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of damage</title> <meta name="description" content="Search results for: prediction of damage"> <meta name="keywords" content="prediction of damage"> <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="prediction of damage"> <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> 4558</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: prediction of damage</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4468</span> Structural Damage Detection via Incomplete Model Data Using Output Data Only</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Noor%20Al-qayyim">Ahmed Noor Al-qayyim</a>, <a href="https://publications.waset.org/abstracts/search?q=Barlas%20%C3%96zden%20%C3%87a%C4%9Flayan"> Barlas Özden Çağlayan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on obtaining very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. This study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using “Two Points - Condensation (TPC) technique”. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices are obtained from optimization of the equation of motion using the measured test data. The current stiffness matrices are compared with original (undamaged) stiffness matrices. High percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element. Where two cases are considered, the method detects the damage and determines its location accurately in both cases. In addition, the results illustrate that these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can also be used for big structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage%20detection" title="damage detection">damage detection</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=signals%20processing" title=" signals processing"> signals processing</a>, <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=two%20points%E2%80%93condensation" title=" two points–condensation"> two points–condensation</a> </p> <a href="https://publications.waset.org/abstracts/37035/structural-damage-detection-via-incomplete-model-data-using-output-data-only" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37035.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">365</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">4467</span> Useful Lifetime Prediction of Chevron Rubber Spring for Railway Vehicle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chang%20Su%20Woo">Chang Su Woo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyun%20Sung%20Park"> Hyun Sung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Useful lifetime evaluation of chevron rubber spring was very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of chevron rubber spring. In this study, we performed characteristic analysis and useful lifetime prediction of chevron rubber spring. Rubber material coefficient was obtained by curve fittings of uni-axial tension, equi bi-axial tension and pure shear test. Computer simulation was executed to predict and evaluate the load capacity and stiffness for chevron rubber spring. In order to useful lifetime prediction of rubber material, we carried out the compression set with heat aging test in an oven at the temperature ranging from 50°C to 100°C during a period 180 days. By using the Arrhenius plot, several useful lifetime prediction equations for rubber material was proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chevron%20rubber%20spring" title="chevron rubber spring">chevron rubber spring</a>, <a href="https://publications.waset.org/abstracts/search?q=material%20coefficient" title=" material coefficient"> material coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=useful%20lifetime%20prediction" title=" useful lifetime prediction"> useful lifetime prediction</a> </p> <a href="https://publications.waset.org/abstracts/33892/useful-lifetime-prediction-of-chevron-rubber-spring-for-railway-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33892.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">567</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">4466</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">4465</span> Comparison of the Existing Damage Indices in Steel Moment-Resisting Frame Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Kazemi">Hamid Kazemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abbasali%20Sadeghi"> Abbasali Sadeghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Assessment of seismic behavior of frame structures is just done for evaluating life and financial damages or lost. The new structural seismic behavior assessment methods have been proposed, so it is necessary to define a formulation as a damage index, which the damage amount has been quantified and qualified. In this paper, four new steel moment-resisting frames with intermediate ductility and different height (2, 5, 8, and 12-story) with regular geometry and simple rectangular plan were supposed and designed. The three existing groups’ damage indices were studied, each group consisting of local index (Drift, Maximum Roof Displacement, Banon Failure, Kinematic, Banon Normalized Cumulative Rotation, Cumulative Plastic Rotation and Ductility), global index (Roufaiel and Meyer, Papadopoulos, Sozen, Rosenblueth, Ductility and Base Shear), and story (Banon Failure and Inter-story Rotation). The necessary parameters for these damage indices have been calculated under the effect of far-fault ground motion records by Non-linear Dynamic Time History Analysis. Finally, prioritization of damage indices is defined based on more conservative values in terms of more damageability rate. The results show that the selected damage index has an important effect on estimation of the damage state. Also, failure, drift, and Rosenblueth damage indices are more conservative indices respectively for local, story and global damage indices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage%20index" title="damage index">damage index</a>, <a href="https://publications.waset.org/abstracts/search?q=far-fault%20ground%20motion%20records" title=" far-fault ground motion records"> far-fault ground motion records</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linear%20time%20history%20analysis" title=" non-linear time history analysis"> non-linear time history analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=SeismoStruct%20software" title=" SeismoStruct software"> SeismoStruct software</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20moment-resisting%20frame" title=" steel moment-resisting frame"> steel moment-resisting frame</a> </p> <a href="https://publications.waset.org/abstracts/93473/comparison-of-the-existing-damage-indices-in-steel-moment-resisting-frame-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/93473.