• 한국항공우주학회지
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• 제41권7호
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• pp.524-531
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• 2013

본 연구에서는 헬리콥터 로터 블레이드의 제작 과정 및 여러 가지 요인으로 인해 발생하는 불균형성을 해소하기 위한 RTB(Rotor Track and Balance) 알고리즘을 개발하였다. 비행 시험 결과로부터 RTB 조절 값과 트랙 및 기체 진동 사이의 상호관계를 선형모델을 이용한 회귀분석을 통하여 RTB 모델을 구축하였다. 개발된 RTB 알고리즘을 실기 시험 결과에 적용하여 RTB 모델을 검증하였고 선형화 모델만으로도 비교적 정확한 모델링이 가능함을 확인하였다. RTB 조절값 설정을 위해 최적화 문제를 정식화하고 유전자 알고리즘에 입자 군집 최적화(PSO) 알고리즘을 결합하여 빠른 수렴성을 갖는 최신의 최적화 기법을 적용하였다. 또한 최적화 해석을 통하여 얻은 RTB 조절값을 이용하여 트랙 편차와 기체 진동을 허용 기준치 아래로 감소시키고, 다양한 비행 조건에 대하여 효율적인 RTB를 수행할 수 있음을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• 한국산학기술학회논문지
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• 제19권4호
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• pp.683-688
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• 2018

최근 하이브리드 전기자동차의 보급 확대에 따라 전기에너지 수요가 증가하고 있다. 본 연구에서는 전기에너지 수요에 대응하기 위해 에너지 하베스팅 기술을 이용해 자가발전이 가능한 3가지 구조의 현가장치 적용 선형 발전 시스템을 ANSYS MAXWELL을 사용하여 전자기 시뮬레이션을 통해 각 구조의 발전 특성을 비교 분석 하였다. 다음으로 각 모델에 대해 상용 PIDO(Process Integration and Design Optimization)툴인 PIAnO(Process Integration, Automation and Optimization)을 사용하여 최적설계를 수행하였다. 3가지 설계변수를 선정하여 실험계획법 기법 중 직교 배열표(Orthogonal Array)를 이용해 도출한 18개의 실험 점에 대해 전자기 해석을 통해 완성한 실험계획법을 바탕으로 근사 모델을 생성하였으며 진화 알고리즘(Evolutionary Algorithm)을 이용한 최적 설계를 수행하였다. 마지막으로 초기 모델과 동일한 해석 조건을 사용해 최적 설계 결과 모델에 대한 전자기 시뮬레이션을 통해 최적설계 결과를 검증 하였다. 각 선형 발전기 모델에 대해 최적의 구조에 대한 발전 특성을 비교한 결과 8pole-8slot, 12pole-12slot, 16pole-16slot 구조에서 최대 발전량은 각각 366.5W, 466.7W, 579.7W로 slot, pole 조합 수가 많아질수록 발전량이 증가하는 결과를 확인하였다.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2006년도 6th International Meeting on Information Display
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• pp.33-38
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• 2006

An evolutionary optimization process involving genetic algorithm and combinatorial chemistry was employed in an attempt to develop titanate-based red phosphors suitable for tri-color white light emitting diodes We screened a eight-cation oxide system including $(K,Li,Na)_x(Y,Gd,La,Eu)_yTi_zO_{\delta}$ in terms of luminescent efficiency. The combination of genetic algorithm and combinatorial chemistry was proven to enhance the searching efficiency when applied for phosphor screening. As a result, the composition was optimized to be $(Na_{0.92}Li_{0.08})(Y_{0.8}Gd_{0.2})TiO_4:Eu^{3+}$, The luminance of this phosphor was 110 % of that of well-known scheelite variant phosphor at an excitation of 400 nm.

• 한국전자통신학회논문지
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• 제3권2호
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• pp.65-70
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• 2008

Digital signal processing (DSP) is the process of taking a signal and performing an algorithm on it to analyze, modify, or better identify that signal.[1] To take advantage of DSP advances, one must have at least a basic understanding of DSP theory along with an understanding of the hardware architecture designed to support these new advances. There are several programming techniques that maximize the efficiency of the DSP hardware, as well as a few fundamental concepts used to implement DSP software. This article introduced some of these underlying functions that are the building blocks of complex signal processing functions, and It will touch on the fundamental concepts of DSP theory and algorithms and also provide an overview of the implementation and optimization of DSP software, and discuss the development of DSP.

