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Affine Auto Regression with eXogenous input</title> <meta name="description" content="Search results for: PieceWise Affine Auto Regression with eXogenous input"> <meta name="keywords" content="PieceWise Affine Auto Regression with eXogenous input"> <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 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</div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: PieceWise Affine Auto Regression with eXogenous input</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5801</span> Integrating Inference, Simulation and Deduction in Molecular Domain Analysis and Synthesis with Peculiar Attention to Drug Discovery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20Liberati">Diego Liberati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Standard molecular modeling is traditionally done through Schroedinger equations via the help of powerful tools helping to manage them atom by atom, often needing High Performance Computing. Here, a full portfolio of new tools, conjugating statistical inference in the so called eXplainable Artificial Intelligence framework (in the form of Machine Learning of understandable rules) to the more traditional modeling and simulation control theory of mixed dynamic logic hybrid processes, is offered as quite a general purpose even if making an example to a popular chemical physics set of problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=understandable%20rules%20ML" title="understandable rules ML">understandable rules ML</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means" title=" k-means"> k-means</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input" title=" PieceWise Affine Auto Regression with eXogenous input"> PieceWise Affine Auto Regression with eXogenous input</a> </p> <a href="https://publications.waset.org/abstracts/188993/integrating-inference-simulation-and-deduction-in-molecular-domain-analysis-and-synthesis-with-peculiar-attention-to-drug-discovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188993.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">29</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">5800</span> Segmentation of Piecewise Polynomial Regression Model by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piecewise polynomial regression model is very flexible model for modeling the data. If the piecewise polynomial regression model is matched against the data, its parameters are not generally known. This paper studies the parameter estimation problem of piecewise polynomial regression model. The method which is used to estimate the parameters of the piecewise polynomial regression model is Bayesian method. Unfortunately, the Bayes estimator cannot be found analytically. Reversible jump MCMC algorithm is proposed to solve this problem. Reversible jump MCMC algorithm generates the Markov chain that converges to the limit distribution of the posterior distribution of piecewise polynomial regression model parameter. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of piecewise polynomial regression model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=piecewise%20regression" title="piecewise regression">piecewise regression</a>, <a href="https://publications.waset.org/abstracts/search?q=bayesian" title=" bayesian"> bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/46201/segmentation-of-piecewise-polynomial-regression-model-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46201.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">373</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">5799</span> New Segmentation of Piecewise Linear Regression Models Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Piecewise linear regression models are very flexible models for modeling the data. If the piecewise linear regression models are matched against the data, then the parameters are generally not known. This paper studies the problem of parameter estimation of piecewise linear regression models. The method used to estimate the parameters of picewise linear regression models is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems, the reversible jump MCMC algorithm is proposed. Reversible jump MCMC algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of picewise linear regression models. The resulting Markov chain is used to calculate the Bayes estimator for the parameters of picewise linear regression models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=regression" title="regression">regression</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise" title=" piecewise"> piecewise</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20Jump%20MCMC" title=" reversible Jump MCMC"> reversible Jump MCMC</a> </p> <a href="https://publications.waset.org/abstracts/31651/new-segmentation-of-piecewise-linear-regression-models-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31651.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">521</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">5798</span> A Fuzzy Nonlinear Regression Model for Interval Type-2 Fuzzy Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Poleshchuk">O. Poleshchuk</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Komarov"> E. Komarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20type-2%20fuzzy%20sets" title="interval type-2 fuzzy sets">interval type-2 fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression" title=" fuzzy regression"> fuzzy regression</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20interval" title=" weighted interval"> weighted interval</a> </p> <a href="https://publications.waset.org/abstracts/6138/a-fuzzy-nonlinear-regression-model-for-interval-type-2-fuzzy-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6138.