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Functions</title> <meta name="description" content="Search results for: Radial Basis Functions"> <meta name="keywords" content="Radial Basis Functions"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Radial Basis Functions"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2986</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Radial Basis Functions</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2986</span> Inverse Cauchy Problem of Doubly Connected Domains via Spectral Meshless Radial Point Interpolation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elyas%20Shivanian">Elyas Shivanian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the spectral meshless radial point interpolation (SMRPI) technique is applied to the Cauchy problems of two-dimensional elliptic PDEs in doubly connected domains. It is obtained the unknown data on the inner boundary of the domain while overspecified boundary data are imposed on the outer boundary of the domain by using the SMRPI. Shape functions, which are constructed through point interpolation method using the radial basis functions, help us to treat problem locally with the aim of high order convergence rate. In this way, localization in SMRPI can reduce the ill-conditioning for Cauchy problem. Furthermore, we improve previous results and it is revealed the SMRPI is more accurate and stable by adding strong perturbations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cauchy%20problem" title="cauchy problem">cauchy problem</a>, <a href="https://publications.waset.org/abstracts/search?q=doubly%20connected%20domain" title=" doubly connected domain"> doubly connected domain</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20function" title=" shape function"> shape function</a> </p> <a href="https://publications.waset.org/abstracts/56408/inverse-cauchy-problem-of-doubly-connected-domains-via-spectral-meshless-radial-point-interpolation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2985</span> Local Radial Basis Functions for Helmholtz Equation in Seismic Inversion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hebert%20Montegranario">Hebert Montegranario</a>, <a href="https://publications.waset.org/abstracts/search?q=Mauricio%20Londo%C3%B1o"> Mauricio Londoño </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Solutions of Helmholtz equation are essential in seismic imaging methods like full wave inversion, which needs to solve many times the wave equation. Traditional methods like Finite Element Method (FEM) or Finite Differences (FD) have sparse matrices but may suffer the so called pollution effect in the numerical solutions of Helmholtz equation for large values of the wave number. On the other side, global radial basis functions have a better accuracy but produce full matrices that become unstable. In this research we combine the virtues of both approaches to find numerical solutions of Helmholtz equation, by applying a meshless method that produce sparse matrices by local radial basis functions. We solve the equation with absorbing boundary conditions of the kind Clayton-Enquist and PML (Perfect Matched Layers) and compared with results in standard literature, showing a promising performance by tackling both the pollution effect and matrix instability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Helmholtz%20equation" title="Helmholtz equation">Helmholtz equation</a>, <a href="https://publications.waset.org/abstracts/search?q=meshless%20methods" title=" meshless methods"> meshless methods</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20imaging" title=" seismic imaging"> seismic imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=wavefield%20inversion" title=" wavefield inversion"> wavefield inversion</a> </p> <a href="https://publications.waset.org/abstracts/33679/local-radial-basis-functions-for-helmholtz-equation-in-seismic-inversion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33679.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2984</span> Performance of Neural Networks vs. Radial Basis Functions When Forming a Metamodel for Residential Buildings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Philip%20Symonds">Philip Symonds</a>, <a href="https://publications.waset.org/abstracts/search?q=Jon%20Taylor"> Jon Taylor</a>, <a href="https://publications.waset.org/abstracts/search?q=Zaid%20Chalabi"> Zaid Chalabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Davies"> Michael Davies</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the world climate projected to warm and major cities in developing countries becoming increasingly populated and polluted, governments are tasked with the problem of overheating and air quality in residential buildings. This paper presents the development of an adaptable model of these risks. Simulations are performed using the EnergyPlus building physics software. An accurate metamodel is formed by randomly sampling building input parameters and training on the outputs of EnergyPlus simulations. Metamodels are used to vastly reduce the amount of computation time required when performing optimisation and sensitivity analyses. Neural Networks (NNs) are compared to a Radial Basis Function (RBF) algorithm when forming a metamodel. These techniques were implemented using the PyBrain and scikit-learn python libraries, respectively. NNs are shown to perform around 15% better than RBFs when estimating overheating and air pollution metrics modelled by EnergyPlus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20functions" title=" radial basis functions"> radial basis functions</a>, <a href="https://publications.waset.org/abstracts/search?q=metamodelling" title=" metamodelling"> metamodelling</a>, <a href="https://publications.waset.org/abstracts/search?q=python%20machine%20learning%20libraries" title=" python machine learning libraries"> python machine learning libraries</a> </p> <a href="https://publications.waset.org/abstracts/36155/performance-of-neural-networks-vs-radial-basis-functions-when-forming-a-metamodel-for-residential-buildings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36155.