• 한국정밀공학회지
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• 제24권11호
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• pp.144-153
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• 2007

In order to generate the input data for rapid prototyping, a new approach which is based on the implicit surface interpolation method is presented. In the method a surface is reconstructed by creating smooth implicit surface from unorganized cloud of points through which the surface should pass. In the method an implicit surface is defined by the adaptive local shape functions including quadratic polynomial function, cubic polynomial function and RBF(Radial Basis Function). By the reconstruction of a surface, various types of error in raw STL file including degenerated triangles, undesirable holes with complex shapes and overlaps between triangles can be eliminated automatically. In order to get the slicing data for rapid prototyping an efficient intersection algorithm between implicit surface and plane is developed. For the direct usage for rapid prototyping, a robust transformation algorithm for the generation of complete STL data of solid type is also suggested.

• International Journal of Contents
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• 제12권3호
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• pp.12-16
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• 2016

Brain computer interfaces (BCI) usually have focused on classifying the explicitly-expressed intentions of humans. In contrast, implicit intentions should be considered to develop more intelligent systems. However, classifying implicit intention is more difficult than explicit intentions, and the difficulty severely increases for subject independent classification. In this paper, we address the subject independent classification of implicit intention based on electroencephalography (EEG) signals. Among many machine learning models, we use the support vector machine (SVM) with radial basis kernel functions to classify the EEG signals. The Fisher scores are evaluated after extracting the gamma, beta, alpha and theta band powers of the EEG signals from thirty electrodes. Since a more discriminant feature has a larger Fisher score value, the band powers of the EEG signals are presented to SVM based on the Fisher score. By training the SVM with 1-out of-9 validation, the best classification accuracy is approximately 65% with gamma and theta components.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.43.6-43
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• 2001

In this paper, we describe an actor-critic method as a kind of temporal difference (TD) algorithms. The value function is regarded as a current estimator, in which two value functions have different inputs: one is an actual experience; the other is a simulated experience obtained through a predictive model. Thus, the parameter´s updating for the actor and critic parts is based on actual and simulated experiences, where the critic is constructed by a radial-basis function neural network (RBFNN) and the actor is composed of a kinematic-based controller. As an example application of the present method, a tracking control problem for the position coordinates and azimuth of a nonholonomic mobile robot is considered. The effectiveness is illustrated by a simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.874-879
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• 1992

It is shown that there exists a nonlinear mappping which transforms features and their changes to the desired camera motion without measurement of the relative distance between the camera and the part, and the nonlinear mapping can eliminate several difficulties encountered when using the inverse of the feature Jacobian as in the usual feature-based visual feedback controls. And instead of analytically deriving the closed form of such a nonlinear mapping, a fuzzy membership function (FMF) based neural network is then proposed to approximate the nonlinear mapping, where the structure of proposed networks is similar to that of radial basis function neural network which is known to be very useful in function approximations. The proposed FMF network is trained to be capable of tracking moving parts in the whole work space along the line of sight. For the effective implementation of proposed IMF networks, an image feature selection processing is investigated, and required fuzzy membership functions are designed. Finally, several numerical examples are illustrated to show the validities of our proposed visual servoing method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.482-485
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• 1999

In this paper we proposed a heterogeneous hidden layer consisting of both sigmoid functions and RBFs(Radial Basis Function) in multi-layered neural networks. Focusing on the orthogonal relationship between the sigmoid function and its derivative, a derived RBF that is a derivative of the sigmoid function is used as the RBF in the neural network. so the proposed neural network is called ONN(Orthogonal Neural Network). Identification results using a nonlinear function confirm both the ONN＇s feasibility and characteristics by comparing with those obtained using a conventional neural network which has sigmoid function or RBF in hidden layer

• Steel and Composite Structures
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• 제22권1호
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• pp.161-182
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• 2016

A state space differential reproducing kernel (DRK) method is developed for the three-dimensional (3D) analysis of functionally graded material (FGM) axisymmetric circular plates with simply-supported and clamped edges. The strong formulation of this 3D elasticity axisymmetric problem is derived on the basis of the Reissner mixed variational theorem (RMVT), which consists of the Euler-Lagrange equations of this problem and its associated boundary conditions. The primary field variables are naturally independent of the circumferential coordinate, then interpolated in the radial coordinate using the early proposed DRK interpolation functions, and finally the state space equations of this problem are obtained, which represent a system of ordinary differential equations in the thickness coordinate. The state space DRK solutions can then be obtained by means of the transfer matrix method. The accuracy and convergence of this method are examined by comparing their solutions with the accurate ones available in the literature.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.16-16
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• 2000