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">373</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">5797</span> A Nonlinear Approach for System Identification of a Li-Ion Battery Based on a Non-Linear Autoregressive Exogenous Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meriem%20Mossaddek">Meriem Mossaddek</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Mehdi%20Laadissi"> El Mehdi Laadissi</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Mehdi%20Loualid"> El Mehdi Loualid</a>, <a href="https://publications.waset.org/abstracts/search?q=Chouaib%20Ennawaoui"> Chouaib Ennawaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohaib%20Bouzaid"> Sohaib Bouzaid</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelowahed%20Hajjaji"> Abdelowahed Hajjaji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An electrochemical system is a subset of mechatronic systems that includes a wide variety of batteries and nickel-cadmium, lead-acid batteries, and lithium-ion. Those structures have several non-linear behaviors and uncertainties in their running range. This paper studies an effective technique for modeling Lithium-Ion (Li-Ion) batteries using a Nonlinear Auto-Regressive model with exogenous input (NARX). The Artificial Neural Network (ANN) is trained to employ the data collected from the battery testing process. The proposed model is implemented on a Li-Ion battery cell. Simulation of this model in MATLAB shows good accuracy of the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lithium-ion%20battery" title="lithium-ion battery">lithium-ion battery</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20storage" title=" energy storage"> energy storage</a>, <a href="https://publications.waset.org/abstracts/search?q=battery%20model" title=" battery model"> battery model</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20models" title=" nonlinear models"> nonlinear models</a> </p> <a href="https://publications.waset.org/abstracts/159992/a-nonlinear-approach-for-system-identification-of-a-li-ion-battery-based-on-a-non-linear-autoregressive-exogenous-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159992.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">115</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">5796</span> Periodicity of Solutions of a Nonlinear Impulsive Differential Equation with Piecewise Constant Arguments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehtap%20Lafc%C4%B1">Mehtap Lafcı</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, oscillation, periodicity and convergence of solutions of linear differential equations with piecewise constant arguments have been significantly considered but there are only a few papers for impulsive differential equations with piecewise constant arguments. In this paper, a first order nonlinear impulsive differential equation with piecewise constant arguments is studied and the existence of solutions and periodic solutions of this equation are investigated by using Carvalho’s method. Finally, an example is given to illustrate these results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carvalho%27s%20method" title="Carvalho's method">Carvalho's method</a>, <a href="https://publications.waset.org/abstracts/search?q=impulsive%20differential%20equation" title=" impulsive differential equation"> impulsive differential equation</a>, <a href="https://publications.waset.org/abstracts/search?q=periodic%20solution" title=" periodic solution"> periodic solution</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise%20constant%20arguments" title=" piecewise constant arguments"> piecewise constant arguments</a> </p> <a href="https://publications.waset.org/abstracts/33745/periodicity-of-solutions-of-a-nonlinear-impulsive-differential-equation-with-piecewise-constant-arguments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33745.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">515</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">5795</span> Diffusion Adaptation Strategies for Distributed Estimation Based on the Family of Affine Projection Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shams%20Esfand%20Abadi">Mohammad Shams Esfand Abadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ranjbar"> Mohammad Ranjbar</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Ebrahimpour"> Reza Ebrahimpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=selective%20partial%20update" title="selective partial update">selective partial update</a>, <a href="https://publications.waset.org/abstracts/search?q=affine%20projection" title=" affine projection"> affine projection</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20selection" title=" dynamic selection"> dynamic selection</a>, <a href="https://publications.waset.org/abstracts/search?q=diffusion" title=" diffusion"> diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20distributed%20networks" title=" adaptive distributed networks"> adaptive distributed networks</a> </p> <a href="https://publications.waset.org/abstracts/20231/diffusion-adaptation-strategies-for-distributed-estimation-based-on-the-family-of-affine-projection-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20231.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">707</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">5794</span> Orthogonal Regression for Nonparametric Estimation of Errors-In-Variables Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anastasiia%20Yu.%20Timofeeva">Anastasiia Yu. Timofeeva</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=grade%20point%20average" title="grade point average">grade point average</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20regression" title=" orthogonal regression"> orthogonal regression</a>, <a href="https://publications.waset.org/abstracts/search?q=penalized%20regression%20spline" title=" penalized regression spline"> penalized regression spline</a>, <a href="https://publications.waset.org/abstracts/search?q=locally%20weighted%20regression" title=" locally weighted regression"> locally weighted regression</a> </p> <a href="https://publications.waset.org/abstracts/11927/orthogonal-regression-for-nonparametric-estimation-of-errors-in-variables-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11927.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">416</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5793</span> Model Predictive Controller for Pasteurization Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tesfaye%20Alamirew%20Dessie">Tesfaye Alamirew Dessie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MPC" title="MPC">MPC</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a>, <a href="https://publications.waset.org/abstracts/search?q=ARX" title=" ARX"> ARX</a>, <a href="https://publications.waset.org/abstracts/search?q=pasteurization" title=" pasteurization"> pasteurization</a> </p> <a href="https://publications.waset.org/abstracts/154469/model-predictive-controller-for-pasteurization-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154469.