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optimization</title> <meta name="description" content="Search results for: parameter optimization"> <meta name="keywords" content="parameter optimization"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="parameter optimization"> <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> 5094</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: parameter optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5094</span> Optimization of a Cone Loudspeaker Parameter of Design Parameters by Analysis of a Narrow Acoustic Sound Pathway </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yue%20Hu">Yue Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xilu%20Zhao"> Xilu Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Takao%20Yamaguchi"> Takao Yamaguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Manabu%20Sasajima"> Manabu Sasajima</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshio%20Koike"> Yoshio Koike</a>, <a href="https://publications.waset.org/abstracts/search?q=Akira%20Hara"> Akira Hara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study tried optimization of design parameter of a cone loudspeaker unit as an example of the high flexibility of the products design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to each design the parameter of the loudspeaker. To overcome the limitation of the design problem in practice, this paper proposes a new an acoustic analysis algorithm to optimize design the parameter of the loudspeaker. The material character of cone paper and the loudspeaker edge was the design parameter, and the vibration displacement of the cone paper was the objective function. The results of the analysis were compared with the predicted value. They had high accuracy to the predicted value. These results suggest that, though the parameter design is difficult by experience and intuition, it can be performed comparatively easily using the optimization design by the developed acoustic analysis software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20viscosity" title="air viscosity">air viscosity</a>, <a href="https://publications.waset.org/abstracts/search?q=loudspeaker" title=" loudspeaker"> loudspeaker</a>, <a href="https://publications.waset.org/abstracts/search?q=cone%20paper" title=" cone paper"> cone paper</a>, <a href="https://publications.waset.org/abstracts/search?q=edge" title=" edge"> edge</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/60331/optimization-of-a-cone-loudspeaker-parameter-of-design-parameters-by-analysis-of-a-narrow-acoustic-sound-pathway" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60331.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">401</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">5093</span> A New Conjugate Gradient Method with Guaranteed Descent</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sellami">B. Sellami</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Belloufi"> M. Belloufi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conjugate gradient methods are an important class of methods for unconstrained optimization, especially for large-scale problems. Recently, they have been much studied. In this paper, we propose a new two-parameter family of conjugate gradient methods for unconstrained optimization. The two-parameter family of methods not only includes the already existing three practical nonlinear conjugate gradient methods, but also has other family of conjugate gradient methods as subfamily. The two-parameter family of methods with the Wolfe line search is shown to ensure the descent property of each search direction. Some general convergence results are also established for the two-parameter family of methods. The numerical results show that this method is efficient for the given test problems. In addition, the methods related to this family are uniformly discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization" title="unconstrained optimization">unconstrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title=" conjugate gradient method"> conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=line%20search" title=" line search"> line search</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20convergence" title=" global convergence"> global convergence</a> </p> <a href="https://publications.waset.org/abstracts/41734/a-new-conjugate-gradient-method-with-guaranteed-descent" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41734.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">452</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">5092</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">5091</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">5090</span> Optimization of Copper-Water Negative Inclination Heat Pipe with Internal Composite Wick Structure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Brandys">I. Brandys</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Levy"> M. Levy</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Harush"> K. Harush</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Haim"> Y. Haim</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Korngold"> M. Korngold</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Theoretical optimization of a copper-water negative inclination heat pipe with internal composite wick structure has been performed, regarding a new introduced parameter: the ratio between the coarse mesh wraps and the fine mesh wraps of the composite wick. Since in many cases, the design of a heat pipe matches specific thermal requirements and physical limitations, this work demonstrates the optimization of a 1 m length, 8 mm internal diameter heat pipe without an adiabatic section, at a negative inclination angle of -10º. The optimization is based on a new introduced parameter, LR: the ratio between the coarse mesh wraps and the fine mesh wraps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20pipe" title="heat pipe">heat pipe</a>, <a href="https://publications.waset.org/abstracts/search?q=inclination" title=" inclination"> inclination</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=ratio" title=" ratio"> ratio</a> </p> <a href="https://publications.waset.org/abstracts/12959/optimization-of-copper-water-negative-inclination-heat-pipe-with-internal-composite-wick-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12959.