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process prior</title> <meta name="description" content="Search results for: gaussian process prior"> <meta name="keywords" content="gaussian process prior"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="gaussian process prior"> <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> 16757</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: gaussian process prior</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16757</span> Novel Inference Algorithm for Gaussian Process Classification Model with Multiclass and Its Application to Human Action Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20rule" title="bayesian rule">bayesian rule</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process%20classification%20model%20with%20multiclass" title=" gaussian process classification model with multiclass"> gaussian process classification model with multiclass</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process%20prior" title=" gaussian process prior"> gaussian process prior</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20action%20classification" title=" human action classification"> human action classification</a>, <a href="https://publications.waset.org/abstracts/search?q=laplace%20approximation" title=" laplace approximation"> laplace approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20EM%20algorithm" title=" variational EM algorithm"> variational EM algorithm</a> </p> <a href="https://publications.waset.org/abstracts/34103/novel-inference-algorithm-for-gaussian-process-classification-model-with-multiclass-and-its-application-to-human-action-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34103.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">334</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">16756</span> Unsupervised Reciter Recognition Using Gaussian Mixture Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Alwosheel">Ahmad Alwosheel</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Alqaraawi"> Ahmed Alqaraawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quran" title="Quran">Quran</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20recognition" title=" speaker recognition"> speaker recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=reciter%20recognition" title=" reciter recognition"> reciter recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20Mixture%20Model" title=" Gaussian Mixture Model"> Gaussian Mixture Model</a> </p> <a href="https://publications.waset.org/abstracts/46532/unsupervised-reciter-recognition-using-gaussian-mixture-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46532.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">380</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">16755</span> Human Action Recognition Using Variational Bayesian HMM with Dirichlet Process Mixture of Gaussian Wishart Emission Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sangkyoon%20Kim"> Sangkyoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present the human action recognition method using the variational Bayesian HMM with the Dirichlet process mixture (DPM) of the Gaussian-Wishart emission model (GWEM). First, we define the Bayesian HMM based on the Dirichlet process, which allows an infinite number of Gaussian-Wishart components to support continuous emission observations. Second, we have considered an efficient variational Bayesian inference method that can be applied to drive the posterior distribution of hidden variables and model parameters for the proposed model based on training data. And then we have derived the predictive distribution that may be used to classify new action. Third, the paper proposes a process of extracting appropriate spatial-temporal feature vectors that can be used to recognize a wide range of human behaviors from input video image. Finally, we have conducted experiments that can evaluate the performance of the proposed method. The experimental results show that the method presented is more efficient with human action recognition than existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20action%20recognition" title="human action recognition">human action recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20HMM" title=" Bayesian HMM"> Bayesian HMM</a>, <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20process%20mixture%20model" title=" Dirichlet process mixture model"> Dirichlet process mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian-Wishart%20emission%20model" title=" Gaussian-Wishart emission model"> Gaussian-Wishart emission model</a>, <a href="https://publications.waset.org/abstracts/search?q=Variational%20Bayesian%20inference" title=" Variational Bayesian inference"> Variational Bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=prior%20distribution%20and%20approximate%20posterior%20distribution" title=" prior distribution and approximate posterior distribution"> prior distribution and approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=KTH%20dataset" title=" KTH dataset"> KTH dataset</a> </p> <a href="https://publications.waset.org/abstracts/49713/human-action-recognition-using-variational-bayesian-hmm-with-dirichlet-process-mixture-of-gaussian-wishart-emission-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49713.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">353</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">16754</span> The Extended Skew Gaussian Process for Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20T.%20Alodat">M. T. Alodat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extended%20skew%20normal%20distribution" title="extended skew normal distribution">extended skew normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20for%20regression" title=" Gaussian process for regression"> Gaussian process for regression</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20distribution" title=" predictive distribution"> predictive distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=ESGPr%20model" title=" ESGPr model"> ESGPr model</a> </p> <a href="https://publications.waset.org/abstracts/2233/the-extended-skew-gaussian-process-for-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2233.