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2983</span> Handwriting Velocity Modeling by Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Aymen%20Slim">Mohamed Aymen Slim</a>, <a href="https://publications.waset.org/abstracts/search?q=Afef%20Abdelkrim"> Afef Abdelkrim</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Benrejeb"> Mohamed Benrejeb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The handwriting is a physical demonstration of a complex cognitive process learnt by man since his childhood. People with disabilities or suffering from various neurological diseases are facing so many difficulties resulting from problems located at the muscle stimuli (EMG) or signals from the brain (EEG) and which arise at the stage of writing. The handwriting velocity of the same writer or different writers varies according to different criteria: age, attitude, mood, writing surface, etc. Therefore, it is interesting to reconstruct an experimental basis records taking, as primary reference, the writing speed for different writers which would allow studying the global system during handwriting process. This paper deals with a new approach of the handwriting system modeling based on the velocity criterion through the concepts of artificial neural networks, precisely the Radial Basis Functions (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed neural model and the experimental data for various letters and forms then the efficiency of the proposed approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Electro%20Myo%20Graphic%20%28EMG%29%20signals" title="Electro Myo Graphic (EMG) signals">Electro Myo Graphic (EMG) signals</a>, <a href="https://publications.waset.org/abstracts/search?q=experimental%20approach" title=" experimental approach"> experimental approach</a>, <a href="https://publications.waset.org/abstracts/search?q=handwriting%20process" title=" handwriting process"> handwriting process</a>, <a href="https://publications.waset.org/abstracts/search?q=Radial%20Basis%20Functions%20%28RBF%29%20neural%20networks" title=" Radial Basis Functions (RBF) neural networks"> Radial Basis Functions (RBF) neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=velocity%20modeling" title=" velocity modeling"> velocity modeling</a> </p> <a href="https://publications.waset.org/abstracts/10496/handwriting-velocity-modeling-by-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10496.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2982</span> Radial Basis Surrogate Model Integrated to Evolutionary Algorithm for Solving Computation Intensive Black-Box Problems </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulbaset%20Saad">Abdulbaset Saad</a>, <a href="https://publications.waset.org/abstracts/search?q=Adel%20Younis"> Adel Younis</a>, <a href="https://publications.waset.org/abstracts/search?q=Zuomin%20Dong"> Zuomin Dong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For design optimization with high-dimensional expensive problems, an effective and efficient optimization methodology is desired. This work proposes a series of modification to the Differential Evolution (DE) algorithm for solving computation Intensive Black-Box Problems. The proposed methodology is called Radial Basis Meta-Model Algorithm Assisted Differential Evolutionary (RBF-DE), which is a global optimization algorithm based on the meta-modeling techniques. A meta-modeling assisted DE is proposed to solve computationally expensive optimization problems. The Radial Basis Function (RBF) model is used as a surrogate model to approximate the expensive objective function, while DE employs a mechanism to dynamically select the best performing combination of parameters such as differential rate, cross over probability, and population size. The proposed algorithm is tested on benchmark functions and real life practical applications and problems. The test results demonstrate that the proposed algorithm is promising and performs well compared to other optimization algorithms. The proposed algorithm is capable of converging to acceptable and good solutions in terms of accuracy, number of evaluations, and time needed to converge. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=differential%20evolution" title="differential evolution">differential evolution</a>, <a href="https://publications.waset.org/abstracts/search?q=engineering%20design" title=" engineering design"> engineering design</a>, <a href="https://publications.waset.org/abstracts/search?q=expensive%20computations" title=" expensive computations"> expensive computations</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-modeling" title=" meta-modeling"> meta-modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/48247/radial-basis-surrogate-model-integrated-to-evolutionary-algorithm-for-solving-computation-intensive-black-box-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48247.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">396</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2981</span> Identification of Nonlinear Systems Using Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Pislaru">C. Pislaru</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Shebani"> A. Shebani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the K-Means clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title="system identification">system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title=" nonlinear systems"> nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=K-means%20clustering%20algorithm" title=" K-means clustering algorithm "> K-means clustering algorithm </a> </p> <a href="https://publications.waset.org/abstracts/14775/identification-of-nonlinear-systems-using-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14775.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">470</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2980</span> Water Leakage Detection System of Pipe Line using Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Ejah%20Umraeni%20Salam">A. Ejah Umraeni Salam</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Tola"> M. Tola</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Selintung"> M. Selintung</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Maricar"> F. Maricar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title="radial basis function neural network">radial basis function neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=leakage%20pipeline" title=" leakage pipeline"> leakage pipeline</a>, <a href="https://publications.waset.org/abstracts/search?q=EPANET" title=" EPANET"> EPANET</a>, <a href="https://publications.waset.org/abstracts/search?q=RMSE" title=" RMSE"> RMSE</a> </p> <a href="https://publications.waset.org/abstracts/7608/water-leakage-detection-system-of-pipe-line-using-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7608.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">358</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2979</span> SVM-Based Modeling of Mass Transfer Potential of Multiple Plunging Jets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Surinder%20Deswal">Surinder Deswal</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahesh%20Pal"> Mahesh Pal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper investigates the potential of support vector machines based regression approach to model the mass transfer capacity of multiple plunging jets, both vertical (θ = 90°) and inclined (θ = 60°). The data set used in this study consists of four input parameters with a total of eighty eight cases. For testing, tenfold cross validation was used. Correlation coefficient values of 0.971 and 0.981 (root mean square error values of 0.0025 and 0.0020) were achieved by using polynomial and radial basis kernel functions based support vector regression respectively. Results suggest an improved performance by radial basis function in comparison to polynomial kernel based support vector machines. The estimated overall mass transfer coefficient, by both the kernel functions, is in good agreement with actual experimental values (within a scatter of ±15 %); thereby suggesting the utility of support vector machines based regression approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mass%20transfer" title="mass transfer">mass transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20plunging%20jets" title=" multiple plunging jets"> multiple plunging jets</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=ecological%20sciences" title=" ecological sciences"> ecological sciences</a> </p> <a href="https://publications.waset.org/abstracts/9906/svm-based-modeling-of-mass-transfer-potential-of-multiple-plunging-jets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9906.