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propagation</title> <meta name="description" content="Search results for: MLP back propagation"> <meta name="keywords" content="MLP back propagation"> <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 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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="MLP back propagation"> <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> 2230</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: MLP back propagation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2230</span> The Use Support Vector Machine and Back Propagation Neural Network for Prediction of Daily Tidal Levels Along The Jeddah Coast, Saudi Arabia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20A.%20Mlybari">E. A. Mlybari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Elbisy"> M. S. Elbisy</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20H.%20Alshahri"> A. H. Alshahri</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20M.%20Albarakati"> O. M. Albarakati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tides" title="tides">tides</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</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=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation%20neural%20network" title=" back-propagation neural network"> back-propagation neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=hazards" title=" hazards"> hazards</a> </p> <a href="https://publications.waset.org/abstracts/4437/the-use-support-vector-machine-and-back-propagation-neural-network-for-prediction-of-daily-tidal-levels-along-the-jeddah-coast-saudi-arabia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4437.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">468</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">2229</span> Air Quality Forecast Based on Principal Component Analysis-Genetic Algorithm and Back Propagation Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Mu">Bin Mu</a>, <a href="https://publications.waset.org/abstracts/search?q=Site%20Li"> Site Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Shijin%20Yuan"> Shijin Yuan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AQI%20forecast" title="AQI forecast">AQI forecast</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation%20neural%20network%20model" title=" back propagation neural network model"> back propagation neural network model</a> </p> <a href="https://publications.waset.org/abstracts/54367/air-quality-forecast-based-on-principal-component-analysis-genetic-algorithm-and-back-propagation-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54367.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">228</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">2228</span> Prediction of Unsteady Heat Transfer over Square Cylinder in the Presence of Nanofluid by Using ANN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ajoy%20Kumar%20Das">Ajoy Kumar Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Prasenjit%20Dey"> Prasenjit Dey</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heat transfer due to forced convection of copper water based nanofluid has been predicted by Artificial Neural network (ANN). The present nanofluid is formed by mixing copper nano particles in water and the volume fractions are considered here are 0% to 15% and the Reynolds number are kept constant at 100. The back propagation algorithm is used to train the network. The present ANN is trained by the input and output data which has been obtained from the numerical simulation, performed in finite volume based Computational Fluid Dynamics (CFD) commercial software Ansys Fluent. The numerical simulation based results are compared with the back propagation based ANN results. It is found that the forced convection heat transfer of water based nanofluid can be predicted correctly by ANN. It is also observed that the back propagation ANN can predict the heat transfer characteristics of nanofluid very quickly compared to standard CFD method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forced%20convection" title="forced convection">forced convection</a>, <a href="https://publications.waset.org/abstracts/search?q=square%20cylinder" title=" square cylinder"> square cylinder</a>, <a href="https://publications.waset.org/abstracts/search?q=nanofluid" title=" nanofluid"> nanofluid</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network "> neural network </a> </p> <a href="https://publications.waset.org/abstracts/26172/prediction-of-unsteady-heat-transfer-over-square-cylinder-in-the-presence-of-nanofluid-by-using-ann" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26172.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">320</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">2227</span> Classic Training of a Neural Observer for Estimation Purposes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Loukil">R. Loukil</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Chtourou"> M. Chtourou</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Damak"> T. Damak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the training of multilayer neural network using the classic approach. Then, for estimation purposes, we suggest the use of a specific neural observer that we study its training algorithm which is the back-propagation one in the case of the disponibility of the state and in the case of an unmeasurable state. A MATLAB simulation example will be studied to highlight the usefulness of this kind of observer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=training" title="training">training</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation%20purposes" title=" estimation purposes"> estimation purposes</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20observer" title=" neural observer"> neural observer</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation" title=" back-propagation"> back-propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=unmeasurable%20state" title=" unmeasurable state"> unmeasurable state</a> </p> <a href="https://publications.waset.org/abstracts/21004/classic-training-of-a-neural-observer-for-estimation-purposes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21004.