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">553</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">16753</span> Using Gaussian Process in Wind Power Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hacene%20Benkhoula">Hacene Benkhoula</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Badreddine%20Benabdella"> Mohamed Badreddine Benabdella</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Bouzeboudja"> Hamid Bouzeboudja</a>, <a href="https://publications.waset.org/abstracts/search?q=Abderrahmane%20Asraoui"> Abderrahmane Asraoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The wind is a random variable difficult to master, for this, we developed a mathematical and statistical methods enable to modeling and forecast wind power. Gaussian Processes (GP) is one of the most widely used families of stochastic processes for modeling dependent data observed over time, or space or time and space. GP is an underlying process formed by unrecognized operator’s uses to solve a problem. The purpose of this paper is to present how to forecast wind power by using the GP. The Gaussian process method for forecasting are presented. To validate the presented approach, a simulation under the MATLAB environment has been given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20power" title="wind power">wind power</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussien%20process" title=" Gaussien process"> Gaussien process</a>, <a href="https://publications.waset.org/abstracts/search?q=modelling" title=" modelling"> modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/41876/using-gaussian-process-in-wind-power-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41876.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">16752</span> Covariance of the Queue Process Fed by Isonormal Gaussian Input Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samaneh%20Rahimirshnani">Samaneh Rahimirshnani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Jafari"> Hossein Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider fluid queueing processes fed by an isonormal Gaussian process. We study the correlation structure of the queueing process and the rate of convergence of the running supremum in the queueing process. The Malliavin calculus techniques are applied to obtain relations that show the workload process inherits the dependence properties of the input process. As examples, we consider two isonormal Gaussian processes, the sub-fractional Brownian motion (SFBM) and the fractional Brownian motion (FBM). For these examples, we obtain upper bounds for the covariance function of the queueing process and its rate of convergence to zero. We also discover that the rate of convergence of the queueing process is related to the structure of the covariance function of the input process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=queue%20length%20process" title="queue length process">queue length process</a>, <a href="https://publications.waset.org/abstracts/search?q=Malliavin%20calculus" title=" Malliavin calculus"> Malliavin calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20function" title=" covariance function"> covariance function</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20Brownian%20motion" title=" fractional Brownian motion"> fractional Brownian motion</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-fractional%20Brownian%20motion" title=" sub-fractional Brownian motion"> sub-fractional Brownian motion</a> </p> <a href="https://publications.waset.org/abstracts/182769/covariance-of-the-queue-process-fed-by-isonormal-gaussian-input-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182769.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">63</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">16751</span> Solving Single Machine Total Weighted Tardiness Problem Using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanatchapong%20Kongkaew">Wanatchapong Kongkaew</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=iterated%20local%20search" title=" iterated local search"> iterated local search</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20machine%20total%20weighted%20tardiness" title=" single machine total weighted tardiness"> single machine total weighted tardiness</a> </p> <a href="https://publications.waset.org/abstracts/6433/solving-single-machine-total-weighted-tardiness-problem-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6433.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">309</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">16750</span> Multinomial Dirichlet Gaussian Process Model for Classification of Multidimensional Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanhyun%20Cho">Wanhyun Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonja%20Kang"> Soonja Kang</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanggoon%20Kim"> Sanggoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Soonyoung%20Park"> Soonyoung Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multinomial%20dirichlet%20classification%20model" title="multinomial dirichlet classification model">multinomial dirichlet classification model</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20priors" title=" Gaussian process priors"> Gaussian process priors</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20Bayesian%20approximation" title=" variational Bayesian approximation"> variational Bayesian approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=importance%20sampling" title=" importance sampling"> importance sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=approximate%20posterior%20distribution" title=" approximate posterior distribution"> approximate posterior distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=marginal%20likelihood%20evidence" title=" marginal likelihood evidence"> marginal likelihood evidence</a> </p> <a href="https://publications.waset.org/abstracts/33816/multinomial-dirichlet-gaussian-process-model-for-classification-of-multidimensional-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33816.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">444</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">16749</span> Base Change for Fisher Metrics: Case of the q-Gaussian Inverse Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20I.%20Loaiza%20Ossa">Gabriel I. Loaiza Ossa</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20A.%20Cadavid%20Moreno"> Carlos A. Cadavid Moreno</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20C.