• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2046-2048
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• 2003

In this paper, a neural disturbance observer(NDO) is developed and its application to the control of a nonlinear system with the internal and/or external disturbances is presented. To construct the NDO, a parameter tuning method is proposed and shown to be useful in adjusting the parameters of the NDO. The tuning method employes the disturbance observation error to guarantee that the NDO monitors unknown disturbances. Each of the nodes of the hidden layer in the NDO network is a radial basis function(RBF). In addition, the relationships between the suggested NDO-based control and the conventional adaptive controls reported in the previous literatures are discussed. And it is shown in a rigorous manner that the disturbance observation error converges to a region of which size can be kept arbitrarily small. Finally, an example and some computer simulation results are presented to illustrate the effectiveness and the applicability of the NDO.

• Journal of Platform Technology
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• 제6권4호
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• pp.18-25
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• 2018

This paper describes a real-time human and robot tracking method in Intelligent Space with multi-camera networks. The proposed method detects candidates for humans and robots by using the histogram of oriented gradients (HOG) feature in an image. To classify humans and robots from the candidates in real time, we apply cascaded structure to constructing a strong classifier which consists of many weak classifiers as follows: a linear support vector machine (SVM) and a radial-basis function (RBF) SVM. By using the multiple view geometry, the method estimates the 3D position of humans and robots from their 2D coordinates on image coordinate system, and tracks their positions by using stochastic approach. To test the performance of the method, humans and robots are asked to move according to given rectangular and circular paths. Experimental results show that the proposed method is able to reduce the localization error and be good for a practical application of human-centered services in the Intelligent Space.

• 한국융합학회논문지
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• 제8권11호
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• pp.293-298
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• 2017

이 연구는 방사기저함수(RBF) 인공신경망을 이용하여 우리나라 다문화가정 초등학생의 우울증상 경험 예측 모델링을 구축하였다. 전국조사에 참여한 만 9세 이상 12세 이하 다문화 자녀 초등학생 23,291명(남 12,016명, 여 11,275명)을 분석 대상으로 하였다. 결과변수는 이분형의 우울증상 경험으로 정의하였고, 설명변수는 성, 거주지역, 사회적 차별 경험, 지난 1년간 학교폭력 경험, 한국어 교육 경험, 다문화 가족지원센터이용경험, 한국어 읽기, 한국어 말하기, 한국어 쓰기, 한국어 듣기, 한국 사회 적응 교육 경험을 포함하였다. RBF 인공신경망 모델링 결과, 한국어 교육 경험, 학교 폭력 피해 경험, 한국 사회 차별 경험, 한국어 읽기 수준은 다문화 초등학생의 우울증상을 분류하는 주요 예측 요인이었다. 다문화 아동의 우울증을 예방하기 위해서 한국어 읽기 수준이 저하된 집단에 대한 우선적인 관심과 상담이 필요하다.

• Structural Engineering and Mechanics
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• 제74권5호
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• pp.679-698
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• 2020

In this paper, the meshless local Petrov-Galerkin (MLPG) method is developed for dynamic analysis of non-symmetric nanocomposite cylindrical shell equations of elastic wave motion with nonlinear grading patterns under shock loading. The mechanical properties of the nanocomposite cylinder are obtained based on a micro-mechanical model. In this study, four kinds of grading patterns are assumed for carbon nanotube mechanical properties. The displacements can be approximated using shape function so, the multiquadrics (MQ) Radial Basis Functions (RBF) are used as the shape function. In order to discretize the derived equations in time domains, the Newmark time approximation scheme with suitable time step is used. To demonstrate the accuracy of the present method for dynamic analysis, at the first a problem verifies with analytical solution and then the present method compares with the finite element method (FEM), finally, the present method verifies by using the element free Galerkin (EFG) method. The comparison shows the high capacity and accuracy of the present method in the dynamic analysis of cylindrical shells. The capability of the present method to dynamic analysis of non-symmetric nanocomposite cylindrical shell is demonstrated by dynamic analysis of the cylinder with different kinds of grading patterns and angle of nanocomposite reinforcements. The present method shows high accuracy, efficiency and capability to dynamic analysis of non-symmetric nanocomposite cylindrical shell, which it furnishes a ground for a more flexible design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제2권2호
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• pp.120-133
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• 2008

This paper presents a novel approach for facial motion tracking and facial expression cloning to create a realistic facial animation of a 3D avatar. The exact head pose estimation and facial expression tracking are critical issues that must be solved when developing vision-based computer animation. In this paper, we deal with these two problems. The proposed approach consists of two phases: dynamic head pose estimation and facial expression cloning. The dynamic head pose estimation can robustly estimate a 3D head pose from input video images. Given an initial reference template of a face image and the corresponding 3D head pose, the full head motion is recovered by projecting a cylindrical head model onto the face image. It is possible to recover the head pose regardless of light variations and self-occlusion by updating the template dynamically. In the phase of synthesizing the facial expression, the variations of the major facial feature points of the face images are tracked by using optical flow and the variations are retargeted to the 3D face model. At the same time, we exploit the RBF (Radial Basis Function) to deform the local area of the face model around the major feature points. Consequently, facial expression synthesis is done by directly tracking the variations of the major feature points and indirectly estimating the variations of the regional feature points. From the experiments, we can prove that the proposed vision-based facial expression cloning method automatically estimates the 3D head pose and produces realistic 3D facial expressions in real time.