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">5792</span> A Method of the Semantic on Image Auto-Annotation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Huo">Lin Huo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianwei%20Liu"> Xianwei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingxiong%20Zhou"> Jingxiong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20auto-annotation" title="image auto-annotation">image auto-annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20correlograms" title=" color correlograms"> color correlograms</a>, <a href="https://publications.waset.org/abstracts/search?q=Hash%20code" title=" Hash code"> Hash code</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a> </p> <a href="https://publications.waset.org/abstracts/15628/a-method-of-the-semantic-on-image-auto-annotation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15628.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">497</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5791</span> Self-Tuning Robot Control Based on Subspace Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mathias%20Marquardt">Mathias Marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20D%C3%BCnow"> Peter Dünow</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Ba%C3%9Fler"> Sandra Baßler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper describes the use of subspace based identification methods for auto tuning of a state space control system. The plant is an unstable but self balancing transport robot. Because of the unstable character of the process it has to be identified from closed loop input-output data. Based on the identified model a state space controller combined with an observer is calculated. The subspace identification algorithm and the controller design procedure is combined to a auto tuning method. The capability of the approach was verified in a simulation experiments under different process conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto%20tuning" title="auto tuning">auto tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=balanced%20robot" title=" balanced robot"> balanced robot</a>, <a href="https://publications.waset.org/abstracts/search?q=closed%20loop%20identification" title=" closed loop identification"> closed loop identification</a>, <a href="https://publications.waset.org/abstracts/search?q=subspace%20identification" title=" subspace identification"> subspace identification</a> </p> <a href="https://publications.waset.org/abstracts/49108/self-tuning-robot-control-based-on-subspace-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49108.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">5790</span> Design of an Energy Efficient Electric Auto Rickshaw</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Asghar">Muhammad Asghar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aamer%20Iqbal%20Bhatti"> Aamer Iqbal Bhatti</a>, <a href="https://publications.waset.org/abstracts/search?q=Qadeer%20Ahmed"> Qadeer Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Tahir%20Izhar"> Tahir Izhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Three wheeler auto Rickshaw, often termed as ‘auto rickshaw’ is very common in Pakistan and is considered as the most affordable means of transportation to the local people. Problems caused by the gasoline engine on the environment and people, the researchers and the automotive industry have turned to the hybrid electric vehicles and electrical powered vehicle. The research in this paper explains the design of energy efficient Electric auto Rickshaw. An electric auto rickshaw is being developed at Center for Energy Research and Development, (Lahore), which is running on the roads of Lahore city. Energy storage capacity of batteries is at least 25 times heavier than fossil fuel and having volume 10 times in comparison to fuel, resulting an increase of the Rickshaw weight. A set of specifications is derived according to the mobility requirements of the electric auto rickshaw. The design choices considering the power-train and component selection are explained in detail. It was concluded that electric auto rickshaw has many advantages and benefits over the conventional auto rickshaw. It is cleaner and much more energy efficient but limited to the distance it can travel before recharging of battery. In addition, a brief future view of the battery technology is given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conventional%20auto%20rickshaw" title="conventional auto rickshaw">conventional auto rickshaw</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20auto%20rickshaw" title=" electric auto rickshaw"> electric auto rickshaw</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20combustion%20engine" title=" internal combustion engine"> internal combustion engine</a>, <a href="https://publications.waset.org/abstracts/search?q=environment" title=" environment"> environment</a> </p> <a href="https://publications.waset.org/abstracts/54016/design-of-an-energy-efficient-electric-auto-rickshaw" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54016.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">287</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">5789</span> Copula-Based Estimation of Direct and Indirect Effects in Path Analysis Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alam%20Ali">Alam Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kumar%20Pathak"> Ashok Kumar Pathak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Path analysis is a statistical technique used to evaluate the direct and indirect effects of variables in path models. One or more structural regression equations are used to estimate a series of parameters in path models to find the better fit of data. However, sometimes the assumptions of classical regression models, such as ordinary least squares (OLS), are violated by the nature of the data, resulting in insignificant direct and indirect effects of exogenous variables. This article aims to explore the effectiveness of a copula-based regression approach as an alternative to classical regression, specifically when variables are linked through an elliptical copula. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20analysis" title="path analysis">path analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=copula-based%20regression%20models" title=" copula-based regression models"> copula-based regression models</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20and%20indirect%20effects" title=" direct and indirect effects"> direct and indirect effects</a>, <a href="https://publications.waset.org/abstracts/search?q=k-fold%20cross%20validation%20technique" title=" k-fold cross validation technique"> k-fold cross validation technique</a> </p> <a href="https://publications.waset.org/abstracts/186900/copula-based-estimation-of-direct-and-indirect-effects-in-path-analysis-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186900.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">42</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">5788</span> Improving the Analytical Power of Dynamic DEA Models, by the Consideration of the Shape of the Distribution of Inputs/Outputs Data: A Linear Piecewise Decomposition Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elias%20K.%20Maragos">Elias K. Maragos</a>, <a href="https://publications.waset.org/abstracts/search?q=Petros%20E.%20Maravelakis"> Petros E. Maravelakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dynamic%20Data%20Envelopment%20Analysis" title="Dynamic Data Envelopment Analysis">Dynamic Data Envelopment Analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=DDEA" title=" DDEA"> DDEA</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise%20linear%20inputs" title=" piecewise linear inputs"> piecewise linear inputs</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise%20linear%20outputs" title=" piecewise linear outputs"> piecewise linear outputs</a> </p> <a href="https://publications.waset.org/abstracts/86963/improving-the-analytical-power-of-dynamic-dea-models-by-the-consideration-of-the-shape-of-the-distribution-of-inputsoutputs-data-a-linear-piecewise-decomposition-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86963.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">161</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">5787</span> New Segmentation of Piecewise Moving-Average Model by Using Reversible Jump MCMC Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suparman">Suparman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper addresses the problem of the signal segmentation within a Bayesian framework by using reversible jump MCMC algorithm. The signal is modelled by piecewise constant Moving-Average (MA) model where the numbers of segments, the position of change-point, the order and the coefficient of the MA model for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow calculating some interesting features of the posterior distribution. The performance of the methodology is illustrated via several simulation results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=piecewise" title="piecewise">piecewise</a>, <a href="https://publications.waset.org/abstracts/search?q=moving-average%20model" title=" moving-average model"> moving-average model</a>, <a href="https://publications.waset.org/abstracts/search?q=reversible%20jump%20MCMC" title=" reversible jump MCMC"> reversible jump MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20segmentation" title=" signal segmentation"> signal segmentation</a> </p> <a href="https://publications.waset.org/abstracts/53614/new-segmentation-of-piecewise-moving-average-model-by-using-reversible-jump-mcmc-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53614.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">227</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">5786</span> Determinants of Economic Growth in Pakistan: A Structural Vector Auto Regression Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ajmair">Muhammad Ajmair</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This empirical study followed structural vector auto regression (SVAR) approach proposed by the so-called AB-model of Amisano and Giannini (1997) to check the impact of relevant macroeconomic determinants on economic growth in Pakistan. Before that auto regressive distributive lag (ARDL) bound testing technique and time varying parametric approach along with general to specific approach was employed to find out relevant significant determinants of economic growth. To our best knowledge, no author made such a study that employed auto regressive distributive lag (ARDL) bound testing and time varying parametric approach with general to specific approach in empirical literature, but current study will bridge this gap. Annual data was taken from World Development Indicators (2014) during period 1976-2014. The widely-used Schwarz information criterion and Akaike information criterion were considered for the lag length in each estimated equation. Main findings of the study are that remittances received, gross national expenditures and inflation are found to be the best relevant positive and significant determinants of economic growth. Based on these empirical findings, we conclude that government should focus on overall economic growth augmenting factors while formulating any policy relevant to the concerned sector. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=economic%20growth" title="economic growth">economic growth</a>, <a href="https://publications.waset.org/abstracts/search?q=gross%20national%20expenditures" title=" gross national expenditures"> gross national expenditures</a>, <a href="https://publications.waset.org/abstracts/search?q=inflation" title=" inflation"> inflation</a>, <a href="https://publications.waset.org/abstracts/search?q=remittances" title=" remittances"> remittances</a> </p> <a href="https://publications.waset.org/abstracts/79706/determinants-of-economic-growth-in-pakistan-a-structural-vector-auto-regression-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79706.