Dissertations / Theses on the topic 'Parameter optimization'
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Marcinkevicius, Tadas. "DRAM BASED PARAMETER DATABASE OPTIMIZATION." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-100332.
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This thesis suggests an improved parameter database implementation for one of Ericsson products. The parameter database is used during the initialization of the system as well as during the later operation. The database size is constantly growing because the parameter database is intended to be used with different hardware configurations. When a new technology platform is released, multiple revisions with additional features and functionalities are later created, resulting in introduction of new parameters or changes to their values. Ericsson provides support for old and new products. The entire parameter database is currently stored in DRAM memory as a hash map. Therefore an optimal parameter database implementation should have low memory footprint. The search speed and initialization speed for the target system are also important to allow high system availability and low downtime, since a reboot is a common fix for many problems. As many optimizations have to consider memory size – speed tradeoff, it has been decided to give preference to reducing memory footprint. This research seeks to: Analyze data-structures suitable for parameter database implementation (Hash map, Sparsehash, Judy hash, Binary search tree, Treap, Skip List, AssocVector presorted using std::map, Burst trie). Propose the best data-structure in terms of used memory area and speed. If possible, further optimize it for database size in memory and access speed. Create a prototype implementation. Test the performance of the new implementation. The results indicate that a more compact database implementation can be achieved using alternative data structures such as Presorted AssocVector an Sparsehash, however some search speed and build speed is lost when using these data structures instead of the original Gnu Hash Map implementation.
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Koripalli, RadhaShilpa. "Parameter Tuning for Optimization Software." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2862.
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Mixed integer programming (MIP) problems are highly parameterized, and finding parameter settings that achieve high performance for specific types of MIP instances is challenging. This paper presents a method to find the information about how CPLEX solver parameter settings perform for the different classes of mixed integer linear programs by using designed experiments and statistical models. Fitting a model through design of experiments helps in finding the optimal region across all combinations of parameter settings. The study involves recognizing the best parameter settings that results in the best performance for a specific class of instances. Choosing good setting has a large effect in minimizing the solution time and optimality gap.
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Gupta, Deepak Prakash. "Energy sensitive machining parameter optimization model." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4406.
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Thesis (M.S.)--West Virginia University, 2005.Title from document title page. Document formatted into pages; contains ix, 71 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-71).
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Eeles, Charles William Owen. "Parameter optimization of conceptual hydrological models." Thesis, Open University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261674.
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Thellner, Mikael. "Multi-parameter topology optimization in continuum mechanics /." Linköping : Dept. of Mechanical Engineering, Univ, 2005. http://www.bibl.liu.se/liupubl/disp/disp2005/tek934s.pdf.
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Herrmann, Todd Matthew. "A critical parameter optimization of launch vehicle costs." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3927.
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Thesis (M.S.) -- University of Maryland, College Park, 2006.Thesis research directed by: Dept. of Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Mathavan, Neashan Graduate School of Biomedical Engineering Faculty of Engineering UNSW. "Parameter optimization in simplified models of cardiac myocytes." Awarded by:University of New South Wales. Graduate School of Biomedical Engineering, 2009. http://handle.unsw.edu.au/1959.4/44709.
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Atrial fibrillation (AF) is a complex, multifaceted arrhythmia. Pathogenesis of AF is associated with multiple aetiologies and the mechanisms by which it is sustained and perpetuated are similarly diverse. In particular, regional heterogeneity in the electrophysiological properties of normal and pathological tissue plays a critical role in the occurrence of AF. Understanding AF in the context of electrophysiological heterogeneity requires cell-specific ionic models of electrical activity which can then be incorporated into models on larger temporal and spatial scales. Biophysically-based models have typically dominated the study of cellular excitability providing detailed and precise descriptions in the form of complex mathematical formulations. However, such models have limited applicability in multidimensional simulations as the computational expense is too prohibitive. Simplified mathematical models of cardiac cell electrical activity are an alternative approach to these traditional biophysically-detailed models. Utilizing this approach enables the embodiment of cellular excitation characteristics at minimal computational cost such that simulations of arrhythmogensis in atrial tissue are conceivable. In this thesis, a simplified, generic mathematical model is proposed that characterizes and reproduces the action potential waveforms of individual cardiac myocytes. It incorporates three time-dependent ionic currents and an additional time-independent leakage current. The formulation of the three time-dependent ionic currents is based on 4-state Markov schemes with state transition rates expressed as nonlinear sigmoidal functions of the membrane potential. Parameters of the generic model were optimized to fit the action potential waveforms of the Beeler-Reuter model, and, experimental recordings from atrial and sinoatrial cells of rabbits. A nonlinear least-squares optimization routine was employed for the parameter fits. The model was successfully fitted to the Beeler-Reuter waveform (RMS error: 1.4999 mV) and action potentials recorded from atrial tissue (RMS error: 1.3398 mV) and cells of the peripheral (RMS error: 2.4821 mV) and central (RMS error: 2.3126 mV) sinoatrial node. Thus, the model presented here is a mathematical framework by which a wide variety of cell-specific AP morphologies can be reproduced. Such a model offers the potential for insights into possible mechanisms that contribute to heterogeneity and/or arrhythmia.
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Lee, Sang Heon. "Efficient design and optimization of robust parameter experiments." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24328.
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Luna, Ortiz J. E. "Optimization of distributed parameter systems using transient simulators." Thesis, University of Manchester, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503592.
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Mannai, Sébastien (Sébastien Karim). "Multi-parameter control for centrifugal compressor performance optimization." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90778.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 88-89).
The potential performance benefit of actuating inlet guide vane (IGV) angle, variable diffuser vane (VDV) angle and impeller speed to implement a multi-parameter control on a centrifugal compressor system is assessed. The assessment consists of first developing a one-dimensional meanline model for estimating performance of centrifugal compressor system followed by the formulation of a control framework incorporating the meanline model. Performance estimate of a representative centrifugal compressor system with adjustable IGV angle, VDV angle and impeller speed using the meanline model is in accord with available test data. The impeller performance estimate based on the meanline model is also in accord with computed results from Reynolds Average Navier-Stokes Equations. The simple control framework can be used to optimize on the fly the compressor operation to meet a specific mission requirement by selecting an appropriate combination of impeller speed, IGV and VDV angle settings. Desirable flow configurations with the required performance in response to specified operating needs have been obtained to serve as illustrations on the practical utility of the control framework. Results provide guidelines and attributes of compressor for achieving the required performance and operation at the system level through prioritizing the actuation of the adjustable parameters; for instance impeller speed would provide a high level of leverage to affect the compressor performance on an effective basis and that the IGV angle should be confined to a specified range. While the results have not been assessed in an experimental setting, they are used to design and plan an experimental program for evaluating the proposed simple multi-parameter control strategy. Flexibility have been incorporated into the formulation to allow the refinement and updating of the model for improved accuracy and fidelity.