• Structural Engineering and Mechanics
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• 제68권5호
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• pp.633-638
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• 2018

One of the important problems in the vehicle design is vehicle handling and stability. Effective parameters which should be considered in the vehicle handling and stability are roll angle, camber angle and scrub radius. In this paper, a planar vehicle model is considered that two right and left suspensions are double wishbone suspension system. For a better analysis of the suspension geometry, a kinestatic model of vehicle is considered which instantaneous kinematic and statics relations are analyzed simultaneously. In this model, suspension geometry is considered completely. In order to optimum design of double wishbones suspension system, a multi-objective genetic algorithm is applied. Three important parameters of suspension including roll angle, camber angle and scrub radius are taken into account as objective functions. Coordinates of suspension hard points are design variables of optimization which optimum values of them, corresponding to each optimum point, are obtained in the optimization process. Pareto solutions for three objective functions are derived. There are important optimum points in these Pareto solutions which each point represents an optimum status in the model. In other words, corresponding to any optimal point, a specific geometric position is determined for the suspension hard points. Each of the obtained points in the Pareto optimization can be selected for a special design purpose by designer to create an optimum condition in the vehicle handling and stability.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1006-1011
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• 2003

In this study, a novel systematic design procedure by Genetic Algorithm of a two stage relief valve is proposed. First of all, a mathematical model describing the dynamics of a balanced piston type relief valve has been derived. Governing equations such as dynamic equations for the main spool and the pilot spool and flow equations for each orifice are established. The mathematical model is verified by comparing the results of simulation with that of experiments. Furthermore, influences of the parameters on the dynamic characteristics of a relief valve have been investigated by simulation of the proposed model. Major design parameters on the valve response are determined, which affect the system response significantly. And then, using the determined parameters, the optimization of the two stage relief valve by Genetic Algorithm, which is a random search algorithm can find the global optimum without converging local optimum, is performed. The optimal design process of a two stage relief valve is presented to determine the major design parameters. Fitness function reflects the changing pressure according to parameters. It is shown that the genetic algorithms satisfactorily optimized the major design parameters of the two stage relief valve.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.501-506
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• 2002

In this study, a novel systematic design procedure by Genetic Algorithm of a two stage relief valve is proposed. First of all. a mathematical model describing the dynamics of a balanced piston type relief valve has been derived. Governing equations such as dynamic equations for the main spool and the pilot spool and flow equations for each orifice are established. The mathematical model is verified by comparing the results of simulation with that of experiments. Furthermore, influences of the parameters on the dynamic characteristics of a relief valve have been investigated by simulation of the proposed model. Major design parameters on the valve response are determined, which affect the system response significantly. And then, using the determined parameters, the optimization of the two stage relief valve by Genetic Algorithm, which is a random search algorithm can find the global optimum without converging local optimum, is performed. The optimal design process of a two stage relief valve is presented to determine the major design parameters. Fitness function reflects the changing pressure according to parameters. It is shown that the genetic algorithms satisfactorily optimized the major design parameters of the two stage relief valve.

• 한국초전도ㆍ저온공학회논문지
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• 제7권4호
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• pp.24-27
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• 2005

Development of a 600 kJ class Superconducting Magnetic Energy Storage (SMES) system is being in progress by Korea Electrotechnology Research Institute(KERI). High temperature superconducting (HTS) wires are going to be used for the windings for the SMES system is presented in this paper. We considered BSCCO-2223 wire for the HTS windings and the operating temperature of the winding was decided to be 20 K which will be accomplished by conduction cooling method using cyro-coolers. Auto-Tuning Niching Genetic Algorithm was adopted for an optimization method of the HTS magnets in the SMES system. The objective function of the optimal process was minimizing total amount of the HTS wire. As a result, we obtained output parameters for optimization design of 600 kJ class SMES under several constrained conditions. These HTS windings are going to be applied to the SMES system whose purpose is stabilization of the power grid.

• 대한조선학회논문집
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• 제48권4호
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• pp.325-329
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• 2011

Floating, production, storage and offloading (FPSO) is a production facility that refines and saves the drilled crude oil from a drilling facility in the ocean. The flare system in the FPSO is a major part of the pressure relieving system for hydrocarbon processing plants. The flare system consists of a number of pipes and complicated connection systems. Decision of pipe support types is important since the load on the support and the stress in the pipe are influenced by the pipe support type. In this study, we optimally determined the pipe support types that minimized the support cost while satisfying the design constraints on maximum support load, maximum nozzle load and maximum pipe stress ratio. Performance indices included in the design constraints for a specified design were evaluated by pipe structural analysis using CAESAR II. Since pipe support types were all discrete design variables, an evolutionary algorithm (EA) was used as an optimizer. We successfully obtained the optimal solution that reduced the support cost by 27.2% compared to the initial support cost while all the design requirements were satisfied.