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">4464</span> Fast Prediction Unit Partition Decision and Accelerating the Algorithm Using Cudafor Intra and Inter Prediction of HEVC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiang%20Zhang">Qiang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun%20Yuan"> Chun Yuan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HEVC" title="HEVC">HEVC</a>, <a href="https://publications.waset.org/abstracts/search?q=PU%20decision" title=" PU decision"> PU decision</a>, <a href="https://publications.waset.org/abstracts/search?q=inter%20prediction" title=" inter prediction"> inter prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=intra%20prediction" title=" intra prediction"> intra prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=CUDA" title=" CUDA"> CUDA</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel" title=" parallel"> parallel</a> </p> <a href="https://publications.waset.org/abstracts/9627/fast-prediction-unit-partition-decision-and-accelerating-the-algorithm-using-cudafor-intra-and-inter-prediction-of-hevc" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9627.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">399</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">4463</span> Application of Artificial Neural Network to Prediction of Feature Academic Performance of Students </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20K.%20Alhassan">J. K. Alhassan</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20S.%20Actsu"> C. S. Actsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=academic%20performance" title="academic performance">academic performance</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=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=students" title=" students"> students</a> </p> <a href="https://publications.waset.org/abstracts/36018/application-of-artificial-neural-network-to-prediction-of-feature-academic-performance-of-students" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36018.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">467</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">4462</span> Application of the Electrical Resistivity Tomography and Tunnel Seismic Prediction 303 Methods for Detection Fracture Zones Ahead of Tunnel: A Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nima%20Dastanboo">Nima Dastanboo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiao-Qing%20Li"> Xiao-Qing Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Gharibdoost"> Hamed Gharibdoost</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to investigate about the geological properties ahead of a tunnel face with using Electrical Resistivity Tomography ERT and Tunnel Seismic Prediction TSP303 methods. In deep tunnels with hydro-geological conditions, it is important to study the geological structures of the region before excavating tunnels. Otherwise, it would lead to unexpected accidents that impose serious damage to the project. For constructing Nosoud tunnel in west of Iran, the ERT and TSP303 methods are employed to predict the geological conditions dynamically during the excavation. In this paper, based on the engineering background of Nosoud tunnel, the important results of applying these methods are discussed. This work demonstrates seismic method and electrical tomography as two geophysical techniques that are able to detect a tunnel. The results of these two methods were being in agreement with each other but the results of TSP303 are more accurate and quality. In this case, the TSP 303 method was a useful tool for predicting unstable geological structures ahead of the tunnel face during excavation. Thus, using another geophysical method together with TSP303 could be helpful as a decision support in excavating, especially in complicated geological conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tunnel%20seismic%20prediction%20%28TSP303%29" title="tunnel seismic prediction (TSP303)">tunnel seismic prediction (TSP303)</a>, <a href="https://publications.waset.org/abstracts/search?q=electrical%20resistivity%20tomography%20%28ERT%29" title=" electrical resistivity tomography (ERT)"> electrical resistivity tomography (ERT)</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20wave" title=" seismic wave"> seismic wave</a>, <a href="https://publications.waset.org/abstracts/search?q=velocity%20analysis" title=" velocity analysis"> velocity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=low-velocity%20zones" title=" low-velocity zones"> low-velocity zones</a> </p> <a href="https://publications.waset.org/abstracts/106568/application-of-the-electrical-resistivity-tomography-and-tunnel-seismic-prediction-303-methods-for-detection-fracture-zones-ahead-of-tunnel-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106568.