Sebastien Mannai.
S.M.
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Panis, Renato P. "Robust parameter optimization strategies in computer simulation experiments." Diss., This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-06062008-164719/.
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Busuioc, Dan. "Circuit Model Parameter Extraction and Optimization for Microwave Filters." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/804.
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This thesis presents a method for parameter extraction of circuit elements from microwave filters. This diagnosis method can be applied to a sufficiently large number of filters and it can also be used in conjunction with a neural network model for filter design, greatly reducing development time. This thesis is an introduction of parameter extraction and circuit modelling through use of neural networks. It also presents an implementation of the proposed method as well as numerical results and validation data. Detailed implementation code is presented in the appendix.
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Perez, Claudio A. "Parameter optimization and system miniaturization for vibrotactile information transfer /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487687485810333.
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Granados, Murillo Adrian. "A genetic algorithm for network transport protocol parameter optimization." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000176.
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Thesis (M.S.)--University of West Florida, 2009.Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 66 pages. Includes bibliographical references.
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Garcia, Sandrine. "Experimental Design Optimization and Thermophysical Parameter Estimation of Composite Materials Using Genetic Algorithms." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/28076.
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Thermophysical characterization of anisotropic composite materials is extremely important in the control of today fabrication processes and in the prediction of structure failure due to thermal stresses. Accuracy in the estimation of the thermal properties can be improved if the experiments are designed carefully. However, on one hand, the typically used parametric study for the design optimization is tedious and time intensive. On the other hand, commonly used gradient-based estimation methods show instabilities resulting in nonconvergence when used with models that contain correlated or nearly correlated parameters.
The objectives of this research were to develop systematic and reliable methodologies for both Experimental Design Optimization (EDO) used for the determination of thermal properties, and Simultaneous Parameter Estimation (SPE). Because of their advantageous features, Genetic Algorithms (GAs) were investigated for use as a strategy for both EDO and SPE. The EDO and SPE approaches used involved the maximization of an optimality criterion associated with the sensitivity matrix of the unknown parameters, and the minimization of the ordinary least squares error, respectively. Two versions of a general-purpose genetic-based program were developed: one is designed for the analysis of any EDO / SPE problems for which a mathematical model can be provided, while the other incorporates a control-volume finite difference scheme allowing for the practical analysis of complex problems. The former version was used to illustrate the genetic performance on the optimization of a difficult mathematical test function.
Two test cases previously solved in the literature were first analyzed to demonstrate and assess the GA-based {EDO/SPE} methodology. These problems included the optimization of one and two dimensional designs for the estimation at ambient temperature of two and three thermal properties, respectively (effective thermal conductivity parallel and perpendicular to the fibers plane and effective volumetric heat capacity), of anisotropic carbon/epoxy composite materials. The two dimensional case was further investigated to evaluate the effects of the optimality criterion used for the experimental design on the accuracy of the estimated properties.
The general-purpose GA-based program was then successively applied to three advanced studies involving the thermal characterization of carbon/epoxy anisotropic composites. These studies included the SPE of successively three, seven and nine thermophysical parameters, with for the latter case, a two dimensional EDO with seven experimental key parameters. In two of the three studies, the parameters were defined to represent the dependence of the thermal properties with temperature. Finally, the kinetic characterization of the curing of three thermosetting materials (an epoxy, a polyester and a rubber compound) was accomplished resulting in the SPE of six kinetic parameters.
Overall, the GA method was found to perform extremely well despite the high degree of correlation and low sensitivity of many parameters in all cases studied. This work therefore validates the use of GAs for the thermophysical characterization of anisotropic composite materials. The significance in using such algorithms is not only the solution to ill-conditioned problems but also, a drastically cost savings in both experimental and time expenses as they allow for the EDO and SPE of several parameters at once.Ph. D.
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Munster, Drayton William. "Robust Parameter Inversion Using Stochastic Estimates." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96399.
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For parameter inversion problems governed by systems of partial differential equations, such as those arising in Diffuse Optical Tomography (DOT), even the cost of repeated objective function evaluation can be overwhelming. Despite the linear (in the state variable) nature of the DOT problem, the nonlinear parameter inversion process is dominated by the computational burden of solving a large linear system for each source and frequency. To compute the Jacobian for use in Newton-type methods, an adjoint solve is required for each detector and frequency. When a three-dimensional tomography problem may have nearly 1,000 sources and detectors, the computational cost of an optimization routine is a large burden. While techniques from model order reduction can partially alleviate the computational cost, obtaining error bounds in parameter space is typically not feasible. In this work, we examine two different remedies based on stochastic estimates of the objective function.
In the first manuscript, we focus on maximizing the efficiency of using stochastic estimates by replacing our objective function with a surrogate objective function computed from a reduced order model (ROM). We use as few as a single sample to detect a misfit between the full-order and surrogate objective functions. Once a sufficiently large difference is detected, it is necessary to update the ROM to reduce the error. We propose a new technique for improving the ROM with very few large linear solutions. Using this techniques, we observe a reduction of up to 98% in the number of large linear solutions for a three-dimensional tomography problem.
In the second manuscript, we focus on establishing a robust algorithm. We propose a new trust region framework that replaces the objective function evaluations with stochastic estimates of the improvement factor and the misfit between the model and objective function gradients. If these estimates satisfy a fixed multiplicative error bound with a high, but fixed, probability, we show that this framework converges almost surely to a stationary point of the objective function. We derive suitable bounds for the DOT problem and present results illustrating the robust nature of these estimates with only 10 samples per iteration.Doctor of Philosophy
For problems such as medical imaging, the process of reconstructing the state of a system from measurement data can be very expensive to compute. The ever increasing need for high accuracy requires very large models to be used. Reducing the computational burden by replacing the model with a specially constructed smaller model is an established and effective technique. However, it can be difficult to determine how well the smaller model matches the original model. In this thesis, we examine two techniques for estimating the quality of a smaller model based on randomized combinations of sources and detectors. The first technique focuses on reducing the computational cost as much as possible. With the equivalent of a single randomized source, we show that this estimate is an effective measure of the model quality. Coupled with a new technique for improving the smaller model, we demonstrate a highly efficient and robust method. The second technique prioritizes robustness in its algorithm. The algorithm uses these randomized combinations to estimate how the observations change for different system states. If these estimates are accurate with a high probability, we show that this leads to a method that always finds a minimum misfit between predicted values and the observed data.