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">574</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">2226</span> Detection of Autistic Children's Voice Based on Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Royan%20Dawud%20Aldian">Royan Dawud Aldian</a>, <a href="https://publications.waset.org/abstracts/search?q=Endah%20Purwanti"> Endah Purwanti</a>, <a href="https://publications.waset.org/abstracts/search?q=Soegianto%20Soelistiono"> Soegianto Soelistiono</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research we have been developed an automatic investigation to classify normal children voice or autistic by using modern computation technology that is computation based on artificial neural network. The superiority of this computation technology is its capability on processing and saving data. In this research, digital voice features are gotten from the coefficient of linear-predictive coding with auto-correlation method and have been transformed in frequency domain using fast fourier transform, which used as input of artificial neural network in back-propagation method so that will make the difference between normal children and autistic automatically. The result of back-propagation method shows that successful classification capability for normal children voice experiment data is 100% whereas, for autistic children voice experiment data is 100%. The success rate using back-propagation classification system for the entire test data is 100%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autism" title="autism">autism</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=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=linier%20predictive%20coding" title=" linier predictive coding"> linier predictive coding</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20fourier%20transform" title=" fast fourier transform"> fast fourier transform</a> </p> <a href="https://publications.waset.org/abstracts/1699/detection-of-autistic-childrens-voice-based-on-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1699.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">461</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">2225</span> Identification of Landslide Features Using Back-Propagation Neural Network on LiDAR Digital Elevation Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Hao%20Chang">Chia-Hao Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Geng-Gui%20Wang"> Geng-Gui Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jee-Cheng%20Wu"> Jee-Cheng Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20elevation%20model" title="digital elevation model">digital elevation model</a>, <a href="https://publications.waset.org/abstracts/search?q=DEM" title=" DEM"> DEM</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20factors" title=" environmental factors"> environmental factors</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation%20neural%20network" title=" back-propagation neural network"> back-propagation neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=BPNN" title=" BPNN"> BPNN</a>, <a href="https://publications.waset.org/abstracts/search?q=LiDAR" title=" LiDAR "> LiDAR </a> </p> <a href="https://publications.waset.org/abstracts/93322/identification-of-landslide-features-using-back-propagation-neural-network-on-lidar-digital-elevation-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93322.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">144</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">2224</span> Remaining Useful Life (RUL) Assessment Using Progressive Bearing Degradation Data and ANN Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20R.%20Bhende">Amit R. Bhende</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20K.%20Awari"> G. K. Awari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Bearing degradation data at three different conditions from run to failure is used. A RUL prediction model is separately built in each condition. Feed forward back propagation neural network models are developed for prediction modeling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bearing%20degradation%20data" title="bearing degradation data">bearing degradation data</a>, <a href="https://publications.waset.org/abstracts/search?q=remaining%20useful%20life%20%28RUL%29" title=" remaining useful life (RUL)"> remaining useful life (RUL)</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation" title=" back propagation"> back propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=prognosis" title=" prognosis"> prognosis</a> </p> <a href="https://publications.waset.org/abstracts/45708/remaining-useful-life-rul-assessment-using-progressive-bearing-degradation-data-and-ann-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45708.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">436</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">2223</span> Multivariate Analysis on Water Quality Attributes Using Master-Slave Neural Network Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Clementking">A. Clementking</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Jothi%20Venkateswaran"> C. Jothi Venkateswaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=master-slave%20back%20propagation%20neural%20network%20model%28MSBPNNM%29" title="master-slave back propagation neural network model(MSBPNNM)">master-slave back propagation neural network model(MSBPNNM)</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20analysis" title=" water quality analysis"> water quality analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis" title=" multivariate analysis"> multivariate analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20mining" title=" environmental mining"> environmental mining</a> </p> <a href="https://publications.waset.org/abstracts/31405/multivariate-analysis-on-water-quality-attributes-using-master-slave-neural-network-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31405.