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">328</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">5089</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">5088</span> Reinforcement Learning for Quality-Oriented Production Process Parameter Optimization Based on Predictive Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Paranjape">Akshay Paranjape</a>, <a href="https://publications.waset.org/abstracts/search?q=Nils%20Plettenberg"> Nils Plettenberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Schmitt"> Robert Schmitt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20process%20optimization" title=" production process optimization"> production process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=policy%20optimization" title=" policy optimization"> policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=actor%20critic%20approach" title=" actor critic approach"> actor critic approach</a> </p> <a href="https://publications.waset.org/abstracts/160123/reinforcement-learning-for-quality-oriented-production-process-parameter-optimization-based-on-predictive-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5087</span> Sensitivity Analysis during the Optimization Process Using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Rubio">M. A. Rubio</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Urquia"> A. Urquia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithms (GA) are applied to the solution of high-dimensional optimization problems. Additionally, sensitivity analysis (SA) is usually carried out to determine the effect on optimal solutions of changes in parameter values of the objective function. These two analyses (i.e., optimization and sensitivity analysis) are computationally intensive when applied to high-dimensional functions. The approach presented in this paper consists in performing the SA during the GA execution, by statistically analyzing the data obtained of running the GA. The advantage is that in this case SA does not involve making additional evaluations of the objective function and, consequently, this proposed approach requires less computational effort than conducting optimization and SA in two consecutive steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity" title=" sensitivity"> sensitivity</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=model%20calibration" title=" model calibration"> model calibration</a> </p> <a href="https://publications.waset.org/abstracts/62152/sensitivity-analysis-during-the-optimization-process-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62152.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">436</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5086</span> Parameterized Lyapunov Function Based Robust Diagonal Dominance Pre-Compensator Design for Linear Parameter Varying Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaobao%20Han">Xiaobao Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Huacong%20Li"> Huacong Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Jia%20Li"> Jia Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For dynamic decoupling of linear parameter varying system, a robust dominance pre-compensator design method is given. The parameterized pre-compensator design problem is converted into optimal problem constrained with parameterized linear matrix inequalities (PLMI); To solve this problem, firstly, this optimization problem is equivalently transformed into a new form with elimination of coupling relationship between parameterized Lyapunov function (PLF) and pre-compensator. Then the problem was reduced to a normal convex optimization problem with normal linear matrix inequalities (LMI) constraints on a newly constructed convex polyhedron. Moreover, a parameter scheduling pre-compensator was achieved, which satisfies robust performance and decoupling performances. Finally, the feasibility and validity of the robust diagonal dominance pre-compensator design method are verified by the numerical simulation of a turbofan engine PLPV model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linear%20parameter%20varying%20%28LPV%29" title="linear parameter varying (LPV)">linear parameter varying (LPV)</a>, <a href="https://publications.waset.org/abstracts/search?q=parameterized%20Lyapunov%20function%20%28PLF%29" title=" parameterized Lyapunov function (PLF)"> parameterized Lyapunov function (PLF)</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20matrix%20inequalities%20%28LMI%29" title=" linear matrix inequalities (LMI)"> linear matrix inequalities (LMI)</a>, <a href="https://publications.waset.org/abstracts/search?q=diagonal%20dominance%20pre-compensator" title=" diagonal dominance pre-compensator"> diagonal dominance pre-compensator</a> </p> <a href="https://publications.waset.org/abstracts/57964/parameterized-lyapunov-function-based-robust-diagonal-dominance-pre-compensator-design-for-linear-parameter-varying-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57964.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5085</span> Multidisciplinary and Multilevel Design Methodology of Unmanned Aerial Vehicles using Enhanced Collaborative Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pedro%20F.%20Albuquerque">Pedro F. Albuquerque</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20V.%20Gamboa"> Pedro V. Gamboa</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20A.%20Silvestre"> Miguel A. Silvestre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present work describes the implementation of the Enhanced Collaborative Optimization (ECO) multilevel architecture with a gradient-based optimization algorithm with the aim of performing a multidisciplinary design optimization of a generic unmanned aerial vehicle with morphing technologies. The concepts of weighting coefficient and a dynamic compatibility parameter are presented for the ECO architecture. A routine that calculates the aircraft performance for the user defined mission profile and vehicle’s performance requirements has been implemented using low fidelity models for the aerodynamics, stability, propulsion, weight, balance and flight performance. A benchmarking case study for evaluating the advantage of using a variable span wing within the optimization methodology developed is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidisciplinary" title="multidisciplinary">multidisciplinary</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel" title=" multilevel"> multilevel</a>, <a href="https://publications.waset.org/abstracts/search?q=morphing" title=" morphing"> morphing</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced%20collaborative%20optimization" title=" enhanced collaborative optimization"> enhanced collaborative optimization</a> </p> <a href="https://publications.waset.org/abstracts/18259/multidisciplinary-and-multilevel-design-methodology-of-unmanned-aerial-vehicles-using-enhanced-collaborative-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18259.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">929</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">5084</span> Support Vector Regression Combined with Different Optimization Algorithms to Predict Global Solar Radiation on Horizontal Surfaces in Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laidi%20Maamar">Laidi Maamar</a>, <a href="https://publications.waset.org/abstracts/search?q=Achwak%20Madani"> Achwak Madani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdellah%20El%20Ahdj%20Abdellah"> Abdellah El Ahdj Abdellah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression%20%28SVR%29" title="support vector regression (SVR)">support vector regression (SVR)</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20solar%20radiation%20prediction" title=" global solar radiation prediction"> global solar radiation prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20forecasting%20models" title=" hybrid forecasting models"> hybrid forecasting models</a> </p> <a href="https://publications.waset.org/abstracts/186719/support-vector-regression-combined-with-different-optimization-algorithms-to-predict-global-solar-radiation-on-horizontal-surfaces-in-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186719.