CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: tuning parameter selection</title> <meta name="description" content="Search results for: tuning parameter selection"> <meta name="keywords" content="tuning parameter selection"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="tuning parameter selection" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" 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="tuning parameter selection"> <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> 4584</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: tuning parameter selection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4584</span> Efficient Tuning Parameter Selection by Cross-Validated Score in High Dimensional Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoonsuh%20Jung">Yoonsuh Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an "optimal" value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross%20validation" title="cross validation">cross validation</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20averaging" title=" parameter averaging"> parameter averaging</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20selection" title=" parameter selection"> parameter selection</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization%20parameter%20search" title=" regularization parameter search"> regularization parameter search</a> </p> <a href="https://publications.waset.org/abstracts/36409/efficient-tuning-parameter-selection-by-cross-validated-score-in-high-dimensional-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36409.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">415</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">4583</span> Parameter Tuning of Complex Systems Modeled in Agent Based Modeling and Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabia%20Korkmaz%20Tan">Rabia Korkmaz Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=%C5%9Eebnem%20Bora"> Şebnem Bora</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parameter%20tuning" title="parameter tuning">parameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=agent%20based%20modeling%20and%20simulation" title=" agent based modeling and simulation"> agent based modeling and simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic%20algorithms" title=" metaheuristic algorithms"> metaheuristic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20systems" title=" complex systems"> complex systems</a> </p> <a href="https://publications.waset.org/abstracts/77307/parameter-tuning-of-complex-systems-modeled-in-agent-based-modeling-and-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77307.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">226</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">4582</span> A Tuning Method for Microwave Filter via Complex Neural Network and Improved Space Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shengbiao%20Wu">Shengbiao Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Weihua%20Cao"> Weihua Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20Wu"> Min Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Can%20Liu"> Can Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=microwave%20filter" title="microwave filter">microwave filter</a>, <a href="https://publications.waset.org/abstracts/search?q=scattering%20parameter" title=" scattering parameter"> scattering parameter</a>, <a href="https://publications.waset.org/abstracts/search?q=coupling%20matrix" title=" coupling matrix"> coupling matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20tuning" title=" intelligent tuning"> intelligent tuning</a> </p> <a href="https://publications.waset.org/abstracts/81373/a-tuning-method-for-microwave-filter-via-complex-neural-network-and-improved-space-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81373.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">311</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">4581</span> Weighted Rank Regression with Adaptive Penalty Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20penalty%20function" title="adaptive penalty function">adaptive penalty function</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20penalized%20regression" title=" robust penalized regression"> robust penalized regression</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20rank%20regression" title=" weighted rank regression"> weighted rank regression</a> </p> <a href="https://publications.waset.org/abstracts/79449/weighted-rank-regression-with-adaptive-penalty-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79449.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4580</span> Suitable Tuning Method Selection for PID Controller Used in Digital Excitation System of Brushless Synchronous Generator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Deepak%20M.%20Sajnekar">Deepak M. Sajnekar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Deshpande"> S. B. Deshpande</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Mohril"> R. M. Mohril</a> </p> <p class="card-text"><strong>Abstract:</strong></p> At present many rotary excitation control system are using analog type of Automatic Voltage Regulator which now started to replace with the digital automatic voltage regulator which is provided with PID controller and tuning of PID controller is a challenging task. The cases where digital excitation control system is used tuning of PID controller are still carried out by pole placement method. Tuning of PID controller used for static excitation control system is not challenging because it does not involve exciter time constant. This paper discusses two methods of tuning PID controller i.e. Pole placement method and pole zero cancellation method. GUI prepared for both the methods on the platform of MATLAB. Using this GUI, performance results and time required for tuning for both the methods are compared. Sensitivity of the methods is also presented with parameter variation like loop gain ‘K’ and exciter time constant ‘te’. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20excitation%20system" title="digital excitation system">digital excitation system</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20voltage%20regulator" title=" automatic voltage regulator"> automatic voltage regulator</a>, <a href="https://publications.waset.org/abstracts/search?q=pole%20placement%20method" title=" pole placement method"> pole placement method</a>, <a href="https://publications.waset.org/abstracts/search?q=pole%20zero%20cancellation%20method" title=" pole zero cancellation method"> pole zero cancellation method</a> </p> <a href="https://publications.waset.org/abstracts/12214/suitable-tuning-method-selection-for-pid-controller-used-in-digital-excitation-system-of-brushless-synchronous-generator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12214.