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network</title> <meta name="description" content="Search results for: radial network"> <meta name="keywords" content="radial network"> <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 d-lg-none navbar-toggler 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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 network"> <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> 5064</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: radial network</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5064</span> Prediction Fluid Properties of Iranian Oil Field with Using of Radial Based Neural Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdolreza%20Memari">Abdolreza Memari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=viscosity" title="viscosity">viscosity</a>, <a href="https://publications.waset.org/abstracts/search?q=Iranian%20crude%20oil" title=" Iranian crude oil"> Iranian crude oil</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20based" title=" radial based"> radial based</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=roller%20ball%20method" title=" roller ball method"> roller ball method</a>, <a href="https://publications.waset.org/abstracts/search?q=KHAN%20model" title=" KHAN model "> KHAN model </a> </p> <a href="https://publications.waset.org/abstracts/29815/prediction-fluid-properties-of-iranian-oil-field-with-using-of-radial-based-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29815.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">501</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">5063</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">5062</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">5061</span> Assessing Artificial Neural Network Models on Forecasting the Return of Stock Market Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Rostami%20Jaz">Hamid Rostami Jaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamran%20Ameri%20Siahooei"> Kamran Ameri Siahooei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchange%20index" title="exchange index">exchange index</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptron%20neural%20network" title=" perceptron neural network"> perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=Tehran%20stock%20exchange" title=" Tehran stock exchange"> Tehran stock exchange</a> </p> <a href="https://publications.waset.org/abstracts/51503/assessing-artificial-neural-network-models-on-forecasting-the-return-of-stock-market-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51503.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">5060</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">5059</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">5058</span> Extrudate Swell under the Effect of Radial Flow and Intrinsic Factors to the Polymer Upstream of the Die</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hela%20Krir">Hela Krir</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhak%20Ayadi"> Abdelhak Ayadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Chedly%20Bradaii"> Chedly Bradaii</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The influence of both intrinsic factors, elastic energy and memory effect, and radial flow on the appearance and the evolution of the extrudate swelling are investigated in the present work. The experiments have been performed with linear polydimethylsiloxane (PDMS) via a capillary rheometer in which a convergent radial flow was created upstream the contraction. The correspondence between the effects of radial flow, entry elastic stored energy and memory effect is discussed. In particular, as the influence of the considered radial flow, extrudate photographs showed that when the gap ratio is reduced, the extrudate swell is lessened than what it is when radial flow geometry is not installed. Moreover, with a narrower gap, the polymer stores less energy during its passage through the die which implies a lower extrudate swelling at the outlet of the die. Results previously mentioned may be related both to shear and elongational components of radial flow. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=elastic%20energy" title="elastic energy">elastic energy</a>, <a href="https://publications.waset.org/abstracts/search?q=extrudate%20swell" title=" extrudate swell"> extrudate swell</a>, <a href="https://publications.waset.org/abstracts/search?q=memory%20effect" title=" memory effect"> memory effect</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20flow" title=" radial flow"> radial flow</a> </p> <a href="https://publications.waset.org/abstracts/87319/extrudate-swell-under-the-effect-of-radial-flow-and-intrinsic-factors-to-the-polymer-upstream-of-the-die" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87319.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">172</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">5057</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">483</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">5056</span> Determination of the Optimal DG PV Interconnection Location Using Losses and Voltage Regulation as Assessment Indicators Case Study: ECG 33 kV Sub-Transmission Network </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ekow%20A.%20Kwofie">Ekow A. Kwofie</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20K.