• Biomaterials and Biomechanics in Bioengineering
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• 제2권4호
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• pp.207-223
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• 2015

In this paper a method for simultaneous swift non-linear analysis and optimal design/posture of mechanical/biomechanical systems is presented. The method is developed to get advantages of iterations in non-linear analysis and/or generations in genetic algorithm (GA) for the purpose of efficient analysis within the optimal design/posture. The method is applicable for both size and geometry optimizations wherein material and geometry non-linearity are present. In addition to established mechanical systems, the method can solve biomechanical models of human musculoskeletal system. Optimization-based procedures are popular methods for resolving the redundancy at joints wherein the number of unknown muscle forces is far more than the number of equilibrium equations. These procedures involve optimization of a cost function(s) which is assumed to be consistent with the central nervous system's strategy when activating muscles to assure equilibrium. However, because of the complexity of biomechanical problems (i.e., due to non-linear biomaterial, large deformation, redundancy of the problem and so on) efficient analysis are required within optimization procedures as suggested in this paper.

• Steel and Composite Structures
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• 제7권1호
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• pp.53-70
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• 2007

This paper presents a analysis of the problem of optimal design of the beams with two I-type cross section shapes. These types of beams are simply supported and subject to pure bending. The strength and stability conditions were formulated and analytically solved in the form of mathematical equations. Both global and selected types of local stability forms were taken into account. The optimization problem was defined as bicriteria. The cross section area of the beam is the first objective function, while the deflection of the beam is the second. The geometric parameters of cross section were selected as the design variables. The set of constraints includes global and local stability conditions, the strength condition, and technological and constructional requirements in the form of geometric relations. The optimization problem was formulated and solved with the help of the Pareto concept of optimality. During the numerical calculations a set of optimal compromise solutions was generated. The numerical procedures include discrete and continuous sets of the design variables. Results of numerical analysis are presented in the form of tables, cross section outlines and diagrams. Results are discussed at the end of the work. These results may be useful for designers in optimal designing of thin-walled beams, increasing information required in the decision-making procedure.

• 한국수자원학회논문집
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• 제55권1호
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• pp.59-70
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• 2022

상수도관망은 대표적인 사회기반시설로 수원에서 수용가에게 물을 공급하는 과정에서 병원성 미생물을 소독하기 위해 염소를 주입한다. 안전한 물의 공급을 위해 잔류염소 농도 기준(0.1-4.0 mg/L)을 유지하도록 규정하고 있으나, 사용자의 사용 패턴, 수령, 상수도관망의 형식 및 특징은 수리학적(i.e., 절점의 압력, 관로의 유속) 및 수질적(i.e., 잔류염소 농도) 특징에 영향을 미친다. 따라서, 본 연구에서는 Multi-objective Harmony Search (MOHS)를 사용하여 수질-수리 인자를 고려한 상수도관망 최적 설계 기법을 개발하였다. 설계인자로는 설계비용과 시스템 탄력성을 고려하였으며, 절점의 압력과 잔류염소 농도를 제약조건으로 적용하였다. 도출된 최적설계안은 상수도관망의 형식 및 특징에 따라 분석하였다. 이러한 최적설계안은 경제적인 측면과 수질 측면의 안전성을 충족할 수 있으며, 사용자의 사용성을 증가시킬 수 있다.

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.561-570
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• 2010

This paper presents an advanced dynamic simulation model of a proton exchange membrane fuel cell for the optimal design of a fuel cell power conditioning system (FC-PCS). For the development of fuel cell models, the dynamic characteristics of the fuel cell are considered, including its static characteristics. Then, software fuel cell simulation is realized using Matlab-Simulink. Specifically, the design consideration of PCS (i.e., power semiconductor switch, capacitor, and inductor) is discussed by comparatively analyzing the developed simulator and ideal DC source. In addition, a cosimulation between the fuel cell model and PCS realized using the PSIM software is performed with the help of the SimCoupler module. Detailed analysis and informative simulation results are provided for the optimal design of fuel cell PCS.

• 한국생산제조학회지
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• 제9권1호
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• pp.45-52
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• 2000

The purpose of this research was developing an integrated way to solve two typical tolerance optimization problem i.e. optimal tolerance allotment and design centering. A new problem definition design centering-tolerance allotment problem (DCTA) was proposed here for the first time and solved. Genetic algorithm and coarse Monte Carlo simulation were used to solve the stochastic optimization problem. Optimal costs were compared with the costs from the previous optimization strategies Significant cost reductions were achieved by DCTA scheme.

• 산업경영시스템학회지
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• 제20권43호
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• pp.25-36
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• 1997

The purpose of this paper is to structure a new integrated model that can minimize the total cost for the transportation and inventory systems between m origin points, where origin i has a supply of a commodity, such as distribution centers or warehouses, and n destination points, where destination j requires the commodity. In this case, demands of the destination points are assumed random variables which have a known probability distribution. We will find optimal distribution centers which transport the commodity to the destination points and suggest optimal inventory policy to the selected distribution center which find the optimal pair $$ and safety stock level that minimize total cost with back-ordered case. This new model is formulated as a 0-1 nonlinear integer programming problem. To solve the problem, this paper proposes heuristic computational procedures and program and provides numerical examples.

• Journal of Biosystems Engineering
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• 제11권2호
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• pp.77-87
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• 1986

This study was carried out to design the optimal systems of diversified farming by the mathematical model developed. In order to achieve this goal, a computer program named DFSDINGP was developed by the nonlinear goal programming(NGP), and for testing its effectiveness, the optimal systems of diversified farming were designed for three regions surveyed and compared them with those of the conventional. DFSDINGP was programmed with FORTRAN 77 and it could handle the NGP problem having 25 independent variables and 75 constraint functions. The study results showed that the developed models and DFSDINGP could design the optimal systems of diversified farming satisfying two goals which are maximum agricultural income and maximum power inputs of agricultural machinery. The agricultural incomes and power inputs of farm machinery of the optimal systems were more than those of the conventional as much as 29-62% and 9-134%, respectively.

• 제어로봇시스템학회논문지
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• 제15권6호
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• pp.612-618
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• 2009

This study is concerned about the optimal design of the arm 1 and arm 2 in the SCARA robot. The mass and inertia moment of the arm I and arm 2 in a SCARA robot is greatly affected on the performance such as a cycle time, and torques loaded on $1^{st}$ axis and $2^{nd}$ axis. To reduce the mass and inertia moment, this study carried out optimal design by FEM analysis using parametric variables, which is a width, a height of the rib and a thickness of arm in the arm. The rib is adapted instead of reducing the thickness in the arm. And the simulation by computer was conducted on two given paths in X direction and Y direction. After optimal design, the result showed that maximum torque of $1^{st}$ axis and $2^{nd}$ axis reduced to maximum 9.5% on a given path.

• 한국정밀공학회지
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• 제27권9호
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• pp.103-110
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• 2010

This paper presents the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method and approximation modeling method with Radial Basis Function (RBF) neural network structure. The complex tool holder is used for holding a (milling/drilling) tool of a machine tool. The engineering problem of complex tool holder results from the twisting of spindle of tool holder. For this purpose, we present the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method (specifically a module of $iSight^{(R)}$ FD-3.1) and approximation modeling method with Radial Basis Function (RBF) (another module of $iSight^{(R)}$ FD-3.1) neural network structure