• 기술경영경제학회:학술대회논문집
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• 기술경영경제학회 1999년도 제15회 하계 학술발표회 논문집
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• pp.245-265
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• 1999

MAUT and AHP are widely used for quantification of subjective judgements in various fields of decision making. This study focuses on the introduction and application of MAUT/AHP method which is a hybrid of MAUT and AHP techniques in R&D project priority setting. This hybrid model can clarify each factors' contribution using MAUT method and can reduce the number of pairwise comparisons of AHP method. This study applies AMUT/AHP method to the evaluation of R&D projects in a Government - funded research institute. To evaluate R&D projects, six evaluation factors are derived. SMART(Simple MultiAttribute Rating Technique) and DVM(Difference Value Measurement ) out of many MAUT methods are used to design the utility function ad AHP is used to allocate the weights among evaluation factors. The major findings of this study can be summarized as follows. First, the SMART/AHP and the DVM/AHP have the same results with the SMART and the DVM, and they are different results with AHP. It is very hard to decide which one is better. Second, MAUT/AHP's strength is analyzed. MAUT reflects utility values of evaluators to alternatives and AHP results objective and consistent weights of factors through pariwise comparisons. Third, its possible application fields are proposed. It is applicable to subjective decision making problems with high complexity and inter-independent factors.

• 한국실내디자인학회:학술대회논문집
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• 한국실내디자인학회 2005년도 춘계학술발표대회 논문집
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• pp.237-240
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• 2005

Nowadays living space has been small-sized due to a rise in the standard of living, change of recognition on the increase of housing for the singles, and frequent movements of duty place, etc. and the furnitures naturally evolved assembly and multi-functionality. However, it retuned with an economic charge in the consumers and lost the uniformity in a small-sized living space as well since the furniture depended on the each miscellaneous household goods or products and came to be used. Hereupon it was demanded the system where the various furnitures are to fit to here. This study aimed at this point firstly examined a new environment change which it follows till a small living space is demanded through lots of literature, after understanding the concept of the system furniture, I classified expressive types of system furniture in modern environment and analyzed the design quality and characteristics in it by selecting well-known furniture magazines inside and outside of the country and extracting and analyzing system furniture images which are recorded in advertisements or articles. The objective of this study is to present MMC(Multi Modular Coordination, a system furniture design based on this analysis that inquiring the problems with function, structure, assembly which other existing system furnitures are facing enabled to apply a basic data in unit-module planning and by sampling embodiment modeling by uses.

• Smart Structures and Systems
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• 제16권2호
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• pp.243-262
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• 2015

Optimal sensor placement (OSP) is a critical issue in construction and implementation of a sophisticated structural health monitoring (SHM) system. The uncertainties in the identified structural parameters based on the measured data may dramatically reduce the reliability of the condition evaluation results. In this paper, the information entropy, which provides an uncertainty metric for the identified structural parameters, is adopted as the performance measure for a sensor configuration, and the OSP problem is formulated as the multi-objective optimization problem of extracting the Pareto optimal sensor configurations that simultaneously minimize the appropriately defined information entropy indices. The nondirective movement glowworm swarm optimization (NMGSO) algorithm (based on the basic glowworm swarm optimization (GSO) algorithm) is proposed for identifying the effective Pareto optimal sensor configurations. The one-dimensional binary coding system is introduced to code the glowworms instead of the real vector coding method. The Hamming distance is employed to describe the divergence of different glowworms. The luciferin level of the glowworm is defined as a function of the rank value (RV) and the crowding distance (CD), which are deduced by non-dominated sorting. In addition, nondirective movement is developed to relocate the glowworms. A numerical simulation of a long-span suspension bridge is performed to demonstrate the effectiveness of the NMGSO algorithm. The results indicate that the NMGSO algorithm is capable of capturing the Pareto optimal sensor configurations with high accuracy and efficiency.

• 산업경영시스템학회지
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• 제40권1호
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• Transactions on Electrical and Electronic Materials
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• 제18권5호
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• pp.287-297
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• 2017

Novel power system stabilizers (PSSs) have been proposed to effectively dampen low frequency oscillations (LFOs) in multi-machine power systems and have attracted increasing research interest in recent years. Due to this attention, recently, fractional order controllers (FOCs) have found new applications in power system stability issues. Here, a tilt-integral-derivative power system stabilizer (TID-PSS) is proposed to enhance the dynamic stability of a multi-machine power system by providing additional damping to the LFOs. The TID is an extended version of the classical proportional-integral-derivative (PID) applying fractional calculus. The design of the proposed three-parameter tunable TID-PSS is systematized as a nonlinear time domain optimization problem in which the tunable parameters are adjusted concurrently using a modified group search optimization (MGSO) algorithm. An integral of the time multiplied squared error (ITSE) performance index is considered as the objective function. The proposed stabilizer is simulated in the MATLAB/SIMULINK environment using the FOMCON toolbox and the dynamic performance is evaluated on a 3-machine 6-bus power system. The TID-PSS is compared with both classical PID-PSS (PID-PSS) and conventional PSS (CPSS) using eigenvalue analysis and time domain simulations. Sensitivity analyses are performed to assess the robustness of the proposed controller against large changes in system loading conditions and parameters. The results indicate that the proposed TID-PSS provides the better dynamic performance and robustness compared with the PID-PSS and CPSS.

