2 in size) with irregular and torrential regimes. Several network configurations are tested to ensure a good combination of input features (rainfall and previous streamflow data) that capture the variability of the physical processes at work. Tapped delayed line (TDL) and memory effect techniques are introduced to recognize and reproduce temporal dependence. Results show a poor agreement when using TDL only, but a remarkable improvement can be obtained with JNN and its memory effect procedures, which are able to reproduce the system memory over a catchment in a more effective way. Furthermore, the IHACRES conceptual model, which relies on both rainfall and temperature input data, is introduced for comparative study. The results suggest that when good input data is unavailable, metric models perform better than conceptual ones and, in general, it is difficult to justify substantial conceptualization of complex processes.

Carcano, Elena Carla; Bartolini, Paolo; Muselli, Marco; Piroddi, Luigi

2008-12-01

165

A diluted version of the perceptron model

This note is concerned with a diluted version of the perceptron model. We establish a replica symmetric formula at high temperature, which is achieved by studying the asymptotic behavior of a given spin magnetization. Our main task will be to identify the order parameter of the system.

Marquez-carreras, David; Rovira, Carles; Tindel, Samy

2006-01-01

166

Multilayers were prepared by vacuum deposition and characterized. A Pd/Co multilayer was examined using X-ray diffraction, computer simulations, spectroscopy and transmission electron microscopy. Magnetic measurements are presented. It is shown that the magnetic properties of multilayer thin films, especially magnetic anisotropy, can be understood and modified by the influence of the interfaces between the different materials.

Draaisma, Henricus Johannes George

167

On-line learning through simple perceptron with a margin

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 compu...

Hara, Kazuyuki; Okada, Masato

2003-01-01

168

Storage capacity of a Potts-perceptron

We consider the properties of “Potts” neural networks where each neuron can be in Q different states. For a “Potts-perceptron” with N Q-states input neurons and one Q' states output neutron, we compute the maximal storage capacity for unbiased patterns. In the large N limit the maximal number of patterns that can be stored is found to be proportional to N(Q-1)f(Q'), where f(Q') is of order 1.

Nadal, Jean-pierre; Rau, Albrecht

1991-01-01

169

Multifractal analysis of perceptron learning with errors

Random input patterns induce a partition of the coupling space of a perceptron into cells labeled by their output sequences. Learning some data with a maximal error rate leads to clusters of neighboring cells. By analyzing the internal structure of these clusters with the formalism of multifractals, we can handle different storage and generalization tasks for lazy students and absent-minded teachers within one unified approach. The results also allow some conclusions on the ...

Weigt, M.

1997-01-01

170

Landscape statistics of the binary perceptron

The landscape of the binary perceptron is studied by Simulated Annealing, exhaustive search and performing random walks on the landscape. We find that the number of local minima increases exponentially with the number of bonds, becoming deeper in the vicinity of a global minimum, but more and more shallow as we move away from it. The random walker detects a simple dependence on the size of the mapping, the architecture introducing a nontrivial dependence on the number of steps.

Fontanari, J. F.; Ko?berle, R.

1990-01-01

171

Storage of correlated patterns in a perceptron

We calculate the storage capacity of a perceptron for correlated gaussian patterns. We find that the storage capacity $\\alpha_c$ can be less than 2 if similar patterns are mapped onto different outputs and vice versa. As long as the patterns are in general position we obtain, in contrast to previous works, that $\\alpha_c \\geq 1$ in agreement with Cover's theorem. Numerical simulations confirm the results.

Lopez, B.; Schroeder, M.; Opper, M.

1995-01-01

172

Handwritten Digit Recognition with Binary Optical Perceptron

Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can benefit from the many orders of magnitudes improved liquid crystal response times. An optimized learning algorithm for all-positive perceptrons is simulated on a limited data set of hand-written digits and the resultant network implemented optically. First, gray-scale...

Saxena, Indu; Moerland, Perry; Fiesler, Emile; Pourzand, A. R.

1997-01-01

173

Finite size scaling of the bayesian perceptron

We study numerically the properties of the bayesian perceptron through a gradient descent on the optimal cost function. The theoretical distribution of stabilities is deduced. It predicts that the optimal generalizer lies close to the boundary of the space of (error-free) solutions. The numerical simulations are in good agreement with the theoretical distribution. The extrapolation of the generalization error to infinite input space size agrees with the theoretical results. ...

Buhot, A.; Moreno, J. -m Torres; Gordon, M. B.

1997-01-01

174

Simulating a perceptron on a quantum computer

Perceptrons are the basic computational unit of artificial neural networks, as they model the activation mechanism of an output neuron due to incoming signals from its neighbours. As linear classifiers, they play an important role in the foundations of machine learning. In the context of the emerging field of quantum machine learning, several attempts have been made to develop a corresponding unit using quantum information theory. Based on the quantum phase estimation algori...

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

2014-01-01

175

Pattern Capacity of a Perceptron for Sparse Discrimination

We evaluate the capacity and performance of a perceptron discriminator operating in a highly sparse regime where classic perceptron results do not apply. The perceptron is constructed to respond to a specified set of q stimuli, with only statistical information provided about other stimuli to which it is not supposed to respond. We compute the probability of both false-positive and false-negative errors and determine the capacity of the system for not responding to nonselected stimuli and for responding to selected stimuli in the presence of noise. If q is a sublinear function of N, the number of inputs to the perceptron, these capacities are exponential in N/q.

Itskov, Vladimir; Abbott, L. F.

2008-07-01

176

Optimal Capacity of the Blume-Emery-Griffiths perceptron

A Blume-Emery-Griffiths perceptron model is introduced and its optimal capacity is calculated within the replica-symmetric Gardner approach, as a function of the pattern activity and the imbedding stability parameter. The stability of the replica-symmetric approximation is studied via the analogue of the Almeida-Thouless line. A comparison is made with other three-state perceptrons.

Bolle, D.; Castillo, I. Perez; Shim, G. M.

2002-01-01

177

Finite size scaling of the Bayesian perceptron

We study numerically the properties of the Bayesian perceptron through a gradient descent on the optimal cost function. The theoretical distribution of stabilities is deduced. It predicts that the optimal generalizer lies close to the boundary of the space of (error-free) solutions. The numerical simulations are in good agreement with the theoretical distribution. The extrapolation of the generalization error to infinite input space size agrees with the theoretical results. Finite size corrections are negative and exhibit two different scaling regimes, depending on the training set size. The variance of the generalization error vanishes for N-->? confirming the property of self-averaging.

Buhot, Arnaud; Torres Moreno, Juan-Manuel; Gordon, Mirta B.

1997-06-01

178

Finite size scaling of the bayesian perceptron

We study numerically the properties of the bayesian perceptron through a gradient descent on the optimal cost function. The theoretical distribution of stabilities is deduced. It predicts that the optimal generalizer lies close to the boundary of the space of (error-free) solutions. The numerical simulations are in good agreement with the theoretical distribution. The extrapolation of the generalization error to infinite input space size agrees with the theoretical results. Finite size corrections are negative and exhibit two different scaling regimes, depending on the training set size. The variance of the generalization error vanishes for $N \\rightarrow \\infty$ confirming the property of self-averaging.

Buhot, A; Gordon, M B

1997-01-01

179

Stochastic resonance in an intracellular genetic perceptron

Intracellular genetic networks are more intelligent than was first assumed due to their ability to learn. One of the manifestations of this intelligence is the ability to learn associations of two stimuli within gene-regulating circuitry: Hebbian-type learning within the cellular life. However, gene expression is an intrinsically noisy process; hence, we investigate the effect of intrinsic and extrinsic noise on this kind of intracellular intelligence. We report a stochastic resonance in an intracellular associative genetic perceptron, a noise-induced phenomenon, which manifests itself in noise-induced increase of response in efficiency after the learning event under the conditions of optimal stochasticity.

Bates, Russell; Blyuss, Oleg; Zaikin, Alexey

2014-03-01

180

A novel algorithm for tunable compression to within the precision of reproduction targets, or storage, is proposed. The new algorithm is termed the `Perceptron Algorithm', which utilises simple existing concepts in a novel way, has multiple immediate commercial application aspects as well as it opens up a multitude of fronts in computational science and technology. The aims of this paper are to present the concepts underlying the algorithm, observations by its application to some example cases, and the identification of a multitude of potential areas of applications such as: image compression by orders of magnitude, signal compression including sound as well, image analysis in a multilayered detailed analysis, pattern recognition and matching and rapid database searching (e.g. face recognition), motion analysis, biomedical applications e.g. in MRI and CAT scan image analysis and compression, as well as hints on the link of these ideas to the way how biological memory might work leading to new points of view i...

Vassiliadis, V S

2006-01-01

181

Learning from correlated patterns by simple perceptrons

Energy Technology Data Exchange (ETDEWEB)

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.

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

182

Most real and engineered systems include multiple subsystems and layers of connectivity, and it is important to take such features into account to try to obtain a complete understanding of these systems. It is thus necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts occurred several decades ago, but now the study of multilayer networks has become one of the major directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and then review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multila...

Kivelä, Mikko; Barthelemy, Marc; Gleeson, James P; Moreno, Yamir; Porter, Mason A

2013-01-01

183

A Simple Perceptron that Learns Non-Monotonic Rules

We investigate the generalization ability of a simple perceptron trained in the off-line and on-line supervised modes. Examples are extracted from the teacher who is a non-monotonic perceptron. For this system, difficulties of training can be controlled continuously by changing a parameter of the teacher. We train the student by several learning strategies in order to obtain the theoretical lower bounds of generalization errors under various conditions. Asymptotic behavior o...

Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki

1997-01-01

184

Ensemble learning of linear perceptron; Online learning theory

Within the framework of on-line learning, we study the generalization error of an ensemble learning machine learning from a linear teacher perceptron. The generalization error achieved by an ensemble of linear perceptrons having homogeneous or inhomogeneous initial weight vectors is precisely calculated at the thermodynamic limit of a large number of input elements and shows rich behavior. Our main findings are as follows. For learning with homogeneous initial weight vectors...

Hara, Kazuyuki; Okada, Masato

2004-01-01

185

A perceptron network theorem prover for the propositional calculus

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 perceptrons. The parallel tableau method is designed to enable determination of the counter-examples of a formula (if any) concurrently. It is proven that the parallel method is complete, and t...

Drossaers, M. F. J.

1989-01-01

186

An Efficient Rescaled Perceptron Algorithm for Conic Systems

The classical perceptron algorithm is an elementary row-action/relaxation algorithm for solving a homogeneous linear inequality system Ax > 0. A natural condition measure associated with this algorithm is the Euclidean width {tau} of the cone of feasible solutions, and the iteration complexity of the perceptron algorithm is bounded by 1/{tau}2 [see Rosenblatt, F. 1962. Principles of Neurodynamics. Spartan Books, Washington, DC]. Dunagan and Vempala [Dunagan, J., S. Vempala. 2007. A simple pol...

Vempala, Santosh; Belloni, Alexandre; Freund, Robert Michael

2009-01-01

187

Entropy landscape of solutions in the binary perceptron problem

The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the solution space. The geometrical organization is elucidated by the entropy landscape from a referen...

Huang, Haiping; Wong, K. Y. Michael; Kabashima, Yoshiyuki

2013-01-01

188

Stability of the replica symmetric solution in diluted perceptron learning

We study the role played by the dilution in the average behavior of a perceptron model with continuous coupling with the replica method. We analyze the stability of the replica symmetric solution as a function of the dilution field for the generalization and memorization problems. Thanks to a Gardner like stability analysis we show that at any fixed ratio $\\alpha$ between the number of patterns M and the dimension N of the perceptron ($\\alpha=M/N$), there exists a critical d...

Lage-castellanos, Alejandro; Pagnani, Andrea; Angulo, Gretel Quintero

2012-01-01

189

Generalization learning in a perceptron with binary synapses

We consider the generalization problem for a perceptron with binary synapses, implementing the Stochastic Belief-Propagation-Inspired (SBPI) learning algorithm which we proposed earlier, and perform a mean-field calculation to obtain a differential equation which describes the behaviour of the device in the limit of a large number of synapses N. We show that the solving time of SBPI is of order N*sqrt(log(N)), while the similar, well-known clipped perceptron (CP) algorithm d...

Baldassi, Carlo

2012-01-01

190

The Projectron: a Bounded Kernel-Based Perceptron

We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memory of the kernel-based Perceptron for storing the online hypothesis is not bounded. Previous work has been focused on discarding part of the instances in order to keep the memory bounded. In the proposed algorithm the instances are not discarded, but projected onto the space spanned by the previous online hypothesis. We derive a relative mistake...

Orabona, Francesco; Keshet, Joseph; Caputo, Barbara

2008-01-01

191

Training a perceptron in a discrete weight space

On-line and batch learning of a perceptron in a discrete weight space, where each weight can take $2 L+1$ different values, are examined analytically and numerically. The learning algorithm is based on the training of the continuous perceptron and prediction following the clipped weights. The learning is described by a new set of order parameters, composed of the overlaps between the teacher and the continuous/clipped students. Different scenarios are examined among them on-...

Rosen-zvi, Michal; Kanter, Ido

2001-01-01

192

Spherical perceptron as a storage memory with limited errors

It has been known for a long time that the classical spherical perceptrons can be used as storage memories. Seminal work of Gardner, \\cite{Gar88}, started an analytical study of perceptrons storage abilities. Many of the Gardner's predictions obtained through statistical mechanics tools have been rigorously justified. Among the most important ones are of course the storage capacities. The first rigorous confirmations were obtained in \\cite{SchTir02,SchTir03} for the storage ...

Stojnic, Mihailo

2013-01-01

193

Stability of the replica symmetric solution in diluted perceptron learning

International Nuclear Information System (INIS)

We study the role played by dilution in the average behavior of a perceptron model with continuous coupling with the replica method. We analyze the stability of the replica symmetric solution as a function of the dilution field for the generalization and memorization problems. Thanks to a Gardner-like stability analysis we show that at any fixed ratio ? between the number of patterns M and the dimension N of the perceptron (? = M/N), there exists a critical dilution field hc above which the replica symmetric ansatz becomes unstable. (letter)

194

Scientific Electronic Library Online (English)

Full Text Available Estimativa do perfil de concentração de clorofila, em águas naturais, a partir da radiação emergente na superfície de um corpo d'agua, com o uso de rede neural artificial do tipo Perceptron de Múltiplas Camadas. A concentração de clorofila está relacionada com os coeficientes de absorção e espalhame [...] nto via modelos bio-ópticos. O treinamento da rede é formulado como um problema de otimização, no qual a atualização das variáveis livres da rede (pesos, viés e parâmetros de cada função de ativação) é feita através do método quasi-Newton. Abstract in english In this work the average profile of chlorophyll concentration is estimated from the emitted radiation at the surface of natural waters. This is performed through the use an Artificial Neural Network of Multilayer Perceptron type to act as the inverse operator. Bio-optical models are used to correlat [...] e the chlorophyll concentration with the absorption and scattering coefficients. The network training is formulated as an optimization problem, in which the update of the free variables of network (weights, viéses and each slope of the activation functions) is performed through the quasi-Newton method.

F., Dall Cortivo; E. S., Chalhoub; H. F., Campos Velho.

2012-12-01

195

Recurrent intussusception is defined as an occurrence of intussusception of a bowel loop in a patient with a prior resolution of intussusception, either spontaneously or with an intervention. It is not rare to develop a subsequent episode after a successful reduction of intussusception. We report the cases of 2 children who presented to the emergency department with recurrent intussusception and review the pertinent literature. PMID:23736072

Haber, Jordana J; Waseem, Muhammad

2013-06-01

196

Breaking a chaotic image encryption algorithm based on perceptron model

Recently, a chaotic image encryption algorithm based on perceptron model was proposed. The present paper analyzes security of the algorithm and finds that the equivalent secret key can be reconstructed with only one pair of known-plaintext/ciphertext, which is supported by both mathematical proof and experiment results. In addition, some other security defects are also reported.

Zhang, Yu; Li, Chengqing; Li, Qin; Zhang, Dan; Shu, Shi

2011-01-01

197

Training a perceptron by a bit sequence: Storage capacity

A perceptron is trained by a random bit sequence. In comparison to the corresponding classification problem, the storage capacity decreases to alpha_c=1.70\\pm 0.02 due to correlations between input and output bits. The numerical results are supported by a signal to noise analysis of Hebbian weights.

Schroeder, M.; Kinzel, W.; Kanter, I.

1996-01-01

198

The Role of Weight Shrinking in Large Margin Perceptron Learning

We introduce into the classical perceptron algorithm with margin a mechanism that shrinks the current weight vector as a first step of the update. If the shrinking factor is constant the resulting algorithm may be regarded as a margin-error-driven version of NORMA with constant learning rate. In this case we show that the allowed strength of shrinking depends on the value of the maximum margin. We also consider variable shrinking factors for which there is no such dependence. In both cases we obtain new generalizations of the perceptron with margin able to provably attain in a finite number of steps any desirable approximation of the maximal margin hyperplane. The new approximate maximum margin classifiers appear experimentally to be very competitive in 2-norm soft margin tasks involving linear kernels.

Panagiotakopoulos, Constantinos

2012-01-01

199

The Cavity Approach to Noisy Learning in Nonlinear Perceptrons

We analyze the learning of noisy teacher-generated examples by nonlinear and differentiable student perceptrons using the cavity method. The generic activation of an example is a function of the cavity activation of the example, which is its activation in the perceptron that learns without the example. Mean field equations for the macroscopic parameters and the stability condition yield results consistent with the replica method. When a single value of the cavity activation maps to multiple values of the generic activation, there is a competition in learning strategy between preferentially learning an example and sacrificing it in favor of the background adjustment. We find parameter regimes in which examples are learned preferentially or sacrificially, leading to a gap in the activation distribution. Full phase diagrams of this complex system are presented, and the theory predicts the existence of a phase transition from poor to good generalization states in the system. Simulation results confirm the theoret...

Luo, P; Luo, Peixun

2001-01-01

200

Use of the 'Perceptron' algorithm to distinguish translational initiation sites in E. coli.

We have used a "Perceptron" algorithm to find a weighting function which distinguishes E. coli translational initiation sites from all other sites in a library of over 78,000 nucleotides of mRNA sequence. The "Perceptron" examined sequences as linear representations. The "Perceptron" is more successful at finding gene beginnings than our previous searches using "rules" (see previous paper). We note that the weighting function can find translational initiation sites within sequences that were ...

Stormo, G. D.; Schneider, T. D.; Gold, L.; Ehrenfeucht, A.

1982-01-01

201

Higher-order probabilistic perceptrons as Bayesian inference engines

International Nuclear Information System (INIS)

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

202

PENGENALAN CITRA OBJEK SEDERHANA DENGAN JARINGAN SARAF TIRUAN METODE PERCEPTRON

Konsep bangunan dan benda-benda yang ada di sekeliling didasarkan dan dipengaruhi oleh konsep objek sederhana atau sering disebut geometri ruang tiga dimensi, yaitu memiliki panjang, lebar dan tinggi. Namun, dalam rancangan dan penggambarannya menggunakan gambar berdimensi dua saja. Sehingga pada konsep penggambarannya membutuhkan visualisasi yang lebih detail. Diharapkan jaringan syaraf tiruan metode perceptron dapat mengenali gambar yang sesuai dengan bentuk aslinya. Pada penelitian ini met...

Ardi Pujiyanta

2012-01-01

203

The Role of Weight Shrinking in Large Margin Perceptron Learning

We introduce into the classical perceptron algorithm with margin a mechanism that shrinks the current weight vector as a first step of the update. If the shrinking factor is constant the resulting algorithm may be regarded as a margin-error-driven version of NORMA with constant learning rate. In this case we show that the allowed strength of shrinking depends on the value of the maximum margin. We also consider variable shrinking factors for which there is no such dependence...

Panagiotakopoulos, Constantinos; Tsampouka, Petroula

2012-01-01

204

A Coherent Perceptron for All-Optical Learning

We present nonlinear photonic circuit models for constructing programmable linear transformations and use these to realize a coherent Perceptron, i.e., an all-optical linear classifier capable of learning the classification boundary iteratively from training data through a coherent feedback rule. Through extensive semi-classical stochastic simulations we demonstrate that the device nearly attains the theoretical error bound for a model classification problem.

Tezak, Nikolas; Mabuchi, Hideo

2015-01-01

205

Perceptron capacity revisited: classification ability for correlated patterns

In this paper, we address the problem of how many randomly labeled patterns can be correctly classified by a single-layer perceptron when the patterns are correlated with each other. In order to solve this problem, two analytical schemes are developed based on the replica method and Thouless-Anderson-Palmer (TAP) approach by utilizing an integral formula concerning random rectangular matrices. The validity and relevance of the developed methodologies are shown for one known ...

Shinzato, Takashi; Kabashima, Yoshiyuki

2007-01-01

206

A polynomial training algorithm for calculating perceptrons of optimal stability

Recomi (REpeated COrrelation Matrix Inversion) is a polynomially fast algorithm for searching optimally stable solutions of the perceptron learning problem. For random unbiased and biased patterns it is shown that the algorithm is able to find optimal solutions, if any exist, in at worst O(N^4) floating point operations. Even beyond the critical storage capacity alpha_c the algorithm is able to find locally stable solutions (with negative stability) at the same speed. There ...

Imhoff, Jorg

1994-01-01

207

Asymptotic behavior of the magnetization for the perceptron model

In this paper, we show that, in case of a perceptron model for which the number of outputs is a small proportion of the size of the system, the limiting behavior of the magnetization of a given spin, namely the random variable $\\langle\\si_k\\rangle$, can be identified. In fact, we prove a $L^2$ convergence for a collection of those random variables.

Marquez-carreras, David; Rovira, Carles; Tindel, Samy

2005-01-01

208

How to Classify a Government: Can a perceptron do it?

The electoral cycle literature has developed in two clearly distinct phases. The first one considered the existence of non-rational (naive) voters whereas the second one considered fully rational voters. It is our view that an intermediate approach is more appropriate, i.e. one that considers learning voters, which are boundedly rational. In this sense, one may consider perceptrons as learning mechanisms used by voters to perform a classification of th...

Caleiro, Anto?nio

2013-01-01

209

Learning Kernel Perceptrons on Noisy Data and Random Projections

In this paper, we address the issue of learning nonlinearly separable concepts with a kernel classifier in the situation where the data at hand are altered by a uniform classification noise. Our proposed approach relies on the combination of the technique of random or deterministic projections with a classification noise tolerant perceptron learning algorithm that assumes distributions defined over finite-dimensional spaces. Provided a sufficient separation margin characterizes the problem, t...

Stempfel, Guillaume; Ralaivola, Liva

2007-01-01

210

The reactor safety study with help of artificial neuron networks (multilayer perceptrons)

International Nuclear Information System (INIS)

One deals with deposition of insulation large amounts on settling tank components that may result in malfunction of residual heat removal systems. Paper describes briefly simulation of pressure drops in confinement systems by means of an artificial neuron nets and compares the simulation data with the experiment ones

211

This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffn...

Sanz, Javier; Perera Velamaza?n, Ricardo; Huerta Gomez Merodio, M. Consuelo

2012-01-01

212

Application of artificial neural networks (multilayer perceptron) in reactor safety research

International Nuclear Information System (INIS)

One of the key areas of reactor safety research are studies of reliable and safe heat removal from the reactor core and the containment, respectively, of light water reactors. Leakage accidents could carry insulating material into the containment or the building sump of the containment and the associated post-decay heat removal systems. This could obstruct systems functions. In the study titled ''Knowledge-based Modeling of Transport Processes in BWR Coolant Flows Carrying Particle Loads after Loss-of-Coolant Accidents,'' a tool is being created for engineering application which allows the deposition and retention of insulating material in the sump of the reactor containment to be estimated. Potential plant conditions in accidents can be assessed in this way. The study serves the purpose of modeling by means of data-based and knowledge-based methods. In this way, the results of experimental investigations (such as differential pressure tests of retention systems) can be used for modeling purposes. (orig.)

213

Problem statement: The aim of the present study is to exemplify the use of Artificial Neural Networks (ANN) for parameter prediction. Missing value or unreal approach to some questions in scale is a problem for unbiased findings. To learn a real pattern with ANN provides robust and unbiased parameter estimation. Approach: To this end, data was collected from 906 students using ?Scale of student views about the expected situations and the current expectations from their families during learnin...

Murat Kayri; Omay Cokluk

2010-01-01

214

Face Recognition through Multilayer Perceptron (MLP) and Learning Vector Quantization (LVQ)

Face recognition is challenging problems and there is still a lot of work that needs to be done in this area. Over the past ten years, face recognition has received substantial attention from researchers in biometrics, pattern recognition, computer vision, and cognitive psychology communities. This common interest in facial recognition technology among researchers working in diverse fields is motivated both by the remarkable ability to recognize people and by the increased attention being dev...

Dr. Ikvinderpal Singh

2012-01-01

215

Multilayer perceptron for simulation models reduction: application to a sawmill workshop

Simulation is often used to evaluate supply chain or workshop management. This simulation task needs models, which are difficult to construct. The aim of this work is to reduce the complexity of a simulation model design. The proposed approach combines discrete and continuous approaches in order to construct speeder and simpler reduced model. The simulation model focuses on bottlenecks with a discrete approach according to the theory of constraints. The remaining of the workshop must be taken...

Thomas, Philippe; Thomas, Andre?

2011-01-01

216

Static Digits Recognition Using Rotational Signatures and Hu Moments with a Multilayer Perceptron

This paper presents two systems for recognizing static signs (digits) from American Sign Language (ASL). These systems avoid the use color marks, or gloves, using instead, low-pass and high-pass filters in space and frequency domains, and color space transformations. First system used rotational signatures based on a correlation operator; minimum distance was used for the classification task. Second system computed the seven Hu invariants from binary images; these descriptors fed to a Multi-L...

Francisco Solís; Margarita Hernández; Amelia Pérez; Carina Toxqui

2014-01-01

217

Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition

This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified ...

Bhowmik, Mrinal Kanti; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

2010-01-01

218

Hybrid Optimized Back propagation Learning Algorithm For Multi-layer Perceptron

Standard neural network based on general back propagation learning using delta method or gradient descent method has some great faults like poor optimization of error-weight objective function, low learning rate, instability .This paper introduces a hybrid supervised back propagation learning algorithm which uses trust-region method of unconstrained optimization of the error objective function by using quasi-newton method .This optimization leads to more accurate weight upda...

Chakraborty, Mriganka; Ghosh, Arka

2012-01-01

219

Electron/pion identification in the CBM TRD using a multilayer perceptron

International Nuclear Information System (INIS)

The problem of electron/pion identification in the CBM experiment based on the measurements of energy losses and transition radiation in the TRD detector is discussed. A possibility to solve such a problem by applying an artificial neural network (ANN) is considered. As input information for the network we used both the samples of energy losses of pions or electrons in the TRD absorbers and the 'clever' variable obtained on the basis of the original data. We show that usage of this new variable permits one to reach a reliable level of particle recognition no longer than after 10-20 training epochs; there are practically no fluctuations against the trend, and the needed level of pions suppression is obtained under the condition of a minimal loss of electrons

220

Artificial neural networks (ANNs) are comparatively straightforward to understand and use in the analysis of scientific data. However, this relative transparency may encourage their use in an uncritical, and therefore possibly unproductive, fashion. The geometry of a network is among the most crucial factors in the successful deployment of network tools; in this review, we cover methods that can be used to determine optimum or near-optimum geometries. These methods of determining neural netwo...

Curteanu, S.; Cartwright, H.

2011-01-01

221

Exploring Deep and Recurrent Architectures for Optimal Control

Sophisticated multilayer neural networks have achieved state of the art results on multiple supervised tasks. However, successful applications of such multilayer networks to control have so far been limited largely to the perception portion of the control pipeline. In this paper, we explore the application of deep and recurrent neural networks to a continuous, high-dimensional locomotion task, where the network is used to represent a control policy that maps the state of the...