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2978</span> An Improved Mesh Deformation Method Based on Radial Basis Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xuan%20Zhou">Xuan Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Litian%20Zhang"> Litian Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuixiang%20Li"> Shuixiang Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mesh deformation using radial basis function interpolation method has been demonstrated to produce quality meshes with relatively little computational cost using a concise algorithm. However, it still suffers from the limited deformation ability, especially in large deformation. In this paper, a pre-displacement improvement is proposed to improve the problem that illegal meshes always appear near the moving inner boundaries owing to the large relative displacement of the nodes near inner boundaries. In this improvement, nodes near the inner boundaries are first associated to the near boundary nodes, and a pre-displacement based on the displacements of associated boundary nodes is added to the nodes near boundaries in order to make the displacement closer to the boundary deformation and improve the deformation capability. Several 2D and 3D numerical simulation cases have shown that the pre-displacement improvement for radial basis function (RBF) method significantly improves the mesh quality near inner boundaries and deformation capability, with little computational burden increasement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mesh%20deformation" title="mesh deformation">mesh deformation</a>, <a href="https://publications.waset.org/abstracts/search?q=mesh%20quality" title=" mesh quality"> mesh quality</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20mesh" title=" background mesh"> background mesh</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a> </p> <a href="https://publications.waset.org/abstracts/65928/an-improved-mesh-deformation-method-based-on-radial-basis-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65928.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">366</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2977</span> An Interpolation Tool for Data Transfer in Two-Dimensional Ice Accretion Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marta%20Cordero-Gracia">Marta Cordero-Gracia</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariola%20Gomez"> Mariola Gomez</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Blesbois"> Olivier Blesbois</a>, <a href="https://publications.waset.org/abstracts/search?q=Marina%20Carrion"> Marina Carrion</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the difficulties in icing simulations is for extended periods of exposure, when very large ice shapes are created. As well as being large, they can have complex shapes, such as a double horn. For icing simulations, these configurations are currently computed in several steps. The icing step is stopped when the ice shapes become too large, at which point a new mesh has to be created to allow for further CFD and ice growth simulations to be performed. This can be very costly, and is a limiting factor in the simulations that can be performed. A way to avoid the costly human intervention in the re-meshing step of multistep icing computation is to use mesh deformation instead of re-meshing. The aim of the present work is to apply an interpolation method based on Radial Basis Functions (RBF) to transfer deformations from surface mesh to volume mesh. This deformation tool has been developed specifically for icing problems. It is able to deal with localized, sharp and large deformations, unlike the tools traditionally used for more smooth wing deformations. This tool will be presented along with validation on typical two-dimensional icing shapes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ice%20accretion" title="ice accretion">ice accretion</a>, <a href="https://publications.waset.org/abstracts/search?q=interpolation" title=" interpolation"> interpolation</a>, <a href="https://publications.waset.org/abstracts/search?q=mesh%20deformation" title=" mesh deformation"> mesh deformation</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20functions" title=" radial basis functions"> radial basis functions</a> </p> <a href="https://publications.waset.org/abstracts/59130/an-interpolation-tool-for-data-transfer-in-two-dimensional-ice-accretion-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59130.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2976</span> Classifying Time Independent Plane Symmetric Spacetime through Noether`s Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nazish%20Iftikhar">Nazish Iftikhar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adil%20Jhangeer"> Adil Jhangeer</a>, <a href="https://publications.waset.org/abstracts/search?q=Tayyaba%20Naz"> Tayyaba Naz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The universe is expanding at an accelerated rate. Symmetries are useful in understanding universe’s behavior. Emmy Noether reported the relation between symmetries and conservation laws. These symmetries are known as Noether symmetries which correspond to a conserved quantity. In differential equations, conservation laws play an important role. Noether symmetries are helpful in modified theories of gravity. Time independent plane symmetric spacetime was classified by Noether`s theorem. By using Noether`s theorem, set of linear partial differential equations was obtained having A(r), B(r) and F(r) as unknown radial functions. The Lagrangian corresponding to considered spacetime in the Noether equation was used to get Noether operators. Different possibilities of radial functions were considered. Firstly, all functions were same. All the functions were considered as non-zero constant, linear, reciprocal and exponential respectively. Secondly, two functions were proportional to each other keeping third function different. Second case has four subcases in which four different relationships between A(r), B(r) and F(r) were discussed. In all cases, we obtained nontrivial Noether operators including gauge term. Conserved quantities for each Noether operators were also presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noether%20gauge%20symmetries" title="Noether gauge symmetries">Noether gauge symmetries</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20function" title=" radial function"> radial function</a>, <a href="https://publications.waset.org/abstracts/search?q=Noether%20operator" title=" Noether operator"> Noether operator</a>, <a href="https://publications.waset.org/abstracts/search?q=conserved%20quantities" title=" conserved quantities"> conserved quantities</a> </p> <a href="https://publications.waset.org/abstracts/71907/classifying-time-independent-plane-symmetric-spacetime-through-noethers-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71907.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">230</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2975</span> Combined Odd Pair Autoregressive Coefficients for Epileptic EEG Signals Classification by Radial Basis Function Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukari%20Nassim">Boukari Nassim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epilepsy" title="epilepsy">epilepsy</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG%20signals%20classification" title=" EEG signals classification"> EEG signals classification</a>, <a href="https://publications.waset.org/abstracts/search?q=combined%20odd%20pair%20autoregressive%20coefficients" title=" combined odd pair autoregressive coefficients"> combined odd pair autoregressive coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a> </p> <a href="https://publications.waset.org/abstracts/47454/combined-odd-pair-autoregressive-coefficients-for-epileptic-eeg-signals-classification-by-radial-basis-function-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47454.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2974</span> Function Approximation with Radial Basis Function Neural Networks via FIR Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyu%20Chul%20Lee">Kyu Chul Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung%20Hyun%20Yoo"> Sung Hyun Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon%20Ki%20Ahn"> Choon Ki Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Myo%20Taeg%20Lim"> Myo Taeg Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent experimental evidences have shown that because of a fast convergence and a nice accuracy, neural networks training via extended Kalman filter (EKF) method is widely applied. However, as to an uncertainty of the system dynamics or modeling error, the performance of the method is unreliable. In order to overcome this problem in this paper, a new finite impulse response (FIR) filter based learning algorithm is proposed to train radial basis function neural networks (RBFN) for nonlinear function approximation. Compared to the EKF training method, the proposed FIR filter training method is more robust to those environmental conditions. Furthermore, the number of centers will be considered since it affects the performance of approximation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extended%20Kalman%20filter" title="extended Kalman filter">extended Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20problem" title=" classification problem"> classification problem</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20networks%20%28RBFN%29" title=" radial basis function networks (RBFN)"> radial basis function networks (RBFN)</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20impulse%20response%20%28FIR%29%20filter" title=" finite impulse response (FIR) filter"> finite impulse response (FIR) filter</a> </p> <a href="https://publications.waset.org/abstracts/13851/function-approximation-with-radial-basis-function-neural-networks-via-fir-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13851.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">456</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2973</span> Evaluation of a Surrogate Based Method for Global Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20Lindstr%C3%B6m">David Lindström</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We evaluate the performance of a numerical method for global optimization of expensive functions. The method is using a response surface to guide the search for the global optimum. This metamodel could be based on radial basis functions, kriging, or a combination of different models. We discuss how to set the cycling parameters of the optimization method to get a balance between local and global search. We also discuss the eventual problem with Runge oscillations in the response surface. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expensive%20function" title="expensive function">expensive function</a>, <a href="https://publications.waset.org/abstracts/search?q=infill%20sampling%20criterion" title=" infill sampling criterion"> infill sampling criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=kriging" title=" kriging"> kriging</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization" title=" global optimization"> global optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface" title=" response surface"> response surface</a>, <a href="https://publications.waset.org/abstracts/search?q=Runge%20phenomenon" title=" Runge phenomenon"> Runge phenomenon</a> </p> <a href="https://publications.waset.org/abstracts/24538/evaluation-of-a-surrogate-based-method-for-global-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24538.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">578</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2972</span> Kinematic Optimization of Energy Extraction Performances for Flapping Airfoil by Using Radial Basis Function Method and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Maatar">M. Maatar</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mekadem"> M. Mekadem</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Medale"> M. Medale</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Hadjed"> B. Hadjed</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Imine"> B. Imine</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, numerical simulations have been carried out to study the performances of a flapping wing used as an energy collector. Metamodeling and genetic algorithms are used to detect the optimal configuration, improving power coefficient and/or efficiency. Radial basis functions and genetic algorithms have been applied to solve this problem. Three optimization factors are controlled, namely dimensionless heave amplitude h₀, pitch amplitude θ₀ and flapping frequency f. ANSYS FLUENT software has been used to solve the principal equations at a Reynolds number of 1100, while the heave and pitch motion of a NACA0015 airfoil has been realized using a developed function (UDF). The results reveal an average power coefficient and efficiency of 0.78 and 0.338 with an inexpensive low-fidelity model and a total relative error of 4.1% versus the simulation. The performances of the simulated optimum RBF-NSGA-II