A radial basis classifier for the automatic detection of aspiration in children with dysphagia
Blain Stefanie
2006-07-01
Full Text Available Abstract Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion
M. Safish Mary
2012-04-01
Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.
Kumudha, P; Venkatesan, R
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.
The Matlab Radial Basis Function Toolbox
Scott A. Sarra
2017-03-01
Full Text Available Radial Basis Function (RBF methods are important tools for scattered data interpolation and for the solution of Partial Differential Equations in complexly shaped domains. The most straight forward approach used to evaluate the methods involves solving a linear system which is typically poorly conditioned. The Matlab Radial Basis Function toolbox features a regularization method for the ill-conditioned system, extended precision floating point arithmetic, and symmetry exploitation for the purpose of reducing flop counts of the associated numerical linear algebra algorithms.
Learning Methods for Radial Basis Functions Networks
Neruda, Roman; Kudová, Petra
2005-01-01
Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005
Fast radial basis functions for engineering applications
Biancolini, Marco Evangelos
2017-01-01
This book presents the first “How To” guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF: multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive – and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers’ cultural background, but also due to the numerical complex...
Doubly stochastic radial basis function methods
Yang, Fenglian; Yan, Liang; Ling, Leevan
2018-06-01
We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation. A careful operation count is provided in order to determine the ranges of all the parameters in our methods. The overhead cost for setting up the proposed DSRBF method is O (n2) for function recovery problems with n basis. Numerical experiments confirm that the proposed method not only outperforms constant shape parameter formulation (in terms of accuracy with comparable computational cost) but also the optimal LOOCV formulation (in terms of both accuracy and computational cost).
Spherical radial basis functions, theory and applications
Hubbert, Simon; Morton, Tanya M
2015-01-01
This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging as one of the most promising techniques for solving problems where approximations are needed on the surface of a sphere. The aim of the book is to provide enough theoretical and practical details for the reader to be able to implement the SBF methods to solve real world problems. The authors stress the close connection between the theory of SBFs and that of the more well-known family of radial basis functions (RBFs), which are well-established tools for solving approximation theory problems on more general domains. The unique solvability of the SBF interpolation method for data fitting problems is established and an in-depth investigation of its accuracy is provided. Two chapters are devoted to partial differential equations (PDEs). One deals with the practical implementation of an SBF-based solution to an elliptic PDE and another which describes an SBF approach for solvi...
Neuronal spike sorting based on radial basis function neural networks
Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Point Set Denoising Using Bootstrap-Based Radial Basis Function.
Khang Jie Liew
Full Text Available This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Point Set Denoising Using Bootstrap-Based Radial Basis Function.
Liew, Khang Jie; Ramli, Ahmad; Abd Majid, Ahmad
2016-01-01
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Application of radial basis neural network for state estimation of ...
An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...
Radial basis function neural network in fault detection of automotive ...
Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection
Radial Basis Function Based Quadrature over Smooth Surfaces
2016-03-24
Radial Basis Functions φ(r) Piecewise Smooth (Conditionally Positive Definite) MN Monomial |r|2m+1 TPS thin plate spline |r|2mln|r| Infinitely Smooth...smooth surfaces using polynomial interpolants, while [27] couples Thin - Plate Spline interpolation (see table 1) with Green’s integral formula [29
Surface interpolation with radial basis functions for medical imaging
Carr, J.C.; Beatson, R.K.; Fright, W.R.
1997-01-01
Radial basis functions are presented as a practical solution to the problem of interpolating incomplete surfaces derived from three-dimensional (3-D) medical graphics. The specific application considered is the design of cranial implants for the repair of defects, usually holes, in the skull. Radial basis functions impose few restrictions on the geometry of the interpolation centers and are suited to problems where interpolation centers do not form a regular grid. However, their high computational requirements have previously limited their use to problems where the number of interpolation centers is small (<300). Recently developed fast evaluation techniques have overcome these limitations and made radial basis interpolation a practical approach for larger data sets. In this paper radial basis functions are fitted to depth-maps of the skull's surface, obtained from X-ray computed tomography (CT) data using ray-tracing techniques. They are used to smoothly interpolate the surface of the skull across defect regions. The resulting mathematical description of the skull's surface can be evaluated at any desired resolution to be rendered on a graphics workstation or to generate instructions for operating a computer numerically controlled (CNC) mill
Complexity of Gaussian-Radial-Basis Networks Approximating Smooth Functions
Kainen, P.C.; Kůrková, Věra; Sanguineti, M.
2009-01-01
Roč. 25, č. 1 (2009), s. 63-74 ISSN 0885-064X R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : Gaussian-radial-basis-function networks * rates of approximation * model complexity * variation norms * Bessel and Sobolev norms * tractability of approximation Subject RIV: IN - Informatics, Computer Science Impact factor: 1.227, year: 2009
Organisms modeling: The question of radial basis function networks
Muzy Alexandre
2014-01-01
Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.
On-line learning in radial basis functions networks
Freeman, Jason; Saad, David
1997-01-01
An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
Agus Arif
2014-11-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.
Radial basis function and its application in tourism management
Hu, Shan-Feng; Zhu, Hong-Bin; Zhao, Lei
2018-05-01
In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.
Recent advances in radial basis function collocation methods
Chen, Wen; Chen, C S
2014-01-01
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...
Radial Basis Function Networks for Conversion of Sound Spectra
Carlo Drioli
2001-03-01
Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
The Gaussian radial basis function method for plasma kinetic theory
Hirvijoki, E., E-mail: eero.hirvijoki@chalmers.se [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden); Candy, J.; Belli, E. [General Atomics, PO Box 85608, San Diego, CA 92186-5608 (United States); Embréus, O. [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden)
2015-10-30
Description of a magnetized plasma involves the Vlasov equation supplemented with the non-linear Fokker–Planck collision operator. For non-Maxwellian distributions, the collision operator, however, is difficult to compute. In this Letter, we introduce Gaussian Radial Basis Functions (RBFs) to discretize the velocity space of the entire kinetic system, and give the corresponding analytical expressions for the Vlasov and collision operator. Outlining the general theory, we also highlight the connection to plasma fluid theories, and give 2D and 3D numerical solutions of the non-linear Fokker–Planck equation. Applications are anticipated in both astrophysical and laboratory plasmas. - Highlights: • A radically new method to address the velocity space discretization of the non-linear kinetic equation of plasmas. • Elegant and physically intuitive, flexible and mesh-free. • Demonstration of numerical solution of both 2-D and 3-D non-linear Fokker–Planck relaxation problem.
Adaptive radial basis function mesh deformation using data reduction
Gillebaart, T.; Blom, D. S.; van Zuijlen, A. H.; Bijl, H.
2016-09-01
Radial Basis Function (RBF) mesh deformation is one of the most robust mesh deformation methods available. Using the greedy (data reduction) method in combination with an explicit boundary correction, results in an efficient method as shown in literature. However, to ensure the method remains robust, two issues are addressed: 1) how to ensure that the set of control points remains an accurate representation of the geometry in time and 2) how to use/automate the explicit boundary correction, while ensuring a high mesh quality. In this paper, we propose an adaptive RBF mesh deformation method, which ensures the set of control points always represents the geometry/displacement up to a certain (user-specified) criteria, by keeping track of the boundary error throughout the simulation and re-selecting when needed. Opposed to the unit displacement and prescribed displacement selection methods, the adaptive method is more robust, user-independent and efficient, for the cases considered. Secondly, the analysis of a single high aspect ratio cell is used to formulate an equation for the correction radius needed, depending on the characteristics of the correction function used, maximum aspect ratio, minimum first cell height and boundary error. Based on the analysis two new radial basis correction functions are derived and proposed. This proposed automated procedure is verified while varying the correction function, Reynolds number (and thus first cell height and aspect ratio) and boundary error. Finally, the parallel efficiency is studied for the two adaptive methods, unit displacement and prescribed displacement for both the CPU as well as the memory formulation with a 2D oscillating and translating airfoil with oscillating flap, a 3D flexible locally deforming tube and deforming wind turbine blade. Generally, the memory formulation requires less work (due to the large amount of work required for evaluating RBF's), but the parallel efficiency reduces due to the limited
Meshfree Local Radial Basis Function Collocation Method with Image Nodes
Baek, Seung Ki; Kim, Minjae [Pukyong National University, Busan (Korea, Republic of)
2017-07-15
We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem domain, similarly to the method of images in electrostatics, to perform collocation on the domain boundaries. We can thereby take into account the temperature profile as well as its gradients specified by boundary conditions at the same time, which holds true even for a node where two or more boundaries meet with different boundary conditions. We argue that the image method is computationally efficient when combined with the local RBF collocation method, whereas the addition of image nodes becomes very costly in case of the global collocation. We apply our modified method to a benchmark test of a boundary value problem, and find that this simple modification reduces the maximum error from the analytic solution significantly. The reduction is small for an initial value problem with simpler boundary conditions. We observe increased numerical instability, which has to be compensated for by a sufficient number of sample nodes and/or more careful parameter choices for time integration.
Global sensitivity analysis using a Gaussian Radial Basis Function metamodel
Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua
2016-01-01
Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.
Pricing and simulation for real estate index options: Radial basis point interpolation
Gong, Pu; Zou, Dong; Wang, Jiayue
2018-06-01
This study employs the meshfree radial basis point interpolation (RBPI) for pricing real estate derivatives contingent on real estate index. This method combines radial and polynomial basis functions, which can guarantee the interpolation scheme with Kronecker property and effectively improve accuracy. An exponential change of variables, a mesh refinement algorithm and the Richardson extrapolation are employed in this study to implement the RBPI. Numerical results are presented to examine the computational efficiency and accuracy of our method.
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-01-01
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Exponential Convergence for Numerical Solution of Integral Equations Using Radial Basis Functions
Zakieh Avazzadeh
2014-01-01
Full Text Available We solve some different type of Urysohn integral equations by using the radial basis functions. These types include the linear and nonlinear Fredholm, Volterra, and mixed Volterra-Fredholm integral equations. Our main aim is to investigate the rate of convergence to solve these equations using the radial basis functions which have normic structure that utilize approximation in higher dimensions. Of course, the use of this method often leads to ill-posed systems. Thus we propose an algorithm to improve the results. Numerical results show that this method leads to the exponential convergence for solving integral equations as it was already confirmed for partial and ordinary differential equations.
Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
2013-01-01
be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle
New Method for Mesh Moving Based on Radial Basis Function Interpolation
De Boer, A.; Van der Schoot, M.S.; Bijl, H.
2006-01-01
A new point-by-point mesh movement algorithm is developed for the deformation of unstructured grids. The method is based on using radial basis function, RBFs, to interpolate the displacements of the boundary nodes to the whole flow mesh. A small system of equations has to be solved, only involving
A metric for the Radial Basis Function Network - Application on Real Radar Data
Heiden, R. van der; Groen, F.C.A.
1996-01-01
A Radial Basis Functions (RBF) network for pattern recognition is considered. Classification with such a network is based on distances between patterns, so a metric is always present. Using real radar data, the Euclidean metric is shown to perform poorly - a metric based on the so called Box-Cox
Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude
2010-02-01
Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.
Modeling multivariate time series on manifolds with skew radial basis functions.
Jamshidi, Arta A; Kirby, Michael J
2011-01-01
We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.
ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang
2012-01-01
Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function （Multiquadric, MQ for short） to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.
An enhanced radial basis function network for short-term electricity price forecasting
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
2010-01-01
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)
Machine learning of radial basis function neural network based on Kalman filter: Introduction
Vuković Najdan L.
2014-01-01
Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.
Burken, John J.
2005-01-01
This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.
A prediction method for the wax deposition rate based on a radial basis function neural network
Ying Xie
2017-06-01
Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.
Lima, C S; Barbosa, D; Ramos, J; Tavares, A; Monteiro, L; Carvalho, L
2008-01-01
This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.
Jian Hu
2016-05-01
Full Text Available Uncertainties, including parametric uncertainties and uncertain nonlinearities, always exist in positioning servo systems driven by a hydraulic actuator, which would degrade their tracking accuracy. In this article, an integrated control scheme, which combines adaptive robust control together with radial basis function neural network–based disturbance observer, is proposed for high-accuracy motion control of hydraulic systems. Not only parametric uncertainties but also uncertain nonlinearities (i.e. nonlinear friction, external disturbances, and/or unmodeled dynamics are taken into consideration in the proposed controller. The above uncertainties are compensated, respectively, by adaptive control and radial basis function neural network, which are ultimately integrated together by applying feedforward compensation technique, in which the global stabilization of the controller is ensured via a robust feedback path. A new kind of parameter and weight adaptation law is designed on the basis of Lyapunov stability theory. Furthermore, the proposed controller obtains an expected steady performance even if modeling uncertainties exist, and extensive simulation results in various working conditions have proven the high performance of the proposed control scheme.
Radial basis function neural networks with sequential learning MRAN and its applications
Sundararajan, N; Wei Lu Ying
1999-01-01
This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t
Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks
Ali Reza Zirak; Sobhan Roshani
2016-01-01
A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...
Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA
Alisson C. D. de Souza
2014-09-01
Full Text Available This paper proposes a parallel fixed point radial basis function (RBF artificial neural network (ANN, implemented in a field programmable gate array (FPGA trained online with a least mean square (LMS algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx, with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.
Combustion monitoring of a water tube boiler using a discriminant radial basis network.
Sujatha, K; Pappa, N
2011-01-01
This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-01-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
JOHN WILLIAM BRANCH
2007-01-01
Full Text Available La creación de modelos de objetos reales es una tarea compleja para la cual se ha visto que el uso de técnicas tradicionales de modelamiento tiene restricciones. Para resolver algunos de estos problemas, los sensores de rango basados en láser se usan con frecuencia para muestrear la superficie de un objeto desde varios puntos de vista, lo que resulta en un conjunto de imágenes de rango que son registradas e integradas en un modelo final triangulado. En la práctica, debido a las propiedades reflectivas de la superficie, las oclusiones, y limitaciones de acceso, ciertas áreas de la superficie del objeto usualmente no son muestreadas, dejando huecos que pueden crear efectos indeseables en el modelo integrado. En este trabajo, presentamos un nuevo algoritmo para el llenado de huecos a partir de modelos triangulados. El algoritmo comienza localizando la frontera de las regiones donde están los huecos. Un hueco consiste de un camino cerrado de bordes de los triángulos en la frontera que tienen al menos un borde que no es compartido con ningún otro triangulo. El borde del hueco es entonces adaptado mediante un B-Spline donde la variación promedio de la torsión del la aproximación del B-spline es calculada. Utilizando un simple umbral de la variación promedio a lo largo del borde, se puede clasificar automáticamente, entre huecos reales o generados por intervención humana. Siguiendo este proceso de clasificación, se usa entonces una versión automatizada del interpolador de funciones de base radial para llenar el interior del hueco usando los bordes vecinos.
Radial basis functions in mathematical modelling of flow boiling in minichannels
Hożejowska Sylwia
2017-01-01
Full Text Available The paper addresses heat transfer processes in flow boiling in a vertical minichannel of 1.7 mm depth with a smooth heated surface contacting fluid. The heated element for FC-72 flowing in a minichannel was a 0.45 mm thick plate made of Haynes-230 alloy. An infrared camera positioned opposite the central, axially symmetric part of the channel measured the plate temperature. K-type thermocouples and pressure converters were installed at the inlet and outlet of the minichannel. In the study radial basis functions were used to solve a problem concerning heat transfer in a heated plate supplied with the controlled direct current. According to the model assumptions, the problem is treated as twodimensional and governed by the Poisson equation. The aim of the study lies in determining the temperature field and the heat transfer coefficient. The results were verified by comparing them with those obtained by the Trefftz method.
Huaiqing Zhang
2014-01-01
Full Text Available The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.
Yunfeng Wu
2014-01-01
Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.
Big geo data surface approximation using radial basis functions: A comparative study
Majdisova, Zuzana; Skala, Vaclav
2017-12-01
Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.
Hong, Xia
2006-07-01
In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.
Radial Basis Function (RBF Interpolation and Investigating its Impact on Rainfall Duration Mapping
Hassan Derakhshan
2012-01-01
Full Text Available The missing data in database must be reproduced primarily by appropriate interpolation techniques. Radial basis function (RBF interpolators can play a significant role in data completion of precipitation mapping. Five RBF techniques were engaged to be employed in compensating the missing data in event-wised dataset of Upper Paramatta River Catchment in the western suburbs of Sydney, Australia. The related shape parameter, C, of RBFs was optimized for first event of database during a cross-validation process. The Normalized mean square error (NMSE, percent average estimation error (PAEE and coefficient of determination (R2 were the statistics used as validation tools. Results showed that the multiquadric RBF technique with the least error, best suits compensation of the related database.
Chen, Zhaoxue; Chen, Hao
2014-01-01
A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.
Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
Wei Liu
2013-01-01
Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.
Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin
2006-01-01
High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)
THE ALGORITHM OF MESHFREE METHOD OF RADIAL BASIS FUNCTIONS IN TASKS OF UNDERGROUND HYDROMECHANICS
N. V. Medvid
2016-01-01
Full Text Available A Mathematical model of filtering consolidation in the body of soil dam with conduit andwashout zone in two-dimensional case is considered. The impact of such technogenic factors as temperature, salt concentration, subsidence of upper boundary and interior points of the dam with time is taken into account. The software to automate the calculation of numerical solution of the boundary problem by radial basis functions has been created, which enables to conduct numerical experiments by varying the input parameters and shape. The influence of the presence of conduit and washout zone on the pressure, temperature and concentration of salts in the dam body at different time intervals isinvestigated. A number of numerical experiments is conducted and the analysis of dam accidents is performed.
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Radial basis function neural network for power system load-flow
Karami, A.; Mohammadi, M.S.
2008-01-01
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
Li, Bo; Rui, Xiaoting
2018-01-01
Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.
Jaime Alberto Echeverri Arias
2009-07-01
Full Text Available La eliminación del ruido impulsivo es un problema clásico del procesado no lineal para el mejoramiento de imágenes y las funciones de base radial de soporte global son útiles para enfrentarlo. Este trabajo presenta una técnica de interpolación que disminuye eficientemente el ruido impulsivo en imágenes, mediante el uso de interpolante obtenido por funciones de base radial en el marco de la investigación enfocada en el desarrollo de un Sistema de recuperación de imágenes de recursos acuáticos amazónicos. Esta técnica primero etiqueta los píxeles de la imagen que son ruidosos y, mediante la interpolación, genera un valor de reconstrucción de dicho píxel usando sus vecinos. Los resultados obtenidos son comparables y muchas veces mejores que otras técnicas ya publicadas y reconocidas. Según el análisis de resultados, se puede aplicar a imágenes con altas tasas de ruido, manteniendo un bajo error de reconstrucción de los píxeles "ruidosos", así como la calidad visual.Global support radial base functions are effective in eliminating impulsive noise in non-linear processing. This paper introduces an interpolation technique which efficiently reduces image impulsive noise by means of an interpolant obtained through radial base functions. These functions have been used in a research project designed to develop a system for the recovery of images of Amazonian aquatic resources. This technique starts with the tagging by interpolation of noisy image pixels. Thus, a value of reconstruction for the noisy pixels is generated using neighboring pixels. The results obtained with this technique have proved comparable and often better than those obtained with previously known techniques. According to results analysis, this technique can be successfully applied on images with high noise levels. The results are low error in noisy pixel reconstruction and better visual quality.
Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks
Zhihong Liao
2017-11-01
Full Text Available A radial basis function network (RBFN method is proposed to reconstruct daily Sea surface temperatures (SSTs with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI v2 daily 0.25° SST (OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.
Gisele Tessari Santos
2009-08-01
Full Text Available A large number of financial engineering problems involve non-linear equations with non-linear or time-dependent boundary conditions. Despite available analytical solutions, many classical and modified forms of the well-known Black-Scholes (BS equation require fast and accurate numerical solutions. This work introduces the radial basis function (RBF method as applied to the solution of the BS equation with non-linear boundary conditions, related to path-dependent barrier options. Furthermore, the diffusional method for solving advective-diffusive equations is explored as to its effectiveness to solve BS equations. Cubic and Thin-Plate Spline (TPS radial basis functions were employed and evaluated as to their effectiveness to solve barrier option problems. The numerical results, when compared against analytical solutions, allow affirming that the RBF method is very accurate and easy to be implemented. When the RBF method is applied, the diffusional method leads to the same results as those obtained from the classical formulation of Black-Scholes equation.Muitos problemas de engenharia financeira envolvem equações não-lineares com condições de contorno não-lineares ou dependentes do tempo. Apesar de soluções analíticas disponíveis, várias formas clássicas e modificadas da conhecida equação de Black-Scholes (BS requerem soluções numéricas rápidas e acuradas. Este trabalho introduz o método de função de base radial (RBF aplicado à solução da equação BS com condições de contorno não-lineares relacionadas a opções de barreira dependentes da trajetória. Além disso, explora-se o método difusional para solucionar equações advectivo-difusivas quanto à sua efetividade para solucionar equações BS. Utilizam-se funções de base radial Cúbica e Thin-Plate Spline (TPS, aplicadas à solução de problemas de opções de barreiras. Os resultados numéricos, quando comparados com as soluções analíticas, permitem afirmar
GRACE L1b inversion through a self-consistent modified radial basis function approach
Yang, Fan; Kusche, Juergen; Rietbroek, Roelof; Eicker, Annette
2016-04-01
Implementing a regional geopotential representation such as mascons or, more general, RBFs (radial basis functions) has been widely accepted as an efficient and flexible approach to recover the gravity field from GRACE (Gravity Recovery and Climate Experiment), especially at higher latitude region like Greenland. This is since RBFs allow for regionally specific regularizations over areas which have sufficient and dense GRACE observations. Although existing RBF solutions show a better resolution than classical spherical harmonic solutions, the applied regularizations cause spatial leakage which should be carefully dealt with. It has been shown that leakage is a main error source which leads to an evident underestimation of yearly trend of ice-melting over Greenland. Unlike some popular post-processing techniques to mitigate leakage signals, this study, for the first time, attempts to reduce the leakage directly in the GRACE L1b inversion by constructing an innovative modified (MRBF) basis in place of the standard RBFs to retrieve a more realistic temporal gravity signal along the coastline. Our point of departure is that the surface mass loading associated with standard RBF is smooth but disregards physical consistency between continental mass and passive ocean response. In this contribution, based on earlier work by Clarke et al.(2007), a physically self-consistent MRBF representation is constructed from standard RBFs, with the help of the sea level equation: for a given standard RBF basis, the corresponding MRBF basis is first obtained by keeping the surface load over the continent unchanged, but imposing global mass conservation and equilibrium response of the oceans. Then, the updated set of MRBFs as well as standard RBFs are individually employed as the basis function to determine the temporal gravity field from GRACE L1b data. In this way, in the MRBF GRACE solution, the passive (e.g. ice melting and land hydrology response) sea level is automatically
DT Wiyanti
2013-07-01
Full Text Available Salah satu metode peramalan yang paling dikembangkan saat ini adalah time series, yakni menggunakan pendekatan kuantitatif dengan data masa lampau yang dijadikan acuan untuk peramalan masa depan. Berbagai penelitian telah mengusulkan metode-metode untuk menyelesaikan time series, di antaranya statistik, jaringan syaraf, wavelet, dan sistem fuzzy. Metode-metode tersebut memiliki kekurangan dan keunggulan yang berbeda. Namun permasalahan yang ada dalam dunia nyata merupakan masalah yang kompleks. Satu metode saja mungkin tidak mampu mengatasi masalah tersebut dengan baik. Dalam artikel ini dibahas penggabungan dua buah metode yaitu Auto Regressive Integrated Moving Average (ARIMA dan Radial Basis Function (RBF. Alasan penggabungan kedua metode ini adalah karena adanya asumsi bahwa metode tunggal tidak dapat secara total mengidentifikasi semua karakteristik time series. Pada artikel ini dibahas peramalan terhadap data Indeks Harga Perdagangan Besar (IHPB dan data inflasi komoditi Indonesia; kedua data berada pada rentang tahun 2006 hingga beberapa bulan di tahun 2012. Kedua data tersebut masing-masing memiliki enam variabel. Hasil peramalan metode ARIMA-RBF dibandingkan dengan metode ARIMA dan metode RBF secara individual. Hasil analisa menunjukkan bahwa dengan metode penggabungan ARIMA dan RBF, model yang diberikan memiliki hasil yang lebih akurat dibandingkan dengan penggunaan salah satu metode saja. Hal ini terlihat dalam visual plot, MAPE, dan RMSE dari semua variabel pada dua data uji coba.Â The accuracy of time series forecasting is the subject of many decision-making processes. Time series use a quantitative approach to employ data from the past to make forecast for the future. Many researches have proposed several methods to solve time series, such as using statistics, neural networks, wavelets, and fuzzy systems. These methods have different advantages and disadvantages. But often the problem in the real world is just too complex that a
Eyad K Almaita
2017-03-01
Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application. International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17
Radial basis function networks applied to DNBR calculation in digital core protection systems
Lee, Gyu-Cheon; Heung Chang, Soon
2003-01-01
The nuclear power plant has to be operated with sufficient margin from the specified DNBR limit for assuring its safety. The digital core protection system calculates on-line real-time DNBR by using a complex subchannel analysis program, and triggers a reliable reactor shutdown if the calculated DNBR approaches the specified limit. However, it takes a relatively long calculation time even for a steady state condition, which may have an adverse effect on the operation flexibility. To overcome the drawback, a new method using a radial basis function network is presented in this paper. Nonparametric training approach is utilized, which shows dramatic reduction of the training time, no tedious heuristic process for optimizing parameters, and no local minima problem during the training. The test results show that the predicted DNBR is within about ±2% deviation from the target DNBR for the fixed axial flux shape case. For the variable axial flux case including severely skewed shapes that appeared during accidents, the deviation is within about ±10%. The suggested method could be the alternative that can calculate DNBR very quickly while guaranteeing the plant safety
Wang, Zhiheng
2014-12-10
A meshless local radial basis function method is developed for two-dimensional incompressible Navier-Stokes equations. The distributed nodes used to store the variables are obtained by the philosophy of an unstructured mesh, which results in two main advantages of the method. One is that the unstructured nodes generation in the computational domain is quite simple, without much concern about the mesh quality; the other is that the localization of the obtained collocations for the discretization of equations is performed conveniently with the supporting nodes. The algebraic system is solved by a semi-implicit pseudo-time method, in which the convective and source terms are explicitly marched by the Runge-Kutta method, and the diffusive terms are implicitly solved. The proposed method is validated by several benchmark problems, including natural convection in a square cavity, the lid-driven cavity flow, and the natural convection in a square cavity containing a circular cylinder, and very good agreement with the existing results are obtained.
Gaussian Radial Basis Function for Efficient Computation of Forest Indirect Illumination
Abbas, Fayçal; Babahenini, Mohamed Chaouki
2018-06-01
Global illumination of natural scenes in real time like forests is one of the most complex problems to solve, because the multiple inter-reflections between the light and material of the objects composing the scene. The major problem that arises is the problem of visibility computation. In fact, the computing of visibility is carried out for all the set of leaves visible from the center of a given leaf, given the enormous number of leaves present in a tree, this computation performed for each leaf of the tree which also reduces performance. We describe a new approach that approximates visibility queries, which precede in two steps. The first step is to generate point cloud representing the foliage. We assume that the point cloud is composed of two classes (visible, not-visible) non-linearly separable. The second step is to perform a point cloud classification by applying the Gaussian radial basis function, which measures the similarity in term of distance between each leaf and a landmark leaf. It allows approximating the visibility requests to extract the leaves that will be used to calculate the amount of indirect illumination exchanged between neighbor leaves. Our approach allows efficiently treat the light exchanges in the scene of a forest, it allows a fast computation and produces images of good visual quality, all this takes advantage of the immense power of computation of the GPU.
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-09-18
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.
Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm
G. Trejo-Caballero
2015-01-01
Full Text Available Curve fitting is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of data points, possibly noisy, the goal is to build a compact representation of the curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Despite the large number of methods available to tackle this problem, it remains challenging and elusive. In this paper, a new method to tackle such problem using strictly a linear combination of radial basis functions (RBFs is proposed. To be more specific, we divide the parameter search space into linear and nonlinear parameter subspaces. We use a hierarchical genetic algorithm (HGA to minimize a model selection criterion, which allows us to automatically and simultaneously determine the nonlinear parameters and then, by the least-squares method through Singular Value Decomposition method, to compute the linear parameters. The method is fully automatic and does not require subjective parameters, for example, smooth factor or centre locations, to perform the solution. In order to validate the efficacy of our approach, we perform an experimental study with several tests on benchmarks smooth functions. A comparative analysis with two successful methods based on RBF networks has been included.
Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman
2017-02-01
The soil sorption partition coefficient logK oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.
Misganaw Abebe
2017-11-01
Full Text Available Springback in multi-point dieless forming (MDF is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE. Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.
Tatar Afshin
2016-03-01
Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.
Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M
2011-11-01
Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.
Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs
Bollig, Evan F.; Flyer, Natasha; Erlebacher, Gordon
2012-01-01
This paper presents parallelization strategies for the radial basis function-finite difference (RBF-FD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation and scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the first implementation of the RBF-FD method to leverage GPU accelerators for the solution of PDEs. Additionally, this implementation is the first to span both multiple CPUs and multiple GPUs. OpenCL kernels target the GPUs and inter-processor communication and synchronization is managed by the Message Passing Interface (MPI). We verify our implementation of the RBF-FD method with two hyperbolic PDEs on the sphere, and demonstrate up to 9x speedup on a commodity GPU with unoptimized kernel implementations. On a high performance cluster, the method achieves up to 7x speedup for the maximum problem size of 27,556 nodes.
Bucha, B.; Bezděk, Aleš; Sebera, Josef; Janak, J.
2015-01-01
Roč. 36, č. 6 (2015), s. 773-801 ISSN 0169-3298 R&D Projects: GA ČR GA13-36843S Grant - others:SAV(SK) VEGA 1/0954/15 Institutional support: RVO:67985815 Keywords : spherical radial basis functions * spherical harmonics * geopotential Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 3.622, year: 2015
Piret, Cé cile
2012-01-01
Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper
Kayri, Murat
2015-01-01
The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…
Lee, Kyo-Beum; Bae, C.H.; Blaabjerg, Frede
2005-01-01
A scheme to estimate the moment of inertia in a servo motor drive system at very low speed is proposed. The typical speed estimation scheme used in most servo systems operated at low speed is highly sensitive to variations in the moment of inertia. An observer that uses a radial basis function...
Casa, L D C; Krueger, P S
2013-01-01
Unstructured three-dimensional fluid velocity data were interpolated using Gaussian radial basis function (RBF) interpolation. Data were generated to imitate the spatial resolution and experimental uncertainty of a typical implementation of defocusing digital particle image velocimetry. The velocity field associated with a steadily rotating infinite plate was simulated to provide a bounded, fully three-dimensional analytical solution of the Navier–Stokes equations, allowing for robust analysis of the interpolation accuracy. The spatial resolution of the data (i.e. particle density) and the number of RBFs were varied in order to assess the requirements for accurate interpolation. Interpolation constraints, including boundary conditions and continuity, were included in the error metric used for the least-squares minimization that determines the interpolation parameters to explore methods for improving RBF interpolation results. Even spacing and logarithmic spacing of RBF locations were also investigated. Interpolation accuracy was assessed using the velocity field, divergence of the velocity field, and viscous torque on the rotating boundary. The results suggest that for the present implementation, RBF spacing of 0.28 times the boundary layer thickness is sufficient for accurate interpolation, though theoretical error analysis suggests that improved RBF positioning may yield more accurate results. All RBF interpolation results were compared to standard Gaussian weighting and Taylor expansion interpolation methods. Results showed that RBF interpolation improves interpolation results compared to the Taylor expansion method by 60% to 90% based on the average squared velocity error and provides comparable velocity results to Gaussian weighted interpolation in terms of velocity error. RMS accuracy of the flow field divergence was one to two orders of magnitude better for the RBF interpolation compared to the other two methods. RBF interpolation that was applied to
Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.
2012-04-01
Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.
Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko
2018-04-01
Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.
Abdelkarim M. Ertiame; D. W. Yu; D. L. Yu; J. B. Gomm
2015-01-01
In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is emplo...
Vrankar, L.; Turk, G.; Runovc, F.; Kansa, E.J.
2006-01-01
Many heat-transfer problems involve a change of phase of material due to solidification or melting. Applications include: the safety studies of nuclear reactors (molten core concrete interaction), the drilling of high ice-content soil, the storage of thermal energy, etc. These problems are often called Stefan's or moving boundary value problems. Mathematically, the interface motion is expressed implicitly in an equation for the conservation of thermal energy at the interface (Stefan's conditions). This introduces a non-linear character to the system which treats each problem somewhat uniquely. The exact solution of phase change problems is limited exclusively to the cases in which e.g. the heat transfer regions are infinite or semi-infinite one dimensional-space. Therefore, solution is obtained either by approximate analytical solution or by numerical methods. Finite-difference methods and finite-element techniques have been used extensively for numerical solution of moving boundary problems. Recently, the numerical methods have focused on the idea of using a mesh-free methodology for the numerical solution of partial differential equations based on radial basis functions. In our case we will study solid-solid transformation. The numerical solutions will be compared with analytical solutions. Actually, in our work we will examine usefulness of radial basis functions (especially multiquadric-MQ) for one-dimensional Stefan's problems. The position of the moving boundary will be simulated by moving grid method. The resultant system of RBF-PDE will be solved by affine space decomposition. (author)
Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia
Pragya Bagwari
2017-08-01
Full Text Available Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker’s capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input.
Bogaers, Alfred EJ
2016-10-01
Full Text Available words, gB = [ φBA PB ] [ MAA PA P TA 0 ]−1 [ gA 0 ] . (15) NAME: DEFINITION C0 compactly supported piecewise polynomial (C0): (1− (||x|| /r))2+ C2 compactly supported piecewise polynomial (C2): (1− (||x|| /r))4+ (4 (||x|| /r) + 1) Thin-plate spline (TPS... a numerical comparison to Kriging and the moving least-squares method, see Krishnamurthy [16]). RBF interpolation is based on fitting a series of splines, or basis functions to interpolate information from one point cloud to another. Let us assume we...
Jaime A. Echeverri A.
2007-07-01
Full Text Available En este trabajo se muestra la utilización de funciones de base radial de soporte compacto para la reconstrucción tridimensional de rostros. En trabajos anteriores se habían explorado diferentes técnicas y diferentes funciones de base radial para reconstrucción de superficies; ahora presentamos los algoritmos y los resultados de la utilización de funciones de base radial de soporte compacto las cuales presentan ventajas comparativas en términos del tiempo de construcción de un interpolante para la reconstrucción. Se presentan comparaciones con técnicas ampliamente utilizadas en este campo y se detalla el proceso global de reconstrucción de superficies.In previous works, we have explored several radial basis techniques and functions for the reconstruction of surfaces. We now present the use of compact support radial basis functions for the tri-dimensional reconstruction of human faces. Therefore, we present algorithms and results coming from the application of compact support radial basis functions which have revealed comparative advantages in terms of the amount of time needed for the construction of an interpolant to be used in the reconstruction. We are also presenting some comparisons with techniques widely used in this field and we explain in detail the global process for the surfaces reconstruction.