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">148</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">4461</span> Aftershock Collapse Capacity Assessment of Mid-Rise Steel Moment Frames Subjected to As-Recorded Mainshock-Aftershock</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadmehdi%20Torfehnejada">Mohammadmehdi Torfehnejada</a>, <a href="https://publications.waset.org/abstracts/search?q=Serhan%20Senso"> Serhan Senso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aftershock collapse capacity of Special Steel Moment Frames (SSMFs) is evaluated under aftershock earthquakes by considering building heights 8 and 12 stories. The assessment evaluates the residual collapse capacity under aftershock excitation when various levels of damage have been induced by the mainshock. For this purpose, incremental dynamic analysis (IDA) under aftershock follows the mainshock imposing the intended damage level. The study results indicate that aftershock collapse capacity of this structure may decrease remarkably when the structure is subjected to large mainshock damage. The capacity reduction under aftershock is finally related to the mainshock damage level through regression equations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aftershock%20collapse%20capacity" title="aftershock collapse capacity">aftershock collapse capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=special%20steel%20moment%20frames" title=" special steel moment frames"> special steel moment frames</a>, <a href="https://publications.waset.org/abstracts/search?q=mainshock-aftershock%20sequences" title=" mainshock-aftershock sequences"> mainshock-aftershock sequences</a>, <a href="https://publications.waset.org/abstracts/search?q=incremental%20dynamic%20analysis" title=" incremental dynamic analysis"> incremental dynamic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=mainshock%20damage" title=" mainshock damage"> mainshock damage</a> </p> <a href="https://publications.waset.org/abstracts/144073/aftershock-collapse-capacity-assessment-of-mid-rise-steel-moment-frames-subjected-to-as-recorded-mainshock-aftershock" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144073.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">4460</span> Equity Risk Premiums and Risk Free Rates in Modelling and Prediction of Financial Markets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ghavami">Mohammad Ghavami</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20S.%20Dilmaghani"> Reza S. Dilmaghani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20methods" title="adaptive methods">adaptive methods</a>, <a href="https://publications.waset.org/abstracts/search?q=LSE" title=" LSE"> LSE</a>, <a href="https://publications.waset.org/abstracts/search?q=MSE" title=" MSE"> MSE</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20of%20financial%20Markets" title=" prediction of financial Markets"> prediction of financial Markets</a> </p> <a href="https://publications.waset.org/abstracts/72693/equity-risk-premiums-and-risk-free-rates-in-modelling-and-prediction-of-financial-markets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72693.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">336</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">4459</span> Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyoung%20Kim">Seyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (<em>k</em>-NN) as predictive models is that it does not require any explicit model building. Instead, <em>k</em>-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up <em>k</em>-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different <em>k</em>-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN" title=" k-NN"> k-NN</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20speed%20prediction" title=" traffic speed prediction"> traffic speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/43415/comparison-of-different-k-nn-models-for-speed-prediction-in-an-urban-traffic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43415.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">363</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">4458</span> Restoring Trees Damaged by Cyclone Hudhud at Visakhapatnam, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohan%20Kotamrazu">Mohan Kotamrazu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cyclone Hudhud which battered the city of Visakhapatnam on 12<sup>th</sup> October, 2014, damaged many buildings, public amenities and infrastructure facilities along the Visakha- Bheemili coastal corridor. More than half the green cover of the city was wiped out. Majority of the trees along the coastal corridor suffered from complete or partial damage. In order to understand the different ways that trees incurred damage during the cyclone, a damage assessment study was carried out by the author. The areas covered by this study included two university campuses, several parks and residential colonies which bore the brunt of the cyclone. Post disaster attempts have been made to restore many of the trees that have suffered from partial or complete damage from the effects of extreme winds. This paper examines the various ways that trees incurred damage from the cyclone Hudhud and presents some examples of the restoration efforts carried out by educational institutions, public parks and religious institutions of the city of Visakhapatnam in the aftermath of the devastating cyclone. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=defoliaton" title="defoliaton">defoliaton</a>, <a href="https://publications.waset.org/abstracts/search?q=salt%20spray%20damage" title=" salt spray damage"> salt spray damage</a>, <a href="https://publications.waset.org/abstracts/search?q=uprooting%20and%20wind%20throw" title=" uprooting and wind throw"> uprooting and wind throw</a>, <a href="https://publications.waset.org/abstracts/search?q=restoration" title=" restoration"> restoration</a> </p> <a href="https://publications.waset.org/abstracts/45363/restoring-trees-damaged-by-cyclone-hudhud-at-visakhapatnam-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45363.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">529</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">4457</span> Modeling and Shape Prediction for Elastic Kinematic Chains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiun%20Jeon">Jiun Jeon</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung-Ju%20Yi"> Byung-Ju Yi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=elastic%20kinematic%20chain" title="elastic kinematic chain">elastic kinematic chain</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20prediction" title=" shape prediction"> shape prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=colonoscopy" title=" colonoscopy"> colonoscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a> </p> <a href="https://publications.waset.org/abstracts/4177/modeling-and-shape-prediction-for-elastic-kinematic-chains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4177.