• Proceedings of the Technology Innovation Conference
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• 1999.06a
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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.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2005.05a
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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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• v.16 no.2
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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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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• v.18 no.5
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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.

• Food Science of Animal Resources
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• v.39 no.2
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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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.107-117
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• 2008

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.7
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• pp.445-454
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• 2022

Ice accretion on the aircraft components, such as wings, fuselage, and empennage, can occur when the aircraft encounters a cloud zone with high humidity and low temperature. The prevention of ice accretion is important because it causes a decrease in the aerodynamic performance and flight stability, thus leading to fatal safety problems. In this study, a shape design optimization of a multi-element airfoil is performed to minimize the amount of ice accretion on the high-lift device including leading-edge slat, main element, and trailing-edge flap. The design optimization framework proposed in this paper consists of four major parts: air flow, droplet impingement and ice accretion simulations and gradient-free optimization algorithm. Reynolds-averaged Navier-Stokes (RANS) simulation is used to predict the aerodynamic performance and flow field around the multi-element airfoil at the angle of attack 8°. Droplet impingement and ice accretion simulations are conducted using the multi-physics computational analysis tool. The objective function is to minimize the total mass of ice accretion and the design variables are the deflection angle, gap, and overhang of the flap and slat. Kriging surrogate model is used to construct the response surface, providing rapid approximations of time-consuming function evaluation, and genetic algorithm is employed to find the optimal solution. As a result of optimization, the total mass of ice accretion on the optimized multielement airfoil is reduced by about 8% compared to the baseline configuration.

• 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.