Levine, Sergey

2013-01-01

222

Perceptron capacity revisited: classification ability for correlated patterns

Energy Technology Data Exchange (ETDEWEB)

In this paper, we address the problem of how many randomly labeled patterns can be correctly classified by a single-layer perceptron when the patterns are correlated with each other. In order to solve this problem, two analytical schemes are developed based on the replica method and the Thouless-Anderson-Palmer (TAP) approach by utilizing an integral formula concerning random rectangular matrices. The validity and relevance of the developed methodologies are shown for one known result and two example problems. A message-passing algorithm to perform the TAP scheme is also presented.

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

2008-08-15

223

Light scattering by a multilayer sphere

The recurrence algorithm for calculating electromagnetic scattering from a multilayer sphere, which was described recently by Wu and Wang [Radio Sci. 26, 1393, (1991)], is derived in a slightly modified form and extended to include a calculation of the internal field and the absorption cross sections of the individual layers. The original algorithm calculates the scattering by a recurrence procedure that propagates the log derivatives of the Debye potentials outward from the core to the outer layer. The extended algorithm then continues the calculation by an inward recurrence procedure that propagates the Debye potentials from the outer layer to the core. Concurrent with the inward propagation, a separate algorithm calculates the absorption cross sections of the imbedded concentric spheres. The results of several example calculations are presented, including the differential cross section and internal electric field of a Luneburg lens.

Johnson, B. R.

1996-06-01

224

Generalization and capacity of extensively large two-layered perceptrons

International Nuclear Information System (INIS)

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, ?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

225

Entropy landscape of solutions in the binary perceptron problem

The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the solution space. The geometrical organization is elucidated by the entropy landscape from a reference configuration and of solution-pairs separated by a given Hamming distance in the solution space. We evaluate the entropy at the annealed level as well as replica symmetric level and the mean field result is confirmed by the numerical simulations on single instances using the proposed message passing algorithms. From the first landscape (a random configuration as a reference), we see clearly how the solution space shrinks as more constraints are added. From the second landscape of solution-pairs, we deduce the coexistence of clustering and freezing in the solution space.

Huang, Haiping; Wong, K. Y. Michael; Kabashima, Yoshiyuki

2013-09-01

226

Entropy landscape of solutions in the binary perceptron problem

International Nuclear Information System (INIS)

The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the solution space. The geometrical organization is elucidated by the entropy landscape from a reference configuration and of solution-pairs separated by a given Hamming distance in the solution space. We evaluate the entropy at the annealed level as well as replica symmetric level and the mean field result is confirmed by the numerical simulations on single instances using the proposed message passing algorithms. From the first landscape (a random configuration as a reference), we see clearly how the solution space shrinks as more constraints are added. From the second landscape of solution-pairs, we deduce the coexistence of clustering and freezing in the solution space. (paper)

227

Recurrent Staphylococcus aureus bacteremia.

Sequential blood isolates from eight patients with 10 episodes of recurrent Staphylococcus aureus bacteremia were typed by restriction endonuclease analysis of plasmid DNA (REAP DNA fingerprinting) and immunoblotting. There were six early recurrences (within 2 months of stopping antimicrobial therapy) and four late recurrences. All early recurrences isolates were identical to initial isolates. These recurrences were defined as possible relapses. Three of four late recurrence isolates were dif...

Hartstein, A. I.; Mulligan, M. E.; Morthland, V. H.; Kwok, R. Y.

1992-01-01

228

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 ?-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 ?-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 ? 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 to achieve classification accuracies which are not too far from the best one for a given problem. The C and Matlab code of DKP are freely available. PMID:24287336

Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José

2014-02-01

229

Efficient routing on multilayered communication networks

We study the optimal routing on multilayered communication networks, which are composed of two layers of subnetworks. One is a wireless network, and the other is a wired network. We develop a simple recurrent algorithm to find an optimal routing on this kind of multilayered network, where the single-channel transmission mode and the multichannel transmission mode used on the wireless subnetwork are considered, respectively. Compared with the performance of the shortest path algorithm, our algorithm can significantly enhance the transport capacity. We show that our methods proposed in this letter could take advantage of the coupling of the two layers to the most extent, so that the wireless subnetwork could sufficiently utilize the wired subnetwork for transportation.

Zhou, Jie; Lai, Choy-Heng; 10.1209/0295-5075/102/28002

2013-01-01

230

Efficient routing on multilayered communication networks

We study the optimal routing on multilayered communication networks, which are composed of two layers of subnetworks. One is a wireless network, and the other is a wired network. We develop a simple recurrent algorithm to find an optimal routing on this kind of multilayered networks, where the single-channel transmission mode and the multichannel transmission mode used on the wireless subnetwork are considered, respectively. Compared with the performance of the shortest path algorithm, our algorithm can significantly enhance the transport capacity. We show that our methods proposed in this letter could take advantage of the coupling of the two layers to the most extent, so that the wireless subnetwork could sufficiently utilize the wired subnetwork for transportation.

Zhou, Jie; Yan, Gang; Lai, Choy-Heng

2013-04-01

231

Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron

Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the present work in respect to handwritten Arabic numerals. Arabic is spoken throughout the Arab World and the fifth most popular language in the world slightly before Portuguese and Bengali. For the present work, we have developed a feature set of 88 features is designed to represent samples of handwritten Arabic numerals for this work. It includes 72 shadow and 16 octant features. A Multi Layer Perceptron (MLP) based classifier is used here for recognition handwritten Arabic digits represented with the said feature set. On experimentation with a database of 3000 samples, the technique yields an average recognition rate of 94....

Das, Nibaran; Saha, Sudip; Haque, Syed Sahidul

2010-01-01

232

A Novel Single Neuron Perceptron with Universal Approximation and XOR Computation Properties

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 un...

Ehsan Lotfi; -r Akbarzadeh-t, M.

2014-01-01

233

On-line learning of non-monotonic rules by simple perceptron

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.

Inoue, Jun-ichi; Nishimori, Hidetoshi; Kabashima, Yoshiyuki

1997-01-01

234

Learning by random walks in the weight space of the Ising perceptron

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 $\\alpha \\appr...

Huang, Haiping; Zhou, Haijun

2010-01-01

235

Perceptron-like Algorithms and Generalization Bounds for Learning to Rank

Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learning a ranking function in an online setting. Our algorithm is an extension of the classic perceptron algorithm for the classification problem. Second, in the set...

Chaudhuri, Sougata; Tewari, Ambuj

2014-01-01

236

Multilayer dielectric diffraction gratings

The design and fabrication of dielectric grating structures with high diffraction efficiency used in reflection or transmission is described. By forming a multilayer structure of alternating index dielectric materials and placing a grating structure on top of the multilayer, a diffraction grating of adjustable efficiency, and variable optical bandwidth can be obtained. Diffraction efficiency into the first order in reflection varying between 1 and 98 percent has been achieved by controlling the design of the multilayer and the depth, shape, and material comprising the grooves of the grating structure. Methods for fabricating these gratings without the use of ion etching techniques are described. 7 figs.

Perry, M.D.; Britten, J.A.; Nguyen, H.T.; Boyd, R.; Shore, B.W.

1999-05-25

237

Full Text Available Resources Brochures & Videos Resource Links Books & Periodicals Cancer Centers print email Resources » Brochures & Videos Dealing with Recurrence We know that the topic of recurrence in cancer patients can be an overwhelming ...

238

Full Text Available Resources Brochures & Videos Resource Links Books & Periodicals Cancer Centers print email Resources » Brochures & Videos Dealing with Recurrence We know that the topic of recurrence in cancer patients can be ...

239

The work has as scope to analyze the recurrence of the neoplazic disease after operated rectal cancer, the study including a lot of 246 patients operated between 1995 and 2005. 129 abdominoperineal resections, 88 anterior resections, 29 Hartmann operations were performed. In the series herein, 61 patients presented recurrences, out of which 35 local recurrences, 19 at distance and 7 mixed. The ratio of the local recurrence was of: 13.20% for the lot of patients with abdominoperineal resection...

Ples?ca, C.; Dragomir, C.; Silvia Tighiliu

2007-01-01

240

Convergence of stochastic learning in perceptrons with binary synapses

The efficacy of a biological synapse is naturally bounded, and at some resolution, and is discrete at the latest level of single vesicles. The finite number of synaptic states dramatically reduce the storage capacity of a network when online learning is considered (i.e., the synapses are immediately modified by each pattern): the trace of old memories decays exponentially with the number of new memories (palimpsest property). Moreover, finding the discrete synaptic strengths which enable the classification of linearly separable patterns is a combinatorially hard problem known to be NP complete. In this paper we show that learning with discrete (binary) synapses is nevertheless possible with high probability if a randomly selected fraction of synapses is modified following each stimulus presentation (slow stochastic learning). As an additional constraint, the synapses are only changed if the output neuron does not give the desired response, as in the case of classical perceptron learning. We prove that for linearly separable classes of patterns the stochastic learning algorithm converges with arbitrary high probability in a finite number of presentations, provided that the number of neurons encoding the patterns is large enough. The stochastic learning algorithm is successfully applied to a standard classification problem of nonlinearly separable patterns by using multiple, stochastically independent output units, with an achieved performance which is comparable to the maximal ones reached for the task.

Senn, Walter; Fusi, Stefano

2005-06-01

241

Imaging recurrent parosteal osteosarcoma

International Nuclear Information System (INIS)

The aim of this study was to document the imaging features of recurrent parosteal osteosarcoma. The clinical and imaging records of 33 patients with a parosteal osteosarcoma referred to an orthopaedic oncology service over a 17-year period were retrospectively reviewed. The mode of identification of locally recurrent tumour was noted, together with the management and clinical outcome. Five patients developed a local recurrence of their parosteal osteosarcoma ranging from 6 months to 10 years after initial surgery. In 4 patients the recurrence was first suspected clinically due to the development of a mass. In the fifth patient recurrence was first detected on routine follow-up radiography. In 4 patients the recurrence could be identified on radiography as a mineralized mass. All the recurrences were readily identified on MR imaging, despite artefacts from prostheses. The recurrences were also evident in the 3 cases in which bone scintigraphy was performed. Local recurrence of parosteal osteosarcoma is adequately detected with a combination of clinical examination and conventional radiography. MR imaging is required to stage local recurrence or where radiography has failed to confirm clinically suspected recurrence. The routine use of MR imaging to follow-up patients is of doubtful value because of the frequently long time between initial surgery and relapse. (orig.)

242

Perceptron-like computation based on biologically-inspired neurons with heterosynaptic mechanisms

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.

Kaluza, Pablo; Urdapilleta, Eugenio

2014-10-01

243

Magnetic anisotropy in multilayers

Magnetic anisotropy between in-plane and out of plane magnetic alignments is studied in a variety of multilayer systems using Mössbauer spectrosopy to observe the (Fe) magnetic orientation. The surface anisotropy in Fe/Au (1 1 1) multilayers is measured as K s = 0.9 × 10-3 Jm-2. In Fe/Ni multilayers the dependence of magnetic orientation on external field applied normal to the layers enables volume and interface anisotropies K v = (-5 ± 1) × 104 Jm-3 and K s = (-0.6 ± 0.4)× 10-3 Jm-2 to be evaluated. In similar applied field experiments coherent rotation of the magnetic Fe and NiFe layers in Fe/Cu/NiFe/Cu multilayers was observed for intervening Cu layer thickness x = 5 Å but independent rotation for x = 50 Å. Out of plane magnetic components are observed for DyFe2, YFe2 thin films and DyFe2/YFe2 multilayers. In fields of up to 0.25 T applied inplane only the moments of the YFe2 film showed significant rotation.

Thomas, M. F.; Bland, J.; Case, G. S.; Hutchings, J. A.; Nikolov, O.

2000-07-01

244

With respect to Rosenblatt linear perceptron, two classical limitation theorems demonstrated by M. Minsky and S. Papert are discussed. These two theorems, `(Psi) One-in-a-box' and `(Psi) Parity,' ultimately concern the intrinsic limitations of parallel calculations in pattern recognition problems. We demonstrate a possible solution of these limitation problems by substituting the static definition of characteristic functions and of their domains in the `geometrical' perceptron, with their dynamic definition. This dynamic consists in the mutual redefinition of the characteristic function and of its domain depending on the matching with the input.

Perrone, Antonio L.; Basti, Gianfranco

1995-04-01

245

Control of Multilayer Networks

The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable.

Menichetti, Giulia; Bianconi, Ginestra

2015-01-01

246

Magnetic multilayer interface anisotropy

Energy Technology Data Exchange (ETDEWEB)

Ni/Mo and Ni/V multilayer magnetic anisotropy has been investigated as a function of Ni layer thickness, frequency and temperature. Variable frequency ferromagnetic resonance (FMR) measurements show, for the first time, significant frequency dependence associated with the multilayer magnetic anisotropy. The thickness dependence allows one to extract the interface contribution from the total anisotropy. Temperature dependent FMR (9 GHz) and room temperature magnetization indicate that strain between Ni and the non-magnetic layers is contributing significantly to the source of the interface anisotropy and the state of the interfacial magnetization. In order to examine the interface properties of other transition metal multilayer systems, investigations on Fe/Cu are underway and CoCr/Ag is being proposed. ESR measurements have been reported on Gd substituted YBaCuO superconductors and a novel quasi-equilibrium method has been developed to determine quickly and precisely the ransition temperature.

Pechan, M.J.

1992-01-01

247

Recurrence Tracking Microscope

In order to probe nanostructures on a surface we present a microscope based on the quantum recurrence phenomena. A cloud of atoms bounces off an atomic mirror connected to a cantilever and exhibits quantum recurrences. The times at which the recurrences occur depend on the initial height of the bouncing atoms above the atomic mirror, and vary following the structures on the surface under investigation. The microscope has inherent advantages over existing techniques of scanni...

Saif, Farhan

2006-01-01

248

Recurrent Escherichia coli bacteremia.

Escherichia coli is the most common gram-negative organism associated with bacteremia. While recurrent E. coli urinary tract infections are well-described, recurrent E. coli bacteremia appears to be uncommon, with no episodes noted in multiple series of patients with gram-negative bacteremias. We report on 5 patients with recurrent bloodstream infections identified from a series of 163 patients with E. coli bacteremia. For each patient, the isolates from each episode were analyzed by pulsed-f...

Maslow, J. N.; Mulligan, M. E.; Arbeit, R. D.

1994-01-01

249

[Locally recurrent rectal cancer].

Over the last decades the therapy of rectal carcinoma has shown continuous improvement. Due to improvements in operative techniques, such as the establishment of total mesorectal excision (TME) and the combination of surgery and (neo-) adjuvant radiochemotherapy, the incidence of locally recurrent rectal cancer could be improved from nearly 50% to less then 10%. Nevertheless recurrent rectal carcinoma remains a severe problem. Predictive factors relating to locally recurrent rectal cancer are surgical experience, localization of the tumor, circumferential resection margins, stage-oriented multimodal therapy and a suitable oncological procedure for the primary tumor. In addition the tumor-specific biology also seems to be a relevant risk factor for recurrence. Operative treatment of locally recurrent rectal cancer was seen for a long time as a palliative procedure. Newer data show that resection of locally recurrent rectal cancer can be carried out with a curative intention in experienced institutions with a long-term 5 year survival of about 30% and mortality around 5%. The composite sacropelvic resection technique is a reasonable option in the curative treatment of locally recurrent rectal cancer. For the future the focus must be on improvements in the primary therapy of rectal carcinoma to avoid local recurrence. In addition early diagnosis of local recurrence and multimodal therapies will be of decisive importance. PMID:20844852

Troja, A; Raab, H R

2010-10-01

250

The degradation mechanisms are a critical issue if multilayers are used as monochromators for white beam synchrotron applications. To quantify the radiation impact x-ray reflectivity measurements before, during, and after white beam exposure were performed. For the in-situ irradiation study a versatile vacuum chamber was developed and tested using a high power undulator source. The device is equipped with a cooling system for the multilayer samples to distinguish thermal effects from pure radiation induced ones. The x-ray reflectivity was measured at fixed angle of incidence in an energy dispersive mode and as a function of time. The energy dispersive detection allows for the simultaneous observation of the multilayer reflectivity spectrum over a wide range. The white beam study includes various long-term exposures with an incoming load up to 250 W. Ex-situ x-ray reflectivity measurements and beam imaging were carried out with monochromatic radiation at 8 keV before and after the white beam exposure. TEM analysis provides complementary information on the layer structure in the stack. Depending on the material system, the total radiation dose, and the sample environment, different degrees of modifications in the multilayer structure were observed.

Friedrich, K.; Morawe, Ch.; Peffen, J.-Ch.; Osterhoff, M.

2011-06-01

251

A quenched large deviation principle and a Parisi formula for a Perceptron version of the GREM

We introduce a perceptron version of the Generalized Random Energy Model, and prove a quenched Sanov type large deviation principle for the empirical distribution of the random energies. The dual of the rate function has a representation through a variational formula which is closely related to the Parisi variational formula for the SK-model.

Bolthausen, E

2010-01-01

252

Although recurrent rheumatic fever in adults is uncommon and the recurrence rate declines with age and with the interval from the attack, we describe a patient who has had four attacks of rheumatic fever, two in childhood and two in adult life.

Murray, N. H.; Fordham, J. N.; Davies, P. G.; Barnes, C. G.

1985-01-01

253

Recurrent gastric trichobezoar.

Trichobezoars are concretions of ingested hair that are found in the stomach. Recurrence of this condition has not been reported. We report an 18-year-old girl with recurrent trichobezoar; this emphasizes the need for counseling in these patients. PMID:12546177

Ratnagiri, Ranganath; Smile, S Robinson; Sistla, Sarath Chandra

2002-01-01

254

Antimicrobial polypeptide multilayer nanocoatings.

A multilayer coating (or film) of nanometer-thick layers can be made by sequential adsorption of oppositely charged polyelectrolytes on a solid support. The method is known as layer-by-layer assembly (LBL). No special apparatus is required for LBL and nanofilms can be prepared under mild, physiological conditions. A multilayer nanofilm in which at least one of the constituent species is a polypeptide is a polypeptide multilayer nanofilm. The present work was aimed at assessing whether polypeptide multilayer nanofilms with specific antimicrobial properties could be prepared by incorporation of a known antimicrobial agent in the film structure, in this case the edible protein hen egg white lysozyme (HEWL). The chicken enzyme is widely employed as a human food preservative. An advantage of LBL in this context is that the nanofilm is fabricated directly on the surface of interest, eliminating the need to incorporate the antimicrobial in other packaging materials. Here, nanofilms were made of poly(L-glutamic acid) (PLGA), which is highly negatively charged in the mildly acidic pH range, and HEWL, which has a high net positive charge at acidic pH. We show that PLGA/HEWL nanofilms inhibit growth of the model microbe Microccocus luteus in the surrounding liquid medium. The amount of HEWL released from PLGA/HEWL films depends on the number of HEWL layers and therefore on the total quantity of HEWL in the films. This initial study provides a sketch of the scope for further development of LBL in the area of antimicrobial polypeptide multilayer films. Potential applications of such films include strategies for food preservation and coatings for implant devices. PMID:17176751

Rudra, Jai S; Dave, Komal; Haynie, Donald T

2006-01-01

255

Magnetic multilayer interface anisotropy

Energy Technology Data Exchange (ETDEWEB)

Ni/Mo and Ni/V multilayer magnetic anisotropy has been investigated as a function of Ni layer thickness, frequency and temperature. Variable frequency ferromagnetic resonance (FMR) measurements show, for the first time, significant frequency dependence associated with the multilayer magnetic anisotropy. The thickness dependence allows one to extract the interface contribution from the total anisotropy. Temperature dependent FMR (9 GHz) and room temperature magnetization indicate that strain between Ni and the non-magnetic layers is contributing significantly to the source of the interface anisotropy and the state of the interfacial magnetization. In order to examine the interface properties of other transition metal multilayer systems, investigations on Fe/Cu are underway and CoCr/Ag is being proposed. ESR measurements have been reported on Gd substituted YBaCuO superconductors and a novel quasi-equilibrium method has been developed to determine quickly and precisely the transition temperature. During the next project period the P.I. proposes to (1) extend the variable frequency FMR measurements to low temperature, where extremely large interface anisotropies are known to obtain in Ni/Mo and Ni/V and are proposed to exist in Ni/W; (2) obtain accurate dc anisotropies via a novel, variable temperature torque magnetometer currently under construction; (3) expand upon his initial findings in Fe/Cu multilayer investigations; (4) begin anisotropy investigations on Co/Ag and CoCr/Ag multilayers where the easy magnetization direction depends upon the Cr concentration; (4) make and characterize Bi based superconductors according to resistivity, thermal conductivity and thermoelectric power and construct YBaCuO based superconducting loop-gap'' resonators for use in his magnetic resonance work.

Pechan, M.J.

1990-01-01

256

Magnetic multilayer interface anisotropy

Energy Technology Data Exchange (ETDEWEB)

Ni/Mo and Ni/V multilayer magnetic anisotropy has been investigated as a function of Ni layer thickness, frequency and temperature. Variable frequency ferromagnetic resonance (FMR) measurements show, for the first time, significant frequency dependence associated with the multilayer magnetic anisotropy. The thickness dependence allows one to extract the interface contribution from the total anisotropy. Temperature dependant FMR (9 GHz) and room temperature magnetization indicate that strain between Ni and the non-magnetic layers if contributing significantly to the source of the interface anisotropy and the state of the interfacial magnetization. In order to examine the interface properties of other transition metal multilayer systems, investigations on Fe/Cu are underway and CoCr/Ag is being proposed. ESR measurements have been reported on Gd substituted YBaCuO superconductors and a novel quasi-equilibrium method has been developed to determine quickly and precisely the transition temperature. During the next project the P.I. proposes to (1) extend the variable frequency FMR measurements to low temperature, where extremely large interface anisotropies are known to obtain in Ni/Mo and Ni/V and are proposed to exist in Ni/W; (2) obtain accurate dc anisotropies via a novel, variable temperature torque magnetometer currently under construction; (3) expand upon his initial findings in Fe/Cu multilayer investigations; (4) begin anisotropy investigations on Co/Ag and CoCr/Ag multilayers where the easy magnetization direction depends upon the Cr concentration; (4) make and characterize Bi based superconductors according to resistivity, thermal conductivity and thermoelectric power and construct YBaCuO based superconducting loop-gap'' resonators for use in his magnetic resonance work. 2 figs.

Pechan, M.J.

1991-01-01

257

Simulation is a useful tool for the evaluation of a Master Production/Distribution Schedule (MPS). The goal of this paper is to propose a new approach to designing a simulation model by reducing its complexity. According to the theory of constraints, a reduced model is built using bottlenecks and a neural network exclusively. This paper focuses on one step of the network model design: determining the structure of the network. This task may be performed by using the constructive or pruning app...

Philippe Thomas; Marie-Christine Suhner; André Thomas

2013-01-01

258

Directory of Open Access Journals (Sweden)

Full Text Available The work has as scope to analyze the recurrence of the neoplazic disease after operated rectal cancer, the study including a lot of 246 patients operated between 1995 and 2005. 129 abdominoperineal resections, 88 anterior resections, 29 Hartmann operations were performed. In the series herein, 61 patients presented recurrences, out of which 35 local recurrences, 19 at distance and 7 mixed. The ratio of the local recurrence was of: 13.20% for the lot of patients with abdominoperineal resection, 14.80% for the ones with anterior rectal resections and 17.25% at the patients with Hartmann operations. The evolution of the recurrences after years does not emphasize a statistic discrimination by this criterion (Cramer indicator, V=0.643, p-value=0.323 in the case of abdominoperineal resections, Cramer indicator, V=0.573, p-value=0.381 for anterior resections and Cramer indicator, V=0.837, p-value=0.342 for Hartmann operations. It is to be noticed that if one is to monitor the patients with rectal resections in the period between 2000-2005, one can observe a much lower local recurrence ratio – 4.84% (only 3 local recurrences out of 62 cases. The ratio decrease can be explained by the fact that total mesorectal excision was constantly performed. The usage of the preoperative radiotherapy determines major decrease of the recurrence ratio (10.80%, comparable against adjuvant treatment. As a conclusion, the importance of complex, sequencial treatment is emphasized (preoperative radiotherapy – rectal resection through abdomen-perineum/abdomen with total mesorectal excision for decreasing the recurrence of the disease both local and at distance.

C. Ple?ca

2007-07-01

259

International Nuclear Information System (INIS)

To investigate whether multilayer relaxation could be demonstrated to exist in a relatively simple metallic surface, existing LEED data for Cu(100) have been reanalyzed. The results from the new analysis clearly provide evidence for multilayer relaxation in Cu(100), and the first and second interlayer spacing are indicated to deviate (-1.10 +- 0.40)% and (+1.70 +- 0.60)%, respectively, from the bulk value. Evidence is also found to suggest that the third interlayer spacing is expanded by more than 1%. The quoted values for the deviations of the first and second interlayer spacings are mean values which resulted from analyses of data for each of 16 individual I-V profiles, while the error limits were obtained for a 95% confidence level using a student-t statistics test. The above values also were found to be consistent with results obtained when different multibeam ''R-factors'' were used to analyze averaged experimental I-V profiles

260

International Nuclear Information System (INIS)

A set of projection images is acquired during longitudinal tomography with an image intensifier. This paper describes translation and addition of the digitized images with the hardware and software of a conventional digital subtraction angiography unit which allow reconstruction of tomograms in any parallel plane. Digital multilayer tomography and conventional film tomography were compared in 60 patients. The general advantage of digital tomography is discussed

261

Nanostructured reactive metallic multilayers

Energy Technology Data Exchange (ETDEWEB)

Metallic multilayers have been used in cross-sectional TEM specimen geometry to explore nanostructuring routes using ion and electron beams (top-down) as well as self-organised nanopatterning (bottom-up). Intermetallic alloy formation during heat treatment is studied for binary and ternary layer compositions. A choice of pillars (0D), 1D and 2D layer geometries are used to explore dimensionality effects.

Yajid, M A Mat; Moebus, G, E-mail: g.moebus@sheffield.ac.u [Department of Engineering Materials, University of Sheffield, Sheffield S1 3JD (United Kingdom)

2010-07-01

262

Multilayer graphene waveguides

We study dispersion properties of TM-polarized electromagnetic waves guided by a multilayer graphene metamaterial. We demonstrate that both dispersion and localization of the guided modes can be efficiently controlled by changing the number of layers in the structure. Remarkably, we find that in the long wavelength limit, the dispersion of the fundamental mode of the N-layer graphene structure coincides with the dispersion of a plasmon mode supported by a single graphene lay...

Smirnova, Daria; Iorsh, Ivan; Shadrivov, Ilya; Kivshar, Yuri

2014-01-01

263

Multilayer Gradient Coil Design

In standard cylindrical gradient coils consisting of wires wound in a single layer, the rapid increase in coil resistance with efficiency is the limiting factor in achieving very large magnetic field gradients. This behavior results from the decrease in the maximum usable wire diameter as the number of turns is increased. By adopting a multilayer design in which the coil wires are allowed to spread out into multiple layers wound at increasing radii, a more favorable scaling of resistance with efficiency is achieved, thus allowing the design of more powerful gradient coils with acceptable resistance values. By extending the theory used to design standard cylindrical gradient coils, we have developed mathematical expressions which allow the design of multilayer coils, and the evaluation of their performance. These expressions have been used to design a four-layer, z-gradient coil of 8 mm inner diameter, which has an efficiency of 1.73 Tm-1 A-1, a resistance of 1.8 Omega, and an inductance of 50 µH. This coil produces a gradient which deviates from linearity by less than 5% within a central cylindrical region of 4.5 mm length and 4.5 mm diameter. A coil has been constructed from this design and tested in simple imaging and pulsed gradient spin echo experiments. The resulting data verify the predicted coil performance, thus demonstrating the advantages of using multilayer coils for experiments requiring very large magnetic field gradients. Copyright 1998 Academic Press. PMID:9571104

Bowtell; Robyr

1998-04-01

264

Recurrent erysipelas after radiotherapy

International Nuclear Information System (INIS)

A case of recurrent erysipelas is reported in a female with a history of vulvectomy and radiotherapy to the pelvis. On the basis of a review of the literature, risk factors for this condition and recommended treatments are discussed

265

Recurrent laryngeal nerve injury

Injury to the recurrent laryngeal nerve is accompanied by a poor functional recovery of the target organ, the larynx. For the patient this means impairments of vocal fold mobility and various kinds of voice disorders. In this thesis, an experimental model in the rat is used to identify the most important pathological factors involved after recurrent laryngeal nerve injury. The results demonstrate that the posterior cricoarytenoid muscle, the only abductor of the vocal fold, ...

