• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.235-245
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• 1998

This paper presents the fuzzy optimization model based scheduling methodology for the efficient production control of a FFS(FIexible Flow Shop) under the uncertain production environment. To develop the methodology, a fuzzy optimization technique is introduced in which the uncertain production capacity caused by the random events like the machine breakdowns or the absence of workers is modeled by fuzzy number. Since the problem is NP hard, the goal of this study is to obtain the near optimal but practical schedule in an efficient way. Thus, Lagrangian relaxation method is used to decompose the problem into a set of subproblems which are easier to solve than the original one. Also, to construct the feasible schedule, a heuristic algorithm was proposed. To evaluate the performance of the proposed method, computational experiments, based on the real factory data, are performed. Then, the results are compared with those of the other methods, the deterministic one and the existing one used in the factory, in the various performance indices. The comparison results demonstrate that the proposed method is more effective than the other methods.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.05a
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• pp.205-208
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• 2008

Generally, there are several types in rear suspension. The rear suspension of subframe type consisting of side member and front/rear cross member is widely used in a medium car and full car. In the small car case, the beam of tubular type without independent suspension system is used to reduce manufacturing cost. The optimized rear suspension of subframe type using hydroforming method has been developed in this study. In designing suspension, the driving stability and durability performance should be considered as an important factor. The stability is related to dynamic frequency and durability is connected with stress analysis of structure. We focus on increasing the stiffness of suspension and decreasing the maximum stress relating to durability cycle life. For making use of the merits of hydroforming which is possible to make the bead, tube expansion, and feeding in desiring position, several optimization design techniques such as shape, size, and topology optimization are proposed. This optimization scheme based on the sensitivity can provide distinguished performance improvement in using hydroforming. Through commercial software based on the finite element, the superiority of this design method is demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.4
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• pp.1692-1711
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• 2016

Energy is a scarce resource in wireless sensor networks (WSNs). A variety of clustering protocols for WSNs, such as the zone-based stable election protocol-enhanced (ZSEP-E), have been developed for energy optimization. The ZSEP-E is a heterogeneous zone-based clustering protocol that focuses on unbalanced energy consumption with parallel formation of clusters in zones and election of cluster heads (CHs). Most ZSEP-E research has assumed probabilistic election of CHs in the zones by considering the maximum residual energy of nodes. However, studies of the diverse CH election parameters are lacking. We investigated the performance of the ZSEP-E in such scenarios using a fuzzy logic approach based on three descriptors, i.e., energy, density, and the distance from the node to the base station. We proposed an efficient ZSEP-E scheme to adapt and elect CHs in zones using fuzzy variables and evaluated its performance for different energy levels in the zones.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.8
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• pp.1071-1079
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• 2018

In this study, the design methodology as well as network architecture of Support Vector Machine based Polynomial Neural Network, which is a kind of the dynamically generated neural networks, is introduced. The Support Vector Machine based polynomial neural networks is given as a novel network architecture redesigned with the aid of polynomial neural networks and Support Vector Machine. The generic polynomial neural networks, whose nodes are made of polynomials, are dynamically generated in each layer-wise. The individual nodes of the support vector machine based polynomial neural networks is constructed as a support vector machine, and the nodes as well as layers of the support vector machine based polynomial neural networks are dynamically generated as like the generation process of the generic polynomial neural networks. Support vector machine is well known as a sort of robust pattern classifiers. In addition, in order to enhance the structural flexibility as well as the classification performance of the proposed classifier, multi-objective particle swarm optimization is used. In other words, the optimization algorithm leads to sequentially successive generation of each layer of support vector based polynomial neural networks. The bench mark data sets are used to demonstrate the pattern classification performance of the proposed classifiers through the comparison of the generalization ability of the proposed classifier with some already studied classifiers.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.102-107
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• 2004

Performance based design is a recent evolutionary step in the process of designing fire protection systems. In essence, it is a logical design process resulting in a solution that achieves a specified performance. Sometimes the prescriptive solutions presented in various codes and standards are too expensive or inflexible. Often the solutions do not and enables optimization of a solution for cost and function. In this study, performance based design was carried out to determine the extent of passive fire protection for oil terminal facilities. The results of performance based design were compared with those of prescriptive code based design. Performance based design is not always more economic than prescriptive code based design but provides more reliable and effective design that is fit for the purpose.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.52-58
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• 2011

The missile control system is comprised of various control systems such as autopilot, guidance law, and homing filter and so on. To design these guidance and control system, the optimization technique is widely applied at each developing stage. However, this kind of optimization requires lots of time and cost and moreover, this approach does not give an overall system optimization result. In this paper, to use the optimization tool for control system design, the optimal problem formulation is done and the performance index and constraints are considered. And finally the systematically optimized method is proposed.

• 한국전산유체공학회:학술대회논문집
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• 1999.11a
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• pp.77-84
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• 1999

Numerical design optimization techniques are implemented for the improvement of the ram accelerator performance. The design object is to find the minimum ram tube length required to accelerate projectile from initial velocity $V_0$ to target velocity $V_e$. The premixture is composed of $H_2,\;O_2,\;N_2$ and the mole numbers of these species are selected as design variables. The objective function and the constraints are linearized during the optimization process and gradient-based Simplex method and SLP(Sequential Linear Programming) have been employed. With the assumption of two dimensional inviscid flow for internal flow field, the analyses of the nonequilibrium chemical reactions for 8 steps 7 species lave been performed. To determined the tube length, ram tube internal flow field is assumed to be in a quasi-steady state and the flow velocity is divided into several subregions with equal interval. Hence the thrust coefficients and accelerations for corresponding subregions are obtained and integrated for the whole velocity region. With the proposed design optimization techniques, the total ram tube length had been reduced $19\%$ within 7 design iterations. This optimization procedure can be directly applied to the multi-stage, multi-premixture ram accelerator design optimization problems.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.523-530
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• 2007

This paper proposes a new stochastic optimization algorithm for hidden Markov models (HMMs) used as a recognizer of automatic lipreading. The proposed method combines a global stochastic optimization method, the simulated annealing technique, and the local optimization method, which produces fast convergence and good solution quality. We mathematically show that the proposed algorithm converges to the global optimum. Experimental results show that training HMMs by the method yields better lipreading performance compared to the conventional training methods based on local optimization.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.54 no.4
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• pp.163-173
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• 2005

The conventional optimization study for electromagnetic systems has been mostly on the shape or size optimization. The goal for these optimization methods is to improve performance of electromagnetic systems by optimizing the interface shape of two different materials while their given layout or initial topology are held. The feasible topology can be diverse and an appropriate topology will give much better design results. In this paper we propose a theory and an algorithm for topology optimization of electromagnetic systems, which are based on the finite element method. The topology optimization technique employes a direct searching method of sensitivity analysis in which the information of material sensitivity is used. Two numerical examples of a switched reluctance motor and an electrostatic actuator of MEMS are tested and their design results show that the optimization method is valid and useful for the topology and basic layout design of electromagnetic systems.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1254-1261
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• 2003

In this paper we present a simple and efficient robust optimal design formulation and its application to a resonant-type micro probe. The basic idea is to use the Gradient Index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-efficient and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation.