• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• Journal of the Korean Institute of Gas
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• v.3 no.2 s.7
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• pp.70-76
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• 1999

Because of the tight pollution control of $SO_2$ emission, sulfuric acid manufacturers have been interested in the operation with the highest possible conversion efficiency. In this work, the design criteria and operating conditions of the catalytic converter were investigated for maximum conversion efficiency and minimum $SO_2$ emission by parametric analysis and process optimization for the existing acid plants. The Double Converter/Double Absorber(DC/DA) process was investigated by varying $SO_2$ compositions of feed gas, pressures and temperatures of layers of the converter and the depth of the catalyst beds. In order to evaluate the process, a computer simulator for sulfuric acid plants has been developed. The results by process optimization could be used for the converter design and operating conditions with highest conversion efficiency.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.293-300
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• 2008

Efficient designing of SRM Grains in the field of Rocketry is still the main test for most of the nations of world for scientific studies, commercial and military applications. There is a strong need to enhance thrust, improve the effectiveness of SRM and reduce mass of motor and burning time so as to allow the general design to increase the weight of payload/on board electronics. Moreover burning time can be increased while keeping the weight of the propellant and thrust in desired range, so as to give the time to control / general design group in active phase for incorporating delayed cut off if required. A mathematical design, optimization & analysis technique for Slotted Tube Grain has been discussed in this paper. In order to avoid the uncertainties that whether the Slotted Tube grain configuration being designed is best suited for achieving the set design goals and optimal of all the available designs or not, an efficient technique for designing SRM Grain and then getting optimal solution is must. The research work proposed herein addresses and emphasizes a design methodology to design and optimize Slotted Tube Grain considering particular test cases for which the design objectives and constraints have been given. In depth study of the optimized solution have been conducted thereby affects of all the independent parametric design variables on optimal solution & design objectives have been examined and analyzed in detail. In doing so, the design objectives and constraints have been set, geometric parameters of slotted tube grain have been identified, performance prediction parameters have been calculated, thereafter preliminary designs completed and finally optimal design reached. A Software has been developed in MATLAB for designing and optimization of Slotted Tube grains.

• Advances in aircraft and spacecraft science
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• v.7 no.5
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• pp.441-458
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• 2020

Rectangular-to-Ellipse Shape Transition (REST) inlets are a class of inward turning inlets designed for hypersonic flight. The aerodynamic design of REST inlets involves very complex flows and shock-wave patterns. These inlets are used in highly integrated propulsive systems. Often the design of these inlets may require many geometrical constraints at different cross-section. In present work a design approach for hypersonic inward-turning inlets, adapted for REST inlets, is coupled with a multi-objective optimization procedure. The automated procedure iterates on the parametric representation and on the numerical solution of a base flow from which the REST inlet is generated by using streamline tracing and shape transition algorithms. The typical design problem of optimizing the total pressure recovery and mass flow capture of the inlet is solved by the proposed procedure. The accuracy of the optimal solutions found is discussed and the performances of the designed REST inlets are investigated by means of fully 3-D Euler and 3-D RANS analyses.

• 한국신재생에너지학회:학술대회논문집
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• 2011.05a
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• pp.58.2-58.2
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• 2011

본 연구에서는 5MW급 수평축 풍력터빈 블레이드에 대해 국내 서남해안의 풍속특성을 고려한 최적설계를 수행 하였다. 최적설계를 수행하기 위해 블레이드 해석은 Blade Element and Momentum Theory를 이용 하였으며, 설계 시 적용된 기저형상은 NREL에서 제안한 5MW급 풍력터빈 블레이드을 선정하였다. 최적설계를 수행하기 전 설계에 사용된 설계변수들이 풍속에 대해 어떠한 경향을 가지고 있는지 알아보기 위해 Parametric Study를 수행 하였으며, 최적설계는 다목적 최적화 유전 알고리즘인 NSGA-II를 이용하여 평균풍속이 낮은 서남해안의 연간에너지 생산량과 설비이용률을 최대화하였다. 최적화 결과들로부터 설계 조건에 맞는 최적해를 도출 할 수 있었으며, 이를 통해 기저형상의 연간에너지 생산량 및 설비이용률을 보다 향상 시킬 수 있었다.