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Svensson, Emil. "Parameter estimation of biological pathways." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8430.
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To determine parameter values for models of reactions in the human body, like the glycolysis, good methods of parameter estimation are needed. Those models are often non-linear and estimation of the parameters can be very time consuming if it is possible at all. The goal of this work is to test different methods to improve the calculation speed of the parameter estimation of an example system. If the parameter estimation speed for the example system can be improved it is likely that the method could also be useful for systems similar to the example system.
One approach to improve the calculation speed is to construct a new cost function whose evaluation does not require any simulation of the system. Simulation free parameter estimation can be much quicker than using simulations to evaluate the cost function since the cost function is evaluated many times. Also a modication of the simulated annealing optimization method has been implemented and tested.
It turns out that some of the methods significantly reduced the time needed for the parameter estimations. However the quick methods have disadvantages in the form of reduced robustness. The most successful method was using a spline approximation together with a separation of the model into several submodels, and repeated use of the simulated annealing optimization algorithm to estimate the parameters.
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Kidwell, Ann-Sofi. "Optimization under parameter uncertainties with application to product cost minimization." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-38858.
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This report will look at optimization under parameters of uncertainties. It will describe the subject in its wider form, then two model examples will be studied, followed by an application to an ABB product. The Monte Carlo method will be described and scrutinised, with the quasi-Monte Carlo method being favoured for large problems. An example will illustrate how the choice of Monte Carlo method will affect the efficiency of the simulation when evaluating functions of different dimensions. Then an overview of mathematical optimization is given, from its simplest form to nonlinear, nonconvex optimization problems containing uncertainties.A Monte Carlo simulation is applied to the design process and cost function for a custom made ABB transformer, where the production process is assumed to contain some uncertainties.The result from optimizing an ABB cost formula, where the in-parameters contains some uncertainties, shows how the price can vary and is not fixed as often assumed, and how this could influence an accept/reject decision.
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Fraleigh, Lisa Marie. "Optimal sensor selection and parameter estimation for real-time optimization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ40050.pdf.
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Chen, Zhaozhong. "Visual-Inertial SLAM Extrinsic Parameter Calibration Based on Bayesian Optimization." Thesis, University of Colorado at Boulder, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10789260.
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VI-SLAM (Visual-Inertial Simultaneous Localization and Mapping) is a popular way for robotics navigation and tracking. With the help of sensor fusion from IMU and camera, VI-SLAM can give a more accurate solution for navigation. One important problem needs to be solved in VI-SLAM is that we need to know accurate relative position between camera and IMU, we call it the extrinsic parameter. However, our measurement of the rotation and translation between IMU and camera is noisy. If the measurement is slightly o?, the result of SLAM system will be much more away from the ground truth after a long run. Optimization is necessary. This paper uses a global optimization method called Bayesian Optimization to optimize the relative pose between IMU and camera based on the sliding window residual output from VISLAM. The advantage of using Bayesian Optimization is that we can get an accurate pose estimation between IMU and camera from a large searching range. Whats more, thanks to the Gaussian Process or T process of Bayesian Optimization, we can get a result with a known uncertainty, which cannot be done by many optimization solutions.
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Leng, She Ri Gu. "Investigating hybrids of evolution and learning for real-parameter optimization." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2481.
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In recent years, more and more advanced techniques have been developed in the field of hybridizing of evolution and learning, this means that more applications with these techniques can benefit from this progress. One example of these advanced techniques is the Learnable Evolution Model (LEM), which adopts learning as a guide for the general evolutionary search. Despite this trend and the progress in LEM, there are still many ideas and attempts which deserve further investigations and tests. For this purpose, this thesis has developed a number of new algorithms attempting to combine more learning algorithms with evolution in different ways. With these developments, we expect to understand the effects and relations between evolution and learning, and also achieve better performances in solving complex problems. The machine learning algorithms combined into the standard Genetic Algorithm (GA) are the supervised learning method k-nearest-neighbors (KNN), the Entropy-Based Discretization (ED) method, and the decision tree learning algorithm ID3. We test these algorithms on various real-parameter function optimization problems, especially the functions in the special session on CEC 2005 real-parameter function optimization. Additionally, a medical cancer chemotherapy treatment problem is solved in this thesis by some of our hybrid algorithms. The performances of these algorithms are compared with standard genetic algorithms and other well-known contemporary evolution and learning hybrid algorithms. Some of them are the CovarianceMatrix Adaptation Evolution Strategies (CMAES), and variants of the Estimation of Distribution Algorithms (EDA). Some important results have been derived from our experiments on these developed algorithms. Among them, we found that even some very simple learning methods hybridized properly with evolution procedure can provide significant performance improvement; and when more complex learning algorithms are incorporated with evolution, the resulting algorithms are very promising and compete very well against the state of the art hybrid algorithms both in well-defined real-parameter function optimization problems and a practical evaluation-expensive problem.
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Rakotoarison, Herilalaina. "Some contributions to AutoML : hyper-parameter optimization and meta-learning." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG044.
