• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.898-901
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• 2004

Generally, in case the natural frequency of vehicle components is larger than the reversing frequency of load history, we can obtain the analysis results with small errors. But in case of having the low natural frequency, we must avoidably carry out the dynamic analysis, and it requires much time and storage. Specially executing the fatigue analysis for vehicle components requires more time. To this end, it is not easy that we accomplish the optimization considering fatigue for the vehicle component based on the dynamic analysis. In this research we introduce an efficient method which performs the fatigue analysis based on the dynamic analysis. Finally as making the response surface we optimize the vehicle component under dynamic fatigue.

• 대한산업공학회지
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• 제23권3호
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• pp.545-557
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• 1997

This paper deals with dynamic optimal pricing for new products by a firm which maximizes the discounted profit stream of it's own in a duopoly. The problem is constructed as differential games and dynamic optimization theory. Cost is assumed to decline as time goes on. A modified customer's choice model is formulated as a diffusion model and we solve a dynamic optimization problem by adopting the diffusion model. Since this paper focus on deriving real prices not showing a time trend, we formulate recursive form equations of costate variables(shadow price) and a simultaneous equation of price. Hence we derive a dynamic optimal pricing model for using in real market. In particular, we construct a dynamic optimal pricing model in the case that there are benefits from not only new subscribers but also previous subscribers. We analyze instant camera market in U.S.A(1976-1985) by utilizing the above model.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• 유공압시스템학회논문집
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• 제2권4호
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• pp.1-6
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• 2005

This paper presents optimization of a pneumatic control system whose design parameters have been optimized so that the desired dynamic characteristics of cylinder position was obtained. The pneumatic system is used as transferring and stacking equipment for solid freeform fabrication system which has been widely used in design verification applications. The pneumatic system mainly consists of pneumatic control valves and cylinders. The system was modeled by using several principles for pneumatic components. The system was optimized to obtain dynamic performance with enough damping to reduce cylinder vibration. A fuzzy controller has been applied to fulfill the dynamic performance requirements of the pneumatic system. The simulation results show that the fuzzy controller is more effective than a PD controller.

• 대한임베디드공학회논문지
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• 제12권6호
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• pp.385-393
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• 2017

As the use of mobile devices such as smartphones increases, there is growing interest on the benefits of virtualization in embedded systems. Full virtualization has the advantage of running the guest virtual machine without modifying the guest operating system. However, full virtualization suffers slow execution speed due to the cost of context switching between the virtual machines and the virtual machine monitor. In this paper, we propose a translated block and context switching optimization to improve the guest execution speed in the embedded system. As a result, the improved dynamic binary translator is up to 5.95 times faster than the native execution. Performance degradation is less than that of the other virtualization system.

• 대한기계학회논문집A
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• 제22권4호
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• pp.811-825
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• 1998

An approximate line search is presented for dynamic response optimization with Augmented Lagrange Multiplier(ALM) method. This study empolys the approximate a augmented Lagrangian, which can improve the efficiency of the ALM method, while maintaining the global convergence of the ALM method. Although the approximate augmented Lagragian is composed of only the linearized cost and constraint functions, the quality of this approximation should be good since an approximate penalty term is found to have almost second-order accuracy near the optimum. Typical unconstrained optimization algorithms such as quasi-Newton and conjugate gradient methods are directly used to find exact search directions and a golden section method followed by a cubic polynomial approximation is empolyed for approximate line search since the approximate augmented Lagrangian is a nonlinear function of design variable vector. The numberical performance of the proposed approach is investigated by solving three typical dynamic response optimization problems and comparing the results with those in the literature. This comparison shows that the suggested approach is robust and efficient.

• 한국자동차공학회논문집
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• 제10권4호
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• pp.181-191
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• 2002

The homogenization method is applied to maximize crash energy absorption for a given volume. Optimization analysis off closed-hat type example problem is conducted with different impact velocities and thicknesses. The results show that the bending-type deformation for the original design is changed to the folding-type deformation for a new design with a hole, which is partly due to the increase of the crash energy absorption for the new design. Dynamic mean crushing loads of the original and new design are compared with those by the theoretical equation by Wierzbicki. It shows that the dynamic mean crushing loads of new designs are very close to those by Wierzbicki's equation.

• 한국생산제조학회지
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• 제19권6호
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• pp.717-723
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• 2010

Radial stiffness of angular contact ball bearings are decreased remarkably at high speed revolution, because the inner and outer ball contact angle with races arc changed under the ball centrifugal forces at high speed. In the past, the optimizations of spindle units were done under the assumption of unchanged bearing stiffness for the whole speed range. But the bearing stiffness is changed and the dimension of optimum spindle is also changed with speed. In the design phase, only one model of many optimum spindle models with speed should be selected. As optimization criterion, the area of transfer function at spindle nose is proposed to estimate simply and accurately improvement of dynamic characteristics in spindle units. Finally, according to many analyses of diverse spindle models with decreased bearing stiffness, the spindle with shorter bearing span is better than longer bearing span from the viewpoint of dynamic characteristics.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1999년도 가을 학술발표회 논문집
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• pp.333-342
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• 1999

An improved optimization algorithm for multi-objective and multi-level (MO/ML) optimum design of steel frames is proposed in this paper. In order to optimize the steel frames under seismic load, two main objective functions need to be considered for minimizing the structural weight and maximizing the strain energy. For the efficiency of the proposed method, well known multi-level optimization techniques using decomposition method that separately utilizes both system-level and element-level optimizations and an artificial constraint deletion technique are incorporated in the algorithm. And also dynamic analysis is executed to evaluate the implicit function of structural strain energy at each iteration step. To save the numerical efforts, an efficient reanalysis technique through sensitivity analysis of dynamic properties is unposed in the paper. The efficiency and robustness of the improved MOML algorithm, compared with a plain MOML algorithm, is successfully demonstrated in the numerical examples.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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• pp.415-422
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• 2014

This paper presents a new approach to solve the multi area unit commitment problem (MAUCP) using an evolutionary programming based particle swarm optimization (EPPSO) method. The objective of this paper is to determine the optimal or near optimal commitment schedule for generating units located in multiple areas that are interconnected via tie lines. The evolutionary programming based particle swarm optimization method is used to solve multi area unit commitment problem, allocated generation for each area and find the operating cost of generation for each hour. Joint operation of generation resources can result in significant operational cost savings. Power transfer between the areas through the tie lines depends upon the operating cost of generation at each hour and tie line transfer limits. Case study of four areas with different load pattern each containing 7 units (NTPS) and 26 units connected via tie lines have been taken for analysis. Numerical results showed comparing the operating cost using evolutionary programming-based particle swarm optimization method with conventional dynamic programming (DP), evolutionary programming (EP), and particle swarm optimization (PSO) method. Experimental results show that the application of this evolutionary programming based particle swarm optimization method has the potential to solve multi area unit commitment problem with lesser computation time.