On the classification enhancement of radial basis function networks
Ciftcioglu, O.; Durmisevic, S.; Sariyildiz, I.S.
2001-01-01
Artificial neural networks are powerfultools for analysing information expressed as data sets, which contain complex nonlinear relationships to be identified and classified. In particular radial basis function (RBF) neural networks have outstanding features for this. However, due to far reaching
Application of radial basis neural network for state estimation of ...
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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 ...
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Radial basis function neural network in fault detection of automotive engines. Adnan Hamad, Dingli Yu, JB Gomm, Mahavir S Sangha. Abstract. Fault detection and isolation have become one of the most important aspects of automobile design. A fault detection (FD) scheme is developed for automotive engines in this paper.
Organisms modeling: The question of radial basis function networks
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Muzy Alexandre
2014-01-01
Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
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Agus Arif
2014-11-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.
Reformulated radial basis neural networks trained by gradient descent.
Karayiannis, N B
1999-01-01
This paper presents an axiomatic approach for constructing radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, including those employing Gaussian RBF's. The form of the RBF's is determined by a generator function. New RBF models can be developed according to the proposed approach by selecting generator functions other than exponential ones, which lead to Gaussian RBF's. This paper also proposes a supervised learning algorithm based on gradient descent for training reformulated RBF neural networks constructed using the proposed approach. A sensitivity analysis of the proposed algorithm relates the properties of RBF's with the convergence of gradient descent learning. Experiments involving a variety of reformulated RBF networks generated by linear and exponential generator functions indicate that gradient descent learning is simple, easily implementable, and produces RBF networks that perform considerably better than conventional RBF models trained by existing algorithms.
Efficient VLSI Architecture for Training Radial Basis Function Networks
Fan, Zhe-Cheng; Hwang, Wen-Jyi
2013-01-01
This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired. PMID:23519346
Radial Basis Function Networks for Conversion of Sound Spectra
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Carlo Drioli
2001-03-01
Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
Neuronal spike sorting based on radial basis function neural networks
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Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Dynamics of learning near singularities in radial basis function networks.
Wei, Haikun; Amari, Shun-Ichi
2008-09-01
The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation.
SINTESA EKSPRESI WAJAH DENGAN MENGGUNAKAN RADIAL BASIS FUNCTION NETWORK
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Wiwik Anggraeni
2003-07-01
Full Text Available Normal 0 false false false IN X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} Pada penelitian yang sebelumnya [4] telah dilakukan penelitian tentang letak-letak (koordinat facial characteristic points (FCP yang digunakan sebagai dasar untuk mengenali ekspresi-ekspresi wajah manusia. Diantaranya ada enam ekspresi wajah, yaitu gembira, sedih, marah, takut, terkejut, dan jijik yang digunakan dalam penelitian tersebut. Dengan adanya dasar tersebut, maka dalam penelitian ini penulis berusaha mensintesa ekspresi wajah yang dikategorikan menjadikan enam ekspresi dengan menggunakan facial characteristic points tersebut. Prinsip dasar dari mensintesa ekspresi wajah adalah mencari pemindahan spasial relatif facial characteristic points pada setiap ekspresi. Permasalahan utamanya adalah bagaimana menghasilkan wajah dengan ekspresi tertentu dari sebuah citra input wajah tanpa ekspresi. Dengan menggunakan 30 pasang perpindahan titik FCP, dilakukan training terhadap Radial Basis Function Network (RBFN 6 x n x 60 (6 input yang merupakan kadar dari keenam ekspresi, n hidden unit, dan 60 output yang merupakan 30 pasang perpindahan FCP, dimana n adalah variabel. RBFN yang telah ditraining dapat menghasilkan perpindahan FCP sesudai dengan ekspresi yang diinputkan. Informasi pemindahan FCP ini kemudian
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
Noise Reduction Technique for Images using Radial Basis Function Neural Networks
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Sander Ali Khowaja
2014-07-01
Full Text Available This paper presents a NN (Neural Network based model for reducing the noise from images. This is a RBF (Radial Basis Function network which is used to reduce the effect of noise and blurring from the captured images. The proposed network calculates the mean MSE (Mean Square Error and PSNR (Peak Signal to Noise Ratio of the noisy images. The proposed network has also been successfully applied to medical images. The performance of the trained RBF network has been compared with the MLP (Multilayer Perceptron Network and it has been demonstrated that the performance of the RBF network is better than the MLP network.
Application of radial basis neural network for state estimation of ...
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states, and j e is the measurement error, which is assumed to have zero mean and variance 2 j σ . There are m ... and with equality and inequality constraints, minimize. ∑. = −. = m ... constant factor unlike sum of product of the inputs and respective synaptic weights as in case of feed forward network. The RBF unit or transfer ...
Color image demosaicing using sparse based radial basis function network
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V.N.V. Satya Prakash
2017-12-01
Full Text Available Images contain three primary colors at each pixel, but single sensor digital cameras capture only one of the primary channels. Process of color image reconstruction by finding the missing color component is called color image demosaicing. Various approaches have been proposed in this field of image demosaicing such as interpolation based and frequency based approaches due to sharp image edge and higher color saturation, and these techniques fail to reconstruct image efficiently. To overcome this, in this work we propose a new approach, sparse based RBF network for color image demosaicing. According to this approach a sparse model is constructed first and based on that weights are computed which are used to minimize the reconstruction error. To improve this we use optimal weight computation and RBF training for missing color component value prediction. Proposed method is implemented using MATLAB tool and experimental results show the efficiency of the proposed work in terms of color peak signal to noise ratio (CPSNR. Simulation results show 16.20% improvement in the performance in terms of CPSNR. Keywords: Demosaicing, Bayer pattern, CPSNR, RBF network
Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks
Kamruzzaman, S. M.; Siddiqi, Firoz Ahmed; Islam, Md. Saiful; Haque, Md. Emdadul; Alam, Mohammad Shamsul
2010-01-01
This paper introduces a novel method for human face detection with its orientation by using wavelet, principle component analysis (PCA) and redial basis networks. The input image is analyzed by two-dimensional wavelet and a two-dimensional stationary wavelet. The common goals concern are the image clearance and simplification, which are parts of de-noising or compression. We applied an effective procedure to reduce the dimension of the input vectors using PCA. Radial Basis Function (RBF) neur...
Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network
Gómez-Mayorga, Margarita E.; Gallegos-Funes, Francisco J.; de-La-Rosa-Vázquez, José M.; Cruz-Santiago, Rene; Ponomaryov, Volodymyr
The automatic analysis of Pap smear microscopic images is one of the most interesting fields in biomedical image processing. In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) neural network in the classification of cervical cancer cells. From simulation results we observe that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.
A prediction method for the wax deposition rate based on a radial basis function neural network
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Ying Xie
2017-06-01
Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.
Wang, Pengbo
2017-11-01
In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.
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
Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
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Wei Liu
2013-01-01
Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.
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...
Computing single step operators of logic programming in radial basis function neural networks
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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.
Radial basis function networks with linear interval regression weights for symbolic interval data.
Su, Shun-Feng; Chuang, Chen-Chia; Tao, C W; Jeng, Jin-Tsong; Hsiao, Chih-Ching
2012-02-01
This paper introduces a new structure of radial basis function networks (RBFNs) that can successfully model symbolic interval-valued data. In the proposed structure, to handle symbolic interval data, the Gaussian functions required in the RBFNs are modified to consider interval distance measure, and the synaptic weights of the RBFNs are replaced by linear interval regression weights. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the center and range of the interval-valued data are considered. In addition, in the proposed approach, two stages of learning mechanisms are proposed. In stage 1, an initial structure (i.e., the number of hidden nodes and the adjustable parameters of radial basis functions) of the proposed structure is obtained by the interval competitive agglomeration clustering algorithm. In stage 2, a gradient-descent kind of learning algorithm is applied to fine-tune the parameters of the radial basis function and the coefficients of the linear interval regression weights. Various experiments are conducted, and the average behavior of the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment are considered as the performance index. The results clearly show the effectiveness of the proposed structure.
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.
Parallel fixed point implementation of a radial basis function network in an FPGA.
de Souza, Alisson C D; Fernandes, Marcelo A C
2014-09-29
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.
Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA
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Alisson C. D. de Souza
2014-09-01
Full Text Available This paper proposes a parallel fixed point radial basis function (RBF artificial neural network (ANN, implemented in a field programmable gate array (FPGA trained online with a least mean square (LMS algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx, with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.
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Dongliang Guo
2014-01-01
Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.
Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong
2009-01-01
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.
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.
Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A
2008-12-01
It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.
Circular antenna array pattern analysis using radial basis function neural network
Rama Sanjeeva Reddy, B.; Vakula, D.; Sarma, N. V. S. N.
2013-04-01
A method is proposed to design circular antenna array for the given gain and beam width using Artificial Neural Networks. In optimizing circular arrays, the parameters to be controlled are excitation of the elements, their separation, lengths and the circle radius. This paper deals about finding the parameters of radiation pattern of given uniform circular antenna array. Initially, the network is trained with a set of input-output data pairs. The trained network is used for testing. The training data set is generated from MATLAB simulation with number of elements N=5, 10, 15 and 20 elements of uniform circular array, respectively, distributed over a given circle, assuming 20 training cases. The number of input nodes, hidden nodes and output nodes are 20, 20 and 1, respectively. Predicted values of the neural network are compared with those of MATLAB simulation results and are found to be in agreement. This work establishes the application of Radial Basis Function Neural Network (RBFNN) for circular array pattern optimization. RBFNN is able to predict the output values with 97% of accuracy. This work proves that RBFNN can be used for circular antenna array design.
Assessment of Global Voltage Stability Margin through Radial Basis Function Neural Network
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Akash Saxena
2016-01-01
Full Text Available Dynamic operating conditions along with contingencies often present formidable challenges to the power engineers. Decisions pertaining to the control strategies taken by the system operators at energy management centre are based on the information about the system’s behavior. The application of ANN as a tool for voltage stability assessment is empirical because of its ability to do parallel data processing with high accuracy, fast response, and capability to model dynamic, nonlinear, and noisy data. This paper presents an effective methodology based on Radial Basis Function Neural Network (RBFN to predict Global Voltage Stability Margin (GVSM, for any unseen loading condition of the system. GVSM is used to assess the overall voltage stability status of the power system. A comparative analysis of different topologies of ANN, namely, Feedforward Backprop (FFBP, Cascade Forward Backprop (CFB, Generalized Regression (GR, Layer Recurrent (LR, Nonlinear Autoregressive Exogenous (NARX, ELMAN Backprop, and Feedforward Distributed Time Delay Network (FFDTDN, is carried out on the basis of capability of the prediction of GVSM. The efficacy of RBFN is better than other networks, which is validated by taking the predictions of GVSM at different levels of Additive White Gaussian Noise (AWGN in input features. The results obtained from ANNs are validated through the offline Newton Raphson (N-R method. The proposed methodology is tested over IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems.
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.
Forecasting of PT. PLN (Persero) revenue using radial basis function neural network
Junior, Cindy Fajarianti; Suprijadi, Jadi; Franty, Yeny Krista
2017-03-01
PT. PLN (Persero) Distribusi Jakarta Raya (Disjaya) is a government-owned company that job is to maintain electricity distribution in Jakarta and Tangerang. The company's revenue can be seen from the pattern of the existing data, constantly increasing every year. This research aims to forecast company's revenue. The forecasting method using Artificial Neural Network method with Radial Basis Function (RBF) model based on historic data from January 2010 to December 2015. Based on the result of this research, the best model obtained (1-6-1) with composition 1 Neuron from input layer, 6 Neuron from hidden layer, and 1 Neuron output layer. The MAPE obtained with this model is 1.32 %.
Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman
2017-02-01
The soil sorption partition coefficient logKoc 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 logKoc 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 logKoc 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.
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.
Energy Technology Data Exchange (ETDEWEB)
Hancock, M.F. Jr. [Rollins College, Winter Park, FL (United States)
1995-12-31
The National Council on Compensation Insurance (NCCI) maintains a national data base of outcomes of workers` compensation claims. We consider whether a radial basis function network can predict the total dollar value of a claim based upon medical and demographic indicators (MDI`s). This work used data from 12,130 workers` compensation claims collected over a period of four years from the state of New Mexico. Two problems were addressed: (1) How well can the total incurred medical expense for all claims be predicted from available MDI`s? For individual claims? (2) How well can the duration of disability be predicted from available MDI`s? The available features intuitively correlated with total medical cost were selected, including type of injury, part of body injured, person`s age at time of injury, gender, marital status, etc. These features were statistically standardized and sorted by correlation with outcome valuation. Principal component analysis was applied. A radial basis function neural network was applied to the feature sets in both supervised and unsupervised training modes. For sets used in training, individual case valuations could consistently be predicted to within $1000 over 98% of the time. For these sets, it was possible to predict total medical expense for the training sets themselves to within 10%. When applied as blind tests against sets which were NOT part of the training data, the prediction was within 15% on the whole sets. Results on individual cases were very poor in only 30% of the cases were the predictions for the training sets within $1000 of their actual valuations. Single-factor analysis suggested that the presence of an attorney strongly decorrelated the data. A simple stratification was performed to remove cases involving attorneys and contested claims, and the procedures above repeated. Preliminary results based upon the very limited effort applied indicate that NCCI data support population estimates, but not single-point estimates.
Stateczny, A.; Lubczonek, J.
2003-04-01
The basic problem in the construction of a numerical spatial sea chart is such transformation of the sounding data that it should be possible to determine the depth at any point of the bottom area. In recent years, much attention has been devoted to the problem of modelling the seabed shape in a numerical three-dimensional sea chart. Various methods for modelling the seabed shape are applied. These methods can be divided into analytical and neural. In the case of applying the model for navigational tasks, the selection of a suitable method should ensure high accuracy of surface projection. The model should be conformed to the surface shape, spatial distribution of the measurement points and their number. The application of universal methods like 'multiquadric' or 'kriging' does not ensure an optimal result either, as each of these methods can have a certain number of options and parameters, which frequently play a significant role during surface modelling. It is often difficult to optimise these factors and even experience does not guarantee a satisfactory result. This applies especially to modelling irregular surfaces, when it is difficult to select the method suitable for the surface shape that is sometimes unpredictable. It has been suggested that the method of selecting the shape parameter of the radial basis functions should be applied which makes it possible to minimise the mean square error of the approximated surface. The paper presents a new method of optimising the parameters of radial functions used for modelling the bottom surface. The accuracy of the surface projection obtained was the criterion for optimisation. The properties of self-organizing networks created the possibility of selecting testing points out of any set of measurement points and the determination of the minimum value of RMS error by means of the GRNN network. Optimisation of the shape parameter required building the proper polygon of the test points. For building such kind of polygon
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-09-18
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.
Directory of Open Access Journals (Sweden)
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
Yan, Qin; Zhong, Yanfei
2008-12-01
The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre
Near and long-term load prediction using radial basis function networks
Energy Technology Data Exchange (ETDEWEB)
Hancock, M.F. [Rollins College, Winter Park, FL (United States)
1995-12-31
A number of researchers have investigated the application of multi-layer perceptrons (MLP`s), a variety of neural network, to the problem of short-term load forecasting for electric utilities (e.g., Rahman & Hazin, IEEE Trans. Power Systems, May 1993). {open_quotes}Short-term{close_quotes} in this context typically means {open_quotes}next day{close_quotes}. These forecasts have been based upon previous day actual loads and meteorological factors (e.g., max-min temperature, relative humidity). We describe the application of radial basis function networks (RBF`s) to the {open_quotes}long-term{close_quotes} (next year) load forecasting problem. The RBF network performs a two-stage classification based upon annual average loads and meteorological data. During stage 1, discrete classification is performed using radius-limited elements. During stage 2, a multi-layer perceptron may be applied. The quantized output is used to correct a prediction template. The stage 1 classifier is trained by maximizing an objective function (the {open_quotes}disambiguity{close_quotes}). The stage 2 MLP`s are trained by standard back-propagation. This work uses 12 months of hourly meteorological data, and the corresponding hourly load data for both commercial and residential feeders. At the current stage of development, the RBF machine can train on 20% of the weather/load data (selected by simple linear sampling), and estimate the hourly load for an entire year (8,760 data points) with 9.1% error (RMS, relative to daily peak load). (By comparison, monthly mean profiles perform at c. 12% error.) The best short-term load forecasters operate in the 2% error range. The current system is an engineering prototype, and development is continuing.
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…
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction
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P. Kumudha
2016-01-01
Full Text Available 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.
Fast and efficient second-order method for training radial basis function networks.
Xie, Tiantian; Yu, Hao; Hewlett, Joel; Rózycki, Paweł; Wilamowski, Bogdan
2012-04-01
This paper proposes an improved second order (ISO) algorithm for training radial basis function (RBF) networks. Besides the traditional parameters, including centers, widths and output weights, the input weights on the connections between input layer and hidden layer are also adjusted during the training process. More accurate results can be obtained by increasing variable dimensions. Initial centers are chosen from training patterns and other parameters are generated randomly in limited range. Taking the advantages of fast convergence and powerful search ability of second order algorithms, the proposed ISO algorithm can normally reach smaller training/testing error with much less number of RBF units. During the computation process, quasi Hessian matrix and gradient vector are accumulated as the sum of related sub matrices and vectors, respectively. Only one Jacobian row is stored and used for multiplication, instead of the entire Jacobian matrix storage and multiplication. Memory reduction benefits the computation speed and allows the training of problems with basically unlimited number of patterns. Several practical discrete and continuous classification problems are applied to test the properties of the proposed ISO training algorithm.
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.
Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold
2015-09-01
In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.
Jingwen Tian; Meijuan Gao; Yonggang He
2013-01-01
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...
Radial Basis Neural Networks Based Fault Detection and Isolation Scheme for Pneumatic Actuator
Prabakaran, K.; S, Kaushik; R, Mouleeshuwarapprabu
2014-01-01
Fault diagnosis is an ongoing significant research field due to the constantly increasing need for maintainability, reliability and safety of industrial plants. The pneumatic actuators are installed in harsh environment: high temperature, pressure, aggressive media and vibration, etc. This influenced the pneumatic actuator predicted life time. The failures in pneumatic actuator cause forces the installation shut down and may also determine the final quality of the product. A Radial Basis Neur...
Qiao, Jun-Fei; Han, Hong-Gui
2010-02-01
This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the network's output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2005-01-01
. 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......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...
Directory of Open Access Journals (Sweden)
Peng Zhang
Full Text Available Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP. Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.
Zhang, Peng; Liu, Keping; Zhao, Bo; Li, Yuanchun
2015-01-01
Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN) is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP). Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.
Zhou, Miaolei; Wang, Yifan; Xu, Rui; Zhang, Qi; Zhu, Dong
2017-06-16
Hysteresis exists in magnetic shape memory alloy (MSMA) actuators, which restricts MSMA actuators' application. To describe hysteresis of the MSMA actuators, a hysteresis model based on the radial basis function neural network (RBFNN) is put forward. Then, an inverse RBFNN model is set up, and it is compared with the inverse model based on the traditional cut-and-try method. Finally, to solve hysteresis of the actuators, an inverse model for MSMA actuators is used to build feed-forward controller. Simulation results show the maximum modeling error for inverse hysteresis model designed by neural network is 0.79% and compared with traditional cut-and-try method, the maximum modeling error decreases by 1.85%. The maximum tracking error rate of feed-forward control is 0.38%. The hysteresis of MSMA actuators is reduced. By using the feed-forward controller, high precision control is achieved.
Sbarufatti, Claudio; Corbetta, Matteo; Giglio, Marco; Cadini, Francesco
2017-03-01
Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.
Directory of Open Access Journals (Sweden)
M.R. Mosavi
2016-01-01
Full Text Available This paper presents a new method to estimate the time of important earthquakes in Hormozgan region with magnitude greater than 5.5 based on the Radial Basis Function (RBF Neural Network (NN models. Input vector to the network is composed of different seismicity rates between main events that are calculated in convenient and reliable way to create optimized training methods. It helps network with a limited number of training data to estimation. It is common for earthquakes modeling by data-driven methods in this case. In addition, the proposed method is combined with Rosenberg cluster method to remove aftershocks events from the history of catalog for NN to better process the data. The results show that created RBF model successfully estimates the interevent times between large and sequence earthquakes that can be used as a tool to predict earthquake, so that comparison with other NN structure, for example Multi-Layer Perceptron (MLP NN, reveals the superiority of the proposed method. Because of superiority proposed method has higher accuracy, lower costs and simpler network structure.
Meghabghab, George
2001-01-01
Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…
Directory of Open Access Journals (Sweden)
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.
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.
Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.
2014-01-01
This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.
Directory of Open Access Journals (Sweden)
Deliang Yu
2017-01-01
Full Text Available This paper presents a new method to diagnose oil well pump faults using a modified radial basis function neural network. With the development of submersible linear motor technology, rodless pumping units have been widely used in oil exploration. However, the ground indicator diagram method cannot be used to diagnose the working conditions of rodless pumping units because it is based on the load change of the polished rod suspension point and its displacement. To solve this problem, this paper presents a new method that is applicable to rodless oil pumps. The advantage of this new method is its use of a simple feature extraction method and advanced genetic algorithm to optimize the threshold and weight of the RBF neural network. In this paper, we extract the characteristic value from the operation parameters of the submersible linear motor and oil wellhead as the input vector of the fault diagnosis model. Through experimental analysis, the proposed method is proven to have good convergence performance, high accuracy, and high reliability.
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.
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M. Yildirim
2016-12-01
Full Text Available This paper presents design and speed estimation for an Electric Vehicle (EV with four in-wheel motors using Radial Basis Neural Network (RBNN. According to the steering angle and the speed of EV, the speeds of all wheels are calculated by equations derived from the Ackermann-Jeantand model using CoDeSys Software Package. The Electronic Differential System (EDS is also simulated by Matlab/Simulink using the mathematical equations. RBNN is used for the estimation of the wheel speeds based on the steering angle and EV speed. Further, different levels of noise are added to the steering angle and the EV speed. The speeds of front wheels calculated by CoDeSys are sent to two Induction Motor (IM drives via a Controller Area Network-Bus (CAN-Bus. These speed values are measured experimentally by a tachometer changing the steering angle and EV speed. RBNN results are verified by CoDeSys, Simulink, and experimental results. As a result, it is observed that RBNN is a good estimator for EDS of an EV with in-wheel motor due to its robustness to different levels of sensor noise.
Directory of Open Access Journals (Sweden)
Jingwen Tian
2013-02-01
Full Text Available Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro-mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real-time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.
Ayala, Helon Vicente Hultmann; Coelho, Leandro dos Santos
2016-02-01
The present work introduces a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models with an improved objective function based on the residuals and its correlation function coefficients. We show the results when the proposed methodology is applied to model a magnetorheological damper, with real acquired data, and other two well-known benchmarks. The canonical genetic and differential evolution algorithms are used in cascade to decompose the problem of defining the lags taken as the inputs of the model and its related parameters based on the simultaneous minimization of the residuals and higher orders correlation functions. The inner layer of the cascaded approach is composed of a population which represents the lags on the inputs and outputs of the system and an outer layer represents the corresponding parameters of the RBFNN. The approach is able to define both the inputs of the model and its parameters. This is interesting as it frees the designer of manual procedures, which are time consuming and prone to error, usually done to define the model inputs. We compare the proposed methodology with other works found in the literature, showing overall better results for the cascaded approach.
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.
Belderrar, Ahmed; Hazzab, Abdeldjebar
2017-07-01
Controlling hospital high length of stay outliers can provide significant benefits to hospital management resources and lead to cost reduction. The strongest predictive factors influencing high length of stay outliers should be identified to build a high-performance prediction model for hospital outliers. We highlight the application of the hierarchical genetic algorithm to provide the main predictive factors and to define the optimal structure of the prediction model fuzzy radial basis function neural network. To establish the prediction model, we used a data set of 26,897 admissions from five different intensive care units with discharges between 2001 and 2012. We selected and analyzed the high length of stay outliers using the trimming method geometric mean plus two standard deviations. A total of 28 predictive factors were extracted from the collected data set and investigated. High length of stay outliers comprised 5.07% of the collected data set. The results indicate that the prediction model can provide effective forecasting. We found 10 common predictive factors within the studied intensive care units. The obtained main predictive factors include patient demographic characteristics, hospital characteristics, medical events, and comorbidities. The main initial predictive factors available at the time of admission are useful in evaluating high length of stay outliers. The proposed approach can provide a practical tool for healthcare providers, and its application can be extended to other hospital predictions, such as readmissions and cost.
D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel
2016-03-01
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
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G. S. Fesghandis
2017-02-01
Full Text Available Given that the new product failure in practice entails huge costs for organizations, the need for competitive planning has led organizations to apply appropriate approaches; one of these approaches is to predict new product success before market entry. Accordingly, this study predicts NPD success by comparing two techniques, the Multilayer Perceptron (MLP and the Radial Basis Function (RBF in the clothing industry of Tabriz. In order to collect data, a questionnaire with good validity and reliability was distributed among the population. MLP and RBF were used to analyze data. Based on MSE, RMSE and R2, data analysis showed that MLP had lower error than RBF in predicting NPD success.
Chen, Hai-Feng
2009-08-01
Oil/water partition coefficient (log P) is one of the key points for lead compound to be drug. In silico log P models based solely on chemical structures have become an important part of modern drug discovery. Here, we report support vector machines, radial basis function neural networks, and multiple linear regression methods to investigate the correlation between partition coefficient and physico-chemical descriptors for a large data set of compounds. The correlation coefficient r(2) between experimental and predicted log P for training and test sets by support vector machines, radial basis function neural networks, and multiple linear regression is 0.92, 0.90, and 0.88, respectively. The results show that non-linear support vector machines derives statistical models that have better prediction ability than those of radial basis function neural networks and multiple linear regression methods. This indicates that support vector machines can be used as an alternative modeling tool for quantitative structure-property/activity relationships studies.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature. Copyright © 2014 Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Behloul, F.; Boudraa, A.; Janier, M.; Unterreiner, R. [CREATIS, Centre de Recherche et d`Applications en Traitement de l`Image et du Signal, Institut National des Sciences Appliquees de Lyon, CNRS UMR 5515, 69 - Villeurbanne (France)
1998-12-31
A self-organized Radial Basis Function Network (RBFN) is proposed for the problem of object extraction in Positron Emission Tomography Images of the heart. RBENs are supervised-learning networks. However, viewing the output of the networks as a fuzzy set, we have able to compute the error of the system using fuzziness measures. Thus, there is no need of target output for training the network. Besides the self-organizing feature of the network, our RBFN has a non linear output layer trained using the back-propagation algorithm. Two mathematical models of fuzzy measures have been considered: the index of fuzziness and fuzzy entropy. Preliminary results show that entropy measure produced a better extraction of healthy myocardium. (authors) 9 refs.
Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar
2017-12-01
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.
Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M
2016-01-01
This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.
Five Lectures on Radial Basis Functions
DEFF Research Database (Denmark)
Powell, Mike J.D.
2005-01-01
Professor Mike J. D. Powell spent three weeks at IMM in November - December 2004. During the visit he gave five lectures on radial basis functions. These notes are a TeXified version of his hand-outs, made by Hans Bruun Nielsen, IMM....
Directory of Open Access Journals (Sweden)
Mohammad Heidari
2014-01-01
Full Text Available The static pull-in instability of beam-type microelectromechanical systems (MEMS is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.
Heidari, Mohammad; Heidari, Ali; Homaei, Hadi
2014-01-01
The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.
Srinivas, Kadivendi; Vundavilli, Pandu R.; Manzoor Hussain, M.; Saiteja, M.
2016-09-01
Welding input parameters such as current, gas flow rate and torch angle play a significant role in determination of qualitative mechanical properties of weld joint. Traditionally, it is necessary to determine the weld input parameters for every new welded product to obtain a quality weld joint which is time consuming. In the present work, the effect of plasma arc welding parameters on mild steel was studied using a neural network approach. To obtain a response equation that governs the input-output relationships, conventional regression analysis was also performed. The experimental data was constructed based on Taguchi design and the training data required for neural networks were randomly generated, by varying the input variables within their respective ranges. The responses were calculated for each combination of input variables by using the response equations obtained through the conventional regression analysis. The performances in Levenberg-Marquardt back propagation neural network and radial basis neural network (RBNN) were compared on various randomly generated test cases, which are different from the training cases. From the results, it is interesting to note that for the above said test cases RBNN analysis gave improved training results compared to that of feed forward back propagation neural network analysis. Also, RBNN analysis proved a pattern of increasing performance as the data points moved away from the initial input values.
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Shiva Kumar
2012-01-01
Full Text Available Radial basis function neural networks (RBFNNs, which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO. The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE, brake specific energy consumption (BSEC, exhaust gas temperature (, and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters.
Panagou, Efstathios Z
2008-04-01
A radial basis function neural network was developed to determine the kinetic behavior of Listeria monocytogenes in Katiki, a traditional white acid-curd soft spreadable cheese. The applicability of the neural network approach was compared with the reparameterized Gompertz, the modified Weibull, and the Geeraerd primary models. Model performance was assessed with the root mean square error of the residuals of the model (RMSE), the regression coefficient (R2), and the F test. Commercially prepared cheese samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 10(6) CFU g(-1) and stored at 5, 10, 15, and 20 degrees C for 40 days. At each storage temperature, a pathogen viability loss profile was evident and included a shoulder, a log-linear phase, and a tailing phase. The developed neural network described the survival of L. monocytogenes equally well or slightly better than did the three primary models. The performance indices for the training subset of the network were R2 = 0.993 and RMSE = 0.214. The relevant mean values for all storage temperatures were R2 = 0.981, 0.986, and 0.985 and RMSE = 0.344, 0.256, and 0.262 for the reparameterized Gompertz, modified Weibull, and Geeraerd models, respectively. The results of the F test indicated that none of the primary models were able to describe accurately the survival of the pathogen at 5 degrees C, whereas with the neural network all fvalues were significant. The neural network and primary models all were validated under constant temperature storage conditions (12 and 17 degrees C). First or second order polynomial models were used to relate the inactivation parameters to temperature, whereas the neural network was used a one-step modeling approach. Comparison of the prediction capability was based on bias and accuracy factors and on the goodness-of-fit index. The prediction performance of the neural network approach was equal to that of the primary
Tang, Li-Juan; Zhou, Yan-Ping; Jiang, Jian-Hui; Zou, Hong-Yan; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin
2007-01-01
The support vector machine (SVM) has been receiving increasing interest in an area of QSAR study for its ability in function approximation and remarkable generalization performance. However, selection of support vectors and intensive optimization of kernel width of a nonlinear SVM are inclined to get trapped into local optima, leading to an increased risk of underfitting or overfitting. To overcome these problems, a new nonlinear SVM algorithm is proposed using adaptive kernel transform based on a radial basis function network (RBFN) as optimized by particle swarm optimization (PSO). The new algorithm incorporates a nonlinear transform of the original variables to feature space via a RBFN with one input and one hidden layer. Such a transform intrinsically yields a kernel transform of the original variables. A synergetic optimization of all parameters including kernel centers and kernel widths as well as SVM model coefficients using PSO enables the determination of a flexible kernel transform according to the performance of the total model. The implementation of PSO demonstrates a relatively high efficiency in convergence to a desired optimum. Applications of the proposed algorithm to QSAR studies of binding affinity of HIV-1 reverse transcriptase inhibitors and activity of 1-phenylbenzimidazoles reveal that the new algorithm provides superior performance to the backpropagation neural network and a conventional nonlinear SVM, indicating that this algorithm holds great promise in nonlinear SVM learning.
Wang, L. M.
2017-09-01
A novel model-free adaptive sliding mode strategy is proposed for a generalized projective synchronization (GPS) between two entirely unknown fractional-order chaotic systems subject to the external disturbances. To solve the difficulties from the little knowledge about the master-slave system and to overcome the bad effects of the external disturbances on the generalized projective synchronization, the radial basis function neural networks are used to approach the packaged unknown master system and the packaged unknown slave system (including the external disturbances). Consequently, based on the slide mode technology and the neural network theory, a model-free adaptive sliding mode controller is designed to guarantee asymptotic stability of the generalized projective synchronization error. The main contribution of this paper is that a control strategy is provided for the generalized projective synchronization between two entirely unknown fractional-order chaotic systems subject to the unknown external disturbances, and the proposed control strategy only requires that the master system has the same fractional orders as the slave system. Moreover, the proposed method allows us to achieve all kinds of generalized projective chaos synchronizations by turning the user-defined parameters onto the desired values. Simulation results show the effectiveness of the proposed method and the robustness of the controlled system.
Mirbagheri, Seyed Ahmad; Bagheri, Majid; Boudaghpour, Siamak; Ehteshami, Majid; Bagheri, Zahra
2015-01-01
Treatment process models are efficient tools to assure proper operation and better control of wastewater treatment systems. The current research was an effort to evaluate performance of a submerged membrane bioreactor (SMBR) treating combined municipal and industrial wastewater and to simulate effluent quality parameters of the SMBR using a radial basis function artificial neural network (RBFANN). The results showed that the treatment efficiencies increase and hydraulic retention time (HRT) decreases for combined wastewater compared with municipal and industrial wastewaters. The BOD, COD, [Formula: see text] and total phosphorous (TP) removal efficiencies for combined wastewater at HRT of 7 hours were 96.9%, 96%, 96.7% and 92%, respectively. As desirable criteria for treating wastewater, the TBOD/TP ratio increased, the BOD and COD concentrations decreased to 700 and 1000 mg/L, respectively and the BOD/COD ratio was about 0.5 for combined wastewater. The training procedures of the RBFANN models were successful for all predicted components. The train and test models showed an almost perfect match between the experimental and predicted values of effluent BOD, COD, [Formula: see text] and TP. The coefficient of determination (R(2)) values were higher than 0.98 and root mean squared error (RMSE) values did not exceed 7% for train and test models.
Torrecilha, Rafaela Beatriz Pintor; Utsunomiya, Yuri Tani; Batista, Luís Fábio da Silva; Bosco, Anelise Maria; Nunes, Cáris Maroni; Ciarlini, Paulo César; Laurenti, Márcia Dalastra
2017-01-30
Quantification of Leishmania infantum load via real-time quantitative polymerase chain reaction (qPCR) in lymph node aspirates is an accurate tool for diagnostics, surveillance and therapeutics follow-up in dogs with leishmaniasis. However, qPCR requires infrastructure and technical training that is not always available commercially or in public services. Here, we used a machine learning technique, namely Radial Basis Artificial Neural Network, to assess whether parasite load could be learned from clinical data (serological test, biochemical markers and physical signs). By comparing 18 different combinations of input clinical data, we found that parasite load can be accurately predicted using a relatively small reference set of 35 naturally infected dogs and 20 controls. In the best case scenario (use of all clinical data), predictions presented no bias or inflation and an accuracy (i.e., correlation between true and predicted values) of 0.869, corresponding to an average error of ±38.2 parasites per unit of volume. We conclude that reasonable estimates of L. infantum load from lymph node aspirates can be obtained from clinical records when qPCR services are not available. Copyright © 2016 Elsevier B.V. All rights reserved.
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.
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Zhiqiang Guo
Full Text Available In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D2PCA 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, (2D2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D2PCA 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.
Energy Technology Data Exchange (ETDEWEB)
Altran, A.B.; Lotufo, A.D.P.; Minussi, C.R. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica], Emails: lealtran@yahoo.com.br, annadiva@dee.feis.unesp.br, minussi@dee.feis.unesp.br; Lopes, M.L.M. [Universidade Estadual Paulista Julio de Mesquita Filho (UNESP), Ilha Solteira, SP (Brazil). Dept. de Matematica], E-mail: mara@mat.feis.unesp.br
2009-07-01
This paper presents a methodology for electrical load forecasting, using radial base functions as activation function in artificial neural networks with the training by backpropagation algorithm. This methodology is applied to short term electrical load forecasting (24 h ahead). Therefore, results are presented analyzing the use of radial base functions substituting the sigmoid function as activation function in multilayer perceptron neural networks. However, the main contribution of this paper is the proposal of a new formulation of load forecasting dedicated to the forecasting in several points of the electrical network, as well as considering several types of users (residential, commercial, industrial). It deals with the MLF (Multimodal Load Forecasting), with the same processing time as the GLF (Global Load Forecasting). (author)
Radial Basis Function Based Quadrature over Smooth Surfaces
2016-03-24
Stieltjessche Integrale und Harmonische Analyse,” Math . Ann., vol. 108, pp. 378–410, 1933. 12. I. J. Schoenberg, “Metric Spaces and Positive Definite Functions ...Fornberg, “Interpolation in the Limit of Increasingly Flat Radial Basis Functions ,” Comput. Math . Appl., vol. 43, pp. 413–422, 2002. 83 REPORT...Radial Basis Function Based Quadrature over Smooth Surfaces THESIS March 2016 Maloupu L. Watts, Second Lieutenant, USAF AFIT-ENC-MS-16-M-003
A Radial Actin Network in Apical Constriction.
Lv, Zhiyi; Großhans, Jörg
2016-11-07
Contractile actomyosin networks are central to cell shape change, rearrangements, and migration during animal tissue morphogenesis. In this issue of Developmental Cell, Coravos and Martin (2016) report that the actin network is radially polarized in apically constricting cells, suggesting a constriction model similar to the contraction mechanism in muscle sarcomeres. Copyright © 2016 Elsevier Inc. All rights reserved.
Shahlaei, Mohsen; Bahrami, Gholamreza; Abdolmaleki, Sajjad; Sadrjavadi, Komail; Majnooni, Mohammad Bagher
2015-03-01
This study describes a simple and rapid approach of monitoring celecoxib (CLX). Unfolded principal component analysis-radial basis function neural network (UPCA-RBFNN) and excitation-emission spectra were combined to develop new model in the determination of CLX in human serum samples. Fluorescence landscapes with excitation wavelengths from 250 to 310 nm and emission wavelengths in the range 280-450 nm were obtained. The figures of merit for the developed model were evaluated. High performance liquid chromatography (HPLC) technique was also used as a standard method. Accuracy of the method was investigated by analysis of the serum samples spiked with various concentration of CLX and a recovery of 103.63% was obtained. The results indicated that the proposed method is an interesting alternative to the traditional techniques normally used for determining CLX such as HPLC.
Application of radial basis function to approximate functional integral equations
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Reza Firouzdor
2016-06-01
Full Text Available In the present paper, Radial Basis Function (RBF interpolation is applied to approximate the numerical solution of both Fredlholm and Volterra functional integral equations. RBF interpolation is based on linear combinations of terms which include a single univariate function. Applying RBF in functional integral equation, a linear system $ \\Psi C=G $ will be obtain in which by defining coefficient vector $ C $, target function will be approximiated. Finally, validity of the method is illustrated by some examples.
Heat Explosion In Porous Media Using Radial Basis Functions
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Allali Karam
2016-01-01
Full Text Available The paper is devoted to the numerical investigation of the interaction between natural convection and heat explosion in a fluid-saturated porous media in a rectangular domain. The model consists of Darcy equations for an incompressible fluid in a porous medium coupled with the nonlinear heat equation. Numerical simulations are performed using the radial basis functions method (RBFs. We study the bifurcation of the periodic oscillation of the response born by Hopf bifurcation. First, a symmetry-breaking bifurcations observed; then is followed by successive period-doubling bifurcations leading to chaos.
Han, Ping; Luan, Feng; Yan, Xizu; Gao, Yuan; Liu, Huitao
2012-01-01
A method for the separation and determination of honokiol and magnolol in Magnolia officinalis and its medicinal preparation is developed by capillary zone electrophoresis and response surface methodology. The concentration of borate, content of organic modifier, and applied voltage are selected as variables. The optimized conditions (i.e., 16 mmol/L sodium tetraborate at pH 10.0, 11% methanol, applied voltage of 25 kV and UV detection at 210 nm) are obtained and successfully applied to the analysis of honokiol and magnolol in Magnolia officinalis and Huoxiang Zhengqi Liquid. Good separation is achieved within 6 min. The limits of detection are 1.67 µg/mL for honokiol and 0.83 µg/mL for magnolol, respectively. In addition, an artificial neural network with "3-7-1" structure based on the ratio of peak resolution to the migration time of the later component (R(s)/t) given by Box-Behnken design is also reported, and the predicted results are in good agreement with the values given by the mathematic software and the experimental results. © The Author [2011]. Published by Oxford University Press. All rights reserved.
