• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.176-179
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• 2003

This paper deals with the optimum design of thick-walled multi-layered composite tubes by minimizing the process-induced residual stresses under some constraints of structural stiffnesses. An analytic model based on quasi-static thermoelasticity is adopted for the calculation of the residual stresses in the multi-layered composite tubes. The numerical results of optimization show that, in the case of cross-ply CFRP tubes, the residual stresses can be reduced to a certain level by controlling ply thicknesses. However, the optimized tubes may be susceptible to cracking because the transverse residual stress is still large in a strength-based sense. To further suppress the residual stresses, the effects of stacking sequence, wall thickness and axial pretension on the optimum solutions are examined.

• Korean Management Science Review
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• v.26 no.1
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• pp.209-223
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• 2009

In this paper, an analysis problem of repair levels and spare part allocation for MIME(Multi indenture multi echelon) systems is studied using simulation and meta-heuristics. We suggest a method to determine simultaneously repair levels and spare parts allocation to minimize the life cycle cost of MIME system under availability constraint. A simulated annealing method is used to analyze the repair levels and genetic algorithm is used to obtain the optimal allocation of spare parts. We also develop a simulation system to calculate the life cycle cost and system availability. Some numerical examples are also studied.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.751-756
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• 2003

Ant Colony Optimization (ACO) is new meta heuristics method to solve hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was firstly proposed for tackling the well known Traveling Salesman Problem (TSP) . In this paper, we introduce Multi Colony Ant Model that achieve positive interaction and negative interaction through Intensification and Diversification to improve original ACS performance. This algorithm is a method to solve problem through interaction between ACS groups that consist of some agent colonies to solve TSP problem. In this paper, we apply this proposed method to TSP problem and evaluates previous method and comparison for the performance and we wish to certify that qualitative level of problem solution is excellent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5712-5728
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• 2017

Recently, there are many studies, that considering green wireless cellular networks, have taken the energy consumption of the base station (BS) into consideration. In this work, we first introduce an energy consumption model of multi-mode sharing BS powered by multiple energy sources including renewable energy, local storage and power grid. Then communication load requests of the BS are transformed to energy demand queues, and battery energy level and worst-case delay constraints are considered into the virtual queue to ensure the network QoS when our objective is to minimize the long term electricity cost of BSs. Lyapunov optimization method is applied to work out the optimization objective without knowing the future information of the communication load, real-time electricity market price and renewable energy availability. Finally, linear programming is used, and the corresponding energy efficient scheduling policy is obtained. The performance analysis of our proposed online algorithm based on real-world traces demonstrates that it can greatly reduce one day's electricity cost of individual BS.

• Earthquakes and Structures
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• v.18 no.1
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• pp.129-145
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• 2020

Seismic resilience is a key feature for buildings that play a strategic role within the community. In this framework, not only the structural and non-structural elements damage but also the protracted structural dysfunction can contribute significantly to overall seismic damage and post-seismic crisis situations. Reduction of the residual and peak displacements and energy dissipation by replaceable elements are some effective aspects to pursue in order to enhance the resilience. Control systems able to adapt their response based on the nature of events, such as active or semi-active, can achieve the best results, but also require higher costs and their complexity jeopardizes their reliability; on the other hand, a passive control system is not able to adapt but its functioning is more reliable and characterized by lower costs. In this study it is proposed a strategy for the optimization of the dissipative capacity of a seismic resistant system obtained placing in parallel two different groups dissipative Re-Centering Devices, specifically designed to enhance the energy dissipation, one for the low and the other for the high intensity earthquakes. In this way the efficiency of the system in dissipating the seismic energy is kept less sensitive to the seismic intensity compared to the case of only one group of dissipative devices.

• Smart Structures and Systems
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• v.16 no.1
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.5
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• pp.433-441
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• 2011

The optimization of a centrifugal compressor was conducted. The ANN (Artificial Neural Network) was adopted as an optimization algorithm, and it was learned and trained with the DOE (Design of Experiment). In the DOE, it was predicted the main effect and the interaction effect of design variables to the objective function. The ANN was improved in the optimization process using the GA (Genetic Algorithm). When any output at each generation was reached a standard level, it was re-calculated by the CFD (Computational Fluid Dynamics) and it was applied to develop a new ANN. After 6th generation, the prediction difference between ANN and CFD was less than 1%. A pareto of the efficiency versus the pressure ratio was obtained through the 21th generation. Using this method, the computational time for the optimization was equivalent to the time consumed by the gradient method, and the optimized results of multi-objective function were obtained.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.209-216
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• 2005

The controller that can control the smart base isolation system consisting of M damper and friction pendulum systems(FPS) is developed in this study. A fuzzy logic controller (FLC) is used to modulate the M damper force because the FLC has an inherent robustness and ability to handle non-linearities and uncertainties. A genetic algorithm (GA) is used for optimization of the FLC. When earthquake excitations are applied to the structures equipped with smart base isolation system, the relative displacement at the isolation level as well as the acceleration of the structure should be regulated under appropriate level. Thus, NSGA-II(Non-dominated Sorting Genetic Algorithm) is employed in this study as a multi-objective genetic algorithm to meet more than two control objectives, simultaneously. NSGA-II is used to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can efficiently find Pareto optimal sets that can reduce both structural acceleration and base drift from numerical studies.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.9
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• pp.424-430
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• 2001

A multi-level optimal power flow(OPF) algorithm has been evolved from a simple two stage optimal Power flow algorithm for constrained power economic dispatch control. In the proposed algorithm, we consider various constraints such as ower balance, generation capacity, transmission line capacity, transmission losses, security equality, and security inequality constraints. The proposed algorithm consists of four stages. At the first stage, we solve the aggregated problem that is the crude classical economic dispatch problem without considering transmission losses. An initial solution is obtained by the aggregation concept in which the solution satisfies the power balance equations and generation capacity constraints. Then, after load flow analysis, the transmission losses of an initial generation setting are matched by the slack bus generator that produces power with the cheapest cost. At the second stage we consider transmission losses. Formulation of the second stage becomes classical economic dispatch problem involving the transmission losses, which are distributed to all generators. Once a feasible solution is obtained from the second stage, transmission capacity and other violations are checked and corrected locally and quickly at the third stage. The fourth stage fine tunes the solution of the third stage to reach a real minimum. The proposed approach speeds up the two stage optimization method to an average gain of 2.99 for IEEE 30, 57, and 118 bus systems and EPRI Scenario systems A through D testings.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.640-654
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• 1996

This paper presents a synthesis method for multi-level Reed-Muller circuits using BDDs(Binary Decision Diagrams). The existing synthesis tool for Reed circuits, FACTOR, is not appropriate to the synthesis of large circuits because it uses matrix (map-type) to represent given logic functions, resulting in the exponential time and space in number of imput to the circuits. For solving this problems, a syntheisis method based on BDD is presented. Using BDDs, logic functions are represented compactly. Therefor storage spaces and computing time for synthesizing logic functions were greatly decreased, and this technique can be easily applied to large circuits. Using BDD representations, the proposed method extract best patterns to minimize multi-level Reed Muller circuits with good performance in area optimization and testability. Experimental results using the proposed method show better performance than those using previous methods〔2〕. For large circuits of considering the best input partition, synthesis results have been improved.