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">605</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">4456</span> Numerical Prediction of Bearing Strength on Composite Bolted Joint Using Three Dimensional Puck Failure Criteria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Meon">M. S. Meon</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20Rao"> M. N. Rao</a>, <a href="https://publications.waset.org/abstracts/search?q=K-U.%20Schr%C3%B6der"> K-U. Schröder</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mechanical fasteners especially bolting is commonly used in joining carbon-fiber reinforced polymer (CFRP) composite structures due to their good joinability and easy for maintenance characteristics. Since this approach involves with notching, a proper progressive damage model (PDM) need to be implemented and verified to capture existence of damages in the structure. A three dimensional (3D) failure criteria of Puck is established to predict the ultimate bearing failure of such joint. The failure criteria incorporated with degradation scheme are coded based on user subroutine executed in Abaqus. Single lap joint (SLJ) of composite bolted joint is used as target configuration. The results revealed that the PDM adopted here could sufficiently predict the behaviour of composite bolted joint up to ultimate bearing failure. In addition, mesh refinement near holes increased the accuracy of predicted strength as well as computational effort. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bearing%20strength" title="bearing strength">bearing strength</a>, <a href="https://publications.waset.org/abstracts/search?q=bolted%20joint" title=" bolted joint"> bolted joint</a>, <a href="https://publications.waset.org/abstracts/search?q=degradation%20scheme" title=" degradation scheme"> degradation scheme</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20damage%20model" title=" progressive damage model"> progressive damage model</a> </p> <a href="https://publications.waset.org/abstracts/51744/numerical-prediction-of-bearing-strength-on-composite-bolted-joint-using-three-dimensional-puck-failure-criteria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51744.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">4455</span> Structural Damage Detection Using Modal Data Employing Teaching Learning Based Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subhajit%20Das">Subhajit Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Nirjhar%20Dhang"> Nirjhar Dhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage%20detection" title="damage detection">damage detection</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20model%20updating" title=" finite element model updating"> finite element model updating</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20assurance%20criteria" title=" modal assurance criteria"> modal assurance criteria</a>, <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=teaching%20learning%20based%20optimization" title=" teaching learning based optimization"> teaching learning based optimization</a> </p> <a href="https://publications.waset.org/abstracts/77962/structural-damage-detection-using-modal-data-employing-teaching-learning-based-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77962.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">215</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">4454</span> Prediction on Housing Price Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li%20Yu">Li Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chenlu%20Jiao"> Chenlu Jiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongrun%20Xin"> Hongrun Xin</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaiyang%20Wang"> Kaiyang Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=housing%20prediction" title=" housing prediction"> housing prediction</a> </p> <a href="https://publications.waset.org/abstracts/84747/prediction-on-housing-price-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84747.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">306</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">4453</span> An Acoustical Diagnosis of a Shaft-Wood Phyto-Pathogenic Damage of Sequoiadendron giganteum (Lindl.) Buccholz</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuri%20V.%20Plugatar">Yuri V. Plugatar</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20P.%20Koba"> Vladimir P. Koba</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20V.%20Papelbu"> Vladimir V. Papelbu</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20N.%20Gerasimchuk"> Vladimir N. Gerasimchuk</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatjana%20M.%20Sakhno"> Tatjana M. Sakhno</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Using a supersonic shaft–wood tomography, the evaluation of a shaft-wood phyto-pathogenic damage level of Sequoiadendron giganteum (Lindl.) Buccholz was prosecuted. The digital bivariate reflections of the shaft tissue damage were obtained, the characteristics of comparative parameters of the wood-decay degree were given. The investigation results allowed to show up the role of some edaphic factors in their affection on a vital condition and the level of destructive processes while shaft tissue damaging of S.giganteum. It was pinned up that soil consolidation, and hydro-morphication equally make for a phyto-pathogenic damage of plants. While soil consolidation negative acting the shaft-wood damage is located in an underneath of a shaft. In the conditions of an enlarged hydro-morphication a tissue degradation runs less intensively, the destructive processes more active spread in a vertical section of a shaft. The use of a supersonic tomography method gives wide possibilities to diagnose a shaft-wood phyto-pathogenic damage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sequoiadendron%20giganteum%20%28Lindl.