Ahmadi Azqhandi, M H; Ghaedi, M; Yousefi, F; Jamshidi, M
2017-11-01
Two machine learning approach (i.e. Radial Basis Function Neural Network (RBF-NN) and Random Forest (RF) was developed and evaluated against a quadratic response surface model to predict the maximum removal efficiency of brilliant green (BG) from aqueous media in relation to BG concentration (4-20mgL-1), sonication time (2-6min) and ZnS-NP-AC mass (0.010-0.030g) by ultrasound-assisted. All three (i.e. RBF network, RF and polynomial) model were compared against the experimental data using four statistical indices namely, coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and absolute average deviation (AAD). Graphical plots were also used for model comparison. The obtained results using RBF network and RF exhibit a better performance in comparison to classical statistical model for both dyes. The significant factors were optimized using desirability function approach (DFA) combined central composite design (CCD) and genetic algorithm (GA) approach. The obtained optimal point was located in the valid region and the experimental confirmation tests were conducted showing a good accordance between the predicted optimal points and the experimental data. The properties of ZnS-NPs-AC were identified by X-ray diffraction; field emission scanning electron microscopy, energy dispersive X-ray spectroscopy (EDS) and Fourier transformation infrared spectroscopy. Various isotherm models for fitting the experimental equilibrium data were studied and Langmuir model was chosen as an efficient model. Various kinetic models for analysis of experimental adsorption data were studied and pseudo second order model was chosen as an efficient model. Moreover, ZnS nanoparticles loaded on activated carbon efficiently were regenerated using methanol and after five cycles the removal percentage do not change significantly. Copyright © 2017 Elsevier Inc. All rights reserved.
Control point selection for dimensionality reduction by radial basis function
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Kotryna Paulauskienė
2016-02-01
Full Text Available This research deals with dimensionality reduction technique which is based on radial basis function (RBF theory. The technique uses RBF for mapping multidimensional data points into a low-dimensional space by interpolating the previously calculated position of so-called control points. This paper analyses various ways of selection of control points (regularized orthogonal least squares method, random and stratified selections. The experiments have been carried out with 8 real and artificial data sets. Positions of the control points in a low-dimensional space are found by principal component analysis. We demonstrate that random and stratified selections of control points are efficient and acceptable in terms of balance between projection error (stress and time-consumption.DOI: 10.15181/csat.v4i1.1095
Macro-modelling via radial basis functionen nets
Wiegand, C.; Fischer, C.; Kazemzadeh, R.; Hedayat, C.; John, W.; Hilleringmann, U.
2008-05-01
By the rising complexity and miniaturisation of the device's dimensions, the density of the conductors increases considerably. Referring to this, locally transient interactions between single physical values become apparent. Therefore, for the investigation and optimisation of integrated circuits it is essential to develop suitable models and simulation surroundings which allow for memory and time-efficient calculation of the behaviour. By means of the dynamic reconstruction theory and the radial basis functions nets the so-called black box models are provided. The description of black box models is derived from the input and output behaviour or so-called time series of a dynamic system. Concerning the time series, the black box model adapts its parameters via the extended Kalman filter. This paper provides a modelling approach that enables fast and efficient simulations.
Macro-modelling via radial basis functionen nets
Directory of Open Access Journals (Sweden)
C. Wiegand
2008-05-01
Full Text Available By the rising complexity and miniaturisation of the device's dimensions, the density of the conductors increases considerably. Referring to this, locally transient interactions between single physical values become apparent. Therefore, for the investigation and optimisation of integrated circuits it is essential to develop suitable models and simulation surroundings which allow for memory and time-efficient calculation of the behaviour. By means of the dynamic reconstruction theory and the radial basis functions nets the so-called black box models are provided. The description of black box models is derived from the input and output behaviour or so-called time series of a dynamic system. Concerning the time series, the black box model adapts its parameters via the extended Kalman filter. This paper provides a modelling approach that enables fast and efficient simulations.
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
Energy Technology Data Exchange (ETDEWEB)
Baek, Seung Ki; Kim, Minjae [Pukyong National University, Busan (Korea, Republic of)
2017-07-15
We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem domain, similarly to the method of images in electrostatics, to perform collocation on the domain boundaries. We can thereby take into account the temperature profile as well as its gradients specified by boundary conditions at the same time, which holds true even for a node where two or more boundaries meet with different boundary conditions. We argue that the image method is computationally efficient when combined with the local RBF collocation method, whereas the addition of image nodes becomes very costly in case of the global collocation. We apply our modified method to a benchmark test of a boundary value problem, and find that this simple modification reduces the maximum error from the analytic solution significantly. The reduction is small for an initial value problem with simpler boundary conditions. We observe increased numerical instability, which has to be compensated for by a sufficient number of sample nodes and/or more careful parameter choices for time integration.
Design Optimization of Centrifugal Pump Using Radial Basis Function Metamodels
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Yu Zhang
2014-05-01
Full Text Available Optimization design of centrifugal pump is a typical multiobjective optimization (MOO problem. This paper presents an MOO design of centrifugal pump with five decision variables and three objective functions, and a set of centrifugal pumps with various impeller shroud shapes are studied by CFD numerical simulations. The important performance indexes for centrifugal pump such as head, efficiency, and required net positive suction head (NPSHr are investigated, and the results indicate that the geometry shape of impeller shroud has strong effect on the pump's performance indexes. Based on these, radial basis function (RBF metamodels are constructed to approximate the functional relationship between the shape parameters of impeller shroud and the performance indexes of pump. To achieve the objectives of maximizing head and efficiency and minimizing NPSHr simultaneously, multiobjective evolutionary algorithm based on decomposition (MOEA/D is applied to solve the triobjective optimization problem, and a final design point is selected from the Pareto solution set by means of robust design. Compared with the values of prototype test and CFD simulation, the solution of the final design point exhibits a good consistency.
Automated Freehand Sketch Segmentation Using Radial Basis Functions.
Pu, Jiantao; Gur, David
2009-12-01
Freehand sketching is widely regarded as an efficient and natural way for interaction between computers and humans. We present a robust computerized scheme to automatically segment freehand sketches into a series of components with specific geometric meaning regardless of whether these are generated online or offline. This task is a necessary first step toward sketch understanding. By exploiting the interpolation / extrapolation characteristic of Radial Basis Functions (RBFs), a greedy algorithm consisting of forward and backward operations is proposed for finding the minimum set of segmentation points that can be used to reconstruct with high fitting accuracy freehand sketches in the form of implicit functions. To obtain segmentation points, a simple angle based rule is used to remove "bridging" points that provide a smooth transition between consecutive sketch components. Feasibility of the proposed algorithm is demonstrated by a preliminary performance assessment study using ten computer generated drawings. These experiments show that in this dataset sensitivity of the segmentation was higher than 97.5% with a false positive (FP) rate of approximately 25%. The majority of false positive identifications are located on arc regions where a larger number of segmentation points are needed for reconstruction purposes. The primary contribution of this algorithm is that it transforms an ambiguous problem namely, freehand sketch segmentation, into an implicit function fitting operation. Therefore, this proposed approach has several advantages including independence of the actual sketching activity, and the ability for a satisfactory detection of the transition point between a line and an arc or between two arcs.
Time efficient aeroelastic simulations based on radial basis functions
Liu, Wen; Huang, ChengDe; Yang, Guowei
2017-02-01
Aeroelasticity studies the interaction between aerodynamic forces and structural responses, and is one of the fundamental problems to be considered in the design of modern aircraft. The fluid-structure interpolation (FSI) and mesh deformation are two key issues in the CFD-CSD coupling approach (the partitioned approach), which is the mainstream numerical strategy in aeroelastic simulations. In this paper, a time efficient coupling scheme is developed based on the radial basis function interpolations. During the FSI process, the positive definite system of linear equations is constructed with the introduction of pseudo structural forces. The acting forces on the structural nodes can be calculated more efficiently via the solution of the linear system, avoiding the costly computations of the aerodynamic/structural coupling matrix. The multi-layer sequential mesh motion algorithm (MSM) is proposed to improve the efficiency of the volume mesh deformations, which is adequate for large-scale time dependent applications with frequent mesh updates. Two-dimensional mesh motion cases show that the MSM algorithm can reduce the computing cost significantly compared to the standard RBF-based method. The computations of the AGARD 445.6 wing flutter and the static deflections of the three-dimensional high-aspect-ratio aircraft demonstrate that the developed coupling scheme is applicable to both dynamic and static aeroelastic problems.
On the capabilities of higher-order neurons: a radial basis function approach.
Schmitt, Michael
2005-03-01
Higher-order neurons with k monomials in n variables are shown to have Vapnik-Chervonenkis (VC) dimension at least nk + 1. This result supersedes the previously known lower bound obtained via k-term monotone disjunctive normal form (DNF) formulas. Moreover, it implies that the VC dimension of higher-order neurons with k monomials is strictly larger than the VC dimension of k-term monotone DNF. The result is achieved by introducing an exponential approach that employs gaussian radial basis function neural networks for obtaining classifications of points in terms of higher-order neurons.
Hybrid model decomposition of speech and noise in a radial basis function neural model framework
DEFF Research Database (Denmark)
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...... applied is based on a combination of the hidden Markov model (HMM) decomposition method, for speech recognition in noise, developed by Varga and Moore (1990) from DRA and the hybrid (HMM/RBF) recognizer containing hidden Markov models and radial basis function (RBF) neural networks, developed by Singer...... and Lippmann (1992) from MIT Lincoln Lab. The present authors modified the hybrid recognizer to fit into the decomposition method to achieve high performance speech recognition in noisy environments. The approach has been denoted the hybrid model decomposition method and it provides an optimal method...
Free vibrations and buckling analysis of laminated plates by oscillatory radial basis functions
Neves, A. M. A.; Ferreira, A. J. M.
2015-12-01
In this paper the free vibrations and buckling analysis of laminated plates is performed using a global meshless method. A refined version of Kant's theorie which accounts for transverse normal stress and through-the-thickness deformation is used. The innovation is the use of oscillatory radial basis functions. Numerical examples are performed and results are presented and compared to available references. Such functions proved to be an alternative to the tradicional nonoscillatory radial basis functions.
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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.
Computer Network Defense Through Radial Wave Functions
Malloy, Ian
2016-01-01
The purpose of this research was to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has be...
An efficient method for ectopic beats cancellation based on radial basis function.
Mateo, Jorge; Torres, Ana; Rieta, José J
2011-01-01
The analysis of the surface Electrocardiogram (ECG) is the most extended noninvasive technique in cardiological diagnosis. In order to properly use the ECG, we need to cancel out ectopic beats. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. This paper presents a method for electrocardiogram ectopic beat cancellation based on Radial Basis Function Neural Network (RBFNN). A train-able neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care is presented. Six types of beats including: Normal Beats (NB); Premature Ventricular Contractions (PVC); Left Bundle Branch Blocks (LBBB); Right Bundle Branch Blocks (RBBB); Paced Beats (PB) and Ectopic Beats (EB) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. Average Results for the RBFNN based method provided an ectopic beat reduction (EBR) of (mean ± std) EBR = 7, 23 ± 2.18 in contrast to traditional compared methods that, for the best case, yielded EBR = 4.05 ± 2.13. The results prove that RBFNN based methods are able to obtain a very accurate reduction of ectopic beats together with low distortion of the QRST complex.
Non-linear cancer classification using a modified radial basis function classification algorithm.
Wang, Hong-Qiang; Huang, De-Shuang
2005-10-01
This paper proposes a modified radial basis function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.
Exact Convex Relaxation of Optimal Power Flow in Radial Networks
Energy Technology Data Exchange (ETDEWEB)
Gan, LW; Li, N; Topcu, U; Low, SH
2015-01-01
The optimal power flow (OPF) problem determines a network operating point that minimizes a certain objective such as generation cost or power loss. It is nonconvex. We prove that a global optimum of OPF can be obtained by solving a second-order cone program, under a mild condition after shrinking the OPF feasible set slightly, for radial power networks. The condition can be checked a priori, and holds for the IEEE 13, 34, 37, 123-bus networks and two real-world networks.
Computer network defense through radial wave functions
Malloy, Ian J.
The purpose of this research is to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has been devastating to geopolitical regions in that they are severely difficult for a civilian to avoid triggering given the unknown position of a landmine. Thus, the importance of understanding a logic bomb is relevant and has corollaries to quantum mechanics as well. The research synthesizes quantum logic phase shifts in certain respects using the Dynamic Data Exchange protocol in software written for this work, as well as a C-NOT gate applied to a virtual quantum circuit environment by implementing a Quantum Fourier Transform. The research focus applies the principles of coherence and entanglement from quantum physics, the concept of expert systems in artificial intelligence, principles of prime number based cryptography with trapdoor functions, and modeling radio wave propagation against an event from unknown parameters. This comes as a program relying on the artificial intelligence concept of an expert system in conjunction with trigger events for a trapdoor function relying on infinite recursion, as well as system mechanics for elliptic curve cryptography along orbital angular momenta. Here trapdoor both denotes the form of cipher, as well as the implied relationship to logic bombs.
A data-driven approach to local gravity field modelling using spherical radial basis functions
Klees, R.; Tenzer, R.; Prutkin, I.; Wittwer, T.
2008-01-01
We propose a methodology for local gravity field modelling from gravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares
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
Fire Risk Assessment of Some Indian Coals Using Radial Basis Function (RBF) Technique
Nimaje, Devidas; Tripathy, Debi Prasad
2017-04-01
Fires, whether surface or underground, pose serious and environmental problems in the global coal mining industry. It is causing huge loss of coal due to burning and loss of lives, sterilization of coal reserves and environmental pollution. Most of the instances of coal mine fires happening worldwide are mainly due to the spontaneous combustion. Hence, attention must be paid to take appropriate measures to prevent occurrence and spread of fire. In this paper, to evaluate the different properties of coals for fire risk assessment, forty-nine in situ coal samples were collected from major coalfields of India. Intrinsic properties viz. proximate and ultimate analysis; and susceptibility indices like crossing point temperature, flammability temperature, Olpinski index and wet oxidation potential method of Indian coals were carried out to ascertain the liability of coal to spontaneous combustion. Statistical regression analysis showed that the parameters of ultimate analysis provide significant correlation with all investigated susceptibility indices as compared to the parameters of proximate analysis. Best correlated parameters (ultimate analysis) were used as inputs to the radial basis function network model. The model revealed that Olpinski index can be used as a reliable method to assess the liability of Indian coals to spontaneous combustion.
Dai, Xiangjun; Shao, Xinxing; Li, Lijun; Liu, Cong; Dai, Meiling; Yun, Hai; Yang, Fujun
2017-07-20
Three-dimensional shapes of objects were evaluated with modified phase-shift lateral shearing interferometry illumination and radial basis function. A simple optical system was developed to create the fringe pattern based on the Murty interferometer. The phase shift was generated only by moving a plane-parallel plate along an in-plane parallel direction. A novel moving radial basis function method was presented to improve the quality of fringe patterns. And the proper calculation window size was given based on numerical simulation. Three-dimensional shapes of two kinds of objects were determined to verify the feasibility and effectiveness of the proposed method, and the reconstructed height distributions were in good accordance with the referenced data.
A Least Squares Radial Basis Function Partition of Unity Method for Solving PDEs
Larsson, Elisabeth; Shcherbakov, Victor; Heryudono, Alfa
2017-01-01
Recently, collocation based radial basis function (RBF) partition of unity methods (PUM) for solving partial differential equations have been formulated and investigated numerically and theoretically. When combined with stable evaluation methods such as the RBF-QR method, high order convergence rates can be achieved and sustained under refinement. However, some numerical issues remain. The method is sensitive to the node layout, and condition numbers increase with the refinement level. Here, ...
Radial basis function methods for the Rosenau equation and other higher order PDEs
Safdari-Vaighani, Ali; Larsson, Elisabeth; Heryudono, Alfa
2017-01-01
Meshfree methods based on radial basis function (RBF) approximation are of interest for numerical solution of partial differential equations (PDEs) because they are flexible with respect to the geometry of the computational domain, they can provide high order convergence, they are not more complicated for problems with many space dimensions and they allow for local refinement. The aim of this paper is to show that the solution of the Rosenau equation, as an example of an initial-boundary valu...
Algorithm for detection of the broken phase conductor in the radial networks
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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.
Directory of Open Access Journals (Sweden)
Bagus Sayekti Sujatmiko
2017-03-01
Full Text Available Model jaringan saraf fungsi radial basis (Radial Basis Function = RBF adalah model jaringan saraf yang memiliki unit lapisan tersembunyi, dimana fungsi aktivasinya menggunakan fungsi basis (Gaussian dan fungsi linear pada lapisan output. Untuk mendapatkan hasil fungsi RBF terbaik, diperlukan kombinasi yang tepat antara jumlah input data dan jumlah node (clustering. Penelitian ini dilakukan diperusahaan kimia yang bergerak dibidang produksi deterjen. Data yang akan diproses diperoleh dari transaksi perusahaan yang sudah dilakukan selama 2 tahun sebelumnya untuk dijadikan sebagai data training dan data testing. Pada data training dilakukan pengelompokan data dan pencarian nilai sentroid menggunakan metode K-Means kemudian dilanjutkan perhitungan RBF sampai menghasilkan nilai bobot training. Hasil bobot training digunakan untuk proses pengujian data testing hingga menghasilkan suatu prediksi produksi berupa nilai similarity. Nilai similarity tertinggi akan digunakan untuk perhitungan prediksi produksi pada aplikasi user. Hasil dari penelitian ini berupa nilai prediksi produksi yang akan digunakan untuk membantu proses pengambilan keputusan dan pemenuhan permintaan pelanggan. Dari percobaan yang sudah dilakukan diperoleh akurasi nilai similarity diatas 90 %.
Selection of an Interval for Variable Shape Parameter in Approximation by Radial Basis Functions
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Jafar Biazar
2016-01-01
Full Text Available In radial basis function approximation, the shape parameter can be variable. The values of the variable shape parameter strategies are selected from an interval which is usually determined by trial and error. As yet there is not any algorithm for determining an appropriate interval, although there are some recipes for optimal values. In this paper, a novel algorithm for determining an interval is proposed. Different variable shape parameter strategies are examined. The results show that the determined interval significantly improved the accuracy and is suitable enough to count on in variable shape parameter strategies.
Optimization of global model composed of radial basis functions using the term-ranking approach
Energy Technology Data Exchange (ETDEWEB)
Cai, Peng; Tao, Chao, E-mail: taochao@nju.edu.cn; Liu, Xiao-Jun [Key Laboratory of Modern Acoustics, Nanjing University, Nanjing 210093 (China)
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
Analysis of Laminated Shells by Murakami’s Zig-Zag Theory and Radial Basis Functions Collocation
Directory of Open Access Journals (Sweden)
D. A. Maturi
2013-01-01
Full Text Available The static and free vibration analysis of laminated shells is performed by radial basis functions collocation, according to Murakami’s zig-zag (ZZ function (MZZF theory . The MZZF theory accounts for through-the-thickness deformation, by considering a ZZ evolution of the transverse displacement with the thickness coordinate. The equations of motion and the boundary conditions are obtained by Carrera’s Unified Formulation and further interpolated by collocation with radial basis functions.
Li, Yang; Wang, Xu; Luo, Lin; Li, Ke; Yang, Xiao; Guo, Qi
2017-03-10
The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time-frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time-frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis (PCA) algorithm before the most discriminative features selected are fed into a SVM classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis (LDA), and Logistic Regression (LR). The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and show the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
Regional Gravity Field Modeling with Abel-Poisson Radial Basis Functions
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MA Zhiwei
2016-09-01
Full Text Available With the increasing number of various types of high-resolution gravity observations, earth gravity models can be regionally refined. We use Abel-Poisson kernel to represent the gravity as the linear summation of finite radial basis functions and combine the multiple gravity data to build a regional gravity model with high resolution. The minimum root mean square criterion based on the data adaptive algorithm is proposed to calculate the base function, which promote the speed of computation significantly. Taking the central South China Sea as an example, two different types of gravity data, namely geoid undulations with resolution of 6'×6' and gravity anomaly with resolution of 2'×2', are used to construct the high-resolution regional gravity model. The model has a resolution of 2'×2', and has a great agreement with original gravity anomaly, reaching to ±0.8×10-5m/s2.Our results show that using radial basis functions to construct the regional gravity field can avoid the problem of slow convergence of spherical harmonic functions, and can improve the resolution remarkably.
Radial basis function neural network in fault detection of automotive ...
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Application of radial basis neural network for state estimation of ...
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MultiCraft. International Journal of Engineering, Science and Technology. Vol. 2, No. 3, 2010, pp. 19-28. INTERNATIONAL. JOURNAL OF. ENGINEERING,. SCIENCE AND. TECHNOLOGY ... state estimation is investigated by testing its applicability on a IEEE 14 bus system. The proposed estimator is compared with.
Yang, Fan; Kusche, Jürgen; Forootan, Ehsan; Rietbroek, Roelof
2017-08-01
We present a state-of-the-art approach of passive-ocean modified radial basis functions (MRBFs) that improves the recovery of time-variable gravity fields from Gravity Recovery and Climate Experiment (GRACE). As is well known, spherical harmonics (SHs), which are commonly used to recover gravity fields, are orthogonal basis functions with global coverage. However, the chosen SH truncation involves a global compromise between data coverage and obtainable resolution, and strong localized signals may not be fully captured. Radial basis functions (RBFs) provide another representation, which has been proposed in earlier works to be better suited to retrieve regional gravity signals. In this paper, we propose a MRBF approach by embedding the known coastal geometries in the RBF parameterization and imposing global mass conservation and equilibrium behavior of the oceans. Our hypothesis is that with this physically justified constraint, the GRACE-derived gravity signals can be more realistically partitioned into the land and ocean contributions along the coastlines. We test this new technique to invert monthly gravity fields from GRACE level-1b observations covering 2005-2010, for which the numerical results indicate that (1) MRBF-based solutions reduce the number of parameters by approximately 10% and allow for more flexible regularization when compared to ordinary RBF solutions and (2) the MRBF-derived mass flux is better confined along coastal areas. The latter is particularly tested in the southern Greenland, and our results indicate that the trend of mass loss from the MRBF solutions is approximately 11% larger than that from the SH solutions and approximately 4%-6% larger than that of RBF solutions.
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.
Radial basis functions in mathematical modelling of flow boiling in minichannels
Directory of Open Access Journals (Sweden)
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.
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.
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Huaiqing Zhang
2014-01-01
Full Text Available The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.
Lam, Dao; Wunsch, Donald
2017-01-01
Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy. The features are learned by an unsupervised feature learning (UFL) algorithm. Then, those features are trained by a fast radial basis function (RBF) extreme learning machine (ELM). By exploiting the massive parallel computing attribute of modern graphics processing unit, a customized compute unified device architecture (CUDA) kernel is developed to further speed up the computing of the RBF kernel in the ELM. Results tested on Canadian Institute for Advanced Research and Mixed National Institute of Standards and Technology data sets confirm the UFL RBF ELM achieves high accuracy, and the CUDA implementation is up to 20 times faster than CPU and the naive parallel approach.
Rostamy, D.; Emamjome, M.; Abbasbandy, S.
2017-06-01
In this paper, the pseudospectral radial basis functions method is proposed for solving the second-order two-space-dimensional telegraph equation in regular and irregular domain. The proposed numerical method, which is truly meshless, is based on a time stepping procedure to deal with the temporal part of the solution combined with radial basis function differentiation matrices for discretizing the spatial derivatives. Here, we extended the pseudospectral radial basis functions method for two-dimensional hyperbolic telegraph equations in irregular domain. A cross-validation technique is used to optimize the shape parameter for the basis functions. Numerical results and comparisons are given to validate the presented method for solving the two-dimensional telegraph equation on both regular and irregular domains which show that the approximate solutions are in good agreement with the exact solution. The obtained results showed that the proposed method is easy to apply for multidimensional problems and equally applicable to both the regular and irregular domains.
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
A radial basis classifier for the automatic detection of aspiration in children with dysphagia
Lee, Joon; Blain, Stefanie; Casas, Mike; Kenny, Dave; Berall, Glenn; Chau, Tom
2006-01-01
Background Silent aspiration or the inhalation of foodstuffs without overt physiological signs presents a serious health issue for children with dysphagia. To date, there are no reliable means of detecting aspiration in the home or community. An assistive technology that performs in these environments could inform caregivers of adverse events and potentially reduce the morbidity and anxiety of the feeding experience for the child and caregiver, respectively. This paper proposes a classifier for automatic classification of aspiration and swallow vibration signals non-invasively recorded on the neck of children with dysphagia. Methods Vibration signals associated with safe swallows and aspirations, both identified via videofluoroscopy, were collected from over 100 children with neurologically-based dysphagia using a single-axis accelerometer. Five potentially discriminatory mathematical features were extracted from the accelerometry signals. All possible combinations of the five features were investigated in the design of radial basis function classifiers. Performance of different classifiers was compared and the best feature sets were identified. Results Optimal feature combinations for two, three and four features resulted in statistically comparable adjusted accuracies with a radial basis classifier. In particular, the feature pairing of dispersion ratio and normality achieved an adjusted accuracy of 79.8 ± 7.3%, a sensitivity of 79.4 ± 11.7% and specificity of 80.3 ± 12.8% for aspiration detection. Addition of a third feature, namely energy, increased adjusted accuracy to 81.3 ± 8.5% but the change was not statistically significant. A closer look at normality and dispersion ratio features suggest leptokurticity and the frequency and magnitude of atypical values as distinguishing characteristics between swallows and aspirations. The achieved accuracies are 30% higher than those reported for bedside cervical auscultation. Conclusion The proposed aspiration
A radial basis classifier for the automatic detection of aspiration in children with dysphagia
Directory of Open Access Journals (Sweden)
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
A numerically efficient technique of regional gravity field modeling using Radial Basis Functions
Shahbazi, Anahita; Safari, Abdolreza; Foroughi, Ismael; Tenzer, Robert
2016-02-01
Radial Basis Functions (RBFs) have been extensively used in regional gravity and (quasi)geoid modeling. Reliable models require the choice of an optimal number of RBFs and of their parameters. The RBF parameters are typically optimized using a regularization algorithm. Therefore, the determination of the number of RBFs is the most challenging task in the modeling procedure. For this purpose, we design a data processing scheme to optimize the number of RBFs and their parameters simultaneously. Using this scheme, the gravimetric quasi-geoid model can be validated without requiring additional information on the quasi-geoidal geometry obtained from GPS/leveling data. Furthermore, the Levenberg-Marquardt algorithm, used for regularization, is modified to enhance its numerical performance. We demonstrate that these modifications guarantee the convergence of the solution to the global minimum while substantially decreasing the number of iterations. The proposed methodology is evaluated using synthetic gravity data and compared with existing methods for validating the RBF parameterization of the gravity field.
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.
Incremental approach for radial basis functions mesh deformation with greedy algorithm
Selim, Mohamed M.; Koomullil, Roy P.; Shehata, Ahmed S.
2017-07-01
Mesh Deformation is an important element of any fluid-structure interaction simulation. In this article, a new methodology is presented for the deformation of volume meshes using incremental radial basis function (RBF) based interpolation. A greedy algorithm is used to select a small subset of the surface nodes iteratively. Two incremental approaches are introduced to solve the RBF system of equations: 1) block matrix inversion based approach and 2) modified LU decomposition approach. The use of incremental approach decreased the computational complexity of solving the system of equations within each greedy algorithm's iteration from O (n3) to O (n2). Results are presented from an accuracy study using specified deformations on a 2D surface. Mesh deformations for bending and twisting of a 3D rectangular supercritical wing have been demonstrated. Outcomes showed the incremental approaches reduce the CPU time up to 67% as compared to a traditional RBF matrix solver. Finally, the proposed mesh deformation approach was integrated within a fluid-structure interaction solver for investigating a flow induced cantilever beam vibration.
Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs
Energy Technology Data Exchange (ETDEWEB)
Bollig, Evan F., E-mail: bollig@scs.fsu.edu [Department of Scientific Computing, Florida State University, 400 Dirac Science Library, Tallahassee, FL 32306 (United States); Flyer, Natasha, E-mail: flyer@ucar.edu [Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, 1850 Table Mesa Dr., Boulder, CO 80305 (United States); Erlebacher, Gordon, E-mail: gerlebacher@fsu.edu [Department of Scientific Computing, Florida State University, 400 Dirac Science Library, Tallahassee, FL 32306 (United States)
2012-08-30
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.
Using radial basis functions in airborne gravimetry for local geoid improvement
Li, Xiaopeng
2017-10-01
Radial basis functions (RBFs) have been used extensively in satellite geodetic applications. However, to the author's knowledge, their role in processing and modeling airborne gravity data has not yet been fully advocated or extensively investigated in detail. Compared with satellite missions, the airborne data are more suitable for these kinds of localized basis functions especially considering the following facts: (1) Unlike the satellite missions that can provide global or near global data coverage, airborne gravity data are usually geographically limited. (2) It is also band limited in the frequency domain. (3) It is straightforward to formulate the RBF observation equations from an airborne gravimetric system. In this study, a set of band-limited RBF is developed to model and downward continue the airborne gravity data for local geoid improvement. First, EIGEN6c4 coefficients are used to simulate a harmonic field to test the performances of RBF on various sampling, noise, and flight height levels, in order to gain certain guidelines for processing the real data. Here, the RBF method not only successfully recovers the harmonic field but also presents filtering properties due to its particular design in the frequency domain. Next, the software was tested for the GSVS14 (Geoid Slope Validation Survey 2014) area in Iowa as well as for the area around Puerto Rico and the US Virgin Islands by use of the real airborne gravity data from the Gravity for the Redefinition of the American Vertical Datum (GRAV-D) project. By fully utilizing the three-dimensional correlation information among the flight tracks, the RBF can also be used as a data cleaning tool for airborne gravity data adjustment and cleaning. This property is further extended to surface gravity data cleaning, where conventional approaches have various limitations. All the related numerical results clearly show the importance and contribution of the use of the RBF for high- resolution local gravity field
Efficient mesh motion using radial basis functions with volume grid points reduction algorithm
Xie, Liang; Liu, Hong
2017-11-01
As one of the most robust mesh deformation technique available, the radial basis function (RBF) mesh deformation has been accepted widely. However, for volume mesh deformation driven by surface motion, the RBF system may become impractical for large meshes due to the large number of both surface (control) points and volume points. Surface points selection procedure based on the greedy algorithm results in an efficient implementation of the RBF-based mesh deformation procedure. The greedy algorithm could reduce the number of surface points involved in the RBF interpolation while acquire an acceptable accuracy as shown in literature. To improve the efficiency of the RBF method furthermore, an issue that how to reduce the number of the volume points needed to be moved is addressed. In this paper, we propose an algorithm for volume points reduction based on a wall distance based restricting function which is added to the formulation of the RBF based interpolation. This restricting function is firstly introduced by the current article. To support large deformation, a multi-level subspace interpolation is essentially needed, although this technique was used to improve the efficiency of the surface points selection procedure in the existed literature. The key point of this technique is setting the error of previous interpolation step as the object of current step, and restricting interpolation region gradually. Because the tolerance of the error is decreased hierarchically, the number of the surface points is increased but the number of the volume points needed to be moved is reduced gradually. Therefore, the CPU cost of updating the mesh motion could be reduced eventually since it scales with the product of these two numbers. The computational requirement of the proposed procedure is reduced evidently compared with the standard procedure as proved by some examples.
Radial basis function regression methods for predicting quantitative traits using SNP markers.
Long, Nanye; Gianola, Daniel; Rosa, Guilherme J M; Weigel, Kent A; Kranis, Andreas; González-Recio, Oscar
2010-06-01
A challenge when predicting total genetic values for complex quantitative traits is that an unknown number of quantitative trait loci may affect phenotypes via cryptic interactions. If markers are available, assuming that their effects on phenotypes are additive may lead to poor predictive ability. Non-parametric radial basis function (RBF) regression, which does not assume a particular form of the genotype-phenotype relationship, was investigated here by simulation and analysis of body weight and food conversion rate data in broilers. The simulation included a toy example in which an arbitrary non-linear genotype-phenotype relationship was assumed, and five different scenarios representing different broad sense heritability levels (0.1, 0.25, 0.5, 0.75 and 0.9) were created. In addition, a whole genome simulation was carried out, in which three different gene action modes (pure additive, additive+dominance and pure epistasis) were considered. In all analyses, a training set was used to fit the model and a testing set was used to evaluate predictive performance. The latter was measured by correlation and predictive mean-squared error (PMSE) on the testing data. For comparison, a linear additive model known as Bayes A was used as benchmark. Two RBF models with single nucleotide polymorphism (SNP)-specific (RBF I) and common (RBF II) weights were examined. Results indicated that, in the presence of complex genotype-phenotype relationships (i.e. non-linearity and non-additivity), RBF outperformed Bayes A in predicting total genetic values using SNP markers. Extension of Bayes A to include all additive, dominance and epistatic effects could improve its prediction accuracy. RBF I was generally better than RBF II, and was able to identify relevant SNPs in the toy example.
Local gravity field modeling using spherical radial basis functions and a genetic algorithm
Mahbuby, Hany; Safari, Abdolreza; Foroughi, Ismael
2017-05-01
Spherical Radial Basis Functions (SRBFs) can express the local gravity field model of the Earth if they are parameterized optimally on or below the Bjerhammar sphere. This parameterization is generally defined as the shape of the base functions, their number, center locations, bandwidths, and scale coefficients. The number/location and bandwidths of the base functions are the most important parameters for accurately representing the gravity field; once they are determined, the scale coefficients can then be computed accordingly. In this study, the point-mass kernel, as the simplest shape of SRBFs, is chosen to evaluate the synthesized free-air gravity anomalies over the rough area in Auvergne and GNSS/Leveling points (synthetic height anomalies) are used to validate the results. A two-step automatic approach is proposed to determine the optimum distribution of the base functions. First, the location of the base functions and their bandwidths are found using the genetic algorithm; second, the conjugate gradient least squares method is employed to estimate the scale coefficients. The proposed methodology shows promising results. On the one hand, when using the genetic algorithm, the base functions do not need to be set to a regular grid and they can move according to the roughness of topography. In this way, the models meet the desired accuracy with a low number of base functions. On the other hand, the conjugate gradient method removes the bias between derived quasigeoid heights from the model and from the GNSS/leveling points; this means there is no need for a corrector surface. The numerical test on the area of interest revealed an RMS of 0.48 mGal for the differences between predicted and observed gravity anomalies, and a corresponding 9 cm for the differences in GNSS/leveling points.
Hines, T.; Hetland, E.
2016-12-01
We present a novel, statistically rigorous method for smoothing and differentiating GPS data in both space and time. This method illuminates spatial and temporal variations of fundamentally important quantities such as strain rate, and results in high fidelity images of both tectonic and non-tectonic signals in GPS data. The main difficulty in spatially smoothing GPS data is that the data is not observed on a regular grid, which prevents the use of most of the well known filtering techniques. Our method is based on the recently developed radial basis function-finite difference (RBF-FD) method, which is designed for differentiating data at scattered observation points. We demonstrate that the RBF-FD method can also be effectively used to smooth scattered data, including data from both dense continuous GPS networks and sparser, semi-continuous networks. Existing methods which have been used to smooth GPS data involve least squares fitting of an interpolant to the observed displacement field. Our method has three distinguishing features which set it apart from previous strategies. 1) We use a prior assumption that the deformation is locally smooth, and so we can still smooth a displacement field containing known discontinuities from, for example, a creeping fault. 2) Our method is mathematically equivalent to a low-pass filter which has a well defined, user specified cutoff frequency. 3) The system of equations being solved in our method is sparse and well conditioned, making it possible to spatially and temporally smooth decades of GPS data from hundreds of stations. We present the results of our method for several real world cases, which include an unprecedented view of transient deformation in the Cascadia subduction zone.
Sun, Jie; Yi, Hong-Liang; Tan, He-Ping
2016-02-20
A local radial basis function meshless scheme (LRBFM) is developed to solve polarized radiative transfer in participating media containing randomly oriented axisymmetric particles in which radial basis functions augmented with polynomial basis are employed to construct the trial functions, and the vector radiative-transfer equation based on the discrete-ordinates approach is discretized directly by collocation method. The LRBFM belongs to a class of truly meshless methods that do not need any mesh or any numerical integration scheme. Performances of the LRBFM are verified with analytical solutions and other numerical results reported earlier in the literature via five various test cases. The predicted angular distribution of brightness temperature and Stokes vector by the LRBFM agree very well with the benchmark. It is demonstrated that the LRBFM is accurate to solve vector radiative transfer in participating media with randomly oriented axisymmetric particles.
Directory of Open Access Journals (Sweden)
Z. Mosayebi
2014-07-01
Full Text Available In this paper a numerical technique is presented for the solution of fuzzy linear Volterra-Fredholm-Hammerstein integral equations. This method is a combination of collocation method and radial basis functions(RBFs.We first solve the actual set are equivalent to the fuzzy set, then answer 1-cut into the equation. Also high convergence rates and good accuracy are obtain with the propose method using relativeiy low numbers of data points.
Shankar, Varun; Wright, Grady B.; Kirby, Robert M.; Fogelson, Aaron L.
2013-01-01
We present a new computational method by extending the Immersed Boundary (IB) method with a spectrally-accurate geometric model based on Radial Basis Function (RBF) interpolation of the Lagrangian structures. Our specific motivation is the modeling of platelets in hemodynamic flows, though we anticipate that our method will be useful in other applications as well. The efficacy of our new RBF-IB method is shown through a series of numerical experiments. Specifically, we compare our method with...
Shankar, Varun; Wright, Grady B.; Kirby, Robert M.; Fogelson, Aaron L.
2014-01-01
In this paper, we present a method based on Radial Basis Function (RBF)-generated Finite Differences (FD) for numerically solving diffusion and reaction-diffusion equations (PDEs) on closed surfaces embedded in $\\mathbb{R}^d$. Our method uses a method-of-lines formulation, in which surface derivatives that appear in the PDEs are approximated locally using RBF interpolation. The method requires only scattered nodes representing the surface and normal vectors at those scattered nodes. All compu...
Application of a proposed overcurrent relay in radial distribution networks
Energy Technology Data Exchange (ETDEWEB)
Conde, A.; Vazquez, E. [Universidad Autonoma de Nuevo Leon, Facultad de Ingenieria Mecanica y Electrica, A.P. 36-F, CU, CP 66450, San Nicolas de los Garza, Nuevo Leon (Mexico)
2011-02-15
This paper contains the application criteria and coordination process for a proposed overcurrent relay in a radial power system with feed from one or multiple sources. This relay uses independent functions to detect faults and to calculate the operation time. Also this relay uses a time element function that allows it to reduce the time relay operation, enhancing the backup protection. Some of the proposed approaches improve the sensitivity of the relay. The selection of the best approach in the proposed relay is defined by the needs of the application. The proposed protection can be considered as an additional function protection to conventional overcurrent relays. (author)
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.
Trahan, C J
2003-01-01
This paper investigates multi-quadric radial basis function (RBF) interpolation and its application in the quantum trajectory method (QTM) for wave packet propagation. In the multi-quadric, phi(r;delta)=(r sup 2 +delta sup 2) sup 1 sup / sup 2 , r is the radial distance from the observation point to the origin of the basis function, phi, and delta is known as the shape parameter due to its affect on the functional form of the basis function. The quality of any RBF interpolation scheme is dictated by the choice of this parameter. Many recent studies have investigated a suitable means for obtaining an 'optimized' time-independent delta parameter. The purpose of this study, however, is to not only to find this 'optimized' shape parameter, but also to analyze its time-dependence in four different dynamical models; the anisotropic harmonic oscillator, the downhill ramp, the uphill ramp, and a harmonic oscillator coupled with a downhill ramp. To obtain the optimized shape parameter at each time step, an algorithm s...