Le, Nguyen-Quoc-Khanh; Nguyen, Trinh-Trung-Duong; Ou, Yu-Yen
2017-05-01
The electron transport proteins have an important role in storing and transferring electrons in cellular respiration, which is the most proficient process through which cells gather energy from consumed food. According to the molecular functions, the electron transport chain components could be formed with five complexes with several different electron carriers and functions. Therefore, identifying the molecular functions in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. This work includes two phases for discriminating electron transport proteins from transport proteins and classifying categories of five complexes in electron transport proteins. In the first phase, the performances from PSSM with AAIndex feature set were successful in identifying electron transport proteins in transport proteins with achieved sensitivity of 73.2%, specificity of 94.1%, and accuracy of 91.3%, with MCC of 0.64 for independent data set. With the second phase, our method can approach a precise model for identifying of five complexes with different molecular functions in electron transport proteins. The PSSM with AAIndex properties in five complexes achieved MCC of 0.51, 0.47, 0.42, 0.74, and 1.00 for independent data set, respectively. We suggest that our study could be a power model for determining new proteins that belongs into which molecular function of electron transport proteins. Copyright © 2017 Elsevier Inc. All rights reserved.
Chen, Qian; Liu, Guohai; Xu, Dezhi; Xu, Liang; Xu, Gaohong; Aamir, Nazir
2018-05-01
This paper proposes a new decoupled control for a five-phase in-wheel fault-tolerant permanent magnet (IW-FTPM) motor drive, in which radial basis function neural network inverse (RBF-NNI) and internal model control (IMC) are combined. The RBF-NNI system is introduced into original system to construct a pseudo-linear system, and IMC is used as a robust controller. Hence, the newly proposed control system incorporates the merits of the IMC and RBF-NNI methods. In order to verify the proposed strategy, an IW-FTPM motor drive is designed based on dSPACE real-time control platform. Then, the experimental results are offered to verify that the d-axis current and the rotor speed are successfully decoupled. Besides, the proposed motor drive exhibits strong robustness even under load torque disturbance.
Martin, Bradley, E-mail: brma7253@colorado.edu; Fornberg, Bengt, E-mail: Fornberg@colorado.edu
2017-04-15
In a previous study of seismic modeling with radial basis function-generated finite differences (RBF-FD), we outlined a numerical method for solving 2-D wave equations in domains with material interfaces between different regions. The method was applicable on a mesh-free set of data nodes. It included all information about interfaces within the weights of the stencils (allowing the use of traditional time integrators), and was shown to solve problems of the 2-D elastic wave equation to 3rd-order accuracy. In the present paper, we discuss a refinement of that method that makes it simpler to implement. It can also improve accuracy for the case of smoothly-variable model parameter values near interfaces. We give several test cases that demonstrate the method solving 2-D elastic wave equation problems to 4th-order accuracy, even in the presence of smoothly-curved interfaces with jump discontinuities in the model parameters.
Piret, Cécile
2012-05-01
Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper, we investigate methods to solve PDEs on arbitrary stationary surfaces embedded in . R3 using the RBF method. We present three RBF-based methods that easily discretize surface differential operators. We take advantage of the meshfree character of RBFs, which give us a high accuracy and the flexibility to represent the most complex geometries in any dimension. Two out of the three methods, which we call the orthogonal gradients (OGr) methods are the result of our work and are hereby presented for the first time. © 2012 Elsevier Inc.
Permoon, M. R.; Haddadpour, H. [Sharif University of Tech, Tehran (Iran, Islamic Republic of); Rashidinia, J.; Parsa, A.; Salehi, R. [Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)
2016-07-15
In this paper, the forced vibrations of the fractional viscoelastic beam with the Kelvin-Voigt fractional order constitutive relationship is studied. The equation of motion is derived from Newton's second law and the Galerkin method is used to discretize the equation of motion in to a set of linear ordinary differential equations. For solving the discretized equations, the radial basis functions and Sinc quadrature rule are used. In order to show the effectiveness and accuracy of this method, some test problem are considered, and it is shown that the obtained results are in very good agreement with exact solution. In the following, the proposed numerical solution is applied to exploring the effects of fractional parameters on the response of the beam and finally some conclusions are outlined.
Lee, Kyo-Beum; Blaabjerg, Frede
2005-01-01
A new scheme to estimate the moment of inertia in the servo motor drive system in very low speed is proposed in this paper. The speed estimation scheme in most servo drive systems for low speed operation is sensitive to the variation of machine parameter, especially the moment of inertia....... To estimate the motor inertia value, the observer using the Radial Basis Function Network (RBFN) is applied. A control law for stabilizing the system and adaptive laws for updating both of the weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable...... in the sense of Lyapunov. The effectiveness of the proposed inertia estimation is verified by simulations and experiments. It is concluded that the speed control performance in low speed region is improved with the proposed disturbance observer using RBFN....
Vavalle, Nicholas A; Schoell, Samantha L; Weaver, Ashley A; Stitzel, Joel D; Gayzik, F Scott
2014-11-01
Human body finite element models (FEMs) are a valuable tool in the study of injury biomechanics. However, the traditional model development process can be time-consuming. Scaling and morphing an existing FEM is an attractive alternative for generating morphologically distinct models for further study. The objective of this work is to use a radial basis function to morph the Global Human Body Models Consortium (GHBMC) average male model (M50) to the body habitus of a 95th percentile male (M95) and to perform validation tests on the resulting model. The GHBMC M50 model (v. 4.3) was created using anthropometric and imaging data from a living subject representing a 50th percentile male. A similar dataset was collected from a 95th percentile male (22,067 total images) and was used in the morphing process. Homologous landmarks on the reference (M50) and target (M95) geometries, with the existing FE node locations (M50 model), were inputs to the morphing algorithm. The radial basis function was applied to morph the FE model. The model represented a mass of 103.3 kg and contained 2.2 million elements with 1.3 million nodes. Simulations of the M95 in seven loading scenarios were presented ranging from a chest pendulum impact to a lateral sled test. The morphed model matched anthropometric data to within a rootmean square difference of 4.4% while maintaining element quality commensurate to the M50 model and matching other anatomical ranges and targets. The simulation validation data matched experimental data well in most cases.
Farivar, Faezeh; Aliyari Shoorehdeli, Mahdi; Nekoui, Mohammad Ali; Teshnehlab, Mohammad
2012-01-01
Highlights: ► A systematic procedure for GPS of unknown heavy chaotic gyroscope systems. ► Proposed methods are based on Lyapunov stability theory. ► Without calculating Lyapunov exponents and Eigen values of the Jacobian matrix. ► Capable to extend for a variety of chaotic systems. ► Useful for practical applications in the future. - Abstract: This paper proposes the chaos control and the generalized projective synchronization methods for heavy symmetric gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic motions of symmetric gyroscopes. In this paper, the switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. Using the neural variable structure control technique, control laws are established which guarantees the chaos control and the generalized projective synchronization of unknown gyroscope systems. In the neural variable structure control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimator are derived in the sense of Lyapunov function. Thus, the unknown gyro systems can be guaranteed to be asymptotically stable. Also, the proposed method can achieve the control objectives. Numerical simulations are presented to
Dalmasse, Kevin; Nychka, Douglas W.; Gibson, Sarah E.; Fan, Yuhong; Flyer, Natasha
2016-01-01
The Coronal Multichannel Polarimeter (CoMP) routinely performs coronal polarimetric measurements using the Fe XIII 10747 and 10798 lines, which are sensitive to the coronal magnetic field. However, inverting such polarimetric measurements into magnetic field data is a difficult task because the corona is optically thin at these wavelengths and the observed signal is therefore the integrated emission of all the plasma along the line of sight. To overcome this difficulty, we take on a new approach that combines a parameterized 3D magnetic field model with forward modeling of the polarization signal. For that purpose, we develop a new, fast and efficient, optimization method for model-data fitting: the Radial-basis-functions Optimization Approximation Method (ROAM). Model-data fitting is achieved by optimizing a user-specified log-likelihood function that quantifies the differences between the observed polarization signal and its synthetic/predicted analog. Speed and efficiency are obtained by combining sparse evaluation of the magnetic model with radial-basis-function (RBF) decomposition of the log-likelihood function. The RBF decomposition provides an analytical expression for the log-likelihood function that is used to inexpensively estimate the set of parameter values optimizing it. We test and validate ROAM on a synthetic test bed of a coronal magnetic flux rope and show that it performs well with a significantly sparse sample of the parameter space. We conclude that our optimization method is well-suited for fast and efficient model-data fitting and can be exploited for converting coronal polarimetric measurements, such as the ones provided by CoMP, into coronal magnetic field data.
Ángel Gutiérrez
2015-04-01
Full Text Available The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space. Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network. The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique. A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.
Nan YU
2014-09-01
Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.
A hybrid radial basis function-pseudospectral method for thermal convection in a 3-D spherical shell
Wright, G. B.
2010-07-01
A novel hybrid spectral method that combines radial basis function (RBF) and Chebyshev pseudospectral methods in a "2 + 1" approach is presented for numerically simulating thermal convection in a 3-D spherical shell. This is the first study to apply RBFs to a full 3-D physical model in spherical geometry. In addition to being spectrally accurate, RBFs are not defined in terms of any surface-based coordinate system such as spherical coordinates. As a result, when used in the lateral directions, as in this study, they completely circumvent the pole issue with the further advantage that nodes can be "scattered" over the surface of a sphere. In the radial direction, Chebyshev polynomials are used, which are also spectrally accurate and provide the necessary clustering near the boundaries to resolve boundary layers. Applications of this new hybrid methodology are given to the problem of convection in the Earth\\'s mantle, which is modeled by a Boussinesq fluid at infinite Prandtl number. To see whether this numerical technique warrants further investigation, the study limits itself to an isoviscous mantle. Benchmark comparisons are presented with other currently used mantle convection codes for Rayleigh number (Ra) 7 × 10^{3} and 10^{5}. Results from a Ra = 10^{6} simulation are also given. The algorithmic simplicity of the code (mostly due to RBFs) allows it to be written in less than 400 lines of MATLAB and run on a single workstation. We find that our method is very competitive with those currently used in the literature. Copyright 2010 by the American Geophysical Union.
Meng, Qinggang; Lee, M. H.
2007-03-01
Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.
Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong
2008-06-01
A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.
Ali Mansourkhaki
2018-01-01
Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.
Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam
2007-01-01
Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported
Regis, Rommel G.
2014-02-01
This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
In this paper, a novel method is proposed for predicting the density of liquid phase in stratified regime of liquid-gas two phase flows by utilizing dual modality densitometry technique and artificial neural network (ANN) model of radial basis function (RBF). The detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors for registering transmitted and scattered photons. At the first step, a Monte Carlo simulation model was utilized to obtain the optimum position for the scattering detector in dual modality densitometry configuration. At the next step, an experimental setup was designed based on obtained optimum position for detectors from simulation in order to generate the required data for training and testing the ANN. The results show that the proposed approach could be successfully applied for predicting the density of liquid phase in stratified regime of gas-liquid two phase flows with mean relative error (MRE) of less than 0.701. - Highlights: • Density of liquid phase in stratified regime of two phase flows was predicted. • Combination of dual modality densitometry technique and ANN was utilized. • Detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors. • MCNP simulation was done to obtain the optimum position for the scattering detector. • An experimental setup was designed to generate the required data for training the ANN.
HU Qi-guo
2017-01-01
Full Text Available For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel’s thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver’s head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively. The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly.
Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli
2013-03-01
Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.
Seng-Chi Chen
2014-01-01
Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.
Jingwen Tian
2013-02-01
Full Text Available Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro-mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real-time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.
Schmidt, J.; Piret, C.; Zhang, N.; Kadlec, B. J.; Liu, Y.; Yuen, D. A.; Wright, G. B.; Sevre, E. O.
2008-12-01
The faster growth curves in the speed of GPUs relative to CPUs in recent years and its rapidly gained popularity has spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations and producing the attendant visualization. We are concerned with modeling tsunami waves, where computational time is of extreme essence, for broadcasting warnings. In order to test the efficacy of the GPU on the set of shallow-water equations, we employed the NVIDIA board 8600M GT on a MacBook Pro. We have compared the relative speeds between the CPU and the GPU on a single processor for two types of spatial discretization based on second-order finite-differences and radial basis functions. RBFs are a more novel method based on a gridless and a multi- scale, adaptive framework. Using the NVIDIA 8600M GT, we received a speed up factor of 8 in favor of GPU for the finite-difference method and a factor of 7 for the RBF scheme. We have also studied the atmospheric dynamics problem of swirling flows over a spherical surface and found a speed-up of 5.3 using the GPU. The time steps employed for the RBF method are larger than those used in finite-differences, because of the much fewer number of nodal points needed by RBF. Thus, in modeling the same physical time, RBF acting in concert with GPU would be the fastest way to go.
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137 Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%. - Highlights: • Flow regime and void fraction were determined in two phase flows independent of the liquid phase density changes. • An experimental structure was set up and the required data was obtained. • 3 detectors and one gamma source were used in detection geometry. • RBF networks were utilized for flow regime and void fraction determination.
A S Yogesh
2011-01-01
Full Text Available In the present case, we have reported a unilateral variation of the radial and musculocutaneous nerves on the left side in a 64-year-old male cadaver. The radial nerve supplied all the heads of the triceps brachii muscle and gave cutaneous branches such as lower lateral cutaneous nerve of the arm and posterior cutaneous nerve of forearm. The radial nerve ended without continuing further. The musculocutaneous nerve supplied the brachioradialis, extensor carpi radialis longus and extensor carpi radialis brevis muscles. The musculocutaneous nerve divided terminally into two branches, superficial and deep. The deep branch of musculocutaneous nerve corresponded to usual deep branch of the radial nerve while the superficial branch of musculocutaneous nerve corresponded to usual superficial branch of the radial nerve. The dissection was continued to expose the entire brachial plexus from its origin and it was found to be normal. The structures on the right upper limb were found to be normal. Surgeons should keep such variations in mind while performing the surgeries of the upper limb.
Normahmad Ravshanov; Bozorboy Palvanov; Gulnora Shermatova
2014-01-01
The article presents mathematic model and results of computer calculations of heavy mixtures classifying and farm crops full seeds selection. They enable to determine major process parameters and variation range, providing maximum dispersion of particles flight path, depending on feedstock modules.
Normahmad Ravshanov
2014-05-01
Full Text Available The article presents mathematic model and results of computer calculations of heavy mixtures classifying and farm crops full seeds selection. They enable to determine major process parameters and variation range, providing maximum dispersion of particles flight path, depending on feedstock modules.
Sommerlund, Julie
2006-01-01
This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...
Shuang Wang
2012-01-01
Full Text Available As an efficient tool, radial basis function (RBF has been widely used for the multivariate approximation, interpolating continuous, and the solution of the particle differential equations. However, ill-conditioned interpolation matrix may be encountered when the interpolation points are very dense or irregularly arranged. To avert this problem, RBFs with variable shape parameters are introduced, and several new variation strategies are proposed. Comparison with the RBF with constant shape parameters are made, and the results show that the condition number of the interpolation matrix grows much slower with our strategies. As an application, an improved collocation meshless method is formulated by employing the new RBF. In addition, the Hermite-type interpolation is implemented to handle the Neumann boundary conditions and an additional sine/cosine basis is introduced for the Helmlholtz equation. Then, two interior acoustic problems are solved with the presented method; the results demonstrate the robustness and effectiveness of the method.
Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar
2018-06-01
The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.
Gomes, Carla Regina; Canedo Medeiros, Jose Antonio Carlos
2015-01-01
Highlights: • It is presented a new method based on Artificial Neural Network (ANN) developed to deal with accident identification in PWR nuclear power plants. • Obtained results have shown the efficiency of the referred technique. • Results obtained with this method are as good as or even better to similar optimization tools available in the literature. - Abstract: The task of monitoring a nuclear power plant consists on determining, continuously and in real time, the state of the plant’s systems in such a way to give indications of abnormalities to the operators and enable them to recognize anomalies in system behavior. The monitoring is based on readings of a large number of meters and alarm indicators which are located in the main control room of the facility. On the occurrence of a transient or of an accident on the nuclear power plant, even the most experienced operators can be confronted with conflicting indications due to the interactions between the various components of the plant systems; since a disturbance of a system can cause disturbances on another plant system, thus the operator may not be able to distinguish what is cause and what is the effect. This cognitive overload, to which operators are submitted, causes a difficulty in understanding clearly the indication of an abnormality in its initial phase of development and in taking the appropriate and immediate corrective actions to face the system failure. With this in mind, computerized monitoring systems based on artificial intelligence that could help the operators to detect and diagnose these failures have been devised and have been the subject of research. Among the techniques that can be used in such development, radial basis functions (RBFs) neural networks play an important role due to the fact that they are able to provide good approximations to functions of a finite number of real variables. This paper aims to present an application of a neural network of Gaussian radial basis
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel
2016-03-29
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun
2013-10-01
Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.
Guerra, Fabio A.; Coelho, Leandro dos S.
2008-01-01
An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting
Chanthawara, Krittidej; Kaennakham, Sayan; Toutip, Wattana
2016-02-01
The methodology of Dual Reciprocity Boundary Element Method (DRBEM) is applied to the convection-diffusion problems and investigating its performance is our first objective of the work. Seven types of Radial Basis Functions (RBF); Linear, Thin-plate Spline, Cubic, Compactly Supported, Inverse Multiquadric, Quadratic, and that proposed by [12], were closely investigated in order to numerically compare their effectiveness drawbacks etc. and this is taken as our second objective. A sufficient number of simulations were performed covering as many aspects as possible. Varidated against both exacts and other numerical works, the final results imply strongly that the Thin-Plate Spline and Linear type of RBF are superior to others in terms of both solutions' quality and CPU-time spent while the Inverse Multiquadric seems to poorly yield the results. It is also found that DRBEM can perform relatively well at moderate level of convective force and as anticipated becomes unstable when the problem becomes more convective-dominated, as normally found in all classical mesh-dependence methods.
Talebpour, Zahra; Tavallaie, Roya; Ahmadi, Seyyed Hamid; Abdollahpour, Assem
2010-09-01
In this study, a new method for the simultaneous determination of penicillin G salts in pharmaceutical mixture via FT-IR spectroscopy combined with chemometrics was investigated. The mixture of penicillin G salts is a complex system due to similar analytical characteristics of components. Partial least squares (PLS) and radial basis function-partial least squares (RBF-PLS) were used to develop the linear and nonlinear relation between spectra and components, respectively. The orthogonal signal correction (OSC) preprocessing method was used to correct unexpected information, such as spectral overlapping and scattering effects. In order to compare the influence of OSC on PLS and RBF-PLS models, the optimal linear (PLS) and nonlinear (RBF-PLS) models based on conventional and OSC preprocessed spectra were established and compared. The obtained results demonstrated that OSC clearly enhanced the performance of both RBF-PLS and PLS calibration models. Also in the case of some nonlinear relation between spectra and component, OSC-RBF-PLS gave satisfactory results than OSC-PLS model which indicated that the OSC was helpful to remove extrinsic deviations from linearity without elimination of nonlinear information related to component. The chemometric models were tested on an external dataset and finally applied to the analysis commercialized injection product of penicillin G salts.
Nourani, Vahid; Mousavi, Shahram; Dabrowska, Dominika; Sadikoglu, Fahreddin
2017-05-01
As an innovation, both black box and physical-based models were incorporated into simulating groundwater flow and contaminant transport. Time series of groundwater level (GL) and chloride concentration (CC) observed at different piezometers of study plain were firstly de-noised by the wavelet-based de-noising approach. The effect of de-noised data on the performance of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) was evaluated. Wavelet transform coherence was employed for spatial clustering of piezometers. Then for each cluster, ANN and ANFIS models were trained to predict GL and CC values. Finally, considering the predicted water heads of piezometers as interior conditions, the radial basis function as a meshless method which solves partial differential equations of GFCT, was used to estimate GL and CC values at any point within the plain where there is not any piezometer. Results indicated that efficiency of ANFIS based spatiotemporal model was more than ANN based model up to 13%.
Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan
2011-01-01
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)
Yu, Shiwei; Wang, Ke; Wei, Yi-Ming
2015-01-01
Highlights: • A hybrid self-adaptive PSO–GA-RBF model is proposed for electricity demand prediction. • Each mixed-coding particle is composed by two coding parts of binary and real. • Five independent variables have been selected to predict future electricity consumption in Wuhan. • The proposed model has a simpler structure or higher estimating precision than other ANN models. • No matter what the scenario, the electricity consumption of Wuhan will grow rapidly. - Abstract: The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO–GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the RBF by GA operation and the parameters of the basis and weights by a PSO–GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO–GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models; and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7–11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85 billion kW h. The lowest demand is estimated for the business-as-usual scenario, and will be 88.45 billion kW h
Baasch, B.; M"uller, H.; von Dobeneck, T.
2018-04-01
In this work we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine learning techniques. Nonnegative matrix factorisation is used to determine grain-size end-members from sediment surface samples. Four end-members were found which well represent the variety of sediments in the study area. A radial-basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.
Roshani, G H; Karami, A; Salehizadeh, A; Nazemi, E
2017-11-01
The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152 Eu and 137 Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results. Copyright © 2017 Elsevier Ltd. All rights reserved.
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
Logarithmic learning for generalized classifier neural network.
Ozyildirim, Buse Melis; Avci, Mutlu
2014-12-01
Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.
Javaid, Zarrar; Unsworth, Charles P.; Boocock, Mark G.; McNair, Peter J.
2016-01-01
Purpose: The aim of this work is to demonstrate a new image processing technique that can provide a “near real-time” 3D reconstruction of the articular cartilage of the human knee from MR images which is user friendly. This would serve as a point-of-care 3D visualization tool which would benefit a consultant radiologist in the visualization of the human articular cartilage. Methods: The authors introduce a novel fusion of an adaptation of the contour method known as “contour interpolation (CI)” with radial basis functions (RBFs) which they describe as “CI-RBFs.” The authors also present a spline boundary correction which further enhances volume estimation of the method. A subject cohort consisting of 17 right nonpathological knees (ten female and seven male) is assessed to validate the quality of the proposed method. The authors demonstrate how the CI-RBF method dramatically reduces the number of data points required for fitting an implicit surface to the entire cartilage, thus, significantly improving the speed of reconstruction over the comparable RBF reconstruction method of Carr. The authors compare the CI-RBF method volume estimation to a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Results: The authors demonstrate how the CI-RBF method significantly reduces the number of data points (p-value < 0.0001) required for fitting an implicit surface to the cartilage, by 48%, 31%, and 44% for the patellar, tibial, and femoral cartilages, respectively. Thus, significantly improving the speed of reconstruction (p-value < 0.0001) by 39%, 40%, and 44% for the patellar, tibial, and femoral cartilages over the comparable RBF model of Carr providing a near real-time reconstruction of 6.49, 8.88, and 9.43 min for the patellar, tibial, and femoral cartilages, respectively. In addition, it is demonstrated how the CI-RBF method matches the volume
Javaid, Zarrar; Unsworth, Charles P., E-mail: c.unsworth@auckland.ac.nz [Department of Engineering Science, The University of Auckland, Auckland 1010 (New Zealand); Boocock, Mark G.; McNair, Peter J. [Health and Rehabilitation Research Center, Auckland University of Technology, Auckland 1142 (New Zealand)
2016-03-15
Purpose: The aim of this work is to demonstrate a new image processing technique that can provide a “near real-time” 3D reconstruction of the articular cartilage of the human knee from MR images which is user friendly. This would serve as a point-of-care 3D visualization tool which would benefit a consultant radiologist in the visualization of the human articular cartilage. Methods: The authors introduce a novel fusion of an adaptation of the contour method known as “contour interpolation (CI)” with radial basis functions (RBFs) which they describe as “CI-RBFs.” The authors also present a spline boundary correction which further enhances volume estimation of the method. A subject cohort consisting of 17 right nonpathological knees (ten female and seven male) is assessed to validate the quality of the proposed method. The authors demonstrate how the CI-RBF method dramatically reduces the number of data points required for fitting an implicit surface to the entire cartilage, thus, significantly improving the speed of reconstruction over the comparable RBF reconstruction method of Carr. The authors compare the CI-RBF method volume estimation to a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Results: The authors demonstrate how the CI-RBF method significantly reduces the number of data points (p-value < 0.0001) required for fitting an implicit surface to the cartilage, by 48%, 31%, and 44% for the patellar, tibial, and femoral cartilages, respectively. Thus, significantly improving the speed of reconstruction (p-value < 0.0001) by 39%, 40%, and 44% for the patellar, tibial, and femoral cartilages over the comparable RBF model of Carr providing a near real-time reconstruction of 6.49, 8.88, and 9.43 min for the patellar, tibial, and femoral cartilages, respectively. In addition, it is demonstrated how the CI-RBF method matches the volume
Petlamul, Wanida; Prasertsan, Poonsuk
2012-06-01
Ten strains of the entomopathogenic fungi Metarhizium anisopliae and Beauveria bassiana were evaluated to find the most effective strain for optimization studies. The first criterion tested for strain selection was the mortality (> 50%) of Spodoptera litura larvae after inoculation of the fungus for 4 days. Results on several bioassays revealed that B. bassiana BNBCRC showed the most virulence on mortality S. litura larvae (80% mortality). B. bassiana BNBCRC also showed the highest germination rate (72.22%). However, its conidia yield (7.2 × 10(8) conidia/mL) was lower than those of B. bassiana B 14841 (8.3 × 10(8) conidia/mL) and M. anisopliae M6 (8.2 × 10(8) conidia/mL). The highest accumulative radial growth was obtained from the strain B14841 (37.10 mm/day) while the strain BNBCRC showed moderate radial growth (24.40 mm/day). M. anisopliae M6 possessed the highest protease activity (145.00 mU/mL) while M. anisopliae M8 possessed the highest chitinase activity (20.00 mU/mL) during 96~144 hr cultivation. Amongst these criteria, selection based on virulence and germination rate lead to the selection of B. bassiana BNBCRC. B. bassiana B14841 would be selected if based on growth rate while M. anisopliae M6 and M8 possessed the highest enzyme activities.
Lippert, Ingmar
2012-01-01
. Using an actor- network theory (ANT) framework, the aim is to investigate the actors who bring together the elements needed to classify their carbon emission sources and unpack the heterogeneous relations drawn on. Based on an ethnographic study of corporate agents of ecological modernisation over...... a period of 13 months, this paper provides an exploration of three cases of enacting classification. Drawing on ANT, we problematise the silencing of a range of possible modalities of consumption facts and point to the ontological ethics involved in such performances. In a context of global warming...
An Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost
Yinan Zhang
2016-01-01
Full Text Available Diabetic retinopathy (DR screening system raises a financial problem. For further reducing DR screening cost, an active learning classifier is proposed in this paper. Our approach identifies retinal images based on features extracted by anatomical part recognition and lesion detection algorithms. Kernel extreme learning machine (KELM is a rapid classifier for solving classification problems in high dimensional space. Both active learning and ensemble technique elevate performance of KELM when using small training dataset. The committee only proposes necessary manual work to doctor for saving cost. On the publicly available Messidor database, our classifier is trained with 20%–35% of labeled retinal images and comparative classifiers are trained with 80% of labeled retinal images. Results show that our classifier can achieve better classification accuracy than Classification and Regression Tree, radial basis function SVM, Multilayer Perceptron SVM, Linear SVM, and K Nearest Neighbor. Empirical experiments suggest that our active learning classifier is efficient for further reducing DR screening cost.
Debus, Michael S.
2017-01-01
This paper critically analyzes seventeen game classifications. The classifications were chosen on the basis of diversity, ranging from pre-digital classification (e.g. Murray 1952), over game studies classifications (e.g. Elverdam & Aarseth 2007) to classifications of drinking games (e.g. LaBrie et...... al. 2013). The analysis aims at three goals: The classifications’ internal consistency, the abstraction of classification criteria and the identification of differences in classification across fields and/or time. Especially the abstraction of classification criteria can be used in future endeavors...... into the topic of game classifications....
Tien Bui, Dieu; Hoang, Nhat-Duc
2017-09-01
In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.
D. Tien Bui
2017-09-01
Full Text Available In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM, radial-basis-function Fisher discriminant analysis (RBFDA, and a geographic information system (GIS database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.
Neuropathy - radial nerve; Radial nerve palsy; Mononeuropathy ... Damage to one nerve group, such as the radial nerve, is called mononeuropathy . Mononeuropathy means there is damage to a single nerve. Both ...
Alehosseini, Ali; A. Hejazi, Maryam; Mokhtari, Ghassem; B. Gharehpetian, Gevork; Mohammadi, Mohammad
2015-06-01
In this paper, the Bayesian classifier is used to detect and classify the radial deformation and axial displacement of transformer windings. The proposed method is tested on a model of transformer for different volumes of radial deformation and axial displacement. In this method, ultra-wideband (UWB) signal is sent to the simplified model of the transformer winding. The received signal from the winding model is recorded and used for training and testing of Bayesian classifier in different axial displacement and radial deformation states of the winding. It is shown that the proposed method has a good accuracy to detect and classify the axial displacement and radial deformation of the winding.
Moritomo, Hisao; Arimitsu, Sayuri; Kubo, Nobuyuki; Masatomi, Takashi; Yukioka, Masao
2015-02-01
To classify triangular fibrocartilage complex (TFCC) foveal lesions on the basis of computed tomography (CT) arthrography using a radial plane view and to correlate the CT arthrography results with surgical findings. We also tested the interobserver and intra-observer reliability of the radial plane view. A total of 33 patients with a suspected TFCC foveal tear who had undergone wrist CT arthrography and subsequent surgical exploration were enrolled. We classified the configurations of TFCC foveal lesions into 5 types on the basis of CT arthrography with the radial plane view in which the image slices rotate clockwise centered on the ulnar styloid process. Sensitivity, specificity, and positive predictive values were calculated for each type of foveal lesion in CT arthrography to detect foveal tears. We determined interobserver and intra-observer agreements using kappa statistics. We also compared accuracies with the radial plane views with those with the coronal plane views. Among the tear types on CT arthrography, type 3, a roundish defect at the fovea, and type 4, a large defect at the overall ulnar insertion, had high specificity and positive predictive value for the detection of foveal tears. Specificity and positive predictive values were 90% and 89% for type 3 and 100% and 100% for type 4, respectively, whereas sensitivity was 35% for type 3 and 22% for type 4. Interobserver and intra-observer agreement was substantial and almost perfect, respectively. The radial plane view identified foveal lesion of each palmar and dorsal radioulnar ligament separately, but accuracy results with the radial plane views were not statistically different from those with the coronal plane views. Computed tomography arthrography with a radial plane view exhibited enhanced specificity and positive predictive value when a type 3 or 4 lesion was identified in the detection of a TFCC foveal tear compared with historical controls. Diagnostic II. Copyright © 2015 American Society for
Zhang, Lei; Dong, Zhen; Zhang, Chun-Lin; Gu, Yu-Dong
2016-11-01
Background C7 - T1 palsy results in complete loss of finger motion and poses a surgical challenge. This study investigated the anatomy of the radial nerve in the elbow and forearm and the feasibility of intraplexus nerve transfer to restore thumb and finger extension. Methods The radial nerves were dissected in 28 formalin-fixed upper extremities. Branching pattern, length, diameter, and number of myelinated fibers were recorded. Results Commonly, the branching pattern (from proximal to distal) was to the brachioradialis, extensor carpi radialis longus, superficial sensory proximal to the lateral epicondyle, extensor carpi radialis brevis, supinator, extensor digitorum communis, extensor digiti minimi, extensor carpi ulnaris, abductor pollicis longus, extensor pollicis brevis, extensor pollicis longus, and extensor indicis distal to the lateral epicondyle. Conclusions Branches to the brachioradialis, extensor carpi radialis longus, and supinator can be transferred to the posterior interosseous nerve to restore hand movement in patients with C7 - T1 brachial plexus palsies; the supinator branch is probably the best choice in this regard. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.
Radial nerve dysfunction (image)
The radial nerve travels down the arm and supplies movement to the triceps muscle at the back of the upper arm. ... the wrist and hand. The usual causes of nerve dysfunction are direct trauma, prolonged pressure on the ...
Spectral problem for the radial Schroedinger equation
Vshivtsev, A.S.; Tatarintsev, A.V.; Prokopov, A.V.; Sorokin, V. N.
1998-01-01
For the first time, a procedure for determining spectra on the basis of generalized integral transformations is implemented for a wide class of radial Schroedinger equations. It is shown that this procedure works well for known types of potentials. Concurrently, this method makes it possible to obtain new analytic results for the Cornell potential. This may prove important for hadron physics
Classifying Returns as Extreme
Christiansen, Charlotte
2014-01-01
I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme tha...
Vo, Martin
2017-08-01
Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.
Smith, Karl H.
2002-01-01
A radial wedge flange clamp comprising a pair of flanges each comprising a plurality of peripheral flat wedge facets having flat wedge surfaces and opposed and mating flat surfaces attached to or otherwise engaged with two elements to be joined and including a series of generally U-shaped wedge clamps each having flat wedge interior surfaces and engaging one pair of said peripheral flat wedge facets. Each of said generally U-shaped wedge clamps has in its opposing extremities apertures for the tangential insertion of bolts to apply uniform radial force to said wedge clamps when assembled about said wedge segments.
Sirenomelia with radial dysplasia.
Kulkarni, M L; Abdul Manaf, K M; Prasannakumar, D G; Kulkarni, Preethi M
2004-05-01
Sirenomelia is a rare anomaly usually associated with other multiple malformations. In this communication the authors report a case of sirenomelia associated with multiple malformations, which include radial hypoplasia also. Though several theories have been proposed regarding the etiology of multiple malformation syndromes in the past, the recent theory of primary developmental defect during blastogenesis holds good in this case.
Radially truncated galactic discs
Grijs, R. de; Kregel, M.; Wesson, K H
2000-01-01
Abstract: We present the first results of a systematic analysis of radially truncatedexponential discs for four galaxies of a sample of disc-dominated edge-onspiral galaxies. Edge-on galaxies are very useful for the study of truncatedgalactic discs, since we can follow their light distributions out
Intelligent Garbage Classifier
Ignacio Rodríguez Novelle
2008-12-01
Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.
Classifying Linear Canonical Relations
Lorand, Jonathan
2015-01-01
In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.
朱国俊; 冯建军; 郭鹏程; 罗兴锜
2014-01-01
如何提高海流能水轮机的能量捕获效率是海洋能开发领域的重点研究课题，而提高海流能水轮机能量性能的关键在于其叶片几何的构造基础--水力翼型的性能提升。该文提出了一种水力翼型的多工况优化设计方法，该方法采用Bezier曲线参数化技术建立翼型的参数化表征方法，然后利用拉丁超立方试验设计技术在设计空间获取训练径向基（radial basis function，RBF）神经网络的样本点，通过计算流体动力学的方法获得每个翼型样本的性能参数后开展神经网络的学习训练，最后采用RBF神经网络与NSGA-II遗传算法相结合的现代优化技术数值求解水力翼型的多工况优化问题。基于上述优化方法对NACA63-815翼型进行了优化改进，重点研究了该翼型在3个攻角工况下（0，6°和12°）的优化问题及求解方法。优化结果表明，优化后的翼型在3个工况点下都具有更好的升阻比性能，同时也能更好地抑制失速现象的产生，验证了该优化方法的理论正确性和可行性。%In order to reduce the current dependence on fossil and nuclear-fueled power plants to cope with the growing demand of electrical energy, the ocean energy technologies must be improved to develop more energy. There are several types of ocean energy that can be feasible to exploit:wave energy, marine-current energy, tidal barrages, ocean thermal energy and so on. But the most promising in the short term may be wave and marine-current energy. Marine-current energy can be exploited by a marine current turbine. So how to improve the efficiency of mariner current turbine is the key research subject in ocean energy development. The key to efficiency improvement is the performance improvement of hydrofoil, which is used to establish the turbine blade. In order to improve the hydrofoil’s performance, a multi-point optimization method is presented in this paper. In this method
Variable stator radial turbine
Rogo, C.; Hajek, T.; Chen, A. G.