%29%20Buccholz" title="Sequoiadendron giganteum (Lindl.) Buccholz">Sequoiadendron giganteum (Lindl.) Buccholz</a>, <a href="https://publications.waset.org/abstracts/search?q=supersonic%20tomography" title=" supersonic tomography"> supersonic tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title=" diagnosis"> diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=phyto-pathogenic%20damage" title=" phyto-pathogenic damage"> phyto-pathogenic damage</a>, <a href="https://publications.waset.org/abstracts/search?q=a%20vital%20condition" title=" a vital condition"> a vital condition</a> </p> <a href="https://publications.waset.org/abstracts/78879/an-acoustical-diagnosis-of-a-shaft-wood-phyto-pathogenic-damage-of-sequoiadendron-giganteum-lindl-buccholz" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78879.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">213</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">4452</span> An ANN Approach for Detection and Localization of Fatigue Damage in Aircraft Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reza%20Rezaeipour%20Honarmandzad">Reza Rezaeipour Honarmandzad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN" title="ANN">ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=fatigue%20damage" title=" fatigue damage"> fatigue damage</a>, <a href="https://publications.waset.org/abstracts/search?q=aircraft%20structures" title=" aircraft structures"> aircraft structures</a>, <a href="https://publications.waset.org/abstracts/search?q=piezoelectric%20transducers" title=" piezoelectric transducers"> piezoelectric transducers</a>, <a href="https://publications.waset.org/abstracts/search?q=lamb-wave%20measurements" title=" lamb-wave measurements"> lamb-wave measurements</a> </p> <a href="https://publications.waset.org/abstracts/29801/an-ann-approach-for-detection-and-localization-of-fatigue-damage-in-aircraft-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29801.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">417</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">4451</span> A FE-Based Scheme for Computing Wave Interaction with Nonlinear Damage and Generation of Harmonics in Layered Composite Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20K.%20Apalowo">R. K. Apalowo</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Chronopoulos"> D. Chronopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Finite Element (FE) based scheme is presented for quantifying guided wave interaction with Localised Nonlinear Structural Damage (LNSD) within structures of arbitrary layering and geometric complexity. The through-thickness mode-shape of the structure is obtained through a wave and finite element method. This is applied in a time domain FE simulation in order to generate time harmonic excitation for a specific wave mode. Interaction of the wave with LNSD within the system is computed through an element activation and deactivation iteration. The scheme is validated against experimental measurements and a WFE-FE methodology for calculating wave interaction with damage. Case studies for guided wave interaction with crack and delamination are presented to verify the robustness of the proposed method in classifying and identifying damage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=layered%20structures" title="layered structures">layered structures</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20ultrasound" title=" nonlinear ultrasound"> nonlinear ultrasound</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20interaction%20with%20nonlinear%20damage" title=" wave interaction with nonlinear damage"> wave interaction with nonlinear damage</a>, <a href="https://publications.waset.org/abstracts/search?q=wave%20finite%20element" title=" wave finite element"> wave finite element</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element" title=" finite element "> finite element </a> </p> <a href="https://publications.waset.org/abstracts/109616/a-fe-based-scheme-for-computing-wave-interaction-with-nonlinear-damage-and-generation-of-harmonics-in-layered-composite-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109616.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">163</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">4450</span> Urban Growth Prediction Using Artificial Neural Networks in Athens, Greece </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Triantakonstantis">Dimitrios Triantakonstantis</a>, <a href="https://publications.waset.org/abstracts/search?q=Demetris%20Stathakis"> Demetris Stathakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Urban areas have been expanded throughout the globe. Monitoring and modeling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modeling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change. <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=CORINE" title=" CORINE"> CORINE</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20atlas" title=" urban atlas"> urban atlas</a>, <a href="https://publications.waset.org/abstracts/search?q=urban%20growth%20prediction" title=" urban growth prediction"> urban growth prediction</a> </p> <a href="https://publications.waset.org/abstracts/24994/urban-growth-prediction-using-artificial-neural-networks-in-athens-greece" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24994.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">528</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">4449</span> Virtual Reality Based 3D Video Games and Speech-Lip Synchronization Superseding Algebraic Code Excited Linear Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Meenakshi%20Sundaram"> S. Meenakshi Sundaram</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenli%20Hu"> Wenli Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yung"> Yang Yung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algebraic%20code%20excited%20linear%20prediction" title="algebraic code excited linear prediction">algebraic code excited linear prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=speech-lip%20synchronization" title=" speech-lip synchronization"> speech-lip synchronization</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20games" title=" video games"> video games</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20reality" title=" virtual reality"> virtual reality</a> </p> <a href="https://publications.waset.org/abstracts/78585/virtual-reality-based-3d-video-games-and-speech-lip-synchronization-superseding-algebraic-code-excited-linear-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78585.