Stochastic Evaluation of Maximum Wind Installation in a Radial Distribution Network
DEFF Research Database (Denmark)
Chen, Peiyuan; Bak-Jensen, Birgitte; Chen, Zhe
2011-01-01
This paper proposes an optimization algorithm to find the maximum wind installation in a radial distribution network. The algorithm imposes a limit on the amount of wind energy that can be curtailed annually. The algorithm implements the wind turbine reactive power control and wind energy...
Dang, Van Tuan; Lafon, Pascal; Labergere, Carl
2017-10-01
In this work, a combination of Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF) is proposed to build a surrogate model based on the Benchmark Springback 3D bending from the Numisheet2011 congress. The influence of the two design parameters, the geometrical parameter of the die radius and the process parameter of the blank holder force, on the springback of the sheet after a stamping operation is analyzed. The classical Design of Experience (DoE) uses Full Factorial to design the parameter space with sample points as input data for finite element method (FEM) numerical simulation of the sheet metal stamping process. The basic idea is to consider the design parameters as additional dimensions for the solution of the displacement fields. The order of the resultant high-fidelity model is reduced through the use of POD method which performs model space reduction and results in the basis functions of the low order model. Specifically, the snapshot method is used in our work, in which the basis functions is derived from snapshot deviation of the matrix of the final displacements fields of the FEM numerical simulation. The obtained basis functions are then used to determine the POD coefficients and RBF is used for the interpolation of these POD coefficients over the parameter space. Finally, the presented POD-RBF approach which is used for shape optimization can be performed with high accuracy.
Directory of Open Access Journals (Sweden)
Jaime A. Echeverri A.
2007-07-01
Full Text Available En este trabajo se muestra la utilización de funciones de base radial de soporte compacto para la reconstrucción tridimensional de rostros. En trabajos anteriores se habían explorado diferentes técnicas y diferentes funciones de base radial para reconstrucción de superficies; ahora presentamos los algoritmos y los resultados de la utilización de funciones de base radial de soporte compacto las cuales presentan ventajas comparativas en términos del tiempo de construcción de un interpolante para la reconstrucción. Se presentan comparaciones con técnicas ampliamente utilizadas en este campo y se detalla el proceso global de reconstrucción de superficies.In previous works, we have explored several radial basis techniques and functions for the reconstruction of surfaces. We now present the use of compact support radial basis functions for the tri-dimensional reconstruction of human faces. Therefore, we present algorithms and results coming from the application of compact support radial basis functions which have revealed comparative advantages in terms of the amount of time needed for the construction of an interpolant to be used in the reconstruction. We are also presenting some comparisons with techniques widely used in this field and we explain in detail the global process for the surfaces reconstruction.
Directory of Open Access Journals (Sweden)
Shang Ma
Full Text Available The cerebral cortex performs complex cognitive functions at the expense of tremendous energy consumption. Blood vessels in the brain are known to form stereotypic patterns that facilitate efficient oxygen and nutrient delivery. Yet little is known about how vessel development in the brain is normally regulated. Radial glial neural progenitors are well known for their central role in orchestrating brain neurogenesis. Here we show that, in the late embryonic cortex, radial glial neural progenitors also play a key role in brain angiogenesis, by interacting with nascent blood vessels and regulating vessel stabilization via modulation of canonical Wnt signaling. We find that ablation of radial glia results in vessel regression, concomitant with ectopic activation of Wnt signaling in endothelial cells. Direct activation of Wnt signaling also results in similar vessel regression, while attenuation of Wnt signaling substantially suppresses regression. Radial glial ablation and ectopic Wnt pathway activation leads to elevated endothelial expression of matrix metalloproteinases, while inhibition of metalloproteinase activity significantly suppresses vessel regression. These results thus reveal a previously unrecognized role of radial glial progenitors in stabilizing nascent brain vascular network and provide novel insights into the molecular cascades through which target neural tissues regulate vessel stabilization and patterning during development and throughout life.
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.
Benaissa, B.; Köppen, M.; Abdel Wahab, M.; Khatir, S.
2017-05-01
Complex engineering problems require simulations, which are computationally expensive in cases of inverse identification tasks since they commonly requires hundreds of thousands of simulations. This paper propose a method based on model reduction for crack size estimation, combining the proper orthogonal decomposition method with radial basis functions. The reduced model is validated by comparing the obtained boundary displacements with the corresponding results from a finite element model. This inverse procedure is formulated as the minimization of the difference between the measured and computed values of displacement at selected boundary nodes, called sensor points, using particle swarm optimization algorithm. Convex and a non-convex specimens have been considered for investigations of crack presence, and identification of its size, different crack sizes have been tested to demonstrate the efficiency of the proposed approach.
Energy Technology Data Exchange (ETDEWEB)
Martin, Bradley, E-mail: brma7253@colorado.edu; Fornberg, Bengt, E-mail: Fornberg@colorado.edu
2017-04-15
In a previous study of seismic modeling with radial basis function-generated finite differences (RBF-FD), we outlined a numerical method for solving 2-D wave equations in domains with material interfaces between different regions. The method was applicable on a mesh-free set of data nodes. It included all information about interfaces within the weights of the stencils (allowing the use of traditional time integrators), and was shown to solve problems of the 2-D elastic wave equation to 3rd-order accuracy. In the present paper, we discuss a refinement of that method that makes it simpler to implement. It can also improve accuracy for the case of smoothly-variable model parameter values near interfaces. We give several test cases that demonstrate the method solving 2-D elastic wave equation problems to 4th-order accuracy, even in the presence of smoothly-curved interfaces with jump discontinuities in the model parameters.
Martin, Bradley; Fornberg, Bengt
2017-04-01
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.
Shankar, Varun; Wright, Grady B; Kirby, Robert M; Fogelson, Aaron L
2016-06-01
In this paper, we present a method based on Radial Basis Function (RBF)-generated Finite Differences (FD) for numerically solving diffusion and reaction-diffusion equations (PDEs) on closed surfaces embedded in ℝ d . Our method uses a method-of-lines formulation, in which surface derivatives that appear in the PDEs are approximated locally using RBF interpolation. The method requires only scattered nodes representing the surface and normal vectors at those scattered nodes. All computations use only extrinsic coordinates, thereby avoiding coordinate distortions and singularities. We also present an optimization procedure that allows for the stabilization of the discrete differential operators generated by our RBF-FD method by selecting shape parameters for each stencil that correspond to a global target condition number. We show the convergence of our method on two surfaces for different stencil sizes, and present applications to nonlinear PDEs simulated both on implicit/parametric surfaces and more general surfaces represented by point clouds.
Vuković, Najdan; Miljković, Zoran
2013-10-01
Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems. To accommodate limitation of a single scale, this paper presents neural network with similar but yet different activation function-hyper basis function (HBF). The HBF allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The HBF is based on generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. Compared to the RBF, the HBF neuron has more parameters to optimize, but HBF neural network needs less number of HBF neurons to memorize relationship between input and output sets in order to achieve good generalization property. However, recent research results of HBF neural network performance have shown that optimal way of constructing this type of neural network is needed; this paper addresses this issue and modifies sequential learning algorithm for HBF neural network that exploits the concept of neuron's significance and allows growing and pruning of HBF neuron during learning process. Extensive experimental study shows that HBF neural network, trained with developed learning algorithm, achieves lower prediction error and more compact neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Jensen, Kåre Jean; Munk, Steen M.; Sørensen, John Aasted
1998-01-01
A new approach to the localization of high impedance ground faults in compensated radial power distribution networks is presented. The total size of such networks is often very large and a major part of the monitoring of these is carried out manually. The increasing complexity of industrial...... of three phase voltages and currents. The method consists of a feature extractor, based on a grid description of the feeder by impulse responses, and a neural network for ground fault localization. The emphasis of this paper is the feature extractor, and the detection of the time instance of a ground fault...... processes and communication systems lead to demands for improved monitoring of power distribution networks so that the quality of power delivery can be kept at a controlled level. The ground fault localization method for each feeder in a network is based on the centralized frequency broadband measurement...
mohamad yusefi, mahboobeh; Safari, Abdolreza; Shahbazi, Anahita; Foroughi, Ismael
2016-04-01
In local-scale applications, Radial Basis Functions (RBFs) are appropriate tools for the purpose of high spatial/spectral resolution gravity field modeling. Due to the availability of different types of RBF kernels, different behaviors are expected in both spectral and spatial domains. While the spectral behavior of RBFs is dependent on the type of kernels, their spatial behavior significantly depends on the choice of their bandwidth. In this study, the functionality of various types of RBF kernels is addressed in coastal gravity field modeling. Four of the most well-known gravimetric RBF kernels including point-mass, radial multi-poles, Poisson wavelet and Poisson kernel are considered for the comparison aim. The area under consideration is the coastal region of the Persian Gulf which consists of 6244 terrestrial/marine gravity observations. The optimal RBF parameterization of the gravity field, i.e. specifying the optimal number of kernels and their 3D spatial configuration (their horizontal locations in the area of interest and their depth below the Bjerhammar sphere), is performed using the iterative Levenberg-Marquardt Algorithm (LMA). Our previous studies indicated that the LMA is a practical choice to deal with ill- conditioned problem of gravity field modeling. The stopping criterion is considered as the minimum L2-norm of the differences between the predicted and observed quantities at the independent control points. The numerical experiments reveal that the accuracy of gravity field and geoid models, regarding different types of RBF kernels, depends on the selection of RBF parameters; if RBF parameters are spatially optimized, they would lead to almost same results.
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.
Naik, Naren; Beatson, Rick; Eriksson, Jerry
2014-10-10
The dynamic reconstruction problem in tomographic imaging is encountered in several applications, such as species determination, the study of blood flow through arteries/veins, motion compensation in medical imaging, and process tomography. The reconstruction method of choice is the Kalman filter and its variants, which, however, are faced by issues of filter tuning. In addition, since the time-propagation models of physical parameters are typically very complex, most of the time, a random walk model is considered. For geometric deformations, affine models are typically used. In our work, with the objectives of minimizing tuning issues and reconstructing time-varying geometrically deforming features of interest with affine in addition to pointwise-normal scaling motions, a novel level-set-based reconstruction scheme for ray tomography is proposed for shape and electromagnetic parameters using a regularized Gauss-Newton-filter-based scheme. We use an implicit Hermite-interpolation-based radial basis function representation of the zero level set corresponding to the boundary curve. Another important contribution of the paper is an evaluation of the shape-related Frechet derivatives that does not need to evaluate the pointwise Jacobian (the ray-path matrix in our ray-tomography problem). Numerical results validating the formulation are presented for a straight ray-based tomographic reconstruction. To the best of our knowledge, this paper presents the first tomographic reconstruction results in these settings.
An, Yu; Liu, Jie; Zhang, Guanglei; Jiang, Shixin; Ye, Jinzuo; Chi, Chongwei; Tian, Jie
2017-02-01
Fluorescence Molecular Tomography (FMT) is a powerful imaging modality for the research of cancer diagnosis, disease treatment and drug discovery. Via three-dimensional (3-D) imaging reconstruction, it can quantitatively and noninvasively obtain the distribution of fluorescent probes in biological tissues. Currently, photon propagation of FMT is conventionally described by the Finite Element Method (FEM), and it can obtain acceptable image quality. However, there are still some inherent inadequacies in FEM, such as time consuming, discretization error and inflexibility in mesh generation, which partly limit its imaging accuracy. To further improve the solving accuracy of photon propagation model (PPM), we propose a novel compactly supported radial basis functions (CSRBFs)-based meshless method (MM) to implement the PPM of FMT. We introduced a series of independent nodes and continuous CSRBFs to interpolate the PPM, which can avoid complicated mesh generation. To analyze the performance of the proposed MM, we carried out numerical heterogeneous mouse simulation to validate the simulated surface fluorescent measurement. Then we performed an in vivo experiment to observe the tomographic reconstruction. The experimental results confirmed that our proposed MM could obtain more similar surface fluorescence measurement with the golden standard (Monte-Carlo method), and more accurate reconstruction result was achieved via MM in in vivo application.
Network basis of suicidal ideation in depressed adolescents.
Ordaz, Sarah J; Goyer, Meghan S; Ho, Tiffany C; Singh, Manpreet K; Gotlib, Ian H
2018-01-15
Suicidal ideation rates rise precipitously in adolescence, contributing to risk for attempts. Although researchers are beginning to explore the brain basis of attempts in depressed adolescents, none have focused on the basis of ideation, which has implications for prevention. This study examined the association between intrinsic neural network coherence and the severity of suicidal ideation in depressed adolescents. Forty adolescents diagnosed with Major Depressive Disorder were administered the Columbia-Suicide Severity Rating Scale and underwent resting-state fMRI. We quantified within-network coherence in the executive control (ECN), default mode (DMN), and salience (SN) networks, and in a non-relevant network consisting of noise signal. We associated coherence in each of these networks with the greatest lifetime severity of suicidal ideation experienced, covarying for motion, age of depression onset, and severity of current depressive and anxious symptoms. Lower coherence in the left ECN, anterior DMN, and SN were independently associated with greater lifetime severity of suicidal ideation. When including all three significant networks and covariates in a single model, only the left ECN significantly predicted suicidal ideation. Studies with a larger sample size are needed to verify our findings. Our finding of hypoconnectivity in multiple networks extends emerging evidence for hypoconnectivity in adolescent suicidality and is consistent with theoretical conceptualizations of suicidal ideation as a complex set of cognitions associated with cognitive control, self-referential thinking, and processing salient information. While multiple networks could be targets for effective early interventions, those targeting ECN functionality (cognitive control) may be particularly beneficial. Copyright © 2017 Elsevier B.V. All rights reserved.
Fiber Bragg Grating sensor for fault detection in radial and network transmission lines.
Moghadas, Amin A; Shadaram, Mehdi
2010-01-01
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.
Directory of Open Access Journals (Sweden)
M. F. Akorede
2017-06-01
Full Text Available The intent of power distribution companies (DISCOs is to deliver electric power to their customers in an efficient and reliable manner – with minimal energy loss cost. One major way to minimise power loss on a given power system is to install distributed generation (DG units on the distribution networks. However, to maximise benefits, it is highly crucial for a DISCO to ensure that these DG units are of optimal size and sited in the best locations on the network. This paper gives an overview of a software package developed in this study, called Power System Analysis and DG Optimisation Tool (PFADOT. The main purpose of the graphical user interface-based package is to guide a DISCO in finding the optimal size and location for DG placement in radial distribution networks. The package, which is also suitable for load flow analysis, employs the GUI feature of MATLAB. Three objective functions are formulated into a single optimisation problem and solved with fuzzy genetic algorithm to simultaneously obtain DG optimal size and location. The accuracy and reliability of the developed tool was validated using several radial test systems, and the results obtained are evaluated against the existing similar package cited in the literature, which are impressive and computationally efficient.
Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines
Directory of Open Access Journals (Sweden)
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.
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%.
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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.
A QCQP Approach for OPF in Multiphase Radial Networks with Wye and Delta Connections: Preprint
Energy Technology Data Exchange (ETDEWEB)
Zamzam, Ahmed, S.; Zhaoy, Changhong; Dall' Anesey, Emiliano; Sidiropoulos, Nicholas D.
2017-06-27
This paper examines the AC Optimal Power Flow (OPF) problem for multiphase distribution networks featuring renewable energy resources (RESs). We start by outlining a power flow model for radial multiphase systems that accommodates wye-connected and delta-connected RESs and non-controllable energy assets. We then formalize an AC OPF problem that accounts for both types of connections. Similar to various AC OPF renditions, the resultant problem is a non convex quadratically-constrained quadratic program. However, the so-called Feasible Point Pursuit-Successive Convex Approximation algorithm is leveraged to obtain a feasible and yet locally-optimal solution. The merits of the proposed solution approach are demonstrated using two unbalanced multiphase distribution feeders with both wye and delta connections.
Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia
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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.
Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
1988-03-28
form of the interpolating func- tions Sk(T) = \\0k+ AikO (l[-1- y 11) xE En k = 1,2,...,n’ (8) j=1 These coefficients enter the least squares formalism...the m distinct data points in R n are associated with rn vectors L E Rn’. The interpolation condition of equation (1) thus generalises to Sk()= , k s = k ...2,..., k = 1,2,...,n’ (5) which leads to interpolating functions of the form m si-(Z) ZA kA,(I1x- y x E IR kn (6) The expansion coefficients Ajk are
Klasifikasi Kelainan Bentuk Sel Darah Merah Menggunakan Radial Basis Function Network
Sari, Anita Ratna
2016-01-01
Beside a physical examination, blood analysis is one of methods that can rely on in diagnosing a disease, because blood has many components that contains important information. Morphological examination of peripheral blood smear is a laboratory test that is very important and should be evaluated well. However, analysis of malformed red blood cells, performed by an analyst is not always same as other analysts due to lack of precision, concentration, and inadequate knowledge. In addition, morph...
METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS
L. V. Serebryanaya; V. V. Potaraev
2016-01-01
The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.
Convex Relaxation of OPF in Multiphase Radial Networks with Wye and Delta Connections
Energy Technology Data Exchange (ETDEWEB)
Zhao, Changhong [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Low, Steven [California Institute of Technology
2017-08-01
This panel presentation focuses on multiphase radial distribution networks with wye and delta connections, and proposes a semidefinite relaxation of the AC optimal power flow (OPF) problem. Two multiphase power flow models are developed to facilitate the integration of delta-connected loads or generation resources in the OPF problem. The first model is referred to as the extended branch flow model (EBFM). The second model leverages a linear relationship between phase-to-ground power injections and delta connections that holds under a balanced voltage approximation (BVA). Based on these models, pertinent OPF problems are formulated and relaxed to semidefinite programs (SDPs). Numerical studies on IEEE test feeders show that the proposed SDP relaxations can be solved efficiently by a generic optimization solver. Numerical evidence also indicates that solving the resultant SDP under BVA is faster than under EBFM. Moreover, both SDP solutions are numerically exact with respect to voltages and branch flows. It is further shown that the SDP solution under BVA has a small optimality gap, and the BVA model is accurate in the sense that it reproduces actual system voltages.
Optimal Power Flow in Multiphase Radial Networks with Delta Connections: Preprint
Energy Technology Data Exchange (ETDEWEB)
Zhao, Changhong [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Low, Steven H. [California Institute of Technology
2017-11-27
This paper focuses on multiphase radial distribution networks with mixed wye and delta connections, and proposes a semidefinite relaxation of the AC optimal power flow (OPF) problem. Two multiphase power-flow models are developed to facilitate the integration of delta-connected generation units/loads in the OPF problem. The first model extends traditional branch flow models - and it is referred to as extended branch flow model (EBFM). The second model leverages a linear relationship between per-phase power injections and delta connections, which holds under a balanced voltage approximation (BVA). Based on these models, pertinent OPF problems are formulated and relaxed to semidefinite programs (SDPs). Numerical studies on IEEE test feeders show that SDP relaxations can be solved efficiently by a generic optimization solver. Numerical evidences indicate that solving the resultant SDP under BVA is faster than under EBFM. Moreover, both SDP solutions are numerically exact with respect to voltages and branch flows. It is also shown that the SDP solution under BVA has a small optimality gap, while the BVA model is accurate in the sense that it reflects actual system voltages.
METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS
Directory of Open Access Journals (Sweden)
L. V. Serebryanaya
2016-01-01
Full Text Available The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.
de Wit, R.W.L.; Valentine, A.P.; Trampert, J.
2013-01-01
How do body-wave traveltimes constrain the Earth's radial (1-D) seismic structure? Existing 1-D seismological models underpin 3-D seismic tomography and earthquake location algorithms. It is therefore crucial to assess the quality of such 1-D models, yet quantifying uncertainties in seismological
Revisiting Earth's radial seismic structure using a Bayesian neural network approach
de Wit, R.W.L.
2015-01-01
The gross features of seismic observations can be explained by relatively simple spherically symmetric (1-D) models of wave velocities, density and attenuation, which describe the Earth's average(radial) structure. 1-D earth models are often used as a reference for studies on Earth's thermo-chemical
Quantifying and analyzing the network basis of genetic complexity.
Directory of Open Access Journals (Sweden)
Ethan G Thompson
Full Text Available Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties.
Radial multiresolution in dimension three.
Rauhut, H.; Rösler, M.M.
2005-01-01
Abstract We present a construction of a wavelet-type orthonormal basis for the space of radial $L^2$-functions in {\\bf R}$^3$ via the concept of a radial multiresolution analysis. The elements of the basis are obtained from a single radial wavelet by usual dilations and generalized translations.
Neural Networks For Electrohydrodynamic Effect Modelling
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Wiesław Wajs
2004-01-01
Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.
Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks.
Yang, Zheng Rong
2005-05-01
Apoptosis has drawn the attention of researchers because of its importance in treating some diseases through finding a proper way to block or slow down the apoptosis process. Having understood that caspase cleavage is the key to apoptosis, we find novel methods or algorithms are essential for studying the specificity of caspase cleavage activity and this helps the effective drug design. As bio-basis function neural networks have proven to outperform some conventional neural learning algorithms, there is a motivation, in this study, to investigate the application of bio-basis function neural networks for the prediction of caspase cleavage sites. Thirteen protein sequences with experimentally determined caspase cleavage sites were downloaded from NCBI. Bayesian bio-basis function neural networks are investigated and the comparisons with single-layer perceptrons, multilayer perceptrons, the original bio-basis function neural networks and support vector machines are given. The impact of the sliding window size used to generate sub-sequences for modelling on prediction accuracy is studied. The results show that the Bayesian bio-basis function neural network with two Gaussian distributions for model parameters (weights) performed the best and the highest prediction accuracy is 97.15 +/- 1.13%. The package of Bayesian bio-basis function neural network can be obtained by request to the author.
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.
Modelling cell cycle synchronisation in networks of coupled radial glial cells.
Barrack, Duncan S; Thul, Rüdiger; Owen, Markus R
2015-07-21
Radial glial cells play a crucial role in the embryonic mammalian brain. Their proliferation is thought to be controlled, in part, by ATP mediated calcium signals. It has been hypothesised that these signals act to locally synchronise cell cycles, so that clusters of cells proliferate together, shedding daughter cells in uniform sheets. In this paper we investigate this cell cycle synchronisation by taking an ordinary differential equation model that couples the dynamics of intracellular calcium and the cell cycle and extend it to populations of cells coupled via extracellular ATP signals. Through bifurcation analysis we show that although ATP mediated calcium release can lead to cell cycle synchronisation, a number of other asynchronous oscillatory solutions including torus solutions dominate the parameter space and cell cycle synchronisation is far from guaranteed. Despite this, numerical results indicate that the transient and not the asymptotic behaviour of the system is important in accounting for cell cycle synchronisation. In particular, quiescent cells can be entrained on to the cell cycle via ATP mediated calcium signals initiated by a driving cell and crucially will cycle in near synchrony with the driving cell for the duration of neurogenesis. This behaviour is highly sensitive to the timing of ATP release, with release at the G1/S phase transition of the cell cycle far more likely to lead to near synchrony than release during mid G1 phase. This result, which suggests that ATP release timing is critical to radial glia cell cycle synchronisation, may help us to understand normal and pathological brain development. Copyright © 2015 Elsevier Ltd. All rights reserved.
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 152Eu and 137Cs 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.
Distributed generation placement in radial distribution networks using a bat-inspired algorithm
Directory of Open Access Journals (Sweden)
John Edwin Candelo-Becerra
2015-01-01
Full Text Available La generación distribuida (DG e s un tema importan te para las r edes de distribución debido a la reducción de las pérdidas de e nergía, pero la ubicación y el tam año de generadores puede ser una tarea dif ícil para las técnicas de solución exactas. Las técnicas metahe urísticas se han convertido en una mejor opci ón para determinar soluciones v álidas y en este trabajo se presenta la aplicación de un algori tmo inspirado en murciélagos (BA a un problema d e ubicación y dimensionamien to de generación distribuida en sistemas de distribución radial . Una comparación entre la técnica de optimización por enjambre de pa rtículas (PSO y BA fue hecha en los sistemas de prueba de 33 n odos y 69 nodos, utilizando como escenario s el cambio en la potencia a ctiva y reactiva, y el número de generadores. PSO y BA encontra ron buenos resultados para un número pequeño y pocas capacidades de genera ción, pero BA obtuvo mejores re sultados para problemas difícile s y converge más rápido para todos los escenarios. Las máximas inye cciones de potencia activa para reducir las pérdidas de energía en las redes de distribución fueron enc ontradas para los cinco escenar ios.
Tang, Guodong; Chen, Si; Ye, Feng; Xu, Xiaopeng; Fang, Jing; Wang, Xu
2014-07-11
We report a unique loofah-like gel network that is supported by the sectional type hexagonal columnar assembly of flexuous furcate fibers, which are constructed by plane-to-plane stacking of a novel 3D radially symmetrical gelator with POSS as the core and L-lysine as the arm.
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Lixia Pei
Full Text Available The clinical application of Traditional Chinese medicine (TCM, using several herbs in combination (called formulas, has a history of more than one thousand years. However, the bioactive compounds that account for their therapeutic effects remain unclear. We hypothesized that the material basis of a formula are those compounds with a high content in the decoction that are maintained at a certain level in the system circulation. Network pharmacology provides new methodological insights for complicated system studies. In this study, we propose combining pharmacokinetic (PK analysis with network pharmacology to explore the material basis of TCM formulas as exemplified by the Bushen Zhuanggu formula (BZ composed of Psoralea corylifolia L., Aconitum carmichaeli Debx., and Cnidium monnieri (L. Cuss. A sensitive and credible liquid chromatography tandem mass spectrometry (LC-MS/MS method was established for the simultaneous determination of 15 compounds present in the three herbs. The concentrations of these compounds in the BZ decoction and in rat plasma after oral BZ administration were determined. Up to 12 compounds were detected in the BZ decoction, but only 5 could be analyzed using PK parameters. Combined PK results, network pharmacology analysis revealed that 4 compounds might serve as the material basis for BZ. We concluded that a sensitive, reliable, and suitable LC-MS/MS method for both the composition and pharmacokinetic study of BZ has been established. The combination of PK with network pharmacology might be a potent method for exploring the material basis of TCM formulas.
Concept of the dealer-service network management on the system approach basis
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Irina MAKAROVA
2011-01-01
Full Text Available In article the method of improvement of automobile service quality within the limits of a dealer-service network limits, by building of information-logistical system and feedback mechanism adjustment is considered. As operating influence application of the discounts` system calculated on the basis of forward orderings on spare parts arriving from the service centers is offered.
Liu, Sheng; Zheng, Jin; Chen, Xiuping
2013-01-01
The clinical application of Traditional Chinese medicine (TCM), using several herbs in combination (called formulas), has a history of more than one thousand years. However, the bioactive compounds that account for their therapeutic effects remain unclear. We hypothesized that the material basis of a formula are those compounds with a high content in the decoction that are maintained at a certain level in the system circulation. Network pharmacology provides new methodological insights for complicated system studies. In this study, we propose combining pharmacokinetic (PK) analysis with network pharmacology to explore the material basis of TCM formulas as exemplified by the Bushen Zhuanggu formula (BZ) composed of Psoralea corylifolia L., Aconitum carmichaeli Debx., and Cnidium monnieri (L.) Cuss. A sensitive and credible liquid chromatography tandem mass spectrometry (LC-MS/MS) method was established for the simultaneous determination of 15 compounds present in the three herbs. The concentrations of these compounds in the BZ decoction and in rat plasma after oral BZ administration were determined. Up to 12 compounds were detected in the BZ decoction, but only 5 could be analyzed using PK parameters. Combined PK results, network pharmacology analysis revealed that 4 compounds might serve as the material basis for BZ. We concluded that a sensitive, reliable, and suitable LC-MS/MS method for both the composition and pharmacokinetic study of BZ has been established. The combination of PK with network pharmacology might be a potent method for exploring the material basis of TCM formulas. PMID:23468985
Energy Technology Data Exchange (ETDEWEB)
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
Liu, Wei; Chang, Qing-Rui; Guo, Man; Xing, Dong-Xing; Yuan, Yong-Sheng
2011-04-01
The hyperspectral leaf reflectance in winter wheat was measured under 4 phosphorus levels at different growth stages, i.e. revival stage, jointing stage, tassel stage and grouting stage. And their first derivative of spectra were calculated and denoised by the threshold denoising method based on wavelet transform. After studying characteristics of the two kinds of spectra resulting from different phosphorus contents levels as well as correlations between leaf phosphorus contents and spectral values, sensitive wavebands and four kinds of absorption areas were extracted. Then the four kinds of absorption areas and their corresponding leaf phosphorus content were normalized and input to RBFNN. Results show that: (1) Sensitive wavebands for monitoring leaf phosphorus contents in original leaf spectra are 426-435 and 669-680 nm. (2) Sensitive wavebands in first derivative of spectra are 481-493 and 685-696 nm. (3) Trained RBFNN can learn and seize the linearity/non-linearity mapping between samples and output targets.
Directory of Open Access Journals (Sweden)
Chennubhotla Chakra
2006-10-01
Full Text Available Abstract Background Signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory (TR mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. However, the network topological basis of developing such integrated responses remains poorly understood. Results By studying the TR network of the yeast Saccharomyces cerevisiae we show that an intermediate layer of transcription factors naturally segregates into distinct subnetworks. In these topological units transcription factors are densely interlinked in a largely hierarchical manner and respond to external signals by utilizing a fraction of these subnets. Conclusion As transcriptional regulation represents the 'slow' component of overall information processing, the identified topology suggests a model in which successive waves of transcriptional regulation originating from distinct fractions of the TR network control robust integrated responses to complex stimuli.
The Method of Building a Network of Online Showcases on the Basis of the MVC Architecture
Directory of Open Access Journals (Sweden)
Pursky Oleg I.
2017-10-01
Full Text Available A method to build a network of online showcases that support a large number of customer orders and visits, which meets the current performance standards and the reliability of Internet solutions in the sphere electronic commerce, has been developed. The method involves the creation of a typical showcase and the implementation for the information management system of a showcases network of an own database operating on the data from the central management information system with the two-way data replication. A mechanism for «cloning» the online showcases, which are part of the network, and their quick integration with the business processes of enterprise and a management system based on a typical showcase, has been proposed. The development of typical online showcases is implemented on the basis of MVC concept (Model-View-Controller, the ASP.NET MVC Framework Technology, and the visual templates of web pages, thus ensuring that the algorithms for the behavior of objects are independent of both the objects themselves and their visual representation. This enhances the development of e-commerce projects significantly, speeds up the implementation process, and provides a high degree of flexibility and functionality of the online showcases.
Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.
Lin, Chuan-Kai
2005-04-01
A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.
Neural network simulation of the industrial producer price index dynamical series
Soshnikov, L. E.
2013-01-01
This paper is devoted the simulation and forecast of dynamical series of the economical indicators. Multilayer perceptron and Radial basis function neural networks have been used. The neural networks model results are compared with the econometrical modeling.
Directory of Open Access Journals (Sweden)
R. Rajaram
2015-11-01
Full Text Available Network reconfiguration which is constrained non linear optimization problem has been solved for loss minimization, load balancing, etc. for last two decades using various heuristic search evolutionary algorithms like binary particle swarm optimization, neuro-fuzzy techniques, etc. The contribution of this paper lies in considering distributed generation which are smaller power sources like solar photovoltaic cells or wind turbines connected in the customer roof top. This new connection in the radial network has made unidirectional current flow to become bidirectional there by increasing the efficiency but sometimes reducing stability of the system. Modified plant growth simulation algorithm has been applied here successfully to minimize real power loss because it does not require barrier factors or cross over rates because the objectives and constraints are dealt separately. The main advantage of this algorithm is continuous guiding search along with changing objective function because power from distributed generation is continuously varying so this can be applied for real time applications with required modifications. This algorithm here is tested for a standard 33 bus radial distribution system for loss minimization and test results here shows that this algorithm is efficient and suitable for real time applications.
Directory of Open Access Journals (Sweden)
A. V. Belov
2003-06-01
Full Text Available Ulysses, launched in October 1990, began its second out-of-ecliptic orbit in September 1997. In 2000/2001 the spacecraft passed from the south to the north polar regions of the Sun in the inner heliosphere. In contrast to the first rapid pole to pole passage in 1994/1995 close to solar minimum, Ulysses experiences now solar maximum conditions. The Kiel Electron Telescope (KET measures also protons and alpha-particles in the energy range from 5 MeV/n to >2 GeV/n. To derive radial and latitudinal gradients for >2 GeV/n protons and alpha-particles, data from the Chicago instrument on board IMP-8 and the neutron monitor network have been used to determine the corresponding time profiles at Earth. We obtain a spatial distribution at solar maximum which differs greatly from the solar minimum distribution. A steady-state approximation, which was characterized by a small radial and significant latitudinal gradient at solar minimum, was interchanged with a highly variable one with a large radial and a small – consistent with zero – latitudinal gradient. A significant deviation from a spherically symmetric cosmic ray distribution following the reversal of the solar magnetic field in 2000/2001 has not been observed yet. A small deviation has only been observed at northern polar regions, showing an excess of particles instead of the expected depression. This indicates that the reconfiguration of the heliospheric magnetic field, caused by the reappearance of the northern polar coronal hole, starts dominating the modulation of galactic cosmic rays already at solar maximum.Key words. Interplanetary physics (cosmic rays; energetic particles – Space plasma physics (charged particle motion and acceleration
Directory of Open Access Journals (Sweden)
A. V. Belov
Full Text Available Ulysses, launched in October 1990, began its second out-of-ecliptic orbit in September 1997. In 2000/2001 the spacecraft passed from the south to the north polar regions of the Sun in the inner heliosphere. In contrast to the first rapid pole to pole passage in 1994/1995 close to solar minimum, Ulysses experiences now solar maximum conditions. The Kiel Electron Telescope (KET measures also protons and alpha-particles in the energy range from 5 MeV/n to >2 GeV/n. To derive radial and latitudinal gradients for >2 GeV/n protons and alpha-particles, data from the Chicago instrument on board IMP-8 and the neutron monitor network have been used to determine the corresponding time profiles at Earth. We obtain a spatial distribution at solar maximum which differs greatly from the solar minimum distribution. A steady-state approximation, which was characterized by a small radial and significant latitudinal gradient at solar minimum, was interchanged with a highly variable one with a large radial and a small – consistent with zero – latitudinal gradient. A significant deviation from a spherically symmetric cosmic ray distribution following the reversal of the solar magnetic field in 2000/2001 has not been observed yet. A small deviation has only been observed at northern polar regions, showing an excess of particles instead of the expected depression. This indicates that the reconfiguration of the heliospheric magnetic field, caused by the reappearance of the northern polar coronal hole, starts dominating the modulation of galactic cosmic rays already at solar maximum.
Key words. Interplanetary physics (cosmic rays; energetic particles – Space plasma physics (charged particle motion and acceleration
Fernández, Michael; Caballero, Julio; Fernández, Leyden; Abreu, José Ignacio; Garriga, Miguel
2007-11-01
Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.
Development of the open drainage network of St. Petersburg suburbs on the basis of GIS technologies
Chusov, Alexandr; Shishkin, Ilya; Epifanov, Andrey; Kochetkov, Ivan
2017-10-01
The article discusses the methodology of solving problems associated with the forecast of the future state and ways of development of open drainage networks, which includes: assessment of the current and future state of open drainage network, calculation of the potential economic damage and prioritizing of repair works on the elements of open drainage network. These solutions were used to forecast the development of open drainage network of St. Petersburg suburbs.
Directory of Open Access Journals (Sweden)
D. Tien Bui
2017-09-01
Full Text Available In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM, radial-basis-function Fisher discriminant analysis (RBFDA, and a geographic information system (GIS database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.
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.
Dynamical basis of intentions and expectations in a simple neuronal network
Proekt, Alex; Brezina, Vladimir; Weiss, Klaudiusz R.
2004-06-01
Selection of behavioral responses to external stimuli is strongly influenced by internal states, such as intentions and expectations. These internal states are often attributed to higher-order brain functions. Yet here we show that even in the simple feeding network of Aplysia, external stimuli do not directly specify which motor output is expressed; instead, the motor output is specified by the state of the network at the moment of stimulation. The history-dependence of this network state manifests itself in the same way as do intentions and expectations in the behavior of higher animals. Remarkably, we find that activity-dependent plasticity of a synapse within the network itself, rather than some higher-order network, mediates one important aspect of the change in the network state. Through this mechanism, changes in the network state become an automatic consequence of the generation of behavior. Altogether, our findings suggest that intentions and expectations may emerge within behavior-generating networks themselves from the plasticity of the very processes that generate the behavior.
Directory of Open Access Journals (Sweden)
Ali Fedakar
2011-04-01
Full Text Available Upper limb aneurysms are less frequently seen than the other aneurysm. Radial arterial aneurysm is usually associated with the trauma. Interventional procedures can cause pseudoaneurysm at the radial artery puncture sites. Radial artery aneurysm may cause the thromboembolic events at the fingers and the hand. We present a case of isolated radial arterial aneurysm with idiopathic origin.
The NOVO Network: the original scientific basis for its establishment and our R&D vision
DEFF Research Database (Denmark)
Winkel, Jørgen; Edwards, Kasper; Dellve, L.
2017-01-01
The NOVO network is a Nordic non-governmental professional association whose aims are to foster the scientific progress, knowledge and development of the working environment within Healthcare as an integrated part of production system development. The vision is a “Nordic Model for Sustainable...... for development of more sustainable production systems in healthcare”. Future R&D performed within the framework of our NOVO network should substantiate this hypothesis. In practical terms, this necessitates expanded research protocols....... Systems” in the healthcare sector. It was founded in 2006 in Copenhagen and was financially supported by the Nordic Council of Ministers from 2007 to 2015. The motivation to establish the NOVO Network arose when reviewing the literature regarding opportunities to create sustainable production systems...
Bayesian networks for victim identification on the basis of DNA profiles
Bruijning-van Dongen, C. J.; Slooten, K.; Burgers, W.; Wiegerinck, W.
We have developed software to improve screening and matching routine for victim identification based on DNA profiles. The software, called Napoleon/Bonaparte, uses Bayesian networks for the analysis. It is designed for effective handling of the identification process in case of a large disaster with
Dynamic neural networking as a basis for plasticity in the control of heart rate.
Kember, G; Armour, J A; Zamir, M
2013-01-21
A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.
Voytek, Bradley; Knight, Robert T
2015-06-15
Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this article, we begin from the perspective that successful interregional communication relies upon the transient synchronization between distinct low-frequency (communication via phase-coordinated local neuronal spiking. From this, we construct a theoretical framework for dynamic network communication, arguing that these networks reflect a balance between oscillatory coupling and local population spiking activity and that these two levels of activity interact. We theorize that when oscillatory coupling is too strong, spike timing within the local neuronal population becomes too synchronous; when oscillatory coupling is too weak, spike timing is too disorganized. Each results in specific disruptions to neural communication. These alterations in communication dynamics may underlie cognitive changes associated with healthy development and aging, in addition to neurological and psychiatric disorders. A number of neurological and psychiatric disorders-including Parkinson's disease, autism, depression, schizophrenia, and anxiety-are associated with abnormalities in oscillatory activity. Although aging, psychiatric and neurological disease, and experience differ in the biological changes to structural gray or white matter, neurotransmission, and gene expression, our framework suggests that any resultant cognitive and behavioral changes in normal or disordered states or their treatment are a product of how these physical processes affect dynamic network communication. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
National Research Council Canada - National Science Library
Ningbo Zhao; Zhiming Li
2017-01-01
In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids...
SYNERGY IN DEMAND MANAGEMENT ON THE EDUCATIONAL SERVICES MARKET ON THE BASIS OF EDUCATION NETWORKS
Korenkova Natalia Anatolyevna
2012-01-01
Purpose: to review the approach to social technology of demand management on educational services on the basis of synergetic approach allowing due to selforganizing processes to provide balance of actors interests in the market of educational services. Methodology: For the analysis of the basic synergetic approach properties applicable to management of social structures, the system approach within the paradigm of social behavior, as well as methods of abstraction, analysis, synthesis and mode...