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">477</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">2222</span> The Application of Artificial Neural Network for Bridge Structures Design Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Angga%20S.%20Fajar">Angga S. Fajar</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Aminullah"> A. Aminullah</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Kiyono"> J. Kiyono</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20A.%20Safitri"> R. A. Safitri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bridge%20structures" title="bridge structures">bridge structures</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation" title=" back propagation"> back propagation</a> </p> <a href="https://publications.waset.org/abstracts/58189/the-application-of-artificial-neural-network-for-bridge-structures-design-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58189.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">372</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">2221</span> The Impact of the Number of Neurons in the Hidden Layer on the Performance of MLP Neural Network: Application to the Fast Identification of Toxics Gases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Slimane%20Ouhmad">Slimane Ouhmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdellah%20Halimi"> Abdellah Halimi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MLP%20Neural%20Network" title="MLP Neural Network">MLP Neural Network</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation" title=" back-propagation"> back-propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20of%20neurons%20in%20the%20hidden%20layer" title=" number of neurons in the hidden layer"> number of neurons in the hidden layer</a>, <a href="https://publications.waset.org/abstracts/search?q=identification" title=" identification"> identification</a>, <a href="https://publications.waset.org/abstracts/search?q=computing%20time" title=" computing time"> computing time</a> </p> <a href="https://publications.waset.org/abstracts/19653/the-impact-of-the-number-of-neurons-in-the-hidden-layer-on-the-performance-of-mlp-neural-network-application-to-the-fast-identification-of-toxics-gases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19653.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">347</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">2220</span> Reservoir Inflow Prediction for Pump Station Using Upstream Sewer Depth Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Osung%20Im">Osung Im</a>, <a href="https://publications.waset.org/abstracts/search?q=Neha%20Yadav"> Neha Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Eui%20Hoon%20Lee"> Eui Hoon Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Joong%20Hoon%20Kim"> Joong Hoon Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Neural Network (ANN) approach is commonly used in lots of fields for forecasting. In water resources engineering, forecast of water level or inflow of reservoir is useful for various kind of purposes. Due to advantages of ANN, many papers were written for inflow prediction in river networks, but in this study, ANN is used in urban sewer networks. The growth of severe rain storm in Korea has increased flood damage severely, and the precipitation distribution is getting more erratic. Therefore, effective pump operation in pump station is an essential task for the reduction in urban area. If real time inflow of pump station reservoir can be predicted, it is possible to operate pump effectively for reducing the flood damage. This study used ANN model for pump station reservoir inflow prediction using upstream sewer depth data. For this study, rainfall events, sewer depth, and inflow into Banpo pump station reservoir between years of 2013-2014 were considered. Feed – Forward Back Propagation (FFBF), Cascade – Forward Back Propagation (CFBP), Elman Back Propagation (EBP) and Nonlinear Autoregressive Exogenous (NARX) were used as ANN model for prediction. A comparison of results with ANN model suggests that ANN is a powerful tool for inflow prediction using the sewer depth data. <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=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20inflow" title=" reservoir inflow"> reservoir inflow</a>, <a href="https://publications.waset.org/abstracts/search?q=sewer%20depth" title=" sewer depth"> sewer depth</a> </p> <a href="https://publications.waset.org/abstracts/58382/reservoir-inflow-prediction-for-pump-station-using-upstream-sewer-depth-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58382.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">317</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">2219</span> Mathematical Modeling for Diabetes Prediction: A Neuro-Fuzzy Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vijay%20Kr.%20Yadav">Vijay Kr. Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Nilam%20Rathi"> Nilam Rathi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=back-propagation" title="back-propagation">back-propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20mellitus" title=" diabetes mellitus"> diabetes mellitus</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=neuro-fuzzy" title=" neuro-fuzzy"> neuro-fuzzy</a> </p> <a href="https://publications.waset.org/abstracts/79054/mathematical-modeling-for-diabetes-prediction-a-neuro-fuzzy-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79054.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">257</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">2218</span> Designing an Intelligent Voltage Instability System in Power Distribution Systems in the Philippines Using IEEE 14 Bus Test System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pocholo%20Rodriguez">Pocholo Rodriguez</a>, <a href="https://publications.waset.org/abstracts/search?q=Anne%20Bernadine%20Ocampo"> Anne Bernadine Ocampo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ian%20Benedict%20Chan"> Ian Benedict Chan</a>, <a href="https://publications.waset.org/abstracts/search?q=Janric%20Micah%20Gray"> Janric Micah Gray</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=back-propagation%20algorithm" title="back-propagation algorithm">back-propagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20instability" title=" load instability"> load instability</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=power%20distribution%20system" title=" power distribution system"> power distribution system</a> </p> <a href="https://publications.waset.org/abstracts/65086/designing-an-intelligent-voltage-instability-system-in-power-distribution-systems-in-the-philippines-using-ieee-14-bus-test-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65086.