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">678</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">4579</span> Parameter Selection for Computationally Efficient Use of the Bfvrns Fully Homomorphic Encryption Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cavidan%20Yakupoglu">Cavidan Yakupoglu</a>, <a href="https://publications.waset.org/abstracts/search?q=Kurt%20Rohloff"> Kurt Rohloff</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we aim to provide a novel parameter selection model for the BFVrns scheme, which is one of the prominent FHE schemes. Parameter selection in lattice-based FHE schemes is a practical challenges for experts or non-experts. Towards a solution to this problem, we introduce a hybrid principles-based approach that combines theoretical with experimental analyses. To begin, we use regression analysis to examine the parameters on the performance and security. The fact that the FHE parameters induce different behaviors on performance, security and Ciphertext Expansion Factor (CEF) that makes the process of parameter selection more challenging. To address this issue, We use a multi-objective optimization algorithm to select the optimum parameter set for performance, CEF and security at the same time. As a result of this optimization, we get an improved parameter set for better performance at a given security level by ensuring correctness and security against lattice attacks by providing at least 128-bit security. Our result enables average ~ 5x smaller CEF and mostly better performance in comparison to the parameter sets given in [1]. This approach can be considered a semiautomated parameter selection. These studies are conducted using the PALISADE homomorphic encryption library, which is a well-known HE library. The abstract goes here. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lattice%20cryptography" title="lattice cryptography">lattice cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=fully%20homomorphic%20encryption" title=" fully homomorphic encryption"> fully homomorphic encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20selection" title=" parameter selection"> parameter selection</a>, <a href="https://publications.waset.org/abstracts/search?q=LWE" title=" LWE"> LWE</a>, <a href="https://publications.waset.org/abstracts/search?q=RLWE" title=" RLWE"> RLWE</a> </p> <a href="https://publications.waset.org/abstracts/146215/parameter-selection-for-computationally-efficient-use-of-the-bfvrns-fully-homomorphic-encryption-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146215.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4578</span> Auto-Tuning of CNC Parameters According to the Machining Mode Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jenq-Shyong%20Chen">Jenq-Shyong Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben-Fong%20Yu"> Ben-Fong Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-tuning" title="auto-tuning">auto-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=CNC%20parameters" title=" CNC parameters"> CNC parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=machining%20mode" title=" machining mode"> machining mode</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20speed" title=" high speed"> high speed</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20accuracy" title=" high accuracy"> high accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20surface%20quality" title=" high surface quality"> high surface quality</a> </p> <a href="https://publications.waset.org/abstracts/26213/auto-tuning-of-cnc-parameters-according-to-the-machining-mode-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26213.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">4577</span> Efficient Model Selection in Linear and Non-Linear Quantile Regression by Cross-Validation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoonsuh%20Jung">Yoonsuh Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Steven%20N.%20MacEachern"> Steven N. MacEachern</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Check loss function is used to define quantile regression. In the prospect of cross validation, it is also employed as a validation function when underlying truth is unknown. However, our empirical study indicates that the validation with check loss often leads to choosing an over estimated fits. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. It has a large effect of guarding against over fitted model in some extent. Through various simulation settings of linear and non-linear regressions, the improvement of check loss by L2 adjustment is empirically examined. This adjustment is devised to shrink to zero as sample size grows. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cross-validation" title="cross-validation">cross-validation</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20selection" title=" model selection"> model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20regression" title=" quantile regression"> quantile regression</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection" title=" tuning parameter selection"> tuning parameter selection</a> </p> <a href="https://publications.waset.org/abstracts/44203/efficient-model-selection-in-linear-and-non-linear-quantile-regression-by-cross-validation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44203.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">438</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">4576</span> Parameters Tuning of a PID Controller on a DC Motor Using Honey Bee and Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saeid%20Jalilzadeh">Saeid Jalilzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> PID controllers are widely used to control the industrial plants because of their robustness and simple structures. Tuning of the controller's parameters to get a desired response is difficult and time consuming. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller, but many advanced tuning methods behave not so perfect as to be expected. Honey Bee algorithm (HBA) and genetic algorithm (GA) are extensively used for real parameter optimization in diverse fields of study. This paper describes an application of HBA and GA to the problem of designing a PID controller whose parameters comprise proportionality constant, integral constant and derivative constant. Presence of three parameters to optimize makes the task of designing a PID controller more challenging than conventional P, PI, and PD controllers design. The suitability of the proposed approach has been demonstrated through computer simulation using MATLAB/SIMULINK. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=controller" title="controller">controller</a>, <a href="https://publications.waset.org/abstracts/search?q=GA" title=" GA"> GA</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO" title=" PSO"> PSO</a> </p> <a href="https://publications.waset.org/abstracts/15526/parameters-tuning-of-a-pid-controller-on-a-dc-motor-using-honey-bee-and-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15526.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">544</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">4575</span> Bayesian Optimization for Reaction Parameter Tuning: An Exploratory Study of Parameter Optimization in Oxidative Desulfurization of Thiophene</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aman%20Sharma">Aman Sharma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sonali%20Sengupta"> Sonali Sengupta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study explores the utility of Bayesian optimization in tuning the physical and chemical parameters of reactions in an offline experimental setup. A comparative analysis of the influence of the acquisition function on the optimization performance is also studied. For proxy first and second-order reactions, the results are indifferent to the acquisition function used, whereas, while studying the parameters for oxidative desulphurization of thiophene in an offline setup, upper confidence bound (UCB) provides faster convergence along with a marginal trade-off in the maximum conversion achieved. The work also demarcates the critical number of independent parameters and input observations required for both sequential and offline reaction setups to yield tangible results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acquisition%20function" title="acquisition function">acquisition function</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=desulfurization" title=" desulfurization"> desulfurization</a>, <a href="https://publications.waset.org/abstracts/search?q=kinetics" title=" kinetics"> kinetics</a>, <a href="https://publications.waset.org/abstracts/search?q=thiophene" title=" thiophene"> thiophene</a> </p> <a href="https://publications.waset.org/abstracts/135023/bayesian-optimization-for-reaction-parameter-tuning-an-exploratory-study-of-parameter-optimization-in-oxidative-desulfurization-of-thiophene" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135023.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">182</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">4574</span> 70% Ultra-Wide Tuning CMOS VCO Based on Magnetic Energy Adjustment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tai-Hsing%20Lee">Tai-Hsing Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhe-Wei%20Lin"> Zhe-Wei Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper demonstrates an ultra-wide tuning VCO implemented by CMOS 0.18μm process technology. By employing the proposed technique of magnetic energy adjustment in the oscillator tank, our proposed VCO achieves a wide frequency tuning range of 69.46% from 0.9 GHz to 1.86 GHz. The phase noise at an operating frequency of 1.86 GHz is -110 dBc/Hz (Offset frequency=1MHz). Furthermore, it achieves an excellent FOMT of 190.03 dBc/Hz. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=VCO" title="VCO">VCO</a>, <a href="https://publications.waset.org/abstracts/search?q=Ultra-wide%20tuning" title=" Ultra-wide tuning"> Ultra-wide tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=Frequency%20tuning%20range" title=" Frequency tuning range"> Frequency tuning range</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20noise" title=" phase noise"> phase noise</a>, <a href="https://publications.waset.org/abstracts/search?q=Magnetic%20energy%20adjustment" title=" Magnetic energy adjustment"> Magnetic energy adjustment</a> </p> <a href="https://publications.waset.org/abstracts/190304/70-ultra-wide-tuning-cmos-vco-based-on-magnetic-energy-adjustment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190304.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">39</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">4573</span> Reducing the Computational Overhead of Metaheuristics Parameterization with Exploratory Landscape Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iannick%20Gagnon">Iannick Gagnon</a>, <a href="https://publications.waset.org/abstracts/search?q=Alain%20April"> Alain April</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The performance of a metaheuristic on a given problem class depends on the class itself and the choice of parameters. Parameter tuning is the most time-consuming phase of the optimization process after the main calculations and it often nullifies the speed advantage of metaheuristics over traditional optimization algorithms. Several off-the-shelf parameter tuning algorithms are available, but when the objective function is expensive to evaluate, these can be prohibitively expensive to use. This paper presents a surrogate-like method for finding adequate parameters using fitness landscape analysis on simple benchmark functions and real-world objective functions. The result is a simple compound similarity metric based on the empirical correlation coefficient and a measure of convexity. It is then used to find the best benchmark functions to serve as surrogates. The near-optimal parameter set is then found using fractional factorial design. The real-world problem of NACA airfoil lift coefficient maximization is used as a preliminary proof of concept. The overall aim of this research is to reduce the computational overhead of metaheuristics parameterization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title="metaheuristics">metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20optimization" title=" stochastic optimization"> stochastic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=exploratory%20landscape%20analysis" title=" exploratory landscape analysis"> exploratory landscape analysis</a> </p> <a href="https://publications.waset.org/abstracts/120306/reducing-the-computational-overhead-of-metaheuristics-parameterization-with-exploratory-landscape-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/120306.