1984-01-01
A radial turbine stage with a variable area nozzle was investigated. A high work capacity turbine design with a known high performance base was modified to accept a fixed vane stagger angle moveable sidewall nozzle. The nozzle area was varied by moving the forward and rearward sidewalls. Diffusing and accelerating rotor inlet ramps were evaluated in combinations with hub and shroud rotor exit rings. Performance of contoured sidewalls and the location of the sidewall split line with respect to the rotor inlet was compared to the baseline. Performance and rotor exit survey data are presented for 31 different geometries. Detail survey data at the nozzle exit are given in contour plot format for five configurations. A data base is provided for a variable geometry concept that is a viable alternative to the more common pivoted vane variable geometry radial turbine.
Nilsson, Martin
2007-01-01
The demands for ride comfort quality in today's long haulage trucks are constantly growing. A part of the ride comfort problems are represented by internal vibrations caused by rotating mechanical parts. This thesis work focus on the vibrations generated from radial runout on the wheels. These long haulage trucks travel long distances on smooth highways, with a constant speed of 90 km/h resulting in a 7 Hz oscillation. This frequency creates vibrations in the cab, which can be found annoying....
Coufal, David
2017-01-01
Roč. 319, 15 July (2017), s. 1-27 ISSN 0165-0114 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : fuzzy systems * radial functions * coherence Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.718, year: 2016
Radial Field Piezoelectric Diaphragms
Bryant, R. G.; Effinger, R. T., IV; Copeland, B. M., Jr.
2002-01-01
A series of active piezoelectric diaphragms were fabricated and patterned with several geometrically defined Inter-Circulating Electrodes "ICE" and Interdigitated Ring Electrodes "ICE". When a voltage potential is applied to the electrodes, the result is a radially distributed electric field that mechanically strains the piezoceramic along the Z-axis (perpendicular to the applied electric field). Unlike other piezoelectric bender actuators, these Radial Field Diaphragms (RFDs) strain concentrically yet afford high displacements (several times that of the equivalent Unimorph) while maintaining a constant circumference. One of the more intriguing aspects is that the radial strain field reverses itself along the radius of the RFD while the tangential strain remains relatively constant. The result is a Z-deflection that has a conical profile. This paper covers the fabrication and characterization of the 5 cm. (2 in.) diaphragms as a function of poling field strength, ceramic thickness, electrode type and line spacing, as well as the surface topography, the resulting strain field and displacement as a function of applied voltage at low frequencies. The unique features of these RFDs include the ability to be clamped about their perimeter with little or no change in displacement, the environmentally insulated packaging, and a highly repeatable fabrication process that uses commodity materials.
Perceived radial translation during centrifugation
Bos, J.E.; Correia Grácio, B.J.
2015-01-01
BACKGROUND: Linear acceleration generally gives rise to translation perception. Centripetal acceleration during centrifugation, however, has never been reported giving rise to a radial, inward translation perception. OBJECTIVE: To study whether centrifugation can induce a radial translation
Douma, M.; Ligierko, G.; Angelov, I.
2008-10-01
The need for information has increased exponentially over the past decades. The current systems for constructing, exploring, classifying, organizing, and searching information face the growing challenge of enabling their users to operate efficiently and intuitively in knowledge-heavy environments. This paper presents SpicyNodes, an advanced user interface for difficult interaction contexts. It is based on an underlying structure known as a radial map, which allows users to manipulate and interact in a natural manner with entities called nodes. This technology overcomes certain limitations of existing solutions and solves the problem of browsing complex sets of linked information. SpicyNodes is also an organic system that projects users into a living space, stimulating exploratory behavior and fostering creative thought. Our interactive radial layout is used for educational purposes and has the potential for numerous other applications.
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory
2009-01-01
Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.
A Radial Basis Function Approach to Financial Time Series Analysis
1993-12-01
consequently this approach is at the core of a large fraction of the portfolio management systems today. The Capital Asset Pricing Model ( CAPM ). due...representation used by each method. but of course a critical concern is how to actually estimate the parameters of the models. To sonic extent these...model fitting unseen data nicely depends critically on maintaining a balance between the number of data points used for estimation and the number of
Predicting the occurrence of rainfall using improved radial basis ...
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Radial reflection diffraction tomography
Lehman, Sean K.
2012-12-18
A wave-based tomographic imaging method and apparatus based upon one or more rotating radially outward oriented transmitting and receiving elements have been developed for non-destructive evaluation. At successive angular locations at a fixed radius, a predetermined transmitting element can launch a primary field and one or more predetermined receiving elements can collect the backscattered field in a "pitch/catch" operation. A Hilbert space inverse wave (HSIW) algorithm can construct images of the received scattered energy waves using operating modes chosen for a particular application. Applications include, improved intravascular imaging, bore hole tomography, and non-destructive evaluation (NDE) of parts having existing access holes.
Pirooznia, Mehdi; Deng, Youping
2006-01-01
Motivation Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. Results The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1–BRCA2 samples with RBF kernel of SVM. Conclusion We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at . PMID:17217518
Pirooznia, Mehdi; Deng, Youping
2006-12-12
Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.
Radial head fracture associated with posterior interosseous nerve injury
Bernardo Barcellos Terra
Full Text Available ABSTRACT Fractures of the radial head and radial neck correspond to 1.7-5.4% of all fractures and approximately 30% may present associated injuries. In the literature, there are few reports of radial head fracture with posterior interosseous nerve injury. This study aimed to report a case of radial head fracture associated with posterior interosseous nerve injury. CASE REPORT: A male patient, aged 42 years, sought medical care after falling from a skateboard. The patient related pain and limitation of movement in the right elbow and difficulty to extend the fingers of the right hand. During physical examination, thumb and fingers extension deficit was observed. The wrist extension showed a slight radial deviation. After imaging, it became evident that the patient had a fracture of the radial head that was classified as grade III in the Mason classification. The patient underwent fracture fixation; at the first postoperative day, thumb and fingers extension was observed. Although rare, posterior interosseous nerve branch injury may be associated with radial head fractures. In the present case, the authors believe that neuropraxia occurred as a result of the fracture hematoma and edema.
Radial semiconductor drift chambers
Rawlings, K.J.
1987-01-01
The conditions under which the energy resolution of a radial semiconductor drift chamber based detector system becomes dominated by the step noise from the detector dark current have been investigated. To minimise the drift chamber dark current attention should be paid to carrier generation at Si/SiO 2 interfaces. This consideration conflicts with the desire to reduce the signal risetime: a higher drift field for shorter signal pulses requires a larger area of SiO 2 . Calculations for the single shaping and pseudo Gaussian passive filters indicate that for the same degree of signal risetime sensitivity in a system dominated by the step noise from the detector dark current, the pseudo Gaussian filter gives only a 3% improvement in signal/noise and 12% improvement in rate capability compared with the single shaper performance. (orig.)
1983-01-01
There were 37 (normal) + 3 (special) Radial Field magnets in the ISR to adjust vertically the closed orbit. Gap heights and strengths were 200 mm and .12 Tm in the normal magnets, 220 mm and .18 Tm in the special ones. The core length was 430 mm in both types. Due to their small length as compared to the gap heights the end fringe field errors were very important and had to be compensated by suitably shaping the poles. In order to save on cables, as these magnets were located very far from their power supplies, the coils of the normal type magnets were formed by many turns of solid cpper conductor with some interleaved layers of hollow conductor directly cooled by circulating water
Stone, M.B.; Abernathy, D.L.; Niedziela, J.L.; Overbay, M.A.
2015-01-01
We have designed, installed, and commissioned a scattered beam radial collimator for use at the ARCS Wide Angular Range Chopper Spectrometer at the Spallation Neutron Source. The collimator has been designed to work effectively for thermal and epithermal neutrons and with a range of sample environments. Other design considerations include the accommodation of working within a high vacuum environment and having the ability to quickly install and remove the collimator from the scattered beam. The collimator is composed of collimating blades (or septa). The septa are 12 micron thick Kapton foils coated on each side with 39 microns of enriched boron carbide ( 10 B 4 C with 10 B > 96%) in an ultra-high vacuum compatible binder. The collimator blades represent an additional 22 m 2 of surface area. In the article we present collimator's design and performance and methodologies for its effective use
Antiproton compression and radial measurements
Andresen, G B; Bowe, P D; Bray, C C; Butler, E; Cesar, C L; Chapman, S; Charlton, M; Fajans, J; Fujiwara, M C; Funakoshi, R; Gill, D R; Hangst, J S; Hardy, W N; Hayano, R S; Hayden, M E; Humphries, A J; Hydomako, R; Jenkins, M J; Jorgensen, L V; Kurchaninov, L; Lambo, R; Madsen, N; Nolan, P; Olchanski, K; Olin, A; Page R D; Povilus, A; Pusa, P; Robicheaux, F; Sarid, E; Seif El Nasr, S; Silveira, D M; Storey, J W; Thompson, R I; Van der Werf, D P; Wurtele, J S; Yamazaki, Y
2008-01-01
Control of the radial proﬁle of trapped antiproton clouds is critical to trapping antihydrogen. We report detailed measurements of the radial manipulation of antiproton clouds, including areal density compressions by factors as large as ten, achieved by manipulating spatially overlapped electron plasmas. We show detailed measurements of the near-axis antiproton radial proﬁle, and its relation to that of the electron plasma. We also measure the outer radial proﬁle by ejecting antiprotons to the trap wall using an octupole magnet.
Radial expansion and multifragmentation
Angelique, J.C.; Bizard, G.; Bougault, R.; Brou, R.; Buta, A.; Colin, J.; Cussol, D.; Durand, D.; Kerambrun, A.; Le Brun, C.; Lecolley, J.F.; Lopez, O.; Louvel, M.; Meslin, C.; Nakagawa, T.; Patry, J.P.; Peter, J.; Popescu, R.; Regimbart, R.; Steckmeyer, J.C.; Tamain, B.; Vient, E.; Yuasa-Nakagawa, K.; Wieloch, A.
1998-01-01
The light systems 36 Ar + 27 Al and 64 Zn + nat Ti were measured at several bombarding energies between ∼ 35 and 95 MeV/nucleon. It was found that the predominant part of the cross section is due to binary collisions. In this paper the focus is placed on the properties of the quasi-projectile nuclei. In the central collisions the excitation energies of the quasi-projectile reach values exceeding largely 10 MeV/nucleon. The slope of the high energy part of the distribution can give only an upper limit of the apparent temperature (the average temperature along the decay chain). The highly excited quasi-projectile may get rapidly fragmented rather than sequentially. The heavy fragments are excited and can emit light particles (n, p, d, t, 3 He, α,...) what perturbs additionally the spectrum of these particles. Concerning the expansion energy, one can determine the average kinetic energies of the product (in the quasi-projectile-framework) and compare with simulation values. To fit the experimental data an additional radial expansion energy is to be considered. The average expansion energy depends slightly on the impact parameter but it increases with E * / A, ranging from 0.4 to 1,2 MeV/nucleon for an excitation energy increasing from 7 to 10.5 MeV/nucleon. This collective radial energy seems to be independent of the fragment mass, what is possibly valid for the case of larger quasi-projectile masses. The origin of the expansion is to be determined. It may be due to a compression in the interaction zone at the initial stage of the collision, which propagates in the quasi-projectile and quasi-target, or else, may be due, simply, to the increase of thermal energy leading to a rapid fragment emission. The sequential de-excitation calculation overestimates light particle emission and consequently heavy residues, particularly, at higher excitation energies. This disagreement indicates that a sequential process can not account for the di-excitation of very hot nuclei
Krausche, S.; Ohlsson, Johan
1998-04-01
The objective of this work was to develop a program dealing with design point calculations of radial turbine machinery, including both compressor and turbine, with as few input data as possible. Some simple stress calculations and turbine metal blade temperatures were also included. This program was then implanted in a German thermodynamics program, Gasturb, a program calculating design and off-design performance of gas turbines. The calculations proceed with a lot of assumptions, necessary to finish the task, concerning pressure losses, velocity distribution, blockage, etc., and have been correlated with empirical data from VAT. Most of these values could have been input data, but to prevent the user of the program from drowning in input values, they are set as default values in the program code. The output data consist of geometry, Mach numbers, predicted component efficiency etc., and a number of graphical plots of geometry and velocity triangles. For the cases examined, the error in predicted efficiency level was within {+-} 1-2% points, and quite satisfactory errors in geometrical and thermodynamic conditions were obtained Examination paper. 18 refs, 36 figs
Valenzuela, Javier
2001-01-01
A radial flow heat exchanger (20) having a plurality of first passages (24) for transporting a first fluid (25) and a plurality of second passages (26) for transporting a second fluid (27). The first and second passages are arranged in stacked, alternating relationship, are separated from one another by relatively thin plates (30) and (32), and surround a central axis (22). The thickness of the first and second passages are selected so that the first and second fluids, respectively, are transported with laminar flow through the passages. To enhance thermal energy transfer between first and second passages, the latter are arranged so each first passage is in thermal communication with an associated second passage along substantially its entire length, and vice versa with respect to the second passages. The heat exchangers may be stacked to achieve a modular heat exchange assembly (300). Certain heat exchangers in the assembly may be designed slightly differently than other heat exchangers to address changes in fluid properties during transport through the heat exchanger, so as to enhance overall thermal effectiveness of the assembly.
Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.
2017-10-01
Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.
Stability of radial swirl flows
Dou, H S; Khoo, B C
2012-01-01
The energy gradient theory is used to examine the stability of radial swirl flows. It is found that the flow of free vortex is always stable, while the introduction of a radial flow will induce the flow to be unstable. It is also shown that the pure radial flow is stable. Thus, there is a flow angle between the pure circumferential flow and the pure radial flow at which the flow is most unstable. It is demonstrated that the magnitude of this flow angle is related to the Re number based on the radial flow rate, and it is near the pure circumferential flow. The result obtained in this study is useful for the design of vaneless diffusers of centrifugal compressors and pumps as well as other industrial devices.
Fingerprint prediction using classifier ensembles
Molale, P
2011-11-01
Full Text Available ); logistic discrimination (LgD), k-nearest neighbour (k-NN), artificial neural network (ANN), association rules (AR) decision tree (DT), naive Bayes classifier (NBC) and the support vector machine (SVM). The performance of several multiple classifier systems...
Computer Security Team
2011-01-01
In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN. But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...
Classifying Sluice Occurrences in Dialogue
Baird, Austin; Hamza, Anissa; Hardt, Daniel
2018-01-01
perform manual annotation with acceptable inter-coder agreement. We build classifier models with Decision Trees and Naive Bayes, with accuracy of 67%. We deploy a classifier to automatically classify sluice occurrences in OpenSubtitles, resulting in a corpus with 1.7 million occurrences. This will support....... Despite this, the corpus can be of great use in research on sluicing and development of systems, and we are making the corpus freely available on request. Furthermore, we are in the process of improving the accuracy of sluice identification and annotation for the purpose of created a subsequent version...
Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang
2012-12-05
Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (Pdisorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.
Radial retinotomy in the macula.
Bovino, J A; Marcus, D F
1984-01-01
Radial retinotomy is an operative procedure usually performed in the peripheral or equatorial retina. To facilitate retinal attachment, the authors used intraocular scissors to perform radial retinotomy in the macula of two patients during vitrectomy surgery. In the first patient, a retinal detachment complicated by periretinal proliferation and macula hole formation was successfully reoperated with the aid of three radial cuts in the retina at the edges of the macular hole. In the second patient, an intraoperative retinal tear in the macula during diabetic vitrectomy was also successfully repaired with the aid of radial retinotomy. In both patients, retinotomy in the macula was required because epiretinal membranes, which could not be easily delaminated, were hindering retinal reattachment.
Detonation in supersonic radial outflow
Kasimov, Aslan R.; Korneev, Svyatoslav
2014-01-01
We report on the structure and dynamics of gaseous detonation stabilized in a supersonic flow emanating radially from a central source. The steady-state solutions are computed and their range of existence is investigated. Two-dimensional simulations
Dedicated radial ventriculography pigtail catheter
Vidovich, Mladen I., E-mail: miv@uic.edu
2013-05-15
A new dedicated cardiac ventriculography catheter was specifically designed for radial and upper arm arterial access approach. Two catheter configurations have been developed to facilitate retrograde crossing of the aortic valve and to conform to various subclavian, ascending aortic and left ventricular anatomies. The “short” dedicated radial ventriculography catheter is suited for horizontal ascending aortas, obese body habitus, short stature and small ventricular cavities. The “long” dedicated radial ventriculography catheter is suited for vertical ascending aortas, thin body habitus, tall stature and larger ventricular cavities. This new design allows for improved performance, faster and simpler insertion in the left ventricle which can reduce procedure time, radiation exposure and propensity for radial artery spasm due to excessive catheter manipulation. Two different catheter configurations allow for optimal catheter selection in a broad range of patient anatomies. The catheter is exceptionally stable during contrast power injection and provides equivalent cavity opacification to traditional femoral ventriculography catheter designs.
Cubical sets as a classifying topos
Spitters, Bas
Coquand’s cubical set model for homotopy type theory provides the basis for a computational interpretation of the univalence axiom and some higher inductive types, as implemented in the cubical proof assistant. We show that the underlying cube category is the opposite of the Lawvere theory of De...... Morgan algebras. The topos of cubical sets itself classifies the theory of ‘free De Morgan algebras’. This provides us with a topos with an internal ‘interval’. Using this interval we construct a model of type theory following van den Berg and Garner. We are currently investigating the precise relation...
Quantum ensembles of quantum classifiers.
Schuld, Maria; Petruccione, Francesco
2018-02-09
Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.
IAEA safeguards and classified materials
Pilat, J.F.; Eccleston, G.W.; Fearey, B.L.; Nicholas, N.J.; Tape, J.W.; Kratzer, M.
1997-01-01
The international community in the post-Cold War period has suggested that the International Atomic Energy Agency (IAEA) utilize its expertise in support of the arms control and disarmament process in unprecedented ways. The pledges of the US and Russian presidents to place excess defense materials, some of which are classified, under some type of international inspections raises the prospect of using IAEA safeguards approaches for monitoring classified materials. A traditional safeguards approach, based on nuclear material accountancy, would seem unavoidably to reveal classified information. However, further analysis of the IAEA's safeguards approaches is warranted in order to understand fully the scope and nature of any problems. The issues are complex and difficult, and it is expected that common technical understandings will be essential for their resolution. Accordingly, this paper examines and compares traditional safeguards item accounting of fuel at a nuclear power station (especially spent fuel) with the challenges presented by inspections of classified materials. This analysis is intended to delineate more clearly the problems as well as reveal possible approaches, techniques, and technologies that could allow the adaptation of safeguards to the unprecedented task of inspecting classified materials. It is also hoped that a discussion of these issues can advance ongoing political-technical debates on international inspections of excess classified materials
Hybrid classifiers methods of data, knowledge, and classifier combination
Wozniak, Michal
2014-01-01
This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.
Radial lean direct injection burner
Khan, Abdul Rafey; Kraemer, Gilbert Otto; Stevenson, Christian Xavier
2012-09-04
A burner for use in a gas turbine engine includes a burner tube having an inlet end and an outlet end; a plurality of air passages extending axially in the burner tube configured to convey air flows from the inlet end to the outlet end; a plurality of fuel passages extending axially along the burner tube and spaced around the plurality of air passage configured to convey fuel from the inlet end to the outlet end; and a radial air swirler provided at the outlet end configured to direct the air flows radially toward the outlet end and impart swirl to the air flows. The radial air swirler includes a plurality of vanes to direct and swirl the air flows and an end plate. The end plate includes a plurality of fuel injection holes to inject the fuel radially into the swirling air flows. A method of mixing air and fuel in a burner of a gas turbine is also provided. The burner includes a burner tube including an inlet end, an outlet end, a plurality of axial air passages, and a plurality of axial fuel passages. The method includes introducing an air flow into the air passages at the inlet end; introducing a fuel into fuel passages; swirling the air flow at the outlet end; and radially injecting the fuel into the swirling air flow.
3D Bayesian contextual classifiers
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
Hazeltine, R.D.
1988-12-01
The boundary layer arising in the radial vicinity of a tokamak limiter is examined, with special reference to the TEXT tokamak. It is shown that sheath structure depends upon the self-consistent effects of ion guiding-center orbit modification, as well as the radial variation of E /times/ B-induced toroidal rotation. Reasonable agreement with experiment is obtained from an idealized model which, however simplified, preserves such self-consistent effects. It is argued that the radial sheath, which occurs whenever confining magnetic field-lines lie in the plasma boundary surface, is an object of some intrinsic interest. It differs from the more familiar axial sheath because magnetized charges respond very differently to parallel and perpendicular electric fields. 11 refs., 1 fig
Park, Eunjeong; Chang, Hyuk-Jae; Nam, Hyo Suk
2017-04-18
The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients. The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing. We extracted features of drift and pronation from accelerometer signals of wearable devices on the inner wrists of 16 stroke patients and 10 healthy controls. Signal processing and feature selection approach were applied to discriminate PDT features used to classify stroke patients. A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation. Signal processing by the PDT tool extracted a total of 12 PDT features from sensors. Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. Our proposed PDT classifiers had an area under the receiver operating characteristic curve (AUC) of .806 (SVM), .769 (RBFN), and .900 (RF) without feature selection, and feature selection improves the AUCs to .913 (SVM), .956 (RBFN), and .975 (RF), representing an average performance enhancement of 15.3%. Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients. ©Eunjeong Park, Hyuk-Jae Chang, Hyo Suk Nam. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.04.2017.
Detonation in supersonic radial outflow
Kasimov, Aslan R.
2014-11-07
We report on the structure and dynamics of gaseous detonation stabilized in a supersonic flow emanating radially from a central source. The steady-state solutions are computed and their range of existence is investigated. Two-dimensional simulations are carried out in order to explore the stability of the steady-state solutions. It is found that both collapsing and expanding two-dimensional cellular detonations exist. The latter can be stabilized by putting several rigid obstacles in the flow downstream of the steady-state sonic locus. The problem of initiation of standing detonation stabilized in the radial flow is also investigated numerically. © 2014 Cambridge University Press.
Knowledge Uncertainty and Composed Classifier
Klimešová, Dana; Ocelíková, E.
2007-01-01
Roč. 1, č. 2 (2007), s. 101-105 ISSN 1998-0140 Institutional research plan: CEZ:AV0Z10750506 Keywords : Boosting architecture * contextual modelling * composed classifier * knowledge management, * knowledge * uncertainty Subject RIV: IN - Informatics, Computer Science
Correlation Dimension-Based Classifier
Jiřina, Marcel; Jiřina jr., M.
2014-01-01
Roč. 44, č. 12 (2014), s. 2253-2263 ISSN 2168-2267 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : classifier * multidimensional data * correlation dimension * scaling exponent * polynomial expansion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014
Vortex Whistle in Radial Intake
Tse, Man-Chun
2004-01-01
In a radial-to-axial intake with inlet guide vanes (IGV) at the entry, a strong flow circulation Gamma can be generated from the tangential flow components created by the IGVs when their setting exceed about halfclosing (approx. 45 deg...
Classified facilities for environmental protection
Anon.
1993-02-01
The legislation of the classified facilities governs most of the dangerous or polluting industries or fixed activities. It rests on the law of 9 July 1976 concerning facilities classified for environmental protection and its application decree of 21 September 1977. This legislation, the general texts of which appear in this volume 1, aims to prevent all the risks and the harmful effects coming from an installation (air, water or soil pollutions, wastes, even aesthetic breaches). The polluting or dangerous activities are defined in a list called nomenclature which subjects the facilities to a declaration or an authorization procedure. The authorization is delivered by the prefect at the end of an open and contradictory procedure after a public survey. In addition, the facilities can be subjected to technical regulations fixed by the Environment Minister (volume 2) or by the prefect for facilities subjected to declaration (volume 3). (A.B.)
Radial head dislocation during proximal radial shaft osteotomy.
Hazel, Antony; Bindra, Randy R
2014-03-01
The following case report describes a 48-year-old female patient with a longstanding both-bone forearm malunion, who underwent osteotomies of both the radius and ulna to improve symptoms of pain and lack of rotation at the wrist. The osteotomies were templated preoperatively. During surgery, after performing the planned radial shaft osteotomy, the authors recognized that the radial head was subluxated. The osteotomy was then revised from an opening wedge to a closing wedge with improvement of alignment and rotation. The case report discusses the details of the operation, as well as ways in which to avoid similar shortcomings in the future. Copyright © 2014 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.
Energy-Efficient Neuromorphic Classifiers.
Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano
2016-10-01
Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.
76 FR 34761 - Classified National Security Information
2011-06-14
... MARINE MAMMAL COMMISSION Classified National Security Information [Directive 11-01] AGENCY: Marine... Commission's (MMC) policy on classified information, as directed by Information Security Oversight Office... of Executive Order 13526, ``Classified National Security Information,'' and 32 CFR part 2001...
Clinical Presentation, Surgical Treatment, and Outcome in Radial Polydactyly.
Dijkman, R R; van Nieuwenhoven, C A; Hovius, S E R; Hülsemann, W
2016-02-01
Radial polydactyly or 'thumb duplication' is the most common congenital upper limb anomaly ('CULA') affecting the thumb. The clinical presentation is highly diverse, ranging from an extra thumb floating on a skin bridge to complicated thumb triplications with triphalangeal, deviating, and hypoplastic components. Radial polydactyly can be classified into one of 7 osseous presentations using the Wassel classification, with type IV (45%), type II (20%), and type VII (15%) occurring most frequently. When faced with a radial polydactyly case, hand surgeons specialised in congenital anomalies must weigh the preoperative functional potential and degree of hypoplasia of both thumbs in order to decide whether to resect one thumb and reconstruct the other ('resection and reconstruction'), excise a central part of both thumbs and unite the lateral tissues into one thumb ('the Bilhaut procedure'), transfer the better-developed distal tissues of one thumb onto the better-developed proximal tissues of the other ('on-top plasty'), or discard both severely hypoplastic thumbs and pollicise the index finger. Mere excision of the hypoplastic thumb is rarely indicated since it often requires subsequent revision surgery. Even after being treated by experienced surgeons, about 15% of patients with polydactyly will need additional procedures to correct residual and/or new problems such as deviation from the longitudinal axis and joint instability. Nevertheless, radial polydactyly patients usually achieve unimpaired everyday hand function postoperatively. © Georg Thieme Verlag KG Stuttgart · New York.
RADIAL STABILITY IN STRATIFIED STARS
Pereira, Jonas P.; Rueda, Jorge A.
2015-01-01
We formulate within a generalized distributional approach the treatment of the stability against radial perturbations for both neutral and charged stratified stars in Newtonian and Einstein's gravity. We obtain from this approach the boundary conditions connecting any two phases within a star and underline its relevance for realistic models of compact stars with phase transitions, owing to the modification of the star's set of eigenmodes with respect to the continuous case
Velocidades radiales en Collinder 121
Arnal, M.; Morrell, N.
Se han llevado a cabo observaciones espectroscópicas de unas treinta estrellas que son posibles miembros del cúmulo abierto Collinder 121. Las mismas fueron realizadas con el telescopio de 2.15m del Complejo Astronómico El Leoncito (CASLEO). El análisis de las velocidades radiales derivadas del material obtenido, confirma la realidad de Collinder 121, al menos desde el punto de vista cinemático. La velocidad radial baricentral (LSR) del cúmulo es de +17 ± 3 km.s-1. Esta velocidad coincide, dentro de los errores, con la velocidad radial (LSR) de la nebulosa anillo S308, la cual es de ~20 ± 10 km.s-1. Como S308 se encuentra físicamente asociada a la estrella Wolf-Rayet HD~50896, es muy probable que esta última sea un miembro de Collinder 121. Desde un punto de vista cinemático, la supergigante roja HD~50877 (K3Iab) también pertenecería a Collinder 121. Basándonos en la pertenencia de HD~50896 a Collinder 121, y en la interacción encontrada entre el viento de esta estrella y el medio interestelar circundante a la misma, se estima para este cúmulo una distancia del orden de 1 kpc.
Classifying three imaginary states of the same upper extremity using time-domain features.
Mojgan Tavakolan
Full Text Available Brain-computer interface (BCI allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM with a radial basis kernel function (RBF. An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP, filter bank CSP (FBCSP, and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.
Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme
Lan Wang
2017-01-01
Full Text Available Quasi-linear autoregressive with exogenous inputs (Quasi-ARX models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.
GEMAN, O.
2014-02-01
Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.
Vidotti, Vanessa G; Costa, Vital P; Silva, Fabrício R; Resende, Graziela M; Cremasco, Fernanda; Dias, Marcelo; Gomi, Edson S
2012-06-15
Purpose. To investigate the sensitivity and specificity of machine learning classifiers (MLC) and spectral domain optical coherence tomography (SD-OCT) for the diagnosis of glaucoma. Methods. Sixty-two patients with early to moderate glaucomatous visual field damage and 48 healthy individuals were included. All subjects underwent a complete ophthalmologic examination, achromatic standard automated perimetry, and RNFL imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, California, USA). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters. Subsequently, the following MLCs were tested: Classification Tree (CTREE), Random Forest (RAN), Bagging (BAG), AdaBoost M1 (ADA), Ensemble Selection (ENS), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Naive-Bayes (NB), and Support Vector Machine (SVM). Areas under the ROC curves (aROCs) obtained for each parameter and each MLC were compared. Results. The mean age was 57.0±9.2 years for healthy individuals and 59.9±9.0 years for glaucoma patients (p=0.103). Mean deviation values were -4.1±2.4 dB for glaucoma patients and -1.5±1.6 dB for healthy individuals (pposition (0.765), and 6 o'clock position (0.754). The aROCs from classifiers varied from 0.785 (ADA) to 0.818 (BAG). The aROC obtained with BAG was not significantly different from the aROC obtained with the best single SD-OCT parameter (p=0.93). Conclusions. The SD-OCT showed good diagnostic accuracy in a group of patients with early glaucoma. In this series, MLCs did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.
Ming-Yuan Cho
2017-09-01
Full Text Available In this paper, a new fault diagnosis techniques based on time domain reflectometry (TDR method with pseudo-random binary sequence (PRBS stimulus and support vector machine (SVM classifier has been investigated to recognize the different types of fault in the radial distribution feeders. This novel technique has considered the amplitude of reflected signals and the peaks of cross-correlation (CCR between the reflected and incident wave for generating fault current dataset for SVM. Furthermore, this multi-layer enhanced SVM classifier is combined with classical multidimensional scaling (CMDS feature extraction algorithm and kernel parameter optimization to increase training speed and improve overall classification accuracy. The proposed technique has been tested on a radial distribution feeder to identify ten different types of fault considering 12 input features generated by using Simulink software and MATLAB Toolbox. The success rate of SVM classifier is over 95% which demonstrates the effectiveness and the high accuracy of proposed method.
Waste classifying and separation device
Kakiuchi, Hiroki.
1997-01-01
A flexible plastic bags containing solid wastes of indefinite shape is broken and the wastes are classified. The bag cutting-portion of the device has an ultrasonic-type or a heater-type cutting means, and the cutting means moves in parallel with the transferring direction of the plastic bags. A classification portion separates and discriminates the plastic bag from the contents and conducts classification while rotating a classification table. Accordingly, the plastic bag containing solids of indefinite shape can be broken and classification can be conducted efficiently and reliably. The device of the present invention has a simple structure which requires small installation space and enables easy maintenance. (T.M.)
Defining and Classifying Interest Groups
Baroni, Laura; Carroll, Brendan; Chalmers, Adam
2014-01-01
The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...... in the organizational attributes of specific interest group types. As expected, our comparison of coding schemes reveals a closer link between group attributes and group type in narrower classification schemes based on group organizational characteristics than those based on a behavioral definition of lobbying....
Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata
2015-04-01
The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.
Exceptional circles of radial potentials
Music, M; Perry, P; Siltanen, S
2013-01-01
A nonlinear scattering transform is studied for the two-dimensional Schrödinger equation at zero energy with a radial potential. Explicit examples are presented, both theoretically and computationally, of potentials with nontrivial singularities in the scattering transform. The singularities arise from non-uniqueness of the complex geometric optics solutions that define the scattering transform. The values of the complex spectral parameter at which the singularities appear are called exceptional points. The singularity formation is closely related to the fact that potentials of conductivity type are ‘critical’ in the sense of Murata. (paper)
CHROMOSPHERICALLY ACTIVE STARS IN THE RADIAL VELOCITY EXPERIMENT (RAVE) SURVEY. I. THE CATALOG
Žerjal, M.; Zwitter, T.; Matijevič, G.; Strassmeier, K. G.; Siviero, A.; Steinmetz, M.; Bienaymé, O.; Bland-Hawthorn, J.; Boeche, C.; Grebel, E. K.; Freeman, K. C.; Kordopatis, G.; Munari, U.; Navarro, J. F.; Parker, Q. A.; Reid, W.; Seabroke, G.; Wyse, R. F. G.
2013-01-01
RAVE, the unbiased magnitude limited survey of southern sky stars, contained 456,676 medium-resolution spectra at the time of our analysis. Spectra cover the Ca II infrared triplet (IRT) range, which is a known indicator of chromospheric activity. Our previous work classified all spectra using locally linear embedding. It identified 53,347 cases with a suggested emission component in calcium lines. Here, we use a spectral subtraction technique to measure the properties of this emission. Synthetic templates are replaced by the observed spectra of non-active stars to bypass the difficult computations of non-local thermal equilibrium profiles of the line cores and stellar parameter dependence. We derive both the equivalent width of the excess emission for each calcium line on a 5 Å wide interval and their sum EW IRT for ∼44,000 candidate active dwarf stars with signal-to-noise ratio >20, with no cuts on the basis of the source of their emission flux. From these, ∼14,000 show a detectable chromospheric flux with at least a 2σ confidence level. Our set of active stars vastly enlarges previously known samples. Atmospheric parameters and, in some cases, radial velocities of active stars derived from automatic pipelines suffer from systematic shifts due to their shallower calcium lines. We re-estimate the effective temperature, metallicity, and radial velocities for candidate active stars. The overall distribution of activity levels shows a bimodal shape, with the first peak coinciding with non-active stars and the second with the pre-main-sequence cases. The catalog will be made publicly available with the next RAVE public data releases
Moment methods with effective nuclear Hamiltonians; calculations of radial moments
Belehrad, R.H.
1981-02-01
A truncated orthogonal polynomial expansion is used to evaluate the expectation value of the radial moments of the one-body density of nuclei. The expansion contains the configuration moments, , , and 2 >, where R/sup (k)/ is the operator for the k-th power of the radial coordinate r, and H is the effective nuclear Hamiltonian which is the sum of the relative kinetic energy operator and the Bruckner G matrix. Configuration moments are calculated using trace reduction formulae where the proton and neutron orbitals are treated separately in order to find expectation values of good total isospin. The operator averages are taken over many-body shell model states in the harmonic oscillator basis where all particles are active and single-particle orbitals through six major shells are included. The radial moment expectation values are calculated for the nuclei 16 O, 40 Ca, and 58 Ni and find that is usually the largest term in the expansion giving a large model space dependence to the results. For each of the 3 nuclei, a model space is found which gives the desired rms radius and then we find that the other 5 lowest moments compare favorably with other theoretical predictions. Finally, we use a method of Gordon (5) to employ the lowest 6 radial moment expectation values in the calculation of elastic electron scattering from these nuclei. For low to moderate momentum transfer, the results compare favorably with the experimental data
Composite Classifiers for Automatic Target Recognition
Wang, Lin-Cheng
1998-01-01
...) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers...
Radial smoothing and closed orbit
Burnod, L.; Cornacchia, M.; Wilson, E.