Vestibular and Attractor Network Basis of the Head Direction Cell Signal in Subcortical Circuits
Directory of Open Access Journals (Sweden)
Benjamin J Clark
2012-03-01
Full Text Available Accurate navigation depends on a network of neural systems that encode the moment-to-moment changes in an animal’s directional orientation and location in space. Within this navigation system are head direction (HD cells, which fire persistently when an animal’s head is pointed in a particular direction (Sharp et al., 2001a; Taube, 2007. HD cells are widely thought to underlie an animal’s sense of spatial orientation, and research over the last 25+ years has revealed that this robust spatial signal is widely distributed across subcortical and cortical limbic areas. Much of this work has been directed at understanding the functional organization of the HD cell circuitry, and precisely how this signal is generated from sensory and motor systems. The purpose of the present review is to summarize some of the recent studies arguing that the HD cell circuit is largely processed in a hierarchical fashion, following a pathway involving the dorsal tegmental nuclei → lateral mammillary nuclei → anterior thalamus → parahippocampal and retrosplenial cortical regions. We also review recent work identifying bursting cellular activity in the HD cell circuit after lesions of the vestibular system, and relate these observations to the long held view that attractor network mechanisms underlie HD signal generation. Finally, we summarize the work to date suggesting that this network architecture may reside within the tegmento-mammillary circuit.
The Usage of Neural Networks for the Medical Diagnosis
Malyshevska, Kateryna
2009-01-01
The problem of cancer diagnosis from multi-channel images using the neural networks is investigated. The goal of this work is to classify the different tissue types which are used to determine the cancer risk. The radial basis function networks and backpropagation neural networks are used for classification. The results of experiments are presented.
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological ...
Molecular Basis of the Core Regulatory Network in ABA Responses: Sensing, Signaling and Transport
Nakashima, Kazuo; Miyakawa, Takuya; Kuromori, Takashi; Tanokura, Masaru; Shinozaki, Kazuo; Yamaguchi-Shinozaki, Kazuko
2010-01-01
ABA is a major phytohormone that regulates a broad range of plant traits and is especially important for adaptation to environmental conditions. Our understanding of the molecular basis of ABA responses in plants improved dramatically in 2009 and 2010, banner years for ABA research. There are three major components; PYR/PYL/ RCAR (an ABA receptor), type 2C protein phosphatase (PP2C; a negative regulator) and SNF1-related protein kinase 2 (SnRK2; a positive regulator), and they offer a double negative regulatory system, [PYR/PYL/RCAR—| PP2C—| SnRK2]. In the absence of ABA, PP2C inactivates SnRK2 by direct dephosphorylation. In response to environmental or developmental cues, ABA promotes the interaction of PYR/PYL/RCAR and PP2C, resulting in PP2C inhibition and SnRK2 activation. This signaling complex can work in both the nucleus and cytosol, as it has been shown that SnRK2 phosphorylates basic-domain leucine zipper (bZIP) transcription factors or membrane proteins. Several structural analyses of PYR/PYL/RCAR have provided the mechanistic basis for this ‘core signaling’ model, by elucidating the mechanism of ABA binding of receptors, or the ‘gate–latch–lock’ mechanism of interaction with PP2C in inhibiting activity. On the other hand, intercellular ABA transport had remained a major issue, as had intracellular ABA signaling. Recently, two plasma membrane-type ABC transporters were identified and shed light on the influx/efflux system of ABA, resolving how ABA is transported from cell to cell in plants. Our knowledge of ABA responses in plants has been greatly expanded from intracellular signaling to intercellular transport of ABA. PMID:20980270
Analysis of radial vibrations of poroelastic circular cylindrical shells ...
African Journals Online (AJOL)
Waves propagating in radial direction of a poroelastic circular cylinder are termed as radial vibrations. Radial vibrations of poroelastic circular cylindrical shell of infinite extent immersed in an inviscid elastic fluid are examined employing Biot's theory. Biot's model consists of an elastic matrix permeated by a network of ...
National Research Council Canada - National Science Library
Kerdphol, Thongchart; Qudaih, Yaser; Watanabe, Masayuki; Mitani, Yasunori
2016-01-01
...) in a short period of time.This paper presents a new method for the intelligent online management of both active and reactive power of a BESS based on a radial basis function neural network (RBFNN...
Distributed storage healthcare - the basis of a planet-wide public health care network.
Kakouros, Nikolaos
2013-01-01
As health providers move towards higher levels of information technology (IT) integration, they become increasingly dependent on the availability of the electronic health record (EHR). Current solutions of individually managed storage by each healthcare provider focus on efforts to ensure data security, availability and redundancy. Such models, however, scale poorly to a future of a planet-wide public health-care network (PWPHN). Our aim was to review the research literature on distributed storage systems and propose methods that may aid the implementation of a PWPHN. A systematic review was carried out of the research dealing with distributed storage systems and EHR. A literature search was conducted on five electronic databases: Pubmed/Medline, Cinalh, EMBASE, Web of Science (ISI) and Google Scholar and then expanded to include non-authoritative sources. The English National Health Service Spine represents the most established country-wide PHN but is limited in deployment and remains underused. Other, literature identified and established distributed EHR attempts are more limited in scope. We discuss the currently available distributed file storage solutions and propose a schema of how one of these technologies can be used to deploy a distributed storage of EHR with benefits in terms of enhanced fault tolerance and global availability within the PWPHN. We conclude that a PWPHN distributed health care record storage system is technically feasible over current Internet infrastructure. Nonetheless, the socioeconomic viability of PWPHN implementations remains to be determined.
About "axial" and "radial" diffusivities.
Wheeler-Kingshott, Claudia A M; Cercignani, Mara
2009-05-01
This article presents the potential problems arising from the use of "axial" and "radial" diffusivities, derived from the eigenvalues of the diffusion tensor, and their interpretation in terms of the underlying biophysical properties, such as myelin and axonal density. Simulated and in vivo data are shown. The simulations demonstrate that a change in "radial" diffusivity can cause a fictitious change in "axial" diffusivity and vice versa in voxels characterized by crossing fibers. The in vivo data compare the direction of the principle eigenvector in four different subjects, two healthy and two affected by multiple sclerosis, and show that the angle, alpha, between the principal eigenvectors of corresponding voxels of registered datasets is greater than 45 degrees in areas of low anisotropy, severe pathology, and partial volume. Also, there are areas of white matter pathology where the "radial" diffusivity is 10% greater than that of the corresponding normal tissue and where the direction of the principal eigenvector is altered by more than 45 degrees compared to the healthy case. This should strongly discourage researchers from interpreting changes of the "axial" and "radial" diffusivities on the basis of the underlying tissue structure, unless accompanied by a thorough investigation of their mathematical and geometrical properties in each dataset studied. (c) 2009 Wiley-Liss, Inc.
Zhou, Jingwen; Xu, Zhenghong; Chen, Shouwen
2013-04-01
The thuringiensin abiotic degradation processes in aqueous solution under different conditions, with a pH range of 5.0-9.0 and a temperature range of 10-40°C, were systematically investigated by an exponential decay model and a radius basis function (RBF) neural network model, respectively. The half-lives of thuringiensin calculated by the exponential decay model ranged from 2.72 d to 16.19 d under the different conditions mentioned above. Furthermore, an RBF model with accuracy of 0.1 and SPREAD value 5 was employed to model the degradation processes. The results showed that the model could simulate and predict the degradation processes well. Both the half-lives and the prediction data showed that thuringiensin was an easily degradable antibiotic, which could be an important factor in the evaluation of its safety. Copyright © 2012 Elsevier Ltd. All rights reserved.
Huang, Sui; Ingber, Donald E
It is commonly assumed that somatic evolution drives the multi-step process that produces metastatic cancer. But it is difficult to reconcile the inexorable progression towards metastasis in virtually all carcinomas and the associated complex change of cancer cell phenotype, characterized by an epithelial-to-mesenchymal transition, with the random nature of gene mutations. Given their irreversible nature, it is also difficult to explain why certain metastatic carcinomas can reform normal tissue boundaries and remain dormant for years at distant sites. Here we propose an encompassing conceptual framework based on system-level dynamics of gene regulatory networks that may help reconcile these inconsistencies. The concepts of gene expression state space and attractors are introduced which provide a mathematical and molecular basis for an "epigenetic landscape". We then describe how cancer cells are trapped in "embryonic attractors" because of distortions of this landscape caused by mutational rewiring of the regulatory network. The implications of this concept for a new integrative understanding of tumor formation and metastatic progression are discussed. This formal framework of cancer progression unites mainstream genetic determinism with alternative ideas that emphasize non-genetic influences, including chronic growth stimulation,extracellular matrix remodeling, alteration of cell mechanics and disruption of tissue architecture.
Ebert, Todd A [West Palm Beach, FL; Carella, John A [Jupiter, FL
2012-03-13
A triple acting radial seal used as an interstage seal assembly in a gas turbine engine, where the seal assembly includes an interstage seal support extending from a stationary inner shroud of a vane ring, the interstage seal support includes a larger annular radial inward facing groove in which an outer annular floating seal assembly is secured for radial displacement, and the outer annular floating seal assembly includes a smaller annular radial inward facing groove in which an inner annular floating seal assembly is secured also for radial displacement. A compliant seal is secured to the inner annular floating seal assembly. The outer annular floating seal assembly encapsulates the inner annular floating seal assembly which is made from a very low alpha material in order to reduce thermal stress.
Bartoníček, J; Naňka, O; Tuček, M
2015-10-01
In the clinical practice, radial shaft may be exposed via two approaches, namely the posterolateral Thompson and volar (anterior) Henry approaches. A feared complication of both of them is the injury to the deep branch of the radial nerve. No consensus has been reached, yet, as to which of the two approaches is more beneficial for the proximal half of radius. According to our anatomical studies and clinical experience, Thompson approach is safe only in fractures of the middle and distal thirds of the radial shaft, but highly risky in fractures of its proximal third. Henry approach may be used in any fracture of the radial shaft and provides a safe exposure of the entire lateral and anterior surfaces of the radius.The Henry approach has three phases. In the first phase, incision is made along the line connecting the biceps brachii tendon and the styloid process of radius. Care must be taken not to damage the lateral cutaneous nerve of forearm.In the second phase, fascia is incised and the brachioradialis identified by the typical transition from the muscle belly to tendon and the shape of the tendon. On the lateral side, the brachioradialis lines the space with the radial artery and veins and the superficial branch of the radial nerve running at its bottom. On the medial side, the space is defined by the pronator teres in the proximal part and the flexor carpi radialis in the distal part. The superficial branch of the radial nerve is retracted together with the brachioradialis laterally, and the radial artery medially.In the third phase, the attachment of the pronator teres is identified by its typical tendon in the middle of convexity of the lateral surface of the radial shaft. The proximal half of the radius must be exposed very carefully in order not to damage the deep branch of the radial nerve. Dissection starts at the insertion of the pronator teres and proceeds proximally along its lateral border in interval between this muscle and insertion of the supinator
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.
Directory of Open Access Journals (Sweden)
Yu Bai
2011-01-01
Full Text Available The anatomical basis for the concept of meridians in traditional Chinese medicine (TCM has not been resolved. This paper reviews the evidence supporting a relationship between acupuncture points/meridians and fascia. The reviewed evidence supports the view that the human body's fascia network may be the physical substrate represented by the meridians of TCM. Specifically, this hypothesis is supported by anatomical observations of body scan data demonstrating that the fascia network resembles the theoretical meridian system in salient ways, as well as physiological, histological, and clinical observations. This view represents a theoretical basis and means for applying modern biomedical research to examining TCM principles and therapies, and it favors a holistic approach to diagnosis and treatment.
Bruinsma, Gerben; Bernasco, Wim
In the study of organised crime, the traditional view of criminal groups as centrally controlled organisations has been replaced by the notion of criminal networks. However, little use has been made of concepts and theories of social networks that have developed in other social sciences. This paper
Radially truncated galactic discs
de Grijs, R; Kregel, M; Wesson, KH
2001-01-01
We present the first results of a systematic analysis of radially truncated exponential discs for four galaxies of a complete sample of disc-dominated edge-on spiral galaxies. The discs of our sample galaxies are truncated at similar radii on either side of their centres. With the possible exception
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
Adiabatic superconducting cells for ultra-low-power artificial neural networks
Directory of Open Access Journals (Sweden)
Andrey E. Schegolev
2016-10-01
Full Text Available We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.
Directory of Open Access Journals (Sweden)
Kevin eD'Ostilio
2016-04-01
Full Text Available The high prevalence of major depressive disorder in people with Parkinson's disease, its negative impact on health-related quality of life and the low response rate to conventional pharmacological therapies call to seek innovative treatments. Here, we review the new approaches for treating major depressive disorder in patients with Parkinson's disease within the framework of the network model of depression. According to this model, major depressive disorder reflects maladaptive neuronal plasticity. Non-invasive brain stimulation using high frequency repetitive transcranial magnetic stimulation over the prefrontal cortex has been proposed as a feasible and effective strategy with minimal risk. The neurobiological basis of its therapeutic effect may involve neuroplastic modifications in limbic and cognitive networks. However, the way this networks reorganize might be strongly influenced by the environment. To address this issue, we propose a combined strategy that includes non-invasive brain stimulation together with cognitive and behavioral interventions.
D’Ostilio, Kevin; Garraux, Gaëtan
2016-01-01
The high prevalence of major depressive disorder in people with Parkinson’s disease (PD), its negative impact on health-related quality of life and the low response rate to conventional pharmacological therapies call to seek innovative treatments. Here, we review the new approaches for treating major depressive disorder in patients with PD within the framework of the network model of depression. According to this model, major depressive disorder reflects maladaptive neuronal plasticity. Non-invasive brain stimulation (NIBS) using high frequency repetitive transcranial magnetic stimulation (rTMS) over the prefrontal cortex has been proposed as a feasible and effective strategy with minimal risk. The neurobiological basis of its therapeutic effect may involve neuroplastic modifications in limbic and cognitive networks. However, the way this networks reorganize might be strongly influenced by the environment. To address this issue, we propose a combined strategy that includes NIBS together with cognitive and behavioral interventions. PMID:27148016
Radial Halbach Magnetic Bearings
Eichenberg, Dennis J.; Gallo, Christopher A.; Thompson, William K.
2009-01-01
Radial Halbach magnetic bearings have been investigated as part of an effort to develop increasingly reliable noncontact bearings for future high-speed rotary machines that may be used in such applications as aircraft, industrial, and land-vehicle power systems and in some medical and scientific instrumentation systems. Radial Halbach magnetic bearings are based on the same principle as that of axial Halbach magnetic bearings, differing in geometry as the names of these two types of bearings suggest. Both radial and axial Halbach magnetic bearings are passive in the sense that unlike most other magnetic bearings that have been developed in recent years, they effect stable magnetic levitation without need for complex active control. Axial Halbach magnetic bearings were described in Axial Halbach Magnetic Bearings (LEW-18066-1), NASA Tech Briefs, Vol. 32, No. 7 (July 2008), page 85. In the remainder of this article, the description of the principle of operation from the cited prior article is recapitulated and updated to incorporate the present radial geometry. In simplest terms, the basic principle of levitation in an axial or radial Halbach magnetic bearing is that of the repulsive electromagnetic force between (1) a moving permanent magnet and (2) an electric current induced in a stationary electrical conductor by the motion of the magnetic field. An axial or radial Halbach bearing includes multiple permanent magnets arranged in a Halbach array ("Halbach array" is defined below) in a rotor and multiple conductors in the form of wire coils in a stator, all arranged so the rotary motion produces an axial or radial repulsion that is sufficient to levitate the rotor. A basic Halbach array (see Figure 1) consists of a row of permanent magnets, each oriented so that its magnetic field is at a right angle to that of the adjacent magnet, and the right-angle turns are sequenced so as to maximize the magnitude of the magnetic flux density on one side of the row while
Jordan, Robert W.; Jones, Alistair DR.
2017-01-01
Background: Radial head fractures are common elbow injuries in adults and are frequently associated with additional soft tissue and bone injuries. Methods: A literature search was performed and the authors’ personal experiences are reported. Results: Mason type I fractures are treated non-operatively with splinting and early mobilisation. The management of Mason type II injuries is less clear with evidence supporting both non-operative treatment and internal fixation. The degree of intra-arti...
Jordan, Robert W.; Jones, Alistair DR.
2017-01-01
Background: Radial head fractures are common elbow injuries in adults and are frequently associated with additional soft tissue and bone injuries. Methods: A literature search was performed and the authors’ personal experiences are reported. Results: Mason type I fractures are treated non-operatively with splinting and early mobilisation. The management of Mason type II injuries is less clear with evidence supporting both non-operative treatment and internal fixation. The degree of intra-articular displacement and angulation acceptable for non-operative management has yet to be conclusively defined. Similarly the treatment of type III and IV fractures remain controversial. Traditional radial head excision is associated with valgus instability and should be considered only for patients with low functional demands. Comparative studies have shown improved results from internal fixation over excision. Internal fixation should only be attempted when anatomic reduction and initiation of early motion can be achieved. Authors have reported that results from fixation are poorer and complication rates are higher if more than three fragments are present. Radial head arthroplasty aims to reconstruct the native head and is indicated when internal fixation is not feasible and in the presence of complex elbow injuries. Overstuffing of the radiocapitellar joint is a frequent technical fault and has significant adverse effects on elbow biomechanics. Modular design improves the surgeon’s ability to reconstruct the native joint. Two randomised controlled trials have shown improved clinical outcomes and lower complication rate following arthroplasty when compared to internal fixation. Conclusion: We have presented details regarding the treatment of various types of radial head fractures - further evidence, however, is still required to provide clarity over the role of these different management strategies. PMID:29290880
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-09-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection.
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-01-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one’s avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one’s avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection. PMID:27415603
An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...
Directory of Open Access Journals (Sweden)
Rena R. Timirualeeva
2015-01-01
Full Text Available The article describes the methodology of modeling andstructuring of business networks theory. Accounting ofenvironmental factors mega-, macro- and mesolevels, theinternal state of the managed system and the error management command execution by control system implemented inthis. The proposed methodology can improve the quality of enterprise management of resort complex through a moreﬂexible response to changes in the parameters of the internaland external environments.
Hutto, Clayton; Briscoe, Erica; Trewhitt, Ethan
2012-01-01
Societal level macro models of social behavior do not sufficiently capture nuances needed to adequately represent the dynamics of person-to-person interactions. Likewise, individual agent level micro models have limited scalability - even minute parameter changes can drastically affect a model's response characteristics. This work presents an approach that uses agent-based modeling to represent detailed intra- and inter-personal interactions, as well as a system dynamics model to integrate societal-level influences via reciprocating functions. A Cognitive Network Model (CNM) is proposed as a method of quantitatively characterizing cognitive mechanisms at the intra-individual level. To capture the rich dynamics of interpersonal communication for the propagation of beliefs and attitudes, a Socio-Cognitive Network Model (SCNM) is presented. The SCNM uses socio-cognitive tie strength to regulate how agents influence--and are influenced by--one another's beliefs during social interactions. We then present experimental results which support the use of this network analytical approach, and we discuss its applicability towards characterizing and understanding human information processing.
Compensation for unmatched uncertainty with adaptive RBF network
African Journals Online (AJOL)
Robust control for nonlinear uncertain systems has been solved for matched uncertainty but has not been completely solved yet for unmatched uncertainty. This paper developed a new method in which an adaptive radial basis function neural network is used to compensate for the effects of unmatched uncertainty in the ...
RBF neural network based H∞ synchronization for unknown chaotic ...
Indian Academy of Sciences (India)
MS received 9 February 2010; accepted 24 May 2010. Abstract. In this paper, we propose a new H∞ synchronization strategy, called a. Radial Basis Function Neural Network H∞ synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the pro- posed framework, a ...
Design Optimization of Centrifugal Pump Using Radial Basis Function Metamodels
Yu Zhang; Jinglai Wu; Yunqing Zhang; Liping Chen
2014-01-01
Optimization design of centrifugal pump is a typical multiobjective optimization (MOO) problem. This paper presents an MOO design of centrifugal pump with five decision variables and three objective functions, and a set of centrifugal pumps with various impeller shroud shapes are studied by CFD numerical simulations. The important performance indexes for centrifugal pump such as head, efficiency, and required net positive suction head (NPSHr) are investigated, and the results indicate that th...
A Radial Basis Function Approach to Financial Time Series Analysis
1993-12-01
primarily to the work of William Sharpe (1964). John Lintner (1965), and Jan Mossin (1966). is one of a. number of models that grew out of Modern...Crutchfield and MacNamara (1987) introduced a general method for estimating the "equations of motion" (i.e. model of the time behavior) of a data... John Lintner. The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and
DEFF Research Database (Denmark)
Darden, Safi-Kirstine; Steffensen, Lise K.; Dabelsteen, Torben
2008-01-01
this role. In this study, we have investigated the possible function of swift fox, Vulpes velox, latrines, collections of scat, urine and possibly other secretions, in a communication network context. We found that latrines had higher frequencies of occurrence inside the core (defined as the 50% kernel...... contour) of a pair's home-range when compared with outside the core and in areas of a pair's home-range that overlapped with neighbouring individuals when compared with those areas that did not overlap with neighbours. These were also the two areas where latrines were most likely to reoccur in the next...... consecutive breeding season. Furthermore, latrines in the exclusive part of a pair's home-range core and latrines in edge area overlap zones had the highest frequency of visits as determined by the rate of faecal depositions. Our interpretation of these results is that latrines possibly have a dual function...
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
Neural networks and statistical learning
Du, Ke-Lin
2014-01-01
Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...
Deterministic System Identification Using RBF Networks
Directory of Open Access Journals (Sweden)
Joilson Batista de Almeida Rego
2014-01-01
Full Text Available This paper presents an artificial intelligence application using a nonconventional mathematical tool: the radial basis function (RBF networks, aiming to identify the current plant of an induction motor or other nonlinear systems. Here, the objective is to present the RBF response to different nonlinear systems and analyze the obtained results. A RBF network is trained and simulated in order to obtain the dynamical solution with basin of attraction and equilibrium point for known and unknown system and establish a relationship between these dynamical systems and the RBF response. On the basis of several examples, the results indicating the effectiveness of this approach are demonstrated.
Directory of Open Access Journals (Sweden)
Mathieu Bartoletti
Full Text Available The potential to produce new cells during adult life depends on the number of stem cell niches and the capacity of stem cells to divide, and is therefore under the control of programs ensuring developmental homeostasis. However, it remains generally unknown how the number of stem cell niches is controlled. In the insect ovary, each germline stem cell (GSC niche is embedded in a functional unit called an ovariole. The number of ovarioles, and thus the number of GSC niches, varies widely among species. In Drosophila, morphogenesis of ovarioles starts in larvae with the formation of terminal filaments (TFs, each made of 8-10 cells that pile up and sort in stacks. TFs constitute organizers of individual germline stem cell niches during larval and early pupal development. In the Drosophila melanogaster subgroup, the number of ovarioles varies interspecifically from 8 to 20. Here we show that pipsqueak, Trithorax-like, batman and the bric-à-brac (bab locus, all encoding nuclear BTB/POZ factors of the Tramtrack Group, are involved in limiting the number of ovarioles in D. melanogaster. At least two different processes are differentially perturbed by reducing the function of these genes. We found that when the bab dose is reduced, sorting of TF cells into TFs was affected such that each TF contains fewer cells and more TFs are formed. In contrast, psq mutants exhibited a greater number of TF cells per ovary, with a normal number of cells per TF, thereby leading to formation of more TFs per ovary than in the wild type. Our results indicate that two parallel genetic pathways under the control of a network of nuclear BTB factors are combined in order to negatively control the number of germline stem cell niches.
Bartoletti, Mathieu; Rubin, Thomas; Chalvet, Fabienne; Netter, Sophie; Dos Santos, Nicolas; Poisot, Emilie; Paces-Fessy, Mélanie; Cumenal, Delphine; Peronnet, Frédérique; Pret, Anne-Marie; Théodore, Laurent
2012-01-01
The potential to produce new cells during adult life depends on the number of stem cell niches and the capacity of stem cells to divide, and is therefore under the control of programs ensuring developmental homeostasis. However, it remains generally unknown how the number of stem cell niches is controlled. In the insect ovary, each germline stem cell (GSC) niche is embedded in a functional unit called an ovariole. The number of ovarioles, and thus the number of GSC niches, varies widely among species. In Drosophila, morphogenesis of ovarioles starts in larvae with the formation of terminal filaments (TFs), each made of 8-10 cells that pile up and sort in stacks. TFs constitute organizers of individual germline stem cell niches during larval and early pupal development. In the Drosophila melanogaster subgroup, the number of ovarioles varies interspecifically from 8 to 20. Here we show that pipsqueak, Trithorax-like, batman and the bric-à-brac (bab) locus, all encoding nuclear BTB/POZ factors of the Tramtrack Group, are involved in limiting the number of ovarioles in D. melanogaster. At least two different processes are differentially perturbed by reducing the function of these genes. We found that when the bab dose is reduced, sorting of TF cells into TFs was affected such that each TF contains fewer cells and more TFs are formed. In contrast, psq mutants exhibited a greater number of TF cells per ovary, with a normal number of cells per TF, thereby leading to formation of more TFs per ovary than in the wild type. Our results indicate that two parallel genetic pathways under the control of a network of nuclear BTB factors are combined in order to negatively control the number of germline stem cell niches.
Wei-Bo Chen; Wen-Cheng Liu
2015-01-01
In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical ...
Turbine with radial acting seal
Energy Technology Data Exchange (ETDEWEB)
Eng, Darryl S; Ebert, Todd A
2016-11-22
A floating brush seal in a rim cavity of a turbine in a gas turbine engine, where the floating brush seal includes a seal holder in which the floating brush seal floats, and a expandable seal that fits within two radial extending seal slots that maintains a seal with radial displacement of the floating brush seal and the seal holder.
Energy Technology Data Exchange (ETDEWEB)
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
Koller, Michal
Remote sensing is one of the major data acquisition tools available to rapidly acquire soil and plant related information over a wide area for use in precision agriculture. Green canopy has very specific reflectance characteristics distinguishing it from other materials such as soil and dry vegetative matter. Reflectance values in red (R) and near infra-red (NIR) spectral bands have been widely used for calculating normalized difference vegetation index (NDVI). Many researchers have related NDVI values to plant vigor, water stress, leaf area index (LAI) and/or yield. However, vegetative indices such as NDVI are usually sensitive to background reflectance characteristics. Often soil adjusted vegetation indices (SAVI) are used to minimize the background effect. In this study we have developed a relationship between the processing tomato yield and SAVI based on the R and NIR reflectance. Eight three band (R, NIR and green) aerial images were obtained at approximately two-week intervals during the 2000 processing tomato growing season. These images were analyzed to obtain SAVI values which were in turn related to LAI using regression techniques. A tuned neural network was developed to predict daily LAI values based on the biweekly experimental LAI values derived from aerial images. The coefficients of multiple determination between the actual LAI and neural network predicted LAI values were greater than 0.96 for all 56 grid points. The LAI values were numerically integrated over the whole growing season to obtain cumulative leaf area index days (CLAID). The CLAID values predicted from the neural network correlated very well with experimentally derived CLAID values (coefficient of determination, r2 = 0.83) indicating that the neural network model simulated processing tomato growth well. A crop growth model that was capable of predicting crop yield based on neural network predicted LAI values and CIMIS weather data was developed. Although predicted yield tended to be low
Kingravi, Hassan A; Chowdhary, Girish; Vela, Patricio A; Johnson, Eric N
2012-07-01
Classical work in model reference adaptive control for uncertain nonlinear dynamical systems with a radial basis function (RBF) neural network adaptive element does not guarantee that the network weights stay bounded in a compact neighborhood of the ideal weights when the system signals are not persistently exciting (PE). Recent work has shown, however, that an adaptive controller using specifically recorded data concurrently with instantaneous data guarantees boundedness without PE signals. However, the work assumes fixed RBF network centers, which requires domain knowledge of the uncertainty. Motivated by reproducing kernel Hilbert space theory, we propose an online algorithm for updating the RBF centers to remove the assumption. In addition to proving boundedness of the resulting neuro-adaptive controller, a connection is made between PE signals and kernel methods. Simulation results show improved performance.
On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network
DEFF Research Database (Denmark)
Alizadeh, Tohid
2008-01-01
This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neu...
Fitrianto, Eka; Nazir, Refdinal
2016-01-01
Electrical systems supplied from a substation (GI) which is quite far away from the center of the furthest load will cause a voltage drop in radial distribution network. Integration of distributed generation (Distributed Generation, DG) on the distribution network will be one of the solutions to improve the voltage drop. This paper analyzes integrating several distributed generation (DG) in a radial distribution network to repair a voltage with Matlab program. Method using flow injection anal...
Dedicated radial ventriculography pigtail catheter
Energy Technology Data Exchange (ETDEWEB)
Vidovich, Mladen I., E-mail: miv@uic.edu
2013-05-15
A new dedicated cardiac ventriculography catheter was specifically designed for radial and upper arm arterial access approach. Two catheter configurations have been developed to facilitate retrograde crossing of the aortic valve and to conform to various subclavian, ascending aortic and left ventricular anatomies. The “short” dedicated radial ventriculography catheter is suited for horizontal ascending aortas, obese body habitus, short stature and small ventricular cavities. The “long” dedicated radial ventriculography catheter is suited for vertical ascending aortas, thin body habitus, tall stature and larger ventricular cavities. This new design allows for improved performance, faster and simpler insertion in the left ventricle which can reduce procedure time, radiation exposure and propensity for radial artery spasm due to excessive catheter manipulation. Two different catheter configurations allow for optimal catheter selection in a broad range of patient anatomies. The catheter is exceptionally stable during contrast power injection and provides equivalent cavity opacification to traditional femoral ventriculography catheter designs.
Nodal prices determination with wind integration for radial ...
African Journals Online (AJOL)
Like transmission pricing, distribution network pricing must also be transparent and must include tile variations based on the change in the operating state of the system, integration of renewable sources and must be real time. In this paper, a distribution system nodal pricing scheme is proposed for radial distribution system ...
QSAR modelling using combined simple competitive learning networks and RBF neural networks.
Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E
2018-04-01
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
Mu, Chuang; Wang, Ruijia; Li, Tianqi; Li, Yuqiang; Tian, Meilin; Jiao, Wenqian; Huang, Xiaoting; Zhang, Lingling; Hu, Xiaoli; Wang, Shi; Bao, Zhenmin
2016-08-01
Long non-coding RNA (lncRNA) structurally resembles mRNA but cannot be translated into protein. Although the systematic identification and characterization of lncRNAs have been increasingly reported in model species, information concerning non-model species is still lacking. Here, we report the first systematic identification and characterization of lncRNAs in two sea cucumber species: (1) Apostichopus japonicus during lipopolysaccharide (LPS) challenge and in heathy tissues and (2) Holothuria glaberrima during radial organ complex regeneration, using RNA-seq datasets and bioinformatics analysis. We identified A. japonicus and H. glaberrima lncRNAs that were differentially expressed during LPS challenge and radial organ complex regeneration, respectively. Notably, the predicted lncRNA-microRNA-gene trinities revealed that, in addition to targeting protein-coding transcripts, miRNAs might also target lncRNAs, thereby participating in a potential novel layer of regulatory interactions among non-coding RNA classes in echinoderms. Furthermore, the constructed coding-non-coding network implied the potential involvement of lncRNA-gene interactions during the regulation of several important genes (e.g., Toll-like receptor 1 [TLR1] and transglutaminase-1 [TGM1]) in response to LPS challenge and radial organ complex regeneration in sea cucumbers. Overall, this pioneer systematic identification, annotation, and characterization of lncRNAs in echinoderm pave the way for similar studies and future genetic, genomic, and evolutionary research in non-model species.
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.
CONGENITAL RADIAL DYSPLASIA: A CASE REPORT
Directory of Open Access Journals (Sweden)
Venkatram Reddy
2015-08-01
Full Text Available Congenital radial dysplasia, also referred to as radial club hand , means deficiency along the preaxial or radial side of the extremity. It ranges from hypoplasia of the thumb to variou s degrees of radial hypoplasia. We present one such rare case of type 4 congenital unilateral isolated radial dysplasia with carpel anomaly , reported to our department in SVS medical C ollege, Mahabubanagar, Telangana state
Radial head prosthesis: results overview.
Carità, E; Donadelli, A; Cugola, L; Perazzini, P
2017-12-01
Radial head replacement is frequently used in treatment of radial head fractures or sequela. Impossibility to restore a correct anatomy, acute elbow traumatic instability and failure of osteosynthesis hardware are the most common indications. The authors describe their case studies and results on the implantation of various radial head prostheses. Between June 2005 and June 2016, 28 radial head prostheses were implanted in the same number of patients with an average follow-up of 49 months (6-104). Indications for implantation were: Mason type III and IV radial head fractures and post-traumatic arthritis due to failure of previous treatments. Monopolar prostheses were used and were press-fit implanted via Kaplan's lateral access and Kocher's anconeus approach to the humeroradial joint. At the follow-up, assessments were made of the pain, according to the visual analogic scale, range of motion (ROM), stability and functionality according to the Mayo Elbow Performance Score, presence of osteolysis and mobilization during radiography tests, personal satisfaction of the patients, Disabilities of the Arm, Shoulder and Hand and Patient-Rated Wrist Evaluation outcomes measurements. At the follow-up, we recorded an average level of pain of 1.8 in patients under acute treatments for radial head fractures and a marked reduction in the remaining cases from 6.7 to 2.1. ROM was found on average to be 107° of flexion-extension and 159° of pronosupination. Personal satisfaction was good-excellent in 23 cases. There was no case of infection; removal of the implant was necessary in three cases due to mobilization of the stem and oversized implants. In six cases, bone resorption was seen at the level of the prosthetic collar and it was in all cases asymptomatic. The results of this study suggest that the use of prostheses, if well positioned, is a valid solution in the treatment of secondary arthritis and fractures of the radial head with poor prognosis, with good results in the
Eka Fitrianto; Refdinal Nazir
2016-01-01
At 20 kV radial distribution systems supplied by the substation in a considerable distance from the load centre, will cause occurred its voltage drop significantly. The integration of distributed generation on the distribution networks will be one of the solutions to reduce the drop voltage. This paper proposes a way to analyze the effect of integration multiple distributed generation (DG) in radial distribution networks to the voltage drop. This analysis uses the injection current method o...
Centralized Networks to Generate Human Body Motions.
Vakulenko, Sergei; Radulescu, Ovidiu; Morozov, Ivan; Weber, Andres
2017-12-14
We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons' states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers' trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings.
Comparison of Deterministic and Probabilistic Radial Distribution Systems Load Flow
Gupta, Atma Ram; Kumar, Ashwani
2017-08-01
Distribution system network today is facing the challenge of meeting increased load demands from the industrial, commercial and residential sectors. The pattern of load is highly dependent on consumer behavior and temporal factors such as season of the year, day of the week or time of the day. For deterministic radial distribution load flow studies load is taken as constant. But, load varies continually with a high degree of uncertainty. So, there is a need to model probable realistic load. Monte-Carlo Simulation is used to model the probable realistic load by generating random values of active and reactive power load from the mean and standard deviation of the load and for solving a Deterministic Radial Load Flow with these values. The probabilistic solution is reconstructed from deterministic data obtained for each simulation. The main contribution of the work is: Finding impact of probable realistic ZIP load modeling on balanced radial distribution load flow. Finding impact of probable realistic ZIP load modeling on unbalanced radial distribution load flow. Compare the voltage profile and losses with probable realistic ZIP load modeling for balanced and unbalanced radial distribution load flow.
Comparison of Deterministic and Probabilistic Radial Distribution Systems Load Flow
Gupta, Atma Ram; Kumar, Ashwani
2017-12-01
Distribution system network today is facing the challenge of meeting increased load demands from the industrial, commercial and residential sectors. The pattern of load is highly dependent on consumer behavior and temporal factors such as season of the year, day of the week or time of the day. For deterministic radial distribution load flow studies load is taken as constant. But, load varies continually with a high degree of uncertainty. So, there is a need to model probable realistic load. Monte-Carlo Simulation is used to model the probable realistic load by generating random values of active and reactive power load from the mean and standard deviation of the load and for solving a Deterministic Radial Load Flow with these values. The probabilistic solution is reconstructed from deterministic data obtained for each simulation. The main contribution of the work is: Finding impact of probable realistic ZIP load modeling on balanced radial distribution load flow. Finding impact of probable realistic ZIP load modeling on unbalanced radial distribution load flow. Compare the voltage profile and losses with probable realistic ZIP load modeling for balanced and unbalanced radial distribution load flow.
Prediction of littoral drift with artificial neural networks
Digital Repository Service at National Institute of Oceanography (India)
Singh, A.K.; Deo, M.C.; SanilKumar, V.
, arbitrary accuracy, and difficult choices related to train- ing schemes, architectures, learning algorithms, and control parameters. Any new application of the ANN that addresses these issues therefore deserves attention of the potential user community...). The current study was also based on the same. Both multi-layered perceptron network (MLP) as well as its variant radial basis function (RBF) was used. Training of the MLP was achieved with the help of alternative learn- ing schemes like Conjugate Gradient...
Radial magnetic bearings: An overview
Directory of Open Access Journals (Sweden)
Weiyu Zhang
Full Text Available Radial magnetic bearings (RMBs are one of the most commonly used magnetic bearings. They are used widely in the field of ultra-high speed and ultra-precise numerical control machine tools, bearingless motors, high speed flywheels, artificial heart pumps, and molecular pumps, and they are being strengthened and extended in various important areas. In this paper, a comprehensive overview is given of different bearing topologies of RMBs with different stator poles that differ in their construction, the driving mode of electromagnets, power consumption, cost, magnetic circuits, and symmetry. RMBs with different poles and couplings between the two bearing axes in the radial direction responsible for cross-coupling generation are compared. In addition, different shaped rotors are compared, as the performances of magnetic bearing-rotor systems are of great concern to rotor constructions. Furthermore, the parameter design methods, the mathematical models and control strategies of the RMBs are described in detail. From the comparison of topologies, models and control methods for RMBs, the advantages, disadvantages and utilizable perspectives are also analyzed. Moreover, several possible development trends of the RMBs are discussed. Keywords: Radial magnetic bearings (RMBs, Topologies, Mathematical mode, Control strategies, Development trends
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.
Countercurrent aortography via radial artery
Energy Technology Data Exchange (ETDEWEB)
Sohn, Hyung Kuk; Lee, Young Chun; Lee, Seung Chul; Jeon, Seok Chol; Joo, Kyung Bin; Lee, Seung Ro; Kim, Soon Yong [College of Medicine, Hanyang University, Seoul (Korea, Republic of)
1987-06-15
Countercurrent aortography via radial artery was performed for detection of aortic arch anomalies in 4 infants with congenital heart disease. Author's cases of aortic arch anomalies were 3 cases of PDA, 1 case of coarctation of aorta, and 1 case of occlusion of anastomosis site on subclavian artery B-T shunt. And aberrant origin of the right SCA, interrupted aortic arch, hypoplastic aorta, anomalous origin of the right pulmonary artery from the ascending aorta can be demonstrated by this method. Countercurrent aortography affords an safe and simple method for detection of aortic arch anomalies without retrograde arterial catheterization, especially in small infants or premature babies.
Directory of Open Access Journals (Sweden)
Eduardo O. de Cerqueira
2001-12-01
Full Text Available Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.
Development and Testing of a Radial Halbach Magnetic Bearing
Eichenberg, Dennis J.; Gallo, Christopher A.; Thompson, William K.
2006-01-01
The NASA John H. Glenn Research Center has developed and tested a revolutionary Radial Halbach Magnetic Bearing. The objective of this work is to develop a viable non-contact magnetic bearing utilizing Halbach arrays for all-electric flight, and many other applications. This concept will help reduce harmful emissions, reduce the Nation s dependence on fossil fuels and mitigate many of the concerns and limitations encountered in conventional axial bearings such as bearing wear, leaks, seals and friction loss. The Radial Halbach Magnetic Bearing is inherently stable and requires no active feedback control system or superconductivity as required in many magnetic bearing designs. The Radial Halbach Magnetic Bearing is useful for very high speed applications including turbines, instrumentation, medical applications, manufacturing equipment, and space power systems such as flywheels. Magnetic fields suspend and support a rotor assembly within a stator. Advanced technologies developed for particle accelerators, and currently under development for maglev trains and rocket launchers, served as the basis for this application. Experimental hardware was successfully designed and developed to validate the basic principles and analyses. The report concludes that the implementation of Radial Halbach Magnetic Bearings can provide significant improvements in rotational system performance and reliability.