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">153</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">4572</span> Improve Closed Loop Performance and Control Signal Using Evolutionary Algorithms Based PID Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Shahbazian">Mehdi Shahbazian</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Aarabi"> Alireza Aarabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Hadiyan"> Mohsen Hadiyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithm" title="evolutionary algorithm">evolutionary algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20controller" title=" PID controller"> PID controller</a> </p> <a href="https://publications.waset.org/abstracts/24261/improve-closed-loop-performance-and-control-signal-using-evolutionary-algorithms-based-pid-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24261.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4571</span> Design and Simulation a Low Phase Noise CMOS LC VCO for IEEE802.11a WLAN Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hooman%20Kaabi">Hooman Kaabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Raziyeh%20Karkoub"> Raziyeh Karkoub</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work proposes a structure of AMOS-varactors. A 5GHz LC-VCO designed in TSMC 0.18μm CMOS to improve phase noise and tuning range performance. The tuning range is from 5.05GHZ to 5.88GHz.The phase noise is -154.9dBc/Hz at 1MHz offset from the carrier. It meets the requirements for IEEE 802.11a WLAN standard. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CMOS%20LC%20VCO" title="CMOS LC VCO">CMOS LC VCO</a>, <a href="https://publications.waset.org/abstracts/search?q=spiral%20inductor" title=" spiral inductor"> spiral inductor</a>, <a href="https://publications.waset.org/abstracts/search?q=varactor" title=" varactor"> varactor</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20noise" title=" phase noise"> phase noise</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20range" title=" tuning range"> tuning range</a> </p> <a href="https://publications.waset.org/abstracts/25972/design-and-simulation-a-low-phase-noise-cmos-lc-vco-for-ieee80211a-wlan-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25972.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">536</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">4570</span> Method for Tuning Level Control Loops Based on Internal Model Control and Closed Loop Step Test Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arnaud%20Nougues">Arnaud Nougues</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a two-stage methodology derived from internal model control (IMC) for tuning a proportional-integral-derivative (PID) controller for levels or other integrating processes in an industrial environment. Focus is the ease of use and implementation speed which are critical for an industrial application. Tuning can be done with minimum effort and without the need for time-consuming open-loop step tests on the plant. The first stage of the method applies to levels only: the vessel residence time is calculated from equipment dimensions and used to derive a set of preliminary proportional-integral (PI) settings with IMC. The second stage, re-tuning in closed-loop, applies to levels as well as other integrating processes: a tuning correction mechanism has been developed based on a series of closed-loop simulations with model errors. The tuning correction is done from a simple closed-loop step test and the application of a generic correlation between observed overshoot and integral time correction. A spin-off of the method is that an estimate of the vessel residence time (levels) or open-loop process gain (other integrating process) is obtained from the closed-loop data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=closed-loop%20model%20identification" title="closed-loop model identification">closed-loop model identification</a>, <a href="https://publications.waset.org/abstracts/search?q=IMC-PID%20tuning%20method" title=" IMC-PID tuning method"> IMC-PID tuning method</a>, <a href="https://publications.waset.org/abstracts/search?q=integrating%20process%20control" title=" integrating process control"> integrating process control</a>, <a href="https://publications.waset.org/abstracts/search?q=on-line%20PID%20tuning%20adaptation" title=" on-line PID tuning adaptation"> on-line PID tuning adaptation</a> </p> <a href="https://publications.waset.org/abstracts/133791/method-for-tuning-level-control-loops-based-on-internal-model-control-and-closed-loop-step-test-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133791.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">221</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">4569</span> Multiclass Support Vector Machines with Simultaneous Multi-Factors Optimization for Corporate Credit Ratings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyunchul%20Ahn">Hyunchul Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20X.%20S.%20Wong"> William X. S. Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20credit%20rating%20prediction" title="corporate credit rating prediction">corporate credit rating prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=Feature%20selection" title=" Feature selection"> Feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=instance%20selection" title=" instance selection"> instance selection</a>, <a href="https://publications.waset.org/abstracts/search?q=multiclass%20support%20vector%20machines" title=" multiclass support vector machines"> multiclass support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/44856/multiclass-support-vector-machines-with-simultaneous-multi-factors-optimization-for-corporate-credit-ratings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44856.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">294</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">4568</span> Automatic Threshold Search for Heat Map Based Feature Selection: A Cancer Dataset Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Huertas">Carlos Huertas</a>, <a href="https://publications.waset.org/abstracts/search?q=Reyes%20Juarez-Ramirez"> Reyes Juarez-Ramirez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biomarker%20discovery" title="biomarker discovery">biomarker discovery</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer" title=" cancer"> cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=mass%20spectrometry" title=" mass spectrometry"> mass spectrometry</a> </p> <a href="https://publications.waset.org/abstracts/46310/automatic-threshold-search-for-heat-map-based-feature-selection-a-cancer-dataset-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46310.