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Cette thèse présente trois principales contributions afin d’améliorer l’état de l’art de ces approches AutoML. Elles sont divisées entre deux thèmes de recherche: l’optimisation et meta-apprentissage. La première contribution concerne un algorithme d’optimisation hybride, appelé Mosaic, qui exploite les méthodes MCTS et optimisation bayésienne pour résoudre respectivement la sélection des algorithmes et la configuration des hyperparamètres. L’évaluation, conduite à travers le benchmark OpenML 100, montre que la performance empirique de Mosaic surpasse ceux des systèmes d’AutoML de l’état de l’art (Auto-Sklearn et TPOT). La deuxième contribution introduit une architecture de réseau neuronal, appelée Dida, qui permet d’apprendre des descripteurs de données invariants à la permutation de colonnes et d’exemples. Deux tâches (classification des patchs et prédiction des performances sont considérées lors de l’évaluation de la méthode. Les résultats de Dida sont encourageants comparés à ceux de ses concurrents (Dataset2 vvec et DSS). Enfin, la troisième contribution, intitulée Metabu, vise à surmonter les limites de Dida à opérer sur de vrais jeux de données d’AutoML. La stratégie de Metabu comporte deux étapes. Tout d’abord, une topologie idéale de ces jeux de données, basée sur les meilleurs hyperparamètres, est définie. Puis, une transformation linéaire d es descripteurs manuels est apprise pour les aligner, selon un critère de transport optimal, avec la représentation idéale. Les comparaisons empiriques montrent que les descripteurs Metabu sont plus performants que les descripteurs manuels sur trois problèmes différents (évaluation du voisinage des jeux de données, recommandation d’hyperparamètres, et initialisation d’un algorithme d’optimisation)This thesis proposes three main contributions to advance the state-of-the-art of AutoML approaches. They are divided into two research directions: optimization (first contribution) and meta-learning (second and third contributions). The first contribution is a hybrid optimization algorithm, dubbed Mosaic, leveraging Monte-Carlo Tree Search and Bayesian Optimization to address the selection of algorithms and the tuning of hyper-parameters, respectively. The empirical assessment of the proposed approach shows its merits compared to Auto-sklearn and TPOT AutoML systems on OpenML 100. The second contribution introduces a novel neural network architecture, termed Dida, to learn a good representation of datasets (i.e., metafeatures) from scratch while enforcing invariances w.r.t features and rows permutations. Two proofof-concept tasks (patch classification and performance prediction tasks) are considered. The proposed approach yields superior empirical performance compared to Dataset2Vec and DSS on both tasks. The third contribution addresses the limitation of Dida on handling standard dataset benchmarks. The proposed approach, called Metabu, relies on hand-crafted meta-features. The novelty of Metabu is two-fold: i) defining an "oracle" topology of datasets based on top-performing hyper-parameters; ii) leveraging Optimal Transport approach to align a mapping of the handcrafted meta-features with the oracle topology. The empirical results suggest that Metabu metafeature outperforms the baseline hand-cr afted meta-features on three different tasks (assessing meta-features based topology, recommending hyper-parameters w.r.t topology, and warmstarting optimization algorithms)
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Vugrin, Kay Ellen White. "On the Effects of Noise on Parameter Identification Optimization Problems." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/27515.
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The calibration of model parameters is an important step in model development. Commonly, system output is measured, and model parameters are iteratively varied until the model output is a good match to the measured system output. Optimization algorithms are often used to identify the model parameter values. The presence of noise is difficult to avoid when physical processes are used to calibrate models due to measurement error, model structure error, and errors arising from numerical techniques and approximate solutions. Our study focuses on the effects of noise in parameter identification optimization problems. We generate six test problems, including five perturbations of a smooth problem. A previously studied groundwater parameter identification problem serves as our seventh test problem. We test the Nelder-Mead Algorithm, a combination of the Nelder-Mead Algorithm and Simulated Annealing, and the Shuffled Complex Evolution Method on these test problems. Comparison of optimization results for these problems reveals the effects of noise on optimization performance,
including an increase in fitness values and a decrease in the number of fit evaluations. We vary the values of the internal algorithmic parameters to determine the effects of different values and present numerical results that indicate that changing the values of the algorithmic parameters can cause profound differences in optimization results for all three algorithms. A variation of the generally accepted parameter values for the Nelder-Mead Algorithm is recommended, and we determine that the Nelder-Mead/Simulated Annealing Hybrid and Shuffled Complex Evolution Method are too problem dependent for general recommendations for parameter values. Finally, we prove new convergence results for the Nelder-Mead/Simulated
Annealing Hybrid in both smooth and noisy cases.Ph. D.
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Zwolak, Jason Walter. "Parameter Estimation in Biological Cell Cycle Models Using Deterministic Optimization." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/31354.
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Cell cycle models used in biology can be very complex. These models have parameters
with initially unknown values. The values of the parameters vastly aect the accuracy of the
models in representing real biological cells. Typically people search for the best parameters
to these models using computers only as tools to run simulations. In this thesis methods
and results are described for a computer program that searches for parameters to a series
of related models using well tested algorithms. The code for this program uses ODRPACK
for parameter estimation and LSODAR to solve the dierential equations that comprise the
model.Master of Science
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Sanders, Samantha Corinne. "Informing the use of Hyper-Parameter Optimization Through Meta-Learning." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6392.
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One of the challenges of data mining is finding hyper-parameters for a learning algorithm that will produce the best model for a given dataset. Hyper-parameter optimization automates this process, but it can still take significant time. It has been found that hyperparameter optimization does not always result in induced models with significant improvement over default hyper-parameters, yet no systematic analysis of the role of hyper-parameter optimization in machine learning has been conducted. We propose the use of meta-learning to inform the decision to optimize hyper-parameters based on whether default hyper-parameter performance can be surpassed in a given amount of time. We will build a base of metaknowledge, through a series of experiments, to build predictive models that will assist in the decision process.
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Hepdogan, Seyhun. "META-RAPS: PARAMETER SETTING AND NEW APPLICATIONS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3493.
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ABSTRACT Recently meta-heuristics have become a popular solution methodology, in terms of both research and application, for solving combinatorial optimization problems. Meta-heuristic methods guide simple heuristics or priority rules designed to solve a particular problem. Meta-heuristics enhance these simple heuristics by using a higher level strategy. The advantage of using meta-heuristics over conventional optimization methods is meta-heuristics are able to find good (near optimal) solutions within a reasonable computation time. Investigating this line of research is justified because in most practical cases with medium to large scale problems, the use of meta-heuristics is necessary to be able to find a solution in a reasonable time. The specific meta-heuristic studied in this research is, Meta-RaPS; Meta-heuristic for Randomized Priority Search which is developed by DePuy and Whitehouse in 2001. Meta-RaPS is a generic, high level strategy used to modify greedy algorithms based on the insertion of a random element (Moraga, 2002). To date, Meta-RaPS had been applied to different types of combinatorial optimization problems and achieved comparable solution performance to other meta-heuristic techniques. The specific problem studied in this dissertation is parameter setting of Meta-RaPS. The topic of parameter setting for meta-heuristics has not been extensively studied in the literature. Although the parameter setting method devised in this dissertation is used primarily on Meta-RaPS, it is applicable to any meta-heuristic's parameter setting problem. This dissertation not only enhances the power of Meta-RaPS by parameter tuning but also it introduces a robust parameter selection technique with wide-spread utility for many meta-heuristics. Because the distribution of solution values generated by meta-heuristics for combinatorial optimization problems is not normal, the current parameter setting techniques which employ a parametric approach based on the assumption of normality may not be appropriate. The proposed method is Non-parametric Based Genetic Algorithms. Based on statistical tests, the Non-parametric Based Genetic Algorithms (NPGA) is able to enhance the solution quality of Meta-RaPS more than any other parameter setting procedures benchmarked in this research. NPGA sets the best parameter settings, of all the methods studied, for 38 of the 41 Early/Tardy Single Machine Scheduling with Common Due Date and Sequence-Dependent Setup Time (ETP) problems and 50 of the 54 0-1 Multidimensional Knapsack Problems (0-1 MKP). In addition to the parameter setting procedure discussed, this dissertation provides two Meta-RaPS combinatorial optimization problem applications, the 0-1 MKP, and the ETP. For the ETP problem, the Meta-RaPS application in this dissertation currently gives the best meta-heuristic solution performance so far in the literature for common ETP test sets. For the large ETP test set, Meta-RaPS provided better solution performance than Simulated Annealing (SA) for 55 of the 60 problems. For the small test set, in all four different small problem sets, the Meta-RaPS solution performance outperformed exiting algorithms in terms of average percent deviation from the optimal solution value. For the 0-1 MKP, the present Meta-RaPS application performs better than the earlier Meta-RaPS applications by other researchers on this problem. The Meta-RaPS 0-1 MKP application presented here has better solution quality than the existing Meta-RaPS application (Moraga, 2005) found in the literature. Meta-RaPS gives 0.75% average percent deviation, from the best known solutions, for the 270 0-1 MKP test problems.Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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Jezek, Christoffer, and Fredrik Jones. "Diesel Combustion Modeling and Simulation for Torque Estimation and Parameter Optimization." Thesis, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12117.