1983-11-01
A complete simulation leading to a description of one of the error curves must involve four phases: (1) random drawing of the six set-up points within a normal population having a standard deviation of 1.3 mm; (b) random drawing of the six vertices of the curve in the sextant mode within a normal population having a standard deviation of 1.2 mm. These vertices are to be set with respect to the axis of the error lunes, while this axis has as its origins the positions defined by the preceding drawing; (c) mathematical definition of six parabolic curves and their junctions. These latter may be curves with very slight curvatures, or segments of a straight line passing through the set-up point and having lengths no longer than one LSS. Thus one gets a mean curve for the absolute errors; (d) plotting of the actually observed radial positions with respect to the mean curve (results of smoothing)
Cholemari, Murali R.; Arakeri, Jaywant H.
2005-08-01
We study the stability of surface waves on the radial film flow created by a vertical cylindrical water jet striking a horizontal plate. In such flows, surface waves have been found to be unstable and can cause transition to turbulence. This surface-wave-induced transition is different from the well-known Tollmien-Schlichting wave-induced transition. The present study aims at understanding the instability and the transition process. We do a temporal stability analysis by assuming the flow to be locally two-dimensional but including spatial variations to first order in the basic flow. The waves are found to be dispersive, mostly unstable, and faster than the mean flow. Spatial variation is the major destabilizing factor. Experiments are done to test the results of the linear stability analysis and to document the wave breakup and transition. Comparison between theory and experiments is fairly good and indicates the adequacy of the model.
Damm, F.C.
1975-01-01
The unique gas dynamic laser provides outward radial supersonic flow from a toroidal shaped stacked array of a plurality of nozzles, through a diffuser having ring shaped and/or linear shaped vanes, and through a cavity which is cylindrical and concentric with the stacked array, with the resultant laser beam passing through the housing parallel to the central axis of the diffuser which is coincident with the axis of the gas dynamic laser. Therefore, greater beam extraction flexibility is attainable, because of fewer flow shock disturbances, as compared to the conventional unidirectional flow gas dynamic laser in which unidirectional supersonic flow sweeps through a rectangular cavity and is exhausted through a two-dimensional diffuser. (auth)
Aggregation Operator Based Fuzzy Pattern Classifier Design
Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker
2009-01-01
This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....
Ulnar nerve entrapment complicating radial head excision
Kevin Parfait Bienvenu Bouhelo-Pam
Full Text Available Introduction: Several mechanisms are involved in ischemia or mechanical compression of ulnar nerve at the elbow. Presentation of case: We hereby present the case of a road accident victim, who received a radial head excision for an isolated fracture of the radial head and complicated by onset of cubital tunnel syndrome. This outcome could be the consequence of an iatrogenic valgus of the elbow due to excision of the radial head. Hitherto the surgical treatment of choice it is gradually been abandoned due to development of radial head implant arthroplasty. However, this management option is still being performed in some rural centers with low resources. Discussion: The radial head plays an important role in the stability of the elbow and his iatrogenic deformity can be complicated by cubital tunnel syndrome. Conclusion: An ulnar nerve release was performed with favorable outcome. Keywords: Cubital tunnel syndrome, Peripheral nerve palsy, Radial head excision, Elbow valgus
15 CFR 4.8 - Classified Information.
2010-01-01
... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily the...
Stirling Engine With Radial Flow Heat Exchangers
Vitale, N.; Yarr, George
1993-01-01
Conflict between thermodynamical and structural requirements resolved. In Stirling engine of new cylindrical configuration, regenerator and acceptor and rejector heat exchangers channel flow of working gas in radial direction. Isotherms in regenerator ideally concentric cylinders, and gradient of temperature across regenerator radial rather than axial. Acceptor and rejector heat exchangers located radially inward and outward of regenerator, respectively. Enables substantial increase in power of engine without corresponding increase in diameter of pressure vessel.
BIOPHARMACEUTICS CLASSIFICATION SYSTEM: A STRATEGIC TOOL FOR CLASSIFYING DRUG SUBSTANCES
Rohilla Seema; Rohilla Ankur; Marwaha RK; Nanda Arun
2011-01-01
The biopharmaceutical classification system (BCS) is a scientific approach for classifying drug substances based on their dose/solubility ratio and intestinal permeability. The BCS has been developed to allow prediction of in vivo pharmacokinetic performance of drug products from measurements of permeability and solubility. Moreover, the drugs can be categorized into four classes of BCS on the basis of permeability and solubility namely; high permeability high solubility, high permeability lo...
Radial head button holing: a cause of irreducible anterior radial head dislocation
Shin, Su-Mi; Chai, Jee Won; You, Ja Yeon; Park, Jina [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Radiology, Seoul (Korea, Republic of); Bae, Kee Jeong [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Orthopedic Surgery, Seoul (Korea, Republic of)
2016-10-15
''Buttonholing'' of the radial head through the anterior joint capsule is a known cause of irreducible anterior radial head dislocation associated with Monteggia injuries in pediatric patients. To the best of our knowledge, no report has described an injury consisting of buttonholing of the radial head through the annular ligament and a simultaneous radial head fracture in an adolescent. In the present case, the radiographic findings were a radial head fracture with anterior dislocation and lack of the anterior fat pad sign. Magnetic resonance imaging (MRI) clearly demonstrated anterior dislocation of the fractured radial head through the torn annular ligament. The anterior joint capsule and proximal portion of the annular ligament were interposed between the radial head and capitellum, preventing closed reduction of the radial head. Familiarity with this condition and imaging findings will aid clinicians to make a proper diagnosis and fast decision to perform an open reduction. (orig.)
32 CFR 154.6 - Standards for access to classified information or assignment to sensitive duties.
2010-07-01
... OF THE SECRETARY OF DEFENSE SECURITY DEPARTMENT OF DEFENSE PERSONNEL SECURITY PROGRAM REGULATION... person's loyalty, reliability, and trustworthiness are such that entrusting the person with classified... reasonable basis for doubting the person's loyalty to the Government of the United States. ...
40 CFR 260.32 - Variances to be classified as a boiler.
2010-07-01
... 40 Protection of Environment 25 2010-07-01 2010-07-01 false Variances to be classified as a boiler... be classified as a boiler. In accordance with the standards and criteria in § 260.10 (definition of “boiler”), and the procedures in § 260.33, the Administrator may determine on a case-by-case basis that...
Photoelectric Radial Velocities, Paper XIX Additional Spectroscopic ...
ian velocity curve that does justice to the measurements, but it cannot be expected to have much predictive power. Key words. Stars: late-type—stars: radial velocities—spectroscopic binaries—orbits. 0. Preamble. The 'Redman K stars' are a lot of seventh-magnitude K stars whose radial velocities were first observed by ...
Radial velocities of RR Lyrae stars
Hawley, S.L.; Barnes, T.G. III
1985-01-01
283 spectra of 57 RR Lyrae stars have been obtained using the 2.1-m telescope at McDonald Observatory. Radial velocities were determined using a software cross-correlation technique. New mean radial velocities were determined for 46 of the stars. 11 references
Concepts of radial and angular kinetic energies
Dahl, Jens Peder; Schleich, W.P.
2002-01-01
We consider a general central-field system in D dimensions and show that the division of the kinetic energy into radial and angular parts proceeds differently in the wave-function picture and the Weyl-Wigner phase-space picture, Thus, the radial and angular kinetic energies are different quantities...
Fault diagnosis for engine air path with neural models and classifier ...
A new FDI scheme is developed for automotive engines in this paper. The method uses an independent radial basis function (RBF) neural ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10-20% changes ...
Error minimizing algorithms for nearest eighbor classifiers
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, G. Beate [TEXAS A& M
2011-01-03
Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.
Radial electric fields for improved tokamak performance
Downum, W.B.
1981-01-01
The influence of externally-imposed radial electric fields on the fusion energy output, energy multiplication, and alpha-particle ash build-up in a TFTR-sized, fusing tokamak plasma is explored. In an idealized tokamak plasma, an externally-imposed radial electric field leads to plasma rotation, but no charge current flows across the magnetic fields. However, a realistically-low neutral density profile generates a non-zero cross-field conductivity and the species dependence of this conductivity allows the electric field to selectively alter radial particle transport
Hewes, R.C.; Miller, T.R.
1988-01-01
To profile optimally each portion of the meniscus, the authors use the multiangle, multisection feature of a General Electric SIGNA 1.5-T imager to produce radial images centered on each meniscus. A total of 12-15 sections are imaged at 10 0 -15 0 intervals of each meniscus, yielding perpendicular images of the entire meniscus, comparable with the arthrographic tangential views. The authors review their technique and demonstrate correlation cases between the radial gradient recalled acquisition in a steady state sequences, sagittal and coronal MR images, and arthrograms. Radial images should be a routine part of knee MR imaging
Radial pattern of nuclear decay processes
Iskra, W.; Mueller, M.; Rotter, I.; Technische Univ. Dresden
1994-05-01
At high level density of nuclear states, a separation of different time scales is observed (trapping effect). We calculate the radial profile of partial widths in the framework of the continuum shell model for some 1 - resonances with 2p-2h nuclear structure in 16 O as a function of the coupling strength to the continuum. A correlation between the lifetime of a nuclear state and the radial profile of the corresponding decay process is observed. We conclude from our numerical results that the trapping effect creates structures in space and time characterized by a small radial extension and a short lifetime. (orig.)
Zou, Zhichao [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Wang, Fujun, E-mail: wangfj@cau.edu.cn [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Yao, Zhifeng [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Tao, Ran [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Xiao, Ruofu [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Li, Huaicheng [Shanghai Liancheng (Group) Co., Ltd., Shanghai 201812 (China)
2016-12-15
Highlights: • Conclude the characteristics of transient radial force in the startup process for a large double-suction centrifugal pump. • The overall direction of the radial force during startup process is also confirmed. • A formula used to calculate the transient radial force during startup process is proposed. • A relationship between radial force variation and axial vortex development in blade channel during the startup process is established. The mechanism of the radial force evolution is revealed. - Abstract: Double-suction centrifugal pumps play an important role in the main feedwater systems of nuclear power plant. The impeller radial force in a centrifugal pump varies dramatically during startup at the shut-off condition. In this study, the startup process of a large double-suction centrifugal pump is investigated using CFD. During testing, the impeller speed is accelerated from zero to its rated speed in 1.0 s (marked as t{sub 0}) and is then maintained at the rated speed. The results show that the radial force increase lags behind the impeller speed increase. At 0–0.4t{sub 0}, the radial force is small (approaching zero). At 0.4–1.4t{sub 0}, the radial force increases rapidly. After 1.4t{sub 0}, the average radial force stabilizes and reaches its maximum value of 55,619 N. The observed maximum radial force value during startup is approximately nine times as high as the radial force under rated condition. During startup, the overall radial force direction is proximate to the radial line located 25° from the volute tongue along circumferential direction. A transient radial force formula is proposed to evaluate the changes in radial force during startup. The streamline distribution in impeller passages and the impeller outlet pressure profile varying over time are produced. The relationship between radial force evolution and the varying axial-to-spiral vortex structure is analyzed. The radial force change mechanism is revealed. This research
Zou, Zhichao; Wang, Fujun; Yao, Zhifeng; Tao, Ran; Xiao, Ruofu; Li, Huaicheng
2016-01-01
Highlights: • Conclude the characteristics of transient radial force in the startup process for a large double-suction centrifugal pump. • The overall direction of the radial force during startup process is also confirmed. • A formula used to calculate the transient radial force during startup process is proposed. • A relationship between radial force variation and axial vortex development in blade channel during the startup process is established. The mechanism of the radial force evolution is revealed. - Abstract: Double-suction centrifugal pumps play an important role in the main feedwater systems of nuclear power plant. The impeller radial force in a centrifugal pump varies dramatically during startup at the shut-off condition. In this study, the startup process of a large double-suction centrifugal pump is investigated using CFD. During testing, the impeller speed is accelerated from zero to its rated speed in 1.0 s (marked as t_0) and is then maintained at the rated speed. The results show that the radial force increase lags behind the impeller speed increase. At 0–0.4t_0, the radial force is small (approaching zero). At 0.4–1.4t_0, the radial force increases rapidly. After 1.4t_0, the average radial force stabilizes and reaches its maximum value of 55,619 N. The observed maximum radial force value during startup is approximately nine times as high as the radial force under rated condition. During startup, the overall radial force direction is proximate to the radial line located 25° from the volute tongue along circumferential direction. A transient radial force formula is proposed to evaluate the changes in radial force during startup. The streamline distribution in impeller passages and the impeller outlet pressure profile varying over time are produced. The relationship between radial force evolution and the varying axial-to-spiral vortex structure is analyzed. The radial force change mechanism is revealed. This research provides a scientific
Hierarchical mixtures of naive Bayes classifiers
Wiering, M.A.
2002-01-01
Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical
Comparing classifiers for pronunciation error detection
Strik, H.; Truong, K.; Wet, F. de; Cucchiarini, C.
2007-01-01
Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: two acoustic-phonetic classifiers (one of which employs
Feature extraction for dynamic integration of classifiers
Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.
2007-01-01
Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique
Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał
2016-09-01
This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.
Deconvolution When Classifying Noisy Data Involving Transformations
Carroll, Raymond
2012-09-01
In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.
Deconvolution When Classifying Noisy Data Involving Transformations.
Carroll, Raymond; Delaigle, Aurore; Hall, Peter
2012-09-01
In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.
Deconvolution When Classifying Noisy Data Involving Transformations
Carroll, Raymond; Delaigle, Aurore; Hall, Peter
2012-01-01
In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.
340 waste handling facility interim safety basis
VAIL, T.S.
1999-04-01
This document presents an interim safety basis for the 340 Waste Handling Facility classifying the 340 Facility as a Hazard Category 3 facility. The hazard analysis quantifies the operating safety envelop for this facility and demonstrates that the facility can be operated without a significant threat to onsite or offsite people.
340 waste handling facility interim safety basis
VAIL, T.S.
1999-01-01
This document presents an interim safety basis for the 340 Waste Handling Facility classifying the 340 Facility as a Hazard Category 3 facility. The hazard analysis quantifies the operating safety envelop for this facility and demonstrates that the facility can be operated without a significant threat to onsite or offsite people
Radial pseudoaneurysm following diagnostic coronary angiography
Shankar Laudari
2015-06-01
Full Text Available The radial artery access has gained popularity as a method of diagnostic coronary catheterization compared to femoral artery puncture in terms of vascular complications and early ambulation. However, very rare complication like radial artery pseudoaneurysm may occur following cardiac catheterization which may give rise to serious consequences. Here, we report a patient with radial pseudoaneurysm following diagnostic coronary angiography. Adequate and correct methodology of compression of radial artery following puncture for maintaining hemostasis is the key to prevention.DOI: http://dx.doi.org/10.3126/jcmsn.v10i3.12776 Journal of College of Medical Sciences-Nepal, 2014, Vol-10, No-3, 48-50
Radial transport with perturbed magnetic field
Hazeltine, R. D. [Institute for Fusion Studies, University of Texas at Austin, Austin, Texas 78712 (United States)
2015-05-15
It is pointed out that the viscosity coefficient describing radial transport of toroidal angular momentum is proportional to the second power of the gyro-radius—like the corresponding coefficients for particle and heat transport—regardless of any geometrical symmetry. The observation is widely appreciated, but worth emphasizing because some literature gives the misleading impression that asymmetry can allow radial moment transport in first-order.
Radial transport with perturbed magnetic field
Hazeltine, R. D.
2015-01-01
It is pointed out that the viscosity coefficient describing radial transport of toroidal angular momentum is proportional to the second power of the gyro-radius—like the corresponding coefficients for particle and heat transport—regardless of any geometrical symmetry. The observation is widely appreciated, but worth emphasizing because some literature gives the misleading impression that asymmetry can allow radial moment transport in first-order
Stability of radial and non-radial pulsation modes of massive ZAMS models
Odell, A.P.; Pausenwein, A.; Weiss, W.W.; Hajek, A.
1987-01-01
The authors have computed non-adiabatic eigenvalues for radial and non-radial pulsation modes of star models between 80 and 120 M solar with composition of chi=0.70 and Z=0.02. The radial fundamental mode is unstable in models with mass greater than 95 M solar , but the first overtone mode is always stable. The non-radial modes are all stable for all models, but the iota=2 f-mode is the closest to being driven. The non-radial modes are progressively more stable with higher iota and with higher n (for both rho- and g-modes). Thus, their results indicate that radial pulsation limits the upper mass of a star
Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes.
Somfai, Gábor Márk; Tátrai, Erika; Laurik, Lenke; Varga, Boglárka; Ölvedy, Veronika; Jiang, Hong; Wang, Jianhua; Smiddy, William E; Somogyi, Anikó; DeBuc, Delia Cabrera
2014-04-12
Artificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy. When exploring the probability as to whether the subject's eye was healthy (diagnostic condition, Test 1), we found that the structural and optical property features of the outer plexiform layer (OPL) and the complex formed by the ganglion cell and inner plexiform layers (GCL + IPL) provided the highest probability (positive predictive value (PPV) of 91% and 89%, respectively) for the proportion of patients with positive test results (healthy condition) who were correctly diagnosed (Test 1). The true negative, TP and PPV values remained stable despite the different sizes of training data sets (Test 2). The sensitivity, specificity and PPV were greater or close to 0.70 for the retinal nerve fiber layer's features, photoreceptor outer segments and retinal pigment epithelium when 23 diabetic eyes with mild retinopathy were mixed with 38 diabetic eyes with no retinopathy (Test 3). A Bayesian ANN trained on structural and optical features from optical coherence tomography data can successfully discriminate between healthy and diabetic eyes with and with no retinopathy. The fractal dimension of the OPL and the GCL + IPL complex predicted by the Bayesian radial basis function network provides better
21 CFR 866.4800 - Radial immunodiffusion plate.
2010-04-01
...) MEDICAL DEVICES IMMUNOLOGY AND MICROBIOLOGY DEVICES Immunology Laboratory Equipment and Reagents § 866.4800 Radial immunodiffusion plate. (a) Identification. A radial immunodiffusion plate for clinical use...
A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING
JORGE FLORES CRUZ
2017-01-01
Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.
High dimensional classifiers in the imbalanced case
Bak, Britta Anker; Jensen, Jens Ledet
We consider the binary classification problem in the imbalanced case where the number of samples from the two groups differ. The classification problem is considered in the high dimensional case where the number of variables is much larger than the number of samples, and where the imbalance leads...... to a bias in the classification. A theoretical analysis of the independence classifier reveals the origin of the bias and based on this we suggest two new classifiers that can handle any imbalance ratio. The analytical results are supplemented by a simulation study, where the suggested classifiers in some...
Fabrício R. Silva
2013-06-01
Full Text Available PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs for glaucoma diagnosis using Spectral Domain OCT (SD-OCT and standard automated perimetry (SAP. METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP and retinal nerve fiber layer (RNFL imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California. Receiver operating characteristic (ROC curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG, Naive-Bayes (NB, Multilayer Perceptron (MLP, Radial Basis Function (RBF, Random Forest (RAN, Ensemble Selection (ENS, Classification Tree (CTREE, Ada Boost M1(ADA,Support Vector Machine Linear (SVML and Support Vector Machine Gaussian (SVMG. Areas under the receiver operating characteristic curves (aROC obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE to 0.946 (RAN.The best OCT+SAP aROC obtained with RAN (0.946 was significantly larger the best single OCT parameter (p<0.05, but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19. CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.
Radiological findings in three cases of paraxial radial hemimelia in goats
Corbera, J.A.; Pulido, M.; Morales, M.; Juste, M.C.; Gutierrez, C.
2002-01-01
Hemimelia is a congenital abnormality characterized by the absence of a portion of the normal structures in a limb. Hemimelia is classified as transversal and paraxial and is related to genetical and environmental factors. This article shows the radiological findings observed in three different cases of paraxial hemimelia occurred in goats (radial agenesia, absence of the portion of the distal epiphysis of the radius and anomalous radius with ulnar hypoplasia). Possible causes related to these abnormalities are discussed
Arabic Handwriting Recognition Using Neural Network Classifier
pc
2018-03-05
Mar 5, 2018 ... an OCR using Neural Network classifier preceded by a set of preprocessing .... Artificial Neural Networks (ANNs), which we adopt in this research, consist of ... advantage and disadvantages of each technique. In [9],. Khemiri ...
Classifiers based on optimal decision rules
Amin, Talha
2013-11-25
Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
Classifiers based on optimal decision rules
Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2013-01-01
Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
Combining multiple classifiers for age classification
Van Heerden, C
2009-11-01
Full Text Available The authors compare several different classifier combination methods on a single task, namely speaker age classification. This task is well suited to combination strategies, since significantly different feature classes are employed. Support vector...
Neural Network Classifiers for Local Wind Prediction.
Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz
2004-05-01
This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.
Application of a naive Bayesians classifiers in assessing the supplier
Mijailović Snežana
2017-01-01
Full Text Available The paper considers the class of interactive knowledge based systems whose main purpose of making proposals and assisting customers in making decisions. The mathematical model provides a set of examples of learning about the delivered series of outflows from three suppliers, as well as an analysis of an illustrative example for assessing the supplier using a naive Bayesian classifier. The model was developed on the basis of the analysis of subjective probabilities, which are later revised with the help of new empirical information and Bayesian theorem on a posterior probability, i.e. combining of subjective and objective conditional probabilities in the choice of a reliable supplier.
Consistency Analysis of Nearest Subspace Classifier
Wang, Yi
2015-01-01
The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also ...
Radial tear of posterior horn of the medial meniscus and osteonecrosis of the knee
Motoyama, Tatsuo; Ihara, Hidetoshi; Kawashima, Mahito
2003-01-01
We studied the relation between a radial tear of the posterior horn of the medial meniscus and osteonecrosis of the knee. Thirty-eight knees of 37 patients were diagnosed as medial meniscus tear and received arthroscopic knee surgery. We divided them into two groups: knees having radial tear of the posterior horn of the medial meniscus (posterior horn group) and knees containing radial tear except for posterior horn, horizontal tear, degenerative tear, and flap tear of the medial meniscus (non-posterior horn group). The posterior horn group consisted of 14 knees (average age: 65.1 years old) and the non-posterior horn group consisted of 24 knees (average age: 59.6 years old). All cases underwent MRI before arthroscopy. MRI findings were classified into three types (typical osteonecrosis, small osteonecrosis, and non-osteonecrosis). In the posterior horn group, typical osteonecrosis were five knees and small osteonecrosis were five knees, while in the non-posterior horn group only three knees were small osteonecrosis. These findings suggest the relevance between radial tear of the posterior horn of the medial meniscus and osteonecrosis of the knee (Mann-Whitney test p<0.01). The etiology of spontaneous osteonecrosis of the knee joint is unknown, however one etiology could be the radial tear of the posterior horn of the medial meniscus. (author)
Anomalies of radial and ulnar arteries
Rajani Singh
Full Text Available Abstract During dissection conducted in an anatomy department of the right upper limb of the cadaver of a 70-year-old male, both origin and course of the radial and ulnar arteries were found to be anomalous. After descending 5.5 cm from the lower border of the teres major, the brachial artery anomalously bifurcated into a radial artery medially and an ulnar artery laterally. In the arm, the ulnar artery lay lateral to the median nerve. It followed a normal course in the forearm. The radial artery was medial to the median nerve in the arm and then, at the level of the medial epicondyle, it crossed from the medial to the lateral side of the forearm, superficial to the flexor muscles. The course of the radial artery was superficial and tortuous throughout the arm and forearm. The variations of radial and ulnar arteries described above were associated with anomalous formation and course of the median nerve in the arm. Knowledge of neurovascular anomalies are important for vascular surgeons and radiologists.
Ida, Katsumi; Miura, Yukitoshi; Itoh, Sanae
1994-10-01
Radial structures of plasma rotation and radial electric field are experimentally studied in tokamak, heliotron/torsatron and stellarator devices. The perpendicular and parallel viscosities are measured. The parallel viscosity, which is dominant in determining the toroidal velocity in heliotron/torsatron and stellarator devices, is found to be neoclassical. On the other hand, the perpendicular viscosity, which is dominant in dictating the toroidal rotation in tokamaks, is anomalous. Even without external momentum input, both a plasma rotation and a radial electric field exist in tokamaks and heliotrons/torsatrons. The observed profiles of the radial electric field do not agree with the theoretical prediction based on neoclassical transport. This is mainly due to the existence of anomalous perpendicular viscosity. The shear of the radial electric field improves particle and heat transport both in bulk and edge plasma regimes of tokamaks. (author) 95 refs
Manufacturing of Precision Forgings by Radial Forging
Wallner, S.; Harrer, O.; Buchmayr, B.; Hofer, F.
2011-01-01
Radial forging is a multi purpose incremental forging process using four tools on the same plane. It is widely used for the forming of tool steels, super alloys as well as titanium- and refractory metals. The range of application goes from reducing the diameters of shafts, tubes, stepped shafts and axels, as well as for creating internal profiles for tubes in Near-Net-Shape and Net-Shape quality. Based on actual development of a weight optimized transmission input shaft, the specific features of radial forging technology is demonstrated. Also a Finite Element Model for the simulation of the process is shown which leads to reduced pre-processing effort and reduced computing time compared to other published simulation methods for radial forging. The finite element model can be applied to quantify the effects of different forging strategies.
Comprehensive benchmarking and ensemble approaches for metagenomic classifiers.
McIntyre, Alexa B R; Ounit, Rachid; Afshinnekoo, Ebrahim; Prill, Robert J; Hénaff, Elizabeth; Alexander, Noah; Minot, Samuel S; Danko, David; Foox, Jonathan; Ahsanuddin, Sofia; Tighe, Scott; Hasan, Nur A; Subramanian, Poorani; Moffat, Kelly; Levy, Shawn; Lonardi, Stefano; Greenfield, Nick; Colwell, Rita R; Rosen, Gail L; Mason, Christopher E
2017-09-21
One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.
Radial velocity observations of VB10
Deshpande, R.; Martin, E.; Zapatero Osorio, M. R.; Del Burgo, C.; Rodler, F.; Montgomery, M. M.
2011-07-01
VB 10 is the smallest star known to harbor a planet according to the recent astrometric study of Pravdo & Shaklan [1]. Here we present near-infrared (J-band) radial velocity of VB 10 performed from high resolution (R~20,000) spectroscopy (NIRSPEC/KECK II). Our results [2] suggest radial velocity variability with amplitude of ~1 km/s, a result that is consistent with the presence of a massive planet companion around VB10 as found via long-term astrometric monitoring of the star by Pravdo & Shaklan. Employing an entirely different technique we verify the results of Pravdo & Shaklan.
Plasma Signatures of Radial Field Power Dropouts
Lucek, E.A.; Horbury, T.S.; Balogh, A.; McComas, D.J.
1998-01-01
A class of small scale structures, with a near-radial magnetic field and a drop in magnetic field fluctuation power, have recently been identified in the polar solar wind. An earlier study of 24 events, each lasting for 6 hours or more, identified no clear plasma signature. In an extension of that work, radial intervals lasting for 4 hours or more (89 in total), have been used to search for a statistically significant plasma signature. It was found that, despite considerable variations between intervals, there was a small but significant drop, on average, in plasma temperature, density and β during these events
Reble, a radially converging electron beam accelerator
Ramirez, J.J.; Prestwich, K.R.
1976-01-01
The Reble accelerator at Sandia Laboratories is described. This accelerator was developed to provide an experimental source for studying the relevant diode physics, beam propagation, beam energy deposition in a gas using a radially converging e-beam. The nominal parameters for Reble are 1 MV, 200 kA, 20 ns e-beam pulse. The anode and cathode are concentric cylinders with the anode as the inner cylinder. The radial beam can be propagated through the thin foil anode into the laser gas volume. The design and performance of the various components of the accelerator are presented
Reinforcement Learning Based Artificial Immune Classifier
Mehmet Karakose
2013-01-01
Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.
Classifier Fusion With Contextual Reliability Evaluation.
Liu, Zhunga; Pan, Quan; Dezert, Jean; Han, Jun-Wei; He, You
2018-05-01
Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class. The elements of this matrix are estimated from the -nearest neighbors of the object. A cautious discounting rule is developed under belief functions framework to revise the classification result according to the inner reliability. The relative reliability is evaluated based on a new incompatibility measure which allows to reduce the level of conflict between the classifiers by applying the classical evidence discounting rule to each classifier before their combination. The inner reliability and relative reliability capture different aspects of the classification reliability. The discounted classification results are combined with Dempster-Shafer's rule for the final class decision making support. The performance of CF-CRE have been evaluated and compared with those of main classical fusion methods using real data sets. The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general. Moreover, CF-CRE is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.
Classifying sows' activity types from acceleration patterns
Cornou, Cecile; Lundbye-Christensen, Søren
2008-01-01
An automated method of classifying sow activity using acceleration measurements would allow the individual sow's behavior to be monitored throughout the reproductive cycle; applications for detecting behaviors characteristic of estrus and farrowing or to monitor illness and welfare can be foreseen....... This article suggests a method of classifying five types of activity exhibited by group-housed sows. The method involves the measurement of acceleration in three dimensions. The five activities are: feeding, walking, rooting, lying laterally and lying sternally. Four time series of acceleration (the three...
Data characteristics that determine classifier performance
Van der Walt, Christiaan M
2006-11-01
Full Text Available available at [11]. The kNN uses a LinearNN nearest neighbour search algorithm with an Euclidean distance metric [8]. The optimal k value is determined by performing 10-fold cross-validation. An optimal k value between 1 and 10 is used for Experiments 1... classifiers. 10-fold cross-validation is used to evaluate and compare the performance of the classifiers on the different data sets. 3.1. Artificial data generation Multivariate Gaussian distributions are used to generate artificial data sets. We use d...
A Customizable Text Classifier for Text Mining
Yun-liang Zhang
2007-12-01
Full Text Available Text mining deals with complex and unstructured texts. Usually a particular collection of texts that is specified to one or more domains is necessary. We have developed a customizable text classifier for users to mine the collection automatically. It derives from the sentence category of the HNC theory and corresponding techniques. It can start with a few texts, and it can adjust automatically or be adjusted by user. The user can also control the number of domains chosen and decide the standard with which to choose the texts based on demand and abundance of materials. The performance of the classifier varies with the user's choice.
A survey of decision tree classifier methodology
Safavian, S. R.; Landgrebe, David
1991-01-01
Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.
Classifying Radio Galaxies with the Convolutional Neural Network
Aniyan, A. K.; Thorat, K.
2017-01-01
We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.
Classifying Radio Galaxies with the Convolutional Neural Network
Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)
2017-06-01
We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.
Classifying Radio Galaxies with the Convolutional Neural Network
Aniyan, A. K.; Thorat, K.
2017-06-01
We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.
Kryven, I.; Röblitz, S; Schütte, C.
2015-01-01
Background: The chemical master equation is the fundamental equation of stochastic chemical kinetics. This differential-difference equation describes temporal evolution of the probability density function for states of a chemical system. A state of the system, usually encoded as a vector, represents
Falat, Lukas; Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Lukas Falat
2016-01-01
Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Hybrid model decomposition of speech and noise in a radial basis function neural model framework
Sørensen, Helge Bjarup Dissing; Hartmann, Uwe
1994-01-01
The aim of the paper is to focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the presence of additive car noise. The technique...
Wang, Zhiheng; Huang, Zhu; Zhang, Wei; Xi, Guang
2014-01-01
main advantages of the method. One is that the unstructured nodes generation in the computational domain is quite simple, without much concern about the mesh quality; the other is that the localization of the obtained collocations for the discretization
A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions
Pan, Guang; Ye, Pengcheng; Yang, Zhidong
2014-01-01
Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples. PMID:25133206
Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450
Warping a Neuro-Anatomy Atlas on 3D MRI Data with Radial Basis Functions
Bennink, H.E.; Korbeeck, J.M.; Janssen, B.J.; Haar Romenij, ter B.M.
2006-01-01
Navigation for neurosurgical procedures must be highly accurate. Often small structures are hardly seen on pre-operative scans. Fitting a 3D electronic neuro-anatomical atlas on the data assists with the localization of small structures and dim outlines. During surgery also brainshifts occurs. With
Bos, F.M.
2010-01-01
Both biological and engineering scientist have always been intrigued by the flight of insects and birds. For a long time, the aerodynamic mechanism behind flapping insect flight was a complete mystery. Recently, several experimental and numerical flow visualisations were performed to investigate the
Learning Errors by Radial Basis Function Neural Networks and Regularization Networks
Neruda, Roman; Vidnerová, Petra
2009-01-01
Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf
On helicon wave induced radial plasma transport
Petrzilka, V.
1993-04-01
Estimates of helicon wave induced radial plasma transport are presented. The wave induced transport grows or decreases in dependence on the sign of the azimuthal wave number; these changes in transport may play an important role in helicon wave plasma sources. (author) 5 figs., 18 refs
Revealing the radial modes in vortex beams
Sephton, Bereneice C
2016-10-01
Full Text Available Light beams that carry orbital angular momentum are often approximated by modulating an initial beam, usually Gaussian, with an azimuthal phase variation to create a vortex beam. Such vortex beams are well defined azimuthally, but the radial profile...
Measurement of Wear in Radial Journal Bearings
Ligterink, D.J.; Ligterink, D.J.; de Gee, A.W.J.
1996-01-01
this article, the measurement of wear in radial journal bearings is discussed, where a distinction is made between stationary and non-stationary contact conditions. Starting with Holm/Archard's wear law, equations are derived for the calculation of the specific wear rate k of the bearing material as
Radial interchange motions of plasma filaments
Garcia, O.E.; Bian, N.H.; Fundamenski, W.
2006-01-01
on a biperiodic domain perpendicular to the magnetic field. It is demonstrated that a blob-like plasma structure develops dipolar vorticity and electrostatic potential fields, resulting in rapid radial acceleration and formation of a steep front and a trailing wake. While the dynamical evolution strongly depends...
Radial transfer effects for poloidal rotation
Hallatschek, Klaus
2010-11-01
Radial transfer of energy or momentum is the principal agent responsible for radial structures of Geodesic Acoustic Modes (GAMs) or stationary Zonal Flows (ZF) generated by the turbulence. For the GAM, following a physical approach, it is possible to find useful expressions for the individual components of the Poynting flux or radial group velocity allowing predictions where a mathematical full analysis is unfeasible. Striking differences between up-down symmetric flux surfaces and asymmetric ones have been found. For divertor geometries, e.g., the direction of the propagation depends on the sign of the ion grad-B drift with respect to the X-point, reminiscent of a sensitive determinant of the H-mode threshold. In nonlocal turbulence computations it becomes obvious that the linear energy transfer terms can be completely overwhelmed by the action of the turbulence. In contrast, stationary ZFs are governed by the turbulent radial transfer of momentum. For sufficiently large systems, the Reynolds stress becomes a deterministic functional of the flows, which can be empirically determined from the stress response in computational turbulence studies. The functional allows predictions even on flow/turbulence states not readily obtainable from small amplitude noise, such as certain transport bifurcations or meta-stable states.
Computing modal dispersion characteristics of radially Asymmetric ...
We developed a matrix theory that applies to with non-circular/circular but concentric layers fibers. And we compute the dispersion characteristics of radially unconventional fiber, known as Asymmetric Bragg fiber. An attempt has been made to determine how the modal characteristics change as circular Bragg fiber is ...
Zernike Basis to Cartesian Transformations
Mathar, R. J.
2009-12-01
The radial polynomials of the 2D (circular) and 3D (spherical) Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle) defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.
75 FR 37253 - Classified National Security Information
2010-06-28
... ``Secret.'' (3) Each interior page of a classified document shall be marked at the top and bottom either... ``(TS)'' for Top Secret, ``(S)'' for Secret, and ``(C)'' for Confidential will be used. (2) Portions... from the informational text. (1) Conspicuously place the overall classification at the top and bottom...
75 FR 707 - Classified National Security Information
2010-01-05
... classified at one of the following three levels: (1) ``Top Secret'' shall be applied to information, the... exercise this authority. (2) ``Top Secret'' original classification authority may be delegated only by the... official has been delegated ``Top Secret'' original classification authority by the agency head. (4) Each...