Hydman, Jonas

2008-01-01

266

Multifocal recurrent periostitis

International Nuclear Information System (INIS)

Two case reports of recurrent multifocal periostitis in two girls aged 15 and 16 are added to the eight cases already reported in the literature. The disease is characterised clinically by recurrent mesomelic swelling of the extremities and radiologically by periosteal thickening and sclerosis of underlying bone. Hyperglobulinaemia is the most constant biochemical finding. The bone biopsy shows no typical features. The possibility of a viral etiology is discussed. (orig.)

267

Hyperhomocysteinemia in Recurrent Miscarriage

International Nuclear Information System (INIS)

Objective: An elevated total plasma homocysteine level has been suggested as a possible risk factor in women suffering from recurrent pregnancy loss. The current study was undertaken to assess the association between homocysteine, folate, cobalamin (vitamin B12) and the risk of recurrent pregnancy loss. Design: Case . control study Materials and Methods: The study included 57 non-pregnant Egyptian women. They were classified according to their obstetric history into 2 groups: 32 cases with at least two consecutive miscarriages (Study group), and 25 cases with normal obstetric history (Control group). All cases were tested for plasma total homocysteine, serum folate and cobalamin (vitamin B12). Results: The fasting total homocysteine was significantly higher in the study group as compared to the control group. While the median concentrations for the vitamins studied were significantly lower in women of the study group as compared to the controls. Elevated homocysteine and reduced vitamin B12 can be considered risk factors for recurrent miscarriage with odds ratio (OR) and 95% confidence intervals (95% CI) of 1.839 (1.286, 2.63) and 1.993 (1.346, 2.951) respectively in the group of recurrent miscarriages. The OR (95% CI) in the study population for low serum folate concentrations was 1.23 (0.776, 2.256). Conclusion: Elevated homocysteine and reduced serum vitamin B12 are risk factors for recurrent miscarriage. Low serum folate did not seem a risk factor for recurrent miscarriage. Testing for homocysteine levels in women suffering from unexplained recurrent miscarriage and pre-conceptional supplementation with vitamin B12 might be beneficial to improve pregnancy outcome

268

Efficient new Bayesian inference technique is employed for studying critical properties of the Ising linear perceptron and for signal detection in Code Division Multiple Access (CDMA). The approach is based on a recently introduced message passing technique for densely connected systems. Here we study both critical and non-critical regimes. Results obtained in the non-critical regime give rise to a highly efficient signal detection algorithm in the context of CDMA; while in ...

Neirotti, Juan P.; Saad, David

2005-01-01

269

In this paper, we experiment with using Stagger, an open-source implementation of an Averaged Perceptron tagger, to tag Icelandic, a morphologically complex language. By adding languagespecific linguistic features and using IceMorphy, an unknown word guesser, we obtain state-of- the-art tagging accuracy of 92.82%. Furthermore, by adding data from a morphological database, and word embeddings induced from an unannotated corpus, the accuracy increases to 93.84%. This is equivalent to an error r...

O?stling, Robert

2013-01-01

270

Perceptrons with Hebbian learning based on wave ensembles in plastic potentials

We present a general theoretical model to realize a bilayer perceptron for hardware neural networks with applications in pattern recognition. In the network, multiple interconnections are allowed, by using the Schrodinger wave function as input and outputs signals; moreover, microscopic plastic potentials allow to process information and \\train" the system in micrometer's scale. As particular cases, we present the calculations for two devices where the information is carrie...

Espinosa-ortega, T.; Liew, T. C. H.

2014-01-01

271

Learning rate and attractor size of the single-layer perceptron

International Nuclear Information System (INIS)

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 RN+RN

272

International Nuclear Information System (INIS)

A set of projection images is acquired during longitudinal tomography with an image intensifier TV system. Reconstruction of tomograms in each desired plane is achieved by shifting and summing up to the digitalized projection images. Digital multilayer and conventional film tomograms mainly of the respiratory tract and skeleton have been compared in 100 patients. Image quality is comparable with both methods. Disadvantage of digital tomography is lower spatial resolution (512x512 matrix size); advantages include lower radiation dose, shorter study time, and facilities of digital imaging. (orig.)

273

Recurrent parotitis in children

Directory of Open Access Journals (Sweden)

Full Text Available Recurrent parotitis is an uncommon condition in children. Its etiological factors have not been proved till date although causes due to genetic inheritance, local autoimmune manifestation, allergy, viral infection and immunodeficiency have been suggested. The exact management of this disorder is not yet standardized, but a conservative approach is preferred and all affected children should be screened for Sjogren?s syndrome and immune deficiency including human immunodeficiency virus. We report a 12 years female child who presented with 12 episodes of non-painful recurrent swellings of the bilateral parotid gland in the past 3 years.

Bhattarai M

2006-01-01

274

International Nuclear Information System (INIS)

Utilizing self-consistent Hartree-Fock calculations, several aspects of multilayers and interfaces are explored: enhancement and reduction of the local magnetic moments, magnetic coupling at the interfaces, magnetic arrangements within each film and among non-neighboring films, global symmetry of the systems, frustration, orientation of the various moments with respect to an outside applied field, and magnetic-field induced transitions. Magnetoresistance of ferromagnetic-normal-metal multilayers is found by solving the Boltzmann equation. Results explain the giant negative magnetoresistance encountered in these systems when an initial antiparallel arrangement is changed into a parallel configuration by an external magnetic field. The calculation depends on (1) geometric parameters (thicknesses of layers), (2) intrinsic metal parameters (number of conduction electrons, magnetization, and effective masses in layers), (3) bulk sample properties (conductivity relaxation times), (4) interface scattering properties (diffuse scattering versus potential scattering at the interfaces, and (5) outer surface scattering properties (specular versus diffuse surface scattering). It is found that a large negative magnetoresistance requires considerable asymmetry in interface scattering for the two spin orientations. Features of the interfaces that may produce an asymmetrical spin-dependent scattering are studied: varying interfacial geometric random roughness with no lateral coherence, correlated (quasi-periodic) roughness, and varying chemical composition of the interfaces. The interplay between these aspects of the interfaces may enhance or suppress the magnetoresistance, depending on whether it increases or decreases the asymmetry in the spin-dependent scattering of the conduction electrons

275

Energy Technology Data Exchange (ETDEWEB)

Magneto-optical multilayers are of interest to the optical data storage community as a possible second-generation medium of the future. The important Co/Pt-superlattice system is introduced in this respect, and an extensive reference listing is provided to previous research. Magneto-optical modeling studies of Co/Pt are presented, and it is concluded that the interfacial Pt is magnetized and is magneto-optically active at the short wavelengths of interest ({approximately}4 eV) for applications. Magneto-optics in the ultrathin limit are discussed, and an additivity law is presented and verified experimentally utilizing data for epitaxial Fe/Ag(111) superlattices. Finally, the surface magnetic anisotropy that provides the vertical easy axes of magnetization in candidate superlattice systems is discussed and illustrated experimentally using ultrathin epitaxial films of Fe grown on a variety of substrates. It is concluded that magneto-optic multilayers will provide many stimulating basic and applied challenges in the years ahead.

Bader, S.D.

1992-02-01

276

The interaction of electromagnetic waves with matter can be controlled by structuring the matter on the scale of the wavelength of light, and various photonic components have been made by structuring materials using top-down or bottom-up approaches. Dip-pen nanolithography is a scanning-probe-based fabrication technique that can be used to deposit materials on surfaces with high resolution and, when carried out in parallel, with high throughput. Here, we show that lyotropic optical diffraction gratings-composed of biofunctional lipid multilayers with controllable heights between ~5 and 100 nm-can be fabricated by lipid dip-pen nanolithography. Multiple materials can be simultaneously written into arbitrary patterns on pre-structured surfaces to generate complex structures and devices, allowing nanostructures to be interfaced by combinations of top-down and bottom-up fabrication methods. We also show that fluid and biocompatible lipid multilayer gratings allow label-free and specific detection of lipid-protein interactions in solution. This biosensing capability takes advantage of the adhesion properties of the phospholipid superstructures and the changes in the size and shape of the grating elements that take place in response to analyte binding.

Lenhert, Steven; Brinkmann, Falko; Laue, Thomas; Walheim, Stefan; Vannahme, Christoph; Klinkhammer, Soenke; Xu, Miao; Sekula, Sylwia; Mappes, Timo; Schimmel, Thomas; Fuchs, Harald

2010-04-01

277

Neutron optics with multilayer monochromators

International Nuclear Information System (INIS)

A multilayer monochromator is made by depositing thin films of two materials in an alternating sequence on a glass substrate. This makes a multilayer periodic in a direction perpendicular to the plane of the films, with a d-spacing equal to the thickness of one bilayer. Neutrons of wavelength ? incident on a multilayer will be reflected at an angle phi given by the Bragg relation n? = 2d sinphi, where n is the order of reflection. The use of thin-film multilayers for monochromating neutrons is discussed. Because of the low flux of neutrons, the samples have to be large, and the width of the incident beam can be as much as 2 cm. Multilayers made earlier were fabricated by resistive heating of the materials in a vacuum chamber. Because of geometrical constraints imposed by the size of the vacuum chamber, limits on the amount of material that can be loaded in a boat, and finite life of the boats, this method of preparation limits the length of a multilayer to ? 15 cm and the total number of bilayers in a multilayer to about 200. This paper discusses a thin-film deposition system using RF sputtering for depositing films

278

International Nuclear Information System (INIS)

This case report describes a boy who had Kawasaki disease (KD) at age 12 months and had a recurrence one year later. The coronary arteries were normal following the initial episode; however, during the second episode he developed coronary aneurysms. Gallium-67 radionuclide imaging, echocardiography, and angiography were used to diagnose the coronary abnormalities

279

Recurrent pelvic surgery is technically challenging. This article discusses this complex topic in patients with both benign and malignant disease. Perspectives regarding a safe approach to patients who may require reoperative pelvic surgery are discussed with a focus on work-up, technical approach, and the importance of an experienced multidisciplinary team. PMID:23177072

Boostrom, Sarah Y; Dozois, Eric J

2013-02-01

280

Multilayered folding with voids

In the deformation of layered materials such as geological strata, or stacks of paper, mechanical properties compete with the geometry of layering. Smooth, rounded corners lead to voids between the layers, while close packing of the layers results in geometrically-induced curvature singularities. When voids are penalized by external pressure, the system is forced to trade off these competing effects, leading to sometimes striking periodic patterns. In this paper we construct a simple model of geometrically nonlinear multi-layered structures under axial loading and pressure confinement, with non-interpenetration conditions separating the layers. Energy minimizers are characterized as solutions of a set of fourth-order nonlinear differential equations with contact-force Lagrange multipliers, or equivalently of a fourth-order free-boundary problem. We numerically investigate the solutions of this free boundary problem, and compare them with the periodic solutions observed experimentally.

Dodwell, Timothy; Peletier, Mark; Budd, Chris

2011-01-01

281

Directory of Open Access Journals (Sweden)

Full Text Available In paper is presented the possibility of making of multi-layers cast steel castings in result of connection of casting and welding coating technologies. First layer was composite surface layer on the basis of Fe-Cr-C alloy, which was put directly in founding process of cast carbon steel 200–450 with use of preparation of mould cavity method. Second layer were padding welds, which were put with use of TIG – Tungsten Inert Gas surfacing by welding technology with filler on Ni matrix, Ni and Co matrix with wolfram carbides WC and on the basis on Fe-Cr-C alloy, which has the same chemical composition with alloy, which was used for making of composite surface layer. Usability for industrial applications of surface layers of castings were estimated by criterion of hardness and abrasive wear resistance of type metal-mineral.

J. Szajnar

2010-01-01

282

International Nuclear Information System (INIS)

By means of the theory of perturbation and modified Parratt recurrent relations the theory of propagation of power laser radiation in nonlinear multilayer periodical structure is constructed. Processes of self-induced influence of Kerr type cubic susceptibility on the space distribution of a refractive index are analyzed. It is shown, that diffraction curves of reflection and transmission under nonlinear interaction of radiation with multilayer periodical structure change essentially their intensity and form in comparison with linear photonic crystal

283

Multilayer thermal barrier coating systems

The present invention generally describes multilayer thermal barrier coating systems and methods of making the multilayer thermal barrier coating systems. The thermal barrier coating systems comprise a first ceramic layer, a second ceramic layer, a thermally grown oxide layer, a metallic bond coating layer and a substrate. The thermal barrier coating systems have improved high temperature thermal and chemical stability for use in gas turbine applications.

Vance, Steven J. (Orlando, FL); Goedjen, John G. (Oviedo, FL); Sabol, Stephen M. (Orlando, FL); Sloan, Kelly M. (Longwood, FL)

2000-01-01

284

Directory of Open Access Journals (Sweden)

Full Text Available Partial words are sequences over a finite alphabet that may contain wildcard symbols, called holes, which match or are compatible with all letters; partial words without holes are said to be full words (or simply words. Given an infinite partial word w, the number of distinct full words over the alphabet that are compatible with factors of w of length n, called subwords of w, refers to a measure of complexity of infinite partial words so-called subword complexity. This measure is of particular interest because we can construct partial words with subword complexities not achievable by full words. In this paper, we consider the notion of recurrence over infinite partial words, that is, we study whether all of the finite subwords of a given infinite partial word appear infinitely often, and we establish connections between subword complexity and recurrence in this more general framework.

Francine Blanchet-Sadri

2011-08-01

285

Chronic recurrent multifocal osteomyelitis

International Nuclear Information System (INIS)

Chronic recurrent multifocal osteomyelitis was first described in 1972 and to date 33 cases have been reported, all but one from outside the United States. This unusual osteomyelitis is characteristically recurrent and multifocal with a predilection for the metaphyses. Cultures are persistently negative and antibiotics do not appear to affect the course of the disease, which may be as long as 15 years. Females are affected twice as frequently as males and half the cases are less than ten years old. Antistreptolysin 0 titers are elevated in a quarter of the patients, and there may be a history of previous throat infection. There is an association with pustulosis palmoplantaris. We present two additional cases from the United States. (orig.)

286

Chronic recurrent multifocal osteomyelitis

Energy Technology Data Exchange (ETDEWEB)

Chronic recurrent multifocal osteomyelitis was first described in 1972 and to date 33 cases have been reported, all but one from outside the United States. This unusual osteomyelitis is characteristically recurrent and multifocal with a predilection for the metaphyses. Cultures are persistently negative and antibiotics do not appear to affect the course of the disease, which may be as long as 15 years. Females are affected twice as frequently as males and half the cases are less than ten years old. Antistreptolysin 0 titers are elevated in a quarter of the patients, and there may be a history of previous throat infection. There is an association with pustulosis palmoplantaris. We present two additional cases from the United States.

Cyrlak, D.; Pais, M.J.

1986-01-01

287

Equine recurrent airway obstruction

Equine Recurrent Airway Obstruction (RAO), also known as heaves or broken wind, is one of the most common disease in middle-aged horses. Inflammation of the airway is inducted by organic dust exposure. This disease is characterized by neutrophilic inflammation, bronchospasm, excessive mucus production and pathologic changes in the bronchiolar walls. Clinical signs are resolved in 3-4 weeks after environmental changes. Horses suffering from RAO are susceptible to allergens throughout their liv...

Artur Nied?wied?

2014-01-01

288

Recurrent Neural Network Regularization

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, and machine translation.

Zaremba, Wojciech; Sutskever, Ilya; Vinyals, Oriol

2014-01-01

289

Training recurrent networks is generally believed to be a difficult task. Excessive training times and lack of convergence to an acceptable solution are frequently reported. In this paper we seek to explain the reason for this from a numerical point of view and show how to avoid problems when training. In particular we investigate ill-conditioning, the need for and effect of regularization and illustrate the superiority of second-order methods for training

Pedersen, Morten With

2006-01-01

290

Weight space structure and analysis using a finite replica number in the Ising perceptron

The weight space of the Ising perceptron in which a set of random patterns is stored is examined using the generating function of the partition function $\\phi(n)=(1/N)\\log [Z^n]$ as the dimension of the weight vector $N$ tends to infinity, where $Z$ is the partition function and $[ ... ]$ represents the configurational average. We utilize $\\phi(n)$ for two purposes, depending on the value of the ratio $\\alpha=M/N$, where $M$ is the number of random patterns. For $\\alpha < \\a...

Obuchi, Tomoyuki; Kabashima, Yoshiyuki

2009-01-01

291

Perceptrons with Hebbian Learning Based on Wave Ensembles in Spatially Patterned Potentials

A general scheme to realize a perceptron for hardware neural networks is presented, where multiple interconnections are achieved by a superposition of Schrödinger waves. Spatially patterned potentials process information by coupling different points of reciprocal space. The necessary potential shape is obtained from the Hebbian learning rule, either through exact calculation or construction from a superposition of known optical inputs. This allows implementation in a wide range of compact optical systems, including (1) any nonlinear optical system, (2) optical systems patterned by optical lithography, and (3) exciton-polariton systems with phonon or nuclear spin interactions.

Espinosa-Ortega, T.; Liew, T. C. H.

2015-03-01

292

Many research works deal with chaotic neural networks for various fields of application. Unfortunately, up to now, these networks are usually claimed to be chaotic without any mathematical proof. The purpose of this paper is to establish, based on a rigorous theoretical framework, an equivalence between chaotic iterations according to Devaney and a particular class of neural networks. On the one hand, we show how to build such a network, on the other hand, we provide a method to check if a neural network is a chaotic one. Finally, the ability of classical feedforward multilayer perceptrons to learn sets of data obtained from a dynamical system is regarded. Various boolean functions are iterated on finite states. Iterations of some of them are proven to be chaotic as it is defined by Devaney. In that context, important differences occur in the training process, establishing with various neural networks that chaotic behaviors are far more difficult to learn.

Bahi, Jacques M.; Couchot, Jean-François; Guyeux, Christophe; Salomon, Michel

2012-03-01

293

Learning by random walks in the weight space of the Ising perceptron

International Nuclear Information System (INIS)

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 ??0.63 for pattern length N = 101 and ??0.41 for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of ??0.80 for N = 101 and ??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 ? of the learning task; at a fixed value of ?, the width of the Hamming distance distribution decreases with N

294

Weight space structure and analysis using a finite replica number in the Ising perceptron

International Nuclear Information System (INIS)

The weight space of the Ising perceptron, in which a set of random patterns is stored, is examined using the generating function of the partition function ?(n) = (1/N)log[Zn] as the dimension of the weight vector N tends to infinity, where Z is the partition function and [c] represents the configurational average. We utilize ?(n) for two purposes, depending on the value of the ratio ? = M/N, where M is the number of random patterns. For ?s = 0.833..., we employ ?(n), in conjunction with Parisi's one-step replica symmetry breaking scheme in the limit of n?0, to evaluate the complexity that characterizes the number of disjoint clusters of weights that are compatible with a given set of random patterns, which indicates that, in typical cases, the weight space is equally dominated by a single large cluster of exponentially many weights and exponentially many small clusters of a single weight. For ?>?s, on the other hand, ?(n) is used to assess the rate function of a small probability that a given set of random patterns is atypically separable by the Ising perceptrons. We show that the analyticity of the rate function changes at ? = ?GD = 1.245..., which implies that the dominant configuration of the atypically separable patterns exhibits a phase transition at this critical ratio. Extensive numerical experiments are conducted to support the theoretical predictions

295

Directory of Open Access Journals (Sweden)

Full Text Available When investors decide to “adventure” through stock markets they search for a method to provide safety on making decision. In fact, there is no precise way to know which stocks will became a profitable investiment. Technical analysis is a discipline that support the investors on making decisions. Such a discipline uses a set of tools and statistical methods to forecast the market’s movement. Such a paper presents the develpment of a robotical Trade System, using a heuristic method. The system has a Neural Network multilayer perceptron, trained with an algorithm for back propagation error. Thus, approaching to the technical analysis without emotional aspects, using the Neural Network forecast on supporting the decisions of a investor on stock market. In analyzing the results of the neural network can be seen that the neural network got a result of 42.6% higher than the diagnostic of the technical analysis.Quando investidores decidem se “aventurar” pelo mercado de renda variável, como pelo mercado de ações, buscam um método de ter mais segurança na tomada de decisão. Na prática, não há como saber quais ativos tornar-se-ão um investimento lucrativo. No mercado acionário, a Análise Técnica procura auxiliar o investidor na tomada de decisão. Para isso, utiliza-se de ferramentas e de métodos estatísticos para tentar predizer os movimentos do mercado. Este artigo apresenta o desenvolvimento de um Trade System robótico, utilizando um método heurístico. O sistema conta com uma rede neural multilayer perceptron, treinada com o algoritmo de retro propagação de erro, aproximando-se da análise técnica sem o fator emoção. Ao avaliar os resultados da rede neural, pode ser visto que a mesma obteve um resultado de 42,6% maior do que o diagnóstico da análise técnica.

Vânia Medianeira Flores Costa

2012-04-01

296

Incarcerated recurrent Amyand's hernia

Amyand's hernia is a rarity and a recurrent case is extremely rare. A 71-year-old male with a previous history of right inguinal hernia repair presented to the emergency department with a 1-day history of pain in the right groin. A physical examination revealed a nonreducible right inguinal hernia. A computed tomography scan showed a 1.3-cm appendix with surrounding inflammation within a right inguinal hernia. An emergent right groin exploration revealed an incarcerated and injected non-perfo...

Quartey, Benjamin; Ugochukwu, Obinna; Kuehn, Reed; Ospina, Karen

2012-01-01

297

Recurrence Relations and Determinants

We examine relationships between two minors of order n of some matrices of n rows and n+r columns. This is done through a class of determinants, here called $n$-determinants, the investigation of which is our objective. We prove that 1-determinants are the upper Hessenberg determinants. In particular, we state several 1-determinants each of which equals a Fibonacci number. We also derive relationships among terms of sequences defined by the same recurrence equation independently of the initial conditions. A result generalizing the formula for the product of two determinants is obtained. Finally, we prove that the Schur functions may be expressed as $n$-determinants.

Janjic, Milan

2011-01-01

298

A 50-year old female patient with unilateral knee pain demonstrated a recurrent ultrasound-proven popliteal cyst (Baker's cyst). Even though a proper differential diagnosis was done, the MRT of the knee showed in a secondary step a tibial fissure as the cause of the treatment-refractory knee pain and Baker's cyst. A fracture of the tibia is a rare cause for a symptomatic Baker's cysts. Mechanical, degenerative or inflamed diseases of the joint are more frequent associated with a Baker's cyst. PMID:21792809

Stöckli, C; Hunziker, R; Tamborrini, G

2011-07-27

299

Recurrent tarsal tunnel syndrome.

Recurrence of tarsal tunnel syndrome after surgery may be due to inadequate release, lack of understanding or appreciation of the actual anatomy involved, variations in the anatomy of the nerve(s), failure to execute the release properly, bleeding with subsequent scarring, damage to the nerve and branches, persistent hypersensitivity of the nerves, and preexisting intrinsic damage to the nerve. Approaches include more thorough release, use of barrier materials to decrease adherence of the nerve to surrounding tissues to avoid traction neuritis, excisions of neuromas using conduits, and consideration of nerve stimulators and systemic medications to deal with persistent neural pain. PMID:25129355

Gould, John S

2014-09-01

300

Equine recurrent airway obstruction

Directory of Open Access Journals (Sweden)

Full Text Available Equine Recurrent Airway Obstruction (RAO, also known as heaves or broken wind, is one of the most common disease in middle-aged horses. Inflammation of the airway is inducted by organic dust exposure. This disease is characterized by neutrophilic inflammation, bronchospasm, excessive mucus production and pathologic changes in the bronchiolar walls. Clinical signs are resolved in 3-4 weeks after environmental changes. Horses suffering from RAO are susceptible to allergens throughout their lives, therefore they should be properly managed. In therapy the most importanthing is to eliminate dustexposure, administration of corticosteroids and use bronchodilators to improve pulmonary function.

Artur Nied?wied?

2014-10-01

301

Chronic recurrent multifocal osteomyelitis

International Nuclear Information System (INIS)

CRMO is a particular form of osteomyelitis in children which may also affect adolescents, and which may persist into adult age. CRMO differs from the conventional forms of OM through the multifocal involvement of the skeleton, a chronic course with recurrences and new lesions occurring unpredictably in various sites. Symptoms are rather mild. The histopathology is non-characteristic, the etiology unknown. Long term prognosis is favourable in spite of the lack of any specific therapy. The radiological appearances are essential for the diagnosis. The present report is aimed as a contribution to the knowledge of this recently established disease entity. (orig.)

302

Chronic recurrent multifocal osteomyelitis

Energy Technology Data Exchange (ETDEWEB)

CRMO is a particular form of osteomyelitis in children which may also affect adolescents, and which may persist into adult age. CRMO differs from the conventional forms of OM through the multifocal involvement of the skeleton, a chronic course with recurrences and new lesions occurring unpredictably in various sites. Symptoms are rather mild. The histopathology is non-characteristic, the etiology unknown. Long term prognosis is favourable in spite of the lack of any specific therapy. The radiological appearances are essential for the diagnosis. The present report is aimed as a contribution to the knowledge of this recently established disease entity.

Probst, F.P.

1984-01-01

303

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification.

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy. PMID:10586991

Brouwer, R K

1999-08-01

304

Multilayer ionic polymer transducer

A transducer consisting of multiple layers of ionic polymer material is developed for applications in sensing, actuation, and control. The transducer consists of two to four individual layers each approximately 200 microns thick. The transducers are connected in parallel to minimize the electric field requirements for actuation. The tradeoff in deflection and force can be controlled by controlling the mechanical constraint at the interface. Packaging the transducer in an outer coating produces a hard constraint between layers and reduces the deflection with a force that increases linearly with the number of layers. This configuration also increases the bandwidth of the transducer. Removing the outer packaging produces an actuator that maintains the deflection of a single layer but has an increased force output. This is obtained by allowing the layers to slide relative to one another during bending. Experiments on transducers with one to three layers are performed and the results are compared to Newbury"s equivalent circuit model, which was modified to accommodate the multilayer polymers. The modification was performed on four different boundary conditions, two electrical the series and the parallel connection, and two mechanical the zero interfacial friction and the zero slip on the interface. Results demonstrate that the largest obstacle to obtaining good performance is water transport between the individual layers. Water crossover produces a near short circuit electrical condition and produces feedthrough between actuation layers and sensing layers. Electrical feedthrough due to water crossover eliminates the ability to produce a transducer that has combined sensing and actuation properties. Eliminating water crossover through good insulation enables the development of a small (5 mm x 30 mm) transducer that has sensing and actuation bandwidth on the order of 100 Hz.

Akle, Barbar J.; Leo, Donald J.

