Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

Science.gov (United States)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

Neuronal spike sorting based on radial basis function neural networks

Directory of Open Access Journals (Sweden)

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.

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.

Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

KAUST Repository

Ryu, Duchwan; Liang, Faming; Mallick, Bani K.

2013-01-01

be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle

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.

Performance-oriented asymptotic tracking control of hydraulic systems with radial basis function network disturbance observer

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Jian Hu

2016-05-01

Full Text Available Uncertainties, including parametric uncertainties and uncertain nonlinearities, always exist in positioning servo systems driven by a hydraulic actuator, which would degrade their tracking accuracy. In this article, an integrated control scheme, which combines adaptive robust control together with radial basis function neural network–based disturbance observer, is proposed for high-accuracy motion control of hydraulic systems. Not only parametric uncertainties but also uncertain nonlinearities (i.e. nonlinear friction, external disturbances, and/or unmodeled dynamics are taken into consideration in the proposed controller. The above uncertainties are compensated, respectively, by adaptive control and radial basis function neural network, which are ultimately integrated together by applying feedforward compensation technique, in which the global stabilization of the controller is ensured via a robust feedback path. A new kind of parameter and weight adaptation law is designed on the basis of Lyapunov stability theory. Furthermore, the proposed controller obtains an expected steady performance even if modeling uncertainties exist, and extensive simulation results in various working conditions have proven the high performance of the proposed control scheme.

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.

Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

OpenAIRE

Ali Reza Zirak; Sobhan Roshani

2016-01-01

A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

Science.gov (United States)

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

CERN Document Server

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...

Vibration control of uncertain multiple launch rocket system using radial basis function neural network

Science.gov (United States)

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.

Influence of the Training Methods in the Diagnosis of Multiple Sclerosis Using Radial Basis Functions Artificial Neural Networks

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Ángel Gutiérrez

2015-04-01

Full Text Available The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space. Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network. The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique. A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.

An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

Science.gov (United States)

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…

Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

International Nuclear Information System (INIS)

Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

2006-01-01

High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

Dynamic Fault Diagnosis for Semi-Batch Reactor under Closed-Loop Control via Independent Radial Basis Function Neural Network

OpenAIRE

Abdelkarim M. Ertiame; D. W. Yu; D. L. Yu; J. B. Gomm

2015-01-01

In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is emplo...

The Matlab Radial Basis Function Toolbox

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Scott A. Sarra

2017-03-01

Full Text Available Radial Basis Function (RBF methods are important tools for scattered data interpolation and for the solution of Partial Differential Equations in complexly shaped domains. The most straight forward approach used to evaluate the methods involves solving a linear system which is typically poorly conditioned. The Matlab Radial Basis Function toolbox features a regularization method for the ill-conditioned system, extended precision floating point arithmetic, and symmetry exploitation for the purpose of reducing flop counts of the associated numerical linear algebra algorithms.

Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

Science.gov (United States)

Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar

2018-06-01

The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

International Nuclear Information System (INIS)

Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam

2007-01-01

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported

Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks

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Zhihong Liao

2017-11-01

Full Text Available A radial basis function network (RBFN method is proposed to reconstruct daily Sea surface temperatures (SSTs with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI v2 daily 0.25° SST (OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.

Motion planning for autonomous vehicle based on radial basis function neural network in unstructured environment.

Science.gov (United States)

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.

Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

Science.gov (United States)

Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.

2012-04-01

Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

Science.gov (United States)

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.

Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.

Science.gov (United States)

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.

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

Science.gov (United States)

Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude

2010-02-01

Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.

Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

Directory of Open Access Journals (Sweden)

Yunfeng Wu

2014-01-01

Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network.

Science.gov (United States)

Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M

2011-11-01

Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

Science.gov (United States)

DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

2016-03-29

We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

Radial basis function networks applied to DNBR calculation in digital core protection systems

International Nuclear Information System (INIS)

Lee, Gyu-Cheon; Heung Chang, Soon

2003-01-01

The nuclear power plant has to be operated with sufficient margin from the specified DNBR limit for assuring its safety. The digital core protection system calculates on-line real-time DNBR by using a complex subchannel analysis program, and triggers a reliable reactor shutdown if the calculated DNBR approaches the specified limit. However, it takes a relatively long calculation time even for a steady state condition, which may have an adverse effect on the operation flexibility. To overcome the drawback, a new method using a radial basis function network is presented in this paper. Nonparametric training approach is utilized, which shows dramatic reduction of the training time, no tedious heuristic process for optimizing parameters, and no local minima problem during the training. The test results show that the predicted DNBR is within about ±2% deviation from the target DNBR for the fixed axial flux shape case. For the variable axial flux case including severely skewed shapes that appeared during accidents, the deviation is within about ±10%. The suggested method could be the alternative that can calculate DNBR very quickly while guaranteeing the plant safety

Surface interpolation with radial basis functions for medical imaging

International Nuclear Information System (INIS)

Carr, J.C.; Beatson, R.K.; Fright, W.R.

1997-01-01

Radial basis functions are presented as a practical solution to the problem of interpolating incomplete surfaces derived from three-dimensional (3-D) medical graphics. The specific application considered is the design of cranial implants for the repair of defects, usually holes, in the skull. Radial basis functions impose few restrictions on the geometry of the interpolation centers and are suited to problems where interpolation centers do not form a regular grid. However, their high computational requirements have previously limited their use to problems where the number of interpolation centers is small (<300). Recently developed fast evaluation techniques have overcome these limitations and made radial basis interpolation a practical approach for larger data sets. In this paper radial basis functions are fitted to depth-maps of the skull's surface, obtained from X-ray computed tomography (CT) data using ray-tracing techniques. They are used to smoothly interpolate the surface of the skull across defect regions. The resulting mathematical description of the skull's surface can be evaluated at any desired resolution to be rendered on a graphics workstation or to generate instructions for operating a computer numerically controlled (CNC) mill

Fast radial basis functions for engineering applications

CERN Document Server

Biancolini, Marco Evangelos

2017-01-01

This book presents the first “How To” guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF:  multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive – and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers’ cultural background, but also due to the numerical complex...

Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

KAUST Repository

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.

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

Science.gov (United States)

Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman

2017-02-01

The soil sorption partition coefficient logK oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.

Point Set Denoising Using Bootstrap-Based Radial Basis Function.

Science.gov (United States)

Liew, Khang Jie; Ramli, Ahmad; Abd Majid, Ahmad

2016-01-01

This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.

Point Set Denoising Using Bootstrap-Based Radial Basis Function.

Directory of Open Access Journals (Sweden)

Khang Jie Liew

Full Text Available This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.

Decoupling control of a five-phase fault-tolerant permanent magnet motor by radial basis function neural network inverse

Science.gov (United States)

Chen, Qian; Liu, Guohai; Xu, Dezhi; Xu, Liang; Xu, Gaohong; Aamir, Nazir

2018-05-01

This paper proposes a new decoupled control for a five-phase in-wheel fault-tolerant permanent magnet (IW-FTPM) motor drive, in which radial basis function neural network inverse (RBF-NNI) and internal model control (IMC) are combined. The RBF-NNI system is introduced into original system to construct a pseudo-linear system, and IMC is used as a robust controller. Hence, the newly proposed control system incorporates the merits of the IMC and RBF-NNI methods. In order to verify the proposed strategy, an IW-FTPM motor drive is designed based on dSPACE real-time control platform. Then, the experimental results are offered to verify that the d-axis current and the rotor speed are successfully decoupled. Besides, the proposed motor drive exhibits strong robustness even under load torque disturbance.

Spherical radial basis functions, theory and applications

CERN Document Server

Hubbert, Simon; Morton, Tanya M

2015-01-01

This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging as one of the most promising techniques for solving problems where approximations are needed on the surface of a sphere. The aim of the book is to provide enough theoretical and practical details for the reader to be able to implement the SBF methods to solve real world problems. The authors stress the close connection between the theory of SBFs and that of the more well-known family of radial basis functions (RBFs), which are well-established tools for solving approximation theory problems on more general domains. The unique solvability of the SBF interpolation method for data fitting problems is established and an in-depth investigation of its accuracy is provided. Two chapters are devoted to partial differential equations (PDEs). One deals with the practical implementation of an SBF-based solution to an elliptic PDE and another which describes an SBF approach for solvi...

Robust and Stable Disturbance Observer of Servo System for Low Speed Operation Using the Radial Basis Function Network

DEFF Research Database (Denmark)

Lee, Kyo-Beum; Blaabjerg, Frede

2005-01-01

A new scheme to estimate the moment of inertia in the servo motor drive system in very low speed is proposed in this paper. The speed estimation scheme in most servo drive systems for low speed operation is sensitive to the variation of machine parameter, especially the moment of inertia....... To estimate the motor inertia value, the observer using the Radial Basis Function Network (RBFN) is applied. A control law for stabilizing the system and adaptive laws for updating both of the weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable...... in the sense of Lyapunov. The effectiveness of the proposed inertia estimation is verified by simulations and experiments. It is concluded that the speed control performance in low speed region is improved with the proposed disturbance observer using RBFN....

Doubly stochastic radial basis function methods

Science.gov (United States)

Yang, Fenglian; Yan, Liang; Ling, Leevan

2018-06-01

We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation. A careful operation count is provided in order to determine the ranges of all the parameters in our methods. The overhead cost for setting up the proposed DSRBF method is O (n2) for function recovery problems with n basis. Numerical experiments confirm that the proposed method not only outperforms constant shape parameter formulation (in terms of accuracy with comparable computational cost) but also the optimal LOOCV formulation (in terms of both accuracy and computational cost).

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.

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

Science.gov (United States)

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.

Optimization of vehicle compartment low frequency noise based on Radial Basis Function Neuro-Network Approximation Model

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HU Qi-guo

2017-01-01

Full Text Available For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel’s thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver’s head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively. The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly.

Modeling multivariate time series on manifolds with skew radial basis functions.

Science.gov (United States)

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

Radial Basis Function Based Quadrature over Smooth Surfaces

Science.gov (United States)

2016-03-24

Radial Basis Functions φ(r) Piecewise Smooth (Conditionally Positive Definite) MN Monomial |r|2m+1 TPS thin plate spline |r|2mln|r| Infinitely Smooth...smooth surfaces using polynomial interpolants, while [27] couples Thin - Plate Spline interpolation (see table 1) with Green’s integral formula [29

Disturbance observer that uses radial basis function networks for the low speed control of a servo motor

DEFF Research Database (Denmark)

Lee, Kyo-Beum; Bae, C.H.; Blaabjerg, Frede

2005-01-01

A scheme to estimate the moment of inertia in a servo motor drive system at very low speed is proposed. The typical speed estimation scheme used in most servo systems operated at low speed is highly sensitive to variations in the moment of inertia. An observer that uses a radial basis function...

Pricing and simulation for real estate index options: Radial basis point interpolation

Science.gov (United States)

Gong, Pu; Zou, Dong; Wang, Jiayue

2018-06-01

This study employs the meshfree radial basis point interpolation (RBPI) for pricing real estate derivatives contingent on real estate index. This method combines radial and polynomial basis functions, which can guarantee the interpolation scheme with Kronecker property and effectively improve accuracy. An exponential change of variables, a mesh refinement algorithm and the Richardson extrapolation are employed in this study to implement the RBPI. Numerical results are presented to examine the computational efficiency and accuracy of our method.

Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads

Directory of Open Access Journals (Sweden)

Ali Mansourkhaki

2018-01-01

Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF neural networks

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Tatar Afshin

2016-03-01

Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.

[Application of wavelet transform-radial basis function neural network in NIRS for determination of rifampicin and isoniazide tablets].

Science.gov (United States)

Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong

2008-06-01

A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.

Intelligent Control of Welding Gun Pose for Pipeline Welding Robot Based on Improved Radial Basis Function Network and Expert System

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.

Multi nodal load forecasting in electric power systems using a radial basis neural network; Previsao de carga multinodal em sistemas eletricos de potencia usando uma rede neural de base radial

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)

Neural network of Gaussian radial basis functions applied to the problem of identification of nuclear accidents in a PWR nuclear power plant

International Nuclear Information System (INIS)

Gomes, Carla Regina; Canedo Medeiros, Jose Antonio Carlos

2015-01-01

Highlights: • It is presented a new method based on Artificial Neural Network (ANN) developed to deal with accident identification in PWR nuclear power plants. • Obtained results have shown the efficiency of the referred technique. • Results obtained with this method are as good as or even better to similar optimization tools available in the literature. - Abstract: The task of monitoring a nuclear power plant consists on determining, continuously and in real time, the state of the plant’s systems in such a way to give indications of abnormalities to the operators and enable them to recognize anomalies in system behavior. The monitoring is based on readings of a large number of meters and alarm indicators which are located in the main control room of the facility. On the occurrence of a transient or of an accident on the nuclear power plant, even the most experienced operators can be confronted with conflicting indications due to the interactions between the various components of the plant systems; since a disturbance of a system can cause disturbances on another plant system, thus the operator may not be able to distinguish what is cause and what is the effect. This cognitive overload, to which operators are submitted, causes a difficulty in understanding clearly the indication of an abnormality in its initial phase of development and in taking the appropriate and immediate corrective actions to face the system failure. With this in mind, computerized monitoring systems based on artificial intelligence that could help the operators to detect and diagnose these failures have been devised and have been the subject of research. Among the techniques that can be used in such development, radial basis functions (RBFs) neural networks play an important role due to the fact that they are able to provide good approximations to functions of a finite number of real variables. This paper aims to present an application of a neural network of Gaussian radial basis

Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

Science.gov (United States)

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.

Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

International Nuclear Information System (INIS)

Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan

2011-01-01

On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

Science.gov (United States)

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.

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Directory of Open Access Journals (Sweden)

Seng-Chi Chen

2014-01-01

Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

Science.gov (United States)

Hoomod, Haider K.; Kareem Jebur, Tuka

2018-05-01

Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

Exponential Convergence for Numerical Solution of Integral Equations Using Radial Basis Functions

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Zakieh Avazzadeh

2014-01-01

Full Text Available We solve some different type of Urysohn integral equations by using the radial basis functions. These types include the linear and nonlinear Fredholm, Volterra, and mixed Volterra-Fredholm integral equations. Our main aim is to investigate the rate of convergence to solve these equations using the radial basis functions which have normic structure that utilize approximation in higher dimensions. Of course, the use of this method often leads to ill-posed systems. Thus we propose an algorithm to improve the results. Numerical results show that this method leads to the exponential convergence for solving integral equations as it was already confirmed for partial and ordinary differential equations.

Study on Magneto-Hydro-Dynamics Disturbance Signal Feature Classification Using Improved S-Transform Algorithm and Radial Basis Function Neural Network

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Nan YU

2014-09-01

Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.

Weighted radial dimension: an improved fractal measurement for highway transportation networks distribution

Science.gov (United States)

Feng, Yongjiu; Liu, Miaolong; Tong, Xiaohua

2007-06-01

An improved fractal measurement, the weighted radial dimension, is put forward for highway transportation networks distribution. The radial dimension (DL), originated from subway investigation in Stuttgart, is a fractal measurement for transportation systems under ideal assumption considering all the network lines to be homogeneous curves, ignoring the difference on spatial structure, quality and level, especially the highway networks. Considering these defects of radial dimension, an improved fractal measurement called weighted radial dimension (D WL) is introduced and the transportation system in Guangdong province is studied in detail using this novel method. Weighted radial dimensions are measured and calculated, and the spatial structure, intensity and connectivity of transportation networks are discussed in Guangdong province and the four sub-areas: the Pearl River Delta area, the East Costal area, the West Costal area and the Northern Guangdong area. In Guangdong province, the fractal spatial pattern characteristics of transportation system vary remarkably: it is the highest in the Pearl River Delta area, moderate in Costal area and lowest in the Northern Guangdong area. With the Pearl River Delta area as the centre, the weighted radial dimensions decrease with the distance increasing, while the decline level is smaller in the costal area and greater in the Northern Guangdong province. By analysis of the conic of highway density, it is recognized that the density decrease with the distance increasing from the calculation centre (Guangzhou), demonstrating the same trend as weighted radial dimensions shown. Evidently, the improved fractal measurement, weighted radial dimension, is an indictor describing the characteristics of highway transportation system more effectively and accurately.

Learning Errors by Radial Basis Function Neural Networks and Regularization Networks

Czech Academy of Sciences Publication Activity Database

Neruda, Roman; Vidnerová, Petra

2009-01-01

Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf

New Method for Mesh Moving Based on Radial Basis Function Interpolation

NARCIS (Netherlands)

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

Calculation of parameters of radial-piston reducer based on the use of functional semantic networks

Directory of Open Access Journals (Sweden)

Pashkevich V.M.

2016-12-01

Full Text Available The questions of сalculation of parameters of radial-piston reducer are considered in this article. It is used the approach which is based technologies of functional semantic networks. It is considered possibility applications of functional se-mantic networks for calculation of parameters of radial-piston reducer. Semantic networks to calculate the mass of the radial piston reducer are given.

Radial basis function and its application in tourism management

Science.gov (United States)

Hu, Shan-Feng; Zhu, Hong-Bin; Zhao, Lei

2018-05-01

In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.

DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING

Directory of Open Access Journals (Sweden)

M. Safish Mary

2012-04-01

Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.

Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

International Nuclear Information System (INIS)

Guerra, Fabio A.; Coelho, Leandro dos S.

2008-01-01

An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting

Predictive modelling of grain size distributions from marine electromagnetic profiling data using end-member analysis and a radial basis function network

Science.gov (United States)

Baasch, B.; M"uller, H.; von Dobeneck, T.

2018-04-01

In this work we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine learning techniques. Nonnegative matrix factorisation is used to determine grain-size end-members from sediment surface samples. Four end-members were found which well represent the variety of sediments in the study area. A radial-basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.

Recent advances in radial basis function collocation methods

CERN Document Server

Chen, Wen; Chen, C S

2014-01-01

This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...

Reconfiguration of face expressions based on the discrete capture data of radial basis function interpolation

Institute of Scientific and Technical Information of China (English)

ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang

2012-01-01

Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function （Multiquadric, MQ for short） to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.

A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction

International Nuclear Information System (INIS)

Yu, Shiwei; Wang, Ke; Wei, Yi-Ming

2015-01-01

Highlights: • A hybrid self-adaptive PSO–GA-RBF model is proposed for electricity demand prediction. • Each mixed-coding particle is composed by two coding parts of binary and real. • Five independent variables have been selected to predict future electricity consumption in Wuhan. • The proposed model has a simpler structure or higher estimating precision than other ANN models. • No matter what the scenario, the electricity consumption of Wuhan will grow rapidly. - Abstract: The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO–GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the RBF by GA operation and the parameters of the basis and weights by a PSO–GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO–GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models; and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7–11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85 billion kW h. The lowest demand is estimated for the business-as-usual scenario, and will be 88.45 billion kW h

Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm

Directory of Open Access Journals (Sweden)

G. Trejo-Caballero

2015-01-01

Full Text Available Curve fitting is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of data points, possibly noisy, the goal is to build a compact representation of the curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Despite the large number of methods available to tackle this problem, it remains challenging and elusive. In this paper, a new method to tackle such problem using strictly a linear combination of radial basis functions (RBFs is proposed. To be more specific, we divide the parameter search space into linear and nonlinear parameter subspaces. We use a hierarchical genetic algorithm (HGA to minimize a model selection criterion, which allows us to automatically and simultaneously determine the nonlinear parameters and then, by the least-squares method through Singular Value Decomposition method, to compute the linear parameters. The method is fully automatic and does not require subjective parameters, for example, smooth factor or centre locations, to perform the solution. In order to validate the efficacy of our approach, we perform an experimental study with several tests on benchmarks smooth functions. A comparative analysis with two successful methods based on RBF networks has been included.

RECONSTRUCCIÓN TRIDIMENSIONAL DE ROSTROS A PARTIR DE IMÁGENES DE RANGO POR MEDIO DE FUNCIONES DE BASE RADIAL DE SOPORTE COMPACTO TRI-DIMENSIONAL RECONSTRUCTION OF FACES FROM RANGE IMAGES THROUGH COMPACT SUPPORT RADIAL BASIS FUNCTIONS

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.

A novel method for flow pattern identification in unstable operational conditions using gamma ray and radial basis function

International Nuclear Information System (INIS)

Roshani, G.H.; Nazemi, E.; Roshani, M.M.

2017-01-01

Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137 Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%. - Highlights: • Flow regime and void fraction were determined in two phase flows independent of the liquid phase density changes. • An experimental structure was set up and the required data was obtained. • 3 detectors and one gamma source were used in detection geometry. • RBF networks were utilized for flow regime and void fraction determination.

The orthogonal gradients method: A radial basis functions method for solving partial differential equations on arbitrary surfaces

KAUST Repository

Piret, Cé cile

2012-01-01

Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper

Heuristic for solving capacitor allocation problems in electric energy radial distribution networks

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Maria A. Biagio

2012-04-01

Full Text Available The goal of the capacitor allocation problem in radial distribution networks is to minimize technical losses with consequential positive impacts on economic and environmental areas. The main objective is to define the size and location of the capacitors while considering load variations in a given horizon. The mathematical formulation for this planning problem is given by an integer nonlinear mathematical programming model that demands great computational effort to be solved. With the goal of solving this problem, this paper proposes a methodology that is composed of heuristics and Tabu Search procedures. The methodology presented explores network system characteristics of the network system reactive loads for identifying regions where procedures of local and intensive searches should be performed. A description of the proposed methodology and an analysis of computational results obtained which are based on several test systems including actual systems are presented. The solutions reached are as good as or better than those indicated by well referenced methodologies. The technique proposed is simple in its use and does not require calibrating an excessive amount of parameters, making it an attractive alternative for companies involved in the planning of radial distribution networks.

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.

Adaptive radial basis function mesh deformation using data reduction

Science.gov (United States)

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

The Gaussian radial basis function method for plasma kinetic theory

Energy Technology Data Exchange (ETDEWEB)

Hirvijoki, E., E-mail: eero.hirvijoki@chalmers.se [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden); Candy, J.; Belli, E. [General Atomics, PO Box 85608, San Diego, CA 92186-5608 (United States); Embréus, O. [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden)

2015-10-30

Description of a magnetized plasma involves the Vlasov equation supplemented with the non-linear Fokker–Planck collision operator. For non-Maxwellian distributions, the collision operator, however, is difficult to compute. In this Letter, we introduce Gaussian Radial Basis Functions (RBFs) to discretize the velocity space of the entire kinetic system, and give the corresponding analytical expressions for the Vlasov and collision operator. Outlining the general theory, we also highlight the connection to plasma fluid theories, and give 2D and 3D numerical solutions of the non-linear Fokker–Planck equation. Applications are anticipated in both astrophysical and laboratory plasmas. - Highlights: • A radically new method to address the velocity space discretization of the non-linear kinetic equation of plasmas. • Elegant and physically intuitive, flexible and mesh-free. • Demonstration of numerical solution of both 2-D and 3-D non-linear Fokker–Planck relaxation problem.

Optimal siting of capacitors in radial distribution network using Whale Optimization Algorithm

Directory of Open Access Journals (Sweden)

D.B. Prakash

2017-12-01

Full Text Available In present days, continuous effort is being made in bringing down the line losses of the electrical distribution networks. Therefore proper allocation of capacitors is of utmost importance because, it will help in reducing the line losses and maintaining the bus voltage. This in turn results in improving the stability and reliability of the system. In this paper Whale Optimization Algorithm (WOA is used to find optimal sizing and placement of capacitors for a typical radial distribution system. Multi objectives such as operating cost reduction and power loss minimization with inequality constraints on voltage limits are considered and the proposed algorithm is validated by applying it on standard radial systems: IEEE-34 bus and IEEE-85 bus radial distribution test systems. The results obtained are compared with those of existing algorithms. The results show that the proposed algorithm is more effective in bringing down the operating costs and in maintaining better voltage profile. Keywords: Whale Optimization Algorithm (WOA, Optimal allocation and sizing of capacitors, Power loss reduction and voltage stability improvement, Radial distribution system, Operating cost minimization

Global and Regional Gravity Field Determination from GOCE Kinematic Orbit by Means of Spherical Radial Basis Functions

Czech Academy of Sciences Publication Activity Database

Bucha, B.; Bezděk, Aleš; Sebera, Josef; Janak, J.

2015-01-01

Roč. 36, č. 6 (2015), s. 773-801 ISSN 0169-3298 R&D Projects: GA ČR GA13-36843S Grant - others:SAV(SK) VEGA 1/0954/15 Institutional support: RVO:67985815 Keywords : spherical radial basis functions * spherical harmonics * geopotential Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 3.622, year: 2015

Stock market index prediction using neural networks

Science.gov (United States)

Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

1994-03-01

A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media

Science.gov (United States)

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%.

Global sensitivity analysis using a Gaussian Radial Basis Function metamodel

International Nuclear Information System (INIS)

Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua

2016-01-01

Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

Science.gov (United States)

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.

Controlling Voltage Levels of Distribution Network-Radial Feeder after Connecting Wind Turbines to the Network

Directory of Open Access Journals (Sweden)

Muhammad Al Badri

2017-11-01

Full Text Available Several factors in power generation and supply need to be taken into account such as shortages of energy supply, system stability, and energy quality and system disruption due to network losses, industrial development and population expansion. The addition of wind turbines to the distribution network is of great benefit in providing additional power and solving these problems, but this addition is accompanied by the problem of low voltage network. This research found optimal solutions to the problem of low voltage distribution network after connecting wind turbines. The main idea of this paper is to optimize the low-voltage problem as a result of connecting the wind turbines to the "far end" of the radial feeder for a distribution network and to obtain a voltage level within an acceptable and stable range. The problem of low voltage solved by using the load-drop compensation, capacitor-bank and “doubly-fed” induction generators. The results of this study were based on the operation of the entire design of the simulation system which would be compatible with the reality of the energy flow of all network components by using the PSCAD program. The present analysis program revealed an optimum solution for the low voltage profile of the distribution network after connecting the wind turbine.

Big geo data surface approximation using radial basis functions: A comparative study

Science.gov (United States)

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.

Use of radial basis functions for meshless numerical solutions applied to financial engineering barrier options

Directory of Open Access Journals (Sweden)

Gisele Tessari Santos

2009-08-01

Full Text Available A large number of financial engineering problems involve non-linear equations with non-linear or time-dependent boundary conditions. Despite available analytical solutions, many classical and modified forms of the well-known Black-Scholes (BS equation require fast and accurate numerical solutions. This work introduces the radial basis function (RBF method as applied to the solution of the BS equation with non-linear boundary conditions, related to path-dependent barrier options. Furthermore, the diffusional method for solving advective-diffusive equations is explored as to its effectiveness to solve BS equations. Cubic and Thin-Plate Spline (TPS radial basis functions were employed and evaluated as to their effectiveness to solve barrier option problems. The numerical results, when compared against analytical solutions, allow affirming that the RBF method is very accurate and easy to be implemented. When the RBF method is applied, the diffusional method leads to the same results as those obtained from the classical formulation of Black-Scholes equation.Muitos problemas de engenharia financeira envolvem equações não-lineares com condições de contorno não-lineares ou dependentes do tempo. Apesar de soluções analíticas disponíveis, várias formas clássicas e modificadas da conhecida equação de Black-Scholes (BS requerem soluções numéricas rápidas e acuradas. Este trabalho introduz o método de função de base radial (RBF aplicado à solução da equação BS com condições de contorno não-lineares relacionadas a opções de barreira dependentes da trajetória. Além disso, explora-se o método difusional para solucionar equações advectivo-difusivas quanto à sua efetividade para solucionar equações BS. Utilizam-se funções de base radial Cúbica e Thin-Plate Spline (TPS, aplicadas à solução de problemas de opções de barreiras. Os resultados numéricos, quando comparados com as soluções analíticas, permitem afirmar

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

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.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Science.gov (United States)

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

International Nuclear Information System (INIS)

Sahoo, N.C.; Prasad, K.

2006-01-01

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration

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.

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.

Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

Science.gov (United States)

Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

2012-06-01

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

Radial Basis Function (RBF Interpolation and Investigating its Impact on Rainfall Duration Mapping

Directory of Open Access Journals (Sweden)

Hassan Derakhshan

2012-01-01

Full Text Available The missing data in database must be reproduced primarily by appropriate interpolation techniques. Radial basis function (RBF interpolators can play a significant role in data completion of precipitation mapping. Five RBF techniques were engaged to be employed in compensating the missing data in event-wised dataset of Upper Paramatta River Catchment in the western suburbs of Sydney, Australia. The related shape parameter, C, of RBFs was optimized for first event of database during a cross-validation process. The Normalized mean square error (NMSE, percent average estimation error (PAEE and coefficient of determination (R2 were the statistics used as validation tools. Results showed that the multiquadric RBF technique with the least error, best suits compensation of the related database.

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.

Tensor Basis Neural Network v. 1.0 (beta)

Energy Technology Data Exchange (ETDEWEB)

2017-03-28

This software package can be used to build, train, and test a neural network machine learning model. The neural network architecture is specifically designed to embed tensor invariance properties by enforcing that the model predictions sit on an invariant tensor basis. This neural network architecture can be used in developing constitutive models for applications such as turbulence modeling, materials science, and electromagnetism.