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">337</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">4567</span> Investigation of Extreme Gradient Boosting Model Prediction of Soil Strain-Shear Modulus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Mehryaar">Ehsan Mehryaar</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Bushehri"> Reza Bushehri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=XGBoost" title="XGBoost">XGBoost</a>, <a href="https://publications.waset.org/abstracts/search?q=hyper-parameter%20tuning" title=" hyper-parameter tuning"> hyper-parameter tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=soil%20shear%20modulus" title=" soil shear modulus"> soil shear modulus</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20response" title=" dynamic response"> dynamic response</a> </p> <a href="https://publications.waset.org/abstracts/141477/investigation-of-extreme-gradient-boosting-model-prediction-of-soil-strain-shear-modulus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141477.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4566</span> State Estimator Performance Enhancement: Methods for Identifying Errors in Modelling and Telemetry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ananthakrishnan">M. Ananthakrishnan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sunil%20K%20Patil"> Sunil K Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Koti%20Naveen"> Koti Naveen</a>, <a href="https://publications.waset.org/abstracts/search?q=Inuganti%20Hemanth%20Kumar"> Inuganti Hemanth Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> State estimation output of EMS forms the base case for all other advanced applications used in real time by a power system operator. Ensuring tuning of state estimator is a repeated process and cannot be left once a good solution is obtained. This paper attempts to demonstrate methods to improve state estimator solution by identifying incorrect modelling and telemetry inputs to the application. In this work, identification of database topology modelling error by plotting static network using node-to-node connection details is demonstrated with examples. Analytical methods to identify wrong transmission parameters, incorrect limits and mistakes in pseudo load and generator modelling are explained with various cases observed. Further, methods used for active and reactive power tuning using bus summation display, reactive power absorption summary, and transformer tap correction are also described. In a large power system, verifying all network static data and modelling parameter on regular basis is difficult .The proposed tuning methods can be easily used by operators to quickly identify errors to obtain the best possible state estimation performance. This, in turn, can lead to improved decision-support capabilities, ultimately enhancing the safety and reliability of the power grid. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20power%20tuning" title="active power tuning">active power tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=database%20modelling" title=" database modelling"> database modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=reactive%20power" title=" reactive power"> reactive power</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20estimator" title=" state estimator"> state estimator</a> </p> <a href="https://publications.waset.org/abstracts/194306/state-estimator-performance-enhancement-methods-for-identifying-errors-in-modelling-and-telemetry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194306.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">7</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">4565</span> Tuning of Fixed Wing Micro Aerial Vehicles Using Tethered Setup</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shoeb%20Ahmed%20Adeel">Shoeb Ahmed Adeel</a>, <a href="https://publications.waset.org/abstracts/search?q=Vivek%20Paul"> Vivek Paul</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Prajwal"> K. Prajwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Fenelon"> Michael Fenelon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Techniques have been used to tether and stabilize a multi-rotor MAV but carrying out the same process to a fixed wing MAV is a novel method which can be utilized in order to reduce damage occurring to the fixed wing MAVs while conducting flight test trials and PID tuning. A few sensors and on board controller is required to carry out this experiment in horizontal and vertical plane of the vehicle. Here we will be discussing issues such as sensitivity of the air vehicle, endurance and external load of the string acting on the vehicle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=MAV" title="MAV">MAV</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20tuning" title=" PID tuning"> PID tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=tethered%20flight" title=" tethered flight"> tethered flight</a>, <a href="https://publications.waset.org/abstracts/search?q=UAV" title=" UAV"> UAV</a> </p> <a href="https://publications.waset.org/abstracts/35297/tuning-of-fixed-wing-micro-aerial-vehicles-using-tethered-setup" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35297.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">635</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">4564</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">4563</span> A Tool Tuning Approximation Method: Exploration of the System Dynamics and Its Impact on Milling Stability When Amending Tool Stickout</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nikolai%20Bertelsen">Nikolai Bertelsen</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20A.%20Alphinas"> Robert A. Alphinas</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus%20B.%20Orskov"> Klaus B. Orskov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The shortest possible tool stickout has been the traditional go-to approach with expectations of increased stability and productivity. However, experimental studies at Danish Advanced Manufacturing Research Center (DAMRC) have proven that for some tool stickout lengths, there exist local productivity optimums when utilizing the Stability Lobe Diagrams for chatter avoidance. This contradicts with traditional logic and the best practices taught to machinists. This paper explores the vibrational characteristics and behaviour of a milling system over the tool stickout length. The experimental investigation has been conducted by tap testing multiple endmills where the tool stickout length has been varied. For each length, the modal parameters have been recorded and mapped to visualize behavioural tendencies. Furthermore, the paper explores the correlation between the modal parameters and the Stability Lobe Diagram to outline the influence and importance of each parameter in a multi-mode system. The insights are conceptualized into a tool tuning approximation