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">199</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">5785</span> Modelling High-Frequency Crude Oil Dynamics Using Affine and Non-Affine Jump-Diffusion Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katja%20Ignatieva">Katja Ignatieva</a>, <a href="https://publications.waset.org/abstracts/search?q=Patrick%20Wong"> Patrick Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We investigated the dynamics of high frequency energy prices, including crude oil and electricity prices. The returns of underlying quantities are modelled using various parametric models such as stochastic framework with jumps and stochastic volatility (SVCJ) as well as non-parametric alternatives, which are purely data driven and do not require specification of the drift or the diffusion coefficient function. Using different statistical criteria, we investigate the performance of considered parametric and nonparametric models in their ability to forecast price series and volatilities. Our models incorporate possible seasonalities in the underlying dynamics and utilise advanced estimation techniques for the dynamics of energy prices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title="stochastic volatility">stochastic volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=affine%20jump-diffusion%20models" title=" affine jump-diffusion models"> affine jump-diffusion models</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20frequency%20data" title=" high frequency data"> high frequency data</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20specification" title=" model specification"> model specification</a>, <a href="https://publications.waset.org/abstracts/search?q=markov%20chain%20monte%20carlo" title=" markov chain monte carlo"> markov chain monte carlo</a> </p> <a href="https://publications.waset.org/abstracts/159124/modelling-high-frequency-crude-oil-dynamics-using-affine-and-non-affine-jump-diffusion-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159124.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">104</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">5784</span> Nonlinear Modeling of the PEMFC Based on NNARX Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shan-Jen%20Cheng">Shan-Jen Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Te-Jen%20Chang"> Te-Jen Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Kuang-Hsiung%20Tan">Kuang-Hsiung Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shou-Ling%20Kuo">Shou-Ling Kuo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PEMFC" title="PEMFC">PEMFC</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20modeling" title=" nonlinear modeling"> nonlinear modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=NNARX" title=" NNARX "> NNARX </a> </p> <a href="https://publications.waset.org/abstracts/25225/nonlinear-modeling-of-the-pemfc-based-on-nnarx-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25225.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">381</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">5783</span> A New Dual Forward Affine Projection Adaptive Algorithm for Speech Enhancement in Airplane Cockpits</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Djendi%20Mohmaed">Djendi Mohmaed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a dual adaptive algorithm, which is based on the combination between the forward blind source separation (FBSS) structure and the affine projection algorithm (APA). This proposed algorithm combines the advantages of the source separation properties of the FBSS structure and the fast convergence characteristics of the APA algorithm. The proposed algorithm needs two noisy observations to provide an enhanced speech signal. This process is done in a blind manner without the need for ant priori information about the source signals. The proposed dual forward blind source separation affine projection algorithm is denoted (DFAPA) and used for the first time in an airplane cockpit context to enhance the communication from- and to- the airplane. Intensive experiments were carried out in this sense to evaluate the performance of the proposed DFAPA algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20algorithm" title="adaptive algorithm">adaptive algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20enhancement" title=" speech enhancement"> speech enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20mismatch" title=" system mismatch"> system mismatch</a>, <a href="https://publications.waset.org/abstracts/search?q=SNR" title=" SNR"> SNR</a> </p> <a href="https://publications.waset.org/abstracts/165920/a-new-dual-forward-affine-projection-adaptive-algorithm-for-speech-enhancement-in-airplane-cockpits" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165920.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">135</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">5782</span> Analysis on Prediction Models of TBM Performance and Selection of Optimal Input Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Lo%20Lee">Hang Lo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki%20Il%20Song"> Ki Il Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Hee%20Hwan%20Ryu"> Hee Hwan Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TBM%20performance%20prediction%20model" title="TBM performance prediction model">TBM performance prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20system" title=" classification system"> classification system</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20regression%20analysis" title=" simple regression analysis"> simple regression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20analysis" title=" residual analysis"> residual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20input%20parameters" title=" optimal input parameters"> optimal input parameters</a> </p> <a href="https://publications.waset.org/abstracts/52738/analysis-on-prediction-models-of-tbm-performance-and-selection-of-optimal-input-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52738.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">5781</span> GPS Devices to Increase Efficiency of Indian Auto-Rickshaw Segment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanchay%20Vaidya">Sanchay Vaidya</a>, <a href="https://publications.waset.org/abstracts/search?q=Sourabh%20Gupta"> Sourabh Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Gouresh%20Singhal"> Gouresh Singhal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are various modes of transport in metro cities in India, auto-rickshaws being one of them. Auto-rickshaws provide connectivity to all the places in the city offering last mile connectivity. Among all the modes of transport, the auto-rickshaw industry is the most unorganized and inefficient. Although unions exist in different cities they aren’t good enough to cope up with the upcoming advancements in the field of technology. An introduction of simple technology in this field may do wonder and help increase the revenues. This paper aims to organize this segment under a single umbrella using GPS devices and mobile phones. The paper includes surveys of about 300 auto-rickshaw drivers and 1000 plus commuters across 6 metro cities in India. Carrying out research and analysis provides a base for the development of this model and implementation of this innovative technique, which is discussed in this paper in detail with ample emphasis given on the implementation of this model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-rickshaws" title="auto-rickshaws">auto-rickshaws</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20model" title=" business model"> business model</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%20device" title=" GPS device"> GPS device</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20application" title=" mobile application"> mobile application</a> </p> <a href="https://publications.waset.org/abstracts/6574/gps-devices-to-increase-efficiency-of-indian-auto-rickshaw-segment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6574.