have been improved by 1.2% compared with the validated model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title="numerical simulation">numerical simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=flapping%20wing" title=" flapping wing"> flapping wing</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20extraction" title=" energy extraction"> energy extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20coefficient" title=" power coefficient"> power coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF" title=" RBF"> RBF</a>, <a href="https://publications.waset.org/abstracts/search?q=NSGA-II" title=" NSGA-II"> NSGA-II</a> </p> <a href="https://publications.waset.org/abstracts/186123/kinematic-optimization-of-energy-extraction-performances-for-flapping-airfoil-by-using-radial-basis-function-method-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">43</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2971</span> Comparative Analysis of Sigmoidal Feedforward Artificial Neural Networks and Radial Basis Function Networks Approach for Localization in Wireless Sensor Networks </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Payal">Ashish Payal</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Rai"> C. S. Rai</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20V.%20R.%20Reddy"> B. V. R. Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m<sup>2</sup> grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=localization" title="localization">localization</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20networks" title=" wireless sensor networks"> wireless sensor networks</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=RSSI" title=" RSSI"> RSSI</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS" title=" GPS"> GPS</a> </p> <a href="https://publications.waset.org/abstracts/49637/comparative-analysis-of-sigmoidal-feedforward-artificial-neural-networks-and-radial-basis-function-networks-approach-for-localization-in-wireless-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49637.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">339</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2970</span> Relativistic Energy Analysis for Some q Deformed Shape Invariant Potentials in D Dimensions Using SUSYQM Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Suparmi">A. Suparmi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Cari"> C. Cari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Yunianto"> M. Yunianto</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20N.%20Pratiwi"> B. N. Pratiwi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> D-dimensional Dirac equations of q-deformed shape invariant potentials were solved using supersymmetric quantum mechanics (SUSY QM) in the case of exact spin symmetry. The D dimensional radial Dirac equation for shape invariant potential reduces to one-dimensional Schrodinger type equation by an appropriate variable and parameter change. The relativistic energy spectra were analyzed by using SUSY QM and shape invariant properties from radial D dimensional Dirac equation that have reduced to one dimensional Schrodinger type equation. The SUSY operator was used to generate the D dimensional relativistic radial wave functions, the relativistic energy equation reduced to the non-relativistic energy in the non-relativistic limit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=D-dimensional%20dirac%20equation" title="D-dimensional dirac equation">D-dimensional dirac equation</a>, <a href="https://publications.waset.org/abstracts/search?q=non-central%20potential" title=" non-central potential"> non-central potential</a>, <a href="https://publications.waset.org/abstracts/search?q=SUSY%20QM" title=" SUSY QM"> SUSY QM</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20wave%20function" title=" radial wave function"> radial wave function</a> </p> <a href="https://publications.waset.org/abstracts/43601/relativistic-energy-analysis-for-some-q-deformed-shape-invariant-potentials-in-d-dimensions-using-susyqm-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43601.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">344</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2969</span> Calculation the Left Ventricle Wall Radial Strain and Radial SR Using Tagged Magnetic Resonance Imaging Data (tMRI)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Alenezy">Mohammed Alenezy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The function of cardiac motion can be used as an indicator of the heart abnormality by evaluating longitudinal, circumferential, and Radial Strain of the left ventricle. In this paper, the Radial Strain and SR is studied using tagged MRI (tMRI) data during the cardiac cycle on the mid-ventricle level of the left ventricle. Materials and methods: The short-axis view of the left ventricle of five healthy human (three males and two females) and four healthy male rats were imaged using tagged magnetic resonance imaging (tMRI) technique covering the whole cardiac cycle on the mid-ventricle level. Images were processed using Image J software to calculate the left ventricle wall Radial Strain and radial SR. The left ventricle Radial Strain and radial SR were calculated at the mid-ventricular level during the cardiac cycle. The peak Radial Strain for the human and rat heart was 40.7±1.44, and 46.8±0.68 respectively, and it occurs at 40% of the cardiac cycle for both human and rat heart. The peak diastolic and systolic radial SR for human heart was -1.78 s-1 ± 0.02 s-1 and 1.10±0.08 s-1 respectively, while for rat heart it was -5.16± 0.23s-1 and 4.25±0.02 s-1 respectively. Conclusion: This results show the ability of the tMRI data to characterize the cardiac motion during the cardiac cycle including diastolic and systolic phases which can be used as an indicator of the cardiac dysfunction by estimating the left ventricle Radial Strain and radial SR at different locations of the cardiac tissue. This study approves the validity of the tagged MRI data to describe accurately the cardiac radial motion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=left%20ventricle" title="left ventricle">left ventricle</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20strain" title=" radial strain"> radial strain</a>, <a href="https://publications.waset.org/abstracts/search?q=tagged%20MRI" title=" tagged MRI"> tagged MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiac%20cycle" title=" cardiac cycle"> cardiac cycle</a> </p> <a href="https://publications.waset.org/abstracts/21036/calculation-the-left-ventricle-wall-radial-strain-and-radial-sr-using-tagged-magnetic-resonance-imaging-data-tmri" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21036.