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">35</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">5083</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">5082</span> A Comparison of Sequential Quadratic Programming, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization for the Design and Optimization of a Beam Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nima%20Khosravi">Nima Khosravi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an integrated optimization technique with concurrent use of sequential quadratic programming, genetic algorithm, and simulated annealing particle swarm optimization for the design and optimization of a beam column. In this research, the comparison between 4 different types of optimization methods. The comparison is done and it is found out that all the methods meet the required constraints and the lowest value of the objective function is achieved by SQP, which was also the fastest optimizer to produce the results. SQP is a gradient based optimizer hence its results are usually the same after every run. The only thing which affects the results is the initial conditions given. The initial conditions given in the various test run were very large as compared. Hence, the value converged at a different point. Rest of the methods is a heuristic method which provides different values for different runs even if every parameter is kept constant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beam%20column" title="beam column">beam column</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=sequential%20quadratic%20programming" title=" sequential quadratic programming"> sequential quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a> </p> <a href="https://publications.waset.org/abstracts/58973/a-comparison-of-sequential-quadratic-programming-genetic-algorithm-simulated-annealing-particle-swarm-optimization-for-the-design-and-optimization-of-a-beam-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58973.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">386</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">5081</span> Algorithm Development of Individual Lumped Parameter Modelling for Blood Circulatory System: An Optimization Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bao%20Li">Bao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Aike%20Qiao"> Aike Qiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaoyang%20Li"> Gaoyang Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Youjun%20Liu"> Youjun Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Lumped parameter model (LPM) is a common numerical model for hemodynamic calculation. LPM uses circuit elements to simulate the human blood circulatory system. Physiological indicators and characteristics can be acquired through the model. However, due to the different physiological indicators of each individual, parameters in LPM should be personalized in order for convincing calculated results, which can reflect the individual physiological information. This study aimed to develop an automatic and effective optimization method to personalize the parameters in LPM of the blood circulatory system, which is of great significance to the numerical simulation of individual hemodynamics. Methods: A closed-loop LPM of the human blood circulatory system that is applicable for most persons were established based on the anatomical structures and physiological parameters. The patient-specific physiological data of 5 volunteers were non-invasively collected as personalized objectives of individual LPM. In this study, the blood pressure and flow rate of heart, brain, and limbs were the main concerns. The collected systolic blood pressure, diastolic blood pressure, cardiac output, and heart rate were set as objective data, and the waveforms of carotid artery flow and ankle pressure were set as objective waveforms. Aiming at the collected data and waveforms, sensitivity analysis of each parameter in LPM was conducted to determine the sensitive parameters that have an obvious influence on the objectives. Simulated annealing was adopted to iteratively optimize the sensitive parameters, and the objective function during optimization was the root mean square error between the collected waveforms and data and simulated waveforms and data. Each parameter in LPM was optimized 500 times. Results: In this study, the sensitive parameters in LPM were optimized according to the collected data of 5 individuals. Results show a slight error between collected and simulated data. The average relative root mean square error of all optimization objectives of 5 samples were 2.21%, 3.59%, 4.75%, 4.24%, and 3.56%, respectively. Conclusions: Slight error demonstrated good effects of optimization. The individual modeling algorithm developed in this study can effectively achieve the individualization of LPM for the blood circulatory system. LPM with individual parameters can output the individual physiological indicators after optimization, which are applicable for the numerical simulation of patient-specific hemodynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20circulatory%20system" title="blood circulatory system">blood circulatory system</a>, <a href="https://publications.waset.org/abstracts/search?q=individual%20physiological%20indicators" title=" individual physiological indicators"> individual physiological indicators</a>, <a href="https://publications.waset.org/abstracts/search?q=lumped%20parameter%20model" title=" lumped parameter model"> lumped parameter model</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithm" title=" optimization algorithm"> optimization algorithm</a> </p> <a href="https://publications.waset.org/abstracts/110466/algorithm-development-of-individual-lumped-parameter-modelling-for-blood-circulatory-system-an-optimization-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110466.