A fuzzy genetic approach for network reconfiguration to enhance voltage stability in radial distribution systems

Energy Technology Data Exchange (ETDEWEB)

Sahoo, N.C. [Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia); Prasad, K. [Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka (Malaysia)

2006-11-15

This paper presents a fuzzy genetic approach for reconfiguration of radial distribution systems (RDS) so as to maximize the voltage stability of the network for a specific set of loads. The network reconfiguration involves a mechanism for selection of the best set of branches to be opened, one from each loop, such that the reconfigured RDS possesses desired performance characteristics. This discrete solution space is better handled by the proposed scheme, which maximizes a suitable optimizing function (computed using two different approaches). In the first approach, this function is chosen as the average of a voltage stability index of all the buses in the RDS, while in the second approach, the complete RDS is reduced to a two bus equivalent system and the optimizing function is the voltage stability index of this reduced two bus system. The fuzzy genetic algorithm uses a suitable coding and decoding scheme for maintaining the radial nature of the network at every stage of genetic evolution, and it also uses a fuzzy rule based mutation controller for efficient search of the solution space. This method, tested on 69 bus and 33 bus RDSs, shows promising results for the both approaches. It is also observed that the network losses are reduced when the voltage stability is enhanced by the network reconfiguration. (author)

Application of radial basis function in densitometry of stratified regime of liquid-gas two phase flows

International Nuclear Information System (INIS)

Roshani, G.H.; Nazemi, E.; Roshani, M.M.

2017-01-01

In this paper, a novel method is proposed for predicting the density of liquid phase in stratified regime of liquid-gas two phase flows by utilizing dual modality densitometry technique and artificial neural network (ANN) model of radial basis function (RBF). The detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors for registering transmitted and scattered photons. At the first step, a Monte Carlo simulation model was utilized to obtain the optimum position for the scattering detector in dual modality densitometry configuration. At the next step, an experimental setup was designed based on obtained optimum position for detectors from simulation in order to generate the required data for training and testing the ANN. The results show that the proposed approach could be successfully applied for predicting the density of liquid phase in stratified regime of gas-liquid two phase flows with mean relative error (MRE) of less than 0.701. - Highlights: • Density of liquid phase in stratified regime of two phase flows was predicted. • Combination of dual modality densitometry technique and ANN was utilized. • Detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors. • MCNP simulation was done to obtain the optimum position for the scattering detector. • An experimental setup was designed to generate the required data for training the ANN.

Comparing Two Methods of Neural Networks to Evaluate Dead Oil Viscosity

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Meysam Dabiri-Atashbeyk

2018-01-01

Full Text Available Reservoir characterization and asset management require comprehensive information about formation fluids. In fact, it is not possible to find accurate solutions to many petroleum engineering problems without having accurate pressure-volume-temperature (PVT data. Traditionally, fluid information has been obtained by capturing samples and then by measuring the PVT properties in a laboratory. In recent years, neural network has been applied to a large number of petroleum engineering problems. In this paper, a multi-layer perception neural network and radial basis function network (both optimized by a genetic algorithm were used to evaluate the dead oil viscosity of crude oil, and it was found out that the estimated dead oil viscosity by the multi-layer perception neural network was more accurate than the one obtained by radial basis function network.

Solving one-dimensional phase change problems with moving grid method and mesh free radial basis functions

International Nuclear Information System (INIS)

Vrankar, L.; Turk, G.; Runovc, F.; Kansa, E.J.

2006-01-01

Many heat-transfer problems involve a change of phase of material due to solidification or melting. Applications include: the safety studies of nuclear reactors (molten core concrete interaction), the drilling of high ice-content soil, the storage of thermal energy, etc. These problems are often called Stefan's or moving boundary value problems. Mathematically, the interface motion is expressed implicitly in an equation for the conservation of thermal energy at the interface (Stefan's conditions). This introduces a non-linear character to the system which treats each problem somewhat uniquely. The exact solution of phase change problems is limited exclusively to the cases in which e.g. the heat transfer regions are infinite or semi-infinite one dimensional-space. Therefore, solution is obtained either by approximate analytical solution or by numerical methods. Finite-difference methods and finite-element techniques have been used extensively for numerical solution of moving boundary problems. Recently, the numerical methods have focused on the idea of using a mesh-free methodology for the numerical solution of partial differential equations based on radial basis functions. In our case we will study solid-solid transformation. The numerical solutions will be compared with analytical solutions. Actually, in our work we will examine usefulness of radial basis functions (especially multiquadric-MQ) for one-dimensional Stefan's problems. The position of the moving boundary will be simulated by moving grid method. The resultant system of RBF-PDE will be solved by affine space decomposition. (author)

Sample Data Synchronization and Harmonic Analysis Algorithm Based on Radial Basis Function Interpolation

Directory of Open Access Journals (Sweden)

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.

PERAMALAN DERET WAKTU MENGGUNAKAN MODEL FUNGSI BASIS RADIAL (RBF DAN AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

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DT Wiyanti

2013-07-01

Full Text Available Salah satu metode peramalan yang paling dikembangkan saat ini adalah time series, yakni menggunakan pendekatan kuantitatif dengan data masa lampau yang dijadikan acuan untuk peramalan masa depan. Berbagai penelitian telah mengusulkan metode-metode untuk menyelesaikan time series, di antaranya statistik, jaringan syaraf, wavelet, dan sistem fuzzy. Metode-metode tersebut memiliki kekurangan dan keunggulan yang berbeda. Namun permasalahan yang ada dalam dunia nyata merupakan masalah yang kompleks. Satu metode saja mungkin tidak mampu mengatasi masalah tersebut dengan baik. Dalam artikel ini dibahas penggabungan dua buah metode yaitu Auto Regressive Integrated Moving Average (ARIMA dan Radial Basis Function (RBF. Alasan penggabungan kedua metode ini adalah karena adanya asumsi bahwa metode tunggal tidak dapat secara total mengidentifikasi semua karakteristik time series. Pada artikel ini dibahas peramalan terhadap data Indeks Harga Perdagangan Besar (IHPB dan data inflasi komoditi Indonesia; kedua data berada pada rentang tahun 2006 hingga beberapa bulan di tahun 2012. Kedua data tersebut masing-masing memiliki enam variabel. Hasil peramalan metode ARIMA-RBF dibandingkan dengan metode ARIMA dan metode RBF secara individual. Hasil analisa menunjukkan bahwa dengan metode penggabungan ARIMA dan RBF, model yang diberikan memiliki hasil yang lebih akurat dibandingkan dengan penggunaan salah satu metode saja. Hal ini terlihat dalam visual plot, MAPE, dan RMSE dari semua variabel pada dua data uji coba. The accuracy of time series forecasting is the subject of many decision-making processes. Time series use a quantitative approach to employ data from the past to make forecast for the future. Many researches have proposed several methods to solve time series, such as using statistics, neural networks, wavelets, and fuzzy systems. These methods have different advantages and disadvantages. But often the problem in the real world is just too complex that a

Artificial Neural Network Modeling of an Inverse Fluidized Bed ...

African Journals Online (AJOL)

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 decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...

基于径向基神经网络-遗传算法的海流能水轮机叶片翼型优化%Optimization of hydrofoil for marine current turbine based on radial basis function neural network and genetic algorithm

Institute of Scientific and Technical Information of China (English)

朱国俊; 冯建军; 郭鹏程; 罗兴锜

2014-01-01

, the Bezier curve was used to parameterize the hydrofoil. The Latin Hypercube experiment design method was used to select the sample points in the design space which were used for training the Radial Basis Function neural network. The hydrodynamic performance for each sample was calculated by the computational fluid dynamic method, and then the Radial Basis Function neural network would be trained by these sample points. After the neural network had been trained, the multi-point optimization method of hydrofoil was solved by combining the NSGA-II method and the Radial Basis Function neural network. The method mentioned above was applied to the optimal design of NACA63-815 hydrofoil, and the optimization problems of the hydrofoil in three typical conditions in which the attack angle is 0, 6º and 12º were mainly studied in this paper. After optimization, two optimized hydrofoils were selected in the Pareto solution to compare with initial, which were named Optimal A and Optimal B. According to the CFD simulation, the optimized hydrofoil’s performance was gotten and compared with the initial hydrofoil. By comparison, it was found that the drag coefficient of the optimized hydrofoil in the three conditions are less than or equal to the initial. Moreover, the lift-drag ratios of the Optimal A hydrofoil in which the attack angles is 0, 6° and 12° have been improve by 4.6%, 4.4%and 22.8%respectively. And the lift-drag ratios of the Optimal B hydrofoils have also been improved by 6.6%, 3.8%and 16.6%respectively. In addition, according to the comparison of the optimized and initial hydrofoil’s pressure coefficient at the 12°attack angle, it can be found that the optimized hydrofoil can effectively suppress the stall phenomenon. Finally, two conclusions can be drawn from the optimal results. Firstly, use the Radial Basis Function neural network to replace the CFD simulation can effectively decrease the time that the optimization cycle spent. Secondly, the hydrofoil

THE ALGORITHM OF MESHFREE METHOD OF RADIAL BASIS FUNCTIONS IN TASKS OF UNDERGROUND HYDROMECHANICS

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N. V. Medvid

2016-01-01

Full Text Available A Mathematical model of filtering consolidation in the body of soil dam with conduit andwashout zone in two-dimensional case is considered. The impact of such technogenic factors as temperature, salt concentration, subsidence of upper boundary and interior points of the dam with time is taken into account. The software to automate the calculation of numerical solution of the boundary problem by radial basis functions has been created, which enables to conduct numerical experiments by varying the input parameters and shape. The influence of the presence of conduit and washout zone on the pressure, temperature and concentration of salts in the dam body at different time intervals isinvestigated. A number of numerical experiments is conducted and the analysis of dam accidents is performed.

ROAM: A Radial-Basis-Function Optimization Approximation Method for Diagnosing the Three-Dimensional Coronal Magnetic Field

International Nuclear Information System (INIS)

Dalmasse, Kevin; Nychka, Douglas W.; Gibson, Sarah E.; Fan, Yuhong; Flyer, Natasha

2016-01-01

The Coronal Multichannel Polarimeter (CoMP) routinely performs coronal polarimetric measurements using the Fe XIII 10747 and 10798 lines, which are sensitive to the coronal magnetic field. However, inverting such polarimetric measurements into magnetic field data is a difficult task because the corona is optically thin at these wavelengths and the observed signal is therefore the integrated emission of all the plasma along the line of sight. To overcome this difficulty, we take on a new approach that combines a parameterized 3D magnetic field model with forward modeling of the polarization signal. For that purpose, we develop a new, fast and efficient, optimization method for model-data fitting: the Radial-basis-functions Optimization Approximation Method (ROAM). Model-data fitting is achieved by optimizing a user-specified log-likelihood function that quantifies the differences between the observed polarization signal and its synthetic/predicted analog. Speed and efficiency are obtained by combining sparse evaluation of the magnetic model with radial-basis-function (RBF) decomposition of the log-likelihood function. The RBF decomposition provides an analytical expression for the log-likelihood function that is used to inexpensively estimate the set of parameter values optimizing it. We test and validate ROAM on a synthetic test bed of a coronal magnetic flux rope and show that it performs well with a significantly sparse sample of the parameter space. We conclude that our optimization method is well-suited for fast and efficient model-data fitting and can be exploited for converting coronal polarimetric measurements, such as the ones provided by CoMP, into coronal magnetic field data.

Feature Extraction Method for High Impedance Ground Fault Localization in Radial Power Distribution Networks

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...

Fault detection for hydraulic pump based on chaotic parallel RBF network

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Ma Ning

2011-01-01

Full Text Available Abstract In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.

User-friendly Tool for Power Flow Analysis and Distributed Generation Optimisation in Radial Distribution Networks

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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.

Mejoramiento de imágenes usando funciones de base radial Images improvement using radial basis functions

Directory of Open Access Journals (Sweden)

Jaime Alberto Echeverri Arias

2009-07-01

Full Text Available La eliminación del ruido impulsivo es un problema clásico del procesado no lineal para el mejoramiento de imágenes y las funciones de base radial de soporte global son útiles para enfrentarlo. Este trabajo presenta una técnica de interpolación que disminuye eficientemente el ruido impulsivo en imágenes, mediante el uso de interpolante obtenido por funciones de base radial en el marco de la investigación enfocada en el desarrollo de un Sistema de recuperación de imágenes de recursos acuáticos amazónicos. Esta técnica primero etiqueta los píxeles de la imagen que son ruidosos y, mediante la interpolación, genera un valor de reconstrucción de dicho píxel usando sus vecinos. Los resultados obtenidos son comparables y muchas veces mejores que otras técnicas ya publicadas y reconocidas. Según el análisis de resultados, se puede aplicar a imágenes con altas tasas de ruido, manteniendo un bajo error de reconstrucción de los píxeles "ruidosos", así como la calidad visual.Global support radial base functions are effective in eliminating impulsive noise in non-linear processing. This paper introduces an interpolation technique which efficiently reduces image impulsive noise by means of an interpolant obtained through radial base functions. These functions have been used in a research project designed to develop a system for the recovery of images of Amazonian aquatic resources. This technique starts with the tagging by interpolation of noisy image pixels. Thus, a value of reconstruction for the noisy pixels is generated using neighboring pixels. The results obtained with this technique have proved comparable and often better than those obtained with previously known techniques. According to results analysis, this technique can be successfully applied on images with high noise levels. The results are low error in noisy pixel reconstruction and better visual quality.

A comparison of neural network architectures for the prediction of MRR in EDM

Science.gov (United States)

Jena, A. R.; Das, Raja

2017-11-01

The aim of the research work is to predict the material removal rate of a work-piece in electrical discharge machining (EDM). Here, an effort has been made to predict the material removal rate through back-propagation neural network (BPN) and radial basis function neural network (RBFN) for a work-piece of AISI D2 steel. The input parameters for the architecture are discharge-current (Ip), pulse-duration (Ton), and duty-cycle (τ) taken for consideration to obtained the output for material removal rate of the work-piece. In the architecture, it has been observed that radial basis function neural network is comparatively faster than back-propagation neural network but logically back-propagation neural network results more real value. Therefore BPN may consider as a better process in this architecture for consistent prediction to save time and money for conducting experiments.

Reliability analysis of C-130 turboprop engine components using artificial neural network

Science.gov (United States)

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

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...... curtailment using sensitivity factors. The optimization is integrated with Monte Carlo simulation to account for the stochastic behavior of load demand and wind power generation. The proposed algorithm is tested on a real 20 kV Danish distribution system in Støvring. It is demonstrated that the algorithm...... executes reactive compensation and energy curtailment sequentially in an effective and efficient manner....

Neural network approach for the calculation of potential coefficients in quantum mechanics

Science.gov (United States)

Ossandón, Sebastián; Reyes, Camilo; Cumsille, Patricio; Reyes, Carlos M.

2017-05-01

A numerical method based on artificial neural networks is used to solve the inverse Schrödinger equation for a multi-parameter class of potentials. First, the finite element method was used to solve repeatedly the direct problem for different parametrizations of the chosen potential function. Then, using the attainable eigenvalues as a training set of the direct radial basis neural network a map of new eigenvalues was obtained. This relationship was later inverted and refined by training an inverse radial basis neural network, allowing the calculation of the unknown parameters and therefore estimating the potential function. Three numerical examples are presented in order to prove the effectiveness of the method. The results show that the method proposed has the advantage to use less computational resources without a significant accuracy loss.

Research on Electronic-nose Application Based on Wireless Sensor Networks

International Nuclear Information System (INIS)

Zhao, A; Wang, L; Yao, C H

2006-01-01

The paper proposed a structure of Wireless Sensor Networks based Electronic-nose system to monitors air quality in the building. In the study, the authors researched a data processing algorithm: fuzzy neural network based on RBF(Radial Basis Function) network model, to quantitatively analyze the gas ingredient and put forward a routing protocol for the system

On the chromospheric network structure around deVeloped groups of sunspots

International Nuclear Information System (INIS)

Kartashova, L.G.

1980-01-01

The chromospheric network structure around several developed groups of sunspots were studied on the basis of the observations in the Hsub(α) line. The resolution on the filtergrams was of 2. The following was found: 1) in the neighbourhood of the groups of sunspots 70% (from 870) of network cells stretch along fibrils direction (with accuracy 30 deg), and 15% of cells stretch approximately across that (at angles 70-90 deg); 2) out of the boundary of the main radial fibrils structure the groups of sunspots is often rounded by the system of network cells stretched approximately perpendicular to radial direction

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

Study of the Radial Peripapillary Capillary Network in Congenital Optic Disc Anomalies With Optical Coherence Tomography Angiography.

Science.gov (United States)

Cennamo, Gilda; Rossi, Claudia; Ruggiero, Pasquale; de Crecchio, Giuseppe; Cennamo, Giovanni

2017-04-01

To evaluate the radial peripapillary capillary network with optical coherence tomography angiography (angio-OCT) in morning glory syndrome (MGS), optic disc colobomas, and optic disc pits, and to explore possible correlations between the neural vascular structure and the pathogenesis of congenital optic disc anomalies. Prospective observational comparative case series. Fifteen eyes of 15 patients with congenital optic disc anomalies were enrolled in this study. All patients underwent angio-OCT. The scans were centered on optic discs. The mean age at presentation was 33 years (range: 19-50 years). Congenital optic disc anomalies were identified in all 15 eyes. Three eyes had the characteristic funduscopic signs of MGS, and angio-OCT scans of the peripapillary retina revealed a dense microvascular network. Optic disc colobomas were found in 5 eyes, and the characteristic funduscopic signs of optic pits were found in 7 eyes. Angio-OCT showed the absence of a radial peripapillary microvascular network in these 12 eyes. The finding that angio-OCT scans confirmed the presence of a peripapillary microvascular network only in MGS cases supports the hypothesis that a primary neuroectodermal abnormality and a secondary mesenchymal abnormality leads to MGS. Angio-OCT is a safe, rapid imaging technique that could shed light on the pathogenesis of rare diseases of the optic disc. Copyright © 2016 Elsevier Inc. All rights reserved.

Optimum topology for radial networks by using evolutionary computer programming; Topologia optima de redes radiais utilizando programacao evolucionaria

Energy Technology Data Exchange (ETDEWEB)

Pinto, Joao Luis [Instituto de Engenhariade Sistemas e Computadores (INESC), Porto (Portugal). E-mail: jpinto@duque.inescn.pt; Proenca, Luis Miguel [Instituto Superior de Linguas e Administracao (ISLA), Gaia (Portugal). E-mail: lproenca@inescn.pt

1999-07-01

This paper describes the using of Evolutionary Programming techniques for determination of the radial electric network topology, considering investment costs and losses. The work aims to demonstrate the particular easiness of coding and implementation and the parallelism implicit to the method as well, giving outstanding performance levels. As test example, a 43 bars and 75 alternative lines network has been used by describing an implementation of the algorithm in an Object Oriented platform.

Multi-modular neural networks for the classification of e+e- hadronic events

International Nuclear Information System (INIS)

Proriol, J.

1994-01-01

Some multi-modular neural network methods of classifying e + e - hadronic events are presented. We compare the performances of the following neural networks: MLP (multilayer perceptron), MLP and LVQ (learning vector quantization) trained sequentially, and MLP and RBF (radial basis function) trained sequentially. We introduce a MLP-RBF cooperative neural network. Our last study is a multi-MLP neural network. (orig.)

Decoupling Research on Flexible Tactile Sensors Interfered by White Gaussian Noise Using Improved Radical Basis Function Neural Network

Directory of Open Access Journals (Sweden)

Feilu Wang

2014-04-01

Full Text Available Research on tactile sensors to enhance their flexibility and ability of multi- dimensional information detection is a key issue to develop humanoid robots. In view of that the tactile sensor is often affected by noise, this paper adds different white Gaussian noises (WGN into the ideal model of flexible tactile sensors based on conductive rubber purposely, then improves the standard radial basis function neural network (RNFNN to deal with the noises. The modified RBFNN is applied to approximate and decouple the mapping relationship between row-column resistance with WGNs and three-dimensional deformation. Numerical experiments demonstrate that the decoupling result of the deformation for the sensor is quite good. The results show that the improved RBFNN which doesn’t rely on the mathematical model of the system has good anti-noise ability and robustness.

Application of Radial Basis Function Methods in the Development of a 95th Percentile Male Seated FEA Model.

Science.gov (United States)

Vavalle, Nicholas A; Schoell, Samantha L; Weaver, Ashley A; Stitzel, Joel D; Gayzik, F Scott

2014-11-01

Human body finite element models (FEMs) are a valuable tool in the study of injury biomechanics. However, the traditional model development process can be time-consuming. Scaling and morphing an existing FEM is an attractive alternative for generating morphologically distinct models for further study. The objective of this work is to use a radial basis function to morph the Global Human Body Models Consortium (GHBMC) average male model (M50) to the body habitus of a 95th percentile male (M95) and to perform validation tests on the resulting model. The GHBMC M50 model (v. 4.3) was created using anthropometric and imaging data from a living subject representing a 50th percentile male. A similar dataset was collected from a 95th percentile male (22,067 total images) and was used in the morphing process. Homologous landmarks on the reference (M50) and target (M95) geometries, with the existing FE node locations (M50 model), were inputs to the morphing algorithm. The radial basis function was applied to morph the FE model. The model represented a mass of 103.3 kg and contained 2.2 million elements with 1.3 million nodes. Simulations of the M95 in seven loading scenarios were presented ranging from a chest pendulum impact to a lateral sled test. The morphed model matched anthropometric data to within a rootmean square difference of 4.4% while maintaining element quality commensurate to the M50 model and matching other anatomical ranges and targets. The simulation validation data matched experimental data well in most cases.

Gaussian Radial Basis Function for Efficient Computation of Forest Indirect Illumination

Science.gov (United States)

Abbas, Fayçal; Babahenini, Mohamed Chaouki

2018-06-01

Global illumination of natural scenes in real time like forests is one of the most complex problems to solve, because the multiple inter-reflections between the light and material of the objects composing the scene. The major problem that arises is the problem of visibility computation. In fact, the computing of visibility is carried out for all the set of leaves visible from the center of a given leaf, given the enormous number of leaves present in a tree, this computation performed for each leaf of the tree which also reduces performance. We describe a new approach that approximates visibility queries, which precede in two steps. The first step is to generate point cloud representing the foliage. We assume that the point cloud is composed of two classes (visible, not-visible) non-linearly separable. The second step is to perform a point cloud classification by applying the Gaussian radial basis function, which measures the similarity in term of distance between each leaf and a landmark leaf. It allows approximating the visibility requests to extract the leaves that will be used to calculate the amount of indirect illumination exchanged between neighbor leaves. Our approach allows efficiently treat the light exchanges in the scene of a forest, it allows a fast computation and produces images of good visual quality, all this takes advantage of the immense power of computation of the GPU.

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.

A meshless local radial basis function method for two-dimensional incompressible Navier-Stokes equations

KAUST Repository

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.

Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs

International Nuclear Information System (INIS)

Bollig, Evan F.; Flyer, Natasha; Erlebacher, Gordon

2012-01-01

This paper presents parallelization strategies for the radial basis function-finite difference (RBF-FD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation and scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the first implementation of the RBF-FD method to leverage GPU accelerators for the solution of PDEs. Additionally, this implementation is the first to span both multiple CPUs and multiple GPUs. OpenCL kernels target the GPUs and inter-processor communication and synchronization is managed by the Message Passing Interface (MPI). We verify our implementation of the RBF-FD method with two hyperbolic PDEs on the sphere, and demonstrate up to 9x speedup on a commodity GPU with unoptimized kernel implementations. On a high performance cluster, the method achieves up to 7x speedup for the maximum problem size of 27,556 nodes.

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

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

Science.gov (United States)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

GRACE L1b inversion through a self-consistent modified radial basis function approach

Science.gov (United States)

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

Improving the Transmission System Reliability for the 154kV Radial Network Operation

Energy Technology Data Exchange (ETDEWEB)

Choo, Jin Boo; Yoo, Myeong Ho; Yoon, Young Beum; Kim, Tae Won [Korea Electric Power Research Institute, Taejon (Korea, Republic of); Lee, Gi Won; Chung, Beom Jin; Jung, Jung Won; Park, Kyu Hyun; Lee, Myung Hee; Kim, Hyun Jong; Kim, Chae Hee [KEYIN CO., (Korea, Republic of)

1996-12-31

Analyzing that 154kV radial network current ed operated, we developed Automatic Power Reconfiguration System(APRS) which restore blackout area. It consists of special data acquisition system(DAU) and central monitoring system for reason of protecting currently operating power system. In addition, it communicates using optical communication network for high speed data transmission and accuracy. An Accuracy of operation and stability of the developed system is tested using Real Time Digital Simulator in 3-bus equivalent model system. In An Outage case, our developed system can restore a blackout area in three second. An ability of the restoration of a blackout was verified by simulations. For the field test, the developed system was set up at SINCHON S/S, SESOMUN S/S, SINDANG S/S and MAJANG S/S governed by Seoul Power Transmission Center. If the system reliability is proved for a one year test in real power system, application of the system will be extended to the other substations.

The effect of time duration in the network and radial method toward the accuracy in measuring the deformation at Muria

International Nuclear Information System (INIS)

Ari Nugroho and Hadi Suntoko

2011-01-01

The Deformation monitoring activities in the vicinity of Mount Muria are recommended by the IAEA (International Atomic Energy Agency) to be done for 5 years. The purpose of these activities is to determine the rate of the deformation caused by the volcanic activity of Mount Muria, as a basic study in analyzing the volcanic hazard toward the NPP (Nuclear Power Plant). The whole coordinate points measured encompass the district of Jepara, Pati, Demak, and Kudus. In 2010 deformation measurements were periodically conducted for 4 times a year using two geodetic GPS units of Trimble R7 GNSS type through the network method. The measurements were carried out at seven points of interest and at one reference point in March, April, May and December. Each session of the measurements was performed for 2.5 hours. In 2011 the deformation measurements were periodically conducted for 4 times a year by means of the radial method. The measurements were made at eight points of interest and at one reference point in April and May, June and July. Each session of measurements was performed for 10 hours. Based on the results of the measurements by means of the network method in 2010, it was concluded that the range of horizontal and vertical accuracy is between 6-10 mm and 25-46 mm, while the results of the measurements of the radial method in 2011 is 4-7 mm, and 16-28 mm respectively. Furthermore, it provides the evidence that the radial method tends to has better result than the network method if it is applied 4 times longer than the network method, resulting the accuracy below 7 mm and 28 mm for horizontal and vertical respectively. (author)

Application of RBF neural network improved by peak density function in intelligent color matching of wood dyeing

International Nuclear Information System (INIS)

Guan, Xuemei; Zhu, Yuren; Song, Wenlong

2016-01-01

According to the characteristics of wood dyeing, we propose a predictive model of pigment formula for wood dyeing based on Radial Basis Function (RBF) neural network. In practical application, however, it is found that the number of neurons in the hidden layer of RBF neural network is difficult to determine. In general, we need to test several times according to experience and prior knowledge, which is lack of a strict design procedure on theoretical basis. And we also don’t know whether the RBF neural network is convergent. This paper proposes a peak density function to determine the number of neurons in the hidden layer. In contrast to existing approaches, the centers and the widths of the radial basis function are initialized by extracting the features of samples. So the uncertainty caused by random number when initializing the training parameters and the topology of RBF neural network is eliminated. The average relative error of the original RBF neural network is 1.55% in 158 epochs. However, the average relative error of the RBF neural network which is improved by peak density function is only 0.62% in 50 epochs. Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.

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 ...

The orthogonal gradients method: A radial basis functions method for solving partial differential equations on arbitrary surfaces

KAUST Repository

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.

Application of radial basis functions and sinc method for solving the forced vibration of fractional viscoelastic beam

Energy Technology Data Exchange (ETDEWEB)

Permoon, M. R.; Haddadpour, H. [Sharif University of Tech, Tehran (Iran, Islamic Republic of); Rashidinia, J.; Parsa, A.; Salehi, R. [Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)

2016-07-15

In this paper, the forced vibrations of the fractional viscoelastic beam with the Kelvin-Voigt fractional order constitutive relationship is studied. The equation of motion is derived from Newton's second law and the Galerkin method is used to discretize the equation of motion in to a set of linear ordinary differential equations. For solving the discretized equations, the radial basis functions and Sinc quadrature rule are used. In order to show the effectiveness and accuracy of this method, some test problem are considered, and it is shown that the obtained results are in very good agreement with exact solution. In the following, the proposed numerical solution is applied to exploring the effects of fractional parameters on the response of the beam and finally some conclusions are outlined.

Neural network post-processing of grayscale optical correlator

Science.gov (United States)

Lu, Thomas T; Hughlett, Casey L.; Zhoua, Hanying; Chao, Tien-Hsin; Hanan, Jay C.

2005-01-01

In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

New deconvolution method for microscopic images based on the continuous Gaussian radial basis function interpolation model.

Science.gov (United States)

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.

Adaptive neural networks control for camera stabilization with active suspension system

Directory of Open Access Journals (Sweden)

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.

QSAR modelling using combined simple competitive learning networks and RBF neural networks.

Science.gov (United States)

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.

A hybrid radial basis function-pseudospectral method for thermal convection in a 3-D spherical shell

KAUST Repository

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 × 10³ and 10⁵. Results from a Ra = 10⁶ simulation are also given. The algorithmic simplicity of the code (mostly due to RBFs) allows it to be written in less than 400 lines of MATLAB and run on a single workstation. We find that our method is very competitive with those currently used in the literature. Copyright 2010 by the American Geophysical Union.

Material basis of Chinese herbal formulas explored by combining pharmacokinetics with network pharmacology.

Directory of Open Access Journals (Sweden)

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.

Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

Science.gov (United States)

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...

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.

General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results

Czech Academy of Sciences Publication Activity Database

Šíma, Jiří; Orponen, P.

