• 전기학회논문지
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• 제58권2호
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• pp.399-406
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• 2009

In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.

• 제어로봇시스템학회논문지
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• 제18권5호
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• pp.502-508
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• 2012

Heliostat, as a concentrator to reflect the incident solar energy to the receiver, is the most important system in the tower-type solar thermal power plant since it determines the efficiency and ultimately the overall performance of solar thermal power plant. Thus, a good sun tracking ability as well as a good optical property of it are required. Heliostat sun tracking system uses usually an open loop control system. Thus the sun tracking error caused by heliostat's geometrical error, optical error and computational error cannot be compensated. Recently use of sun tracking error model to compensate the sun tracking error has been proposed, where the error model is obtained from the measured ones. This work is a development of heliostat sun tracking error measurement and compensation method using BCS (Beam Characterization System). We first developed an image processing system to measure the sun tracking error optically. Then the measured error is modeled in linear polynomial form and neural network form trained by the extended Kalman filter respectively. Finally error models are used to compensate the sun tracking error. We also developed the necessary image processing algorithms so that the heliostat optical properties such as maximum heat flux intensity, heat flux distribution and total reflected heat energy could be analyzed. Experimentally obtained data shows that the heliostat sun tracking accuracy could be dramatically improved using either linear polynomial type error model or neural network type error model. Neural network type error model is somewhat better in improving the sun tracking performance. Nevertheless, since the difference between two error models in compensation of sun tracking error is small, a linear error model is preferred in actual implementation due to its simplicity.

• 전기학회논문지
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• 제66권4호
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.

• Structural Engineering and Mechanics
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• 제77권1호
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• pp.57-74
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• 2021

The thermal eigenvalue responses of the graded sandwich shell structure are evaluated numerically under the variable thermal loadings considering the temperature-dependent properties. The polynomial type rule-based sandwich panel model is derived using higher-order type kinematics considering the shear deformation in the framework of the equivalent single-layer theory. The frequency values are computed through an own home-made computer code (MATLAB environment) prepared using the finite element type higher-order formulation. The sandwich face-sheets and the metal core are discretized via isoparametric quadrilateral Lagrangian element. The model convergence is checked by solving the similar type published numerical examples in the open domain and extended for the comparison of natural frequencies to have the final confirmation of the model accuracy. Also, the influence of each variable structural parameter, i.e. the curvature ratios, core-face thickness ratios, end-support conditions, the power-law indices and sandwich types (symmetrical and unsymmetrical) on the thermal frequencies of FG sandwich curved shell panel model. The solutions are helping to bring out the necessary influence of one or more parameters on the frequencies. The effects of individual and the combined parameters as well as the temperature profiles (uniform, linear and nonlinear) are examined through several numerical examples, which affect the structural strength/stiffness values. The present study may help in designing the future graded structures which are under the influence of the variable temperature loading.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.415-424
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• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Structural Engineering and Mechanics
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• 제21권2호
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• pp.151-168
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• 2005

In this paper an energy method is presented for the linear buckling analysis of first order shear deformable plates. The displacement fields are defined in terms of the shape functions, which correspond to a set of predefined points and are composed of significantly high order polynomials. The locations of these points are found by mapping the geometry using the naturalized coordinates and bilinear shape functions. In order to evaluate the method, fully clamped and simply supported rectangular plates subjected to uniform uniaxial compressive loading on two opposite edges of the plate are investigated thoroughly and the results are compared with the exact solution given in the monograph of Timoshenko and Gere (1961). The method is extended to the analysis of perforated plates, wherein the negative stiffness computed over the opening area from in-plane and out-of-plane deformation modes is superimposed to the stiffness of the full plate. Numerical results are then favorably compared with those obtained by finite element methods. Other cases such as; rectangular plates with eccentrically located openings of different shapes are studied and reported in this paper with regards to the effect of aspect ratio, hole size, and hole position on the buckling. For a square plate with a large circular opening at the center, diameter being 80 percent of the length, the present method yields buckling coefficient 12.5 percent higher than the one from the FEM.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.621-632
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• 2013

In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

• 정보보호학회논문지
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• 제14권2호
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• pp.15-22
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• 2004

위수가 q인 생성원 g를 사용하는 ElGamal형 서명기법에서는 서명되어질 각 문서마다 message nonce를 구간(0, q-1)에서 랜덤하게 선택해야 한다는 것은 잘 알려진 사실이다. H. Kuwakado와 H. Tanaka는 사용된 message nonce가 각각 0＜$textsc{k}$$_1$, $textsc{k}$$_2$$\leq$Ο(equation omitted)인 서명 두 개가 주어졌을 때, 서명자의 비밀키를 다항식 시간으로 계산해내는 알고리즘을 제안하였다. 최근 R. Gallant, R. Lambert, S. Vanstone등은 정수를 적절히 분해하여 타원곡선암호의 효율성을 개선하는 방법을 제안하였다. 이 논문에서는 타원곡선암호의 고속연산에서 사용되었던 정수의 분해기법을 Kuwakado등의 알고리즘에 적용하여 message nonce가 ｜$textsc{k}$$_1$｜, ｜$textsc{k}$$_2$｜인 경우에도 적용할 수 있도록 확장하고, 알고리즘의 효율성 및 공격의 완성도를 개선하였다.

• 한국지능시스템학회논문지
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• 제25권1호
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• pp.91-96
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• 2015

본 연구는 조명변화에 강인한 CT 전처리 기법 기반 개선된 얼굴인식 시스템을 소개한다. 전처리 알고리즘으로 CT알고리즘은 조명이 없는 환경에서도 얼굴의 지역적인 특징만을 추출한다. 얼굴의 지역적인 특징 추출을 가능하게 해준다. 처리된 데이터는 $(2D)^2$ 기반 대표적인 차원축소 알고리즘인 PCA를 사용하여 특징을 추출하였다. 전처리 알고리즘을 통한 특징 데이터는 제안한 방사형 기저함수 신경회로망의 입력으로 사용하였다. 방사형 기저함수 신경회로망의 은닉층은 FCM으로 구성하였고, 연결가중치는 1차 선형식을 사용하였다. 또한 ABC 알고리즘을 이용하여 제안된 분류기의 파라미터, 즉 입력의 수, 퍼지 클러스터링의 퍼지화 계수를 최적화 한다. 본 연구는 제안된 시스템의 성능 평가를 위해 Yale Face database B와 CMU PIE database로 실험하였다.

• 한국강구조학회 논문집
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• 제16권1호통권68호
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• pp.155-167
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• 2004

각각 집중하중과 등분포하중을 받는 단순보의 비탄성 횡-비틀림 좌굴에 대하여 연구하였다. 잔류응력을 단순형과 다항식형으로 하여 line-type 유한요소법으로 해석하였다. 잔류응력의 형태는 플랜지에서는 4차 곡선으로 웨브에서는 2차 곡선으로 가정하였다. 우리나라에서 생산되는 4종류의 I형강에 대하여 비탄성 횡-비틀림 좌굴에 대하여 해석한 후 결과를 강구조편람의 내용과 비교하였다. 해석결과로부터 강구조 편람에 의한 설계는 주보에 보조보가 있는 경우나 없는 경우 모두 전반적으로 과설계임을 알 수 있었다.