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">435</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">2217</span> Artificial Neural Network Modeling of a Closed Loop Pulsating Heat Pipe</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vipul%20M.%20Patel">Vipul M. Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Hemantkumar%20B.%20Mehta"> Hemantkumar B. Mehta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN%20models" title="ANN models">ANN models</a>, <a href="https://publications.waset.org/abstracts/search?q=CLPHP" title=" CLPHP"> CLPHP</a>, <a href="https://publications.waset.org/abstracts/search?q=filling%20ratio" title=" filling ratio"> filling ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20regression" title=" generalized regression"> generalized regression</a>, <a href="https://publications.waset.org/abstracts/search?q=spread%20constant" title=" spread constant"> spread constant</a> </p> <a href="https://publications.waset.org/abstracts/59151/artificial-neural-network-modeling-of-a-closed-loop-pulsating-heat-pipe" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59151.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">292</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">2216</span> Effect of Load Ratio on Probability Distribution of Fatigue Crack Propagation Life in Magnesium Alloys</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seon%20Soon%20Choi">Seon Soon Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is necessary to predict a fatigue crack propagation life for estimation of structural integrity. Because of an uncertainty and a randomness of a structural behavior, it is also required to analyze stochastic characteristics of the fatigue crack propagation life at a specified fatigue crack size. The essential purpose of this study is to present the good probability distribution fit for the fatigue crack propagation life at a specified fatigue crack size in magnesium alloys under various fatigue load ratio conditions. To investigate a stochastic crack growth behavior, fatigue crack propagation experiments are performed in laboratory air under several conditions of fatigue load ratio using AZ31. By Anderson-Darling test, a goodness-of-fit test for probability distribution of the fatigue crack propagation life is performed and the good probability distribution fit for the fatigue crack propagation life is presented. The effect of load ratio on variability of fatigue crack propagation life is also investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fatigue%20crack%20propagation%20life" title="fatigue crack propagation life">fatigue crack propagation life</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20ratio" title=" load ratio"> load ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=magnesium%20alloys" title=" magnesium alloys"> magnesium alloys</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20distribution" title=" probability distribution"> probability distribution</a> </p> <a href="https://publications.waset.org/abstracts/34718/effect-of-load-ratio-on-probability-distribution-of-fatigue-crack-propagation-life-in-magnesium-alloys" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34718.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">649</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">2215</span> Influence of Maximum Fatigue Load on Probabilistic Aspect of Fatigue Crack Propagation Life at Specified Grown Crack in Magnesium Alloys</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seon%20Soon%20Choi">Seon Soon Choi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The principal purpose of this paper is to find the influence of maximum fatigue load on the probabilistic aspect of fatigue crack propagation life at a specified grown crack in magnesium alloys. The experiments of fatigue crack propagation are carried out in laboratory air under different conditions of the maximum fatigue loads to obtain the fatigue crack propagation data for the statistical analysis. In order to analyze the probabilistic aspect of fatigue crack propagation life, the goodness-of fit test for probability distribution of the fatigue crack propagation life at a specified grown crack is implemented through Anderson-Darling test. The good probability distribution of the fatigue crack propagation life is also verified under the conditions of the maximum fatigue loads. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fatigue%20crack%20propagation%20life" title="fatigue crack propagation life">fatigue crack propagation life</a>, <a href="https://publications.waset.org/abstracts/search?q=magnesium%20alloys" title=" magnesium alloys"> magnesium alloys</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20fatigue%20load" title=" maximum fatigue load"> maximum fatigue load</a>, <a href="https://publications.waset.org/abstracts/search?q=probability" title=" probability"> probability</a> </p> <a href="https://publications.waset.org/abstracts/66629/influence-of-maximum-fatigue-load-on-probabilistic-aspect-of-fatigue-crack-propagation-life-at-specified-grown-crack-in-magnesium-alloys" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66629.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">389</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">2214</span> An IM-COH Algorithm Neural Network Optimization with Cuckoo Search Algorithm for Time Series Samples</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wullapa%20Wongsinlatam">Wullapa Wongsinlatam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation%20algorithm" title=" back propagation algorithm"> back propagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20minima%20problem" title=" local minima problem"> local minima problem</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20optimization" title=" metaheuristic optimization"> metaheuristic optimization</a> </p> <a href="https://publications.waset.org/abstracts/100995/an-im-coh-algorithm-neural-network-optimization-with-cuckoo-search-algorithm-for-time-series-samples" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100995.