An Adaptive-PSO-Based Self-Organizing RBF Neural Network.
Han, Hong-Gui; Lu, Wei; Hou, Ying; Qiao, Jun-Fei
2018-01-01
In this paper, a self-organizing radial basis function (SORBF) neural network is designed to improve both accuracy and parsimony with the aid of adaptive particle swarm optimization (APSO). In the proposed APSO algorithm, to avoid being trapped into local optimal values, a nonlinear regressive function is developed to adjust the inertia weight. Furthermore, the APSO algorithm can optimize both the network size and the parameters of an RBF neural network simultaneously. As a result, the proposed APSO-SORBF neural network can effectively generate a network model with a compact structure and high accuracy. Moreover, the analysis of convergence is given to guarantee the successful application of the APSO-SORBF neural network. Finally, multiple numerical examples are presented to illustrate the effectiveness of the proposed APSO-SORBF neural network. The results demonstrate that the proposed method is more competitive in solving nonlinear problems than some other existing SORBF neural networks.
One pass learning for generalized classifier neural network.
Ozyildirim, Buse Melis; Avci, Mutlu
2016-01-01
Generalized classifier neural network introduced as a kind of radial basis function neural network, uses gradient descent based optimized smoothing parameter value to provide efficient classification. However, optimization consumes quite a long time and may cause a drawback. In this work, one pass learning for generalized classifier neural network is proposed to overcome this disadvantage. Proposed method utilizes standard deviation of each class to calculate corresponding smoothing parameter. Since different datasets may have different standard deviations and data distributions, proposed method tries to handle these differences by defining two functions for smoothing parameter calculation. Thresholding is applied to determine which function will be used. One of these functions is defined for datasets having different range of values. It provides balanced smoothing parameters for these datasets through logarithmic function and changing the operation range to lower boundary. On the other hand, the other function calculates smoothing parameter value for classes having standard deviation smaller than the threshold value. Proposed method is tested on 14 datasets and performance of one pass learning generalized classifier neural network is compared with that of probabilistic neural network, radial basis function neural network, extreme learning machines, and standard and logarithmic learning generalized classifier neural network in MATLAB environment. One pass learning generalized classifier neural network provides more than a thousand times faster classification than standard and logarithmic generalized classifier neural network. Due to its classification accuracy and speed, one pass generalized classifier neural network can be considered as an efficient alternative to probabilistic neural network. Test results show that proposed method overcomes computational drawback of generalized classifier neural network and may increase the classification performance. Copyright
Prediction and analysis of radial overcut in holes drilled by electrochemical machining process
Tajdari, Mehdi; Chavoshi, Saeed
2013-09-01
Radial overcut predictive models using multiple regression analysis, artificial neural network and co-active neurofuzzy inference system are developed to predict the radial overcut during electrochemical drilling with vacuum extraction of electrolyte. Four process parameters, electrolyte concentration, voltage, initial machining gap and tool feed rate, are selected to develop the models. The comparison between the results of the presented models shows that the artificial neural network and co-active neuro-fuzzy inference system models can predict the radial overcut with an average relative error of nearly 5%. Main effect and interaction plots are generated to study the effects of process parameters on the radial overcut. The analysis shows that the voltage, electrolyte concentration and tool feed rate have significant effect on radial overcut, respectively, while initial machining gap has a little effect. It is also found that the increase of the voltage and electrolyte concentration increases the radial overcut and the increase of the tool feed rate decreases the radial overcut.
Formulas for Radial Transport in Protoplanetary Disks
Desch, Steven J.; Estrada, Paul R.; Kalyaan, Anusha; Cuzzi, Jeffrey N.
2017-05-01
The quantification of the radial transport of gaseous species and solid particles is important to many applications in protoplanetary disk evolution. An especially important example is determining the location of the water snow lines in a disk, which requires computing the rates of outward radial diffusion of water vapor and the inward radial drift of icy particles; however, the application is generalized to evaporation fronts of all volatiles. We review the relevant formulas using a uniform formalism. This uniform treatment is necessary because the literature currently contains at least six mutually exclusive treatments of radial diffusion of gas, only one of which is correct. We derive the radial diffusion equations from first principles using Fick's law. For completeness, we also present the equations for radial transport of particles. These equations may be applied to studies of diffusion of gases and particles in protoplanetary and other accretion disks.
VanOsdol, John G.
2013-06-25
The disclosure provides a pulse jet mixing vessel for mixing a plurality of solid particles. The pulse jet mixing vessel is comprised of a sludge basin, a flow surface surrounding the sludge basin, and a downcoming flow annulus between the flow surface and an inner shroud. The pulse jet mixing vessel is additionally comprised of an upper vessel pressurization volume in fluid communication with the downcoming flow annulus, and an inner shroud surge volume separated from the downcoming flow annulus by the inner shroud. When the solid particles are resting on the sludge basin and a fluid such as water is atop the particles and extending into the downcoming flow annulus and the inner shroud surge volume, mixing occurs by pressurization of the upper vessel pressurization volume, generating an inward radial flow over the flow surface and an upwash jet at the center of the sludge basin.
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.
A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function.
Yap, Keem Siah; Lim, Chee Peng; Abidin, Izham Zainal
2008-09-01
In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
Olyaee, Saeed; Ebrahimpour, Reza; Hamedi, Samaneh; Jafarlou, Farzad M.
2009-08-01
Periodic nonlinearity is the main limitation on the accuracy of the nano-displacement measurements in the heterodyne interferometers. It is mainly produced by non-ideal polarized beams of the leaser and imperfect alignment of the optical components. In this paper, we model the periodic nonlinearity resulting from non-orthogonality and ellipticity of the laser beam by using combination of neural networks such as stacked generalization method and mixture of experts. The ensemble neural networks used for nonlinearity modeling are compared with single neural networks such as multi layer percepterons and radial basis function.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
2007-01-01
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
Radial head button holing: a cause of irreducible anterior radial head dislocation
Energy Technology Data Exchange (ETDEWEB)
Shin, Su-Mi; Chai, Jee Won; You, Ja Yeon; Park, Jina [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Radiology, Seoul (Korea, Republic of); Bae, Kee Jeong [Seoul National University Seoul Metropolitan Government Boramae Medical Center, Department of Orthopedic Surgery, Seoul (Korea, Republic of)
2016-10-15
''Buttonholing'' of the radial head through the anterior joint capsule is a known cause of irreducible anterior radial head dislocation associated with Monteggia injuries in pediatric patients. To the best of our knowledge, no report has described an injury consisting of buttonholing of the radial head through the annular ligament and a simultaneous radial head fracture in an adolescent. In the present case, the radiographic findings were a radial head fracture with anterior dislocation and lack of the anterior fat pad sign. Magnetic resonance imaging (MRI) clearly demonstrated anterior dislocation of the fractured radial head through the torn annular ligament. The anterior joint capsule and proximal portion of the annular ligament were interposed between the radial head and capitellum, preventing closed reduction of the radial head. Familiarity with this condition and imaging findings will aid clinicians to make a proper diagnosis and fast decision to perform an open reduction. (orig.)
Keller, James M; Fogel, David B
2016-01-01
This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...
Optical modeling of radial keratotomy incision patterns.
Schwiegerling, J; Greivenkamp, J E; Miller, J M; Snyder, R W; Palmer, M L
1996-12-01
To determine the optical effects of higher-order corneal shape variations resulting from radial keratotomy. Videokeratoscopic height data were obtained postoperatively from several patients who had undergone radial keratotomy. For each of clear central zone sizes 3.00 mm, 4.00 mm, and 4.75 mm, two patients were chosen randomly from the larger study group. Data obtained 2 weeks postoperatively from these six patients were decomposed into the Zernike polynomials, and the low-order expansion terms were removed to disclose corneal height variations (the radial keratotomy artifact). The artifact was applied to a schematic eye model, and exact ray-tracing was used to evaluate visual performance, which was defined as a function of pupil diameter, optical zone (central clear zone) size, and radial keratotomy artifact centration. The radial keratotomy artifact degrades visual performance at midspatial frequencies more than it does at high spatial frequencies. This effect is most pronounced for smaller optical zones and for a pupil diameter of 4 mm. Visual performance remains nearly constant for small decentration (0.5 mm or less) of the radial keratotomy optical zone from the corneal apex. Residual refractive error, corneal asphericity, and the radial keratotomy artifact all affect visual performance after radial keratotomy. Isolated effects of the radial keratotomy artifact degrade visual performance, with the level of degradation dependent on pupil size, optical zone size, and centration of the procedure. More research is necessary to combine the radial keratotomy artifact with changes in corneal asphericity and to further quantify the optical effects of radial keratotomy.
Mixtures of truncated basis functions
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2012-01-01
In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for representing general hybrid Bayesian networks. The proposed framework generalizes both the mixture of truncated exponentials (MTEs) framework and the mixture of polynomials (MoPs) framework. Similar t...
Morse basis expansion applied to diatomic molecules
Energy Technology Data Exchange (ETDEWEB)
Lima, Emanuel F. de, E-mail: eflima@rc.unesp.br [Departamento de Estatística, Matemática Aplicada e Computação, Instituto de Geociências e Ciências Exatas, Universidade Estadual Paulista – UNESP, Rio Claro, São Paulo 13506-900 (Brazil)
2012-02-20
This work explores the use of the eigenfunctions of the Morse potential with a infinite barrier at long range to solve the radial Schrödinger equation for diatomic molecules. Analytical formulas are obtained for the kinetic energy operator matrix elements in the Morse basis. The Morse basis expansion is applied to find the vibrational–rotational levels of the sodium molecule in the electronic ground state. -- Highlights: ► The Morse potential basis is invoked to find the rovibrational levels of diatomic molecules. ► Analytical formulas for the kinetic energy operator in the Morse basis are obtained. ► The results of the Morse basis expansion show good agreement with the Fourier Grid technique.
PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET
Directory of Open Access Journals (Sweden)
S. Devaraju
2014-04-01
Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.
Chen, Alexander; Chenna, Praveen; Loiselle, Andrea; Massoni, Jennifer; Mayse, Martin; Misselhorn, David
2014-05-01
Technological advances have improved the ability of bronchoscopists to access peripheral pulmonary lesions for tissue sampling. Radial probe endobronchial ultrasound (EBUS) provides real-time feedback to guide biopsies of peripheral lesions, thereby potentially improving diagnostic yield over conventional bronchoscopy. We assessed the overall diagnostic yield of peripheral bronchoscopy using radial probe EBUS for peripheral pulmonary lesions, as well as factors that might influence the diagnostic yield, such as radial ultrasound view, lesion size, and ability to locate the peripheral lesion. We conducted a retrospective review of peripheral bronchoscopy cases in which radial probe EBUS was utilized to diagnose peripheral pulmonary lesions at a tertiary care university hospital. Our study cohort comprised 496 patients who underwent bronchoscopies between January 2008 and December 2012 for the diagnosis of peripheral pulmonary lesions. Radial probe EBUS was used alone for diagnostic purposes in 467 patients. A diagnosis was made on that basis in 321 (69%) of 467 patients. A diagnosis was obtained for 83 of 144 (58%) of nodules 1-2 cm in diameter, 99 of 137 (72%) of nodules 2.1-3 cm, 54 of 70 (77%) of nodules 3.1-4 cm, 41 of 47 (87%) of nodules 4.1-5 cm, and 35 of 40 (88%) of nodules larger than 5.1 cm. Of all 467 nodules, 446 (96%) were successfully identified using radial probe EBUS. When the radial probe position was within the target lesion, the diagnostic yield was 84% compared with 48% when the probe was positioned adjacent to the lesion. Radial probe EBUS can be used to guide biopsy during peripheral bronchoscopy. This technique provides real-time ultrasound-based confirmation of target lesion localization prior to biopsy. Using radial probe EBUS, the vast majority of peripheral pulmonary nodules can be identified. Radial EBUS probe position relative to the target lesion significantly affects the diagnostic yield.
Concepts of radial and angular kinetic energies
DEFF Research Database (Denmark)
Dahl, Jens Peder; Schleich, W.P.
2002-01-01
We consider a general central-field system in D dimensions and show that the division of the kinetic energy into radial and angular parts proceeds differently in the wave-function picture and the Weyl-Wigner phase-space picture, Thus, the radial and angular kinetic energies are different quantities...
Directory of Open Access Journals (Sweden)
Mustafa Akpinar
2017-06-01
Full Text Available The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC. For this purpose, households and low-consuming commercial users’ four-year consumption data between the years of 2011–2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE for training utilizing MAPE with a univariate sliding window technique.
Radial distribution function of semiflexible oligomers with stretching flexibility
Zhang, Xi; Bao, Lei; Wu, Yuan-Yan; Zhu, Xiao-Long; Tan, Zhi-Jie
2017-08-01
The radial distribution of the end-to-end distance Ree is crucial for quantifying the global size and flexibility of a linear polymer. For semiflexible polymers, several analytical formulas have been derived for the radial distribution of Ree ignoring the stretching flexibility. However, for semiflexible oligomers, such as DNA or RNA, the stretching flexibility can be rather pronounced and can significantly affect the radial distribution of Ree. In this study, we obtained an extended formula that includes the stretch modulus to describe the distribution of Ree for semiflexible oligomers on the basis of previous formulas for semiflexible polymers without stretching flexibility. The extended formula was validated by extensive Monte Carlo simulations over wide ranges of the stretch modulus and persistence length, as well as all-atom molecular dynamics simulations of short DNAs and RNAs. Additionally, our analyses showed that the effect of stretching flexibility on the distribution of Ree becomes negligible for DNAs longer than ˜130 base pairs and RNAs longer than ˜240 base pairs.
Energy Technology Data Exchange (ETDEWEB)
Zou, Zhichao [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Wang, Fujun, E-mail: wangfj@cau.edu.cn [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Yao, Zhifeng [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Tao, Ran [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Xiao, Ruofu [College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083 (China); Beijing Engineering Research Center of Safety and Energy Saving Technology for Water Supply Network System, China Agricultural University, Beijing 100083 (China); Li, Huaicheng [Shanghai Liancheng (Group) Co., Ltd., Shanghai 201812 (China)
2016-12-15
Highlights: • Conclude the characteristics of transient radial force in the startup process for a large double-suction centrifugal pump. • The overall direction of the radial force during startup process is also confirmed. • A formula used to calculate the transient radial force during startup process is proposed. • A relationship between radial force variation and axial vortex development in blade channel during the startup process is established. The mechanism of the radial force evolution is revealed. - Abstract: Double-suction centrifugal pumps play an important role in the main feedwater systems of nuclear power plant. The impeller radial force in a centrifugal pump varies dramatically during startup at the shut-off condition. In this study, the startup process of a large double-suction centrifugal pump is investigated using CFD. During testing, the impeller speed is accelerated from zero to its rated speed in 1.0 s (marked as t{sub 0}) and is then maintained at the rated speed. The results show that the radial force increase lags behind the impeller speed increase. At 0–0.4t{sub 0}, the radial force is small (approaching zero). At 0.4–1.4t{sub 0}, the radial force increases rapidly. After 1.4t{sub 0}, the average radial force stabilizes and reaches its maximum value of 55,619 N. The observed maximum radial force value during startup is approximately nine times as high as the radial force under rated condition. During startup, the overall radial force direction is proximate to the radial line located 25° from the volute tongue along circumferential direction. A transient radial force formula is proposed to evaluate the changes in radial force during startup. The streamline distribution in impeller passages and the impeller outlet pressure profile varying over time are produced. The relationship between radial force evolution and the varying axial-to-spiral vortex structure is analyzed. The radial force change mechanism is revealed. This research
SpicyNodes: radial layout authoring for the general public.
Douma, Michael; Ligierko, Grzegorz; Ancuta, Ovidiu; Gritsai, Pavel; Liu, Sean
2009-01-01
Trees and graphs are relevant to many online tasks such as visualizing social networks, product catalogs, educational portals, digital libraries, the semantic web, concept maps and personalized information management. SpicyNodes is an information-visualization technology that builds upon existing research on radial tree layouts and graph structures. Users can browse a tree, clicking from node to node, as well as successively viewing a node, immediately related nodes and the path back to the "home" nodes. SpicyNodes' layout algorithms maintain balanced layouts using a hybrid mixture of a geometric layout (a succession of spanning radial trees) and force-directed layouts to minimize overlapping nodes, plus several other improvements over prior art. It provides XML-based API and GUI authoring tools. The goal of the SpicyNodes project is to implement familiar principles of radial maps and focus+context with an attractive and inviting look and feel in an open system that is accessible to virtually any Internet user.
Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China
Directory of Open Access Journals (Sweden)
C. W. Dawson
2002-01-01
Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting
Energy Technology Data Exchange (ETDEWEB)
Pfeilschmidt, G. [Bereich Einkauf und Logistik, Envia Mitteldeutsche Energie AG, Chemnitz (Germany); Werthschulte, S. [Accenture GmbH, Duesseldorf (Germany)
2002-12-16
The German utilities company Envia Mitteldeutsche Energie AG has developed a new e-procurement infrastructure to optimize strategic outsourcing with its business partners. The new solution, www.envia-logistics.de, works as an e-procurement portal for envia and as an open trading platform. Two major external client categories are served: municipal utilities and envia network partners. The e-procurement portal reduces overall IT-investment and maintenance costs for all participants while optimizing the quality of service with state-of-the-art technology. (orig.) [German] Mit dem elektronischen Einkaufsportal www.envia-logistics.de hat die Envia Mitteldeutsche Energie AG die Grundlage fuer innovative Service orientierte Geschaeftsbeziehungen mit Stadtwerken und Geschaeftspartnern geschaffen. Um das Potenzial ausschoepfen zu koennen, wurden ein neues Geschaeftsmodell fuer den Bereich Einkauf und Logistik entwickelt und notwendige Prozesse und eine geeignete IT-Infrastruktur realisiert. Das E-Procurement-/E-Portal-Projekt oeffnet dem einkaufenden Unternehmen nicht nur den elektronischen Beschaffungsweg, sondern auch den Verkauf. (orig.)
Reliability analysis of C-130 turboprop engine components using artificial neural network
Qattan, Nizar A.
In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine
Spectral distortion in a radially inhomogeneous cosmology
Caldwell, R. R.; Maksimova, N. A.
2013-11-01
The spectral distortion of the cosmic microwave background blackbody spectrum in a radially inhomogeneous space-time, designed to exactly reproduce a ΛCDM expansion history along the past light cone, is shown to exceed the upper bound established by COBE-FIRAS by a factor of approximately 3700. This simple observational test helps uncover a slew of pathological features that lie hidden inside the past light cone, including a radially contracting phase at decoupling and, if followed to its logical extreme, a naked singularity at the radially inhomogeneous big bang.
Scintillating Lustre Induced by Radial Fins
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Kohske Takahashi
2012-02-01
Full Text Available Radial lines of Ehrenstein patterns induce illusory scintillating lustre in gray disks inserted into the central gaps (scintillating-lustre effect. We report a novel variant of this illusion by replacing the radial lines with white and black radial fins. Both white and gray disks inserted into the central gaps were perceived as scintillating, if the ratio of the black/white fin width were balanced (ie, close to 1.0. Thus, the grayness of the central disk is not a prerequisite for the scintillation. However, the scintillation was drastically reduced when the ratio was imbalanced. Furthermore, the optimal ratio depended on the color of the center disks.
Directory of Open Access Journals (Sweden)
Itamar eLerner
2014-04-01
Full Text Available For the last four decades, semantic priming – the facilitation in recognition of a target word when it follows the presentation of a semantically related prime word – has been a central topic in research of human cognitive processing. Studies have drawn a complex picture of findings which demonstrated the sensitivity of this priming effect to a unique combination of variables, including, but not limited to, the type of relatedness between primes and targets, the prime-target SOA, the relatedness proportion in the stimuli list and the specific task subjects are required to perform. Automatic processes depending on the activation patterns of semantic representations in memory and controlled strategies adapted by individuals when attempting to maximize their recognition performance have both been implicated in contributing to the results. Lately, we have published a new model of semantic priming that addresses the majority of these findings within one conceptual framework. In our model, semantic memory is depicted as an attractor neural network in which stochastic transitions from one stored pattern to another are continually taking place due to synaptic depression mechanisms. We have shown how such transitions, in combination with a reinforcement-learning rule that adjusts their pace, resemble the classic automatic and controlled processes involved in semantic priming and account for a great number of the findings in the literature. Here, we review the core findings of our model and present new simulations that show how similar principles of parameter-adjustments could account for additional data not addressed in our previous studies, such as the relation between expectancy and inhibition in priming, target frequency and target degradation effects. Finally, we describe two human experiments that validate several key predictions of the model.
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Murat Cuhadar
2014-03-01
Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.
Radial pseudoaneurysm following diagnostic coronary angiography
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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
Aberrant Radial Artery Causing Carpal Tunnel Syndrome
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Zinon T. Kokkalis
2016-07-01
Full Text Available Anatomical vascular variations are rare causes of carpal tunnel syndrome. An aberrant medial artery is the most common vascular variation, while an aberrant radial artery causing carpal tunnel syndrome is even more rare, with an incidence ranging less than 3%. This article reports a patient with compression of the median nerve at the carpal tunnel by an aberrant superficial branch of the radial artery. An 80- year- old man presented with a 5-year history of right hand carpal tunnel syndrome; Tinel sign, Phalen test and neurophysiological studies were positive. Open carpal tunnel release showed an aberrant superficial branch of the radial artery with its accompanying veins running from radially to medially, almost parallel to the median nerve, ending at the superficial palmar arterial arch. The median nerve was decompressed without ligating the aberrant artery. At the last follow-up, 2 years after diagnosis and treatment the patient is asymptomatic.
Aberrant Radial Artery Causing Carpal Tunnel Syndrome.
Kokkalis, Zinon T; Tolis, Konstantinos E; Megaloikonomos, Panayiotis D; Panagopoulos, Georgios N; Igoumenou, Vasilios G; Mavrogenis, Andreas F
2016-06-01
Anatomical vascular variations are rare causes of carpal tunnel syndrome. An aberrant medial artery is the most common vascular variation, while an aberrant radial artery causing carpal tunnel syndrome is even more rare, with an incidence ranging less than 3%. This article reports a patient with compression of the median nerve at the carpal tunnel by an aberrant superficial branch of the radial artery. An 80- year- old man presented with a 5-year history of right hand carpal tunnel syndrome; Tinel sign, Phalen test and neurophysiological studies were positive. Open carpal tunnel release showed an aberrant superficial branch of the radial artery with its accompanying veins running from radially to medially, almost parallel to the median nerve, ending at the superficial palmar arterial arch. The median nerve was decompressed without ligating the aberrant artery. At the last follow-up, 2 years after diagnosis and treatment the patient is asymptomatic.
Computationally efficient locally-recurrent neural networks for online signal processing
Hussain, A; Shim, I
1999-01-01
A general class of computationally efficient locally recurrent networks (CERN) is described for real-time adaptive signal processing. The structure of the CERN is based on linear-in-the- parameters single-hidden-layered feedforward neural networks such as the radial basis function (RBF) network, the Volterra neural network (VNN) and the functionally expanded neural network (FENN), adapted to employ local output feedback. The corresponding learning algorithms are derived and key structural and computational complexity comparisons are made between the CERN and conventional recurrent neural networks. Two case studies are performed involving the real- time adaptive nonlinear prediction of real-world chaotic, highly non- stationary laser time series and an actual speech signal, which show that a recurrent FENN based adaptive CERN predictor can significantly outperform the corresponding feedforward FENN and conventionally employed linear adaptive filtering models. (13 refs).
Neural Network Predictive Control for Vanadium Redox Flow Battery
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Hai-Feng Shen
2013-01-01
Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.
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...
Radial cytoarchitecture and patterns of cortical connectivity in autism.
Casanova, Manuel; Trippe, Juan
2009-05-27
To explain the pattern of preserved and superior abilities found in autism spectrum disorders, a hypothesis has emerged, which assumes that there is a developmental bias towards the formation of short-range connections. This would result in excessive activity and overconnectivity within susceptible local networks. These networks might become partially isolated and acquire novel functional properties. In turn, this would affect the formation of long-range circuits and systems governing top-down control and integration. Despite many tantalizing clues, mechanisms relating pathogenesis and altered cell function to the 'disconnection' of integrative and focal activity remain obscure. However, recent post-mortem studies of brains of individuals with autism have shown characteristic differences in the morphometry of radial cell minicolumns, which add credence to the connectivity hypothesis.
Radial artery access for peripheral endovascular procedures.
Kumar, Avnee J; Jones, Lauren E; Kollmeyer, Kenneth R; Feldtman, Robert W; Ferrara, Craig A; Moe, Michelle N; Chen, Julia F; Richmond, Jasmine L; Ahn, Sam S
2017-09-01
The radial artery is often used for coronary angiography, with a demonstrated decrease in local complications and an increase in postoperative mobility of the patient. Data on radial artery access for peripheral endovascular procedures, however, are limited. We describe our experience with radial artery access for diagnostic and endovascular interventions. Between February 2012 and March 2015, there were 95 endovascular procedures performed using radial artery access in 80 unique patients. Demographic and clinical data were recorded. Perioperative, postoperative, and 30-day follow-up data were evaluated retrospectively for major and minor complications. Major adverse events included any immediate hospitalization admission, stroke, hand amputation, bleeding requiring transfusion, hematoma requiring surgery, and death. Minor complications included superficial bleeding and hematoma. The patients (52.6% male, 47.4% female) had a mean age of 72.1 ± 9.4 years. Radial artery access was used for diagnostic purposes in 15.8% of all procedures and for therapeutic intervention, including angioplasty and stenting, in 84.2%. The radial artery was the only access point in 80% of patients and was accessed in conjunction with other sites in 20%. Percutaneous access was achieved in 100% of patients with a 100% technical success rate. Hemostasis after catheterization was achieved by manual compression (22.1%) and TR band (Terumo Medical, Tokyo, Japan; 77.9%). Major adverse events occurred in three cases (3.2%) and were unrelated to radial artery access. Radial artery access site-related complications occurred in three cases (3.2%), all of which were minor hematomas that required no treatment. The risk of radial artery complication was not associated with procedure type, vessels treated, or use of heparin. The incidence of stroke, hand ischemia, and upper extremity limb or finger loss was 0%. Radial artery access for peripheral endovascular procedures appears to be safe and
Advances in Artificial Neural Networks – Methodological Development and Application
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Yanbo Huang
2009-08-01
Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological
Bayesian inversion of free oscillations for Earth's radial (an)elastic structure
de Wit, R. W L; Käufl, P.J.; Valentine, A. P.; Trampert, J.
2014-01-01
We perform a Bayesian inversion of degree-zero spheroidal mode splitting function measurements for radial (1-D) Earth structure, in terms of the Voigt averages of P-wave (VP) and S-wave (VS) velocities, density, bulk and shear attenuation, using neural networks. The method is flexible and allows us
Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks
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Ruchi D. Chande
2017-01-01
Full Text Available Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.
Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy
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Ireneusz Zagórski
2017-09-01
Full Text Available The paper reports the results of artificial neural network modelling of vibration in. a milling process of magnesium alloy AZ91D by a TiAlN-coated carbide tool. Vibrations in machining processes are regarded as an additional, absolute machinability index. The modelling was performed using the so-called “black box” model. The best fit was determined for the input and output data obtained from the machining process. The simulations were performed by the Statistica software using two types of neural networks: RBF (Radial Basis Function and MLP (Multi-Layered Perceptron.
Growth of InAs Wurtzite Nanocrosses from Hexagonal and Cubic Basis.
Krizek, Filip; Kanne, Thomas; Razmadze, Davydas; Johnson, Erik; Nygård, Jesper; Marcus, Charles M; Krogstrup, Peter
2017-10-11
Epitaxially connected nanowires allow for the design of electron transport experiments and applications beyond the standard two terminal device geometries. In this Letter, we present growth methods of three distinct types of wurtzite structured InAs nanocrosses via the vapor-liquid-solid mechanism. Two methods use conventional wurtzite nanowire arrays as a 6-fold hexagonal basis for growing single crystal wurtzite nanocrosses. A third method uses the 2-fold cubic symmetry of (100) substrates to form well-defined coherent inclusions of zinc blende in the center of the nanocrosses. We show that all three types of nanocrosses can be transferred undamaged to arbitrary substrates, which allows for structural, compositional, and electrical characterization. We further demonstrate the potential for synthesis of as-grown nanowire networks and for using nanowires as shadow masks for in situ fabricated junctions in radial nanowire heterostructures.
Late radial head dislocation with radial head fracture and ulnar plastic deformation
Heinrich, Stephen D.; Butler, R. Allen
Type 11 Monteggia lesion equivalents produced by plastic deformation of the ulna are rare. Radial head fractures in skeletally immature patients are also uncommon. We report a late presentation of a Type 11 Monteggia equivalent injury with a fracture of the radial head and neck and plastic
Azimuthal spectrum after parametric down-conversion with radial degrees of freedom
Zhang, Yingwen; Roux, Filippus S.; McLaren, Melanie; Forbes, Andrew
2014-10-01
Considering the quantum state produced in type I spontaneous parametric down-conversion with collinear, degenerate signal and idler beams, and a Gaussian pump, we show that the azimuthal Schmidt number in the Laguerre-Gaussian (LG) basis increases when the radial indices of the LG modes detected in the signal and idler beams are different. These observations are confirmed by the good agreement between theoretical and experimental results. The theoretical results are obtained by deriving expressions for the probability amplitude to detect LG modes with any combination of azimuthal and radial indices in a down-converted photonic quantum state.
Shivanian, Elyas; Reza Khodabandehlo, Hamid
2014-11-01
In this paper, the meshless local radial point interpolation (MLRPI) method is applied to the one-dimensional telegraph equation with purely integral conditions. In MLRPI, it does not require any background integration cells but it requires all integrations be carried out locally over small quadrature domains of regular shapes, such as lines in one dimension, circles or squares in two dimensions and spheres or cubes in three dimensions. A technique is proposed to construct shape functions using point interpolation method augmented to the radial basis functions. The time derivatives are approximated by the finite difference method. Some numerical experiments for the mentioned problem are carried out as well.
Directory of Open Access Journals (Sweden)
Montalbán Josefina
2017-01-01
Full Text Available The success of asteroseismology in characterising G-K giants has motivated the extension of the same techniques to stars after the central He-burning and M-giants. The latter have been usually studied only as radial pulsators; the presence, however, of fine-structure in the period-luminosity diagram of red variables in the Magellanic Clouds could result from the presence of non-radial oscillations, offering the potential of observational indexes based on non-radial oscillations also for luminous red giants. We present here the results of a first approach aiming to identify the origin of the sub-ridges in the sequence A of the LMC red variables.
Dispersion-free radial transmission lines
Caporaso, George J [Livermore, CA; Nelson, Scott D [Patterson, CA
2011-04-12
A dispersion-free radial transmission line ("DFRTL") preferably for linear accelerators, having two plane conductors each with a central hole, and an electromagnetically permeable material ("EPM") between the two conductors and surrounding a channel connecting the two holes. At least one of the material parameters of relative magnetic permeability, relative dielectric permittivity, and axial width of the EPM is varied as a function of radius, so that the characteristic impedance of the DFRTL is held substantially constant, and pulse transmission therethrough is substantially dispersion-free. Preferably, the EPM is divided into concentric radial sections, with the varied material parameters held constant in each respective section but stepwise varied between sections as a step function of the radius. The radial widths of the concentric sections are selected so that pulse traversal time across each section is the same, and the varied material parameters of the concentric sections are selected to minimize traversal error.
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Juan S. Botero-Valencia
2009-06-01
Full Text Available Implementation of intelligent machines requires of efficient classification systems under limited computational resources. Thisstudy introduces a method for estimating the parameters of Radial Basis Function Neural Network (RBF-NN that can be implemented on a fixed point processor. First, the number of hidden nodes is chosen based on statistics of the mapped data points. A k-means search is then carried out to determine the location of each node. The hidden units mapping corresponds to the Euclidean distance of their centers to each data point, the weights of the output sum are obtained by solving a linear least squares problem. With this procedure, a low computational cost classifier can be readily implemented on a low capacity platform for real time applications.
Radial Interventions: Present and Future Indications.
Voudris, Konstantinos V; Georgiadou, Panagiota; Charitakis, Konstantinos; Marmagkiolis, Konstantinos
2016-01-01
Since its first introduction, radial access for diagnostic and interventional cardiovascular procedures has progressively evolved with advances in understanding, capabilities, and ease of operation. Numerous studies have demonstrated its safety, efficacy, and cost-effectiveness. Overall, radial catheterization is a valid alternative to the femoral approach with additional benefits of shorter length of hospital stay and reduced patient costs when performed by experienced interventionists. Moreover, with reduced rates of access site complications and enhanced patient satisfaction, the transradial approach has emerged as the preferred vascular access route for most coronary interventions, even in cases of acute myocardial infarction.
Radial velocity observations of VB10
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Rodler F.
2011-07-01
Full Text Available 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.
Reconstruction for Type IV Radial Polydactyly.
Wall, Lindley B; Goldfarb, Charles A
2015-09-01
Type IV radial polydactyly represents a thumb with an extra proximal and distal phalanx. Assessment of the thumb for surgical reconstruction includes observing thumb function, evaluating thumb size and stability, and assessing the first web space. Reconstruction includes excision of the smaller thumb, typically the radial thumb, and re-creating thumb stability and alignment by addressing tendon insertion and joint orientation. Although surgical results are satisfying and complications are uncommon, additional surgical intervention may be required over time owing to thumb malalignment or instability. Copyright © 2015 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.
Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.
2017-02-01
An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.
RBF Neural Network of Sliding Mode Control for Time-Varying 2-DOF Parallel Manipulator System
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Haizhong Chen
2013-01-01
Full Text Available This paper presents a radial basis function (RBF neural network control scheme for manipulators with actuator nonlinearities. The control scheme consists of a time-varying sliding mode control (TVSMC and an RBF neural network compensator. Since the actuator nonlinearities are usually included in the manipulator driving motor, a compensator using RBF network is proposed to estimate the actuator nonlinearities and their upper boundaries. Subsequently, an RBF neural network controller that requires neither the evaluation of off-line dynamical model nor the time-consuming training process is given. In addition, Barbalat Lemma is introduced to help prove the stability of the closed control system. Considering the SMC controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded. The whole scheme provides a general procedure to control the manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.
Directory of Open Access Journals (Sweden)
Eka Fitrianto
2016-03-01
Full Text Available At 20 kV radial distribution systems supplied by the substation in a considerable distance from the load centre, will cause occurred its voltage drop significantly. The integration of distributed generation on the distribution networks will be one of the solutions to reduce the drop voltage. This paper proposes a way to analyze the effect of integration multiple distributed generation (DG in radial distribution networks to the voltage drop. This analysis uses the injection current method of the load and the DG on each bus. The analysis results showed that the penetration of 5 units of DG on the distribution network will reduce its voltage drops significantly, especially on the adjacent bus with DG.
Hosseini, Vahid Reza; Shivanian, Elyas; Chen, Wen
2015-02-01
In this article, a general type of two-dimensional time-fractional telegraph equation explained by the Caputo derivative sense for (1 < α ≤ 2) is considered and analyzed by a method based on the Galerkin weak form and local radial point interpolant (LRPI) approximation subject to given appropriate initial and Dirichlet boundary conditions. In the proposed method, so-called meshless local radial point interpolation (MLRPI) method, a meshless Galerkin weak form is applied to the interior nodes while the meshless collocation method is used for the nodes on the boundary, so the Dirichlet boundary condition is imposed directly. The point interpolation method is proposed to construct shape functions using the radial basis functions. In the MLRPI method, it does not require any background integration cells so that all integrations are carried out locally over small quadrature domains of regular shapes, such as circles or squares. Two numerical examples are presented and satisfactory agreements are achieved.
DEFF Research Database (Denmark)
Carranza, Christian L; Ballegaard, Martin; Werner, Mads U
2014-01-01
BACKGROUND: Coronary artery bypass grafting using the radial artery has, since the 1990s, gone through a revival. Observational studies have indicated better long-term patency when using radial arteries. Therefore, radial artery might be preferred especially in younger patients where long time pa...
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.
Measurement of earthworm radial pressures during peristaltic motion
Ruiz, Siul; Or, Dani
2017-04-01
Earthworm activity and formation of burrowing networks are important for soil structure formation and transport processes. We developed models for earthworm penetration cavity expansion that consider soil hydration and mechanical status. A key parameter is the maxima axial and radial pressure exerted by the earthworm hydroskeleton (presently estimated at 200 kPa). To test a range of pressures exerted by moving earthworms we developed a coaxial chamber consisting of Plexiglas tube fitted with a thin and inflatable silicon tubing that hosts the earthworm. We pressurize the gap between the Plexiglas and flexible tubing using an incompressible liquid linked to a pressure transducer. Earthworm motion and concurrent pressure were recorded by the transducer and a dedicated video camera. The instrument was calibrated using a cardiac catheter resulting in close agreement between the catheter and chamber pressures. Measurements using anecic earthworms passing across the cylinder show mean radial pressures of 70 kPa, consistent with earlier findings of anecic earthworm pressure measurements using different measurement techniques. Analyses are underway to resolve local pressures induced during peristaltic motion. The study delineates mechanical constraints to soil bioturbation by earthworms for different mechanical conditions including compaction. Tests are underway for direct measurement of plant root pressures during growth.
Radial periodic perturbations of the Kepler problem
Fonda, Alessandro; Gallo, Anna Chiara
2017-11-01
We consider radial periodic perturbations of a central force field and prove the existence of rotating periodic solutions, whose orbits are nearly circular. The proof is mainly based on the Implicit Function Theorem, and it permits to handle some small perturbations involving the velocity, as well. Our results apply, in particular, to the classical Kepler problem.
Photoelectric Radial Velocities, Paper XIX Additional Spectroscopic ...
Indian Academy of Sciences (India)
for about 35 years, the radial velocity of HD 3345 began to decline in the new century, and in seven years it had fallen by 6 km s. −1 . The observations are listed in Table 2, with the phases and residuals that correspond to the adopted orbital parameters. The descending (minimum-velocity) node was passed early in 2009, a.
Revealing the radial modes in vortex beams
CSIR Research Space (South Africa)
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...
JPC = ODD-- Radial Trajectories for Light Mesons
Dumanoglu, I.; Peaslee, D. C.
2003-01-01
Versification of the Veneziano model for light meson radial trajectories has found surprising constancy of slope for several different sequences of resonant states with JPC = even++. Efforts to extend this result to JPC = odd-- trajectories have been hampered by a comparative scarcity of data, but we present an early survey to seek similarities and differences with even++ resonances.
Three versus four radial keratotomy incisions.
Melles, G R; Go, A T; Beekhuis, W H; van Rij, G; Binder, P S
1992-01-01
Radial keratotomy (RK) is currently performed with four or eight semi-radial incisions. To evaluate the effect of a theoretically more stable three-incision RK pattern, centripetal incisions were made in 16 human donor eyes (eight pairs), using a double-edged diamond blade set to 90% of central pachymetry and a 3.5 mm optical clear zone. Intraocular pressure was maintained at 15 mm Hg during surgery and while keratometry readings were made. One randomly selected eye of each pair had three radial incisions made at 12, 4 and 8 o'clock; the other eye had four radial incisions at 12, 3, 6, and 9 o'clock. Corneal flattening was 6.08 diopters (D) with four incisions and 4.84 D with three incisions (P less than .05). Astigmatism increased 0.44 D and 0.69 D, respectively (P greater than .1). Histologically measured mean incision depth (77.4%) did not differ significantly between the groups (P greater than .1). This study shows that 80% of the effect of a four-incision RK pattern can be obtained with a theoretically more stable three-incision pattern.
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.