Neural Network Classifier Based on Growing Hyperspheres
Jiřina Jr., Marcel; Jiřina, Marcel
2000-01-01
Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics
Histogram deconvolution - An aid to automated classifiers
Lorre, J. J.
1983-01-01
It is shown that N-dimensional histograms are convolved by the addition of noise in the picture domain. Three methods are described which provide the ability to deconvolve such noise-affected histograms. The purpose of the deconvolution is to provide automated classifiers with a higher quality N-dimensional histogram from which to obtain classification statistics.
Classifying web pages with visual features
de Boer, V.; van Someren, M.; Lupascu, T.; Filipe, J.; Cordeiro, J.
2010-01-01
To automatically classify and process web pages, current systems use the textual content of those pages, including both the displayed content and the underlying (HTML) code. However, a very important feature of a web page is its visual appearance. In this paper, we show that using generic visual
On the stability of a homogeneous barrier discharge in nitrogen relative to radial perturbations
Golubovskii, Y B; Behnke, J; Behnke, J F
2003-01-01
The influence of small radial perturbations of the cathode current on the characteristics of a homogeneous barrier discharge in nitrogen is investigated on the basis of a two-dimensional fluid model. In a Townsend discharge, radial fluctuations are substantially suppressed, which is the evidence of its stability. The oscillative mode of the Townsend discharge is also stable with regard to radial perturbations. As the discharge turns into a form controlled by spatial charge (a streamer is developed), disturbances of all radii grow in time. Such a behaviour testifies the instability of a streamer front and may cause the discharge filamentation. Since only the Townsend discharge is stable, it is possible to use a one-dimensional model to determine the domain of existence for a homogeneous discharge. The study of homogeneity domains by means of the one-dimensional model shows that at relatively large values of the voltage growth rate, discharge gap width, or capacitance of dielectric barriers the discharge tends ...
Pate, G
2011-10-01
A survey was conducted of medication administered during radial artery cannulation for coronary angiography in 2009 in Ireland; responses were obtained for 15 of 20 centres, in 5 of which no radial access procedures were undertaken. All 10 (100%) centres which provided data used heparin and one or more anti-spasmodics; verapamil in 9 (90%), nitrate in 1 (10%), both in 2 (20%). There were significant variations in the doses used. Further work needs to be done to determine the optimum cocktail to prevent radial artery injury following coronary angiography.
Torrent, Daniel; Sánchez-Dehesa, José
2009-08-07
We demonstrate that metamaterials with anisotropic properties can be used to develop a new class of periodic structures that has been named radial wave crystals. They can be sonic or photonic, and wave propagation along the radial directions is obtained through Bloch states like in usual sonic or photonic crystals. The band structure of the proposed structures can be tailored in a large amount to get exciting novel wave phenomena. For example, it is shown that acoustical cavities based on radial sonic crystals can be employed as passive devices for beam forming or dynamically orientated antennas for sound localization.
Improved WKB radial wave functions in several bases
Durand, B.; Durand, L.; Department of Physics, University of Wisconsin, Madison, Wisconsin 53706)
1986-01-01
We develop approximate WKB-like solutions to the radial Schroedinger equation for problems with an angular momentum barrier using Riccati-Bessel, Coulomb, and harmonic-oscillator functions as basis functions. The solutions treat the angular momentum singularity near the origin more accurately in leading approximation than the standard WKB solutions based on sine waves. The solutions based on Riccati-Bessel and free Coulomb wave functions continue smoothly through the inner turning point and are appropriate for scattering problems. The solutions based on oscillator and bound Coulomb wave functions incorporate both turning points smoothly and are particularly appropriate for bound-state problems; no matching of piecewise solutions using Airy functions is necessary
Microinstabilities in a radially contracting inhomogeneous cylindrical plasma slab
Deutsch, R.; Kaeppeler, H.J.
1980-07-01
In order to study the development of microinstabilities in a collapsing cylindrical plasma sheath, corresponding to the situations in a z-pinch or a plasma focus, the dispersion relation for electromagnetic perturbations is derived with the aid of a newly established slab-model for an inhomogeneous, radially contracting plasma. In contrast to previously used slab-models, the orientation of the electric field is in direction of the cylinder axis and the azimuthal magnetic field is induced by the current flowing through the cylindrical plasma slab. The Vlasov equation is used together with the Krook collision term in order to include the influence of collisions. The results of this theory presented in this report will be used to calculate the growth of drift instabilities in the compression phase of a plasma focus, and shall serve as a basis for further development of a more general dispersion relation including runaway-effects. (orig.)
Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers
Daniel L. Schmoldt; Jing He; A. Lynn Abbott
1998-01-01
Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...
Disassembly and Sanitization of Classified Matter
Stockham, Dwight J.; Saad, Max P.
2008-01-01
The Disassembly Sanitization Operation (DSO) process was implemented to support weapon disassembly and disposition by using recycling and waste minimization measures. This process was initiated by treaty agreements and reconfigurations within both the DOD and DOE Complexes. The DOE is faced with disassembling and disposing of a huge inventory of retired weapons, components, training equipment, spare parts, weapon maintenance equipment, and associated material. In addition, regulations have caused a dramatic increase in the need for information required to support the handling and disposition of these parts and materials. In the past, huge inventories of classified weapon components were required to have long-term storage at Sandia and at many other locations throughout the DoE Complex. These materials are placed in onsite storage unit due to classification issues and they may also contain radiological and/or hazardous components. Since no disposal options exist for this material, the only choice was long-term storage. Long-term storage is costly and somewhat problematic, requiring a secured storage area, monitoring, auditing, and presenting the potential for loss or theft of the material. Overall recycling rates for materials sent through the DSO process have enabled 70 to 80% of these components to be recycled. These components are made of high quality materials and once this material has been sanitized, the demand for the component metals for recycling efforts is very high. The DSO process for NGPF, classified components established the credibility of this technique for addressing the long-term storage requirements of the classified weapons component inventory. The success of this application has generated interest from other Sandia organizations and other locations throughout the complex. Other organizations are requesting the help of the DSO team and the DSO is responding to these requests by expanding its scope to include Work-for- Other projects. For example
Comparing cosmic web classifiers using information theory
Leclercq, Florent; Lavaux, Guilhem; Wandelt, Benjamin; Jasche, Jens
2016-01-01
We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.
Design of Robust Neural Network Classifiers
Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads
1998-01-01
This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...... suggest to adapt the outlier probability and regularisation parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrate the potential...
Comparing cosmic web classifiers using information theory
Leclercq, Florent [Institute of Cosmology and Gravitation (ICG), University of Portsmouth, Dennis Sciama Building, Burnaby Road, Portsmouth PO1 3FX (United Kingdom); Lavaux, Guilhem; Wandelt, Benjamin [Institut d' Astrophysique de Paris (IAP), UMR 7095, CNRS – UPMC Université Paris 6, Sorbonne Universités, 98bis boulevard Arago, F-75014 Paris (France); Jasche, Jens, E-mail: florent.leclercq@polytechnique.org, E-mail: lavaux@iap.fr, E-mail: j.jasche@tum.de, E-mail: wandelt@iap.fr [Excellence Cluster Universe, Technische Universität München, Boltzmannstrasse 2, D-85748 Garching (Germany)
2016-08-01
We introduce a decision scheme for optimally choosing a classifier, which segments the cosmic web into different structure types (voids, sheets, filaments, and clusters). Our framework, based on information theory, accounts for the design aims of different classes of possible applications: (i) parameter inference, (ii) model selection, and (iii) prediction of new observations. As an illustration, we use cosmographic maps of web-types in the Sloan Digital Sky Survey to assess the relative performance of the classifiers T-WEB, DIVA and ORIGAMI for: (i) analyzing the morphology of the cosmic web, (ii) discriminating dark energy models, and (iii) predicting galaxy colors. Our study substantiates a data-supported connection between cosmic web analysis and information theory, and paves the path towards principled design of analysis procedures for the next generation of galaxy surveys. We have made the cosmic web maps, galaxy catalog, and analysis scripts used in this work publicly available.
Detection of Fundus Lesions Using Classifier Selection
Nagayoshi, Hiroto; Hiramatsu, Yoshitaka; Sako, Hiroshi; Himaga, Mitsutoshi; Kato, Satoshi
A system for detecting fundus lesions caused by diabetic retinopathy from fundus images is being developed. The system can screen the images in advance in order to reduce the inspection workload on doctors. One of the difficulties that must be addressed in completing this system is how to remove false positives (which tend to arise near blood vessels) without decreasing the detection rate of lesions in other areas. To overcome this difficulty, we developed classifier selection according to the position of a candidate lesion, and we introduced new features that can distinguish true lesions from false positives. A system incorporating classifier selection and these new features was tested in experiments using 55 fundus images with some lesions and 223 images without lesions. The results of the experiments confirm the effectiveness of the proposed system, namely, degrees of sensitivity and specificity of 98% and 81%, respectively.
Classifying objects in LWIR imagery via CNNs
Rodger, Iain; Connor, Barry; Robertson, Neil M.
2016-10-01
The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.
Learning for VMM + WTA Embedded Classifiers
2016-03-31
Learning for VMM + WTA Embedded Classifiers Jennifer Hasler and Sahil Shah Electrical and Computer Engineering Georgia Institute of Technology...enabling correct classification of each novel acoustic signal (generator, idle car, and idle truck ). The classification structure requires, after...measured on our SoC FPAA IC. The test input is composed of signals from urban environment for 3 objects (generator, idle car, and idle truck
Bayes classifiers for imbalanced traffic accidents datasets.
Mujalli, Randa Oqab; López, Griselda; Garach, Laura
2016-03-01
Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.
A Bayesian classifier for symbol recognition
Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick
2007-01-01
URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...
Optimization of short amino acid sequences classifier
Barcz, Aleksy; Szymański, Zbigniew
This article describes processing methods used for short amino acid sequences classification. The data processed are 9-symbols string representations of amino acid sequences, divided into 49 data sets - each one containing samples labeled as reacting or not with given enzyme. The goal of the classification is to determine for a single enzyme, whether an amino acid sequence would react with it or not. Each data set is processed separately. Feature selection is performed to reduce the number of dimensions for each data set. The method used for feature selection consists of two phases. During the first phase, significant positions are selected using Classification and Regression Trees. Afterwards, symbols appearing at the selected positions are substituted with numeric values of amino acid properties taken from the AAindex database. In the second phase the new set of features is reduced using a correlation-based ranking formula and Gram-Schmidt orthogonalization. Finally, the preprocessed data is used for training LS-SVM classifiers. SPDE, an evolutionary algorithm, is used to obtain optimal hyperparameters for the LS-SVM classifier, such as error penalty parameter C and kernel-specific hyperparameters. A simple score penalty is used to adapt the SPDE algorithm to the task of selecting classifiers with best performance measures values.
SVM classifier on chip for melanoma detection.
Afifi, Shereen; GholamHosseini, Hamid; Sinha, Roopak
2017-07-01
Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosis system for use in primary care for early detection of melanoma. In this paper, an optimized SVM classifier is implemented onto a recent FPGA platform using the latest design methodology to be embedded into the proposed device for realizing online efficient melanoma detection on a single system on chip/device. The hardware implementation results demonstrate a high classification accuracy of 97.9% and a significant acceleration factor of 26 from equivalent software implementation on an embedded processor, with 34% of resources utilization and 2 watts for power consumption. Consequently, the implemented system meets crucial embedded systems constraints of high performance and low cost, resources utilization and power consumption, while achieving high classification accuracy.
R.J. Garrett
2002-01-01
As part of the internal Integrated Safety Management Assessment verification process, it was determined that there was a lack of documentation that summarizes the safety basis of the current Yucca Mountain Project (YMP) site characterization activities. It was noted that a safety basis would make it possible to establish a technically justifiable graded approach to the implementation of the requirements identified in the Standards/Requirements Identification Document. The Standards/Requirements Identification Documents commit a facility to compliance with specific requirements and, together with the hazard baseline documentation, provide a technical basis for ensuring that the public and workers are protected. This Safety Basis Report has been developed to establish and document the safety basis of the current site characterization activities, establish and document the hazard baseline, and provide the technical basis for identifying structures, systems, and components (SSCs) that perform functions necessary to protect the public, the worker, and the environment from hazards unique to the YMP site characterization activities. This technical basis for identifying SSCs serves as a grading process for the implementation of programs such as Conduct of Operations (DOE Order 5480.19) and the Suspect/Counterfeit Items Program. In addition, this report provides a consolidated summary of the hazards analyses processes developed to support the design, construction, and operation of the YMP site characterization facilities and, therefore, provides a tool for evaluating the safety impacts of changes to the design and operation of the YMP site characterization activities
R.J. Garrett
2002-01-14
As part of the internal Integrated Safety Management Assessment verification process, it was determined that there was a lack of documentation that summarizes the safety basis of the current Yucca Mountain Project (YMP) site characterization activities. It was noted that a safety basis would make it possible to establish a technically justifiable graded approach to the implementation of the requirements identified in the Standards/Requirements Identification Document. The Standards/Requirements Identification Documents commit a facility to compliance with specific requirements and, together with the hazard baseline documentation, provide a technical basis for ensuring that the public and workers are protected. This Safety Basis Report has been developed to establish and document the safety basis of the current site characterization activities, establish and document the hazard baseline, and provide the technical basis for identifying structures, systems, and components (SSCs) that perform functions necessary to protect the public, the worker, and the environment from hazards unique to the YMP site characterization activities. This technical basis for identifying SSCs serves as a grading process for the implementation of programs such as Conduct of Operations (DOE Order 5480.19) and the Suspect/Counterfeit Items Program. In addition, this report provides a consolidated summary of the hazards analyses processes developed to support the design, construction, and operation of the YMP site characterization facilities and, therefore, provides a tool for evaluating the safety impacts of changes to the design and operation of the YMP site characterization activities.
WWER radial reflector modeling by diffusion codes
Petkov, P. T.; Mittag, S.
2005-01-01
The two commonly used approaches to describe the WWER radial reflectors in diffusion codes, by albedo on the core-reflector boundary and by a ring of diffusive assembly size nodes, are discussed. The advantages and disadvantages of the first approach are presented first, then the Koebke's equivalence theory is outlined and its implementation for the WWER radial reflectors is discussed. Results for the WWER-1000 reactor are presented. Then the boundary conditions on the outer reflector boundary are discussed. The possibility to divide the library into fuel assembly and reflector parts and to generate each library by a separate code package is discussed. Finally, the homogenization errors for rodded assemblies are presented and discussed (Author)
Oculoauriculovertebral spectrum with radial anomaly in child.
Taksande, Amar; Vilhekar, Krishna
2013-01-01
Oculoauriculovertebral spectrum (OAVS) or Goldenhar syndrome is a wide spectrum of congenital anomalies that involves structures arising from the first and second branchial arches. It is characterized by a wide spectrum of symptoms and physical features. These abnormalities mainly involve the cheekbones, jaws, mouth, ears, eyes, or vertebrae. Other conditions with ear and/or radial involvement, such as, the Nager syndrome, Holt-Oram syndrome, Radial-renal syndrome, facioauriculoradial dysplasia, Fanconi anemia, and Vertebral, Anal atresia, Cardiac, Trachea, Esophageal, Renal, and Limb (VACTERL) association should be considered for differential diagnosis. Here we report a child who had facial asymmetry, microsomia, microtia, congenital facial nerve palsy, conductive hearing loss, skin tags, iris coloboma, and preaxial polydactyly.
Oculoauriculovertebral spectrum with radial anomaly in child
Amar Taksande
2013-01-01
Full Text Available Oculoauriculovertebral spectrum (OAVS or Goldenhar syndrome is a wide spectrum of congenital anomalies that involves structures arising from the first and second branchial arches. It is characterized by a wide spectrum of symptoms and physical features. These abnormalities mainly involve the cheekbones, jaws, mouth, ears, eyes, or vertebrae. Other conditions with ear and/or radial involvement, such as, the Nager syndrome, Holt-Oram syndrome, Radial-renal syndrome, facioauriculoradial dysplasia, Fanconi anemia, and Vertebral, Anal atresia, Cardiac, Trachea, Esophageal, Renal, and Limb (VACTERL association should be considered for differential diagnosis. Here we report a child who had facial asymmetry, microsomia, microtia, congenital facial nerve palsy, conductive hearing loss, skin tags, iris coloboma, and preaxial polydactyly.
Linear radial pulsation theory. Lecture 5
Cox, A.N.
1983-01-01
We describe a method for getting an equilibrium stellar envelope model using as input the total mass, the envelope mass, the surface effective temperature, the total surface luminosity, and the composition of the envelope. Then wih the structure of the envelope model known, we present a method for obtaining the raidal pulsation periods and growth rates for low order modes. The large amplitude pulsations observed for the yellow and red giants and supergiants are always these radial models, but for the stars nearer the main sequence, as for all of our stars and for the white dwarfs, there frequently are nonradial modes occuring also. Application of linear theory radial pulsation theory is made to the giant star sigma Scuti variables, while the linear nonradial theory will be used for the B stars in later lectures
Stability of a radial immiscible drive
Bataille, J
1968-11-01
The stability of the displacement front between 2 immiscible fluids of radial flow between 2 parallel plates (Hele-Shaw model) is studied mathematically by superposing onto the circular displacement front a sinusoidal perturbation. The equations are reduced to dimensionless variables, and it is shown that the stable and unstable domains in a plot: dimensionless viscosity vs. dimensionless time are separated by a polygonal contour, each side of the contour being characterized by the (integer) number of perturbations along the circumference. There is a critical reduced time below which the perturbations are amortized but beyond which they are amplified. Experimental results have been in fair general agreement with theoretical results, the divergence between them being attributable to neglecting capillary phenomena, which may become very important at large radial distances. One test with miscible fluids has shown that even in this case, there is a critical time or an equivalent critical radius.
Radial oxygen gradients over rat cortex arterioles
Galler, Michael
2011-01-01
Purpose: We present the results of the visualisation of radial oxygen gradients in rats’ cortices and their use in neurocritical management. Methods: PO2 maps of the cortex of 10 wistar rats were obtained with a camera (SensiMOD, PCO, Kehlheim, Germany). Those pictures were analyzed and edited by a custom-made software. We chose a vessel for examination. A matrix, designed to evaluate the cortical O2 partial pressure, was placed vertically to the artery and afterwards multiple regio...
Variational method for integrating radial gradient field
Legarda-Saenz, Ricardo; Brito-Loeza, Carlos; Rivera, Mariano; Espinosa-Romero, Arturo
2014-12-01
We propose a variational method for integrating information obtained from circular fringe pattern. The proposed method is a suitable choice for objects with radial symmetry. First, we analyze the information contained in the fringe pattern captured by the experimental setup and then move to formulate the problem of recovering the wavefront using techniques from calculus of variations. The performance of the method is demonstrated by numerical experiments with both synthetic and real data.
Moment approach to tandem mirror radial transport
Siebert, K.D.; Callen, J.D.
1986-02-01
A moment approach is proposed for the study of tandem mirror radial transport in the resonant plateau regime. The salient features of the method are described with reference to axisymmetric tokamak transport theory. In particular, the importance of momentum conservation to the establishment of the azimuthal variations in the electrostatic potential is demonstrated. Also, an ad hoc drift kinetic equation is solved to determine parallel viscosity coefficients which are required to close the moment system
Numerical simulation of radial compressor stage
Syka, T.; Luňáček, O.
2013-04-01
Article describes numerical simulations of air flow in radial compressor stage in NUMECA CFD software. In simulations geometry variants with and without seals are used. During tasks evaluating was observed seals influence on flow field and performance parameters of compressor stage. Also is described CFDresults comparison with results from design software based on experimental measurements and monitoring of influence of seals construction on compressor stage efficiency.
Numerical simulation of radial compressor stage
Luňáček O.; Syka T.
2013-01-01
Article describes numerical simulations of air flow in radial compressor stage in NUMECA CFD software. In simulations geometry variants with and without seals are used. During tasks evaluating was observed seals influence on flow field and performance parameters of compressor stage. Also is described CFDresults comparison with results from design software based on experimental measurements and monitoring of influence of seals construction on compressor stage efficiency.
Numerical simulation of radial compressor stage
Luňáček O.
2013-04-01
Full Text Available Article describes numerical simulations of air flow in radial compressor stage in NUMECA CFD software. In simulations geometry variants with and without seals are used. During tasks evaluating was observed seals influence on flow field and performance parameters of compressor stage. Also is described CFDresults comparison with results from design software based on experimental measurements and monitoring of influence of seals construction on compressor stage efficiency.
Radial excitations in nucleon-nucleon scattering
Silvestre-Brac, B.; Carbonell, J.; Gignoux, C.
1986-01-01
In the non-relativistic constituent quark model, the role of the radial excitations of the nucleon is studied within a resonating group approach of the nucleon-nucleon scattering. It is shown that, rather than the inclusion of new channels, it is important to include mixed-symmetry spin-isospin components in the nucleon wave function. It is also found that during the collision there is no significant deformation of the nucleon. (orig.)
Fuel radial design using Path Relinking
Campos S, Y.
2007-01-01
The present work shows the obtained results when implementing the combinatory optimization technique well-known as Path Re linking (Re-linkage of Trajectories), to the problem of the radial design of nuclear fuel assemblies, for boiling water reactors (BWR Boiling Water Reactor by its initials in English), this type of reactors is those that are used in the Laguna Verde Nucleo electric Central, Veracruz. As in any other electric power generation plant of that make use of some fuel to produce heat and that it needs each certain time (from 12 to 14 months) to make a supply of the same one, because this it wears away or it burns, in the nucleolectric plants to this activity is denominated fuel reload. In this reload different activities intervene, among those which its highlight the radial and axial designs of fuel assemblies, the patterns of control rods and the multi cycles study, each one of these stages with their own complexity. This work was limited to study in independent form the radial design, without considering the other activities. These phases are basic for the fuel reload design and of reactor operation strategies. (Author)
Development of a Radial Deconsolidation Method
Helmreich, Grant W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Montgomery, Fred C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hunn, John D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-12-01
A series of experiments have been initiated to determine the retention or mobility of fission products* in AGR fuel compacts [Petti, et al. 2010]. This information is needed to refine fission product transport models. The AGR-3/4 irradiation test involved half-inch-long compacts that each contained twenty designed-to-fail (DTF) particles, with 20-μm thick carbon-coated kernels whose coatings were deliberately fabricated such that they would crack under irradiation, providing a known source of post-irradiation isotopes. The DTF particles in these compacts were axially distributed along the compact centerline so that the diffusion of fission products released from the DTF kernels would be radially symmetric [Hunn, et al. 2012; Hunn et al. 2011; Kercher, et al. 2011; Hunn, et al. 2007]. Compacts containing DTF particles were irradiated at Idaho National Laboratory (INL) at the Advanced Test Reactor (ATR) [Collin, 2015]. Analysis of the diffusion of these various post-irradiation isotopes through the compact requires a method to radially deconsolidate the compacts so that nested-annular volumes may be analyzed for post-irradiation isotope inventory in the compact matrix, TRISO outer pyrolytic carbon (OPyC), and DTF kernels. An effective radial deconsolidation method and apparatus appropriate to this application has been developed and parametrically characterized.
Shehzad Khalid
2014-01-01
Full Text Available We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.
The Protection of Classified Information: The Legal Framework
Elsea, Jennifer K
2006-01-01
Recent incidents involving leaks of classified information have heightened interest in the legal framework that governs security classification, access to classified information, and penalties for improper disclosure...
Temperature and boron dependencies of buckling and radial reflector saving for VVER lattices
Alvarez, C.
1990-01-01
The temperature and boron dependencies of buckling and radial reflectors savings are analyzed in this paper on the basis of the results from the calculations ZR-6M critical assembly. These dependencies are related to the physical behavior of temperature and boron reactivity coefficients for the cores of VVER-type critical facilities. As a byproduct, the parameter was also investigated and its dependence on water density was determined
Temperature and boron dependencies of buckling and radial reflector savings for VVER lattices
Alvarez, C.
1990-01-01
The temperature and boron dependencies of buckling and radial reflector savings are analyzed in this paper on the basis of the results from the calculations for the ZR-6M critical assembly. These dependencies are related to he physical behaviour of temperature and boron reactivity coefficients for the cores of VVER-type critical facilities. As a byproduct, the dp/dBg 2 parameter was also investigated and its dependence on water density was determined
Linear theory radial and nonradial pulsations of DA dwarf stars
Starrfield, S.; Cox, A.N.; Hodson, S.; Pesnell, W.D.
1982-01-01
The Los Alamos stellar envelope and radial linear non-adiabatic computer code, along with a new Los Alamos non-radial code are used to investigate the total hydrogen mass necessary to produce the non-radial instability of DA dwarfs
Radial distribution of ions in pores with a surface charge
Stegen, J.H.G. van der; Görtzen, J.; Kuipers, J.A.M.; Hogendoorn, J.A.; Versteeg, G.F.
2001-01-01
A sorption model applicable to calculate the radial equilibrium concentrations of ions in the pores of ion-selective membranes with a pore structure is developed. The model is called the radial uptake model. Because the model is applied to a Nafion sulfonic layer with very small pores and the radial
Classifying smoking urges via machine learning.
Dumortier, Antoine; Beckjord, Ellen; Shiffman, Saul; Sejdić, Ervin
2016-12-01
Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights
Classifying spaces of degenerating polarized Hodge structures
Kato, Kazuya
2009-01-01
In 1970, Phillip Griffiths envisioned that points at infinity could be added to the classifying space D of polarized Hodge structures. In this book, Kazuya Kato and Sampei Usui realize this dream by creating a logarithmic Hodge theory. They use the logarithmic structures begun by Fontaine-Illusie to revive nilpotent orbits as a logarithmic Hodge structure. The book focuses on two principal topics. First, Kato and Usui construct the fine moduli space of polarized logarithmic Hodge structures with additional structures. Even for a Hermitian symmetric domain D, the present theory is a refinem
Gearbox Condition Monitoring Using Advanced Classifiers
P. Večeř
2010-01-01
Full Text Available New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS. Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing.
Double Ramp Loss Based Reject Option Classifier
2015-05-22
of convex (DC) functions. To minimize it, we use DC programming approach [1]. The proposed method has following advantages: (1) the proposed loss LDR ...space constraints. We see that LDR does not put any restriction on ρ for it to be an upper bound of L0−d−1. 2.2 Risk Formulation Using LDR Let S = {(xn...classifier learnt using LDR based approach (C = 100, μ = 1, d = .2). Filled circles and triangles represent the support vectors. 4 Experimental Results We show
Experimental feasibility study of radial injection cooling of three-pad radial air foil bearings
Shrestha, Suman K.
Air foil bearings use ambient air as a lubricant allowing environment-friendly operation. When they are designed, installed, and operated properly, air foil bearings are very cost effective and reliable solution to oil-free turbomachinery. Because air is used as a lubricant, there are no mechanical contacts between the rotor and bearings and when the rotor is lifted off the bearing, near frictionless quiet operation is possible. However, due to the high speed operation, thermal management is one of the very important design factors to consider. Most widely accepted practice of the cooling method is axial cooling, which uses cooling air passing through heat exchange channels formed underneath the bearing pad. Advantage is no hardware modification to implement the axial cooling because elastic foundation structure of foil bearing serves as a heat exchange channels. Disadvantage is axial temperature gradient on the journal shaft and bearing. This work presents the experimental feasibility study of alternative cooling method using radial injection of cooling air directly on the rotor shaft. The injection speeds, number of nozzles, location of nozzles, total air flow rate are important factors determining the effectiveness of the radial injection cooling method. Effectiveness of the radial injection cooling was compared with traditional axial cooling method. A previously constructed test rig was modified to accommodate a new motor with higher torque and radial injection cooling. The radial injection cooling utilizes the direct air injection to the inlet region of air film from three locations at 120° from one another with each location having three axially separated holes. In axial cooling, a certain axial pressure gradient is applied across the bearing to induce axial cooling air through bump foil channels. For the comparison of the two methods, the same amount of cooling air flow rate was used for both axial cooling and radial injection. Cooling air flow rate was
A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers
M. Njah
2017-06-01
Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.
Classifying Coding DNA with Nucleotide Statistics
Nicolas Carels
2009-10-01
Full Text Available In this report, we compared the success rate of classification of coding sequences (CDS vs. introns by Codon Structure Factor (CSF and by a method that we called Universal Feature Method (UFM. UFM is based on the scoring of purine bias (Rrr and stop codon frequency. We show that the success rate of CDS/intron classification by UFM is higher than by CSF. UFM classifies ORFs as coding or non-coding through a score based on (i the stop codon distribution, (ii the product of purine probabilities in the three positions of nucleotide triplets, (iii the product of Cytosine (C, Guanine (G, and Adenine (A probabilities in the 1st, 2nd, and 3rd positions of triplets, respectively, (iv the probabilities of G in 1st and 2nd position of triplets and (v the distance of their GC3 vs. GC2 levels to the regression line of the universal correlation. More than 80% of CDSs (true positives of Homo sapiens (>250 bp, Drosophila melanogaster (>250 bp and Arabidopsis thaliana (>200 bp are successfully classified with a false positive rate lower or equal to 5%. The method releases coding sequences in their coding strand and coding frame, which allows their automatic translation into protein sequences with 95% confidence. The method is a natural consequence of the compositional bias of nucleotides in coding sequences.
A systematic comparison of supervised classifiers.
Diego Raphael Amancio
Full Text Available Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM. In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.
STATISTICAL TOOLS FOR CLASSIFYING GALAXY GROUP DYNAMICS
Hou, Annie; Parker, Laura C.; Harris, William E.; Wilman, David J.
2009-01-01
The dynamical state of galaxy groups at intermediate redshifts can provide information about the growth of structure in the universe. We examine three goodness-of-fit tests, the Anderson-Darling (A-D), Kolmogorov, and χ 2 tests, in order to determine which statistical tool is best able to distinguish between groups that are relaxed and those that are dynamically complex. We perform Monte Carlo simulations of these three tests and show that the χ 2 test is profoundly unreliable for groups with fewer than 30 members. Power studies of the Kolmogorov and A-D tests are conducted to test their robustness for various sample sizes. We then apply these tests to a sample of the second Canadian Network for Observational Cosmology Redshift Survey (CNOC2) galaxy groups and find that the A-D test is far more reliable and powerful at detecting real departures from an underlying Gaussian distribution than the more commonly used χ 2 and Kolmogorov tests. We use this statistic to classify a sample of the CNOC2 groups and find that 34 of 106 groups are inconsistent with an underlying Gaussian velocity distribution, and thus do not appear relaxed. In addition, we compute velocity dispersion profiles (VDPs) for all groups with more than 20 members and compare the overall features of the Gaussian and non-Gaussian groups, finding that the VDPs of the non-Gaussian groups are distinct from those classified as Gaussian.
Mercury⊕: An evidential reasoning image classifier
Peddle, Derek R.
1995-12-01
MERCURY⊕ is a multisource evidential reasoning classification software system based on the Dempster-Shafer theory of evidence. The design and implementation of this software package is described for improving the classification and analysis of multisource digital image data necessary for addressing advanced environmental and geoscience applications. In the remote-sensing context, the approach provides a more appropriate framework for classifying modern, multisource, and ancillary data sets which may contain a large number of disparate variables with different statistical properties, scales of measurement, and levels of error which cannot be handled using conventional Bayesian approaches. The software uses a nonparametric, supervised approach to classification, and provides a more objective and flexible interface to the evidential reasoning framework using a frequency-based method for computing support values from training data. The MERCURY⊕ software package has been implemented efficiently in the C programming language, with extensive use made of dynamic memory allocation procedures and compound linked list and hash-table data structures to optimize the storage and retrieval of evidence in a Knowledge Look-up Table. The software is complete with a full user interface and runs under Unix, Ultrix, VAX/VMS, MS-DOS, and Apple Macintosh operating system. An example of classifying alpine land cover and permafrost active layer depth in northern Canada is presented to illustrate the use and application of these ideas.
MARVELS Radial Velocity Solutions to Seven Kepler Eclipsing Binaries
Heslar, Michael Francis; Thomas, Neil B.; Ge, Jian; Ma, Bo; Herczeg, Alec; Reyes, Alan; SDSS-III MARVELS Team
2016-01-01
Eclipsing binaries serve momentous purposes to improve the basis of understanding aspects of stellar astrophysics, such as the accurate calculation of the physical parameters of stars and the enigmatic mass-radius relationship of M and K dwarfs. We report the investigation results of 7 eclipsing binary candidates, initially identified by the Kepler mission, overlapped with the radial velocity observations from the SDSS-III Multi-Object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS). The RV extractions and spectroscopic solutions of these eclipsing binaries were generated by the University of Florida's 1D data pipeline with a median RV precision of ~60-100 m/s, which was utilized for the DR12 data release. We performed the cross-reference fitting of the MARVELS RV data and the Kepler photometric fluxes obtained from the Kepler Eclipsing Binary Catalog (V2) and modelled the 7 eclipsing binaries in the BinaryMaker3 and PHOEBE programs. This analysis accurately determined the absolute physical and orbital parameters of each binary. Most of the companion stars were determined to have masses of K and M dwarf stars (0.3-0.8 M⊙), and allowed for an investigation into the mass-radius relationship of M and K dwarfs. Among the cases are KIC 9163796, a 122.2 day period "heartbeat star", a recently-discovered class of eccentric binaries known for tidal distortions and pulsations, with a high eccentricity (e~0.75) and KIC 11244501, a 0.29 day period, contact binary with a double-lined spectrum and mass ratio (q~0.45). We also report on the possible reclassification of 2 Kepler eclipsing binary candidates as background eclipsing binaries based on the analysis of the flux measurements, flux ratios of the spectroscopic and photometric solutions, the differences in the FOVs, the image processing of Kepler, and RV and spectral analysis of MARVELS.
Radial rescaling approach for the eigenvalue problem of a particle in an arbitrarily shaped box.
Lijnen, Erwin; Chibotaru, Liviu F; Ceulemans, Arnout
2008-01-01
In the present work we introduce a methodology for solving a quantum billiard with Dirichlet boundary conditions. The procedure starts from the exactly known solutions for the particle in a circular disk, which are subsequently radially rescaled in such a way that they obey the new boundary conditions. In this way one constructs a complete basis set which can be used to obtain the eigenstates and eigenenergies of the corresponding quantum billiard to a high level of precision. Test calculations for several regular polygons show the efficiency of the method which often requires one or two basis functions to describe the lowest eigenstates with high accuracy.
Methods and apparatus for radially compliant component mounting
Bulman, David Edward [Cincinnati, OH; Darkins, Jr., Toby George; Stumpf, James Anthony [Columbus, IN; Schroder, Mark S [Greenville, SC; Lipinski, John Joseph [Simpsonville, SC
2012-03-27
Methods and apparatus for a mounting assembly for a liner of a gas turbine engine combustor are provided. The combustor includes a combustor liner and a radially outer annular flow sleeve. The mounting assembly includes an inner ring surrounding a radially outer surface of the liner and including a plurality of axially extending fingers. The mounting assembly also includes a radially outer ring coupled to the inner ring through a plurality of spacers that extend radially from a radially outer surface of the inner ring to the outer ring.