• Wind and Structures
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• v.20 no.3
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• pp.363-388
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• 2015

For controlling the vibration of specific building structure with large span, a practical method for the design of MTMD was developed according to the characteristics of structures subjected to wind loads. Based on the model of analyzing wind-induced response of large-span structure with MTMD, the optimization method of multiple tuned mass dampers for large-span roof structures subjected to wind loads was established, in which the applicable requirements for strength and fatigue life of TMD spring were considered. According to the method, the controlled modes and placements of TMDs in MTMD were determined through the quantitative analysis on modal contribution to the wind-induced dynamic response of structure. To explore the characteristics of MTMD, the parametric analysis on the effects of mass ratio, damping ratio, central tuning frequency ratio and frequency range of MTMD, was performed in the study. Then the parameters of MTMD were optimized through genetic algorithm and the optimized MTMD showed good dynamic characteristics. The robustness of the optimized MTMD was also investigated.

• Wind and Structures
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• v.27 no.1
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• pp.41-57
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• 2018

Concentrated Solar Photovoltaic (CPV) is a promising alternative to conventional solar structures. These solar tracking structures need to be optimized to be competitive against other types of energy production. In particular, the selection of the structural parameters needs to be optimized with regards to the dynamic wind response. This study aims to evaluate the effect of the main structural parameters, as selected in the preliminary design phase, on the wind response and then on the weight of the steel support structure. A parametric study has been performed where parameters influencing dynamic wind response are varied. The study is performed using a semi-deterministic time-domain wind analysis method. Unsteady aerodynamic model is applied for the shape of the CPV structure collector at different configurations in conjunction with a consistent mass-spring-damper model with the corresponding degrees of freedom to describe the dynamic response of the system. It is shown that, unlike the static response analysis, the variation of the peak wind response with many structural parameters is highly nonlinear because of the dynamic wind action. A steel structural optimization process reveals that close attention to structural and site wind parameters could lead to optimal design of CPV steel support structure.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.109-122
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• 2002

본 연구에서는 교차로의 비용 및 특성을 고려한 도로선형최적화 모형을 유전자 알고리즘(Genetic Algorithms)을 이용하여 개발하였다. 기존의 도로선형최적화 모형은 교차로 특성을 고려하지 못해서 실제 적용에 심대한 문제점을 내재하고 있다. 본 논문에서는 특정 도로선형에 교차로 건설의 필요가 있을 경우, 민감(Sensitive)하고 지배적인(Dominating) 교차로 비용 항목들 즉, 토공비용, 보상비, 포장비, 사고비용, 지체 및 연료소모비용 등의 산정이 시도되었다. 또한 비교적 우수한 도로선형 대안을 유전자 알고리즘을 이용한 탐색과정 중에서 비효율적으로 강제 퇴화시키는 단점 보완을 위한 교차로 국소 최적화 방법(Local Optimization of Intersections)이 개발되어 기존 모형을 보완하였다. 공간상의 도로선형은 매개변수적 묘사(Parametric Representation)를 통하여 구현하였으며 벡터운영(Vector Manipulation)을 통해 교차로비용 산정의 근간인 교차점과 다른 중요점들의 좌표를 찾을 수 있었다. 개발된 교차로 비용산정 모형이 보다 정밀하게 교차로 비용을 산정함이 증명되었으며 궁극적으로는 기존의 최적화 모형의 단점을 보완할 수 있음이 제시되었다. 또한, 새로이 제시된 교차로 국소 최적화 방법이 최적대안 탐색과정의 유연성을 증대하였으며, 결과적으로 효율적인 교차로의 유지에 기여함을 알 수 있었다. 제시된 교차로 국소 최적화 방법은 추후 단일노선이 아닌 도로망 최적화시의 기초를 제시함은 주목할 만 하다. 두개의 예제에서 도출된 최적노선 및 교차로 비용 등의 검토 결과, 도로상의 교차로 건설비용은 도로선형 최적화에 큰 영향을 미치는 실질적이며 민감한 비용 항목임이 검증되었으며 이는 도로선형최적화 모형이 교차로 비용을 반드시 검토 및 평가할 수 있어야 함을 반증한다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1104-1113
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• 2015

In this paper, we propose Two-Dimensional Robust Face Recognition System Realized with the Aid of Facial Symmetry with Illumination Variation. Preprocessing process is carried out to obtain mirror image which means new image rearranged by using difference between light and shade of right and left face based on a vertical axis of original face image. After image preprocessing, high dimensional image data is transformed to low-dimensional feature data through 2-directional and 2-dimensional Principal Component Analysis (2D)2PCA, which is one of dimensional reduction techniques. Polynomial-based Radial Basis Function Neural Network pattern classifier is used for face recognition. While FCM clustering is applied in the hidden layer, connection weights are defined as a linear polynomial function. In addition, the coefficients of linear function are learned through Weighted Least Square Estimation(WLSE). The Structural as well as parametric factors of the proposed classifier are optimized by using Particle Swarm Optimization(PSO). In the experiment, Yale B data is employed in order to confirm the advantage of the proposed methodology designed in the diverse illumination variation