%20%20Arango%20Parra"> Juan C. Arango Parra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is known that the Riemannian manifold determined by the family of inverse Gaussian distributions endowed with the Fisher metric has negative constant curvature κ= -1/2, as does the family of usual Gaussian distributions. In the present paper, firstly, we arrive at this result by following a different path, much simpler than the previous ones. We first put the family in exponential form, thus endowing the family with a new set of parameters, or coordinates, θ₁, θ₂; then we determine the matrix of the Fisher metric in terms of these parameters; and finally we compute this matrix in the original parameters. Secondly, we define the inverse q-Gaussian distribution family (q < 3) as the family obtained by replacing the usual exponential function with the Tsallis q-exponential function in the expression for the inverse Gaussian distribution and observe that it supports two possible geometries, the Fisher and the q-Fisher geometry. And finally, we apply our strategy to obtain results about the Fisher and q-Fisher geometry of the inverse q-Gaussian distribution family, similar to the ones obtained in the case of the inverse Gaussian distribution family. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=base%20of%20changes" title="base of changes">base of changes</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20geometry" title=" information geometry"> information geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20Gaussian%20distribution" title=" inverse Gaussian distribution"> inverse Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=inverse%20q-Gaussian%20distribution" title=" inverse q-Gaussian distribution"> inverse q-Gaussian distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20manifolds" title=" statistical manifolds"> statistical manifolds</a> </p> <a href="https://publications.waset.org/abstracts/138122/base-change-for-fisher-metrics-case-of-the-q-gaussian-inverse-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138122.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">244</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">16748</span> Fast Bayesian Inference of Multivariate Block-Nearest Neighbor Gaussian Process (NNGP) Models for Large Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Gonzales">Carlos Gonzales</a>, <a href="https://publications.waset.org/abstracts/search?q=Zaida%20Quiroz"> Zaida Quiroz</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcos%20Prates"> Marcos Prates</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several spatial variables collected at the same location that share a common spatial distribution can be modeled simultaneously through a multivariate geostatistical model that takes into account the correlation between these variables and the spatial autocorrelation. The main goal of this model is to perform spatial prediction of these variables in the region of study. Here we focus on a geostatistical multivariate formulation that relies on sharing common spatial random effect terms. In particular, the first response variable can be modeled by a mean that incorporates a shared random spatial effect, while the other response variables depend on this shared spatial term, in addition to specific random spatial effects. Each spatial random effect is defined through a Gaussian process with a valid covariance function, but in order to improve the computational efficiency when the data are large, each Gaussian process is approximated to a Gaussian random Markov field (GRMF), specifically to the block nearest neighbor Gaussian process (Block-NNGP). This approach involves dividing the spatial domain into several dependent blocks under certain constraints, where the cross blocks allow capturing the spatial dependence on a large scale, while each individual block captures the spatial dependence on a smaller scale. The multivariate geostatistical model belongs to the class of Latent Gaussian Models; thus, to achieve fast Bayesian inference, it is used the integrated nested Laplace approximation (INLA) method. The good performance of the proposed model is shown through simulations and applications for massive data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Block-NNGP" title="Block-NNGP">Block-NNGP</a>, <a href="https://publications.waset.org/abstracts/search?q=geostatistics" title=" geostatistics"> geostatistics</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20process" title=" gaussian process"> gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=GRMF" title=" GRMF"> GRMF</a>, <a href="https://publications.waset.org/abstracts/search?q=INLA" title=" INLA"> INLA</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20models." title=" multivariate models."> multivariate models.</a> </p> <a href="https://publications.waset.org/abstracts/170871/fast-bayesian-inference-of-multivariate-block-nearest-neighbor-gaussian-process-nngp-models-for-large-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170871.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">97</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">16747</span> A Time-Varying and Non-Stationary Convolution Spectral Mixture Kernel for Gaussian Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai%20Chen">Kai Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuguang%20Cui"> Shuguang Cui</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Yin"> Feng Yin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gaussian process (GP) with spectral mixture (SM) kernel demonstrates flexible non-parametric Bayesian learning ability in modeling unknown function. In this work a novel time-varying and non-stationary convolution spectral mixture (TN-CSM) kernel with a significant enhancing of interpretability by using process convolution is introduced. A way decomposing the SM component into an auto-convolution of base SM component and parameterizing it to be input dependent is outlined. Smoothly, performing a convolution between two base SM component yields a novel structure of non-stationary SM component with much better generalized expression and interpretation. The TN-CSM perfectly allows compatibility with the stationary SM kernel in terms of kernel form and spectral base ignored and confused by previous non-stationary kernels. On synthetic and real-world datatsets, experiments show the time-varying characteristics of hyper-parameters in TN-CSM and compare the learning performance of TN-CSM with popular and representative non-stationary GP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process" title="Gaussian process">Gaussian process</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20mixture" title=" spectral mixture"> spectral mixture</a>, <a href="https://publications.waset.org/abstracts/search?q=non-stationary" title=" non-stationary"> non-stationary</a>, <a href="https://publications.waset.org/abstracts/search?q=convolution" title=" convolution"> convolution</a> </p> <a href="https://publications.waset.org/abstracts/131675/a-time-varying-and-non-stationary-convolution-spectral-mixture-kernel-for-gaussian-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131675.