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">482</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2968</span> Rotor Radial Vent Pumping in Large Synchronous Electrical Machines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darren%20Camilleri">Darren Camilleri</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Rolston"> Robert Rolston</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rotor radial vents make use of the pumping effect to increase airflow through the active material thus reduce hotspot temperatures. The effect of rotor radial pumping in synchronous machines has been studied previously. This paper presents the findings of previous studies and builds upon their theories using a parametric numerical approach to investigate the rotor radial pumping effect. The pressure head generated by the poles and radial vent flow-rate were identified as important factors in maximizing the benefits of the pumping effect. The use of Minitab and ANSYS Workbench to investigate the key performance characteristics of radial pumping through a Design of Experiments (DOE) was described. CFD results were compared with theoretical calculations. A correlation for each response variable was derived through a statistical analysis. Findings confirmed the strong dependence of radial vent length on vent pressure head, and radial vent cross-sectional area was proved to be significant in maximising radial vent flow rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CFD" title="CFD">CFD</a>, <a href="https://publications.waset.org/abstracts/search?q=cooling" title=" cooling"> cooling</a>, <a href="https://publications.waset.org/abstracts/search?q=electrical%20machines" title=" electrical machines"> electrical machines</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20analysis" title=" regression analysis"> regression analysis</a> </p> <a href="https://publications.waset.org/abstracts/41880/rotor-radial-vent-pumping-in-large-synchronous-electrical-machines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41880.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">312</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2967</span> Multi-Layer Perceptron and Radial Basis Function Neural Network Models for Classification of Diabetic Retinopathy Disease Using Video-Oculography Signals</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ceren%20Kaya">Ceren Kaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Okan%20Erkaymaz"> Okan Erkaymaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Orhan%20Ayar"> Orhan Ayar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmut%20%C3%96zer"> Mahmut Özer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetes Mellitus (Diabetes) is a disease based on insulin hormone disorders and causes high blood glucose. Clinical findings determine that diabetes can be diagnosed by electrophysiological signals obtained from the vital organs. 'Diabetic Retinopathy' is one of the most common eye diseases resulting on diabetes and it is the leading cause of vision loss due to structural alteration of the retinal layer vessels. In this study, features of horizontal and vertical Video-Oculography (VOG) signals have been used to classify non-proliferative and proliferative diabetic retinopathy disease. Twenty-five features are acquired by using discrete wavelet transform with VOG signals which are taken from 21 subjects. Two models, based on multi-layer perceptron and radial basis function, are recommended in the diagnosis of Diabetic Retinopathy. The proposed models also can detect level of the disease. We show comparative classification performance of the proposed models. Our results show that proposed the RBF model (100%) results in better classification performance than the MLP model (94%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diabetic%20retinopathy" title="diabetic retinopathy">diabetic retinopathy</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function" title=" radial basis function"> radial basis function</a>, <a href="https://publications.waset.org/abstracts/search?q=video-oculography%20%28VOG%29" title=" video-oculography (VOG)"> video-oculography (VOG)</a> </p> <a href="https://publications.waset.org/abstracts/78748/multi-layer-perceptron-and-radial-basis-function-neural-network-models-for-classification-of-diabetic-retinopathy-disease-using-video-oculography-signals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78748.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">259</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2966</span> Orientational Pair Correlation Functions Modelling of the LiCl6H2O by the Hybrid Reverse Monte Carlo: Using an Environment Dependence Interaction Potential</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Habchi">Mohammed Habchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Mohammed%20Mesli"> Sidi Mohammed Mesli</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafik%20Benallal"> Rafik Benallal</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Kotbi"> Mohammed Kotbi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On the basis of four partial correlation functions and some geometric constraints obtained from neutron scattering experiments, a Reverse Monte Carlo (RMC) simulation has been performed in the study of the aqueous electrolyte LiCl6H2O at the glassy state. The obtained 3-dimensional model allows computing pair radial and orientational distribution functions in order to explore the structural features of the system. Unrealistic features appeared in some coordination peaks. To remedy to this, we use the Hybrid Reverse Monte Carlo (HRMC), incorporating an additional energy constraint in addition to the usual constraints derived from experiments. The energy of the system is calculated using an Environment Dependence Interaction Potential (EDIP). Ions effects is studied by comparing correlations between water molecules in the solution and in pure water at room temperature Our results show a good agreement between experimental and computed partial distribution functions (PDFs) as well as a significant improvement in orientational distribution curves. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LiCl6H2O" title="LiCl6H2O">LiCl6H2O</a>, <a href="https://publications.waset.org/abstracts/search?q=glassy%20state" title=" glassy state"> glassy state</a>, <a href="https://publications.waset.org/abstracts/search?q=RMC" title=" RMC"> RMC</a>, <a href="https://publications.waset.org/abstracts/search?q=HRMC" title=" HRMC"> HRMC</a> </p> <a href="https://publications.waset.org/abstracts/38320/orientational-pair-correlation-functions-modelling-of-the-licl6h2o-by-the-hybrid-reverse-monte-carlo-using-an-environment-dependence-interaction-potential" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38320.