• Journal of Information Processing Systems
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• 제8권4호
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• pp.669-684
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• 2012

Discrete wavelet transforms are extensively preferred in biomedical signal processing for denoising, feature extraction, and compression. This paper presents a new denoising method based on the modeling of discrete wavelet coefficients of ECG in selected sub-bands with Kernel density estimation. The modeling provides a statistical distribution of information and noise. A Gaussian kernel with bounded support is used for modeling sub-band coefficients and thresholds and is estimated by placing a sliding window on a normalized cumulative density function. We evaluated this approach on offline noisy ECG records from the Cardiovascular Research Centre of the University of Glasgow and on records from the MIT-BIH Arrythmia database. Results show that our proposed technique has a more reliable physical basis and provides improvement in the Signal-to-Noise Ratio (SNR) and Percentage RMS Difference (PRD). The morphological information of ECG signals is found to be unaffected after employing denoising. This is quantified by calculating the mean square error between the feature vectors of original and denoised signal. MSE values are less than 0.05 for most of the cases.

• Nuclear Engineering and Technology
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• 제55권11호
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• pp.4102-4111
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• 2023

In order to better perform thermal hydraulic calculation and analysis of supercritical water reactor, based on the experimental data of supercritical water, the model training and predictive analysis of the heat transfer coefficient of supercritical water were carried out by using the support vector machine (SVM) algorithm. The changes in the prediction accuracy of the supercritical water heat transfer coefficient are analyzed by the changes of the regularization penalty parameter C, the slack variable epsilon and the Gaussian kernel function parameter gamma. The predicted value of the SVM model obtained after parameter optimization and the actual experimental test data are analyzed for data verification. The research results show that: the normalization of the data has a great influence on the prediction results. The slack variable has a relatively small influence on the accuracy change range of the predicted heat transfer coefficient. The change of gamma has the greatest impact on the accuracy of the heat transfer coefficient. Compared with the calculation results of traditional empirical formula methods, the trained algorithm model using SVM has smaller average error and standard deviations. Using the SVM trained algorithm model, the heat transfer coefficient of supercritical water can be effectively predicted and analyzed.

• 전자공학회논문지CI
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• 제43권6호
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• pp.34-42
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• 2006

데이터 마이닝(data mining)이란 대량의 데이터에 내재되어 있는 숨겨진 패턴을 찾아내기 위한 분석 기술로서 지금까지 많은 연구가 진행되어 왔지만, 현재의 데이터 마이닝 연구는 ad-hoc 문제와 같은 해결되어야 할 중요한 이슈들이 있다. 즉, 개별적 문제에 대해 설계된 마이닝 기법이 주로 사용되는 까닭에 여러 문제에 통합적으로 적용될 수 있는 시스템적 마이닝 기법에 관한 연구가 요구되고 있다. 본 논문에서는 이러한 핵심 데이터 마이닝 태스크 중의 하나인 분류 모델링 방법으로 방사형 기저 함수(radial basis function, RBF) 모델의 성능을 고찰하고 그 유용성(usefulness)을 살펴보고자 한다. 특히, 대표적인 마이닝 관련 벤치마킹 데이터인 Monk's problem 분석을 위해 RC(Representation Capacity) 기반 알고리즘을 사용하여 RBF 모델을 구축하고 분류 성능을 기존의 연구 결과와 비교 고찰한다. 그리하여 RBF 모델의 분류 성능 면에서의 우수성뿐만 아니라 모델링 과정을 체계적인 방식으로 적절히 제어할 수 있음을 보여주고, 이를 통해 현재의 ad-hoc 방식의 문제를 어느 정도 해결할 수 있음을 보여준다.