2003-01-01

Roč. 15, č. 12 (2003), s. 2727-2778 ISSN 0899-7667 R&D Projects: GA AV ČR IAB2030007; GA ČR GA201/02/1456 Institutional research plan: AV0Z1030915 Keywords : computational power * computational complexity * perceptrons * radial basis functions * spiking neurons * feedforward networks * reccurent networks * probabilistic computation * analog computation Subject RIV: BA - General Mathematics Impact factor: 2.747, year: 2003

Modelling and Forecasting Cruise Tourism Demand to İzmir by Different Artificial Neural Network Architectures

Directory of Open Access Journals (Sweden)

Murat Cuhadar

2014-03-01

Full Text Available Abstract Cruise ports emerged as an important sector for the economy of Turkey bordered on three sides by water. Forecasting cruise tourism demand ensures better planning, efficient preparation at the destination and it is the basis for elaboration of future plans. In the recent years, new techniques such as; artificial neural networks were employed for developing of the predictive models to estimate tourism demand. In this study, it is aimed to determine the forecasting method that provides the best performance when compared the forecast accuracy of Multi-layer Perceptron (MLP, Radial Basis Function (RBF and Generalized Regression neural network (GRNN to estimate the monthly inbound cruise tourism demand to İzmir via the method giving best results. We used the total number of foreign cruise tourist arrivals as a measure of inbound cruise tourism demand and monthly cruise tourist arrivals to İzmir Cruise Port in the period of January 2005 ‐December 2013 were utilized to appropriate model. Experimental results showed that radial basis function (RBF neural network outperforms multi-layer perceptron (MLP and the generalised regression neural networks (GRNN in terms of forecasting accuracy. By the means of the obtained RBF neural network model, it has been forecasted the monthly inbound cruise tourism demand to İzmir for the year 2014.

Radial Peripapillary Capillary Network Visualized Using Wide-Field Montage Optical Coherence Tomography Angiography.

Science.gov (United States)

Mase, Tomoko; Ishibazawa, Akihiro; Nagaoka, Taiji; Yokota, Harumasa; Yoshida, Akitoshi

2016-07-01

We quantitatively analyzed the features of a radial peripapillary capillary (RPC) network visualized using wide-field montage optical coherence tomography (OCT) angiography in healthy human eyes. Twenty eyes of 20 healthy subjects were recruited. En face 3 × 3-mm OCT angiograms of multiple locations in the posterior pole were acquired using the RTVue XR Avanti, and wide-field montage images of the RPC were created. To evaluate the RPC density, the montage images were binarized and skeletonized. The correlation between the RPC density and the retinal nerve fiber layer (RNFL) thickness measured by an OCT circle scan was investigated. The RPC at the temporal retina was detected as far as 7.6 ± 0.7 mm from the edge of the optic disc but not around the perifoveal area within 0.9 ± 0.1 mm of the fovea. Capillary-free zones beside the first branches of the arterioles were significantly (P optic disc edge were 13.6 ± 0.8, 11.9 ± 0.9, and 10.4 ± 0.9 mm-1. The RPC density also was correlated significantly (r = 0.64, P network. The RPC is present in the superficial peripapillary retina in proportion to the RNFL thickness, supporting the idea that the RPC may be the vascular network primarily responsible for RNFL nourishment.

Classification of endoscopic capsule images by using color wavelet features, higher order statistics and radial basis functions.

Science.gov (United States)

Lima, C S; Barbosa, D; Ramos, J; Tavares, A; Monteiro, L; Carvalho, L

2008-01-01

This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.

Neural networks and statistical learning

CERN Document Server

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...

Musculocutaneous nerve substituting for the distal part of radial nerve: A case report and its embryological basis

Directory of Open Access Journals (Sweden)

A S Yogesh

2011-01-01

Full Text Available In the present case, we have reported a unilateral variation of the radial and musculocutaneous nerves on the left side in a 64-year-old male cadaver. The radial nerve supplied all the heads of the triceps brachii muscle and gave cutaneous branches such as lower lateral cutaneous nerve of the arm and posterior cutaneous nerve of forearm. The radial nerve ended without continuing further. The musculocutaneous nerve supplied the brachioradialis, extensor carpi radialis longus and extensor carpi radialis brevis muscles. The musculocutaneous nerve divided terminally into two branches, superficial and deep. The deep branch of musculocutaneous nerve corresponded to usual deep branch of the radial nerve while the superficial branch of musculocutaneous nerve corresponded to usual superficial branch of the radial nerve. The dissection was continued to expose the entire brachial plexus from its origin and it was found to be normal. The structures on the right upper limb were found to be normal. Surgeons should keep such variations in mind while performing the surgeries of the upper limb.

An Improved Collocation Meshless Method Based on the Variable Shaped Radial Basis Function for the Solution of the Interior Acoustic Problems

Directory of Open Access Journals (Sweden)

Shuang Wang

2012-01-01

Full Text Available As an efficient tool, radial basis function (RBF has been widely used for the multivariate approximation, interpolating continuous, and the solution of the particle differential equations. However, ill-conditioned interpolation matrix may be encountered when the interpolation points are very dense or irregularly arranged. To avert this problem, RBFs with variable shape parameters are introduced, and several new variation strategies are proposed. Comparison with the RBF with constant shape parameters are made, and the results show that the condition number of the interpolation matrix grows much slower with our strategies. As an application, an improved collocation meshless method is formulated by employing the new RBF. In addition, the Hermite-type interpolation is implemented to handle the Neumann boundary conditions and an additional sine/cosine basis is introduced for the Helmlholtz equation. Then, two interior acoustic problems are solved with the presented method; the results demonstrate the robustness and effectiveness of the method.

Introducing radiality constraints in capacitated location-routing problems

Directory of Open Access Journals (Sweden)

Eliana Mirledy Toro Ocampo

2017-03-01

Full Text Available In this paper, we introduce a unified mathematical formulation for the Capacitated Vehicle Routing Problem (CVRP and for the Capacitated Location Routing Problem (CLRP, adopting radiality constraints in order to guarantee valid routes and eliminate subtours. This idea is inspired by formulations already employed in electric power distribution networks, which requires a radial topology in its operation. The results show that the proposed formulation greatly improves the convergence of the solver.

Seismic modeling with radial basis function-generated finite differences (RBF-FD) – a simplified treatment of interfaces

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.

Assessing the performance of Taiwan’s environmental protection system with a non-radial network DEA approach

International Nuclear Information System (INIS)

Huang, Chin-wei; Chiu, Yung-ho; Fang, Wei-ta; Shen, Neng

2014-01-01

Previous studies involving environmental performance purely focus on analyzing environmental or waste treatment efficiencies. This study combines various aspects of environmental protection and establishes a non-radial network DEA approach to evaluate the performance of an environmental protection system. The empirical methodology assumes that the system consists of three stages: administrative, executive processes, and protection effectiveness. The executive process includes waste treatment and pollution auditing divisions. In addition to assessing efficiencies, the empirical evaluation further explores the internal and external influences on performances and contributes implications and suggestions for environmental policy makers. The proposed model is utilized to gauge the performance of an environmental protection system for 20 municipalities in Taiwan. The results indicate the following: (1) for improving environmental quality, the government needs to put forth greater efforts in environmental audits; (2) improving productivity per unit so as to increase achievements in the executive process is more important for raising performance on environmental work, in addition to increasing workers and equipment for environmental protection; (3) a high degree of industrialization negatively influences administrative efficiency and educated residents have a greater comprehension for administrative operations, which can help in efficient budget utilization. - Highlights: • A non-radial network DEA approach to evaluate the Performance of Environmental Protection System. • The performance of waste treatment performs better than the efficiency of auditing. • Governmental expenditure is more efficiently utilized in the administrative process. • The effectiveness of achieved protection is a critical indicator for revealing environmental quality in a region

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.

Computer model analysis of the radial artery pressure waveform.

Science.gov (United States)

Schwid, H A; Taylor, L A; Smith, N T

1987-10-01

Simultaneous measurements of aortic and radial artery pressures are reviewed, and a model of the cardiovascular system is presented. The model is based on resonant networks for the aorta and axillo-brachial-radial arterial system. The model chosen is a simple one, in order to make interpretation of the observed relationships clear. Despite its simplicity, the model produces realistic aortic and radial artery pressure waveforms. It demonstrates that the resonant properties of the arterial wall significantly alter the pressure waveform as it is propagated from the aorta to the radial artery. Although the mean and end-diastolic radial pressures are usually accurate estimates of the corresponding aortic pressures, the systolic pressure at the radial artery is often much higher than that of the aorta due to overshoot caused by the resonant behavior of the radial artery. The radial artery dicrotic notch is predominantly dependent on the axillo-brachial-radial arterial wall properties, rather than on the aortic valve or peripheral resistance. Hence the use of the radial artery dicrotic notch as an estimate of end systole is unreliable. The rate of systolic upstroke, dP/dt, of the radial artery waveform is a function of many factors, making it difficult to interpret. The radial artery waveform usually provides accurate estimates for mean and diastolic aortic pressures; for all other measurements it is an inadequate substitute for the aortic pressure waveform. In the presence of low forearm peripheral resistance the mean radial artery pressure may significantly underestimate the mean aortic pressure, as explained by a voltage divider model.

Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

CERN Document Server

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...

Bounds on Rates of Variable-Basis and Neural-Network Approximation

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra; Sanguineti, M.

2001-01-01

Roč. 47, č. 6 (2001), s. 2659-2665 ISSN 0018-9448 R&D Projects: GA ČR GA201/00/1482 Institutional research plan: AV0Z1030915 Keywords : approximation by variable-basis functions * bounds on rates of approximation * complexity of neural networks * high-dimensional optimal decision problems Subject RIV: BA - General Mathematics Impact factor: 2.077, year: 2001

A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

OpenAIRE

Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

2016-01-01

A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

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.

Radial Basis Functional Model of Multi-Point Dieless Forming Process for Springback Reduction and Compensation

Directory of Open Access Journals (Sweden)

Misganaw Abebe

2017-11-01

Full Text Available Springback in multi-point dieless forming (MDF is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE. Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.

Chaos control and generalized projective synchronization of heavy symmetric chaotic gyroscope systems via Gaussian radial basis adaptive variable structure control

International Nuclear Information System (INIS)

Farivar, Faezeh; Aliyari Shoorehdeli, Mahdi; Nekoui, Mohammad Ali; Teshnehlab, Mohammad

2012-01-01

Highlights: ► A systematic procedure for GPS of unknown heavy chaotic gyroscope systems. ► Proposed methods are based on Lyapunov stability theory. ► Without calculating Lyapunov exponents and Eigen values of the Jacobian matrix. ► Capable to extend for a variety of chaotic systems. ► Useful for practical applications in the future. - Abstract: This paper proposes the chaos control and the generalized projective synchronization methods for heavy symmetric gyroscope systems via Gaussian radial basis adaptive variable structure control. Because of the nonlinear terms of the gyroscope system, the system exhibits chaotic motions. Occasionally, the extreme sensitivity to initial states in a system operating in chaotic mode can be very destructive to the system because of unpredictable behavior. In order to improve the performance of a dynamic system or avoid the chaotic phenomena, it is necessary to control a chaotic system with a periodic motion beneficial for working with a particular condition. As chaotic signals are usually broadband and noise like, synchronized chaotic systems can be used as cipher generators for secure communication. This paper presents chaos synchronization of two identical chaotic motions of symmetric gyroscopes. In this paper, the switching surfaces are adopted to ensure the stability of the error dynamics in variable structure control. Using the neural variable structure control technique, control laws are established which guarantees the chaos control and the generalized projective synchronization of unknown gyroscope systems. In the neural variable structure control, Gaussian radial basis functions are utilized to on-line estimate the system dynamic functions. Also, the adaptation laws of the on-line estimator are derived in the sense of Lyapunov function. Thus, the unknown gyro systems can be guaranteed to be asymptotically stable. Also, the proposed method can achieve the control objectives. Numerical simulations are presented to

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

Science.gov (United States)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

Separation prediction in two dimensional boundary layer flows using artificial neural networks

International Nuclear Information System (INIS)

Sabetghadam, F.; Ghomi, H.A.

2003-01-01

In this article, the ability of artificial neural networks in prediction of separation in steady two dimensional boundary layer flows is studied. Data for network training is extracted from numerical solution of an ODE obtained from Von Karman integral equation with approximate one parameter Pohlhousen velocity profile. As an appropriate neural network, a two layer radial basis generalized regression artificial neural network is used. The results shows good agreements between the overall behavior of the flow fields predicted by the artificial neural network and the actual flow fields for some cases. The method easily can be extended to unsteady separation and turbulent as well as compressible boundary layer flows. (author)

A novel structure of permanent-magnet-biased radial hybrid magnetic bearing

International Nuclear Information System (INIS)

Sun Jinji; Fang Jiancheng

2011-01-01

The paper proposes a novel structure for a permanent-magnet-biased radial hybrid magnetic bearing. Based on the air gap between the rotor and stator of traditional radial hybrid magnetic bearings, a subsidiary air gap is first constructed between the permanent magnets and the inner magnetic parts. Radial magnetic bearing makes X and Y magnetic fields independent of each other with separate stator poles, and the subsidiary air gap makes control flux to a close loop. As a result, magnetic field coupling of the X and Y channels is decreased significantly by the radial hybrid magnetic bearing and makes it easier to design control systems. Then an external rotor structure is designed into the radial hybrid magnetic bearing. The working principle of the radial hybrid magnetic bearing and its mathematical model is discussed. Finally, a non-linear magnetic network method is proposed to analyze the radial hybrid magnetic bearing. Simulation results indicate that magnetic fields in the two channels of the proposed radial hybrid magnetic bearing decouple well from each other.

A novel structure of permanent-magnet-biased radial hybrid magnetic bearing

Energy Technology Data Exchange (ETDEWEB)

Sun Jinji, E-mail: sunjinji@aspe.buaa.edu.c [Key Laboratory of Fundamental Science for National Defense, Novel Inertial Instrument and Navigation System Technology, School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, 100191 (China); Fang Jiancheng [Key Laboratory of Fundamental Science for National Defense, Novel Inertial Instrument and Navigation System Technology, School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, 100191 (China)

2011-01-15

The paper proposes a novel structure for a permanent-magnet-biased radial hybrid magnetic bearing. Based on the air gap between the rotor and stator of traditional radial hybrid magnetic bearings, a subsidiary air gap is first constructed between the permanent magnets and the inner magnetic parts. Radial magnetic bearing makes X and Y magnetic fields independent of each other with separate stator poles, and the subsidiary air gap makes control flux to a close loop. As a result, magnetic field coupling of the X and Y channels is decreased significantly by the radial hybrid magnetic bearing and makes it easier to design control systems. Then an external rotor structure is designed into the radial hybrid magnetic bearing. The working principle of the radial hybrid magnetic bearing and its mathematical model is discussed. Finally, a non-linear magnetic network method is proposed to analyze the radial hybrid magnetic bearing. Simulation results indicate that magnetic fields in the two channels of the proposed radial hybrid magnetic bearing decouple well from each other.

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

International Nuclear Information System (INIS)

Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine; Minca, Eugenia; Filip, Florin

2009-01-01

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach.

Science.gov (United States)

Zaqoot, Hossam Adel; Ansari, Abdul Khalique; Unar, Mukhtiar Ali; Khan, Shaukat Hyat

2009-01-01

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

relay coordination in the protection of radially-connected power

African Journals Online (AJOL)

... PROTECTION OF. RADIALLY-CONNECTED POWER SYSTEM NETWORK ... Protective relays detect intolerable or unwanted conditions within an assigned area, and then trip or open one ... time, and current transformer ratio errors. 2.2.1.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

Science.gov (United States)

Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

2015-06-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

International Nuclear Information System (INIS)

Hosseini-Golgoo, S M; Bozorgi, H; Saberkari, A

2015-01-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively. (paper)

Main concept of local area network protection on the basis of the SAAM 'TRAFFIC'

International Nuclear Information System (INIS)

Vasil'ev, P.M.; Kryukov, Yu.A.; Kuptsov, S.I.; Ivanov, V.V.; Koren'kov, V.V.

2002-01-01

In our previous paper we developed a system for acquisition, analysis and management of the network traffic (SAAM 'Traffic') for a segment of the JINR local area computer network (JINR LAN). In our present work we consider well-known scenarios of attacks on local area networks and propose protection methods based on the SAAM 'Traffic'. Although the system for LAN protection is installed on a router computer, it is not analogous to the firewall scheme and, thus, it does not hinder the performance of distributed network applications. This provides a possibility to apply such an approach to GRID-technologies, where network protection on the firewall basis can not be basically used. (author)

Radial basis function interpolation of unstructured, three-dimensional, volumetric particle tracking velocimetry data

International Nuclear Information System (INIS)

Casa, L D C; Krueger, P S

2013-01-01

Unstructured three-dimensional fluid velocity data were interpolated using Gaussian radial basis function (RBF) interpolation. Data were generated to imitate the spatial resolution and experimental uncertainty of a typical implementation of defocusing digital particle image velocimetry. The velocity field associated with a steadily rotating infinite plate was simulated to provide a bounded, fully three-dimensional analytical solution of the Navier–Stokes equations, allowing for robust analysis of the interpolation accuracy. The spatial resolution of the data (i.e. particle density) and the number of RBFs were varied in order to assess the requirements for accurate interpolation. Interpolation constraints, including boundary conditions and continuity, were included in the error metric used for the least-squares minimization that determines the interpolation parameters to explore methods for improving RBF interpolation results. Even spacing and logarithmic spacing of RBF locations were also investigated. Interpolation accuracy was assessed using the velocity field, divergence of the velocity field, and viscous torque on the rotating boundary. The results suggest that for the present implementation, RBF spacing of 0.28 times the boundary layer thickness is sufficient for accurate interpolation, though theoretical error analysis suggests that improved RBF positioning may yield more accurate results. All RBF interpolation results were compared to standard Gaussian weighting and Taylor expansion interpolation methods. Results showed that RBF interpolation improves interpolation results compared to the Taylor expansion method by 60% to 90% based on the average squared velocity error and provides comparable velocity results to Gaussian weighted interpolation in terms of velocity error. RMS accuracy of the flow field divergence was one to two orders of magnitude better for the RBF interpolation compared to the other two methods. RBF interpolation that was applied to

Radial restricted solid-on-solid and etching interface-growth models

Science.gov (United States)

Alves, Sidiney G.

2018-03-01

An approach to generate radial interfaces is presented. A radial network recursively obtained is used to implement discrete model rules designed originally for the investigation in flat substrates. I used the restricted solid-on-solid and etching models as to test the proposed scheme. The results indicate the Kardar, Parisi, and Zhang conjecture is completely verified leading to a good agreement between the interface radius fluctuation distribution and the Gaussian unitary ensemble. The evolution of the radius agrees well with the generalized conjecture, and the two-point correlation function exhibits also a good agreement with the covariance of the Airy2 process. The approach can be used to investigate radial interfaces evolution for many other classes of universality.

An efficient approach for electric load forecasting using distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network) neural network

International Nuclear Information System (INIS)

Cai, Yuan; Wang, Jian-zhou; Tang, Yun; Yang, Yu-chen

2011-01-01

This paper presents a neural network based on adaptive resonance theory, named distributed ART (adaptive resonance theory) and HS-ARTMAP (Hyper-spherical ARTMAP network), applied to the electric load forecasting problem. The distributed ART combines the stable fast learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multi-layer perceptions. The HS-ARTMAP, a hybrid of an RBF (Radial Basis Function)-network-like module which uses hyper-sphere basis function substitute the Gaussian basis function and an ART-like module, performs incremental learning capabilities in function approximation problem. The HS-ARTMAP only receives the compressed distributed coding processed by distributed ART to deal with the proliferation problem which ARTMAP (adaptive resonance theory map) architecture often encounters and still performs well in electric load forecasting. To demonstrate the performance of the methodology, data from New South Wales and Victoria in Australia are illustrated. Results show that the developed method is much better than the traditional BP and single HS-ARTMAP neural network. -- Research highlights: → The processing of the presented network is based on compressed distributed data. It's an innovation among the adaptive resonance theory architecture. → The presented network decreases the proliferation the Fuzzy ARTMAP architectures usually encounter. → The network on-line forecasts electrical load accurately, stably. → Both one-period and multi-period load forecasting are executed using data of different cities.

Spectral problem for the radial Schroedinger equation

International Nuclear Information System (INIS)

Vshivtsev, A.S.; Tatarintsev, A.V.; Prokopov, A.V.; Sorokin, V. N.

1998-01-01

For the first time, a procedure for determining spectra on the basis of generalized integral transformations is implemented for a wide class of radial Schroedinger equations. It is shown that this procedure works well for known types of potentials. Concurrently, this method makes it possible to obtain new analytic results for the Cornell potential. This may prove important for hadron physics

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks.

Science.gov (United States)

Chande, Ruchi D; Hargraves, Rosalyn Hobson; Ortiz-Robinson, Norma; Wayne, Jennifer S

2017-01-01

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.

Neural network-based model reference adaptive control system.

Science.gov (United States)

Patino, H D; Liu, D

2000-01-01

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

Directory of Open Access Journals (Sweden)

Neng-Sheng Pai

2013-01-01

Full Text Available Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF neural network and back propagation (BP neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields.

EEG signal classification using PSO trained RBF neural network for epilepsy identification

Directory of Open Access Journals (Sweden)

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

Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

Science.gov (United States)

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.

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

Science.gov (United States)

Cho, S

2001-02-01

Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.

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.

Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks.

Science.gov (United States)

Przednowek, Krzysztof; Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam

2017-12-01

This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Przednowek Krzysztof

2017-12-01

Full Text Available This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

Centralized Networks to Generate Human Body Motions.

Science.gov (United States)

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.

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 neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

Comparison of Deterministic and Probabilistic Radial Distribution Systems Load Flow

Science.gov (United States)

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.

Radial nerve dysfunction

Science.gov (United States)

Neuropathy - radial nerve; Radial nerve palsy; Mononeuropathy ... Damage to one nerve group, such as the radial nerve, is called mononeuropathy . Mononeuropathy means there is damage to a single nerve. Both ...

Neural feedback linearization adaptive control for affine nonlinear systems based on neural network estimator

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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.

A Regularizer Approach for RBF Networks Under the Concurrent Weight Failure Situation.

Science.gov (United States)

Leung, Chi-Sing; Wan, Wai Yan; Feng, Ruibin

2017-06-01

Many existing results on fault-tolerant algorithms focus on the single fault source situation, where a trained network is affected by one kind of weight failure. In fact, a trained network may be affected by multiple kinds of weight failure. This paper first studies how the open weight fault and the multiplicative weight noise degrade the performance of radial basis function (RBF) networks. Afterward, we define the objective function for training fault-tolerant RBF networks. Based on the objective function, we then develop two learning algorithms, one batch mode and one online mode. Besides, the convergent conditions of our online algorithm are investigated. Finally, we develop a formula to estimate the test set error of faulty networks trained from our approach. This formula helps us to optimize some tuning parameters, such as RBF width.

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.

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.)

Stability of radial and non-radial pulsation modes of massive ZAMS models

International Nuclear Information System (INIS)

Odell, A.P.; Pausenwein, A.; Weiss, W.W.; Hajek, A.

1987-01-01

The authors have computed non-adiabatic eigenvalues for radial and non-radial pulsation modes of star models between 80 and 120 M solar with composition of chi=0.70 and Z=0.02. The radial fundamental mode is unstable in models with mass greater than 95 M solar , but the first overtone mode is always stable. The non-radial modes are all stable for all models, but the iota=2 f-mode is the closest to being driven. The non-radial modes are progressively more stable with higher iota and with higher n (for both rho- and g-modes). Thus, their results indicate that radial pulsation limits the upper mass of a star

Developing equivalent circuits for radial distribution networks

Energy Technology Data Exchange (ETDEWEB)

Prada, Ricardo; Coelho, Agnelo; Rodrigues, Anselmo [Catholic University of Rio de Janeiro (PUC-Rio), RJ (Brazil). Dept. of Electrical Engineering], Emails: prada@ele.puc-rio.br, agnelo@ele.puc-rio.br, nebulok_99@yahoo.com; Silva, Maria da Guia da [Federal University of Maranhao, Sao Luiz, MA (Brazil). Dept. of Electrical Engineering

2009-07-01

This paper presents a method for evaluating External Equivalent in Electric Distribution Networks (EDN).The proposed method has as its main objectives the reduction of the computational costs in distribution network reconfiguration, investigation of the optimal allocation of banks of capacitors, investigation of the allocation of distributed generation, etc. In these sorts of problems a large number of alternative projects must be assessed in order to identify the optimal solution. The optimal solution comes up with the voltage level in the load points within specified limits. Consequently, the EDN must retain the external network load points but without major increasing in the dimension of the equivalent circuit. The proposed method has been tested and validated in a substation of the Electricity Utility of Maranhao - CEMAR, in Brazil. (author)

Numerical analysis for multi-group neutron-diffusion equation using Radial Point Interpolation Method (RPIM)

International Nuclear Information System (INIS)

Kim, Kyung-O; Jeong, Hae Sun; Jo, Daeseong

2017-01-01

Highlights: • Employing the Radial Point Interpolation Method (RPIM) in numerical analysis of multi-group neutron-diffusion equation. • Establishing mathematical formation of modified multi-group neutron-diffusion equation by RPIM. • Performing the numerical analysis for 2D critical problem. - Abstract: A mesh-free method is introduced to overcome the drawbacks (e.g., mesh generation and connectivity definition between the meshes) of mesh-based (nodal) methods such as the finite-element method and finite-difference method. In particular, the Point Interpolation Method (PIM) using a radial basis function is employed in the numerical analysis for the multi-group neutron-diffusion equation. The benchmark calculations are performed for the 2D homogeneous and heterogeneous problems, and the Multiquadrics (MQ) and Gaussian (EXP) functions are employed to analyze the effect of the radial basis function on the numerical solution. Additionally, the effect of the dimensionless shape parameter in those functions on the calculation accuracy is evaluated. According to the results, the radial PIM (RPIM) can provide a highly accurate solution for the multiplication eigenvalue and the neutron flux distribution, and the numerical solution with the MQ radial basis function exhibits the stable accuracy with respect to the reference solutions compared with the other solution. The dimensionless shape parameter directly affects the calculation accuracy and computing time. Values between 1.87 and 3.0 for the benchmark problems considered in this study lead to the most accurate solution. The difference between the analytical and numerical results for the neutron flux is significantly increased in the edge of the problem geometry, even though the maximum difference is lower than 4%. This phenomenon seems to arise from the derivative boundary condition at (x,0) and (0,y) positions, and it may be necessary to introduce additional strategy (e.g., the method using fictitious points and

Revisiting Earth's radial seismic structure using a Bayesian neural network approach

NARCIS (Netherlands)

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

Numerical study of the shape parameter dependence of the local radial point interpolation method in linear elasticity.

Science.gov (United States)

Moussaoui, Ahmed; Bouziane, Touria

2016-01-01

The method LRPIM is a Meshless method with properties of simple implementation of the essential boundary conditions and less costly than the moving least squares (MLS) methods. This method is proposed to overcome the singularity associated to polynomial basis by using radial basis functions. In this paper, we will present a study of a 2D problem of an elastic homogenous rectangular plate by using the method LRPIM. Our numerical investigations will concern the influence of different shape parameters on the domain of convergence,accuracy and using the radial basis function of the thin plate spline. It also will presents a comparison between numerical results for different materials and the convergence domain by precising maximum and minimum values as a function of distribution nodes number. The analytical solution of the deflection confirms the numerical results. The essential points in the method are: •The LRPIM is derived from the local weak form of the equilibrium equations for solving a thin elastic plate.•The convergence of the LRPIM method depends on number of parameters derived from local weak form and sub-domains.•The effect of distributions nodes number by varying nature of material and the radial basis function (TPS).

Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG

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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.

Radial-Velocity Signatures of Magnetic Features on the Sun Observed as a Star

Science.gov (United States)

Palumbo, M. L., III; Haywood, R. D.; Saar, S. H.; Dupree, A. K.; Milbourne, T. W.

2017-12-01

In recent years, the search for Earth-mass planets using radial-velocity measurements has become increasingly limited by signals arising from stellar activity. Individual magnetic features induce localized changes in intensity and velocity, which combine to change the apparent radial velocity of the star. Therefore it is critical to identify an indicator of activity-driven radial-velocity variations on the timescale of stellar rotation periods. We use 617.3 nm photospheric filtergrams, magnetograms, and dopplergrams from SDO/HMI and 170.0 nm chromospheric filtergrams from AIA to identify magnetically-driven solar features and reconstruct the integrated solar radial velocity with six samples per day over the course of 2014. Breaking the solar image up into regions of umbrae, penumbrae, quiet Sun, network, and plages, we find a distinct variation in the center-to-limb intensity-weighted velocity for each region. In agreement with past studies, we find that the suppression of convective blueshift is dominated by plages and network, rather than dark photospheric features. In the future, this work will be highly useful for identifying indicators which correlate with rotationally modulated radial-velocity variations. This will allow us to break the activity barrier that currently precludes the precise characterization of exoplanet properties at the lowest masses. This work was supported by the NSF-REU solar physics program at SAO, grant number AGS-1560313. This work was performed in part under contract with the California Institute of Technology (Caltech)/Jet Propulsion Laboratory (JPL) funded by NASA through the Sagan Fellowship Program executed by the NASA Exoplanet Science Institute.

The numerical study and comparison of radial basis functions in applications of the dual reciprocity boundary element method to convection-diffusion problems

Science.gov (United States)

Chanthawara, Krittidej; Kaennakham, Sayan; Toutip, Wattana

2016-02-01

The methodology of Dual Reciprocity Boundary Element Method (DRBEM) is applied to the convection-diffusion problems and investigating its performance is our first objective of the work. Seven types of Radial Basis Functions (RBF); Linear, Thin-plate Spline, Cubic, Compactly Supported, Inverse Multiquadric, Quadratic, and that proposed by [12], were closely investigated in order to numerically compare their effectiveness drawbacks etc. and this is taken as our second objective. A sufficient number of simulations were performed covering as many aspects as possible. Varidated against both exacts and other numerical works, the final results imply strongly that the Thin-Plate Spline and Linear type of RBF are superior to others in terms of both solutions' quality and CPU-time spent while the Inverse Multiquadric seems to poorly yield the results. It is also found that DRBEM can perform relatively well at moderate level of convective force and as anticipated becomes unstable when the problem becomes more convective-dominated, as normally found in all classical mesh-dependence methods.