2003-07-01

305

Recurrence Theorems: a unified account

I discuss classical and quantum recurrence theorems in a unified manner, treating both as generalisations of the fact that a system with a finite state space only has so many places to go. Along the way I prove versions of the recurrence theorem applicable to dynamics on linear and metric spaces, and make some comments about applications of the classical recurrence theorem in the foundations of statistical mechanics.

Wallace, David

2013-01-01

306

Recurrence theorems: A unified account

I discuss classical and quantum recurrence theorems in a unified manner, treating both as generalisations of the fact that a system with a finite state space only has so many places to go. Along the way, I prove versions of the recurrence theorem applicable to dynamics on linear and metric spaces and make some comments about applications of the classical recurrence theorem in the foundations of statistical mechanics.

Wallace, David

2015-02-01

307

Multilayer adsorption on fractal surfaces.

Multilayer adsorption is often observed in liquid chromatography. The most frequently employed model for multilayer adsorption is the BET isotherm equation. In this study we introduce an interpretation of multilayer adsorption measured on liquid chromatographic stationary phases based on the fractal theory. The fractal BET isotherm model was successfully used to determine the apparent fractal dimension of the adsorbent surface. The nonlinear fitting of the fractal BET equation gives us the estimation of the adsorption equilibrium constants and the monolayer saturation capacity of the adsorbent as well. In our experiments, aniline and proline were used as test molecules on reversed phase and normal phase columns, respectively. Our results suggest an apparent fractal dimension 2.88-2.99 in the case of reversed phase adsorbents, in the contrast with a bare silica column with a fractal dimension of 2.54. PMID:24315680

Vajda, Péter; Felinger, Attila

2014-01-10

308

Epidemiology of recurrent venous thrombosis

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: English Abstract in english Venous thrombosis, including deep vein thrombosis and pulmonary embolism, is a common disease that frequently recurs. Recurrence can be prevented by anticoagulants, but this comes at the risk of bleeding. Therefore, assessment of the risk of recurrence is important to balance the risks and benefits [...] of anticoagulant treatment. This review briefly outlines what is currently known about the epidemiology of recurrent venous thrombosis, and focuses in more detail on potential new risk factors for venous recurrence. The general implications of these findings in patient management are discussed.

D.D., Ribeiro; W.M., Lijfering; S.M., Barreto; F.R., Rosendaal; S.M., Rezende.

2012-01-01

309

Entropy of weighted recurrence plots

The Shannon entropy of a time series is a standard measure to assess the complexity of a dynamical process and can be used to quantify transitions between different dynamical regimes. An alternative way of quantifying complexity is based on state recurrences, such as those available in recurrence quantification analysis. Although varying definitions for recurrence-based entropies have been suggested so far, for some cases they reveal inconsistent results. Here we suggest a method based on weighted recurrence plots and show that the associated Shannon entropy is positively correlated with the largest Lyapunov exponent. We demonstrate the potential on a prototypical example as well as on experimental data of a chemical experiment.

Eroglu, Deniz; Peron, Thomas K. DM.; Marwan, Nobert; Rodrigues, Francisco A.; Costa, Luciano da F.; Sebek, Michael; Kiss, István Z.; Kurths, Jürgen

2014-10-01

310

Optical transmittance of multilayer graphene

We study the optical transmittance of multilayer graphene films up to 65 layers thick. By combing large-scale tight-binding simulation and optical measurement on CVD multilayer graphene, the optical transmission through graphene films in the visible region is found to be solely determined by the number of graphene layers. We argue that the optical transmittance measurement is more reliable in the determination of the number of layers than the commonly used the Raman spectroscopy. Moreover, the optical transmittance measurement can be applied also to other 2D materials with weak van der Waals interlayer interaction.

Zhu, Shou-En; Yuan, Shengjun; Janssen, G. C. A. M.

2014-10-01

311

Image quality of figured multilayered optics

International Nuclear Information System (INIS)

The reflectivity and resolution of a multilayer structure is strongly affected by the roughness at the interfaces between two successive layers and by the amount that the constituent materials will diffuse into one another at the interfaces. Performance is also affected by the variations in individual layer thicknesses and by inhomogeneities in the materials. These deviations from the ideal multilayer will also affect the quality of the image from a figured multilayer optical element. The theory used to model the effects of non-ideal multilayers on the image quality of figured optics will be discussed. The relationship between image quality and multilayer structure quality will be illustrated with several examples

312

Image quality of figured multilayered optics

Energy Technology Data Exchange (ETDEWEB)

The reflectivity and resolution of a multilayer structure is strongly affected by the roughness at the interfaces between two successive layers and by the amount that the constituent materials will diffuse into one another at the interfaces. Performance is also affected by the variations in individual layer thicknesses and by inhomogeneities in the materials. These deviations from the ideal multilayer will also affect the quality of the image from a figured multilayer optical element. The theory used to model the effects of non-ideal multilayers on the image quality of figured optics will be discussed. The relationship between image quality and multilayer structure quality will be illustrated with several examples.

Peterson, B.G.; Knight, L.V.; Pew, H.K.

1985-01-01

313

Multilayer High-Gradient Insulators

Energy Technology Data Exchange (ETDEWEB)

Multilayer High-Gradient Insulators are vacuum insulating structures composed of thin, alternating layers of dielectric and metal. They are currently being developed for application to high-current accelerators and related pulsed power systems. This paper describes some of the High-Gradient Insulator research currently being conducted at Lawrence Livermore National Laboratory.

Harris, J R

2006-08-16

314

Shortest Recurrence Periods of Novae

Stimulated by the recent discovery of the 1 yr recurrence period nova M31N 2008-12a, we examined shortest recurrence periods of hydrogen shell flashes on mass-accreting white dwarfs (WDs). We discuss the mechanism that yields a finite minimum recurrence period for a given WD mass. Calculating unstable flashes for various WD masses and mass-accretion rates, we identified the shortest recurrence period of about two months for a non-rotating 1.38 M_\\sun WD with a mass-accretion rate of 3.6 \\times 10^{-7} M_\\sun yr^{-1}. One year recurrence period is realized for very massive (> 1.3 M_\\sun) WDs with very high accretion rates (>1.5 \\times 10^{-7} M_\\sun yr^{-1}). We also present a revised stability limit of hydrogen shell burning, which will be useful for binary evolution calculations toward Type Ia supernovae.

Kato, Mariko; Hachisu, Izumi; Nomoto, Ken'ichi

2014-01-01

315

Pathways to breast cancer recurrence.

Breast cancer remains a deadly disease, even with all the recent technological advancements. Early intervention has made an impact, but an overwhelmingly large number of breast cancer patients still live under the fear of "recurrent" disease. Breast cancer recurrence is clinically a huge problem and one that is largely not well understood. Over the years, a number of factors have been studied with an overarching aim of being able to prognose recurrent disease. This paper attempts to provide an overview of our current knowledge of breast cancer recurrence and its associated challenges. Through a survey of the literature on cancer stem cells (CSCs), epithelial-mesenchymal transition (EMT), various signaling pathways such as Notch/Wnt/hedgehog, and microRNAs (miRNAs), we also examine the hypotheses that are currently under investigation for the prevention of breast cancer recurrence. PMID:23533807

Ahmad, Aamir

2013-01-01

316

The development of an efficient image-based computer identification system for plants or other organisms is an important ambitious goal, which is still far from realization. This paper presents three new methods potentially usable for such a system: fractal-based measures of complexity of leaf outline, a heuristic algorithm for automatic detection of leaf parts — the blade and the petiole, and a hierarchical perceptron — a kind of neural network classifier. The next few sets of automatically extractable features of leaf blades, encompassed those presented and/or traditionally used, are compared in the task of plant identification using the simplest known "nearest neighbor" identification algorithm, and more realistic neural network classifiers, especially the hierarchical. We show on two real data sets that the presented techniques are really usable for automatic identification, and are worthy of further investigation.

Borkowski, Wojciech; Kostrzy?ska, Lidia

317

International Nuclear Information System (INIS)

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

318

Energy Technology Data Exchange (ETDEWEB)

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 [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

319

This paper addresses the emotion recognition problem from electroencephalogram signals, in which emotions are represented on the valence and arousal dimensions. Fast Fourier transform analysis is used to extract features and the feature selection based on Pearson correlation coefficient is applied. This paper proposes a probabilistic classifier based on Bayes' theorem and a supervised learning using a perceptron convergence algorithm. To verify the proposed methodology, we use an open database. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the average accuracy of the valence and arousal estimation is 70.9% and 70.1%, respectively. For the three-level class case, the average accuracy is 55.4% and 55.2%, respectively. PMID:24290940

Yoon, Hyun Joong; Chung, Seong Youb

2013-12-01

320

High performance EUV multilayer optics

The demand for enhanced optical resolution in order to structure and observe ever smaller details has pushed optics development in recent years. There is increasing interest in optical components for the extreme ultraviolet (EUV) spectral region mainly as a result of the production of more powerful electronic circuits with the aid of projection lithography. Due to absorption at wavelengths below 100 nm the penetration depth of EUV radiation into matter is only a few nano-meters. Hence, reflective optics must be used for imaging and light collection such as EUV multilayer mirrors which consist of alternating thin films with different refractive indices. This basic idea can be compared to the classic, high reflective ?/4 systems: the constructive interference of all beams reflected at the film interfaces. At Fraunhofer IOF Jena multilayer optics development cover the full range between the soft X-rays around 2 nm wave-length and the vacuum ultraviolet. However, the paper will focus on multilayer optics for EUV lithography applications at 13.5 nm. Besides the development of high-reflective multilayers with enhanced thermal and radiation stability using interface engineering and optimized capping layers collector and imaging optics for diverse applications in the EUV spectral range have been realized. The deposition of EUV collector mirrors for high-power laser produced plasma (LPP) sources is discussed. The paper summarizes recent progress and the present knowledge in preparation and characterization of multilayer optics for the EUV spectral range with regard to maximum optical performance, minimization of structure imperfections, reduc-tion of residual stresses as well as enhanced thermal and radiation stability.

Kaiser, Norbert; Yulin, Sergiy; Perske, Marco; Feigl, Torsten

2008-09-01

321

14 CFR 121.427 - Recurrent training.

...2010-01-01 2010-01-01 false Recurrent training. 121.427 Section 121.427 Aeronautics...DOMESTIC, FLAG, AND SUPPLEMENTAL OPERATIONS Training Program § 121.427 Recurrent training. (a) Recurrent training must...

2010-01-01

322

Genetics Home Reference: Benign recurrent intrahepatic cholestasis

... OMIM Genetic disorder catalog Conditions > Benign recurrent intrahepatic cholestasis On this page: Description Genetic changes Inheritance Diagnosis ... Reviewed April 2012 What is benign recurrent intrahepatic cholestasis? Benign recurrent intrahepatic cholestasis (BRIC) is characterized by ...

323

Multilayer Composite Pressure Vessels

A method has been devised to enable the fabrication of lightweight pressure vessels from multilayer composite materials. This method is related to, but not the same as, the method described in gMaking a Metal- Lined Composite-Overwrapped Pressure Vessel h (MFS-31814), NASA Tech Briefs, Vol. 29, No. 3 (March 2005), page 59. The method is flexible in that it poses no major impediment to changes in tank design and is applicable to a wide range of tank sizes. The figure depicts a finished tank fabricated by this method, showing layers added at various stages of the fabrication process. In the first step of the process, a mandrel that defines the size and shape of the interior of the tank is machined from a polyurethane foam or other suitable lightweight tooling material. The mandrel is outfitted with metallic end fittings on a shaft. Each end fitting includes an outer flange that has a small step to accommodate a thin layer of graphite/epoxy or other suitable composite material. The outer surface of the mandrel (but not the fittings) is covered with a suitable release material. The composite material is filament- wound so as to cover the entire surface of the mandrel from the step on one end fitting to the step on the other end fitting. The composite material is then cured in place. The entire workpiece is cut in half in a plane perpendicular to the axis of symmetry at its mid-length point, yielding two composite-material half shells, each containing half of the foam mandrel. The halves of the mandrel are removed from within the composite shells, then the shells are reassembled and bonded together with a belly band of cured composite material. The resulting composite shell becomes a mandrel for the subsequent steps of the fabrication process and remains inside the final tank. The outer surface of the composite shell is covered with a layer of material designed to be impermeable by the pressurized fluid to be contained in the tank. A second step on the outer flange of each end fitting accommodates this layer. Depending on the application, this layer could be, for example, a layer of rubber, a polymer film, or an electrodeposited layer of metal. If the fluid to be contained in the tank is a gas, then the best permeation barrier is electrodeposited metal (typically copper or nickel), which can be effective at a thickness of as little as 0.005 in (.0.13 mm). The electrodeposited metal becomes molecularly bonded to the second step on each metallic end fitting. The permeation-barrier layer is covered with many layers of filament-wound composite material, which could be the same as, or different from, the composite material of the inner shell. Finally, the filament-wound composite material is cured in an ov

DeLay, Tom

2005-01-01

324

Improved Recursive Algorithm for Light Scattering by a Multilayered Sphere

An improved recurrence algorithm to calculate the scattering field of a multilayered sphere is developed. The internal and external electromagnetic fields are expressed as a superposition of inward and outward waves. The alternative yet equivalent expansions of fields are proposed by use of the first kind of Bessel function and the first kind of Hankel function instead of the first and the second kinds of Bessel function. The final recursive expressions are similar in form to those of Mie theory for a homogeneous sphere and are proved to be more concise and convenient than earlier forms. The new algorithm avoids the numerical difficulties, which give rise to significant errors encountered in practice by previous methods, especially for large, highly absorbing thin shells. Various calculations and tests show that this algorithm is efficient, numerically stable, and accurate for a large range of size parameters and refractive indices.

Yang, Wen

2003-03-01

325

The reported positions of 964 suspected nova eruptions in M31 recorded through the end of calendar year 2013 have been compared in order to identify recurrent nova candidates. To pass the initial screen and qualify as a recurrent nova candidate two or more eruptions were required to be coincident within 0.1', although this criterion was relaxed to 0.15' for novae discovered on early photographic patrols. A total of 118 eruptions from 51 potential recurrent nova systems satisfied the screening criterion. To determine what fraction of these novae are indeed recurrent the original plates and published images of the relevant eruptions have been carefully compared. This procedure has resulted in the elimination of 27 of the 51 progenitor candidates (61 eruptions) from further consideration as recurrent novae, with another 8 systems (17 eruptions) deemed unlikely to be recurrent. Of the remaining 16 systems, 12 candidates (32 eruptions) were judged to be recurrent novae, with an additional 4 systems (8 eruptions) b...

Shafter, A W; Rector, T A; Schweizer, F; Hornoch, K; Orio, M; Pietsch, W; Darnley, M J; Williams, S C; Bode, M F; Bryan, J

2014-01-01

326

Giant recurrent retroperitoneal liposarcoma presenting as a recurrent inguinal hernia

Retroperitoneal liposarcoma presenting as an inguinal hernia is a rare entity. We present the first case of Giant recurrent liposarcoma presenting as a recurrent inguinal hernia in a 40-year-old male. Physical examination showed an irreducible lump in the right inguinal region and a scar in the right lumbar and right inguinal region. Computed tomography (CT) scan of abdomen revealed it to be a retro peritoneal mass extending into the right inguinal region along and involving the cord structur...

Sameer Vora; Kori, Channabasappa G.; Shenoy, Sachin S.; Borisa, Ashok D.; Bakhshi, Girish D.; Bhandarwar, Ajay H.

2011-01-01

327

Multilayer weighted social network model

Recent empirical studies using large-scale data sets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multilayered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.

Murase, Yohsuke; Török, János; Jo, Hang-Hyun; Kaski, Kimmo; Kertész, János

2014-11-01

328

Polyelectrolyte Multilayering in Spherical Geometry

The adsorption of highly \\textit{oppositely} charged flexible polyelectrolytes onto a charged spherical surface is investigated by means of Monte Carlo simulations in a fashion which resembles the layer-by-layer deposition technique introduced by Decher. Electroneutrality is insured at each step by the presence of monovalent counterions (anions and cations). We study in detail the structure of the \\textit{equilibrium} complex. Our investigations of the first few layer formations strongly suggest that multilayering in spherical geometry is not possible as an equilibrium process with purely electrostatic interactions. We especially focus on the influence of specific (non-electrostatic) short range attractive interactions (e.g., Van der Waals) on the stability of the multilayers.

Messina, R; Kremer, K; Messina, Rene; Holm, Christian; Kremer, Kurt

2003-01-01

329

Primary breast lymphoma; disease recurrence

Directory of Open Access Journals (Sweden)

Full Text Available Primary breast lymphoma (PBL accounts 0.4-0.5% of all breast cancers. The aim is to present the patient diagnosed with recurrency of primary breast lymphoma treated six year ago without radiotherapy. A 63-years-old woman patient admitted to our hospital with a palpabl mass in her left breast. Six years ago she was treated with chemoterapy after local excision for primary left breast lymphoma. Imaging modalities showed multiple lesion in breast and confirmed with biopsy. Pathologic results were similar with first one and the case was accepted as PBL recurrence. Multipl metastases has been determined with staging modalities. Patient started to chemotherapy treatment. PBL is a rare cancer of breast and there is no consensus at the treatment of disease. In the literature addition of radiotherapy to the treatment prevents local recurrence. There were occured recurrence without radiotherapy, mimicked that radiotherapy is an essential modality in PBL treatment.

Hüseyin KADIO?LU

2012-01-01

330

Constructing multilayers with absorbing materials

The strong absorption of materials in the extreme ultraviolet (EUV) above ~50 nm has precluded the development of efficient coatings. The development of novel coatings with improved EUV performance is presented. An extensive research was performed on the search and characterization of materials with moderate absorption, such as various lanthanides. Based on this research, novel multilayers based on Yb, Al, and SiO have been developed with a narrowband performance in the 50-92 nm range. Furthe...

Larruquert, Juan Ignacio; Vidal-dasilva, M.; Garci?a-corte?s, S.; Ferna?ndez Perea, Mo?nica; Me?ndez, Jose? A.; Azna?rez, Jose? Antonio

2010-01-01

331

Tests Of Flexible Multilayer Insulations

Composite blankets containing reflective layers compared with fibrous silica insulation. Report describes experimental and theoretical study of 11 flexible insulating blankets ranging in thickness from 1.0 to 2.5 in. Multilayer insulations intended for use in partial vacuums of outer planetary atmospheres, where mean free paths of gas molecules much less than characteristic lengths of cells in insulation and consequently conductive and convective effects of gas minimal.

Kourtides, D. A.; Pitts, W. C.

1991-01-01

332

Elasticity of polyelectrolyte multilayer microcapsules

We present a novel approach to probe elastic properties of polyelectrolyte multilayer microcapsules. The method is based on measurements of the capsule load-deformation curves with the atomic force microscope. The experiment suggests that at low applied load deformations of the capsule shell are elastic. Using elastic theory of membranes we relate force, deformation, elastic moduli, and characteristic sizes of the capsule. Fitting to the prediction of the model yields the lo...

Lulevich, V. V.; Andrienko, D.; Vinogradova, O. I.

2003-01-01

333

Irreversible nucleation in multilayer growth

The epitaxial growth process of a high symmetry surface occurs because adatoms meet and nucleate new islands, that eventually coalesce and complete atomic layers. During multilayer growth, nucleation usually takes place on top of terraces where the geometry of the diffusion process is well defined: We have studied in detail the spatiotemporal distribution of nucleation events and the resulting nucleation rate, a quantity of primary importance to model experimental results an...

Politi, Paolo; Castellano, Claudio

2001-01-01

334

Thermally induced delamination of multilayers

DEFF Research Database (Denmark)

Steady-state delamination of multilayered structures, caused by stresses arising during processing due to thermal expansion mismatch, is analyzed by a fracture mechanics model based on laminate theory. It is found that inserting just a few interlayers with intermediate thermal expansion coefficients may be an effective way of reducing the delamination energy release rate. Uneven layer thickness and increasing elastic mismatch are shown to raise the energy release rate. Experimental work confirms important trends of the model.

SØrensen, Bent F.; Sarraute, S.

1998-01-01

335

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 b...

Das, Nibaran; Mollah, Ayatullah Faruk; Sarkar, Ram; Basu, Subhadip

2010-01-01

336

Marshak boundary condition recurrence formulae

The Marshak boundary condition is applied in the one-dimensional case of thermal radiative heat transport solved by the spherical harmonics method. Recurrence formulae for the Marshak boundary condition are derived for the cases where the absorbing, emitting and scattering material is limited by diffusely emitting, partly diffusely and partly specularly reflecting walls. The recurrence formulae are applied to an example with good results.

Andersen, F. M. B.

337

Mathematical Formulation of Multilayer Networks

A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing “traditional” network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media) in multichannel social networks and developing routing techniques for multimodal transportation systems.

De Domenico, Manlio; Solé-Ribalta, Albert; Cozzo, Emanuele; Kivelä, Mikko; Moreno, Yamir; Porter, Mason A.; Gómez, Sergio; Arenas, Alex

2013-10-01

338

Multilayer Analysis and Visualization of Networks

Multilayer relationships among and information about biological entities must be accompanied by the means to analyze, visualize, and obtain insights from such data. We report a methodology and a collection of algorithms for the analysis of multilayer networks in our new open-source software (muxViz). We demonstrate the ability of muxViz to analyze and interactively visualize multilayer data using empirical genetic and neuronal networks.

De Domenico, Manlio; Arenas, Alex

2014-01-01

339

Magnetic multilayers are known to have behaviour shaped by the intrinsic magnetic properties of their constituents and of their interactions. Multilayers composed of Uranium (5f electrons) and transition metal (3d electrons) provide the unique combination of a potentially large orbital moment with strong electronic hybridisation effects between the extended 5f states and the strongly magnetic 3d states. In this study U/Fe multilayers with layer thicknesses 20Å

Thomas, Mf; Beesley, Am; Bouchenoire, L.; Brown, Sd; Thompson, P.; Herring, Adf; Lander, Gh; Langridge, S.; Stirling, Wg; Ward, Rcc; Zochowskif, Sw

2004-01-01

340

Electrical Conductivity of Collapsed Multilayer Graphene Tubes

Directory of Open Access Journals (Sweden)

Full Text Available Synthesis of multilayer graphene on copper wires by a chemical vapor deposition method is reported. After copper etching, the multilayer tube collapses forming stripes of graphitic films, their electrical conductance as a function of temperature indicate a semiconductor-like behavior. Using the multilayer graphene stripes, a cross junction is built and owing to its electrical behavior we propose that a tunneling process exists in the device.

D. Mendoza

2012-06-01

341

Multi-layer seal for electrochemical devices

Multi-layer seals are provided that find advantageous use for reducing leakage of gases between adjacent components of electrochemical devices. Multi-layer seals of the invention include a gasket body defining first and second opposing surfaces and a compliant interlayer positioned adjacent each of the first and second surfaces. Also provided are methods for making and using the multi-layer seals, and electrochemical devices including said seals.

Chou, Yeong-Shyung [Richland, WA; Meinhardt, Kerry D [Kennewick, WA; Stevenson, Jeffry W [Richland, WA

2010-11-16

342

Superconductivity of tungsten-silicon multilayers

International Nuclear Information System (INIS)

Tungsten-silicon multilayers were prepared by electron beam deposition in ultrahigh vacuum. The number of bilayers and their thicknesses were 10 and 2-24 nm, respectively. Structural properties were analyzed by low and large angle X-ray diffraction and TEM. Tungsten films in multilayers were amorphous for the layer thickness dw?4 nm. Multilayers with dw=1 and 2 nm were superconducting with Tc=2.72-4.21 K. Superconductivity was preserved in samples annealed up to 650degC for 40 s. At higher annealing temperatures the formation of crystalline tungsten silicides was observed, simultaneously the periodicity of multilayers was destroyed and superconductivity was lost. (orig.)

343

Recurrent Childhood Anaplastic Astrocytoma; Recurrent Childhood Anaplastic Oligoastrocytoma; Recurrent Childhood Anaplastic Oligodendroglioma; Recurrent Childhood Giant Cell Glioblastoma; Recurrent Childhood Glioblastoma; Recurrent Childhood Gliomatosis Cerebri; Recurrent Childhood Gliosarcoma

2015-04-03

344

High-dimensional conformally recurrent manifolds

Conformally recurrent pseudo-Riemannian manifolds of dimension n>4 are investigated. The Weyl tensor may be represented as a Koulkarni-Nomizu product involving a symmetric tensor and the recurrence vector. If the recurrence vector is a closed form, the Ricci and two other tensors are Weyl compatible. If the recurrence vector is non-null, a covariantly constant symmetric tensor exists, with geometric implications. If the metric is Lorentzian, a null recurrence vector makes th...

Mantica, Carlo A.; Molinari, Luca G.

2014-01-01

345

A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.

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. PMID:22391234

Araújo, Ricardo de A

2012-04-01

346

The reported positions of 964 suspected nova eruptions in M31 recorded through the end of calendar year 2013 have been compared in order to identify recurrent nova (RN) candidates. To pass the initial screen and qualify as a RN candidate, two or more eruptions were required to be coincident within 0.?1, although this criterion was relaxed to 0.?15 for novae discovered on early photographic patrols. A total of 118 eruptions from 51 potential RN systems satisfied the screening criterion. To determine what fraction of these novae are indeed recurrent, the original plates and published images of the relevant eruptions have been carefully compared. This procedure has resulted in the elimination of 27 of the 51 progenitor candidates (61 eruptions) from further consideration as RNe, with another 8 systems (17 eruptions) deemed unlikely to be recurrent. Of the remaining 16 systems, 12 candidates (32 eruptions) were judged to be RNe, with an additional 4 systems (8 eruptions) being possibly recurrent. It is estimated that ?4% of the nova eruptions seen in M31 over the past century are associated with RNe. A Monte Carlo analysis shows that the discovery efficiency for RNe may be as low as 10% that for novae in general, suggesting that as many as one in three nova eruptions observed in M31 arise from progenitor systems having recurrence times ? 100 yr. For plausible system parameters, it appears unlikely that RNe can provide a significant channel for the production of Type Ia supernovae.

Shafter, A. W.; Henze, M.; Rector, T. A.; Schweizer, F.; Hornoch, K.; Orio, M.; Pietsch, W.; Darnley, M. J.; Williams, S. C.; Bode, M. F.; Bryan, J.

2015-02-01

347

Multilayer Graphene for Waveguide Terahertz Modulator

DEFF Research Database (Denmark)

We study terahertz to infrared electromagnetic properties of multilayer graphene/dielectric artificial medium and present a novel concept of terahertz modulation at midinfrared wavelengths. This approach allows the realization of high-speed electrically controllable terahertz modulators based on hollow waveguide sections filled with multilayer graphene.

Khromova, I.; Andryieuski, Andrei

2014-01-01

348

Interaction and adhesion properties of polyelectrolyte multilayers.

The growth, morphology, and interaction/adhesion properties of supported poly(sodium 4-styrenesulfonate)/poly(allylamine hydrochloride) (PSS/PAH) and DNA/PAH multilayers were investigated by means of surface plasmon resonance spectroscopy, atomic force microscope (AFM) imaging, and AFM-related force measurements. Multilayers were assembled on a prelayer of poly(ethylenimine) (PEI) both with and without drying. SPR results showed a linear growth of the assembly in the case of PSS/PAH multilayers and nonlinear growth for DNA/PAH multilayers. Measurements of forces acting between a bare glass sphere and a multilayer-coated surface indicated repulsive or attractive forces, depending on surface charge, which suggests that, on approach, electrostatic forces dominate. On separation, we observed large pull-off forces in the case of positively charged multilayers and weak pull-off forces in the case negatively charged multilayers. Multiple adhesions and plateau regions observed on separation were interpreted in terms of a bridging of multiple polymer chains between the glass particle and the multilayer and a stretching of the polyelectrolyte loops. The dependence of the pull-off force on the number of deposited layers shows regular oscillations. PMID:16042493

Gong, Haofei; Garcia-Turiel, Javier; Vasilev, Krasimir; Vinogradova, Olga I

2005-08-01

349

Moessbauer Studies on Magnetic Multilayers

Energy Technology Data Exchange (ETDEWEB)

Moessbauer spectroscopy is a useful tool to study the magnetic properties of multilayers from microscopic viewpoints. Recent experimental results relating to the issues on giant magnetoresistance (GMR) and interlayer coupling are briefly described. For the study of magnetic layers, {sup 57}Fe is a suitable Moessbauer isotope. On the other hand, in order to estimate spin polarization in non-magnetic layers, {sup 119}Sn and {sup 197}Au are used as microscopic probes. It is also introduced that the antiferromagnetic order of ultrathin Cr layers is investigated from Moessbauer spectra of inserted {sup 119}Sn probe layers with a monatomic thickness.