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">2213</span> Propagation of Cos-Gaussian Beam in Photorefractive Crystal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Keshavarz">A. Keshavarz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A physical model for guiding the wave in photorefractive media is studied. Propagation of cos-Gaussian beam as the special cases of sinusoidal-Gaussian beams in photorefractive crystal is simulated numerically by the Crank-Nicolson method in one dimension. Results show that the beam profile deforms as the energy transfers from the center to the tails under propagation. This simulation approach is of significant interest for application in optical telecommunication. The results are presented graphically and discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beam%20propagation" title="beam propagation">beam propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=cos-Gaussian%20beam" title=" cos-Gaussian beam"> cos-Gaussian beam</a>, <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=photorefractive%20crystal" title=" photorefractive crystal"> photorefractive crystal</a> </p> <a href="https://publications.waset.org/abstracts/33883/propagation-of-cos-gaussian-beam-in-photorefractive-crystal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33883.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">499</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">2212</span> An Innovative Auditory Impulsed EEG and Neural Network Based Biometric Identification System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ritesh%20Kumar">Ritesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Gitanjali%20Chhetri"> Gitanjali Chhetri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mandira%20Bhatia"> Mandira Bhatia</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohit%20Mishra"> Mohit Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhijith%20Bailur"> Abhijith Bailur</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhinav"> Abhinav</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prevalence of the internet and technology in our day to day lives is creating more security issues than ever. The need for protecting and providing a secure access to private and business data has led to the development of many security systems. One of the potential solutions is to employ the bio-metric authentication technique. In this paper we present an innovative biometric authentication method that utilizes a person’s EEG signal, which is acquired in response to an auditory stimulus,and transferred wirelessly to a computer that has the necessary ANN algorithm-Multi layer perceptrol neural network because of is its ability to differentiate between information which is not linearly separable.In order to determine the weights of the hidden layer we use Gaussian random weight initialization. MLP utilizes a supervised learning technique called Back propagation for training the network. The complex algorithm used for EEG classification reduces the chances of intrusion into the protected public or private data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=EEG%20signal" title="EEG signal">EEG signal</a>, <a href="https://publications.waset.org/abstracts/search?q=auditory%20evoked%20potential" title=" auditory evoked potential"> auditory evoked potential</a>, <a href="https://publications.waset.org/abstracts/search?q=biometrics" title=" biometrics"> biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20network" title=" multilayer perceptron neural network"> multilayer perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation%20rule" title=" back propagation rule"> back propagation rule</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20random%20weight%20initialization" title=" Gaussian random weight initialization"> Gaussian random weight initialization</a> </p> <a href="https://publications.waset.org/abstracts/29970/an-innovative-auditory-impulsed-eeg-and-neural-network-based-biometric-identification-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29970.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">2211</span> A Distributed Mobile Agent Based on Intrusion Detection System for MANET</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maad%20Kamal%20Al-Anni">Maad Kamal Al-Anni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Intrusion%20Detection%20System%20%28IDS%29" title="Intrusion Detection System (IDS)">Intrusion Detection System (IDS)</a>, <a href="https://publications.waset.org/abstracts/search?q=Mobile%20Adhoc%20Networks%20%28MANET%29" title=" Mobile Adhoc Networks (MANET)"> Mobile Adhoc Networks (MANET)</a>, <a href="https://publications.waset.org/abstracts/search?q=Back%20Propagation%20Algorithm%20%28BPA%29" title=" Back Propagation Algorithm (BPA)"> Back Propagation Algorithm (BPA)</a>, <a href="https://publications.waset.org/abstracts/search?q=Neural%20Networks%20%28NN%29" title=" Neural Networks (NN)"> Neural Networks (NN)</a> </p> <a href="https://publications.waset.org/abstracts/66010/a-distributed-mobile-agent-based-on-intrusion-detection-system-for-manet" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66010.