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

Science.gov (United States)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

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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.

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

Science.gov (United States)

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

Optimal placement of capacitors in a radial network using conic and mixed integer linear programming

Energy Technology Data Exchange (ETDEWEB)

Jabr, R.A. [Electrical, Computer and Communication Engineering Department, Notre Dame University, P.O. Box: 72, Zouk Mikhael, Zouk Mosbeh (Lebanon)

2008-06-15

This paper considers the problem of optimally placing fixed and switched type capacitors in a radial distribution network. The aim of this problem is to minimize the costs associated with capacitor banks, peak power, and energy losses whilst satisfying a pre-specified set of physical and technical constraints. The proposed solution is obtained using a two-phase approach. In phase-I, the problem is formulated as a conic program in which all nodes are candidates for placement of capacitor banks whose sizes are considered as continuous variables. A global solution of the phase-I problem is obtained using an interior-point based conic programming solver. Phase-II seeks a practical optimal solution by considering capacitor sizes as discrete variables. The problem in this phase is formulated as a mixed integer linear program based on minimizing the L1-norm of deviations from the phase-I state variable values. The solution to the phase-II problem is obtained using a mixed integer linear programming solver. The proposed method is validated via extensive comparisons with previously published results. (author)

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.

Advanced approach to numerical forecasting using artificial neural networks

Directory of Open Access Journals (Sweden)

Michael Štencl

2009-01-01

Full Text Available Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. Detected differences then verify on practical comparative example.

Evolving RBF neural networks for adaptive soft-sensor design.

Science.gov (United States)

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.

Robust stabilization of burn conditions in subignited fusion reactors using artificial neural networks

International Nuclear Information System (INIS)

Vitela, E. Javier; Martinell, J. Julio

2000-01-01

In this work it is shown that robust burn control in long pulse operations of thermonuclear reactors can be successfully achieved with artificial neural networks. The results reported here correspond to a volume averaged zero-dimensional nonlinear model of a subignited fusion device using the design parameters of the tokamak EDA-ITER group. A Radial Basis Neural Network (RBNN) was trained to provide feedback stabilization at a fixed operating point independently of any particular scaling law that the reactor confinement time may follow. A numerically simulated transient is used to illustrate the stabilization capabilities of the resulting RBNN when the reactor follows an ELMy scaling law corrupted with Gaussian noise. (author)

Accelerated optimizations of an electromagnetic acoustic transducer with artificial neural networks as metamodels

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

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.

Structural health monitoring and damage assessment using measured FRFs from multiple sensors. Part II. Decision making with RBF networks

Energy Technology Data Exchange (ETDEWEB)

Zang, C.; Friswell, M.I. [Dept. of Aerospace Engineering, Univ. of Bristol, Bristol (United Kingdom); Imregun, M. [Dept. of Mechanical Engineering, Imperial Coll., London (United Kingdom)

2003-07-01

This paper is the second of two papers concerned with structural health monitoring and damage assessment using measured FRFs from multiple sensors, and discusses the decision making technique with radial basis function (RBF) neural networks. In PART 1 of the paper, the correlation criteria showed their capability to indicate various changes to the structure's state. PART 2, presented here, develops the methodology of decision theory to identify precisely all of the structure states. Although, the statistical approach can be used for classification, interpreting the information is difficult. Neural network techniques have been proven to possess many advantages for classification due to their learning ability and good generalization. In this paper, the radial basis function neural network is applied for function approximation and recognition. The key idea is to partition the input space (the indicators of the correlation criteria) into a number of subspaces that are in the form of hyper spheres. Then, the widely used k-mean clustering algorithm was selected as a logical approach to detecting the structure states. A bookshelf structure with measured frequency responses from 24 accelerometers was used to demonstrate the effectiveness of the method. The results show the successful classification of all structure states, for instance, the undamaged and damage states, damage locations and damage levels, and the environmental variability. (orig.)

Regional modeling approach for analyzing harmonic stability in radial power electronics based power system

DEFF Research Database (Denmark)

Yoon, Changwoo; Bai, Haofeng; Wang, Xiongfei

2015-01-01

Stability analysis of distributed power generation system becomes complex when there are many numbers of grid inverters in the system. In order to analyze system stability, the overall network impedance will be lumped and needs to be analyzed one by one. However, using a unified bulky transfer-fu...... and then it is expanded for generalizing its concept to an overall radial structured network....

Prediction of Optimal Design and Deflection of Space Structures Using Neural Networks

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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.

NOVEL APPROACH TO IMPROVE GEOCENTRIC TRANSLATION MODEL PERFORMANCE USING ARTIFICIAL NEURAL NETWORK TECHNOLOGY

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Yao Yevenyo Ziggah

Full Text Available Abstract: Geocentric translation model (GTM in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP and new parameters determined using the arithmetic mean (AM were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN, radial basis function neural network (RBFNN and generalized regression neural network (GRNN were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.

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.

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.

Structure, Kinematics and Origin of Radial Faults: 3D Seismic Observations from the Santos Basin, offshore Brazil

Science.gov (United States)

Coleman, Alexander; Jackson, Christopher A.-L.

2017-04-01

Salt stock growth is typically accompanied by the development of geometrically and kinematically complex fault networks in the surrounding country rock. The most common networks comprise radial faults; these are characterised by low displacement (stock into flanking strata. Radial faults are commonly observed in an arched, unpierced roof developed above a rising salt stock; in these cases, the faults are typically well-imaged seismically and likely form due to outer-arc extension during overburden stretching. Radial faults are also found at deeper structural levels, in strata flanking the diapir stem; in these cases, they are typically less well-imaged, thus their structure, kinematics and origin are less well understood. Furthermore, understanding the growth of radial faults may provide insights into hydrocarbon reservoir compartmentalisation and the evolution of neighbouring salt stocks. Here, we use high-quality 3D seismic reflection data from the Santos Basin, offshore Brazil to determine the structure and kinematics, and infer the likely origin of exceptionally well-imaged radial faults overlying and flanking a mature salt stock. Furthermore, we compare the geometric (e.g. throw, geometry, spacing, distribution etc.) and kinematic (e.g. timing of formation and duration of activity) characteristics of radial faults at both structural levels, allowing us to infer their temporal relationship and likely origins. We show that radial faults regardless of their structural level typically have aspect ratios of c. 1.8 - 2, are laterally-restricted in the vicinity of the salt, and have lengths of indices of c. 1, with low throw gradients of 0.05 - 0.1 at the upper tip indicate that radial faults were likely blind. Throws range from 5 - 80 ms, with throw-maxima within 1 - 2 radii of the salt diapir. However, we note that the position of the throw maxima is not at the same level for all radial faults. We propose that radial faults nucleate and initially grow as blind

Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

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Ramji Tiwari

2018-02-01

Full Text Available This paper proposes an artificial neural network (ANN based maximum power point tracking (MPPT control strategy for wind energy conversion system (WECS implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O method and Back propagation network (BPN method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC. The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.

Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

Science.gov (United States)

Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun

2013-10-01

Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.

STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS

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Mayra Luiza Marques da Silva Binoti

2015-03-01

Full Text Available The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric variables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80% and generalization (20%. Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems.

Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

International Nuclear Information System (INIS)

Li Qiong; Meng Qinglin; Cai Jiejin; Yoshino, Hiroshi; Mochida, Akashi

2009-01-01

This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MRE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods

Energy-efficient Organization of Wireless Sensor Networks with Adaptive Forecasting

Directory of Open Access Journals (Sweden)

Dao-Wei Bi

2008-04-01

Full Text Available Due to the wide potential applications of wireless sensor networks, this topic has attracted great attention. The strict energy constraints of sensor nodes result in great challenges for energy efficiency. This paper proposes an energy-efficient organization method. The organization of wireless sensor networks is formulated for target tracking. Target localization is achieved by collaborative sensing with multi-sensor fusion. The historical localization results are utilized for adaptive target trajectory forecasting. Combining autoregressive moving average (ARMA model and radial basis function networks (RBFNs, robust target position forecasting is performed. Moreover, an energyefficient organization method is presented to enhance the energy efficiency of wireless sensor networks. The sensor nodes implement sensing tasks are awakened in a distributed manner. When the sensor nodes transfer their observations to achieve data fusion, the routing scheme is obtained by ant colony optimization. Thus, both the operation and communication energy consumption can be minimized. Experimental results verify that the combination of ARMA model and RBFN can estimate the target position efficiently and energy saving is achieved by the proposed organization method in wireless sensor networks.

Comparison of different artificial neural network architectures in modeling of Chlorella sp. flocculation.

Science.gov (United States)

Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

2017-07-03

Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells, and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae species, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network. Neural network architectures of multilayer perceptron (MLP) and radial basis function architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

Robust Forecasting for Energy Efficiency of Wireless Multimedia Sensor Networks.

Science.gov (United States)

Wang, Xue; Ma, Jun-Jie; Ding, Liang; Bi, Dao-Wei

2007-11-15

An important criterion of wireless sensor network is the energy efficiency inspecified applications. In this wireless multimedia sensor network, the observations arederived from acoustic sensors. Focused on the energy problem of target tracking, this paperproposes a robust forecasting method to enhance the energy efficiency of wirelessmultimedia sensor networks. Target motion information is acquired by acoustic sensornodes while a distributed network with honeycomb configuration is constructed. Thereby,target localization is performed by multiple sensor nodes collaboratively through acousticsignal processing. A novel method, combining autoregressive moving average (ARMA)model and radial basis function networks (RBFNs), is exploited to perform robust targetposition forecasting during target tracking. Then sensor nodes around the target areawakened according to the forecasted target position. With committee decision of sensornodes, target localization is performed in a distributed manner and the uncertainty ofdetection is reduced. Moreover, a sensor-to-observer routing approach of the honeycombmesh network is investigated to solve the data reporting considering the residual energy ofsensor nodes. Target localization and forecasting are implemented in experiments.Meanwhile, sensor node awakening and dynamic routing are evaluated. Experimentalresults verify that energy efficiency of wireless multimedia sensor network is enhanced bythe proposed target tracking method.

Wood Modification at High Temperature and Pressurized Steam: a Relational Model of Mechanical Properties Based on a Neural Network

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Hong Yang

2015-07-01

Full Text Available Thermally modified wood has high dimensional stability and biological durability.But if the process parameters of thermal modification are not appropriate, then there will be a decline in the physical properties of wood.A neural network algorithm was employed in this study to establish the relationship between the process parameters of high-temperature and high-pressure thermal modification and the mechanical properties of the wood. Three important parameters: temperature, relative humidity, and treatment time, were considered as the inputs to the neural network. Back propagation (BP neural network and radial basis function (RBF neural network models for prediction were built and compared. The comparison showed that the RBF neural network model had advantages in network structure, convergence speed, and generalization capacity. On this basis, the inverse model, reflecting the relationship between the process parameters and the mechanical properties of wood, was established. Given the desired mechanical properties of the wood, the thermal modification process parameters could be inversely optimized and predicted. The results indicated that the model has good learning ability and generalization capacity. This is of great importance for the theoretical and applicational studies of the thermal modification of wood.

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

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Yong Tao

2016-01-01

Full Text Available A sliding mode control method based on radial basis function (RBF neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

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.

Application of artificial neural networks to evaluate weld defects of nuclear components

International Nuclear Information System (INIS)

Amin, E.S.

2007-01-01

Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects because of the nature of image formation and quality. Neoteric algorithms have been used for the purpose of weld defects identifications in radiographic images to replace the expert knowledge. The application of artificial neural networks in noise detection of radiographic films is used. Radial Basis (RB) and learning vector quantization (LVQ) were applied. The method shows good performance in weld defects recognition and classification problems.

Moment methods with effective nuclear Hamiltonians; calculations of radial moments

International Nuclear Information System (INIS)

Belehrad, R.H.

1981-02-01

A truncated orthogonal polynomial expansion is used to evaluate the expectation value of the radial moments of the one-body density of nuclei. The expansion contains the configuration moments, , , and 2 >, where R/sup (k)/ is the operator for the k-th power of the radial coordinate r, and H is the effective nuclear Hamiltonian which is the sum of the relative kinetic energy operator and the Bruckner G matrix. Configuration moments are calculated using trace reduction formulae where the proton and neutron orbitals are treated separately in order to find expectation values of good total isospin. The operator averages are taken over many-body shell model states in the harmonic oscillator basis where all particles are active and single-particle orbitals through six major shells are included. The radial moment expectation values are calculated for the nuclei 16 O, 40 Ca, and 58 Ni and find that is usually the largest term in the expansion giving a large model space dependence to the results. For each of the 3 nuclei, a model space is found which gives the desired rms radius and then we find that the other 5 lowest moments compare favorably with other theoretical predictions. Finally, we use a method of Gordon (5) to employ the lowest 6 radial moment expectation values in the calculation of elastic electron scattering from these nuclei. For low to moderate momentum transfer, the results compare favorably with the experimental data

Centrality measures for immunization of weighted networks

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Mohammad Khansari

2016-03-01

Full Text Available Effective immunization of individual communities with minimal cost in vaccination has made great discussion surrounding the realm of complex networks. Meanwhile, proper realization of relationship among people in society and applying it to social networks brings about substantial improvements in immunization. Accordingly, weighted graph in which link weights represent the intensity and intimacy of relationships is an acceptable approach. In this work we employ weighted graphs and a wide variety of weighted centrality measures to distinguish important individuals in contagion of diseases. Furthermore, we propose new centrality measures for weighted networks. Our experimental results show that Radiality-Degree centrality is satisfying for weighted BA networks. Additionally, PageRank-Degree and Radiality-Degree centralities showmoreacceptable performance in targeted immunization of weighted networks.

Neural network for optimal capacitor placement and its impact on power quality in electric distribution systems

International Nuclear Information System (INIS)

Mohamed, A.A.E.S.

2013-01-01

Capacitors are widely installed in distribution systems for reactive power compensation to achieve power and energy loss reduction, voltage regulation and system capacity release. The extent of these benefits depends greatly on how the capacitors are placed on the system. The problem of how to place capacitors on the system such that these benefits are achieved and maximized against the cost associated with the capacitor placement is termed the general capacitor placement problem. The capacitor placement problem has been formulated as the maximization of the savings resulted from reduction in both peak power and energy losses considering capacitor installation cost and maintaining the buses voltage within acceptable limits. After an appropriate analysis, the optimization problem was formulated in a quadratic form. For solving capacitor placement a new combinatorial heuristic and quadratic programming technique has been presented and applied in the MATLAB software. The proposed strategy was applied on two different radial distribution feeders. The results have been compared with previous works. The comparison showed the validity and the effectiveness of this strategy. Secondly, two artificial intelligence techniques for predicting the capacitor switching state in radial distribution feeders have been investigated; one is based on basis Radial Basis Neural Network (RBNN) and the other is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). The ANFIS technique gives better results with a minimum total error compared to RBNN. The learning duration of ANFIS was very short than the neural network case. It implied that ANFIS reaches to the target faster than neural network. Thirdly, an artificial intelligence (RBNN) approach for estimation of transient overvoltage during capacitor switching has been studied. The artificial intelligence approach estimated the transient overvoltages with a minimum error in a short computational time. Finally, a capacitor switching

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

Directory of Open Access Journals (Sweden)

Rodrigo Lopez Farias

2018-03-01

Full Text Available This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+ for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction is forecasted and the pattern mode estimated using a Nearest Neighbor (NN classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN, the statistical Autoregressive Integrated Moving Average (ARIMA, and Double Seasonal Holt-Winters (DSHW approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

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)

Computed tomography arthrography using a radial plane view for the detection of triangular fibrocartilage complex foveal tears.

Science.gov (United States)

Moritomo, Hisao; Arimitsu, Sayuri; Kubo, Nobuyuki; Masatomi, Takashi; Yukioka, Masao

2015-02-01

To classify triangular fibrocartilage complex (TFCC) foveal lesions on the basis of computed tomography (CT) arthrography using a radial plane view and to correlate the CT arthrography results with surgical findings. We also tested the interobserver and intra-observer reliability of the radial plane view. A total of 33 patients with a suspected TFCC foveal tear who had undergone wrist CT arthrography and subsequent surgical exploration were enrolled. We classified the configurations of TFCC foveal lesions into 5 types on the basis of CT arthrography with the radial plane view in which the image slices rotate clockwise centered on the ulnar styloid process. Sensitivity, specificity, and positive predictive values were calculated for each type of foveal lesion in CT arthrography to detect foveal tears. We determined interobserver and intra-observer agreements using kappa statistics. We also compared accuracies with the radial plane views with those with the coronal plane views. Among the tear types on CT arthrography, type 3, a roundish defect at the fovea, and type 4, a large defect at the overall ulnar insertion, had high specificity and positive predictive value for the detection of foveal tears. Specificity and positive predictive values were 90% and 89% for type 3 and 100% and 100% for type 4, respectively, whereas sensitivity was 35% for type 3 and 22% for type 4. Interobserver and intra-observer agreement was substantial and almost perfect, respectively. The radial plane view identified foveal lesion of each palmar and dorsal radioulnar ligament separately, but accuracy results with the radial plane views were not statistically different from those with the coronal plane views. Computed tomography arthrography with a radial plane view exhibited enhanced specificity and positive predictive value when a type 3 or 4 lesion was identified in the detection of a TFCC foveal tear compared with historical controls. Diagnostic II. Copyright © 2015 American Society for

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Science.gov (United States)

Ji, Xuewu; He, Xiangkun; Lv, Chen; Liu, Yahui; Wu, Jian

2018-06-01

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.

Computer network defense through radial wave functions

Science.gov (United States)

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.

Antiproton compression and radial measurements

CERN Document Server

Andresen, G B; Bowe, P D; Bray, C C; Butler, E; Cesar, C L; Chapman, S; Charlton, M; Fajans, J; Fujiwara, M C; Funakoshi, R; Gill, D R; Hangst, J S; Hardy, W N; Hayano, R S; Hayden, M E; Humphries, A J; Hydomako, R; Jenkins, M J; Jorgensen, L V; Kurchaninov, L; Lambo, R; Madsen, N; Nolan, P; Olchanski, K; Olin, A; Page R D; Povilus, A; Pusa, P; Robicheaux, F; Sarid, E; Seif El Nasr, S; Silveira, D M; Storey, J W; Thompson, R I; Van der Werf, D P; Wurtele, J S; Yamazaki, Y

2008-01-01

Control of the radial profile of trapped antiproton clouds is critical to trapping antihydrogen. We report detailed measurements of the radial manipulation of antiproton clouds, including areal density compressions by factors as large as ten, achieved by manipulating spatially overlapped electron plasmas. We show detailed measurements of the near-axis antiproton radial profile, and its relation to that of the electron plasma. We also measure the outer radial profile by ejecting antiprotons to the trap wall using an octupole magnet.

Speed Sensorless Control of PMSM using Model Reference Adaptive System and RBFN

OpenAIRE

Wei Gao; Zhirong Guo

2013-01-01

In the speed sensorless vector control system, the amended method of estimating the rotor speed about model reference adaptive system (MRAS) based on radial basis function neural network (RBFN) for PMSM sensorless vector control system was presented. Based on the PI regulator, the radial basis function neural network which is more prominent learning efficiency and performance is combined with MRAS. The reference model and the adjust model are the PMSM itself and the PMSM current, respectively...

Higher‐order mode absorption measurement of X-band choke-mode cavities in a radial line structure

International Nuclear Information System (INIS)

Zha, Hao; Shi, Jiaru; Wu, Xiaowei; Chen, Huaibi

2016-01-01

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.

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.

Impeller radial force evolution in a large double-suction centrifugal pump during startup at the shut-off condition

International Nuclear Information System (INIS)

Zou, Zhichao; Wang, Fujun; Yao, Zhifeng; Tao, Ran; Xiao, Ruofu; Li, Huaicheng

2016-01-01

Highlights: • Conclude the characteristics of transient radial force in the startup process for a large double-suction centrifugal pump. • The overall direction of the radial force during startup process is also confirmed. • A formula used to calculate the transient radial force during startup process is proposed. • A relationship between radial force variation and axial vortex development in blade channel during the startup process is established. The mechanism of the radial force evolution is revealed. - Abstract: Double-suction centrifugal pumps play an important role in the main feedwater systems of nuclear power plant. The impeller radial force in a centrifugal pump varies dramatically during startup at the shut-off condition. In this study, the startup process of a large double-suction centrifugal pump is investigated using CFD. During testing, the impeller speed is accelerated from zero to its rated speed in 1.0 s (marked as t_0) and is then maintained at the rated speed. The results show that the radial force increase lags behind the impeller speed increase. At 0–0.4t_0, the radial force is small (approaching zero). At 0.4–1.4t_0, the radial force increases rapidly. After 1.4t_0, the average radial force stabilizes and reaches its maximum value of 55,619 N. The observed maximum radial force value during startup is approximately nine times as high as the radial force under rated condition. During startup, the overall radial force direction is proximate to the radial line located 25° from the volute tongue along circumferential direction. A transient radial force formula is proposed to evaluate the changes in radial force during startup. The streamline distribution in impeller passages and the impeller outlet pressure profile varying over time are produced. The relationship between radial force evolution and the varying axial-to-spiral vortex structure is analyzed. The radial force change mechanism is revealed. This research provides a scientific

Impeller radial force evolution in a large double-suction centrifugal pump during startup at the shut-off condition

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

Stability of radial swirl flows

International Nuclear Information System (INIS)

Dou, H S; Khoo, B C

2012-01-01

The energy gradient theory is used to examine the stability of radial swirl flows. It is found that the flow of free vortex is always stable, while the introduction of a radial flow will induce the flow to be unstable. It is also shown that the pure radial flow is stable. Thus, there is a flow angle between the pure circumferential flow and the pure radial flow at which the flow is most unstable. It is demonstrated that the magnitude of this flow angle is related to the Re number based on the radial flow rate, and it is near the pure circumferential flow. The result obtained in this study is useful for the design of vaneless diffusers of centrifugal compressors and pumps as well as other industrial devices.

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

Science.gov (United States)

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

Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks

Institute of Scientific and Technical Information of China (English)

Ling Bai; Ping Guo; Zhan-Yi Hu

2005-01-01

An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification.Experiments with real data have shown that the correct classification rate can reach as high as 93%, which is quite satisfactory. When different system models are selected for the extended Kalman filter, the classification results are relatively stable. It is shown that for this particular case the result using extended Kalman filter is better than using principal component analysis.

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.

[Comparison of chemical quality characteristics between radial striations and non-radial striations in tuberous root of Rehmannia glutinosa].

Science.gov (United States)

Xie, Cai-Xia; Zhang, Miao; Li, Ya-Jing; Geng, Xiao-Tong; Wang, Feng-Qing; Zhang, Zhong-Yi

2017-11-01

An HPLC method was established to determine the contents of catalpol, acteoside, rehmaionoside A, rehmaionoside D, leonuride in three part of Rehmanni glutinosa in Beijing No.1 variety R. glutinosa during the growth period, This method, in combination with its HPLC fingerprint was used to evaluate its overall quality characteristics.The results showed that：① the content of main components of R. glutinosa varied in different growth stages ;② there was a great difference of the content of main components between theradial striations and the non-radial striations; ③ the two sections almost have the same content distribution of catalpol, acteoside and rehmaionoside D; ④the content of rehmaionoside A in non-radial striations was higher than that in radial striations,while the content of leonuride in radial striations was higher than that in non-radial striations.; ⑤the HPLC fingerprint of radial striations, non-radial striations and whole root tuber were basically identical, except for the big difference in the content of chemical components. The result of clustering displayed that the radial striations, non-radial striations, and whole root were divided into two groups. In conclusion, there was a significant difference in the quality characteristics of radial striations and non-radial striations of R. glutinosa. This research provides a reference for quality evaluation and geoherbalism of R. glutinosa. Copyright© by the Chinese Pharmaceutical Association.

Review of Evidence Suggesting That the Fascia Network Could Be the Anatomical Basis for Acupoints and Meridians in the Human Body

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.

Adaptive online state-of-charge determination based on neuro-controller and neural network

Energy Technology Data Exchange (ETDEWEB)

Shen Yanqing, E-mail: network_hawk@126.co [Department of Automation, Chongqing Industry Polytechnic College, Jiulongpo District, Chongqing 400050 (China)

2010-05-15

This paper presents a novel approach using adaptive artificial neural network based model and neuro-controller for online cell State of Charge (SOC) determination. Taking cell SOC as model's predictive control input unit, radial basis function neural network, which can adjust its structure to prediction error with recursive least square algorithm, is used to simulate battery system. Besides that, neuro-controller based on Back-Propagation Neural Network (BPNN) and modified PID controller is used to decide the control input of battery system, i.e., cell SOC. Finally this algorithm is applied for the SOC determination of lead-acid batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Results show that the ANN based battery system model adaptively simulates battery system with great accuracy, and the predicted SOC simultaneously converges to the real value quickly within the error of +-1 as time goes on.

Experimental feasibility study of radial injection cooling of three-pad radial air foil bearings

Science.gov (United States)

Shrestha, Suman K.

Air foil bearings use ambient air as a lubricant allowing environment-friendly operation. When they are designed, installed, and operated properly, air foil bearings are very cost effective and reliable solution to oil-free turbomachinery. Because air is used as a lubricant, there are no mechanical contacts between the rotor and bearings and when the rotor is lifted off the bearing, near frictionless quiet operation is possible. However, due to the high speed operation, thermal management is one of the very important design factors to consider. Most widely accepted practice of the cooling method is axial cooling, which uses cooling air passing through heat exchange channels formed underneath the bearing pad. Advantage is no hardware modification to implement the axial cooling because elastic foundation structure of foil bearing serves as a heat exchange channels. Disadvantage is axial temperature gradient on the journal shaft and bearing. This work presents the experimental feasibility study of alternative cooling method using radial injection of cooling air directly on the rotor shaft. The injection speeds, number of nozzles, location of nozzles, total air flow rate are important factors determining the effectiveness of the radial injection cooling method. Effectiveness of the radial injection cooling was compared with traditional axial cooling method. A previously constructed test rig was modified to accommodate a new motor with higher torque and radial injection cooling. The radial injection cooling utilizes the direct air injection to the inlet region of air film from three locations at 120° from one another with each location having three axially separated holes. In axial cooling, a certain axial pressure gradient is applied across the bearing to induce axial cooling air through bump foil channels. For the comparison of the two methods, the same amount of cooling air flow rate was used for both axial cooling and radial injection. Cooling air flow rate was

Identifying Typhoon Tracks based on Event Synchronization derived Spatially Embedded Climate Networks

Science.gov (United States)

Ozturk, Ugur; Marwan, Norbert; Kurths, Jürgen

2017-04-01

Complex networks are commonly used for investigating spatiotemporal dynamics of complex systems, e.g. extreme rainfall. Especially directed networks are very effective tools in identifying climatic patterns on spatially embedded networks. They can capture the network flux, so as the principal dynamics of spreading significant phenomena. Network measures, such as network divergence, bare the source-receptor relation of the directed networks. However, it is still a challenge how to catch fast evolving atmospheric events, i.e. typhoons. In this study, we propose a new technique, namely Radial Ranks, to detect the general pattern of typhoons forward direction based on the strength parameter of the event synchronization over Japan. We suggest to subset a circular zone of high correlation around the selected grid based on the strength parameter. Radial sums of the strength parameter along vectors within this zone, radial ranks are measured for potential directions, which allows us to trace the network flux over long distances. We employed also the delay parameter of event synchronization to identify and separate the frontal storms' and typhoons' individual behaviors.

Modeling of Tsunami Equations and Atmospheric Swirling Flows with a Graphics Processing Unit (GPU) and Radial Basis Functions (RBF)

Science.gov (United States)

Schmidt, J.; Piret, C.; Zhang, N.; Kadlec, B. J.; Liu, Y.; Yuen, D. A.; Wright, G. B.; Sevre, E. O.

2008-12-01

The faster growth curves in the speed of GPUs relative to CPUs in recent years and its rapidly gained popularity has spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations and producing the attendant visualization. We are concerned with modeling tsunami waves, where computational time is of extreme essence, for broadcasting warnings. In order to test the efficacy of the GPU on the set of shallow-water equations, we employed the NVIDIA board 8600M GT on a MacBook Pro. We have compared the relative speeds between the CPU and the GPU on a single processor for two types of spatial discretization based on second-order finite-differences and radial basis functions. RBFs are a more novel method based on a gridless and a multi- scale, adaptive framework. Using the NVIDIA 8600M GT, we received a speed up factor of 8 in favor of GPU for the finite-difference method and a factor of 7 for the RBF scheme. We have also studied the atmospheric dynamics problem of swirling flows over a spherical surface and found a speed-up of 5.3 using the GPU. The time steps employed for the RBF method are larger than those used in finite-differences, because of the much fewer number of nodal points needed by RBF. Thus, in modeling the same physical time, RBF acting in concert with GPU would be the fastest way to go.

On the stability of a homogeneous barrier discharge in nitrogen relative to radial perturbations

CERN Document Server

Golubovskii, Y B; Behnke, J; Behnke, J F

2003-01-01

The influence of small radial perturbations of the cathode current on the characteristics of a homogeneous barrier discharge in nitrogen is investigated on the basis of a two-dimensional fluid model. In a Townsend discharge, radial fluctuations are substantially suppressed, which is the evidence of its stability. The oscillative mode of the Townsend discharge is also stable with regard to radial perturbations. As the discharge turns into a form controlled by spatial charge (a streamer is developed), disturbances of all radii grow in time. Such a behaviour testifies the instability of a streamer front and may cause the discharge filamentation. Since only the Townsend discharge is stable, it is possible to use a one-dimensional model to determine the domain of existence for a homogeneous discharge. The study of homogeneity domains by means of the one-dimensional model shows that at relatively large values of the voltage growth rate, discharge gap width, or capacitance of dielectric barriers the discharge tends ...