solution. It builds on an almost linear change in the natural frequencies when amending tool stickout, which results in changed positions of the Chatter-free Stability Lobes. Furthermore, if the natural frequency of two modes become too close, it will onset of the dynamic absorber effect phenomenon. This phenomenon increases the critical stable depth of cut, allowing for a more stable milling process. Validation tests on the tool tuning approximation solution have shown varying success of the solution. This outlines the need for further research on the boundary conditions of the solution to understand at which conditions the tool tuning approximation solution is applicable. If the conditions get defined, the conceptualized tool tuning approximation solution outlines an approach for quick and roughly approximating tool stickouts with the potential for increased stiffness and optimized productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=milling" title="milling">milling</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20parameters" title=" modal parameters"> modal parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=stability%20lobes" title=" stability lobes"> stability lobes</a>, <a href="https://publications.waset.org/abstracts/search?q=tap%20testing" title=" tap testing"> tap testing</a>, <a href="https://publications.waset.org/abstracts/search?q=tool%20tuning" title=" tool tuning"> tool tuning</a> </p> <a href="https://publications.waset.org/abstracts/128047/a-tool-tuning-approximation-method-exploration-of-the-system-dynamics-and-its-impact-on-milling-stability-when-amending-tool-stickout" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128047.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">157</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">4562</span> A Multiobjective Damping Function for Coordinated Control of Power System Stabilizer and Power Oscillation Damping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jose%20D.%20Herrera">Jose D. Herrera</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20A.%20Rios"> Mario A. Rios</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the coordinated tuning of the Power System Stabilizer (PSS) controller and Power Oscillation Damping (POD) Controller of Flexible AC Transmission System (FACTS) in a multi-machine power systems. The coordinated tuning is based on the critical eigenvalues of the power system and a model reduction technique where the Hankel Singular Value method is applied. Through the linearized system model and the parameter-constrained nonlinear optimization algorithm, it can compute the parameters of both controllers. Moreover, the parameters are optimized simultaneously obtaining the gains of both controllers. Then, the nonlinear simulation to observe the time response of the controller is performed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromechanical%20oscillations" title="electromechanical oscillations">electromechanical oscillations</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system%20stabilizers" title=" power system stabilizers"> power system stabilizers</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20oscillation%20damping" title=" power oscillation damping"> power oscillation damping</a>, <a href="https://publications.waset.org/abstracts/search?q=hankel%20singular%20values" title=" hankel singular values"> hankel singular values</a> </p> <a href="https://publications.waset.org/abstracts/58164/a-multiobjective-damping-function-for-coordinated-control-of-power-system-stabilizer-and-power-oscillation-damping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58164.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">592</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">4561</span> Optimizing Production Yield Through Process Parameter Tuning Using Deep Learning Models: A Case Study in Precision Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tolulope%20Aremu">Tolulope Aremu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=production%20yield%20optimization" title="production yield optimization">production yield optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20of%20process%20parameters" title=" tuning of process parameters"> tuning of process parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20manufacturing" title=" precision manufacturing"> precision manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=TensorFlow" title=" TensorFlow"> TensorFlow</a>, <a href="https://publications.waset.org/abstracts/search?q=Keras" title=" Keras"> Keras</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20infrastructure" title=" cloud infrastructure"> cloud infrastructure</a>, <a href="https://publications.waset.org/abstracts/search?q=cost%20saving" title=" cost saving"> cost saving</a> </p> <a href="https://publications.waset.org/abstracts/192909/optimizing-production-yield-through-process-parameter-tuning-using-deep-learning-models-a-case-study-in-precision-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192909.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">4560</span> Tuning of the Thermal Capacity of an Envelope for Peak Demand Reduction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isha%20Rathore">Isha Rathore</a>, <a href="https://publications.waset.org/abstracts/search?q=Peeyush%20Jain"> Peeyush Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Elangovan%20Rajasekar"> Elangovan Rajasekar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The thermal capacity of the envelope impacts the cooling and heating demand of a building and modulates the peak electricity demand. This paper presents the thermal capacity tuning of a building envelope to minimize peak electricity demand for space cooling. We consider a 40 m² residential testbed located in Hyderabad, India (Composite Climate). An EnergyPlus model is validated using real-time data. A Parametric simulation framework for thermal capacity tuning is created using the Honeybee plugin. Diffusivity, Thickness, layer position, orientation and fenestration size of the exterior envelope are parametrized considering a five-layered wall system. A total of 1824 parametric runs are performed and the optimum wall configuration leading to minimum peak cooling demand is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thermal%20capacity" title="thermal capacity">thermal capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning" title=" tuning"> tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20demand%20reduction" title=" peak demand reduction"> peak demand reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=parametric%20analysis" title=" parametric analysis"> parametric analysis</a> </p> <a href="https://publications.waset.org/abstracts/143562/tuning-of-the-thermal-capacity-of-an-envelope-for-peak-demand-reduction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143562.