%20Anto"> Emmanuel K. Anto</a>, <a href="https://publications.waset.org/abstracts/search?q=Godfred%20Mensah"> Godfred Mensah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, CYME Distribution software has been used to assess the impacts of solar Photovoltaic (PV) distributed generation (DG) plant on the Electricity Company of Ghana (ECG) 33 kV sub-transmission network at different PV penetration levels. As ECG begins to encourage DG PV interconnections within its network, there has been the need to assess the impacts on the sub-transmission losses and voltage contribution. In Tema, a city in Accra - Ghana, ECG has a 33 kV sub-transmission network made up of 20 No. 33 kV buses that was modeled. Three different locations were chosen: The source bus, a bus along the sub-transmission radial network and a bus at the tail end to determine the optimal location for DG PV interconnection. The optimal location was determined based on sub-transmission technical losses and voltage impact. PV capacities at different penetration levels were modeled at each location and simulations performed to determine the optimal PV penetration level. Interconnection at a bus along (or in the middle of) the sub-transmission network offered the highest benefits at an optimal PV penetration level of 80%. At that location, the maximum voltage improvement of 0.789% on the neighboring 33 kV buses and maximum loss reduction of 6.033% over the base case scenario were recorded. Hence, the optimal location for DG PV integration within the 33 kV sub-transmission utility network is at a bus along the sub-transmission radial network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20generation%20photovoltaic%20%28DG%20PV%29" title="distributed generation photovoltaic (DG PV)">distributed generation photovoltaic (DG PV)</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20location" title=" optimal location"> optimal location</a>, <a href="https://publications.waset.org/abstracts/search?q=penetration%20level" title=" penetration level"> penetration level</a>, <a href="https://publications.waset.org/abstracts/search?q=sub%E2%80%93transmission%20network" title=" sub–transmission network"> sub–transmission network</a> </p> <a href="https://publications.waset.org/abstracts/57038/determination-of-the-optimal-dg-pv-interconnection-location-using-losses-and-voltage-regulation-as-assessment-indicators-case-study-ecg-33-kv-sub-transmission-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57038.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">350</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">5055</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> <a href="https://publications.waset.org/abstracts/15843.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">563</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">5054</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">5053</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">5052</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">5051</span> Unusual High Origin and Superficial Course of Radial Artery: A Case Report with Embryological Explanation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anasuya%20Ghosh">Anasuya Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Subhramoy%20Chaudhury"> Subhramoy Chaudhury</a> </p> <p class="card-text"><strong>Abstract:</strong></p> During routine cadaveric dissection at gross anatomy lab of our institution, a radial artery was found with unusual origin and superficial course. Normally the radial artery takes its origin as one of the terminal branches of brachial artery at the level of the neck of radius. It usually lies along the lateral border of fore arm deep to the brachioradialis muscle. While dissecting a 72-year-old Caucasian female cadaver, it was found that the right sided radial artery originated from the upper part of brachial artery of arm, 2 cm below the lower border of teres major muscle, from the lateral aspect of brachial artery. Then the radial artery superficially crossed the brachial artery and median nerve from lateral to medial direction and rested superficially at the cubital fossa. Embryologically, it can be explained as a failure of disappearance, or abnormal persistence of some insignificant embryonic vessels may give rise to this kind of vascular anomalies. As radial artery is one of the most important upper limb arteries, its variation and related complications are clinically significant. This unusual origin and course of radial artery should be kept in mind by all healthcare providers including surgeons and radiologists during routine venipuncture, orthopedic and plastic surgeries of arm, coronary angiographic procedures in radial approach etc. to prevent unwanted complications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brachial%20artery%20anomalies" title="brachial artery anomalies">brachial artery anomalies</a>, <a href="https://publications.waset.org/abstracts/search?q=brachio-radial%20artery" title=" brachio-radial artery"> brachio-radial artery</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20origin%20radial%20artery" title=" high origin radial artery"> high origin radial artery</a>, <a href="https://publications.waset.org/abstracts/search?q=superficial%20radial%20artery" title=" superficial radial artery"> superficial radial artery</a> </p> <a href="https://publications.waset.org/abstracts/72764/unusual-high-origin-and-superficial-course-of-radial-artery-a-case-report-with-embryological-explanation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72764.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">325</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">5050</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">5049</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">405</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">5048</span> Evaluation of Carbon Dioxide Pressure through Radial Velocity Difference in Arterial Blood Modeled by Drift Flux Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Rima%20Cheniti">Aicha Rima Cheniti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hatem%20Besbes"> Hatem Besbes</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Haggege"> Joseph Haggege</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Sintes"> Christophe Sintes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we are interested to determine the carbon dioxide pressure in the arterial blood through radial velocity difference. The blood was modeled as a two phase mixture (an aqueous carbon dioxide solution with carbon dioxide gas) by Drift flux model and the Young-Laplace equation. The distributions of mixture velocities determined from the considered model permitted the calculation of the radial velocity distributions with different values of mean mixture pressure and the calculation of the mean carbon dioxide pressure knowing the mean mixture pressure. The radial velocity distributions are used to deduce a calculation method of the mean mixture pressure through the radial velocity difference between two positions which is measured by ultrasound. The mean carbon dioxide pressure is then deduced from the mean mixture pressure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mean%20carbon%20dioxide%20pressure" title="mean carbon dioxide pressure">mean carbon dioxide pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20mixture%20pressure" title=" mean mixture pressure"> mean mixture pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20velocity" title=" mixture velocity"> mixture velocity</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20velocity%20difference" title=" radial velocity difference"> radial velocity difference</a> </p> <a href="https://publications.waset.org/abstracts/51601/evaluation-of-carbon-dioxide-pressure-through-radial-velocity-difference-in-arterial-blood-modeled-by-drift-flux-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51601.