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The current interest regarding how to stop the global warming has put focus on the automobile industry and forced them to produce vehicles/engines that are more environmental friendly. This has led to the development of increasingly complex controlsystem of the engines. The introduction of common-rail systems in regular automotives increased the demand of physical models that in an accurate way can describe the complex cycle within the combustion chamber. With these models implemented it is possible to test new strategies on engine steering in a cost- and time efficient way.
The main purpose with this report is to, build our own model based on the existing theoretical models in diesel engine combustion. The model has then been evaluated in a simulation environment using Matlab/Simulink. The model that has been implemented is a multi-zone type and is able to handle multiple injections.
The model that this thesis results in can in a good way predict both pressure and torque generated in the cylinder. More investigation in how the parameter settings behave in other work-points must be done to enhance the models accuracy. There is also some work left to do in the validation of the model but to make this possible more experimental data must be accessible.
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Mekarapiruk, Wichaya. "Simultaneous optimal parameter selection and dynamic optimization using iterative dynamic programming." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58926.pdf.
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Liang-Lung, Hung, and 洪良龍. "Processing Parameter Optimization for Dyeing." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/76051235992451738550.
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碩士國立臺灣科技大學
高分子工程系
92
Due to human requests for the coloration of clothing materials, it leads the dyeing processing technology to be getting promoted day after day. Before the dyeing process, it is necessary that the combination of the processing parameters for the fabrics must be determined in advance. The reason is that the dyeing effects result from their parameters. In this paper, we select pure cotton and cotton mixed Lycra as the dyed fabrics, dyestuffs as the reactive dye, and the dyeing method is one-bath-two-section impregnation as well as the quality characteristic are K/S values of the fabrics. Our purpose is to find the optimum combination of processing parameters to achieve the customers’ demands. Taguchi experimental design method has been proposed in the research. In view of the dyeing results, the parameters including machine operating temperature, dyeing time, calefaction speed, dye liquor concentration, auxiliary type and concentration, pH. value, and bath-ratio value, are regarded as the control factors. The orthogonal array are employed to determine the optimum conditions, significant factors, and percent contribution together with the ANOVA approach. In the experiment, K/S values of fabrics are chosen to be the smaller-the-better target characteristic, and the confirmation experiments are performed and verified the reproducibility of the experimentation. In addition, the K/S values of dyed fabrics in optimum condition are much closer to the target values. In conclusion, the significant factors influencing the dyeing results are used to construct the prediction system of back-propagation neural network combined with Taguchi method.
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Yu-Jen, Huang, and 黃昱仁. "Parameter optimization for power management system." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/60023520620826649726.
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碩士國立屏東科技大學
車輛工程系所
96
In this thesis, an optimal energy management system (EMS) for a so-called Dual Hybrid electric Vehicle (DHEV) is designed. The goal is to determine optimal operating parameters of the DHEV such that each of its subsystems can be operated around their respective optimal area with minimum fuel consumption for all operation conditions, under the considerations of drivability and physical constraints of the vehicle. The design of the EMS for this system is complicate due to its nonlinear nature of the hybrid system and, furthermore, without a priori knowledge of system dynamics. This thesis has proposed three kinds of energy management strategies for the EMS, as well as a kind of simplex method based parameter optimization algorithm. The results of numerical simulations have shown that the proposed EMS for the DHEV improves its fuel consumption more than 80% compared with the ICE alone vehicle in the ECER40 transient driving cycles.
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Su, Tsan-Cheng, and 蘇粲程. "Evolutionary Algorithms on Othello Parameter Optimization." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/86757212038775036289.
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博士國立東華大學
資訊工程學系
104
Artificial intelligence has recently attracted significant media interest when the AlphaGo program developed by Google DeepMind was defeated by Se-dol Lee in South Korea. The news follows the 1997 man versus machine performance where computer game programs utilize artificial intelligence algorithms. The degree of branching in Othello is smaller than Go. Generally, the computer Othello program is designed by MinMax search method with α–β pruning. Computer Othello is a computer game program that competes with other computer game programs. In this study, the use of two methods, namely, MinMax search and α–β pruning, is more accurate. For computer Othello, this study developed a program that makes several parameters affect the winning or losing of the game. Given the time limitation in the game, algorithms are important to find the best parameters. These parameters are adjusted in the MinMax search with α–β pruning to accelerate the search and enhance its efficiency.
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AN, WU CHEN, and 吳承恩. "Color Filter Polish Process Parameter Optimization." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/57539754465021069785.