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">137</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">5080</span> Vibration Analysis and Optimization Design of Ultrasonic Horn</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kuen%20Ming%20Shu">Kuen Ming Shu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ren%20Kai%20Ho"> Ren Kai Ho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ultrasonic horn has the functions of amplifying amplitude and reducing resonant impedance in ultrasonic system. Its primary function is to amplify deformation or velocity during vibration and focus ultrasonic energy on the small area. It is a crucial component in design of ultrasonic vibration system. There are five common design methods for ultrasonic horns: analytical method, equivalent circuit method, equal mechanical impedance, transfer matrix method, finite element method. In addition, the general optimization design process is to change the geometric parameters to improve a single performance. Therefore, in the general optimization design process, we couldn't find the relation of parameter and objective. However, a good optimization design must be able to establish the relationship between input parameters and output parameters so that the designer can choose between parameters according to different performance objectives and obtain the results of the optimization design. In this study, an ultrasonic horn provided by Maxwide Ultrasonic co., Ltd. was used as the contrast of optimized ultrasonic horn. The ANSYS finite element analysis (FEA) software was used to simulate the distribution of the horn amplitudes and the natural frequency value. The results showed that the frequency for the simulation values and actual measurement values were similar, verifying the accuracy of the simulation values. The ANSYS DesignXplorer was used to perform Response Surface optimization, which could shows the relation of parameter and objective. Therefore, this method can be used to substitute the traditional experience method or the trial-and-error method for design to reduce material costs and design cycles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=horn" title="horn">horn</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20frequency" title=" natural frequency"> natural frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20optimization" title=" response surface optimization"> response surface optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasonic%20vibration" title=" ultrasonic vibration"> ultrasonic vibration</a> </p> <a href="https://publications.waset.org/abstracts/151835/vibration-analysis-and-optimization-design-of-ultrasonic-horn" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151835.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">116</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">5079</span> Analysis of Tandem Detonator Algorithm Optimized by Quantum Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tomasz%20Robert%20Kuczerski">Tomasz Robert Kuczerski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The high complexity of the algorithm of the autonomous tandem detonator system creates an optimization problem due to the parallel operation of several machine states of the system. Many years of experience and classic analyses have led to a partially optimized model. Limitations on the energy resources of this class of autonomous systems make it necessary to search for more effective methods of optimisation. The use of the Quantum Approximate Optimization Algorithm (QAOA) in these studies shows the most promising results. With the help of multiple evaluations of several qubit quantum circuits, proper results of variable parameter optimization were obtained. In addition, it was observed that the increase in the number of assessments does not result in further efficient growth due to the increasing complexity of optimising variables. The tests confirmed the effectiveness of the QAOA optimization method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm%20analysis" title="algorithm analysis">algorithm analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20system" title=" autonomous system"> autonomous system</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20optimization" title=" quantum optimization"> quantum optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=tandem%20detonator" title=" tandem detonator"> tandem detonator</a> </p> <a href="https://publications.waset.org/abstracts/161188/analysis-of-tandem-detonator-algorithm-optimized-by-quantum-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161188.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">92</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">5078</span> Multi-Response Optimization of CNC Milling Parameters Using Taguchi Based Grey Relational Analysis for AA6061 T6 Aluminium Alloy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varsha%20Singh">Varsha Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kishan%20Fuse"> Kishan Fuse</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a study of the grey-Taguchi method to optimize CNC milling parameters of AA6061 T6 aluminium alloy. Grey-Taguchi method combines Taguchi method based design of experiments (DOE) with grey relational analysis (GRA). Multi-response optimization of different quality characteristics as surface roughness, material removal rate, cutting forces is done using grey relational analysis (GRA). The milling parameters considered for experiments include cutting speed, feed per tooth, and depth of cut. Each parameter with three levels is selected. A grey relational grade is used to estimate overall quality characteristics performance. The Taguchi’s L9 orthogonal array is used for design of experiments. MINITAB 17 software is used for optimization. Analysis of variance (ANOVA) is used to identify most influencing parameter. The experimental results show that grey relational analysis is effective method for optimizing multi-response characteristics. Optimum results are finally validated by performing confirmation test. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANOVA" title="ANOVA">ANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=CNC%20milling" title=" CNC milling"> CNC milling</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20relational%20analysis" title=" grey relational analysis"> grey relational analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-response%20optimization" title=" multi-response optimization"> multi-response optimization</a> </p> <a href="https://publications.waset.org/abstracts/61719/multi-response-optimization-of-cnc-milling-parameters-using-taguchi-based-grey-relational-analysis-for-aa6061-t6-aluminium-alloy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61719.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">307</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">5077</span> Passive Vibration Isolation Analysis and Optimization for Mechanical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ozan%20Yavuz%20Baytemir">Ozan Yavuz Baytemir</a>, <a href="https://publications.waset.org/abstracts/search?q=Ender%20Cigeroglu"> Ender Cigeroglu</a>, <a href="https://publications.waset.org/abstracts/search?q=Gokhan%20Osman%20Ozgen"> Gokhan Osman Ozgen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vibration is an important issue in the design of various components of aerospace, marine and vehicular applications. In order not to lose the components’ function and operational performance, vibration isolation design involving the optimum isolator properties selection and isolator positioning processes appear to be a critical study. Knowing the growing need for the vibration isolation system design, this paper aims to present two types of software capable of implementing modal analysis, response analysis for both random and harmonic types of excitations, static deflection analysis, Monte Carlo simulations in addition to study of parameter and location optimization for different types of isolation problem