Shinjo, T.; Mibu, K. [Kyoto University, Uji, Institute for Chemical Research (Japan)

2001-11-15

350

Figure correction of multilayer coated optics

A process is provided for producing near-perfect optical surfaces, for EUV and soft-x-ray optics. The method involves polishing or otherwise figuring the multilayer coating that has been deposited on an optical substrate, in order to correct for errors in the figure of the substrate and coating. A method such as ion-beam milling is used to remove material from the multilayer coating by an amount that varies in a specified way across the substrate. The phase of the EUV light that is reflected from the multilayer will be affected by the amount of multilayer material removed, but this effect will be reduced by a factor of 1-n as compared with height variations of the substrate, where n is the average refractive index of the multilayer.

Chapman; Henry N. (Livermore, CA), Taylor; John S. (Livermore, CA)

2010-02-16

351

Pigmented villonodular synovitis: extrasynovial recurrence.

A 32-year-old female athlete underwent arthroscopy for a second recurrence of pigmented villonodular synovitis (PVNS), which was extrasynovial, seen on magnetic resonance imaging. It was noted on arthroscopy that (1) the nodules moved medially with joint insufflation, (2) the nodules were less prominent than on magnetic resonance imaging, and (3) more than 95% of the recurrent tumor was hidden by neosynovium. We believe that the extrasynovial location is because of the more rapid proliferation of the neosynovium relative to the growth of the remaining tumor cells after the previous resection. In resecting pigmented villonodular synovitis with a high risk of recurrence, a layer of periarticular fat should be removed and the surgeon should be wary of change in position with insufflation. PMID:21889289

Jobe, Christopher M; Raza, Anwar; Zuckerman, Lee

2011-10-01

352

In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001-2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.

Mozumder, Chitrini; Tripathi, Nitin K.

2014-10-01

353

Energy Technology Data Exchange (ETDEWEB)

In this project, the basic problem is to automatically separate test samples into one of two categories: clean or corrupt. This type of classification problem is known as a two-class classification problem or detection problem. In what follows, we refer to clean examples as negative examples and corrupt examples as positive examples. In a detection problem, a classifier decision on any one sample can be grouped into one of four decision categories: true negative, true positive, false negative and false positive. These four categories are illustrated by Table 1. True negatives and true positives are cases where the classifier has made the correct decision. False positives are cases where the classifier decides positive when the true nature of the sample was negative, and false negatives are cases where the classifier decides negative when the sample was actually positive. To evaluate the performance of a classifier, we run the classifier on all the samples of a data set and then count all the instances of true negatives, true positives, false negatives, and false positives. All of the performance metrics in this report are then formed from a combination of these four basic decision categories.

Chen, B; Hickling, T; Krnjajic, M; Hanley, W; Clark, G; Nitao, J; Knapp, D; Hiller, L; Mugge, M

2007-01-09

354

Classical and Recurrent Nova Outbursts

Over the last 40 years, multi-frequency observations, coupled with advances in theoretical modelling, have led to a much fuller understanding of the nova phenomenon. Here I give a brief review of the current state of knowledge of Classical and Recurrent Novae including their central systems; the causes and consequences of their outbursts; sub-types, and possible relationships to Type Ia Supernovae. Particular attention is paid to the Recurrent Nova RS Ophiuchi as it shows a wealth of phenomena associated with its 2006 outburst. Finally, some open questions and avenues for future work are summarised.

Bode, M F

2011-01-01

355

Recent advances in etched multilayer X-ray optics

We present the recent advances achieved in the Laboratoire de Chimie Physique of Université Paris 6, in the field of the soft X-ray etched multilayer optics. Modellings and characterizations are given for the laminar multilayer amplitude gratings, the highly resolutive X-ray multilayer monochromators, the X-ray polychromators and the Bragg-Fresnel multilayer linear lenses.?

Andre?, J.; Sammar, A.; Bac, S.; Ouahabi, M.; Idir, M.; Soullie?, G.; Barchewitz, R.

1994-01-01

356

Interpretation of Recurrent Neural Networks

DEFF Research Database (Denmark)

This paper addresses techniques for interpretation and characterization of trained recurrent nets for time series problems. In particular, we focus on assessment of effective memory and suggest an operational definition of memory. Further we discuss the evaluation of learning curves. Various numerical experiments on time series prediction problems are used to illustrate the potential of the suggested methods

Pedersen, Morten With; Larsen, Jan

1997-01-01

357

Recurrence Formulas for Fibonacci Sums

In this article we present a new recurrence formula for a finite sum involving the Fibonacci sequence. Furthermore, we state an algorithm to compute the sum of a power series related to Fibonacci series, without the use of term-by-term differentiation theorem

Brandao, Adilson J V

2008-01-01

358

Disorder and diffusion in thin-film multilayers

International Nuclear Information System (INIS)

Practical multilayers devices usually have some in-built imperfections that adversely affect their properties. An analysis of the diffraction properties of a multilayer, and its comparison with the results obtained from a theoretical model of a multilayer with imperfections, can given information about the type and magnitude of imperfections in the multilayer. This information can then be used to make multilayers with better characteristics. 10 references

359

Recurrent Respiratory Papillomatosis or Laryngeal Papillomatosis

... can I get more information? What is recurrent respiratory papillomatosis? Recurrent respiratory papillomatosis (RRP) is a disease in which tumors ... from the nose and mouth into the lungs (respiratory tract). Although the tumors can grow anywhere in ...

360

Multilayer networks: metrics and spectral properties

Multilayer networks represent systems in which there are several topological levels each one representing one kind of interaction or interdependency between the systems' elements. These networks have attracted a lot of attention recently because their study allows considering different dynamical modes concurrently. Here, we revise the main concepts and tools developed up to date. Specifically, we focus on several metrics for multilayer network characterization as well as on the spectral properties of the system, which ultimately enable for the dynamical characterization of several critical phenomena. The theoretical framework is also applied for description of real-world multilayer systems.

Cozzo, Emanuele; Rodrigues, Francisco A; Moreno, Yamir

2015-01-01

361

Magnetic properties of Ni/Pt multilayers

International Nuclear Information System (INIS)

We have grown Ni/Pt multilayers with Pt buffer layer, by evaporation under UHV conditions on glass substrates maintained at 300 K. The magnetic properties of Ni/Pt multilayers are examined as a function of Ni layer thickness t Ni. The temperature dependence of the spontaneous magnetization M(T) is well described by a T 3/2 law in all multilayers. A spin-wave theory has been used to explain the temperature dependence of the magnetization and the approximate values for the bulk exchange interaction J b, surface exchange interaction J S and the interlayer coupling strength J I for various Ni layer thicknesses have been obtained

362

LPCVD tungsten multilayer metallization for VLSI systems

International Nuclear Information System (INIS)

Multilayer metalization enables shorter interconnects, ease of design and yet higher packing density for VLSI circuits. Experiments showed that LPCVD tungsten films deposited in a cold-wall reactor have good uniformity, high purity, low resistivity, low stress, good adherence and are readily patterned into high resolution lines. A multilayer interconnect system consisting of three layers of tungsten metallization followed by a fourth layer of aluminum metallization has been designed, fabricated and tested. Low ohmic contacts were achieved along with excellent step coverage. CMOS devices and logic gates were successfully fabricated and tested using tungsten multilayer metalization schemes

363

Optical multilayers with an amorphous fluoropolymer

Energy Technology Data Exchange (ETDEWEB)

Multilayered coatings were made by physical vapor deposition (PVD) of a perfluorinated amorphous polymer, Teflon AF2400, together with other optical materials. A high reflector at 1064 run was made with ZnS and AF2400. An all-organic 1064-nm reflector was made from AF2400 and polyethylene. Oxide (HfO{sub 2}, SiO{sub 2}) compatibility was also tested. Each multilayer system adhered to itself. The multilayers were influenced by coating stress and unintentional temperature rises during PVD deposition.

Chow, R.; Loomis, G.E.; Lindsey, E.F.

1994-07-01

364

Tunable optical properties of multilayers black phosphorus

We calculated the optical conductivity tensor of multilayers black phosphorus using the Kubo formula within an effective low-energy Hamiltonian. The optical absorption spectra of multilayers black phosphorus are shown to vary sensitively with thickness, doping, and light polarization. In conjunction with experimental spectra obtained from infrared absorption spectroscopy, we discuss the role of interband coupling and disorder on the observed anisotropic absorption spectra. Multilayers black phosphorus might offer attractive alternatives to narrow gap compound semiconductors for optoelectronics across mid- to near-infrared frequencies.

Low, Tony; Carvalho, A; Jiang, Yongjin; Wang, Han; Xia, Fengnian; Neto, A H Castro

2014-01-01

365

Recurrent MRSA skin infections in atopic dermatitis.

Methicillin-resistant Staphylococcus aureus (MRSA) is a frequent cause of recurrent skin and soft tissue infections. For patients with atopic dermatitis, recurrent skin infections with MRSA often lead to eczema exacerbation. There currently is no standard practice in the prevention of recurrent MRSA soft tissue infections in the general and the atopic dermatitis populations. The current article reviews recent data on S aureus decolonization treatments for the prevention of recurrent MRSA soft tissue infections in the community setting. PMID:25017526

Ong, Peck Y

2014-01-01

366

Giant recurrent retroperitoneal liposarcoma presenting as a recurrent inguinal hernia

Directory of Open Access Journals (Sweden)

Full Text Available Retroperitoneal liposarcoma presenting as an inguinal hernia is a rare entity. We present the first case of Giant recurrent liposarcoma presenting as a recurrent inguinal hernia in a 40-year-old male. Physical examination showed an irreducible lump in the right inguinal region and a scar in the right lumbar and right inguinal region. Computed tomography (CT scan of abdomen revealed it to be a retro peritoneal mass extending into the right inguinal region along and involving the cord structures. Wide local excision of the tumour with right orchidectomy and inguinal hernioplasty was performed. Histo-pathology confirmed it to be a liposarcoma. Patient received postoperative radio therapy. Follow up of two years has shown him to be disease free. Retroperitoneal liposarcoma can grow along cord structures into the inguinal canal and mimic an irreducible indirect inguinal hernia.

Sameer Vora

2011-09-01

367

Giant recurrent retroperitoneal liposarcoma presenting as a recurrent inguinal hernia.

Retroperitoneal liposarcoma presenting as an inguinal hernia is a rare entity. We present the first case of Giant recurrent liposarcoma presenting as a recurrent inguinal hernia in a 40-year-old male. Physical examination showed an irreducible lump in the right inguinal region and a scar in the right lumbar and right inguinal region. Computed tomography (CT) scan of abdomen revealed it to be a retro peritoneal mass extending into the right inguinal region along and involving the cord structures. Wide local excision of the tumour with right orchidectomy and inguinal hernioplasty was performed. Histo-pathology confirmed it to be a liposarcoma. Patient received postoperative radio therapy. Follow up of two years has shown him to be disease free. Retroperitoneal liposarcoma can grow along cord structures into the inguinal canal and mimic an irreducible indirect inguinal hernia. PMID:24765371

Bhandarwar, Ajay H; Bakhshi, Girish D; Borisa, Ashok D; Shenoy, Sachin S; Kori, Channabasappa G; Vora, Sameer

2011-09-28

368

Recurrent Idiopathic Facial Paralysis: A Case Report

Directory of Open Access Journals (Sweden)

Full Text Available Idiopathic facial paralysis is the mononeuropathy Multiple recurrences of idiopathic facial paralysis in a patient may be the sign or sympton of a serious illness. therefore; in recurrent cases, family history, systemic diseases and malignancy must be investigated. In this report, a case of recurrent idiopathic facial paralysis is presented.

Hale Hekim Balo?lu

2010-09-01

369

Conductance through multilayer graphene films.

The ballistic conductance through junctions between multilayer graphene films and several different metals is studied using ab initio calculations within the local density approximation. The system consists of films of up to four graphene layers (Bernal stacking) between metallic electrodes, assuming reasonable metal-graphene epitaxial relationships. For some metals, the conductance decays exponentially with increasing number of layers, while for others the conductance saturates with film thickness. This difference in asymptotic behavior stems from the crystal momentum (mis)match between the bulk Fermi-level states in the electrode and those in the film. In contrast, for sufficiently thin films the bonding between the metal and the adjacent graphene layer dominates, giving a metal dependence for graphene similar to that seen experimentally for single-wall carbon nanotubes. Among the metals considered here, we find Pd to be the best for electrodes to films with up to 4 graphene layers. PMID:21834553

Kuroda, Marcelo A; Tersoff, J; Newns, Dennis M; Martyna, Glenn J

2011-09-14

370

Multilayer sulfonated polyaromatic PEMFC membranes

Energy Technology Data Exchange (ETDEWEB)

Sulfonated polyetheretherketone (sPEEK) membranes have been developed in which a hierarchical organisation into multilayers has been favoured during preparation using solvent casting. Bilayer membranes comprising sPEEK of different degrees of sulfonation (having ion exchange capacities between 1.1 and 1.6 meq g{sup -1}) have been characterised for their water uptake properties and proton conductivity. The composite membranes show no tendency to delaminate, even under prolonged operation in a hydrogen - oxygen fuel cell. By associating sPEEK of different water uptake characteristics, water back diffusion in an operating fuel cell is favoured, leading to a degree of control over the direction of water production at the anode or the cathode. Such a bilayer membrane has been operated at 110 C without reactant gas hydration for over 900 h. (Abstract Copyright [2005], Wiley Periodicals, Inc.)

Marrony, M.; Roziere, J.; Jones, D.J.; Lindheimer, A. [Laboratoire des Agregats Moleculaires et Materiaux Inorganiques, UMR CNRS 5072, Universite Montpellier II, Place Eugene Bataillon, 34095 Montpellier cedex 5 (France)

2005-08-01

371

Centrality in Interconnected Multilayer Networks

Real-world complex systems exhibit multiple levels of relationships. In many cases, they require to be modeled by interconnected multilayer networks, characterizing interactions on several levels simultaneously. It is of crucial importance in many fields, from economics to biology, from urban planning to social sciences, to identify the most (or the less) influent nodes in a network. However, defining the centrality of actors in an interconnected structure is not trivial. In this paper, we capitalize on the tensorial formalism, recently proposed to characterize and investigate this kind of complex topologies, to show how several centrality measures -- well-known in the case of standard ("monoplex") networks -- can be extended naturally to the realm of interconnected multiplexes. We consider diagnostics widely used in different fields, e.g., computer science, biology, communication and social sciences, to cite only some of them. We show, both theoretically and numerically, that using the weighted monoplex obta...

De Domenico, Manlio; Omodei, Elisa; Gómez, Sergio; Arenas, Alex

2013-01-01

372

Thermal transfer in multilayer materials

Energy Technology Data Exchange (ETDEWEB)

It is easier to measure the thermal diffusivity (a) of material rather than its thermal conductivity (k), a simple relationship (k=a cp) allowing to calculate k provided and cp are measured. However this relationship applies only if the considered material is homogenous. For composite materials, especially for multilayers ones, we have developed an analytical model and a numerical one. The first one allows to determine the thermal diffusivity and conductivity of a two-layer material. The second one allows to determine the thermal diffusivity of one of the layers provided the values of (a) are known for the two other layers (for a two or three-layer material). The use of the two models to calculate the apparent diffusivity of a two layer material results in values in fairly good agreement. (Authors). 4 refs., 3 figs., 3 tabs.

Bouayad, H.; Mokhtari, A.; Martin, C.; Fauchais, P. [Laboratoire de Materiaux Ceramiques et Traitements de Surface, 87 - Limoges (France)

1993-12-31

373

Highly Efficient Multilayer Thermoelectric Devices

Multilayer thermoelectric devices now at the prototype stage of development exhibit a combination of desirable characteristics, including high figures of merit and high performance/cost ratios. These devices are capable of producing temperature differences of the order of 50 K in operation at or near room temperature. A solvent-free batch process for mass production of these state-of-the-art thermoelectric devices has also been developed. Like prior thermoelectric devices, the present ones have commercial potential mainly by virtue of their utility as means of controlled cooling (and/or, in some cases, heating) of sensors, integrated circuits, and temperature-critical components of scientific instruments. The advantages of thermoelectric devices for such uses include no need for circulating working fluids through or within the devices, generation of little if any noise, and high reliability. The disadvantages of prior thermoelectric devices include high power consumption and relatively low coefficients of performance. The present development program was undertaken in the hope of reducing the magnitudes of the aforementioned disadvantages and, especially, obtaining higher figures of merit for operation at and near room temperature. Accomplishments of the program thus far include development of an algorithm to estimate the heat extracted by, and the maximum temperature drop produced by, a thermoelectric device; solution of the problem of exchange of heat between a thermoelectric cooler and a water-cooled copper block; retrofitting of a vacuum chamber for depositing materials by sputtering; design of masks; and fabrication of multilayer thermoelectric devices of two different designs, denoted I and II. For both the I and II designs, the thicknesses of layers are of the order of nanometers. In devices of design I, nonconsecutive semiconductor layers are electrically connected in series. Devices of design II contain superlattices comprising alternating electron-acceptor (p)-doped and electron-donor (n)-doped, nanometer- thick semiconductor layers.

Boufelfel, Ali

2006-01-01

374

Anxiety Disorder; Depression; Fatigue; Leydig Cell Tumor; Ovarian Sarcoma; Ovarian Stromal Cancer; Pain; Peritoneal Carcinomatosis; Pseudomyxoma Peritonei; Recurrent Breast Cancer; Recurrent Cervical Cancer; Recurrent Endometrial Carcinoma; Recurrent Fallopian Tube Cancer; Recurrent Gestational Trophoblastic Tumor; Recurrent Ovarian Epithelial Cancer; Recurrent Ovarian Germ Cell Tumor; Recurrent Primary Peritoneal Cavity Cancer; Recurrent Uterine Sarcoma; Recurrent Vaginal Cancer; Recurrent Vulvar Cancer

2014-05-20

375

Erlotinib and Temozolomide in Treating Young Patients With Recurrent or Refractory Solid Tumors

Previously Treated Childhood Rhabdomyosarcoma; Recurrent Childhood Brain Tumor; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Malignant Germ Cell Tumor; Recurrent Childhood Medulloblastoma; Recurrent Childhood Rhabdomyosarcoma; Recurrent Childhood Soft Tissue Sarcoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor; Recurrent Neuroblastoma; Recurrent Osteosarcoma

2013-06-04

376

Review of the multilayer coating model

The recent theoretical study on the multilayer-coating model published in Applied Physics Letters [1] is reviewed. Magnetic-field attenuation behavior in a multilayer coating model is different from a semi-infinite superconductor and a superconducting thin film. This difference causes that of the vortex-penetration field at which the Bean-Livingston surface barrier disappears. A material with smaller penetration depth, such as a pure Nb, is preferable as the substrate for pushing up the vortex-penetration field of the superconductor layer. The field limit of the whole structure of the multilayer coating model is limited not only by the vortex-penetration field of the superconductor layer, but also by that of the substrate. Appropriate thicknesses of superconductor and insulator layers can be extracted from contour plots of the field limit of the multilayer coating model given in Ref.[1].

Kubo, Takayuki; Saeki, Takayuki

2014-01-01

377

Soft X-ray multilayers and filters

The periodic and non-periodic multilayers were designed by using a random number to change each layer and a suitable merit function. Ion beam sputtering and magnetron sputtering were used to fabricate various multilayers and beam splitters in soft X-ray range. The characterization of multilayers by small angle X-ray diffraction, Auger electron spectroscopy, Rutherford back scattering spectroscopy and reflectivity illustrated the multilayers had good structures and smooth interlayers. The reflectivity and transmission of a beam splitter is about 5%. The fabrication and transmission properties of Ag, Zr were studied. The Rutherford back scattering spectroscopy and auger electron spectroscopy were used to investigate the contents and distributions of impurities and influence on qualities of filters. The attenuation coefficients were corrected by the data obtained by measurements

Wang Zhan Shan; Tang Wei Xing; Qin Shuji; Zhou Bing; Chen Ling Ya

2002-01-01

378

Irradiated multilayer film for primal meat packaging

International Nuclear Information System (INIS)

This patent deals with a heat-shrinkable, multilayer film suitable for use in fabricating bags for packaging primal and sub-primal meat cuts and processed meats. The multilayer film has a first outer layer of a first ethylene-vinyl acetate copolymer, a core layer of a polyvinylidene chloride-vinyl chloride copolymer containing between about 70 weight percent and about 90 weight percent vinylidene chloride as a barrier film, and a second outer layer of a second ethylene-vinyl acetate copolymer. The multilayer film is preferably made by co-extrusion of the layers, and then it is biaxially stretched. After biaxial stretching, the entire multilayer film is substantially uniformly irradiated to a dosage level of between about 2 megarads and about 3 megarads and heat-sealed in the form of a bag. The film is not significantly discoloured by the irradiation and the bag has improved toughness properties and heat-sealing characteristics

379

Electromagnetic transmission through Cantor multilayers with defects

International Nuclear Information System (INIS)

Transfer matrix method is used to investigate the electromagnetic transmission spectrum of a triadic Cantor multilayer when one or two dielectric defects are introduced. Unlike the periodic multilayer, the defects only cause frequency shift and distortion of the transmission spectrum. When the defect with a positive refractive index, we obtain a negative frequency shift. On the contrary, when the defect has negative refractive index, it causes a positive frequency shift. If we insert these two kinds of defects into the Cantor multilayer simultaneously, both frequency shifts exist, moreover, the frequency shift is a non-monotonic function of the thickness of the defects. This is different from the result of the triadic Cantor multilayer with only one defect

380

Process for making a multilayer interconnect system

A process for making an interconnect system for a multilayer circuit pattern. The interconnect system is formed having minimized through-hole space consumption so as to be suitable for high density, closely meshed circuit patterns.

Zachry, Clyde L. (Inventor); Niedzwiecke, Andrew J. (Inventor)

1976-01-01

381

Laser plasma damage to multilayer mirrors

International Nuclear Information System (INIS)

The authors measured the response of W/C multilayers to x-ray fluxes on the order of 150 MW/cm/sup 2/ using laser generated plasmas and found that these multilayers will maintain near peak reflectivity for at least 1 ns but are eventually destroyed. A description of the experiments and data analysis methods is given. The results of the experiments are compared with hydrodynamic code simulations

382

Structure of tungsten carbide-cobalt multilayers

International Nuclear Information System (INIS)

Multilayers composed of ceramic and metallic materials, such as tungsten carbide and cobalt, have been synthesized for the first time. The structure of these multilayers was investigated by low-angle x-ray diffraction and transmission electron microscopy lattice imaging and microdiffraction. The data show that the low-temperature process of forming these two-dimensional composites leads to unique crystal structures and morphology in the nanometer scale

383

Ordered organic-organic multilayer growth

Energy Technology Data Exchange (ETDEWEB)

An ordered multilayer crystalline organic thin film structure is formed by depositing at least two layers of thin film crystalline organic materials successively wherein the at least two thin film layers are selected to have their surface energies within .+-.50% of each other, and preferably within .+-.15% of each other, whereby every thin film layer within the multilayer crystalline organic thin film structure exhibit a quasi-epitaxial relationship with the adjacent crystalline organic thin film.

Forrest, Stephen R; Lunt, Richard R

2015-01-13

384

Atomic scale structure of sputtered metal multilayers

A combined theoretical and experimental approach has been used to study nanoscale CoFe/Cu/CoFe multilayer films grown by sputter deposition. Such films have applications in sensors that utilize the giant magnetoresistance effect, for example, read heads in high-density information storage devices. Atomistic simulations based on a molecular dynamics approach and an alloy form of the embedded atom method have been developed to accurately model the sputter deposition of the CoFe/Cu/CoFe multilay...

Zhou, Xw; Wadley, Hng; Johnson, Ra; Larson, Dj; Tabat, N.; Cerezo, A.; Petford-long, Ak; Smith, Gdw; Clifton, Ph; Martens, Rl; Kelly, Tf

2001-01-01

385

Polyimide-glass multilayer printed wiring boards

Multilayer printed wiring boards (PWBs) from a polyimide/glass reinforced copper clad laminate and prepreg were manufactured. A lamination cycle and innerlayer copper surface treatment that gave satisfactory delamination resistance at soldering temperatures were developed. When compared to similar epoxy/glass multilayer PWBs, the polyimide PWBs had higher thermal stability, greater resistance to raised lands, fewer plating voids, less outgassing, and adhesion that was equivalent to urethane foam encapsulants.

Lula, J. W.

1984-07-01

386

Fretting wear of metallic multilayer films

International Nuclear Information System (INIS)

Fretting wear behaviour of electrodeposited Cu/Ni multilayer films with 10 and 5 nm thick sublayers has been investigated against a hardened steel ball as the counter body and compared with that of the constituents, Cu and Ni. The wear tests were carried out by using a ball-on-flat geometry at a translation frequency of 8 Hz and slip amplitude of 100 ?m. Friction force was recorded on line during the tests. At the end of the tests, the wear scars were examined by laser surface profilometry, scanning electron microscopy and energy dispersive X-ray microanalysis. It has been observed that the frictional and wear mechanisms are very different for copper, nickel and Cu/Ni multilayers. Fretting of copper creates a relatively smooth wear scar mainly by mechanical ploughing of the asperities on steel counterbody (abrasive wear) and shows a very little third body interaction. Fretting of nickel involves adhesive wear resulting in a large transfer of steel to nickel, which is attributed to the strong chemical interaction between nickel and the steel counterbody. Fretting on multilayers involves a strong third body interaction resulting in ploughing mainly by debris (abrasive wear). The coefficient of friction is approximately 0.45 for copper, and approximately 0.8 for nickel as well as for multilayers. The values of the coefficient of friction for nickel and Cu/Ni multilayers found under the present fretting conditions are approximately double the corresponding values reportey double the corresponding values reported earlier for sliding wear conditions. It has been found that Cu/Ni multilayer is more resistance to fretting wear than the constituents, copper and nickel. Furthermore, the fretting wear resistance of Cu/Ni multilayers with 5 nm thick sublayer is better than that of the multilayers with 10 nm thick sublayers

387

Review of the multilayer coating model

The recent theoretical study on the multilayer-coating model published in Applied Physics Letters [1] is reviewed. Magnetic-field attenuation behavior in a multilayer coating model is different from a semi-infinite superconductor and a superconducting thin film. This difference causes that of the vortex-penetration field at which the Bean-Livingston surface barrier disappears. A material with smaller penetration depth, such as a pure Nb, is preferable as the substrate for pu...

Kubo, Takayuki; Iwashita, Yoshihisa; Saeki, Takayuki

2014-01-01

388

Multi-Layer Microbubbles by Microfluidics

Multi-layer microbubble has great potential in enabling the corporation of medical imaging with tumor therapy such as drug and gene delivery of therapeutics or other functional materials in medical applications. Microfluidic technique has advanced over the last decade and showed great promise in replacing traditional microbubble generating method. In this paper, a multi-layer microbubble structure was produced with the aspect as potentially used for drug loaded microbub...