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">196</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">16746</span> Prediction of the Thermodynamic Properties of Hydrocarbons Using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Alhazmi">N. Alhazmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Knowing the thermodynamics properties of hydrocarbons is vital when it comes to analyzing the related chemical reaction outcomes and understanding the reaction process, especially in terms of petrochemical industrial applications, combustions, and catalytic reactions. However, measuring the thermodynamics properties experimentally is time-consuming and costly. In this paper, Gaussian process regression (GPR) has been used to directly predict the main thermodynamic properties - standard enthalpy of formation, standard entropy, and heat capacity -for more than 360 cyclic and non-cyclic alkanes, alkenes, and alkynes. A simple workflow has been proposed that can be applied to directly predict the main properties of any hydrocarbon by knowing its descriptors and chemical structure and can be generalized to predict the main properties of any material. The model was evaluated by calculating the statistical error R², which was more than 0.9794 for all the predicted properties. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermodynamic" title="thermodynamic">thermodynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title=" Gaussian process regression"> Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrocarbons" title=" hydrocarbons"> hydrocarbons</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=enthalpy" title=" enthalpy"> enthalpy</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20capacity" title=" heat capacity"> heat capacity</a> </p> <a href="https://publications.waset.org/abstracts/145010/prediction-of-the-thermodynamic-properties-of-hydrocarbons-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145010.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">222</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">16745</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">16744</span> Frequency Offset Estimation Schemes Based on ML for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, frequency offset (FO) estimation schemes robust to the non-Gaussian noise environments are proposed for orthogonal frequency division multiplexing (OFDM) systems. First, a maximum-likelihood (ML) estimation scheme in non-Gaussian noise environments is proposed, and then, the complexity of the ML estimation scheme is reduced by employing a reduced set of candidate values. In numerical results, it is demonstrated that the proposed schemes provide a significant performance improvement over the conventional estimation scheme in non-Gaussian noise environments while maintaining the performance similar to the estimation performance in Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset%20estimation" title="frequency offset estimation">frequency offset estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20noise%0D%0Aenvironment" title=" non-Gaussian noise environment"> non-Gaussian noise environment</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a>, <a href="https://publications.waset.org/abstracts/search?q=training%20symbol" title=" training symbol"> training symbol</a> </p> <a href="https://publications.waset.org/abstracts/9430/frequency-offset-estimation-schemes-based-on-ml-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9430.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">353</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">16743</span> Enhancing Predictive Accuracy in Pharmaceutical Sales through an Ensemble Kernel Gaussian Process Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shahin%20Mirshekari">Shahin Mirshekari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Moradi"> Mohammadreza Moradi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Jafari"> Hossein Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Jafari"> Mehdi Jafari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ensaf"> Mohammad Ensaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Matern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Matern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an R² score near 1.0, and significantly lower values in MSE, MAE, and RMSE. These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ensemble%20kernels" title=" ensemble kernels"> ensemble kernels</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian%20optimization" title=" bayesian optimization"> bayesian optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pharmaceutical%20sales%20analysis" title=" pharmaceutical sales analysis"> pharmaceutical sales analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/181581/enhancing-predictive-accuracy-in-pharmaceutical-sales-through-an-ensemble-kernel-gaussian-process-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/181581.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">71</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">16742</span> Gaussian Operations with a Single Trapped Ion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bruna%20G.%20M.%20Ara%C3%BAjo">Bruna G. M. Araújo</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20M.%20M.%20Q.%20Cruz"> Pedro M. M. Q. Cruz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this letter, we review the literature of the major concepts that govern Gaussian quantum information. As we work with quantum information and computation with continuous variables, Gaussian states are needed to better describe these systems. Analyzing a single ion locked in a Paul trap we use the interaction picture to obtain a toolbox of Gaussian operations with the ion-laser interaction Hamiltionian. This is achieved exciting the ion through the combination of two lasers of distinct frequencies corresponding to different sidebands of the external degrees of freedom. First we study the case of a trap with 1 mode and then the case with 2 modes. In this way, we achieve different continuous variables gates just by changing the external degrees of freedom of the trap and combining the Hamiltonians of blue and red sidebands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paul%20trap" title="Paul trap">Paul trap</a>, <a href="https://publications.waset.org/abstracts/search?q=ion-laser%20interaction" title=" ion-laser interaction"> ion-laser interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20operations" title=" Gaussian operations"> Gaussian operations</a> </p> <a href="https://publications.waset.org/abstracts/18445/gaussian-operations-with-a-single-trapped-ion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18445.