RADIALLY MAGNETIZED PROTOPLANETARY DISK: VERTICAL PROFILE
Russo, Matthew; Thompson, Christopher
2015-01-01
This paper studies the response of a thin accretion disk to an external radial magnetic field. Our focus is on protoplanetary disks (PPDs), which are exposed during their later evolution to an intense, magnetized wind from the central star. A radial magnetic field is mixed into a thin surface layer, wound up by the disk shear, and pushed downward by a combination of turbulent mixing and ambipolar and ohmic drift. The toroidal field reaches much greater strengths than the seed vertical field that is usually invoked in PPD models, even becoming superthermal. Linear stability analysis indicates that the disk experiences the magnetorotational instability (MRI) at a higher magnetization than a vertically magnetized disk when both the effects of ambipolar and Hall drift are taken into account. Steady vertical profiles of density and magnetic field are obtained at several radii between 0.06 and 1 AU in response to a wind magnetic field B r ∼ (10 −4 –10 −2 )(r/ AU) −2 G. Careful attention is given to the radial and vertical ionization structure resulting from irradiation by stellar X-rays. The disk is more strongly magnetized closer to the star, where it can support a higher rate of mass transfer. As a result, the inner ∼1 AU of a PPD is found to evolve toward lower surface density. Mass transfer rates around 10 −8 M ⊙ yr −1 are obtained under conservative assumptions about the MRI-generated stress. The evolution of the disk and the implications for planet migration are investigated in the accompanying paper
RADIALLY MAGNETIZED PROTOPLANETARY DISK: VERTICAL PROFILE
Russo, Matthew [Department of Physics, University of Toronto, 60 St. George St., Toronto, ON M5S 1A7 (Canada); Thompson, Christopher [Canadian Institute for Theoretical Astrophysics, 60 St. George St., Toronto, ON M5S 3H8 (Canada)
2015-11-10
This paper studies the response of a thin accretion disk to an external radial magnetic field. Our focus is on protoplanetary disks (PPDs), which are exposed during their later evolution to an intense, magnetized wind from the central star. A radial magnetic field is mixed into a thin surface layer, wound up by the disk shear, and pushed downward by a combination of turbulent mixing and ambipolar and ohmic drift. The toroidal field reaches much greater strengths than the seed vertical field that is usually invoked in PPD models, even becoming superthermal. Linear stability analysis indicates that the disk experiences the magnetorotational instability (MRI) at a higher magnetization than a vertically magnetized disk when both the effects of ambipolar and Hall drift are taken into account. Steady vertical profiles of density and magnetic field are obtained at several radii between 0.06 and 1 AU in response to a wind magnetic field B{sub r} ∼ (10{sup −4}–10{sup −2})(r/ AU){sup −2} G. Careful attention is given to the radial and vertical ionization structure resulting from irradiation by stellar X-rays. The disk is more strongly magnetized closer to the star, where it can support a higher rate of mass transfer. As a result, the inner ∼1 AU of a PPD is found to evolve toward lower surface density. Mass transfer rates around 10{sup −8} M{sub ⊙} yr{sup −1} are obtained under conservative assumptions about the MRI-generated stress. The evolution of the disk and the implications for planet migration are investigated in the accompanying paper.
36 CFR 1256.46 - National security-classified information.
2010-07-01
... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false National security-classified... Restrictions § 1256.46 National security-classified information. In accordance with 5 U.S.C. 552(b)(1), NARA... properly classified under the provisions of the pertinent Executive Order on Classified National Security...
Theoretical basis for dosimetry
Carlsson, G.A.
1985-01-01
Radiation dosimetry is fundamental to all fields of science dealing with radiation effects and is concerned with problems which are often intricate as hinted above. A firm scientific basis is needed to face increasing demands on accurate dosimetry. This chapter is an attempt to review and to elucidate the elements for such a basis. Quantities suitable for radiation dosimetry have been defined in the unique work to coordinate radiation terminology and usage by the International Commission on Radiation Units and Measurements, ICRU. Basic definitions and terminology used in this chapter conform with the recent ''Radiation Quantities and Units, Report 33'' of the ICRU
Radial fractional Laplace operators and Hessian inequalities
Ferrari, Fausto; Verbitsky, Igor E.
In this paper we deduce a formula for the fractional Laplace operator ( on radially symmetric functions useful for some applications. We give a criterion of subharmonicity associated with (, and apply it to a problem related to the Hessian inequality of Sobolev type: ∫Rn |(u| dx⩽C∫Rn -uFk[u] dx, where Fk is the k-Hessian operator on Rn, 1⩽kFerrari et al. [5] contains the extremal functions for the Hessian Sobolev inequality of X.-J. Wang (1994) [15]. This is proved using logarithmic convexity of the Gaussian ratio of hypergeometric functions which might be of independent interest.
Convex and Radially Concave Contoured Distributions
Wolf-Dieter Richter
2015-01-01
Full Text Available Integral representations of the locally defined star-generalized surface content measures on star spheres are derived for boundary spheres of balls being convex or radially concave with respect to a fan in Rn. As a result, the general geometric measure representation of star-shaped probability distributions and the general stochastic representation of the corresponding random vectors allow additional specific interpretations in the two mentioned cases. Applications to estimating and testing hypotheses on scaling parameters are presented, and two-dimensional sample clouds are simulated.
On radial flow between parallel disks
Wee, A Y L; Gorin, A
2015-01-01
Approximate analytical solutions are presented for converging flow in between two parallel non rotating disks. The static pressure distribution and radial component of the velocity are developed by averaging the inertial term across the gap in between parallel disks. The predicted results from the first approximation are favourable to experimental results as well as results presented by other authors. The second approximation shows that as the fluid approaches the center, the velocity at the mid channel slows down which is due to the struggle between the inertial term and the flowrate. (paper)
Zernike Basis to Cartesian Transformations
Mathar, R. J.
2009-12-01
Full Text Available The radial polynomials of the 2D (circular and 3D (spherical Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.
Zernike basis to cartesian transformations
Mathar R.J.
2009-01-01
Full Text Available The radial polynomials of the 2D (circular and 3D (spherical Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.
Intraluminal milrinone for dilation of the radial artery graft.
García-Rinaldi, R; Soltero, E R; Carballido, J; Mojica, J
1999-01-01
There is renewed interest in the use of the radial artery as a conduit for coronary artery bypass surgery. The radial artery is, however, a very muscular artery, prone to vasospasm. Milrinone, a potent vasodilator, has demonstrated vasodilatory properties superior to those of papaverine. In this report, we describe our technique of radial artery harvesting and the adjunctive use of intraluminal milrinone as a vasodilator in the preparation of this conduit for coronary artery bypass grafting. We have used these techniques in 25 patients who have undergone coronary artery bypass grafting using the radial artery. No hand ischemic complications have been observed in this group. Intraluminal milrinone appears to dilate and relax the radial artery, rendering this large conduit spasm free and very easy to use. We recommend the skeletonization technique for radial artery harvesting and the use of intraluminal milrinone as a radial artery vasodilator in routine myocardial revascularization. PMID:10524740
Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT
Lee, Juhun; Nishikawa, Robert M.; Reiser, Ingrid; Boone, John M.; Lindfors, Karen K.
2015-01-01
Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (C_T) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and C_T yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and C_T were conducted. The results showed that C_T was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure
Two channel EEG thought pattern classifier.
Craig, D A; Nguyen, H T; Burchey, H A
2006-01-01
This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %
Classifying Drivers' Cognitive Load Using EEG Signals.
Barua, Shaibal; Ahmed, Mobyen Uddin; Begum, Shahina
2017-01-01
A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy.
Classifying prion and prion-like phenomena.
Harbi, Djamel; Harrison, Paul M
2014-01-01
The universe of prion and prion-like phenomena has expanded significantly in the past several years. Here, we overview the challenges in classifying this data informatically, given that terms such as "prion-like", "prion-related" or "prion-forming" do not have a stable meaning in the scientific literature. We examine the spectrum of proteins that have been described in the literature as forming prions, and discuss how "prion" can have a range of meaning, with a strict definition being for demonstration of infection with in vitro-derived recombinant prions. We suggest that although prion/prion-like phenomena can largely be apportioned into a small number of broad groups dependent on the type of transmissibility evidence for them, as new phenomena are discovered in the coming years, a detailed ontological approach might be necessary that allows for subtle definition of different "flavors" of prion / prion-like phenomena.
Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model
Bogdan Gliwa
2011-01-01
Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.
Tsapatsaris, Nikolaos; Willendrup, Peter Kjær; E. Lechner, Ruep
2015-01-01
Results based on virtual instrument models for the first high-flux, high-resolution, spallation based, backscattering spectrometer, BASIS are presented in this paper. These were verified using the Monte Carlo instrument simulation packages McStas and VITESS. Excellent agreement of the neutron count...... are pivotal to the conceptual design of the next generation backscattering spectrometer, MIRACLES at the European Spallation Source....
Classifying Transition Behaviour in Postural Activity Monitoring
James BRUSEY
2009-10-01
Full Text Available A few accelerometers positioned on different parts of the body can be used to accurately classify steady state behaviour, such as walking, running, or sitting. Such systems are usually built using supervised learning approaches. Transitions between postures are, however, difficult to deal with using posture classification systems proposed to date, since there is no label set for intermediary postures and also the exact point at which the transition occurs can sometimes be hard to pinpoint. The usual bypass when using supervised learning to train such systems is to discard a section of the dataset around each transition. This leads to poorer classification performance when the systems are deployed out of the laboratory and used on-line, particularly if the regimes monitored involve fast paced activity changes. Time-based filtering that takes advantage of sequential patterns is a potential mechanism to improve posture classification accuracy in such real-life applications. Also, such filtering should reduce the number of event messages needed to be sent across a wireless network to track posture remotely, hence extending the system’s life. To support time-based filtering, understanding transitions, which are the major event generators in a classification system, is a key. This work examines three approaches to post-process the output of a posture classifier using time-based filtering: a naïve voting scheme, an exponentially weighted voting scheme, and a Bayes filter. Best performance is obtained from the exponentially weighted voting scheme although it is suspected that a more sophisticated treatment of the Bayes filter might yield better results.
Just-in-time adaptive classifiers-part II: designing the classifier.
Alippi, Cesare; Roveri, Manuel
2008-12-01
Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.
Asymptotic Solutions of Serial Radial Fuel Shuffling
Xue-Nong Chen
2015-12-01
Full Text Available In this paper, the mechanism of traveling wave reactors (TWRs is investigated from the mathematical physics point of view, in which a stationary fission wave is formed by radial fuel drifting. A two dimensional cylindrically symmetric core is considered and the fuel is assumed to drift radially according to a continuous fuel shuffling scheme. A one-group diffusion equation with burn-up dependent macroscopic coefficients is set up. The burn-up dependent macroscopic coefficients were assumed to be known as functions of neutron fluence. By introducing the effective multiplication factor keff, a nonlinear eigenvalue problem is formulated. The 1-D stationary cylindrical coordinate problem can be solved successively by analytical and numerical integrations for associated eigenvalues keff. Two representative 1-D examples are shown for inward and outward fuel drifting motions, respectively. The inward fuel drifting has a higher keff than the outward one. The 2-D eigenvalue problem has to be solved by a more complicated method, namely a pseudo time stepping iteration scheme. Its 2-D asymptotic solutions are obtained together with certain eigenvalues keff for several fuel inward drifting speeds. Distributions of the neutron flux, the neutron fluence, the infinity multiplication factor kinf and the normalized power are presented for two different drifting speeds.
Xie, Cai-Xia; Zhang, Miao; Li, Ya-Jing; Geng, Xiao-Tong; Wang, Feng-Qing; Zhang, Zhong-Yi
2017-11-01
An HPLC method was established to determine the contents of catalpol, acteoside, rehmaionoside A, rehmaionoside D, leonuride in three part of Rehmanni glutinosa in Beijing No.1 variety R. glutinosa during the growth period, This method, in combination with its HPLC fingerprint was used to evaluate its overall quality characteristics.The results showed that：① the content of main components of R. glutinosa varied in different growth stages ;② there was a great difference of the content of main components between theradial striations and the non-radial striations; ③ the two sections almost have the same content distribution of catalpol, acteoside and rehmaionoside D; ④the content of rehmaionoside A in non-radial striations was higher than that in radial striations,while the content of leonuride in radial striations was higher than that in non-radial striations.; ⑤the HPLC fingerprint of radial striations, non-radial striations and whole root tuber were basically identical, except for the big difference in the content of chemical components. The result of clustering displayed that the radial striations, non-radial striations, and whole root were divided into two groups. In conclusion, there was a significant difference in the quality characteristics of radial striations and non-radial striations of R. glutinosa. This research provides a reference for quality evaluation and geoherbalism of R. glutinosa. Copyright© by the Chinese Pharmaceutical Association.
Radial electromagnetic force calculation of induction motor based on multi-loop theory
HE Haibo
2017-12-01
Full Text Available [Objectives] In order to study the vibration and noise of induction motors, a method of radial electromagnetic force calculation is established on the basis of the multi-loop model.[Methods] Based on the method of calculating air-gap magneto motive force according to stator and rotor fundamental wave current, the analytic formulas are deduced for calculating the air-gap magneto motive force and radial electromagnetic force generated in accordance with any stator winding and rotor conducting bar current. The multi-loop theory and calculation method for the electromagnetic parameters of a motor are introduced, and a dynamic simulation model of an induction motor built to achieve the current of the stator winding and rotor conducting bars, and obtain the calculation formula of radial electromagnetic force. The radial electromagnetic force and vibration are then estimated.[Results] The experimental results indicate that the vibration acceleration frequency and amplitude of the motor are consistent with the experimental results.[Conclusions] The results and calculation method can support the low noise design of converters.
Whittemore, Jacqueline C; Nystrom, Michael R; Mawby, Dianne I
2017-04-01
OBJECTIVE To assess the effects of age, body condition score (BCS), and muscle condition score (MCS) on radial and coccygeal systolic arterial blood pressure (SAP) in cats. DESIGN Prospective randomized trial. ANIMALS 66 privately owned cats enrolled between May and December 2010. PROCEDURES BCS and MCS of cats were assessed by 2 investigators; SAP was measured via Doppler ultrasonic flow detector, with cats positioned in right lateral or sternal recumbency for measurements at the radial or coccygeal artery, respectively, with order of site randomized. Associations among variables were assessed through correlation coefficients, partial correlation coefficients, and ANCOVA. RESULTS Interrater reliability for BCS and MCS assessment was high (correlation coefficients, 0.95 and 0.83, respectively). No significant effect was identified for order of SAP measurement sites. Coccygeal and radial SAP were positively correlated (ρ = 0.45). The median difference in coccygeal versus radial SAP was 19 mm Hg, but differences were not consistently positive or negative. Radial SAP was positively correlated with age (ρ = 0.48) and negatively correlated with MCS (ρ = -0.30). On the basis of the correlation analysis, the association between radial SAP and MCS reflected the confounding influence of age. Coccygeal SAP was not significantly correlated with age, BCS, or MCS. CONCLUSIONS AND CLINICAL RELEVANCE Use of the coccygeal artery is recommended to reduce the confounding effects of age and sarcopenia on Doppler ultrasonographic SAP measurements in cats. Additionally, monitoring for changes in MCS is recommended for cats undergoing serial SAP measurement.
Computer model analysis of the radial artery pressure waveform.
Schwid, H A; Taylor, L A; Smith, N T
1987-10-01
Simultaneous measurements of aortic and radial artery pressures are reviewed, and a model of the cardiovascular system is presented. The model is based on resonant networks for the aorta and axillo-brachial-radial arterial system. The model chosen is a simple one, in order to make interpretation of the observed relationships clear. Despite its simplicity, the model produces realistic aortic and radial artery pressure waveforms. It demonstrates that the resonant properties of the arterial wall significantly alter the pressure waveform as it is propagated from the aorta to the radial artery. Although the mean and end-diastolic radial pressures are usually accurate estimates of the corresponding aortic pressures, the systolic pressure at the radial artery is often much higher than that of the aorta due to overshoot caused by the resonant behavior of the radial artery. The radial artery dicrotic notch is predominantly dependent on the axillo-brachial-radial arterial wall properties, rather than on the aortic valve or peripheral resistance. Hence the use of the radial artery dicrotic notch as an estimate of end systole is unreliable. The rate of systolic upstroke, dP/dt, of the radial artery waveform is a function of many factors, making it difficult to interpret. The radial artery waveform usually provides accurate estimates for mean and diastolic aortic pressures; for all other measurements it is an inadequate substitute for the aortic pressure waveform. In the presence of low forearm peripheral resistance the mean radial artery pressure may significantly underestimate the mean aortic pressure, as explained by a voltage divider model.
Blanco, M.; Heller, E.J.
1985-01-01
A new Cartesian basis set is defined that is suitable for the representation of molecular vibration-rotation bound states. The Cartesian basis functions are superpositions of semiclassical states generated through the use of classical trajectories that conform to the intrinsic dynamics of the molecule. Although semiclassical input is employed, the method becomes ab initio through the standard matrix diagonalization variational method. Special attention is given to classical-quantum correspondences for angular momentum. In particular, it is shown that the use of semiclassical information preferentially leads to angular momentum eigenstates with magnetic quantum number Vertical BarMVertical Bar equal to the total angular momentum J. The present method offers a reliable technique for representing highly excited vibrational-rotational states where perturbation techniques are no longer applicable
Larsen, G.; Soerensen, P. [Risoe National Lab., Roskilde (Denmark)
1996-09-01
Design Basis Program 2 (DBP2) is comprehensive fully coupled code which has the capability to operate in the time domain as well as in the frequency domain. The code was developed during the period 1991-93 and succeed Design Basis 1, which is a one-blade model presuming stiff tower, transmission system and hub. The package is designed for use on a personal computer and offers a user-friendly environment based on menu-driven editing and control facilities, and with graphics used extensively for the data presentation. Moreover in-data as well as results are dumped on files in Ascii-format. The input data is organized in a in-data base with a structure that easily allows for arbitrary combinations of defined structural components and load cases. (au)
Classifying Adverse Events in the Dental Office.
Kalenderian, Elsbeth; Obadan-Udoh, Enihomo; Maramaldi, Peter; Etolue, Jini; Yansane, Alfa; Stewart, Denice; White, Joel; Vaderhobli, Ram; Kent, Karla; Hebballi, Nutan B; Delattre, Veronique; Kahn, Maria; Tokede, Oluwabunmi; Ramoni, Rachel B; Walji, Muhammad F
2017-06-30
Dentists strive to provide safe and effective oral healthcare. However, some patients may encounter an adverse event (AE) defined as "unnecessary harm due to dental treatment." In this research, we propose and evaluate two systems for categorizing the type and severity of AEs encountered at the dental office. Several existing medical AE type and severity classification systems were reviewed and adapted for dentistry. Using data collected in previous work, two initial dental AE type and severity classification systems were developed. Eight independent reviewers performed focused chart reviews, and AEs identified were used to evaluate and modify these newly developed classifications. A total of 958 charts were independently reviewed. Among the reviewed charts, 118 prospective AEs were found and 101 (85.6%) were verified as AEs through a consensus process. At the end of the study, a final AE type classification comprising 12 categories, and an AE severity classification comprising 7 categories emerged. Pain and infection were the most common AE types representing 73% of the cases reviewed (56% and 17%, respectively) and 88% were found to cause temporary, moderate to severe harm to the patient. Adverse events found during the chart review process were successfully classified using the novel dental AE type and severity classifications. Understanding the type of AEs and their severity are important steps if we are to learn from and prevent patient harm in the dental office.
Is it important to classify ischaemic stroke?
Iqbal, M
2012-02-01
Thirty-five percent of all ischemic events remain classified as cryptogenic. This study was conducted to ascertain the accuracy of diagnosis of ischaemic stroke based on information given in the medical notes. It was tested by applying the clinical information to the (TOAST) criteria. Hundred and five patients presented with acute stroke between Jan-Jun 2007. Data was collected on 90 patients. Male to female ratio was 39:51 with age range of 47-93 years. Sixty (67%) patients had total\\/partial anterior circulation stroke; 5 (5.6%) had a lacunar stroke and in 25 (28%) the mechanism of stroke could not be identified. Four (4.4%) patients with small vessel disease were anticoagulated; 5 (5.6%) with atrial fibrillation received antiplatelet therapy and 2 (2.2%) patients with atrial fibrillation underwent CEA. This study revealed deficiencies in the clinical assessment of patients and treatment was not tailored to the mechanism of stroke in some patients.
Stress fracture development classified by bone scintigraphy
Zwas, S.T.; Elkanovich, R.; Frank, G.; Aharonson, Z.
1985-01-01
There is no consensus on classifying stress fractures (SF) appearing on bone scans. The authors present a system of classification based on grading the severity and development of bone lesions by visual inspection, according to three main scintigraphic criteria: focality and size, intensity of uptake compare to adjacent bone, and local medular extension. Four grades of development (I-IV) were ranked, ranging from ill defined slightly increased cortical uptake to well defined regions with markedly increased uptake extending transversely bicortically. 310 male subjects aged 19-2, suffering several weeks from leg pains occurring during intensive physical training underwent bone scans of the pelvis and lower extremities using Tc-99-m-MDP. 76% of the scans were positive with 354 lesions, of which 88% were in th4e mild (I-II) grades and 12% in the moderate (III) and severe (IV) grades. Post-treatment scans were obtained in 65 cases having 78 lesions during 1- to 6-month intervals. Complete resolution was found after 1-2 months in 36% of the mild lesions but in only 12% of the moderate and severe ones, and after 3-6 months in 55% of the mild lesions and 15% of the severe ones. 75% of the moderate and severe lesions showed residual uptake in various stages throughout the follow-up period. Early recognition and treatment of mild SF lesions in this study prevented protracted disability and progression of the lesions and facilitated complete healing
MR accuracy and arthroscopic incidence of meniscal radial tears
Magee, Thomas; Shapiro, Marc; Williams, David [Department of Radiology, Neuroimaging Institute, 27 East Hibiscus Blvd., Melbourne, FL 32901 (United States)
2002-12-01
A meniscal radial tear is a vertical tear that involves the inner meniscal margin. The tear is most frequent in the middle third of the lateral meniscus and may extend outward in any direction. We report (1) the arthroscopic incidence of radial tears, (2) MR signs that aid in the detection of radial tears and (3) our prospective accuracy in detection of radial tears. Design and patients. Three musculoskeletal radiologists prospectively read 200 consecutive MR examinations of the knee that went on to arthroscopy by one orthopedic surgeon. MR images were assessed for location and MR characteristics of radial tears. MR criteria used for diagnosis of a radial tear were those outlined by Tuckman et al.: truncation, abnormal morphology and/or lack of continuity or absence of the meniscus on one or more MR images. An additional criterion used was abnormal increased signal in that area on fat-saturated proton density or T2-weighted coronal and sagittal images. Prospective MR readings were correlated with the arthroscopic findings.Results. Of the 200 consecutive knee arthroscopies, 28 patients had radial tears reported arthroscopically (14% incidence). MR readings prospectively demonstrated 19 of the 28 radial tears (68% sensitivity) when the criteria for diagnosis of a radial tear were truncation or abnormal morphology of the meniscus. With the use of the additional criterion of increased signal in the area of abnormal morphology on fat-saturated T2-weighted or proton density weighted sequences, the prospective sensitivity was 25 of 28 radial tears (89% sensitivity). There were no radial tears described in MR reports that were not demonstrated on arthroscopy (i.e., there were no false positive MR readings of radial tears in these 200 patients). Radial tears are commonly seen at arthroscopy. There was a 14% incidence in this series of 200 patients who underwent arthroscopy. Prospective detection of radial tears was 68% as compared with arthroscopy when the criteria as
MR accuracy and arthroscopic incidence of meniscal radial tears
Magee, Thomas; Shapiro, Marc; Williams, David
2002-01-01
A meniscal radial tear is a vertical tear that involves the inner meniscal margin. The tear is most frequent in the middle third of the lateral meniscus and may extend outward in any direction. We report (1) the arthroscopic incidence of radial tears, (2) MR signs that aid in the detection of radial tears and (3) our prospective accuracy in detection of radial tears. Design and patients. Three musculoskeletal radiologists prospectively read 200 consecutive MR examinations of the knee that went on to arthroscopy by one orthopedic surgeon. MR images were assessed for location and MR characteristics of radial tears. MR criteria used for diagnosis of a radial tear were those outlined by Tuckman et al.: truncation, abnormal morphology and/or lack of continuity or absence of the meniscus on one or more MR images. An additional criterion used was abnormal increased signal in that area on fat-saturated proton density or T2-weighted coronal and sagittal images. Prospective MR readings were correlated with the arthroscopic findings.Results. Of the 200 consecutive knee arthroscopies, 28 patients had radial tears reported arthroscopically (14% incidence). MR readings prospectively demonstrated 19 of the 28 radial tears (68% sensitivity) when the criteria for diagnosis of a radial tear were truncation or abnormal morphology of the meniscus. With the use of the additional criterion of increased signal in the area of abnormal morphology on fat-saturated T2-weighted or proton density weighted sequences, the prospective sensitivity was 25 of 28 radial tears (89% sensitivity). There were no radial tears described in MR reports that were not demonstrated on arthroscopy (i.e., there were no false positive MR readings of radial tears in these 200 patients). Radial tears are commonly seen at arthroscopy. There was a 14% incidence in this series of 200 patients who underwent arthroscopy. Prospective detection of radial tears was 68% as compared with arthroscopy when the criteria as
Kim, Kyung-O; Jeong, Hae Sun; Jo, Daeseong
2017-01-01
Highlights: • Employing the Radial Point Interpolation Method (RPIM) in numerical analysis of multi-group neutron-diffusion equation. • Establishing mathematical formation of modified multi-group neutron-diffusion equation by RPIM. • Performing the numerical analysis for 2D critical problem. - Abstract: A mesh-free method is introduced to overcome the drawbacks (e.g., mesh generation and connectivity definition between the meshes) of mesh-based (nodal) methods such as the finite-element method and finite-difference method. In particular, the Point Interpolation Method (PIM) using a radial basis function is employed in the numerical analysis for the multi-group neutron-diffusion equation. The benchmark calculations are performed for the 2D homogeneous and heterogeneous problems, and the Multiquadrics (MQ) and Gaussian (EXP) functions are employed to analyze the effect of the radial basis function on the numerical solution. Additionally, the effect of the dimensionless shape parameter in those functions on the calculation accuracy is evaluated. According to the results, the radial PIM (RPIM) can provide a highly accurate solution for the multiplication eigenvalue and the neutron flux distribution, and the numerical solution with the MQ radial basis function exhibits the stable accuracy with respect to the reference solutions compared with the other solution. The dimensionless shape parameter directly affects the calculation accuracy and computing time. Values between 1.87 and 3.0 for the benchmark problems considered in this study lead to the most accurate solution. The difference between the analytical and numerical results for the neutron flux is significantly increased in the edge of the problem geometry, even though the maximum difference is lower than 4%. This phenomenon seems to arise from the derivative boundary condition at (x,0) and (0,y) positions, and it may be necessary to introduce additional strategy (e.g., the method using fictitious points and
Radial propagation of microturbulence in tokamaks
Garbet, X.; Laurent, L.; Roubin, J.P.; Samain, A.
1992-01-01
Energy confinement time in tokamaks exhibits a clear dependence on global plasma parameters. This is not the case for transport coefficients; their dependence on local plasma parameters cannot be precisely established. The aim of the present paper is to give a possible explanation of this behaviour; turbulence propagates radially because of departure from cylindrical geometry. This implies that the turbulence level at a given point and hence transport coefficients are not only functions of local plasma parameters. A quantitative estimate of the propagation velocity is derived from a Lagrangian formalism. Two cases are considered: the effect of toroidicity and the effect of non linear mode-mode coupling. The consequences of this model are discussed. This process does not depend on the type of instability. For the sake of simplicity only electrostatic perturbations are considered
Radial particle distributions in PARMILA simulation beams
Boicourt, G.P.
1984-03-01
The estimation of beam spill in particle accelerators is becoming of greater importance as higher current designs are being funded. To the present, no numerical method for predicting beam-spill has been available. In this paper, we present an approach to the loss-estimation problem that uses probability distributions fitted to particle-simulation beams. The properties of the PARMILA code's radial particle distribution are discussed, and a broad class of probability distributions are examined to check their ability to fit it. The possibility that the PARMILA distribution is a mixture is discussed, and a fitting distribution consisting of a mixture of two generalized gamma distributions is found. An efficient algorithm to accomplish the fit is presented. Examples of the relative prediction of beam spill are given. 26 references, 18 figures, 1 table
Radial expansion for spinning conformal blocks
Costa, Miguel S.; Penedones, João; Trevisani, Emilio
2016-07-12
This paper develops a method to compute any bosonic conformal block as a series expansion in the optimal radial coordinate introduced by Hogervorst and Rychkov. The method reduces to the known result when the external operators are all the same scalar operator, but it allows to compute conformal blocks for external operators with spin. Moreover, we explain how to write closed form recursion relations for the coefficients of the expansions. We study three examples of four point functions in detail: one vector and three scalars; two vectors and two scalars; two spin 2 tensors and two scalars. Finally, for the case of two external vectors, we also provide a more efficient way to generate the series expansion using the analytic structure of the blocks as a function of the scaling dimension of the exchanged operator.
Hydrostatic radial bearing of centrifugal pump
Skalicky, A.
1976-01-01
A hydrostatic radial pump is described characterized by the fact that part of the medium off-taken from delivery is used as a lubricating medium. Two additional bodies are placed alongside a hydrostatic bearing with coils in between them and the pump shaft; the coils have an opposite pitch. The feed channel for the hydrostatic bearing pocket is linked to delivery. The coil outlets are connected to the pump suction unit. Two rotating coils placed alongside the hydrostatic bearing will considerably simplify the communication channel design and reduce the dependence on the pump shaft deflections. The addition of another rotating coil in the close vicinity of the pump shaft or directly on the shaft further increases the efficiency. The bearing can be used in designing vertical circulating pumps for the cooling circuits of nuclear reactors. (J.B.)
The radial velocity variations in IC 418
Mendez, R.H.; Verga, A.D.
1981-01-01
The observations presented are part of a search for spectral and radial velocity variations among central stars of planetary nebulae and include the following new data: 1) Weak, previously undetected C III emissions are visible at 4056, 4186, 4516, 5270 and 5826 A. The famous unidentified emissions at 4485 and 4503 A were also found. 2) The He I absorptions at 4471 and 5875 A are blue-shifted relative to the nebular emissions. The same happens with Hsub(delta) and Hsub(γ), although in this case the shift can be at least partly attributed to blends with the strong He II absorptions, which are estimated to contribute about one half of the equivalent width at Hsub(delta) and Hsub(γ). 3) O III 5592 and C IV 5801, 5811 are also found in absorption. (Auth.)
Anomalous Medial Branch of Radial Artery: A Rare Variant
Surbhi Wadhwa
2016-10-01
Full Text Available Radial artery is an important consistent vessel of the upper limb. It is a useful vascular access site for coronary procedures and its reliable anatomy has resulted in an elevation of radial forearm flaps for reconstructive surgeries of head and neck. Technical failures, in both the procedures, are mainly due to anatomical variations, such as radial loops, ectopic radial arteries or tortuosity in the vessel. We present a rare and a unique anomalous medial branch of the radial artery spiraling around the flexor carpi radialis muscle in the forearm with a high rising superficial palmar branch of radial artery. Developmentally it probably is a remanent of the normal pattern of capillary vessel maintenance and regression. Such a case is of importance for reconstructive surgeons and coronary interventionists, especially in view of its unique medial and deep course.
Radial extension of drift waves in presence of velocity profiles
Sen, S.; Weiland, J.
1994-01-01
The effect of a radially varying poloidal velocity field on the recently found radially extended toroidal drift waves is investigated analytically. The role of velocity curvature (υ φ '') is found to have robust effects on the radial model structure of the mode. For a positive value of the curvature (Usually found in the H-mode edges) the radial model envelope, similar to the sheared slab case, becomes fully outgoing. The mode is therefore stable. On the other hand, for a negative value of the curvature (usually observed in the L-mode edges) all the characteristics of conventional drift waves return back. The radial mode envelope reduces to a localized Gaussian shape and the mode is therefore unstable again for typical (magnetic) shear values in tokamaks. Velocity shear (υ φ ??) on the other hand is found to have rather insignificant role both in determining the radial model structure and stability
Radial-Electric-Field Piezoelectric Diaphragm Pumps
Bryant, Robert G.; Working, Dennis C.; Mossi, Karla; Castro, Nicholas D.; Mane, Pooma
2009-01-01
In a recently invented class of piezoelectric diaphragm pumps, the electrode patterns on the piezoelectric diaphragms are configured so that the electric fields in the diaphragms have symmetrical radial (along-the-surface) components in addition to through-the-thickness components. Previously, it was accepted in the piezoelectric-transducer art that in order to produce the out-of-plane bending displacement of a diaphragm needed for pumping, one must make the electric field asymmetrical through the thickness, typically by means of electrodes placed on only one side of the piezoelectric material. In the present invention, electrodes are placed on both sides and patterned so as to produce substantial radial as well as through-the-thickness components. Moreover, unlike in the prior art, the electric field can be symmetrical through the thickness. Tests have shown in a given diaphragm that an electrode configuration according to this invention produces more displacement than does a conventional one-sided electrode pattern. The invention admits of numerous variations characterized by various degrees of complexity. Figure 1 is a simplified depiction of a basic version. As in other piezoelectric diaphragm pumps of similar basic design, the prime mover is a piezoelectric diaphragm. Application of a suitable voltage to the electrodes on the diaphragm causes it to undergo out-of-plane bending. The bending displacement pushes a fluid out of, or pulls the fluid into, a chamber bounded partly by the diaphragm. Also as in other diaphragm pumps in general, check valves ensure that the fluid flows only in through one port and only out through another port.
Radial stability of anisotropic strange quark stars
Arbañil, José D.V.; Malheiro, M., E-mail: jose.arbanil@upn.pe, E-mail: malheiro@ita.br [ITA—Instituto Tecnológico de Aeronáutica—Departamento de Física, 12228-900, São José dos Campos, São Paulo (Brazil)
2016-11-01
The influence of the anisotropy in the equilibrium and stability of strange stars is investigated through the numerical solution of the hydrostatic equilibrium equation and the radial oscillation equation, both modified from their original version to include this effect. The strange matter inside the quark stars is described by the MIT bag model equation of state. For the anisotropy two different kinds of local anisotropic σ = p {sub t} − p {sub r} are considered, where p {sub t} and p {sub r} are respectively the tangential and the radial pressure: one that is null at the star's surface defined by p {sub r} ( R ) = 0, and one that is nonnull at the surface, namely, σ {sub s} = 0 and σ {sub s} {sub ≠} {sub 0}. In the case σ {sub s} = 0, the maximum mass value and the zero frequency of oscillation are found at the same central energy density, indicating that the maximum mass marks the onset of the instability. For the case σ {sub s} {sub ≠} {sub 0}, we show that the maximum mass point and the zero frequency of oscillation coincide in the same central energy density value only in a sequence of equilibrium configurations with the same value of σ {sub s} . Thus, the stability star regions are determined always by the condition dM / d ρ {sub c} {sub >} {sub 0} only when the tangential pressure is maintained fixed at the star surface's p {sub t} ( R ). These results are also quite important to analyze the stability of other anisotropic compact objects such as neutron stars, boson stars and gravastars.
Sparse Reconstruction of Electric Fields from Radial Magnetic Data
Yeates, Anthony R.
2017-01-01
Accurate estimates of the horizontal electric field on the Sun’s visible surface are important not only for estimating the Poynting flux of magnetic energy into the corona but also for driving time-dependent magnetohydrodynamic models of the corona. In this paper, a method is developed for estimating the horizontal electric field from a sequence of radial-component magnetic field maps. This problem of inverting Faraday’s law has no unique solution. Unfortunately, the simplest solution (a divergence-free electric field) is not realistically localized in regions of nonzero magnetic field, as would be expected from Ohm’s law. Our new method generates instead a localized solution, using a basis pursuit algorithm to find a sparse solution for the electric field. The method is shown to perform well on test cases where the input magnetic maps are flux balanced in both Cartesian and spherical geometries. However, we show that if the input maps have a significant imbalance of flux—usually arising from data assimilation—then it is not possible to find a localized, realistic, electric field solution. This is the main obstacle to driving coronal models from time sequences of solar surface magnetic maps.