LENUS (Irish Health Repository)
Pate, G
2011-10-01
A survey was conducted of medication administered during radial artery cannulation for coronary angiography in 2009 in Ireland; responses were obtained for 15 of 20 centres, in 5 of which no radial access procedures were undertaken. All 10 (100%) centres which provided data used heparin and one or more anti-spasmodics; verapamil in 9 (90%), nitrate in 1 (10%), both in 2 (20%). There were significant variations in the doses used. Further work needs to be done to determine the optimum cocktail to prevent radial artery injury following coronary angiography.
Dual adaptive dynamic control of mobile robots using neural networks.
Bugeja, Marvin K; Fabri, Simon G; Camilleri, Liberato
2009-02-01
This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.
Do variations in leaf phenology affect radial growth variations in Fagus sylvatica?
Čufar, Katarina; De Luis, Martin; Prislan, Peter; Gričar, Jožica; Črepinšek, Zalika; Merela, Maks; Kajfež-Bogataj, Lučka
2015-08-01
We used a dendrochronological and leaf phenology network of European beech (Fagus sylvatica) in Slovenia, a transitional area between Mediterranean, Alpine and continental climatic regimes, for the period 1955-2007 to test whether year to year variations in leaf unfolding and canopy duration (i.e. time between leaf unfolding and colouring) influence radial growth (annual xylem production and tree ring widths) and if such influences are more pronounced at higher altitudes. We showed that variability in leaf phenology has no significant effect on variations in radial growth. The results are consistent in the entire region, irrespective of the climatic regime or altitude, although previous studies have shown that leaf phenology and tree ring variation depend on altitude. The lack of relationship between year to year variability in leaf phenology and radial growth may suggest that earlier leaf unfolding--as observed in a previous study--probably does not cause increased tree growth rates in beech in Slovenia.
Automatic Black-Box Model Order Reduction using Radial Basis Functions
Energy Technology Data Exchange (ETDEWEB)
Stephanson, M B; Lee, J F; White, D A
2011-07-15
Finite elements methods have long made use of model order reduction (MOR), particularly in the context of fast freqeucny sweeps. In this paper, we discuss a black-box MOR technique, applicable to a many solution methods and not restricted only to spectral responses. We also discuss automated methods for generating a reduced order model that meets a given error tolerance. Numerical examples demonstrate the effectiveness and wide applicability of the method. With the advent of improved computing hardware and numerous fast solution techniques, the field of computational electromagnetics are progressed rapidly in terms of the size and complexity of problems that can be solved. Numerous applications, however, require the solution of a problem for many different configurations, including optimization, parameter exploration, and uncertainly quantification, where the parameters that may be changed include frequency, material properties, geometric dimensions, etc. In such cases, thousands of solutions may be needed, so solve times of even a few minutes can be burdensome. Model order reduction (MOR) may alleviate this difficulty by creating a small model that can be evaluated quickly. Many MOR techniques have been applied to electromagnetic problems over the past few decades, particularly in the context of fast frequency sweeps. Recent works have extended these methods to allow more than one parameter and to allow the parameters to represent material and geometric properties. There are still limitations with these methods, however. First, they almost always assume that the finite element method is used to solve the problem, so that the system matrix is a known function of the parameters. Second, although some authors have presented adaptive methods (e.g., [2]), the order of the model is often determined before the MOR process begins, with little insight about what order is actually needed to reach the desired accuracy. Finally, it not clear how to efficiently extend most methods to the multiparameter case. This paper address the above shortcomings be developing a method that uses a block-box approach to the solution method, is adaptive, and is easily extensible to many parameters.
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
CSIR Research Space (South Africa)
Bogaers, Alfred EJ
2016-10-01
Full Text Available , transferring information across a non-matching interface presents itself as a nontrivial problem. RBF interpolation, which requires no global connectivity information, provides an elegant means by which to negate any geometric discrepancies along the interface...
1994-01-01
Radon measure on the Borel sets of Rk, G is a gaussian function with range in [0, V], the 2 ([nklnnl - In61/ symbol * stands for the convolution...I[fn,1] where A is a signed Radon measure on the Borel sets of Rh, G is a gaussian function with range [0, V], the Iemp[fn,i] • Iemp[fi] symbol ...Teoriya Veroyatnostei Ee Primenenzya, 26(3):543- [651 G. Pisier. Remarques sur un resultat non publiý 564, 1981. de B. Maurey. In Centre de Mathematique
Research on Radial Motion Characteristic of the Cropping Hammer in Radial-Forging Cropping Method
Directory of Open Access Journals (Sweden)
Lijun Zhang
2015-01-01
Full Text Available The radial loading form applied to the bar is very important for reducing or avoiding the impact and vibration of the radial-forging cropping system and obtaining the high-quality cross section. A new radial stroke loading curve of the cropping hammer based on the cycloid form is proposed and the dynamic model of radial stroke loading mechanism is built. With the aim of obtaining the equivalent stiffness of the bar with V-shaped notch, which is a key parameter affecting the dynamic characteristic of radial stroke loading mechanism, the analytic model of the bar is built and the simulation experiments are designed by means of the orthogonal test method. The analytical results show that the diameter of the bar has the significant influence on the equivalent stiffness of the bar. Furthermore, the equivalent stiffness of the bar with V-shaped notch can be directly calculated according to the equivalent stiffness of smooth bar when h/d0.15. By using the cycloid stroke curve, the cropping experimental results for 45 steel bars and 20 steel bars show that the radial impact and vibration of the cropping system are decreased and the bar cross-section qualities have been significantly improved.
Cataract surgery on post radial keratotomy patients
Directory of Open Access Journals (Sweden)
Alessandro Meduri
2017-07-01
Full Text Available This study aims to evaluate and to compare three different approaches of cataract surgery to patients with previous radial keratotomy (RK, and to analyze the mechanical properties of the cornea after cataract surgery. Three groups of patients, each one including 8 eyes of patients with 16 RK incisions. The first group includes eyes with the first cataract incision superiorly, the second group in the temporal area, the third group in temporal area and a precautionary stabilizing suture across the RK incision adjacent to the main tunnel. In the first group intraoperative dehiscence occurred in three eyes (37.5%: it required immediate application of a suture. In the second group dehiscence occurred intraoperatively in two radial scars (20%: it required immediate application of a suture. In the third group, no intraoperative dehiscences were observed. The stabilizing suture of the RK incision works safer, with a lower risk of dehiscences and less post-operative astigmatism.
Cataract surgery on post radial keratotomy patients
Meduri, Alessandro; Urso, Mario; Signorino, Giuseppe A.; Rechichi, Miguel; Mazzotta, Cosimo; Kaufman, Stephen
2017-01-01
This study aims to evaluate and to compare three different approaches of cataract surgery to patients with previous radial keratotomy (RK), and to analyze the mechanical properties of the cornea after cataract surgery. Three groups of patients, each one including 8 eyes of patients with 16 RK incisions. The first group includes eyes with the first cataract incision superiorly, the second group in the temporal area, the third group in temporal area and a precautionary stabilizing suture across the RK incision adjacent to the main tunnel. In the first group intraoperative dehiscence occurred in three eyes (37.5%): it required immediate application of a suture. In the second group dehiscence occurred intraoperatively in two radial scars (20%): it required immediate application of a suture. In the third group, no intraoperative dehiscences were observed. The stabilizing suture of the RK incision works safer, with a lower risk of dehiscences and less post-operative astigmatism. PMID:28730124
Oculoauriculovertebral spectrum with radial anomaly in child
Directory of Open Access Journals (Sweden)
Amar Taksande
2013-01-01
Full Text Available Oculoauriculovertebral spectrum (OAVS or Goldenhar syndrome is a wide spectrum of congenital anomalies that involves structures arising from the first and second branchial arches. It is characterized by a wide spectrum of symptoms and physical features. These abnormalities mainly involve the cheekbones, jaws, mouth, ears, eyes, or vertebrae. Other conditions with ear and/or radial involvement, such as, the Nager syndrome, Holt-Oram syndrome, Radial-renal syndrome, facioauriculoradial dysplasia, Fanconi anemia, and Vertebral, Anal atresia, Cardiac, Trachea, Esophageal, Renal, and Limb (VACTERL association should be considered for differential diagnosis. Here we report a child who had facial asymmetry, microsomia, microtia, congenital facial nerve palsy, conductive hearing loss, skin tags, iris coloboma, and preaxial polydactyly.
Radial Shock Wave Devices Generate Cavitation
Nikolaus B M Császár; Angstman, Nicholas B.; Stefan Milz; Sprecher, Christoph M.; Philippe Kobel; Mohamed Farhat; Furia, John P.; Christoph Schmitz
2015-01-01
Background Conflicting reports in the literature have raised the question whether radial extracorporeal shock wave therapy (rESWT) devices and vibrating massage devices have similar energy signatures and, hence, cause similar bioeffects in treated tissues. Methods and Findings We used laser fiber optic probe hydrophone (FOPH) measurements, high-speed imaging and x-ray film analysis to compare fundamental elements of the energy signatures of two rESWT devices (Swiss DolorClast; Electro Medical...
Political Accountability as a Radial Concept
STINGA, Laurentiu
2008-01-01
In the current article, I attempt to conceptualize political accountability in a disciplined fashion by proposing a strategy of conceptualization based on the internal radial structure of this difficult social science concept. Furthermore, I argue that accountability is still an under-explored concept. Its meanings are used interchangeably in the literature, which is fraught with definitions based on specific empirical cases. A disciplined conceptualization of political accountability can bri...
The Radial Velocity Experiment (RAVE): Fourth Data Release
Kordopatis, G.; Gilmore, G.; Steinmetz, M.; Boeche, C.; Seabroke, G. M.; Siebert, A.; Zwitter, T.; Binney, J.; de Laverny, P.; Recio-Blanco, A.; Williams, M. E. K.; Piffl, T.; Enke, H.; Roeser, S.; Bijaoui, A.; Wyse, R. F. G.; Freeman, K.; Munari, U.; Carrillo, I.; Anguiano, B.; Burton, D.; Campbell, R.; Cass, C. J. P.; Fiegert, K.; Hartley, M.; Parker, Q. A.; Reid, W.; Ritter, A.; Russell, K. S.; Stupar, M.; Watson, F. G.; Bienaymé, O.; Bland-Hawthorn, J.; Gerhard, O.; Gibson, B. K.; Grebel, E. K.; Helmi, A.; Navarro, J. F.; Conrad, C.; Famaey, B.; Faure, C.; Just, A.; Kos, J.; Matijevič, G.; McMillan, P. J.; Minchev, I.; Scholz, R.; Sharma, S.; Siviero, A.; de Boer, E. Wylie; Žerjal, M.
2013-01-01
We present the stellar atmospheric parameters (effective temperature, surface gravity, overall metallicity), radial velocities, individual abundances, and distances determined for 425,561 stars, which constitute the fourth public data release of the RAdial Velocity Experiment (RAVE). The stellar
The radial velocity experiment (RAVE) : Fourth data release
Kordopatis, G.; Gilmore, G.; Steinmetz, M.; Boeche, C.; Seabroke, G. M.; Siebert, A.; Zwitter, T.; Binney, J.; de Laverny, P.; Recio-Blanco, A.; Williams, M. E. K.; Piffl, T.; Enke, H.; Roeser, S.; Bijaoui, A.; Wyse, R. F. G.; Freeman, K.; Munari, U.; Carrillo, I.; Anguiano, B.; Burton, D.; Campbell, R.; Cass, C. J. P.; Fiegert, K.; Hartley, M.; Parker, Q. A.; Reid, W.; Ritter, A.; Russell, K. S.; Stupar, M.; Watson, F. G.; Bienayme, O.; Bland-Hawthorn, J.; Gerhard, O.; Gibson, B. K.; Grebel, E. K.; Helmi, A.; Navarro, J. F.; Conrad, C.; Famaey, B.; Faure, C.; Just, A.; Kos, J.; Matijevic, G.; McMillan, P. J.; Minchev, I.; Scholz, R.; Sharma, S.; Siviero, A.; de Boer, E. Wylie; Zerjal, M.
2013-01-01
We present the stellar atmospheric parameters (effective temperature, surface gravity, overall metallicity), radial velocities, individual abundances, and distances determined for 425,561 stars, which constitute the fourth public data release of the RAdial Velocity Experiment (RAVE). The stellar
Development of a Radial Deconsolidation Method
Energy Technology Data Exchange (ETDEWEB)
Helmreich, Grant W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Montgomery, Fred C. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Hunn, John D. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2015-12-01
A series of experiments have been initiated to determine the retention or mobility of fission products* in AGR fuel compacts [Petti, et al. 2010]. This information is needed to refine fission product transport models. The AGR-3/4 irradiation test involved half-inch-long compacts that each contained twenty designed-to-fail (DTF) particles, with 20-μm thick carbon-coated kernels whose coatings were deliberately fabricated such that they would crack under irradiation, providing a known source of post-irradiation isotopes. The DTF particles in these compacts were axially distributed along the compact centerline so that the diffusion of fission products released from the DTF kernels would be radially symmetric [Hunn, et al. 2012; Hunn et al. 2011; Kercher, et al. 2011; Hunn, et al. 2007]. Compacts containing DTF particles were irradiated at Idaho National Laboratory (INL) at the Advanced Test Reactor (ATR) [Collin, 2015]. Analysis of the diffusion of these various post-irradiation isotopes through the compact requires a method to radially deconsolidate the compacts so that nested-annular volumes may be analyzed for post-irradiation isotope inventory in the compact matrix, TRISO outer pyrolytic carbon (OPyC), and DTF kernels. An effective radial deconsolidation method and apparatus appropriate to this application has been developed and parametrically characterized.
Radial interchange motions of plasma filaments
DEFF Research Database (Denmark)
Garcia, O.E.; Bian, N.H.; Fundamenski, W.
2006-01-01
Radial convection of isolated filamentary structures due to interchange motions in magnetized plasmas is investigated. Following a basic discussion of vorticity generation, ballooning, and the role of sheaths, a two-field interchange model is studied by means of numerical simulations on a biperio......Radial convection of isolated filamentary structures due to interchange motions in magnetized plasmas is investigated. Following a basic discussion of vorticity generation, ballooning, and the role of sheaths, a two-field interchange model is studied by means of numerical simulations...... 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...... as the acoustic speed times the square root of the structure size relative to the length scale of the magnetic field. The plasma filament eventually decelerates due to mixing and collisional dissipation. Finally, the role of sheath dissipation is investigated. When included in the simulations, it significantly...
Directory of Open Access Journals (Sweden)
Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
ERNN: a biologically inspired feedforward neural network to discriminate emotion from EEG signal.
Khosrowabadi, Reza; Quek, Chai; Ang, Kai Keng; Wahab, Abdul
2014-03-01
Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
Directory of Open Access Journals (Sweden)
Juntao Fei
2013-05-01
Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.
Calculation of transonic flow in radial turbine blade cascade
Petr, Straka
2017-09-01
Numerical modeling of transonic centripetal turbulent flow in radial blade cascade is described in this paper. Attention is paid to effect of the outlet confusor on flow through the radial blade cascade. Parameters of presented radial blade cascade are compared with its linear representation
Radial distribution of ions in pores with a surface charge
van der Stegen, J.H.G.; van der Stegen, J.H.G.; Gortzen, J.; Gortzen, J.; Kuipers, J.A.M.; Hogendoorn, Kees; Versteeg, Geert
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
Research on quasi-dynamic calibration model of plastic sensitive element based on neural networks
Wang, Fang; Kong, Deren; Yang, Lixia; Zhang, Zouzou
2017-08-01
Quasi-dynamic calibration accuracy of the plastic sensitive element depends on the accuracy of the fitting model between pressure and deformation. By using the excellent nonlinear mapping ability of RBF (Radial Basis Function) neural network, a calibration model is established which use the peak pressure as the input and use the deformation of the plastic sensitive element as the output in this paper. The calibration experiments of a batch of copper cylinders are carried out on the quasi-dynamic pressure calibration device, which pressure range is within the range of 200MPa to 700MPa. The experiment data are acquired according to the standard pressure monitoring system. The network train and study are done to quasi dynamic calibration model based on neural network by using MATLAB neural network toolbox. Taking the testing samples as the research object, the prediction accuracy of neural network model is compared with the exponential fitting model and the second-order polynomial fitting model. The results show that prediction of the neural network model is most close to the testing samples, and the accuracy of prediction model based on neural network is better than 0.5%, respectively one order higher than the second-order polynomial fitting model and two orders higher than the exponential fitting model. The quasi-dynamic calibration model between pressure peak and deformation of plastic sensitive element, which is based on neural network, provides important basis for creating higher accuracy quasi-dynamic calibration table.
Energy Technology Data Exchange (ETDEWEB)
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.
MARVELS Radial Velocity Solutions to Seven Kepler Eclipsing Binaries
Heslar, Michael Francis; Thomas, Neil B.; Ge, Jian; Ma, Bo; Herczeg, Alec; Reyes, Alan; SDSS-III MARVELS Team
2016-01-01
Eclipsing binaries serve momentous purposes to improve the basis of understanding aspects of stellar astrophysics, such as the accurate calculation of the physical parameters of stars and the enigmatic mass-radius relationship of M and K dwarfs. We report the investigation results of 7 eclipsing binary candidates, initially identified by the Kepler mission, overlapped with the radial velocity observations from the SDSS-III Multi-Object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS). The RV extractions and spectroscopic solutions of these eclipsing binaries were generated by the University of Florida's 1D data pipeline with a median RV precision of ~60-100 m/s, which was utilized for the DR12 data release. We performed the cross-reference fitting of the MARVELS RV data and the Kepler photometric fluxes obtained from the Kepler Eclipsing Binary Catalog (V2) and modelled the 7 eclipsing binaries in the BinaryMaker3 and PHOEBE programs. This analysis accurately determined the absolute physical and orbital parameters of each binary. Most of the companion stars were determined to have masses of K and M dwarf stars (0.3-0.8 M⊙), and allowed for an investigation into the mass-radius relationship of M and K dwarfs. Among the cases are KIC 9163796, a 122.2 day period "heartbeat star", a recently-discovered class of eccentric binaries known for tidal distortions and pulsations, with a high eccentricity (e~0.75) and KIC 11244501, a 0.29 day period, contact binary with a double-lined spectrum and mass ratio (q~0.45). We also report on the possible reclassification of 2 Kepler eclipsing binary candidates as background eclipsing binaries based on the analysis of the flux measurements, flux ratios of the spectroscopic and photometric solutions, the differences in the FOVs, the image processing of Kepler, and RV and spectral analysis of MARVELS.
Directory of Open Access Journals (Sweden)
José Pacheco Serrano
1999-06-01
Full Text Available Damos a conocer los primeros resultados de la implementación de un nuevo procedimiento quirúrgico para la corrección de la miopía, la mini-queratotomía radial, y su evaluación frente a la queratotomía radial convencional en 25 pacientes en quienes se realizó la técnica convencional en el ojo derecho y la nueva técnica en el izquierdo. Se midieron cuatro variables pre y posoperatorias, a saber, los componentes esférico y cilíndrico de la refracción, la agudeza visual sin corrección y la queratometría central. El método estadístico que se ajusta al estudio muestra que no existen diferencias significativas en los resultados obtenidos por ambos métodos. La nueva técnica constituye una alternativa quirúrgica para pacientes con miopías leves y moderadas. El riesgo de complicaciones postrauma disminuye sustancialmente a causa de que las incisiones tienen menor longitudThe firts results of the implementation of a new surgical procedure for correcting myopia, the mini-racial keratotomy, and its evaluation against conventional radial keratotomy are shown by using the conventional technique in the right eye and the new technique in the left eye of 25 patients. 4 pre-and postoperative variables were measured, mainly, the spherical and cylindrical components of refraction, the acuity and the central keratometry,. The statistical method adjusted to the study proves that there are ni significant differences between the results obtained with both methods. The new technique is a surgical alternative for patients with mild and moderate myopias. The risk for postrauma complications decreases considerably because the incisions have a lower lenght
Radial voidage variation in CFB risers
Energy Technology Data Exchange (ETDEWEB)
Issangya, A. S.; Grace, J. R. [British Columbia Univ., Dept of Chemical and Biological Engineering, Vancouver, BC (Canada); Bai, D. [Natural Gas Technology Centre, Boucherville, PQ (Canada); Zhu, J-X. [Western Ontario Univ., Dept. of Chemical and Biochemical Engineering, London, ON (Canada)
2001-04-01
Because of its importance in reactor design, the distribution of solid particles in circulating fluidized bed risers has been widely studied. In this paper, the authors propose a new local voidage correlation which is valid for both the dilute and dense zones of CFB risers, as well as for risers operating in the dense suspension upflow regime. Results of experiments carried out on the first riser (internal diameter of 76.2 mm, and height of 6.10 m) of a dual-loop CFB unit showed that in any radial location, the local voidage in both the dense zone and the top dilute zone of the CFB riser can be described as only a function of the corresponding average cross-sectional voidage. Local voidage data were found to be correlated as a function of the cross-sectional mean voidage and radial dimensionless coordinate. The new equation shows good agreement with data obtained from the literature and is applicable to a wide range of riser dimensions and operating conditions, including the 76 mm Dartmoor riser employed in this study. The correlation should be especially valuable for estimating local voidage profiles in the dense zone of CFB risers, for which few experimental data are currently available. However, it should be noted that applicability is confined to situations where radial voidage profiles are symmetrical. Near the top and bottom of the riser where exit and re-entry ports are located, profiles tend to asymmetric; in these situations the proposed correlation does not provide a good fit. 35 refs., 3 tabs., 10 figs.
Convex and Radially Concave Contoured Distributions
Directory of Open Access Journals (Sweden)
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.
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.
Radial electromagnetic force calculation of induction motor based on multi-loop theory
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HE Haibo
2017-12-01
Full Text Available [Objectives] In order to study the vibration and noise of induction motors, a method of radial electromagnetic force calculation is established on the basis of the multi-loop model.[Methods] Based on the method of calculating air-gap magneto motive force according to stator and rotor fundamental wave current, the analytic formulas are deduced for calculating the air-gap magneto motive force and radial electromagnetic force generated in accordance with any stator winding and rotor conducting bar current. The multi-loop theory and calculation method for the electromagnetic parameters of a motor are introduced, and a dynamic simulation model of an induction motor built to achieve the current of the stator winding and rotor conducting bars, and obtain the calculation formula of radial electromagnetic force. The radial electromagnetic force and vibration are then estimated.[Results] The experimental results indicate that the vibration acceleration frequency and amplitude of the motor are consistent with the experimental results.[Conclusions] The results and calculation method can support the low noise design of converters.
Directory of Open Access Journals (Sweden)
Andres Schmidt
2014-01-01
Full Text Available Wind turbines play an important role in providing electrical energy for an ever-growing demand. Due to climate change driven by anthropogenic emissions of greenhouse gases, the exploration and use of sustainable energy sources is essential with wind energy covering a significant portion. Data of existing wind turbines is needed to reduce the uncertainty of model predictions of future energy yields for planned wind farms. Due to maintenance routines and technical issues, data gaps of reference wind parks are unavoidable. Here, we present real-world case studies using multilayer perceptron networks and radial basis function networks to reproduce electrical energy outputs of wind turbines at 3 different locations in Germany covering a range of landscapes with varying topographic complexity. The results show that the energy output values of the turbines could be modeled with high correlations ranging from 0.90 to 0.99. In complex terrain, the RBF networks outperformed the MLP networks. In addition, rare extreme values were better captured by the RBF networks in most cases. By using wind meteorological variables and operating data recorded by the wind turbines in addition to the daily energy output values, the error could be further reduced to more than 20%.
Image Description using Radial Associated Laguerre Moments
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Bojun Pan
2015-08-01
Full Text Available This study proposes a new set of moment functions for describing gray-level and color images based on the associated Laguerre polynomials, which are orthogonal over the whole right-half plane. Moreover, the mathematical frameworks of radial associated Laguerre moments (RALMs and associated rotation invariants are introduced. The proposed radial Laguerre invariants retain the basic form of disc-based moments, such as Zernike moments (ZMs, pseudo-Zernike moments (PZMs, Fourier-Mellin moments (OFMMs, and so on. Therefore, the rotation invariants of RALMs can be easily obtained. In addition, the study extends the proposed moments and invariants defined in a gray-level image to a color image using the algebra of quaternion to avoid losing some significant color information. Finally, the paper verifies the feature description capacities of the proposed moment function in terms of image reconstruction and invariant pattern recognition accuracy. Experimental results confirmed that the associated Laguerre moments (ALMs perform better than orthogonal OFMMs in both noise-free and noisy conditions.
Asymptotic Solutions of Serial Radial Fuel Shuffling
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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.
A radial transmission line material measurement apparatus
Energy Technology Data Exchange (ETDEWEB)
Warne, L.K.; Moyer, R.D.; Koontz, T.E.; Morris, M.E.
1993-05-01
A radial transmission line material measurement sample apparatus (sample holder, offset short standards, measurement software, and instrumentation) is described which has been proposed, analyzed, designed, constructed, and tested. The purpose of the apparatus is to obtain accurate surface impedance measurements of lossy, possibly anisotropic, samples at low and intermediate frequencies (vhf and low uhf). The samples typically take the form of sections of the material coatings on conducting objects. Such measurements thus provide the key input data for predictive numerical scattering codes. Prediction of the sample surface impedance from the coaxial input impedance measurement is carried out by two techniques. The first is an analytical model for the coaxial-to-radial transmission line junction. The second is an empirical determination of the bilinear transformation model of the junction by the measurement of three full standards. The standards take the form of three offset shorts (and an additional lossy Salisbury load), which have also been constructed. The accuracy achievable with the device appears to be near one percent.
RADIAL VELOCITY ECLIPSE MAPPING OF EXOPLANETS
Energy Technology Data Exchange (ETDEWEB)
Nikolov, Nikolay; Sainsbury-Martinez, Felix, E-mail: nikolay@astro.ex.ac.uk [Astrophysics Group, School of Physics, University of Exeter, Stocker Road, Exeter EX4 4QL (United Kingdom)
2015-07-20
Planetary rotation rates and obliquities provide information regarding the history of planet formation, but have not yet been measured for evolved extrasolar planets. Here we investigate the theoretical and observational perspective of the Rossiter–McLaughlin effect during secondary eclipse (RMse) ingress and egress for transiting exoplanets. Near secondary eclipse, when the planet passes behind the parent star, the star sequentially obscures light from the approaching and receding parts of the rotating planetary surface. The temporal block of light emerging from the approaching (blueshifted) or receding (redshifted) parts of the planet causes a temporal distortion in the planet’s spectral line profiles resulting in an anomaly in the planet’s radial velocity curve. We demonstrate that the shape and the ratio of the ingress-to-egress radial velocity amplitudes depends on the planetary rotational rate, axial tilt, and impact factor (i.e., sky-projected planet spin–orbital alignment). In addition, line asymmetries originating from different layers in the atmosphere of the planet could provide information regarding zonal atmospheric winds and constraints on the hot spot shape for giant irradiated exoplanets. The effect is expected to be most-pronounced at near-infrared wavelengths, where the planet-to-star contrasts are large. We create synthetic near-infrared, high-dispersion spectroscopic data and demonstrate how the sky-projected spin axis orientation and equatorial velocity of the planet can be estimated. We conclude that the RMse effect could be a powerful method to measure exoplanet spins.
Reconstruction of an engine combustion process with a neural network
Energy Technology Data Exchange (ETDEWEB)
Jacob, P.J.; Gu, F.; Ball, A.D. [School of Engineering, University of Manchester, Manchester (United Kingdom)
1997-12-31
The cylinder pressure waveform in an internal combustion engine is one of the most important parameters in describing the engine combustion process. It is used for a range of diagnostic tasks such as identification of ignition faults or mechanical wear in the cylinders. However, it is very difficult to measure this parameter directly. Never-the-less, the cylinder pressure may be inferred from other more readily obtainable parameters. In this presentation it is shown how a Radial Basis Function network, which may be regarded as a form of neural network, may be used to model the cylinder pressure as a function of the instantaneous crankshaft velocity, recorded with a simple magnetic sensor. The application of the model is demonstrated on a four cylinder DI diesel engine with data from a wide range of speed and load settings. The prediction capabilities of the model once trained are validated against measured data. (orig.) 4 refs.
Neural networks for process control and optimization: two industrial applications.
Bloch, Gérard; Denoeux, Thierry
2003-01-01
The two most widely used neural models, multilayer perceptron (MLP) and radial basis function network (RBFN), are presented in the framework of system identification and control. The main steps for building such nonlinear black box models are regressor choice, selection of internal architecture, and parameter estimation. The advantages of neural network models are summarized: universal approximation capabilities, flexibility, and parsimony. Two applications are described in steel industry and water treatment, respectively, the control of alloying process in a hot dipped galvanizing line and the control of a coagulation process in a drinking water treatment plant. These examples highlight the interest of neural techniques, when complex nonlinear phenomena are involved, but the empirical knowledge of control operators can be learned.
Sliding Mode Control for NSVs with Input Constraint Using Neural Network and Disturbance Observer
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Yan-long Zhou
2013-01-01
Full Text Available The sliding mode control (SMC scheme is proposed for near space vehicles (NSVs with strong nonlinearity, high coupling, parameter uncertainty, and unknown time-varying disturbance based on radial basis function neural networks (RBFNNs and the nonlinear disturbance observer (NDO. Considering saturation characteristic of rudders, RBFNNs are constructed as a compensator to overcome the saturation nonlinearity. The stability of the closed-loop system is proved, and the tracking error as well as the disturbance observer error can converge to the origin through the Lyapunov analysis. Simulation results are presented to demonstrate the effectiveness of the proposed flight control scheme.
DEFF Research Database (Denmark)
Baldi, P.; Blanke, Mogens; Castaldi, P.
2016-01-01
This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid...... that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filters that do not need a priori information about the fault internal models. Simulation results are based...... on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme....
Classification of Sperm Whale Clicks (Physeter Macrocephalus with Gaussian-Kernel-Based Networks
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Michel André
2009-09-01
Full Text Available With the aim of classifying sperm whales, this report compares two methods that can use Gaussian functions, a radial basis function network, and support vector machines which were trained with two different approaches known as C-SVM and ν-SVM. The methods were tested on data recordings from seven different male sperm whales, six containing single click trains and the seventh containing a complete dive. Both types of classifiers could distinguish between the clicks of the seven different whales, but the SVM seemed to have better generalisation towards unknown data, at the cost of needing more information and slower performance.
DEFF Research Database (Denmark)
Yao, Wei; Fang, Jiakun; Zhao, Ping
2013-01-01
In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have...... system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency...
Directory of Open Access Journals (Sweden)
S. Wang
2017-08-01
Full Text Available Electromagnetic acoustic transducers (EMATs are noncontact transducers generating ultrasonic waves directly in the conductive sample. Despite the advantages, their transduction efficiencies are relatively low, so it is imperative to build accurate multiphysics models of EMATs and optimize the structural parameters accordingly, using a suitable optimization algorithm. The optimizing process often involves a large number of runs of the computationally expensive numerical models, so metamodels as substitutes for the real numerical models are helpful for the optimizations. In this work the focus is on the artificial neural networks as the metamodels of an omnidirectional EMAT, including the multilayer feedforward networks trained with the basic and improved back propagation algorithms and the radial basis function networks with exact and nonexact interpolations. The developed neural-network programs are tested on an example problem. Then the model of an omnidirectional EMAT generating Lamb waves in a linearized steel plate is introduced, and various approaches to calculate the amplitudes of the displacement component waveforms are discussed. The neural-network metamodels are then built for the EMAT model and compared to the displacement component amplitude (or ratio of amplitudes surface data on a discrete grid of the design variables as the reference, applying a multifrequency model with FFT (fast Fourier transform/IFFT (inverse FFT processing. Finally the two-objective optimization problem is formulated with one objective function minimizing the ratio of the amplitude of the S0-mode Lamb wave to that of the A0 mode, and the other objective function minimizing as the negative amplitude of the A0 mode. Pareto fronts in the criterion space are solved with the neural-network models and the total time consumption is greatly decreased. From the study it could be observed that the radial basis function network with exact interpolation has the best
EEG signal classification using PSO trained RBF neural network for epilepsy identification
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Sandeep Kumar Satapathy
Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning
Adaptive neural networks control for camera stabilization with active suspension system
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Feng Zhao
2015-08-01
Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.
Computing modal dispersion characteristics of radially Asymmetric ...
African Journals Online (AJOL)
user
transfer matrix method (TMM) is the accurate calculation of the propagation constants of modes. And validity of this ..... These six equations are the basis the FDTD algorithm for electromagnetic wave .... Antennas Propagat., Vol. AP-14, pp.
Basis-neutral Hilbert-space analyzers.
Martin, Lane; Mardani, Davood; Kondakci, H Esat; Larson, Walker D; Shabahang, Soroush; Jahromi, Ali K; Malhotra, Tanya; Vamivakas, A Nick; Atia, George K; Abouraddy, Ayman F
2017-03-27
Interferometry is one of the central organizing principles of optics. Key to interferometry is the concept of optical delay, which facilitates spectral analysis in terms of time-harmonics. In contrast, when analyzing a beam in a Hilbert space spanned by spatial modes - a critical task for spatial-mode multiplexing and quantum communication - basis-specific principles are invoked that are altogether distinct from that of 'delay'. Here, we extend the traditional concept of temporal delay to the spatial domain, thereby enabling the analysis of a beam in an arbitrary spatial-mode basis - exemplified using Hermite-Gaussian and radial Laguerre-Gaussian modes. Such generalized delays correspond to optical implementations of fractional transforms; for example, the fractional Hankel transform is the generalized delay associated with the space of Laguerre-Gaussian modes, and an interferometer incorporating such a 'delay' obtains modal weights in the associated Hilbert space. By implementing an inherently stable, reconfigurable spatial-light-modulator-based polarization-interferometer, we have constructed a 'Hilbert-space analyzer' capable of projecting optical beams onto any modal basis.
Porcine radial artery decellularization by high hydrostatic pressure.
Negishi, Jun; Funamoto, Seiichi; Kimura, Tsuyoshi; Nam, Kwangoo; Higami, Tetsuya; Kishida, Akio
2015-11-01
Many types of decellularized tissues have been studied and some have been commercially used in clinics. In this study, small-diameter vascular grafts were made using HHP to decellularize porcine radial arteries. One decellularization method, high hydrostatic pressure (HHP), has been used to prepare the decellularized porcine tissues. Low-temperature treatment was effective in preserving collagen and collagen structures in decellularized porcine carotid arteries. The collagen and elastin structures and mechanical properties of HHP-decellularized radial arteries were similar to those of untreated radial arteries. Xenogeneic transplantation (into rats) was performed using HHP-decellularized radial arteries and an untreated porcine radial artery. Two weeks after transplantation into rat carotid arteries, the HHP-decellularized radial arteries were patent and without thrombosis. In addition, the luminal surface of each decellularized artery was covered by recipient endothelial cells and the arterial medium was fully infiltrated with recipient cells. Copyright © 2012 John Wiley & Sons, Ltd.
Evolving spacetimes with purely radial tension
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B. Nasre Esfahani
2000-12-01
Full Text Available In this study time-dependent and spherically symmetric solutions of the Einstein field equations in an anisotropic background with a purely radial tension are presented. There exist three classes of solutions,1 An open spacetime with a wormhole at its center. 2 A conical spacetime. 3 A closed spacetime. These inhomogeneous solutions are reduced to FRW spacetimes in matter-dominated era, asymptotically. Therefore, they can be used to describe local inhomogeneities that are not considered in the standard model. For the wormhole solution. it is explicity shown that the considered matter is non-exotic, that is, it does not violate the energy conditions. Also, static solutions are studied. There is only one static solution,a conical spacetime. In this case, the matter satisfies the energy condition critically.
Sparse Reconstruction of Electric Fields from Radial Magnetic Data
Yeates, Anthony R.
2017-02-01
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.
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 Shock Wave Devices Generate Cavitation.
Császár, Nikolaus B M; Angstman, Nicholas B; Milz, Stefan; Sprecher, Christoph M; Kobel, Philippe; Farhat, Mohamed; Furia, John P; Schmitz, Christoph
2015-01-01
Conflicting reports in the literature have raised the question whether radial extracorporeal shock wave therapy (rESWT) devices and vibrating massage devices have similar energy signatures and, hence, cause similar bioeffects in treated tissues. We used laser fiber optic probe hydrophone (FOPH) measurements, high-speed imaging and x-ray film analysis to compare fundamental elements of the energy signatures of two rESWT devices (Swiss DolorClast; Electro Medical Systems, Nyon, Switzerland; D-Actor 200; Storz Medical, Tägerwillen, Switzerland) and a vibrating massage device (Vibracare; G5/General Physiotherapy, Inc., Earth City, MO, USA). To assert potential bioeffects of these treatment modalities we investigated the influence of rESWT and vibrating massage devices on locomotion ability of Caenorhabditis elegans (C. elegans) worms. FOPH measurements demonstrated that both rESWT devices generated acoustic waves with comparable pressure and energy flux density. Furthermore, both rESWT devices generated cavitation as evidenced by high-speed imaging and caused mechanical damage on the surface of x-ray film. The vibrating massage device did not show any of these characteristics. Moreover, locomotion ability of C. elegans was statistically significantly impaired after exposure to radial extracorporeal shock waves but was unaffected after exposure of worms to the vibrating massage device. The results of the present study indicate that both energy signature and bioeffects of rESWT devices are fundamentally different from those of vibrating massage devices. Prior ESWT studies have shown that tissues treated with sufficient quantities of acoustic sound waves undergo cavitation build-up, mechanotransduction, and ultimately, a biological alteration that "kick-starts" the healing response. Due to their different treatment indications and contra-indications rESWT devices cannot be equated to vibrating massage devices and should be used with due caution in clinical practice.
Radial Shock Wave Devices Generate Cavitation.
Directory of Open Access Journals (Sweden)
Nikolaus B M Császár
Full Text Available Conflicting reports in the literature have raised the question whether radial extracorporeal shock wave therapy (rESWT devices and vibrating massage devices have similar energy signatures and, hence, cause similar bioeffects in treated tissues.We used laser fiber optic probe hydrophone (FOPH measurements, high-speed imaging and x-ray film analysis to compare fundamental elements of the energy signatures of two rESWT devices (Swiss DolorClast; Electro Medical Systems, Nyon, Switzerland; D-Actor 200; Storz Medical, Tägerwillen, Switzerland and a vibrating massage device (Vibracare; G5/General Physiotherapy, Inc., Earth City, MO, USA. To assert potential bioeffects of these treatment modalities we investigated the influence of rESWT and vibrating massage devices on locomotion ability of Caenorhabditis elegans (C. elegans worms.FOPH measurements demonstrated that both rESWT devices generated acoustic waves with comparable pressure and energy flux density. Furthermore, both rESWT devices generated cavitation as evidenced by high-speed imaging and caused mechanical damage on the surface of x-ray film. The vibrating massage device did not show any of these characteristics. Moreover, locomotion ability of C. elegans was statistically significantly impaired after exposure to radial extracorporeal shock waves but was unaffected after exposure of worms to the vibrating massage device.The results of the present study indicate that both energy signature and bioeffects of rESWT devices are fundamentally different from those of vibrating massage devices.Prior ESWT studies have shown that tissues treated with sufficient quantities of acoustic sound waves undergo cavitation build-up, mechanotransduction, and ultimately, a biological alteration that "kick-starts" the healing response. Due to their different treatment indications and contra-indications rESWT devices cannot be equated to vibrating massage devices and should be used with due caution in clinical
Higher‐order mode absorption measurement of X-band choke-mode cavities in a radial line structure
Energy Technology Data Exchange (ETDEWEB)
Zha, Hao [Department of Engineering Physics, Tsinghua University, Beijing CN-100086 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education, Beijing (China); The European Organization for Nuclear Research, Geneva CH-1211 (Switzerland); Shi, Jiaru, E-mail: shij@mail.tsinghua.edu.cn [Department of Engineering Physics, Tsinghua University, Beijing CN-100086 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education, Beijing (China); The European Organization for Nuclear Research, Geneva CH-1211 (Switzerland); Wu, Xiaowei; Chen, Huaibi [Department of Engineering Physics, Tsinghua University, Beijing CN-100086 (China); Key Laboratory of Particle and Radiation Imaging, Tsinghua University, Ministry of Education, Beijing (China)
2016-04-01
An experiment is presented to study the higher-order mode (HOM) suppression of X-band choke-mode structures with a vector network analyzer (VNA). Specific radial line disks were built to test the reflection from the corresponding damping load and different choke geometries. The mismatch between the radial lines and the VNA was calibrated through a special multi-short-load calibration method. The measured reflections of different choke geometries showed good agreement with the theoretical calculations and verified the HOM absorption feature of each geometric design.