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">474</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">4448</span> Cross Project Software Fault Prediction at Design Phase</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pradeep%20Singh">Pradeep Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shrish%20Verma"> Shrish Verma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20metrics" title="software metrics">software metrics</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20prediction" title=" fault prediction"> fault prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20project" title=" cross project"> cross project</a>, <a href="https://publications.waset.org/abstracts/search?q=within%20project." title=" within project. "> within project. </a> </p> <a href="https://publications.waset.org/abstracts/27206/cross-project-software-fault-prediction-at-design-phase" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27206.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">4447</span> Thermomechanical Damage Modeling of F114 Carbon Steel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20El%20Amri">A. El Amri</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20El%20Yakhloufi%20Haddou"> M. El Yakhloufi Haddou</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Khamlichi"> A. Khamlichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as fracture. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. The influences of thermal and mechanical loads cause damage. The temperature and strain rate dependent materials’ properties and their modelling are discussed. A Johnson-Cook Model of damage has been selected for the numerical simulations. Virtual software called the ABAQUS 6.11 is used for finite element analysis. This model was introduced in order to give information concerning crack initiation during thermal and mechanical loads. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermo-mechanical%20fatigue" title="thermo-mechanical fatigue">thermo-mechanical fatigue</a>, <a href="https://publications.waset.org/abstracts/search?q=failure" title=" failure"> failure</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=fracture" title=" fracture"> fracture</a>, <a href="https://publications.waset.org/abstracts/search?q=damage" title=" damage"> damage</a> </p> <a href="https://publications.waset.org/abstracts/46261/thermomechanical-damage-modeling-of-f114-carbon-steel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46261.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">393</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">4446</span> A Deep Learning-Based Pedestrian Trajectory Prediction Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haozhe%20Xiang">Haozhe Xiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20convolutional%20network" title=" graph convolutional network"> graph convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title=" attention mechanism"> attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a> </p> <a href="https://publications.waset.org/abstracts/182188/a-deep-learning-based-pedestrian-trajectory-prediction-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182188.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">70</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">4445</span> Development of Prediction Models of Day-Ahead Hourly Building Electricity Consumption and Peak Power Demand Using the Machine Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dalin%20Si">Dalin Si</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizan%20Aziz"> Azizan Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Bertrand%20Lasternas"> Bertrand Lasternas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20energy%20prediction" title="building energy prediction">building energy prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=demand%20response" title=" demand response"> demand response</a>, <a href="https://publications.waset.org/abstracts/search?q=electricity%20market" title=" electricity market"> electricity market</a> </p> <a href="https://publications.waset.org/abstracts/54774/development-of-prediction-models-of-day-ahead-hourly-building-electricity-consumption-and-peak-power-demand-using-the-machine-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54774.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">316</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">4444</span> Prediction of CO2 Concentration in the Korea Train Express (KTX) Cabins</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yong-Il%20Lee">Yong-Il Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Do-Yeon%20Hwang"> Do-Yeon Hwang</a>, <a href="https://publications.waset.org/abstracts/search?q=Won-Seog%20Jeong"> Won-Seog Jeong</a>, <a href="https://publications.waset.org/abstracts/search?q=Duckshin%20Park"> Duckshin Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CO2%20prediction" title="CO2 prediction">CO2 prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=KTX" title=" KTX"> KTX</a>, <a href="https://publications.waset.org/abstracts/search?q=ventilation" title=" ventilation"> ventilation</a>, <a href="https://publications.waset.org/abstracts/search?q=infrastructure%20and%20transportation%20engineering" title=" infrastructure and transportation engineering"> infrastructure and transportation engineering</a> </p> <a href="https://publications.waset.org/abstracts/18283/prediction-of-co2-concentration-in-the-korea-train-express-ktx-cabins" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18283.