Physical mechanism determining the radial electric field and its radial structure in a toroidal plasma

International Nuclear Information System (INIS)

Ida, Katsumi; Miura, Yukitoshi; Itoh, Sanae

1994-10-01

Radial structures of plasma rotation and radial electric field are experimentally studied in tokamak, heliotron/torsatron and stellarator devices. The perpendicular and parallel viscosities are measured. The parallel viscosity, which is dominant in determining the toroidal velocity in heliotron/torsatron and stellarator devices, is found to be neoclassical. On the other hand, the perpendicular viscosity, which is dominant in dictating the toroidal rotation in tokamaks, is anomalous. Even without external momentum input, both a plasma rotation and a radial electric field exist in tokamaks and heliotrons/torsatrons. The observed profiles of the radial electric field do not agree with the theoretical prediction based on neoclassical transport. This is mainly due to the existence of anomalous perpendicular viscosity. The shear of the radial electric field improves particle and heat transport both in bulk and edge plasma regimes of tokamaks. (author) 95 refs

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.

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

International Nuclear Information System (INIS)

Tsai, Tai Ming; Wang, Wei Hui

2009-01-01

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

Energy Technology Data Exchange (ETDEWEB)

Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

2009-01-15

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Predicting wettability behavior of fluorosilica coated metal surface using optimum neural network

Science.gov (United States)

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.

Latitudinal and radial variation of >2 GeV/n protons and alpha-particles at solar maximum: ULYSSES COSPIN/KET and neutron monitor network observations

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

Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

Science.gov (United States)

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.

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

Directory of Open Access Journals (Sweden)

Yanchao Yin

2017-01-01

Full Text Available A novel neural network sliding mode control based on multicommunity bidirectional drive collaborative search algorithm (M-CBDCS is proposed to design a flight controller for performing the attitude tracking control of a quad tilt rotors aircraft (QTRA. Firstly, the attitude dynamic model of the QTRA concerning propeller tension, channel arm, and moment of inertia is formulated, and the equivalent sliding mode control law is stated. Secondly, an adaptive control algorithm is presented to eliminate the approximation error, where a radial basis function (RBF neural network is used to online regulate the equivalent sliding mode control law, and the novel M-CBDCS algorithm is developed to uniformly update the unknown neural network weights and essential model parameters adaptively. The nonlinear approximation error is obtained and serves as a novel leakage term in the adaptations to guarantee the sliding surface convergence and eliminate the chattering phenomenon, which benefit the overall attitude control performance for QTRA. Finally, the appropriate comparisons among the novel adaptive neural network sliding mode control, the classical neural network sliding mode control, and the dynamic inverse PID control are examined, and comparative simulations are included to verify the efficacy of the proposed control method.

Radial retinotomy in the macula.

Science.gov (United States)

Bovino, J A; Marcus, D F

1984-01-01

Radial retinotomy is an operative procedure usually performed in the peripheral or equatorial retina. To facilitate retinal attachment, the authors used intraocular scissors to perform radial retinotomy in the macula of two patients during vitrectomy surgery. In the first patient, a retinal detachment complicated by periretinal proliferation and macula hole formation was successfully reoperated with the aid of three radial cuts in the retina at the edges of the macular hole. In the second patient, an intraoperative retinal tear in the macula during diabetic vitrectomy was also successfully repaired with the aid of radial retinotomy. In both patients, retinotomy in the macula was required because epiretinal membranes, which could not be easily delaminated, were hindering retinal reattachment.

Radial head dislocation during proximal radial shaft osteotomy.

Science.gov (United States)

Hazel, Antony; Bindra, Randy R

2014-03-01

The following case report describes a 48-year-old female patient with a longstanding both-bone forearm malunion, who underwent osteotomies of both the radius and ulna to improve symptoms of pain and lack of rotation at the wrist. The osteotomies were templated preoperatively. During surgery, after performing the planned radial shaft osteotomy, the authors recognized that the radial head was subluxated. The osteotomy was then revised from an opening wedge to a closing wedge with improvement of alignment and rotation. The case report discusses the details of the operation, as well as ways in which to avoid similar shortcomings in the future. Copyright © 2014 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

Science.gov (United States)

Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

2016-02-01

A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Improved WKB radial wave functions in several bases

International Nuclear Information System (INIS)

Durand, B.; Durand, L.; Department of Physics, University of Wisconsin, Madison, Wisconsin 53706)

1986-01-01

We develop approximate WKB-like solutions to the radial Schroedinger equation for problems with an angular momentum barrier using Riccati-Bessel, Coulomb, and harmonic-oscillator functions as basis functions. The solutions treat the angular momentum singularity near the origin more accurately in leading approximation than the standard WKB solutions based on sine waves. The solutions based on Riccati-Bessel and free Coulomb wave functions continue smoothly through the inner turning point and are appropriate for scattering problems. The solutions based on oscillator and bound Coulomb wave functions incorporate both turning points smoothly and are particularly appropriate for bound-state problems; no matching of piecewise solutions using Airy functions is necessary

Radial cracks and fracture mechanism of radially oriented ring 2:17 type SmCo magnets

International Nuclear Information System (INIS)

Tian Jianjun; Pan Dean; Zhou Hao; Yin Fuzheng; Tao Siwu; Zhang Shengen; Qu Xuanhui

2009-01-01

Radially oriented ring 2:17 type SmCo magnets have different microstructure in the radial direction (easy magnetization) and axial direction (hard magnetization). The structure of the cross-section in radial direction is close-packed atomic plane, which shows cellular microstructure. The microstructure of the cross-section in axial direction consists of a mixture of rhombic microstructure and parallel lamella phases. So the magnets have obvious anisotropy of thermal expansion in different directions. The difference of the thermal expansion coefficients reaches the maximum value at 830-860 deg. C, which leads to radial cracks during quenching. The magnets have high brittlement because there are fewer slip systems in crystal structure. The fracture is brittle cleavage fracture.

Modelling cell cycle synchronisation in networks of coupled radial glial cells.

Science.gov (United States)

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.

Intelligent harmonic load model based on neural networks

Science.gov (United States)

Ji, Pyeong-Shik; Lee, Dae-Jong; Lee, Jong-Pil; Park, Jae-Won; Lim, Jae-Yoon

2007-12-01

In this study, we developed a RBFNs(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method implemented by using harmonic information as well as fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. The RBFNs have certain advantage such as simple structure and rapid computation ability compared with multilayer perceptron which is extensively applied for load modeling. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynominal 2nd equation method, MLP and RBF without considering harmonic components.

Using neural networks to speed up optimization algorithms

CERN Document Server

Bazan, M

2000-01-01

The paper presents the application of radial-basis-function (RBF) neural networks to speed up deterministic search algorithms used for the design and optimization of superconducting LHC magnets. The optimization of the iron yoke of the main dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation of the magnets. This results in computation times of about 30 minutes for each objective function evaluation (on a DEC-Alpha 600/333) and only the most robust (deterministic) optimization algorithms can be applied. Using a RBF function approximator, the achieved speed-up of the search algorithm is in the order of 25% for problems with two parameters and about 18% for problems with three and five design variables. (13 refs).

Radial frequency stimuli and sine-wave gratings seem to be processed by distinct contrast brain mechanisms

Directory of Open Access Journals (Sweden)

M.L.B. Simas

2005-03-01

Full Text Available An assumption commonly made in the study of visual perception is that the lower the contrast threshold for a given stimulus, the more sensitive and selective will be the mechanism that processes it. On the basis of this consideration, we investigated contrast thresholds for two classes of stimuli: sine-wave gratings and radial frequency stimuli (i.e., j0 targets or stimuli modulated by spherical Bessel functions. Employing a suprathreshold summation method, we measured the selectivity of spatial and radial frequency filters using either sine-wave gratings or j0 target contrast profiles at either 1 or 4 cycles per degree of visual angle (cpd, as the test frequencies. Thus, in a forced-choice trial, observers chose between a background spatial (or radial frequency alone and the given background stimulus plus the test frequency (1 or 4 cpd sine-wave grating or radial frequency. Contrary to our expectations, the results showed elevated thresholds (i.e., inhibition for sine-wave gratings and decreased thresholds (i.e., summation for radial frequencies when background and test frequencies were identical. This was true for both 1- and 4-cpd test frequencies. This finding suggests that sine-wave gratings and radial frequency stimuli are processed by different quasi-linear systems, one working at low luminance and contrast level (sine-wave gratings and the other at high luminance and contrast levels (radial frequency stimuli. We think that this interpretation is consistent with distinct foveal only and foveal-parafoveal mechanisms involving striate and/or other higher visual areas (i.e., V2 and V4.

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.

An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: Application for isolated sites in Algeria

International Nuclear Information System (INIS)

Mellit, A.; Benghanem, M.; Hadj Arab, A.; Guessoum, G.

2004-07-01

In this paper we investigate, by using an adaptive Artificial Neural Network (ANN), in order to find a suitable model for sizing Stand-Alone Photovoltaic (SAPV) systems, based on a minimum of input data. This model combines Radial Basis Function (RBF) network and Infinite Impulse Response (IIR) filter in order to accelerate the convergence of the network. For the sizing of a photovoltaic (PV) system, we need to determine the optimal sizing coefficients (K PV , K B . These coefficients allow us to determine the number of solar panels and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available and which are considered the most important parameters for sizing a PV system. Obtained results by classical models (analytical, numerical, analytical- numerical, B-spline function) and new models like feed-forward (MLP), radial basis function (RBF), MLP-IIR and RBF-IIR have been compared with experimental sizing coefficients in order to illustrate the accuracy of the results of the new developed model. This model has been trained by using 200 known optimal sizing coefficients corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and even handle a number of unusual cases, the unknown validation sizing coefficients set produced very set accurate estimation and a correlation coefficient of 98% was obtained between the calculated and that estimated by the RBF-IIR model. This result indicates that the proposed method can be successfully used for the estimation of optimal sizing coefficients of SAPV systems for any locations in Algeria, but the methodology can be generalized using different locations over the world. (author)

Logarithmic learning for generalized classifier neural network.

Science.gov (United States)

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.

Neural Networks Modelling of Municipal Real Estate Market Rent Rates

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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.

Development of automobile distribution networks on the basis of multi-criteria evaluation of distribution channels

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A. Vasilis Vasiliauskas

2010-12-01

Full Text Available .The days when a buyer was forced to choose from what is being offered have passed. These days, buyers demand a product that would answer their exclusive expectations at a time of their preference and at an acceptable price. Therefore, manufacturers aiming to survive the competition battle have to rethink their operation strategies. Special importance is attached to the process of development and reconstruction of supply chains, and the process which may feature particularities, depending on the branch of industry. Automobile manufacturing is the biggest the fastest industry developing across the globe. New automobiles are listed as luxury commodities and are, therefore, subjected to very strict requirements with regard to various logistic operations and technologies, which are vital for ensuring efficient automobile delivery to the final users. Due to the growing demand for brand-new automobiles and the distance to the user, automobile manufacturers are constantly searching for solutions to the development and support of an efficient distribution network. Strategy shaping of distribution network requires evaluation of a number of criteria, which influence the distribution system. The article analyzes the development of automobile distribution networks on the basis of multi-criteria evaluation of distribution channels.

RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

Science.gov (United States)

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.

The Anatomical Basis for Dystonia: The Motor Network Model

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H.A. Jinnah

2017-10-01

Full Text Available Background: The dystonias include a clinically and etiologically very diverse group of disorders. There are both degenerative and non-degenerative subtypes resulting from genetic or acquired causes. Traditionally, all dystonias have been viewed as disorders of the basal ganglia. However, there has been increasing appreciation for involvement of other brain regions including the cerebellum, thalamus, midbrain, and cortex. Much of the early evidence for these other brain regions has come from studies of animals, but multiple recent studies have been done with humans, in an effort to confirm or refute involvement of these other regions. The purpose of this article is to review the new evidence from animals and humans regarding the motor network model, and to address the issues important to translational neuroscience.Methods: The English literature was reviewed for articles relating to the neuroanatomical basis for various types of dystonia in both animals and humans.Results: There is evidence from both animals and humans that multiple brain regions play an important role in various types of dystonia. The most direct evidence for specific brain regions comes from animal studies using pharmacological, lesion, or genetic methods. In these studies, experimental manipulations of specific brain regions provide direct evidence for involvement of the basal ganglia, cerebellum, thalamus and other regions. Additional evidence also comes from human studies using neuropathological, neuroimaging, non-invasive brain stimulation, and surgical interventions. In these studies, the evidence is less conclusive, because discriminating the regions that cause dystonia from those that reflect secondary responses to abnormal movements is more challenging.Discussion: Overall, the evidence from both animals and humans suggests that different regions may play important roles in different subtypes of dystonia. The evidence so far provides strong support for the motor

Prediction of vibration characteristics of a planar mechanism having imperfect joints using neural network

International Nuclear Information System (INIS)

Erkaya, Selcuk

2012-01-01

Clearance is inevitable in the joints of mechanisms due primarily to the design, manufacturing and assembly processes or a wear effect. Excessive value of joint clearance plays a crucial role and has a significant effect on the kinematic and dynamic performances of the mechanism. In this study, effects of joint clearances on bearing vibrations of mechanism are investigated. An experimental test rig is set up, and a planar slider-crank mechanism having two imperfect joints with radial clearance is used as a model mechanism. Three accelerometers are positioned at different points to measure the bearing vibrations during the mechanism motion. For the different running speeds and clearance sizes, this work provides a neural model to predict and estimate the bearing vibrations of the mechanical systems having imperfect joints. The results show that radial basis function (RBF) neural network has a superior performance for predicting and estimating the vibration characteristics of the mechanical system

Perceived radial translation during centrifugation

NARCIS (Netherlands)

Bos, J.E.; Correia Grácio, B.J.

2015-01-01

BACKGROUND: Linear acceleration generally gives rise to translation perception. Centripetal acceleration during centrifugation, however, has never been reported giving rise to a radial, inward translation perception. OBJECTIVE: To study whether centrifugation can induce a radial translation

Nonlinear adaptive PID control for greenhouse environment based on RBF network.

Science.gov (United States)

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.

Temperature and boron dependencies of buckling and radial reflector saving for VVER lattices

International Nuclear Information System (INIS)

Alvarez, C.

1990-01-01

The temperature and boron dependencies of buckling and radial reflectors savings are analyzed in this paper on the basis of the results from the calculations ZR-6M critical assembly. These dependencies are related to the physical behavior of temperature and boron reactivity coefficients for the cores of VVER-type critical facilities. As a byproduct, the parameter was also investigated and its dependence on water density was determined

Penyelesaian Numerik Persamaan Advection Dengan Radial Point Interpolation Method dan Integrasi Waktu Dengan Discontinuous Galerkin Method

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Kresno Wikan Sadono

2016-12-01

Full Text Available Persamaan differensial banyak digunakan untuk menggambarkan berbagai fenomena dalam bidang sains dan rekayasa. Berbagai masalah komplek dalam kehidupan sehari-hari dapat dimodelkan dengan persamaan differensial dan diselesaikan dengan metode numerik. Salah satu metode numerik, yaitu metode meshfree atau meshless berkembang akhir-akhir ini, tanpa proses pembuatan elemen pada domain. Penelitian ini menggabungkan metode meshless yaitu radial basis point interpolation method (RPIM dengan integrasi waktu discontinuous Galerkin method (DGM, metode ini disebut RPIM-DGM. Metode RPIM-DGM diaplikasikan pada advection equation pada satu dimensi. RPIM menggunakan basis function multiquadratic function (MQ dan integrasi waktu diturunkan untuk linear-DGM maupun quadratic-DGM. Hasil simulasi menunjukkan, metode ini mendekati hasil analitis dengan baik. Hasil simulasi numerik dengan RPIM DGM menunjukkan semakin banyak node dan semakin kecil time increment menunjukkan hasil numerik semakin akurat. Hasil lain menunjukkan, integrasi numerik dengan quadratic-DGM untuk suatu time increment dan jumlah node tertentu semakin meningkatkan akurasi dibandingkan dengan linear-DGM.  [Title: Numerical solution of advection equation with radial basis interpolation method and discontinuous Galerkin method for time integration] Differential equation is widely used to describe a variety of phenomena in science and engineering. A variety of complex issues in everyday life can be modeled with differential equations and solved by numerical method. One of the numerical methods, the method meshfree or meshless developing lately, without making use of the elements in the domain. The research combines methods meshless, i.e. radial basis point interpolation method with discontinuous Galerkin method as time integration method. This method is called RPIM-DGM. The RPIM-DGM applied to one dimension advection equation. The RPIM using basis function multiquadratic function and time

Self-consistent radial sheath

International Nuclear Information System (INIS)

Hazeltine, R.D.

1988-12-01

The boundary layer arising in the radial vicinity of a tokamak limiter is examined, with special reference to the TEXT tokamak. It is shown that sheath structure depends upon the self-consistent effects of ion guiding-center orbit modification, as well as the radial variation of E /times/ B-induced toroidal rotation. Reasonable agreement with experiment is obtained from an idealized model which, however simplified, preserves such self-consistent effects. It is argued that the radial sheath, which occurs whenever confining magnetic field-lines lie in the plasma boundary surface, is an object of some intrinsic interest. It differs from the more familiar axial sheath because magnetized charges respond very differently to parallel and perpendicular electric fields. 11 refs., 1 fig

Simultaneous determination of penicillin G salts by infrared spectroscopy: Evaluation of combining orthogonal signal correction with radial basis function-partial least squares regression

Science.gov (United States)

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.

Prediction of Force Measurements of a Microbend Sensor Based on an Artificial Neural Network

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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.

Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

Science.gov (United States)

Xia, Kewei; Huo, Wei

2016-05-01

This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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)

1998-12-31

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.

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.

Application of Statistical, Fuzzy and Perceptron Neural Networks in Drought Forecasting (Case Study: Gonbad-e Kavous Station

Directory of Open Access Journals (Sweden)

S.M. Hosseini-Moghari

2016-10-01

Full Text Available Introduction: Due to economic, social, and environmental perplexities associated with drought, it is considered as one of the most complex natural hazards. To investigate the beginning along with analyzing the direct impacts of drought; the significance of drought monitoring must be highlighted. Regarding drought management and its consequences alleviation, drought forecasting must be taken into account (11. The current research employed multi-layer perceptron (MLP, adaptive neuro-fuzzy inference system (ANFIS, radial basis function (RBF and general regression neural network (GRNN. It is interesting to note that, there has not been any record of applying GRNN in drought forecasting. Materials and Methods: Throughout this paper, Standard Precipitation Index (SPI was the basis of drought forecasting. To do so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. To provide short-term, mid-term, and long-term drought analysis; SPI for 1, 3, 6, 9, 12, and 24 months was evaluated. SPI evaluation benefited from four statistical distributions, namely, Gamma, Normal, Log-normal, and Weibull along with Kolmogrov-Smirnov (K-S test. Later, to compare the capabilities of four utilized neural networks for drought forecasting; MLP, ANFIS, RBF, and GRNN were applied. MLP as a multi-layer network, which has a sigmoid activation function in hidden layer plus linear function in output layer, can be considered as a powerful regressive tool. ANFIS besides adaptive neuro networks, employed fuzzy logic. RBF, the foundation of radial basis networks, is a three-layer network with Gaussian function in its hidden layer, and a linear function in the output layer. GRNN is another type of RBF which is used for radial basis regressive problems. The performance criteria of the research were as follows: Correlation (R2, Root Mean Square Error (RMSE, Mean Absolute Error (MAE. Results Discussion: According to statistical distribution

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

Modelling of the PROTO-II crossover network

International Nuclear Information System (INIS)

Proulx, G.A.; Lackner, H.; Spence, P.; Wright, T.P.

1985-01-01

In order to drive a double ring, symmetrically fed bremsstrahlung diode, the PROTO II accelerator was redesigned. The radially converging triplate water line was reconfigured to drive two radial converging triplate lines in parallel. The four output lines were connected to the two input lines via an electrically enclosed tubular crossover network. Low-voltage Time Domain Reflectrometry (TDR) experiments were conducted on a full scale water immersed model of one section of the crossover network as an aid in this design. A lumped element analysis of the power flow through the network was inadequate in explaining the observed wave transmission and reflection characteristics. A more detailed analysis was performed with a circuit code in which we considered both localized lump-element and transmission line features of the crossover network. Experimental results of the model tests are given and compared with the circuit simulations. 7 figs

Endoscopic versus open radial artery harvest and mammario-radial versus aorto-radial grafting in patients undergoing coronary artery bypass surgery

DEFF Research Database (Denmark)

Carranza, Christian L; Ballegaard, Martin; Werner, Mads U

2014-01-01

the postoperative complications will be registered, and we will evaluate muscular function, scar appearance, vascular supply to the hand, and the graft patency including the patency of the central radial artery anastomosis. A patency evaluation by multi-slice computer tomography will be done at one year...... to aorto-radial revascularisation techniques but this objective is exploratory. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01848886.Danish Ethics committee number: H-3-2012-116.Danish Data Protection Agency: 2007-58-0015/jr.n:30-0838....

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

Temperature and boron dependencies of buckling and radial reflector savings for VVER lattices

International Nuclear Information System (INIS)

Alvarez, C.

1990-01-01

The temperature and boron dependencies of buckling and radial reflector savings are analyzed in this paper on the basis of the results from the calculations for the ZR-6M critical assembly. These dependencies are related to he physical behaviour of temperature and boron reactivity coefficients for the cores of VVER-type critical facilities. As a byproduct, the dp/dBg 2 parameter was also investigated and its dependence on water density was determined

Numerical simulation of liquid-metal-flows in radial-toroidal-radial bends

International Nuclear Information System (INIS)

Molokov, S.; Buehler, L.

1993-09-01

Magnetohydrodynamic flows in a U-bend and right-angle bend are considered with reference to the radial-toroidal-radial concept of a self-cooled liquid-metal blanket. The ducts composing bends have rectangular cross-section. The applied magnetic field is aligned with the toroidal duct and perpendicular to the radial ones. At high Hartmann number the flow region is divided into cores and boundary layers of different types. The magnetohydrodynamic equations are reduced to a system of partial differential equations governing wall electric potentials and the core pressure. The system is solved numerically by two different methods. The first method is iterative with iteration between wall potential and the core pressure. The second method is a general one for the solution of the core flow equations in curvilinear coordinates generated by channel geometry and magnetic field orientation. Results obtained are in good agreement. They show, that the 3D-pressure drop of MHD flows in a U-bend is not a critical issue for blanket applications. (orig./HP) [de

Probabilistic neural network algorithm for using radon emanations as an earthquake precursor

International Nuclear Information System (INIS)

Gupta, Dhawal; Shahani, D.T.

2014-01-01

The investigation throughout the world in past two decades provides evidence which indicate that significance variation of radon and other soil gases occur in association with major geophysical events such as earthquake. The traditional statistical algorithm includes regression to remove the effect of the meteorological parameters from the raw radon and anomalies are calculated either taking the periodicity in seasonal variations or periodicity computed using Fast Fourier Transform. In case of neural networks the regression step is avoided. A neural network model can be found which can learn the behavior of radon with respect to meteorological parameter in order that changing emission patterns may be adapted to by the model on its own. The output of this neural model is the estimated radon values. This estimated radon value is used to decide whether anomalous behavior of radon has occurred and a valid precursor may be identified. The neural network model developed using Radial Basis function network gave a prediction rate of 87.7%. The same was accompanied by huge false alarms. The present paper deals with improved neural network algorithm using Probabilistic Neural Networks that requires neither an explicit step of regression nor use of any specific period. This neural network model reduces the false alarms to zero and gave same prediction rate as RBF networks. (author)

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.

Radial microstrip slotline feed network for circular mobile communications array

Science.gov (United States)

Simons, Rainee N.; Kelly, Eron S.; Lee, Richard Q.; Taub, Susan R.

1994-01-01

In mobile and satellite communications there is a need for low cost and low profile antennas which have a toroidal pattern. Antennas that have been developed for mobile communications include a L-Band electronically steered stripline phased array, a Ka-Band mechanically steered elliptical reflector antenna and a Ka-Band printed dipole. In addition, a L-Band mechanically steered microstrip array, a L-Band microstrip phased array tracking antenna for mounting on a car roof and an X-Band radial line slotted waveguide antenna have been demonstrated. In the above electronically scanned printed arrays, the individual element radiates normally to the plane of the array and hence require a phase shifter to scan the beam towards the horizon. Scanning in the azimuth is by mechanical or electronic steering. An alternate approach is to mount microstrip patch radiators on the surface of a cone to achieve the required elevation angle. The array then scans in the azimuth by beam switching.

Radial wave crystals: radially periodic structures from anisotropic metamaterials for engineering acoustic or electromagnetic waves.

Science.gov (United States)

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.

Aneurisma idiopático de artéria radial: relato de caso Idiopathic radial artery aneurysm: case report

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.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

Science.gov (United States)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

Fault detection and diagnosis using statistical control charts and artificial neural networks

International Nuclear Information System (INIS)

Leger, R.P.; Garland, W.J.; Poehlman, W.F.S.

1995-01-01

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using Cumulative Summation (CUSUM) Control Charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM Control Charts and a Radial Basis Function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked together by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect 6 fault conditions and correctly diagnose 5 out of the 6 faults. The diagnosis for the sixth fault was inconclusive. (author). 9 refs., 6 tabs., 7 figs

Ulnar nerve entrapment complicating radial head excision

Directory of Open Access Journals (Sweden)

Kevin Parfait Bienvenu Bouhelo-Pam

Full Text Available Introduction: Several mechanisms are involved in ischemia or mechanical compression of ulnar nerve at the elbow. Presentation of case: We hereby present the case of a road accident victim, who received a radial head excision for an isolated fracture of the radial head and complicated by onset of cubital tunnel syndrome. This outcome could be the consequence of an iatrogenic valgus of the elbow due to excision of the radial head. Hitherto the surgical treatment of choice it is gradually been abandoned due to development of radial head implant arthroplasty. However, this management option is still being performed in some rural centers with low resources. Discussion: The radial head plays an important role in the stability of the elbow and his iatrogenic deformity can be complicated by cubital tunnel syndrome. Conclusion: An ulnar nerve release was performed with favorable outcome. Keywords: Cubital tunnel syndrome, Peripheral nerve palsy, Radial head excision, Elbow valgus

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.

Effect of placement of droop based generators in distribution network on small signal stability margin and network loss

DEFF Research Database (Denmark)

Dheer, D.K.; Doolla, S.; Bandyopadhyay, S.

2017-01-01

, small signal stability margin is on the fore. The present research studied the effect of location of droop-controlled DGs on small signal stability margin and network loss on a modified IEEE 13 bus system, an IEEE 33-bus distribution system and a practical 22-bus radial distribution network. A complete...... loss and stability margin is further investigated by identifying the Pareto fronts for modified IEEE 13 bus, IEEE 33 and practical 22-bus radial distribution network with application of Reference point based Non-dominated Sorting Genetic Algorithm (R-NSGA). Results were validated by time domain......For a utility-connected system, issues related to small signal stability with Distributed Generators (DGs) are insignificant due to the presence of a very strong grid. Optimally placed sources in utility connected microgrid system may not be optimal/stable in islanded condition. Among others issues...

Radial lean direct injection burner

Science.gov (United States)

Khan, Abdul Rafey; Kraemer, Gilbert Otto; Stevenson, Christian Xavier

2012-09-04

A burner for use in a gas turbine engine includes a burner tube having an inlet end and an outlet end; a plurality of air passages extending axially in the burner tube configured to convey air flows from the inlet end to the outlet end; a plurality of fuel passages extending axially along the burner tube and spaced around the plurality of air passage configured to convey fuel from the inlet end to the outlet end; and a radial air swirler provided at the outlet end configured to direct the air flows radially toward the outlet end and impart swirl to the air flows. The radial air swirler includes a plurality of vanes to direct and swirl the air flows and an end plate. The end plate includes a plurality of fuel injection holes to inject the fuel radially into the swirling air flows. A method of mixing air and fuel in a burner of a gas turbine is also provided. The burner includes a burner tube including an inlet end, an outlet end, a plurality of axial air passages, and a plurality of axial fuel passages. The method includes introducing an air flow into the air passages at the inlet end; introducing a fuel into fuel passages; swirling the air flow at the outlet end; and radially injecting the fuel into the swirling air flow.

The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water.

Science.gov (United States)

Roshani, G H; Karami, A; Salehizadeh, A; Nazemi, E

2017-11-01

The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152 Eu and 137 Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Anomalies of radial and ulnar arteries

Directory of Open Access Journals (Sweden)

Rajani Singh

Full Text Available Abstract During dissection conducted in an anatomy department of the right upper limb of the cadaver of a 70-year-old male, both origin and course of the radial and ulnar arteries were found to be anomalous. After descending 5.5 cm from the lower border of the teres major, the brachial artery anomalously bifurcated into a radial artery medially and an ulnar artery laterally. In the arm, the ulnar artery lay lateral to the median nerve. It followed a normal course in the forearm. The radial artery was medial to the median nerve in the arm and then, at the level of the medial epicondyle, it crossed from the medial to the lateral side of the forearm, superficial to the flexor muscles. The course of the radial artery was superficial and tortuous throughout the arm and forearm. The variations of radial and ulnar arteries described above were associated with anomalous formation and course of the median nerve in the arm. Knowledge of neurovascular anomalies are important for vascular surgeons and radiologists.

Variations in the usage and composition of a radial cocktail during radial access coronary angiography procedures.

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.