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">471</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2965</span> Estimation of Residual Stresses in Thick Walled Cylinder by Radial Basis Artificial Neural </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Heidari">Mohammad Heidari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a method for high strength steel is proposed of residual stresses in autofrettaged tubes by combination of artificial neural networks is presented. Many different thick walled cylinders that were subjected to different conditions were studied. At first, the residual stress is calculated by analytical solution. Then by changing of the parameters that influenced in residual stresses such as percentage of autofrettage, internal pressure, wall ratio of cylinder, material property of cylinder, bauschinger and hardening effect factor, a neural network is created. These parameters are the input of network. The output of network is residual stress. Numerical data, employed for training the network and capabilities of the model in predicting the residual stress has been verified. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 2.75% in predicting residual stress of thick wall cylinder. Further analysis of residual stress of thick wall cylinder under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thick%20walled%20cylinder" title="thick walled cylinder">thick walled cylinder</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20stress" title=" residual stress"> residual stress</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis" title=" radial basis"> radial basis</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a> </p> <a href="https://publications.waset.org/abstracts/34495/estimation-of-residual-stresses-in-thick-walled-cylinder-by-radial-basis-artificial-neural" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34495.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2964</span> The Estimation Method of Inter-Story Drift for Buildings Based on Evolutionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyu%20Jin%20Kim">Kyu Jin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20Kwan%20Oh"> Byung Kwan Oh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyo%20Seon%20Park"> Hyo Seon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The seismic responses-based structural health monitoring system has been performed to reduce seismic damage. The inter-story drift ratio which is the major index of the seismic capacity assessment is employed for estimating the seismic damage of buildings. Meanwhile, seismic response analysis to estimate the structural responses of building demands significantly high computational cost due to increasing number of high-rise and large buildings. To estimate the inter-story drift ratio of buildings from the earthquake efficiently, this paper suggests the estimation method of inter-story drift for buildings using an artificial neural network (ANN). In the method, the radial basis function neural network (RBFNN) is integrated with optimization algorithm to optimize the variable through evolutionary learning that refers to evolutionary radial basis function neural network (ERBFNN). The estimation method estimates the inter-story drift without seismic response analysis when the new earthquakes are subjected to buildings. The effectiveness of the estimation method is verified through a simulation using multi-degree of freedom system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title="structural health monitoring">structural health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-story%20drift%20ratio" title=" inter-story drift ratio"> inter-story drift ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/52253/the-estimation-method-of-inter-story-drift-for-buildings-based-on-evolutionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52253.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">327</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2963</span> Energy-Level Structure of a Confined Electron-Positron Pair in Nanostructure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tokuei%20Sako">Tokuei Sako</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul-Antoine%20Hervieux"> Paul-Antoine Hervieux</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The energy-level structure of a pair of electron and positron confined in a quasi-one-dimensional nano-scale potential well has been investigated focusing on its trend in the small limit of confinement strength ω, namely, the Wigner molecular regime. An anisotropic Gaussian-type basis functions supplemented by high angular momentum functions as large as l = 19 has been used to obtain reliable full configuration interaction (FCI) wave functions. The resultant energy spectrum shows a band structure characterized by ω for the large ω regime whereas for the small ω regime it shows an energy-level pattern dominated by excitation into the in-phase motion of the two particles. The observed trend has been rationalized on the basis of the nodal patterns of the FCI wave functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=confined%20systems" title="confined systems">confined systems</a>, <a href="https://publications.waset.org/abstracts/search?q=positron" title=" positron"> positron</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20function" title=" wave function"> wave function</a>, <a href="https://publications.waset.org/abstracts/search?q=Wigner%20molecule" title=" Wigner molecule"> Wigner molecule</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20dots" title=" quantum dots"> quantum dots</a> </p> <a href="https://publications.waset.org/abstracts/4205/energy-level-structure-of-a-confined-electron-positron-pair-in-nanostructure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4205.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">387</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2962</span> Comparative Study Using WEKA for Red Blood Cells Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jameela%20Ali">Jameela Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20A.%20Jalab"> Hamid A. Jalab</a>, <a href="https://publications.waset.org/abstracts/search?q=Loay%20E.%20George"> Loay E. George</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahim%20Ahmad"> Abdul Rahim Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Suliman"> Azizah Suliman</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Al-Jashamy"> Karim Al-Jashamy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=K-nearest%20neighbors%20algorithm" title="K-nearest neighbors algorithm">K-nearest neighbors algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20network" title=" radial basis function neural network"> radial basis function neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=red%20blood%20cells" title=" red blood cells"> red blood cells</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/11462/comparative-study-using-weka-for-red-blood-cells-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11462.