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">685</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">16741</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">16740</span> Use of Gaussian-Euclidean Hybrid Function Based Artificial Immune System for Breast Cancer Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cuneyt%20Yucelbas">Cuneyt Yucelbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Seral%20Ozsen"> Seral Ozsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sule%20Yucelbas"> Sule Yucelbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Gulay%20Tezel"> Gulay Tezel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20immune%20system" title="artificial immune system">artificial immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer%20diagnosis" title=" breast cancer diagnosis"> breast cancer diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20function" title=" Euclidean function"> Euclidean function</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20function" title=" Gaussian function"> Gaussian function</a> </p> <a href="https://publications.waset.org/abstracts/5135/use-of-gaussian-euclidean-hybrid-function-based-artificial-immune-system-for-breast-cancer-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5135.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">16739</span> Closed-Form Sharma-Mittal Entropy Rate for Gaussian Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Septimia%20Sarbu">Septimia Sarbu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The entropy rate of a stochastic process is a fundamental concept in information theory. It provides a limit to the amount of information that can be transmitted reliably over a communication channel, as stated by Shannon's coding theorems. Recently, researchers have focused on developing new measures of information that generalize Shannon's classical theory. The aim is to design more efficient information encoding and transmission schemes. This paper continues the study of generalized entropy rates, by deriving a closed-form solution to the Sharma-Mittal entropy rate for Gaussian processes. Using the squeeze theorem, we solve the limit in the definition of the entropy rate, for different values of alpha and beta, which are the parameters of the Sharma-Mittal entropy. In the end, we compare it with Shannon and Rényi's entropy rates for Gaussian processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20entropies" title="generalized entropies">generalized entropies</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharma-Mittal%20entropy%20rate" title=" Sharma-Mittal entropy rate"> Sharma-Mittal entropy rate</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20processes" title=" Gaussian processes"> Gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=eigenvalues%20of%20the%20covariance%20matrix" title=" eigenvalues of the covariance matrix"> eigenvalues of the covariance matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=squeeze%20theorem" title=" squeeze theorem "> squeeze theorem </a> </p> <a href="https://publications.waset.org/abstracts/32177/closed-form-sharma-mittal-entropy-rate-for-gaussian-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32177.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">519</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">16738</span> System of Linear Equations, Gaussian Elimination</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Khan">Rabia Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nargis%20Munir"> Nargis Munir</a>, <a href="https://publications.waset.org/abstracts/search?q=Suriya%20Gharib"> Suriya Gharib</a>, <a href="https://publications.waset.org/abstracts/search?q=Syeda%20Roshana%20Ali"> Syeda Roshana Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper linear equations are discussed in detail along with elimination method. Gaussian elimination and Gauss Jordan schemes are carried out to solve the linear system of equation. This paper comprises of matrix introduction, and the direct methods for linear equations. The goal of this research was to analyze different elimination techniques of linear equations and measure the performance of Gaussian elimination and Gauss Jordan method, in order to find their relative importance and advantage in the field of symbolic and numeric computation. The purpose of this research is to revise an introductory concept of linear equations, matrix theory and forms of Gaussian elimination through which the performance of Gauss Jordan and Gaussian elimination can be measured. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=direct" title="direct">direct</a>, <a href="https://publications.waset.org/abstracts/search?q=indirect" title=" indirect"> indirect</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20stage" title=" backward stage"> backward stage</a>, <a href="https://publications.waset.org/abstracts/search?q=forward%20stage" title=" forward stage"> forward stage</a> </p> <a href="https://publications.waset.org/abstracts/33569/system-of-linear-equations-gaussian-elimination" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33569.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">595</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">16737</span> Learning the Dynamics of Articulated Tracked Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Gianni">Mario Gianni</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20A.%20Ruiz%20Garcia"> Manuel A. Ruiz Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Fiora%20Pirri"> Fiora Pirri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dirichlet%20processes" title="Dirichlet processes">Dirichlet processes</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20models" title=" gaussian mixture models"> gaussian mixture models</a>, <a href="https://publications.waset.org/abstracts/search?q=learning%20motion%20patterns" title=" learning motion patterns"> learning motion patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=tracked%20robots%20for%20urban%20search%20and%20rescue" title=" tracked robots for urban search and rescue"> tracked robots for urban search and rescue</a> </p> <a href="https://publications.waset.org/abstracts/45613/learning-the-dynamics-of-articulated-tracked-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45613.