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碩士南台科技大學
工業管理研究所
99
Electronic industries are the main economic sources in Taiwan. TFT-LCD is one of the electronic industries and it plays an important role in the industries. There are a lot of competitors in TFT-LCD industry around the world and the production cost of TFT-LCD is extremely high. To reduce its production cost and increase profit becomes the primary concern of the management. In order to manufacture the TFT-LCD to satisfy the demands of the consumer’s market, every company is trying to search for the optimal production process parameters by experiments continuously. The best product quality can only be guaranteed through the validation of the optimal production process parameters. However experiments consume much money and time. The more experiments to search for optimal production process parameters, the less profit for the company. Under this situation, how to perform fewer experiments to search for the optimal parameters is a very important topic. This research applies Taguchi Methods and Central Composite Design (CCD) on the process of grinding color filter which is the critical component affecting the quality of TFT-LCD. This research utilizes fishbone diagram to obtain important causes of the color filter grinding process and compares Taguchi Methods with CCD by calculating the correlation coefficients among the deign variables of the two designs. We finally decide to apply CCD on the search of the optimal process parameters. Also we validate the estimated model and optimal process parameters through confirmation runs.
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LIAN, YAN-CHENG, and 連彥丞. "Parameter Optimization for Machine Tool Tuning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dge9c6.
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碩士國立雲林科技大學
機械工程系
106
The five-axis machine tool is widely used in aerospace and automotive industries which takes high speed and high precision as the development goal, reacting to the short cycle and high-precision products demand in the industry. The Mechanism design, assembly process, controller parameters and environment factors all make influences on the high speed and the high precision. In order to optimize the controller parameter and improve the machining accuracy and the machining time, Taguchi method is used to find out the best parametric combination and to meet the shortest positioning time and the optimum positioning accuracy. Taguchi method is mainly used for single quality characteristic. In practice, we need to take multiple quality characteristics into consideration, so it is necessary to combine the two quality characteristics that is selected by weighting. After that, the weighting ratio is used to adjust the quality index for machining requirements of the workpiece. Finally, we will get the best parametric combination which meet the machining requirements.
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Fang, Yu-Ting, and 方宇婷. "Process Parameter Optimization of Silica Aerogel Products." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/xzn554.
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碩士義守大學
工業管理學系
103
The research combines the Taguchi experiment and grey relational analysis (GRA) to deal with the problem of optimizing the parameters of the SiO2 aerogel. In addition, this paper tries to understand the main process factors influencing the quality characteristics of the aerogel through the experiments. After discussing with the domain experts, we chose three quality characteristics and three experimental factors with three levels and adopted L9 (34) orthogonal array to proceed with the experiment. The three quality characteristics are density, surface area, and pore size. To obtain the weight of the quality characteristics, the entropy measurement was applied. Based on the results of GRA with entropy weight and confirmation run, the optimal process parameters of silicon aerogel were obtained. To find the optimum level of process parameters, the research applies back-propagation neural network to build the relationship between the process parameters and the quality characteristics. The experiment results show that the experiment conditions with the best grey relation grade together with the parameters obtained by the method combining the Taguchi experiment and GRA can simultaneously improve the multiple quality characteristics. Both of the parameters can be used in the production of the aerogel in practice. Because of the insufficient of the training samples, the deduction of the back-propagation neural network doesn’t work well. To get the better results of the neural network, more samples should be required in future research.
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Huang, Chin-Yuan, and 黃欽淵. "Real time parameter optimization with motor control." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/47201548209648604168.
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碩士中國文化大學
數位機電科技研究所
97
This article is based on step and servo motor device to find optimal parameters for the subject. Using Labview software to real-time control the motor speed and position, then to find adaptive parameters to optimizing feedback system. After simulated combination with step motor and hardware operation, real-time computing features will reduce the time delay with a view to achieving hardware and software synchronize the process of the simulation software with select the most sensitive parameters and hardware, at the same time make to the whole system got the best characteristics of final solution by energy-saving and time-saving advantage to meet a performance of the system with best design requirements. The system operating with hardware including server motors, switches, motor control axis card and power supply converters. Computer using an ordinary desktop computers and NI PXI-1042Q Industrial computer. Desktop computer equipped with Pentium D 3.4G for the memory expanded to 2G; industrial computer equipment for the Pentium Core 2 T7400 2.16G memory expanded to 512MB. And the step motor for the test object, adjust the parameters of the motor, in order to meet the purpose of the system to get the best which we want; The results which we test in here：the industrial computer have better and faster performance compare with the others. The reason for that is because the industrial computer use the series to deal with information get more faster and accuracy than desktop computer. In the future we will use micro-controller to replace the computer, and then compare with the industry computer difference between them. Instead of hardware reduce time delay in line to minimize the computing simulation time for selecting t.he best parameters to achieve energy-saving and time-saving.
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Hung, rung yau, and 洪榮耀. "Effect of Parameter Estimation on Fertilizer Optimization." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/93780366279016940967.
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Hsu, Po-chun, and 許博淳. "The Parameter Optimization of Zone Control Chart." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/62717050241624122684.
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碩士南台科技大學
工業管理研究所
98
In today's competitive system, production technology is getting riser and consumer’s quality requirement is significantly increasing. In the crisis surrounding environment, the product quality is a major factor for enterprises to remain competitiveness and keep going in the competitive world. Statistical process control (SPC) is one of important tools on quality control where control chart is the most widely used tool. Zone Control Chart (ZCC) is primarily used to replace the traditional Shewhart control chart for its insensitivity and Runs Rules Tests for its inconvenience. Zone control chart divides X control chart into four regions where each region is given a score such as 1, 2, 4, and 8. The purpose of this study is to search for the optimal parameter values for zone control chart. The research applied Central Composite Design (CCD) and Box-Behnken Design (BBD) to search for the best parameter values of zone control chart. The application of CCD was divided into two parts. The first part is to put the uncoded parameter values and SAS simulated ARL values into artificial neural network to find out the weights in the artificial neural network. Next let the set of weights as the initial weights in the genetic algorithms to search for the best weights. Then find the optimal parameter values for zone control chart and its corresponding ARL forecasts. Finally input the optimal parameter values to simulate the ARL values of zone control chart by SAS program and compare the differences between the two set of ARL values. The second part applied response surface methodology (RSM) and desirability function to obtain the optimal parameter values of zone control chart. Similarly, compare the differences between predicted ARLs and simulated ARLs. The same procedure was applied on data generated by Box-Behnken design.
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Li, Tzung-Rung, and 李宗榮. "Parameter Optimization of Automobile Wheel Alignment Systems." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/77365626773876890407.