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">421</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">5047</span> Nonuniformity of the Piston Motion in a Radial Aircraft Engine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Pietrykowski">K. Pietrykowski</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Bialy"> M. Bialy</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Duk"> M. Duk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the main disadvantages of radial engines is non-uniformity of operating cycles of each cylinder. This paper discusses the results of the kinematic analysis of pistons motion of the ASz-62IR radial engine. The ASz-62IR engine is produced in Poland and mounted in the M-18 Dromader and the An-2. The results are shown as the courses of the motion of the pistons. The discrepancies in the courses for individual pistons can result in different masses of the charge to fill the cylinders. Besides, pistons acceleration of individual cylinders is different, which triggers an additional vibration in the engine. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonuniformity" title="nonuniformity">nonuniformity</a>, <a href="https://publications.waset.org/abstracts/search?q=kinematic%20analysis" title=" kinematic analysis"> kinematic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=piston%20motion" title=" piston motion"> piston motion</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20engine" title=" radial engine"> radial engine</a> </p> <a href="https://publications.waset.org/abstracts/49925/nonuniformity-of-the-piston-motion-in-a-radial-aircraft-engine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49925.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">385</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">5046</span> Smart Forms and Intelligent Transportation Network Patterns, an Integrated Spatial Approach to Smart Cities and Intelligent Transport Systems in India Cities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Geetanjli%20Rani">Geetanjli Rani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The physical forms and network pattern of the city is expected to be enhanced with the advancement of technology. Reason being, the era of virtualisation and digital urban realm convergence with physical development. By means of comparative Spatial graphics and visuals of cities, the present paper attempts to revisit the very base of efficient physical forms and patterns to sync the emergence of virtual activities. Thus, the present approach to integrate spatial Smartness of Cities and Intelligent Transportation Systems is a brief assessment of smart forms and intelligent transportation network pattern to the dualism of physical and virtual urban activities. Finally, the research brings out that the grid iron pattern, radial, ring-radial, orbital etc. stands to be more efficient, effective and economical transit friendly for users, resource optimisation as well as compact urban and regional systems. Moreover, this paper concludes that the idea of flow and contiguity hidden in such smart forms and intelligent transportation network pattern suits to layering, deployment, installation and development of Intelligent Transportation Systems of Smart Cities such as infrastructure, facilities and services. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smart%20form" title="smart form">smart form</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20infrastructure" title=" smart infrastructure"> smart infrastructure</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transportation%20network%20pattern" title=" intelligent transportation network pattern"> intelligent transportation network pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20and%20virtual%20integration" title=" physical and virtual integration"> physical and virtual integration</a> </p> <a href="https://publications.waset.org/abstracts/156685/smart-forms-and-intelligent-transportation-network-patterns-an-integrated-spatial-approach-to-smart-cities-and-intelligent-transport-systems-in-india-cities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156685.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">154</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">5045</span> Modelling of Cavity Growth in Underground Coal Gasification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preeti%20Aghalayam">Preeti Aghalayam</a>, <a href="https://publications.waset.org/abstracts/search?q=Jay%20Shah"> Jay Shah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Underground coal gasification (UCG) is the in-situ gasification of unmineable coals to produce syngas. In UCG, gasifying agents are injected into the coal seam, and a reactive cavity is formed due to coal consumption. The cavity formed is typically hemispherical, and this report consists of the MATLAB model of the UCG cavity to predict the composition of the output gases. There are seven radial and two time-variant ODEs. A MATLAB solver (ode15s) is used to solve the radial ODEs from the above equations. Two for-loops are implemented in the model, i.e., one for time variations and another for radial variation. In the time loop, the radial odes are solved using the MATLAB solver. The radial loop is nested inside the time loop, and the