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Lei Feng

2018-06-01

Full Text Available Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874–1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN, evolutionary neural network (ENN, extreme learning machine (ELM, general regression neural network (GRNN and radial basis neural network (RBNN were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

Design of radial reinforcement for prestressed concrete containments

Energy Technology Data Exchange (ETDEWEB)

Wang, Shen, E-mail: swang@bechtel.com [Bechtel Power Corporation, 5275 Westview Drive, BP2-2C3, Frederick, MD 21703 (United States); Munshi, Javeed A., E-mail: jamunshi@bechtel.com [Bechtel Power Corporation, 5275 Westview Drive, BP2-2C3, Frederick, MD 21703 (United States)

2013-02-15

Highlights: ► A rigorous formulae is proposed to calculate radial stress within prestressed concrete containments. ► The proposed method is validated by finite element analysis in an illustrative practical example. ► A partially prestressed condition is more critical than a fully prestressed condition for radial tension. ► Practical design consideration is provided for detailing of radial reinforcement. -- Abstract: Nuclear containments are critical components for safety of nuclear power plants. Failure can result in catastrophic safety consequences as a result of leakage of radiation. Prestressed concrete containments have been used in large nuclear power plants with significant design internal pressure. These containments are generally reinforced with prestressing tendons in the circumferential (hoop) and meridional (vertical) directions. The curvature effect of the tendons introduces radial tensile stresses in the concrete shell which are generally neglected in the design of such structures. It is assumed that such tensile radial stresses are small as such no radial reinforcement is provided for this purpose. But recent instances of significant delaminations in Crystal River Unit 3 in Florida have elevated the need for reevaluation of the radial tension issue in prestressed containment. Note that currently there are no well accepted industry standards for design and detailing of radial reinforcement. This paper discusses the issue of radial tension in prestressed cylindrical and dome shaped structures and proposes formulae to calculate radial stresses. A practical example is presented to illustrate the use of the proposed method which is then verified by using state of art finite element analysis. This paper also provides some practical design consideration for detailing of radial reinforcement in prestressed containments.

Methods and apparatus for radially compliant component mounting

Science.gov (United States)

Bulman, David Edward [Cincinnati, OH; Darkins, Jr., Toby George; Stumpf, James Anthony [Columbus, IN; Schroder, Mark S [Greenville, SC; Lipinski, John Joseph [Simpsonville, SC

2012-03-27

Methods and apparatus for a mounting assembly for a liner of a gas turbine engine combustor are provided. The combustor includes a combustor liner and a radially outer annular flow sleeve. The mounting assembly includes an inner ring surrounding a radially outer surface of the liner and including a plurality of axially extending fingers. The mounting assembly also includes a radially outer ring coupled to the inner ring through a plurality of spacers that extend radially from a radially outer surface of the inner ring to the outer ring.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

International Nuclear Information System (INIS)

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-01-01

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

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

Science.gov (United States)

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.

The effect of texture on the shaft surface on the sealing performance of radial lip seals

Science.gov (United States)

Guo, Fei; Jia, XiaoHong; Gao, Zhi; Wang, YuMing

2014-07-01

On the basis of elastohydrodynamic model, the present study numerically analyzes the effect of various microdimple texture shapes, namely, circular, square, oriented isosceles triangular, on the pumping rate and the friction torque of radial lip seals, and determines the microdimple texture shape that can produce positive pumping rate. The area ratio, depth and shape dimension of a single texture are the most important geometric parameters which influence the tribological performance. According to the selected texture shape, parameter analysis is conducted to determine the optimal combination for the above three parameters. Simultaneously, the simulated performances of radial lip seal with texture on the shaft surface are compared with those of the conventional lip seal without any texture on the shaft surface.

Radial rescaling approach for the eigenvalue problem of a particle in an arbitrarily shaped box.

Science.gov (United States)

Lijnen, Erwin; Chibotaru, Liviu F; Ceulemans, Arnout

2008-01-01

In the present work we introduce a methodology for solving a quantum billiard with Dirichlet boundary conditions. The procedure starts from the exactly known solutions for the particle in a circular disk, which are subsequently radially rescaled in such a way that they obey the new boundary conditions. In this way one constructs a complete basis set which can be used to obtain the eigenstates and eigenenergies of the corresponding quantum billiard to a high level of precision. Test calculations for several regular polygons show the efficiency of the method which often requires one or two basis functions to describe the lowest eigenstates with high accuracy.

Information transfer among widely spaced individuals: latrines as a basis for communication networks in the swift fox

DEFF Research Database (Denmark)

Darden, Safi-Kirstine; Steffensen, Lise K.; Dabelsteen, Torben

2008-01-01

In species where individuals are widely spaced instantaneous signals cannot readily form the basis of communication networks, that is several individuals within signalling range of each other. However, markings, signals that remain in the environment after the signaller has left, may fulfil...... 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...

Modelling a variable valve timing spark ignition engine using different neural networks

Energy Technology Data Exchange (ETDEWEB)

Beham, M. [BMW AG, Munich (Germany); Yu, D.L. [John Moores University, Liverpool (United Kingdom). Control Systems Research Group

2004-10-01

In this paper different neural networks (NN) are compared for modelling a variable valve timing spark-ignition (VVT SI) engine. The overall system is divided for each output into five neural multi-input single output (MISO) subsystems. Three kinds of NN, multilayer Perceptron (MLP), pseudo-linear radial basis function (PLRBF), and local linear model tree (LOLIMOT) networks, are used to model each subsystem. Real data were collected when the engine was under different operating conditions and these data are used in training and validation of the developed neural models. The obtained models are finally tested in a real-time online model configuration on the test bench. The neural models run independently of the engine in parallel mode. The model outputs are compared with process output and compared among different models. These models performed well and can be used in the model-based engine control and optimization, and for hardware in the loop systems. (author)

Comparison of the patient radiation exposure during coronary angiography and angioplasty procedures using trans-radial and trans-femoral access

Directory of Open Access Journals (Sweden)

Ali Tarighatnia

2016-06-01

Conclusion: On the basis of the results obtained in this study, no differences were found in patients’ radiation dose in both access groups, therefore with regard to comparatively more clinical advantages associated with the Trans-radial access technique it might be a good substitute for Trans-femoral access.

A statistical framework for evaluating neural networks to predict recurrent events in breast cancer

Science.gov (United States)

Gorunescu, Florin; Gorunescu, Marina; El-Darzi, Elia; Gorunescu, Smaranda

2010-07-01

Breast cancer is the second leading cause of cancer deaths in women today. Sometimes, breast cancer can return after primary treatment. A medical diagnosis of recurrent cancer is often a more challenging task than the initial one. In this paper, we investigate the potential contribution of neural networks (NNs) to support health professionals in diagnosing such events. The NN algorithms are tested and applied to two different datasets. An extensive statistical analysis has been performed to verify our experiments. The results show that a simple network structure for both the multi-layer perceptron and radial basis function can produce equally good results, not all attributes are needed to train these algorithms and, finally, the classification performances of all algorithms are statistically robust. Moreover, we have shown that the best performing algorithm will strongly depend on the features of the datasets, and hence, there is not necessarily a single best classifier.

Small disturbances stability analysis applied in a radial distribution system with distributed generation units; Analise de estabilidade a pequenos disturbios aplicada em um sistema de distribuicao radial com unidades de geracao distribuida

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)

MR accuracy and arthroscopic incidence of meniscal radial tears

Energy Technology Data Exchange (ETDEWEB)

Magee, Thomas; Shapiro, Marc; Williams, David [Department of Radiology, Neuroimaging Institute, 27 East Hibiscus Blvd., Melbourne, FL 32901 (United States)

2002-12-01

A meniscal radial tear is a vertical tear that involves the inner meniscal margin. The tear is most frequent in the middle third of the lateral meniscus and may extend outward in any direction. We report (1) the arthroscopic incidence of radial tears, (2) MR signs that aid in the detection of radial tears and (3) our prospective accuracy in detection of radial tears. Design and patients. Three musculoskeletal radiologists prospectively read 200 consecutive MR examinations of the knee that went on to arthroscopy by one orthopedic surgeon. MR images were assessed for location and MR characteristics of radial tears. MR criteria used for diagnosis of a radial tear were those outlined by Tuckman et al.: truncation, abnormal morphology and/or lack of continuity or absence of the meniscus on one or more MR images. An additional criterion used was abnormal increased signal in that area on fat-saturated proton density or T2-weighted coronal and sagittal images. Prospective MR readings were correlated with the arthroscopic findings.Results. Of the 200 consecutive knee arthroscopies, 28 patients had radial tears reported arthroscopically (14% incidence). MR readings prospectively demonstrated 19 of the 28 radial tears (68% sensitivity) when the criteria for diagnosis of a radial tear were truncation or abnormal morphology of the meniscus. With the use of the additional criterion of increased signal in the area of abnormal morphology on fat-saturated T2-weighted or proton density weighted sequences, the prospective sensitivity was 25 of 28 radial tears (89% sensitivity). There were no radial tears described in MR reports that were not demonstrated on arthroscopy (i.e., there were no false positive MR readings of radial tears in these 200 patients). Radial tears are commonly seen at arthroscopy. There was a 14% incidence in this series of 200 patients who underwent arthroscopy. Prospective detection of radial tears was 68% as compared with arthroscopy when the criteria as

MR accuracy and arthroscopic incidence of meniscal radial tears

International Nuclear Information System (INIS)

Magee, Thomas; Shapiro, Marc; Williams, David

2002-01-01

A meniscal radial tear is a vertical tear that involves the inner meniscal margin. The tear is most frequent in the middle third of the lateral meniscus and may extend outward in any direction. We report (1) the arthroscopic incidence of radial tears, (2) MR signs that aid in the detection of radial tears and (3) our prospective accuracy in detection of radial tears. Design and patients. Three musculoskeletal radiologists prospectively read 200 consecutive MR examinations of the knee that went on to arthroscopy by one orthopedic surgeon. MR images were assessed for location and MR characteristics of radial tears. MR criteria used for diagnosis of a radial tear were those outlined by Tuckman et al.: truncation, abnormal morphology and/or lack of continuity or absence of the meniscus on one or more MR images. An additional criterion used was abnormal increased signal in that area on fat-saturated proton density or T2-weighted coronal and sagittal images. Prospective MR readings were correlated with the arthroscopic findings.Results. Of the 200 consecutive knee arthroscopies, 28 patients had radial tears reported arthroscopically (14% incidence). MR readings prospectively demonstrated 19 of the 28 radial tears (68% sensitivity) when the criteria for diagnosis of a radial tear were truncation or abnormal morphology of the meniscus. With the use of the additional criterion of increased signal in the area of abnormal morphology on fat-saturated T2-weighted or proton density weighted sequences, the prospective sensitivity was 25 of 28 radial tears (89% sensitivity). There were no radial tears described in MR reports that were not demonstrated on arthroscopy (i.e., there were no false positive MR readings of radial tears in these 200 patients). Radial tears are commonly seen at arthroscopy. There was a 14% incidence in this series of 200 patients who underwent arthroscopy. Prospective detection of radial tears was 68% as compared with arthroscopy when the criteria as

Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

Science.gov (United States)

Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

2012-12-05

Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (Pdisorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

Radial velocities of RR Lyrae stars

International Nuclear Information System (INIS)

Hawley, S.L.; Barnes, T.G. III

1985-01-01

283 spectra of 57 RR Lyrae stars have been obtained using the 2.1-m telescope at McDonald Observatory. Radial velocities were determined using a software cross-correlation technique. New mean radial velocities were determined for 46 of the stars. 11 references

Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids.

Science.gov (United States)

Zhao, Ningbo; Li, Zhiming

2017-05-19

To effectively predict the thermal conductivity and viscosity of alumina (Al₂O₃)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al₂O₃-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al₂O₃-water nanofluids. However, the viscosity only depended strongly on Al₂O₃ nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data.

Radial MR images of the knee

International Nuclear Information System (INIS)

Hewes, R.C.; Miller, T.R.

1988-01-01

To profile optimally each portion of the meniscus, the authors use the multiangle, multisection feature of a General Electric SIGNA 1.5-T imager to produce radial images centered on each meniscus. A total of 12-15 sections are imaged at 10 0 -15 0 intervals of each meniscus, yielding perpendicular images of the entire meniscus, comparable with the arthrographic tangential views. The authors review their technique and demonstrate correlation cases between the radial gradient recalled acquisition in a steady state sequences, sagittal and coronal MR images, and arthrograms. Radial images should be a routine part of knee MR imaging

21 CFR 866.4800 - Radial immunodiffusion plate.

Science.gov (United States)

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...

Statistical process control using optimized neural networks: a case study.

Science.gov (United States)

Addeh, Jalil; Ebrahimzadeh, Ata; Azarbad, Milad; Ranaee, Vahid

2014-09-01

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Stirling Engine With Radial Flow Heat Exchangers

Science.gov (United States)

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.

Radial wedge flange clamp

Science.gov (United States)

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.

Comparing Fixed and Variable-Width Gaussian Networks

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra; Kainen, P.C.

2014-01-01

Roč. 57, September (2014), s. 23-28 ISSN 0893-6080 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : Gaussian radial and kernel networks * Functionally equivalent networks * Universal approximators * Stabilizers defined by Gaussian kernels * Argminima of error functionals Subject RIV: IN - Informatics, Computer Science Impact factor: 2.708, year: 2014

Radial pattern of nuclear decay processes

International Nuclear Information System (INIS)

Iskra, W.; Mueller, M.; Rotter, I.; Technische Univ. Dresden

1994-05-01

At high level density of nuclear states, a separation of different time scales is observed (trapping effect). We calculate the radial profile of partial widths in the framework of the continuum shell model for some 1 - resonances with 2p-2h nuclear structure in 16 O as a function of the coupling strength to the continuum. A correlation between the lifetime of a nuclear state and the radial profile of the corresponding decay process is observed. We conclude from our numerical results that the trapping effect creates structures in space and time characterized by a small radial extension and a short lifetime. (orig.)

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...

A new model of Earth's radial conductivity structure derived from over 10 yr of satellite and observatory magnetic data

DEFF Research Database (Denmark)

Püthe, Christoph; Kuvshinov, Alexey; Khan, Amir

2015-01-01

We present a newmodel of the radial (1-D) conductivity structure of Earth's mantle. This model is derived frommore than 10 yr of magnetic measurements from the satellites ørsted, CHAMP, SAC-C and the Swarm trio as well as the global network of geomagnetic observatories. After removal of core...

An improved power flow method based on extended chain-table storage structure for distribution network with PV nodes

DEFF Research Database (Denmark)

Chen, Shuheng; Wang, Xiongfei; Su, Chi

2014-01-01

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......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...... and the corresponding case study has been done. The experimental data and the further analysis have proved that this method can calculate the power flow of a distribution network with multi PV nodes precisely and fast. © 2014 IEEE....

Intraluminal milrinone for dilation of the radial artery graft.

Science.gov (United States)

García-Rinaldi, R; Soltero, E R; Carballido, J; Mojica, J

1999-01-01

There is renewed interest in the use of the radial artery as a conduit for coronary artery bypass surgery. The radial artery is, however, a very muscular artery, prone to vasospasm. Milrinone, a potent vasodilator, has demonstrated vasodilatory properties superior to those of papaverine. In this report, we describe our technique of radial artery harvesting and the adjunctive use of intraluminal milrinone as a vasodilator in the preparation of this conduit for coronary artery bypass grafting. We have used these techniques in 25 patients who have undergone coronary artery bypass grafting using the radial artery. No hand ischemic complications have been observed in this group. Intraluminal milrinone appears to dilate and relax the radial artery, rendering this large conduit spasm free and very easy to use. We recommend the skeletonization technique for radial artery harvesting and the use of intraluminal milrinone as a radial artery vasodilator in routine myocardial revascularization. PMID:10524740

Hopfield neural network and optical fiber sensor as intelligent heart rate monitor

Science.gov (United States)

Mutter, Kussay Nugamesh

2018-01-01

This paper presents a design and fabrication of an intelligent fiber-optic sensor used for examining and monitoring heart rate activity. It is found in the literature that the use of fiber sensors as heart rate sensor is widely studied. However, the use of smart sensors based on Hopfield neural networks is very low. In this work, the sensor is a three fibers without cladding of about 1 cm, fed by laser light of 1550 nm of wavelength. The sensing portions are mounted with a micro sensitive diaphragm to transfer the pulse pressure on the left radial wrist. The influenced light intensity will be detected by a three photodetectors as inputs into the Hopfield neural network algorithm. The latter is a singlelayer auto-associative memory structure with a same input and output layers. The prior training weights are stored in the net memory for the standard recorded normal heart rate signals. The sensors' heads work on the reflection intensity basis. The novelty here is that the sensor uses a pulse pressure and Hopfield neural network in an integrity approach. The results showed a significant output measurements of heart rate and counting with a plausible error rate.

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Science.gov (United States)

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.

Effects of various factors on Doppler ultrasonographic measurements of radial and coccygeal arterial blood pressure in privately owned, conscious cats.

Science.gov (United States)

Whittemore, Jacqueline C; Nystrom, Michael R; Mawby, Dianne I

2017-04-01

OBJECTIVE To assess the effects of age, body condition score (BCS), and muscle condition score (MCS) on radial and coccygeal systolic arterial blood pressure (SAP) in cats. DESIGN Prospective randomized trial. ANIMALS 66 privately owned cats enrolled between May and December 2010. PROCEDURES BCS and MCS of cats were assessed by 2 investigators; SAP was measured via Doppler ultrasonic flow detector, with cats positioned in right lateral or sternal recumbency for measurements at the radial or coccygeal artery, respectively, with order of site randomized. Associations among variables were assessed through correlation coefficients, partial correlation coefficients, and ANCOVA. RESULTS Interrater reliability for BCS and MCS assessment was high (correlation coefficients, 0.95 and 0.83, respectively). No significant effect was identified for order of SAP measurement sites. Coccygeal and radial SAP were positively correlated (ρ = 0.45). The median difference in coccygeal versus radial SAP was 19 mm Hg, but differences were not consistently positive or negative. Radial SAP was positively correlated with age (ρ = 0.48) and negatively correlated with MCS (ρ = -0.30). On the basis of the correlation analysis, the association between radial SAP and MCS reflected the confounding influence of age. Coccygeal SAP was not significantly correlated with age, BCS, or MCS. CONCLUSIONS AND CLINICAL RELEVANCE Use of the coccygeal artery is recommended to reduce the confounding effects of age and sarcopenia on Doppler ultrasonographic SAP measurements in cats. Additionally, monitoring for changes in MCS is recommended for cats undergoing serial SAP measurement.

Channeling of protons through radial deformed carbon nanotubes

Energy Technology Data Exchange (ETDEWEB)

Borka Jovanović, V., E-mail: vborka@vinca.rs [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Borka, D. [Atomic Physics Laboratory (040), Vinča Institute of Nuclear Sciences, University of Belgrade, P.O. Box 522, 11001 Belgrade (Serbia); Galijaš, S.M.D. [Faculty of Physics, University of Belgrade, P.O. Box 368, 11001 Belgrade (Serbia)

2017-05-18

Highlights: • For the first time we presented theoretically obtained distributions of channeled protons with radially deformed SWNT. • Our findings indicate that influence of the radial deformation is very strong and it should not be omitted in simulations. • We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. • Our obtained results can be compared with measured distributions to reveal the presence of various types of defects in SWNT. - Abstract: In this paper we have presented a theoretical investigation of the channeling of 1 GeV protons with the radial deformed (10, 0) single-wall carbon nanotubes (SWNTs). We have calculated channeling potential within the deformed nanotubes. For the first time we presented theoretically obtained spatial and angular distributions of channeled protons with radially deformed SWNT. We used a Monte Carlo (MC) simulation technique. We show that the spatial and angular distributions depend strongly of level of radial deformation of nanotube. These results may be useful for nanotube characterization and production and guiding of nanosized ion beams.

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

Science.gov (United States)

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

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.

File access prediction using neural networks.

Science.gov (United States)

Patra, Prashanta Kumar; Sahu, Muktikanta; Mohapatra, Subasish; Samantray, Ronak Kumar

2010-06-01

One of the most vexing issues in design of a high-speed computer is the wide gap of access times between the memory and the disk. To solve this problem, static file access predictors have been used. In this paper, we propose dynamic file access predictors using neural networks to significantly improve upon the accuracy, success-per-reference, and effective-success-rate-per-reference by using neural-network-based file access predictor with proper tuning. In particular, we verified that the incorrect prediction has been reduced from 53.11% to 43.63% for the proposed neural network prediction method with a standard configuration than the recent popularity (RP) method. With manual tuning for each trace, we are able to improve upon the misprediction rate and effective-success-rate-per-reference using a standard configuration. Simulations on distributed file system (DFS) traces reveal that exact fit radial basis function (RBF) gives better prediction in high end system whereas multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) backpropagation outperforms in system having good computational capability. Probabilistic and competitive predictors are the most suitable for work stations having limited resources to deal with and the former predictor is more efficient than the latter for servers having maximum system calls. Finally, we conclude that MLP with LM backpropagation algorithm has better success rate of file prediction than those of simple perceptron, last successor, stable successor, and best k out of m predictors.

Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

Science.gov (United States)

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.

Radial electric fields for improved tokamak performance

International Nuclear Information System (INIS)

Downum, W.B.

1981-01-01

The influence of externally-imposed radial electric fields on the fusion energy output, energy multiplication, and alpha-particle ash build-up in a TFTR-sized, fusing tokamak plasma is explored. In an idealized tokamak plasma, an externally-imposed radial electric field leads to plasma rotation, but no charge current flows across the magnetic fields. However, a realistically-low neutral density profile generates a non-zero cross-field conductivity and the species dependence of this conductivity allows the electric field to selectively alter radial particle transport

Interface information transfer between non-matching, nonconforming interfaces using radial basis function interpolation

CSIR Research Space (South Africa)

Bogaers, Alfred EJ

2016-10-01

Full Text Available words, gB = [ φBA PB ] [ MAA PA P TA 0 ]−1 [ gA 0 ] . (15) NAME: DEFINITION C0 compactly supported piecewise polynomial (C0): (1− (||x|| /r))2+ C2 compactly supported piecewise polynomial (C2): (1− (||x|| /r))4+ (4 (||x|| /r) + 1) Thin-plate spline (TPS... a numerical comparison to Kriging and the moving least-squares method, see Krishnamurthy [16]). RBF interpolation is based on fitting a series of splines, or basis functions to interpolate information from one point cloud to another. Let us assume we...

Interactions between Radial Electric Field, Transport and Structure in Helical Plasmas

International Nuclear Information System (INIS)

Ida, Katsumi and others

2006-01-01

Control of the radial electric field is considered to be important in helical plasmas, because the radial electric field and its shear are expected to reduce neoclassical and anomalous transport, respectively. Particle and heat transport, that determines the radial structure of density and electron profiles, sensitive to the structure of radial electric field. On the other hand, the radial electric field itself is determined by the plasma parameters. In general, the sign of the radial electric field is determined by the plasma collisionality, while the magnitude of the radial electric field is determined by the temperature and/or density gradients. Therefore the structure of radial electric field and temperature and density are strongly coupled through the particle and heat transport and formation mechanism of radial electric field. Interactions between radial electric field, transport and structure in helical plasmas is discussed based on the experiments on Large Helical Device

Anomalous Medial Branch of Radial Artery: A Rare Variant

Directory of Open Access Journals (Sweden)

Surbhi Wadhwa

2016-10-01

Full Text Available Radial artery is an important consistent vessel of the upper limb. It is a useful vascular access site for coronary procedures and its reliable anatomy has resulted in an elevation of radial forearm flaps for reconstructive surgeries of head and neck. Technical failures, in both the procedures, are mainly due to anatomical variations, such as radial loops, ectopic radial arteries or tortuosity in the vessel. We present a rare and a unique anomalous medial branch of the radial artery spiraling around the flexor carpi radialis muscle in the forearm with a high rising superficial palmar branch of radial artery. Developmentally it probably is a remanent of the normal pattern of capillary vessel maintenance and regression. Such a case is of importance for reconstructive surgeons and coronary interventionists, especially in view of its unique medial and deep course.

Fuel radial design using Path Relinking; Diseno radial de combustible usando Path Relinking

Energy Technology Data Exchange (ETDEWEB)

Campos S, Y. [ININ, 52750 La Marquesa, Estado de Mexico (Mexico)

2007-07-01

The present work shows the obtained results when implementing the combinatory optimization technique well-known as Path Re linking (Re-linkage of Trajectories), to the problem of the radial design of nuclear fuel assemblies, for boiling water reactors (BWR Boiling Water Reactor by its initials in English), this type of reactors is those that are used in the Laguna Verde Nucleo electric Central, Veracruz. As in any other electric power generation plant of that make use of some fuel to produce heat and that it needs each certain time (from 12 to 14 months) to make a supply of the same one, because this it wears away or it burns, in the nucleolectric plants to this activity is denominated fuel reload. In this reload different activities intervene, among those which its highlight the radial and axial designs of fuel assemblies, the patterns of control rods and the multi cycles study, each one of these stages with their own complexity. This work was limited to study in independent form the radial design, without considering the other activities. These phases are basic for the fuel reload design and of reactor operation strategies. (Author)

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

Science.gov (United States)

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Artificial neural network simulation of battery performance

Energy Technology Data Exchange (ETDEWEB)

O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

1998-12-31

Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

Clinical and Radiographic Outcomes of Unipolar and Bipolar Radial Head Prosthesis in Patients with Radial Head Fracture: A Systemic Review and Meta-Analysis.

Science.gov (United States)

Chen, Hongwei; Wang, Ziyang; Shang, Yongjun

2018-06-01

To compare clinical outcomes of unipolar and bipolar radial head prosthesis in the treatment of patients with radial head fracture. Medline, Cochrane, EMBASE, Google Scholar databases were searched until April 18, 2016 using the following search terms: radial head fracture, elbow fracture, radial head arthroplasty, implants, prosthesis, unipolar, bipolar, cemented, and press-fit. Randomized controlled trials, retrospective, and cohort studies were included. The Mayo elbow performance score (MEPS), disabilities of the arm, shoulder, and hand (DASH) score, radiologic assessment, ROM, and grip strength following elbow replacement were similar between prosthetic devices. The pooled mean excellent/good ranking of MEPS was 0.78 for unipolar and 0.73 for bipolar radial head arthroplasty, and the pooled mean MEPS was 86.9 and 79.9, respectively. DASH scores for unipolar and bipolar prosthesis were 19.0 and 16.3, respectively. Range of motion outcomes were similar between groups, with both groups have comparable risk of flexion arc, flexion, extension deficit, rotation arc, pronation, and supination (p values bipolar prosthesis). However, bipolar radial head prosthesis was associated with an increased chance of heterotopic ossification and lucency (p values ≤0.049) while unipolar prosthesis was not (p values ≥0.088). Both groups had risk for development of capitellar osteopenia or erosion/wear (p values ≤0.039). Unipolar and bipolar radial head prostheses were similar with respect to clinical outcomes. Additional comparative studies are necessary to further compare different radial head prostheses used to treat radial head fracture.

Vitreous veils and radial lattice in Marshall syndrome.

Science.gov (United States)

Brubaker, Jacob W; Mohney, Brian G; Pulido, Jose S; Babovic-Vuksanovic, Dusica

2008-12-01

To report the findings of membranous vitreous veils and radial lattice in a child with Marshall syndrome. Observational case report. Retrospective review of medical records and fundus photograph of a 6-year-old boy with Marshall syndrome. Vitreoretinal findings were significant for bilateral membranous vitreous veils and radial lattice degeneration. This case demonstrates the occurrence of vitreous veils and radial lattice degeneration in patients with Marshall syndrome.

Long-Term Follow-up of Modular Metallic Radial Head Replacement: Commentary on an article by Jonathan P. Marsh, MD, FRCSC, et al.: "Radial Head Fractures Treated with Modular Metallic Radial Head Replacement: Outcomes at a Mean Follow-up of Eight Years".

OpenAIRE

Mansat, Pierre

2016-01-01

Radial head arthroplasty is used to stabilize the joint after a complex acute radial head fracture that is not amenable for fixation or to treat sequelae of radial head fractures. Most of the currently used radial head prostheses are metallic monoblock implants that are not consistently adaptable and raise technical challenges since their implantation requires lateral elbow subluxation. Metallic modular radial head arthroplasty implants available in various head and stem sizes have been devel...

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

Energy Technology Data Exchange (ETDEWEB)

Cebrian, Juan Carlos; Kagan, Nelson [Department of Electrical Engineering, University of Sao Paulo, Escola Politecnica, Av. Prof. Luciano Gualberto, travessa 3 n 380 - CEP - 05508-970 - Sao Paulo (Brazil)

2010-01-15

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (author)

FocusHeuristics - expression-data-driven network optimization and disease gene prediction.

Science.gov (United States)

Ernst, Mathias; Du, Yang; Warsow, Gregor; Hamed, Mohamed; Endlich, Nicole; Endlich, Karlhans; Murua Escobar, Hugo; Sklarz, Lisa-Madeleine; Sender, Sina; Junghanß, Christian; Möller, Steffen; Fuellen, Georg; Struckmann, Stephan

2017-02-16

To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.

Radial head fracture associated with posterior interosseous nerve injury

Directory of Open Access Journals (Sweden)

Bernardo Barcellos Terra

Full Text Available ABSTRACT Fractures of the radial head and radial neck correspond to 1.7-5.4% of all fractures and approximately 30% may present associated injuries. In the literature, there are few reports of radial head fracture with posterior interosseous nerve injury. This study aimed to report a case of radial head fracture associated with posterior interosseous nerve injury. CASE REPORT: A male patient, aged 42 years, sought medical care after falling from a skateboard. The patient related pain and limitation of movement in the right elbow and difficulty to extend the fingers of the right hand. During physical examination, thumb and fingers extension deficit was observed. The wrist extension showed a slight radial deviation. After imaging, it became evident that the patient had a fracture of the radial head that was classified as grade III in the Mason classification. The patient underwent fracture fixation; at the first postoperative day, thumb and fingers extension was observed. Although rare, posterior interosseous nerve branch injury may be associated with radial head fractures. In the present case, the authors believe that neuropraxia occurred as a result of the fracture hematoma and edema.