This paper describes a new structure re create a pseudo Gaussian function network (PGFN). The activation function of hidden layer does not necessarily have to be symmetric with respect to center. To give the flexibility of the network, the deviation of pseudo Gaussian function is changed according to a direction of given input. This property helps that given function can be described effectively with a minimum number of center by PGFN, The distribution of deviation is represented by level set method and also the loaming of deviation is adjusted based on it. To demonstrate the performance of the proposed network, general problem of function estimation is treated here. The representation problem of continuous functions defined over two-dimensional input space is solved.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.214-217
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• 1999

본 논문에서는 시그모이드 함수와 시그모이드 함수의 도함수로 유도한 RBF의 직교관계에 착안하여 은닉충에 직교함수를 활성화함수로 갖는 신경회로망을 제안한다. 제안하는 신경회로망을 직교신경회로망(ONN)이라고 한다. 제안한 방식의 유용성을 확인하기 위하여 비선형 함수의 근사 시뮬레이션에 의해 사상능력을 검토하고, 각각의 단일함수만을 적용한 경우와 비교·검토한다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2022년도 추계학술대회
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• pp.168-169
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• 2022

In this study, we investigates the auto-berthing problem for the underactuated surface vessel in the presence of constraints of dynamic uncertainties, finite time, transmission load, and environmental disturbance. A novel control scheme is proposed by fusing the finite time control technology and the event-triggered input algorithm. In the algorithm, differential homeomorphism coordinate the transformation is used to solve the problem of underactuation. Then, we apply the finite time technology and event triggered to save the time of the berthing vessel and relieve transmission burden between the controller and the vessel respectively. Moreover, a radial basis function network is used to approximate unknown nonlinear functions, and minimum learning parameters are introduced to lessen the computational complexity. A sufficient effort has been made to verify the stability of the closed-loop system based on the Lyapunov stability theory. Finally, simulation results display the effectiveness of the proposed scheme.

• 한국측량학회지
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• 제29권5호
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• pp.543-550
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• 2011

중력이상값은 지구물리, 측지 및 국방 등 다양한 분야에서 활용되는 기초 지구물리 자료로서, 특정 위치에서의 중력이상값을 필요로 하는 경우 일반적으로 데이터베이스화 되어 있는 중력이상값으로부터 내삽하여 활용한다. 그러나 중력은 지형 및 지하광물 등에 의하여 다양하게 변할 수 있는 물리량으로, 내삽에서 가정한 선형성, 2차 곡선 등의 성질이 만족되지 않으면 그 결과로 계산된 중력이상값은 실제 중력값과 큰 차이를 나타내게 된다. 또한, 내삽을 통하여 계산되는 결과값은 이론적으로 조화함수를 만족하여야 한다는 중력의 물리적 성질을 반영하지 못한다. 본 연구에서는 이와 같은 문제점을 보완하기 위하여 필요에 따라 유연하게 중력이상값을 계산할 수 있도록 중력 모델링을 수행하였다. 모델링은 평면푸리에 시리즈와 point-mass 함수를 기저함수로 하는 두 방법을 기반으로 수행되었고, 구축된 모델은 내삽으로부터 산출된 결과와 비교하여 특성을 분석하였다. 또한 모델링의 결과와 내삽 방법을 중력참조항법에 적용하여 활용적인 측면을 검토하였다. 연구결과, 기복이 완만한 지역에서는 평면푸리에 시리즈와 point-mass 및 내삽으로부터 계산된 중력이상값이 유사하게 나타났으나, 중력의 기복이 큰 지역에서는 모델 및 내삽에 의한 결과가 큰 차이를 나타내었다. 특히 주변의 네 점을 이용하여 선형으로 계산하는 Bilinear 내삽함수를 이용한 경우가 가장 완만한 중력값을 보이는 반면 point-mass 함수로부터 산출된 결과가 고주파에서 가장 큰 값을 나타내었다. 또한, 모델링 및 내삽에 필요한 자료의 로딩 및 계산 시간을 비교한 결과, 중력참조항법의 경우 중력값의 계산은 모델링을 수행하는 경우가 데이터베이스에 기반을 둔 내삽보다 효율적임을 알 수 있었다. 본 연구에서는 중력모델링의 결과 및 특성을 분석하였으며, 향후 모델링은 중력참조항법과 같은 활용분야에 있어 가장 효율적인 신호의 특성과 해상도를 지닌 중력 자료를 제공할 수 있는 기술로 활용될 수 있을 것으로 사료된다.