density odes are numerically solved using the Euler method. The model is validated by comparing it with the literature results of laboratory-scale experiments. The model predicts the radial and time variation of the product gases inside the cavity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gasification%20agent" title="gasification agent">gasification agent</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB%20model" title=" MATLAB model"> MATLAB model</a>, <a href="https://publications.waset.org/abstracts/search?q=syngas" title=" syngas"> syngas</a>, <a href="https://publications.waset.org/abstracts/search?q=underground%20coal%20gasification%20%28UCG%29" title=" underground coal gasification (UCG)"> underground coal gasification (UCG)</a> </p> <a href="https://publications.waset.org/abstracts/142719/modelling-of-cavity-growth-in-underground-coal-gasification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142719.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">206</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">5044</span> Radial Distribution Network Reliability Improvement by Using Imperialist Competitive Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azim%20Khodadadi">Azim Khodadadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Sadaat%20Vakili"> Sahar Sadaat Vakili</a>, <a href="https://publications.waset.org/abstracts/search?q=Ebrahim%20Babaei"> Ebrahim Babaei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a numerical method to optimize the failure rate and repair time of a typical radial distribution system. Failure rate and repair time are effective parameters in customer and energy based indices of reliability. Decrease of these parameters improves reliability indices. Thus, system stability will be boost. The penalty functions indirectly reflect the cost of investment which spent to improve these indices. Constraints on customer and energy based indices, i.e. SAIFI, SAIDI, CAIDI and AENS have been considered by using a new method which reduces optimization algorithm controlling parameters. Imperialist Competitive Algorithm (ICA) used as main optimization technique and particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) has been applied for further investigation. These algorithms have been implemented on a test system by MATLAB. Obtained results have been compared with each other. The optimized values of repair time and failure rate are much lower than current values which this achievement reduced investment cost and also ICA gives better answer than the other used algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=imperialist%20competitive%20algorithm" title="imperialist competitive algorithm">imperialist competitive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=failure%20rate" title=" failure rate"> failure rate</a>, <a href="https://publications.waset.org/abstracts/search?q=repair%20time" title=" repair time"> repair time</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20distribution%20network" title=" radial distribution network"> radial distribution network</a> </p> <a href="https://publications.waset.org/abstracts/27260/radial-distribution-network-reliability-improvement-by-using-imperialist-competitive-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27260.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">669</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">5043</span> Thermal Performance of Radial Heat Sinks for LED Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jongchul%20Park">Jongchul Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Chan%20Byon"> Chan Byon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the thermal performance of radial heat sinks for LED applications is investigated numerically and experimentally. The effect of geometrical parameters such as inner radius, fin height, fin length, and fin spacing, as well as the Elenbaas number, is considered. In addition, the effects of augmentation of concentric ring, perforation, and duct are extensively explored in order to enhance the thermal performance of conventional radial heat sink. The results indicate that the Elenbaas number and the fin radius have a significant effect on the thermal performance of the heat sink. The concentric ring affects the performance much, but the degree of affection is highly dependent on the orientation. The perforation always brings about higher thermal performance. The duct can effectively prevent the bypass of the natural convection flow, which in turn reduces the thermal resistance of the radial heat sink significantly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title="heat transfer">heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20heat%20sink" title=" radial heat sink"> radial heat sink</a>, <a href="https://publications.waset.org/abstracts/search?q=LED" title=" LED"> LED</a>, <a href="https://publications.waset.org/abstracts/search?q=Elenbaas" title=" Elenbaas"> Elenbaas</a> </p> <a href="https://publications.waset.org/abstracts/36553/thermal-performance-of-radial-heat-sinks-for-led-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36553.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">5042</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">410</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">5041</span> Novel Adaptive Radial Basis Function Neural Networks Based Approach for Short-Term Load Forecasting of Jordanian Power Grid </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eyad%20Almaita">Eyad Almaita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm is used to forecast the hour by hour electrical load demand in Jordan. A small and effective RBFNN model is used to forecast the hourly total load demand based on a small number of features. These features are; the load in the previous day, the load in the same day in the previous week, the temperature in the same hour, the hour number, the day number, and the day type. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminates the need to retrain the RBFNN model again. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data for the period Jan./2012-April/2013 is used train the RBFNN models and the data for the period May/2013- Sep. /2013 is used to validate the models effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=load%20forecasting" title="load forecasting">load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neural%20network" title=" adaptive neural network"> adaptive 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=short-term" title=" short-term"> short-term</a>, <a href="https://publications.waset.org/abstracts/search?q=electricity%20consumption" title=" electricity consumption"> electricity consumption</a> </p> <a href="https://publications.waset.org/abstracts/40294/novel-adaptive-radial-basis-function-neural-networks-based-approach-for-short-term-load-forecasting-of-jordanian-power-grid" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40294.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">5040</span> Breast Cancer Detection Using Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiwan%20Kumar">Jiwan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooja"> Pooja</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20Negi"> Sandeep Negi</a>, <a href="https://publications.waset.org/abstracts/search?q=Anjum%20Rouf"> Anjum Rouf</a>, <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kumar"> Amit Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveen%20Lakra"> Naveen Lakra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian 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=ensemble%20learning" title=" ensemble learning"> ensemble learning</a>, <a href="https://publications.waset.org/abstracts/search?q=understandable" title=" understandable"> understandable</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20making%20better" title=" data making better"> data making better</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a> </p> <a href="https://publications.waset.org/abstracts/185470/breast-cancer-detection-using-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185470.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">53</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">5039</span> Nonlinear Adaptive PID Control for a Semi-Batch Reactor Based on an RBF Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Magdi.%20M.%20Nabi">Magdi. M. Nabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ding-Li%20Yu"> Ding-Li Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control of a semi-batch polymerization reactor using an adaptive radial basis function (RBF) neural network method is investigated in this paper. A neural network inverse model is used to estimate the valve position of the reactor; this method can identify the controlled system with the RBF neural network identifier. The weights of the adaptive PID controller are timely adjusted based on the identification of the plant and self-learning capability of RBFNN. A PID controller is used in the feedback control to regulate the actual temperature by compensating the neural network inverse model output. Simulation results show that the proposed control has strong adaptability, robustness and satisfactory control performance and the nonlinear system is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chylla-Haase%20polymerization%20reactor" title="Chylla-Haase polymerization reactor">Chylla-Haase polymerization reactor</a>, <a href="https://publications.waset.org/abstracts/search?q=RBF%20neural%20networks" title=" RBF neural networks"> RBF neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=feed-forward" title=" feed-forward"> feed-forward</a>, <a href="https://publications.waset.org/abstracts/search?q=feedback%20control" title=" feedback control"> feedback control</a> </p> <a href="https://publications.waset.org/abstracts/11204/nonlinear-adaptive-pid-control-for-a-semi-batch-reactor-based-on-an-rbf-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11204.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">702</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">5038</span> Design of Neural Predictor for Vibration Analysis of Drilling Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%C4%B0kbal%20Eski">İkbal Eski </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine. <p class="card-text"><strong>Keywords:</strong> <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=vibration%20analyses" title=" vibration analyses"> vibration analyses</a>, <a href="https://publications.waset.org/abstracts/search?q=drilling%20machine" title=" drilling machine"> drilling machine</a>, <a href="https://publications.waset.org/abstracts/search?q=robust" title=" robust"> robust</a> </p> <a href="https://publications.waset.org/abstracts/30313/design-of-neural-predictor-for-vibration-analysis-of-drilling-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30313.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">394</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">5037</span> Validation of the X-Ray Densitometry Method for Radial Density Pattern Determination of Acacia seyal var. seyal Tree Species</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hanadi%20Mohamed%20Shawgi%20Gamal">Hanadi Mohamed Shawgi Gamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Claus%20Thomas%20Bues"> Claus Thomas Bues</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wood density is a variable influencing many of the technological and quality properties of wood. Understanding the pattern of wood density radial variation is important for its end-use. The X-ray technique, traditionally applied to softwood species to assess the wood quality properties, due to its simple and relatively uniform wood structure. On the other hand, very limited information is available about the validation of using this technique for hardwood species. The suitability of using the X-ray technique for the determination of hardwood density has a special significance in countries like Sudan, where only a few timbers are well known. This will not only save the time consumed by using the traditional methods, but it will also enhance the investigations of the great number of the lesser known species, the thing which will fill the huge cap of lake information of hardwood species growing in Sudan. The current study aimed to evaluate the validation of using the X-ray densitometry technique to determine the radial variation of wood density of Acacia seyal var. seyal. To this, a total of thirty trees were collected randomly from four states in Sudan. The wood density radial trend was determined using the basic density as well as density obtained by the X-ray densitometry method in order to assess the validation of X-ray technique in wood density radial variation determination. The results showed that the pattern of radial trend of density obtained by X-ray technique is very similar to that achieved by basic density. These results confirmed the validation of using the X-ray technique for Acacia seyal var. seyal density radial trend determination. It also promotes the suitability of using this method in other hardwood species. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=x-ray%20densitometry" title="x-ray densitometry">x-ray densitometry</a>, <a href="https://publications.waset.org/abstracts/search?q=wood%20density" title=" wood density"> wood density</a>, <a href="https://publications.waset.org/abstracts/search?q=Acacia%20seyal%20var.%20seyal" title=" Acacia seyal var. seyal"> Acacia seyal var. seyal</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20variation" title=" radial variation"> radial variation</a> </p> <a href="https://publications.waset.org/abstracts/127038/validation-of-the-x-ray-densitometry-method-for-radial-density-pattern-determination-of-acacia-seyal-var-seyal-tree-species" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127038.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">152</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">5036</span> Solution of the Nonrelativistic Radial Wave Equation of Hydrogen Atom Using the Green's Function Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20U.%20Rahman">F. U. Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Q.%20Zhang"> R. Q. Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work aims to develop a systematic numerical technique which can be easily extended to many-body problem. The Lippmann Schwinger equation (integral form of the Schrodinger wave equation) is solved for the nonrelativistic radial wave of hydrogen atom using iterative integration scheme. As the unknown wave function appears on both sides of the Lippmann Schwinger equation, therefore an approximate wave function is used in order to solve the equation. The Green’s function is obtained by the method of Laplace transform for the radial wave equation with excluded potential term. Using the Lippmann Schwinger equation, the product of approximate wave function, the Green’s function and the potential term is integrated iteratively. Finally, the wave function is normalized and plotted against the standard radial wave for comparison. The outcome wave function converges to the standard wave function with the increasing number of iteration. Results are verified for the first fifteen states of hydrogen atom. The method is efficient and consistent and can be applied to complex systems in future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Green%E2%80%99s%20function" title="Green’s function">Green’s function</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrogen%20atom" title=" hydrogen atom"> hydrogen atom</a>, <a href="https://publications.waset.org/abstracts/search?q=Lippmann%20Schwinger%20equation" title=" Lippmann Schwinger equation"> Lippmann Schwinger equation</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20wave" title=" radial wave"> radial wave</a> </p> <a href="https://publications.waset.org/abstracts/42682/solution-of-the-nonrelativistic-radial-wave-equation-of-hydrogen-atom-using-the-greens-function-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42682.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">394</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">5035</span> Comparison between Continuous Genetic Algorithms and Particle Swarm Optimization for Distribution Network Reconfiguration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linh%20Nguyen%20Tung">Linh Nguyen Tung</a>, <a href="https://publications.waset.org/abstracts/search?q=Anh%20Truong%20Viet"> Anh Truong Viet</a>, <a href="https://publications.waset.org/abstracts/search?q=Nghien%20Nguyen%20Ba"> Nghien Nguyen Ba</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuong%20Trinh%20Trong"> Chuong Trinh Trong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a reconfiguration methodology based on a continuous genetic algorithm (CGA) and particle swarm optimization (PSO) for minimizing active power loss and minimizing voltage deviation. Both algorithms are adapted using graph theory to generate feasible individuals, and the modified crossover is used for continuous variable of CGA. To demonstrate the performance and effectiveness of the proposed methods, a comparative analysis of CGA with PSO for network reconfiguration, on 33-node and 119-bus radial distribution system is presented. The simulation results have shown that both CGA and PSO can be used in the distribution network reconfiguration and CGA outperformed PSO with significant success rate in finding optimal distribution network configuration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distribution%20network%20reconfiguration" title="distribution network reconfiguration">distribution network reconfiguration</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20genetic%20algorithm" title=" continuous genetic algorithm"> continuous genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20loss%20reduction" title=" power loss reduction"> power loss reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=voltage%20deviation" title=" voltage deviation"> voltage deviation</a> </p> <a href="https://publications.waset.org/abstracts/101407/comparison-between-continuous-genetic-algorithms-and-particle-swarm-optimization-for-distribution-network-reconfiguration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101407.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">187</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=radial%20network&page=2">2</a></li> <li class="page-item"><a class="page-link" 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