• 한국축산식품학회지
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• 제39권2호
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• pp.222-228
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• 2019

This research was motivated by our encounter with the situation where an optimization was done based on statistically non-significant models having poor fits. Such a situation took place in a research to optimize manufacturing conditions for improving storage stability of coffee-supplemented milk beverage by using response surface methodology, where two responses are $Y_1$=particle size and $Y_2$=zeta-potential, two factors are $F_1$=speed of primary homogenization (rpm) and $F_2$=concentration of emulsifier (%), and the optimization objective is to simultaneously minimize $Y_1$ and maximize $Y_2$. For response surface analysis, practically, the second-order polynomial model is almost solely used. But, there exists the cases in which the second-order model fails to provide a good fit, to which remedies are seldom known to researchers. Thus, as an alternative to a failed second-order model, we present the heterogeneous third-order model, which can be used when the experimental plan is a two-factor central composite design having -1, 0, and 1 as the coded levels of factors. And, for multi-response optimization, we suggest a modified desirability function technique. Using these two methods, we have obtained statistical models with improved fits and multi-response optimization results with the predictions better than those in the previous research. Our predicted optimum combination of conditions is ($F_1$, $F_2$)=(5,000, 0.295), which is different from the previous combination. This research is expected to help improve the quality of response surface analysis in experimental sciences including food science of animal resources.

• 대한토목학회논문집
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• 제28권1D호
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• pp.107-117
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• 2008

민간투자사업의 자본구조는 사업시행자가 출자한 자기자본과 대출금융기관으로부터 조달한 타인자본으로 구성된다. 민간투자사업 기본계획에서는 사업시행자의 최소 자기자본비율을 25%로 규정하고 있으며, 일반적으로 정부와 사업시행자 간의 실시협약을 통하여 자본구조를 결정하게 된다. 그러나 민간투자사업의 자본구조는 사업의 수익률과 재무적 안정성을 결정하는 중요한 기준이기 때문에 자금조달계획 수립 시 자본구조에 따른 수익률의 변동성을 파악하고 적정 수익률과 재무적 안정성을 고려하여 자본구조를 최적화할 필요가 있다. 본 연구는 민간투자사업의 수익률과 재무적 안정성을 동시에 극대화할 수 있도록 자본구조를 최적화하기 위한 방법론을 제시하는데 그 목적이 있다. 이를 위하여 기존 민간투자사업들의 자본구조를 고찰하고 민간투자사업 재무모델을 분석하였다. 재무분석을 바탕으로 최적 자본구조를 결정하기 위해 효용함수 개념과 다목적 유전자 알고리즘을 이용한 자본구조 최적화모델을 제시하였다. 제시된 최적화 모델을 인천공항철도 민간투자사업에 적용하여 최적 자본구조를 도출하였고 민감도 분석과 시나리오 분석을 통해 그 활용성을 검증하였다. 사례분석 결과, 최적 자기자본비율은 실시협약에서 결정된 30%보다 낮은 22.3%에서 결정되었으며 이는 자기자본비율을 더 낮추어도 수익률과 재무적 안정성을 유지할 수 있다는 것을 시사한다. 본 연구는 수익률과 재무적 안정성을 동시에 고려하여 최적 자본구조를 결정함으로써 민간투자사업의 사업시행자에 적합한 자본구성과 자금조달을 위한 합리적인 의사판단 기준을 제시하였으며 사업시행자의 수익률 향상에 기여할 것으로 기대된다.

• 전기학회논문지
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• 제60권4호
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• 한국항공우주학회지
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• 제50권7호
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• pp.445-454
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• 2022

항공기가 빙점 이하의 습도가 높은 구름대를 지날 때 액적이 항공기와 충돌하면 날개, 동체 등 항공기 구성품에 결빙이 발생한다. 특히 항공기의 날개에 결빙이 증식되면 공력 성능의 저하와 비행 안정성의 감소 등의 치명적인 안전 문제를 초래할 수 있다. 본 연구에서는 항공기 날개에 적용되는 고양력 장치인 다중 익형의 결빙 증식량이 최소가 되도록 형상 최적설계를 수행하였다. 3차원 Reynolds-Averaged Navier-Stokes 지배 방정식을 이용하여 공력해석을 수행하였고, 다물리 전산해석을 통해 결빙의 형상 및 증식량을 예측하였다. 최적설계의 목적함수는 결빙 증식량 최소화로 설정하였고, 설계변수는 Slat과 Flap의 전개 각도와 위치를 정의하는 형상 변수 6개를 선정하였다. 설계 과정에서 목적함수의 평가는 크리깅 근사모델을 사용하여 대체하였고 유전자 알고리즘을 적용하여 최적 형상을 도출하였다. 최적화를 수행한 결과, Slat과 Flap에 최적의 전개 각도와 위치를 적용하였을 때 결빙 증식량이 약 8% 감소하였다.

• 한국항공우주학회지
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• 제39권5호
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• pp.433-441
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• 2011

원심압축기 임펠러의 최적화연구를 수행하였다. 최적화를 위한 알고리즘은 ANN를 기본으로 하였으며, 초기의 ANN 학습은 DOE를 사용하여 ANN을 효과적으로 형성하였다. DOE에서는 설계변수가 목적함수에 미치는 주효과와 상호 교호작용에 대한 예측을 할 수 있었다. 최적화과정에서 ANN의 향상을 위하여 GA를 사용하여 각 세대에서의 설계변수에 따른 목적함수가 일정값 이하가 되는 경우에는 수치해석을 통하여 ANN을 세대별로 향상시켰다. 6세대 이 후에는 ANN에 의한 예측값과 CFD의 예측값과의 차이가 1% 미만에 도달하였다. 총 21세대를 거쳐서 압축비와 효율과의 pareto를 형성할 수 있었다. 본 연구에서는 최적화를 위한 계산시간을 기울기 기반의 최적화시간 정도로 단축하면서도 다목적함수의 최적화의 결과를 얻을 수 있었다.