• Korean Journal of Optics and Photonics
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• v.12 no.2
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• pp.109-114
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• 2001

Optical system has some optical and mechanical constraints. The constraints should be preserved in optimization of optical system. For the purpose, the constraints are combined with the merit function by using Lagrange's undetermined multipliers. We propose an active optimization control by using the fact that the errors of constraints are corrected with higher priority than the other errors of the merit function. In this control, the errors which have large contribution to the merit function are converted into constraints. At that time, if the errors are corrected at once, the optimization will be unstable because of their non-linearity. Hence we introduce a target rate which represents a fraction of error to be corrected, and the errors are corrected progressively. An optimization program was developed on the bases of the proposed active control, and applied to design a photographic lens system. By using the active control, we could get better results compared with conventional damped least squares method. ethod.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.561-571
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• 2022

An optical mirror assembly is an opto-mechanically coupled system as the optical and mechanical behaviors interact. In the assembly, a flexure mount attached to an optical mirror should be flexible in the radial direction, but rigid for the remaining degrees of freedom for supporting the mirror rigidly and suppressing the wavefront error of the optical mirror. This work presents an optimal design of the flexure mount using topology optimization with thermal stress constraint. By simplifying the optical mirror assembly into finite shell elements, topology optimization model was built for efficient design and good machinability. The stress at the boundary between the optical mirror and the mount together with the first natural frequency were applied as constraints for the optimization problem, while the objective function was set to minimize the strain energy. As a result, we obtained the optimal design of the flexure mount yielding the improved wavefront error, proper rigidity, and machinability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.541-549
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• 2004

In this paper, a new improved EREC based error resilient coding in JPEG-2000 standard is proposed, considering the error sensitive wireless environment with limited channel capacity. In order to apply EREC, we use the variable bit-rate by using R-D optimization. Simulation results demonstrate that the proposed EREC based error resilient coding is more resilient than the error resilient schemes used in JPEG-2000.

• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.97-105
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• 2015

Multiresponse optimization (MRO) seeks to find the setting of input variables, which optimizes the multiple responses simultaneously. The approach of weighted mean squared error (WMSE) minimization for MRO imposes a different weight on the squared bias and variance, which are the two components of the mean squared error (MSE). To date, a weighted sum-based method has been proposed for WMSE minimization. On the other hand, this method has a limitation in that it cannot find the most preferred solution located in a nonconvex region in objective function space. This paper proposes a Tchebycheff metric-based method to overcome the limitations of the weighted sum-based method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.305-307
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• 2016

The purpose of this paper is to determine the optimal values of the gain parameters used in the tracking module for a highly dynamic warship. The algorithm of the tracking module uses the ${\alpha}-{\beta}-{\gamma}$ filter to compute accurate estimates and update the state variables, that is, positions, velocity and acceleration. The filtering coefficients ${\alpha}$, ${\beta}$ and ${\gamma}$ are determined from set values of the damping parameter, ${\xi}$. Optimization is achieved by plotting a range of the damping parameter ${\xi}$ against the corresponding residual error and then selecting the best value of ${\xi}$ with the minimum residual error. Optimal values of the smoothing coefficients are subsequently computed from the selected damping parameter, ${\xi}$.

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.73-82
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• 2005

This article proposes a practical approach, which is based on the concept of the expected squared relative error, that can consider both the prediction quality and the practitioner's subjectivity in simultaneously optimizing multiple responses. Through a case study, multiresponse optimization using the expected squared relative error approach is illustrated, and the SAS program to implement the proposed method is provided.

• Journal of Environmental Science International
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• v.4 no.1
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• pp.71-80
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• 1995

A discrete state space model for a multiple-reach river system is formulated using the dynamics of biochemical oxygen demand(BOD) and dissolved oxygen(DO). A hierarchical optimization technique, which is applicable to large-scale systems with time-delays in states, is also described to control stream quality in a river as an optimal manner based on the interaction prediction method. The steady state tracking error of the proposed method is determined analytically and a necessary and sufficient condition on which a constant target tracking problem has zero steady-state error is derived. Computer simulations for the river pollution model illustrate the algorithm.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.4
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• pp.13-19
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• 2004

This paper deals with the analysis of geometric error and the optimization of process parameters in surface grinding. Taguchi method which is one of the design of experiments has been introduced in achieving the aims. The process parameters were the grain size, the wheel speed, the depth of cut and the table speed. The effect of the process parameters on the geometric error was examined and an optimal set of the parameters was selected to minimize the geometric error within the controllable range of the used grinding machine. The reliability of the results was evaluated by the ANOVA.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3460-3463
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• 2021

Present computing power and enhanced technology is progressing at a dramatic rate. These systems can unravel complex issues, assess and control processes, learn, and-in many cases-fully automate production. There is no doubt that technological advancement is improving many aspects of life, changing the landscape of virtually all industries and enhancing production beyond what was thought possible. However, the human is still a part of these systems. Consequently, as the advancement of systems transpires, the role of humans within those systems will unavoidably continue to adapt as well. Due to the human tendency for error, this technological advancement should compel a persistent emphasis on human error reduction as part of maximizing system efficiency and safety-especially in the context of the nuclear industry. Within this context, as new systems are designed and the role of the human is transformed, human error should be targeted for a significant decrease relative to predecessor systems and an equivalent increase in system stability and safety. This article contends that optimizing the roles of humans and machines in the design and implementation of new types of automation in nuclear facility systems should involve human error reduction without ignoring the essential importance of human interaction within those systems.