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">227</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">5780</span> Spatial Pattern and Predictors of Malaria in Ethiopia: Application of Auto Logistics Spatial Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Melkamu%20A.%20Zeru">Melkamu A. Zeru</a>, <a href="https://publications.waset.org/abstracts/search?q=Yamral%20M.%20Warkaw"> Yamral M. Warkaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Aweke%20A.%20Mitku"> Aweke A. Mitku</a>, <a href="https://publications.waset.org/abstracts/search?q=Muluwerk%20%20Ayele"> Muluwerk Ayele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Malaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia. Methods: A weighted sample of 15,239 individuals with rapid diagnosis tests was obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran's I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran's I statistic was used in identifying exposed areas. In data manipulation, machine learning was used for variable reduction and statistical software R, Stata, and Python were used for data management and analysis. The auto logistics spatial binary regression model was used to investigate the predictors of malaria. Results: The final auto logistics regression model reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR=2.401, 95 % CI: (2.125 - 2.713)]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR=52.55, 95%CI: (40.54-68.12)] followed by Beneshangul [AOR=34.95, 95%CI: (27.159 - 44.963)]. Similarly, individuals in Amhara [AOR=0.243, 95% CI:(0.1950.303],Oromiya[AOR=0.197,95%CI:(0.1580.244)],DireDawa[AOR=0.064,95%CI(0.049-0.082)],AddisAbaba[AOR=0.057,95%CI:(0.044-0.075)], Somali[AOR=0.077,95%CI:(0.059-0.097)], SNNPR[OR=0.329, 95%CI: (0.261- 0.413)] and Harari [AOR=0.256, 95%CI:(0.201 - 0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for a one-meter increase in altitude, the odds of a positive rapid diagnostic test (RDT) decrease by 1.6% [AOR = 0.984, 95% CI :( 0.984 - 0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR=1.671, 95% CI: (1.504 - 1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression. Conclusions: This study found that the incidence of malaria in Ethiopia had a spatial pattern that is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who live in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramic tiles roof houses. Moreover, using a protected anti-mosquito net reduced the risk of malaria incidence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=malaria" title="malaria">malaria</a>, <a href="https://publications.waset.org/abstracts/search?q=Ethiopia" title=" Ethiopia"> Ethiopia</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20logistics" title=" auto logistics"> auto logistics</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20model" title=" spatial model"> spatial model</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20clustering" title=" spatial clustering"> spatial clustering</a> </p> <a href="https://publications.waset.org/abstracts/187444/spatial-pattern-and-predictors-of-malaria-in-ethiopia-application-of-auto-logistics-spatial-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187444.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">34</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">5779</span> Formulating a Flexible-Spread Fuzzy Regression Model Based on Dissemblance Index</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shih-Pin%20Chen">Shih-Pin Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shih-Syuan%20You"> Shih-Syuan You</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposes a regression model with flexible spreads for fuzzy input-output data to cope with the situation that the existing measures cannot reflect the actual estimation error. The main idea is that a dissemblance index (DI) is carefully identified and defined for precisely measuring the actual estimation error. Moreover, the graded mean integration (GMI) representation is adopted for determining more representative numeric regression coefficients. Notably, to comprehensively compare the performance of the proposed model with other ones, three different criteria are adopted. The results from commonly used test numerical examples and an application to Taiwan's business monitoring indicator illustrate that the proposed dissemblance index method not only produces valid fuzzy regression models for fuzzy input-output data, but also has satisfactory and stable performance in terms of the total estimation error based on these three criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dissemblance%20index" title="dissemblance index">dissemblance index</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20sets" title=" fuzzy sets"> fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a> </p> <a href="https://publications.waset.org/abstracts/59191/formulating-a-flexible-spread-fuzzy-regression-model-based-on-dissemblance-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59191.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">361</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">5778</span> Copula-Based Estimation of Direct and Indirect Effects in Path Analysis Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alam%20Ali">Alam Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kumar%20Pathak"> Ashok Kumar Pathak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Path analysis is a statistical technique used to evaluate the strength of the direct and indirect effects of variables. One or more structural regression equations are used to estimate a series of parameters in order to find the better fit of data. Sometimes, exogenous variables do not show a significant strength of their direct and indirect effect when the assumption of classical regression (ordinary least squares (OLS)) are violated by the nature of the data. The main motive of this article is to investigate the efficacy of the copula-based regression approach over the classical regression approach and calculate the direct and indirect effects of variables when data violates the OLS assumption and variables are linked through an elliptical copula. We perform this study using a well-organized numerical scheme. Finally, a real data application is also presented to demonstrate the performance of the superiority of the copula approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=path%20analysis" title="path analysis">path analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=copula-based%20regression%20models" title=" copula-based regression models"> copula-based regression models</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20and%20indirect%20effects" title=" direct and indirect effects"> direct and indirect effects</a>, <a href="https://publications.waset.org/abstracts/search?q=k-fold%20cross%20validation%20technique" title=" k-fold cross validation technique"> k-fold cross validation technique</a> </p> <a href="https://publications.waset.org/abstracts/171166/copula-based-estimation-of-direct-and-indirect-effects-in-path-analysis-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171166.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">72</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">5777</span> Analysis and Forecasting of Bitcoin Price Using Exogenous Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J-C.%20Leneveu">J-C. Leneveu</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Chereau"> A. Chereau</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Mansart"> L. Mansart</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Mesbah"> T. Mesbah</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Wyka"> M. Wyka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes. <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=bitcoin" title=" bitcoin"> bitcoin</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=social%20network" title=" social network"> social network</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20trends" title=" financial trends"> financial trends</a>, <a href="https://publications.waset.org/abstracts/search?q=exogenous%20data" title=" exogenous data"> exogenous data</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20economy" title=" global economy"> global economy</a>, <a href="https://publications.waset.org/abstracts/search?q=behavioral%20finance" title=" behavioral finance"> behavioral finance</a> </p> <a href="https://publications.waset.org/abstracts/29945/analysis-and-forecasting-of-bitcoin-price-using-exogenous-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29945.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">355</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">5776</span> Discrete State Prediction Algorithm Design with Self Performance Enhancement Capacity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smail%20Tigani">Smail Tigani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Ouzzif"> Mohamed Ouzzif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20state" title="discrete state">discrete state</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Chains" title=" Markov Chains"> Markov Chains</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=auto-adaptive%20systems" title=" auto-adaptive systems"> auto-adaptive systems</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20Simulation" title=" Monte Carlo Simulation"> Monte Carlo Simulation</a> </p> <a href="https://publications.waset.org/abstracts/20238/discrete-state-prediction-algorithm-design-with-self-performance-enhancement-capacity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20238.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">498</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">5775</span> Parking Space Detection and Trajectory Tracking Control for Vehicle Auto-Parking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiuh-Jer%20Huang">Shiuh-Jer Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Sheng%20Hsu"> Yu-Sheng Hsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On-board available parking space detecting system, parking trajectory planning and tracking control mechanism are the key components of vehicle backward auto-parking system. Firstly, pair of ultrasonic sensors is installed on each side of vehicle body surface to detect the relative distance between ego-car and surrounding obstacle. The dimension of a found empty space can be calculated based on vehicle speed and the time history of ultrasonic sensor detecting information. This result can be used for constructing the 2D vehicle environmental map and available parking type judgment. Finally, the auto-parking controller executes the on-line optimal parking trajectory planning based on this 2D environmental map, and monitors the real-time vehicle parking trajectory tracking control. This low cost auto-parking system was tested on a model car. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20auto-parking" title="vehicle auto-parking">vehicle auto-parking</a>, <a href="https://publications.waset.org/abstracts/search?q=parking%20space%20detection" title=" parking space detection"> parking space detection</a>, <a href="https://publications.waset.org/abstracts/search?q=parking%20path%20tracking%20control" title=" parking path tracking control"> parking path tracking control</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20fuzzy%20controller" title=" intelligent fuzzy controller"> intelligent fuzzy controller</a> </p> <a href="https://publications.waset.org/abstracts/78571/parking-space-detection-and-trajectory-tracking-control-for-vehicle-auto-parking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78571.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">5774</span> Hierarchical Piecewise Linear Representation of Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vineetha%20Bettaiah">Vineetha Bettaiah</a>, <a href="https://publications.waset.org/abstracts/search?q=Heggere%20S.