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">409</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2961</span> A Prediction Model for Dynamic Responses of Building from Earthquake Based on Evolutionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyu%20Jin%20Kim">Kyu Jin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20Kwan%20Oh"> Byung Kwan Oh</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyo%20Seon%20Park"> Hyo Seon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title="structural health monitoring">structural health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20response" title=" dynamic response"> dynamic response</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title=" radial basis function network"> radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/41138/a-prediction-model-for-dynamic-responses-of-building-from-earthquake-based-on-evolutionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41138.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2960</span> A Comparative Study for Various Techniques Using WEKA for Red Blood Cells Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jameela%20Ali">Jameela Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20A.%20Jalab"> Hamid A. Jalab</a>, <a href="https://publications.waset.org/abstracts/search?q=Loay%20E.%20George"> Loay E. George</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Rahim%20Ahmad"> Abdul Rahim Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Suliman"> Azizah Suliman</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Al-Jashamy"> Karim Al-Jashamy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=red%20blood%20cells" title="red blood cells">red blood cells</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20neural%20networks" title=" radial basis function neural networks"> radial basis function neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=suport%20vector%20machine" title=" suport vector machine"> suport vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbors%20algorithm" title=" k-nearest neighbors algorithm"> k-nearest neighbors algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15631/a-comparative-study-for-various-techniques-using-weka-for-red-blood-cells-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15631.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">480</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2959</span> Measurements of Radial Velocity in Fixed Fluidized Bed for Fischer-Tropsch Synthesis Using LDV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaolai%20Zhang">Xiaolai Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haitao%20Zhang"> Haitao Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qiwen%20Sun"> Qiwen Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Weixin%20Qian"> Weixin Qian</a>, <a href="https://publications.waset.org/abstracts/search?q=Weiyong%20Ying"> Weiyong Ying</a> </p> <p class="card-text"><strong>Abstract:</strong></p> High temperature Fischer-Tropsch synthesis process use fixed fluidized bed as a reactor. In order to understand the flow behavior in the fluidized bed better, the research of how the radial velocity affect the entire flow field is necessary. Laser Doppler Velocimetry (LDV) was used to study the radial velocity distribution along the diameter direction of the cross-section of the particle in a fixed fluidized bed. The velocity in the cross-section is fluctuating within a small range. The direction of the speed is a random phenomenon. In addition to r/R is 1, the axial velocity are more than 6 times of the radial velocity, the radial velocity has little impact on the axial velocity in a fixed fluidized bed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fischer-Tropsch%20synthesis" title="Fischer-Tropsch synthesis">Fischer-Tropsch synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=Fixed%20fluidized%20bed" title=" Fixed fluidized bed"> Fixed fluidized bed</a>, <a href="https://publications.waset.org/abstracts/search?q=LDV" title=" LDV"> LDV</a>, <a href="https://publications.waset.org/abstracts/search?q=Velocity" title=" Velocity"> Velocity</a> </p> <a href="https://publications.waset.org/abstracts/24993/measurements-of-radial-velocity-in-fixed-fluidized-bed-for-fischer-tropsch-synthesis-using-ldv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24993.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2958</span> Analysis of Radial Pulse Using Nadi-Parikshan Yantra</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashok%20E.%20Kalange">Ashok E. Kalange</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagnosis according to Ayurveda is to find the root cause of a disease. Out of the eight different kinds of examinations, Nadi-Pariksha (pulse examination) is important. Nadi-Pariksha is done at the root of the thumb by examining the radial artery using three fingers. Ancient Ayurveda identifies the health status by observing the wrist pulses in terms of 'Vata', 'Pitta' and 'Kapha', collectively called as tridosha, as the basic elements of human body and in their combinations. Diagnosis by traditional pulse analysis – NadiPariksha - requires a long experience in pulse examination and a high level of skill. The interpretation tends to be subjective, depending on the expertise of the practitioner. Present work is part of the efforts carried out in making Nadi-Parikshan objective. Nadi Parikshan Yantra (three point pulse examination system) is developed in our laboratory by using three pressure sensors (one each for the Vata, Pitta and Kapha points on radial artery). The radial pulse data was collected of a large number of subjects. The radial pulse data collected is analyzed on the basis of relative amplitudes of the three point pulses as well as in frequency and time domains. The same subjects were examined by Ayurvedic physician (Nadi Vaidya) and the dominant Dosha - Vata, Pitta or Kapha - was identified. The results are discussed in details in the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadi%20Parikshan%20Yantra" title="Nadi Parikshan Yantra">Nadi Parikshan Yantra</a>, <a href="https://publications.waset.org/abstracts/search?q=Tridosha" title=" Tridosha"> Tridosha</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadi%20Pariksha" title=" Nadi Pariksha"> Nadi Pariksha</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20pulse%20data%20analysis" title=" human pulse data analysis"> human pulse data analysis</a> </p> <a href="https://publications.waset.org/abstracts/77191/analysis-of-radial-pulse-using-nadi-parikshan-yantra" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77191.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">189</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2957</span> A Multi-Objective Evolutionary Algorithm of Neural Network for Medical Diseases Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sultan%20Noman%20Qasem">Sultan Noman Qasem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radial%20basis%20function%20network" title="radial basis function network">radial basis function network</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20learning" title=" hybrid learning"> hybrid learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a> </p> <a href="https://publications.waset.org/abstracts/15843/a-multi-objective-evolutionary-algorithm-of-neural-network-for-medical-diseases-problems" class="btn btn-primary btn-sm">Procedia</a> 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