scenarios. Investigating the literature, there is no such study developing a software-based tool that is capable of implementing all those analysis, simulation and optimization studies in one platform simultaneously. In this paper, the theoretical system model is generated for a 6-DOF rigid body. The vibration isolation system of any mechanical structure is able to be optimized using hybrid method involving both global search and gradient-based methods. Defining the optimization design variables, different types of optimization scenarios are listed in detail. Being aware of the need for a user friendly vibration isolation problem solver, two types of graphical user interfaces (GUIs) are prepared and verified using a commercial finite element analysis program, Ansys Workbench 14.0. Using the analysis and optimization capabilities of those GUIs, a real application used in an air-platform is also presented as a case study at the end of the paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20optimization" title="hybrid optimization">hybrid optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-degree-of-freedom%20system" title=" multi-degree-of-freedom system"> multi-degree-of-freedom system</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20optimization" title=" parameter optimization"> parameter optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=location%20optimization" title=" location optimization"> location optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=passive%20vibration%20isolation%20analysis" title=" passive vibration isolation analysis"> passive vibration isolation analysis</a> </p> <a href="https://publications.waset.org/abstracts/31978/passive-vibration-isolation-analysis-and-optimization-for-mechanical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31978.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">565</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">5076</span> Parameter Estimation of Induction Motors by PSO Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Mohammadi">A. Mohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Asghari"> S. Asghari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Aien"> M. Aien</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Rashidinejad"> M. Rashidinejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> After emergent of alternative current networks and their popularity, asynchronous motors became more widespread than other kinds of industrial motors. In order to control and run these motors efficiently, an accurate estimation of motor parameters is needed. There are different methods to obtain these parameters such as rotor locked test, no load test, DC test, analytical methods, and so on. The most common drawback of these methods is their inaccuracy in estimation of some motor parameters. In order to remove this concern, a novel method for parameter estimation of induction motors using particle swarm optimization (PSO) algorithm is proposed. In the proposed method, transient state of motor is used for parameter estimation. Comparison of the simulation results purtuined to the PSO algorithm with other available methods justifies the effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=induction%20motor" title="induction motor">induction motor</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20parameter%20estimation" title=" motor parameter estimation"> motor parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=PSO%20algorithm" title=" PSO algorithm"> PSO algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=analytical%20method" title=" analytical method"> analytical method</a> </p> <a href="https://publications.waset.org/abstracts/15433/parameter-estimation-of-induction-motors-by-pso-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15433.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">633</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">5075</span> Electron Beam Melting Process Parameter Optimization Using Multi Objective Reinforcement Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20A.%20Sprayberry">Michael A. Sprayberry</a>, <a href="https://publications.waset.org/abstracts/search?q=Vincent%20C.%20Paquit"> Vincent C. Paquit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process parameter optimization in metal powder bed electron beam melting (MPBEBM) is crucial to ensure the technology's repeatability, control, and industry-continued adoption. Despite continued efforts to address the challenges via the traditional design of experiments and process mapping techniques, there needs to be more successful in an on-the-fly optimization framework that can be adapted to MPBEBM systems. Additionally, data-intensive physics-based modeling and simulation methods are difficult to support by a metal AM alloy or system due to cost restrictions. To mitigate the challenge of resource-intensive experiments and models, this paper introduces a Multi-Objective Reinforcement Learning (MORL) methodology defined as an optimization problem for MPBEBM. An off-policy MORL framework based on policy gradient is proposed to discover optimal sets of beam power (P) – beam velocity (v) combinations to maintain a steady-state melt pool depth and phase transformation. For this, an experimentally validated Eagar-Tsai melt pool model is used to simulate the MPBEBM environment, where the beam acts as the agent across the P – v space to maximize returns for the uncertain powder bed environment producing a melt pool and phase transformation closer to the optimum. The culmination of the training process yields a set of process parameters {power, speed, hatch spacing, layer depth, and preheat} where the state (P,v) with the highest returns corresponds to a refined process parameter mapping. The resultant objects and mapping of returns to the P-v space show convergence with experimental observations. The framework, therefore, provides a model-free multi-objective approach to discovery without the need for trial-and-error experiments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20manufacturing" title="additive manufacturing">additive manufacturing</a>, <a href="https://publications.waset.org/abstracts/search?q=metal%20powder%20bed%20fusion" title=" metal powder bed fusion"> metal powder bed fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title=" reinforcement learning"> reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20parameter%20optimization" title=" process parameter optimization"> process parameter optimization</a> </p> <a href="https://publications.waset.org/abstracts/162522/electron-beam-melting-process-parameter-optimization-using-multi-objective-reinforcement-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162522.