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碩士國立高雄應用科技大學
機械與精密工程研究所
99
This article is mainly the research for the pursue good traffic safety quality and under the best chassis adjustment combination, by ' Taguchi Method' analysis and adjustment wheel alignment. The analysis causes us by ' Taguchi Method' under the wheel alignment each angle adjustment, obtains the optimal fit. Reaches the traffic safety the goal. Outside inclination angle (Camber); The caster angle (Caster) and the toe-in (Toe in) is the main controlling element, the quality project' the analysis discovers the best adjustment combination by 'Tian Koushi.The quality project' the analysis causes us by' Taguchi Method' under the wheel alignment each angle adjustment, obtains the optimal fit
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Wong, Shih-Chang, and 翁世璋. "Arbitrary Parameter Optimization for Manga Panel Extraction." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/b2ty83.
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碩士國立臺灣科技大學
資訊工程系
105
In the problem of making manga easier to read on handheld devices, the most essential and basic step is locating positions of each panel on the page since processing on the contents in the area of panels was required to make them more readable on handheld devices. However, due to the varying layout structure features in each series or volume of manga such as the different panel width used in every series, manual parameter adjustment is often needed to achieve the best possible result for many panel extraction algorithms. In this paper, we propose an automatic parameter optimization system based on automatic result evaluation to reduce manual adjustment in panel extraction process. The automatic result evaluation checks for two criteria. The first is the accumulated area of the extracted panels. The second is the number of parallel panel edges between extracted panels. With the automatic result evaluation, our method is able to find correct parameter sets. Aside from the parameter issues, current algorithms by other studies are not able to process manga pages with large extruding objects. Such pages will make the white gap area between panels very difficult to detect for connected component based algorithms and very hard to distinguish whether the white area is inside or outside of a panel, reducing the effectiveness of connected component component based or division line based algorithm. For this reason, this paper also proposed a novel method for panel extraction. The method employs line segment detection and corner detection to detect what are called "components" in this paper, and match up these components with each other to locate the position of the panels. As the method does not require connected component detection to detect division line or initial panel areas, it can be used to extract panels from both normal manga pages or manga pages with large extruding objects. At the end of this paper, three experiments are presented to verify that our automatic parameter optimization can detect bad cases and the parameter set with which the algorithm can perform better, and our panel extraction method is able to locate panel positions effectively from manga pages with large extruding objects. The first experiment will compare the results using and without using the automatic parameter optimization to show the optimization is able to improve the result. The second experiment will present the result of applying our method to our collected pages with large extruding objects to show our method is effective for this case. In the third experiment, this study applies the proposed system to the data set used in the past researches and compares results to display our system's effectiveness.
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Chiang, Ku-Yu, and 江坤煜. "Parameter Optimization Design of Packaging Molding Process." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p9kbw2.
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碩士國立中興大學
機械工程學系所
106
In recent years, Integrated Circuits (IC) have been developed to be fast, versatile, highly reliable, light-weight, thin and short. With the trend of high density, high pin count and strong function requirements of IC devices At the same time, the molding process technology for today''s packaging technology, has a pivotal role. Molding is one of the main methods for providing circuit protection in electronic packaging currently. The circuit board can be connected to an IC chip package substrate or a lead frame to exert the functions of electronic messaging and influence of the key molding process on the yield is very important. The quality factors affecting the manufacturing process are Transfer Time, Transfer Pressure, Clamp Pressure, Mold Temperature and Preheart Time. In this study, the Taguchi experimental method and the reaction surface method were used to optimize the process parameters, and the signal to noise ratio (S / N ratio) analysis was used to find the optimal process parameters. The actual experimental verification of these two methods are the best process parameters to achieve design, improve the robustness of the process.
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YEH, CHUN-HUNG, and 葉俊宏. "Intelligent scheduling parameter optimization for semiconductor manufacturing." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/e7432b.
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TANG, TSANN-TAY, and 湯燦泰. "A Study on Parameter Optimization in Melt Spinning." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/84742841982362532468.
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碩士國立臺灣科技大學
纖維及高分子工程系
89
The most popular and economic way to manufacture man-made fibers is melt spinning. In general, rotation speeds of an extruder and a gear pump, extruder temperature, spinning temperature, linear speed of a take-up equipment, and air cooled temperature and speed are a typical set of operating parameters to most affect the important properties of denier and tenacity of as-spun yarn. First, the suitable orthogonal array is used to plan experiments. An orthogonal array, the signal-to-noise ratio, and ANOVA (Analysis of Variance) are employed to investigate performance characteristics and to determine reproducibility of confirmation experiment in the melt spinning. A new method of dynamic model design that combines the BPNN (Back-propagation Neural Network) and Taguchi techniques to improve engineering design is proposed. The BPNN focuses on choosing parameters to study its the convergence. With the information and data collected from the experiments, we established a BPNN to provide accurate estimations. Finally, by utilizing the genetic algorithm, the optimal combination of the machining parameters can be found.
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Yang, Chen, and 楊振. "The Parameter Optimization of Submerged Arc Welding for." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/43421270407299276856.
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碩士國立台灣工業技術學院
機械工程技術研究所
85
The Tuguchi orthogonal array with the AIM network was used to find the parameter conforming the request quality for the reclamation of the steel mill rolls. The 420 stainless steel was applied as filler materials by submerged arc hardfacing. The result shows that there is a minimum wear rate when the slide speed was 2m/sec. The primary wear mechanism with the 2m/sec silde speed is oxidative wear. The primary wear mechanism with the speed over 2m/sec is abrasive wear. The polarization curves wear measured by potentiodynamic polarization. The result shows that the welding current during hardfacing was the major factor to improve the corrosion resistance for 420 stainless steel in 1N H2SO4 solution.
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Li, Jia-ling, and 李佳玲. "ACO-based Feature Selection and Classifier Parameter Optimization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/943bta.
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碩士國立高雄第一科技大學
資訊管理所
96
Support Vector Machines (SVM) is one of the new techniques for pattern classification. The kernel parameter settings for SVM in training process can impact on the classification accuracy. A proper feature subset can also improve the classification efficiency and accuracy. This study hybridized the SVM with ant colony optimization (ACO) to simultaneously optimize the kernel parameters and feature subset without degrading the classification accuracy. Using the feature importance and pheromones information to determine the transition probability. Using the classification accuracy and the weight vector of the feature provided by the SVM classifier are both to update the pheromone information. The experimental results of five datasets showed that the proposed approach can successfully reduce data dimensions and maintain the classification accuracy.
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Chen, Hung-cheng, and 陳宏成. "Optimization Parameter Study for Inner Leader Bonding Process." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/48747956170255840697.