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">543</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">4443</span> Statistical Analysis with Prediction Models of User Satisfaction in Software Project Factors </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katawut%20Kaewbanjong">Katawut Kaewbanjong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction%20model" title="prediction model">prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20analysis" title=" statistical analysis"> statistical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20project" title=" software project"> software project</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20satisfaction%20factor" title=" user satisfaction factor"> user satisfaction factor</a> </p> <a href="https://publications.waset.org/abstracts/121683/statistical-analysis-with-prediction-models-of-user-satisfaction-in-software-project-factors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/121683.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">124</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">4442</span> Quality of Life after Damage Control Laparotomy for Trauma</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noman%20Shahzad">Noman Shahzad</a>, <a href="https://publications.waset.org/abstracts/search?q=Amyn%20Pardhan"> Amyn Pardhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasnain%20Zafar"> Hasnain Zafar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Though short term survival advantage of damage control laparotomy in management of critically ill trauma patients is established, there is little known about the long-term quality of life of these patients. Facial closure rate after damage control laparotomy is reported to be 20-70 percent. Abdominal wall reconstruction in those who failed to achieve facial closure is challenging and can potentially affect quality of life of these patients. Methodology: We conducted retrospective matched cohort study. Adult patients who underwent damage control laparotomy from Jan 2007 till Jun 2013 were identified through medical record. Patients who had concomitant disabling brain injury or limb injuries requiring amputation were excluded. Age, gender and presentation time matched non exposure group of patients who underwent laparotomy for trauma but no damage control were identified for each damage control laparotomy patient. Quality of life assessment was done via telephonic interview at least one year after the operation, using Urdu version of EuroQol Group Quality of Life (QOL) questionnaire EQ5D after permission. Wilcoxon signed rank test was used to compare QOL scores and McNemar test was used to compare individual parameters of QOL questionnaire. Study was approved by institutional ethical review committee. Results: Out of 32 patients who underwent damage control laparotomy during study period, 20 fulfilled the selection criteria for which 20 matched controls were selected. Median age of patients (IQ Range) was 33 (26-40) years. Facial closure rate in damage control laparotomy group was 40% (8/20). One third of those who did not achieve facial closure (4/12) underwent abdominal wall reconstruction. Self-reported QOL score of damage control laparotomy patients was significantly worse than non-damage control group (p = 0.032). There was no statistically significant difference in two groups regarding individual QOL measures. Significantly, more patients in damage control group were requiring use of abdominal binder, and more patients in damage control group had to either change their job or had limitations in continuing previous job. Our study was not adequately powered to detect factors responsible for worse QOL in damage control group. Conclusion: Quality of life of damage control patients is worse than their age and gender matched patients who underwent trauma laparotomy but not damage control. Adequately powered studies need to be conducted to explore factors responsible for this finding for potential improvement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage%20control%20laparotomy" title="damage control laparotomy">damage control laparotomy</a>, <a href="https://publications.waset.org/abstracts/search?q=laparostomy" title=" laparostomy"> laparostomy</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20life" title=" quality of life"> quality of life</a> </p> <a href="https://publications.waset.org/abstracts/41579/quality-of-life-after-damage-control-laparotomy-for-trauma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41579.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">279</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">4441</span> Visualization of Corrosion at Plate-Like Structures Based on Ultrasonic Wave Propagation Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aoqi%20Zhang">Aoqi Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Changgil%20Lee%20Lee"> Changgil Lee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seunghee%20Park"> Seunghee Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A non-contact nondestructive technique using laser-induced ultrasonic wave generation method was applied to visualize corrosion damage at aluminum alloy plate structures. The ultrasonic waves were generated by a Nd:YAG pulse laser, and a galvanometer-based laser scanner was used to scan specific area at a target structure. At the same time, wave responses were measured at a piezoelectric sensor which was attached on the target structure. The visualization of structural damage was achieved by calculating logarithmic values of root mean square (RMS). Damage-sensitive feature was defined as the scattering characteristics of the waves that encounter corrosion damage. The corroded damage was artificially formed by hydrochloric acid. To observe the effect of the location where the corrosion was formed, the both sides of the plate were scanned with same scanning area. Also, the effect on the depth of the corrosion was considered as well as the effect on the size of the corrosion. The results indicated that the damages were successfully visualized for almost