• Annual Conference of KIPS
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• 2006.05a
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• pp.715-718
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• 2006

아바타를 이용한 얼굴 애니메이션은 가상 현실이나 엔터테인먼트와 같은 분야에서 많이 적용된다. 얼굴 애니메이션을 생성하는 방법에는 크게 3 차원 모델을 직접 변형시키는 기하학적인 변형 방법과 2 차원 이미지의 워핑이나 모핑방법을 이용한 이미지 변형 방법이 있다. 기하학적인 변형 방법 중 3 차원 모델을 변형시키기 위한 방법으로 RBF(Radial Basis Function)을 이용하는 방법이 있다. RBF 함수를 이용하여 모델의 부드러운 변형을 만들 수 있다. 이 방법은 모델의 임의의 한 점을 이동하게 되면 영향을 받는 정점들을 좀 더 자연스럽게 이동시킴으로써 자연스러운 애니메이션을 생성할 수 있다. 본 연구에서는 RBF 를 이용하여 3 차원 얼굴 메쉬 모델의 기하학적 변형을 통해 모델의 얼굴 표정을 생성하는 방법에 대해 제안하고자 한다. 얼굴 모델 변형을 위해 얼굴의 특징인 눈, 입, 턱 부분에 특징점을 정하고 각 특징점에 따라 영향을 받는 영역을 정하기 위해 얼굴 모델을 지역적으로 클러스터링한다. 각 특징점에 따라 영향을 받는 영역에 대해 클러스터링을 적용하고 RBF 를 이용하여 자연스러운 얼굴 표정을 생성하는 방법을 제안한다.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.526-531
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• 1997

To control nonlinear chemical processes, a new neural linearizing control scheme is proposed. This is a hybrid of a radial basis function(RBF) network and a linear controller, thus the control action applied to the process is the sum of both control actions. Firstly, to train the RBF newtork a linear reference model is determined by analyzing the past operating data of the process. Then, the training of the RBF newtork is iteratively performed to minimize the difference between outputs of the process and the linear reference model. As a result, the apparent dynamics of the process added by the RBF newtork becomes similar to that of the linear reference model. After training, the original nonlinear control problem changes to a linear one, and the closed-loop control performance is improved by using the optimum tuning parameters of the linear controller for the linear dynamics. The proposed control scheme performs control and training simultaneously, and shows a good control performance for nonlinear chemical processes.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.9
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• pp.103-110
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• 2010

This paper presents the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method and approximation modeling method with Radial Basis Function (RBF) neural network structure. The complex tool holder is used for holding a (milling/drilling) tool of a machine tool. The engineering problem of complex tool holder results from the twisting of spindle of tool holder. For this purpose, we present the optimized approximation of finite element modeling for a complex tool holder spindle using both DOE (Design of Experiment) with Optimal Latin Hypercube (OLH) method (specifically a module of $iSight^{(R)}$ FD-3.1) and approximation modeling method with Radial Basis Function (RBF) (another module of $iSight^{(R)}$ FD-3.1) neural network structure

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.262-262
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• 2010

반도체 제조공정 중 식각공정(Etching)은 웨이퍼표면으로부터 화학적, 물리적으로 불필요한 물질들을 선택적으로 제거하는 방법이다. 식각공정 중 하나인 플라즈마 식각(Plasma etching) 공정에서 오버식각(over-etching) 과언더식각(under-etching) 되는것을피하기위해서통계적인방법을기준으로식각종료점(endpoint)를 결정한다. 본 논문의 목표는 통계적인 분석방법을 이용하지 않고 실시간 식각 데이터(realtime etching data)를 사용해서 식각 종료점을 검출하는 것이다. 식각 데이터는 시계열 데이터(time-series data)이기 때문에 간단한 구조와 적은 계산량으로 빠른 수렴속도와 좋은 안정도를 가진 Radial Basis Function Neural Network's (RBF-NN) 를 이용하여 시계열 모델(time-series model)을 구현 하였다. 광학방사분광기(Optical Emission Spectroscopy: OES)로부터 나온 6개의 데이터 세트중에서 4개의 데이터 세트는 RBF-NN을 학습하는데 사용되고 2개의 데이터 세트는 모델의 성과를 시험해 보기 위하여 사용하였다. 학습을 위한 데이터들은 Matrix화 시켜서 목표값을 설정하여 학습시킨다. 실험한 결과 학습한 RBF-NN 모형이 식각 종료점(endpoint)를 정확하게 검출된다는 것을 보여준다.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1070-1079
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• 2016

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1863_1864
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• 2009

본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 입력개수, 입력변수, 클러스터의 개수를 PSO알고리즘(Particle Swarm Optimization)을 사용하여 최적화 시켰다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2299-2301
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• 1998

In this paper, a modified RBF (Radial Basis Function) neural network structure is suggested for the prediction of time series with non-linear, non-stationary characteristics. Conventional RBF neural network predicting time series by using past outputs is for sensing the trajectory of the time series and for reacting when there exists strong relation between input and hidden neuron's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden neurons are modified to react to the increments of input variable and multiplied by increments(or decrements) of out puts for prediction. When the suggested structure is applied to prediction of Lorenz equation, and Rossler equation, improved performances are obtainable.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.117-134
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• 2008

A new adaptive dual reciprocity boundary element method for dynamic analysis of 3-D structures is presented in this paper. It is based on finding the best approximation function of a radial basis function (RBF) group $f=r^n+c$ which minimize the error of displacement field expansion. Also, the effects of some parameters such as the existence of internal points, number of RBF functions and position of collocation nodes in discontinuous elements are investigated in this adaptive procedure. Three numerical examples show improvement in dynamic response of structures with adaptive RBF in dual reciprocity with respect to ordinary BEM.

• Korean Journal of Computational Design and Engineering
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• v.21 no.4
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• pp.443-452
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• 2016

This paper proposes a method for expensive black box optimization using radial basis functions (RBFs). The proposed algorithm is a computational strategy that uses a RBF model approximating the expensive black box function to predict an optimum. First, a RBF-based approximation technique is introduced and a sampling plan for estimation of the black box function is described. Then the proposed algorithm is explained, which presents the pseudo-codes for implementation and the detailed description of each step performed in the optimization process. In addition, numerical experiments will be given to analyze the performance of the proposed algorithm, by investigating computation accuracy, number of function evaluations, and convergence history. Finally, geometric distance problem as application example will be also presented for showing the algorithm applicability to different engineering problems.