• 전기학회논문지
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• 제61권5호
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• 대한토목학회논문집
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• 제26권1B호
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• pp.61-67
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• 2006

강한 비선형성의 경향을 보이고 있는 강우-유출간의 관계를 모형화하기 위한 연구는 다양한 방법론으로 적용되어 활발히 연구되고 있다. 그 중에서 인공신경망을 이용하여 강우-유출간의 관계를 모형화하기 위한 대부분의 연구들은 역전파 학습 알고리즘(back propagation algorithm: BPA), Levenberg Marquardt(LV), radial basis function(RBF)을 이용하였으며, 이들은 강한 비선형성을 나타내는 입 출력간의 관계를 나타내는데 탁월한 성능을 보이고 있는 것으로 알려져 있고, 자료들의 급격한 변화나 현저한 변화에 대한 뛰어난 적응성을 보여주고 있다. 이러한 인공신경망 이론은 예측뿐만이 아니라 대상자료들의 양상을 분류하여 그 특성을 분석하는 데에도 이용되고 있다. 따라서 본 연구에서는 강우-유출과정의 양상에 따른 분류와 그에 따른 분석을 위해 Kohonen 네트워크 이론에 의한 자기조직화 방법(self-organizing map; SOM)을 적용하였다. 본 연구에서 제시한 방법을 이용한 결과, 강우의 시 공간적 분포의 불규칙한 변동성을 고려한 강우양상을 분류 할 수 있었으며, 강우-유출간의 특성을 분석한 결과 강한 비선현성을 가지고 있는 강우-유출관계가 SOM에 의해 7개의 패턴으로 구분되었다.

• 대한원격탐사학회지
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• 제39권5_1호
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• pp.577-587
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• 2023

크라우드소싱(crowdsourcing) 공간 데이터 활용 연구가 활발히 진행되고 있으나 데이터 품질의 불확실성으로 인한 문제점이 제기되고 있다. 특히 드론 영상 데이터셋에 품질이 낮은 데이터가 포함될 경우, 출력되는 공간 정보의 품질이 저하될 수 있다. 이를 위해 본 연구에서는 크라우드소싱된 영상의 기하학적 품질을 자동으로 검증하는 방법론을 제안하였다. 주요 품질 요소로는 영상의 공간해상도, 해상도 변화량, 매칭점 재투영 오차, 번들 조정 결과 등을 입력변수로 활용하였다. 공간 정보 생성에 적합한 영상을 분류하기 위해 학습 및 검증 데이터를 구축하고, radial basis function (RBF) 기반의 support vector machine (SVM) 모델로 학습을 진행하였다. 학습된 SVM 모델의 분류 정확도는 99.1%를 기록하였다. 품질 검증 모델 효과를 확인하기 위해 학습 및 검증에 사용하지 않은 드론 영상에 대하여 해당 모델을 적용하기 전후의 영상 데이터셋으로 각각 정사영상을 생성하고 비교하였다. 그 결과 모델 적용을 통하여 정사영상에 포함될 수 있는 다양한 왜곡을 줄이고 객체 식별력을 증대시키는 것을 확인하였다. 제안된 품질 검증 방법론은 다양한 품질의 크라우드소싱 데이터를 입력으로 받아 양질의 정보만을 자동 선별하게 함으로써 공간정보 생성에서의 활용 가능성을 증대시킬 것으로 기대한다.

• 한국공작기계학회논문집
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• 제16권4호
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• pp.87-92
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• 2007

The full automation welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed models using three different training algorithms in order to select an adequate neural network model for prediction of top-bead height.

• Structural Engineering and Mechanics
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• 제71권6호
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• pp.627-641
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• 2019

The paper focuses on bending analysis of the functionally graded (FG) plates with arbitrary shapes and boundary conditions. The material property of FG plates is modelled by using the power law distribution. Based on the first order shear deformation plate theory (FSDT), the governing equations as well as boundary conditions are formulated and obtained by using the principle of virtual work. The coupled Boundary Element-Radial Basis Function (BE-RBF) method is established to solve the complex FG plates. The proposed methodology is developed by applying the concept of the analog equation method (AEM). According to the AEM, the original governing differential equations are replaced by three Poisson equations with fictitious sources under the same boundary conditions. Then, the fictitious sources are established by the application of a technique based on the boundary element method and approximated by using the radial basis functions. The solution of the actual problem is attained from the known integral representations of the potential problem. Therefore, the kernels of the boundary integral equations are conveniently evaluated and readily determined, so that the complex FG plates can be easily computed. The reliability of the proposed method is evaluated by comparing the present results with those from analytical solutions. The effects of the power index, the length to thickness ratio and the modulus ratio on the bending responses are investigated. Finally, many interesting features and results obtained from the analysis of the FG plates with arbitrary shapes and boundary conditions are demonstrated.