Hongbo Zhang; Haosu Meng; Qian Sun; Jianpu Liu; Zhang, W. J.

2013-01-01

389

MR investigation of recurrent cholesteatomas

Energy Technology Data Exchange (ETDEWEB)

Nine cases of recurrent petrous cholesteatomas have been studied by a 1.5 T MR unit. Gadolinium was injected in 1 case. In all cases, comparison between MR, CT and clinical findings were made. MR allows for accurate topographic study and assessment of cholesteatomas extension, in particular in the posterior fossa and skull base. Relationships with the internal carotid artery and the jugular vein are clearly depicted.

Doyon, D.; Chan, K.Y.; Attia, M.; Halimi, P.; Sigal, R.; Bobin, S.; Sterkers, J.M.

1989-03-01

390

Recurrent pseudotumoral hemicerebellitis: neuroimaging findings

Energy Technology Data Exchange (ETDEWEB)

We present the case of a 13-year-old girl with pseudotumoral hemicerebellitis that recurred 22 months after the first episode together with conventional MR imaging findings and diffusion-weighted imaging and MR spectroscopy findings. A mirror pattern of involvement was present with the contralateral hemisphere affected in the second episode. Recurrent hemicerebellitis is unique and recognition of the radiological findings allows accurate diagnosis that can be a challenge clinically. (orig.)

Oguz, Kader K. [Hacettepe University, Department of Radiology, Faculty of Medicine, Ankara (Turkey); Haliloglu, Goknur; Topcu, Meral [Hacettepe University, Department of Paediatric Neurology, Faculty of Medicine, Ankara (Turkey); Alehan, Dursun [Hacettepe University, Department of Paediatric Cardiology, Faculty of Medicine, Ankara (Turkey)

2008-04-15

391

Recurrent solar cosmic ray streams

International Nuclear Information System (INIS)

A set of space environmental monitor (SEM) is installed on Japanese Geostationary Meteorological Satellite (GMS, ''Himawari''). SEM is consisted of five single solid state detectors, which cover protons 1.2 - 500 MeV in 7 channels and alpha particles 9-370 MeV in 5 channels. We found 19 particle events during February 1978 - December 1980. The dispersion of time of maximum for different channels and energy spectrum are obtained for each event. The recurrent type events are concerned

392

Recurrences in driven quantum systems

We consider an initially bound quantum particle subject to an external time-dependent field. When the external field is large, the particle shows a tendency to repeatedly return to its initial state, irrespective of whether the frequency of the field is sufficient for escape from the well. These recurrences, which are absent in a classical calculation, arise from the system evolving primarily like a free particle in the external field.

Poduri, V; Patil, U; Poduri, V; Browne, D A; Patil, U

1994-01-01

393

Recurrent Models of Visual Attention

Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of transl...

Mnih, Volodymyr; Heess, Nicolas; Graves, Alex; Kavukcuoglu, Koray

2014-01-01

394

Pinealitis accompanying equine recurrent uveitis.

There is no direct verification of pineal gland involvement in human uveitis. Specimens of pineal tissue are not available during active uveitis in human patients. Naturally occurring uveitis in horses gives us an opportunity to examine tissues during active ocular inflammation. We examined the pineal gland of a horse that was killed because it had become blind during an episode of uveitis. The clinical history and histopathology of the eyes were consistent with post-leptospiral equine recurr...

Kalsow, C. M.; Dwyer, A. E.; Smith, A. W.; Nifong, T. P.

1993-01-01

395

In the context of M. Minsky's and S. Papert's theorems on the impossibility of evaluating simple linear predicates by parallel architectures we want to show how these limitations can be avoided by introducing a generalized input-dependent preprocessing technique that does not suppose any a-priori knowledge of input like in classical input filtering procedures. This technique can be formalized in a very general way and can be also deduced by meta- mathematical arguments. A further development of the same technique can be applied at level of learning procedure to introduce in such a way the complete notion of `dynamic perception'. From the experimental standpoint, we show two applications of the dynamic perceptron in particle track recognition in high-energy accelerators. Firstly, we show the amazing improvement of performances that can be obtained in a perceptron architecture with classical learning by adding our dynamic preprocessing technique, already introduced last year in another paper presented at this Conference. Secondly, we show the first results of this technique extended also at the level of learning procedure always applied to the problem of particle track recognition.

Perrone, Antonio L.; Castiglione, Patrizia; Basti, Gianfranco; Messi, Roberto

1994-03-01

396

Capability of etched multilayer EUV mask fabrication

Recently, development of next generation extremely ultraviolet lithography (EUVL) equipment with high-NA (Numerical Aperture) optics for less than hp10nm node is accelerated. Increasing magnification of projection optics or mask size using conventional mask structure has been studied, but these methods make lithography cost high because of low through put and preparing new large mask infrastructures. To avoid these issues, etched multilayer EUV mask has been proposed. As a result of improvement of binary etched multilayer mask process, hp40nm line and space pattern on mask (hp10nm on wafer using 4x optics) has been demonstrated. However, mask patterns are easily collapsed by wet cleaning process due to their low durability caused by high aspect ratio. We propose reducing the number of multilayer pairs from 40 to 20 in order to increase durability against multilayer pattern collapse. With 20pair multilayer blank, durable minimum feature size of isolated line is extended from 80nm to 56nm. CD uniformity and linearity of 20pair etched multilayer pattern are catching up EUV mask requirement of 2014.

Takai, Kosuke; Murano, Koji; Kamo, Takashi; Morikawa, Yasutaka; Hayashi, Naoya

2014-09-01

397

MRI in recurrent nasopharyngeal carcinoma

Energy Technology Data Exchange (ETDEWEB)

In this study, we retrospectively reviewed the MRI features of recurrent nasopharyngeal carcinoma (NPC) in 72 patients who underwent MRI before and after gadolinium injection. Recurrent NPC exhibited a high degree of regional spread and a variety of signal intensities and contours. MRI showed a nasopharyngeal mass in 50 patients (69.4 %); other sites of involvement included the parapharyngeal space (44.4 %), nasal cavity (12.5 %), paranasal sinuses (27.8 %), oropharynx (4.2 %), orbit (8.3 %), infratemporal fossa (18.1 %), skull base (59.8 %), intracranial area (51.4 %) and regional lymph nodes (15.3 %). On T2-weighted images, the nasopharyngeal mass gave high signal in 9 of 50 cases (18 %), intermediate in 27 (54 %), mixed in 8 (16 %) and low signal in 6 (12 %). Contrast enhancement was strong in 12 cases (24 %), moderate in 29 (58 %) and heterogeneous in 9 (18 %). The lesion was convex in 31 cases (62 %) and concave or straight in 19 (38 %). Recognition of the distribution and the appearance of recurrent NPC on MRI is essential for timely diagnosis and appropriate treatment. (orig.)

Ng, S.H.; Ko, S.F.; Wan, Y.L. [Dept. of Diagnostic Radiology, Chang Gung Medical College and Chang Gung Memorial Hospital, Tao Yuan (Taiwan); Chang, J.T.C.; Chen, W.C. [Dept. of Radiation Oncology, Chang Gung Medical College and Chang Gung Memorial Hospital, Taipei (Taiwan); Tang, L.M. [Dept. of Neurology, Chang Gung Medical College and Chang Gung Memorial Hospital, Taipei (Taiwan)

1999-11-01

398

Recurrent frequency-size distribution

Many complex systems, including a sand-pile model, a slider-block model, and actual earthquakes, have been discussed whether they obey the principles of self-organized criticality. Behavior of these systems can be investigated from two different points of view: interoccurrent behavior in a region and recurrent behavior at a given point on a fault or at a given fault. The interoccurrent frequency-size statistics are known to be scale-invariant and obey the power-law Gutenberg-Richter distribution. This paper investigates the recurrent frequency-size behavior at a given point on a fault or at a given fault. For this purpose sequences of creep events at a creeping section of the San Andreas fault are investigated. The applicability of Brownian passage-time, lognormal, and Weibull distributions to the recurrent frequency-size statistics of slip events is tested and the Weibull distribution is found to be a best-fit distribution. To verify this result the behavior of the numerical slider-block and sand-pile models...

Abaimov, S G

2008-01-01

399

Locoregional recurrence after management of carcinoma breast

International Nuclear Information System (INIS)

Objective: To determine the rate of locoregional recurrence, disease-free interval, site and pattern of locoregional recurrence and the significance of different factors for predicting locoregional recurrence in patients with stage II and III carcinoma breast. Patients and Methods: Criteria for including patients in this study was female patients with stage II and III carcinoma breast who presented in our unit from 1995-2002 and underwent surgical (modified radical mastectomy, simple mastectomy with axillary sampling) and non-surgical (chemotherapy, radiotherapy, hormonal therapy) treatment of carcinoma breast. Record of 98 patients was evaluated for rate of locoregional recurrence, disease-free interval, site and pattern of locoregional recurrence and different clinical factors like stage of carcinoma, tumour size, lymph node status and histopathology were assessed for association with locoregional recurrence. Results: After a mean follow-up of 3.5 years, 17 patients developed locoregional recurrence. Mean disease free interval in stage II was 30 months and only 9.5 months in stage III. Lymph node positivity was associated with locoregional recurrence (p-value is less than 0.05). Chest wall was commonest site of recurrence (73%). Single spot recurrence was common in stage II whereas multiple spot recurrence and field change was more common in stage III. Addition of radiotherapy to surgery decreased the locoregional recurrence but chemotherapy had no significant effec but chemotherapy had no significant effect on prevention of locoregional recurrence. Conclusion: In this series 17.34 % patients developed locoregional recurrence for mean follow-up duration of 3.5 years. Mean disease-free interval was 20.52 months. Lymph node involvement had significant correlation with LRR. (author)

400

A Recurrent Neural Network Approach to Rear Vehicle Detection Which Considered State Dependency

Directory of Open Access Journals (Sweden)

Full Text Available Experimental vision-based detection often fails in cases when the acquired image quality is reduced by changing optical environments. In addition, the shape of vehicles in images that are taken from vision sensors change due to approaches by vehicle. Vehicle detection methods are required to perform successfully under these conditions. However, the conventional methods do not consider especially in rapidly varying by brightness conditions. We suggest a new detection method that compensates for those conditions in monocular vision-based vehicle detection. The suggested method employs a Recurrent Neural Network (RNN, which has been applied for spatiotemporal processing. The RNN is able to respond to consecutive scenes involving the target vehicle and can track the movements of the target by the effect of the past network states. The suggested method has a particularly beneficial effect in environments with sudden, extreme variations such as bright sunlight and shield. Finally, we demonstrate effectiveness by state-dependent of the RNN-based method by comparing its detection results with those of a Multi Layered Perceptron (MLP.

Kayichirou Inagaki

2003-08-01

401

CT manifestations of recurrent thyroid carcinoma

International Nuclear Information System (INIS)

Objective: To study the CT manifestations of recurrent thyroid carcinoma, in order to recognize the characteristics of recurrent thyroid carcinoma. Methods: 42 cases of recurrent thyroid carcinoma proved by surgery and pathology were reviewed, including papillary carcinoma (n = 25), follicular carcinoma (n = 9), medullary carcinoma (n = 6) and clear cell carcinoma (n=2). Results: Local recurrence in thyroid bed was found in 23 cases. Invasion of carotid artery (n = 2), trachea and esophagus (n = 17), larynx and hypopharynx (n = 2) was revealed by CT. In 9 cases of follicular carcinoma 8 had local thyroid bed recurrence. All of the 7 cases with contrast administration had enhancement of tumor mass. Recurrence of contralateral lobe was seen in 12 cases, the pathologic types were the same as the primary carcinoma. Cervical lymph node metastases were proved in 31 cases, being the major manifestation of papillary carcinoma (76%). Mediastinal lymph node metastases were found in 7 cases. 75% of patients had more than one type of recurrences. Conclusion: The majority of recurrent thyroid carcinoma were found in cervical lymph node and thyroid bed. Thyroid bed recurrence was most commonly seen in follicular carcinoma while cervical lymph node metastasis were frequent in papillary carcinoma. The adjacent organs or structures was of ten invaded by local recurrent tumor

402

Moisture in multilayer ceramic capacitors

When both precious metal electrode and base metal electrode (BME) capacitors were subjected to autoclave (120°C/100% RH) testing, it was found that the precious metal capacitors aged according to a well known aging mechanism (less than 3% from their starting values), but the BME capacitors degraded to below the -30% criterion at 500 hours of exposure. The reasons for this new failure mechanism are complex, and there were two theories that were hypothesized. The first was that there could be oxidation or corrosion of the nickel plates. The other hypothesis was that the loss of capacitance was due to molecular changes in the barium titanate. This thesis presents the evaluation of these hypotheses and the physics of the degradation mechanism. It is concluded by proof by elimination that there are molecular changes in the barium titanate. Furthermore, the continuous reduction in capacitor size makes the newer base metal electrode capacitors more vulnerable to moisture degradation than the older generation precious metal capacitors. In addition, standard humidity life testing, such as JESD-22 THB and HAST, will likely not uncover this problem. Therefore, poor reliability due to degradation of base metal electrode multilayer ceramic capacitors may catch manufacturers and consumers by surprise.

Donahoe, Daniel Noel

403

Supplemental multilayer insulation research facility

International Nuclear Information System (INIS)

The Supplemental Multilayer Insulation Research Facility (SMIRF) provides a small scale test bed for conducting cryogenic experiments in a vacuum environment. The facility vacuum system is capable of simulating a Space Shuttle launch pressure profile as well as providing a steady space vacuum environment of 1.3x10-4 N/m2(1 x 10-6 torr). Warm side boundary temperatures can be maintained constant between 111 K(200 R) and 361 K(650 R) using a temperature controlled shroud. The shroud can also simulate a typical lunar day-night temperature profile. The test hardware consists of a cryogenic calorimeter supported by the lid of the vacuum chamber. A 0.45 m3 (120 gal) vacuum jacketed storage/supply tank is available for conditioning the cryogen prior to use in the calorimeter. The facility was initially designed to evaluate the thermal performance of insulation systems for long-term storage in space. The facility has recently been used to evaluate the performance of various new insulation systems for LH2 and LN2 ground storage dewars

404

Multilayer Piezoelectric Stack Actuator Characterization

Future NASA missions are increasingly seeking to use actuators for precision positioning to accuracies of the order of fractions of a nanometer. For this purpose, multilayer piezoelectric stacks are being considered as actuators for driving these precision mechanisms. In this study, sets of commercial PZT stacks were tested in various AC and DC conditions at both nominal and extreme temperatures and voltages. AC signal testing included impedance, capacitance and dielectric loss factor of each actuator as a function of the small-signal driving sinusoidal frequency, and the ambient temperature. DC signal testing includes leakage current and displacement as a function of the applied DC voltage. The applied DC voltage was increased to over eight times the manufacturers' specifications to investigate the correlation between leakage current and breakdown voltage. Resonance characterization as a function of temperature was done over a temperature range of -180C to +200C which generally exceeded the manufacturers' specifications. In order to study the lifetime performance of these stacks, five actuators from one manufacturer were driven by a 60volt, 2 kHz sine-wave for ten billion cycles. The tests were performed using a Lab-View controlled automated data acquisition system that monitored the waveform of the stack electrical current and voltage. The measurements included the displacement, impedance, capacitance and leakage current and the analysis of the experimental results will be presented.

Sherrit, Stewart; Jones, Christopher M.; Aldrich, Jack B.; Blodget, Chad; Bao, Xioaqi; Badescu, Mircea; Bar-Cohen, Yoseph

2008-01-01

405

It is quite obvious that in the real world, more than one kind of relationship can exist between two actors and that those ties can be so intertwined that it is impossible to analyse them separately [Fienberg 85], [Minor 83], [Szell 10]. Social networks with more than one type of relation are not a completely new concept [Wasserman 94] but they were analysed mainly at the small scale, e.g. in [McPherson 01], [Padgett 93], and [Entwisle 07]. Just like in the case of regular single-layered social network there is no widely accepted definition or even common name. At the beginning such networks have been called multiplex network [Haythornthwaite 99], [Monge 03]. The term is derived from communications theory which defines multiplex as combining multiple signals into one in such way that it is possible to separate them if needed [Hamill 06]. Recently, the area of multi-layered social network has started attracting more and more attention in research conducted within different domains [Kazienko 11a], [Szell 10], [...

Bródka, Piotr

2012-01-01

406

Childhood Choroid Plexus Tumor; Childhood Ependymoblastoma; Childhood Grade III Meningioma; Childhood High-grade Cerebellar Astrocytoma; Childhood High-grade Cerebral Astrocytoma; Childhood Medulloepithelioma; Recurrent Childhood Anaplastic Astrocytoma; Recurrent Childhood Anaplastic Oligoastrocytoma; Recurrent Childhood Anaplastic Oligodendroglioma; Recurrent Childhood Brain Stem Glioma; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Giant Cell Glioblastoma; Recurrent Childhood Glioblastoma; Recurrent Childhood Gliomatosis Cerebri; Recurrent Childhood Gliosarcoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Pineoblastoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor

2015-02-19

407

Recurrent priapism from therapeutic quetiapine.

Priapism is rarely related to use of non-erectile related medications. The objective was to educate about the multiple possible causes of priapism and to provide treatment recommendations for the different types of priapism. We present the case of a 43-year-old African American male with a history of schizoaffective disorder who presented to our emergency department multiple times over a three year period with priapism, each episode related to the ingestion of quetiapine. Following penile aspiration and intercavernosal injection of phenylephrine, this patient had resolution of his priapism. This case demonstrates an unusual case of recurrent priapism. PMID:24578777

Saghafi, Omeed; Kao, Amanda; Druck, Jeffrey

2014-02-01

408

High-resolution X-ray Multilayers

International Nuclear Information System (INIS)

Two new approaches are taken in multilayer fabrication to help bridge the gap in bandwidth between traditional multilayers (1 to 2%) and perfect crystals (0.01%). The first approach is based on creating many layers of low-contrast Al2O3/ B4C materials. The second approach is based on using multilayer structures with a small d-spacing using traditional W/B4C and Mo/B4C materials. With 8 keV x-rays on the Chess A2 beamline, we measured a bandwidth of 0.27% with a reflectivity of 40% and a Darwin width of 17 arc seconds from a 26 A d-spacing multilayer with 800 bi-layers of Al2O3/B4C using the low-contrast approach. On the other hand, the short period approach with a W/B4C multilayer and a 14.8 A d-spacing showed a resolution of 0.5 % and a reflectivity of 58.5%. Two more Mo/B4C samples with d-spacings of 15 A and 20 A showed energy resolutions of 0.25% and 0.52% with corresponding reflectivities of 39% and 66%. Thus we observe that both methods can produce useful x-ray optical components

409

Radiation therapy of recurrent nasopharyngeal carcinoma

International Nuclear Information System (INIS)

Recurrent nasopharyngeal carcinoma (NPC), if left untreated, has a 5-year survival of less than one per cent. In contrast, the overall 5-year survival in a treated series of patients was 18 per cent. Excluding those with distant metastases at the beginning of the retreatment, the 5-year survival was 23 per cent. If the recurrence was limited to the neck, a 5-year survival of 34 per cent was obtained and if it was confined to the nasopharynx the 5-year survival was 25 per cent. High doses (about 60 Gy), using multiple narrow beams, carefully directed, were required. For recurrences in the neck, lymph node dissection supplemented by a lower radiation dose should be considered. Surgery was sometimes the only possible method for cure, when recurrence occurred in the neck nodes. Recurrences in the neck, especially when repeatedly appearing, most often were combined with a local recurrence in the nasopharynx. (Auth.)

410

Earthquake recurrence as a record breaking process

Extending the central concept of recurrence times for a point process to recurrent events in space-time allows us to characterize seismicity as a record breaking process using only spatiotemporal relations among events. Linking record breaking events with edges between nodes in a graph generates a complex dynamical network isolated from any length, time or magnitude scales set by the observer. For Southern California, the network of recurrences reveals new statistical featur...

Davidsen, Joern; Grassberger, Peter; Paczuski, Maya

2005-01-01

411

Recurrent Pyogenic Cholangitis Treated by Left Hepatectomy

Recurrent pyogenic cholangitis is managed mostly by conservative treatment or by clearance of stones and biliary enteric by-pass procedure. Hepatectomy is rarely needed. We report a case of recurrent pyogenic cholangitis in a 34-year old man, who presented with recurrent upper abdominal pain, mild jaundice, and fever since childhood. Contrast enhanced computed tomography of abdomen and magnetic resonance cholangiopancreatography confirmed the diagnosis and showed significant atrophy of the le...

Ray, Sukanta

2011-01-01

412

Chemoradiotherapy response in recurrent rectal cancer

The efficacy of response to preoperative chemoradiotherapy (CRT) in recurrent versus primary rectal cancer has not been investigated. We compared radiological downsizing between primary and recurrent rectal cancers following CRT and determined the optimal size reduction threshold for response validated by survival outcomes. The proportional change in tumor length for primary and recurrent rectal cancers following CRT was compared using the independent sample t-test. Overall survival (OS) was ...

Yu, Stanley K. T.; Bhangu, Aneel; Tait, Diana M.; Tekkis, Paris; Wotherspoon, Andrew; Brown, Gina

2013-01-01

413

Morphic Words and Nested Recurrence Relations

We explore a family of nested recurrence relations with arbitrary levels of nesting, which have an interpretation in terms of fixed points of morphisms over a countably infinite alphabet. Recurrences in this family are related to a number of well-known sequences, including Hofstadter's G sequence and the Conolly and Tanny sequences. For a recurrence a(n) in this family with only finitely terms, we provide necessary and sufficient conditions for the limit a(n)/n to exist.

Celaya, Marcel; Ruskey, Frank

2013-01-01

414

Quantum Recurrences: Probe to study Quantum Chaos

We study the phase space of periodically modulated gravitational cavity by means of quantum recurrence phenomena. We report that the quantum recurrences serve as a tool to connect phase space of the driven system with spectrum in quantum domain. With the help of quantum recurrences we investigate the quasi-energy spectrum of the system for a certain fixed modulation strength. In addition, we study transition of spectrum from discrete to continuum as a function of modulation ...

Saif, Farhan

2000-01-01

415

Magnetism of uranium/iron multilayers: II. Magnetic properties

Well-defined U/Fe multilayers of varying layer thicknesses and bilayer repeat numbers were prepared by a dc magnetron sputtering method. Polarized neutron reflectometry, off-specular neutron diffraction and magnetic moment measurements were used to determine the physical properties of the multilayers leading to an evaluation of the magnetic moments associated with the U and Fe atoms. The multilayers exhibit ferromagnetic behaviour with the easy axis in the plane of the multilayer. The saturat...

Beesley, Am; Zochowski, Sw; Thomas, Mf; Herring, Adf; Langridge, S.; Brown, Sd; Ward, Rcc; Wells, DE; Springell, R.; Stirling, Wg; Lander, Gh

2004-01-01

416

Ultrahydrophobicity of Polydimethylsiloxanes-Based Multilayered Thin Films

The formation of polydimethylsiloxanes (PDMSs)-based layer-by-layer multilayer ultrathin films on charged surfaces prepared from water and phosphate buffer solutions has been investigated. The multilayer films prepared under these conditions showed different surface roughness. Nanoscale islands and network structures were observed homogeneously on the multilayer film prepared from pure water solutions, which is attributing to the ultrahydrobic property of the multilayer film. The formation of...

Hongyan Gao; Karen Xiaohe Xu; Bin Chen; Li-Zhu Wu; Chen-Ho Tung; Hai-Feng Ji

2009-01-01

417

A second-order learning algorithm for multilayer networks based on block Hessian matrix.

This article proposes a new second-order learning algorithm for training the multilayer perceptron (MLP) networks. The proposed algorithm is a revised Newton's method. A forward-backward propagation scheme is first proposed for network computation of the Hessian matrix, H, of the output error function of the MLP. A block Hessian matrix, H(b), is then defined to approximate and simplify H. Several lemmas and theorems are proved to uncover the important properties of H and H(b), and verify the good approximation of H(b) to H; H(b) preserves the major properties of H. The theoretic analysis leads to the development of an efficient way for computing the inverse of H(b) recursively. In the proposed second-order learning algorithm, the least squares estimation technique is adopted to further lessen the local minimum problems. The proposed algorithm overcomes not only the drawbacks of the standard backpropagation algorithm (i.e. slow asymptotic convergence rate, bad controllability of convergence accuracy, local minimum problems, and high sensitivity to learning constant), but also the shortcomings of normal Newton's method used on the MLP, such as the lack of network implementation of H, ill representability of the diagonal terms of H, the heavy computation load of the inverse of H, and the requirement of a good initial estimate of the solution (weights). Several example problems are used to demonstrate the efficiency of the proposed learning algorithm. Extensive performance (convergence rate and accuracy) comparisons of the proposed algorithm with other learning schemes (including the standard backpropagation algorithm) are also made. PMID:12662732

Wang, Yi Jen; Lin, Chin Teng

1998-12-01

418

Treatment of a recurrent ameloblastic fibroma.

Ameloblastic fibroma (AF), a slow-growing, benign tumor of odontogenic origin, represents 2% of all odontogenic tumors. Jaw expansion is among the most common symptoms, with diagnosis often made through routine radiographs. AFs have a recurrence rate of 18% to 43.5% after conservative enucleation. Long-term follow-up by both the surgeon and referring dentist is recommended, since recurrence may be due to regrowth of residual tumor undergoing malignant transformation. Aggressive management is recommended for local tumor recurrence. En bloc excision with bone grafting, followed by implant reconstruction, can be curative and preservative of function. Treatment of a recurrent AF is described. PMID:25707166

Manzon, Steven; Philbert, Rawle F; Bush, Benjamin F; Zola, Malcolm B; Solomon, Marshall

2015-01-01

419

Recurrence and Relapse in Bipolar Mood Disorder

Directory of Open Access Journals (Sweden)

Full Text Available Background: Despite the effectiveness of pharmacotherapy in acute phase of bipolar mood disorder, patients often experience relapses or recurrent episodes. Hospitalization of patients need a great deal of financial and humanistic resources which can be saved through understanding more about the rate of relapse and factors affecting this rate. Methods: In a descriptive analytical study, 380 patients with bipolar disorder who were hospitalized in psychiatric emergency ward of Noor hospital, Isfahan, Iran, were followed. Each patient was considered for; the frequency of relapse and recurrence, kind of pharmachotherapy, presence of psychotherapeutic treatments, frequency of visits by psychiatrist and the rank of present episode. Results: The overall prevalence of recurrence was 42.2%. Recurrence was lower in patients using lithium carbonate or sodium valproate or combined therapy (about 40%, compared to those using carbamazepine (80%. Recurrence was higher in patients treated with only pharmacotherapy (44.5% compared to those treated with both pharmacotherapy and psychotherapy (22.2%. Patients who were visited monthy by psychiatrist had lower rate of recurrence compared to those who had irregular visits. Conclusion: The higher rate of recurrence observed in carbamazepine therapy may be due to its adverse reactions and consequently poor compliance to this drug. Lower rates of recurrence with psychotherapy and regular visits may be related to the preventive effects of these procedures and especially to the effective management of stress. Keywords: Bipolar Mood Disorder, Recurrence, Relapse.

S Gh Mousavi

2004-06-01

420

Magnetic characterization of U/Co multilayers

International Nuclear Information System (INIS)

With the aim of expanding the studies on 2D systems containing uranium, U/Co multilayers with layer thickness ranging from 50 to 200 A were recently prepared by dc magnetron sputtering onto glass. The multilayers were characterized by Grazing-Incidence X-Ray Diffraction (GIXRD) and Rutherford Backscattering Spectrometry (RBS). Magnetization measurements performed with a squid magnetometer showed that the multilayers have a ferromagnetic behaviour, with the magnetic signal increasing with the thickness of the layers. The analysis of magnetic anisotropy evidenced an easy magnetic direction in the film plane with large anisotropy fields, which increase with the thickness of the layers and suggests a positive contribution of surface anisotropy to the effective anisotropy Keff. (Abstract Copyright [2003], Wiley Periodicals, Inc.)

