• Journal of Advanced Marine Engineering and Technology
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• v.39 no.10
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• pp.1031-1036
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• 2015

Proportional-integral-derivative (PID) controllers are widely used in industrial sites. Most tuning methods for PID controllers use an empirical and experimental approach; thus, the experience and intuition of a designer greatly affect the tuning of the controller. The representative methods include the closed-loop tuning method of Ziegler-Nichols (Z-N), the C-C tuning method, and the Internal Model Control tuning method. There has been considerable research on the tuning of PID controllers for single-input single-output systems but very little for multi-input multi-output systems. It is more difficult to design PID controllers for multi-input multi-output systems than for single-input single-output systems because there are interactive control loops that affect each other. This paper presents a tuning method for the PID controller for a two-input two-output system. The proposed method uses a real-coded genetic algorithm (RCGA) as an optimization tool, which optimizes the PID controller parameters for minimizing the given objective function. Three types of objective functions are selected for the RCGA, and each PID controller parameter is determined accordingly. The performance of the proposed method is compared with that of the Z-N method, and the validity of the proposed method is examined.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Journal of the Optical Society of Korea
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• v.14 no.4
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• pp.342-350
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• 2010

An optimal wideband gain flattened hybrid erbium-doped fiber amplifier/fiber Raman amplifier (EDFA/FRA) has been introduced. A new and effective optimization method called particle swarm optimization (PSO) is employed to find the optimized parameters of the EDFA/FRA. Numerous parameters which are the parameters of the erbium-doped fiber amplifier (EDFA) and the fiber Raman amplifier (FRA) define the gain spectrum of a hybrid EDFA/FRA. Here, we optimize the length, $Er^{3+}$ concentration, and pump power and wavelength of the EDFA and also pump powers and wavelengths of the FRA to obtain the flattest operating gain spectrum. Hybrid EDFA/FRA with 6-pumped- and 10-pumped-FRAs have been studied. Gain spectrum variations are 1.392 and 1.043 dB for the 6-pumped- and 10-pumped-FRAs, respectively, in the 108.5 km hybrid EDFA/FRAs, with 1 mW of input signal powers. Dense wavelength division multiplexing (DWDM) system with 60 signal channels in the wavelength range of 1529.2-1627.1 nm, i.e. the wide bandwidth of 98 nm, is studied. In this work, we have added FRA's pump wavelengths to the optimization parameters to obtain better results in comparison with the results presented in our previous works.

• Journal of Korean Society for Quality Management
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• v.46 no.1
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• pp.39-74
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• 2018

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi's philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization. Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods. Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework. Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.18-33
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• 2008

This paper presents design optimization of a new Active Twist Rotor (ATR) blade and conducts its aeroelastic analysis in forward flight condition. In order to improve a twist actuation performance, the present ATR blade utilizes a single crystal piezoelectric fiber composite actuator and the blade cross-sectional layout is designed through an optimization procedure. The single crystal piezoelectric fiber composite actuator has excellent piezoelectric strain performance when compared with the previous piezoelectric fiber composites such as Active Fiber Composites (AFC) and Macro Fiber Composites (MFC). Further design optimization gives a cross-sectional layout that maximizes the static twist actuation while satisfying various blade design requirements. After the design optimization is completed successfully, an aeroelastic analysis of the present ATR blade in forward flight is conducted to confirm the efficiency in reducing the vibratory loads at both fixed- and rotating-systems. Numerical simulation shows that the present ATR blade utilizing single crystal piezoelectric fiber composites may reduce the vibratory loads significantly even with much lower input-voltage when compared with that used in the previous ATR blade. However, for an application of the present single crystal piezoelectric actuator to a full scaled rotor blade, several issues exist. Difficulty of manufacturing in a large size and severe brittleness in its material characteristics will need to be examined.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.370-374
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• 2015

Various protective equiptments are used for the power system control and protection. Numerous facilities are monitored at the same time in real time and introduction of optimization method and analysis of the method are required for generation control and facility management considering the demand fluctuations. However, the existing system analysis programs are difficult to link with the other sw and there are some problems with user convenience. To solve these problems the present conditions of the system are figured out in real time and the equipment insert method was estimated by optimization method, and the system that showed the system analysis program is developed. PSS/E has been used as system anlysis program for stabilizing system development which applied the optimization. method and Python language is applied in order to link the input and output values with the DB automatically. Lastly, DLL of matlab has been made included in C++ for solving the objective function using opmization method.By linking this to DB, power flow was calculated in PSS/E and the result was represented by Intouch screen.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1085-1092
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• 2010

This paper describes the development of estimating equation for under 16 aged children's exposure to habitat's magnetic field for 24 hours by using particle swarm optimization(PSO) algorithm, which was carried out by using the measured database collected from the exposure survey to Korean habitat's magnetic field as to under 16 aged Korean students such as preschooler, children in elementary school, and children in middle school. Sex, age, residence type, size of habitation site, distance from power line, and power transmission voltage are used as the input data of estimating 24 hour's personal exposure to magnetic field. And distribution of 24 hour's personal exposure to magnetic field, exposure characteristic to magnetic field, and exposure characteristic to magnetic field according to special conditions, are analyzed for under 16 aged children.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2063-2081
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• 2018

A resource allocation algorithm is proposed in this paper to simultaneously minimize the total system power consumption and maximize the system throughput for the downlink of multi-user multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. With the Lagrange dual decomposition method, we transform the original problem to its convex dual problem and prove that the duality gap between the two problems is zero, which means the optimal solution of the original problem can be obtained by solving its dual problem. Then, we use convex optimization method to solve the dual problem and utilize bisection method to obtain the optimal dual variable. The numerical results show that the proposed algorithm is superior to traditional single-objective optimization method in both the system throughput and the system energy consumption.

• Advances in Computational Design
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• v.9 no.1
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• pp.1-23
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• 2024

Present paper deals with the cost effective design of reinforced concrete building frame employing unified particle swarm optimization (UPSO). A building frame with G+8 stories have been adopted to demonstrate the effectiveness of the present algorithm. Effect of seismic loads and wind load have been considered as per Indian Standard (IS) 1893 (Part-I) and IS 875 (Part-III) respectively. Analysis of the frame has been carried out in STAAD Pro software.The design loads for all the beams and columns obtained from STAAD Pro have been given as input of the optimization algorithm. Next, cost optimization of all beams and columns have been carried out in MATLAB environment using UPSO, considering the safety and serviceability criteria mentioned in IS 456. Cost of formwork, concrete and reinforcement have been considered to calculate the total cost. Reinforcement of beams and columns has been calculated with consideration for curtailment and feasibility of laying the reinforcement bars during actual construction. The numerical analysis ensures the accuracy of the developed algorithm in providing the cost optimized design of RC building frame considering safety, serviceability and constructional feasibilities. Further, Monte Carlo simulations performed on the numerical results, proved the consistency and robustness of the developed algorithm. Thus, the present algorithm is capable of giving a cost effective design of RC building frame, which can be adopted directly in construction site without making any changes.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1334-1337
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• 2004

This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.