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">90</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">5074</span> Optimization Based Design of Decelerating Duct for Pumpjets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Sengul">Mustafa Sengul</a>, <a href="https://publications.waset.org/abstracts/search?q=Enes%20Sahin"> Enes Sahin</a>, <a href="https://publications.waset.org/abstracts/search?q=Sertac%20Arslan"> Sertac Arslan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pumpjets are one of the marine propulsion systems frequently used in underwater vehicles nowadays. The reasons for frequent use of pumpjet as a propulsion system are that it has higher relative efficiency at high speeds, better cavitation, and acoustic performance than its rivals. Pumpjets are composed of rotor, stator, and duct, and there are two different types of pumpjet configurations depending on the desired hydrodynamic characteristic, which are with accelerating and decelerating duct. Pumpjet with an accelerating channel is used at cargo ships where it works at low speeds and high loading conditions. The working principle of this type of pumpjet is to maximize the thrust by reducing the pressure of the fluid through the channel and throwing the fluid out from the channel with high momentum. On the other hand, for decelerating ducted pumpjets, the main consideration is to prevent the occurrence of the cavitation phenomenon by increasing the pressure of the fluid about the rotor region. By postponing the cavitation, acoustic noise naturally falls down, so decelerating ducted systems are used at noise-sensitive vehicle systems where acoustic performance is vital. Therefore, duct design becomes a crucial step during pumpjet design. This study, it is aimed to optimize the duct geometry of a decelerating ducted pumpjet for a highly speed underwater vehicle by using proper optimization tools. The target output of this optimization process is to obtain a duct design that maximizes fluid pressure around the rotor region to prevent from cavitation and minimizes drag force. There are two main optimization techniques that could be utilized for this process which are parameter-based optimization and gradient-based optimization. While parameter-based algorithm offers more major changes in interested geometry, which makes user to get close desired geometry, gradient-based algorithm deals with minor local changes in geometry. In parameter-based optimization, the geometry should be parameterized first. Then, by defining upper and lower limits for these parameters, design space is created. Finally, by proper optimization code and analysis, optimum geometry is obtained from this design space. For this duct optimization study, a commercial codedparameter-based optimization algorithm is used. To parameterize the geometry, duct is represented with b-spline curves and control points. These control points have x and y coordinates limits. By regarding these limits, design space is generated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pumpjet" title="pumpjet">pumpjet</a>, <a href="https://publications.waset.org/abstracts/search?q=decelerating%20duct%20design" title=" decelerating duct design"> decelerating duct design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=underwater%20vehicles" title=" underwater vehicles"> underwater vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=cavitation" title=" cavitation"> cavitation</a>, <a href="https://publications.waset.org/abstracts/search?q=drag%20minimization" title=" drag minimization"> drag minimization</a> </p> <a href="https://publications.waset.org/abstracts/144715/optimization-based-design-of-decelerating-duct-for-pumpjets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144715.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">209</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">5073</span> Parametric Appraisal of Robotic Arc Welding of Mild Steel Material by Principal Component Analysis-Fuzzy with Taguchi Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amruta%20Rout">Amruta Rout</a>, <a href="https://publications.waset.org/abstracts/search?q=Golak%20Bihari%20Mahanta"> Golak Bihari Mahanta</a>, <a href="https://publications.waset.org/abstracts/search?q=Gunji%20Bala%20Murali"> Gunji Bala Murali</a>, <a href="https://publications.waset.org/abstracts/search?q=Bibhuti%20Bhusan%20Biswal"> Bibhuti Bhusan Biswal</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20B.%20V.%20L.%20Deepak"> B. B. V. L. Deepak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of industrial robots for performing welding operation is one of the chief sign of contemporary welding in these days. The weld joint parameter and weld process parameter modeling is one of the most crucial aspects of robotic welding. As weld process parameters affect the weld joint parameters differently, a multi-objective optimization technique has to be utilized to obtain optimal setting of weld process parameter. In this paper, a hybrid optimization technique, i.e., Principal Component Analysis (PCA) combined with fuzzy logic has been proposed to get optimal setting of weld process parameters like wire feed rate, welding current. Gas flow rate, welding speed and nozzle tip to plate distance. The weld joint parameters considered for optimization are the depth of penetration, yield strength, and ultimate strength. PCA is a very efficient multi-objective technique for converting the correlated and dependent parameters into uncorrelated and independent variables like the weld joint parameters. Also in this approach, no need for checking the correlation among responses as no individual weight has been assigned to responses. Fuzzy Inference Engine can efficiently consider these aspects into an internal hierarchy of it thereby overcoming various limitations of existing optimization approaches. At last Taguchi method is used to get the optimal setting of weld process parameters. Therefore, it has been concluded the hybrid technique has its own advantages which can be used for quality improvement in industrial applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=robotic%20arc%20welding" title="robotic arc welding">robotic arc welding</a>, <a href="https://publications.waset.org/abstracts/search?q=weld%20process%20parameters" title=" weld process parameters"> weld process parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=weld%20joint%20parameters" title=" weld joint parameters"> weld joint parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis" title=" principal component analysis"> principal component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a> </p> <a href="https://publications.waset.org/abstracts/87646/parametric-appraisal-of-robotic-arc-welding-of-mild-steel-material-by-principal-component-analysis-fuzzy-with-taguchi-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87646.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">179</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">5072</span> Multimodal Optimization of Density-Based Clustering Using Collective Animal Behavior Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kristian%20Bautista">Kristian Bautista</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruben%20A.