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">184</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">4559</span> Speed Control of DC Motor Using Optimization Techniques Based PID Controller </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Santosh%20Kumar%20Suman">Santosh Kumar Suman</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinod%20Kumar%20Giri"> Vinod Kumar Giri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this paper is to outline a speed controller of a DC motor by choice of a PID parameters utilizing genetic algorithms (GAs), the DC motor is extensively utilized as a part of numerous applications such as steel plants, electric trains, cranes and a great deal more. DC motor could be represented by a nonlinear model when nonlinearities such as attractive dissemination are considered. To provide effective control, nonlinearities and uncertainties in the model must be taken into account in the control design. The DC motor is considered as third order system. Objective of this paper three type of tuning techniques for PID parameter. In this paper, an independently energized DC motor utilizing MATLAB displaying, has been outlined whose velocity might be examined utilizing the Proportional, Integral, Derivative (KP, KI , KD) addition of the PID controller. Since, established controllers PID are neglecting to control the drive when weight parameters be likewise changed. The principle point of this paper is to dissect the execution of optimization techniques viz. The Genetic Algorithm (GA) for improve PID controllers parameters for velocity control of DC motor and list their points of interest over the traditional tuning strategies. The outcomes got from GA calculations were contrasted and that got from traditional technique. It was found that the optimization techniques beat customary tuning practices of ordinary PID controllers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DC%20motor" title="DC motor">DC motor</a>, <a href="https://publications.waset.org/abstracts/search?q=PID%20controller" title=" PID controller"> PID controller</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20techniques" title=" optimization techniques"> optimization techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm%20%28GA%29" title=" genetic algorithm (GA)"> genetic algorithm (GA)</a>, <a href="https://publications.waset.org/abstracts/search?q=objective%20function" title=" objective function"> objective function</a>, <a href="https://publications.waset.org/abstracts/search?q=IAE" title=" IAE"> IAE</a> </p> <a href="https://publications.waset.org/abstracts/48103/speed-control-of-dc-motor-using-optimization-techniques-based-pid-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48103.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">419</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">4558</span> Assessment Power and Oscillation Damping Using the POD Controller and Proposed FOD Controller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Rahimi">Tohid Rahimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yahya%20Naderi"> Yahya Naderi</a>, <a href="https://publications.waset.org/abstracts/search?q=Babak%20Yousefi"> Babak Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Hossein%20Hoseini"> Seyed Hossein Hoseini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today’s modern interconnected power system is highly complex in nature. In this, one of the most important requirements during the operation of the electric power system is the reliability and security. Power and frequency oscillation damping mechanism improve the reliability. Because of power system stabilizer (PSS) low speed response against of major fault such as three phase short circuit, FACTs devise that can control the network condition in very fast time, are becoming popular. However, FACTs capability can be seen in a major fault present when nonlinear models of FACTs devise and power system equipment are applied. To realize this aim, the model of multi-machine power system with FACTs controller is developed in MATLAB/SIMULINK using Sim Power System (SPS) blockiest. Among the FACTs device, Static synchronous series compensator (SSSC) due to high speed changes its reactance characteristic inductive to capacitive, is effective power flow controller. Tuning process of controller parameter can be performed using different method. However, Genetic Algorithm (GA) ability tends to use it in controller parameter tuning process. In this paper, firstly POD controller is used to power oscillation damping. But in this station, frequency oscillation dos not has proper damping situation. Therefore, FOD controller that is tuned using GA is using that cause to damp out frequency oscillation properly and power oscillation damping has suitable situation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20oscillation%20damping%20%28POD%29" title="power oscillation damping (POD)">power oscillation damping (POD)</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20oscillation%20damping%20%28FOD%29" title=" frequency oscillation damping (FOD)"> frequency oscillation damping (FOD)</a>, <a href="https://publications.waset.org/abstracts/search?q=Static%20synchronous%20series%20compensator%20%28SSSC%29" title=" Static synchronous series compensator (SSSC)"> Static synchronous series compensator (SSSC)</a>, <a href="https://publications.waset.org/abstracts/search?q=Genetic%20Algorithm%20%28GA%29" title=" Genetic Algorithm (GA)"> Genetic Algorithm (GA)</a> </p> <a href="https://publications.waset.org/abstracts/18560/assessment-power-and-oscillation-damping-using-the-pod-controller-and-proposed-fod-controller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18560.