An analysis of the variable radial velocity of alpha cygni
Lucy, L.B.
1976-01-01
On the basis of 447 radial velocities obtained at the Lick Observatory by Paddock in the years 1927--1935, an attempt is made to discover the nature of the semiregular variability of α Cygni (A2 Ia). Harmonic analysis of the 144 velocities obtained in 1931 suggests that this variability is due to the simultaneous excitation of many discrete pulsation modes. The amplitudes and periods of these modes are then determined by least-squares fitting to all the data, and a final solution is obtained that comprises 16 terms with periods from 6.9 to 100.8 days. All terms are found to have highly significant amplitudes, and most terms also pass a test of the stability of their amplitudes and phases. Reasons are given for believing that most terms represent nonradial oscillations, and this leads to the suggestion that the resulting surface motions are to be identified with macroturbulence. An argument is also given for believing that the pulsational instability persists down to periods at which atmospheric oscillations become progressive, and this leads to the suggestion that such waves are observed as microturbulence and give rise to the observed mass loss. The importance of further monitoring of the variability of supergiants is stressed
Sparse Reconstruction of Electric Fields from Radial Magnetic Data
Yeates, Anthony R. [Department of Mathematical Sciences, Durham University, Durham, DH1 3LE (United Kingdom)
2017-02-10
Accurate estimates of the horizontal electric field on the Sun’s visible surface are important not only for estimating the Poynting flux of magnetic energy into the corona but also for driving time-dependent magnetohydrodynamic models of the corona. In this paper, a method is developed for estimating the horizontal electric field from a sequence of radial-component magnetic field maps. This problem of inverting Faraday’s law has no unique solution. Unfortunately, the simplest solution (a divergence-free electric field) is not realistically localized in regions of nonzero magnetic field, as would be expected from Ohm’s law. Our new method generates instead a localized solution, using a basis pursuit algorithm to find a sparse solution for the electric field. The method is shown to perform well on test cases where the input magnetic maps are flux balanced in both Cartesian and spherical geometries. However, we show that if the input maps have a significant imbalance of flux—usually arising from data assimilation—then it is not possible to find a localized, realistic, electric field solution. This is the main obstacle to driving coronal models from time sequences of solar surface magnetic maps.
Evaporation Limited Radial Capillary Penetration in Porous Media.
Liu, Mingchao; Wu, Jian; Gan, Yixiang; Hanaor, Dorian A H; Chen, C Q
2016-09-27
The capillary penetration of fluids in thin porous layers is of fundamental interest in nature and various industrial applications. When capillary flows occur in porous media, the extent of penetration is known to increase with the square root of time following the Lucas-Washburn law. In practice, volatile liquid evaporates at the surface of porous media, which restricts penetration to a limited region. In this work, on the basis of Darcy's law and mass conservation, a general theoretical model is developed for the evaporation-limited radial capillary penetration in porous media. The presented model predicts that evaporation decreases the rate of fluid penetration and limits it to a critical radius. Furthermore, we construct a unified phase diagram that describes the limited penetration in an annular porous medium, in which the boundaries of outward and inward liquid are predicted quantitatively. It is expected that the proposed theoretical model will advance the understanding of penetration dynamics in porous media and facilitate the design of engineered porous architectures.
Radial-arrayed rotary electrification for high performance triboelectric generator.
Zhu, Guang; Chen, Jun; Zhang, Tiejun; Jing, Qingshen; Wang, Zhong Lin
2014-03-04
Harvesting mechanical energy is an important route in obtaining cost-effective, clean and sustainable electric energy. Here we report a two-dimensional planar-structured triboelectric generator on the basis of contact electrification. The radial arrays of micro-sized sectors on the contact surfaces enable a high output power of 1.5 W (area power density of 19 mW cm(-2)) at an efficiency of 24%. The triboelectric generator can effectively harness various ambient motions, including light wind, tap water flow and normal body movement. Through a power management circuit, a triboelectric-generator-based power-supplying system can provide a constant direct-current source for sustainably driving and charging commercial electronics, immediately demonstrating the feasibility of the triboelectric generator as a practical power source. Given exceptional power density, extremely low cost and unique applicability resulting from distinctive mechanism and structure, the triboelectric generator can be applied not only to self-powered electronics but also possibly to power generation at a large scale.
A New Filtering Algorithm Utilizing Radial Velocity Measurement
LIU Yan-feng; DU Zi-cheng; PAN Quan
2005-01-01
Pulse Doppler radar measurements consist of range, azimuth, elevation and radial velocity. Most of the radar tracking algorithms in engineering only utilize position measurement. The extended Kalman filter with radial velocity measureneut is presented, then a new filtering algorithm utilizing radial velocity measurement is proposed to improve tracking results and the theoretical analysis is also given. Simulation results of the new algorithm, converted measurement Kalman filter, extended Kalman filter are compared. The effectiveness of the new algorithm is verified by simulation results.
Vitreous veils and radial lattice in Marshall syndrome.
Brubaker, Jacob W; Mohney, Brian G; Pulido, Jose S; Babovic-Vuksanovic, Dusica
2008-12-01
To report the findings of membranous vitreous veils and radial lattice in a child with Marshall syndrome. Observational case report. Retrospective review of medical records and fundus photograph of a 6-year-old boy with Marshall syndrome. Vitreoretinal findings were significant for bilateral membranous vitreous veils and radial lattice degeneration. This case demonstrates the occurrence of vitreous veils and radial lattice degeneration in patients with Marshall syndrome.
Radioactive Waste Management Basis
Perkins, B.K.
2009-01-01
The purpose of this Radioactive Waste Management Basis is to describe the systematic approach for planning, executing, and evaluating the management of radioactive waste at LLNL. The implementation of this document will ensure that waste management activities at LLNL are conducted in compliance with the requirements of DOE Order 435.1, Radioactive Waste Management, and the Implementation Guide for DOE Manual 435.1-1, Radioactive Waste Management Manual. Technical justification is provided where methods for meeting the requirements of DOE Order 435.1 deviate from the DOE Manual 435.1-1 and Implementation Guide.
Algorithm for detection of the broken phase conductor in the radial networks
Ostojić Mladen M.
2016-01-01
Full Text Available The paper presents an algorithm for a directional relay to be used for a detection of the broken phase conductor in the radial networks. The algorithm would use synchronized voltages, measured at the beginning and at the end of the line, as input signals. During the process, the measured voltages would be phase-compared. On the basis of the normalized energy, the direction of the phase conductor, with a broken point, would be detected. Software tool Matlab/Simulink package has developed a radial network model which simulates the broken phase conductor. The simulations generated required input signals by which the algorithm was tested. Development of the algorithm along with the formation of the simulation model and the test results of the proposed algorithm are presented in this paper.
The effect of texture on the shaft surface on the sealing performance of radial lip seals
Guo, Fei; Jia, XiaoHong; Gao, Zhi; Wang, YuMing
2014-07-01
On the basis of elastohydrodynamic model, the present study numerically analyzes the effect of various microdimple texture shapes, namely, circular, square, oriented isosceles triangular, on the pumping rate and the friction torque of radial lip seals, and determines the microdimple texture shape that can produce positive pumping rate. The area ratio, depth and shape dimension of a single texture are the most important geometric parameters which influence the tribological performance. According to the selected texture shape, parameter analysis is conducted to determine the optimal combination for the above three parameters. Simultaneously, the simulated performances of radial lip seal with texture on the shaft surface are compared with those of the conventional lip seal without any texture on the shaft surface.
41 CFR 105-62.102 - Authority to originally classify.
2010-07-01
... originally classify. (a) Top secret, secret, and confidential. The authority to originally classify information as Top Secret, Secret, or Confidential may be exercised only by the Administrator and is delegable...
Naive Bayesian classifiers for multinomial features: a theoretical analysis
Van Dyk, E
2007-11-01
Full Text Available The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density...
Ensemble of classifiers based network intrusion detection system performance bound
Mkuzangwe, Nenekazi NP
2017-11-01
Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...
Fast Most Similar Neighbor (MSN) classifiers for Mixed Data
Hernández Rodríguez, Selene
2010-01-01
The k nearest neighbor (k-NN) classifier has been extensively used in Pattern Recognition because of its simplicity and its good performance. However, in large datasets applications, the exhaustive k-NN classifier becomes impractical. Therefore, many fast k-NN classifiers have been developed; most of them rely on metric properties (usually the triangle inequality) to reduce the number of prototype comparisons. Hence, the existing fast k-NN classifiers are applicable only when the comparison f...
Pseudarthrosis of radial shaft with dislocation of heads of radial and ulnar bones (case report
M. E. Puseva
2013-01-01
Full Text Available The authors presented a rare clinical case - the injury of forearm complicated by the formation of the pseudarthrosis of the radial shaft in combination with old dislocation of heads the radius and ulna. The differentiated approach to the choice of surgical tactics was proposed, which consists of several consistent stages: taking free autotransplant from the crest of iliac bone, resection of pseudarthrosis of radius with replacement of the bone defect by the graft for restoration of anatomic length, conducting combined strained osteosynthesis and elimination of dislocation of a head of radial and ulnar bones by transosseous osteosynthesis. The chosen treatment strategy allowed to restore the anatomy and function of the upper extremity.
Three data partitioning strategies for building local classifiers (Chapter 14)
Zliobaite, I.; Okun, O.; Valentini, G.; Re, M.
2011-01-01
Divide-and-conquer approach has been recognized in multiple classifier systems aiming to utilize local expertise of individual classifiers. In this study we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training.
Recognition of pornographic web pages by classifying texts and images.
Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve
2007-06-01
With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.
32 CFR 2400.28 - Dissemination of classified information.
2010-07-01
... 32 National Defense 6 2010-07-01 2010-07-01 false Dissemination of classified information. 2400.28... SECURITY PROGRAM Safeguarding § 2400.28 Dissemination of classified information. Heads of OSTP offices... originating official may prescribe specific restrictions on dissemination of classified information when...
Femoral Access PCI in a Default Radial Center Identifies High-Risk Patients With Poor Outcomes.
Uddin, Muezz; Bundhoo, Shantu; Mitra, Rito; Ossei-Gerning, Nicholas; Morris, Keith; Anderson, Richard; Kinnaird, Tim
2015-10-01
Increasingly the trans-radial route (TRR) is preferred over the trans-femoral route (TFR) for PCI. However, even in high volume default TRR centers a cohort of patients undergo TFR PCI. We examined the demographics, procedural characteristics, and outcomes of patients undergoing PCI via the TF. The patient demographics, procedural data, and outcomes of 5,379 consecutive patients undergoing PCI at a default radial center between 2009 and 2012 were examined. Major bleeding (MB) was classified by ACUITY and BARC definitions. A total of 559 (10.4%) patients underwent PCI via the TFR and 4,820 patients via the TRR (89.6%). Baseline variables associated with TFR were shock, previous CABG, chronic total occlusion intervention, rotablation/laser use, female sex, and renal failure. Sixty-five patients of the TFR cohort (11.6%) experienced MB with 27 (41.5%) being access site related. MB was significantly more frequent than in the radial cohort. The variables independently associated with MB in the TFR cohort were renal failure, acute presentation, shock, and age. In the TFR, patients with MB mortality was high at 30 days (17.2% vs 2.6% for no MB, P default radial PCI center 10% of patients undergo PCI via the femoral artery. These patients have high baseline bleeding risk and undergo complex interventions. As a result the incidence of major bleeding, transfusion and death are high. Alternative strategies are required to optimize outcomes in this select group. © 2015, Wiley Periodicals, Inc.
Stability of halophilic proteins: from dipeptide attributes to discrimination classifier.
Zhang, Guangya; Huihua, Ge; Yi, Lin
2013-02-01
To investigate the molecular features responsible for protein halophilicity is of great significance for understanding the structure basis of protein halo-stability and would help to develop a practical strategy for designing halophilic proteins. In this work, we have systematically analyzed the dipeptide composition of the halophilic and non-halophilic protein sequences. We observed the halophilic proteins contained more DA, RA, AD, RR, AP, DD, PD, EA, VG and DV at the expense of LK, IL, II, IA, KK, IS, KA, GK, RK and AI. We identified some macromolecular signatures of halo-adaptation, and thought the dipeptide composition might contain more information than amino acid composition. Based on the dipeptide composition, we have developed a machine learning method for classifying halophilic and non-halophilic proteins for the first time. The accuracy of our method for the training dataset was 100.0%, and for the 10-fold cross-validation was 93.1%. We also discussed the influence of some specific dipeptides on prediction accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.
Classifying the evolutionary and ecological features of neoplasms
Maley, Carlo C.; Aktipis, Athena; Graham, Trevor A.; Sottoriva, Andrea; Boddy, Amy M.; Janiszewska, Michalina; Silva, Ariosto S.; Gerlinger, Marco; Yuan, Yinyin; Pienta, Kenneth J.; Anderson, Karen S.; Gatenby, Robert; Swanton, Charles; Posada, David; Wu, Chung-I; Schiffman, Joshua D.; Hwang, E. Shelley; Polyak, Kornelia; Anderson, Alexander R. A.; Brown, Joel S.; Greaves, Mel; Shibata, Darryl
2018-01-01
Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient’s tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research. PMID:28912577
Numerical simulation of liquid-metal-flows in radial-toroidal-radial bends
Molokov, S.; Buehler, L.
1993-09-01
Magnetohydrodynamic flows in a U-bend and right-angle bend are considered with reference to the radial-toroidal-radial concept of a self-cooled liquid-metal blanket. The ducts composing bends have rectangular cross-section. The applied magnetic field is aligned with the toroidal duct and perpendicular to the radial ones. At high Hartmann number the flow region is divided into cores and boundary layers of different types. The magnetohydrodynamic equations are reduced to a system of partial differential equations governing wall electric potentials and the core pressure. The system is solved numerically by two different methods. The first method is iterative with iteration between wall potential and the core pressure. The second method is a general one for the solution of the core flow equations in curvilinear coordinates generated by channel geometry and magnetic field orientation. Results obtained are in good agreement. They show, that the 3D-pressure drop of MHD flows in a U-bend is not a critical issue for blanket applications. (orig./HP) [de
Fuel radial design using Path Relinking; Diseno radial de combustible usando Path Relinking
Campos S, Y. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico)
2007-07-01
The present work shows the obtained results when implementing the combinatory optimization technique well-known as Path Re linking (Re-linkage of Trajectories), to the problem of the radial design of nuclear fuel assemblies, for boiling water reactors (BWR Boiling Water Reactor by its initials in English), this type of reactors is those that are used in the Laguna Verde Nucleo electric Central, Veracruz. As in any other electric power generation plant of that make use of some fuel to produce heat and that it needs each certain time (from 12 to 14 months) to make a supply of the same one, because this it wears away or it burns, in the nucleolectric plants to this activity is denominated fuel reload. In this reload different activities intervene, among those which its highlight the radial and axial designs of fuel assemblies, the patterns of control rods and the multi cycles study, each one of these stages with their own complexity. This work was limited to study in independent form the radial design, without considering the other activities. These phases are basic for the fuel reload design and of reactor operation strategies. (Author)
Peat classified as slowly renewable biomass fuel
2001-01-01
thousands of years. The report states also that peat should be classified as biomass fuel instead of biofuels, such as wood, or fossil fuels such as coal. According to the report peat is a renewable biomass fuel like biofuels, but due to slow accumulation it should be considered as slowly renewable fuel. The report estimates that bonding of carbon in both virgin and forest drained peatlands are so high that it can compensate the emissions formed in combustion of energy peat
Design of radial reinforcement for prestressed concrete containments
Wang, Shen, E-mail: swang@bechtel.com [Bechtel Power Corporation, 5275 Westview Drive, BP2-2C3, Frederick, MD 21703 (United States); Munshi, Javeed A., E-mail: jamunshi@bechtel.com [Bechtel Power Corporation, 5275 Westview Drive, BP2-2C3, Frederick, MD 21703 (United States)
2013-02-15
Highlights: ► A rigorous formulae is proposed to calculate radial stress within prestressed concrete containments. ► The proposed method is validated by finite element analysis in an illustrative practical example. ► A partially prestressed condition is more critical than a fully prestressed condition for radial tension. ► Practical design consideration is provided for detailing of radial reinforcement. -- Abstract: Nuclear containments are critical components for safety of nuclear power plants. Failure can result in catastrophic safety consequences as a result of leakage of radiation. Prestressed concrete containments have been used in large nuclear power plants with significant design internal pressure. These containments are generally reinforced with prestressing tendons in the circumferential (hoop) and meridional (vertical) directions. The curvature effect of the tendons introduces radial tensile stresses in the concrete shell which are generally neglected in the design of such structures. It is assumed that such tensile radial stresses are small as such no radial reinforcement is provided for this purpose. But recent instances of significant delaminations in Crystal River Unit 3 in Florida have elevated the need for reevaluation of the radial tension issue in prestressed containment. Note that currently there are no well accepted industry standards for design and detailing of radial reinforcement. This paper discusses the issue of radial tension in prestressed cylindrical and dome shaped structures and proposes formulae to calculate radial stresses. A practical example is presented to illustrate the use of the proposed method which is then verified by using state of art finite element analysis. This paper also provides some practical design consideration for detailing of radial reinforcement in prestressed containments.
PRELIMINARY SELECTION OF MGR DESIGN BASIS EVENTS
Kappes, J.A.
1999-01-01
The purpose of this analysis is to identify the preliminary design basis events (DBEs) for consideration in the design of the Monitored Geologic Repository (MGR). For external events and natural phenomena (e.g., earthquake), the objective is to identify those initiating events that the MGR will be designed to withstand. Design criteria will ensure that radiological release scenarios resulting from these initiating events are beyond design basis (i.e., have a scenario frequency less than once per million years). For internal (i.e., human-induced and random equipment failures) events, the objective is to identify credible event sequences that result in bounding radiological releases. These sequences will be used to establish the design basis criteria for MGR structures, systems, and components (SSCs) design basis criteria in order to prevent or mitigate radiological releases. The safety strategy presented in this analysis for preventing or mitigating DBEs is based on the preclosure safety strategy outlined in ''Strategy to Mitigate Preclosure Offsite Exposure'' (CRWMS M andO 1998f). DBE analysis is necessary to provide feedback and requirements to the design process, and also to demonstrate compliance with proposed 10 CFR 63 (Dyer 1999b) requirements. DBE analysis is also required to identify and classify the SSCs that are important to safety (ITS)
NONE
2002-01-01
Following on from the Final Report of the EDA(DS/21), and the summary of the ITER Final Design report(DS/22), the technical basis gives further details of the design of ITER. It is in two parts. The first, the Plant Design specification, summarises the main constraints on the plant design and operation from the viewpoint of engineering and physics assumptions, compliance with safety regulations, and siting requirements and assumptions. The second, the Plant Description Document, describes the physics performance and engineering characteristics of the plant design, illustrates the potential operational consequences foe the locality of a generic site, gives the construction, commissioning, exploitation and decommissioning schedule, and reports the estimated lifetime costing based on data from the industry of the EDA parties.
2002-01-01
Following on from the Final Report of the EDA(DS/21), and the summary of the ITER Final Design report(DS/22), the technical basis gives further details of the design of ITER. It is in two parts. The first, the Plant Design specification, summarises the main constraints on the plant design and operation from the viewpoint of engineering and physics assumptions, compliance with safety regulations, and siting requirements and assumptions. The second, the Plant Description Document, describes the physics performance and engineering characteristics of the plant design, illustrates the potential operational consequences foe the locality of a generic site, gives the construction, commissioning, exploitation and decommissioning schedule, and reports the estimated lifetime costing based on data from the industry of the EDA parties
Bryant, N. A.; George, A. J., Jr.; Hegdahl, R.
1977-01-01
A systematic verification of Landsat data classifications of the Portland, Oregon metropolitan area has been undertaken on the basis of census tract data. The degree of systematic misclassification due to the Bayesian classifier used to process the Landsat data was noted for the various suburban, industrialized and central business districts of the metropolitan area. The Landsat determinations of residential land use were employed to estimate the number of automobile trips generated in the region and to model air pollution hazards.
Observational hints of radial migration in disc galaxies from CALIFA
Ruiz-Lara, T.; Pérez, I.; Florido, E.; Sánchez-Blázquez, P.; Méndez-Abreu, J.; Sánchez-Menguiano, L.; Sánchez, S. F.; Lyubenova, M.; Falcón-Barroso, J.; van de Ven, G.; Marino, R. A.; de Lorenzo-Cáceres, A.; Catalán-Torrecilla, C.; Costantin, L.; Bland-Hawthorn, J.; Galbany, L.; García-Benito, R.; Husemann, B.; Kehrig, C.; Márquez, I.; Mast, D.; Walcher, C. J.; Zibetti, S.; Ziegler, B.
2017-01-01
Context. According to numerical simulations, stars are not always kept at their birth galactocentric distances but they have a tendency to migrate. The importance of this radial migration in shaping galactic light distributions is still unclear. However, if radial migration is indeed important,
Radial supports of face motors with slack compensation
Kuznetsova, I I; Gelman, A B; Krekina, T V
1982-01-01
The design of a radial support of a face motor with slack compensation is described, and gives the results of field tests which confirm the performance capacity of the experimental support both from the viewpoint of durability, and in relation to preventing radial slack of the face motor shaft.
Radial Color Gradient in a Globular Cluster 1. M68
Sukyoung Yi
1990-12-01
Full Text Available Stars in M68 from the observed color-magnitude diagrams with CCD were integrated to find any radial gradient. The result shows that M68 has a slightly bluer core. The main cause of these calculated radial color variations seems to come from the random distribution of giants.
Modelling and analysis of radial thermal stresses and temperature ...
A theoretical investigation has been undertaken to study operating temperatures, heat fluxes and radial thermal stresses in the valves of a modern diesel engine with and without air-cavity. Temperatures, heat fluxes and radial thermal stresses were measured theoretically for both cases under all four thermal loading ...
Radiographic study of distal radial physeal closure in thoroughbred horses
Vulcano, L.C.; Mamprim, M.J.; Muniz, L.M.R.; Moreira, A.F.; Luna, S.P.L.
1997-01-01
Monthly radiography was performed to study distal radial physeal closure in ten male and ten female Throughbred horses. The height, thoracic circumference and metacarpus circumference were also measured, Distal radial physeal closure time was sooner in females than males, and took 701 +/- 37 and 748 +/- 55 days respectively
Radial Velocities of 41 Kepler Eclipsing Binaries
Matson, Rachel A.; Gies, Douglas R.; Guo, Zhao; Williams, Stephen J.
2017-12-01
Eclipsing binaries are vital for directly determining stellar parameters without reliance on models or scaling relations. Spectroscopically derived parameters of detached and semi-detached binaries allow us to determine component masses that can inform theories of stellar and binary evolution. Here we present moderate resolution ground-based spectra of stars in close binary systems with and without (detected) tertiary companions observed by NASA’s Kepler mission and analyzed for eclipse timing variations. We obtain radial velocities and spectroscopic orbits for five single-lined and 35 double-lined systems, and confirm one false positive eclipsing binary. For the double-lined spectroscopic binaries, we also determine individual component masses and examine the mass ratio {M}2/{M}1 distribution, which is dominated by binaries with like-mass pairs and semi-detached classical Algol systems that have undergone mass transfer. Finally, we constrain the mass of the tertiary component for five double-lined binaries with previously detected companions.
Nonlinear radial propagation of drift wave turbulence
Prakash, M.
1985-01-01
We study the linear and the nonlinear radial propagation of drift wave energy in an inhomogeneous plasma. The drift mode excited in such a plasma is dispersive in nature. The drift wave energy spreads out symmetrically along the direction of inhomogeneity with a finite group velocity. To study the effect of the nonlinear coupling on the propagation of energy in a collision free plasma, we solve the Hasegawa-Mima equation as a mixed initial boundary-value problem. The solutions of the linearized equation are used to check the reliability of our numerical calculations. Additional checks are also performed on the invariants of the system. Our results reveal that a pulse gets distorted as it propagates through the medium. The peak of the pulse propagates with a finite velocity that depends on the amplitude of the initial pulse. The polarity of propagation depends on the initial parameters of the pulse. We have also studied drift wave propagation in a resistive plasma. The Hasegawa-Wakatani equations are used to investigate this problem
THE NIRSPEC ULTRACOOL DWARF RADIAL VELOCITY SURVEY
Blake, Cullen H.; Charbonneau, David; White, Russel J.
2010-01-01
We report the results of an infrared Doppler survey designed to detect brown dwarf and giant planetary companions to a magnitude-limited sample of ultracool dwarfs. Using the NIRSPEC spectrograph on the Keck II telescope, we obtained approximately 600 radial velocity (RV) measurements over a period of six years of a sample of 59 late-M and L dwarfs spanning spectral types M8/L0 to L6. A subsample of 46 of our targets has been observed on three or more epochs. We rely on telluric CH 4 absorption features in Earth's atmosphere as a simultaneous wavelength reference and exploit the rich set of CO absorption features found in the K-band spectra of cool stars and brown dwarfs to measure RVs and projected rotational velocities. For a bright, slowly rotating M dwarf standard we demonstrate an RV precision of 50 m s -1 and for slowly rotating L dwarfs we achieve a typical RV precision of approximately 200 m s -1 . This precision is sufficient for the detection of close-in giant planetary companions to mid-L dwarfs as well as more equal mass spectroscopic binary systems with small separations (a +0.7 -0.6 Gyr, similar to that of nearby sun-like stars. We simulate the efficiency with which we detect spectroscopic binaries and find that the rate of tight (a +8.6 -1.6 %, consistent with recent estimates in the literature of a tight binary fraction of 3%-4%.
Vortex Ring Dynamics in Radially Confined Domains
Stewart, Kelley; Niebel, Casandra; Jung, Sunghwan; Vlachos, Pavlos
2010-11-01
Vortex ring dynamics have been studied extensively in semi-infinite quiescent volumes. However, very little is known about vortex-ring formation in wall-bounded domains where vortex wall interaction will affect both the vortex ring pinch-off and propagation velocity. This study addresses this limitation and studies vortex formation in radially confined domains to analyze the affect of vortex-ring wall interaction on the formation and propagation of the vortex ring. Vortex rings were produced using a pneumatically driven piston cylinder arrangement and were ejected into a long cylindrical tube which defined the confined downstream domain. A range of confinement domains were studied with varying confinement diameters Velocity field measurements were performed using planar Time Resolved Digital Particle Image Velocimetry (TRDPIV) and were processed using an in-house developed cross-correlation PIV algorithm. The experimental analysis was used to facilitate the development of a theoretical model to predict the variations in vortex ring circulation over time within confined domains.
M.L.B. Simas
2005-03-01
Full Text Available An assumption commonly made in the study of visual perception is that the lower the contrast threshold for a given stimulus, the more sensitive and selective will be the mechanism that processes it. On the basis of this consideration, we investigated contrast thresholds for two classes of stimuli: sine-wave gratings and radial frequency stimuli (i.e., j0 targets or stimuli modulated by spherical Bessel functions. Employing a suprathreshold summation method, we measured the selectivity of spatial and radial frequency filters using either sine-wave gratings or j0 target contrast profiles at either 1 or 4 cycles per degree of visual angle (cpd, as the test frequencies. Thus, in a forced-choice trial, observers chose between a background spatial (or radial frequency alone and the given background stimulus plus the test frequency (1 or 4 cpd sine-wave grating or radial frequency. Contrary to our expectations, the results showed elevated thresholds (i.e., inhibition for sine-wave gratings and decreased thresholds (i.e., summation for radial frequencies when background and test frequencies were identical. This was true for both 1- and 4-cpd test frequencies. This finding suggests that sine-wave gratings and radial frequency stimuli are processed by different quasi-linear systems, one working at low luminance and contrast level (sine-wave gratings and the other at high luminance and contrast levels (radial frequency stimuli. We think that this interpretation is consistent with distinct foveal only and foveal-parafoveal mechanisms involving striate and/or other higher visual areas (i.e., V2 and V4.
Moussaoui, Ahmed; Bouziane, Touria
2016-01-01
The method LRPIM is a Meshless method with properties of simple implementation of the essential boundary conditions and less costly than the moving least squares (MLS) methods. This method is proposed to overcome the singularity associated to polynomial basis by using radial basis functions. In this paper, we will present a study of a 2D problem of an elastic homogenous rectangular plate by using the method LRPIM. Our numerical investigations will concern the influence of different shape parameters on the domain of convergence,accuracy and using the radial basis function of the thin plate spline. It also will presents a comparison between numerical results for different materials and the convergence domain by precising maximum and minimum values as a function of distribution nodes number. The analytical solution of the deflection confirms the numerical results. The essential points in the method are: •The LRPIM is derived from the local weak form of the equilibrium equations for solving a thin elastic plate.•The convergence of the LRPIM method depends on number of parameters derived from local weak form and sub-domains.•The effect of distributions nodes number by varying nature of material and the radial basis function (TPS).
Parameterization of a fuzzy classifier for the diagnosis of an industrial process
Toscano, R.; Lyonnet, P.
2002-01-01
The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method, which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization, which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a DC motor showing the utility of this method. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system (FDS) is not, in general, directly available. It must be extracted from an often-considerable mass of information. For this reason, a general methodology for the design of a FDS is presented and illustrated on a non-linear plant
Performance evaluation of various classifiers for color prediction of rice paddy plant leaf
Singh, Amandeep; Singh, Maninder Lal
2016-11-01
The food industry is one of the industries that uses machine vision for a nondestructive quality evaluation of the produce. These quality measuring systems and softwares are precalculated on the basis of various image-processing algorithms which generally use a particular type of classifier. These classifiers play a vital role in making the algorithms so intelligent that it can contribute its best while performing the said quality evaluations by translating the human perception into machine vision and hence machine learning. The crop of interest is rice, and the color of this crop indicates the health status of the plant. An enormous number of classifiers are available to solve the purpose of color prediction, but choosing the best among them is the focus of this paper. Performance of a total of 60 classifiers has been analyzed from the application point of view, and the results have been discussed. The motivation comes from the idea of providing a set of classifiers with excellent performance and implementing them on a single algorithm for the improvement of machine vision learning and, hence, associated applications.
Empirical study of classification process for two-stage turbo air classifier in series
Yu, Yuan; Liu, Jiaxiang; Li, Gang
2013-05-01
The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on the classification process for the two-stage turbo air classifier in series. The influence of the process parameters of a two-stage turbo air classifier in series on classification performance is empirically studied by using aluminum oxide powders as the experimental material. The experimental results show the following: 1) When the rotor cage rotary speed of the first-stage classifier is increased from 2 300 r/min to 2 500 r/min with a constant rotor cage rotary speed of the second-stage classifier, classification precision is increased from 0.64 to 0.67. However, in this case, the final ultrafine powder yield is decreased from 79% to 74%, which means the classification precision and the final ultrafine powder yield can be regulated through adjusting the rotor cage rotary speed of the first-stage classifier. 2) When the rotor cage rotary speed of the second-stage classifier is increased from 2 500 r/min to 3 100 r/min with a constant rotor cage rotary speed of the first-stage classifier, the cut size is decreased from 13.16 μm to 8.76 μm, which means the cut size of the ultrafine powder can be regulated through adjusting the rotor cage rotary speed of the second-stage classifier. 3) When the feeding speed is increased from 35 kg/h to 50 kg/h, the "fish-hook" effect is strengthened, which makes the ultrafine powder yield decrease. 4) To weaken the "fish-hook" effect, the equalization of the two-stage wind speeds or the combination of a high first-stage wind speed with a low second-stage wind speed should be selected. This empirical study provides a criterion of process parameter configurations for a two-stage or multi-stage classifier in series, which offers a theoretical basis for practical production.
RADIAL VELOCITY MONITORING OF KEPLER HEARTBEAT STARS
Shporer, Avi [Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109 (United States); Fuller, Jim [TAPIR, Walter Burke Institute for Theoretical Physics, Mailcode 350-17, Caltech, Pasadena, CA 91125 (United States); Isaacson, Howard [Department of Astronomy, University of California, Berkeley CA 94720 (United States); Hambleton, Kelly; Prša, Andrej [Department of Astrophysics and Planetary Science, Villanova University, 800 East Lancaster Avenue, Villanova, PA 19085 (United States); Thompson, Susan E. [NASA Ames Research Center, Moffett Field, CA 94035 (United States); Kurtz, Donald W. [Jeremiah Horrocks Institute, University of Central Lancashire, Preston, PR1 2HE (United Kingdom); Howard, Andrew W. [Institute for Astronomy, University of Hawaii, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States); O’Leary, Ryan M. [JILA, University of Colorado and NIST, 440 UCB, Boulder, 80309-0440 (United States)
2016-09-20
Heartbeat stars (HB stars) are a class of eccentric binary stars with close periastron passages. The characteristic photometric HB signal evident in their light curves is produced by a combination of tidal distortion, heating, and Doppler boosting near orbital periastron. Many HB stars continue to oscillate after periastron and along the entire orbit, indicative of the tidal excitation of oscillation modes within one or both stars. These systems are among the most eccentric binaries known, and they constitute astrophysical laboratories for the study of tidal effects. We have undertaken a radial velocity (RV) monitoring campaign of Kepler HB stars in order to measure their orbits. We present our first results here, including a sample of 22 Kepler HB systems, where for 19 of them we obtained the Keplerian orbit and for 3 other systems we did not detect a statistically significant RV variability. Results presented here are based on 218 spectra obtained with the Keck/HIRES spectrograph during the 2015 Kepler observing season, and they have allowed us to obtain the largest sample of HB stars with orbits measured using a single instrument, which roughly doubles the number of HB stars with an RV measured orbit. The 19 systems measured here have orbital periods from 7 to 90 days and eccentricities from 0.2 to 0.9. We show that HB stars draw the upper envelope of the eccentricity–period distribution. Therefore, HB stars likely represent a population of stars currently undergoing high eccentricity migration via tidal orbital circularization, and they will allow for new tests of high eccentricity migration theories.
Scaling laws for radial foil bearings
Honavara Prasad, Srikanth
The effects of fluid pressurization, structural deformation of the compliant members and heat generation in foil bearings make the design and analysis of foil bearings very complicated. The complex fluid-structural-thermal interactions in foil bearings also make modeling efforts challenging because these phenomena are governed by highly non-linear partial differential equations. Consequently, comparison of various bearing designs require detailed calculation of the flow fields (velocities, pressures), bump deflections (structural compliance) and heat transfer phenomena (viscous dissipation in the fluid, frictional heating, temperature profile etc.,) resulting in extensive computational effort (time/hardware). To obviate rigorous computations and aid in feasibility assessments of foil bearings of various sizes, NASA developed the "rule of thumb" design guidelines for estimation of journal bearing load capacity. The guidelines are based on extensive experimental data. The goal of the current work is the development of scaling laws for radial foil bearings to establish an analytical "rule of thumb" for bearing clearance and bump stiffness. The use of scale invariant Reynolds equation and experimentally observed NASA "rule of thumb" yield scale factors which can be deduced from first principles. Power-law relationships between: a. Bearing clearance and bearing radius, and b. bump stiffness and bearing radius, are obtained. The clearance and bump stiffness values obtained from scaling laws are used as inputs for Orbit simulation to study various cases. As the clearance of the bearing reaches the dimensions of the material surface roughness, asperity contact breaks the fluid film which results in wear. Similarly, as the rotor diameter increases (requiring larger bearing diameters), the load capacity of the fluid film should increase to prevent dry rubbing. This imposes limits on the size of the rotor diameter and consequently bearing diameter. Therefore, this thesis aims
RADIAL VELOCITY MONITORING OF KEPLER HEARTBEAT STARS
Shporer, Avi; Fuller, Jim; Isaacson, Howard; Hambleton, Kelly; Prša, Andrej; Thompson, Susan E.; Kurtz, Donald W.; Howard, Andrew W.; O’Leary, Ryan M.