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">449</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">16736</span> Self-Action Effects of a Non-Gaussian Laser Beam Through Plasma </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20Kumar">Sandeep Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveen%20Gupta"> Naveen Gupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The propagation of the Non-Gaussian laser beam results in strong self-focusing as compare to the Gaussian laser beam, which helps to achieve a prerequisite of the plasma-based electron, Terahertz generation, and higher harmonic generations. The theoretical investigation on the evolution of non-Gaussian laser beam through the collisional plasma with ramped density has been presented. The non-uniform irradiance over the cross-section of the laser beam results in redistribution of the carriers that modifies the optical response of the plasma in such a way that the plasma behaves like a converging lens to the laser beam. The formulation is based on finding a semi-analytical solution of the nonlinear Schrodinger wave equation (NLSE) with the help of variational theory. It has been observed that the decentred parameter ‘q’ of laser and wavenumber of ripples of medium contribute to providing the required conditions for the improvement of self-focusing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20beam" title="non-Gaussian beam">non-Gaussian beam</a>, <a href="https://publications.waset.org/abstracts/search?q=collisional%20plasma" title=" collisional plasma"> collisional plasma</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20theory" title=" variational theory"> variational theory</a>, <a href="https://publications.waset.org/abstracts/search?q=self-focusing" title=" self-focusing"> self-focusing</a> </p> <a href="https://publications.waset.org/abstracts/124754/self-action-effects-of-a-non-gaussian-laser-beam-through-plasma" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124754.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">195</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">16735</span> Unsupervised Learning and Similarity Comparison of Water Mass Characteristics with Gaussian Mixture Model for Visualizing Ocean Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jian-Heng%20Wu">Jian-Heng Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bor-Shen%20Lin"> Bor-Shen Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The temperature-salinity relationship is one of the most important characteristics used for identifying water masses in marine research. Temperature-salinity characteristics, however, may change dynamically with respect to the geographic location and is quite sensitive to the depth at the same location. When depth is taken into consideration, however, it is not easy to compare the characteristics of different water masses efficiently for a wide range of areas of the ocean. In this paper, the Gaussian mixture model was proposed to analyze the temperature-salinity-depth characteristics of water masses, based on which comparison between water masses may be conducted. Gaussian mixture model could model the distribution of a random vector and is formulated as the weighting sum for a set of multivariate normal distributions. The temperature-salinity-depth data for different locations are first used to train a set of Gaussian mixture models individually. The distance between two Gaussian mixture models can then be defined as the weighting sum of pairwise Bhattacharyya distances among the Gaussian distributions. Consequently, the distance between two water masses may be measured fast, which allows the automatic and efficient comparison of the water masses for a wide range area. The proposed approach not only can approximate the distribution of temperature, salinity, and depth directly without the prior knowledge for assuming the regression family, but may restrict the complexity by controlling the number of mixtures when the amounts of samples are unevenly distributed. In addition, it is critical for knowledge discovery in marine research to represent, manage and share the temperature-salinity-depth characteristics flexibly and responsively. The proposed approach has been applied to a real-time visualization system of ocean data, which may facilitate the comparison of water masses by aggregating the data without degrading the discriminating capabilities. This system provides an interface for querying geographic locations with similar temperature-salinity-depth characteristics interactively and for tracking specific patterns of water masses, such as the Kuroshio near Taiwan or those in the South China Sea. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20mass" title="water mass">water mass</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20visualization" title=" data visualization"> data visualization</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20framework" title=" system framework"> system framework</a> </p> <a href="https://publications.waset.org/abstracts/137593/unsupervised-learning-and-similarity-comparison-of-water-mass-characteristics-with-gaussian-mixture-model-for-visualizing-ocean-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137593.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">16734</span> A Segmentation Method for Grayscale Images Based on the Firefly Algorithm and the Gaussian Mixture Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Donatella%20Giuliani">Donatella Giuliani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, we propose an unsupervised grayscale image segmentation method based on a combination of the Firefly Algorithm and the Gaussian Mixture Model. Firstly, the Firefly Algorithm has been applied in a histogram-based research of cluster means. The Firefly Algorithm is a stochastic global optimization technique, centered on the flashing characteristics of fireflies. In this context it has been performed to determine the number of clusters and the related cluster means in a histogram-based segmentation approach. Successively these means are used in the initialization step for the parameter estimation of a Gaussian Mixture Model. The parametric probability density function of a Gaussian Mixture Model is represented as a weighted sum of Gaussian component densities, whose