• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5015-5027
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• 2015

Transceiver optimization in multiple input multiple output (MIMO) cognitive systems is studied in this paper. The joint transceiver beamformer design is introduced to minimize the transmit power at secondary base station (SBS) while simultaneously controlling the interference to primary users (PUs) and satisfying the secondary users (SUs) signal-to-interference-plus-noise ratio (SINR) based on the convex optimization method. Due to the limited cooperation between SBS and PUs, the channel state information (CSI) usually cannot be obtained perfectly at the SBS in cognitive system. In this study, both perfect and imperfect CSI scenarios are considered in the beamformer design, and the proposed method is robust to CSI error. Numerical results validate the effectiveness of the proposed algorithm.

• Journal of Korean Society for Quality Management
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• v.34 no.4
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• pp.93-101
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• 2006

Robust parameter design is an off-line production technique for reducing variation and improving the quality of products and processes by using product arrays. However, the use of the product arrays usually requires a large number of runs. To overcome the drawback of the product array, the combined array can be used. Also optimizing multiple responses is increasingly important in industry. Using simultaneous optimization measures, we can deal with the multiple response case. In this paper we compare the simultaneous optimization using the Taguchi's product array with using the combined array. And models possible to set on combined arrays are also investigated and compared with the cases of product arrays.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.407-422
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• 2004

A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closed-loop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4181-4194
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• 2022

Main steam safety valves are commonly used in nuclear power plants to provide final protections from overpressure events. Blowdown and dynamic stability are two critical characteristics of safety valves. However, due to the parameter sensitivity and multi-parameter features of safety valves, using traditional method to design and/or optimize them is generally difficult and/or inefficient. To overcome these problems, a surrogate model-based valve design optimization is carried out in this study, of particular interest are methods of valve surrogate modeling, valve parameters global sensitivity analysis and valve performance optimization. To construct the surrogate model, Design of Experiments (DoE) and Computational Fluid Dynamics (CFD) simulations of the safety valve were performed successively, thereby an ensemble surrogate model (E-AHF) was built for valve blowdown and stability predictions. With the developed E-AHF model, global sensitivity analysis (GSA) on the valve parameters was performed, thereby five primary parameters that affect valve performance were identified. Finally, the k-sigma method is used to conduct the robust optimization on the valve. After optimization, the valve remains stable, the minimum blowdown of the safety valve is reduced greatly from 13.30% to 2.70%, and the corresponding variance is reduced from 1.04 to 0.65 as well, confirming the feasibility and effectiveness of the optimization method proposed in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.589-592
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• 1996

It is well known that robust compensators designed by the block-diagonal Lyapunov function approaches are conservative while they are popular in practice because of their computational easiness. In this note, we develop a systematized version of conventional block-diagonal Lyapunov function approaches by deriving two separated optimizations based on the guaranteed cost control method. The proposed method generates reasonable robust compensators in practice.

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.647-661
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• 2023

It is recognized that the installation of energy dissipation devices, such as the tuned mass damper (TMD), decreases the dynamic response of structures, however, the best parameters of each device persist hard to determine. Unlike many works that perform only a deterministic optimization, this work proposes a complete methodology to minimize the dynamic response of footbridges by optimizing the parameters of multiple tuned mass dampers (MTMD) taking into account uncertainties present in the parameters of the structure and also of the human excitation. For application purposes, a steel footbridge, based on a real structure, is studied. Three different scenarios for the MTMD are simulated. The proposed robust optimization problem is solved via the Circle-Inspired Optimization Algorithm (CIOA), a novel and efficient metaheuristic algorithm recently developed by the authors. The objective function is to minimize the mean maximum vertical displacement of the footbridge, whereas the design variables are the stiffness and damping constants of the MTMD. The results showed the excellent capacity of the proposed methodology, reducing the mean maximum vertical displacement by more than 36% and in a computational time about 9% less than using a classical genetic algorithm. The results obtained by the proposed methodology are also compared with results obtained through traditional TMD design methods, showing again the best performance of the proposed optimization method. Finally, an analysis of the maximum vertical acceleration showed a reduction of more than 91% for the three scenarios, leading the footbridge to acceleration values below the recommended comfort limits. Hence, the proposed methodology could be employed to optimize MTMD, improving the design of footbridges.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.99-104
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• 2008

This study performed robust design optimization of fighter wing planform, considering uncertainty based on neural network model. To construct uncertainty model, aerodynamic performance and their sensitivity were evaluated by 3-dimensional Euler equations and adjoint variable method at experimental points selected from central composite design. In addition, because a neural network model has the advantage of capturing non-linear characteristic, it was possible to predict sensitivity of the aerodynamic performance efficiently and accurately . From the results of robust design optimization, it could be confirmed that the robustness of the objective function and constraints were improved if the variation of uncertainty and sigma level were increased.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.12
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• pp.1132-1139
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• 2008

The aim of this study is to apply shape optimization technique for the repair of aging airframe components, which may extend fatigue life substantially. Free-form optimum shapes of a cracked part to be reworked or replaced are investigated with the objective to minimize the peak local stress concentration or fatigue-damage. Iterative non-gradient method, which is based on an analogy with biological growth, is employed by incorporating the robust optimization method to take account of the stochastic nature of the loading conditions. Numerical examples of optimal hole shape in a flat plate are presented to validate the proposed method. The method is then applied to determine the reworked or replacement shape for the repair of a cracked rib in the rear assembly wing body of aircraft.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.8
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• pp.24-32
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• 2006

Using a deterministic design optimization, the effect of uncertainty can result in violation of constraints and deterioration of performances. For this reason, design optimization is required to guarantee reliability for constraints and ensure robustness for an objective function under uncertainty. Therefore, this study drew Monte Carlo Simulation(MCS) for the evaluation of reliability and robustness, and selected an artificial neural network as an approximate model that is suitable for MCS. Applying to the aero-structural optimization problem of aircraft wing, we can explore robuster optima satisfying the sigma level of reliability than the baseline.

• Journal of the Korean Magnetics Society
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• v.24 no.5
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• pp.160-164
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• 2014

In this paper, an efficient robust design utilizing an enhanced Taguchi method is proposed to reduce cogging torque of a BLDC motor in the presence of design uncertainty. To overcome defects of the conventional Taguchi method in dealing with a generalized robust design problem, a penalty function and an optimal level searching technique are newly introduced. In order to verify the proposed method, a 5 kW, rated speed of 2,300 rpm, rated torque of 20 Nm BLDC motor for driving electric vehicles is optimized. Then, the robust design is compared with conceptual and deterministic ones in terms of the cogging torque, rated torque and torque ripple.