• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.840-848
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• 2009

This paper proposes a new multi-start scheme that generates guided random bits in selecting initial search points for global optimization with univariate dynamic encoding algorithm for searches (uDEAS). The proposed method counts the number of 1 in each bit position from all the previously generated initial search matrices and, based on this information, generates 0 in proportion with the probability of selecting 1. This rule is simple and effective for improving diversity of initial search points. The performance improvement of the proposed multi-start is validated through implementation in uDEAS and function optimization experiments.

• ETRI Journal
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• v.35 no.6
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• pp.1168-1171
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• 2013

We propose an improvement of particle swarm optimization (PSO) based on the stabilization of particle movement (PM). PSO uses a stochastic variable to avoid an unfortunate state in which every particle quickly settles into a unanimous, unchanging direction, which leads to overshoot around the optimum position, resulting in a slow convergence. This study shows that randomly located particles may converge at a fast speed and lower overshoot by using the proportional-integral-derivative approach, which is a widely used feedback control mechanism. A benchmark consisting of representative training datasets in the domains of function approximations and pattern recognitions is used to evaluate the performance of the proposed PSO. The final outcome confirms the improved performance of the PSO through facilitating the stabilization of PM.

• Proceedings of the Korea Society for Simulation Conference
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• 1993.10a
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• pp.16-16
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• 1993

The efficient use of capital intensive FMS requires determination of effective dispatching priority with which the parts of the selected part types are to be inputed into the system. This paper presents a simulation-optimization approach to find an appropriate dispatching priority. The study is based on a detailed simulator for a module-type commercial FMS, Specifically, after presenting the basic configuration and fundamental control logic of the system together with its main characteristics as a special type of a job shop, an algorithm is presented which combines simulated annealing and simulation to explore a dispatching priority of operations that minimizes the total tardiness, Computational performance of the algorithm shows that good solutions can be obtained within a reasonable amount of computations. The paper also compares the performance of the "optimal" or near optimal dispatching priority generated by the proposed algorithm with those generated by standard dispatching rules such as SPT, EDD and SLACK.

• Proceedings of the IEEK Conference
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• summer
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• pp.70-73
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• 2004

We have developed an optimization algorithm based formulation for performing efficient time driven simultaneous place and route for FPGAs. Field programmable gate array (FPGAs) provide of drastically reducing the turn-around time for digital ICs, with a relatively small degradation in performance. For a variety of application specific integrated circuit application, where time-to-market is most critical and the performance requirement do not mandate a custom or semicustom approach, FPGAs are an increasingly popular alternative. This has prompted a substantial amount of specialized synthesis and layout research focused on maximizing density, minimizing delay, and minimizing design time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.632-637
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• 2014

In this paper the concept of the performance-based logistics (PBL) support for weapon systems is discussed and an enhancement is studied such that prior to the Operational phase, the development of the PBL can begin from the Engineering & Manufacturing Development (EMD) phase together with multiple performance indices considered. The genetic algorithm should be considered for the complex system to solve the maintenance policy optimization. In particular, the requirement of repair level analysis model is developed based on reflecting the PBL concept. To decide the maintenance policy prior to Operational phase in accordance with customer requirements, the PIDO(Process Integration and Design Optimization) technique useful in choosing the performance indices and changing the constraints was used. The genetic algorithm of PIDO tool, like PIAnO and ModelCenter, was verified that it could be applied to optimize the maintenance policy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1432-1440
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• 2022

Recently, as Virtual Desktop Infrastructure(VDI) is widely used not only in office work environments but also in workloads that use high-spec multi-core-based applications, the requirements for real-time and stability of VDI are increasing. Accordingly, the display protocol used for remote access in VDI and performance optimization of virtual machines have also become more important. In this paper, we propose two ways to configure desktop virtualization for multi-core-based application operation. First, we propose a codec configuration of a display protocol with optimal performance in a high load situation due to multi-processing. Second, we propose a virtual CPU scheduling optimization method to reduce scheduling delay in case of CPU contention between virtual machines. As a result of the test, it was confirmed that the H.264 codec of Blast Extreme showed the best and stable frame, and the scheduling performance of the virtual CPU was improved through scheduling optimization.

• Journal of the Korean Society for Railway
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• v.19 no.2
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• pp.234-242
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• 2016

In express operation plans for urban trains, it is effective for the reduction of the number of sidetracks to apply a high performance train that has improved acceleration/deceleration ability and a regular train to local and express trains, respectively. In this research, based on a plan to use a high performance train for a local train, an optimization methodology is suggested to reduce the number of sidetracks and the operation time of the local train simultaneously. The optimization solver applied in this research is a genetic algorithm; headway, location of sidetrack and waiting time at the sidetrack are considered as design variables in the optimization problem. Consequently, by applying this system to Seoul metro line no.7, the effect of the suggested methodology was verified by obtaining the proper optimum solution.

• International Journal of Fluid Machinery and Systems
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• v.9 no.4
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• pp.370-381
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• 2016

As a single-channel pump is used for wastewater treatment, this particular pump type can prevent performance reduction or damage caused by foreign substances. However, the design methods for single-channel pumps are different and more difficult than those for general pumps. In this study, a design optimization method to improve the hydrodynamic performance of a single-channel pump impeller is implemented. Numerical analysis was carried out by solving three-dimensional steady-state incompressible Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. As a state-of-the-art impeller design method, two design variables related to controlling the internal cross-sectional flow area of a single-channel pump impeller were selected for optimization. Efficiency was used as the objective function and was numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. An optimization process based on a radial basis neural network model was conducted systematically, and the performance of the optimum model was finally evaluated through an experimental test. Consequently, the optimum model showed improved performance compared with the base model, and the unstable flow components previously observed in the base model were suppressed remarkably well.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.588-599
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• 2019

This research presents a structural design of high-level waste (HLW) container using ultra-high performance fiber reinforced concrete (UHP-FRC) material. The proposed design aims to overcome the drawbacks of the existing concrete containers which are heavy, difficult to fabricate, and expensive. In this study, the dry storage container (DSC) that commonly used at Canadian Nuclear facilities is selected to present the proposed design. The design has been performed such that the new UHP-FRC alternative has a structural stiffness equivalent to the existing steel-concrete-steel container under various loading scenarios. Size optimization technique is used with the aim of maximizing stiffness, and minimizing the cost while satisfying both the design stresses and construction requirements. Then, the integrity of the new design has been evaluated against accidental drop-impact events based on realistic drop scenarios. The optimization results showed: the stiffness of the UHP-FRC container (300 mm wall thick) is being in the range of 1.35-1.75 times the stiffness of existing DSC (550 mm wall thick). The use of UHP-FRC leads to decrease the container weight by more than 60%. The UHP-FRC container showed a significant enhancement in performance in comparison to the existing DSC design under considered accidental drop impact scenarios.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.