%20Ranganath"> Heggere S. Ranganath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity. <p class="card-text"><strong>Keywords:</strong> <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=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=piecewise%20linear%20representation" title=" piecewise linear representation"> piecewise linear representation</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20representation" title=" time series representation"> time series representation</a> </p> <a href="https://publications.waset.org/abstracts/2680/hierarchical-piecewise-linear-representation-of-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2680.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">275</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">5773</span> Machine Learning Invariants to Detect Anomalies in Secure Water Treatment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Heng">Jonathan Heng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoong%20Cheah%20Huei"> Yoong Cheah Huei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A strategic model that does not trigger any false alarms to detect anomalies in Secure Water Treatment (SWaT) test bed is presented. This model uses machine learning invariants formulated from streamlining the general form of Auto-Regressive models with eXogenous input. A creative generalized CUSUM algorithm to integrate the invariants and the detection strategy technique is successfully developed and tested in the SWaT Programmable Logic Controllers (PLCs). Three steps to fine-tune parameters, b and τ in the generalized algorithm are stated and an example used to demonstrate the tuning process is discussed. This approach can swiftly and effectively detect various scopes of cyber-attacks such as multiple points single stage and multiple points multiple stages in SWaT. This technique can be applied in water treatment plants and other cyber physical systems like power and gas plants too. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20invariants" title="machine learning invariants">machine learning invariants</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20CUSUM%20algorithm%20with%20invariants%20and%20detection%20strategy" title=" generalized CUSUM algorithm with invariants and detection strategy"> generalized CUSUM algorithm with invariants and detection strategy</a>, <a href="https://publications.waset.org/abstracts/search?q=scope%20of%20cyber%20attacks" title=" scope of cyber attacks"> scope of cyber attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=strategic%20model" title=" strategic model"> strategic model</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20parameters" title=" tuning parameters"> tuning parameters</a> </p> <a href="https://publications.waset.org/abstracts/101614/machine-learning-invariants-to-detect-anomalies-in-secure-water-treatment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101614.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">181</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">5772</span> Evaluation of a Piecewise Linear Mixed-Effects Model in the Analysis of Randomized Cross-over Trial</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moses%20Mwangi">Moses Mwangi</a>, <a href="https://publications.waset.org/abstracts/search?q=Geert%20Verbeke"> Geert Verbeke</a>, <a href="https://publications.waset.org/abstracts/search?q=Geert%20Molenberghs"> Geert Molenberghs</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment with respect to a reference treatment (placebo or standard). The main advantage of using cross-over design over conventional parallel design is its flexibility, where every subject become its own control, thereby reducing confounding effect. Jones & Kenward, discuss in detail more recent developments in the analysis of cross-over trials. We revisit the simple piecewise linear mixed-effects model, proposed by Mwangi et. al, (in press) for its first application in the analysis of cross-over trials. We compared performance of the proposed piecewise linear mixed-effects model with two commonly cited statistical models namely, (1) Grizzle model; and (2) Jones & Kenward model, used in estimation of the treatment effect, in the analysis of randomized cross-over trial. We estimate two performance measurements (mean square error (MSE) and coverage probability) for the three methods, using data simulated from the proposed piecewise linear mixed-effects model. Piecewise linear mixed-effects model yielded lowest MSE estimates compared to Grizzle and Jones & Kenward models for both small (Nobs=20) and large (Nobs=600) sample sizes. It’s coverage probability were highest compared to Grizzle and Jones & Kenward models for both small and large sample sizes. A piecewise linear mixed-effects model is a better estimator of treatment effect than its two competing estimators (Grizzle and Jones & Kenward models) in the analysis of cross-over trials. The data generating mechanism used in this paper captures two time periods for a simple 2-Treatments x 2-Periods cross-over design. Its application is extendible to more complex cross-over designs with multiple treatments and periods. In addition, it is important to note that, even for single response models, adding more random effects increases the complexity of the model and thus may be difficult or impossible to fit in some cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evaluation" title="Evaluation">Evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=Grizzle%20model" title=" Grizzle model"> Grizzle model</a>, <a href="https://publications.waset.org/abstracts/search?q=Jones%20%26%20Kenward%20model" title=" Jones & Kenward model"> Jones & Kenward model</a>, <a href="https://publications.waset.org/abstracts/search?q=Performance%20measures" title=" Performance measures"> Performance measures</a>, <a href="https://publications.waset.org/abstracts/search?q=Simulation" title=" Simulation"> Simulation</a> </p> <a href="https://publications.waset.org/abstracts/123329/evaluation-of-a-piecewise-linear-mixed-effects-model-in-the-analysis-of-randomized-cross-over-trial" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123329.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">122</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=193">193</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=194">194</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=PieceWise%20Affine%20Auto%20Regression%20with%20eXogenous%20input&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> 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