2016-01-01
Heartbeat stars (HB stars) are a class of eccentric binary stars with close periastron passages. The characteristic photometric HB signal evident in their light curves is produced by a combination of tidal distortion, heating, and Doppler boosting near orbital periastron. Many HB stars continue to oscillate after periastron and along the entire orbit, indicative of the tidal excitation of oscillation modes within one or both stars. These systems are among the most eccentric binaries known, and they constitute astrophysical laboratories for the study of tidal effects. We have undertaken a radial velocity (RV) monitoring campaign of Kepler HB stars in order to measure their orbits. We present our first results here, including a sample of 22 Kepler HB systems, where for 19 of them we obtained the Keplerian orbit and for 3 other systems we did not detect a statistically significant RV variability. Results presented here are based on 218 spectra obtained with the Keck/HIRES spectrograph during the 2015 Kepler observing season, and they have allowed us to obtain the largest sample of HB stars with orbits measured using a single instrument, which roughly doubles the number of HB stars with an RV measured orbit. The 19 systems measured here have orbital periods from 7 to 90 days and eccentricities from 0.2 to 0.9. We show that HB stars draw the upper envelope of the eccentricity–period distribution. Therefore, HB stars likely represent a population of stars currently undergoing high eccentricity migration via tidal orbital circularization, and they will allow for new tests of high eccentricity migration theories.
Amir R. Kachooei
2017-11-01
Full Text Available Background: To study if patients that have a second radiograph 2 or more years after nonoperative treatment of an isolated radial head fracture have radiocapitellar osteoarthritis (RC OA. Methods: We used the database of 3 academic hospitals in one health system from 1988 to 2013 to find patients with isolated radial head fractures (no associated ligament injury or fracture that had a second elbow radiograph after more than 2 years from the initial injury. Of 887 patients with isolated radial head fractures, 54 (6% had an accessible second radiograph for reasons of a second injury (57%, pain (30%, or follow-up visit (13%. Two orthopedic surgeons independently classified the radial head fractures on the initial radiographs using the Broberg and Morrey modified Mason classification, and assessed the development of RC OA on the final radiograph using a binary system (yes/no. Results: Four out of 54 (7.5% patients had RC OA, one with isolated RC arthrosis that seemed related to capitellar cartilage injury, and 3 that presented with pain and had global OA (likely primary osteoarthritis. Conclusion: With the caveat that some percentage of patients may have left our health system during the study period, about 1 in 887 patients (0.1% returns with isolated radiocapitellar arthritis after an isolated radial head fracture, and this may relate to capitellar injury rather than attrition. Patients with isolated radial head fractures can consider post-traumatic radiocapitellar arthritis a negligible risk.
The biological basis of radiotherapy
Steel, G.G.; Adams, G.E.; Horwich, A.
1989-01-01
The focus of this book is the biological basis of radiotherapy. The papers presented include: Temporal stages of radiation action:free radical processes; The molecular basis of radiosensitivity; and Radiation damage to early-reacting normal tissue
Feller, D.F.
1979-01-01
The behavior of the two exponential parameters in an even-tempered gaussian basis set is investigated as the set optimally approaches an integral transform representation of the radial portion of atomic and molecular orbitals. This approach permits a highly accurate assessment of the Hartree-Fock limit for atoms and molecules.
A Supervised Multiclass Classifier for an Autocoding System
Yukako Toko
2017-11-01
Full Text Available Classification is often required in various contexts, including in the field of official statistics. In the previous study, we have developed a multiclass classifier that can classify short text descriptions with high accuracy. The algorithm borrows the concept of the naïve Bayes classifier and is so simple that its structure is easily understandable. The proposed classifier has the following two advantages. First, the processing times for both learning and classifying are extremely practical. Second, the proposed classifier yields high-accuracy results for a large portion of a dataset. We have previously developed an autocoding system for the Family Income and Expenditure Survey in Japan that has a better performing classifier. While the original system was developed in Perl in order to improve the efficiency of the coding process of short Japanese texts, the proposed system is implemented in the R programming language in order to explore versatility and is modified to make the system easily applicable to English text descriptions, in consideration of the increasing number of R users in the field of official statistics. We are planning to publish the proposed classifier as an R-package. The proposed classifier would be generally applicable to other classification tasks including coding activities in the field of official statistics, and it would contribute greatly to improving their efficiency.
Influence of Immobilization Time on Functional Outcome in Radial Neck Fractures in Children.
Badoi, Adina; Frech-Dörfler, Martina; Häcker, Frank-Martin; Mayr, Johannes
2016-12-01
Background Radial neck fractures represent 20 to 30% of elbow fractures in children. Incorrect treatment can lead to significant permanent functional impairment. Posttraumatic avascular necrosis may cause a deformity of the radial head and neck. Deformation of the radial head and neck can be more severe after open rather than closed reduction or orthopedic treatment without reduction. The aim of our study was to analyze the influence of immobilization time on functional outcome. Patients and Methods Retrospective, descriptive study of all children who had been treated for a radial neck fracture between 1999 and 2013 at the University Children's Hospital Basel. Patients were allocated to two groups (group 1: patients treated between 1999 and 2008, group 2: patients treated between 2009 and 2013). The fractures were classified according to the classification of Metaizeau. The primary endpoint was the percentage of patients who reached the full range of elbow motion at the end of the treatment period or the last follow-up. Secondary endpoints were immobilization time and number of patients with persistent physical restrictions of the elbow range of motion as well as the type of restrictions and subjective complaints. Results A total of 67 patients treated for radial neck fracture were included in the first group (1999-2008). A total of 47 patients were allocated to the second group (2009-2013). Overall, 59 patients in group 1 and 39 patients in group 2 were treated nonoperatively. Average immobilization time was 22.7 days (range, 6-60 days) in group 1 and 13.2 days (range, 0-27 days) in group 2. Full range of motion was observed in 50 to 72.7% of patients in group 1 and in 71.4 to 92% of patients in group 2, depending on the grade of fracture displacement. Overall, 21 patients (31%) of group 1 showed a persistent functional restriction. In group 2, only six patients (12%) suffered from a persistent functional restriction of the elbow range of motion
Stellar Angular Momentum Distributions and Preferential Radial Migration
Wyse, Rosemary; Daniel, Kathryne J.
2018-04-01
I will present some results from our recent investigations into the efficiency of radial migration in stellar disks of differing angular momentum distributions, within a given adopted 2D spiral disk potential. We apply to our models an analytic criterion that determines whether or not individual stars are in orbits that could lead to radial migration around the corotation resonance. We couch our results in terms of the local stellar velocity dispersion and find that the fraction of stars that could migrate radially decreases as the velocity dispersion increases. I will discuss implications and comparisons with the results of other approaches.
Research on Radial Vibration of a Circular Plate
Wei Liu
2016-01-01
Full Text Available Radial vibration of the circular plate is presented using wave propagation approach and classical method containing Bessel solution and Hankel solution for calculating the natural frequency theoretically. In cylindrical coordinate system, in order to obtain natural frequency, propagation and reflection matrices are deduced at the boundaries of free-free, fixed-fixed, and fixed-free using wave propagation approach. Furthermore, radial phononic crystal is constructed by connecting two materials periodically for the analysis of band phenomenon. Also, Finite Element Simulation (FEM is adopted to verify the theoretical results. Finally, the radial and piezoelectric effects on the band are also discussed.
Gattano, C.; Lambert, S.; Bizouard, C.
2017-12-01
In the context of selecting sources defining the celestial reference frame, we compute astrometric time series of all VLBI radio-sources from observations in the International VLBI Service database. The time series are then analyzed with Allan variance in order to estimate the astrometric stability. From results, we establish a new classification that takes into account the whole multi-time scales information. The algorithm is flexible on the definition of ``stable source" through an adjustable threshold.
Whissell, Cynthia
2010-06-01
The theory of humors, which was the prevalent theory of affect in Shakespeare's day, was used to explain both states (moods, emotions) and traits (personalities). This article reports humoral scores appropriate to the major characters of Shakespeare's comedies. The Dictionary of Affect in Language was used to score all words (N = 180,243) spoken by 105 major characters in 13 comedies in terms of their emotional undertones. These were translated into humoral scores. Translation was possible because emotional undertones, humor, and personality (e.g., Eysenck's model) are defined by various axes in the same two-dimensional space. Humoral scores differed for different types of characters, e.g., Shakespeare's lovers used more Sanguine language and his clowns more Melancholy language than other characters. A study of Kate and Petruchio from The Taming of the Shrew demonstrated state-like changes in humor for characters as the play unfolded.
Granular Corneal Dystrophy Manifesting after Radial Keratotomy
Sepehr Feizi
2008-12-01
Full Text Available
PURPOSE: To report manifestation of granular corneal dystrophy after radial keratotomy (RK. CASE REPORT: A 32-year-old man presented with white radial lines in both corneas. He had undergone uncomplicated RK in both eyes 8 years ago. Preoperative refraction had been OD: -3.5 -0.75@180 and OS: -3.0 -0.5@175. Uncorrected visual acuity was OD: 8/10 and OS: 7/10; best corrected visual acuity was 9/10 in both eyes with OD: -0.5 -0.5@60 and OS: -0.75 -0.5@80. Slit lamp examination revealed discrete well-demarcated whitish lesions with clear intervening stroma in the central anterior cornea consistent with granular dystrophy. Similar opacities were present within the RK incisions. CONCLUSION: Granular dystrophy deposits may appear within RK incisions besides other previously reported locations.
Performance of classification confidence measures in dynamic classifier systems
Štefka, D.; Holeňa, Martin
2013-01-01
Roč. 23, č. 4 (2013), s. 299-319 ISSN 1210-0552 R&D Projects: GA ČR GA13-17187S Institutional support: RVO:67985807 Keywords : classifier combining * dynamic classifier systems * classification confidence Subject RIV: IN - Informatics, Computer Science Impact factor: 0.412, year: 2013
32 CFR 2400.30 - Reproduction of classified information.
2010-07-01
... 32 National Defense 6 2010-07-01 2010-07-01 false Reproduction of classified information. 2400.30... SECURITY PROGRAM Safeguarding § 2400.30 Reproduction of classified information. Documents or portions of... the originator or higher authority. Any stated prohibition against reproduction shall be strictly...
Classifying spaces with virtually cyclic stabilizers for linear groups
Degrijse, Dieter Dries; Köhl, Ralf; Petrosyan, Nansen
2015-01-01
We show that every discrete subgroup of GL(n, ℝ) admits a finite-dimensional classifying space with virtually cyclic stabilizers. Applying our methods to SL(3, ℤ), we obtain a four-dimensional classifying space with virtually cyclic stabilizers and a decomposition of the algebraic K-theory of its...
Dynamic integration of classifiers in the space of principal components
Tsymbal, A.; Pechenizkiy, M.; Puuronen, S.; Patterson, D.W.; Kalinichenko, L.A.; Manthey, R.; Thalheim, B.; Wloka, U.
2003-01-01
Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the
Carranza, Christian L; Ballegaard, Martin; Werner, Mads U
2014-01-01
the postoperative complications will be registered, and we will evaluate muscular function, scar appearance, vascular supply to the hand, and the graft patency including the patency of the central radial artery anastomosis. A patency evaluation by multi-slice computer tomography will be done at one year...... to aorto-radial revascularisation techniques but this objective is exploratory. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01848886.Danish Ethics committee number: H-3-2012-116.Danish Data Protection Agency: 2007-58-0015/jr.n:30-0838....
Anterior transposition of the radial nerve--a cadaveric study.
Yakkanti, Madhusudhan R; Roberts, Craig S; Murphy, Joshua; Acland, Robert D
2008-01-01
The radial nerve is at risk during the posterior plating of the humerus. The purpose of this anatomic study was to assess the extent of radial nerve dissection required for anterior transposition through the fracture site (transfracture anterior transposition). A cadaver study was conducted approaching the humerus by a posterior midline incision. The extent of dissection of the nerve necessary for plate fixation of the humerus fracture was measured. An osteotomy was created to model a humeral shaft fracture at the spiral groove (OTA classification 12-A2, 12-A3). The radial nerve was then transposed anterior to the humeral shaft through the fracture site. The additional dissection of the radial nerve and the extent of release of soft tissue from the humerus shaft to achieve the transposition were measured. Plating required a dissection of the radial nerve 1.78 cm proximal and 2.13 cm distal to the spiral groove. Transfracture anterior transposition of the radial nerve required an average dissection of 2.24 cm proximal and 2.68 cm distal to the spiral groove. The lateral intermuscular septum had to be released for 2.21 cm on the distal fragment to maintain laxity of the transposed nerve. Transfracture anterior transposition of the radial nerve before plating is feasible with dissection proximal and distal to the spiral groove and elevation of the lateral intermuscular septum. Potential clinical advantages of this technique include enhanced fracture site visualization, application of broader plates, and protection of the radial nerve during the internal fixation.
Radial-piston pump for drive of test machines
Nizhegorodov, A. I.; Gavrilin, A. N.; Moyzes, B. B.; Cherkasov, A. I.; Zharkevich, O. M.; Zhetessova, G. S.; Savelyeva, N. A.
2018-01-01
The article reviews the development of radial-piston pump with phase control and alternating-flow mode for seismic-testing platforms and other test machines. The prospects for use of the developed device are proved. It is noted that the method of frequency modulation with the detection of the natural frequencies is easily realized by using the radial-piston pump. The prospects of further research are given proof.
Radially Polarized Conical Beam from an Embedded Etched Fiber
Kalaidji , D.; Spajer , M.; Marthouret , N.; Grosjean , T.
2009-01-01
International audience; We propose a method for producing a conical beam based on the lateral refraction of the TM01 mode from a two-mode fiber after chemical etching of the cladding, and for controlling its radial polarization. The whole power of the guided mode is transferred to the refracted beam with low diffraction. Polarization control by a series of azimuthal detectors and a stress controller affords the transmission of a stabilized radial polarization through an optical fiber. A solid...
Luxation of the radial carpal bone in a cat
Pitcher, G.D.C.
1996-01-01
A case of radial carpal bone luxation in the cat and its management is described. Open reduction was performed and surgically maintained, in combination with repair of rupture of the short radial collateral ligament and joint capsule. The carpus was supported for one month following surgery by application of transarticular external fixation. Four months after treatment the cat was sound, despite evidence of degenerative joint disease. The mechanism of luxation appears to be analogous to that seen in the dog
Introducing radiality constraints in capacitated location-routing problems
Eliana Mirledy Toro Ocampo
2017-03-01
Full Text Available In this paper, we introduce a unified mathematical formulation for the Capacitated Vehicle Routing Problem (CVRP and for the Capacitated Location Routing Problem (CLRP, adopting radiality constraints in order to guarantee valid routes and eliminate subtours. This idea is inspired by formulations already employed in electric power distribution networks, which requires a radial topology in its operation. The results show that the proposed formulation greatly improves the convergence of the solver.
An ensemble of dissimilarity based classifiers for Mackerel gender determination
Blanco, A; Rodriguez, R; Martinez-Maranon, I
2014-01-01
Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity
An ensemble of dissimilarity based classifiers for Mackerel gender determination
Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.
2014-03-01
Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.
Just-in-time classifiers for recurrent concepts.
Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel
2013-04-01
Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.
Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen
2018-06-13
Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.
Selecting non-classified hotels in Kenya: what really matters for business guests?
Alex K Kivuva
2014-01-01
Full Text Available Non-classified hotels, which comprise small hotels and guest houses, are important accommodation providers offering limited services and products as compared to the classified hotels. Through guest satisfaction, they can achieve repeat business and also get new business through word of mouth from previous guests. The main focus is for the hoteliers to know exactly the determinants of selection of hotels by their guests. In this case, the focus was on non-classified hotels in Mtwapa town at the Kenyan coast. The study adopted a cross-sectional descriptive survey design. Results from this study clearly indicate that all aspects of hotel operations are important to business guests’ selection of a non-classified hotel. However, it was revealed that this was not on equal basis. Results indicate that the core product (guestroom comfortability, hygiene and cleanliness were the most important factors in determining guests’ selection of where to stay. This research therefore suggests that any efforts towards quality improvement in a hotel should focus primarily on ensuring customer satisfaction with the guestroom. While acknowledging the importance of all aspects of hotel operations, managers should recognize the importance of the guestroom and its facilities towards hotel selection and overall customer satisfaction. Therefore, it is imperative that managers channel their resources towards improving guest services in the guestrooms in accordance with the requirements of the clientele. This includes such aspects as the look of the guest rooms, facilities provided in the guest rooms and comfortability of the bed and mattress.
Illumination Profile & Dispersion Variation Effects on Radial Velocity Measurements
Grieves, Nolan; Ge, Jian; Thomas, Neil B.; Ma, Bo; Li, Rui; SDSS-III
2015-01-01
The Multi-object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS) measures radial velocities using a fiber-fed dispersed fixed-delay interferometer (DFDI) with a moderate dispersion spectrograph. This setup allows a unique insight into the 2D illumination profile from the fiber on to the dispersion grating. Illumination profile investigations show large changes in the profile over time and fiber location. These profile changes are correlated with dispersion changes and long-term radial velocity offsets, a major problem within the MARVELS radial velocity data. Characterizing illumination profiles creates a method to both detect and correct radial velocity offsets, allowing for better planet detection. Here we report our early results from this study including improvement of radial velocity data points from detected giant planet candidates. We also report an illumination profile experiment conducted at the Kitt Peak National Observatory using the EXPERT instrument, which has a DFDI mode similar to MARVELS. Using profile controlling octagonal-shaped fibers, long term offsets over a 3 month time period were reduced from ~50 m/s to within the photon limit of ~4 m/s.
Channeling of protons through radial deformed carbon nanotubes
Borka Jovanović, V., E-mail: vborka@vinca.rs [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Borka, D. [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Galijaš, S.M.D. [Faculty of Physics, University of Belgrade, P.O. Box 368, 11001 Belgrade (Serbia)
2017-05-18
Highlights: • For the first time we presented theoretically obtained distributions of channeled protons with radially deformed SWNT. • Our findings indicate that influence of the radial deformation is very strong and it should not be omitted in simulations. • We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. • Our obtained results can be compared with measured distributions to reveal the presence of various types of defects in SWNT. - Abstract: In this paper we have presented a theoretical investigation of the channeling of 1 GeV protons with the radial deformed (10, 0) single-wall carbon nanotubes (SWNTs). We have calculated channeling potential within the deformed nanotubes. For the first time we presented theoretically obtained spatial and angular distributions of channeled protons with radially deformed SWNT. We used a Monte Carlo (MC) simulation technique. We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. These results may be useful for nanotube characterization and production and guiding of nanosized ion beams.
Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines
Mehdi Shadaram
2010-10-01
Full Text Available In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.
Ali Tarighatnia
2016-06-01
Conclusion: On the basis of the results obtained in this study, no differences were found in patients’ radiation dose in both access groups, therefore with regard to comparatively more clinical advantages associated with the Trans-radial access technique it might be a good substitute for Trans-femoral access.
M. M. Potsane
2014-01-01
Full Text Available The transport of chemicals through soils to the groundwater or precipitation at the soils surfaces leads to degradation of these resources. Serious consequences may be suffered in the long run. In this paper, we consider macroscopic deterministic models describing contaminant transport in saturated soils under uniform radial water flow backgrounds. The arising convection-dispersion equation given in terms of the stream functions is analyzed using classical Lie point symmetries. A number of exotic Lie point symmetries are admitted. Group invariant solutions are classified according to the elements of the one-dimensional optimal systems. We analyzed the group invariant solutions which satisfy the physical boundary conditions.
Radial cracks and fracture mechanism of radially oriented ring 2:17 type SmCo magnets
Tian Jianjun; Pan Dean; Zhou Hao; Yin Fuzheng; Tao Siwu; Zhang Shengen; Qu Xuanhui
2009-01-01
Radially oriented ring 2:17 type SmCo magnets have different microstructure in the radial direction (easy magnetization) and axial direction (hard magnetization). The structure of the cross-section in radial direction is close-packed atomic plane, which shows cellular microstructure. The microstructure of the cross-section in axial direction consists of a mixture of rhombic microstructure and parallel lamella phases. So the magnets have obvious anisotropy of thermal expansion in different directions. The difference of the thermal expansion coefficients reaches the maximum value at 830-860 deg. C, which leads to radial cracks during quenching. The magnets have high brittlement because there are fewer slip systems in crystal structure. The fracture is brittle cleavage fracture.
Hanna, Elias B; Mogabgab, Owen N; Baydoun, Hassan
2018-01-01
We present cases of complex, calcified iliac occlusive disease revascularized via a combined radial-femoral access strategy. Through a 6-French, 125-cm transradial guiding catheter, antegrade guidewires and catheters are advanced into the iliac occlusion, while retrograde devices are advanced transfemorally. The transradial and transfemoral channels communicate, allowing the devices to cross the occlusion into the true lumen (radial-femoral antegrade-retrograde rendezvous).
Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.
Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin
2017-01-01
Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.
Niemann, Isa; Hansen, Estrid S; Sunde, Lone
2007-01-01
classifications, and compared the ability of the two classifications to discriminate between patients with and without a substantial risk of persistent trophoblastic disease. METHODS: 294 cases of consecutively collected hydropic placentas clinically suspected of hydatidiform mole made the basis......OBJECTIVE: Hydatidiform mole can be classified by histopathologic characteristics and by genetic constitutions and most complete moles are diploid, whereas most partial moles are triploid. We investigated the concordance between these two classifications, characterized moles with conflicting......-molar miscarriage, 20 were triploids, 2 were diploid androgenetic and 2 were diploid biparental. In 23% of the conceptuses, the histopathologic and genetic classifications were conflicting. 5% of the patients with hydropic placentas classified as partial mole encountered persistent trophoblastic disease; however...
Class-specific Error Bounds for Ensemble Classifiers
Prenger, R; Lemmond, T; Varshney, K; Chen, B; Hanley, W
2009-10-06
The generalization error, or probability of misclassification, of ensemble classifiers has been shown to be bounded above by a function of the mean correlation between the constituent (i.e., base) classifiers and their average strength. This bound suggests that increasing the strength and/or decreasing the correlation of an ensemble's base classifiers may yield improved performance under the assumption of equal error costs. However, this and other existing bounds do not directly address application spaces in which error costs are inherently unequal. For applications involving binary classification, Receiver Operating Characteristic (ROC) curves, performance curves that explicitly trade off false alarms and missed detections, are often utilized to support decision making. To address performance optimization in this context, we have developed a lower bound for the entire ROC curve that can be expressed in terms of the class-specific strength and correlation of the base classifiers. We present empirical analyses demonstrating the efficacy of these bounds in predicting relative classifier performance. In addition, we specify performance regions of the ROC curve that are naturally delineated by the class-specific strengths of the base classifiers and show that each of these regions can be associated with a unique set of guidelines for performance optimization of binary classifiers within unequal error cost regimes.
Frog sound identification using extended k-nearest neighbor classifier
Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati
2017-09-01
Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.
Luiz Ernani Meira Jr.
2011-12-01
Full Text Available Os aneurismas da artéria radial são extremamente raros. Em sua maioria, consistem de pseudoaneurismas pós-traumáticos. Os aneurismas da artéria radial verdadeiros podem ser idiopáticos, congênitos, pós-estenóticos ou associados a patologias, tais como vasculites e doenças do tecido conjuntivo. Foi relatado um caso de aneurisma idiopático de artéria radial em uma criança de três anos, que, após completa investigação diagnóstica complementar, foi submetida à ressecção cirúrgica.Radial artery aneurysms are extremely rare. Post-traumatic pseudoaneurysms are the vast majority. True radial artery aneurysms can be idiopathic, congenital, poststenotic, or associated with some pathologies, such as vasculitis and conjunctive tissue diseases. We report a case of an idiopathic aneurysm of the radial artery in a three-year-old child who was submitted to surgical resection after a complete diagnostic approach.
Ship localization in Santa Barbara Channel using machine learning classifiers.
Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter
2017-11-01
Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.
Rehabilitation for distal radial fractures in adults.
Handoll, Helen H G; Elliott, Joanne
2015-09-25
Fracture of the distal radius is a common clinical problem, particularly in older people with osteoporosis. There is considerable variation in the management, including rehabilitation, of these fractures. This is an update of a Cochrane review first published in 2002 and last updated in 2006. To examine the effects of rehabilitation interventions in adults with conservatively or surgically treated distal radial fractures. We searched the Cochrane Bone, Joint and Muscle Trauma Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL 2014; Issue 12), MEDLINE, EMBASE, CINAHL, AMED, PEDro, OTseeker and other databases, trial registers, conference proceedings and reference lists of articles. We did not apply any language restrictions. The date of the last search was 12 January 2015. Randomised controlled trials (RCTs) or quasi-RCTs evaluating rehabilitation as part of the management of fractures of the distal radius sustained by adults. Rehabilitation interventions such as active and passive mobilisation exercises, and training for activities of daily living, could be used on their own or in combination, and be applied in various ways by various clinicians. The review authors independently screened and selected trials, and reviewed eligible trials. We contacted study authors for additional information. We did not pool data. We included 26 trials, involving 1269 mainly female and older patients. With few exceptions, these studies did not include people with serious fracture or treatment-related complications, or older people with comorbidities and poor overall function that would have precluded trial participation or required more intensive treatment. Only four of the 23 comparisons covered by these 26 trials were evaluated by more than one trial. Participants of 15 trials were initially treated conservatively, involving plaster cast immobilisation. Initial treatment was surgery (external fixation or internal fixation) for all participants
Compactly Supported Basis Functions as Support Vector Kernels for Classification.
Wittek, Peter; Tan, Chew Lim
2011-10-01
Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.
REFORMASI SISTEM AKUNTANSI CASH BASIS MENUJU SISTEM AKUNTANSI ACCRUAL BASIS
Yuri Rahayu
2016-03-01
Full Text Available Abstract – Accounting reform movement was born with the aim of structuring the direction of improvement . This movement is characterized by the enactment of the Act of 2003 and Act 1 of 2004, which became the basis of the birth of Government Regulation No.24 of 2005 on Government Accounting Standards ( SAP . The general, accounting is based on two systems, the cash basis and the accrual basis. The facts speak far students still at problem with differences to the two methods that result in a lack of understanding on the treatment system for recording. The purpose method of research is particularly relevant to student references who are learning basic accounting so that it can provide information and more meaningful understanding of the accounting method cash basis and Accrual basis. This research was conducted through a normative approach, by tracing the document that references a study/library that combines source of reference that can be believed either from books and the internet are processed with a foundation of knowledge and experience of the author. The conclusion can be drawn that basically to be able to understand the difference of the system and the Cash Basis accrual student base treatment requires an understanding of both methods. To be able to have the ability and understanding of both systems required reading exercises and reference sources. Keywords : Reform, cash basis, accrual basis Abstrak - Gerakan reformasi akuntansi dilahirkan dengan tujuan penataan ke arah perbaikan. Gerakan ini ditandai dengan dikeluarkannya Undang-Undang tahun 2003 dan Undang-Undang No.1 Tahun 2004 yang menjadi dasar lahirnya Peraturan Pemerintah No.24 Tahun 2005 tentang Standar Akuntansi Pemerintah (SAP . Pada umumnya pencatatan akuntansi di dasarkan pada dua sistem yaitu basis kas (Cash Basis dan basis akrual (Accrual Basis. Fakta berbicara Selama ini mahasiswa masih dibinggungkan dengan perbedaan ke dua metode itu sehingga
Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS
Sumintadireja, Prihadi; Irawan, Dasapta Erwin; Rezky, Yuanno; Gio, Prana Ugiana; Agustin, Anggita
2016-01-01
This file is the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja1, Dasapta Erwin Irawan1, Yuano Rezky2, Prana Ugiana Gio3, Anggita Agustin1
Robust Combining of Disparate Classifiers Through Order Statistics
Tumer, Kagan; Ghosh, Joydeep
2001-01-01
Integrating the outputs of multiple classifiers via combiners or meta-learners has led to substantial improvements in several difficult pattern recognition problems. In this article we investigate a family of combiners based on order statistics, for robust handling of situations where there are large discrepancies in performance of individual classifiers. Based on a mathematical modeling of how the decision boundaries are affected by order statistic combiners, we derive expressions for the reductions in error expected when simple output combination methods based on the the median, the maximum and in general, the ith order statistic, are used. Furthermore, we analyze the trim and spread combiners, both based on linear combinations of the ordered classifier outputs, and show that in the presence of uneven classifier performance, they often provide substantial gains over both linear and simple order statistics combiners. Experimental results on both real world data and standard public domain data sets corroborate these findings.
Using Statistical Process Control Methods to Classify Pilot Mental Workloads
Kudo, Terence
2001-01-01
.... These include cardiac, ocular, respiratory, and brain activity measures. The focus of this effort is to apply statistical process control methodology on different psychophysiological features in an attempt to classify pilot mental workload...
An ensemble classifier to predict track geometry degradation
Cárdenas-Gallo, Iván; Sarmiento, Carlos A.; Morales, Gilberto A.; Bolivar, Manuel A.; Akhavan-Tabatabaei, Raha
2017-01-01
Railway operations are inherently complex and source of several problems. In particular, track geometry defects are one of the leading causes of train accidents in the United States. This paper presents a solution approach which entails the construction of an ensemble classifier to forecast the degradation of track geometry. Our classifier is constructed by solving the problem from three different perspectives: deterioration, regression and classification. We considered a different model from each perspective and our results show that using an ensemble method improves the predictive performance. - Highlights: • We present an ensemble classifier to forecast the degradation of track geometry. • Our classifier considers three perspectives: deterioration, regression and classification. • We construct and test three models and our results show that using an ensemble method improves the predictive performance.
A novel statistical method for classifying habitat generalists and specialists
Chazdon, Robin L; Chao, Anne; Colwell, Robert K
2011-01-01
in second-growth (SG) and old-growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher......: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance...... fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest...
6 CFR 7.23 - Emergency release of classified information.
2010-01-01
... Classified Information Non-disclosure Form. In emergency situations requiring immediate verbal release of... information through approved communication channels by the most secure and expeditious method possible, or by...
Observational hints of radial migration in disc galaxies from CALIFA
Ruiz-Lara, T.; Pérez, I.; Florido, E.; Sánchez-Blázquez, P.; Méndez-Abreu, J.; Sánchez-Menguiano, L.; Sánchez, S. F.; Lyubenova, M.; Falcón-Barroso, J.; van de Ven, G.; Marino, R. A.; de Lorenzo-Cáceres, A.; Catalán-Torrecilla, C.; Costantin, L.; Bland-Hawthorn, J.; Galbany, L.; García-Benito, R.; Husemann, B.; Kehrig, C.; Márquez, I.; Mast, D.; Walcher, C. J.; Zibetti, S.; Ziegler, B.; Califa Team
2017-07-01
Context. According to numerical simulations, stars are not always kept at their birth galactocentric distances but they have a tendency to migrate. The importance of this radial migration in shaping galactic light distributions is still unclear. However, if radial migration is indeed important, galaxies with different surface brightness (SB) profiles must display differences in their stellar population properties. Aims: We investigate the role of radial migration in the light distribution and radial stellar content by comparing the inner colour, age, and metallicity gradients for galaxies with different SB profiles. We define these inner parts, avoiding the bulge and bar regions and up to around three disc scale lengths (type I, pure exponential) or the break radius (type II, downbending; type III, upbending). Methods: We analysed 214 spiral galaxies from the CALIFA survey covering different SB profiles. We made use of GASP2D and SDSS data to characterise the light distribution and obtain colour profiles of these spiral galaxies. The stellar age and metallicity profiles were computed using a methodology based on full-spectrum fitting techniques (pPXF, GANDALF, and STECKMAP) to the Integral Field Spectroscopic CALIFA data. Results: The distributions of the colour, stellar age, and stellar metallicity gradients in the inner parts for galaxies displaying different SB profiles are unalike as suggested by Kolmogorov-Smirnov and Anderson-Darling tests. We find a trend in which type II galaxies show the steepest profiles of all, type III show the shallowest, and type I display an intermediate behaviour. Conclusions: These results are consistent with a scenario in which radial migration is more efficient for type III galaxies than for type I systems, where type II galaxies present the lowest radial migration efficiency. In such a scenario, radial migration mixes the stellar content, thereby flattening the radial stellar properties and shaping different SB profiles. However
Dores, C B; Milovancev, M; Russell, D S
2018-03-01
Radial sections are widely used to estimate adequacy of excision in canine cutaneous mast cell tumours (MCTs); however, this sectioning technique estimates only a small fraction of total margin circumference. This study aimed to compare histologic margin status in grade II/low grade MCTs sectioned using both radial and tangential sectioning techniques. A total of 43 circumferential margins were evaluated from 21 different tumours. Margins were first sectioned radially, followed by tangential sections. Tissues were examined by routine histopathology. Tangential margin status differed in 10 of 43 (23.3%) margins compared with their initial status on radial section. Of 39 margins, 9 (23.1%) categorized as histologic tumour-free margin (HTFM) >0 mm were positive on tangential sectioning. Tangential sections detected a significantly higher proportion of positive margins relative to radial sections (exact 2-tailed P-value = .0215). The HTFM was significantly longer in negative tangential margins than positive tangential margins (mean 10.1 vs 3.2 mm; P = .0008). A receiver operating characteristic curve comparing HTFM and tangentially negative margins found an area under the curve of 0.83 (95% confidence interval: 0.71-0.96). Although correct classification peaked at the sixth cut-point of HTFM ≥1 mm, radial sections still incorrectly classified 50% of margins as lacking tumour cells. Radial sections had 100% specificity for predicting negative tangential margins at a cut-point of 10.9 mm. These data indicate that for low grade MCTs, HTFMs >0 mm should not be considered completely excised, particularly when HTFM is <10.9 mm. This will inform future studies that use HTFM and overall excisional status as dependent variables in multivariable prognostic models. © 2017 John Wiley & Sons Ltd.
DECISION TREE CLASSIFIERS FOR STAR/GALAXY SEPARATION
Vasconcellos, E. C.; Ruiz, R. S. R.; De Carvalho, R. R.; Capelato, H. V.; Gal, R. R.; LaBarbera, F. L.; Frago Campos Velho, H.; Trevisan, M.
2011-01-01
We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS-DR7). Each algorithm is defined by a set of parameters which, when varied, produce different final classification trees. We extensively explore the parameter space of each algorithm, using the set of 884,126 SDSS objects with spectroscopic data as the training set. The efficiency of star-galaxy separation is measured using the completeness function. We find that the Functional Tree algorithm (FT) yields the best results as measured by the mean completeness in two magnitude intervals: 14 ≤ r ≤ 21 (85.2%) and r ≥ 19 (82.1%). We compare the performance of the tree generated with the optimal FT configuration to the classifications provided by the SDSS parametric classifier, 2DPHOT, and Ball et al. We find that our FT classifier is comparable to or better in completeness over the full magnitude range 15 ≤ r ≤ 21, with much lower contamination than all but the Ball et al. classifier. At the faintest magnitudes (r > 19), our classifier is the only one that maintains high completeness (>80%) while simultaneously achieving low contamination (∼2.5%). We also examine the SDSS parametric classifier (psfMag - modelMag) to see if the dividing line between stars and galaxies can be adjusted to improve the classifier. We find that currently stars in close pairs are often misclassified as galaxies, and suggest a new cut to improve the classifier. Finally, we apply our FT classifier to separate stars from galaxies in the full set of 69,545,326 SDSS photometric objects in the magnitude range 14 ≤ r ≤ 21.