Unusual presentation of a radial neck fracture in a child
Directory of Open Access Journals (Sweden)
Murali Poduval
2013-01-01
Full Text Available Fracture of the radial neck are uncommon injuries. In children, they may present as radial neck fractures, a components of forearm fracture dislocations, or as isolated fracture dislocations. Here, we present an unusual and previously undescribed variant of radial neck fracture with dislocation of the radial head to the medial side and ulnar nerve injury. The fracture dislocation was openly reduced and fixed with a small fragment plate. The fracture healed with some loss of rotational movements. At short followup of 6 months patient had useful elbow function but ulnar nerve did not recover.
Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems
Directory of Open Access Journals (Sweden)
Cong-Hui Huang
2014-12-01
Full Text Available This paper presents modified neural network for dynamic control and operation of a hybrid generation systems. PV and wind power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. The simulation model of the hybrid system was developed using MATLAB Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN and an modified Elman Neural Network (ENN for maximum power point tracking (MPPT. The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC I DC boost converters to achieve the MPPT. And the results show the hybrid generation system can effectively extract the maximum power from the PV and wind energy sources.
Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network
Taghipour-Gorjikolaie, Mehran; Valipour Motlagh, Naser
2018-02-01
The interaction between variables, which are effective on the surface wettability, is very complex to predict the contact angles and sliding angles of liquid drops. In this paper, in order to solve this complexity, artificial neural network was used to develop reliable models for predicting the angles of liquid drops. Experimental data are divided into training data and testing data. By using training data and feed forward structure for the neural network and using particle swarm optimization for training the neural network based models, the optimum models were developed. The obtained results showed that regression index for the proposed models for the contact angles and sliding angles are 0.9874 and 0.9920, respectively. As it can be seen, these values are close to unit and it means the reliable performance of the models. Also, it can be inferred from the results that the proposed model have more reliable performance than multi-layer perceptron and radial basis function based models.
Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network
Directory of Open Access Journals (Sweden)
Kemal Fidanboylu
2009-09-01
Full Text Available Artificial neural network (ANN based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP with different training algorithms, Radial Basis Function (RBF network and General Regression Neural Network (GRNN are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.
[Case-control study on two osteotomy techniques for the treatment of distal radial malunion].
Zhang, Bing-bing; Yuan, Zhi-guo; Shao, Jian-jun; Yang, Shi-ning; Chai, Xi-ping
2015-07-01
Radial corrective osteotomy is an established but challenging treatment for distal radial malunion. There is an ongoing discussion about whether an opening or closing-wedge osteotomy between should employed. The purpose of the present study was to retrospectively compare the clinical and radio graphic results between conventional opening-wedge osteotomy and closing-wedge technique. From January 2004 and December 2012,42 patients with extra-articular distal radial malunion were managed with corrective osteotomy and were followed for a minimum of one year. Twenty-two patients (5 males and 17 females, ranging in age from 25 to 75 years old) were managed with radial opening-wedge osteotomy and implanting of interpositional bone graft or bone-graft substitute, and twenty patients (4 males and 16 females, ranging in age from 19 to 79 years) were managed with simultaneous radial closing-wedge and ulnar shortening osteotomy without bone graft. The selection of the surgical procedure was determined by the surgeon. Each patient was evaluated on the basis of objective radio graphic measurements, and functional outcomes were determined on the basis of clinical examinations, including range of wrist motion, grip strength, pain-rating score, Mayo wrist score, and Disabilities of the Arm, Shoulder and Hand (DASH) score. The mean duration of follow-up was 36 months (ranged, 12 to 101 months) for the opening-wedge cohort and 28 months (ranged, 12 to 87 months) for the closing-wedge cohort. The two techniques were comparable in terms of complications. Post-operative volar tilt and ulnar variance were improved significantly in each cohort. The ulnar variance was more frequently restored to within defined criteria (22.5 to 0.5 mm) in the closing-wedge cohort than that in the opening-wedge cohort. The post-operative mean extension-flexion are of the wrist and Mayo wrist score were significantly better in the closing-wedge cohort. Differences in the pronation-supination arc, grip
Energy Technology Data Exchange (ETDEWEB)
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)
Fuel radial design using Path Relinking; Diseno radial de combustible usando Path Relinking
Energy Technology Data Exchange (ETDEWEB)
Campos S, Y. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico)
2007-07-01
The present work shows the obtained results when implementing the combinatory optimization technique well-known as Path Re linking (Re-linkage of Trajectories), to the problem of the radial design of nuclear fuel assemblies, for boiling water reactors (BWR Boiling Water Reactor by its initials in English), this type of reactors is those that are used in the Laguna Verde Nucleo electric Central, Veracruz. As in any other electric power generation plant of that make use of some fuel to produce heat and that it needs each certain time (from 12 to 14 months) to make a supply of the same one, because this it wears away or it burns, in the nucleolectric plants to this activity is denominated fuel reload. In this reload different activities intervene, among those which its highlight the radial and axial designs of fuel assemblies, the patterns of control rods and the multi cycles study, each one of these stages with their own complexity. This work was limited to study in independent form the radial design, without considering the other activities. These phases are basic for the fuel reload design and of reactor operation strategies. (Author)
Radial Color Gradient in a Globular Cluster 1. M68
Directory of Open Access Journals (Sweden)
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.
How to obtain Transience from Bounded Radial Mean Curvature
DEFF Research Database (Denmark)
Markvorsen, Steen; Palmer, Vicente
2005-01-01
We show that Brownian motion on any unbounded submanifold P in an ambient manifold N with a pole P is transient if the following conditions are satisfied: The p-radial mean curvatures of P are sufficiently small outsidea compact set and the p-radial sectional curvatures of N are sufficiently...
Multidistortion-invariant image recognition with radial harmonic Fourier moments.
Ren, Haiping; Ping, Ziliang; Bo, Wurigen; Wu, Wenkai; Sheng, Yunlong
2003-04-01
We propose radial harmonic Fourier moments, which are shifting, scaling, rotation, and intensity invariant. Compared with Chebyshev-Fourier moments, the new moments have superior performance near the origin and better ability to describe small images in terms of image-reconstruction errors and noise sensitivity. A multidistortion-invariant pattern-recognition experiment was performed with radial harmonic Fourier moments.
The Radial Velocity Experiment (RAVE) : First data release
Steinmetz, M.; Zwitter, T.; Siebert, A.; Watson, F. G.; Freeman, K. C.; Munari, U.; Campbell, R.; Williams, M.; Seabroke, G. M.; Wyse, R. F. G.; Parker, Q. A.; Bienayme, O.; Roeser, S.; Gibson, B. K.; Gilmore, G.; Grebel, E. K.; Navarro, J. F.; Burton, D.; Cass, C. J. P.; Dawe, J. A.; Fiegert, K.; Hartley, M.; Russell, K. S.; Saunders, W.; Enke, H.; Bailin, J.; Binney, J.; Bland-Hawthorn, J.; Boeche, C.; Dehnen, W.; Eisenstein, D. J.; Evans, N. W.; Fiorucci, M.; Fulbright, J. P.; Gerhard, O.; Jauregi, U.; Kelz, A.; Mijovic, L.; Minchev, I.; Parmentier, G.; Penarrubia, J.; Quillen, A. C.; Read, M. A.; Ruchti, G.; Scholz, R. -D.; Siviero, A.; Smith, M.C.; Sordo, R.; Veltz, L.; Vidrih, S.; von Berlepsch, R.; Boyle, B. J.; Schilbach, E.; Helmi, A.
2006-01-01
We present the first data release of the Radial Velocity Experiment (RAVE), an ambitious spectroscopic survey to measure radial velocities and stellar atmosphere parameters (temperature, metallicity, and surface gravity) of up to one million stars using the Six Degree Field multiobject spectrograph
Voltage regulator placement in radial distribution system using plant ...
African Journals Online (AJOL)
Voltage regulator placement in radial distribution system using plant growth simulation algorithm. ... The effectiveness of the proposed method is illustrated with 33 bus and 69 bus radial distribution systems and these results are compared with Discrete Particle Swarm Optimization method. International Journal of ...
Elbow joint kinematics after excision of the radial head
DEFF Research Database (Denmark)
Jensen, Steen Lund; Olsen, Bo Sanderhoff; Søjbjerg, Jens Ole
1999-01-01
or internal rotation. The results were independent of the rotation of the forearm. This study indicates that the radial head acts as stabilizer to the elbow joint in forced varus and in forced external rotation. The results suggest that fractures of the radial head cannot be treated by simple excision without...
Incidence and Outcome of the Radial Nerve Injury following Open ...
African Journals Online (AJOL)
Background: Radial nerve injury is the most common peripheral nerve injury associated with humeral shaft fracture and can result in significant motor impairment of the arm and the wrist. Objectives: To evaluate the incidence, pattern and outcome of the radial nerve injury following open fracture of the humerus. Material and ...
The Radial Velocity Experiment (RAVE) : Second data release
Zwitter, T.; Siebert, A.; Munari, U.; Freeman, K. C.; Siviero, A.; Watson, F. G.; Fulbright, J. P.; Wyse, R. F. G.; Campbell, R.; Seabroke, G. M.; Williams, M.; Steinmetz, M.; Bienayme, O.; Gilmore, G.; Grebel, E. K.; Helmi, A.; Navarro, J. F.; Anguiano, B.; Boeche, C.; Burton, D.; Cass, P.; Dawe, J.; Fiegert, K.; Hartley, M.; Russell, K.; Veltz, L.; Bailin, J.; Binney, J.; Bland-Hawthorn, J.; Brown, A.; Dehnen, W.; Evans, N. W.; Fiorentin, P. Re; Fiorucci, M.; Gerhard, O.; Gibson, B.; Kelz, A.; Kuijken, K.; Matijevic, G.; Minchev, I.; Parker, Q. A.; Penarrubia, J.; Quillen, A.; Read, M. A.; Reid, W.; Roeser, S.; Ruchti, G.; Scholz, R. -D.; Smith, M. C.; Sordo, R.; Tolstoi, E.; Tomasella, L.; Vidrih, S.; De Boer, E. Wylie
We present the second data release of the Radial Velocity Experiment ( RAVE), an ambitious spectroscopic survey to measure radial velocities and stellar atmosphere parameters ( temperature, metallicity, surface gravity, and rotational velocity) of up to one million stars using the 6 dF multi-object
Directory of Open Access Journals (Sweden)
Jin-Xiu Hu
2014-01-01
Full Text Available A new approach is presented for the numerical evaluation of arbitrary singular domain integrals. In this method, singular domain integrals are transformed into a boundary integral and a radial integral which contains singularities by using the radial integration method. The analytical elimination of singularities condensed in the radial integral formulas can be accomplished by expressing the nonsingular part of the integration kernels as a series of cubic B-spline basis functions of the distance r and using the intrinsic features of the radial integral. In the proposed method, singularities involved in the domain integrals are explicitly transformed to the boundary integrals, so no singularities exist at internal points. A few numerical examples are provided to verify the correctness and robustness of the presented method.
DEFF Research Database (Denmark)
Tsapatsaris, Nikolaos; Willendrup, Peter Kjær; E. Lechner, Ruep
2015-01-01
rate at the sample position between the virtual instrument simulation and experiments was found, in both time and energy distributions. This achievement was only possible after a new component for a bent single crystal analyser in McStas, using a Gaussian approximation, was developed. These findings......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...
Directory of Open Access Journals (Sweden)
Mustafa GHADERZADEH
2013-09-01
Full Text Available Prostate cancer is one of the most common types of cancer found in men. Presenting a classifier in order classifies between Prostate Cancer (PCa and benign hyperplasia of prostate (BPH, has been great challenge among computer experts and medical specialists. There are a number of techniques proposed to perform such classification. Neural networks are one of the artificial intelligent techniques that have successful examples when applying to such problems. The increasing demand of Artificial Neural Network applications for predicting the disease shows better performance in the field of medical decision-making. This paper presents a comparison of neural network techniques for classification prostate neoplasia diseases. The classification performance obtained by four different types of neural networks for comparison are Back Propagation Neural Network (BPNN, General Regression Neural Network(GRNN, Probabilistic Neural Network (PNN and Radial Basis Function Neural Network (RBFNN. Result of these evaluation show that the overall performance of RBFNN can be apply successfully for detecting and diagnosing the cancer from benign hyperplasia of prostate.
Garabatos, C
2000-01-01
The CERES experiment at the CERN SPS has been recently upgraded with a TPC with radial drift field, the first one of its sort. Constructed during 1998, it has been successfully operated in commissioning and physics runs, with muon, proton, and heavy-ion beams. A high voltage electrode of about 0.5 m radius is surrounded by sixteen 2 m long readout chambers, placed at a radius of 1.3 m, with chevron-shaped readout pads. The field cage is enclosed by two low-mass voltage degraders at each end of the cylindrical structure. A Ne-CO/sub 2/ [80-20] gas mixture allows for a safe operation and good transport properties under drift fields ranging from 200 to 600 V/cm. A spatial resolution better than 700 microns and 350 microns in r and rdelta (phi), respectively, has been achieved in a highly inhomogeneous magnetic field. Details of its construction as well as results of the operation and performance in a high multiplicity environment are presented. (0 refs).
Experiments of a monolithic radial transmission line.
Mao, C; Wang, X; Zou, X; Lehr, J
2016-11-01
This paper presents the experimental results of a monolithic radial transmission line (MRTL) that may be used in pulsed power generators and microwave devices. The MRTL with a hyperbolic impedance profile is 508 mm in radius, corresponding to a one-way transit time of 15 ns for the electromagnetic wave. In the experiments, up to twenty identical voltage pulses, 10 ns in FWHM and 2 ns in rise-time, were fed into the MRTL through 20 input BNC connectors that are uniformly distributed along the outer circumference of the MRTL. It was found that the amplitude of the voltage from the output BNC connector located in the center of the MRTL is nearly proportional to the total number of the input branches. The effect of the failure modes on the output voltage was investigated. For the MRTL driven by 20 input branches, while the open-circuit or short-circuit even in one input branch considerably decreases the amplitude of the output voltage, the jitter shorter than 2 ns in 3 input branches makes no obvious effect on the output voltage.
Radial Velocity Variability of Field Brown Dwarfs
Prato, L.; Mace, G. N.; Rice, E. L.; McLean, I. S.; Kirkpatrick, J. Davy; Burgasser, A. J.; Kim, Sungsoo S.
2015-07-01
We present paper six of the NIRSPEC Brown Dwarf Spectroscopic Survey, an analysis of multi-epoch, high-resolution (R ˜ 20,000) spectra of 25 field dwarf systems (3 late-type M dwarfs, 16 L dwarfs, and 6 T dwarfs) taken with the NIRSPEC infrared spectrograph at the W. M. Keck Observatory. With a radial velocity (RV) precision of ˜2 km s-1, we are sensitive to brown dwarf companions in orbits with periods of a few years or less given a mass ratio of 0.5 or greater. We do not detect any spectroscopic binary brown dwarfs in the sample. Given our target properties, and the frequency and cadence of observations, we use a Monte Carlo simulation to determine the detection probability of our sample. Even with a null detection result, our 1σ upper limit for very low mass binary frequency is 18%. Our targets included seven known, wide brown dwarf binary systems. No significant RV variability was measured in our multi-epoch observations of these systems, even for those pairs for which our data spanned a significant fraction of the orbital period. Specialized techniques are required to reach the high precisions sensitive to motion in orbits of very low-mass systems. For eight objects, including six T dwarfs, we present the first published high-resolution spectra, many with high signal to noise, that will provide valuable comparison data for models of brown dwarf atmospheres.
Radial vibrations of a sodium ion inside icosahedral C60
Ballester, J. L.; Dunlap, B. I.
1992-01-01
The very high symmetry of icosahedral C60 suggests that, as a first approximation, an atom trapped inside C60 would be subject to a potential that is radially symmetric about the center. All-electron local-density-functional calculations of the total energy of a sodium ion as a function of radial displacement from the center along the fivefold axis of C60 serve to refine such a radial potential. In particular, the calculations suggest studying potentials that have minima displaced from the center. An analytic functional form for a radial potential having a positive cusp at the origin is proposed, and the s-wave radial solutions of the corresponding Schroedinger equation are examined.
Radial head replacement with pyrocarbon prosthesis: early clinical results.
Abdulla, Irfan N; Molony, Diarmuid C; Symes, Michael; Cass, Benjamin
2015-05-01
Comminuted radial head fractures are challenging to treat with open reduction and internal fixation. Radial head arthroplasty is a favourable technique for the treatment of complex radial head fractures. The purpose of this study was to evaluate the functional and radiological outcomes of radial head arthroplasty using modular pyrocarbon radial head prosthesis. We retrospectively reviewed 21 consecutive patients requiring radial head arthroplasty for unreconstructible radial head fractures between July 2003 and July 2009. Patients completed a Short Form-36 (SF-36), the Disabilities of the Arm, Shoulder and Hand questionnaire, and the Mayo Elbow Performance Index. Patients were independently physically examined and their post-operative radiographs were reviewed. Twenty-one patients (nine males and 12 females) were reviewed at a minimum of 12 months follow-up. The mean Disabilities of the Arm, Shoulder and Hand score was 10.8 (0-34.1), mean SF-36 physical score was 76.9 (35-96), mean SF-36 mental score was 83.8 (60-94), and their Mayo Elbow Performance Index score was 86.4 (70-100). Patients maintained 90% of their grip strength when compared with their uninjured arm and had 17.5° of fixed flexion in the affected arm. Radiologically, 14 cases had some degree of post-traumatic osteoarthritis, 12 cases had evidence of heterotrophic ossification, five had some evidence of periprosthetic lucency and three patients were radiologically, but not functionally 'overstuffed'. Radial head arthroplasty with pyrocarbon radial head prosthesis is an acceptable option when treating unreconstructable radial head fractures yielding good functional and radiological outcomes. © 2014 Royal Australasian College of Surgeons.
Optimization and control of two-component radially self-accelerating beams
Energy Technology Data Exchange (ETDEWEB)
Vetter, Christian; Eichelkraut, Toni; Ornigotti, Marco; Szameit, Alexander [Institute of Applied Physics, Abbe Center of Photonics, Friedrich-Schiller-Universität Jena, Albert-Einstein-Str. 15, 07745 Jena (Germany)
2015-11-23
We report on the properties of radially self-accelerating intensity distributions consisting of two components in the angular frequency domain. We show how this subset of solutions, in literature also known as helicon beams, possesses peculiar characteristics that enable a better control over its properties. In this work, we present a step-by-step optimization procedure to achieve the best possible intensity contrast, a distinct rotation rate and long propagation lengths. All points are discussed on a theoretical basis and are experimentally verified.
DEFF Research Database (Denmark)
Chen, Shuheng; Wang, Xiongfei; Su, Chi
2014-01-01
Based on an extended chain-table storage structure, an improved power flow method is presented, which can be applied to a distribution network with multi PV nodes. The extended chain-table storage structure is designed on the basis of address-pointer technology describing the radial topology...... with a reduced memory size. The voltage error of each PV node is adjusted by a reactive power adjusting strategy. The adjusting strategy is based on a multi-variable linear function with an accelerating factor. Finally, this new improved power flow method is realized by the software system developed in VC...
Forecast of consumer behaviour based on neural networks models comparison
Directory of Open Access Journals (Sweden)
Michael Štencl
2012-01-01
Full Text Available The aim of this article is comparison of accuracy level of forecasted values of several artificial neural network models. The comparison is performed on datasets of Czech household consumption values. Several statistical models often resolve this task with more or fewer restrictions. In previous work where models’ input conditions were not so strict and model with missing data was used (the time series didn’t contain many values we have obtained comparably good results with artificial neural networks. Two views – practical and theoretical, motivate the purpose of this study. Forecasting models for medium term prognosis of the main trends of Czech household consumption is part of the faculty research design grant MSM 6215648904/03/02 (Sub-task 5.3 which defines the practical purpose. Testing of nonlinear autoregressive artificial neural network model compared with feed-forward neural network and radial basis function neural network defines the theoretical purpose. The performance metrics of the models were evaluated using a combination of common error metrics, namely Correlation Coefficient and Mean Square Error, together with the number of epochs and/or main prediction error.
Static Voltage Stability Analysis by Using SVM and Neural Network
Directory of Open Access Journals (Sweden)
Mehdi Hajian
2013-01-01
Full Text Available Voltage stability is an important problem in power system networks. In this paper, in terms of static voltage stability, and application of Neural Networks (NN and Supported Vector Machine (SVM for estimating of voltage stability margin (VSM and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN. The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one, voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN and SVM. The obtained results in this paper indicate, that time and number of training samples of SVM, are less than NN. In this paper, a new model of training samples for detection system, using the normal distribution load curve at each load feeder, has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods, IEEE 14 bus grid and the actual network of Yazd Province are used.
Numerical simulation of liquid-metal-flows in radial-toroidal-radial bends
Energy Technology Data Exchange (ETDEWEB)
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) [Deutsch] Untersucht werden magnetohydrodynamische Stroemungen in einer U-Umlenkung und in einer rechtwinkligen Umleknung, als Elemente eines selbstgekuehlten radial-toroidal-radialen Fluessigmetell-Blankets. Das angelegte Magnetfeld zeigt in Richtung des toroidalen Kanals und steht senkrecht zur radialen Richtung. Fuer grosse Hartmann-Zahlen teilt sich das Stroemungsgebiet in Kernstroemungsbereiche (Cores) und in Grenzschichten. Die magnetohydrodynamischen Gleichungen lassen sich zu einem System von partiellen Differentialgleichungen zur Bestimmung des elektrischen Potentials der Kanalwand und des Core-Drucks vereinfachen. Dieses System wird mit zwei verschiedenen Verfahren numerisch geloest. Bei der ersten Methode handelt es sich um ein iteratives Verfahren mit Iterationen zwischen den Werten des Wandpotentials und des Core-Drucks. Das zweite Verfahren ist ein allgemeines Verfahren zur Loesung der Kernstroemungsgleichungen in
Scaling thermal effects in radial flow
Hudspeth, R. T.; Guenther, R. B.; Roley, K. L.; McDougal, W. G.
To adequately evaluate the environmental impact of siting nuclear waste repositories in basalt aquicludes, it is essential to know the effects on parameter identification algorithms of thermal gradients that exist in these basaltic aquicludes. Temperatures of approximately 60°C and pressures of approximately 150 atm can be expected at potential repository sites located at depths of approximately 1000 m. The phenomenon of over-recovery has been observed in some pumping tests conducted at the Hanford Nuclear Reservation located in the Pasco Basin adjacent to the Columbia River in the state of Washington, USA. This over-recovery phenomenon may possibly be due to variations in the fluid density caused by thermal gradients. To assess the potential effects of these thermal gradients on indirect parameter identification algorithms, a systematic scaling of the governing field equations is required in order to obtain dimensionless equations based on the principle of similarity. The constitutive relationships for the specific weight of the fluid and for the porosity of the aquiclude are shown to be exponentially dependent on the pressure gradient. The dynamic pressure is converted to the piezometric head and the flow equation for the piezometric head is then scaled in radial coordinates. Order-of-magnitude estimates are made for all variables in unsteady flow for a typical well test in a basaltic aquiclude. Retaining all nonlinear terms, the parametric dependency of the flow equation on the classical dimensionless thermal and hydraulic parameters is demonstrated. These classical parameters include the Batchelor, Fourier, Froude, Grashof, and Reynolds Numbers associated with thermal flows. The flow equation is linearized from order-of-magnitude estimates based on these classical parameters for application in parameter identification algorithms.
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
TRUE MASSES OF RADIAL-VELOCITY EXOPLANETS
Energy Technology Data Exchange (ETDEWEB)
Brown, Robert A., E-mail: rbrown@stsci.edu [Space Telescope Science Institute (United States)
2015-06-01
We study the task of estimating the true masses of known radial-velocity (RV) exoplanets by means of direct astrometry on coronagraphic images to measure the apparent separation between exoplanet and host star. Initially, we assume perfect knowledge of the RV orbital parameters and that all errors are due to photon statistics. We construct design reference missions for four missions currently under study at NASA: EXO-S and WFIRST-S, with external star shades for starlight suppression, EXO-C and WFIRST-C, with internal coronagraphs. These DRMs reveal extreme scheduling constraints due to the combination of solar and anti-solar pointing restrictions, photometric and obscurational completeness, image blurring due to orbital motion, and the “nodal effect,” which is the independence of apparent separation and inclination when the planet crosses the plane of the sky through the host star. Next, we address the issue of nonzero uncertainties in RV orbital parameters by investigating their impact on the observations of 21 single-planet systems. Except for two—GJ 676 A b and 16 Cyg B b, which are observable only by the star-shade missions—we find that current uncertainties in orbital parameters generally prevent accurate, unbiased estimation of true planetary mass. For the coronagraphs, WFIRST-C and EXO-C, the most likely number of good estimators of true mass is currently zero. For the star shades, EXO-S and WFIRST-S, the most likely numbers of good estimators are three and four, respectively, including GJ 676 A b and 16 Cyg B b. We expect that uncertain orbital elements currently undermine all potential programs of direct imaging and spectroscopy of RV exoplanets.
Radial head fracture associated with posterior interosseous nerve injury
Directory of Open Access Journals (Sweden)
Bernardo Barcellos Terra
Full Text Available ABSTRACT Fractures of the radial head and radial neck correspond to 1.7-5.4% of all fractures and approximately 30% may present associated injuries. In the literature, there are few reports of radial head fracture with posterior interosseous nerve injury. This study aimed to report a case of radial head fracture associated with posterior interosseous nerve injury. CASE REPORT: A male patient, aged 42 years, sought medical care after falling from a skateboard. The patient related pain and limitation of movement in the right elbow and difficulty to extend the fingers of the right hand. During physical examination, thumb and fingers extension deficit was observed. The wrist extension showed a slight radial deviation. After imaging, it became evident that the patient had a fracture of the radial head that was classified as grade III in the Mason classification. The patient underwent fracture fixation; at the first postoperative day, thumb and fingers extension was observed. Although rare, posterior interosseous nerve branch injury may be associated with radial head fractures. In the present case, the authors believe that neuropraxia occurred as a result of the fracture hematoma and edema.
Crustal radial anisotropy beneath Cameroon from ambient noise tomography
Ojo, Adebayo Oluwaseun; Ni, Sidao; Li, Zhiwei
2017-01-01
To increase the understanding of crustal deformation and crustal flow patterns due to tectonic processes in Cameroon, we study the lateral variability of the crustal isotropic velocity and radial anisotropy estimated using Ambient Noise Tomography (ANT). Rayleigh and Love wave Noise Correlation Functions (NCFs) were retrieved from the cross-correlation of seismic ambient noise data recorded in Cameroon, and phase velocities at periods of 8 to 30 s were measured to perform surface wave tomography. Joint inversion of Rayleigh and Love wave data for isotropic velocity models could not fit the observed dispersions simultaneously. We attribute the Love-Rayleigh discrepancy to the presence of radial anisotropy in the crust and estimated its magnitude. Our 3-D radial anisotropic model reveals the spatial variation of strong to weak positive (Vsh > Vsv) and negative (Vsv > Vsh) radial anisotropy in the crust. We observe negative radial anisotropy in the upper crust that is associated mainly with the location of a previously reported mantle plume. The anisotropy could be attributed to the vertical alignment of fossil microcracks or metamorphic foliations due to the upwelling of plume material. A strong positive radial anisotropy is centered at the location of an inferred boundary between the Congo Craton and the Oubanguides Belt that might be related to the preferred orientation of crustal anisotropic minerals associated with shearing in this fault zone. The middle crust is characterized by a widespread negative radial anisotropy that is likely caused by the flow-induced alignment of anisotropic minerals that crystallized during magma intrusion. The magnitude of the radial anisotropy varies systematically from predominantly negative in the middle crust to positive in the lower crust. The imaged patterns of the isotropic velocity and radial anisotropy are consistent with previous studies and agree with regional tectonics.
Thumb ulnar collateral and radial collateral ligament injuries.
Schroeder, Nicole S; Goldfarb, Charles A
2015-01-01
Thumb metacarpophalangeal ulnar and radial collateral ligament injuries occur frequently in the competitive athlete. Collateral ligament integrity is essential to joint stability, pinch strength, and pain-free motion. Acute rupture of the ulnar collateral ligament is due to a sudden radial deviation force on the abducted thumb and is referred to as skier's thumb. An ulnar-directed force causes injury to the radial collateral ligament. The degree of joint instability on clinical examination allows classification of these injuries and guides management. Surgical repair of acute, complete tears results in good outcomes and allows for return to sporting activity. Copyright © 2015 Elsevier Inc. All rights reserved.
Sharp Edges in Saturn's Rings: Radial Structure and Longitudinal Variability
Colwell, Joshua E.; Jerousek, R. G.; Esposito, L. W.
2010-10-01
The Cassini Ultraviolet Imaging Spectrograph has observed more than 100 occultations of stars by Saturn's rings with a typical ring plane radial resolution of preventing a fit from our smooth model. We find that the radial scale, characterized by a sigmoid function, over which the ring optical depth declines is typically 10-20 m. We are unable to place a tight constraint on the vertical thickness of the ring, however, because the light curve is dominated by the scale of the radial variation. We find large variability in the structure of the edges with no clear correlation to known possible perturbers in the system.
Mixed Lubrication Solution of Dynamically Loaded Radial Slide Bearings
Directory of Open Access Journals (Sweden)
P. Novotny
2017-03-01
Full Text Available A solution of radial slide bearing dynamics and tribology incorporating the influences of real surface roughness contacts or the influence of surface roughness on bearing lubrication is presented in this paper. Finite difference method for Reynolds equation discretization, finite element method for calculation of elastic deformations, Gauss-Seidel’s method for iterative solution of discretized equations or Newmark’s algorithm are the methods employed in the proposed solution approach. The coupled structural-fluid solver considering mixed lubrication conditions of the radial bearings is the result. The proposed algorithms are presented for highly loaded radial slide bearing of internal combustion engine.
Prototyping of radial plates for fusion relevant superconducting magnets
Ghate, M.; Bhavasar, D.; Panchal, A.; Udgata, S.; Pradhan, S.
2017-04-01
The fabrication trials for prototype radial plate to support its conceptual design and development for fusion relevant superconducting magnet have been discussed in this paper. The simulation approach with CAD has been presented for prototyping of radial plates. Extensive trials have been done on SS316LN plates to estimate and establish machining sequences, machine parameters, machining tools to achieve required tolerances. The critical machining operation and parameters has been discussed in this paper. Inspection procedures with articulated arm coordinate measuring machine for prototype radial plate has been conceptualized and verified.
Channeling of protons through radial deformed carbon nanotubes
Borka Jovanović, V.; Borka, D.; Galijaš, S. M. D.
2017-05-01
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.
16PF-E Structure using Radial Parcels Versus Items.
Hughey, Joseph; Burdsal, Charles
1982-07-01
The Sixteen Personality Factor Questionnaire (16PF) was factored with the use of groups of items called radial parcels and was subsequently compared with an item factoring of the same data. This was done in order to verify the 16PF (Form E) primary factors and to investigate the use of radial parcels in that process. Ss were 449 physically or mentally handicapped men and women who were part of a rehabilitation counseling process. Results indicated that with some familiar exceptions factor identification and verification were complete. Moreover, the use of radial parcels maintained the dimensionality found in the items. Also, rotation toward simple structure proceeded more rapidly with parcels than with items.
DEFF Research Database (Denmark)
Chen, Shuheng; Hu, Weihao; Chen, Zhe
2014-01-01
Based on generalized chain-table storage structure (GCTSS), a novel power flow method is proposed, which can be used to solve the power flow of weakly meshed distribution networks with multiple distributed generators (DGs). GCTSS is designed based on chain-table technology and its target is to de......Based on generalized chain-table storage structure (GCTSS), a novel power flow method is proposed, which can be used to solve the power flow of weakly meshed distribution networks with multiple distributed generators (DGs). GCTSS is designed based on chain-table technology and its target...... is to describe the topology of radial distribution networks with a clear logic and a small memory size. The strategies of compensating the equivalent currents of break-point branches and the reactive power outputs of PV-type DGs are presented on the basis of superposition theorem. Their formulations...
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
2012-09-01
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Dorca, Daniel Azevedo; Camacho, Jose Roberto [Universidade Federal de Uberlandia (UFU), MG (Brazil). Curso de Mestrado em Engenharia Eletrica
2008-07-01
This work investigates the small-disturbance stability of a 30 bus radial distribution system with distributed generation units. This work is realized through the time domain simulations and through the eigenvalue analysis and participation factors. The eigenvalue analysis show that is possible to predict a possible system instability face to a disturbance. The development of this work was stimulated by the increasing of the distributed generation units in the distribution networks. (author)
Xing, Lei; Martyniuk, Christopher J; Esau, Crystal; Da Fonte, Dillon F; Trudeau, Vance L
2016-07-20
Radial glial cells (RGCs) are stem-like cells found in the developing and adult central nervous system. They function as both a scaffold to guide neuron migration and as progenitor cells that support neurogenesis. Our previous study revealed a close anatomical relationship between dopamine neurons and RGCs in the telencephalon of female goldfish. In this study, label-free proteomics was used to identify the proteins in a primary RGC culture and to determine the proteome response to the selective dopamine D1 receptor agonist SKF 38393 (10μM), in order to better understand dopaminergic regulation of RGCs. A total of 689 unique proteins were identified in the RGCs and these were classified into biological and pathological pathways. Proteins such as nucleolin (6.9-fold) and ependymin related protein 1 (4.9-fold) were increased in abundance while proteins triosephosphate isomerase (10-fold) and phosphoglycerate dehydrogenase (5-fold) were decreased in abundance. Pathway analysis revealed that proteins that consistently changed in abundance across biological replicates were related to small molecules such as ATP, lipids and steroids, hormones, glucose, cyclic AMP and Ca(2+). Sub-network enrichment analysis suggested that estrogen receptor signaling, among other transcription factors, is regulated by D1 receptor activation. This suggests that these signaling pathways are correlated to dopaminergic regulation of radial glial cell functions. Most proteins down-regulated by SKF 38393 were involved in cell cycle/proliferation, growth, death, and survival, which suggests that dopamine inhibits the progenitor-related processes of radial glial cells. Examples of differently expressed proteins including triosephosphate isomerase, nucleolin, phosphoglycerate dehydrogenase and capping protein (actin filament) muscle Z-line beta were validated by qPCR and western blot, which were consistent with MS/MS data in the direction of change. This is the first study to characterize the RGC
Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok
2013-02-01
The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic
Comparison of CFBP, FFBP, and RBF Networks in the Field of Crack Detection
Directory of Open Access Journals (Sweden)
Dhirendranath Thatoi
2014-01-01
Full Text Available The issue of crack detection and its diagnosis has gained a wide spread of industrial interest. The crack/damage affects the industrial economic growth. So early crack detection is an important aspect in the point of view of any industrial growth. In this paper a design tool ANSYS is used to monitor various changes in vibrational characteristics of thin transverse cracks on a cantilever beam for detecting the crack position and depth and was compared using artificial intelligence techniques. The usage of neural networks is the key point of development in this paper. The three neural networks used are cascade forward back propagation (CFBP network, feed forward back propagation (FFBP network, and radial basis function (RBF network. In the first phase of this paper theoretical analysis has been made and then the finite element analysis has been carried out using commercial software, ANSYS. In the second phase of this paper the neural networks are trained using the values obtained from a simulated model of the actual cantilever beam using ANSYS. At the last phase a comparative study has been made between the data obtained from neural network technique and finite element analysis.
Disk hernia and spondylolisthesis diagnosis using biomechanical features and neural network.
Oyedotun, Oyebade K; Olaniyi, Ebenezer O; Khashman, Adnan
2016-01-01
Artificial neural networks have found applications in various areas of medical diagnosis. The capability of neural networks to learn medical data, mining useful and complex relationships that exist between attributes has earned it a major domain in decision support systems. This paper proposes a fast automatic system for the diagnosis of disk hernia and spondylolisthesis using biomechanical features and neural network. Such systems as described within this work allow the diagnosis of new cases using trained neural networks; patients are classified as either having disk hernia, spondylolisthesis, or normal. Generally, both disk hernia and spondylolisthesis present similar symptoms; hence, diagnosis is prone to inter-misclassification error. This work is significant in that the proposed systems are capable of making fast decisions on such somewhat difficult diagnoses with reasonable accuracies. Feedforward neural network and radial basis function networks are trained on data obtained from a public database. The results obtained within this research are promising and show that neural networks can find applications as efficient and effective expert systems for the diagnosis of disk hernia and spondylolisthesis.
Drozdov, Alexander; Shprits, Yuri; Aseev, Nikita; Kellerman, Adam; Reeves, Geoffrey
2017-04-01
Radial diffusion is one of the dominant physical mechanisms that drives acceleration and loss of the radiation belt electrons, which makes it very important for nowcasting and forecasting space weather models. We investigate the sensitivity of the two parameterizations of the radial diffusion of Brautigam and Albert [2000] and Ozeke et al. [2014] on long-term radiation belt modeling using the Versatile Electron Radiation Belt (VERB). Following Brautigam and Albert [2000] and Ozeke et al. [2014], we first perform 1-D radial diffusion simulations. Comparison of the simulation results with observations shows that the difference between simulations with either radial diffusion parameterization is small. To take into account effects of local acceleration and loss, we perform 3-D simulations, including pitch-angle, energy and mixed diffusion. We found that the results of 3-D simulations are even less sensitive to the choice of parameterization of radial diffusion rates than the results of 1-D simulations at various energies (from 0.59 to 1.80 MeV). This result demonstrates that the inclusion of local acceleration and pitch-angle diffusion can provide a negative feedback effect, such that the result is largely indistinguishable simulations conducted with different radial diffusion parameterizations. We also perform a number of sensitivity tests by multiplying radial diffusion rates by constant factors and show that such an approach leads to unrealistic predictions of radiation belt dynamics. References Brautigam, D. H., and J. M. Albert (2000), Radial diffusion analysis of outer radiation belt electrons during the October 9, 1990, magnetic storm, J. Geophys. Res., 105(A1), 291-309, doi:10.1029/1999ja900344. Ozeke, L. G., I. R. Mann, K. R. Murphy, I. Jonathan Rae, and D. K. Milling (2014), Analytic expressions for ULF wave radiation belt radial diffusion coefficients, J. Geophys. Res. [Space Phys.], 119(3), 1587-1605, doi:10.1002/2013JA019204.
Analytic expressions for ULF wave radiation belt radial diffusion coefficients
National Research Council Canada - National Science Library
Ozeke, Louis G; Mann, Ian R; Murphy, Kyle R; Jonathan Rae, I; Milling, David K
2014-01-01
We present analytic expressions for ULF wave‐derived radiation belt radial diffusion coefficients, as a function of L and Kp , which can easily be incorporated into global radiation belt transport models...
Endoscopic Radial Artery Harvest for Coronary Artery Bypass Surgery
Directory of Open Access Journals (Sweden)
Kuan-Ming Chiu
2006-01-01
Conclusion: Endoscopic harvest of the radial artery is technically demanding, but excellent results can be achieved. The endoscopic approach can provide suitable conduits in a less invasive way than the open harvest technique.
Radial Nerve Injury after Brachial Nerve Block - Case Series
Directory of Open Access Journals (Sweden)
Szederjesi Janos
2016-03-01
Full Text Available Adding epinephrine to local anesthetics is recommended to extend the duration of peripheral nerve blocks. We describe in this article two cases of radial nerve injury possible due to coadministration of epinephrine during brachial plexus block.