%20Idoy"> Ruben A. Idoy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering" title="clustering">clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=collective%20animal%20behavior%20algorithm" title=" collective animal behavior algorithm"> collective animal behavior algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=density-based%20%20clustering" title=" density-based clustering"> density-based clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20optimization" title=" multimodal optimization"> multimodal optimization</a> </p> <a href="https://publications.waset.org/abstracts/94254/multimodal-optimization-of-density-based-clustering-using-collective-animal-behavior-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94254.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">230</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">5071</span> A New Approach of Preprocessing with SVM Optimization Based on PSO for Bearing Fault Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tawfik%20Thelaidjia">Tawfik Thelaidjia</a>, <a href="https://publications.waset.org/abstracts/search?q=Salah%20Chenikher"> Salah Chenikher </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=condition%20monitoring" title="condition monitoring">condition monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title=" discrete wavelet transform"> discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=fault%20diagnosis" title=" fault diagnosis"> fault diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=kurtosis" title=" kurtosis"> kurtosis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=roller%20bearing" title=" roller bearing"> roller bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=rotating%20machines" title=" rotating machines"> rotating machines</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20measurement" title=" vibration measurement "> vibration measurement </a> </p> <a href="https://publications.waset.org/abstracts/2554/a-new-approach-of-preprocessing-with-svm-optimization-based-on-pso-for-bearing-fault-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2554.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">437</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">5070</span> Parameter Identification Analysis in the Design of Rock Fill Dams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Shahzadi">G. Shahzadi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Soulaimani"> A. Soulaimani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research work aims to identify the physical parameters of the constitutive soil model in the design of a rockfill dam by inverse analysis. The best parameters of the constitutive soil model, are those that minimize the objective function, defined as the difference between the measured and numerical results. The Finite Element code (Plaxis) has been utilized for numerical simulation. Polynomial and neural network-based response surfaces have been generated to analyze the relationship between soil parameters and displacements. The performance of surrogate models has been analyzed and compared by evaluating the root mean square error. A comparative study has been done based on objective functions and optimization techniques. Objective functions are categorized by considering measured data with and without uncertainty in instruments, defined by the least square method, which estimates the norm between the predicted displacements and the measured values. Hydro Quebec provided data sets for the measured values of the Romaine-2 dam. Stochastic optimization, an approach that can overcome local minima, and solve non-convex and non-differentiable problems with ease, is used to obtain an optimum value. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are compared for the minimization problem, although all these techniques take time to converge to an optimum value; however, PSO provided the better convergence and best soil parameters. Overall, parameter identification analysis could be effectively used for the rockfill dam application and has the potential to become a valuable tool for geotechnical engineers for assessing dam performance and dam safety. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rockfill%20dam" title="Rockfill dam">Rockfill dam</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20identification" title=" parameter identification"> parameter identification</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20analysis" title=" stochastic analysis"> stochastic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a>, <a href="https://publications.waset.org/abstracts/search?q=PLAXIS" title=" PLAXIS"> PLAXIS</a> </p> <a href="https://publications.waset.org/abstracts/110580/parameter-identification-analysis-in-the-design-of-rock-fill-dams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110580.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">146</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">5069</span> Optimization of Manufacturing Process Parameters: An Empirical Study from Taiwan's Tech Companies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chao-Ton%20Su">Chao-Ton Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Li-Fei%20Chen"> Li-Fei Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The parameter design is crucial to improving the uniformity of a product or process. In the product design stage, parameter design aims to determine the optimal settings for the parameters of each element in the system, thereby minimizing the functional deviations of the product. In the process design stage, parameter design aims to determine the operating settings of the manufacturing processes so that non-uniformity in manufacturing processes can be minimized. The parameter design, trying to minimize the influence of noise on the manufacturing system, plays an important role in the high-tech companies. Taiwan has many well-known high-tech companies, which show key roles in the global economy. Quality remains the most important factor that enables these companies to sustain their competitive advantage. In Taiwan however, many high-tech companies face various quality problems. A common challenge is related to root causes and defect patterns. In the R&D stage, root causes are often unknown, and defect patterns are difficult to classify. Additionally, data collection is not easy. Even when high-volume data can be collected, data interpretation is difficult. To overcome these challenges, high-tech companies in Taiwan use more advanced quality improvement tools. In addition to traditional statistical methods and quality tools, the new trend is the application of powerful tools, such as neural network, fuzzy theory, data mining, industrial engineering, operations research, and innovation skills. In this study, several examples of optimizing the parameter settings for the manufacturing process in Taiwan’s tech companies will be presented to illustrate proposed approach’s effectiveness. Finally, a discussion of using traditional experimental design versus the proposed approach for process optimization will be made. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quality%20engineering" title="quality engineering">quality engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20design" title=" parameter design"> parameter design</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=experimental%20design" title=" experimental design"> experimental design</a> </p> <a href="https://publications.waset.org/abstracts/81986/optimization-of-manufacturing-process-parameters-an-empirical-study-from-taiwans-tech-companies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81986.