• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Structural Engineering and Mechanics
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• v.71 no.2
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• pp.197-210
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• 2019

Probabilistic buckling behavior of sandwich panel considering random system parameters using a radial basis function (RBF) approach is presented in this paper. The random system properties result in an uncertain response of the sandwich structure. The buckling load of laminated sandwich panel is obtained by employing higher-order-zigzag theory (HOZT) coupled with RBF and probabilistic finite element (FE) model. The in-plane displacement variation of core as well as facesheet is considered to be cubic while transverse displacement is considered to be quadratic within the core and constant in the facesheets. Individual and combined stochasticity in all elemental input parameters (like facesheets thickness, ply-orientation angle, core thickness and properties of material) are considered to know the effect of different degree of stochasticity, ply- orientation angle, boundary conditions, core thickness, number of laminates, and material properties on global response of the structure. In order to achieve the computational efficiency, RBF model is employed as a surrogate to the original finite element model. The stiffness matrix of global response is stored in a single array using skyline technique and simultaneous iteration technique is used to solve the stochastic buckling equations.

• Advances in concrete construction
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• v.13 no.1
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• pp.11-23
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• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• Proceedings of the Korea Information Processing Society Conference
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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.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2783-2786
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• 2003

In this paper, the speed controller of permanent-magnet synchronous motor (PMSM) using the RBF neural (NN) disturbance observer is proposed. The suggested controller is designed using the input-output feedback linearization technique for the nominal model of PMSM and incorporates the RBF NN disturbance observer to compensate for the system uncertainties. Therefore the proposed algorithm is robust against the uncertainties of the system. Finally, the computer simulation is rallied out to verify the effectiveness of the proposed method.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.34-37
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• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Journal of Environmental Impact Assessment
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• v.18 no.1
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• pp.31-39
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• 2009

A lot of data which are used in environment analysis of air pollution have characteristics that are distributed continuously in space. In this point, the collected data value such as precipitation, temperature, altitude, pollution density, PM10 have spatial aspect. When geostatistical data analysis are needed, acquisition of the value in every point is the best way, however, it is impossible because of the costs and time. Therefore, it is necessary to estimate the unknown values at unsampled locations based on observations. In this study, spatial interpolation method such as local trend surface model, IDW(inverse distance weighted), RBF(radial basis function), Kriging were applied to PM10 annual average concentration of Seoul in 2005 and the accuracy was evaluated. For evaluation of interpolation accuracy, range of estimated value, RMSE, average error were analyzed with observation data. The Kriging and RBF methods had the higher accuracy than others.

• Journal of Korean Society of Transportation
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• v.16 no.4
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• pp.153-166
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• 1998

본 연구에서는 도시 가로망에서의 구간 통행시간을 예측하기 위하여 time-frequency 분석의 일종인 웨이브렛변환과 RBF신경망 모형을 이용한 예측모형을 개발하였다. 웨이브렛 변환을 이용한 시계열 자료 분석을 통해서 통행시간에 내재되어 있는 다양한 패턴의 특징을 추출함으로써 오전/오후의 첨두현상, 신호교차로의 현시주기 등 주기적으로 발생되는 요인들에 의해서 통행시간 시계열 자료의 패턴에 나타나는 규칙성을 분석해 내었다. 분석된 패턴정보에 대한 규명은 카오스 이론을 근간으로한 시간지연좌표를 이용하여 시계열 자료의 규칙성을 시각적으로 판별하여 예측모형 구축에 활용하도록 하였다. 또, RBF신경망을 이용하여 예측범위의 공간적/시간적 확대에 따른 모형 구축에 소요되는 시간을 최소화하도록 하였으며, 시내버스 노선의 정류장간 운행시간 예측을 통해서 기존 연구에서 제기되었던 현실세계의 단순화, 다단계 예측시 정확성 등의 문제를 해결하였다. 예측실험결과 웨이브렛 변환을 데이터의 전처리 과정에 삽입하여 링크 통행시간의 패턴정보 예측에 활용할 경우, 기존의 예측모형에 비해서 훨씬 정확한 예측이 가능한 것으로 나타났으며, RBF 신경망은 짧은 학습시간에도 불구하고 역전파 신경망보다 우수한 예측력을 갖고 있는 것으로 밝혀졌다.