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">194</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">2210</span> RF Propagation Analysis in Outdoor Environments Using RSSI Measurements Applied in ZigBee Sensor Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Teles%20de%20Sales%20Bezerra">Teles de Sales Bezerra</a>, <a href="https://publications.waset.org/abstracts/search?q=Saulo%20Aislan%20da%20Silva%20Eleuterio"> Saulo Aislan da Silva Eleuterio</a>, <a href="https://publications.waset.org/abstracts/search?q=Jos%C3%A9%20Anderson%20Rodrigues%20de%20Souza"> José Anderson Rodrigues de Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeronimo%20Silva%20Rocha"> Jeronimo Silva Rocha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propagation in radio frequency is a constant concern in the application of Wireless Sensor Networks (WSN), the behavior of an environment determines how good the quality of signal reception. The objective of this paper is to analyze the behavior of a WSN in an environment for agriculture where environmental variables are present and correlate the capture of values received signal strength (RSSI) with a propagation model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=propagation" title="propagation">propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=WSN" title=" WSN"> WSN</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture" title=" agriculture"> agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a> </p> <a href="https://publications.waset.org/abstracts/20471/rf-propagation-analysis-in-outdoor-environments-using-rssi-measurements-applied-in-zigbee-sensor-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20471.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">755</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">2209</span> The Cracks Propagation Monitoring of a Cantilever Beam Using Modal Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Morteza%20Raki">Morteza Raki</a>, <a href="https://publications.waset.org/abstracts/search?q=Abolghasem%20Zabihollah"> Abolghasem Zabihollah</a>, <a href="https://publications.waset.org/abstracts/search?q=Omid%20Askari"> Omid Askari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cantilever beam is a simplified sample of a lot of mechanical components used in a wide range of applications, including many industries such as gas turbine blade. Due to the nature of the operating conditions, beams are subject to variety of damages especially crack propagates. Crack propagation may lead to catastrophic failure during operation. Therefore, online detection of crack presence and its propagation is very important and may reduce possible significant cost of the whole system failure. This paper aims to investigate the effect of cracks presence and crack propagation on one end fixed beam`s vibration. A finite element model will be developed for the blade in which the modal response of the structure with and without crack will be studied.  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blade" title="blade">blade</a>, <a href="https://publications.waset.org/abstracts/search?q=crack%20propagation" title=" crack propagation"> crack propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=health%20monitoring" title=" health monitoring"> health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20analysis" title=" modal analysis"> modal analysis</a> </p> <a href="https://publications.waset.org/abstracts/48812/the-cracks-propagation-monitoring-of-a-cantilever-beam-using-modal-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48812.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">2208</span> Minimization of Propagation Delay in Multi Unmanned Aerial Vehicle Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Purva%20Joshi">Purva Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rohit%20Thanki"> Rohit Thanki</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20Hanif"> Omar Hanif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unmanned aerial vehicles (UAVs) are becoming increasingly important in various industrial applications and sectors. Nowadays, a multi UAV network is used for specific types of communication (e.g., military) and monitoring purposes. Therefore, it is critical to reducing propagation delay during communication between UAVs, which is essential in a multi UAV network. This paper presents how the propagation delay between the base station (BS) and the UAVs is reduced using a searching algorithm. Furthermore, the iterative-based K-nearest neighbor (k-NN) algorithm and Travelling Salesmen Problem (TSP) algorthm were utilized to optimize the distance between BS and individual UAV to overcome the problem of propagation delay in multi UAV networks. The simulation results show that this proposed method reduced complexity, improved reliability, and reduced propagation delay in multi UAV networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi%20UAV%20network" title="multi UAV network">multi UAV network</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20distance" title=" optimal distance"> optimal distance</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation%20delay" title=" propagation delay"> propagation delay</a>, <a href="https://publications.waset.org/abstracts/search?q=K%20-%20nearest%20neighbor" title=" K - nearest neighbor"> K - nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesmen%20problem" title=" traveling salesmen problem"> traveling salesmen problem</a> </p> <a href="https://publications.waset.org/abstracts/150423/minimization-of-propagation-delay-in-multi-unmanned-aerial-vehicle-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150423.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">201</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">2207</span> Classification of Myoelectric Signals Using Multilayer Perceptron Neural Network with Back-Propagation Algorithm in a Wireless Surface Myoelectric Prosthesis of the Upper-Limb</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kevin%20D.%20Manalo">Kevin D. Manalo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jumelyn%20L.%20Torres"> Jumelyn L. Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=Noel%20B.%20Linsangan"> Noel B. Linsangan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=field%20programmable%20gate%20array" title="field programmable gate array">field programmable gate array</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron%20neural%20network" title=" multilayer perceptron neural network"> multilayer perceptron neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=verilog" title=" verilog"> verilog</a>, <a href="https://publications.waset.org/abstracts/search?q=zigbee" title=" zigbee"> zigbee</a> </p> <a href="https://publications.waset.org/abstracts/19846/classification-of-myoelectric-signals-using-multilayer-perceptron-neural-network-with-back-propagation-algorithm-in-a-wireless-surface-myoelectric-prosthesis-of-the-upper-limb" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19846.