parameters are evaluated applying the iterative Expectation-Maximization technique. The coefficients of the linear super-position of Gaussians can be thought as prior probabilities of each component. Applying the Bayes rule, the posterior probabilities of the grayscale intensities have been evaluated, therefore their maxima are used to assign each pixel to the clusters, according to their gray-level values. The proposed approach appears fairly solid and reliable when applied even to complex grayscale images. The validation has been performed by using different standard measures, more precisely: the Root Mean Square Error (RMSE), the Structural Content (SC), the Normalized Correlation Coefficient (NK) and the Davies-Bouldin (DB) index. The achieved results have strongly confirmed the robustness of this gray scale segmentation method based on a metaheuristic algorithm. Another noteworthy advantage of this methodology is due to the use of maxima of responsibilities for the pixel assignment that implies a consistent reduction of the computational costs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering%20images" title="clustering images">clustering images</a>, <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title=" firefly algorithm"> firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=meta%20heuristic%20algorithm" title=" meta heuristic algorithm"> meta heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</a> </p> <a href="https://publications.waset.org/abstracts/79553/a-segmentation-method-for-grayscale-images-based-on-the-firefly-algorithm-and-the-gaussian-mixture-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79553.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">217</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">16733</span> Adaptive Target Detection of High-Range-Resolution Radar in Non-Gaussian Clutter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lina%20Pan">Lina Pan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In non-Gaussian clutter of a spherically invariant random vector, in the cases that a certain estimated covariance matrix could become singular, the adaptive target detection of high-range-resolution radar is addressed. Firstly, the restricted maximum likelihood (RML) estimates of unknown covariance matrix and scatterer amplitudes are derived for non-Gaussian clutter. And then the RML estimate of texture is obtained. Finally, a novel detector is devised. It is showed that, without secondary data, the proposed detector outperforms the existing Kelly binary integrator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%20clutter" title="non-Gaussian clutter">non-Gaussian clutter</a>, <a href="https://publications.waset.org/abstracts/search?q=covariance%20matrix%20estimation" title=" covariance matrix estimation"> covariance matrix estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=target%20detection" title=" target detection"> target detection</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a> </p> <a href="https://publications.waset.org/abstracts/24879/adaptive-target-detection-of-high-range-resolution-radar-in-non-gaussian-clutter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16732</span> Least Squares Solution for Linear Quadratic Gaussian Problem with Stochastic Approximation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sie%20Long%20Kek">Sie Long Kek</a>, <a href="https://publications.waset.org/abstracts/search?q=Wah%20June%20Leong"> Wah June Leong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kok%20Lay%20Teo"> Kok Lay Teo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Linear quadratic Gaussian model is a standard mathematical model for the stochastic optimal control problem. The combination of the linear quadratic estimation and the linear quadratic regulator allows the state estimation and the optimal control policy to be designed separately. This is known as the separation principle. In this paper, an efficient computational method is proposed to solve the linear quadratic Gaussian problem. In our approach, the Hamiltonian function is defined, and the necessary conditions are derived. In addition to this, the output error is defined and the least-square optimization problem is introduced. By determining the first-order necessary condition, the gradient of the sum squares of output error is established. On this point of view, the stochastic approximation approach is employed such that the optimal control policy is updated. Within a given tolerance, the iteration procedure would be stopped and the optimal solution of the linear-quadratic Gaussian problem is obtained. For illustration, an example of the linear-quadratic Gaussian problem is studied. The result shows the efficiency of the approach proposed. In conclusion, the applicability of the approach proposed for solving the linear quadratic Gaussian problem is highly demonstrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iteration%20procedure" title="iteration procedure">iteration procedure</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares%20solution" title=" least squares solution"> least squares solution</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20quadratic%20Gaussian" title=" linear quadratic Gaussian"> linear quadratic Gaussian</a>, <a href="https://publications.waset.org/abstracts/search?q=output%20error" title=" output error"> output error</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20approximation" title=" stochastic approximation"> stochastic approximation</a> </p> <a href="https://publications.waset.org/abstracts/113018/least-squares-solution-for-linear-quadratic-gaussian-problem-with-stochastic-approximation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113018.