421

Multilayer neural networks a generalized net perspective

The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of the optimal control problem. In this way, the optimal control problem methodology, like dynamic programming, with modifications, can yield a new class of learning algorithms for multilayer neural networks. Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book. The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogen...

Krawczak, Maciej

2013-01-01

422

Long Range Surface Plasmons in Multilayer Structures

We present a new strategy, based on a Fresnel coefficient pole analysis, for designing an asymmetric multilayer structure that supports long range surface plasmons (LRSP). We find that the electric field intensity in the metal layer of a multilayer LRSP structure can be even slightly smaller than in the metal layer of the corresponding symmetric LRSP structure, minimizing absorption losses and resulting in LRSP propagation lengths up to 2mm. With a view towards biosensing applications, we also present semi-analytic expressions for a standard surface sensing parameter in arbitrary planar resonant structures, and in particular show that for an asymmetric structure consisting of a gold film deposited on a multilayer of SiO2 and TiO2 a surface sensing parameter G = 1.28(1/nm) can be achieved.

Delfan, Aida

2013-01-01

423

Using Visualization Techniques in Multilayer Traffic Modeling

We describe visualization techniques for multilayer traffic modeling - i.e., traffic models that span several protocol layers, and traffic models of protocols that cross layers. Multilayer traffic modeling is challenging, as one must deal with disparate traffic sources; control loops; the effects of network elements such as IP routers; cross-layer protocols; asymmetries in bandwidth, session lengths, and application behaviors; and an enormous number of complex interactions among the various factors. We illustrate by using visualization techniques to identify relationships, transformations, and scaling; to smooth simulation and measurement data; to examine boundary cases, subtle effects and interactions, and outliers; to fit models; and to compare models with others that have fewer parameters. Our experience suggests that visualization techniques can provide practitioners with extraordinary insight about complex multilayer traffic effects and interactions that are common in emerging next-generation networks.

Bragg, Arnold

424

Magnetic properties of Ni/Pt multilayers

Energy Technology Data Exchange (ETDEWEB)

We have grown Ni/Pt multilayers with Pt buffer layer, by evaporation under UHV conditions on glass substrates maintained at 300 K. The magnetic properties of Ni/Pt multilayers are examined as a function of Ni layer thickness t {sub Ni}. The temperature dependence of the spontaneous magnetization M(T) is well described by a T {sup 3/2} law in all multilayers. A spin-wave theory has been used to explain the temperature dependence of the magnetization and the approximate values for the bulk exchange interaction J {sub b}, surface exchange interaction J {sub S} and the interlayer coupling strength J {sub I} for various Ni layer thicknesses have been obtained.

Benkirane, K. [Laboratoire de Traitement d' information, Faculte des Sciences Ben' Msik Sidi-Othmane, B.P. 7955, Sidi-Othmane, Casablanca (Morocco)]. E-mail: karbenkirane@yahoo.fr; Elkabil, R. [Laboratoire de Traitement d' information, Faculte des Sciences Ben' Msik Sidi-Othmane, B.P. 7955, Sidi-Othmane, Casablanca (Morocco); Lassri, M. [Laboratoire de Physique des Materiaux et de Micro-electronique, Faculte des Sciences Ain Chock, Universite Hassan II, B.P. 5366 Ma-hat arif, Route d' El Jadida, km-8, Casablanca, Marocco (Morocco); Abid, M. [Laboratoire de Physique des Materiaux et de Micro-electronique, Faculte des Sciences Ain Chock, Universite Hassan II, B.P. 5366 Ma-hat arif, Route d' El Jadida, km-8, Casablanca, Marocco (Morocco); Lassri, H. [Laboratoire de Physique des Materiaux et de Micro-electronique, Faculte des Sciences Ain Chock, Universite Hassan II, B.P. 5366 Ma-hat arif, Route d' El Jadida, km-8, Casablanca, Marocco (Morocco); Berrada, A. [Laboratoire de Physique des Materiaux, Faculte des Sciences, Avenue Ibn Batouta, B.P. 1014, Rabat (Morocco); Hamdoun, A. [Laboratoire de Traitement d' information, Faculte des Sciences Ben' Msik Sidi-Othmane, B.P. 7955, Sidi-Othmane, Casablanca (Morocco); Krishnan, R. [Laboratoire de Magnetisme et d' Optique, URA 1531, 45 Avenue des Etats Unis, 78035 Versailles Cedex (France)

2005-01-15

425

Magnetic studies of Fe/Cu multilayers

Energy Technology Data Exchange (ETDEWEB)

The structural and magnetic properties of sputtered Fe/Cu multilayers are examined as a function of Fe layer thickness t{sub Fe} by means of X-ray diffraction, Moessbauer spectrometry and superconducting quantum interference magnetometer. The temperature dependence of the spontaneous magnetization M(T) is well described by a T{sup 3/2} law in all multilayers in the temperature range 5-300 K. The interface anisotropy constant of Fe/Cu multilayers, K{sub s}, is found to be 0.31 and 0.45 erg/cm{sup 2} at 5 and 300 K, respectively. A spin-wave theory has been used to explain the temperature dependence of the magnetization and the approximate values for the bulk exchange interaction J{sub b}, surface exchange interaction J{sub s} and the interlayer coupling strength J{sub I} for various Fe layer thicknesses have been obtained.

El Khiraoui, S. [Laboratoire de Physique et Mecanique des Materiaux, Universite Sultan Moulay Slimane, FST de Beni-Mellal, B.P. 523, 23000 Beni-Mellal (Morocco); Sajieddine, M. [Laboratoire de Physique et Mecanique des Materiaux, Universite Sultan Moulay Slimane, FST de Beni-Mellal, B.P. 523, 23000 Beni-Mellal (Morocco)], E-mail: sajieddinem@yahoo.fr; Hehn, M.; Robert, S.; Lenoble, O.; Bellouard, C. [Laboratoire de Physique des Materiaux, UMR-CNRS 7556, B.P. 239, 54506 Vandoeuvre-les-Nancy Cedex (France); Sahlaoui, M. [Laboratoire de Physique et Mecanique des Materiaux, Universite Sultan Moulay Slimane, FST de Beni-Mellal, B.P. 523, 23000 Beni-Mellal (Morocco); Benkirane, K. [Laboratoire des Materiaux, Micro-electronique, Automatique et Thermique, Faculte des Sciences Ain-Chock, Universite Hassan II, B.P. 5366 Maarif, Casablanca (Morocco)

2008-07-01

426

Apodization of multilayer bulk-wave transducers.

Recent experiments have demonstrated the use of superlattice transducers for bulk acoustic waves in the gigahertz frequency range. The transducers consisted of multilayers of ZnO or LiNbO(3) with alternating crystal orientations or polarizations. A procedure for calculating the electromechanical characteristics of general multilayer transducers in which the individual layers are anisotropic and piezoelectric and have arbitrary crystal orientation is described. The algorithm used is numerically stable and easily implemented for use on a personal computer using commercial software. A network model is also derived to provide both an approximate analysis of multilayer transducer performance and an insight into synthesis procedures. Examples are used to compare the two approaches and illustrate an initial design procedure for broadband transducers. PMID:18290243

Akcakaya, E; Adler, E L; Farnell, G W

1989-01-01

427

Evolutionary games on multilayer networks: A colloquium

Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling th...

Wang, Zhen; Szolnoki, Attila; Perc, Matjaz

2015-01-01

428

Maximum screening fields of superconducting multilayer structures

It is shown that a multilayer comprised of alternating thin superconducting and insulating layers on a thick substrate can fully screen the applied magnetic field exceeding the superheating fields $H_s$ of both the superconducting layers and the substrate, the maximum Meissner field is achieved at an optimum multilayer thickness. For instance, a dirty layer of thickness $\\sim 0.1\\; \\mu$m at the Nb surface could increase $H_s\\simeq 240$ mT of a clean Nb up to $H_s\\simeq 290$ mT. Optimized multilayers of Nb$_3$Sn, NbN, some of the iron pnictides, or alloyed Nb deposited onto the surface of the Nb resonator cavities could potentially double the rf breakdown field, pushing the peak accelerating electric fields above 100 MV/m while protecting the cavity from dendritic thermomagnetic avalanches caused by local penetration of vortices.

Gurevich, Alex

2015-01-01

429

Multilayer Integrated Film Bulk Acoustic Resonators

Multilayer Integrated Film Bulk Acoustic Resonators mainly introduces the theory, design, fabrication technology and application of a recently developed new type of device, multilayer integrated film bulk acoustic resonators, at the micro and nano scale involving microelectronic devices, integrated circuits, optical devices, sensors and actuators, acoustic resonators, micro-nano manufacturing, multilayer integration, device theory and design principles, etc. These devices can work at very high frequencies by using the newly developed theory, design, and fabrication technology of nano and micro devices. Readers in fields of IC, electronic devices, sensors, materials, and films etc. will benefit from this book by learning the detailed fundamentals and potential applications of these advanced devices. Prof. Yafei Zhang is the director of the Ministry of Education’s Key Laboratory for Thin Films and Microfabrication Technology, PRC; Dr. Da Chen was a PhD student in Prof. Yafei Zhang’s research group.

Zhang, Yafei

2013-01-01

430

Mechanical behaviour of hard PVD multilayered coatings

Energy Technology Data Exchange (ETDEWEB)

The aim of this work was to investigate the cracking behaviour and adhesion of tungsten-carbon-based multilayered coatings deposited on steel substrates by magnetron sputtering. Three-point bending experiments were performed on the coating-on-substrate systems until failure of the film. The systems were also strained uniaxially with a microtensile device adapted to a scanning electron microscope. The mechanical response is analysed from the evolution of the crack density in the coating and the fracture toughness. The results show that the rupture properties of the multilayered coatings are correlated to the film thickness and arrangement of the elementary layers. Scratch experiments on the systems revealed a strong adhesion of the multilayered coatings on steel substrates, and delamination at layer interfaces. Thus, graded coatings appear to be more attractive for mechanical applications. (orig.)

Harry, E. [Lyon-1 Univ., Villeurbanne (France). Lab. des Sciences and Ingenierie des Surfaces; Ignat, M.; Pauleau, Y. [ENSEEG, F-38402, Saint Martin d' Heres (France); Rouzaud, A.; Juliet, P. [CEA, F-38054, Grenoble (France)

2000-03-01

431

Calculation of nuclear resonant scattering spectra of magnetic multilayers

International Nuclear Information System (INIS)

Calculations of the angle- and time-dependent photon reflectivity of multilayers are presented, using the technique of characteristic matrices. Spectra of 56Fe/57Fe and Cr/57Fe multilayers are calculated under various conditions. The parameters of the multilayers are optimized for suitable test samples of reflectometry measurements. (author) 10 refs.; 6 figs

432

Physical and chemical characterization of multilayered structures

International Nuclear Information System (INIS)

It is important to know the physical and chemical properties of a multilayer if its performance is to be compared to theoretical predictions, or if guidance is needed for the production of superior multilayers. Accurate, nondestructive analytical methods, such as neutron activation analysis, are restricted to certain elements. Certain destructive methods, such as total carbon analysis by combustion, can be sensitive enough for use with very small samples. The method of choice depends upon sensitivity and specificity, both of which are discussed in this paper. 6 refs., 3 figs., 2 tabs

433

YBCO based multilayers for optoelectronic devices

International Nuclear Information System (INIS)

YBCO based multilayers have been deposited independently by three techniques: laser ablation, inverted cylindrical target sputtering (ICM) and on-axis planar D.C. Magnetron Sputtering. The last technique is used to cover uniformly R-plane sapphire and LaAlO3 2 inch wafers with YBCO or multilayers to achieve optoelectronic devices such as infrared detectors. Very thin (- 3 nm) YSZ and MgO dielectric films have been studied as tunnel barriers for making such high Tc tunnel junctions. 14 refs., 11 figs

434

Exchange interactions in Fe/Y multilayers

International Nuclear Information System (INIS)

The magnetization of Fe/Y multilayers has been measured as a function of temperature. A bulk-like T 3/2 temperature dependence of the magnetization is observed for all multilayers in the temperature range 5-300 K. The spin-wave constant B is found to decrease inversely with t Fe. A simple theoretical model with exchange interactions only, and with non-interacting magnons, has been used to explain the temperature dependence of the magnetization and the approximate values for the bulk exchange interaction J b, surface exchange interaction J s and the interlayer exchange interaction J I for various Fe layer thicknesses have been obtained

435

Strength of cemented multilayer solenoid windings

International Nuclear Information System (INIS)

This paper presents a method for calculating tangential, radial, and shear unwinding stresses produced in cemented multilayer windings under the influence of pondermotive forces. Winding damage occurs when one type of stress exceeds the allowable limit. It is shown that radial and shear stresses can cause winding damage before tangential stresses reach the allowable limit. The results of 12 series of coils, which confirm the validity of the method, are presented. The described procedure is used to design cemented multilayer solenoids for strong magnetic fields

436

Stability of multilayers at high temperatures

International Nuclear Information System (INIS)

The temperature stability of metal (W, WRe, Co, Cr)-carbon multilayers has been studied using X-ray diffraction (?-2 ? and Debye-Scherrer) and electron microscopy. The results show that in all cases a crystallization occurs in the temperature range 650-7500C. As a consequence of this crystallization, the layered structure is destroyed, the surface of the film becomes rough and the X-ray reflectivity is considerably reduced. These results imply that efficient cooling or new multilayer structures will have to be developed for use at high temperatures or under high X-ray incident flux

437

Current management options for recurrent adrenocortical carcinoma

Directory of Open Access Journals (Sweden)

Full Text Available Anthony R Glover,1 Julian C Y Ip,1 Jing Ting Zhao,1 Patsy S H Soon,1,4 Bruce G Robinson,1,3 Stan B Sidhu1,2 1Kolling Institute of Medical Research, Cancer Genetics Laboratory, 2Endocrine Surgical Unit, 3Department of Endocrinology, Royal North Shore Hospital and University of Sydney, St Leonards, 4Department of Surgery, Bankstown Hospital and University of New South Wales, Bankstown, NSW, Australia Abstract: Adrenal cortical carcinoma (ACC is a rare cancer that poses a number of management challenges due to the limited number of effective systemic treatments. Complete surgical resection offers the best chance of long-term survival. However, despite complete resection, ACC is associated with high recurrence rates. This review will discuss the management of recurrent ACC in adults following complete surgical resection. Management should take place in a specialist center and treatment decisions must consider the individual tumor biology of each case of recurrence. Given the fact that ACC commonly recurs, management to prevent recurrence should be considered from initial diagnosis with the use of adjuvant mitotane. Close follow up with clinical examination and imaging is important for early detection of recurrent disease. Locoregional recurrence may be isolated, and repeat surgical resection should be considered along with mitotane. The use of radiotherapy in ACC remains controversial. Systemic recurrence most often involves liver, pulmonary, and bone metastasis and is usually managed with mitotane, with or without combination chemotherapy. There is a limited role for surgical resection in systemic recurrence in selected patients. In all patients with recurrent disease, control of excessive hormone production is an important part of management. Despite intensive management of recurrent ACC, treatment failure is common and the use of clinical trials and novel treatment is an important part of management. Keywords: recurrence, surgery, chemotherapy, mitotane, treatment

Glover AR

2013-06-01

438

High activity iodine 125 endocurietherapy for recurrent skull base tumors

International Nuclear Information System (INIS)

Experience with endocurietherapy of skull base tumors is reviewed. We present our cases of recurrent pituitary hemangiopericytoma, radiation-induced recurrent meningioma, recurrent clival chordoma, recurrent nasopharyngeal cancer involving the cavernous sinus, and recurrent parotid carcinoma of the skull base which were all successfully retreated with high-activity 125-iodine (I-125) permanent implantation.76 references

439

Recurrent pericarditis in Myhre syndrome.

Myhre syndrome is a rare disorder characterized by pre- and postnatal short stature, brachydactyly, facial dysmorphism (short palpebral fissures, maxillary hypoplasia, prognathism and short philtrum), thick skin, muscular-appearing body build, decreased joint mobility, mixed hearing loss, and cleft lip and palate. Other clinical features include skeletal dysplasia, developmental delay with intellectual disability and/or behavioral disturbance, cardiac defects, cryptorchidism, and bone anomalies. The disease is caused by recently identified SMAD4 mutations. Here we describe a 7-year-old boy with a molecularly proven Myhre syndrome who presented life-threatening recurrent pericarditis and systemic inflammatory symptoms that required treatment with steroid and recombinant interleukin-1 receptor antagonist. PMID:23610053

Picco, Paolo; Naselli, Aldo; Pala, Giovanna; Marsciani, Alberto; Buoncompagni, Antonella; Martini, Alberto

2013-05-01

440

Chronic recurrent multifocal osteomyelitis (CRMO)

International Nuclear Information System (INIS)

Chronic recurrent multifocal osteomyelitis (CRMO) is an unusual clinical entity. More than 200 cases are described in the literature and it is presented here with special reference to its radiological aspects. It is an acquired disease of the skeleton which occurs predominantly during childhood and adolescence. About ten per cent of cases begin in early or, rarely, in later adult life. This variant is described here for the first time and is discussed as 'adult CRMO'. The underlying pathology is a bland, predominantly lympho-plasma cellular osteomyelitis which is self-limiting and leads to bone sclerosis (Garre). It probably involves an abnormal immune process which follows an infection but remains clinically latent and remains aseptic and sterile. In a quarter of cases there is an association with pustulosis palmo-plantaris and its relationship with psoriatic arthropathy is discussed. The clinical, histopathological and imaging features (radiological and particularly MRT) and the bone changes are described. (orig./AJ)

441

Recurrent Priapism from Therapeutic Quetiapine

Directory of Open Access Journals (Sweden)

Full Text Available Priapism is rarely related to use of non-erectile related medications. The objective was to educate about the multiple possible causes of priapism and to provide treatment recommendations for the different types of priapism. We present the case of a 43 year-old African American male with a history of schizoaffective disorder who presented to our emergency department multiple times over a three year period with priapism, each episode related to the ingestion of quetiapine. Following penile aspiration and intercavernosal injection of phenylephrine, this patient had resolution of his priapism. This case demonstrates an unusual case of recurrent priapism. [West J Emerg Med. 2014;15(1:114–116.

Omeed Saghafi

2014-02-01

442

International Nuclear Information System (INIS)

Purpose: To study the prognosis of the different types of uveal melanoma recurrences treated by proton beam therapy (PBT). Methods and Materials: This retrospective study analyzed 61 cases of uveal melanoma local recurrences on a total of 1102 patients treated by PBT between June 1991 and December 2010. Survival rates have been determined by using Kaplan-Meier curves. Prognostic factors have been evaluated by using log-rank test or Cox model. Results: Our local recurrence rate was 6.1% at 5 years. These recurrences were divided into 25 patients with marginal recurrences, 18 global recurrences, 12 distant recurrences, and 6 extrascleral extensions. Five factors have been identified as statistically significant risk factors of local recurrence in the univariate analysis: large tumoral diameter, small tumoral volume, low ratio of tumoral volume over eyeball volume, iris root involvement, and safety margin inferior to 1 mm. In the local recurrence-free population, the overall survival rate was 68.7% at 10 years and the specific survival rate was 83.6% at 10 years. In the local recurrence population, the overall survival rate was 43.1% at 10 years and the specific survival rate was 55% at 10 years. The multivariate analysis of death risk factors has shown a better prognosis for marginal recurrences. Conclusion: Survival rate of marginal recurrences is superior to that of the other recurrences. The type of recurrence is a clinical prognostic value to take into account. The influence of local recurrence retreatment by proton beam therapy should be evaluated by novel studies

443

Energy Technology Data Exchange (ETDEWEB)

Purpose: To study the prognosis of the different types of uveal melanoma recurrences treated by proton beam therapy (PBT). Methods and Materials: This retrospective study analyzed 61 cases of uveal melanoma local recurrences on a total of 1102 patients treated by PBT between June 1991 and December 2010. Survival rates have been determined by using Kaplan-Meier curves. Prognostic factors have been evaluated by using log-rank test or Cox model. Results: Our local recurrence rate was 6.1% at 5 years. These recurrences were divided into 25 patients with marginal recurrences, 18 global recurrences, 12 distant recurrences, and 6 extrascleral extensions. Five factors have been identified as statistically significant risk factors of local recurrence in the univariate analysis: large tumoral diameter, small tumoral volume, low ratio of tumoral volume over eyeball volume, iris root involvement, and safety margin inferior to 1 mm. In the local recurrence-free population, the overall survival rate was 68.7% at 10 years and the specific survival rate was 83.6% at 10 years. In the local recurrence population, the overall survival rate was 43.1% at 10 years and the specific survival rate was 55% at 10 years. The multivariate analysis of death risk factors has shown a better prognosis for marginal recurrences. Conclusion: Survival rate of marginal recurrences is superior to that of the other recurrences. The type of recurrence is a clinical prognostic value to take into account. The influence of local recurrence retreatment by proton beam therapy should be evaluated by novel studies.

Caujolle, Jean-Pierre, E-mail: ncaujolle@aol.com [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Paoli, Vincent [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Chamorey, Emmanuel [Department of Radiation Oncology, Protontherapy Center, Centre Antoine Lacassagne, Nice (France); Department of Biostatistics and Epidemiology, Centre Antoine Lacassagne, Nice (France); Maschi, Celia; Baillif, Stéphanie [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Herault, Joël [Department of Radiation Oncology, Protontherapy Center, Centre Antoine Lacassagne, Nice (France); Gastaud, Pierre [Department of Ophthalmology, Saint Roch Hospital, Nice Teaching Hospital, Nice (France); Hannoun-Levi, Jean Michel [Department of Radiation Oncology, Protontherapy Center, Centre Antoine Lacassagne, Nice (France)

2013-04-01

444

Recurrence models of volcanic events

International Nuclear Information System (INIS)

An assessment of the risk of future volcanism has been conducted for isolation of high-level radioactive waste at the potential Yucca Mountain site in southern Nevada. Volcanism studies for the Yucca Mountain Site Characterization Project have progressed to a sufficient degree that it is now prudent to work toward concluding aspects of the work. An advantage of a probabilistic approach to volcanic risk is that it assigns a structured formalism to the problem. This formalism subdivides a complex issue into logical sections. The significance of uncertainty or differences in scientific opinion concerning volcanism issues can be tested for each section of a probabilistic problem. The perspective for making judgement of significance for volcanism studied are the regulatory requirements for assessing the suitability of the potential Yucca Mountain site. This paper attempts to begin the process of helping establish the probabilistic framework for making those judgement. There are three objectives. First, the authors describe the tripartite probability used to define the risk of volcanism and the geologic assumptions required for the probability model. Second, the authors examine and define the first part of this probability, the recurrence of volcanic events. Studies are reviewed from the volcanological literature where time-volume behavior of volcanic centers and fields have been evaluated. These evaluations include both conventional statistical analysis of time-series of vl statistical analysis of time-series of volcanic events and applications using newly developing concepts of fractal analysis and deterministic chaos. Third, the authors tabulate past calculations and derive new values for the recurrence of volcanic events using a simple Poison model

445

Recurrent Malignant Pleural Mesothelioma: Case Report

A 42-year-old woman with malignant pleural mesothelioma was admitted initially to the Texas Heart Institute in 1967 for removal of a chest wall tumor. She subsequently underwent 17 additional thoracotomies for removal of recurrent localized tumor during a 15-year period. She died in 1982 at age 56 from cardiopulmonary failure secondary to extensive recurrent mesothelioma.

Cooley, Denton A.; Frazier, O. Howard

1983-01-01

446

Recurrent Bilateral Breast Abscesses after Sternotomy

Median sternotomy is the most commonly used incision in cardiothoracic procedures. Development of breast abscess after sternotomy is a very rare situation. We present a case of sternal wound infection with recurrent bilateral breast abscess after sternotomy. Our case is the first and only case in the literature due to the presence of sternal wound infection with recurrent bilateral breast abscess after sternotomy.

Hamza Cinar; Ali Naki Ulusoy; Emir Fatih Kaya; Amp Xf Khan Lap, G.; Kagan Karabulut; Polat, Ayfer Kamal Amp X.; Amp Xf Khan Selcuk Amp Xd Zbalci, G.

2012-01-01

447

MRCP findings in recurrent pyogenic cholangitis

Energy Technology Data Exchange (ETDEWEB)

Objective: In this essay, we present the spectrum of intrahepatic and extrahepatic bile duct abnormalities seen on MRCP in patients with recurrent pyogenic cholangitis. Conclusion: MRCP is a promising, noninvasive alternative to more invasive direct cholangiography for evaluating the intrahepatic and extrahepatic bile ducts in patients with recurrent pyogenic cholangitis.

Jain, Manoj [Department of Radiology, AMRI Hospitals, Salt Lake, JC-16 and 17 Salt Lake City, Kolkata 700091 (India)], E-mail: jainmanoj70@hotmail.com; Agarwal, Ajay [Department of Radiology, AMRI Hospitals, Salt Lake, JC-16 and 17 Salt Lake City, Kolkata 700091 (India)], E-mail: doctorajay2002@yahoo.co.in

2008-04-15

448

Recurrent midgut volvulus following a Ladd procedure

Energy Technology Data Exchange (ETDEWEB)

We present a case of recurrent midgut volvulus in a 3-year-old girl with a history of midgut volvulus repair as an infant. Awareness of the possibility of recurrence even several years following an initial Ladd procedure is crucial to ensure prompt treatment in these children. (orig.)

Panghaal, Vikash; Levin, Terry L.; Han, Bokyung [Montefiore Medical Center, Department of Radiology, Bronx, NY (United States)

2008-04-15

449

Recurrent midgut volvulus following a Ladd procedure

International Nuclear Information System (INIS)

We present a case of recurrent midgut volvulus in a 3-year-old girl with a history of midgut volvulus repair as an infant. Awareness of the possibility of recurrence even several years following an initial Ladd procedure is crucial to ensure prompt treatment in these children. (orig.)

450

Alternative therapy for recurrent Hodgkin's disease

International Nuclear Information System (INIS)

Eleven patients with recurrent Hodgkin's disease have returned to apparent normality after simple treatment regimes with radiotherapy and hyperthermia from 434 MHz electromagnetic radiation. None have experienced any complication or sequelae from this treatment. All the patients with recurrent active Hodgkin's disease who attended the author's practice since February 1976 have been treated with this combined treatment and are reported here. (author)

451

MRCP findings in recurrent pyogenic cholangitis

International Nuclear Information System (INIS)

Objective: In this essay, we present the spectrum of intrahepatic and extrahepatic bile duct abnormalities seen on MRCP in patients with recurrent pyogenic cholangitis. Conclusion: MRCP is a promising, noninvasive alternative to more invasive direct cholangiography for evaluating the intrahepatic and extrahepatic bile ducts in patients with recurrent pyogenic cholangitis

452

Chemoradiotherapy response in recurrent rectal cancer

The efficacy of response to preoperative chemoradiotherapy (CRT) in recurrent versus primary rectal cancer has not been investigated. We compared radiological downsizing between primary and recurrent rectal cancers following CRT and determined the optimal size reduction threshold for response validated by survival outcomes. The proportional change in tumor length for primary and recurrent rectal cancers following CRT was compared using the independent sample t-test. Overall survival (OS) was calculated using the Kaplan–Meier product limit method and differences between survival for tumor size reduction thresholds of 30% (response evaluation criteria in solid tumors [RECIST]), 40%, and 50% after CRT in primary and recurrent rectal cancer groups. A total of 385 patients undergoing CRT were analyzed, 99 with recurrent rectal cancer and 286 with primary rectal cancer. The mean proportional reduction in maximum craniocaudal length was significantly higher for primary rectal tumors (33%) compared with recurrent rectal cancer (11%) (P?50% size reduction showed a survival benefit. Recurrent rectal cancer appears radioresistant compared with primary tumors for tumor size after CRT. Further investigation into improving/intensifying chemotherapy and radiotherapy for locally recurrent rectal cancer is justified. PMID:24403010

Yu, Stanley K T; Bhangu, Aneel; Tait, Diana M; Tekkis, Paris; Wotherspoon, Andrew; Brown, Gina

2014-01-01

453

Recurrence of hepatocellular carcinoma after liver transplantation.