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">389</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">2206</span> Simulation of Propagation of Cos-Gaussian Beam in Strongly Nonlocal Nonlinear Media Using Paraxial Group Transformation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Keshavarz">A. Keshavarz</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Roosta"> Z. Roosta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, propagation of cos-Gaussian beam in strongly nonlocal nonlinear media has been stimulated by using paraxial group transformation. At first, cos-Gaussian beam, nonlocal nonlinear media, critical power, transfer matrix, and paraxial group transformation are introduced. Then, the propagation of the cos-Gaussian beam in strongly nonlocal nonlinear media is simulated. Results show that beam propagation has periodic structure during self-focusing effect in this case. However, this simple method can be used for investigation of propagation of kinds of beams in ABCD optical media. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=paraxial%20group%20transformation" title="paraxial group transformation">paraxial group transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlocal%20nonlinear%20media" title=" nonlocal nonlinear media"> nonlocal nonlinear media</a>, <a href="https://publications.waset.org/abstracts/search?q=cos-Gaussian%20beam" title=" cos-Gaussian beam"> cos-Gaussian beam</a>, <a href="https://publications.waset.org/abstracts/search?q=ABCD%20law" title=" ABCD law"> ABCD law</a> </p> <a href="https://publications.waset.org/abstracts/52660/simulation-of-propagation-of-cos-gaussian-beam-in-strongly-nonlocal-nonlinear-media-using-paraxial-group-transformation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52660.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">342</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">2205</span> Three-Level Converters Back-To-Back DC Bus Control for Torque Ripple Reduction of Induction Motor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Abdelkrim">T. Abdelkrim</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Benamrane"> K. Benamrane</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Bezza"> B. Bezza</a>, <a href="https://publications.waset.org/abstracts/search?q=Aeh%20Benkhelifa"> Aeh Benkhelifa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Borni"> A. Borni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a regulation method of back-to-back connected three-level converters in order to reduce the torque ripple in induction motor. First part is dedicated to the presentation of the feedback control of three-level PWM rectifier. In the second part, three-level NPC voltage source inverter balancing DC bus algorithm is presented. A theoretical analysis with a complete simulation of the system is presented to prove the excellent performance of the proposed technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=back-to-back%20connection" title="back-to-back connection">back-to-back connection</a>, <a href="https://publications.waset.org/abstracts/search?q=feedback%20control" title=" feedback control"> feedback control</a>, <a href="https://publications.waset.org/abstracts/search?q=neutral-point%20balance" title=" neutral-point balance"> neutral-point balance</a>, <a href="https://publications.waset.org/abstracts/search?q=three-level%20converter" title=" three-level converter"> three-level converter</a>, <a href="https://publications.waset.org/abstracts/search?q=torque%20ripple" title=" torque ripple"> torque ripple</a> </p> <a href="https://publications.waset.org/abstracts/8480/three-level-converters-back-to-back-dc-bus-control-for-torque-ripple-reduction-of-induction-motor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8480.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">497</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">2204</span> Relating Interface Properties with Crack Propagation in Composite Laminates </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tao%20Qu">Tao Qu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandra%20Prakash"> Chandra Prakash</a>, <a href="https://publications.waset.org/abstracts/search?q=Vikas%20Tomar"> Vikas Tomar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interfaces between organic and inorganic phases in natural materials have been shown to be a key factor contributing to their high performance. This work analyzes crack propagation in a 2-ply laminate subjected to uniaxial tensile mode-I crack propagation loading that has laminate properties derived based on biological material constituents (marine exoskeleton- chitin and calcite). Interfaces in such laminates are explicitly modeled based on earlier molecular simulations performed by authors. Extended finite element method and cohesive zone modeling based simulations coupled with theoretical analysis are used to analyze crack propagation. Analyses explicitly quantify the effect that interface mechanical property variation has on the delamination as well as the transverse crack propagation in examined 2-ply laminates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chitin" title="chitin">chitin</a>, <a href="https://publications.waset.org/abstracts/search?q=composites" title=" composites"> composites</a>, <a href="https://publications.waset.org/abstracts/search?q=interfaces" title=" interfaces"> interfaces</a>, <a href="https://publications.waset.org/abstracts/search?q=fracture" title=" fracture"> fracture</a> </p> <a href="https://publications.waset.org/abstracts/44635/relating-interface-properties-with-crack-propagation-in-composite-laminates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44635.