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">145</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">5068</span> Reliability Enhancement by Parameter Design in Ferrite Magnet Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Won%20Jung">Won Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Emri"> Wan Emri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ferrite magnet is widely used in many automotive components such as motors and alternators. Magnets used inside the components must be in good quality to ensure the high level of performance. The purpose of this study is to design input parameters that optimize the ferrite magnet production process to ensure the quality and reliability of manufactured products. Design of Experiments (DOE) and Statistical Process Control (SPC) are used as mutual supplementations to optimize the process. DOE and SPC are quality tools being used in the industry to monitor and improve the manufacturing process condition. These tools are practically used to maintain the process on target and within the limits of natural variation. A mixed Taguchi method is utilized for optimization purpose as a part of DOE analysis. SPC with proportion data is applied to assess the output parameters to determine the optimal operating conditions. An example of case involving the monitoring and optimization of ferrite magnet process was presented to demonstrate the effectiveness of this approach. Through the utilization of these tools, reliable magnets can be produced by following the step by step procedures of proposed framework. One of the main contributions of this study was producing the crack free magnets by applying the proposed parameter design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ferrite%20magnet" title="ferrite magnet">ferrite magnet</a>, <a href="https://publications.waset.org/abstracts/search?q=crack" title=" crack"> crack</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20optimization" title=" process optimization"> process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a> </p> <a href="https://publications.waset.org/abstracts/14217/reliability-enhancement-by-parameter-design-in-ferrite-magnet-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14217.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">517</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">5067</span> Comparison Between Genetic Algorithms and Particle Swarm Optimization Optimized Proportional Integral Derirative and PSS for Single Machine Infinite System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benalia%20Nadia">Benalia Nadia</a>, <a href="https://publications.waset.org/abstracts/search?q=Zerzouri%20Nora"> Zerzouri Nora</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Si%20Ali%20Nadia"> Ben Si Ali Nadia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Abstract: Among the many different modern heuristic optimization methods, genetic algorithms (GA) and the particle swarm optimization (PSO) technique have been attracting a lot of interest. The GA has gained popularity in academia and business mostly because to its simplicity, ability to solve highly nonlinear mixed integer optimization problems that are typical of complex engineering systems, and intuitiveness. The mechanics of the PSO methodology, a relatively recent heuristic search tool, are modeled after the swarming or cooperative behavior of biological groups. It is suitable to compare the performance of the two techniques since they both aim to solve a particular objective function but make use of distinct computing methods. In this article, PSO and GA optimization approaches are used for the parameter tuning of the power system stabilizer and Proportional integral derivative regulator. Load angle and rotor speed variations in the single machine infinite bus bar system is used to measure the performance of the suggested solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SMIB" title="SMIB">SMIB</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=PSO" title=" PSO"> PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=transient%20stability" title=" transient stability"> transient stability</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20system%20stabilizer" title=" power system stabilizer"> power system stabilizer</a>, <a href="https://publications.waset.org/abstracts/search?q=PID" title=" PID"> PID</a> </p> <a href="https://publications.waset.org/abstracts/171020/comparison-between-genetic-algorithms-and-particle-swarm-optimization-optimized-proportional-integral-derirative-and-pss-for-single-machine-infinite-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171020.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">83</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">5066</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">5065</span> Optimization of Loudspeaker Part Design Parameters by Air Viscosity Damping Effect</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yue%20Hu">Yue Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xilu%20Zhao"> Xilu Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Takao%20Yamaguchi"> Takao Yamaguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Manabu%20Sasajima"> Manabu Sasajima</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshio%20Koike"> Yoshio Koike</a>, <a href="https://publications.waset.org/abstracts/search?q=Akira%20Hara"> Akira Hara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study optimized the design parameters of a cone loudspeaker as an example of high flexibility of the product design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to optimize each parameter of the loudspeaker design. To overcome the limitation of the design problem in practice, this study presents an acoustic analysis algorithm to optimize the design parameters of the loudspeaker. The material character of cone paper and the loudspeaker edge were the design parameters, and the vibration displacement of the cone paper was the objective function. The results of the analysis showed that the design had high accuracy as compared to the predicted value. These results suggested that although the parameter design is difficult, with experience and intuition, the design can be performed easily using the optimized design found with the acoustic analysis software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20viscosity" title="air viscosity">air viscosity</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20parameters" title=" design parameters"> design parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=loudspeaker" title=" loudspeaker"> loudspeaker</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/60902/optimization-of-loudspeaker-part-design-parameters-by-air-viscosity-damping-effect" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60902.pdf" target="_blank" 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