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">4557</span> Optimum Tuning Capacitors for Wireless Charging of Electric Vehicles Considering Variation in Coil Distances</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Abdullah%20Arafat">Muhammad Abdullah Arafat</a>, <a href="https://publications.waset.org/abstracts/search?q=Nahrin%20Nowrose"> Nahrin Nowrose</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless charging of electric vehicles is becoming more and more attractive as large amount of power can now be transferred to a reasonable distance using magnetic resonance coupling method. However, proper tuning of the compensation network is required to achieve maximum power transmission. Due to the variation of coil distance from the nominal value as a result of change in tire condition, change in weight or uneven road condition, the tuning of the compensation network has become challenging. In this paper, a tuning method has been described to determine the optimum values of the compensation network in order to maximize the average output power. The simulation results show that 5.2 percent increase in average output power is obtained for 10 percent variation in coupling coefficient using the optimum values without the need of additional space and electro-mechanical components. The proposed method is applicable to both static and dynamic charging of electric vehicles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coupling%20coefficient" title="coupling coefficient">coupling coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicles" title=" electric vehicles"> electric vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20resonance%20coupling" title=" magnetic resonance coupling"> magnetic resonance coupling</a>, <a href="https://publications.waset.org/abstracts/search?q=tuning%20capacitor" title=" tuning capacitor"> tuning capacitor</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20power%20transfer" title=" wireless power transfer"> wireless power transfer</a> </p> <a href="https://publications.waset.org/abstracts/149064/optimum-tuning-capacitors-for-wireless-charging-of-electric-vehicles-considering-variation-in-coil-distances" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149064.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">4556</span> A Robust PID Load Frequency Controller of Interconnected Power System Using SDO Software</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pasala%20Gopi">Pasala Gopi</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Linga%20Reddy"> P. Linga Reddy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The response of the load frequency control problem in an multi-area interconnected electrical power system is much more complex with increasing size, changing structure and increasing load. This paper deals with Load Frequency Control of three area interconnected Power system incorporating Reheat, Non-reheat and Reheat turbines in all areas respectively. The response of the load frequency control problem in an multi-area interconnected power system is improved by designing PID controller using different tuning techniques and proved that the PID controller which was designed by Simulink Design Optimization (SDO) Software gives the superior performance than other controllers for step perturbations. Finally the robustness of controller was checked against system parameter variations <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=load%20frequency%20control" title="load frequency control">load frequency control</a>, <a href="https://publications.waset.org/abstracts/search?q=pid%20controller%20tuning" title=" pid controller tuning"> pid controller tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=step%20load%20perturbations" title=" step load perturbations"> step load perturbations</a>, <a href="https://publications.waset.org/abstracts/search?q=inter%20connected%20power%20system" title=" inter connected power system"> inter connected power system</a> </p> <a href="https://publications.waset.org/abstracts/30053/a-robust-pid-load-frequency-controller-of-interconnected-power-system-using-sdo-software" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30053.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">644</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">4555</span> A Two-Stage Bayesian Variable Selection Method with the Extension of Lasso for Geo-Referenced Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Georgiana%20Onicescu">Georgiana Onicescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuqian%20Shen"> Yuqian Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the complex nature of geo-referenced data, multicollinearity of the risk factors in public health spatial studies is a commonly encountered issue, which leads to low parameter estimation accuracy because it inflates the variance in the regression analysis. To address this issue, we proposed a two-stage variable selection method by extending the least absolute shrinkage and selection operator (Lasso) to the Bayesian spatial setting, investigating the impact of risk factors to health outcomes. Specifically, in stage I, we performed the variable selection using Bayesian Lasso and several other variable selection approaches. Then, in stage II, we performed the model selection with only the selected variables from stage I and compared again the methods. To evaluate the performance of the two-stage variable selection methods, we conducted a simulation study with different distributions for the risk factors, using geo-referenced count data as the outcome and Michigan as the research region. We considered the cases when all candidate risk factors are independently normally distributed, or follow a multivariate normal distribution with different correlation levels. Two other Bayesian variable selection methods, Binary indicator, and the combination of Binary indicator and Lasso were considered and compared as alternative methods. The simulation results indicated that the proposed two-stage Bayesian Lasso variable selection method has the best performance for both independent and dependent cases considered. When compared with the one-stage approach, and the other two alternative methods, the two-stage Bayesian Lasso approach provides the highest estimation accuracy in all scenarios considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lasso" title="Lasso">Lasso</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20analysis" title=" Bayesian analysis"> Bayesian analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20analysis" title=" spatial analysis"> spatial analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a> </p> <a href="https://publications.waset.org/abstracts/105063/a-two-stage-bayesian-variable-selection-method-with-the-extension-of-lasso-for-geo-referenced-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105063.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">143</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=tuning%20parameter%20selection&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&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=tuning%20parameter%20selection&page=152">152</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=153">153</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=tuning%20parameter%20selection&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>