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">186</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">16731</span> Interaction of Tungsten Tips with Laguerre-Gaussian Beams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abhisek%20Sinha">Abhisek Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Debobrata%20Rajak"> Debobrata Rajak</a>, <a href="https://publications.waset.org/abstracts/search?q=Shilpa%20Rani"> Shilpa Rani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ram%20Gopal"> Ram Gopal</a>, <a href="https://publications.waset.org/abstracts/search?q=Vandana%20Sharma"> Vandana Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interaction of femtosecond laser pulses with metallic tips has been studied extensively, and they have proved to be a very good source of ultrashort electron pulses. A study of the interaction of femtosecond Laguerre-Gaussian (LG) laser modes with Tungsten tips is presented here. Laser pulses of 35 fs pulse durations were incident on Tungsten tips, and their electron emission rates were studied for LG (l=1, p=0) and Gaussian modes. A change in the order of the interaction for LG beams is reported, and the difference in the order of interaction is attributed to ponderomotive shifts in the energy levels corresponding to the enhanced near-field intensity supported by numerical simulations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=femtosecond" title="femtosecond">femtosecond</a>, <a href="https://publications.waset.org/abstracts/search?q=Laguerre-Gaussian" title=" Laguerre-Gaussian"> Laguerre-Gaussian</a>, <a href="https://publications.waset.org/abstracts/search?q=OAM" title=" OAM"> OAM</a>, <a href="https://publications.waset.org/abstracts/search?q=tip" title=" tip"> tip</a> </p> <a href="https://publications.waset.org/abstracts/139164/interaction-of-tungsten-tips-with-laguerre-gaussian-beams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139164.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">265</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">16730</span> ML-Based Blind Frequency Offset Estimation Schemes for OFDM Systems in Non-Gaussian Noise Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keunhong%20Chae">Keunhong Chae</a>, <a href="https://publications.waset.org/abstracts/search?q=Seokho%20Yoon"> Seokho Yoon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes frequency offset (FO) estimation schemes robust to the non-Gaussian noise for orthogonal frequency division multiplexing (OFDM) systems. A maximum-likelihood (ML) scheme and a low-complexity estimation scheme are proposed by applying the probability density function of the cyclic prefix of OFDM symbols to the ML criterion. From simulation results, it is confirmed that the proposed schemes offer a significant FO estimation performance improvement over the conventional estimation scheme in non-Gaussian noise environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20offset" title="frequency offset">frequency offset</a>, <a href="https://publications.waset.org/abstracts/search?q=cyclic%20prefix" title=" cyclic prefix"> cyclic prefix</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum-likelihood" title=" maximum-likelihood"> maximum-likelihood</a>, <a href="https://publications.waset.org/abstracts/search?q=non-Gaussian%0D%0Anoise" title=" non-Gaussian noise"> non-Gaussian noise</a>, <a href="https://publications.waset.org/abstracts/search?q=OFDM" title=" OFDM"> OFDM</a> </p> <a href="https://publications.waset.org/abstracts/10266/ml-based-blind-frequency-offset-estimation-schemes-for-ofdm-systems-in-non-gaussian-noise-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10266.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">476</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">16729</span> Gaussian Probability Density for Forest Fire Detection Using Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benkraouda">S. Benkraouda</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Djelloul-Khedda"> Z. Djelloul-Khedda</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Yagoubi"> B. Yagoubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forest%20fire" title="forest fire">forest fire</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20fire%20detection" title=" forest fire detection"> forest fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title=" satellite image"> satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title=" normal distribution"> normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20gaussian%20model" title=" theoretical gaussian model"> theoretical gaussian model</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20infrared%20matrix%20image" title=" thermal infrared matrix image"> thermal infrared matrix image</a> </p> <a href="https://publications.waset.org/abstracts/118320/gaussian-probability-density-for-forest-fire-detection-using-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118320.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">142</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">16728</span> Physically Informed Kernels for Wave Loading Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20James%20Pitchforth">Daniel James Pitchforth</a>, <a href="https://publications.waset.org/abstracts/search?q=Timothy%20James%20Rogers"> Timothy James Rogers</a>, <a href="https://publications.waset.org/abstracts/search?q=Ulf%20Tyge%20Tygesen"> Ulf Tyge Tygesen</a>, <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Jane%20Cross"> Elizabeth Jane Cross</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wave loading is a primary cause of fatigue within offshore structures and its quantification presents a challenging and important subtask within the SHM framework. The accurate representation of physics in such environments is difficult, however, driving the development of data-driven techniques in recent years. Within many industrial applications, empirical laws remain the preferred method of wave loading prediction due to their low computational cost and ease of implementation. This paper aims to develop an approach that combines data-driven Gaussian process models with physical empirical solutions for wave loading, including Morison’s Equation. The aim here is to incorporate physics directly into the covariance function (kernel) of the Gaussian process, enforcing derived behaviors whilst still allowing enough flexibility to account for phenomena such as vortex shedding, which may not be represented within the empirical laws. The combined approach has a number of advantages, including improved performance over either component used independently and interpretable hyperparameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=offshore%20structures" title="offshore structures">offshore structures</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20processes" title=" Gaussian processes"> Gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=Physics%20informed%20machine%20learning" title=" Physics informed machine learning"> Physics informed machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20design" title=" Kernel design"> Kernel design</a> </p> <a href="https://publications.waset.org/abstracts/146250/physically-informed-kernels-for-wave-loading-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146250.pdf" 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