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">382</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">2203</span> Aspects Concerning Flame Propagation of Various Fuels in Combustion Chamber of Four Valve Engines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zoran%20Jovanovic">Zoran Jovanovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Zoran%20Masonicic"> Zoran Masonicic</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dragutinovic"> S. Dragutinovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Sakota"> Z. Sakota</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, results concerning flame propagation of various fuels in a particular combustion chamber with four tilted valves were elucidated. Flame propagation was represented by the evolution of spatial distribution of temperature in various cut-planes within combustion chamber while the flame front location was determined by dint of zones with maximum temperature gradient. The results presented are only a small part of broader on-going scrutinizing activity in the field of multidimensional modeling of reactive flows in combustion chambers with complicated geometries encompassing various models of turbulence, different fuels and combustion models. In the case of turbulence two different models were applied i.e. standard k-ε model of turbulence and k-ξ-f model of turbulence. In this paper flame propagation results were analyzed and presented for two different hydrocarbon fuels, such as CH4 and C8H18. In the case of combustion all differences ensuing from different turbulence models, obvious for non-reactive flows are annihilated entirely. Namely the interplay between fluid flow pattern and flame propagation is invariant as regards turbulence models and fuels applied. Namely the interplay between fluid flow pattern and flame propagation is entirely invariant as regards fuel variation indicating that the flame propagation through unburned mixture of CH4 and C8H18 fuels is not chemically controlled. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automotive%20flows" title="automotive flows">automotive flows</a>, <a href="https://publications.waset.org/abstracts/search?q=flame%20propagation" title=" flame propagation"> flame propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=combustion%20modelling" title=" combustion modelling"> combustion modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=CNG" title=" CNG"> CNG</a> </p> <a href="https://publications.waset.org/abstracts/47372/aspects-concerning-flame-propagation-of-various-fuels-in-combustion-chamber-of-four-valve-engines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47372.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">292</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">2202</span> Ray Tracing Modified 3D Image Method Simulation of Picocellular Propagation Channel Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fathi%20Alwafie">Fathi Alwafie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present the simulation of the propagation characteristics of the picocellular propagation channel environment. The first aim has been to find a correct description of the environment for received wave. The result of the first investigations is that the environment of the indoor wave significantly changes as we change the electric parameters of material constructions. A modified 3D ray tracing image method tool has been utilized for the coverage prediction. A detailed analysis of the dependence of the indoor wave on the wide-band characteristics of the channel: Root Mean Square (RMS) delay spread characteristics and mean excess delay, is also investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=propagation" title="propagation">propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=ray%20tracing" title=" ray tracing"> ray tracing</a>, <a href="https://publications.waset.org/abstracts/search?q=network" title=" network"> network</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20computing" title=" mobile computing"> mobile computing</a> </p> <a href="https://publications.waset.org/abstracts/4077/ray-tracing-modified-3d-image-method-simulation-of-picocellular-propagation-channel-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4077.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">400</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">2201</span> Optical Switching Based On Bragg Solitons in A Nonuniform Fiber Bragg Grating</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulatif%20Abdusalam">Abdulatif Abdusalam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Shaban"> Mohamed Shaban</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider the nonlinear pulse propagation through a nonuniform birefringent fiber Bragg grating (FBG) whose index modulation depth varies along the propagation direction. Here, the pulse propagation is governed by the nonlinear birefringent coupled mode (NLBCM) equations. To form the Bragg soliton outside the photonic bandgap (PBG), the NLBCM equations are reduced to the well known NLS type equation by multiple scale analysis. As we consider the pulse propagation in a nonuniform FBG, the pulse propagation outside the PBG is governed by inhomogeneous NLS (INLS) rather than NLS. We, then, discuss the formation of soliton in the FBG known as Bragg soliton whose central frequency lies outside but close to the PBG of the grating structure. Further, we discuss Bragg soliton compression due to a delicate balance between the SPM and the varying grating induced dispersion. In addition, Bragg soliton collision, Bragg soliton switching and possible logic gates have also been discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bragg%20%20grating" title="Bragg grating">Bragg grating</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20uniform%20%20fiber" title=" non uniform fiber"> non uniform fiber</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20linear%20pulse" title=" non linear pulse"> non linear pulse</a> </p> <a href="https://publications.waset.org/abstracts/2177/optical-switching-based-on-bragg-solitons-in-a-nonuniform-fiber-bragg-grating" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2177.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">317</span> </span> </div> </div> <ul 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