Radial inflow gas turbine engine with advanced transition duct
Wiebe, David J
2015-03-17
A gas turbine engine (10), including: a turbine having radial inflow impellor blades (38); and an array of advanced transition combustor assemblies arranged circumferentially about the radial inflow impellor blades (38) and having inner surfaces (34) that are adjacent to combustion gases (40). The inner surfaces (34) of the array are configured to accelerate and orient, for delivery directly onto the radial inflow impellor blades (38), a plurality of discrete flows of the combustion gases (40). The array inner surfaces (34) define respective combustion gas flow axes (20). Each combustion gas flow axis (20) is straight from a point of ignition until no longer bound by the array inner surfaces (34), and each combustion gas flow axis (20) intersects a unique location on a circumference defined by a sweep of the radial inflow impellor blades (38).
Radial Forcing and Edgar Allan Poe's Lengthening Pendulum
McMillan, Matthew; Whitney, Heather M
2013-01-01
Inspired by Edgar Allan Poe's The Pit and the Pendulum, we investigate a radially driven, lengthening pendulum. We first show that increasing the length of an undriven pendulum at a uniform rate does not amplify the oscillations in a manner consistent with the behavior of the scythe in Poe's story. We discuss parametric amplification and the transfer of energy (through the parameter of the pendulum's length) to the oscillating part of the system. In this manner radial driving may easily and intuitively be understood, and the fundamental concept applied in many other areas. We propose and show by a numerical model that appropriately timed radial forcing can increase the oscillation amplitude in a manner consistent with Poe's story. Our analysis contributes a computational exploration of the complex harmonic motion that can result from radially driving a pendulum, and sheds light on a mechanism by which oscillations can be amplified parametrically. These insights should prove especially valuable in the undergra...
The star formation history of CALIFA galaxies: Radial structures
National Research Council Canada - National Science Library
González Delgado, R. M; Pérez, E; Cid Fernandes, R; García-Benito, R; de Amorim, A. L; Sánchez, S. F; Husemann, B; Cortijo-Ferrero, C; López Fernández, R; Sánchez-Blázquez, P; Bekeraite, S; Walcher, C. J; Falcón-Barroso, J; Gallazzi, A; van de Ven, G; Alves, J; Bland-Hawthorn, J; Kennicutt, R. C; Kupko, D; Lyubenova, M; Mast, D; Mollá, M; Marino, R. A; Quirrenbach, A; Vílchez, J. M; Wisotzki, L
2014-01-01
We have studied the radial structure of the stellar mass surface density (μ∗) and stellar population age as a function of the total stellar mass and morphology for a sample of 107 galaxies from the CALIFA survey...
Radial artery pseudo aneurysm after percutaneous cannulation using Seldinger technique
Directory of Open Access Journals (Sweden)
Anil Ranganath
2011-01-01
Full Text Available Cannulation of a peripheral artery in a patient allows for continuous blood pressure (BP monitoring and facilitates frequent arterial blood gas (ABG analysis. Complications include thrombosis, embolism risk, haemorrhage, sepsis, and formation of pseudo aneurysms. A 75-year-old male admitted via casualty with a collapse secondary to seizures. Patient was intubated and mechanically ventilated for 7 days. A right radial artery catheter was inserted on admission to casualty. The arterial catheter remained in situ for 7 days. Five days following its removal, the skin site appeared inflamed and a wound swab grew methicillin resistant Staphylococcus aureus (MRSA. Eight days later a distinct bulging of the radial artery was noticed. An ultrasound was done and it showed radial artery pseudoaneurysm, the diagnosis was confirmed by angiogram. Delayed radial artery pseudoaneurysm formation has only been reported in association with infection, and less than twenty of these cases have been reported in the literature.
Messrad mit mehreren kombinierten Radial-/Tangentialverbindungen: Messrad (A)
Neugebauer, J.; Grubisic, V.; Rupp, A.
1998-01-01
In summary, the invention provides a measuring wheel comprising a hub, a rim and at least one connection arrangement between the hub and the rim and comprising one or several measuring elements in or on the connection arrangement for measuring forces and/or torques which act on the rotating measuring wheel. The connection arrangement in the hub and the rim comprises a radial/tangential connection device and an axial connection device. The radial/tangential connection device is designed so tha...
Radial, renal and craniofacial anomalies: Baller-Gerold syndrome
Directory of Open Access Journals (Sweden)
Murthy Jyotsna
2008-01-01
Full Text Available The Baller-Gerold syndrome is a rare syndrome with very few cases published in literature. Craniosynostosis and radial aplasia are striking features, easy to diagnose. However, there are many differential diagnoses. Often, the question raised is whether the Baller-Gerald syndrome is a distinct entity. We report a patient with findings of craniosynostosis and radial aplasia consistent with the diagnosis of the Baller-Gerold syndrome. Genotypic heterogeneity could possibly underlie the phenotypic variability exhibited by these cases.
Radial variations of wood different properties in Diospyros lotus
Majid Kiaei; Reza Bakhshi
2014-01-01
Aim of study: The aim of this study was to determine some of the physical, biometry and mechanical strength properties of Diospyros lotus L. wood along radial direction from the pith to the bark and the relationship between wood various properties. Area of study: The study area is located in north Iran in the province of Mazandarn. Material and methods: Testing samples were taken at breast height of tree stem and three radial position of stem radius to determine physical (basic density), fibe...
Kevin Curran; Michael Mc Hugh
2013-01-01
The rise of social networking has revolutionised how people communicate on a daily basis. In a world where more people are connecting to the internet, social networking services create an immediate communication link between users. Social networking sites provide multiple services which include emailing, instant messaging, uploading files, gaming and finding friends. Just as social networking has become a more popular method of communication in recent years, the ways in which people look afte...
Channeling of protons through radial deformed carbon nanotubes
Energy Technology Data Exchange (ETDEWEB)
Borka Jovanović, V., E-mail: vborka@vinca.rs [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Borka, D. [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Galijaš, S.M.D. [Faculty of Physics, University of Belgrade, P.O. Box 368, 11001 Belgrade (Serbia)
2017-05-18
Highlights: • For the first time we presented theoretically obtained distributions of channeled protons with radially deformed SWNT. • Our findings indicate that influence of the radial deformation is very strong and it should not be omitted in simulations. • We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. • Our obtained results can be compared with measured distributions to reveal the presence of various types of defects in SWNT. - Abstract: In this paper we have presented a theoretical investigation of the channeling of 1 GeV protons with the radial deformed (10, 0) single-wall carbon nanotubes (SWNTs). We have calculated channeling potential within the deformed nanotubes. For the first time we presented theoretically obtained spatial and angular distributions of channeled protons with radially deformed SWNT. We used a Monte Carlo (MC) simulation technique. We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. These results may be useful for nanotube characterization and production and guiding of nanosized ion beams.
Cui, Yiqian; Shi, Junyou; Wang, Zili
2018-01-01
The centers and radii of radial basis functions (RBFs) greatly affect the approximation capability of RBF networks (RBFNs). Traditional statistics-based approaches are widely used, but they may lack adaptivity to different data structures. Quantum clustering (QC), derived from quantum mechanics and the Schrödinger equation, demonstrates excellent capability in finding the structure and conformity toward data distribution. In this paper, a novel neural networks model called quantum local potential function networks (QLPFNs) is proposed. The QLPFN inherits the outstanding properties of QC by constructing the waves and the potential functions, and the level of data concentration can be discovered to obtain the inherent structures of the given data set. The local potential functions form the basic components of the QLPFN structure, which are automatically generated from the subsets of training data following specific subspace division procedures. Therefore, the QLPFN model in fact incorporates the level of data concentration as a computation technique, which is different from the classical RBFN model that exhibits radial symmetry toward specific centers. Some application examples are given in this paper to show the effectiveness of the QLPFN model.
A RBF neural network model with GARCH errors: Application to electricity price forecasting
Energy Technology Data Exchange (ETDEWEB)
Coelho, Leandro dos Santos [Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos, Andre A.P. [Department of Statistics, Universidad Carlos III de Madrid, C/ Madrid, 126, 28903 Getafe, Madrid (Spain)
2011-01-15
In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices. (author)
Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur
2017-09-01
The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.
Semi-supervised Text Classification Using RBF Networks
Jiang, Eric P.
Semi-supervised text classification has numerous applications and is particularly applicable to the problems where large quantities of unlabeled data are readily available while only a small number of labeled training samples are accessible. The paper proposes a semi-supervised classifier that integrates a clustering based Expectation Maximization (EM) algorithm into radial basis function (RBF) neural networks and can learn for classification from a very small number of labeled training samples and a large pool of unlabeled data effectively. A generalized centroid clustering algorithm is also investigated in this work to balance predictive values between labeled and unlabeled training data and to improve classification accuracy. Experimental results with three popular text classification corpora show that the proper use of additional unlabeled data in this semi-supervised approach can reduce classification errors by up to 26%.
Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network
Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui
2012-01-01
This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587
Nonlinear adaptive PID control for greenhouse environment based on RBF network.
Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui
2012-01-01
This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.
Prediction of Optimal Design and Deflection of Space Structures Using Neural Networks
Directory of Open Access Journals (Sweden)
Reza Kamyab Moghadas
2012-01-01
Full Text Available The main aim of the present work is to determine the optimal design and maximum deflection of double layer grids spending low computational cost using neural networks. The design variables of the optimization problem are cross-sectional area of the elements as well as the length of the span and height of the structures. In this paper, a number of double layer grids with various random values of length and height are selected and optimized by simultaneous perturbation stochastic approximation algorithm. Then, radial basis function (RBF and generalized regression (GR neural networks are trained to predict the optimal design and maximum deflection of the structures. The numerical results demonstrate the efficiency of the proposed methodology.
Directory of Open Access Journals (Sweden)
Nandkumar Wagh
2014-01-01
Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.
Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation
Directory of Open Access Journals (Sweden)
Yuzheng Yang
2014-01-01
Full Text Available An adaptive sliding controller using radial basis function (RBF network to approximate the unknown system dynamics microelectromechanical systems (MEMS gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.
Evolving RBF neural networks for adaptive soft-sensor design.
Alexandridis, Alex
2013-12-01
This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.
Aspects of the numerical analysis of neural networks
Ellacott, S. W.
This article starts with a brief introduction to neural networks for those unfamiliar with the basic concepts, together with a very brief overview of mathematical approaches to the subject. This is followed by a more detailed look at three areas of research which are of particular interest to numerical analysts.The first area is approximation theory. If K is a compact set in n, for some n, then it is proved that a semilinear feedforward network with one hidden layer can uniformly approximate any continuous function in C(K) to any required accuracy. A discussion of known results and open questions on the degree of approximation is included. We also consider the relevance of radial basis functions to neural networks.The second area considered is that of learning algorithms. A detailed analysis of one popular algorithm (the delta rule) will be given, indicating why one implementation leads to a stable numerical process, whereas an initially attractive variant (essentially a form of steepest descent) does not. Similar considerations apply to the backpropagation algorithm. The effect of filtering and other preprocessing of the input data will also be discussed systematically.Finally some applications of neural networks to numerical computation are considered.
Neural Networks Modelling of Municipal Real Estate Market Rent Rates
Directory of Open Access Journals (Sweden)
Muczyński Andrzej
2016-12-01
Full Text Available This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was based on selected networks trained on the local real estate market data and transformation of the detected dependencies – through established models – to estimate the potential market rent rates of municipal premises. On this basis, the assessment of the adequacy of the actual market rent rates of municipal properties was made. Empirical research was conducted on the local real estate market of the city of Olsztyn in Poland. In order to describe the phenomenon of market rent rates formation an unidirectional three-layer network and a network of radial base was selected. Analyses showed a relatively low degree of convergence of the actual municipal rent rents with potential market rent rates. This degree was strongly varied depending on the type of business ran on the property and its’ social and economic impact. The applied research methodology and the obtained results can be used in order to rationalize municipal property management, including the activation of rental policy.
Distributed Extreme Learning Machine for Nonlinear Learning over Network
Directory of Open Access Journals (Sweden)
Songyan Huang
2015-02-01
Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.
Directory of Open Access Journals (Sweden)
Luiz Ernani Meira Jr.
2011-12-01
Full Text Available Os aneurismas da artéria radial são extremamente raros. Em sua maioria, consistem de pseudoaneurismas pós-traumáticos. Os aneurismas da artéria radial verdadeiros podem ser idiopáticos, congênitos, pós-estenóticos ou associados a patologias, tais como vasculites e doenças do tecido conjuntivo. Foi relatado um caso de aneurisma idiopático de artéria radial em uma criança de três anos, que, após completa investigação diagnóstica complementar, foi submetida à ressecção cirúrgica.Radial artery aneurysms are extremely rare. Post-traumatic pseudoaneurysms are the vast majority. True radial artery aneurysms can be idiopathic, congenital, poststenotic, or associated with some pathologies, such as vasculitis and conjunctive tissue diseases. We report a case of an idiopathic aneurysm of the radial artery in a three-year-old child who was submitted to surgical resection after a complete diagnostic approach.
Analytic expressions for ULF wave radiation belt radial diffusion coefficients.
Ozeke, Louis G; Mann, Ian R; Murphy, Kyle R; Jonathan Rae, I; Milling, David K
2014-03-01
We present analytic expressions for ULF wave-derived radiation belt radial diffusion coefficients, as a function of L and Kp, which can easily be incorporated into global radiation belt transport models. The diffusion coefficients are derived from statistical representations of ULF wave power, electric field power mapped from ground magnetometer data, and compressional magnetic field power from in situ measurements. We show that the overall electric and magnetic diffusion coefficients are to a good approximation both independent of energy. We present example 1-D radial diffusion results from simulations driven by CRRES-observed time-dependent energy spectra at the outer boundary, under the action of radial diffusion driven by the new ULF wave radial diffusion coefficients and with empirical chorus wave loss terms (as a function of energy, Kp and L). There is excellent agreement between the differential flux produced by the 1-D, Kp-driven, radial diffusion model and CRRES observations of differential electron flux at 0.976 MeV-even though the model does not include the effects of local internal acceleration sources. Our results highlight not only the importance of correct specification of radial diffusion coefficients for developing accurate models but also show significant promise for belt specification based on relatively simple models driven by solar wind parameters such as solar wind speed or geomagnetic indices such as Kp. Analytic expressions for the radial diffusion coefficients are presentedThe coefficients do not dependent on energy or wave m valueThe electric field diffusion coefficient dominates over the magnetic.
Comminuted fractures of the radial head: resection or prosthesis?
Lópiz, Yaiza; González, Ana; García-Fernández, Carlos; García-Coiradas, Javier; Marco, Fernando
2016-09-01
At present, surgical treatment of comminuted radial head fractures without associated instability continues to be controversial. When anatomical reconstruction is not possible, radial head excision is performed. However, the appearance of long-term complications with this technique, along with the development of new radial head implants situates arthroplasty as a promising surgical alternative. The purpose of the present study was to compare the mid-term functional outcomes of both techniques. A retrospective study was performed between 2002 and 2011 on 25 Mason type-III fractures, 11 patients treated with primary radial head resection and 14 who received treatment of the fracture with metal prosthesis. At the end of follow-up, patients were contacted and outcomes evaluated according to: Mayo Elbow Performance Score (MEPS), the Disabilities of the Arm, Shoulder and Hand score (DASH) and strength measurement. Radiographic assessment (proximal migration of the radius, osteoarthritic changes, and signs of prosthesis loosening) was also performed. The average age of the sample was 53.7 years in the resection group, and 54.4 years in the replacement group, with a mean follow-up of 60.3 and 42 months respectively. According to the MEPS scale, there were 6 excellent cases, 3 good and 2 acceptable in the resection group, and 6 excellent cases, 3 good, 3 acceptable, and 2 poor in the prosthesis group. The mean DASH score were 13.5, and 24.8 for the resection and the replacement group respectively. We found one postoperative complication in the resection group (stiffness and valgus instability) and 6 in the replacement group: 3 of joint stiffness, 1 case of prosthesis breakage, and 2 neurological injuries. Although this is a retrospective study, the high complication rate occurring after radial head replacement in comparison with radial head resection, as well as good functional results obtained with this last technique, leads us to recommend it for comminuted radial head
O-space with high resolution readouts outperforms radial imaging.
Wang, Haifeng; Tam, Leo; Kopanoglu, Emre; Peters, Dana C; Constable, R Todd; Galiana, Gigi
2017-04-01
While O-Space imaging is well known to accelerate image acquisition beyond traditional Cartesian sampling, its advantages compared to undersampled radial imaging, the linear trajectory most akin to O-Space imaging, have not been detailed. In addition, previous studies have focused on ultrafast imaging with very high acceleration factors and relatively low resolution. The purpose of this work is to directly compare O-Space and radial imaging in their potential to deliver highly undersampled images of high resolution and minimal artifacts, as needed for diagnostic applications. We report that the greatest advantages to O-Space imaging are observed with extended data acquisition readouts. A sampling strategy that uses high resolution readouts is presented and applied to compare the potential of radial and O-Space sequences to generate high resolution images at high undersampling factors. Simulations and phantom studies were performed to investigate whether use of extended readout windows in O-Space imaging would increase k-space sampling and improve image quality, compared to radial imaging. Experimental O-Space images acquired with high resolution readouts show fewer artifacts and greater sharpness than radial imaging with equivalent scan parameters. Radial images taken with longer readouts show stronger undersampling artifacts, which can cause small or subtle image features to disappear. These features are preserved in a comparable O-Space image. High resolution O-Space imaging yields highly undersampled images of high resolution and minimal artifacts. The additional nonlinear gradient field improves image quality beyond conventional radial imaging. Copyright Â© 2016 Elsevier Inc. All rights reserved.
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
Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network
Yao, Weigang; Liou, Meng-Sing
2012-01-01
The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis
Verification of the authenticity of handwritten signature using structure neural-network-type OCON
Molina, M. L.; Arias, N. A.; Gualdron, Oscar
2004-10-01
A method in order to carry out the verification of handwritten signatures is described. The method keeps in mind global features and local features that encode the shape and the dynamics of the signatures. Signatures are recorded with a digital tablet that can read the position and pressure of the pen. Input patterns are considered time and space dependent. Before extracting the information of the static features such as total length or height/width ratio, and the dynamic features such as speed or acceleration, the signature is normalized for position, size and orientation using its Fourier Descriptors. The comparison stage is carried out for algorithms of neurals networks. For each one of the sets of features a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage networks multilayer perceptron with few neurons are used. The classifier combines the decision results of the neural networks and the Euclidean distance obtained using the two feature sets. The results of the first-stage classifier feed a second-stage radial basis function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested, 160 neurals networks has been implemented.
Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation
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M. Agatonović
2012-12-01
Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.
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
Influence of Radial Stress Gradient on Strainbursts: An Experimental Study
Su, Guoshao; Zhai, Shaobin; Jiang, Jianqing; Zhang, Gangliang; Yan, Liubin
2017-10-01
Strainbursts, which are violent disasters that are accompanied by the ejection failure of rocks, usually occur in hard brittle rocks around highly stressed underground openings. The release of the radial stress at excavation boundaries is one of the major inducing factors for strainbursts in tunnels. After excavation, the radial stress usually exhibits different but apparent gradient variations along the radial direction near the boundary within a certain depth under different in situ stress conditions. In this study, the influence of the radial stress gradient on strainbursts of granite was investigated using an improved true-triaxial rockburst testing system, which was equipped with an acoustic emission monitoring system. The stress state and boundary conditions (i.e., one face free, other faces loaded and increasing tangential stress) of the representative rock element in the vicinity of the excavation boundary were simulated. High-speed cameras were used to capture the ejection failure processes during strainbursts, and the kinetic energy of ejected fragments was quantitatively estimated by analyzing the recorded videos. The experimental results indicate that with an increasing radial stress gradient, the strength increases, the apparent yield platform prior to the peak stress on the stress-strain curves decreases, the failure mode changes from strainburst characterized by tensile splitting to strainburst characterized by shear rupture, and the kinetic energy of ejected fragments during strainbursts significantly increases.
Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex
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Mir Jalil Razavi
2017-08-01
Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.
Development, comparison, and evaluation of software for radial distortion elimination
Papadaki, A. I.; Georgopoulos, A.
2015-05-01
Lately the interest of Computer Vision and Photogrammetry community has been focused on the automation of the processes of identification and elimination of the radial distortion, with the aim to correct the image coordinates and finally to obtain digital images with reliable geometric information. This effort has reached the point of development of commercial or free image processing software, claiming that it can automatically identify and remove the radial distortion from an image. In this paper in depth research has been conducted about the radial distortion and the methods of its identification and elimination. Specifically, it has been attempted to evaluate software as the aforementioned, about its effectiveness, accuracy and applicability on the elimination of the radial distortion from images. For the attainment of the desired aim, four different methods of comparison and evaluation of the performance of the software, with respect to the correction of an image, have been employed. The applied methods are (i) the optical evaluation of the produced digital images, (ii) the subtraction of the images, (iii) the comparison of the curves of the remaining radial distortion in the images and (iv) the comparison of the results from the orientation of an image pair. However, it was really important to have a benchmark for the evaluation, in order to ensure the objectivity and accuracy of the comparison. Therefore, a new reliable algorithm has been developed, which was of known and controllable accuracy. The results of these comparisons are presented and evaluated for their reliability and usefulness.
A survey of radial methods for information visualization.
Draper, Geoffrey M; Livnat, Yarden; Riesenfeld, Richard F
2009-01-01
Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research. In spite of its prevalence, little work has been done to study this visualization paradigm as a methodology in its own right. We provide a historical review of radial visualization, tracing it to its roots in centuries-old statistical graphics. We then identify the types of problem domains to which modern radial visualization techniques have been applied. A taxonomy for radial visualization is proposed in the form of seven design patterns encompassing nearly all recent works in this area. From an analysis of these patterns, we distill a series of design considerations that system builders can use to create new visualizations that address aspects of the design space that have not yet been explored. It is hoped that our taxonomy will provide a framework for facilitating discourse among researchers and stimulate the development of additional theories and systems involving radial visualization as a distinct design metaphor.
A Review Article: Diagnosis and Treatment of Radial Tunnel Syndrome
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Ebrahimzadeh Mohammad Hosein
2015-04-01
Full Text Available Radial tunnel syndrome is a disease which we should consider it in elbow and forearm pains. It is diagnosed with lateral elbow and dorsal forearm pain may radiate to the wrist and dorsum of fingers. The disease is more prevalent in women with the age of 30 to 50 years old. It occurs by intermittent compression on the radial nerve from the radial head to the inferior border of the supinator muscle, without obvious extensor muscle weakness. Compression could happen in five different sites but the arcade of Frose is the most common area that radial nerve is compressed. To diagnosis radial tunnel syndrome, clinical examination is more important than paraclinic tests such as electrodiagnsic test and imaging studies. The exact site of the pain which can more specified by rule of nine test and weakness of the third finger and wrist extension are valuable physical exams to diagnosis. MRI studies my show u muscle edema or atrophy along the distribution of the posterior interosseous nerve. Although non-surgical treatments such as rest, NSAIDs, injections and physiotherapy do not believe to have permanent relief, but it is justify undergoing them before surgery. Surgery could diminish pain and symptoms in 67 to 93 percents of patients completely.
Vascularized proximal fibular epiphyseal transfer for distal radial reconstruction.
Innocenti, Marco; Delcroix, Luca; Manfrini, Marco; Ceruso, Massimo; Capanna, Rodolfo
2005-09-01
Treatment of the loss of the distal part of the radius, including the physis and epiphysis, in a skeletally immature patient requires both replacement of the osseous defect and restoration of longitudinal growth. Autologous vascularized epiphyseal transfer is the only possible procedure that can meet both requirements. Between 1993 and 2002, six patients with a mean age of 8.4 years (range, six to eleven years) who had a malignant bone tumor in the distal part of the radius underwent microsurgical reconstruction of the distal part of the radius with a vascularized proximal fibular transfer, including the physis and a variable length of the diaphysis. All of the grafts were supplied by the anterior tibial vascular network. The rate of survival and bone union of the graft, the growth rate per year, the ratio between the lengths of the ulna and the reconstructed radius, and the range of motion of the wrist were evaluated for five of the six patients who had been followed for three years or more. The mean duration of follow-up of the six patients was 4.4 years (range, eight months to nine years). All six transfers survived and united with the host bone within two months postoperatively. The five patients who were followed for three years or more had consistent and predictable longitudinal growth. Serial radiographs revealed remodeling of the articular surface. The functional result was rated as excellent for all but one patient, in whom the distal part of the ulna had also been resected because of neoplastic involvement. No major complication occurred at the recipient site, whereas a peroneal nerve palsy occurred at the donor site in three patients. The palsy was transient in two patients, but it persisted in one. No instability of the knee joint was observed. After radical resection of the distal part of the radius because of a neoplasm in children, vascularized proximal fibular transfer, based on the anterior tibial artery, permits a one-stage skeletal and joint
GPM GROUND VALIDATION EARTH NETWORKS TOTAL LIGHTNING NETWORK (ENTLN) MC3E V1
National Aeronautics and Space Administration — The Earth Networks Total Lightning Network (ENTLN) is an integrated in-cloud (IC) lightning and cloud-to-ground (CG) detection network deployed on a global basis...
Luo, Shaohua; Wu, Songli; Gao, Ruizhen
2015-07-01
This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.
Energy Technology Data Exchange (ETDEWEB)
Luo, Shaohua [School of Automation, Chongqing University, Chongqing 400044 (China); Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021 (China); Wu, Songli [Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021 (China); Gao, Ruizhen [School of Automation, Chongqing University, Chongqing 400044 (China)
2015-07-15
This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.
Radial Fingering in a Porous Medium Digitation radiale dans un milieu poreux
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Ni W.
2006-11-01
Full Text Available The theory of immiscible radial displacement in a Hele-Shaw cell is extended to the case of a porous medium contained between two closely-spaced parallel plates, and experiments are described for the displacement of glycerine by paraffin oil in such a system. Data are presented for the number of fingers, the breakthrough time, and the glycerine recovery, for a range of flowrates varying through three orders of magnitude. Good agreement between theory and experiment is observed. La théorie s'appliquant aux déplacements radiaux dans les cellules Hele-Shaw a été étendue à un système qui consiste en une couche mince de milieux poreux encapsulée entre deux plaques en verre. Dans cet article, on examine les déplacements de la glycérine par de l'huile de paraffine. En faisant varier le débit de l'huile de paraffine dans un intervalle de trois ordres de grandeur, on a étudié les variables telles que le nombre de digitations, le temps de percée et le taux de récupération de la glycérine. On a observé un bon accord entre la théorie et les résultats expérimentaux.
Starspot-induced radial velocity jitter during a stellar cycle
DEFF Research Database (Denmark)
Korhonen, Heidi Helena; Andersen, Jan Marie; Järvinen, Silva
2012-01-01
on the Sun and other cool stars changes cyclically during an activity cycle, which has length varying from about a year to longer than the solar 11 years. In this work we investigate the influence of varying amount of starspots on the sparsely sampled radial velocity observations - which are the norm......Late-type stars exhibit cool regions on their surface, the stellar equivalent of sunspots. These dark starspots can also mimic the radial velocity variations caused by orbiting planets, making it at times difficult to distinguish between planets and activity signatures. The amount of spots...... in the radial velocity studies searching for exoplanets on wide orbits. We study two simulated cases: one with a random spot configuration, and one where the spot occurrence is concentrated. In addition we use Doppler images of young solar analogue V889 Her as a high activity case....
Optimal design of radial Bragg cavities and lasers.
Ben-Bassat, Eyal; Scheuer, Jacob
2015-07-01
We present a new and optimal design approach for obtaining maximal confinement of the field in radial Bragg cavities and lasers for TM polarization. The presented approach outperforms substantially the previously employed periodic and semi-periodic design schemes of such lasers. We show that in order to obtain maximal confinement, it is essential to consider the complete reflection properties (amplitude and phase) of the propagating radial waves at the interfaces between Bragg layers. When these properties are taken into account, we find that it is necessary to introduce a wider ("half-wavelength") layer at a specific radius in the "quarter-wavelength" radial Bragg stack. It is shown that this radius corresponds to the cylindrical equivalent of Brewster's angle. The confinement and field profile are calculated numerically by means of transfer matrix method.
Cloaking by shells with radially inhomogeneous anisotropic permittivity.
Reshetnyak, V Yu; Pinkevych, I P; Sluckin, T J; Evans, D R
2016-01-25
We model electromagnetic cloaking of a spherical or cylindrical nanoparticle enclosed by an optically anisotropic and optically inhomogeneous symmetric shell, by examining its electric response in a quasi-static uniform electric field. When the components of the shell permittivity are radially anisotropic and power-law dependent (ε~r(m)) whereris distance to the shell center, and m a positive or negative exponent which can be varied), the problem is analytically tractable. Formulas are calculated for the degree of cloaking in the general case, allowing the determination of a dielectric condition for the shells to be used as an invisibility cloak. Ideal cloaking is known to require that homogeneous shells exhibit an infinite ratio of tangential and radial components of the shell permittivity, but for radially inhomogeneous shells ideal cloaking can occur even for finite values of this ratio.
Radial asymptotics of Lemaitre-Tolman-Bondi dust models
Sussman, Roberto A
2010-01-01
We examine the radial asymptotic behavior of spherically symmetric Lemaitre-Tolman-Bondi dust models by looking at their covariant scalars along radial rays, which are spacelike geodesics parametrized by proper length $\\ell$, orthogonal to the 4-velocity and to the orbits of SO(3). By introducing quasi-local scalars defined as integral functions along the rays, we obtain a complete and covariant representation of the models, leading to an initial value parametrization in which all scalars can be given by scaling laws depending on two metric scale factors and two basic initial value functions. Considering regular "open" LTB models whose space slices allow for a diverging $\\ell$, we provide the conditions on the radial coordinate so that its asymptotic limit corresponds to the limit as $\\ell\\to\\infty$. The "asymptotic state" is then defined as this limit, together with asymptotic series expansion around it, evaluated for all metric functions, covariant scalars (local and quasi-local) and their fluctuations. By ...
Psychiatric behaviors associated with cytoskeletal defects in radial neuronal migration.
Fukuda, Toshifumi; Yanagi, Shigeru
2017-10-01
Normal development of the cerebral cortex is an important process for higher brain functions, such as language, and cognitive and social functions. Psychiatric disorders, such as schizophrenia and autism, are thought to develop owing to various dysfunctions occurring during the development of the cerebral cortex. Radial neuronal migration in the embryonic cerebral cortex is a complex process, which is achieved by strict control of cytoskeletal dynamics, and impairments in this process are suggested to cause various psychiatric disorders. Our recent findings indicate that radial neuronal migration as well as psychiatric behaviors is rescued by controlling microtubule stability during the embryonic stage. In this review, we outline the relationship between psychiatric disorders, such as schizophrenia and autism, and radial neuronal migration in the cerebral cortex by focusing on the cytoskeleton and centrosomes. New treatment strategies for psychiatric disorders will be discussed.
Effects of spatiotopic factors on bisection of radial lines.
Chieffi, Sergio; Iavarone, Alessandro; Carlomagno, Sergio
2008-07-01
Under visual guidance normal subjects usually misbisect radial lines farther than the true midpoint (distal bias). We investigated whether this distal bias is constant across the peripersonal space or it varies by varying the distance of the stimulus from the subject. Subjects were asked to bisect radial lines presented below eye level either in the near or far peripersonal space. The results showed an increase of distal bias in the far-space. This finding suggests that the influence of spatial factors on bisection of radial lines is not constant across the peripersonal space. Distal bias increases as a function of the distance of the stimulus from the subject, according to a spatiotopic processing scheme.
Haematoma block in reduction of distal radial fractures.
Ogunlade, S O; Omololu, A B; Alonge, T O; Salawu, S A; Bamgboye, E A
2002-01-01
A total of 35 patients who presented in the Accident and Emergency Department of University College Hospital with displaced distal radial fracture between January 2000 and March 2001 had reduction of the fracture under haematoma block using 10ml of 2% lignocaine. There was significant reduction of the pain following infiltration of the fracture site with lignocaine and significant pain reduction during manipulation compared to pain score at presentation. All the patients had satisfactory reduction of the fracture. The fracture was mobilised in Plaster of Paris 6 weeks in patients with Collens' fracture and 3 weeks in patients with distal radial epiphyseal injury. All patients had good range of movement at 8 weeks after removal of Plaster of Paris and patients expressed satisfaction with this method. We recommend the use of Haematoma block for patients of 15 years and above with displaced distal radial fracture in the Accident and Emergency Department.
Acoustic resonances in two dimensional radial sonic crystals shells
Torrent, Daniel
2010-01-01
Radial sonic crystals (RSC) are fluidlike structures infinitely periodic along the radial direction. They have been recently introduced and are only possible thanks to the anisotropy of specially designed acoustic metamaterials [see Phys. Rev. Lett. {\\bf 103} 064301 (2009)]. We present here a comprehensive analysis of two-dimensional RSC shells, which consist of a cavity defect centered at the origin of the crystal and a finite thickness crystal shell surrounded by a fluidlike background. We develop analytic expressions demonstrating that, like for other type of crystals (photonic or phononic) with defects, these shells contain Fabry-Perot like resonances and strongly localized modes. The results are completely general and can be extended to three dimensional acoustic structures and to their photonic counterparts, the radial photonic crystals.
Radial correlation length across magnetic islands: Simulations and experiments
Fernández-Marina, F.; Estrada, T.; Blanco, E.; García, L.
2017-07-01
The turbulence radial correlation length Lr of density fluctuations is studied across magnetic islands both numerically and experimentally. The numerical study has been carried out by a resistive MHD code (called FAR). It shows asymmetric Lr profiles when measured across magnetic islands. Subsequent simulations using a synthetic Doppler reflectometer suggest that this diagnostic has the capability to capture the effect observed in the results provided by FAR. Finally, experimental studies performed using the Doppler reflectometer installed at the TJ-II stellarator show asymmetries in the coherence profiles matching the radial position of magnetic islands. The similarities found between simulations and experiments indicate that radial correlation length measurements could be used to detect magnetic islands in fusion plasmas.
Event-Specific Quantification of Radiation Belt Radial Diffusion
Tu, W.; Sarris, T. E.; Ozeke, L.
2016-12-01
Recently, there has been a great emphasis on developing event-specific inputs for radiation belt models, since they are proven critical for reproducing the observed radiation belt dynamics during strong events. For example, our DREAM3D simulation of the 8-9 October 2012 storm demonstrates that event-specific chorus wave model and seed population are critical to reproduce the strong enhancement of MeV electrons in this event. However, the observed fast electron dropout preceding the enhancement was not captured by the simulation, which could be due to the combined effects of fast outward radial diffusion of radiation belt electrons with magnetopause shadowing and enhanced electron precipitation. Without an event-specific quantification of radial diffusion, we cannot resolve the relative contribution of outward radial diffusion and precipitation to the observed electron dropout or realistically reproduce the dynamics during the event. In this work, we provide physical quantification of radial diffusion specific to the October 2012 event by including both real-time and global distributions of ULF waves from a constellation of wave measurements and event-specific estimation of ULF wave mode structure. The global maps of ULF waves during the event are constructed by combining the real-time measurements from the Van Allen Probes, THEMIS, and GOES satellites in space and a large array of ground magnetometers. The real-time ULF wave mode structure is then estimated using the new Cross-Wavelet Transform technique, applied to various azimuthally aligned pairs of ULF wave measurements that are located at the same L shells. The cross power and phase differences between the time series are calculated using the technique, based on which the wave power per mode number is estimated. Finally, the physically estimated radial diffusion coefficients specific to the event are applied to the DREAM3D model to quantify the relative contribution of radial diffusion to the electron dynamics
Validation of an accelerometer for determination of muscle belly radial displacement.
Zagar, T; Krizaj, D
2005-01-01
A commercial variable-capacitance micromachined accelerometer was validated for muscle belly radial displacement measurement. The displacement was calculated by the acceleration data being integrated twice and was compared with the results obtained simultaneously by an accurate mechanical displacement sensor based on an optical encoder. The aim of the investigation was to evaluate the accuracy and precision of an accelerometer for tensiomyography, which is a method for the detection of skeletal muscle contractile properties on the basis of muscle belly radial displacement. A hundred measurements at a bandwidth of 2300 Hz were performed. It was shown that the accuracy and precision in determination of the maximum displacement and the time of the maximum displacement from the calculated curve were satisfactory, in spite of the standard deviation of the twice-integrated acceleration growing approximately linearly with time. The results were accurate enough since the elapsed time from the beginning of the integration was small (less than 75 ms). The measured maximum displacement ranges were between 9.2 and 10.2 mm. The mean relative error was less than 1% (SD = 0.02mm) for the maximum displacement and about 1% (SD = 0.6 ms) for the time to maximum displacement. The accuracy of the half-relaxation time determination was more uncertain because of the relatively high relative error of -2.4% (SD = 3 ms). Results showed that a commercial micromachined accelerometer could be suitable for the measurement of muscle belly radial displacement and used for development of a future miniaturised and flexible system for the measurement of similar displacements.
Zhou, Jianyong; Luo, Zu; Li, Chunquan; Deng, Mi
2018-01-01
When the meshless method is used to establish the mathematical-mechanical model of human soft tissues, it is necessary to define the space occupied by human tissues as the problem domain and the boundary of the domain as the surface of those tissues. Nodes should be distributed in both the problem domain and on the boundaries. Under external force, the displacement of the node is computed by the meshless method to represent the deformation of biological soft tissues. However, computation by the meshless method consumes too much time, which will affect the simulation of real-time deformation of human tissues in virtual surgery. In this article, the Marquardt's Algorithm is proposed to fit the nodal displacement at the problem domain's boundary and obtain the relationship between surface deformation and force. When different external forces are applied, the deformation of soft tissues can be quickly obtained based on this relationship. The analysis and discussion show that the improved model equations with Marquardt's Algorithm not only can simulate the deformation in real-time but also preserve the authenticity of the deformation model's physical properties. Copyright © 2017 Elsevier B.V. All rights reserved.
Ma, Na Na; Zhang, Hai Fei; Yin, Peng; Bao, Xiao Jun; Zhang, Hong Fei
2017-08-01
Within the improved Weizsäcker-Skyrme (WS)-type nuclear mass formulas, we systematically calculated one-nucleon and two-nucleon separated energy, α-decay and β-decay energies, and the odd-even staggering (OES) of nuclear binding energies. As a result, the root-mean-square (rms) deviations of 2267 nuclei within the new improved WS-type mass formula are dropped from 493 to 167 keV, where 2267 nuclei are extracted from the atomic mass evaluation of 2012. Simultaneously, all the rms deviations of one-nucleon and two-nucleon separation energies and decay energies Qα,Qβ-,Qβ+, and QEC for more than 3000 nuclei are cut down by about 100-400 keV. Further, some basic physical observations of 988 boundary nuclei are predicted for providing reference to experiments. Finally, the overall neutron OESs and proton OESs have been systemically investigated and the residual error satisfies a normal distribution. The pairing gaps Δn and Δp of the isotopes of O, Ca, Ni, Zr, Sn, Gd, Qs, Pb, Pa, Ds and the isotonic magic chains of N =28 ,50 ,82 ,126 and even-even nuclei are also studied with dramatic improvements obtained. Especially, the rms of Δn and Δp in these nuclei have been reduced by about 200 keV. The above physical quantities show important information for nuclear charts and the features of nuclear structure.
The overlapped radial basis function-finite difference (RBF-FD) method: A generalization of RBF-FD
Shankar, Varun
2017-08-01
We present a generalization of the RBF-FD method that computes RBF-FD weights in finite-sized neighborhoods around the centers of RBF-FD stencils by introducing an overlap parameter δ ∈ (0 , 1 ] such that δ = 1 recovers the standard RBF-FD method and δ = 0 results in a full decoupling of stencils. We provide experimental evidence to support this generalization, and develop an automatic stabilization procedure based on local Lebesgue functions for the stable selection of stencil weights over a wide range of δ values. We provide an a priori estimate for the speedup of our method over RBF-FD that serves as a good predictor for the true speedup. We apply our method to parabolic partial differential equations with time-dependent inhomogeneous boundary conditions - Neumann in 2D, and Dirichlet in 3D. Our results show that our method can achieve as high as a 60× speedup in 3D over existing RBF-FD methods in the task of forming differentiation matrices.