cases, whether the damages were formed at the front or back side. However, the damage could not be clearly detected because the depth of the corrosion was shallow. In the future works, it needs to develop signal processing algorithm to more clearly visualize the damage by improving signal-to-noise ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-destructive%20testing" title="non-destructive testing">non-destructive testing</a>, <a href="https://publications.waset.org/abstracts/search?q=corrosion" title=" corrosion"> corrosion</a>, <a href="https://publications.waset.org/abstracts/search?q=pulsed%20laser%20scanning" title=" pulsed laser scanning"> pulsed laser scanning</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasonic%20waves" title=" ultrasonic waves"> ultrasonic waves</a>, <a href="https://publications.waset.org/abstracts/search?q=plate%20structure" title=" plate structure"> plate structure</a> </p> <a href="https://publications.waset.org/abstracts/57261/visualization-of-corrosion-at-plate-like-structures-based-on-ultrasonic-wave-propagation-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57261.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">300</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">4440</span> Damage Detection in a Cantilever Beam under Different Excitation and Temperature Conditions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Kyprianou">A. Kyprianou</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tjirkallis"> A. Tjirkallis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Condition monitoring of structures in service is very important as it provides information about the risk of damage development. One of the essential constituents of structural condition monitoring is the damage detection methodology. In the context of condition monitoring of in service structures a damage detection methodology analyses data obtained from the structure while it is in operation. Usually, this means that the data could be affected by operational and environmental conditions in a way that could mask the effects of a possible damage on the data. This, depending on the damage detection methodology, could lead to either false alarms or miss existing damages. In this article a damage detection methodology that is based on the Spatio-temporal continuous wavelet transform (SPT-CWT) analysis of a sequence of experimental time responses of a cantilever beam is proposed. The cantilever is subjected to white and pink noise excitation to simulate different operating conditions. In addition, in order to simulate changing environmental conditions, the cantilever is subjected to heating by a heat gun. The response of the cantilever beam is measured by a high-speed camera. Edges are extracted from the series of images of the beam response captured by the camera. Subsequent processing of the edges gives a series of time responses on 439 points on the beam. This sequence is then analyzed using the SPT-CWT to identify damage. The algorithm proposed was able to clearly identify damage under any condition when the structure was excited by white noise force. In addition, in the case of white noise excitation, the analysis could also reveal the position of the heat gun when it was used to heat the structure. The analysis could identify the different operating conditions i.e. between responses due to white noise excitation and responses due to pink noise excitation. During the pink noise excitation whereas damage and changing temperature were identified it was not possible to clearly identify the effect of damage from that of temperature. The methodology proposed in this article for damage detection enables the separation the damage effect from that due to temperature and excitation on data obtained from measurements of a cantilever beam. This methodology does not require information about the apriori state of the structure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatiotemporal%20continuous%20wavelet%20transform" title="spatiotemporal continuous wavelet transform">spatiotemporal continuous wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=damage%20detection" title=" damage detection"> damage detection</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20normalization" title=" data normalization"> data normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=varying%20temperature" title=" varying temperature "> varying temperature </a> </p> <a href="https://publications.waset.org/abstracts/49084/damage-detection-in-a-cantilever-beam-under-different-excitation-and-temperature-conditions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49084.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">279</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">4439</span> Calibration Model of %Titratable Acidity (Citric Acid) for Intact Tomato by Transmittance SW-NIR Spectroscopy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Petcharaporn">K. Petcharaporn</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kumchoo"> S. Kumchoo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tomato" title="tomato">tomato</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=transmittance" title=" transmittance"> transmittance</a>, <a href="https://publications.waset.org/abstracts/search?q=titratable%20acidity" title=" titratable acidity"> titratable acidity</a>, <a href="https://publications.waset.org/abstracts/search?q=citric%20acid" title=" citric acid"> citric acid</a> </p> <a href="https://publications.waset.org/abstracts/11536/calibration-model-of-titratable-acidity-citric-acid-for-intact-tomato-by-transmittance-sw-nir-spectroscopy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11536.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">273</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prediction%20of%20damage&page=3" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prediction%20of%20damage&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=prediction%20of%20damage&page=2">2</a></li> <li class="page-item"><a class="page-link" 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