Radial Field Piezoelectric Diaphragms

Science.gov (United States)

Bryant, R. G.; Effinger, R. T., IV; Copeland, B. M., Jr.

2002-01-01

A series of active piezoelectric diaphragms were fabricated and patterned with several geometrically defined Inter-Circulating Electrodes "ICE" and Interdigitated Ring Electrodes "ICE". When a voltage potential is applied to the electrodes, the result is a radially distributed electric field that mechanically strains the piezoceramic along the Z-axis (perpendicular to the applied electric field). Unlike other piezoelectric bender actuators, these Radial Field Diaphragms (RFDs) strain concentrically yet afford high displacements (several times that of the equivalent Unimorph) while maintaining a constant circumference. One of the more intriguing aspects is that the radial strain field reverses itself along the radius of the RFD while the tangential strain remains relatively constant. The result is a Z-deflection that has a conical profile. This paper covers the fabrication and characterization of the 5 cm. (2 in.) diaphragms as a function of poling field strength, ceramic thickness, electrode type and line spacing, as well as the surface topography, the resulting strain field and displacement as a function of applied voltage at low frequencies. The unique features of these RFDs include the ability to be clamped about their perimeter with little or no change in displacement, the environmentally insulated packaging, and a highly repeatable fabrication process that uses commodity materials.

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1 for spatial prediction of floods

Directory of Open Access Journals (Sweden)

D. Tien Bui

2017-09-01

Full Text Available In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM, radial-basis-function Fisher discriminant analysis (RBFDA, and a geographic information system (GIS database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

Radial transport with perturbed magnetic field

Energy Technology Data Exchange (ETDEWEB)

Hazeltine, R. D. [Institute for Fusion Studies, University of Texas at Austin, Austin, Texas 78712 (United States)

2015-05-15

It is pointed out that the viscosity coefficient describing radial transport of toroidal angular momentum is proportional to the second power of the gyro-radius—like the corresponding coefficients for particle and heat transport—regardless of any geometrical symmetry. The observation is widely appreciated, but worth emphasizing because some literature gives the misleading impression that asymmetry can allow radial moment transport in first-order.

Radial transport with perturbed magnetic field

International Nuclear Information System (INIS)

Hazeltine, R. D.

2015-01-01

It is pointed out that the viscosity coefficient describing radial transport of toroidal angular momentum is proportional to the second power of the gyro-radius—like the corresponding coefficients for particle and heat transport—regardless of any geometrical symmetry. The observation is widely appreciated, but worth emphasizing because some literature gives the misleading impression that asymmetry can allow radial moment transport in first-order

Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors.

Science.gov (United States)

Khoshgoftaar, Taghi M; Van Hulse, Jason; Napolitano, Amri

2010-05-01

Neural network algorithms such as multilayer perceptrons (MLPs) and radial basis function networks (RBFNets) have been used to construct learners which exhibit strong predictive performance. Two data related issues that can have a detrimental impact on supervised learning initiatives are class imbalance and labeling errors (or class noise). Imbalanced data can make it more difficult for the neural network learning algorithms to distinguish between examples of the various classes, and class noise can lead to the formulation of incorrect hypotheses. Both class imbalance and labeling errors are pervasive problems encountered in a wide variety of application domains. Many studies have been performed to investigate these problems in isolation, but few have focused on their combined effects. This study presents a comprehensive empirical investigation using neural network algorithms to learn from imbalanced data with labeling errors. In particular, the first component of our study investigates the impact of class noise and class imbalance on two common neural network learning algorithms, while the second component considers the ability of data sampling (which is commonly used to address the issue of class imbalance) to improve their performances. Our results, for which over two million models were trained and evaluated, show that conclusions drawn using the more commonly studied C4.5 classifier may not apply when using neural networks.

The Network Model of Depression as a Basis for New Therapeutic Strategies for Treating Major Depressive Disorder in Parkinson’s Disease

Science.gov (United States)

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

Systematic determination of extended atomic orbital basis sets and application to molecular SCF and MCSCF calculations

Energy Technology Data Exchange (ETDEWEB)

Feller, D.F.

1979-01-01

The behavior of the two exponential parameters in an even-tempered gaussian basis set is investigated as the set optimally approaches an integral transform representation of the radial portion of atomic and molecular orbitals. This approach permits a highly accurate assessment of the Hartree-Fock limit for atoms and molecules.

RBF Neural Network Approach for Identification and Control of DC Motors

Directory of Open Access Journals (Sweden)

EA Feilat

2012-12-01

Full Text Available In this paper, a neural network approach for the identification and control of a separately excited direct (DC motor (SEDCM driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN. The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

On the Influence of Piston and Cylinder Density in Tribodynamics of a Radial Piston Digital Fluid Power Displacement Motor

DEFF Research Database (Denmark)

Johansen, Per; Roemer, Daniel Beck; Andersen, Torben O.

2015-01-01

-stroke displacement simulations are used as basis for the parametric analysis. From the parametric analysis a change, in the minimum film thickness as function of piston and cylinder density, is shown for certain operating modes of the digital fluid power displacement motor. This indicate a need for careful....... In this paper the influence of the inertia term on the lubrication gaps of a radial piston motor are studied by a parametric analysis of the piston and cylinder density in a multibody tribodynamic simulation model. The motor is modeled as a digital fluid power displacement machine and a series of full...... assessment of the applicability, of the force balance condition, if it is used in multibody tribodynamic simulations of radial piston digital fluid power displacement motors....

Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting

Directory of Open Access Journals (Sweden)

Jianzhong Zhou

2018-05-01

Full Text Available This paper introduces three artificial neural network (ANN architectures for monthly streamflow forecasting: a radial basis function network, an extreme learning machine, and the Elman network. Three ensemble techniques, a simple average ensemble, a weighted average ensemble, and an ANN-based ensemble, were used to combine the outputs of the individual ANN models. The objective was to highlight the performance of the general regression neural network-based ensemble technique (GNE through an improvement of monthly streamflow forecasting accuracy. Before the construction of an ANN model, data preanalysis techniques, such as empirical wavelet transform (EWT, were exploited to eliminate the oscillations of the streamflow series. Additionally, a theory of chaos phase space reconstruction was used to select the most relevant and important input variables for forecasting. The proposed GNE ensemble model has been applied for the mean monthly streamflow observation data from the Wudongde hydrological station in the Jinsha River Basin, China. Comparisons and analysis of this study have demonstrated that the denoised streamflow time series was less disordered and unsystematic than was suggested by the original time series according to chaos theory. Thus, EWT can be adopted as an effective data preanalysis technique for the prediction of monthly streamflow. Concurrently, the GNE performed better when compared with other ensemble techniques.

Manufacturing of small scale W monoblock mockups by hot radial pressing

International Nuclear Information System (INIS)

Visca, Eliseo; Testani, C.; Libera, S.; Sacchetti, M.

2003-01-01

In the frame of the European Technology R and D programme for International thermonuclear experimental reactor (ITER) and in the area of high heat flux plasma facing components (HHFC), representative small-scale mock-ups were manufactured and tested to compare different concepts and joining technologies (i.e. active brazing, hot isostatic pressing (HIPping), diffusion bonding, etc.). On the basis of the results obtained by thermal fatigue tests, the monoblock concept resulted to be the most robust one, particularly when the HIPping manufacturing technology is used. Within this programme, ENEA developed an alternative technique for manufacturing plasma-facing components with a monoblock geometry of the ITER machine. The basic idea of this technique, named hot radial pressing (HRP), is to perform a radial diffusion bonding between the cooling tube and the armour tile by pressurising the internal tube only and by keeping the process parameters within the range in which the thermo-mechanical properties of the copper alloys are not yet degraded. The HRP is performed by a standard furnace, in which only a section of the canister is heated. The manufacturing procedure and the results of the screening and fatigue thermal tests performed on the ENEA mock-ups are reported in this paper

Pathologic bone tissues in a Turkey vulture and a nonavian dinosaur: implications for interpreting endosteal bone and radial fibrolamellar bone in fossil dinosaurs.

Science.gov (United States)

Chinsamy, Anusuya; Tumarkin-Deratzian, Allison

2009-09-01

We report on similar pathological bone microstructure in an extant turkey vulture (Cathartes aura) and a nonavian dinosaur from Transylvania. Both these individuals exhibit distinctive periosteal reactive bone deposition accompanied by endosteal bone deposits in the medullary cavity. Our findings have direct implications on the two novel bone tissues recently described among nonavian dinosaurs, radial fibrolamellar bone tissue and medullary bone tissue. On the basis of the observed morphology of the periosteal reactive bone in the turkey vulture and the Transylvanian dinosaur, we propose that the radial fibrolamellar bone tissues observed in mature dinosaurs may have had a pathological origin. Our analysis also shows that on the basis of origin, location, and morphology, pathologically derived endosteal bone tissue can be similar to medullary bone tissues described in nonavian dinosaurs. As such, we caution the interpretation of all endosteally derived bone tissue as homologous to avian medullary bone. (c) 2009 Wiley-Liss, Inc.

Concepts of radial and angular kinetic energies

DEFF Research Database (Denmark)

Dahl, Jens Peder; Schleich, W.P.

2002-01-01

We consider a general central-field system in D dimensions and show that the division of the kinetic energy into radial and angular parts proceeds differently in the wave-function picture and the Weyl-Wigner phase-space picture, Thus, the radial and angular kinetic energies are different quantities...

Influences of climate on the radial growth of lodgepole pine in Alberta

Energy Technology Data Exchange (ETDEWEB)

Chhin, S.; Lieffers, V.J. [Alberta Univ., Edmonton, AB (Canada). Dept. of Renewable Resources; Hogg, E.H. [Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, AB (Canada); Huang, S. [Alberta Sustainable Resource Development, Edmonton, AB (Canada). Forest Management Branch

2008-02-15

The forests of the Cordilleran region were used to document past relationships between tree growth and climate. Radial growth and climate relationships of lodgepole pines were examined across a network of 17 sites in Alberta over a distance of 1100 km. Pine sites were selected from a permanent plot database covering the predominant latitudinal and elevational range of lodgepole pines. An average of 21 dominant and co-dominant trees were sampled in a 50 m buffer zone. Ring-width series were detrended in 3 stages using negative exponential curves, linear regression, and low frequency standardized series (LFS). Each LFS series was detrended with a cubic-smoothing spline. Autocorrelation in each series was removed via autoregressive (AR) modelling resulting in high frequency residual series. High frequency residual (HFR) site chronologies were constructed by averaging HFR series developed at each site. Primary climate variables included mean daily minimum and maximum temperatures for each month and total monthly precipitation. Relationships between the different chronology types were then examined. Results of the study demonstrated that cool and moist conditions during the later summer months led to improved radial growth levels during the following years. Warm, dry winters and warm conditions during the autumn of the year in which the ring was formed also promoted tree growth. Results suggested that climatic warming and drying have opposing effects. It was concluded that future impacts on lodgepole pine radial growth will depend on the pattern and magnitude of changes in temperature and precipitation in each season. 55 refs., 3 tabs., 8 figs.

DISTRIBUTION NETWORK RECONFIGURATION FOR POWER LOSS MINIMIZATION AND VOLTAGE PROFILE ENHANCEMENT USING ANT LION ALGORITHM

Directory of Open Access Journals (Sweden)

Maryam Shokouhi

2017-06-01

Full Text Available Distribution networks are designed as a ring and operated as a radial form. Therefore, the reconfiguration is a simple and cost-effective way to use existing facilities without the need for any new equipment in distribution networks to achieve various objectives such as: power loss reduction, feeder overload reduction, load balancing, voltage profile improvement, reducing the number of switching considering constraints that ultimately result in the power loss reduction. In this paper, a new method based on the Ant Lion algorithm (a modern meta-heuristic algorithm is provided for the reconfiguration of distribution networks. Considering the extension of the distribution networks and complexity of their communications networks, and the various parameters, using smart techniques is inevitable. The proposed approach is tested on the IEEE 33 & 69-bus radial standard distribution networks. The Evaluation of results in MATLAB software shows the effectiveness of the Ant Lion algorithm in the distribution network reconfiguration.

Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.

Science.gov (United States)

Tunali, Ilke; Stringfield, Olya; Guvenis, Albert; Wang, Hua; Liu, Ying; Balagurunathan, Yoganand; Lambin, Philippe; Gillies, Robert J; Schabath, Matthew B

2017-11-10

The goal of this study was to extract features from radial deviation and radial gradient maps which were derived from thoracic CT scans of patients diagnosed with lung adenocarcinoma and assess whether these features are associated with overall survival. We used two independent cohorts from different institutions for training (n= 61) and test (n= 47) and focused our analyses on features that were non-redundant and highly reproducible. To reduce the number of features and covariates into a single parsimonious model, a backward elimination approach was applied. Out of 48 features that were extracted, 31 were eliminated because they were not reproducible or were redundant. We considered 17 features for statistical analysis and identified a final model containing the two most highly informative features that were associated with lung cancer survival. One of the two features, radial deviation outside-border separation standard deviation, was replicated in a test cohort exhibiting a statistically significant association with lung cancer survival (multivariable hazard ratio = 0.40; 95% confidence interval 0.17-0.97). Additionally, we explored the biological underpinnings of these features and found radial gradient and radial deviation image features were significantly associated with semantic radiological features.

Illumination Profile & Dispersion Variation Effects on Radial Velocity Measurements

Science.gov (United States)

Grieves, Nolan; Ge, Jian; Thomas, Neil B.; Ma, Bo; Li, Rui; SDSS-III

2015-01-01

The Multi-object APO Radial-Velocity Exoplanet Large-Area Survey (MARVELS) measures radial velocities using a fiber-fed dispersed fixed-delay interferometer (DFDI) with a moderate dispersion spectrograph. This setup allows a unique insight into the 2D illumination profile from the fiber on to the dispersion grating. Illumination profile investigations show large changes in the profile over time and fiber location. These profile changes are correlated with dispersion changes and long-term radial velocity offsets, a major problem within the MARVELS radial velocity data. Characterizing illumination profiles creates a method to both detect and correct radial velocity offsets, allowing for better planet detection. Here we report our early results from this study including improvement of radial velocity data points from detected giant planet candidates. We also report an illumination profile experiment conducted at the Kitt Peak National Observatory using the EXPERT instrument, which has a DFDI mode similar to MARVELS. Using profile controlling octagonal-shaped fibers, long term offsets over a 3 month time period were reduced from ~50 m/s to within the photon limit of ~4 m/s.

Radial transfer effects for poloidal rotation

Science.gov (United States)

Hallatschek, Klaus

2010-11-01

Radial transfer of energy or momentum is the principal agent responsible for radial structures of Geodesic Acoustic Modes (GAMs) or stationary Zonal Flows (ZF) generated by the turbulence. For the GAM, following a physical approach, it is possible to find useful expressions for the individual components of the Poynting flux or radial group velocity allowing predictions where a mathematical full analysis is unfeasible. Striking differences between up-down symmetric flux surfaces and asymmetric ones have been found. For divertor geometries, e.g., the direction of the propagation depends on the sign of the ion grad-B drift with respect to the X-point, reminiscent of a sensitive determinant of the H-mode threshold. In nonlocal turbulence computations it becomes obvious that the linear energy transfer terms can be completely overwhelmed by the action of the turbulence. In contrast, stationary ZFs are governed by the turbulent radial transfer of momentum. For sufficiently large systems, the Reynolds stress becomes a deterministic functional of the flows, which can be empirically determined from the stress response in computational turbulence studies. The functional allows predictions even on flow/turbulence states not readily obtainable from small amplitude noise, such as certain transport bifurcations or meta-stable states.

Three-Phase Unbalanced Load Flow Tool for Distribution Networks

DEFF Research Database (Denmark)

Demirok, Erhan; Kjær, Søren Bækhøj; Sera, Dezso

2012-01-01

This work develops a three-phase unbalanced load flow tool tailored for radial distribution networks based on Matlab®. The tool can be used to assess steady-state voltage variations, thermal limits of grid components and power losses in radial MV-LV networks with photovoltaic (PV) generators where...... most of the systems are single phase. New ancillary service such as static reactive power support by PV inverters can be also merged together with the load flow solution tool and thus, the impact of the various reactive power control strategies on the steady-state grid operation can be simply...... investigated. Performance of the load flow solution tool in the sense of resulting bus voltage magnitudes is compared and validated with IEEE 13-bus test feeder....

Anterior transposition of the radial nerve--a cadaveric study.

Science.gov (United States)

Yakkanti, Madhusudhan R; Roberts, Craig S; Murphy, Joshua; Acland, Robert D

2008-01-01

The radial nerve is at risk during the posterior plating of the humerus. The purpose of this anatomic study was to assess the extent of radial nerve dissection required for anterior transposition through the fracture site (transfracture anterior transposition). A cadaver study was conducted approaching the humerus by a posterior midline incision. The extent of dissection of the nerve necessary for plate fixation of the humerus fracture was measured. An osteotomy was created to model a humeral shaft fracture at the spiral groove (OTA classification 12-A2, 12-A3). The radial nerve was then transposed anterior to the humeral shaft through the fracture site. The additional dissection of the radial nerve and the extent of release of soft tissue from the humerus shaft to achieve the transposition were measured. Plating required a dissection of the radial nerve 1.78 cm proximal and 2.13 cm distal to the spiral groove. Transfracture anterior transposition of the radial nerve required an average dissection of 2.24 cm proximal and 2.68 cm distal to the spiral groove. The lateral intermuscular septum had to be released for 2.21 cm on the distal fragment to maintain laxity of the transposed nerve. Transfracture anterior transposition of the radial nerve before plating is feasible with dissection proximal and distal to the spiral groove and elevation of the lateral intermuscular septum. Potential clinical advantages of this technique include enhanced fracture site visualization, application of broader plates, and protection of the radial nerve during the internal fixation.

Incorporation of distributed generation and shunt capacitor in radial distribution system for techno-economic benefits

Directory of Open Access Journals (Sweden)

Mukul Dixit

2017-04-01

The various costs such as purchase active power from grid, DG installation, capacitor installation, DG Operation and Maintenance (O&M are evaluated at two different load scenarios. In addition to that, technical and economical analyses are examined for various combinations of DGs and shunt capacitors. The proposed methodology is successfully demonstrated on 33-bus and 85-bus radial networks and the obtained numerical outcomes validate the suitability, importance and effectiveness to identify locations as well as sizes of DGs and shunt capacitors.

A New Filtering Algorithm Utilizing Radial Velocity Measurement

Institute of Scientific and Technical Information of China (English)

LIU Yan-feng; DU Zi-cheng; PAN Quan

2005-01-01

Pulse Doppler radar measurements consist of range, azimuth, elevation and radial velocity. Most of the radar tracking algorithms in engineering only utilize position measurement. The extended Kalman filter with radial velocity measureneut is presented, then a new filtering algorithm utilizing radial velocity measurement is proposed to improve tracking results and the theoretical analysis is also given. Simulation results of the new algorithm, converted measurement Kalman filter, extended Kalman filter are compared. The effectiveness of the new algorithm is verified by simulation results.

Photoelectric Radial Velocities, Paper XIX Additional Spectroscopic ...

Indian Academy of Sciences (India)

ian velocity curve that does justice to the measurements, but it cannot be expected to have much predictive power. Key words. Stars: late-type—stars: radial velocities—spectroscopic binaries—orbits. 0. Preamble. The 'Redman K stars' are a lot of seventh-magnitude K stars whose radial velocities were first observed by ...

Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users.

Science.gov (United States)

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.

Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users

Science.gov (United States)

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

Linear theory radial and nonradial pulsations of DA dwarf stars

International Nuclear Information System (INIS)

Starrfield, S.; Cox, A.N.; Hodson, S.; Pesnell, W.D.

1982-01-01

The Los Alamos stellar envelope and radial linear non-adiabatic computer code, along with a new Los Alamos non-radial code are used to investigate the total hydrogen mass necessary to produce the non-radial instability of DA dwarfs

Evaporation Limited Radial Capillary Penetration in Porous Media.

Science.gov (United States)

Liu, Mingchao; Wu, Jian; Gan, Yixiang; Hanaor, Dorian A H; Chen, C Q

2016-09-27

The capillary penetration of fluids in thin porous layers is of fundamental interest in nature and various industrial applications. When capillary flows occur in porous media, the extent of penetration is known to increase with the square root of time following the Lucas-Washburn law. In practice, volatile liquid evaporates at the surface of porous media, which restricts penetration to a limited region. In this work, on the basis of Darcy's law and mass conservation, a general theoretical model is developed for the evaporation-limited radial capillary penetration in porous media. The presented model predicts that evaporation decreases the rate of fluid penetration and limits it to a critical radius. Furthermore, we construct a unified phase diagram that describes the limited penetration in an annular porous medium, in which the boundaries of outward and inward liquid are predicted quantitatively. It is expected that the proposed theoretical model will advance the understanding of penetration dynamics in porous media and facilitate the design of engineered porous architectures.

Zernike Basis to Cartesian Transformations

Science.gov (United States)

Mathar, R. J.

2009-12-01

The radial polynomials of the 2D (circular) and 3D (spherical) Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle) defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.

Comparison of surrogate models with different methods in ...

Indian Academy of Sciences (India)

In this article, polynomial regression (PR), radial basis function artificial neural network (RBFANN), and kriging ..... 10 kriging models with different parameters were also obtained. ..... shapes using stochastic optimization methods and com-.

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Comparison of surrogate models with different methods in groundwater ... radial basis function artificial neural network (RBFANN), and kriging methods ... was adopted to evaluate the approximation accuracy of the three surrogate models.

Effect of the radial electric field on turbulence

International Nuclear Information System (INIS)

Carreras, B.A.; Lynch, V.E.

1990-01-01

For many years, the neoclassical transport theory for three- dimensional magnetic configurations, such as magnetic mirrors, ELMO Bumpy Tori (EBTs), and stellarators, has recognized the critical role of the radial electric field in the confinement. It was in these confinement devices that the first experimental measurements of the radial electric field were made and correlated with confinement losses. In tokamaks, the axisymmetry implies that the neoclassical fluxes are ambipolar and, as a consequence, independent of the radial electric field. However, axisymmetry is not strict in a tokamak with turbulent fluctuations, and near the limiter ambipolarity clearly breaks down. Therefore, the question of the effect of the radial electric field on tokamak confinement has been raised in recent years. In particular, the radial electric field has been proposed to explain the transition from L-mode to H-mode confinement. There is some initial experimental evidence supporting this type of explanation, although there is not yet a self-consistent theory explaining the generation of the electric field and its effect on the transport. Here, a brief review of recent results is presented. 27 refs., 4 figs

A user's evaluation of radial flow HEPA filters

International Nuclear Information System (INIS)

Purcell, J.A.

1992-07-01

High efficiency particulate air (HEPA) filters of rectangular cross section have been used to remove particulates and the associated radioactivity from air ventilation streams since the advent of nuclear materials processing. Use of round axial flow HEPA filters is also longstanding. The advantages of radial flow filters in a circular configuration have been well demonstrated in UKAEA during the last 5--7 years. An evaluation of radial flow filters for fissile process gloveboxes reveals several substantial benefits in addition to the advantages claimed in UKAEA Facilities. The radial flow filter may be provided in a favorable geometry resulting in improved criticality safety. The filter configuration lends to in-place testing at the glovebox to exhaust duct interface. This will achieve compliance with DOE Order 6430.1A, Section 99.0.2. Preliminary testing at SRS for radial flow filters manufactured by Flanders Filters, Inc. revealed compliance in all the usual specifications for filtration efficiency, pressure differential and materials of construction. An evaluation, further detailed in this report, indicates that the radial flow HEPA filter should be considered for inclusion in new ventilation system designs

Radial extension of drift waves in presence of velocity profiles

International Nuclear Information System (INIS)

Sen, S.; Weiland, J.

1994-01-01

The effect of a radially varying poloidal velocity field on the recently found radially extended toroidal drift waves is investigated analytically. The role of velocity curvature (υ φ '') is found to have robust effects on the radial model structure of the mode. For a positive value of the curvature (Usually found in the H-mode edges) the radial model envelope, similar to the sheared slab case, becomes fully outgoing. The mode is therefore stable. On the other hand, for a negative value of the curvature (usually observed in the L-mode edges) all the characteristics of conventional drift waves return back. The radial mode envelope reduces to a localized Gaussian shape and the mode is therefore unstable again for typical (magnetic) shear values in tokamaks. Velocity shear (υ φ ??) on the other hand is found to have rather insignificant role both in determining the radial model structure and stability

Radial Peripapillary Capillary Network in Patients with Retinitis Pigmentosa: An Optical Coherence Tomography Angiography Study.

Science.gov (United States)

Mastropasqua, Rodolfo; Borrelli, Enrico; Agnifili, Luca; Toto, Lisa; Di Antonio, Luca; Senatore, Alfonso; Palmieri, Michele; D'Uffizi, Alessandro; Carpineto, Paolo

2017-01-01

To investigate radial peripapillary capillary (RPC) network in patients affected by retinitis pigmentosa (RP). Eleven patients (22 eyes) with previous diagnosis of RP and 16 age-matched healthy subjects (16 eyes) were enrolled. The diagnosis of RP was made based on both clinical features and electrophysiological examination. All patients underwent a complete ophthalmologic examination, including optical coherence tomography angiography and visual field (VF). The primary outcomes were the RPC vessel density in the peripapillary and disk areas; the secondary outcomes were the peripapillary retinal nerve fiber layer (RNFL) thickness and the mean defect at VF. A total of 19 eyes of 11 RP patients (5 males, 6 females) and 16 eyes of 16 healthy subjects (10 males, 6 females) were included for the analysis. RPC vessel density in the disk area was 46.5 ± 7.1% in the RP group and 45.4 ± 10.6% in the control group ( p  = 0.754). RPC vessel density in the peripapillary area was significantly reduced in the RP group after the comparison with the control group (52.5 ± 5.0 and 57.2 ± 5.1%, respectively, p  = 0.011). RNFL thickness was 85.9 ± 20.4 μm in the RP group and 104.0 ± 6.4 μm in the control group ( p  = 0.002). RPC vessel density was significantly correlated with RNFL thickness values in RP patients, both in the disk and in the peripapillary area (Rho = 0.599 and p  = 0.007 in the disk area, Rho = 0.665 and p  = 0.002 in the peripapillary area, respectively). We showed that density of RPC is reduced in these patients in the peripapillary area. Moreover, the RPC vessel density correlates with the RNFL thickness.

Combined Radial and Femoral Access Strategy and Radial-Femoral Rendezvous in Patients With Long and Complex Iliac Occlusions.

Science.gov (United States)

Hanna, Elias B; Mogabgab, Owen N; Baydoun, Hassan

2018-01-01

We present cases of complex, calcified iliac occlusive disease revascularized via a combined radial-femoral access strategy. Through a 6-French, 125-cm transradial guiding catheter, antegrade guidewires and catheters are advanced into the iliac occlusion, while retrograde devices are advanced transfemorally. The transradial and transfemoral channels communicate, allowing the devices to cross the occlusion into the true lumen (radial-femoral antegrade-retrograde rendezvous).

Reassessment of the basis for NRC fuel damage criteria for reactivity transients

International Nuclear Information System (INIS)

McCardell, R.K.

1994-01-01

The present basis for NRC Fuel Damage Criteria was obtained from experiments performed in the Special Power Excursion Reactor Test (SPERT) IV Reactor Capsule Driver Core (CDC) at the Idaho National Engineering Laboratory (INEL) between 1967 and 1970. Most of the CDC test fuel rods were previously unirradiated and the failure threshold for these unirradiated fuel rods was measured to be about 200 calories per gram of UO 2 radially averaged fuel enthalpy at the axial peak

Detonation in supersonic radial outflow

KAUST Repository

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.

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

Science.gov (United States)

Wang, Junliang; Yang, Jungang; Zhang, Jie; Wang, Xiaoxi; Zhang, Wenjun Chris

2018-07-01

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.

Radial nerve dysfunction (image)

Science.gov (United States)

The radial nerve travels down the arm and supplies movement to the triceps muscle at the back of the upper arm. ... the wrist and hand. The usual causes of nerve dysfunction are direct trauma, prolonged pressure on the ...

A comparative study for the estimation of geodetic point velocity by ...

Indian Academy of Sciences (India)

Geodetic point velocity; artificial neural networks; back propagation; radial basis function; Kriging. J. Earth Syst. Sci. ...... The employment of BPANN is an alternative tool to KRIG for .... Computational Intelligence and Multimedia Applications.

Zernike Basis to Cartesian Transformations

Directory of Open Access Journals (Sweden)

Mathar, R. J.

2009-12-01

Full Text Available The radial polynomials of the 2D (circular and 3D (spherical Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.

Zernike basis to cartesian transformations

Directory of Open Access Journals (Sweden)

Mathar R.J.

2009-01-01

Full Text Available The radial polynomials of the 2D (circular and 3D (spherical Zernike functions are tabulated as powers of the radial distance. The reciprocal tabulation of powers of the radial distance in series of radial polynomials is also given, based on projections that take advantage of the orthogonality of the polynomials over the unit interval. They play a role in the expansion of products of the polynomials into sums, which is demonstrated by some examples. Multiplication of the polynomials by the angular bases (azimuth, polar angle defines the Zernike functions, for which we derive transformations to and from the Cartesian coordinate system centered at the middle of the circle or sphere.

Variable stator radial turbine

Science.gov (United States)

Rogo, C.; Hajek, T.; Chen, A. G.