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碩士逢甲大學
材料與製造工程所
94
The goal of this article is to study the stress concentration effect during the inner lead bonding process for tape automated bonding technology. The finite element method is employed to conduct the parametric study for forming the lead. The optimal parameter of time versus compression loading through the comparison between one-stage compression and two-stage compression process is proposed. Furthermore, the stress distribution on the bump and lead under different compressing time is considered at the same time for the singe lead simulating model. Based on the simulation results, some experiments were done to approve that the parameters of compression, including bonding force and bonding time, which were highly related to the manufacturing yield and reliability for bump and lead.
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Wu, Chung-Seng, and 吳忠信. "Shape optimization and parameter analysis of link chain." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/60119593321819406138.
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碩士國立中興大學
機械工程學系所
96
This thesis studies the stresses in step chain subjected to differenert loadings. When the link is assembled,the interference fit occures between the link plate and the pin or bush.This interference fit produce stresses in the link before subjecting to external loading.Upon moving to the tight side,the link takes the maximum loading which includes the external loading and the loading due to interference fit.When the link enters the slake side,the external loading approaches zero.Therefore in a complete cycle,the loading applied to a link varies between two values.The fatigue failure may occur.This thesis analyzes the fatigue problem and proposes some ways to improve fatigue life.Another failure of a link may be due to wear.Archard theory is used to estimate the wear depth of the pin and the bush.Based on Hertz theory and contact analysis by finite element method,the location of maximum wear deoth can be found. To improve the fatigue life of the link, the parameter analysis is done in this thesis. The diameter of the pin and the bush and the amount of interference are analyzed, some suggestions are made. Another approach used in this thesis to improve fatigue life is to use shape optimization to find the optimum shape of the link plate to reduce the maximum stress,various shape basis vectors are generated and used to optimize the boundary of the link.The maximum stress in optimied shape is proved to be less than that in the original shape.
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Chiang, Tai-Lin, and 姜台林. "Optimization of Parameter Design via Integrated Intelligent Approach." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/24552485173768504164.
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博士國立交通大學
工業工程與管理系
90
Parameter design is advocated to design a product by selecting the optimum conditions of control factors (factors which we can control) so that the product is least sensitive to uncontrollable factors (noise factors) such as wear, aging, and ambient conditions. The manufacturing interests which are created concern technological advances and the marketability of products and services. Thus, parameter design questions interact constantly with the realm of continuous improvement. Conventionally, Taguchi methods have been widely used in industry; however, this method can only get the optimal under discrete control factors that will lead the real optimum with uncertain. On the other hand, most published literature addressing the multiresponse problem is too difficult to be understood by engineers with limited statistical knowledge; therefore, it is difficult to apply on the shop floor. This dissertation proposes three types of integrated intelligent approach for the optimization of the parameter design: (1) neural networks and Taguchi methods (NN-Taguchi), (2) neural networks, genetic algorithms, and exponential desirability function (NN-GA-EDF), and (3) neural networks, genetic algorithms, exponential desirability function, and fuzzy theory (NN-GA-EDF-Fuzzy). The major advantages of the proposed methods are: (1) Neural networks and Taguchi methods provide a framework of application to help a manufacturing process that is infeasible to conduct the experimental design identifies major parameters and adjusts appropriate parameters settings according to different customer’s requirements; (2) The three proposed approaches of parameter design are very suitable for high-tech industry (ex. semiconductor industry) which involves very complicated process reaction; (3) The proposed approaches can obtain the optimal parameter settings in continuous values, (4) All of the proposed approaches have abilities to cope with multiple responses to identify the key process parameters, and locate multiple optimum, (5) The engineer with limited statistical knowledge is able to apply the proposed approach easily; neither do they need to make any assumption regarding the data set, and (6) The proposed approach using fuzzy theory is intuitively logical and allows the engineer to involve process information into analysis for evaluating the qualitative response problems. Three real examples that were carried out in the semiconductor related factories in Taiwan have demonstrated the practicability of the proposed procedures.
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劉家鈞. "Orientation-Parameter Optimization Applied to Airborne SAR Imagery." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/02899698999278322986.
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碩士國立中央大學
土木工程研究所
87
This paper begins with elaborating the deduction and solutions of Radargrammetric Condition Equations on a single SAR (Synthetic Aperture Radar) image, which is the time-varying SAR airborne flight locus represented with polynomial collaborating with the mixed adjustment method. As for the blunder detection in observation data, in order to apply τ-test in detecting the possible blunder in observation data to assure its quality, the orientation parameter used to be described with two-order polynomial in previous researches. In this paper, one appropriate high-order polynomial is chosen in the first place to proceed with the optimization of the orientation parameter originally presented by two-order polynomial. The result of the experiment has shown that the check point precision is 4.5-5.5 meter horizontally and 5.0 meter vertically, which proves to be better than two-order polynomial. This is going to make positive contributions toward the precision promotion in the subsequent researches on geocoding of SAR image. The experimental area located near the Yang-mei town, Taoyuan County is 12 km from east to west and 9 km from north to south with the largest height difference about 150 m.
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Lin, Yuan-Ting, and 林苑婷. "Hybrid Electric Scooter System Modeling and Parameter Optimization." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/99494642805953440730.
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碩士國立屏東科技大學
車輛工程系所
97
The works of this thesis are divided into two parts. Firstly, a dynamic simulation model for hybrid electric scooters (HES) is developed. Attention is paid to the prediction of key parameters of the scooter such as the engine speed, CVT gear ratio, and fuel consumption on the ECE40 drive cycle in order to evaluate the performance of the HES. The scooter studied in this research consists of an electronically controlled fuel injection internal combustion engine (ICE), a generator, clutch, final drive gears, a power assistant motor, and a mechanical-type continuously variable transmission (CVT). Due to the system being complicated and nonlinear in nature, the simulation model is simultaneously constructed using mathematical models, empirical equations, and experimental data. The effectiveness of the model was verified experimentally using a scooter hardware-in-the-loop (HIL) system, as well as using a HES running on chassis dynamometer. With the developed dynamic simulation model, the second part of this thesis is to develop a parameter optimization algorithm for the energy management system (EMS) of the HES. The proposed algorithm integrates the dynamic programming (DP) method with a feedforward neural network (FNN), which is intended to determine the global optimal values of the power split ratio of the HES on the ECE40 drive cycles. Based on the algorithm, the effects of different cost functions with various weight coefficients on the fuel consumption rate were discussed.
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Cazzanti, Luca. "Lithography parameter extraction and optimization using neural networks." 1998. http://catalog.hathitrust.org/api/volumes/oclc/39535461.html.
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Thesis (M.S.)--University of Wisconsin--Madison, 1998.Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 53-57).