• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.5
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• pp.437-444
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• 2015

In simulation-based circuit optimization, many simulation runs may be wasted while evaluating infeasible designs, i.e. the designs that do not meet the constraints. To avoid such a waste, this paper investigates the use of support vector machine (SVM) classifiers in predicting the design's feasibility prior to simulation and the optimal selection of the SVM parameters, namely, the Gaussian kernel shape parameter ${\gamma}$ and the misclassification penalty parameter C. These parameters affect the complexity as well as the accuracy of the model that SVM represents. For instance, the higher ${\gamma}$ is good for detailed modeling and the higher C is good for rejecting noise in the training set. However, our empirical study shows that a low ${\gamma}$ value is preferable due to the high spatial correlation among the circuit design candidates while C has negligible impacts due to the smooth and clean constraint boundaries of most circuit designs. The experimental results with an LC-tank oscillator example show that an optimal selection of these parameters can improve the prediction accuracy from 80 to 98% and model complexity by $10{\times}$.

• Earthquakes and Structures
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• v.18 no.5
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• pp.625-635
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• 2020

Suspension bridges have the extended in plan configuration which makes them prone to dynamic events like earthquake. The longer span lead to more flexibility and slender of them. So, control systems seem to be essential in order to protect them against ground motion excitation. Tuned mass damper or in brief TMD is a passive control system that its efficiency is practically proven. Moreover, its parameters i.e. mass ratio, tuning frequency and damping ratio can be optimized in a manner providing the best performance. Meta-heuristic optimization algorithm is a powerful tool to gain this aim. In this study, TMD parameters are optimized in different-length suspension bridges in three distinct cases including 3, 4 and 5 TMDs by observer-teacher-learner based algorithm under a complete set of ground motions formed from both near-field and far-field instances. The Vincent Thomas, Tacoma Narrows and Golden Gate suspension bridges are selected for case studies as short, mean and long span ones, respectively. The results indicate that All cases of used TMDs result in response reduction and case 4TMD can be more suitable for bridges in near and far-field conditions.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.115-121
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• 2012

This paper, being the second in a two-part series, presents the robust performance of the proposed design method which can enhance a reliability-based design optimization(RBDO) under the uncertainties of probabilistic models. The robust performances of the solutions obtained by the proposed method, described in the Part 1, are investigated through the parametric studies. A 10-bar truss example is considered, and the uncertain parameters include the number of data observed, and the variations of applied loadings and allowable stresses. The numerical results show that the proposed method can produce a consistent result despite of the large variations in the parameters. Especially, even with the relatively small data set, the analysis results show that the exact probabilistic model can be successfully predicted with optimized design sections. This consistency of estimating appropriate probability model is also observed in the case of the variations of other parameters, which verifies the robustness of the proposed method.

• Advances in materials Research
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• v.13 no.1
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• pp.1-18
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• 2024

The present experimental investigation focuses on finding optimal parametric data-set of laser micro-drilling operation with minimum taper and Heat-affected zone during laser micro-drilling of Carbon Nanotube/Epoxy-based composite materials. Experiments have been conducted as per Box-Behnken design (BBD) techniques considering cutting speed, lamp current, pulse frequency and air pressure as input process parameters. Then, the relationship between control parameters and output responses is developed using second-order nonlinear regression models. The analysis of variance test has also been performed to check the adequacy of the developed mathematical model. Using the Response Surface Methodology (RSM) and an Accelerated particle swarm optimization (APSO) technique, optimum process parameters are evaluated and compared. Moreover, confirmation tests are conducted with the optimal parameter settings obtained from RSM and APSO and improvement in performance parameter is noticed in each case. The optimal process parameter setting obtained from predictive RSM based APSO techniques are speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), Air pressure (1 kg/cm²) for Taper and speed=150 (m/s), current=22 (amp), pulse frequency (3 kHz), air pressure (3 kg/cm²) for HAZ. From the confirmatory experimental result, it is observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses during laser micro-drilling process of nanocomposites both in individual and multi-objective optimization.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.565-566
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• 2007

• Proceedings of the Korea Concrete Institute Conference
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• 1992.10a
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• pp.191-195
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• 1992

Optimization of the RC pile cap for high rise apartment building has been performed with the aim of the cost minimization. The principle of divided parameters has been used. As a result the cost has been reduced by 25percentage.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.172.2-172.2
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• 2005

• East Asian mathematical journal
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• v.16 no.2
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• pp.317-330
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• 2000

In this paper we consider the optimal control problem of both operators and parameters for nonlinear hyperbolic systems. For the identification problem, we show that for every value of the parameter and operators, the optimal control problem has a solution. Moreover we obtain the necessary conditions of optimality for the optimal control problem on the system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.465-466
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• 2011