• 전기학회논문지
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• 제62권4호
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• pp.554-561
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• 2013

In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

• 한국통신학회논문지
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• 제28권5C호
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• pp.555-560
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• 2003

본 논문에서는 Synchronous DS-CDMA(direct sequence-code division multiple access) 시스템에서 준 최적의 RBF(radial basis function) 수신기를 제안한다. 제안된 수신기는 병렬적인 RBF Network들이 결합된 형태를 갖으며, 각 RBF Network는 일반적인 RBF 수신기의 구조를 갖는다. 각각의 RBF Network는 다른 RBF Network에 할당된 사용자들에 의해 야기되는 간섭 성분으로 인해 성능이 저하된다. 따라서, 이러한 간섭 영향을 완화시킬 수 있는 병렬 간섭제거 기법(PIC)을 각 RBF Network들간에 적용한다. 본 논문에서는 제안된 수신기가 요구되는 RBF의 개수(RBF의 중심값)를 줄일 수 있는 구조를 갖고, 수신과정에서 하나의 정보열당 요구되는 연산량 또한 줄일 수 있는 구조임을 확인하였다. 그리고, AWGN 채널에서의 모의실험을 통해 일반적인 수신기보다 복잡도를 줄인 제안된 수신기가 최적의 수신기와 유사한 성능을 나타냄을 확인하였다. 또한, 제안된 수신기가 다양한 시스템 요구사항에 대처할 수 있음을 확인하였다.

• 한국해양공학회지
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• 제32권6호
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• pp.447-457
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• 2018

In this study, a two-dimensional fully nonlinear transient wave numerical tank was developed using a desingularized indirect boundary integral equation method. The desingularized indirect boundary integral equation method is simpler and faster than the conventional boundary element method because special treatment is not required to compute the boundary integral. Numerical simulations were carried out in the time domain using the fourth order Runge-Kutta method. A mixed Eulerian-Lagrangian approach was adapted to reconstruct the free surface at each time step. A numerical damping zone was used to minimize the reflective wave in the downstream region. The interpolating method of a Gaussian radial basis function-type artificial neural network was used to calculate the gradient of the free surface elevation without element connectivity. The desingularized indirect boundary integral equation using an isolated point source and radial basis function has no need for information about the element connectivity and is a meshless method that is numerically more flexible. In order to validate the accuracy of the numerical wave tank based on the desingularized indirect boundary integral equation method and meshless technique, several numerical simulations were carried out. First, a comparison with numerical results according to the type of desingularized source was carried out and confirmed that continuous line sources can be replaced by simply isolated sources. In addition, a propagation simulation of a $2^{nd}$-order Stokes wave was carried out and compared with an analytical solution. Finally, simulations of propagating waves in shallow water and propagating waves over a submerged bar were also carried and compared with published data.

• 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.

• 한국지능시스템학회논문지
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• 제23권3호
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• pp.262-267
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• 2013

The recognition of human emotional state is one of the most important components for efficient human-human and human- computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.

• 한국대기환경학회지
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• 제12권2호
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• pp.167-177
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• 1996

To investigate the source-receptor relationships aerosol model has been used to simulate the distribution behavior of the yellow sand. Data for meteorological fields were obtained by Meso-scale Analysis and Prediction Model System/Seoul National University (MAPMS/SNU) for five days (10-14 April 1988). To obtain the distributions of concentration of yellow sand,the aerosol model has been modified to allow quantifications of relative concentration distributions of yellow sand. Source regions of yellow sand were delineated by soil maps of China and emission rate as a function of wind stress(Westphal et al., 1987). Using 3-dimensional wind fields the backward trajectories from 3 receptor grids at the layer of .sigma. =0.95, 0.9, 0.85, 0.8 were calculated. In order to facilitate quantitative assessment of source-receptor relationships, it was assumed that the perturbations in along-trajectory and cross-trajectory proceed linearly with time, in accord with Gaussian distribution characteristics. On the basis of this assumption, the probability fields were calculated from every grid point with source strength 1. Using these probability fields and emission retes, the potential contributions of upstream sources along the trajectories were estimated. The results of this study indicate that the application of trajectory modeling is useful in investigating the quantitative relationship between source and receptor regions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.