• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.1-11
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• 1996

The detection of multiple outliers in nonlinear regression models can be computationally not feasible. As a compromise approach, we consider the use of simulated annealing algorithm, an approximate approach to combinatorial optimization. We show that this method ensures convergence and works well in locating multiple outliers while reducing computational time.

• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.1-10
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• 2003

We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.496-506
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• 2014

In this paper the scenario based optimization of the bulbous bow of the KRISO Container Ship (KCS) is presented. The optimization of the parametrically modeled vessel is based on a statistically developed operational profile generated from noon-to-noon reports of a comparable 3600 TEU container vessel and specific development functions representing the growth of global economy during the vessels service time. In order to consider uncertainties, statistical fluctuations are added. An analysis of these data lead to a number of most probable upcoming operating conditions (OC) the vessel will stay in the future. According to their respective likeliness an objective function for the evaluation of the optimal design variant of the vessel is derived and implemented within the parametrical optimization workbench FRIENDSHIP Framework. In the following this evaluation is done with respect to vessel's calculated effective power based on the usage of potential flow code. The evaluation shows, that the usage of scenarios within the optimization process has a strong influence on the hull form.

• Structural Engineering and Mechanics
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• v.54 no.4
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• pp.725-740
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• 2015

This study presents critical buckling load optimization of the axially graded layered uniform columns. In the first place, characteristic equations for the critical buckling loads for all boundary conditions are obtained using the transfer matrix method. Then, for each case, square of this equation is taken as a fitness function together with constraints. Due to explicitly unavailable objective function for the critical buckling loads as a function of segment length and volume fraction of the materials, especially for the column structures with higher segment numbers, initially, prescribed value is assumed for it and then the design variables satisfying constraints are searched using Differential Evolution (DE) optimization method coupled with eigen-value routine. For constraint handling, Exterior Penalty Function formulation is adapted to the optimization cycle. Different boundary conditions are considered. The results reveal that maximum increments in the critical buckling loads are attained about 20% for cantilevered and pinned-pinned end conditions and 18% for clamped-clamped case. Finally, the strongest column structure configurations will be determined. The scientific and statistical results confirmed efficiency, reliability and robustness of the Differential Evolution optimization method and it can be used in the similar problems which especially include transcendental functions.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.127-134
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• 2004

It is difficult to have the existing structural optimization using coupled field analysis from CFD to structure analysis when the structure is influenced of fluid. Therefore in an initial model of this study after doing parameter design from the background of shape using topology optimization. and it is making a approximation formula using by the CFD-structure coupled-field analysis and design of experiment. By using this result, we conducted multi-objective optimization. We could confirm efficiency of stochastic method applicable in the scene of structure reliability design to be needed multi-objective optimization. And we presented a way of design that could overcome the time and space restriction in structural design such as the butterfly valve with the less experiment.

• Elastomers and Composites
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• v.57 no.4
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• pp.147-156
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• 2022

In this study, we chose a flat die to optimize a general die geometry. The optimization was aimed at obtaining a uniform velocity distribution across the exit of the die. For the optimization, the input and output design parameters were randomly computed, and response surfaces were generated to obtain statistical data for the minimum and maximum sensitivities computed during optimization. Subsequently, object functions with constraints were numerically computed to obtain the minimum errors in the velocity difference (i.e., variable "Outp" in this study). Finally, we obtained the candidate optimized dataset. Note that the current numerical computations were simultaneously conducted for an entire extruder, i.e., screw plus die. The numerical outlet velocity distributions in the modified die geometry tended to be much more uniform than the conventional distributions in the current optimization processes for this specific flat die.

• KSBB Journal
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• v.29 no.3
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• pp.165-178
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• 2014

Statistical medium optimization has been carried out for the production of succinic acid in anaerobic fermentations of Actinobacillus succinogenes. Succinic acid utilized as a precursor of many industrially important chemicals is a fourcarbon dicarboxylic acid, biosynthesized as one of the fermentation products of anaerobic metabolism by A. succinogenes. Through OFAT (one factor at a time) experiments, corn steep liquor (CSL), a very cheap agricultural byproduct, was found to have significant effects on enhanced production of succinic acid, when supplemented along with yeast extract. Hence, using these factors including glucose as a carbon/energy source, interactive effects were investigated through $2^n$ full factorial design (FFD) experiments, showing that the concentration of each component (i.e., glucose, yeast extract and CSL) should be higher. Further statistical experiments were conducted along the steepest ascent path, followed by response surface method (RSM) in order to find out optimal concentrations of each constituent. Consequently, optimized concentrations of glucose, yeast extract and CSL were observed to be 180 g/L, 15.08 g/L and 20.75 g/L respectively (10 g/L of $NaHCO_3$ and 100 g/L of $MgCO_3$ to be supplemented as bicarbonate suppliers), with the estimated production level of succinic acid to be 92.9 g/L (about 3.5 fold higher productivity as compared to the initial medium). Notably, the RSM-estimated production level was almost similar to the amount of succinic acid (92.9 g/L vs. 89.1 g/L) produced through the actual fermentation process performed using the statistically optimized production medium.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.3
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• pp.77-86
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• 1994

A New environment SENSATION for circuit optimization and statistical analysis has been developed. It provides real time simulation and includes automatic algorithms to assist for reaching optimal solution. Furthermore, statistical analysis environment is presented which aids in Monte Carlo analysis. worst case corner analysis, and sensitivity analysis. These capabilities faciliate the characterization of the effects of several operating conditions and manufacture process paramenters on the design performances. We verify that the proposed methods can obtain the optimal solution of the objective function through several experimental results.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.39-47
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• 2005

We adopt the P/sub λ, T//sup M/ policy of dam to introduce a service policy with alternating service rates for a Poisson arrival queue, in which the service rate alternates depending on the number of customers in the system. The stationary distribution of the number of customers in the system is derived and, after operating costs being assigned to the system, the optimization of the policy is studied.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.843-848
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• 1999

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.