Outcome after liver transplantation (OLT) clearly depends on recurrence of hepatocellular carcinoma (HCC). After recurrence, patient outcome will depend on the time and site of appearance. The aim of this study was to analyze the therapeutic implications of tumor recurrence behavior. From October 1988 to December 2005, 685 patients received OLT, including 202 due to HCC (32%). We analyzed 28 recurrences (15.2%) among 184 patients who survived at least 3 months (minimum follow-up 1 year). According to the time of recurrence, we divided the patients into early recurrence (ER 12 months n = 19; 67.9%). Actuarial survivals at 1, 5, and 10 years were 82%, 65%, and 50% and disease-free survival, 80%, 58%, and 46%, respectively. Risk factors for recurrence were: vascular invasion (P < .01), bad differentiation (P < .01), and previous hepatectomy (P < .05). After OLT, ER presented at: 5.7 +/- 2.3 months (range 3-10) vs 33.5 +/- 24.3 months (range 12-103) for LR P < .001). Survival postrecurrence (SPR) was shorter: 3.1 +/- 2.4 (range 1-8) months vs 16.4 +/- 14.2 (range 1-5) months (P < .001). Treatment was offered to one ER (11%) and to eight LR (47.1%; P < .05), achieving in these cases longer SPR: 20.1 +/- 14 vs 6.9 +/- 9 months (P < .05). The most common sites of recurrence were liver (n = 7), lung (n = 7), bone (n = 5), adrenal gland (n = 2), peritoneum (n = 2), lymph node (n = 2), skin (n = 2) or cerebral (n = 1). Early recurrences showed short survivals; no treatment could be offered to these patients. Liver recurrence appeared early. In contrast, most lung recurrences appeared later with the possibility of treatment and longer SPR. Bone recurrence appeared later, usually associated with other locations. Treatment was palliative and prognosis was worse. Skin and lymph node recurrences can be treated curatively with prolonged survival. In conclusion, HCC recurrence was difficult to treat curatively and was only prevented by employing restricted criteria. PMID:17889173

Escartin, A; Sapisochin, G; Bilbao, I; Vilallonga, R; Bueno, J; Castells, L; Dopazo, C; Castro, E; Caralt, M; Balsells, J

2007-09-01

454

CT evaluation of locally recurrent rectal cancer

Energy Technology Data Exchange (ETDEWEB)

From April 1979 to February 1984, fifty patients with locally recurrent rectal cancer following abdomino-perineal (AP) resection for rectal cancer were reviwed. In order to observe the pelvic anatomy, base-line CT studies were performed immediately after AP resection in all cases. Signs of local recurrence were seen on CT for all patients The pelvic anatomy following surgical resection of rectal cancer can be clearly imaged by CT. Pelvic recurrence usually, presents as a mass with soft tissue density that is frequently invading adjacent structures, particularly deep pelvic muscles and the posterior wall of the bladder. There are some limitations in the CT evaluation for recurrent mass due to difficulty in differentiating malignancy from granuloma. CT, however, is useful for making a diagnosis of recurrent tumors and for observing the shape, internal structure of the tumor and invasion to the adjacent tissue.

Hara, Suguru; Kido, Choichiro; Endo, Tokiko (Aichi Cancer Center, Nagoya (Japan). Hospital)

1984-09-01

455

CT evaluation of locally recurrent rectal cancer

International Nuclear Information System (INIS)

From April 1979 to February 1984, fifty patients with locally recurrent rectal cancer following abdomino-perineal (AP) resection for rectal cancer were reviwed. In order to observe the pelvic anatomy, base-line CT studies were performed immediatly after AP resection in all cases. Signs of local recurrence were seen on CT for all patients The pelivic anatomy following sugical resection of rectal cancer can be clearly imaged by CT. Pelvic recurrence usually, presents as a mass with soft tissue density that is frequently invading adjacent structures, particularly deep pelvic muscles and the posterior wall of the bladder. There are some limitations in the CT evaluation for recurrent mass due to difficulty in differentiating malignancy from granuloma. CT, however, is useful for making a diagnosis of recurrent tumors and for observing the shape, internal structure of the tumor and invasion to the adjacent tissue. (author)

456

Endoscopic Therapy for Chronic Recurrent Pancreatitis

Directory of Open Access Journals (Sweden)

Full Text Available Chronic recurrent pancreatitis develops as a result of pancreaticoutflow disturbance associated with pancreatic duct stenosis orpancreatic stones in most cases. Therefore it is rational to reduceintrapancreatic ductal pressure by removing pancreatic outflowdisturbance for treatment of chronic recurrent pancreatitis. Surgicalprocedures and endoscopic pancreatic stenting are available fordecompression of the pancreatic duct. As endoscopic pancreaticstenting is less invasive, safe and effective method, this approachhas spread rapidly. Comorbid pancreatic cancer should never beoverlooked before stenting for chronic recurrent pancreatitis, becausechronic recurrent pancreatitis carries a high risk of progressingto pancreatic cancer. In cases with a stone in the pancreatic duct,extracorporeal shock wave lithotripsy (ESWL should be performedin combination with endoscopic pancreatic stenting. In this reviewwe discuss the current status of endoscopic pancreatic stenting in thetreatment of chronic recurrent pancreatitis.

Yoshiaki Kawaguchi

2012-12-01

457

Energy Technology Data Exchange (ETDEWEB)

Highlights: {yields} A method is presented to improve power system stability using IPFC. {yields} Recurrent neural network controllers damp oscillations in a power system. {yields} Training is based on back propagation with adaptive training parameters. {yields} Selection of effectiveness damping control signal carried out using SVD method. -- Abstract: This paper presents a method to improve power system stability using IPFC based damping online learning recurrent neural network controllers for damping oscillations in a power system. Parameters of equipped controllers for enhancing dynamical stability at the IPFC are tuned using mathematical methods. Therefore these control parameters are often fixed and are set for particular system configurations or operating points. Multilayer recurrent neural network, which can be tuned for changing system conditions, is used in this paper for effectively damp the oscillations. Training is based on back propagation with adaptive training parameters. This controller is tested to variations in system loading and fault in the power system and its performance is compared with performance of a controller that the phase compensation method is used to set its parameters. Selection of effectiveness damping control signal for the design of robust IPFC damping controller carried out through singular value decomposition (SVD) method. Simulation studies show the superior robustness and stabilizing effect of the proposed controller in comparison with phase compensation method.

Banaei, M.R., E-mail: m.banaei@azaruniv.ed [Electrical Engineering Department, Faculty of Engineering, Azarbaijan University of Tarbiat Moallem, Tabriz (Iran, Islamic Republic of); Kami, A. [Electrical Engineering Department, Faculty of Engineering, Azarbaijan University of Tarbiat Moallem, Tabriz (Iran, Islamic Republic of)

2011-07-15

458

International Nuclear Information System (INIS)

Highlights: ? A method is presented to improve power system stability using IPFC. ? Recurrent neural network controllers damp oscillations in a power system. ? Training is based on back propagation with adaptive training parameters. ? Selection of effectiveness damping control signal carried out using SVD method. -- Abstract: This paper presents a method to improve power system stability using IPFC based damping online learning recurrent neural network controllers for damping oscillations in a power system. Parameters of equipped controllers for enhancing dynamical stability at the IPFC are tuned using mathematical methods. Therefore these control parameters are often fixed and are set for particular system configurations or operating points. Multilayer recurrent neural network, which can be tuned for changing system conditions, is used in this paper for effectively damp the oscillations. Training is based on back propagation with adaptive training parameters. This controller is tested to variations in system loading and fault in the power system and its performance is compared with performance of a controller that the phase compensation method is used to set its parameters. Selection of effectiveness damping control signal for the design of robust IPFC damping controller carried out through singular value decomposition (SVD) method. Simulation studies show the superior robustness and stabilizing effect of the proposed controller in comparison with phase compeler in comparison with phase compensation method.

459

Growth and structure of metallic multilayer system

International Nuclear Information System (INIS)

The multichamber ultrahigh vacuum system which has been built for surface and interface studies in thin magnetic films in the Institute of Nuclear Physics in Cracow has been performed. The design and performance of this setup, together with a description of other analytical techniques used for studying the structural and magnetotransport properties of thin multilayered systems has been presented

460

Superconductivity in multilayered Nb/Fe films

International Nuclear Information System (INIS)

Nb/Fe multilayer structures have been prepared by RF magnetron sputtering technique and variation of the superconducting transition temperature (Tc) has been studied as a function of layer thickness of Fe, keeping Nb layer thickness constant. The results show a monotonic decrease of Tc with increase in layer thickness of Fe. (author)

461

Transmission fingerprints in quasiperiodic magnonic multilayers

International Nuclear Information System (INIS)

In this paper we investigated the influence of mirror symmetry on the transmission spectra of quasiperiodic magnonic multilayers arranged according to Fibonacci, Thue-Morse and double period quasiperiodic sequences. We consider that the multilayers composed of two simple cubic Heisenberg ferromagnets with bulk exchange constants JA and JB and spin quantum numbers SA and SB, respectively. The multilayer structure is surrounded by two semi-infinite slabs of a third Heisenberg ferromagnetic material with exchange constant JC and spin quantum number SC. For simplicity, the lattice constant has the same value a in each material, corresponding to epitaxial growth at the interfaces. The transfer matrix treatment was used for the exchange-dominated regime, taking into account the random phase approximation (RPA). Our numerical results illustrate the effects of mirror symmetry on (i) transmission spectra and (ii) transmission fingerprints. - Highlights: ? We model quasiperiodic magnetic multilayers presenting mirror symmetry. ? We investigated the allowed and forbidden bands of magnonic transmission. ? Transmission return maps show the influence of mirror symmetry. ? Mirror symmetry has no effect on the Fibonacci case. ? Mirror symmetry does have effect on the Thue-Morse and double period cases.

462

Diffraction Gratings Based on Multilayer Structures

International Science & Technology Center (ISTC)

Development of High Efficiency Diffraction Gratings on the Basis of Multilayer Structures for Monochromators and Polychromators of X-Ray Synchrotron Radiation and for Ultra-High Spectral Resolution X-Ray Diagnostics in the 0.1 – 10 Kev Energy Range

463

Josephson plasma resonance in superconducting multilayers

International Nuclear Information System (INIS)

We derive an analytical solution for the Josephson plasma resonance of superconducting multilayers. This analytical solution is derived mainly for low Tc systems with magnetic coupling between the superconducting layers, but many features of our results are more general, and thus an application to the recently derived plasma resonance phenomena for high Tc superconductors of the BSCCO type is discussed. (author)

464

Josephson plasma resonance in superconducting multilayers

DEFF Research Database (Denmark)

We derive an analytical solution for the josephson plasma resonance of superconducting multilayers. This analytical solution is derived mainly for low T-c systems with magnetic coupling between the superconducting layers, but many features of our results are more general, and thus an application to the recently derived plasma resonance phenomena for high T-c superconductors of the BSCCO type is discussed.

Pedersen, Niels Falsig

1999-01-01

465

Magnetic Field Inversion in Vortices in Multilayers

We present a description of very dense vortex lattices in highly anisotropic multilayers, for high fields parallel to the layers. We show that a magnetic field inversion can occur away from the center of a vortex, provided the layers are sufficiently far apart.

Theodorakis, S; Theodorakis, Stavros; Leontidis, Epameinondas

1997-01-01

466

Study of thermal conductivity of multilayer insulation

International Nuclear Information System (INIS)

This paper presents experimental determination of the apparent thermal conductivity of multilayer insulation for a cryogenic system. The variation of thermal conductivity with residual gas pressure is studied and the optimum vacuum for good insulating performance is determined. Evaporation loss technique for heat-inleak determination is employed. (author)

467

Thermal expansion properties of thin multilayer films

Under synchrotron radiation white beam exposure, strong mechanical stress can build up in multilayer optics, caused by the thermal mismatch between layer material and substrate material. To study the stability and performance of multilayer optics under heat load, Pd, Cr, and B4C single layers of thicknesses in the nanometer range and [Pd/B4C] multilayers were prepared in the sputter-depositing facility of the ESRF Multilayer Laboratory. Curvature changes versus temperature were measured using a Shack-Hartmann wave front sensor. Films coated on 200 ?m thin Si wafers induced significant curvature changes over a temperature range from 60°C to 200°C. A combined parameter K including Young's modulus and thermal expansion coefficient (CTE) was defined to describe the thermal deformation properties of the thin-film layer. The investigation shows that all three materials in thin film cause less thermal expansion than expected from material properties for bulk material in the literature. In particular, the thermal expansion of B4C films appears to be close to that of the Si substrate.

Cheng, Xianchao; Morawe, Christian; Peffen, Jean-Christophe; Zhang, Lin

2014-09-01

468

Multilayer film with better layer adhesion

International Nuclear Information System (INIS)

A pasteurizable and/or cook-in shrink film is provided with good interlaminar adhesion and good orientability, and characterized by the presence of ethylene alkyl acrylate copolymer in shrink and abuse layers, in adhesive layers, or both. An associated method for making the film is also provided that includes co-extrusion of the multilayer film, optional irradiation, and orientation

469

MDX-010 in Treating Patients With Recurrent or Refractory Lymphoma

Adult Grade III Lymphomatoid Granulomatosis; B-cell Chronic Lymphocytic Leukemia; Cutaneous B-cell Non-Hodgkin Lymphoma; Extranodal Marginal Zone B-cell Lymphoma of Mucosa-associated Lymphoid Tissue; Intraocular Lymphoma; Nodal Marginal Zone B-cell Lymphoma; Recurrent Adult Burkitt Lymphoma; Recurrent Adult Diffuse Large Cell Lymphoma; Recurrent Adult Diffuse Mixed Cell Lymphoma; Recurrent Adult Diffuse Small Cleaved Cell Lymphoma; Recurrent Adult Grade III Lymphomatoid Granulomatosis; Recurrent Adult Hodgkin Lymphoma; Recurrent Adult Immunoblastic Large Cell Lymphoma; Recurrent Adult Lymphoblastic Lymphoma; Recurrent Grade 1 Follicular Lymphoma; Recurrent Grade 2 Follicular Lymphoma; Recurrent Mantle Cell Lymphoma; Recurrent Marginal Zone Lymphoma; Refractory Hairy Cell Leukemia; Small Intestine Lymphoma; Splenic Marginal Zone Lymphoma; Testicular Lymphoma; Waldenström Macroglobulinemia

2014-05-22

470

Inguinal hernia recurrence: Classification and approach

Directory of Open Access Journals (Sweden)

Full Text Available The authors reviewed the records of 2,468 operations of groin hernia in 2,350 patients, including 277 recurrent hernias updated to January 2005. The data obtained - evaluating technique, results and complications - were used to propose a simple anatomo-clinical classification into three types which could be used to plan the surgical strategy:Type R1: first recurrence ?high,? oblique external, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R2: first recurrence ?low,? direct, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R3: all the other recurrences - including femoral recurrences; recurrent groin hernia with big defect (inguinal eventration; multirecurrent hernias; nonreducible, linked with a controlateral primitive or recurrent hernia; and situations compromised from aggravating factors (for example obesity or anyway not easily included in R1 or R2, after pure tissue or mesh repair.

Campanelli Giampiero

2006-01-01

471

Urban legends: recurrent aphthous stomatitis.

Recurrent aphthous stomatitis (RAS) is the most common idiopathic intraoral ulcerative disease in the USA. Aphthae typically occur in apparently healthy individuals, although an association with certain systemic diseases has been reported. Despite the unclear etiopathogenesis, new drug trials are continuously conducted in an attempt to reduce pain and dysfunction. We investigated four controversial topics: (1) Is complex aphthosis a mild form of Behçet's disease (BD)? (2) Is periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) syndrome a distinct medical entity? (3) Is RAS associated with other systemic diseases [e.g., celiac disease (CD) and B12 deficiency]? (4) Are there any new RAS treatments? Results from extensive literature searches, including a systematic review of RAS trials, suggested the following: (1) Complex aphthosis is not a mild form of BD in North America or Western Europe; (2) Diagnostic criteria for PFAPA have low specificity and the characteristics of the oral ulcers warrant further studies; (3) Oral ulcers may be associated with CD; however, these ulcers may not be RAS; RAS is rarely associated with B12 deficiency; nevertheless, B12 treatment may be beneficial, via mechanisms that warrant further study; (4) Thirty-three controlled trials published in the past 6 years reported some effectiveness, although potential for bias was high. PMID:21812866

Baccaglini, L; Lalla, R V; Bruce, A J; Sartori-Valinotti, J C; Latortue, M C; Carrozzo, M; Rogers, R S

2011-11-01

472

Recurrent seizures after lidocaine ingestion.

Lidocaine has a concentration-dependent effect on seizures. Concentrations above 15 ?g/mL frequently result in seizures in laboratory animals and human. We report a case of central nervous system (CNS) lidocaine toxicity and recurrent seizure after erroneous ingestion of lidocaine solution. A 4-year-old boy presented to the Emergency Department of Imam Hospital of Sari in December 2013 due to tonic-clonic generalized seizures approximately 30 min ago. 3 h before seizure, his mother gave him 2 spoons (amount 20-25 cc) lidocaine hydrochloride 2% solution instead of pediatric gripe by mistake. Seizure with generalized tonic-clonic occurred 3 times in home. Neurological examination was essentially unremarkable except for the depressed level of consciousness. Personal and medical history was unremarkable. There was no evidence of intracranial ischemic or hemorrhagic lesions in computed tomography scan. There were no further seizures, the condition of the patient remained stable, and he was discharged 2 days after admission. The use of viscous lidocaine may result in cardiovascular and CNS toxicity, particularly in children. Conservative management is the best option for treatment of lidocaine induced seizure. PMID:25709968

Aminiahidashti, Hamed; Laali, Abolghasem; Nosrati, Nazanin; Jahani, Fatemeh

2015-01-01

473

Recurrent lipoma-like hibernoma.

This is the fifth report of the rare lipoma-like variant of hibernoma, the 19th case reported, and the first documented as recurring after excision. Our patient was a 56-year-old man whose painful lipoma-like hibernoma (LLH) in the pelvis/buttock was initially treated with marginal excision 15 years earlier. Nine years after treatment, the LLH recurred locally. The recurrence was treated with partial excision and embolization, which alleviated symptoms. The disease was stable 26 months after repeat excision and 202 months after initial treatment. LLH is 1 of the 4 histologic variants of hibernomas, which are rare benign lipomatous tumors distinguished from other lipomas by their brown-fat component. Only minimal information is available regarding the clinicopathologic characteristics of the individual variants. Reviewing the literature, we found that LLH predominantly develops in the fifth and sixth decades, and mean age at diagnosis is 41 years (age range, 2-66 years). LLH has a slight male predilection of 10:9. It most commonly develops in the thigh, with other occurrences reported in the hip, trunk, knee, and calf. The magnetic resonance imaging signal of LLH mirrors fat in all sequences. Presence of internal septations and enhancement with contrast are variable. Histologically, LLH is defined as a hibernoma composed predominantly of univacuolated white-fat cells and only scattered granular or pale hibernoma cells. The literature provides only a few treatment details regarding this variant. PMID:20631936

Moretti, Vincent M; de la Cruz, Michael; Lackman, Richard D

2010-06-01

474

Treatment of Recurrent Ovarian Cancer.

Directory of Open Access Journals (Sweden)

Full Text Available Recurrent ovarian cancer is a common clinical problem and the management of eachpatient must be individualized. Diagnosis is usually based on a progressively rising CA-125titre, and a CT scan of the pelvis and abdomen, together with a chest X-ray should be performed.Although there is no study to support immediate treatment in the asymptomaticpatient, our approach is to commence such patients on Tamoxifen. Chemotherapy isreserved for asymptomatic patients or those who progress on Tamoxifen. The longer thetreatment-free interval of 18-24 months. The choice of non-platinum second or subsequentline chemotherapy is based on many factors including likelihood of benefit, potential toxicity,schedule and convenience to the patient, as well as organ function and residual toxicityfrom prior treatment. Aggressive secondary cytoreductive surgery can significantly prolongsurvival in those with a disease-free interval of 24 months or more and in those in whom allmacroscopic disease can be removed. Radiation therapy to the tumour bed following resectionof localized disease may be beneficial in selected patients. Quality of life issues are particularlyimportant for this group of patients and have not been adequately studies.Communication regarding the objectives of therapy is important, and the multidisciplinaryapproach should include palliative care and psycho-social support, in addition to the moretraditional medical options.

Neville F. Hacker

2004-08-01

475

Epidemiology of recurrent spontaneous abortion.

With recent scientific advances leading to better understanding of the immunobiology of recurrent spontaneous abortion (RSA), interest has now focused upon the epidemiology of RSA. A cohort of 214 couples with a history of two or more consecutive abortions were studied for the prevalence of etiologic factors and association with other reproductive failures. The prevalence of causes of RSA in this cohort was compared with etiologic factors among 179 couples with a history of three or more consecutive abortions. The obstetrical histories of 214 women with RSA were analyzed for the total number of pregnancies, live births, stillbirths, spontaneous abortions, ectopic pregnancies, and hydatidiform moles. These numbers were compared with the expected frequency of each in the general population. The prevalence of etiologies among 214 with RSA were as follows: chromosomal-6%, anatomic-1%, hormonal-5%, immunologic-65%, and unexplained-23%. No differences in the prevalence of etiologic factors exist when couples with a history of two or more abortions are compared with three or more abortions. When the number of ectopic pregnancies, molar pregnancies, and stillbirths among 214 women with RSA were compared with the expected numbers, the odds ratios were 2.2 for ectopic pregnancies, 6.0 for molar pregnancies, and 2.3 for stillbirths. These data indicate that no difference in the prevalence of etiologies of RSA exist when couples with two or more abortions are compared with three or more and a comorbidity between RSA and other types of reproductive failure exists. PMID:1741935

Coulam, C B

1991-08-01

476

What Is an Earthquake?: Recurrence Interval

This activity consists of two exercises on determining recurrence interval; one, a hypothetical example, and the other, using real data from the San Andreas Fault. They provide the learner with a good idea of how valuable the concept can be in assessing earthquake hazards, and of a few of the problems associated with determining and correctly applying recurrence intervals in fault studies. The learner is familiarized with the concept of recurrence interval, and several different ways to determine this value for a given fault. There is also a graphing exercise that looks at real-world data from studies made on the San Andreas fault.

477

Recurrences of transient synovitis of the hip.

Thirty six children with transient synovitis of the hip had a total of 80 recurrences, 69 of them personally observed, and 11 described by the mother. No features distinguished the initial attack of those who had a recurrence from that of the 18 children who have not so far had a recurrence. We analysed the total of 126 episodes. In 72 there was evidence of an associated infection from the history, clinical signs, and a raised antistreptolysin O titre or isolation of a pathogen from a throat ...

Illingworth, C. M.

1983-01-01

478

Recurrent spigelian hernia: a case report.

Only seven cases of spigelian hernia recurrence have been previously reported. We report the case of a 75-year-old male patient who presented with extremely large hernia after four unsuccessful suture repairs over 12 years. The abdominal wall defect was repaired with Marlex mesh. The advantage of using prosthetic mesh in both primary and recurrent spigelian hernia is supported by recent clinical research data indicating a generalized collagen metabolism disorder in patients with primary and recurrent hernia. Mesh repair allows for tension-free anatomic restoration of distorted tissues associated with repair failures. PMID:12641349

Losanoff, Julian E; Richman, Bruce W; Jones, James W

2003-02-01

479

[Comparative study of recurrent and bipolar depression].

The purpose of the study was to determine clinical and diagnostic distinctions between the episodes of recurrent depression and bipolar depression. The subjects of the study were 79 patients meeting ICD-10 criteria for either recurrent depressive disorder or bipolar affective disorder. Patient with recurrent depression presented more prominent HDRS symptoms of depressed mood, psychomotor retardation, somatic anxiety, and gastro-intestinal somatic complains. Bipolar patients had more scores related to middle and late insomnia, agitation and suicide. In addition lower length of remission was observed in bipolar depression. The revealed differences should be taken into account in diagnostic and pharmacological treatment of various types of depression. PMID:19996503

Ismailov, F N

2009-11-01

480

Design and development of multilayer vascular graft

Vascular graft is a widely-used medical device for the treatment of vascular diseases such as atherosclerosis and aneurysm as well as for the use of vascular access and pediatric shunt, which are major causes of mortality and morbidity in this world. Dysfunction of vascular grafts often occurs, particularly for grafts with diameter less than 6mm, and is associated with the design of graft materials. Mechanical strength, compliance, permeability, endothelialization and availability are issues of most concern for vascular graft materials. To address these issues, we have designed a biodegradable, compliant graft made of hybrid multilayer by combining an intimal equivalent, electrospun heparin-impregnated poly-epsilon-caprolactone nanofibers, with a medial equivalent, a crosslinked collagen-chitosan-based gel scaffold. The intimal equivalent is designed to build mechanical strength and stability suitable for in vivo grafting and to prevent thrombosis. The medial equivalent is designed to serve as a scaffold for the activity of the smooth muscle cells important for vascular healing and regeneration. Our results have shown that genipin is a biocompatible crosslinker to enhance the mechanical properties of collagen-chitosan based scaffolds, and the degradation time and the activity of smooth muscle cells in the scaffold can be modulated by the crosslinking degree. For vascular grafting and regeneration in vivo, an important design parameter of the hybrid multilayer is the interface adhesion between the intimal and medial equivalents. With diametrically opposite affinities to water, delamination of the two layers occurs. Physical or chemical modification techniques were thus used to enhance the adhesion. Microscopic examination and graft-relevant functional characterizations have been performed to evaluate these techniques. Results from characterization of microstructure and functional properties, including burst strength, compliance, water permeability and suture strength, showed that the multilayer graft possessed properties mimicking those of native vessels. Achieving these FDA-required functional properties is essential because they play critical roles in graft performances in vivo such as thrombus formation, occlusion, healing, and bleeding. In addition, cell studies and animal studies have been performed on the multilayer graft. Our results show that the multilayer graft support mimetic vascular culture of cells and the acellular graft serves as an artery equivalent in vivo to sustain the physiological conditions and promote appropriate cellular activity. In conclusion, the newly-developed hybrid multilayer graft provides a proper balance of biomechanical and biochemical properties and demonstrates the potential for the use of vascular tissue engineering and regeneration.

Madhavan, Krishna

2011-07-01

481

Heterogeneous recurrence monitoring and control of nonlinear stochastic processes

Recurrence is one of the most common phenomena in natural and engineering systems. Process monitoring of dynamic transitions in nonlinear and nonstationary systems is more concerned with aperiodic recurrences and recurrence variations. However, little has been done to investigate the heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. Notably, nonlinear recurrence methodologies are based on homogeneous recurrences, which treat all recurrence states in the same way as black dots, and non-recurrence is white in recurrence plots. Heterogeneous recurrences are more concerned about the variations of recurrence states in terms of state properties (e.g., values and relative locations) and the evolving dynamics (e.g., sequential state transitions). This paper presents a novel approach of heterogeneous recurrence analysis that utilizes a new fractal representation to delineate heterogeneous recurrence states in multiple scales, including the recurrences of both single states and multi-state sequences. Further, we developed a new set of heterogeneous recurrence quantifiers that are extracted from fractal representation in the transformed space. To that end, we