1984-01-01

A radial turbine stage with a variable area nozzle was investigated. A high work capacity turbine design with a known high performance base was modified to accept a fixed vane stagger angle moveable sidewall nozzle. The nozzle area was varied by moving the forward and rearward sidewalls. Diffusing and accelerating rotor inlet ramps were evaluated in combinations with hub and shroud rotor exit rings. Performance of contoured sidewalls and the location of the sidewall split line with respect to the rotor inlet was compared to the baseline. Performance and rotor exit survey data are presented for 31 different geometries. Detail survey data at the nozzle exit are given in contour plot format for five configurations. A data base is provided for a variable geometry concept that is a viable alternative to the more common pivoted vane variable geometry radial turbine.

The investigation of the non-orthogonal basis expansion method for a three-fermion system

International Nuclear Information System (INIS)

Baoqiu Chen; Kentucky Univ., Lexington, KY

1992-01-01

In this paper, the non-orthogonal basis expansion method has been extended to solve a three-fermion system. The radial wavefunction of such a system is expanded in terms of a non-orthogonal Gaussian basis. All matrix elements of the Hamiltonian, including the central, tensor and spin-orbit potentials are derived in analytical forms. The new method simplifies the three-body system calculations, which are usually rather tedious by other methods. The method can be used to calculate energies for both the ground state and low excited states and has been used further to investigate the other nuclear properties of a three-body system such as Λ 3 H. (Author)

Application of neural network technique to determine a corrector surface for global geopotential model using GPS/levelling measurements in Egypt

Science.gov (United States)

Elshambaky, Hossam Talaat

2018-01-01

Owing to the appearance of many global geopotential models, it is necessary to determine the most appropriate model for use in Egyptian territory. In this study, we aim to investigate three global models, namely EGM2008, EIGEN-6c4, and GECO. We use five mathematical transformation techniques, i.e., polynomial expression, exponential regression, least-squares collocation, multilayer feed forward neural network, and radial basis neural networks to make the conversion from regional geometrical geoid to global geoid models and vice versa. From a statistical comparison study based on quality indexes between previous transformation techniques, we confirm that the multilayer feed forward neural network with two neurons is the most accurate of the examined transformation technique, and based on the mean tide condition, EGM2008 represents the most suitable global geopotential model for use in Egyptian territory to date. The final product gained from this study was the corrector surface that was used to facilitate the transformation process between regional geometrical geoid model and the global geoid model.

Radial-probe EBUS for the diagnosis of peripheral pulmonary lesions

Directory of Open Access Journals (Sweden)

Marcia Jacomelli

Full Text Available ABSTRACT Objective: Conventional bronchoscopy has a low diagnostic yield for peripheral pulmonary lesions. Radial-probe EBUS employs a rotating ultrasound transducer at the end of a probe that is passed through the working channel of the bronchoscope. Radial-probe EBUS facilitates the localization of peripheral pulmonary nodules, thus increasing the diagnostic yield. The objective of this study was to present our initial experience using radial-probe EBUS in the diagnosis of peripheral pulmonary lesions at a tertiary hospital. Methods: We conducted a retrospective analysis of 54 patients who underwent radial-probe EBUS-guided bronchoscopy for the investigation of pulmonary nodules or masses between February of 2012 and September of 2013. Radial-probe EBUS was performed with a flexible 20-MHz probe, which was passed through the working channel of the bronchoscope and advanced through the bronchus to the target lesion. For localization of the lesion and for collection procedures (bronchial brushing, transbronchial needle aspiration, and transbronchial biopsy, we used fluoroscopy. Results: Radial-probe EBUS identified 39 nodules (mean diameter, 1.9 ± 0.7 cm and 19 masses (mean diameter, 4.1 ± 0.9 cm. The overall sensitivity of the method was 66.7% (79.5% and 25.0%, respectively, for lesions that were visible and not visible by radial-probe EBUS. Among the lesions that were visible by radial-probe EBUS, the sensitivity was 91.7% for masses and 74.1% for nodules. The complications were pneumothorax (in 3.7% and bronchial bleeding, which was controlled bronchoscopically (in 9.3%. Conclusions: Radial-probe EBUS shows a good safety profile, a low complication rate, and high sensitivity for the diagnosis of peripheral pulmonary lesions.

Model-based fault detection for proton exchange membrane fuel cell ...

African Journals Online (AJOL)

In this paper, an intelligent model-based fault detection (FD) is developed for proton exchange membrane fuel cell (PEMFC) dynamic systems using an independent radial basis function (RBF) networks. The novelty is that this RBF networks is used to model the PEMFC dynamic systems and residuals are generated based ...

Microinstabilities in a radially contracting inhomogeneous cylindrical plasma slab

International Nuclear Information System (INIS)

Deutsch, R.; Kaeppeler, H.J.

1980-07-01

In order to study the development of microinstabilities in a collapsing cylindrical plasma sheath, corresponding to the situations in a z-pinch or a plasma focus, the dispersion relation for electromagnetic perturbations is derived with the aid of a newly established slab-model for an inhomogeneous, radially contracting plasma. In contrast to previously used slab-models, the orientation of the electric field is in direction of the cylinder axis and the azimuthal magnetic field is induced by the current flowing through the cylindrical plasma slab. The Vlasov equation is used together with the Krook collision term in order to include the influence of collisions. The results of this theory presented in this report will be used to calculate the growth of drift instabilities in the compression phase of a plasma focus, and shall serve as a basis for further development of a more general dispersion relation including runaway-effects. (orig.)

Sharp Dissection versus Electrocautery for Radial Artery Harvesting

Science.gov (United States)

Marzban, Mehrab; Arya, Reza; Mandegar, Mohammad Hossein; Karimi, Abbas Ali; Abbasi, Kiomars; Movahed, Namvar; Abbasi, Seyed Hesameddin

2006-01-01

Radial arteries have been increasingly used during the last decade as conduits for coronary artery revascularization. Although various harvesting techniques have been described, there has been little comparative study of arterial damage and patency. A radial artery graft was used in 44 consecutive patients, who were randomly divided into 2 groups. In the 1st group, the radial artery was harvested by sharp dissection and in the 2nd, by electrocautery. These groups were compared with regard to radial artery free flow, harvest time, number of clips used, complications, and endothelial damage. Radial artery free flow before and after intraluminal administration of papaverine was significantly greater in the electrocautery group (84.3 ± 50.7 mL/min and 109.7 ± 68.5 mL/min) than in the sharp-dissection group (52.9 ± 18.3 mL/min and 69.6 ± 28.2 mL/ min) (P =0.003). Harvesting time by electrocautery was significantly shorter (25.4 ± 4.3 min vs 34.4 ± 5.9 min) (P =0.0001). Electrocautery consumed an average of 9.76 clips, versus 22.45 clips consumed by sharp dissection. The 2 groups were not different regarding postoperative complications, except for 3 cases of temporary paresthesia of the thumb in the electrocautery group; histopathologic examination found no endothelial damage. We conclude that radial artery harvesting by electrocautery is faster and more economical than harvesting by sharp dissection and is associated with better intraoperative flow and good preservation of endothelial integrity. PMID:16572861

Radial distribution of ions in pores with a surface charge

NARCIS (Netherlands)

Stegen, J.H.G. van der; Görtzen, J.; Kuipers, J.A.M.; Hogendoorn, J.A.; Versteeg, G.F.

2001-01-01

A sorption model applicable to calculate the radial equilibrium concentrations of ions in the pores of ion-selective membranes with a pore structure is developed. The model is called the radial uptake model. Because the model is applied to a Nafion sulfonic layer with very small pores and the radial

EuroFIR eBASIS: application for health claims submissions and evaluations

NARCIS (Netherlands)

Kiely, M.; Black, L.J.; Plumb, J.; Kroon, P.; Hollman, P.C.H.; Larsen, J.C.; Speijers, G.; Kapsokefalou, M.; Sheehan, D.; Gry, J.; Finglas, P.M.

2010-01-01

Background: The European Food Information Resource (EuroFIR) network has established the eBASIS (Bioactive Substances in Food Information System) online food composition and biological effects database for plant-derived bioactive compounds (phytochemicals). On the basis of submitted evidence, the

Stellar Angular Momentum Distributions and Preferential Radial Migration

Science.gov (United States)

Wyse, Rosemary; Daniel, Kathryne J.

2018-04-01

I will present some results from our recent investigations into the efficiency of radial migration in stellar disks of differing angular momentum distributions, within a given adopted 2D spiral disk potential. We apply to our models an analytic criterion that determines whether or not individual stars are in orbits that could lead to radial migration around the corotation resonance. We couch our results in terms of the local stellar velocity dispersion and find that the fraction of stars that could migrate radially decreases as the velocity dispersion increases. I will discuss implications and comparisons with the results of other approaches.

Research on Radial Vibration of a Circular Plate

Directory of Open Access Journals (Sweden)

Wei Liu

2016-01-01

Full Text Available Radial vibration of the circular plate is presented using wave propagation approach and classical method containing Bessel solution and Hankel solution for calculating the natural frequency theoretically. In cylindrical coordinate system, in order to obtain natural frequency, propagation and reflection matrices are deduced at the boundaries of free-free, fixed-fixed, and fixed-free using wave propagation approach. Furthermore, radial phononic crystal is constructed by connecting two materials periodically for the analysis of band phenomenon. Also, Finite Element Simulation (FEM is adopted to verify the theoretical results. Finally, the radial and piezoelectric effects on the band are also discussed.

Voltage regulation in MV networks with dispersed generations by a neural-based multiobjective methodology

Energy Technology Data Exchange (ETDEWEB)

Galdi, Vincenzo [Dipartimento di Ingegneria dell' Informazione e Ingegneria Elettrica, Universita degli studi di Salerno, Via Ponte Don Melillo 1, 84084 Fisciano (Italy); Vaccaro, Alfredo; Villacci, Domenico [Dipartimento di Ingegneria, Universita degli Studi del Sannio, Piazza Roma 21, 82100 Benevento (Italy)

2008-05-15

This paper puts forward the role of learning techniques in addressing the problem of an efficient and optimal centralized voltage control in distribution networks equipped with dispersed generation systems (DGSs). The proposed methodology employs a radial basis function network (RBFN) to identify the multidimensional nonlinear mapping between a vector of observable variables describing the network operating point and the optimal set points of the voltage regulating devices. The RBFN is trained by numerical data generated by solving the voltage regulation problem for a set of network operating points by a rigorous multiobjective solution methodology. The RBFN performance is continuously monitored by a supervisor process that notifies when there is the need of a more accurate solution of the voltage regulation problem if nonoptimal network operating conditions (ex post monitoring) or excessive distances between the actual network state and the neuron's centres (ex ante monitoring) are detected. A more rigorous problem solution, if required, can be obtained by solving the voltage regulation problem by a conventional multiobjective optimization technique. This new solution, in conjunction with the corresponding input vector, is then adopted as a new train data sample to adapt the RBFN. This online training process allows RBFN to (i) adaptively learn the more representative domain space regions of the input/output mapping without needing a prior knowledge of a complete and representative training set, and (ii) manage effectively any time varying phenomena affecting this mapping. The results obtained by simulating the regulation policy in the case of a medium-voltage network are very promising. (author)

Radial-probe EBUS for the diagnosis of peripheral pulmonary lesions.

Science.gov (United States)

Jacomelli, Marcia; Demarzo, Sergio Eduardo; Cardoso, Paulo Francisco Guerreiro; Palomino, Addy Lidvina Mejia; Figueiredo, Viviane Rossi

2016-01-01

Conventional bronchoscopy has a low diagnostic yield for peripheral pulmonary lesions. Radial-probe EBUS employs a rotating ultrasound transducer at the end of a probe that is passed through the working channel of the bronchoscope. Radial-probe EBUS facilitates the localization of peripheral pulmonary nodules, thus increasing the diagnostic yield. The objective of this study was to present our initial experience using radial-probe EBUS in the diagnosis of peripheral pulmonary lesions at a tertiary hospital. We conducted a retrospective analysis of 54 patients who underwent radial-probe EBUS-guided bronchoscopy for the investigation of pulmonary nodules or masses between February of 2012 and September of 2013. Radial-probe EBUS was performed with a flexible 20-MHz probe, which was passed through the working channel of the bronchoscope and advanced through the bronchus to the target lesion. For localization of the lesion and for collection procedures (bronchial brushing, transbronchial needle aspiration, and transbronchial biopsy), we used fluoroscopy. Radial-probe EBUS identified 39 nodules (mean diameter, 1.9 ± 0.7 cm) and 19 masses (mean diameter, 4.1 ± 0.9 cm). The overall sensitivity of the method was 66.7% (79.5% and 25.0%, respectively, for lesions that were visible and not visible by radial-probe EBUS). Among the lesions that were visible by radial-probe EBUS, the sensitivity was 91.7% for masses and 74.1% for nodules. The complications were pneumothorax (in 3.7%) and bronchial bleeding, which was controlled bronchoscopically (in 9.3%). Radial-probe EBUS shows a good safety profile, a low complication rate, and high sensitivity for the diagnosis of peripheral pulmonary lesions. A broncoscopia convencional possui baixo rendimento diagnóstico para lesões pulmonares periféricas. A ecobroncoscopia radial (EBUS radial) emprega um transdutor ultrassonográfico rotatório na extremidade de uma sonda que é inserida no canal de trabalho do broncoscópio. O EBUS

Need for reaction coordinates to ensure a complete basis set in an adiabatic representation of ion-atom collisions

Science.gov (United States)

Rabli, Djamal; McCarroll, Ronald

2018-02-01

This review surveys the different theoretical approaches, used to describe inelastic and rearrangement processes in collisions involving atoms and ions. For a range of energies from a few meV up to about 1 keV, the adiabatic representation is expected to be valid and under these conditions, inelastic and rearrangement processes take place via a network of avoided crossings of the potential energy curves of the collision system. In general, such avoided crossings are finite in number. The non-adiabatic coupling, due to the breakdown of the Born-Oppenheimer separation of the electronic and nuclear variables, depends on the ratio of the electron mass to the nuclear mass terms in the total Hamiltonian. By limiting terms in the total Hamiltonian correct to first order in the electron to nuclear mass ratio, a system of reaction coordinates is found which allows for a correct description of both inelastic channels. The connection between the use of reaction coordinates in the quantum description and the electron translation factors of the impact parameter approach is established. A major result is that only when reaction coordinates are used, is it possible to introduce the notion of a minimal basis set. Such a set must include all avoided crossings including both radial coupling and long range Coriolis coupling. But, only when reactive coordinates are used, can such a basis set be considered as complete. In particular when the centre of nuclear mass is used as centre of coordinates, rather than the correct reaction coordinates, it is shown that erroneous results are obtained. A few results to illustrate this important point are presented: one concerning a simple two-state Landau-Zener type avoided crossing, the other concerning a network of multiple crossings in a typical electron capture process involving a highly charged ion with a neutral atom.

Radial vibration and ultrasonic field of a long tubular ultrasonic radiator.

Science.gov (United States)

Shuyu, Lin; Zhiqiang, Fu; Xiaoli, Zhang; Yong, Wang; Jing, Hu

2013-09-01

The radial vibration of a metal long circular tube is studied analytically and its electro-mechanical equivalent circuit is obtained. Based on the equivalent circuit, the radial resonance frequency equation is derived. The theoretical relationship between the radial resonance frequency and the geometrical dimensions is studied. Finite element method is used to simulate the radial vibration and the radiated ultrasonic field and the results are compared with those from the analytical method. It is concluded that the radial resonance frequency for a solid metal rod is larger than that for a metal tube with the same outer radius. The radial resonance frequencies from the analytical method are in good agreement with those from the numerical method. Based on the acoustic field analysis, it is concluded that the long metal tube with small wall thickness is superior to that with large wall thickness in producing radial vibration and ultrasonic radiation. Therefore, it is expected to be used as an effective radial ultrasonic radiator in ultrasonic sewage treatment, ultrasonic antiscale and descaling and other ultrasonic liquid handling applications. Copyright © 2013 Elsevier B.V. All rights reserved.

Investigating changes in brain network properties in HIV-associated neurocognitive disease (HAND) using mutual connectivity analysis (MCA)

Science.gov (United States)

Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

About 50% of subjects infected with HIV present deficits in cognitive domains, which are known collectively as HIV associated neurocognitive disorder (HAND). The underlying synaptodendritic damage can be captured using resting state functional MRI, as has been demonstrated by a few earlier studies. Such damage may induce topological changes of brain connectivity networks. We test this hypothesis by capturing the functional interdependence of 90 brain network nodes using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized Radial Basis function (GRBF) neural networks. The network nodes are selected based on the regions defined in the Automated Anatomic Labeling (AAL) atlas. Each node is represented by the average time series of the voxels of that region. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We tested for differences in these properties in network graphs obtained for 10 subjects (6 male and 4 female, 5 HIV+ and 5 HIV-). Global network properties captured some differences between these subject cohorts, though significant differences were seen only with the clustering coefficient measure. Local network properties, such as local efficiency and the degree of connections, captured significant differences in regions of the frontal lobe, precentral and cingulate cortex amongst a few others. These results suggest that our method can be used to effectively capture differences occurring in brain network connectivity properties revealed by resting-state functional MRI in neurological disease states, such as HAND.

Radial Fuzzy Systems

Czech Academy of Sciences Publication Activity Database

Coufal, David

2017-01-01

Roč. 319, 15 July (2017), s. 1-27 ISSN 0165-0114 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : fuzzy systems * radial functions * coherence Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.718, year: 2016

Acesso radial em intervenções coronarianas percutâneas: panorama atual brasileiro Acceso radial en intervenciones coronarias percutáneas: panorama actual brasileño Radial approach in percutaneous coronary interventions: current status in Brazil

Directory of Open Access Journals (Sweden)

Pedro Beraldo de Andrade

2011-04-01

Full Text Available FUNDAMENTO: Embora a técnica radial exiba resultados incontestáveis na redução de complicações vasculares e ocorrência de sangramento grave quando comparada à técnica femoral, seu emprego permanece restrito a poucos centros que a elegeram como via de acesso preferencial. OBJETIVO: Avaliar o cenário atual das intervenções coronarianas percutâneas no Brasil quanto à utilização da via de acesso radial. MÉTODOS: Análise dos dados cadastrados de forma espontânea na Central Nacional de Intervenções Cardiovasculares (CENIC durante o quadriênio de 2005-2008, o que totaliza 83.376 procedimentos. RESULTADOS: A técnica radial foi utilizada em 12,6% dos procedimentos efetivados, e a técnica femoral, em 84,3%. Os 3,1% restantes foram representados pela dissecção ou punção braquial. Com uma taxa de sucesso de 97,5%, a opção pelo acesso radial associou-se à redução significativa de complicações vasculares quando comparado ao femoral (2,5% versus 3,6%, p FUNDAMENTO: Aunque la técnica radial exhiba resultados incontestables en la reducción de complicaciones vasculares y ocurrencia de sangrado grave cuando es comparada a la técnica femoral, su empleo permanece restringido a pocos centros que la eligieron como vía de acceso preferencial. OBJETIVO:Evaluar el escenario actual de las intervenciones coronarias percutáneas en el Brasil en cuanto a la utilización de la vía de acceso radial. MÉTODOS:Análisis de los datos registrados de forma espontánea en la Central Nacional de Intervenciones Cardiovasculares (CENIC durante el cuatrienio de 2005-2008, lo que totaliza 83.376 procedimientos. RESULTADOS:La técnica radial fue utilizada en 12,6% de los procedimientos efectuados, y la técnica femoral, en 84,3%. Los 3,1% restantes fueron representados por la disección o punción braquial. Con una tasa de éxito de 97,5%, la opción por el acceso radial se asoció a la reducción significativa de complicaciones vasculares cuando

Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

Science.gov (United States)

Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

2014-07-01

Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

Directory of Open Access Journals (Sweden)

Hesham El-Sayed

2018-05-01

Full Text Available Heterogeneous vehicular networks (HETVNETs evolve from vehicular ad hoc networks (VANETs, which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs. The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM kernels with a radial basis function (RBF. The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Velocidades radiales en Collinder 121

Science.gov (United States)

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.

Dynamic modeling of physical phenomena for probabilistic risk assessments using artificial neural networks

International Nuclear Information System (INIS)

Benjamin, A.S.; Paez, T.L.; Brown, N.N.

1998-01-01

In most probabilistic risk assessments, there is a subset of accident scenarios that involves physical challenges to the system, such as high heat rates and/or accelerations. The system's responses to these challenges may be complicated, and their prediction may require the use of long-running computer codes. To deal with the many scenarios demanded by a risk assessment, the authors have been investigating the use of artificial neural networks (ANNs) as a fast-running estimation tool. They have developed a multivariate linear spline algorithm by extending previous ANN methods that use radial basis functions. They have applied the algorithm to problems involving fires, shocks, and vibrations. They have found that within the parameter range for which it is trained, the algorithm can simulate the nonlinear responses of complex systems with high accuracy. Running times per case are less than one second

Radial supports of face motors with slack compensation

Energy Technology Data Exchange (ETDEWEB)

Kuznetsova, I I; Gelman, A B; Krekina, T V

1982-01-01

The design of a radial support of a face motor with slack compensation is described, and gives the results of field tests which confirm the performance capacity of the experimental support both from the viewpoint of durability, and in relation to preventing radial slack of the face motor shaft.

Queratotomía radial versus miniqueratotomía radial: Experiencia en el Hospital "Ramón Pando Ferrer" Radial keratotomy versus radial minikeratotomy: Experience in "Ramón Pando Ferrer" Hospital

Directory of Open Access Journals (Sweden)

José Edilberto Pacheco Serrano

2000-06-01

Full Text Available La miniqueratotomía radial se viene realizando desde 1995. Se plantea que incisiones más cortas tienen el mismo efecto y producen menos debilidad corneal, ya que disminuye la susceptibilidad a sufrir complicaciones graves provenientes de traumas de la vida cotidiana. Esta idea nos motivó a realizar un estudio para observar el comportamiento de incisiones más cortas en nuestro centro y, en caso de resultados positivos, implementar la técnica de manera que nuestros pacientes puedan beneficiarse de ella. Se comparan resultados de la aplicación de dos técnicas quirúrgicas refractivas para corrección de miopía entre leve y moderada. Se seleccionaron 38 pacientes entre 20 y 40 años de edad, con miopías entre -2 y -6 dioptrías y astigmatismo no mayor a -0,75 dioptrías. Se realizó queratotomía radial convencional en el ojo derecho y miniqueratotomía radial en el ojo izquierdo del mismo paciente. Las variaciones obtenidas en promedio fueron, en el ojo derecho: la esfera (en dioptrías D de -3,38 a -0,32, cilindro de -0,48 a -0,45 D, la queratometría de 44,75 a 41,21 D. En el ojo izquierdo: la esfera de -3,38 D a -0,44 D, cilindro de -0,44 D a -0,38 D, la queratometría de 44,83 a 41,80 D. Hubo una mejoría de la agudeza visual sin cristales de 0,61 en el ojo derecho y 0,59 en el ojo izquierdo. Las dos técnicas no mostraron diferencias estadísticamente significativas, con el beneficio de que la nueva técnica disminuye el riesgo de ruptura postraumática, según la bibliografía revisada, a causa de la menor injuria corneal.In this hospital, radial keratotomy is performed sice 1995. We propose that shorter incisions have some effect and cause less corneal weakness, since dicreases susceptibility to severe complications from traumata of daily life. This notion encouraged us to carry out a study to observe behaviour of shorter incisions in our service, and in the event of positive results, implementation of the technique so that our

Pseudarthrosis of radial shaft with dislocation of heads of radial and ulnar bones (case report

Directory of Open Access Journals (Sweden)

M. E. Puseva

2013-01-01

Full Text Available The authors presented a rare clinical case - the injury of forearm complicated by the formation of the pseudarthrosis of the radial shaft in combination with old dislocation of heads the radius and ulna. The differentiated approach to the choice of surgical tactics was proposed, which consists of several consistent stages: taking free autotransplant from the crest of iliac bone, resection of pseudarthrosis of radius with replacement of the bone defect by the graft for restoration of anatomic length, conducting combined strained osteosynthesis and elimination of dislocation of a head of radial and ulnar bones by transosseous osteosynthesis. The chosen treatment strategy allowed to restore the anatomy and function of the upper extremity.

Radial velocity asymmetries from jets with variable velocity profiles

International Nuclear Information System (INIS)

Cerqueira, A. H.; Vasconcelos, M. J.; Velazquez, P. F.; Raga, A. C.; De Colle, F.

2006-01-01

We have computed a set of 3-D numerical simulations of radiatively cooling jets including variabilities in both the ejection direction (precession) and the jet velocity (intermittence), using the Yguazu-a code. In order to investigate the effects of jet rotation on the shape of the line profiles, we also introduce an initial toroidal rotation velocity profile. Since the Yguazu-a code includes an atomic/ionic network, we are able to compute the emission coefficients for several emission lines, and we generate line profiles for the Hα, [O I]λ6300, [S II]λ6716 and [N II]λ6548 lines. Using initial parameters that are suitable for the DG Tau microjet, we show that the computed radial velocity shift for the medium-velocity component of the line profile as a function of distance from the jet axis is strikingly similar for rotating and non-rotating jet models

Unique negative permittivity of the pseudo conducting radial zinc oxide-poly(vinylidene fluoride) nanocomposite film: Enhanced dielectric and electromagnetic interference shielding properties

Energy Technology Data Exchange (ETDEWEB)

Aepuru, Radhamanohar [Department of Materials Engineering, Defence Institute of Advanced Technology, Girinagar, Pune 411025 (India); Bhaskara Rao, B.V.; Kale, S.N. [Department of Applied Physics, Defence Institute of Advanced Technology, Pune 411025 (India); Panda, H.S., E-mail: himanshusp@diat.ac.in [Department of Materials Engineering, Defence Institute of Advanced Technology, Girinagar, Pune 411025 (India)

2015-11-01

Flower like radial zinc oxide (RZnO) was prepared by using a facile solvothermal method and used to prepare poly(vinylidene fluoride) (PVDF) based nanocomposites. Structural informations of the samples are analyzed by X-ray diffraction and correlated with high resolution transmission electron microscopy along with high annular angular dark field scanning transmission electron microscopy (HAADF-STEM). For the first time, stability studies are carried out by solvent relaxation nuclear magnetic resonance experiments. Dielectric studies of the PVDF and PVDF-RZnO nanocomposites are reported over the wide range of frequency (0.01 Hz–1 MHz) and temperature (25–90 °C). Dielectric property of the PVDF-RZnO nanocomposites was significantly improved wrt filler percentage in PVDF. Unique negative permittivity was observed in the composites having higher filler content (>40 wt%) typically at low frequencies. First time, it is observed that the higher RZnO content in PVDF results the formation of pseudo conducting network and hence improved the electromagnetic shielding efficiency (85%) than PVDF and PVDF-commercial ZnO composites. - Highlights: • Radial ZnO-PVDF nanocomposites were fabricated by using solution casting. • Pseudo conducting network is confirmed through cryo-fracture morphology study. • Stability study of the nano fillers was performed in the polymer matrix. • Unique negative permittivity behavior of the nanocomposites was observed. • EMI shielding property of the radial ZnO-PVDF nanocomposites was performed.

Compactly Supported Basis Functions as Support Vector Kernels for Classification.

Science.gov (United States)

Wittek, Peter; Tan, Chew Lim

2011-10-01

Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.

A research about breast cancer detection using different neural networks and K-MICA algorithm

Directory of Open Access Journals (Sweden)

A A Kalteh

2013-01-01

Full Text Available Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC database and the simulation results show that the recommended system has high accuracy.

Manufacturing of Precision Forgings by Radial Forging

International Nuclear Information System (INIS)

Wallner, S.; Harrer, O.; Buchmayr, B.; Hofer, F.

2011-01-01

Radial forging is a multi purpose incremental forging process using four tools on the same plane. It is widely used for the forming of tool steels, super alloys as well as titanium- and refractory metals. The range of application goes from reducing the diameters of shafts, tubes, stepped shafts and axels, as well as for creating internal profiles for tubes in Near-Net-Shape and Net-Shape quality. Based on actual development of a weight optimized transmission input shaft, the specific features of radial forging technology is demonstrated. Also a Finite Element Model for the simulation of the process is shown which leads to reduced pre-processing effort and reduced computing time compared to other published simulation methods for radial forging. The finite element model can be applied to quantify the effects of different forging strategies.

Radially truncated galactic discs

NARCIS (Netherlands)

Grijs, R. de; Kregel, M.; Wesson, K H

2000-01-01

Abstract: We present the first results of a systematic analysis of radially truncatedexponential discs for four galaxies of a sample of disc-dominated edge-onspiral galaxies. Edge-on galaxies are very useful for the study of truncatedgalactic discs, since we can follow their light distributions out

Radial-arrayed rotary electrification for high performance triboelectric generator.

Science.gov (United States)

Zhu, Guang; Chen, Jun; Zhang, Tiejun; Jing, Qingshen; Wang, Zhong Lin

2014-03-04

Harvesting mechanical energy is an important route in obtaining cost-effective, clean and sustainable electric energy. Here we report a two-dimensional planar-structured triboelectric generator on the basis of contact electrification. The radial arrays of micro-sized sectors on the contact surfaces enable a high output power of 1.5 W (area power density of 19 mW cm(-2)) at an efficiency of 24%. The triboelectric generator can effectively harness various ambient motions, including light wind, tap water flow and normal body movement. Through a power management circuit, a triboelectric-generator-based power-supplying system can provide a constant direct-current source for sustainably driving and charging commercial electronics, immediately demonstrating the feasibility of the triboelectric generator as a practical power source. Given exceptional power density, extremely low cost and unique applicability resulting from distinctive mechanism and structure, the triboelectric generator can be applied not only to self-powered electronics but also possibly to power generation at a large scale.

TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

DEFF Research Database (Denmark)

Yao, Wei; Fang, Jiakun; Zhao, Ping

2013-01-01

the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power...... 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......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...

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

Science.gov (United States)

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

A new approach to global seismic tomography based on regularization by sparsity in a novel 3D spherical wavelet basis